# document

id: 625255c1be599be1379aa85445a54b7
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 13021
content:
"9 Sv of overturning \r\noccurring across the Labrador Sea, as found with the OSNAP 53degN \r\narray spanning the mouth, calling into question the validity of the \r\nLabrador Sea convection-AMOC link (Lozier et al., 2019). Although \r\nthese results are derived from only the first 21 months of data from \r\nmonitoring since 2014, hydrographic observations during 1990-1997 \r\npreviously found small overturning (1-2 Sv) in the Labrador Sea \r\n(Pickart and Spall, 2007). However, previous estimates of Labrador \r\nSea Water formation (obtained with different techniques) suggest \r\nlarger overturning (Haine et al., 2008). Part of this controversy could \r\nbe explained if a large fraction of newly formed Labrador Sea Water \r\nis not exported from the Labrador Sea"


id: 501f1dc1c49bd618376c7ee67affa5bc
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 10045
content:
" The OSNAP observations are \r\nsupported by previous hydrographic measurements in showing strong \r\neast-west symmetry in isopycnal slope in the Labrador Sea in periods \r\nof both strong and weak convection; this implies compensating \r\nnorthward and southward transport above and below the potential \r\ndensity surface that separates the upper and lower overturning \r\nlimbs (Lozier et al., 2019), despite large deep convection variability \r\n(Yashayaev, 2007; Yashayaev and Loder, 2016). New observations \r\nof deep winter mixing in the Irminger Basin (de  Jong et  al., 2018; \r\nJosey et  al., 2019) support the assertion that the Irminger Sea, in \r\naddition to the Nordic Seas (Chafik and Rossby, 2019), are the main \r\nsources of overturning in the eastern subpolar gyre, consistent with \r\nOSNAP (Petit et al., 2020)"


id: f55800d052509199e6699ef770fa895e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 36123
content:
" It is unclear to what extent models are in \r\ndisagreement with this view of overturning in the subpolar gyre, as \r\na direct comparison with OSNAP of model analyses partitioning the \r\noverturning into a western and eastern part is mostly lacking, with \r\na notable exception (Menary et al., 2020a). Other results give rise to \r\nconsiderable uncertainty over veracity of the models in simulating \r\nthe overturning partitioning between east and west and the role of \r\nvarious drivers of AMOC variability, including: the analysis of water \r\nmass formation in CMIP6 models (Heuze, 2021); the analysis between \r\nLabrador Sea Water formation and AMOC in a  suite of ocean-only \r\nmodels (Danabasoglu et al., 2014); and the fact that when the OSNAP \r\nobserving system design was tested in an eddy-permitting ocean \r\nmodel comparable amounts of overturning in the western and eastern \r\nsubpolar gyre were found (Susan Lozier et al., 2017)"


id: 67edf43f674ce18f31161cbf9876eca6
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 13939
content:
" Disagreement \r\nbetween models and OSNAP observations may decrease in higher\u0002resolution models (Menary et  al., 2020a). In summary, multiple \r\nlines of evidence provide medium agreement between models and \r\nobservations on drivers of change and variability in the AMOC and, in \r\nparticular, the role of Labrador Sea deep convection in constituting \r\nAMOC variability."


id: 9540560d39a76a1404de088f931db9b3
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20981
content:
"The SROCC (Collins et al., 2019) assessed that in situ observations \r\n(2004-2017) and sea surface temperature reconstructions indicate \r\nthat AMOC has weakened relative to 1850-1900 (medium \r\nconfidence). However, SROCC also assessed that there is insufficient \r\ndata to quantify the magnitude of the weakening, or to properly \r\nattribute it to anthropogenic forcing, due to the limited length of \r\nthe observational record. Here, this assessment is adjusted to low \r\nconfidence in the weakening (as also discussed in Sections 2.3.3.4.1 \r\nand 3.5.4.1). The CMIP5 multi-model mean showed no 20th century \r\ntrend in AMOC (Cheng et al., 2013). The CMIP6 multi-model mean \r\nslightly opposes the reconstructed decline due to a strong increase \r\nin the 1940-1985 period (Menary et al., 2020b; Weijer et al., 2020), \r\nthought to be in response to aerosol forcing (Section  3.5.4.1), \r\nfollowed by a  smaller decline since the 1990s"


id: 41dc033ef1dc6f3e35c0f58933e2d090
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 7763
content:
" Also, agreement \r\nbetween different proxy-based reconstructions is weak in many \r\ndetails (Moffa-Sanchez et  al., 2019) and questions can be raised \r\nregarding various proxies used in reconstructions (Section 2.3.3.4.1). \r\nFor instance, SST-based proxies can be influenced by atmospheric \r\nand other processes acting on different time scales (Moffa-Sanchez \r\net  al., 2019; Jackson and Wood, 2020). In addition, many proxies \r\nare indirect and based on AMOC-related processes assumed to be \r\nsimilar to those found in models, such as the link between AMOC \r\nand Labrador Sea convection, which has been questioned recently \r\n(see above). In addition, the subpolar gyre from which many AMOC \r\nproxies are taken may vary independently of AMOC, with similar \r\npatterns in SST and OHC driven by wind variability (Williams et al., \r\n2014; Piecuch et  al., 2017)"


id: e32b13ee37e6a26fea36f67f4bbc4db7
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 33235
content:
" Finally, a  new dynamic reconstruction \r\nof the Atlantic inflow to the Nordic Seas suggests no slowdown \r\nover the past 70 to 100 years (Rossby et al., 2020), in contrast to \r\na  new compilation of proxy reconstructions which suggests that \r\nAMOC is presently in its weakest state in the last millennium (Caesar \r\net  al., 2021), reinforcing the evidence that motivated the previous \r\nSROCC assessment. Section  3.5.4.1 also questions the veracity of \r\nthe models' forced AMOC response during the 20th century. Given \r\nthe  large discrepancy between modelled and reconstructed AMOC "


id: 9f830890b56a6487033c54c835afd3d1
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 22638
content:
"The strength of AMOC has been measured directly since 2004 using \r\nthe RAPID Array (Section 2.3.3.4.1; Smeed et al., 2018). RAPID-based \r\nestimates show a  large amount of variability compared to CMIP \r\nmodels (Roberts et  al., 2014). Observed changes since 2004 are \r\ntoo short for the evaluation of a long-term trend given the decadal \r\nscale internal variability (Section  2.3.3.4.1). Nevertheless, Smeed \r\net al. (2018) argue that, between 2007 and 2011, AMOC shifted to \r\na state of reduced overturning - decreasing from 18.8 Sv between \r\n2004 and 2008 to 16.1 Sv after 2008. A shift in AMOC strength of \r\nthis magnitude is not captured by CMIP5 and CMIP6 models, which \r\ngenerally underestimate interannual to decadal AMOC variability \r\n(Section  3.5.4.1). Additional evidence since SROCC also raises the \r\ninconsistency between the RAPID weakening in the 3000-5000 m \r\ndepth range and the relative constancy of deep overflows from the \r\nArctic (Osterhus et al"


id: 613865edbc4512948899b9179c625ef0
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 12869
content:
", 2019), implying that the recent decrease in \r\nAMOC at 26.5degN (Smeed et  al., 2018) is not caused by overflow \r\nweakening or reduced overturning in the Nordic Seas, although the \r\nweakening occurred almost exclusively in the 3000-5000 m depth \r\nrange associated with a reduction of Lower NADW (Section 9.2.2.3). "


id: 1c559914713d01d6123878d1c61d1e66
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 1911
content:
"Figure 9.10 | Atlantic Meridional Overturning Circulation (AMOC) strength in simulations and sensitivity to resolution and forcing. (Top left) AMOC \r\nmagnitude (units: Sverdrup (Sv) = 109 kg s-1) in Paleoclimate Modelling Intercomparison Project (PMIP) experiments. (Top right) Time series of AMOC from Coupled Model \r\nIntercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) based on (Menary et al., 2020b). (Bottom left) Percent change in AMOC strength per year at different resolutions \r\nover the 1950-2050 period with colours for model families (Roberts et al., 2020). (Bottom right) A compilation of percentage changes in the simulated AMOC after applying \r\nan additional freshwater flux in the subpolar North Atlantic at the surface for a limited time (de Vries and Weber, 2005; Stouffer et al., 2006; Yin and Stouffer, 2007; Jackson, \r\n2013; Liu and Liu, 2013; Jackson and Wood, 2018; Haskins et al., 2019)"


id: eee13ed5b2934034d92349d656d07e28
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 35057
content:
" Symbols indicate whether the AMOC recovers within 200 years (circles), is starting to recover (upwards \r\narrow), or does not recover within 200 years (downwards arrow). Symbol size indicates rate of freshwater input. Further details on data sources and processing are available \r\nin the chapter data table (Table 9.SM.9)."


id: 36de3db9ca3c6ab6bb3c0b8c331cd351
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 6020
content:
"climate models to represent the Southern Ocean persist due to most \r\nCMIP6 models still using parameterized mesoscale eddy processes, \r\nwhich are limited in projecting the future response of the horizontal \r\nand vertical circulation under climate warming, and also because \r\nof the continued absence of active ice-shelf and ice-sheet coupling \r\nin the CMIP6 model suite, therefore ignoring basal meltwater \r\nand calving feedback on the circulation (Meredith et al., 2019). In \r\naddition, two important limitations of CMIP6 models of the Southern \r\nOcean involve processes that were not assessed in SROCC. First, \r\nthe poor representation of dense overflows causes most of the \r\nAntarctic Bottom Water (AABW) to be formed by spurious open \r\nocean convection rather than by dense overflows from the Antarctic \r\ncontinental shelves that feed the lower overturning cell (Snow et al., \r\n2015; Dufour et al., 2017; Heuze, 2021)"


id: 79672cc08600b82faa0576b4edabc0d2
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 16672
content:
" Second, Antarctic continental \r\nshelf waters are poorly simulated because potentially important \r\ncontrolling mechanisms tend to be too small and transient to \r\nobserve and resolve in CMIP ocean models. These small processes \r\ninclude: the heterogeneity of observed sub-ice-shelf melt with \r\nwarm water driving narrow basal channels that cut underneath \r\nthe ice (Drews, 2015; Alley et al., 2016; Marsh et al., 2016; Milillo \r\net al., 2019); eddies and tides (Stewart et al., 2018; Jourdain et al., \r\n2019; Hausmann et  al., 2020), which can drive Circumpolar Deep \r\nWater (CDW) onto the continental shelves or dynamically increase \r\nmelting (Section  9.2.3.6); and feedback mechanisms between \r\nocean, atmosphere and cryosphere that can weaken or amplify \r\ninitial perturbations (Donat-Magnin et al., 2017; Spence et al., 2017; \r\nTurner et al., 2017; Silvano et al., 2018; Webber et al., 2019; Hazel \r\nand Stewart, 2020)"


id: fd93f7ad4831c22b6ff2da91159aed6b
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 37412
content:
" In addition, the Southern Ocean in CMIP5 and \r\nCMIP6 models exhibit surface temperature biases (Section 9.2.1.1), \r\nwhich have been linked in CMIP5 models to errors in atmospheric \r\nmodel cloud-related shortwave radiation (Hyder et  al., 2018) and \r\nare somewhat improved in High Resolution Model Intercomparison \r\nProject (HighResMIP) models (Figure 9.3). In summary, there is high \r\nconfidence that future change in the subpolar Southern Ocean region, \r\nincluding sea ice cover and ocean temperature change on Antarctic \r\ncontinental shelves, depends on feedback mechanisms involving the \r\nocean, atmosphere and cryosphere that are poorly understood and \r\nnot represented in the current generation of climate models. This \r\nresults in large uncertainty and low confidence in the future sea \r\nice cover (Section 9.3.2) and in ocean temperature change on the \r\nAntarctic continental shelf (Section 9.4.2.3)."


id: 8f3fe74000e10a3140794d3a92b8092a
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20082
content:
"the models. In theoretical models of the thermohaline circulation, the \r\ncirculation strength is proportional to a density or pressure difference \r\nbetween the subpolar North Atlantic and subtropical South Atlantic \r\n(Kuhlbrodt et al., 2007; Weijer et al., 2019). In all models, the north\u0002south pressure gradient decreases in the 21st century, as subpolar \r\nwaters warm faster than subtropical waters, and an enhanced \r\nhydrological cycle drives freshening at subpolar latitudes, while \r\nsubtropical latitudes feature more evaporation and salinification \r\n(Section  9.2.1). As a  result, surface waters at subpolar latitudes \r\nbecome more buoyant and more stable, so that deep water formation \r\ndriving the AMOC declines (Section 9.2.1.3). Projected AMOC decline \r\nby 2100 ranges from 24 [4 to 46] % in SSP1-2.6 to 39 [17-55] % \r\nin SSP5-8.5 (medium  confidence) (Section  4.3.2.3)"


id: 737848952823f7ded835e5add66e9b26
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 15787
content:
" Note that \r\nthese ranges are based on ensemble means of individual models, \r\nlargely smoothing out internal variability. If single realizations are \r\nconsidered, the ranges become wider, especially by lowering the low \r\nend of the range (Section 4.3.2.3). In summary, it is very likely that \r\nAMOC will decline in the 21st century, but there is low confidence in \r\nthe model's projected timing and magnitude. In addition, freshwater \r\nfrom the melting of the Greenland Ice Sheet (Sections 9.4.1.3 and \r\n9.4.1.4) could further enhance the future weakening of AMOC in the \r\n21st century (Collins et al., 2019; Golledge et al., 2019)."


id: a8928f8fb2e24808c1dd1a081588616e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 24051
content:
"Both AR5 (Collins et  al., 2013) and SROCC (Collins et  al., 2019) \r\nassessed that an abrupt collapse of AMOC before 2100 was very \r\nunlikely, but SROCC added that, by 2300, an AMOC collapse was \r\nas likely as not for high-emissions scenarios. The SROCC also \r\nassessed that model bias may considerably affect the sensitivity of \r\nthe modelled AMOC to freshwater forcing. Tuning towards stability \r\nand model biases (Valdes, 2011; Liu et  al., 2017; Mecking et  al., \r\n2017; Weijer et al., 2019) provides CMIP models a tendency toward \r\nunrealistic stability (medium confidence). By correcting for existing \r\nsalinity biases, Liu et al. (2017) demonstrated that AMOC behaviour \r\nmay change dramatically on centennial to millennial time scales, \r\nand that the probability of a collapsed state increases. None of the \r\nCMIP6 models features an abrupt AMOC collapse in the 21st century, \r\nbut they neglect meltwater release from the Greenland Ice Sheet"


id: 85702305f0a68127e55f291d8368542b
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 18525
content:
" \r\nAlso, a  recent process study reveals that a  collapse of AMOC \r\ncan be induced, even by small-amplitude changes in freshwater \r\nforcing (Lohmann and Ditlevsen, 2021). As a result, we change the \r\nassessment of an abrupt collapse before 2100 to medium confidence \r\nthat it will not occur."


id: 4736255d58b5921d11f3c7a9f4727359
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 8581
content:
"Despite these challenges, the CMIP6 ensemble does represent the \r\nmain Southern Ocean circulation characteristics: the simulated \r\nAntarctic Circumpolar Current (ACC) transport is generally lower than \r\nobservation-based values but consistent when considering ensemble \r\nspread, and the inter-model spread in ACC transport has greatly \r\nreduced from previous generations of climate models from CMIP3 \r\nto CMIP6 (Beadling et  al., 2019, 2020). The structure (but not the \r\nmagnitude) of the two-cell zonally averaged overturning is captured \r\nby most CMIP6 models (Russell et al., 2018; Beadling et al., 2019). \r\nIn addition, while issues remain, CMIP6 climate models show clear \r\nimprovements in their representation of AABW compared to CMIP5: \r\nseveral models correctly represent or parameterize Antarctic shelf \r\nprocesses, fewer models exhibit Southern Ocean deep convection, \r\nbottom density biases are reduced, and abyssal overturning is more \r\nrealistic (Heuze, 2021)"


id: 59e08d352f4938093fb11b4a686ecadb
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 11641
content:
"models show an improvement compared to CMIP5 models, with \r\nan overall reduction in the equatorward bias of the annual mean \r\nwesterly jet from 1.9deg in CMIP5 to 0.4deg in CMIP6, but in contrast, \r\nthey show no such overall improvements for their representation of \r\nthe Amundsen Sea Low (Bracegirdle et al., 2020; Lyu et al., 2020a), \r\nwhich can be critical in driving variability of water masses on the \r\nAntarctic continental shelf in west Antarctica, the Weddell Sea or \r\nthe Ross Sea (Holland et al., 2019; Silvano et al., 2020).\r\nThe SROCC (Meredith et al., 2019) established that, while trends in \r\nthe atmospheric forcing of the Southern Ocean have been dominated \r\nby a  strengthening of the Southern Hemisphere westerly winds in \r\nrecent decades, there is medium confidence that ACC transport is \r\nweakly sensitive to changes in winds"


id: 5445d604fc8d4efa89890293abff04f6
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 10718
content:
" It also reported that, instead of \r\nincreasing the mean ACC transport, additional energy input associated \r\nwith increased wind stress cascades into the eddy field (medium \r\nconfidence). In contrast with the AR5 assessment (Rhein et al., 2013), \r\nSROCC evaluated that it was unlikely that there has been a  net \r\nsouthward migration of the mean ACC position over the past 20 years. \r\nThere is no additional evidence to revisit SROCC assessment on wind \r\nsensitivity. However, new evidence does suggest that air-sea buoyancy \r\nforcing associated with idealized 4xCO2 forcing leads to an increase in \r\nACC transport (limited evidence) (Shi et al., 2020). The SROCC noted \r\nthat, if the general strengthening in westerly winds is sustained, then \r\nit is very likely that the eddy field will continue to increase in intensity, \r\nand it is likely that the mean position and strength of the ACC will \r\nremain only weakly sensitive to winds"


id: 91f863a67490226376a91448ddf514d9
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20488
content:
" In the future, the strength of \r\nthe Southern Hemisphere westerly wind jet results from a competition \r\nbetween decrease due to ozone hole recovery and increase due to \r\nincreased radiative forcing (Section 4.3.3.1). This competition results \r\nin an increased atmospheric jet by 2100 compared to present day \r\nunder SSP2-4.5, SSP3-7.0, and SSP5-8.5, but a decreased jet by 2100 \r\nunder SSP1-2.6 (Bracegirdle et  al., 2020). There is little inter-model \r\nspread in the CMIP6 future response of the atmospheric westerly \r\njet, providing high confidence in this assessment (in contrast, CMIP6 \r\nmodels show no consistency in their future projection of easterly wind \r\nchange along the Antarctic continental shelf break; Bracegirdle et al., \r\n2020)"


id: 92861de420c8e3eba9e24a5988eb58d2
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20574
content:
" Paleo-oceanographic evidence suggests that ACC flow through \r\nDrake Passage was consistently stronger during warm intervals of the \r\npast (both during interstadials and interglacials), but with relatively \r\nlittle change and no consensus on the sign of change in other regions \r\n(Lamy et al., 2015; Toyos et al., 2020). In summary, additional evidence \r\nsince SROCC confirms that there is medium confidence that the ACC \r\nhas been weakly sensitive to Southern Hemisphere atmospheric jet \r\nincrease in the past decades. New evidence since SROCC suggests that \r\nthere is high confidence that the Southern Hemisphere atmospheric jet \r\nwill increase in the 21st century for all scenarios (except for SSP1-1.9 \r\nand SSP1-2.6; Section  4.3.3.1) with a  greater increase for larger \r\nradiative forcing"


id: ccbdf24bbbf1e532bcd72b5961c81eb0
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 29750
content:
" An increase in westerly winds will very likely force \r\nan increase of the eddy field in the ACC, and while there is medium \r\nconfidence that the ACC is weakly sensitive to wind change, new \r\nadvances since SROCC provide limited evidence that the ACC transport \r\nwill nevertheless increase in response to wind and buoyancy fluxes.\r\nFor the upper cell overturning circulation, SROCC concluded that: \r\nits transport has experienced significant inter-decadal variability in \r\nresponse to wind forcing since the 1990s; and there is low confidence"


id: 49173eb8603839008343a3f4045f0d2b
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 8886
content:
"in the assessments of a long-term increase in upper-ocean overturning. \r\nConsistent with SROCC, the importance of eddy processes and winds \r\nin driving long-term change and variability have been reinforced, with \r\na potential fast wind response partially counteracted by a slower eddy \r\nresponse (Doddridge et al., 2019; Waugh et al., 2019; Stewart et al., \r\n2020). Eddy parametrizations affect the strength of overturning, its \r\nsensitivity to winds and the ACC transport (Mak et al., 2017). Even \r\nin eddy-resolving simulations, sub-gridscale dissipation affects the \r\noverturning and ACC (Pearson et  al., 2017). In addition, there has \r\nbeen progress in understanding the importance of Antarctic Ice Shelf \r\nmeltwater and sea ice, in driving the observed changes in the near \r\nsurface and in the upper overturning cell over the past decades, on \r\ntop of changes induced by winds and eddies (Bronselaer et al., 2020; \r\nHaumann et al., 2020; Jeong et al., 2020; Rye et al., 2020)"


id: 332fcfa3afa2eabb7bf9a250d830cf57
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 5474
content:
" In particular, \r\nincreased stratification caused by increased freshwater flux to the \r\nsurface ocean (Section 9.2.1.3) can cause a shoaling and warming of \r\nthe CDW layer, and create a positive feedback, enhancing basal melt \r\nof the Antarctic Ice Sheet (Section 9.4.2.1; Bronselaer et al., 2018; \r\nGolledge et al., 2019; Schloesser et al., 2019; Sadai et al., 2020). There \r\nis medium confidence in the existence of this feedback mechanism \r\nbut low agreement on the magnitude of the feedback. The SROCC \r\nreported that CMIP5 models project that the  overall transport \r\nof upper-ocean overturning cell will increase by up to 20% in the \r\n21st century, and no new studies alter that assessment."


id: 73c89dc410dcc8c5456f332421249295
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 15830
content:
"For the lower cell overturning circulation, SROCC assessed that \r\na slowdown of its transport is consistent with the observed decrease \r\nin volume (medium confidence) of AABW in the global ocean \r\n(Section  9.2.2.3). Additional evidence since SROCC strengthens \r\nconfidence that increased glacial meltwater flux will reduce the \r\ndensity of bottom waters during the 21st century. It will eventually \r\nreach a  point where deep convection will be curtailed, and shelf \r\nwater will become too buoyant to sink to the ocean interior, thereby \r\nslowing the lower cell overturning circulation (Bronselaer et al., 2018; \r\nGolledge et al., 2019; Lago and England, 2019; Moorman et al., 2020). \r\nWhile such changes are consistent with the observed freshening \r\nand decreased volume of the AABW layer reported in SROCC \r\n(as discussed in Section 9.2.2"


id: 189245a66fea5d8f60abef76bb797c09
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 1359
content:
"3), new observation-based studies have \r\nhighlighted how the lower cell overturning can episodically increase \r\nas a  response to climate anomalies, temporally counteracting the \r\ntendency for melt to reduce AABW formation (Abrahamsen et  al., \r\n2019; Castagno et al., 2019; Gordon et al., 2020; Silvano et al., 2020). \r\nIn addition, while the opening of open ocean polynyas can affect \r\nthe lower cell on decadal to centennial time scales, there is limited \r\nevidence and low agreement in the role of open ocean polynyas \r\nin driving observed trends of the lower cell in the last decade \r\n(Section 9.2.2.3). Based on CMIP5 models, SROCC reported with low \r\nconfidence that formation and export of AABW associated with the \r\nlower overturning cell will decrease in the 21st century, and there \r\nis no new evidence to revisit that assessment from climate models"


id: d92bfb6ba4c673bd921937927dbb255f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 31675
content:
" \r\nHowever, additional paleo evidence from marine sediments suggests \r\nthat AABW formation/ventilation was vulnerable to freshwater \r\nfluxes during past interglacials (Hayes et  al., 2014; Huang et  al., \r\n2020; Turney et  al., 2020) and that AABW formation was strongly \r\nreduced (Skinner et al., 2010; Gottschalk et al., 2016; Jaccard et al., \r\n2016) or possibly totally curtailed (Huang et  al., 2020) during the \r\nLast Glacial Maximum (LGM) and transient cold intervals of marine "


id: 8f9652d2e14c13056fa5f4499152af
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20139
content:
"isotope stages 2 and 3 (MIS2 and MIS3). Specifically, sedimentary \r\nreconstructions show a  transient reduction in AABW ventilation \r\nin the Atlantic sector of the Southern Ocean during MIS5e, which \r\nis assessed to have been warmer than modern climate (Thomas \r\net  al., 2020). However, long multi-centennial or millennial model \r\nruns under higher-than-pre-industrial CO2 concentrations show that, \r\nafter 500-1000  years, ventilation in the Southern Ocean resumes, \r\nand possibly overshoots with enhanced convection in the Weddell \r\nand Ross seas, leading to enhanced bottom water ventilation globally \r\n(Yamamoto et  al., 2015; Frolicher et  al., 2020). AABW ventilation \r\nincreased at the onset of the last deglacial transition, promoting \r\nthe release of previously sequestered CO2 to the atmosphere on \r\ncentennial to millennial time scales (Bauska et al., 2016; Jaccard et al., \r\n2016; Rae et al"


id: 2d986be82dde857b65bf380f178ca42d
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 4614
content:
", 2018), concomitant with a southward shift of the \r\nSouthern Hemisphere westerly wind belt (Denton et al., 2010; Jaccard \r\net al., 2016) and reduced sea ice cover (Ferrari et al., 2014; Stein et al., \r\n2020). In summary, the combination of observational, numerical and \r\npaleoclimate evidence provides us with medium confidence that the \r\nlower cell will continue decreasing in the 21st century as a result of \r\nincreased basal melt from the Antarctic Ice Sheet."


id: 15180dd4a112b0975444f46e34277ca0
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 826
content:
"The AR5 (Rhein et al., 2013) assessed with medium to high confidence\r\nthat the North Pacific subpolar gyre, the South Pacific subtropical gyre, \r\nand the subtropical cells have intensified. They also reported that the \r\nNorth Pacific subtropical gyre had expanded since the 1990s, and \r\nthat, overall, the changes in gyre systems were likely predominantly \r\ndue to interannual-to-decadal variability. The SROCC (Meredith et al., \r\n2019) complemented the AR5 assessment by reporting that the polar \r\nBeaufort Gyre in the Arctic expanded to the north-west between \r\n2003 and 2014, contemporaneous with changes in its freshwater \r\naccumulation and alterations in wind forcing. Consistent with the \r\nreported change over the gyres, both AR5 and SROCC (Bindoff et al., \r\n2019; Collins et al., 2019) reported that western boundary currents \r\n(WBCs) have intensified (Figure  9.11), and expanded poleward, \r\nexcept for the Gulf Stream and the Kuroshio. Section  2.3.3"


id: 15ac86aca823fe308c10b2f2e1accc3c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 927
content:
"4 \r\nprovides an overall assessment of gyres and WBCs, including an \r\nassessment of change from paleoclimate archives. Section  2.3.3.4 \r\nassesses that, while WBC strength is highly variable at multi-decadal \r\nscale (high confidence), WBCs and subtropical gyres have shifted \r\npoleward since 1993 (medium confidence), at a rate on the order of \r\n0.04-0.1 degree per decade during 1993-2018. Figure 9.11 shows \r\nthat CMIP5 and CMIP6 models agree in projecting a  weaker Gulf \r\nStream and Gulf Stream Extension, while the Kuroshio changes less \r\n(Sen Gupta et al., 2016)."


id: f4f0d210c2ec478134489c4472c8ee10
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 36046
content:
"The effect of tropical modes of variability on climate and their \r\nlong-term changes are reviewed in detail in Annex IV, while changes \r\nto the tropical ocean are assessed throughout the report and \r\nbriefly summarized here. Section  2.4 concludes that a  sustained \r\nshift beyond multi-centennial variability has not been observed \r\nfor El  Nino-Southern Oscillation (ENSO) (medium confidence) and \r\nthat there is limited evidence and limited agreement about the \r\nlong-term behaviour of other tropical modes. Section  3.7 assesses \r\nwith high confidence that human influence has not affected the \r\nprincipal tropical modes of interannual climate variability and their \r\nassociated regional teleconnections beyond the range of internal \r\nvariability. Section  4.3.3.2 assesses with medium confidence\r\nthat there is no consensus from models for a  systematic change \r\nin the amplitude of ENSO sea surface temperature variability over \r\nthe 21st century"


id: 257879e8910be68f8d24e4f9590b1168
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 3319
content:
" The related change in tropical SSTs is covered in \r\nSection 9.2.1.1. The projected changes in SST have implications for \r\nmarine heat wave characteristics, which are assessed in Box  9.2. \r\nSST changes in the tropics are related to changes in the atmospheric \r\ncirculation, including surface equatorial easterly trade winds and \r\nWalker Circulation (Section 4.5.3.2), and the weakening Indonesian \r\nThroughflow and strengthening Agulhas Extension and leakage \r\n(Section  9.2.3.4). Weakening trade winds under climate change \r\n(Vecchi and Soden, 2007) will tend to decrease upwelling, along \r\nisopycnals in the eastern Pacific and diapycnal upwelling in  the "


id: 2d48bf1baa989778e4f829119c43c3e6
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 4567
content:
"Although the observed wind stress curl shows systematic \r\npoleward shift in each basin as a result of anthropogenic warming \r\n(Section 2.3.1.4; Chen and Wu, 2012; Wu et al., 2012; Zhai et al., 2014), \r\nwhich has caused a  systematic shift of the WBCs and subtropical \r\ngyres since 1993 (Wu et  al., 2012; Yang et  al., 2016, 2020), the \r\nresponse of current strength is more complex and inconsistent across \r\nregions (Sloyan and O'Kane, 2015; Y.-L. Wang et al., 2016; Elipot and \r\nBeal, 2018; McCarthy et al., 2018; Wang and Wu, 2018; Dong et al., \r\n2019). The strength of WBCs and gyres exhibit inconsistent responses \r\nbecause they are dependent on wind stress forcing and because \r\nmulti-scale interaction and air-sea interaction have an important \r\nrole in their long-term trends and variability (Zhang et  al., 2020)"


id: f3389fa2948176f1aa1603df69d1aa6e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 35777
content:
" \r\nObserved changes in gyre circulation are dominated by interannual \r\nand decadal modes of variability globally (Qiu and Chen, 2012; Melzer \r\nand Subrahmanyam, 2017; McCarthy et al., 2018; Hu et al., 2020). \r\nThe North Atlantic subpolar gyre is strongly modulated by variability \r\nassociated with the NAO and AMV (Annex IV; Robson et al., 2016). "


id: b41a3bec22d671b04fda7b6b678841e0
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 25853
content:
"Subpolar gyre systems can change abruptly due to a  positive \r\nfeedback between convective mixing and salinity transport (Born \r\net  al., 2013, 2016) and air-sea interaction (Moffa-Sanchez et  al., \r\n2014; Moreno-Chamarro et al., 2017) within the gyre. In the Arctic, \r\nboth the Beaufort gyre and mesoscale eddies strengthened between \r\n2003 and 2014 (Armitage et al., 2017), which might be partly due \r\nto increased wind stress (Oldenburg et al., 2018) or reduced sea ice \r\nthickness and changes in sea ice pack morphology (van der Linden \r\net  al., 2019). Presently, there is limited evidence in attributing \r\ncausality to these changes for any of the proposed mechanisms. \r\nIn the North Pacific, there has been an increasing trend in the Alaska \r\nGyre from 1993 to 2017 (Cummins and Masson, 2018), which might \r\nbe attributed to Pacific Decadal Oscillation (low confidence) (Hristova \r\net al., 2019)"


id: ef993a61ab72f0d1db0308b39f8f8bec
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 35196
content:
" In the Southern Ocean, limited evidence indicates that \r\nthe subpolar gyres respond to Southern Hemisphere atmospheric \r\nmodes of variability at interannual time scale (Armitage et al., 2018; \r\nDotto et al., 2018)."


id: 368927d11e8fb8e242025885d60fe134
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 5970
content:
"mesoscale eddies, and also improves simulation of the strength and \r\nposition of WBCs such as the Kuroshio Current, Gulf Stream, and \r\nEast Australian Current (very high confidence) (Sasaki et al., 2004; \r\nChassignet and Marshall, 2008; Delworth et al., 2012; Yu et al., 2012; \r\nSmall et  al., 2014; Haarsma et  al., 2016; Chassignet et  al., 2017, \r\n2020; Hewitt et  al., 2020). Improved boundary current location \r\nrelates to improved recirculation regions (Jayne et al., 2009), mean \r\npath and variability, and existence of multiple stable paths (Qiu \r\net al., 2005; Delman et al., 2015), air-sea fluxes (Small et al., 2014), \r\nand related coastal weather patterns (Kaspi and Schneider, 2011). \r\nThe wind-current feedback, implemented by considering relative \r\nvelocity of currents and wind, realistically dampens mesoscale \r\neddies and WBCs, through mesoscale air-sea interaction (Ma et al., \r\n2016; Renault et al"


id: 148e219cfd987489e1744305511def83
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 742
content:
", 2016, 2019), even though sub-mesoscale wind\u0002current damping feedback is missing in these models (medium \r\nconfidence) (Z. Zhang et al., 2016). As eddies potentially play a role \r\nin determining the strength of gyre circulations and their low\u0002frequency variability (Fox-Kemper and Pedlosky, 2004; Berloff et al., \r\n2007), it is expected that eddy-present and eddy-rich models will \r\ndiffer in their decadal variability and sensitivity to changes in the \r\nwind stress of gyres from eddy-parameterized models (medium \r\nconfidence). Nonetheless, important aspects of gyre strength \r\ndepend primarily on forcing and not resolution, allowing long-term \r\nchanges in gyre strength to be investigated with low-resolution \r\nclimate models (Hughes and de Cuevas, 2001; Yeager, 2015)."


id: 34661955e1a123a8bf9ecf7aff5a7a5d
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 5662
content:
"Eastern boundary upwelling systems (EBUS) exist where trade winds \r\ndraw cold and generally low-pH/low-oxygen waters upward. Coastal \r\nupwelling plays a key role in supplying the food chain with nutrients, \r\nhence the richness and productivity of EBUS (Bindoff et al., 2019). \r\nThe SROCC (Bindoff et al., 2019) assessed with high confidence that \r\nthree out of the four major EBUS have experienced large-scale wind \r\nintensification in the past 60 years (only the trend for the Canary \r\nCurrent is considered uncertain). However, it also emphasized that \r\nvarious processes can also modulate, or even reverse, wind trends \r\nlocally (Bindoff et  al., 2019). Here we revisit SROCC assessment \r\n(Bindoff et  al., 2019) based on evidence showing low agreement\r\nbetween studies that have investigated trends over past decadess of \r\nupwelling-favourable winds (Varela et al., 2015)"


id: e0a68b94e7eaf559bf94f2628fd1f146
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 32850
content:
" This low agreement\r\nhas been related to differences in wind products, season of interest, \r\nand length of the considered time series (Varela et al., 2015). Based \r\non this, we assess that only the California Current system has \r\nexperienced large-scale upwelling-favorable wind intensification over \r\nthe period 1982-2010, albeit with regional differences (Garcia-Reyes \r\nand Largier, 2010; Seo et  al., 2012). In the Benguela, Canary, and \r\nHumboldt systems, large-scale, upwelling-favourable wind trends \r\nare ambiguous, owing to low confidence in long-term in situ \r\nmarine wind data (Cardone et al., 1990; Bakun et al., 2010) and low \r\nagreement among available studies (Narayan et al., 2010; Sydeman \r\net  al., 2014; Varela et  al., 2015). Our assessment confirms SROCC \r\nassessment (Bindoff et al"


id: 9ab9c4754074296400a793357dd3b0d8
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 21875
content:
", 2019) in that high natural variability of \r\nEBUS and their inadequate representation by most climate models \r\ngives low confidence in attribution of observed changes, while \r\nanthropogenic changes are projected to emerge primarily in the \r\nsecond half of the 21st century (limited evidence: one model and one \r\nstudy) (Brady et al., 2017)."


id: 8e7ed181569c872dd7ee485820be8edc
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 19940
content:
"The SROCC (Collins et al., 2019) concluded with high confidence that \r\nIndonesian Throughflow (ITF) transport from the Pacific Ocean to \r\nthe Indian Ocean has increased in the past two decades as a result \r\n(medium confidence) of an unprecedented intensification of the \r\nequatorial Pacific trade wind system. Section 2.3.3.4 assesses that \r\nthere is high confidence that the increase in the ITF over the past \r\ntwo decades is linked to multi-decadal scale variability rather than \r\na  longer-term trend. Consistently, in the future, as winds change \r\nunder increased radiative forcing, most models project a decline of \r\nthe ITF on the centennial time scale (Figure 9.11). One of the clearest \r\nchanges of ocean current transport simulated by climate models is \r\na  weakening of the Indonesian Throughflow, projected in CMIP5 \r\nsimulations under RCP4.5 and RCP8.5 scenarios (Sen Gupta et al., \r\n2016; Stellema et  al., 2019), and in CMIP6 simulations under the \r\nSSP5-8"


id: b6460bde0dabac62ff7711338be7c2d3
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 26202
content:
"The SROCC reports with high confidence that the Agulhas leakage \r\nfrom the Indian to the Atlantic Ocean has increased in the past two \r\ndecades (Collins et  al., 2019), and there is no additional evidence \r\nsince then allowing this assessment to be revisited (Biastoch et al., \r\n2015; Loveday et  al., 2015; Lubbecke et  al., 2015). There is low \r\nconfidence in future projections of Agulhas leakage because most \r\nCMIP models cannot directly simulate it, due to coarse resolution. \r\nHowever, there is medium evidence that the strength of the Southern \r\nHemisphere westerlies controls Agulhas leakage  (Durgadoo et  al., \r\n2013; Biastoch et al., 2015; Loveday et al., 2015), and high confidence\r\nthat the strength of the Southern Hemisphere westerlies will increase \r\nunder increased radiative forcing, except in lower warming scenarios \r\n(SSP1-1.9, SSP1.2-6; Section 4.3.3.1; Bracegirdle et al., 2020)"


id: 6979a2a92648df242cc967cb0774ccf1
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 14190
content:
" There \r\nis also evidence that increasing Agulhas leakage is consistent with \r\nobserved change of the temperature and salinity structure in the \r\nAtlantic ocean, and with variability of the AMOC (Section  9.2.3.1; \r\nBiastoch et al., 2015). This range of indirect evidence provides medium \r\nconfidence that the Agulhas leakage will increase in the 21st century, \r\nexcept for the strongest mitigation scenario (Figure 9.11)."


id: 90d1423cc819ffa4a23ffd3f15cd861c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20327
content:
"challenged. Changes in sea level pressure and wind fields in EBUS \r\nappear to be primarily tied  to those affecting subtropical highs \r\n(Garcia-Reyes et al., 2013). Poleward expansion of the Hadley cell \r\n(Section  2.3.1.4.1;  Staten et  al., 2018) and the related poleward \r\nmigration of subtropical highs (He et al., 2017; Cherchi et al., 2018), \r\nproduce robust patterns of changes of reduced upwelling at low \r\nlatitude and enhanced upwelling at high latitude (Echevin et  al., \r\n2012; Belmadani et al., 2014; Bettencourt et al., 2015; Rykaczewski \r\net al., 2015; Sousa et al., 2017; Lamont et al., 2018; Sylla et al., 2019). \r\nThese patterns are most apparent in summer in both hemispheres. \r\nSynoptic variability of upwelling winds, important to the functioning \r\nof upwelling ecosystems (Garcia-Reyes et  al., 2014), may also be \r\naffected by climate change (Aguirre et al., 2019)"


id: 7cc3780cd88cfa35b7668c5aa70237de
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 17227
content:
" However, coarse \r\nresolution model projections of winds in upwelling regions may be \r\nmore consistent than higher-resolution projections, as these regions \r\nare highly sensitive to resolution (Small et al., 2015)."


id: 509f2d7ce95422f0619078f9c794f727
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 10130
content:
"circulations and mixing, leading to indirect connections between \r\nthe inner shelves and coastlines and offshore conditions. Coastal \r\nprocesses link to large-scale metrics of climate and regional effects, \r\nfrom changing rivers and estuaries, melt and runoff to deep water, \r\nto how changes offshore affect regional and coastal conditions.\r\nShelf-deep ocean exchanges involve eddying, tidal, or turbulent \r\nmotions and small-scale topography such as submarine canyons; \r\nhigh-resolution observations and models are needed to capture \r\nthese effects (Greenberg et al., 2007; Capet et al., 2008; Allen and \r\nDurrieu de Madron, 2009; Colas et  al., 2012; Trotta et  al., 2017). \r\nExample coastal processes that introduce uncertainty into large-scale \r\nprojections are exchange of CDW across the Antarctic shelf-break, \r\nwhich affects AABW formation and Antarctic ice-shelf-ocean \r\ninteraction (Sections  9.2.2.3 and 9.2.3"


id: fd6b1aefa6d7ca5f0012c47f705760d2
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 37384
content:
"2; Stewart and Thompson, \r\n2013, 2015), river and estuarine plumes and their responses to water \r\nlevel and hydrology change (Banas et al., 2009; Sun et al., 2017), \r\nfjord dynamics linked to glacial outflows (Straneo and Cenedese, \r\n2015; Torsvik et al., 2019), and changing formation of water masses \r\nin marginal seas (Kim et al., 2001; Greene and Pershing, 2007; Giorgi \r\nand Lionello, 2008; Renner et  al., 2009). Downscaling projections \r\nto the local level allows process detail (Foreman et  al., 2014; \r\nMathis and Pohlmann, 2014; Meier, 2015; Tinker et al., 2016). Some \r\nprocesses can only be simulated when coastal models are forced by \r\nlarger-scale models of the atmosphere, cryosphere, or hydrosphere \r\n(Seo et al., 2007, 2008; Somot et al., 2008; Oerder et al., 2015; Renault \r\net al., 2016; Y. Zhang et al., 2016; Wahlin et al., 2020), including the \r\naddition of tides (Janekovic and Powell, 2012; Timko et  al., 2013; \r\nTinker et al., 2015; Pickering et al., 2017; Hausmann et al"


id: 9aa883c77841e6737dfede8142680ec4
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 21861
content:
", 2020). \r\nDue to coastal process complexity and small scale, linking the \r\neffects of coastal ocean changes to global ocean changes requires \r\nhigh-resolution modelling (Holt et al., 2017, 2018), two-way nesting, \r\nor local mesh refinement (Fringer et al., 2006; Zhang and Baptista, \r\n2008; Mason et al., 2010; Dietrich et al., 2012; Hellmer et al., 2012; \r\nRingler et al., 2013; Q. Wang et al., 2014; Zangl et al., 2015; Y.J. Zhang \r\net al., 2016; Soto-Navarro et al., 2020). Coarse climate models and \r\nHighResMIP models do not represent some coastal phenomena such \r\nas cross-shelf exchanges and sub-mesoscale eddies, which require \r\n1 km or finer resolution. Thus, there is low confidence in projecting \r\ncentennial scale coastal climate change where regional downscaling \r\nor refinement is lacking. There is high confidence in the ability of \r\nregional coupled models to improve coastal climate change process \r\nunderstanding and provide regional information (Section 12"


id: fd98b4a4a5f58b9008be9a5601c714f0
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 37416
content:
"Projected future annual cumulative upwelling wind changes at most \r\nlocations, and seasons remain within  +-10-20% of present-day \r\nvalues in the 21st  century, even in the context of high-end \r\nemissions scenarios (4xCO2 or RCP8.5)  (medium confidence). \r\nChanges due to wind stress curl and alongshore pressure gradients \r\ntend to agree with alongshore wind changes (Oerder et al., 2015; \r\nSylla et  al., 2019). Direct  estimation of oceanic upward transport \r\n(Oyarzun and Brierley, 2019; Sylla et  al., 2019) and nutrient flux \r\ninto the euphotic layer (Jacox et  al., 2018) provide a  meaningful \r\nestimator of upwelling, integrating all relevant processes, including \r\nchanges in wind stress curl. However,  there is limited evidence\r\nfrom vertical velocity of climate models and missing processes in \r\ncoarse-resolution climate models that presently limit this approach. \r\nChange in upper-ocean stratification (Section 9.2.1"


id: 6639356b76b1fe8c2cf00ed6ba58c7c7
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 13674
content:
"3) is projected \r\nto increase confinement of upwelling vertical velocities to near the \r\nocean surface (high confidence) (Oerder et al., 2015; Oyarzun and \r\nBrierley, 2019)."


id: 6f0c773751599227d498a8a82acef4b1
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 15095
content:
"Ocean dynamic sea level change is strongly affected by internal \r\nvariability (Section 9.6.1.4), partly from interannual to decadal coupled \r\natmosphere-ocean modes of variability via wind-driven redistribution \r\n(Annex IV; Griffies et  al., 2014; Han et  al., 2017) and partly from \r\nintrinsic ocean variability, particularly in higher-resolution simulations \r\n(such as HighResMIP), which statistically resemble observations, even \r\non short time scales (Figure 9.12; Griffies et al., 2014; Serazin et al., \r\n2016; Llovel et  al., 2018; Chassignet et  al., 2020). High-resolution \r\nsimulations are not used in relative sea level projections (Section 9.6.3) \r\ndue to the limited range of forcing scenarios. The most marked feature \r\nof long-term regional sea level change in the continuous satellite \r\naltimetry record, beginning in 1992, is the east-west dipole in the \r\nPacific Ocean (rising more rapidly in the east, see also Section 9.6.1"


id: 75684281d4ae4eb4639a9f0f46cdcf25
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 16057
content:
"3), \r\nwhich persisted until 2015, and can be explained by anomalously \r\nstrong trade winds (Merrifield et al., 2012; England et al., 2014; Griffies \r\net al., 2014; Takahashi and Watanabe, 2016; Han et al., 2017) together \r\nwith associated changes in surface heat flux (Piecuch et al., 2019). \r\nThe most notable features of sub-annual variability in altimetry are \r\neddies and tides, which are directly simulated only in high-resolution \r\nmodels (Haigh et al., 2019; Chassignet et al., 2020)."


id: 54cfd0c35299417c396b54170294d3b0
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 10796
content:
"Projections of ocean dynamic sea level change (Box  9.1) on \r\nmulti-annual time scales resemble the patterns of steric sea level \r\nchange in the open ocean (Figures 9.11 and 9.12; Lowe and Gregory, \r\n2006; Pardaens et al., 2011; Couldrey et al., 2021). On shorter time \r\nscales, especially in extratropical coastal areas, there may be an \r\nimportant barotropic component (also called bottom pressure change) \r\ndue mostly to changes in wind-driven circulation and eddies apparent \r\nin the variance of ocean dynamic sea level (Figure 9.12; Roberts et al., \r\n2016; Hughes et al., 2018). This component is highly sensitive to ocean \r\nmodel resolution (Chassignet et al., 2020). Steric sea level change is \r\nassociated with local changes in temperature and salinity, which \r\ncome about through changes in surface fluxes of heat and freshwater \r\n(Section 9.2.1.2) and through redistribution of existing water masses \r\nby changed ocean circulation and mixing processes (Figure  9.12\r\nand Sections 9.2.2"


id: b4b79143efae73ef838b6bf21c7207d6
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 25962
content:
"1 and 9.2.3). Redistribution of water masses \r\noften involves anticorrelated thermosteric and halosteric changes \r\n(Figure 9.12), especially in the Atlantic (Pardaens et al., 2011; Bouttes \r\net al., 2014; Durack et al., 2014; Griffies et al., 2014; Han et al., 2017)."


id: 7530cb0d7e860c441c8478f31acdd826
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 16033
content:
"Figure 9.12 | (a-f) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean projected change contributions to relative sea level change \r\nin (a, d) steric sea level anomaly, (b, e) thermosteric sea level anomaly, and (c, f) halosteric sea level anomaly between 1995-2014 and 2081-2100 using \r\na method that does not require a reference level (Landerer et al., 2007). Global mean change has been removed from these figures, consistent with the methods \r\nin Sections 9.6.3 and 9.SM.4 and the definitions of Gregory et al. (2019). (Gregory et al., 2019). See Figure 9.27 for global mean sea level (GMSL). (g-i) Standard deviation \r\nof ocean dynamic sea level change from (g) Aviso observations (10-day high-pass filter); (h) five-day mean of high-resolution Ocean Model Intercomparison Project phase 2 \r\n(OMIP-2) models forced with observed fluxes; and (i) five-day mean of low-resolution OMIP-2 models which are comparable in resolution to the models in (a-f)"


id: 32fe47b40a39652cc7717e4d4fad8ef9
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 5447
content:
" No overlay \r\nindicates regions with high model agreement, where >=80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% \r\nof models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data \r\ntable (Table 9.SM.9)."


id: 8ab4d52edf75dedf2835a7ce061189d3
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 19363
content:
"Projections of the pattern and amplitude of regional ocean dynamic \r\nsea level change in CMIP6 and previous model generations show \r\na large model spread, of a similar size to the geographical spread \r\n(Figure  9.12). The model spread derives from model dependence \r\nof changes both in surface fluxes (Section  9.2.1.2) and in the \r\nocean response (Section 9.2.2). The spread is similar in CMIP6 and \r\nCMIP5, and is largest in regions with large projected variations in \r\nensemble-mean ocean dynamic sea level change (Lyu et al., 2020a), \r\nsuch as the Southern Ocean Dipole with an ocean dynamic sea level \r\nrise north of the ACC and a  fall to the south, the Atlantic Dipole \r\nwith a  sea level rise north of 40degN and a  fall in 20degN-40degN, the \r\nNorthwest Pacific Dipole, and the large sea level rise in the Arctic \r\n(Church et  al.,  2013b; Slangen et  al., 2014a, 2015; Bilbao et  al., \r\n2015; Gregory et al., 2016; C. Chen et al., 2019; Lyu et al., 2020a; \r\nCouldrey et  al., 2021)"


id: 37b3bfe8cac073b239ab8f49dba7ff45
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 6138
content:
" Patterns of change are consistent between \r\nmodel simulations and observations (medium confidence). The major \r\nmodel ensemble-mean features resemble thermosteric sea level \r\nchange, as expected from altered input of heat to the ocean \r\nwithout changing circulation, while model spread results from the \r\ndiversity in redistribution of the heat content of the unperturbed \r\nocean (Section 9.2.2.1; Bouttes and Gregory, 2014; Gregory et  al., \r\n2016; Huber and Zanna, 2017; Lyu et al., 2020b; Todd et al., 2020; \r\nCouldrey et al., 2021)."


id: 47bd83703f476d94c0beb173784b000e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 8666
content:
"The observed decrease of Arctic sea ice area is a  key indicator of \r\nlarge-scale climate change (Section 2.3.2.1.1, Cross-Chapter Box 2.2). \r\nThe SROCC (Meredith et al., 2019) assesses that sea ice extent, which \r\nis the total area of all grid cells with at least 15% sea ice concentration, \r\nhas declined since 1979 in each month of the year (very high \r\nconfidence). In contrast to SROCC, we assess changes in sea ice area \r\n(the actual area of the ocean covered by sea ice) rather than sea ice \r\nextent, because sea ice area is geophysically more relevant and not \r\ngrid-dependent (Notz, 2014; Ivanova et al., 2016; Notz et al., 2016; \r\nNotz and SIMIP Community, 2020). Arctic sea ice area is calculated \r\nbased on measurements by passive microwave satellite sensors that \r\nprovide near-continuous measurements of gridded, pan-Arctic sea \r\nice concentration from 1979 onwards"


id: ace55861e0072e04103309fdef83d845
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 24752
content:
" Irreducible uncertainties in the \r\nconversion of thermal microwave brightness temperature to sea ice \r\nconcentration, and choices in algorithm design, cause uncertainties in \r\nobserved Arctic sea ice area, which are far smaller than the observed \r\nsea ice loss (e.g., Comiso et al., 2017a; Niederdrenk and Notz, 2018; \r\nAlekseeva et al., 2019; Kern et al., 2019; Meier and Stewart, 2019). \r\nSea ice area has decreased in every month of the year from 1979 to \r\nthe present (very high confidence) (Figure  9.13). The absolute and \r\nthe relative ice losses are highest in late summer-early autumn (high \r\nconfidence) (Figure 9.13). Averaged over the decade 2010-2019, the \r\nmonthly Arctic sea ice area from August to October has been around \r\n2 million km2 (or about 25%) smaller than during 1979-1988 (high \r\nconfidence) (Figure 9.13)."


id: a07b19306750acf91918b44192f5f871
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 22786
content:
"The North Atlantic sea level change dipole is forced by a reduction \r\nin heat loss from the ocean north of 40degN (i.e., net heat uptake), \r\nwhich in all Earth system models leads to a weakening of the AMOC, \r\nalthough the magnitude has a large model spread (Section 9.2.3.1; \r\nGregory et al., 2016; Huber and Zanna, 2017). The reduced northward \r\ntransport of warm, salty water (Section 9.2.2) causes further ocean \r\ndynamic sea level change, whose details are model-dependent. North \r\nof 40degN, this redistribution leads to a sea level rise, predominantly \r\nhalosteric, reinforcing the thermosteric effect of heat uptake (Couldrey \r\net al., 2021). Comparison of observed Atlantic OHC for 1955-2017 \r\nwith a  reconstruction assuming no change in circulation indicates \r\nthat the thermosteric sea level change resulting from southward \r\nredistribution of heat may be detectable (Zanna et  al., 2019)"


id: 8f973ebdfc1563bb5b395818d3998df2
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20140
content:
" This \r\nredistribution causes a tendency for SST cooling north of 40degN and \r\nanomalous heat input from the atmosphere, and thus a  positive \r\nfeedback on AMOC weakening (Winton et al., 2013; Gregory et al., \r\n2016; Todd et  al., 2020; Couldrey et  al., 2021). Many climate and \r\nocean models agree that the AMOC weakening is associated with "


id: f293516106e9660ea3ecf443c4e1774e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 35694
content:
"reconstructions  (SSIA,Sep = 0.2 million km2) (medium confidence\r\nbecause of limited reliability of longer-term sea ice reconstructions) \r\n(Brennan et  al., 2020). Internal variability has been estimated to \r\nhave contributed 30 to 50% of the observed Arctic summer sea ice \r\nloss since 1979 (Kay et al., 2011; Stroeve et al., 2012; Ding et al., \r\n2017, 2019; England et  al., 2019). However, this estimate from \r\nmodels might be biased towards internal over forced variability \r\nbecause of the models' high internal variability and because the \r\nCMIP5 simulated September sea ice sensitivity to forcing is lower \r\nthan observed, even if internal variability is taken into account (Notz \r\nand Stroeve, 2016; Rosenblum and Eisenman, 2017). Most CMIP6 \r\nmodels fail to simulate the observed sensitivity of sea ice loss to CO2\r\nemissions (as a proxy for time) and to temperature simultaneously"


id: e3d4d7a9be0a63684731107fcbae6346
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 33322
content:
" \r\nHowever, they better capture the observed sensitivity of sea ice loss \r\nto CO2 emissions than CMIP5 models (Section 3.4.1; Figure 9.14h; \r\nNotz and SIMIP Community, 2020). "


id: 27733b32a031ff8b6f502734cc3cc62c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 3610
content:
"The SROCC assessed that approximately half of the satellite-observed \r\nArctic summer sea ice loss is driven by increased concentrations \r\nof atmospheric greenhouse gases (medium confidence). Recent \r\nattribution studies now allow the strengthened assessment that it \r\nis very likely that more than half of the observed Arctic sea ice loss \r\nin summer is anthropogenic (Section  3.4.1.1). This assessment is \r\nconfirmed by process-based analyses of Arctic sea ice loss not assessed \r\nby SROCC. Similar to the paleorecord, the satellite record of Arctic \r\nsea ice area from 1979 onwards is strongly and linearly correlated \r\nwith global mean temperature on decadal and longer time scales \r\n(Figures 9.14a,e) (e.g., Gregory et al., 2002; Rosenblum and Eisenman, \r\n2017). The correlation holds across all months with R2 ranging from \r\n0.61 to 0.81 (Niederdrenk and Notz, 2018)"


id: e3af3a45fe45e4aee888d631f20f7a7e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 33303
content:
" However, in contrast \r\nto paleorecords, sea ice fluctuations during the satellite period are \r\nonly weakly correlated with Northern Hemisphere insolation (Notz \r\nand Marotzke, 2012); modern Northern Hemisphere sea ice area \r\nis more strongly correlated with atmospheric carbon dioxide (CO2) \r\nconcentration (Johannessen, 2008; Notz and Marotzke, 2012) and \r\ncumulative anthropogenic CO2 emissions (Figures 9.14b,f; Zickfeld \r\net al., 2012; Herrington and Zickfeld, 2014; Notz and Stroeve, 2016). \r\nThe R2 values of the correlation between sea ice area and cumulative \r\nCO2 emissions range across all months from 0.76 to 0.92 (Stroeve \r\nand Notz, 2018). In summary, there is high confidence that satellite\u0002observed Arctic sea ice area is strongly correlated with global mean \r\ntemperature, CO2 concentration and cumulative anthropogenic \r\nCO2 emissions."


id: e2e8dc0c4bee9111ae3ef747d9da906a
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 33199
content:
"In examining temperature thresholds for the loss of Arctic summer \r\nsea ice, the Special Report on Global Warming of 1.5degC (SR1.5; \r\nHoegh-Guldberg et  al., 2018) and SROCC assess that a  reduction \r\nof September mean sea ice area to below 1 million km2, practically \r\na  sea ice-free Arctic Ocean, is more probable for a  global mean \r\nwarming of 2degC compared to global mean warming of 1.5degC \r\n(high confidence). Analyses of CMIP6 simulations (Notz and SIMIP \r\nCommunity, 2020) confirm this result, as they show that, on decadal \r\nand longer time scales, Arctic summer sea ice area will remain highly \r\ncorrelated with global mean temperature until the summer sea \r\nice has vanished (Figure  9.14a,e). Quantitatively, existing studies \r\n(Screen and Williamson, 2017; Jahn, 2018; Ridley and Blockley, 2018; \r\nSigmond et al., 2018; Notz and SIMIP Community, 2020) also show \r\nthat, for a  warming between 1"


id: 7066a62e9403bbf5bec02465a218359
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 15303
content:
"5 and 2degC, the Arctic will only be \r\npractically sea ice free in September in some years, while at 3degC \r\nwarming, the Arctic is practically sea ice free in September in most \r\nyears, with longer practically sea ice-free periods at higher warming \r\nlevels (medium confidence). However, because of the CMIP5 and \r\nCMIP6 models' generally too low sensitivity of sea ice loss to global \r\nwarming, there is only low confidence regarding the specific warming \r\nlevel at which the Arctic Ocean first becomes practically sea ice free \r\n(Section 4.3.2.1; Notz and SIMIP Community, 2020)."


id: dc6affe53b95de1f644951c5c8abadd5
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 32181
content:
"In addition to changes in the external forcing, internal variability \r\nsubstantially affects Arctic sea ice, evidenced from both paleorecords \r\n(e.g., Chan et al., 2017; Horner et al., 2017; Kolling et al., 2018) and \r\nsatellites after 1979 (e.g.,  Notz and Stroeve, 2018; Roberts et  al., \r\n2020) (high confidence). Most of the internal variability on annual \r\ntime scales is related to atmospheric temperature fluctuations, \r\nfor example linked to cyclone activities (Wernli and Papritz, 2018; \r\nOlonscheck et  al., 2019), while multi-decadal internal variability \r\nis primarily related to changes in oceanic heat transport (Zhang, \r\n2015; Halloran et  al., 2020). These mechanisms are represented \r\nin current climate models (Olonscheck et al., 2019; Halloran et al., \r\n2020), but the resulting internal variability of September sea \r\nice area in CMIP5 and CMIP6 models, as given by the ensemble \r\nmean standard deviation SSIA,Sep = 0"


id: 875c491808b1fea142dba83410302064
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 18840
content:
"5 million km2 (Olonscheck \r\nand Notz, 2017; Notz and SIMIP Community, 2020), exceeds the \r\nestimated internal variability for the period 1850 to 1979 from \r\nboth reanalyses (SSIA,Sep = 0.3 million km2) and direct observational "


id: cde4b53b05be8e58daa5cc4d2beb3c35
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 29914
content:
"There is an indication that CMIP6 simulations of Arctic sea ice \r\nhave improved relative to CMIP5 (Section  3.4.1.1), but detailed \r\nevaluation studies exist mainly for CMIP5 models. These studies \r\nfound that CMIP5 model projections and reanalyses show a  large \r\nspread of simulated regional Arctic sea ice concentration (Laliberte \r\net al., 2016; Chevallier et al., 2017), which remains true for CMIP6 \r\nmodels (Shu et al., 2020; Wei et al., 2020). In addition, both CMIP5 \r\nand CMIP6 models show a large spread in the simulated seasonal \r\ncycle of Arctic sea ice area, with too high a sea ice area in March in \r\nthe ensemble mean (Notz and SIMIP Community, 2020). The CMIP5 \r\nmodels have also had difficulty simulating realistic landfast sea ice \r\n(Laliberte et  al., 2018)"


id: 82343f2a400c197a1207900c3102903e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 18013
content:
" These findings imply that both CMIP5 and \r\nCMIP6 models do not realistically capture the regional and seasonal \r\nprocesses governing observed Arctic sea ice evolution, causing low \r\nconfidence in the models' projections of future regional sea ice \r\nevolution, including updated projections for shipping routes across \r\nthe Northern Sea Route and Northwest Passage (Wei et al., 2020)."


id: 604261efba848f09fb632a65605711b8
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 12719
content:
"The CMIP5 models also have issues with capturing the seasonal \r\ncycle of observed changes in Arctic sea ice drift speed, which affects \r\ntheir simulation of regional sea ice concentration patterns. Direct \r\nmeasurements of Arctic sea ice from drift buoys and satellites show \r\nthat drift speed of Arctic sea ice has increased over the satellite \r\nperiod in all seasons (e.g., Rampal et al., 2009; Docquier et al., 2017). \r\nIn summer, CMIP5 models show a slowdown of Arctic sea ice drift \r\nrather than the observed acceleration (Tandon et al., 2018). In winter, \r\nCMIP5 models generally capture the observed acceleration of Arctic \r\ndrift speed. The drift acceleration is primarily caused by the decrease \r\nin concentration and thickness in the observational record (Rampal \r\net al., 2009; Spreen et al., 2011; Olason and Notz, 2014; Docquier \r\net al., 2017) and, for winter, in CMIP5 models (Tandon et al., 2018)"


id: 5087282ad50d65a078867dbc140545f2
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 10114
content:
" \r\nChanges in wind speed are less important for the observed large-scale \r\nchanges (Spreen et al., 2011; Vihma et al., 2012; Olason and Notz, \r\n2014; Docquier et al., 2017; Tandon et al., 2018). In summary, there \r\nis high confidence that Arctic sea ice drift has accelerated because of \r\nthe decrease in sea ice concentration and thickness."


id: 4e78b6f9b72fe7fd3224dbbb90c85efb
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 9775
content:
"Observations of regional changes in sea ice thickness vary in quality. \r\nAnalysis of submarine data in the central Arctic Ocean suggests that \r\nits sea ice has thinned by about 75 cm compared to the mid-1970s \r\n(Section 2.3.2.1.1). For smaller regions, data are too sparse to allow \r\nfor quantitative estimates of long-term trends (King et  al., 2017; \r\nRosel et al., 2018), but a clear thinning signal over 10 to 20 years has \r\nbeen found for sea ice in the Fram Strait (Spreen et al., 2020), north of \r\nCanada (Haas et al., 2017) and for landfast ice in the Kongsfjorden/\r\nSvalbard Arctic border (Pavlova et al., 2019). The CMIP5 models and \r\nreanalyses fail to capture the observed distribution (Stroeve et al., \r\n2014; Shu et al., 2015) and evolution (Chevallier et al., 2017) of Arctic \r\nsea ice thickness. Most CMIP6 models do not capture the observed \r\nspatial distribution of sea ice thickness realistically (Wei et al., 2020)"


id: 57f5c88579485f45e0a0f1c6aab1208f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 11343
content:
" \r\nThis leads to low confidence in estimates of thickness from reanalyses \r\nand from CMIP5 and CMIP6 models and in their projections of sea \r\nice volume."


id: a47bf504db0ee9c33ef9c7f206197da8
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 23446
content:
"The SR1.5 assessed with high confidence that there is no hysteresis \r\nin the loss of Arctic summer sea ice. In addition, there is no tipping \r\npoint or critical threshold in global mean temperature beyond which \r\nthe loss of summer sea ice becomes self-accelerating and irreversible \r\n(high confidence). This is because stabilizing feedbacks during winter \r\nrelated to increased heat loss through thin ice and thin snow, and \r\nincreased emission of longwave radiation from open water, dominate \r\nover the amplifying ice albedo feedback (see Section 7.4.2 for details \r\non the  individual feedbacks; e.g.,  Eisenman, 2012; Wagner and \r\nEisenman, 2015; Notz and Stroeve, 2018). Observed and modelled \r\nArctic summer sea ice and global mean temperature are linked \r\nwith little temporal delay, and the summer sea ice loss is reversible \r\non decadal time scales (Armour et  al., 2011; Ridley et  al., 2012; \r\nLi et al., 2013; Jahn, 2018)"


id: b72ec8279b090bb81a70121198f61eee
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 26354
content:
" The loss of winter sea ice is reversible \r\nas well, but the loss of winter sea ice area per degree of warming \r\nin CMIP5 and CMIP6 projections increases as the ice retreats from \r\nthe continental shore lines, because these limit the possible areal \r\nfluctuations (high confidence) (Section 4.3.2.1; Bathiany et al., 2016, \r\n2020; Meccia et al., 2020)."


id: 6df50caf82f0b17a8bf62f8231a0f52f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 14913
content:
"The SROCC assessed that the regional trends are closely related \r\nto meridional wind trends (high confidence). This is the case as the \r\nregional trends in the maximum northward extent of the ice cover \r\n(Figure 9.15) are determined by the balance between the northward \r\nadvection of the ice that is formed in polynyas near the continental \r\nmargin, and the lateral and subsurface melting through oceanic heat \r\nfluxes. The advection of the sea ice is strongly correlated with winds \r\nand cyclones (Schemm, 2018; Vichi et al., 2019; Alberello et al., 2020). \r\nAccordingly, the increasing sea ice area in the Ross Sea can be linked \r\nto a  strengthening of the Amundsen Sea low (e.g.,  Holland et  al., \r\n2017b, 2018), while other regional sea ice trends in the austral autumn \r\ncan be linked to changes in westerly winds, cyclone activity and the \r\nSouthern Annular Mode (SAM) in summer and spring (Doddridge and \r\nMarshall, 2017; Holland et al., 2017a; Schemm, 2018)"


id: 97a9323734da9a5a50bdadd31cd64ca1
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 21387
content:
" In addition to \r\nthe wind-driven changes, increased near-surface ocean stratification \r\n(Section 9.2.1.3) has contributed to the observed increase in sea ice \r\ncoverage (e.g., Purich et al., 2018; L. Zhang et al., 2019) as it tends \r\nto cool the surface ocean (Sections 9.2.1.1 and 9.2.3.2). The changes \r\nin stratification result partly from surface freshening (De  Lavergne \r\net al., 2014), associated with increased northward sea ice advection \r\n(Haumann et al., 2020) and/or melting of the Antarctic ice sheet (medium \r\nconfidence) (e.g.,  Haumann et  al., 2020; Jeong et  al., 2020; Mackie \r\net al., 2020), and amplified by local ice-ocean feedbacks (Goosse and \r\nZunz, 2014; Lecomte et al., 2017; Goosse et al., 2018). In the Amundsen \r\nSea, strong ice shelf melting can cause local sea ice melt next to the \r\nice shelf front by entraining warm circumpolar deep water to the ice \r\nshelf cavity and surface ocean (medium confidence) (Sections 9.2.3.2 \r\nand 9.4.2.2; Jourdain et al"


id: 25b1744874289bdf3c6cf19472de4bd6
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 3353
content:
", 2017; Merino et al., 2018). It has also been \r\nsuggested that the observed regional increase in sea ice coverage since "


id: 6c0ecd58e7e1cbee91b5f8828448282f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 14630
content:
"Figure 9.15 | Antarctic sea ice historical records and Coupled Model Intercomparison Project Phase 6 (CMIP6) projections. (Left) Absolute anomaly of \r\nobserved monthly mean Antarctic sea ice area during the period 1979-2019 relative to the average monthly mean Antarctic sea ice area during the period 1979-2008. \r\n(Right) Sea ice coverage in the Antarctic as given by the average of the three most widely used satellite-based estimates for September and February, which usually are the \r\nmonths of maximum and minimum sea ice coverage, respectively. First column: Mean sea ice coverage during the decade 1979-1988. Second column: Mean sea ice coverage \r\nduring the decade 2010-2019. Third column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. Fourth \r\ncolumn: Number of available CMIP6 models that simulate a mean sea ice concentration above 15% for the decade 2045-2054"


id: 352b416c5bd24c1984bb0d8033e2ea27
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 5778
content:
" The average observational record of sea ice area \r\nis derived from the UHH sea ice area product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979-2015, OSI-430b for 2016-2019) \r\n(Lavergne et al., 2019), NASA Team (version 1, 1979-2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979-2019) (Comiso, 2017) that is also used for the figure panels \r\nshowing observed sea ice concentration. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)."


id: c2b10d8893d6d0091cf509a590b5e81f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 28215
content:
"Ice Sheet (Bronselaer et  al., 2018). Compared to the interannual \r\nvariability during the satellite record from 1979 onwards, models \r\nsimulate too much variability in both CMIP5 (Zunz et al., 2013) and \r\nCMIP6 (Roach et al., 2020). The seasonal cycle in sea ice coverage is \r\nmisrepresented in most CMIP5 (e.g., Holmes et al., 2019) and CMIP6 \r\nmodels (Roach et al., 2020), but the multi-model mean seasonal cycle \r\nin CMIP5 and CMIP6 agrees well with observations (Shu et al., 2015; \r\nRoach et al., 2020). Most CMIP5 models do not realistically simulate \r\nthe evolution of Antarctic sea ice volume (Shu et  al., 2015) and \r\nconsistently overestimate the amount of low concentration sea ice, \r\nand underestimate the amount of high concentration sea ice (Roach \r\net al., 2018a). In contrast, CMIP6 models simulate a more realistic \r\ndistribution of regional sea ice coverage (Roach et al., 2020). Most \r\nCMIP5 models poorly represent Antarctic sea ice drift (e.g., Schroeter \r\net  al"


id: 4e6f6dc5fd0882fb719f02061b9c9899
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 9769
content:
", 2018; Holmes et  al., 2019), affecting simulated historical \r\ntrends, with models that simulate a strong sea ice motion showing \r\nmore variability in sea ice coverage than models with weaker sea ice \r\nmotion (Schroeter et al., 2018). Owing to limited agreement between \r\nmodel simulations and observations, limited reliable observations on \r\na process level, and a lack of process understanding of the substantial \r\nspread in CMIP5 and CMIP6 model simulations, there remains low \r\nconfidence in existing future projections of Antarctic sea ice decrease \r\nand lack of decrease."


id: daef346ec336a6afedd4bfdd1cc90183
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 31964
content:
"The analysis and understanding of the long-term evolution of the \r\nAntarctic sea ice cover is hindered by the scarcity of observational \r\nrecords before the satellite period, and the scarcity of paleorecords \r\n(see Section  2.3.2.1.2 for further details). Such long records are \r\nparticularly relevant given that the Southern Ocean response \r\nto external forcing takes longer than the length of the available \r\ndirect observational record (Goosse and Renssen, 2001; Armour \r\net al., 2016). There is only limited evidence for large-scale decadal \r\nfluctuations in sea ice coverage caused by large-scale temperature \r\nand wind forcing. Sparse direct pre-satellite observations suggest \r\na decrease in sea ice coverage from the 1950s to the 1970s (Fan \r\net  al., 2014). Paleo-proxy data indicate that, on multi-decadal to \r\nmulti-centennial time scales, sea ice coverage of the Southern \r\nOcean follows large-scale temperature trends (e.g.,  Crosta et  al., \r\n2018; Chadwick et  al"


id: 1c171fdfa47effc1e1b95ad7aa251c39
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 1881
content:
", 2020; Lamping et  al., 2020), for example \r\nlinked to fluctuations in the El  Nino-Southern Oscillation and \r\nSouthern Annular Mode (Crosta et al., 2021), and that during the \r\nLast Glacial Maximum, Antarctic sea ice extended to about the \r\npolar front latitude in most regions during winter, whereas the \r\nextent during summer is less well understood (e.g.,  Benz et  al., \r\n2016; Xiao et al., 2016; Nair et al., 2019)."


id: 14bbfe8383752b1ed27b51db219d178c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 771
content:
"The SROCC assessed that observations are too sparse to reliably \r\nestimate long-term trends in Antarctic sea ice thickness. This remains \r\ntrue, and only qualitative statements on prevailing thicknesses are \r\npossible. Data from ICESat-1 laser altimetry (Kurtz and Markus, 2012), \r\nfrom Operation IceBridge (Kwok and Kacimi, 2018), and long-term \r\nshipboard observations collected in the Antarctic Sea Ice Processes \r\nand Climate (ASPeCt) dataset (Worby et  al., 2008) suggest that \r\nsea ice thicker than 1 m prevails in regions of multi-year ice along \r\nthe eastern coast of the Antarctic Peninsula in the Weddell Sea, in the \r\nhigh-latitude embayment of the Weddell Sea, and along the coast \r\nof the Amundsen Sea, with remaining regions dominated by thinner \r\nfirst-year sea ice (high confidence). Regional patterns in ice thickness \r\nare affected by areas of high snow deposition and resulting snow-ice \r\nformation (Massom et  al"


id: a6d634a7e396eb2907923851d84299bf
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 23765
content:
", 2001; Maksym and Markus, 2008), and \r\ndeformation, ridging, and rafting that regionally cause formation of \r\nvery thick sea ice (Massom et  al., 2006; G. Williams et  al., 2015). \r\nIn addition, near ice shelves a sub-ice platelet layer from supercooled \r\nwater can significantly increase sea ice thickness (Hoppmann et al., \r\n2020; Haas et  al., 2021). Regarding snow thickness, observations "


id: 567e5f2aa591ec1a54c543ffb1030a0f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 11084
content:
"There is low confidence in the long-term trend of Antarctic sea ice \r\nthickness. Both ASPeCt and ICESat-1 measurements are biased low \r\nin regions with thick ice (Kern and Spreen, 2015), compared to results \r\nfrom reanalyses (Massonnet et al., 2013; Haumann et al., 2016) and \r\nobservations with autonomous vehicles under sea ice (G. Williams \r\net al., 2015). Estimates of sea ice thickness from CryoSat-2 do not \r\nsubstantially reduce uncertainty, primarily because of the unknown \r\nsnow thickness and radar scattering above the snow-ice interface \r\n(Bunzel et  al., 2018; Kwok and Kacimi, 2018; Kacimi and Kwok, \r\n2020). Isolated in situ time series show no clear long-term trend \r\nin landfast ice thickness in the Weddell Sea (Arndt et  al.,  2020). \r\nReanalyses suggest overall increasing sea ice thickness and volume \r\nbetween 1980 and 2010 (Holland et  al., 2014; Zhang, 2014; \r\nMassonnet et  al., 2015), while CMIP5 (Shu et  al., 2015; Schroeter \r\net al"


id: efec019339502f907e1ce1a9062df7a5
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 35254
content:
", 2018) and CMIP6 models simulate a decrease in Antarctic sea \r\nice volume over the historical period. Because of this discrepancy, \r\nand the unclear reliability of the reanalyses (Uotila et al., 2019), there \r\nis low confidence in CMIP5 and CMIP6 simulated future Antarctic \r\nsea ice thickness."


id: a0bd7a118f3fad87c4643c7776318bbf
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 22834
content:
"Post-1992, SROCC stated that it is extremely likely that the rate \r\nof mass change of Greenland Ice Sheet was more negative during \r\n2012-2016 than during 1992-2001, with very high confidence\r\nthat summer melting has increased since the 1990s to a  level \r\nunprecedented over at least the last 350 years. Since SROCC, the \r\nupdated synthesis of satellite observations by the Ice Sheet Mass \r\nBalance Intercomparison Exercise (The IMBIE Team, 2020) and the \r\nGRACE Follow-On (GRACE-FO) Mission (Abich et al., 2019; Kornfeld \r\net al., 2019), have confirmed the mass change record, and the record \r\nhas been extended to 2020 (The IMBIE Team, 2021) as presented \r\nin 2.3.2.4. The Greenland Ice Sheet lost 4890 [4140-5640] Gt of ice \r\nbetween 1992 and 2020, causing sea level to rise by 13.5 [11.4 to \r\n15.6]  mm (The IMBIE Team, 2021; see also Section  2.3.2.4.1, \r\nFigure 9.16 and Table 9.5). The IMBIE Team's (2020) estimates are \r\nconsistent with other post-AR5 reviews (Figure 9.17, Table 9.SM"


id: c92edb2d7488e0c9026f02c897e0ab1e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 29215
content:
"1; \r\nBamber et al., 2018a; Cazenave et al., 2018; Mouginot et al., 2019; \r\nSlater et  al.,  2021). Recent GRACE-FO data (Sasgen et  al., 2020; \r\nVelicogna et al., 2020) show that, after two cold summers in 2017 \r\nand 2018, with relatively moderate mass change of about -100 \r\nGt yr-1, the 2019 mass change (-532 +- 58 Gt yr-1) was the largest \r\nannual mass loss in the record. The high agreement across a variety \r\nof methods confirms SROCC and Chapter 2 assessments. The mass\u0002loss rate was, on average, 39 [-3 to 80] Gt yr-1 over the period \r\n1992-1999, 175 [131 to 220] Gt yr -1 over the period 2000-2009 \r\nand 243 [197 to 290] Gt yr-1 over the period 2010-2019 (see Table 9.\r\nSM.1)."


id: aeed3a649cf0a0f850453d6affaac66b
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 25056
content:
"The SROCC assessed with high confidence that surface mass balance \r\n(SMB), rather than discharge, has started to dominate the mass loss \r\nof the Greenland Ice Sheet (due to increased surface melting and \r\nrunoff), increasing from 42% of the total mass loss for 2000-2005 \r\nto 68% for 2009-2012. While these estimates have been confirmed \r\nsince SROCC (Mouginot et  al., 2019), the new longer record, as \r\nwell as further comprehensive studies (Khan et  al., 2015; Colgan \r\net  al., 2019; Mottram et  al., 2019; The IMBIE Team, 2020) and \r\ndetailed discharge records (King et al., 2020; Mankoff et al., 2020)\r\nreveal a  more complex picture than the continuous trajectory this \r\nstatement may have implied. Discharge was relatively constant from \r\n1972-1999, varying by around 6% for the whole ice sheet, while SMB \r\nvaried by a factor of over two interannually, leading to either mass \r\ngain or loss in a given year (Figure 9.16)"


id: 7ede13d217e839eb66c489c6175f2827
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 17522
content:
" During 2000-2005, the rate \r\nof discharge increased by 18%, then remained fairly constant again \r\n(increasing by 6% from 2006-2018). After 2000, SMB decreased "


id: 7b9a6eafab86d20fdf5da07d38ccd21b
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 17043
content:
"of western Greenland, reducing meltwater retention capacity. \r\nMoreover, meltwater infiltration into firn can be strongly limited \r\nby low-permeability ice slabs created by refreezing of infiltrated \r\nmeltwater (Machguth et al., 2016). Recent observations and modelling \r\nefforts indicate that rapidly expanding low-permeability layers have \r\nled to an increase in runoff area since 2001 (MacFerrin et al., 2019). \r\nIn summary, there is medium confidence that meltwater storage and \r\nrefreezing can temporarily buffer a  large-scale melt increase, but \r\nlimiting factors have been identified.\r\nThe SROCC reported that there was medium confidence that ocean \r\ntemperatures near the grounding zone of tidewater glaciers are \r\ncritically important to their calving rate, but there was low confidence \r\nin understanding their response to ocean forcing. The increase in ice \r\ndischarge in the late 1990s and early 2000s (Mouginot et al., 2019; \r\nKing et  al., 2020; Mankoff et  al"


id: 8cf5194a86536013b91058c9020601a6
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 19699
content:
", 2020) has been associated with \r\na period of widespread tidewater glacier retreat (Murray et al., 2015; \r\nWood et al., 2021) and speed up (Moon et al., 2020). Since SROCC, \r\nnew studies provide strong evidence for rapid submarine melting \r\nat tidewater glaciers (Sutherland et al., 2019; Wagner et al., 2019; \r\nBunce et al., 2020; R.H. Jackson et al., 2020). Changes in submarine \r\nmelting and subglacial meltwater discharge can trigger increased \r\nice discharge by reducing the buttressing to ice flow and promoting \r\ncalving (Benn et al., 2017; Todd et al., 2018; Ma and Bassis, 2019; \r\nMercenier et  al., 2020); through undercutting (Rignot et  al., 2015; \r\nD.A.  Slater et  al., 2017; Wood et  al., 2018; Fried et  al., 2019) and \r\nfrontal incision (Cowton et al., 2019). Warming ocean waters have \r\nbeen implicated in the recent thinning and breakup of floating ice \r\ntongues in north-eastern and north-western Greenland (Mouginot \r\net al., 2015; Wilson et al., 2017; Mayer et al"


id: f7e636092d6b69a05e2e5146210db8d9
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 36520
content:
", 2018; Washam et al., \r\n2018; An et al., 2021; Wood et al., 2021). On decadal time scales, \r\ntidewater glacier terminus position correlates with submarine melting \r\n(Slater et al., 2019). Over shorter time scales, individual glaciers or \r\nclusters of glaciers can behave differently and asynchronously (Bunce \r\net  al., 2018; Vijay et  al., 2019; An et  al., 2021), and there are not \r\nalways clear associations between water temperature and glacier \r\ncalving rates (Motyka et al., 2017), retreat or speed-up (Joughin et al., \r\n2020; Solgaard et al., 2020). Variations in ice melange at the front \r\nof a glacier, associated with changes in ocean and air temperature, \r\nhave also emerged as a plausible control on calving (Burton et al., \r\n2018; Xie et  al., 2019; Joughin et  al., 2020)"


id: ca83165827777f9159407839824c14f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 29426
content:
" In summary, there is \r\nhigh confidence that warmer ocean waters and increased subglacial \r\ndischarge of surface melt at the margins of marine-terminating \r\nglaciers increase submarine melt, which leads to increased ice \r\ndischarge. There is medium confidence that this contributed to the \r\nincreased rate of mass loss from Greenland, particularly in the period \r\n2000-2010 when increased discharge was observed in the south-east \r\nand north-west."


id: 608d6a1c65e18594a94f31213aef24f6
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 12763
content:
"The SROCC did not assess the role of cloud changes in detail. Studies \r\nsince AR5 have shown that higher incident shortwave radiation in \r\nconjunction with reduced cloud cover leads to increased melt rates, \r\nparticularly over the low-albedo ablation zone in the southern \r\npart of the Greenland Ice Sheet (Hofer et al., 2017; Niwano et al., \r\n2019; Ruan et  al., 2019). Conversely, an increase in cloud cover \r\nover the high-albedo central parts of the ice sheet, leading to \r\nhigher downwelling longwave radiation, was shown to lead either \r\nto increased melt (Bennartz et  al., 2013) or reduced refreezing of \r\nmeltwater (van Tricht et al., 2016). The elevation dependence of the \r\ncloud radiative effect and its control on surface meltwater generation \r\nand refreezing (W. Wang et al., 2019; Hahn et al., 2020) can induce \r\na spatially consistent response of the integrated Greenland Ice Sheet \r\nmelt to dominant patterns of cloud and atmospheric variability"


id: 27539c97e1a23d69e46685a8357a2125
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 3589
content:
" \r\nThe shortwave and longwave radiation effects on surface melt by \r\nclouds have been shown to compensate for each other during strong \r\natmospheric river events, and the increase in melt is caused by \r\nincreased sensible heat fluxes during such events (Mattingly et al., \r\n2020). In summary, there is medium confidence that cloud cover \r\nchanges are an important driver of the increasing melt rates in the \r\nsouthern and western part of the Greenland Ice Sheet."


id: 6124ece251f7697784d6ffb6f5b68d4f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 12860
content:
"The SROCC stated with high confidence that positive albedo feedbacks \r\ncontributed substantially to the post-1990s Greenland Ice Sheet melt \r\nincrease. Several (mostly positive) feedbacks involving surface albedo \r\noperate on ice sheets (e.g.,  Fyke et  al., 2018). Melt amplification \r\nby the observed increase of bare ice exposure through snowline \r\nmigration to higher parts of the ice sheet since 2000 (Shimada et al., \r\n2016; Ryan et al., 2019) was five times stronger than the effect of \r\nhydrological and biological processes that lead to reduced bare \r\nice albedo (Ryan et al., 2019). Impurities, in part biologically active \r\n(Ryan et al., 2018), have been observed to lead to albedo reduction \r\n(Stibal et al., 2017) and are estimated to have increased runoff from \r\nbare ice in the southwestern sector of the Greenland Ice Sheet by \r\nabout 10% (Cook et al., 2020)"


id: 39577bd081d83f662b15d00e44bab220
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 6399
content:
" In summary, new studies confirm that \r\nthere is high confidence that the Greenland Ice Sheet melt increase \r\nsince about 2000 has been amplified by positive albedo feedbacks, \r\nwith the expansion of bare ice extent being the dominant factor, and \r\nalbedo in the bare ice zone being primarily controlled by distributed \r\nbiologically active impurities (see also Section 7.3.4.3)."


id: 42c3d16d52a9047646f51c1675452ad2
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 7896
content:
"energy balance models (Punge et  al., 2012; Cullather et  al., 2014; \r\nvan  Kampenhout  et  al.,  2017,  2020; Alexander et  al., 2019) or use \r\nelevation classes to compensate for their coarser resolution (Lipscomb \r\net al., 2013; Sellevold et al., 2019; Gregory et al., 2020; Muntjewerf \r\net  al., 2020a, b). Resulting SMB simulations compare better with \r\nregional climate models and observations (Alexander et  al., 2019; \r\nvan Kampenhout et  al., 2020), but the remaining shortcomings \r\nlead to problems reproducing a  present-day ice-sheet state close to \r\nobservations. In summary, there is medium confidence in quantitative \r\nsimulations of the present-day state of the Greenland Ice Sheet in ESMs.\r\nThe SROCC (Meredith et al., 2019) stated that there is low confidence\r\nin understanding coastal glacier response to ocean forcing because \r\nsubmarine melt rates, calving rates, bed and fjord geometry and \r\nthe roles of ice melange and subglacial discharge are poorly \r\nunderstood"


id: 64e664152b9eca0d6093614882db46df
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 13440
content:
" Ice-ocean interactions remain poorly understood and \r\ndifficult to model, with parametrizations often used for calving of \r\nmarine-terminating glaciers (Mercenier et al., 2018) and submarine \r\nand plume-driven melt (Beckmann et  al., 2019). Due to the \r\ndifficulties of modelling the large number of marine-terminating \r\nglaciers and limited availability of high-resolution bedrock data, the \r\nmajority of recent modelling work on Greenland outlet glaciers is \r\nfocused on individual or a  limited number of glaciers (Krug et  al., \r\n2014; Bondzio et al., 2016, 2017; Morlighem et al., 2016b; Muresan \r\net al., 2016; Choi et al., 2017; Beckmann et al., 2019), or a specific \r\nregion (Morlighem et al., 2019). Since SROCC, using a flowline model \r\nthat includes calving and submarine melting, Beckmann et al. (2019) \r\nconcluded that the AR5 upscaling of contributions from four of the \r\nlargest glaciers (Nick et  al"


id: ea1a48074f92e2cda16ce4471fcaabf0
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 34331
content:
", 2013) overestimated the total glacier \r\ncontribution from the Greenland Ice Sheet, due to differences in \r\nresponse between large and small glaciers. The regional study of \r\nMorlighem et al. (2019) confirms that ice-ocean interactions have \r\nthe potential to trigger extensive glacier retreat over decadal time \r\nscales, as indicated by observations (Section  9.4.1.1). One focus \r\nof  continental ice-sheet models has been the improved treatment \r\nof marine-terminating glaciers via the inclusion of calving processes \r\nand freely moving calving fronts (Aschwanden et  al., 2019; Choi \r\net al., 2021). An improved bedrock topographic dataset (Morlighem \r\net al., 2017) allows for ice discharge to be better captured for outlet \r\nglaciers in continental ice-sheet models, and simulations indicate \r\nthat bedrock topography controls the magnitude and rate of retreat \r\n(Aschwanden et al., 2019; Ruckamp et al., 2020)"


id: dbcc79c76650e36f52340ade0650f55
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 32095
content:
" Overall, although \r\nthere is high confidence that the dynamic response of Greenland \r\noutlet glaciers is controlled by bedrock topography, there is low \r\nconfidence in quantification of future mass loss from Greenland \r\ntriggered by warming ocean conditions, due to limitations in the \r\ncurrent understanding of ice-ocean interactions, its implementation \r\nin ice-sheet models, and knowledge of bedrock topography."


id: cd0a9a6ba4c619835a2b151f0c5332fd
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 29808
content:
"The SROCC stated that climate model simulations of Greenland SMB \r\nhad improved since AR5, giving medium confidence in the ability of \r\nclimate models to simulate changes in Greenland SMB. Since SROCC, \r\na multi-model intercomparison study (Fettweis et al., 2020) of regional \r\nand global climate models has shown that the greatest inter-model \r\nspread occurs in the ablation zone, due to deficiencies in an accurate \r\nmodel representation of the ablation zone extent and processes \r\nrelated to surface melt and runoff, confirming SROCC statement that \r\nthere is large uncertainty in the bare ice model (Ryan et al., 2019). \r\nThis intercomparison showed that simple, well-tuned SMB models \r\nusing positive degree day melt schemes can perform as well as more \r\ncomplex physically based models (Figure Atlas 30). Furthermore, the \r\nensemble mean of the models produced the best estimate of the \r\npresent-day SMB relative to observations (particularly in the ablation \r\nzone)"


id: cf3c7642d8fcdeba3b4beda31096bbd3
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 30143
content:
" Further assessment of Greenland Ice Sheet regional SMB can \r\nbe found in Section Atlas.11.2.3. Recent progress confirms SROCC \r\nassessment that there is medium confidence in the ability of climate \r\nmodels to simulate changes in Greenland SMB."


id: dc32a496317f4279153d1ae33250502a
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 32155
content:
"Since SROCC, projections of the Greenland Ice Sheet are also \r\navailable from The Ice Sheet Model Intercomparison Project for \r\nCMIP6 (ISMIP6) (Box  9.3; Annex II; Figure  9.17; Nowicki et  al., \r\n2016, 2020a). ISMIP6 multi-model projections are corrected with an \r\nassessment of the historical dynamical response to pre-2015 climate \r\nforcing (Box 9.3). For the period 2015-2100, the ISMIP6 uncorrected \r\nmulti-model ensemble projects sea level contributions ranging from \r\n0.01 to 0.05 m under RCP2.6, 0.04 to 0.14 m under RCP8.5 (Goelzer \r\net  al., 2020), 0.02 to 0.06 m under SSP1-2.6, and 0.08 to 0.25 m \r\nunder SSP5-8.5 (Table 9.2; Payne et al., 2021). The higher mass loss \r\nin the SSPs is attributed to a larger decrease in SMB due to the high \r\nclimate sensitivity of the models used (Payne et al., 2021). This finding \r\nis confirmed by Choi et al. (2021), where CMIP6 SSP5-8.5 SMB leads \r\nto larger ice loss than CMIP5 RCP8.5, while ice discharge is similar"


id: 851a6cbd9594d769f0aa6f9a8cf91152
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 18470
content:
" \r\nAs the ISMIP6 framework considers a  subset of the RCPs/SSPs \r\nand CMIP models, SSP-based projections have been inferred from \r\nmultiple approaches. First, the ISMIP6 CMIP5-forced (Goelzer et al., \r\n2020) and CMIP6-forced (Payne et  al., 2021) combined ensemble \r\nprojections were corrected with the historical trend (Box 9.3) using \r\nbootstrapping. Second, an emulator of the ISMIP6 projections \r\n(Box 9.3; Edwards et al., 2021) is forced by distributions of global \r\nsurface air temperature for each SSP from a two-layer energy budget \r\nemulator (Supplementary Material 7.SM.2) and then corrected with \r\nthe historical trend in the same way. These two approaches result \r\nin projections that are similar in their median values to AR5 and \r\nSROCC projections (Table 9.2), but differ in their range. Similar results \r\nare obtained when the AR5 parametric fit is applied to the ISMIP6 \r\nmodels (Table 9.2, Supplementary Material 9.SM.4"


id: f0d3b38bc168cdc88b27eefc1a1bf396
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 35379
content:
"4), which is used \r\nto estimate rates of change and post-2100 projections (Sections \r\n9.4.1.4 and 9.6.3.2)."


id: 11e61ab4ae5662852fe6e16f56dede56
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 314
content:
"The AR5 and SROCC projected that changes in Greenland SMB will \r\ncontribute to sea level in 2100 by 0.03 (0.01 to 0.07) m sea level \r\nequivalent (SLE) under RCP2.6, and 0.07 (0.03 to 0.16) m SLE under \r\nRCP8.5. New since SROCC are the projections of SMB obtained by \r\nan ESM, two regional climate models, and reconstructions based on \r\ntemperature from the CMIP5 and CMIP6 ensembles (Hofer et al., 2020; \r\nNoel et al., 2021). The range of sea level contribution from Greenland \r\nSMB in Noel et al. (2021) is comparable to the AR5 assessment when \r\neither CMIP5 or CMIP6 models are used, while Hofer et al. (2020) \r\nfind a greater mass loss across all CMIP6 emissions scenarios when \r\ncompared to CMIP5 scenarios. Using SSP5-8.5 instead of RCP8.5 \r\nincreases the mean projected sea level from 2005-2100 by up to \r\n0.06 m in the regional climate model simulations of Hofer et al"


id: d78858611ca890ade028107299419ef1
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 31429
content:
" (2020) \r\nwho attribute the difference mainly to a greater Arctic amplification \r\nand associated cloud and sea ice feedbacks in the CMIP6 SSP5-8.5 \r\nsimulations. In summary, these new projections with fixed ice-sheet \r\ntopography do not provide sufficient evidence to change the AR5 and \r\nSROCC assessments."


id: da6b101dd6bb0f7bcb256e7611c74af1
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 31890
content:
"Reviewing modelling studies since AR5 (Church et al., 2013b), SROCC \r\n(Oppenheimer et  al., 2019) assessed Greenland's contribution to \r\nfuture sea level to be relatively similar to AR5 (Table 9.2). The baseline \r\nfor projections has shifted from 1986-2005 in SROCC, to 1995-2014 \r\nin this Report. Adjusted to the new 1995-2014 baseline by subtracting \r\n0.01 m, SROCC projected a likely contribution of 0.07 (0.0-0.11) m \r\nSLE under RCP2.6, and 0.14 (0.08-0.27) m SLE under RCP8.5 by 2100. \r\nSince SROCC, new projections for the 21st  century have included \r\ndynamic ice sheets coupled to ESMs (Muntjewerf et  al., 2020a; \r\nVan Breedam et al., 2020) or regional atmospheric models (Table 9.2; \r\nLe  clec'h et  al., 2019). The coupled ESM-ice-sheet model CESM2-\r\nCISM2 (Community Earth System Model Version 2 and Community Ice \r\nSheet Model 2) projects a sea level rise of 0.109 m in 2100 relative \r\nto 2015 under SSP5-8.5 (Muntjewerf et  al"


id: 7127b290fa8dae20045caf6b5dcfb2db
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 15437
content:
", 2020a) and a  similar \r\ncontribution under the idealized 1% yr-1 increase in CO2 scenario \r\n(Muntjewerf et  al., 2020b). The CESM2-CISM2 simulations include \r\nice-sheet-atmosphere interactions and ice-sheet surface meltwater \r\nrouted to the ocean. The coupled regional atmospheric model and \r\nice-sheet model MAR-GRISLI (Modele Atmospherique Regional and \r\nGrenoble ice sheet and land ice model) projects a sea level rise of \r\n0.079 m in 2100 relative to 2000 under RCP8.5 (Le Clec'h et al., 2019). \r\nAn  ESM of lower complexity coupled to an ice-sheet model gives \r\na sea level contribution of 0.025 to 0.064 m under RCP2.6 and 0.056 \r\nto 0.12 m under RCP8.5 (the range is due to four simulations with \r\ndifferent parameter sets for the atmosphere model) (Van  Breedam \r\net  al., 2020). Van Breedam et  al. (2020) identify a  simulation with \r\na  preferred parameter set that projects 0.034 m for RCP2.6 and "


id: 14c605aefe84fd499ab3751ac79d4108
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 777
content:
"The SROCC noted that the study by Aschwanden et al. (2019) projects \r\na significantly higher Greenland contribution to sea level than the \r\nassessed likely range in AR5 and SROCC. Under RCP8.5, Aschwanden \r\net al. (2019) found that Greenland could contribute up to 0.33 m to \r\nsea level by 2100 relative to 2000 (the ensemble member that best \r\nreproduces the 2000-2015 mean SMB from a regional climate model \r\nprojects Greenland mass losses of 0.08 m SLE under RCP2.6 and \r\n0.18 m SLE under RCP8.5). The SROCC noted that the potentially high \r\nsea level contribution in this study could be due to the assumption \r\nof spatially uniform warming, which can overestimate surface melt \r\nrates. However, it also reflects the deep uncertainty surrounding \r\natmospheric forcing, surface processes, submarine melt, calving \r\nand ice dynamics. Goelzer et al"


id: f5888b19e15db57fe8c3aa8825820f20
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 36153
content:
" (2020) ascribe 40% of the ISMIP6 \r\nmulti-model ensemble spread to ice-sheet model uncertainty, 40% to \r\nclimate model uncertainty and 20% to ocean forcing uncertainty. \r\nWe note that this finding reflects the current challenges associated \r\nwith the representation of ice-ocean interactions in models, and \r\nthe uncertainty in basal conditions (Section 9.4.1.2). However, this \r\nfinding is consistent with the work of Aschwanden et  al. (2019) "


id: 65d29e174f71b233f15b8446d383e717
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 13601
content:
"In summary, it is virtually certain that the Greenland Ice Sheet will \r\ncontinue to lose mass this century under all emissions scenarios, \r\nand high confidence that total mass loss by 2100 will increase with \r\ncumulative emissions. The sea level assessment (Section  9.6.3.3) is \r\nbased on the emulated ISMIP6 projections, allowing a more consistent \r\napproach to a wider range of climate and ocean forcings. The Greenland \r\nIce Sheet is likely to contribute 0.06 (0.01 to 0.10) m under SSP1-2.6 and \r\n0.13 (0.09 to 0.18) m under SSP5-8.5 by 2100 relative to 1995-2014. \r\nThese projections (as well as those of AR5 and SROCC) are lower \r\nthan the study of Aschwanden et al. (2019) or the range of possible \r\nsea level changes resulting from Structured Expert Judgement (SEJ; \r\nSection 9.6.3.2; Bamber et al., 2019), contributing to the deep uncertainty\r\nin projected sea level (Box 9.4)"


id: db570f4da84585519e1f04c9d5a0fbf9
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 32017
content:
" There is, however, high confidence that \r\nthe loss from Greenland will become increasingly dominated by SMB \r\nand surface melt, as the ocean-forced dynamic response of glaciers will \r\ndiminish as marine margins retreat to higher grounds."


id: 19b70c7f9a7ccdff43b224a4ee0dcae7
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 1517
content:
"The SROCC stated that surface processes, rather than ice \r\ndischarged into the ocean, will dominate Greenland ice loss \r\nover the 21st  century, regardless of the emissions scenario (high \r\nconfidence). This is confirmed by the ISMIP6 projections (Goelzer \r\net al., 2020; Payne et al., 2021). The projected mass loss of Greenland \r\nis predominantly due to increased surface meltwater and loss in \r\nrefreezing capacity resulting in decreasing SMB (high confidence), \r\nconcurrent with rising temperatures and darkening of the ice-sheet \r\nsurface (Fettweis et al., 2013; Vizcaino et al., 2015; Le Clec'h et al., \r\n2019; Muntjewerf et  al., 2020a, b; Sellevold and Vizcaino, 2020). \r\nMass changes due to SMB and outlet glacier dynamics are linked \r\n(Goelzer et  al., 2013; Furst et  al., 2015; Ruckamp et  al., 2020), as \r\nmass loss by one process decreases mass loss by the other  - for \r\nexample, SMB removes ice before it can reach the marine glacier \r\nterminus"


id: e859d940695e0f19c9afaf23a8843807
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 34062
content:
" There is medium confidence that the mass loss through ice \r\ndischarge will decrease in the future (Furst et al., 2015; Aschwanden \r\net al., 2019; Golledge et al., 2019), because an increase in mass loss \r\n(via increased discharge or surface runoff) leads, in most areas, to \r\na retreat of the glacier margin onto land above sea level, isolating the \r\nice sheet from marine influence."


id: 8a521d9cb7af0418d0d88a9e33487830
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 19300
content:
"2015; Calov et  al., 2018; Aschwanden et  al., 2019). Since SROCC, \r\na new study gives a sea level contribution of 0.11 to 0.20 m in low\u0002emissions scenarios and 0.61 to 1.29 m in high-emissions scenarios \r\n(Van Breedam et  al., 2020). The low-emissions projections by Van \r\nBreedam et al. (2020) encompass AR5's assessed contribution, while \r\nthe high emissions projections are higher than that from AR5. The \r\n'optimal' ensemble member of Aschwanden et  al. (2019) (see also \r\nSection  9.4.1.3) indicates that Greenland could contribute 0.25  m \r\nunder RCP2.6 and 1.74 m under RCP8.5. Structured expert judgement \r\n(Bamber et al., 2019) projects Greenland losses of 0.54 (0.28-1.28) m \r\nunder 2degC warming and 0.97 (0.4-2.23) m under 5degC warming. These \r\nstudies therefore agree that the AR5 and SROCC assessments are \r\nat the low end of the range of projections. In addition, observations \r\nsuggest that Greenland Ice Sheet losses are tracking the upper range \r\nof AR5 projections (T"


id: 779e016302ae87fcb6cbd5d5884ed9a7
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 16377
content:
" Slater et al., 2020). Therefore, we update the likely\r\nrange for the contribution of the Greenland Ice Sheet to global mean \r\nsea level (GMSL) by 2300 to 0.11-0.25 m under RCP2.6/SSP1-2.6 and \r\n0.31-1.74 m under RCP8.5/SSP5-8.5. However, given the uncertainty in \r\nclimatic drivers used to project ice-sheet change over the 21st century \r\n(Goelzer et  al., 2020; Hofer et  al., 2020; Noel et  al., 2021) and the \r\nlarge range in simulations since AR5 extending beyond 2100, we only \r\nhave low confidence in the contribution to GMSL by 2300 and beyond. "


id: 6241a4c70e501356d5a15cefadf4e73f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 13013
content:
"Accounting for more detailed feedbacks between the atmosphere \r\nand the ice sheet (Gregory et al., 2020) found a gradual relationship \r\nbetween sustained global mean warming and the corresponding near\u0002equilibrium ice-sheet volume, in contrast to a sharp threshold as found \r\nby Robinson et al. (2012). Rather than a climatically controlled tipping \r\npoint for irreversible loss of the Greenland Ice Sheet, Gregory et  al. \r\n(2020) found a threshold of irreversibility linked to ice-sheet size, similar \r\nto previous work (Ridley et al., 2010). The results of Gregory et al. (2020) \r\nshow that, if the ice sheet loses mass equivalent to about 3-3.5 m of \r\nsea level rise, it would not regrow to its present state, and 2 m of the sea \r\nlevel rise would be irreversible. The point in time at which the current \r\nice sheet might reach this critical volume depends on oceanic and \r\natmospheric conditions, ice dynamics, and climate-ice sheet feedbacks \r\n(Gregory et al., 2020; Van Breedam et al., 2020)"


id: a7c1f23613e47288429e6a408c677f6a
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 23904
content:
" Therefore, projections \r\ndiffer in the magnitude and rate of temperature change to cross the \r\nthreshold for irreversible loss. Projections from a  large ensemble \r\nindicate that the mass threshold may be reached in as early as 400 years \r\nunder extended RCP8.5 if warming reaches 10degC or more above present \r\nlevels (Aschwanden et al., 2019). In summary, there is high confidence\r\nin the existence of threshold behaviour of the Greenland Ice Sheet in \r\na warmer climate; however, there is low agreement on the nature of the \r\nthresholds and the associated tipping points."


id: 944187a293068f7e9834b27f87d0c299
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20835
content:
"The ISMIP6 initial state intercomparison projects (initMIP) for the Greenland (Goelzer et al., 2018) and Antarctic (Seroussi et al., \r\n2019) ice sheets were designed to understand the uncertainty in sea level projections resulting from the choice of initialization \r\nprocedures used for projections of sea level (Nowicki et al., 2016). Participating modelling groups (Annex II) were free to decide \r\non the initialization method used to bring ice-sheet models to a  present-day state, with the effect of these choices captured in \r\na control simulation (starting from the present-day state, with no further climate forcing applied), which measures intrinsic model drift. \r\nCompared to the earlier SeaRISE intercomparison project (Bindschadler et al., 2013; Nowicki et al., 2013), the modelled present-day \r\nice sheets are in closer agreement with observations, and the model drift has been reduced (Goelzer et al., 2018; Seroussi et al., \r\n2019)"


id: 5cf0963d6c0963bb6d3f993e7bb426a7
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 12138
content:
" Nonetheless, historical simulations remain challenging for ice-sheet models, due to limited ice-sheet observations prior to the \r\nsatellite era and biases in the historical atmospheric and oceanic forcings from climate models (Nowicki and Seroussi, 2018). ISMIP6 \r\nand LARMIP-2 therefore did not provide a protocol for the historical runs used to bring the ice sheets to present day, nor criteria for \r\nsub-selecting models from the multi-model ensemble based on the ability to reproduce historical changes (Levermann et al., 2020; \r\nNowicki et al., 2020a)."


id: 3d11767242a4ae3ed734439d9c5d5312
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 6969
content:
"ISMIP6 projections for the Greenland and Antarctic ice sheets\r\nThe ISMIP6 projection protocol (Nowicki et al., 2016, 2020a) was designed to sample the uncertainty in future sea level due to climate \r\nscenarios (via the use of high- and low-emissions scenarios and multiple climate models), ice-ocean interactions and inland response \r\nto ice-shelf collapse, and ice-sheet model diversity. The participating ice-sheet models are listed in Annex II. For each ice sheet, forcing \r\nwas selected (Barthel et al., 2020) from the CMIP5 (Taylor et al., 2012) and CMIP6 (Eyring et al., 2016) models. Atmospheric forcing fields \r\nconsisted of anomalies in SMB and surface air temperatures; these were generated directly from the CMIP models for the Antarctic \r\nIce Sheet and downscaled using the regional climate model (MAR) for the Greenland Ice Sheet (Hofer et al., 2020)"


id: 85830036806b35def01a1dbc2891f6bd
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 18537
content:
" To sample the \r\nuncertainty due to ocean forcings, models used either a model-specific scheme with the ISMIP6-provided oceanic dataset or a standard \r\nISMIP6 approach. For the Greenland Ice Sheet, the oceanic dataset consists of thermal forcing (temperature minus freezing temperature) \r\nextrapolated into fjords and subglacial runoff. The standard approach uses timelines of tidewater glacier retreat (D.A. Slater et al., 2019, \r\n2020). For the Antarctic Ice Sheet, the oceanic dataset consists of salinity, thermal forcing and temperature added to an observationally \r\nderived climatology and extrapolated under ice shelves. The standard approach is a basal melt rate that depends quadratically on thermal \r\nforcing, adapted from Favier et al. (2019), with two different calibrations (Figure 9.19, Jourdain et al., 2020) that reproduce observed \r\nbasal melt rates across Antarctica or Pine Island Glacier, respectively (Sections 9.4.2.2, 9.4.2.3)"


id: 839345185ebce204fd1a608ec19aaf43
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 18234
content:
" Antarctic ice-shelf disintegration datasets \r\n(Nowicki et al., 2020a) assume that ice shelves disintegrate when annual surface melt reaches a threshold (Trusel et al., 2015)."


id: f7a4e940c979b6857f2a1751488f5539
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 36477
content:
"The ISMIP6 projections (Goelzer et al., 2020; Seroussi et al., 2020; Payne et al., 2021) are reported as experiment minus control and \r\nrepresent the sea level resulting from future climate change only. The control simulation, which has constant climate conditions \r\nstarting in 2015 from the historical run, captures drift associated with the choices made for the initialization method and historical \r\nrun. Subtraction of this control removes any long-term dynamic response of the ice sheet to pre-2015 climate change. This response \r\nhas been assessed using dynamic discharge derived from observations over the last 40 years (Mouginot et al., 2019; Rignot et al., \r\n2019), under an assumption that it persists at the past rate until 2100, rather than diminishing. The dynamic response to historical \r\nforcing is estimated as 0.19 +- 0.10 mm yr-1 for the Greenland Ice Sheet (Section 9.4.1.3) and 0.33 +- 0.16 mm yr-1 for the Antarctic \r\nIce Sheet (Section 9.4.2.5)"


id: 8db05c9b968e82c69b3e2e7cb729a96b
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 19816
content:
" Over the period 2015-2100, this leads to an additional sea level contribution of 1.7 cm for Greenland and \r\n2.8 cm for Antarctica."


id: 42941e9a07a38ca66096b461a58b241
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 7865
content:
"LARMIP-2 is focused on the uncertainty in the ocean forcing and associated ice-shelf melting (Levermann et al., 2014, 2020) with \r\nthe majority of the models also participating in ISMIP6 (Annex II). The experiments start from present day and impose an additional \r\nbasal ice-shelf melting of 8 m yr -1 at the beginning of the 100-year simulation. A control run is used to remove drift resulting from \r\ninitialization. The time derivative of the ice-sheet response yields a linear response function, which is then convoluted with a forcing of \r\nbasal shelf melt time series for five Antarctic regions. The forcing time series for RCP2.6, 4.5, 6.0 and 8.5 were obtained from a random \r\ncombination of global mean temperature for each Representative Concentration Pathway (RCP) from MAGICC-6.0 (Meinshausen \r\net  al"


id: deaa88761d68c18fb76b360602f8bfb7
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 32531
content:
", 2011), a  scaling factor and time delay for the relationship between global surface air temperature and subsurface ocean \r\nwarming in a given sector of the Southern Ocean from one of 19 CMIP5 models (Taylor et al., 2012) and a basal melting sensitivity \r\nfrom the interval [7-16] m yr-1 degC-1 to convert the regional subsurface warming into basal ice-shelf melting. This process is repeated \r\n20,000 times to obtain a probability distribution of the sea level contribution for five Antarctic sectors. The linear response framework \r\ncaptures complex temporal responses of the ice sheets resulting from an increase in basal ice-shelf melting, but neglects the response \r\nto SMB and any self-dampening or self-amplifying processes, such as marine ice shelf instability (MISI). The LARMIP-2 method is"


id: 32da0c9e568a25275554d239854e01c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 5415
content:
"The ISMIP6 and GlacierMIP projections are primarily based on a limited number of CMIP5 RCPs and CMIP6 SSPs, and a limited sampling \r\nof ice-ocean interaction parameters and ice-shelf collapse simulations. Emulators provide a method for expanding these projections \r\nto a range of SSPs with more comprehensive sampling of climate, ice-sheet and glacier modelling uncertainties. Sections 9.4.1.3, \r\n9.4.2.5 and 9.5.1.3 show estimates from the emulator of Edwards et al. (2021). This is a Gaussian Process, rather than a physically \r\nbased (Cross-Chapter Box 7.1) model derived from the ISMIP6 and GlacierMIP simulations; projections use distributions of global \r\nsurface air temperature (GSAT) from the two-layer emulator (Supplementary Material 7.SM.2) and ice-sheet parameters as inputs, \r\nand include estimates of the emulator uncertainty"


id: 66bbe765d4b45784160349d5e3cc6f4f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 13754
content:
" Therefore, probability intervals are not inflated by a further factor, as is often the \r\ncase for multi-model ensemble projections, to account for missing uncertainties (Section 9.6.3.2). The emulator is used in Section 9.6.3 \r\nto provide projections of the land ice contribution to sea level that are fully consistent with each other, ocean heat content, and the \r\nassessed equilibrium climate sensitivity and projections of GSAT across the entire report."


id: ee5955c1eca135d9de90c5514ecaf436
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 34968
content:
"The SROCC reported with very high confidence that the acceleration, \r\nretreat and thinning of the principal West Antarctic outlet glaciers \r\nhas dominated the observed Antarctic mass loss over the last \r\ndecades, and stated with high confidence that these losses were \r\ndriven by melting of ice shelves by warm ocean waters. The average \r\nWest Antarctic Ice Sheet (WAIS) mass loss of 82 +- 9 Gt yr-1 between \r\n1992 and 2017 (The IMBIE Team, 2021) leads to substantial observed \r\nsurface lowering (e.g., Schroder et al., 2019; Shepherd et al., 2019), \r\nparticularly in coastal regions (Figure  9.18). Recent studies using \r\nsatellite altimetry (Schroder et al., 2019) and the input-output method \r\n(Rignot et  al., 2019) consistently show mass loss in these coastal \r\nregions since the late 1970s (Figure  9.16). Because of consistent \r\nmultiple lines of evidence, there is high confidence in mass loss of the \r\nTotten Glacier in East Antarctica (Miles et al., 2013; X. Li et al"


id: 7342d9a36efb0468475703246498779b
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 15750
content:
", 2016; \r\nMohajerani et al., 2018; Rignot et al., 2019; Schroder et al., 2019; \r\nShepherd et al., 2019) since about 2000, dominated by changes in \r\ncoastal ice dynamics (X. Li et al., 2016). It is currently unclear whether \r\nmass loss of the EAIS over the last three decades has been significant \r\n(Rignot et al., 2019) or, at 5 +- 46 Gt yr-1 between 1992 and 2017, \r\nessentially zero within uncertainties (The IMBIE Team, 2018). \r\nIn summary, WAIS losses, through acceleration, retreat and thinning \r\nof the principal outlet glaciers, dominated the AIS mass losses over \r\nthe last decades (very high confidence) and there is high confidence"


id: 967d37a8790744d9962edcca4271c721
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 21197
content:
"As stated in Section 2.3.2.4, satellite observations by Ice Sheet Mass \r\nBalance Intercomparison Exercise (IMBIE) combining multi-team \r\nestimates based on altimetry, gravity anomalies (GRACE) and the \r\ninput-output method, already presented in SROCC (Meredith et al., \r\n2019), are updated and extended to 2020 (The IMBIE Team, 2021). \r\nThe Antarctic Ice Sheet (AIS) lost 2670 [1800 to 3540] Gt mass over \r\nthe period 1992-2020, equivalent to 7.4 [5.0 to 9.8] mm GMSL rise \r\n(for contribution to sea level budget, see Figures 9.16 and 9.18, and \r\nTable 9.5). Within uncertainties, this estimate agrees with a review of \r\npost-AR5 studies up to 2016 (Bamber et al., 2018b) and is consistent \r\nwith recent single studies based on satellite laser altimetry (Smith \r\net  al., 2020), the input-output method (Rignot et  al., 2019) and \r\ngravimetry (Velicogna et al., 2020)"


id: 7a104a89d15576ecd22de3db0023f6e0
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 16777
content:
" The mass-loss rate was on average \r\n49 [-2 to 100] Gt yr-1 over the period 1992-1999, 70 [22 to 119] \r\nGt yr-1 over the period 2000-2009, and 148 [94 to 202] Gt yr -1 over \r\nthe period 2010-2016 (see Figures 9.16 and 9.18, and Table 9.SM.1). \r\nHowever, recent work suggests that the mass loss has not further \r\nincreased since 2016 because of regional mass gains in Dronning \r\nMaud Land (Velicogna et al., 2020). Mass loss of the West Antarctic \r\nand Antarctic Peninsula ice sheets has increased since about 2000 \r\n(very high confidence), essentially due to increased ice discharge \r\n(Harig and Simons, 2015; Paolo et al., 2015; Forsberg et al., 2017; "


id: d716a5337db9f164e5555c58d0a06627
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 31353
content:
"As stated in SROCC, snowfall and glacier flow are the largest \r\ncomponents determining AIS mass changes, with glacier flow \r\nacceleration (dynamic thinning) on the WAIS and the Antarctic \r\nPeninsula driving total loss trends in recent decades (very high \r\nconfidence), and a partial offset of the dominating dynamic-thinning \r\nlosses by increased snowfall (high confidence). The SROCC attributed \r\nmedium confidence to estimates of 20th-century snowfall increases \r\nequivalent to a sea level change of -7.7 +- 4.0 mm on the EAIS, and \r\n-2.8 +- 1.7 mm on the WAIS, respectively (Medley and Thomas, 2019). \r\nLoss of buttressing, which can be caused by ice-shelf thinning, gradual \r\nice-shelf front retreat or ice-shelf disintegration, has been linked to \r\ninstantaneous ice velocity increases, and thus dynamic thinning, since \r\nthe early 1990s. This link is clearly evident in the Amundsen and, to \r\na lesser degree, Bellingshausen sectors (Gudmundsson et al"


id: 3131b0143e36bdf544e836d354824d45
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 5158
content:
", 2019), \r\nwhere passive shelf ice (ice that can be removed without major \r\neffects on the ice-shelf dynamics) is very limited or absent (Furst \r\net  al., 2016). Surface mass balance (SMB) changes, dominated by \r\nsnowfall, exhibit strong regional and temporal variability, for example "


id: 235368776ca5e27b8e9e8e68ededfe84
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 3011
content:
"Figure 9.18 | Antarctic Ice Sheet cumulative mass change and equivalent sea level contribution. (a) A p-box (Section 9.6.3.2) based estimate of the range of values of \r\npaleo Antarctic ice sheet mass and sea level equivalents relative to present day and the median over all central estimates (Bamber et al., 2009; Argus and Peltier, 2010; Dolan et al., \r\n2011; Mackintosh et al., 2011; Golledge et al., 2012, 2013, 2014, 2015, 2017b; K.G. Miller et al., 2012; Whitehouse et al., 2012; Ivins et al., 2013; Argus et al., 2014; Briggs et al., \r\n2014; Maris et al., 2014; de Boer et al., 2015, 2017; Dutton et al., 2015; Pollard et al., 2015; DeConto and Pollard, 2016; Gasson et al., 2016; Goelzer et al., 2016; Yan et al., 2016; \r\nKopp et al., 2017; Simms et al., 2019); (b left) cumulative mass loss (and sea level equivalent) since 2015, with satellite observations shown from 1993 (Bamber et al"


id: 9e677e34101ae1a9b1300313cc1a4c5c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 22450
content:
", 2018a; The \r\nIMBIE Team, 2018; WCRP Global Sea Level Budget Group, 2018) and observations from 1979 (Rignot et al., 2019), and projections from Ice Sheet Model Intercomparison Project \r\nfor CMIP6 (ISMIP6) to 2100 under RCP8.5/SSP5-8.5 and RCP2.6/SSP1-2.6 scenarios (thin lines from Seroussi et al., 2020; Edwards et al., 2021; Payne et al., 2021) and ISMIP6 \r\nemulator under SSP5-8.5 and SSP1-2.6 to 2100 (shades and bold line; Edwards et al., 2021); (b, right) 17th-83rd, 5th-95th percentile ranges for ISMIP6, ISMIP6 emulator, and \r\nLARMIP-2 including surface mass balance (SMB) at 2100. (c-e) Schematic interpretations of individual reconstructions (Anderson et al., 2002; Bentley et al., 2014; de Boer et al., \r\n2015; Goelzer et al., 2016) of the spatial extent of the Antarctic Ice Sheet are shown for the: (c) mid-Pliocene Warm Period, (d) Last Interglacial; and (e) Last Glacial Maximum \r\n(Fretwell et al., 2013): grey shading shows extent of grounded ice"


id: 4ebc24b87b4705ddb60cb52ba8911e6e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 9814
content:
" (f-g) Maps of mean elevation changes (f) 1978-2017 derived from multi-mission satellite altimetry (Schroder \r\net al., 2019) and (g) ISMIP6: 2061-2100 projected changes for an ensemble using the Norwegian Climate Center's Earth System Model (NorESM1-M) climate model under the \r\nRCP8.5 scenario (Seroussi et al., 2020). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)."


id: 2e0ebaccdc525083ae1996b90879f89c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 4682
content:
"in  this context. Antarctic ice-shelf basal meltwater flux varied \r\nbetween about 1100  +- 150 Gt yr-1 in the mid-1990s and about \r\n1570  +- 140  Gt  yr-1 in the late 2000s before decreasing to \r\n1160 +- 150 Gt yr-1 in 2018, and basal melt rates strongly vary with \r\ngeographical position and depth, as a function of the surrounding \r\nwater temperature (Adusumilli et al., 2020). Section 9.2.2.3 assesses \r\nthat the intrusion of warm Circumpolar Deep Water (CDW), which \r\nhas warmed and shoaled since the 1980s, has been at least partially \r\ncontrolled by forcing with significant decadal variability. Limited \r\nevidence suggests that, beyond strong internal decadal wind \r\nvariability, increased greenhouse gas forcing has slightly modified the \r\nmean local winds between 1920 and 2018, facilitating the intrusion \r\nof CDW heat on the Amundsen-Bellingshausen continental shelf, \r\nand increased ice shelf melt (Section 9.2.2.3)"


id: 4180d852f471be16f39cf48af619fe19
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 7707
content:
" However, theoretical \r\nunderstanding is still incomplete and in situ measurements within \r\nthe ice-ocean boundary layer are sparse (Wahlin et  al., 2020). \r\nModelling, and therefore attribution of ice shelf basal melt, remains \r\nchallenging because of insufficient process understanding, required \r\nspatial resolution, the paucity of in situ observations (Dinniman et al., \r\n2016; Asay-Davis et al., 2017; Turner et al., 2017), and uncertainties \r\nof bathymetric datasets under ice-shelf cavities (Goldberg et  al., \r\n2019, 2020; Morlighem et al., 2020). In summary, ice-shelf thinning, \r\nmainly driven by basal melt, is widespread around the Antarctic coast \r\nand particularly strong around the WAIS (high confidence), although \r\nbasal melt rates show substantial spatio-temporal variability."


id: 8a2cd5263822aa37f040bd708f269a02
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 19268
content:
"Satellite observations suggest that changes in sea ice coverage and \r\nthickness can modulate iceberg calving, ice shelf flow and glacier \r\nterminus position around Antarctica (Miles et al., 2013, 2016, 2017; \r\nMassom et al., 2015; Greene et al., 2018; Bevan et al., 2019), either \r\nthrough mechanical coupling or via changes to ocean stratification, \r\ninfluencing basal melting. A combined observational and modelling \r\nstudy (Massom et al., 2018) showed that regional loss of a protective \r\nsea ice buffer played a role in the rapid disintegration events of the \r\nLarsen A  and B and Wilkins ice shelves in the Antarctic Peninsula \r\nbetween 1995 and 2009, by exposing damaged (rifted) outer ice shelf \r\nmargins to enhanced flexure by storm-generated ocean swells. One \r\nobservational study (Sun et al"


id: d122847104a2863a88ba33f732fdf22d
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 30437
content:
", 2019) suggests that the absence of sea \r\nice in front of ice shelves, which leads to strengthened topographic \r\nwaves, favours higher ice-shelf basal melt rates by increasing the \r\nbaroclinic (depth varying) ocean heat flux which can enter the cavity \r\n(Wahlin et al., 2020). Paleo evidence for sea ice control on ice sheets \r\nis lacking, but geologic evidence shows a  concordance between \r\nperiods of ice-sheet growth and the expansion of sea ice (Patterson \r\net al., 2014; Levy et al., 2019), both being favoured by reduced sea \r\nsurface temperatures. Modelling confirms that sea ice controls the \r\nstrength of ice melange (Robel, 2017; Schlemm and Levermann, 2021) \r\nand thus influences ice-shelf flexure and calving rates and stability of \r\nfloating ice margins, but one model shows this had negligible effect \r\non AIS retreat rates during past warm periods (Pollard et al., 2018)"


id: 9cf7b16e8ff598fc441d05f242dfb963
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 22235
content:
" \r\nLoss of ice-shelf-proximal sea ice is also associated with increased \r\nsolar heating of surface waters and increased sub-shelf melting \r\n(Bendtsen et al., 2017; Stewart et al., 2019). In summary, although \r\nin some cases sea ice decrease and glacier and ice-shelf flow and \r\nterminus position changes can have the same common cause, there \r\nis medium confidence that sea ice decrease ultimately favours the \r\nmass loss of nearby ice shelves through a variety of processes."


id: 5b7326eb6cb463fd08b12109428af3d2
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 11887
content:
"The SROCC stated with medium confidence that sustained mass \r\nlosses of several major glaciers in the Amundsen Sea Embayment \r\n(ASE) are compatible with the onset of marine ice sheet instability \r\n(MISI). However, whether unstable WAIS retreat had begun, or was \r\nimminent, remained a  critical uncertainty. New publications since \r\nSROCC have not substantially clarified this question. One study \r\nthat combined satellite measurements with a numerical model and \r\nprescribed ice-shelf thinning (Gudmundsson et  al., 2019) suggests \r\nthat MISI is not required to explain the observed current mass \r\nloss rates of the WAIS, because they are consistent with external \r\nclimate drivers. Furthermore, the fast grounding line retreat of the \r\nPine Island Glacier in the ASE, which was triggered in the 1940s \r\n(Smith et al., 2017), observed after 1992 (Rignot et al., 2014) and \r\npreviously interpreted as a sign of MISI (Favier et al., 2014), seems to \r\nhave stabilized recently (Milillo et al"


id: 1cb64d69df2434db90a34eb1c22020ed
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 1971
content:
", 2017; Konrad et al., 2018), and \r\nits current flow patterns do not suggest ongoing or imminent MISI \r\n(Bamber and Dawson, 2020). However, sustained fast grounding line \r\nretreat has been observed for the Smith Glacier in the ASE (Scheuchl \r\net  al., 2016), and an analysis of flow patterns and grounding line \r\nretreat of the ASE Thwaites Glacier between 1992 and 2017 (Milillo \r\net  al., 2019) showed sustained, albeit spatially heterogeneous, \r\ngrounding line retreat, highlighting ice-ocean interactions that \r\nlead to increased basal melt. In addition, Denman Glacier in East \r\nAntarctica was shown to hold potential for unstable retreat (Brancato \r\net al., 2020). In summary, the observed evolution of the ASE glaciers \r\nis compatible with, but not unequivocally indicating an ongoing MISI \r\n(medium confidence)."


id: 13240bd6a241c8e89f66514072d9cffc
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 527
content:
"the advantage of connecting melt rates to emissions scenarios, \r\na large variety of melt parametrizations exist (DeConto and Pollard, \r\n2016; Lazeroms et al., 2018; Reese et al., 2018; Hoffman et al., 2019; \r\nPelle et al., 2019; Jourdain et al., 2020), and there is low agreement\r\ndue to limited observational constraints (ocean temperature, salinity, \r\nvelocity, and ice shelf draft)(Jourdain et al., 2020), uncertainty in the \r\nphysics of parametrized processes, missing processes (e.g.,  tides), \r\nand uncertainty in the treatment of ice-sheet-climate feedbacks \r\n(Donat-Magnin et al., 2017; Bronselaer et al., 2018; Golledge et al., \r\n2019). Parametrizations are usually calibrated to present-day melt \r\nrates, but can respond differently to projected ocean warming (Favier \r\net  al., 2019; Jourdain et  al., 2020). Two different calibrations were \r\nused in ISMIP6 (Box 9.3; Jourdain et al., 2020; Nowicki et al"


id: 8238e60f6ff5e883568a05d9c2d0241e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 18015
content:
", 2020b): \r\none reproducing melt rates averaged around the whole continent \r\n(MeanAnt: Figure 9.19), and the other reproducing melt rates near the \r\ngrounding line of Pine Island Glacier (PIGL; see Figure 9.19), leading to \r\nlarge differences in melt rates. Evaluation with observations and two \r\ncavity-resolving models suggests that the MeanAnt parametrization \r\nbetter reproduces observed melt rates and projected increases in both \r\nthe warm Amundsen Sea Embayment and cold Ronne-Filchner shelf \r\ncavity, as well as total Antarctic melting (Jourdain et al., 2020). The \r\nPIGL calibration represents the upper end for increased basal melt \r\nsensitivity that would be caused by continent-wide changes to ocean \r\nwater properties and circulation under strong future forcing (Jourdain \r\net  al., 2020). The basal sliding law also has a  strong influence \r\non grounding line retreat and glacier acceleration in response to \r\nperturbations, and varies spatially (Sun et  al., 2020)"


id: 5d9b34f5eaf2889cad8a98373d4f0ea
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 12251
content:
" Sliding laws \r\n(Joughin et al., 2019) can only be constrained with observations in \r\nregions experiencing significant change, and with sufficiently long \r\nobservational records."


id: 431c953735e558bea43f7507aedb10cc
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 7957
content:
"Section 9.2.3.2 assesses that there is low confidence in simulations \r\nof Southern Ocean temperature. Few ocean models resolve ice-shelf \r\ncavities, and biases in present-day melt rates can be substantial in \r\nsome sectors, including the key region of the Amundsen Sea (e.g., an \r\nexception is the FESOM simulation in Figure  9.19 includes ice\u0002shelf cavities and simulates ice-shelf basal melting and refreezing) \r\n(Naughten et  al., 2018). An  increasing number of observational \r\nstudies from which basal melt rates are calculated (Huhn et al., 2018; \r\nAdusumilli et al., 2020; Das et al., 2020; Hirano et al., 2020; Stevens \r\net al., 2020), combined with improved understanding of influences \r\nspecific to water-masses and modes of melting or dissolving (Silvano \r\net al., 2018; Adusumilli et al., 2020; Malyarenko et al., 2020; Wahlin \r\net al., 2020), may help to refine these models in the future"


id: 58cc84ea26bbac5db232c51403546450
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 11466
content:
" However, \r\ngiven the limited number of available models and their biases, there \r\nis currently low confidence in the sub-shelf melt rates simulated by \r\nocean models."


id: eb509e9393b050b569861dfd6bd05a02
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 34521
content:
"The SROCC noted that AIS simulations are increasingly evaluated \r\nor formally calibrated with modern observations and/or paleodata - \r\nto obtain more realistic initial conditions (ice-sheet geometry, \r\nvelocity and forcing) and to constrain uncertainty in probabilistic \r\nprojections. This trend continues (Nias et  al., 2019; Gilford et  al., \r\n2020; Hamlington et  al., 2020b; Wernecke et  al., 2020). However, \r\nwhile the large-scale characteristics of the initial ice-sheet state \r\nhave improved significantly (Box  9.3), capturing the smaller-scale \r\nrates of change, including mass trends, remains challenging for \r\nmany models (Goldberg et  al., 2015; Reese et  al., 2020; Seroussi \r\net  al., 2020; Siegert et  al., 2020). This  increases uncertainty in \r\nprojections, especially for the 21st  century (Section  9.4.2.5)"


id: 8d0dc2a7c8ee32dc9c281a11fb81e0c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 19718
content:
" \r\nHowever, uncertainties in ice-sheet model simulations have been \r\nmuch better quantified since AR5, through model intercomparison \r\nprojects (in  particular, ISMIP6 and LARMIP-2; see Box  9.3), \r\nperturbed parameter ensembles, and increasing use of statistical \r\nemulation (Gilford et  al., 2020; Levermann et  al., 2020; Wernecke \r\net al., 2020; DeConto et al., 2021; Edwards et al., 2021) to better \r\nsample the parameter space. By exploring uncertainties more fully, \r\nthese methods have the potential to identify better simulations of \r\nthe historical period."


id: f7a3929130c33b0e20e6d023d81117b5
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 36471
content:
"Improvements in the representation of grounding line evolution in \r\nice-sheet models since AR5 (such as sub-grid schemes for basal friction \r\nand ice-shelf melt, and local grid refinement) means that most of the \r\nmodel simulations presented in SROCC were dominated by physical \r\nprocesses. Since then, these advances have been applied in several \r\nmodel intercomparison projects  - such as ISMIP6 and LARMIP-2 \r\n(see Box 9.3); MISMIP+ (Cornford et al. 2020); and ABUMIP (Sun et al. \r\n2020). All models participating in ISMIP6 and LARMIP-2 simulate \r\nice-shelf and grounding-line evolution, and include sub-shelf melt \r\nparametrization, which was not the case in the Sea-level Response to \r\nIce Sheet Evolution (SeaRISE) project intercomparison (Bindschadler \r\net  al., 2013; Nowicki et  al., 2013). Simulations of grounding line \r\nevolution (Seroussi et al., 2017, 2020) have benefitted from improved \r\nbedrock topography (Morlighem et al., 2020)"


id: d9020c70584838aea58e79d861cc2084
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 31650
content:
" Treatment of sub-shelf \r\nmelting, however, remains one of the causes of large differences in \r\nAIS models, particularly for partially floating grid cells in models with \r\ncoarse resolution (Levermann et al., 2020; Edwards et al., 2021). Due \r\nto the limitations in resolving cavities in ocean models, as described \r\nabove, basal melt rates are generally parameterized at the ice shelf \r\nbase, based on ocean model simulations of temperatures and salinity \r\ninstead (Nowicki et al., 2020b; Seroussi et al., 2020). While this has "


id: 9016dc8fab4215bec9c8b240514390c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20203
content:
"The AR5 projected a  negative contribution from Antarctic surface \r\nmass balance (SMB) changes to sea level over the 21st  century \r\n(i.e., mitigating sea level rise), due to increased snowfall associated \r\nwith warmer air temperatures. Sensitivity of SMB to Antarctic \r\nsurface air temperature change varied from 3.7 to 7% degC-1, and \r\nthe sea level projections assumed a sensitivity of 5.1 +- 1.5% degC-1\r\nfrom CMIP3 era models (Gregory and Huybrechts, 2006) to estimate \r\nSMB changes from Antarctic temperatures in the CMIP5 ensemble. \r\nSince the AR5, analyses of CMIP5 and CMIP6 models have found \r\nAntarctic temperature sensitivity for accumulation (precipitation \r\nminus sublimation) of 3.5 to 8.7% degC-1 (Frieler et al., 2015), for SMB \r\nof 6.0 to 9.9% degC-1 (Previdi and Polvani, 2016) and for precipitation of \r\naround 4 to 9% degC-1 (+-1 standard deviation ranges; Bracegirdle et al., \r\n2020)"


id: 2fff30f1f515b98676a379e20c841d5c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 4979
content:
" An accumulation sensitivity estimate derived from ice core \r\ndata lies in the middle of the range, around 6% degC-1 (Frieler et al., \r\n2015). These are consistent, within uncertainties, with each other and \r\nAR5, under the approximation that SMB is dominated by snowfall."


id: 858c1fad5d78908a05cb056c93458149
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 18546
content:
"The AR5 found that the median and likely sea level contributions \r\ndue to SMB from 1986-2005 to 2100 were -0.05 (-0.09 to \r\n-0.02) m under RCP8.5 and -0.02 (-0.05 to 0.00) m under RCP2.6. \r\nThe SROCC did not present a  separate SMB contribution, instead \r\nshowing total Antarctic projections derived from ice-sheet models \r\n(Section 9.4.2.5). Projections of the SMB contribution to sea level \r\ntend to be slightly more negative since AR5, due at least in part to \r\nthe higher range in equilibrium climate sensitivity values in CMIP6 \r\n(Payne et  al., 2021). Mean and +-1 standard deviation ranges for \r\ngrounded Antarctic Ice Sheet SMB changes from 2000 to 2100 \r\ncomputed from CMIP5 models are -0.08 (-0.13 to -0.04) m sea \r\nlevel equivalent (SLE) for RCP8.5 and, similarly for CMIP6 models, \r\nare -0.07 (-0.11 to -0.03) m for SSP5-8.5 (Gorte et al., 2020). The \r\ngeneral circulation models (GCMs) used to drive ice-sheet models \r\nin ISMIP6 (Box 9"


id: 7ec9c88698e0ab9e435f961a9ca011d6
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 17511
content:
"3) project mean grounded AIS SMB changes from \r\n2005 to 2100 of -0.06 (range -0.08 to -0.03) m SLE under RCP8.5 for \r\nthe six CMIP5 models (Seroussi et al., 2020) and -0.09 (range -0.10 \r\nto -0.07) m SLE under SSP5-8.5 for the four CMIP6 models, which \r\nhave climate sensitivity values of 4.8degC -5.3degC (Payne et al., 2021). \r\nWe apply the AR5 parametric AIS SMB model (Section 9.6.3.2) to \r\nupdated projections of global mean temperature from a two-layer \r\nenergy budget emulator (Supplementary Material  7.SM.2), which \r\ngives a  median -0.05 (5-95% range -0.07 to -0.02) m SLE for \r\nSSP5-8.5 (Section  9.4.2.5, Table  9.3), that is, similar to the AR5 \r\nassessment and slightly smaller than the CMIP6 estimate. This \r\nestimate is used to augment the LARMIP-2 dynamic projections \r\n(Box 9.3) in Sections 9.4.2.5 and 9.4.2.6"


id: 941ea993d2cd667e5800aa22368bc453
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20814
content:
" Overall, CMIP5 and CMIP6 \r\nGCM simulations of sea level fall by 2100 due to Antarctic SMB \r\nincreases are around 2-4 cm greater than estimates derived with \r\nthe statistical method used in AR5. Further details about projections \r\nof Antarctic temperature, precipitation and SMB are provided in \r\nSection Atlas.11.1.4, which assesses that, due to the challenges of \r\nmodel evaluation (Section 9.4.2.2) and the possibility of increased \r\nmeltwater runoff (Kittel et  al., 2021), there is only medium \r\nconfidence that the future contribution of Antarctic SMB to sea level \r\nthis century will be negative under all greenhouse gas emissions \r\nscenarios. Longer time scales are discussed in 9.4.2.6."


id: a153972df79f0bc8beb5052693d6c29c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 22939
content:
"The response of sub-shelf melting to ocean warming is also poorly \r\nconstrained. A  key unknown is whether, and when, cold ice-shelf \r\ncavities might become more similar to the Amundsen Sea Embayment, \r\nnot only in ocean temperature but also ice-ocean  heat exchange, \r\nwhich depends on the cavity geometry and ocean circulation \r\n(Little et al., 2009). Only two ocean models with ice-shelf cavities have \r\nbeen used to make sub-shelf basal melting projections for Special \r\nReport on Emissions Scenarios and Representative Concentration \r\nPathway (RCP) scenarios (Hellmer et  al., 2012; Timmermann and \r\nHellmer, 2013; Timmermann and Goeller, 2017; Naughten et  al., \r\n2018). The FESOM simulation, forced by a CMIP5 multi-model mean \r\nunder RCP8.5, projects a  90% increase in melting (Figure  9.19), \r\nalthough this could be overestimated due to an underestimation \r\nof present-day melt  rates (Section  9.4.2.2; Naughten et  al., 2018)"


id: 973f0d5743c8724075f6878df3889fbd
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 21312
content:
" \r\nThe temperature-melt relationship was parameterized by ISMIP6 in \r\nterms of heat exchange velocity in m a-1, and by LARMIP-2 as basal \r\nmelt sensitivity in m a-1 degC-1 (Box 9.3; Jourdain et al., 2020; Levermann \r\net  al., 2020; Reese et  al., 2020), and both vary widely around the \r\ncontinent, depending on cavity type. Median values of ISMIP6 heat \r\nexchange velocity vary by a  factor of 5-10 when calibrating to \r\neither mean Antarctic or high Pine Island Glacier observed melt rates \r\n(Section 9.4.2.2; Box 9.3; Jourdain et al., 2020). Basal melt sensitivities \r\nnear the grounding line estimated by Reese et al. (2020) with a box \r\nmodel of ocean overturning range from 3.9 m a-1 degC-1 for the Weddell \r\nSea to 10.5 m a-1 degC-1 for the Amundsen Sea region, with a continental \r\nmean of 5.3 m a-1 degC-1. Similarly high Amundsen Sea sensitivities \r\nare estimated in coupled ice-ocean simulations of Thwaites Glacier \r\n(mean 9.4 m a-1 degC-1; range 6-16 m a-1 degC-1) (Seroussi et al"


id: ef4a83fa7fe4f4bdf1b1e6e059c6e393
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 35124
content:
", 2017). \r\nThese large variations lead to large differences in basal melt rates and \r\nprojected sea level contributions when applied to the whole ice sheet \r\nin ISMIP6 and LARMIP-2 (Box 9.3). Projections of melt rates from the \r\ntwo ISMIP6 calibrations are higher than those from FESOM, driven \r\nby a  CMIP5 multi-model mean (Figure  9.19; Jourdain et  al., 2020). \r\nThe ISMIP6 ensemble mostly uses the mean Antarctic calibration, but \r\nincludes some simulations with the Pine Island Glacier calibration, \r\nand the ISMIP6 emulator samples more of these higher values; \r\nLARMIP-2 uses basal melt sensitivities (7-16 m a-1 degC-1) consistent \r\nwith estimates for the Amundsen Sea Embayment. Due to the limited \r\navailability of cavity-resolving ocean models, and the wide regional \r\nvariation in estimates of basal melt sensitivity to ocean temperature, \r\nthere is only low confidence in projected future sub-ice-shelf melt \r\nrates"


id: bd55756f66f9e270b643cc88a9887a24
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 27353
content:
" The impact of this uncertainty on AIS model projections to 2100 \r\nis discussed in Section 9.4.2.5."


id: fb4bffad6bc8cb19d4bc45cc0a303b07
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 37045
content:
"The SROCC highlighted that an important ongoing deficiency in \r\nprojections of Antarctic sub-shelf melting is the lack of ice-ocean \r\ncoupling in most continental-scale studies. Increased basal melting is \r\nmainly caused by warmer CDW (Section 9.2.2.3) on the continental \r\nshelves, and warming surface waters intruding under ice shelves \r\n(Naughten et al., 2018). Predicting whether or not open ocean water \r\nmasses will freely penetrate ice shelf cavities, or will be partially \r\nblocked by ocean density gradients, is complex (Wahlin et al., 2020); \r\nwhile melting related to CDW inflow is currently dominant in the \r\nAmundsen Sea Embayment, melt in other embayments is limited \r\nby deep inflows of high-salinity shelf water or seasonally warmed \r\nshallow incursions of Antarctic Surface Water (Stewart et al., 2019; \r\nAdusumilli et  al., 2020). There is little consensus regarding future \r\nchange in CDW (Section 9.2.2"


id: 8730ec8256894d6769573c95afcd8232
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 18802
content:
"3), and more generally low confidence\r\nin future change in the temperature of Antarctic ice-shelf cavities \r\n(Section 9.2.3.2)."


id: 10947584a53794d78d78fd0a1ff27fa7
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 88
content:
"Since SROCC, further studies have highlighted the modelling \r\nuncertainties in this area. Coastal surface air temperature projections \r\nin CMIP6 models show large inter-model differences driven by \r\nsea ice retreat and exhibit more warming relative to global mean \r\ntemperature under low emissions than high, due to delayed response \r\nof the Southern Ocean to stabilized emissions and stratospheric ozone \r\nrecovery (Bracegirdle et  al., 2020). The updated study of DeConto \r\net  al. (2021) includes improvements to the climate simulations \r\nrelative to those in DeConto and Pollard (2016), and the resulting \r\nsurface meltwater projections are now consistent with Trusel et al. \r\n(2015). However, the net effect of meltwater feedbacks on ice shelves \r\nis uncertain. Ice discharge is expected to lead to surface ocean and \r\natmosphere cooling: this increases ocean stratification and sub-shelf \r\nmelting, but also reduces ice-shelf surface melting and delays \r\nhydrofracturing (Golledge et al"


id: fe5743c413fc8162cf76b7d9e5242e33
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 37549
content:
", 2019; Sadai et al., 2020; DeConto \r\net al., 2021). The new studies are insufficient to change SROCC's low \r\nconfidence assessment on ice-shelf loss. The consequence of this \r\nuncertainty on projections is discussed in Section 9.4.2.5 and Box 9.4."


id: 57cb01a8b8b6d0bfa5d593eaf087d524
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 11310
content:
"The SROCC noted limited evidence from geological records and \r\nice-sheet modelling, suggesting that parts of the AIS experienced \r\nrapid (centennial) retreat likely due to MISI between 20,000 and \r\n9,000 years ago, and also described more uncertain evidence for the \r\nLast Interglacial (LIG) and mid-Pliocene Warm Period (MPWP). Recent \r\nsupport for past MISI is provided by model simulations of the WAIS \r\nduring the LIG (Clark et al., 2020), the British Ice Sheet during the last \r\ntermination (Gandy et al., 2018) and the Laurentide Ice Sheet during \r\nthe Younger Dryas (Pico et al., 2019), which show progressive retreat \r\ndespite declining temperatures, indicative of a  true (ice  dynamic) \r\ninstability. Direct observational evidence of rapid paleo ice-sheet \r\ngrounding line retreat is rare but, on the Larsen continental shelf, \r\nretreat rates of >10 km yr-1 during the deglaciation have been \r\nestimated (Dowdeswell et al., 2020)"


id: bf5edfc9818e7be2c02a08712132eb66
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 27663
content:
" MISI has also been inferred from \r\nsedimentological evidence of ice loss from Wilkes Subglacial Basin, \r\nEast Antarctica (Bertram et al., 2018; Wilson et al., 2018; Blackburn \r\net al., 2020) but these reconstructions cannot unambiguously identify \r\nunstable from progressive retreat. Therefore, there is limited evidence\r\nto identify the operation of instability mechanisms such as MISI \r\nin paleo ice-sheet retreat."


id: 91a95e529d28ad69f74c264535e18a01
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20459
content:
"MISI is a  proposed self-reinforcing mechanism within marine ice \r\nsheets that lie on a  bed that slopes down towards the interior of \r\nthe ice sheet, whereby, in the absence of ice-shelf buttressing, \r\nthe position of the grounding line is inherently unstable until \r\nreaching an upward sloping bed. The SROCC (Meredith et al., 2019) \r\nnoted advances in modelling MISI since AR5, but that 'significant \r\ndiscrepancies' remained in projections due to poor understanding \r\nof mechanisms, and lack of observational data to constrain the \r\nmodels. Since SROCC, modelling uncertainties have been more \r\nthoroughly explored, rather than constrained (compatibility of \r\ncurrent observations in the Amundsen Sea Embayment with MISI is \r\nassessed in Section 9.4.2.1). Internal climate variability might either \r\nslow (Hoffman et al., 2019) or amplify (Robel et al"


id: 9ebefbcbbf4a5081b20a0a9a240f08eb
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 22513
content:
", 2019) MISI, and \r\nstable grounding line positions can be reached on downward sloping \r\nbeds if ice shelves provide buttressing (Sergienko and Wingham, \r\n2019; Cornford et al., 2020). Ice-sheet model simulations that remove \r\nall Antarctic ice shelves (and prevent them from reforming) show \r\n2-10 m SLE Antarctic mass loss after 500 years due to MISI, of which \r\nWAIS collapse contributes 2-5 m (Sun et al., 2020), with the majority \r\nof the mass loss in the first one to two centuries. Much of the multi\u0002model variation is due to the sliding law (Section 9.4.2.2). However, \r\nit is not expected that widespread ice-shelf loss will occur before the \r\nend of the 21st century (Section 9.4.2.3; Box 9.4). A recent update \r\nof bed topography that unveiled large and overdeepened subglacial "


id: a4482281eb693272f1502f48ac1128
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 23402
content:
"occurrence in the present or the past (Section 9.4.2.2), its potential \r\nto affect future sea level rise was very uncertain. Since SROCC, new \r\nsimulations show later ice-shelf disintegration, in agreement with \r\nother models (Section 9.4.2.3; DeConto et al., 2021), and therefore \r\nlower projections at 2100 (Section  9.4.2.5). New theoretical \r\nevidence suggests that ice-cliff collapse may only occur after very \r\nrapid ice shelf disintegration caused by unusually high meltwater \r\nproduction (Clerc et al., 2019; Robel and Banwell, 2019), and that \r\nthe subsequent rate of retreat depends on the terminus geometry \r\n(Bassis and Ultee, 2019). As SROCC noted, only Crane Glacier on \r\nthe Peninsula has shown retreat consistent with MICI, after the \r\nLarsen B ice shelf collapsed, and MICI-style behaviour at Jakobshavn \r\nand Helheim Glaciers in Greenland might not be representative of \r\nwider Antarctic glaciers"


id: 69cffeab35d17613cbcf234d71508680
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 14250
content:
" Observations from Greenland show that \r\nsteep cliffs commonly evolve into short floating extensions, rather \r\nthan collapsing catastrophically (Joughin et al., 2020). As assessed \r\nin Section  9.4.2.2 and 9.4.2.3, there is therefore low confidence\r\nin simulating mechanisms that have the potential to cause \r\nwidespread, sustained and very rapid ice loss from Antarctica this \r\ncentury through MICI, and low confidence in projecting the driver of \r\nice-shelf disintegration."


id: f343c552a74839cb6fbef9488613d5cc
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 35786
content:
"Since SROCC, new projections have arisen from multi-model \r\nintercomparison projects ISMIP6 and LARMIP-2 (Box 9.3) and one \r\nmodel that includes MICI (Section 9.4.2.4; Table 9.3; DeConto et al., \r\n2021). Corrections are added to allow comparison: all ISMIP6-derived \r\nprojections have an estimate of the historical dynamical response \r\nto pre-2015 climate forcing added, which increases contributions \r\n(Box  9.3; Figure  9.18); the LARMIP-2 dynamic projections are \r\ncombined with an estimate of SMB, which decreases contributions \r\n(Sections 9.4.2.3 and 9.6.3.2); and the ISMIP6 emulated and LARMIP-2 \r\nprojections were re-estimate using the global surface air temperature \r\ndistributions from the two-layer energy budget emulator described in \r\nSupplementary Material 7.SM.2. The majority of the new projections \r\nindicate that, under all emissions scenarios, the AIS will lose mass \r\noverall and contribute to sea level rise"


id: 26940fde433df893994e6699ef20e1d6
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 3476
content:
" Most thinning occurs in the \r\nAmundsen Sea sector in WAIS and Totten Glacier in EAIS (Figure 9.18). \r\nThe most negative contribution is -0.02 m (5th percentile of ISMIP6 \r\ncombined RCP8.5 and SSP5-8.5 projections after correction) and \r\nthe largest contribution is 0.57 m SLE (95th percentile; Levermann \r\net al., 2020), or 0.63 m SLE with MICI (95th percentile; DeConto et al., \r\n2021). ISMIP6 ensemble ranges are wider for the high scenarios \r\n(RCP8.5/SSP5-8.5) than the low (RCP2.6/SSP1-2.6), in part because \r\nmore simulations were available. The ISMIP6 simulations that apply \r\nan ice-shelf collapse scenario based on exceedance of a  surface \r\nmeltwater threshold (Trusel et al., 2015), driven by CMIP5 models, \r\nshow only a small increase in mass loss (around 0-0.04 m), mostly \r\nfrom the Peninsula, due in part to the small number of ice shelves \r\npredicted to collapse this century (Seroussi et al., 2020)"


id: ac07f703ab437959b47415bedaccebe1
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 24587
content:
" Simulations \r\ndriven by the CMIP5 model HadGEM2-ES, which has unusually \r\nextreme warming in the Ross Sea (Barthel et al., 2020), show a larger \r\nmass loss (up to about 0.05 m) in East Antarctica under ice-shelf \r\ncollapse (Edwards et al., 2021). The ISMIP6 projections do not include \r\nthe efficient meltwater drainage or atmospheric feedbacks that could \r\nreduce mass loss further (Seroussi et al., 2020)."


id: 15d3511f2688065090dd5eb5d44efa5c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 956
content:
"The AR5 assessed the median and likely (66-100% probability) sea \r\nlevel contributions of the AIS in 2100 relative to 1986-2005 to be \r\n0.06 (-0.04 to +0.16) m SLE under RCP2.6 and 0.04 (-0.08 to +0.14) m \r\nSLE under RCP8.5 (Table 9.3; no change when using the AR6 baseline). \r\nThe AR5 stated that only the collapse of the marine-based sectors of \r\nthe AIS, if initiated, could cause GMSL to rise substantially above the \r\nlikely range during the 21st century, with medium confidence that \r\nthis would not exceed several tenths of a metre during this period. \r\nThe assessment of the dynamical contribution had no dependence on \r\nemissions scenarios, due to the lack of literature, so the decrease in \r\nsea level contribution in the higher-emissions scenario was solely due \r\nto increased SMB (Section 9.4.2.3). The SROCC (Oppenheimer et al"


id: 9ab640514cff3006e7e8f94cc2769cf9
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 21871
content:
", \r\n2019) assessed the total contribution based on five new ice-sheet \r\nmodelling studies that incorporated marine ice-sheet dynamics, \r\ncombining their estimates and interpreting the 5-95th percentile \r\nrange of the resulting distribution as the likely range (17-83% \r\nprobability interval, i.e., not open-ended as in the AR5). The median \r\nand likely range contributions by 2100 were 0.04 (0.01-0.11) m \r\nunder RCP2.6 and 0.12 (0.03-0.28)  m under RCP8.5 (Table  9.3). \r\nThe positive scenario-dependence in SROCC  - where increases in \r\ndynamic losses driven by ocean warming and ice-shelf disintegration \r\nunder higher emissions (Section 9.4.2.3) dominate over increases in \r\nSMB - arose from a combination of physical processes and model \r\nlimitations. Modelling improvements in these studies included \r\nimproved representations of grounding line response to drivers, more "


id: 6e12a6c19f428f971d798c28545c5378
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 14932
content:
"noted that deep uncertainty remained beyond 2100: while solid \r\nEarth feedbacks could reduce ice loss over multi-century time scales, \r\nMICI (Section 9.4.2.4) might give contributions higher than the likely\r\nranges. The SROCC also presented structured expert judgement \r\n(SEJ) projections for comparison (Bamber et al., 2019), which give \r\nhigher values. Since SROCC, three studies have made projections \r\nto 2300: (i) Rodehacke et  al. (2020) assessed two methods for \r\nimplementing precipitation changes (based on repeating 2071-2100 \r\nforcings beyond 2100), which both gave negative projections at \r\n2300 because the dynamic response was very small (-0.11 to \r\n-0.01 m SLE for RCP2.6; -0.25 to -0.07 m for RCP8.5 forcing); (ii) In \r\ncontrast, simulations forced by 2081-2100 ocean-only projections \r\nunder RCP8.5/SSP5-8.5 beyond 2100, using two implementations \r\nof the ISMIP6 'non-local' basal melt parametrizations (Box 9.3 and \r\nSection 9.4.2.2) and two sliding laws, are all positive (0"


id: d2fc688e8a82637d6e61975e897ad76c
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 30715
content:
"08 m to 0.96 m \r\nSLE by 2300), though these do not include the negative contribution \r\nfrom SMB changes (Lipscomb et al., 2021); (iii) Finally, DeConto et al. \r\n(2021) update projections for the MICI hypothesis (Section 9.4.2.4) \r\nusing the extensions of the RCPs to 2300, and obtain far higher \r\ncontributions: median (17-83%) ranges of 1.09 (0.71-1.35) m SLE \r\nunder RCP2.6 and 9.60 (6.87-13.54) m SLE under RCP8.5. These \r\nare larger than previous estimates (DeConto and Pollard, 2016), \r\nparticularly at the upper end: 0.68 (0.29-1.13) m SLE for RCP2.6 \r\nand 8.40 (7.47-9.76) m for RCP8.5 (Edwards et al., 2019), which can \r\nlargely be explained by the higher maximum ice cliff calving rate. \r\nLARMIP-2 dynamic projections (Box  9.3) are also estimated under \r\nthe extended SSPs and corrected with SMB (as in Section 9.4.2.5), \r\ngiving median (17-83%) ranges of 0.40 (0.18-0.78)  m SLE at \r\n2300 under SSP1-2.6 and 1.57 (0.68-3.14) m under SSP5-8.5"


id: a2a1c8369a0fcce443ff800457788b62
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 23150
content:
" The \r\nlonger time scale may invalidate the linear response assumption of \r\nLARMIP-2, which neglects any self-dampening or self-amplifying \r\nprocesses. The ranges of projections for 2300 without MICI (Golledge \r\net al., 2015; Bulthuis et al., 2019; Levermann et al., 2020; Rodehacke \r\net al., 2020; Lipscomb et al., 2021; 'assessed ice-sheet contributions' \r\nin Section 9.6.3.5 are -0.14 to +0.78 m SLE under RCP2.6/SSP1-2.6, \r\nand -0.27 to 3.14 m SLE under RCP8.5/SSP5-8.5). The lower bounds \r\nare the 5th percentile of Bulthuis et al. (2019) and the lowest mean/\r\nmedian from Rodehacke et al. (2020), respectively; the upper bounds \r\nare the 83% percentiles of the LARMIP-2 estimates. These ranges \r\nare wider than SROCC likely ranges, and more consistent with the \r\nSEJ (Bamber et al., 2019). However, projections in which Antarctica \r\ncontributes much more than the assessed ranges under sustained \r\nvery high greenhouse gas emissions - that is, around 7-14 m to GMSL \r\nby 2300 (DeConto et al"


id: a34ff1016efa962a4b6da0a2b394b73b
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 23257
content:
", 2021), cannot be ruled out, and are taken \r\nas a sensitivity case (Section 9.6.3.5; Table 9.11). In summary, there \r\nis high confidence that Antarctic mass loss will be greater beyond \r\n2100 under high greenhouse gas emissions, but the large range \r\nof projections mean we have only low confidence in the likely AIS \r\ncontribution to GMSL by 2300 for a given scenario. Deep uncertainty\r\nremains in the role of AIS instabilities under very high emissions."


id: 67db75e5ab0e93e3a73ba9bd8eaf3203
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 13931
content:
"It is not possible to distinguish which of ISMIP6 and LARMIP-2 is \r\nmore realistic, due to limitations in historical simulations (Box 9.3) \r\nand understanding of basal melting (Section  9.4.2.3.2), so the \r\nprojections are combined using a 'p-box' approach (Section 9.6.3.2). \r\nThe mean of the ISMIP6 emulated and LARMIP-2 medians gives the \r\nassessed median projections, and the outer edges of the 17-83% \r\nranges give the outer edges of the assessed likely (17-83%) ranges - \r\nthat is, encompassing the structural and parametric uncertainties \r\nof both methods, giving medium confidence in their combined \r\nprojections. The main difference between this assessment and SROCC \r\nis to increase the medians of the lower scenarios by 0.05-0.07 m, \r\nso that all SSPs are similar to SROCC assessment of RCP8.5, and \r\nto substantially increase the upper ends of the likely ranges: by \r\n0.14-0.16 m for RCP2.6/SSP1-2.6 and RCP4.5/SSP2-4.5, and 0.06 m \r\nfor RCP8.5/SSP5-8.5"


id: 99472dc3e02e9749524c341e91a6a09c
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 21629
content:
" The increase relative to SROCC is partly due to \r\nthe increase in LARMIP-2 projections relative to the original LARMIP \r\nstudy (Levermann et  al., 2014), arising from the larger number of \r\nparticipating ice-sheet models (Levermann et al., 2020). The historical \r\ndynamic response to pre-2015 climate forcing applied to the ISMIP6 \r\nemulator could be overestimated, due to the assumption of a constant \r\nfuture rate (Box 9.3). This assessment encompasses SROCC and all \r\nprojections since, except the 83rd percentiles of projections that \r\nallow MICI under RCP8.5 (DeConto et al., 2021) and the Structured \r\nExpert Judgement (SEJ) under 5degC shown in SROCC (Bamber et al., \r\n2019). Both are used in further p-box estimates to give the outer \r\nlimits of low confidence assessments (Section 9.6.3.2)."


id: 1c68ef9583c390f765301c4e8586ff57
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 1926
content:
"of a  further study (Bulthuis et  al., 2019), SROCC confirmed that \r\nlow emissions would limit Antarctic ice loss over multi-century \r\ntime scales (high confidence), but it was not possible to determine \r\nwhether this was sufficient to prevent substantial ice loss (medium \r\nconfidence). Since SROCC, new studies have revisited this topic \r\n(Garbe et  al., 2020; Rodehacke et  al., 2020; Van Breedam et  al., \r\n2020; DeConto et al., 2021; Lipscomb et al., 2021), allowing a more \r\ncomplete assessment along with other studies (Feldmann and \r\nLevermann, 2015; Clark et al., 2016; Golledge et al., 2017a; Edwards \r\net  al., 2019) and the extension to LARMIP-2 above. The majority \r\nproject 0-1.3 m SLE on multi-century time scales under scenarios of \r\n1degC-2degC warming. Projections can increase up to 2 m SLE under high \r\nbasal melt sensitivity to ocean warming (Section 9.4.2.3; Lipscomb \r\net al., 2021) or MICI (Section 9.4.2.4)"


id: d0f3d184d51f06e104c67be298621f14
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 30406
content:
" On multi-millennial time scales \r\n(>=2,000 years), many projections remain below 1.6 m SLE under \r\n1degC-2degC warming - that is, less than about half of the WAIS in SLE \r\n(see also Section  9.6.3.5 and Figure  9.30). Other studies project \r\nmajority or total loss of WAIS under 1degC-2degC warming, exceeding \r\n2 m SLE, under the higher end of the warming range (>=1.5degC), or high \r\nocean warming (>=0.5degC) and/or high basal melting around WAIS, or \r\nMICI. All but two of these multi-millennial studies use variants of \r\nthe same ice-sheet model, though different modelling choices mean \r\nthey can be considered quasi-independent. Simulations of previous \r\ninterglacial periods often show near or total WAIS disintegration, with \r\nmass loss exceeding 3 m SLE (e.g. Figure 9.18), although limitations \r\nof these studies or inferences that can be drawn under different \r\nforcings limit confidence in the robustness of these as quantitative \r\nanalogues (Sections 9.4.2.4 and 9.6.2)"


id: 5f4f535aade676d79ccefaa9dfd69368
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 12565
content:
" Overall, increased evidence \r\nand agreement on the time scales and drivers of mass loss confirm \r\nthe SR1.5 assessment that a threshold for WAIS instability may be \r\nclose to 1.5degC-2degC (medium confidence), and that the probability \r\nof passing a  threshold is larger for 2degC warming than for 1.5degC \r\n(medium confidence), particularly under strong ocean warming. New \r\nprojections agree with previous studies that only part of WAIS would \r\nbe lost on multi-century time scales if warming remains less than \r\n2degC (medium confidence). There is limited agreement about whether \r\ncomplete disintegration would eventually occur at this level of \r\nwarming, but medium confidence this would take millennia."


id: 4a8283da8d14e11c1aa289962bd6616c
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 9124
content:
"The SROCC (Meredith et al., 2019; Oppenheimer et al., 2019) assessed \r\nthat Antarctic mass losses could be irreversible over decades to \r\nmillennia (low confidence). Garbe et  al. (2020) show that the AIS \r\nis always volumetrically smaller when regrowing under a  given \r\nwarming level than when it retreats under the same forcing. Even if \r\nretreat followed by regrowth results in a net zero change in volume, \r\nthe spatial distribution of mass may be altered, especially in parts of \r\nWest Antarctica vulnerable to MISI. Projections that start reducing \r\nCO2 concentrations from 2030 onwards, reaching pre-industrial \r\nlevels around 2300, show sea level contributions exceeding 1 m \r\nby 2500 when including MICI (DeConto et al., 2021). New research \r\ntherefore confirms SROCC assessment that mass loss from the AIS is \r\nirreversible on decadal to millennial time scales (low confidence) (FAQ \r\n9"


id: af5409528b59b6220434e091c87e318d
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 25102
content:
"1), and suggests that reducing atmospheric CO2 concentrations or \r\ntemperatures to pre-industrial levels may not be sufficient to prevent \r\nor reverse substantial Antarctic mass losses (low confidence)."


id: cfa9a2b3c9d16659eec23b5ca0896e3b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 30205
content:
"The IPCC's Fifth Assessment Report (AR5; Vaughan et  al., 2013) \r\nassessed glacier changes from studies based on the regions defined \r\nin the Randolph Glacier Inventory (RGI; RGI version 2.0): a satellite \r\nobservation-based, global inventory of glacier outlines for the \r\nyear 2000. Following Special Report on the Ocean and Cryosphere \r\nin a  Changing Climate (SROCC; Hock et  al., 2019b; Meredith \r\net  al., 2019), we report on studies based on RGI version 6.0 (RGI \r\nConsortium, 2017). Increased volume of satellite observations and \r\nthe inclusion of detailed regional glacier inventories has resulted in \r\nan improved inventory (RGI Consortium, 2017). A  new consensus \r\nestimate for the ice thickness distribution of all glaciers in RGI 6.0 \r\nwas obtained from an ensemble of five numerical models. However, \r\nonly one out of five models covered all regions (Farinotti et al"


id: 6713836da37179701638d23ba4bf234b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 13807
content:
", 2019), \r\nand was, where possible, calibrated and validated with the worldwide \r\nGlacier Thickness Database (GlaThiDa 3.0: GlaThiDa Consortium, \r\n2019; Welty et  al., 2020). The updated inventory shows decreases \r\nin estimated glacier volume in the Arctic, High Mountain Asia and \r\nSouthern Andes, partially compensated by increases in Antarctica. \r\n15% of the total glacier volume is estimated to be below sea level \r\nand would not contribute to sea level rise if melted (Farinotti et al., \r\n2019). Supplementary Material Table  9.SM.2 shows the inventory \r\nglacier area and mass for each region in the year 2000."


id: 4af3b84190bfcc1f10d4fa105173b1e3
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 9206
content:
"Under around 2degC-3degC peak warming, complete or near-complete \r\nloss of the WAIS is projected in most studies after multiple millennia \r\n(low confidence), with continent-wide mass losses of around 2-5 m \r\nSLE or more; this could occur on multi-century time scales under very \r\nhigh basal melting (Lipscomb et al., 2021) or widespread ice-shelf \r\nloss and/or MICI (low confidence) (Sun et al., 2020; DeConto et al., \r\n2021). Mass losses under around 2degC-3degC warming could be less \r\nthan 2 m SLE, particularly for multi-century time scales, low basal \r\nmelting, or less responsive sliding laws. If warming exceeds around \r\n3degC above pre-industrial, part of the EAIS (typically the Wilkes \r\nSubglacial Basin) is projected to be lost on multi-millennial time \r\nscales (low confidence), with total AIS mass loss equivalent to around \r\n6-12 m or more sea level rise; mass loss could be much smaller if the \r\ndynamic response is small (Bulthuis et al., 2019; Rodehacke et al"


id: 56077f576f4d5e1e47bc0229ca21c5ba
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 11025
content:
", \r\n2020), or much faster under widespread ice-shelf loss and/or MICI \r\n(Sun et al., 2020; DeConto et al., 2021). A study by Garbe et al. (2020) \r\nsuggests that 6degC sustained warming and associated mass loss of \r\nabout 12 m SLE may be a  critical threshold beyond which the ice "


id: fd882fde7579b191f5c512b8b33d9b0b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 37407
content:
"The SROCC found a  globally coherent trend of glacier decline in \r\nthe last decades, despite large annual variability and regional \r\ndifferences (very high confidence). Section  2.3.2.3 assesses the \r\nglobal glacier mass changes for the whole 20th  century (see \r\nTable  9.5 for contribution to the sea level budget. Note that the \r\nperipheral glaciers in Greenland and Antarctica are added to the ice \r\nsheets for the budget). The AR6 assessment is based on Marzeion \r\net  al. (2015), using glacier-length reconstructions (Leclercq et  al., \r\n2011) and a glacier model forced by gridded climate observations \r\n(Marzeion et  al., 2012), and not considering the estimated mass \r\nloss of uncharted glaciers (100 +- 50 Gt yr-1; Parkes and Marzeion, \r\n2018). The time series are assumed independent, resulting in larger \r\nuncertainty than presented in SROCC (see also Section 9.6.1)"


id: f474dcf8568ef3d820cb2013354ad9e6
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 35962
content:
" The rate \r\nof global glacier mass loss (excluding the periphery of ice sheets) for \r\nthe period 1901-1990 is estimated to be very likely 210 +- 90 Gt yr -1, \r\nrepresenting 16 [28 to 7] % of the glacier mass in 1901, in agreement \r\nwith SROCC within uncertainty estimates."


id: 2e460b8f09568af28c293e863ef574a1
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 4716
content:
"The glacier mass loss rate from geodetic mass balance \r\nassessments in the Southern Andes during 2006-2015 was smaller \r\n(720  +-  70  kg  m-2  yr -1; Braun et  al., 2019; Dussaillant et  al., \r\n2019; Hugonnet et  al., 2021) than previously assessed in SROCC \r\n(860 +- 160 kg m-2 yr -1), though within uncertainties. In the Central \r\nand Desert regions of the Southern Andes, an increase in mass loss \r\nfrom 2000-2009 to 2010-2018, and a high loss rate in Patagonia for \r\nthe whole period, are observed (Dussaillant et al., 2019). Records of \r\nglacier mass loss in Peru (Seehaus et al., 2019a) and Bolivia (Seehaus \r\net al., 2019b) in the period 2000-2016 show an increase in mass \r\nloss towards the end of the observation period. In western North \r\nAmerica, outside of Alaska and western Yukon, there was a fourfold \r\nincrease in mass loss for 2009-2018 (860 +- 320 kg  m-2  yr -1) \r\ncompared to 2000-2009 (203 +- 214 kg m-2 yr -1; Menounos et al"


id: 59a84aa061a8e4c48955e6f78df81b11
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 11603
content:
", \r\n2019), and in the Canadian Arctic there was a  doubling of mass \r\nloss in the last two decades compared with pre-1996 (Noel et al., \r\n2018; Cook et  al., 2019). The peripheral glaciers in NE Greenland \r\nexperienced a 23% increase in mass loss in 1980-2014 compared \r\nto the period 1910 to 1978-1987 (Carrivick et al., 2019). In Iceland, \r\n16 +- 4% of the around 1890 glacier mass has been lost; about half \r\nof that loss occurred in the period 1994-2019 (Adalgeirsdottir et al., \r\n2020). Glacier records starting in 1960 in Norway show that half of \r\nthe observed glaciers advanced in the 1990s but all have retreated \r\nsince 2000 (Andreassen et  al., 2020). In Svalbard, glaciers have \r\nbeen losing mass since the 1960s, with a tendency towards more \r\nnegative mass balance since 2000 (Deschamps-Berger et al., 2019; \r\nVan Pelt et al., 2019; Morris et al., 2020; Noel et al., 2020; Schuler \r\net al., 2020)"


id: 5078f9f828fc051f3df54ef9e52e9577
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 10106
content:
" A similar increase in mass loss has been observed for \r\nFranz Josef Land in the Russian Arctic (Zheng et al., 2018). Rapid \r\nretreat and downwasting throughout the European Alps in the \r\nearly 21st century is reported (Sommer et al., 2020) and long-term \r\nrecords, although limited, indicate sustained glacier mass loss in \r\nHigh Mountain Asia since around 1850, with increased mass loss \r\nin recent decades (Shean et  al., 2020). In summary, although \r\ninterannual variability is high in many regions, glacier mass records \r\nthroughout the world show with very high confidence that the \r\nloss rate has been increasing in the last two decades (see also \r\nSection 8.3.1.7.1 and 12.4 for regional glacier assessment)."


id: a4ba4645d925acf405a135bed8d1115e
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 23488
content:
"Since SROCC, new regional estimates for the Andes (Dussaillant \r\net  al., 2019), High Mountain Asia (Shean et  al., 2020), Iceland \r\n(Adalgeirsdottir et al., 2020), the European Alps (Davaze et al., 2020; \r\nSommer et al., 2020) and Svalbard (Schuler et al., 2020), two new \r\nglobal (Ciraci et  al., 2020; Hugonnet et  al., 2021) and an ad  hoc \r\nestimate for the latest glaciological observations (Zemp et al., 2020) \r\nhave extended the glacier mass change time series up to 2018-2019 \r\n(Figure 9.21 and Supplementary Material Table 9.SM.3). A reconciled \r\nglobal estimate for the period 1962-2019 has been compiled by Slater \r\net al. (2021). However, in contrast to Slater et al. (2021), after 2000 \r\nthis assessment is based on the first globally complete and consistent \r\nestimate of 21st-century glacier mass change from differencing \r\nof digital elevation models (Hugonnet et  al., 2021) covering \r\n94"


id: fa72074a55bd62710525191085274678
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 36922
content:
"7% of glacier area with glacier mass change for each glacier in \r\nthe inventory produced with unprecedented accuracy. The estimates \r\nfrom Hugonnet et al. (2021) agree within uncertainties with new and \r\nprevious estimates at global (Hock et al., 2019b; Wouters et al., 2019; \r\nZemp et al., 2019; Ciraci et al., 2020; Slater et al., 2021) and regional \r\nscale (Dussaillant et  al., 2019; Adalgeirsdottir et  al., 2020; Schuler \r\net al., 2020; Shean et al., 2020). Excluding peripheral glaciers of ice \r\nsheets (RGI regions 5 and 19), glacier mass loss rate was very likely\r\n170 +- 80 Gt yr -1 for the period 1971 to 2019 (8 [4 to 14] % of 1971 \r\nglacier mass), 210 +- 50 Gt yr -1 over the period 1993-2019 (6 [4 to \r\n8] % of 1993 glacier mass) and 240 +- 40 Gt yr-1 over the period \r\n2006-2019 (3 [2 to 4] % of 2006 glacier mass; Sections  2.3.2.3 \r\nand 9.6.1, Table  9.5,4 and Cross-Chapter Box  9.1)"


id: e061f0250efb5ff65fb3bec6b6eea252
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 32803
content:
" Including the \r\nperipheral glaciers of the ice sheets, the global glacier mass loss rate \r\nin the period 2000-2019 is very likely 266 +- 16 Gt yr -1 (4 [3 to 6] % \r\nof glacier mass in 2000) with an increase in the mass loss rate from \r\n240 +- 9 Gt yr-1 in 2000-2009 to 290 +- 10 Gt yr-1 in 2010-2019 (high \r\nconfidence). These estimates are in agreement with SROCC estimate \r\nand extend the period to 2018-2019. In summary, new evidence \r\npublished since SROCC shows that, during the decade 2010-2019, \r\nglaciers lost more mass than in any other decade since the beginning \r\nof the observational record (very high confidence) (Section 8.3.1.7.1 \r\nand Figure 9.20)."


id: 9696c2d9a6321447ea3bebfa23240a11
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 21213
content:
"Section 2.3.2.3 assesses that the rate and global character of glacier \r\nretreat in the latter part of 20th century, and finds that the first decades \r\nof the 21st century appear to be unusual in the context of the Holocene \r\n(medium confidence) and the global glacier recession in the beginning \r\nof the 21st  century to be unprecedented in the last 2000 years \r\n(medium confidence). These assessments are supported by regional \r\nevidence. New reconstructions of the Patagonian Ice Sheet suggest \r\nthat 20th-century glacial recession occurred faster than at any time \r\nduring the Holocene (Davies et al., 2020). The reconstructions of glacier \r\nvariations show that the glaciers in some regions are now smaller \r\nthan previously recorded: since the mid-16th  century in the Mont \r\nBlanc and Grindelwald regions of the European Alps (Nussbaumer and \r\nZumbuhl, 2012), since the 9th century in Norway (Nesje et al., 2012), \r\nand for the past 1800 years in north-west Iceland (Harning et  al"


id: b2593a067a9625479e02c1a49dd8f9f
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 25572
content:
", \r\n2016, 2018). In Arctic Canada and Svalbard, many glaciers are now \r\nsmaller than they have been in at least 4000 years (Lowell et al., 2013; \r\nMiller et al., 2013, 2017; Schweinsberg et al., 2017, 2018) and more \r\nthan 40,000 years in Baffin Island (Pendleton et al., 2019). Although \r\nthe millennial glacier length variation records are incomplete and \r\ndiscontinuous, and glacier fluctuations depend on multiple factors \r\n(e.g., temperature, precipitation, topography, internal glacial dynamics), \r\nthere is a coherent relationship between rising temperatures, negative \r\nmass balance and glacier retreat on centennial time scales across most \r\nof the world. Glaciological and geodetic observations show that the \r\nrates of early 21st-century mass loss are the highest since 1850 (Zemp \r\net al., 2015). For all regions with long-term observations, glacier mass \r\nin the decade 2010-2019 was the smallest since at least the beginning \r\nof the 20th century (medium confidence)."


id: 5310266f71144513a18e19ac3ff17a0b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 10535
content:
"In the Northern Hemisphere, where summer insolation decreased \r\nduring the Holocene (Section 2.2.1), glaciers generally waxed (Briner \r\net al., 2016; Kaufman et al., 2016; Lecavalier et al., 2017; Zhang et al., \r\n2017; Axford et al., 2019; Geirsdottir et al., 2019; Larsen et al., 2019; \r\nLuckman et al., 2020). Conversely, in the Southern Hemisphere, where \r\nsummer insolation increased during the Holocene, glaciers generally \r\nwaned (Solomina et al., 2015; Kaplan et al., 2016; Reynhout et al., \r\n2019). However, these general global trends were modulated by \r\nregional climate variations in temperature and precipitation (Murari \r\net al., 2014; Kaplan et al., 2016; Batbaatar et al., 2018; Saha et al., \r\n2018) and there are a number of examples of this. A precipitation \r\nincrease led to a local early Holocene (7-8 ka) glacier maximum in arid \r\nMongolia (Gichginii Range)"


id: 913cfaa13fdd71d7b21dca8bcd9bec7a
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 20388
content:
" Glacier advances at about 9 ka in south\u0002west Greenland have been suggested to be a consequence of the \r\nfreshwater pulse from the Laurentide Ice Sheet, which led to cooling \r\nin the Baffin Bay area (Schweinsberg et al., 2018). Lake sediments \r\nindicate that the glaciers in the region were smaller than today, or \r\nabsent between 8.6 and 1.4 ka (Larocca et  al., 2020). Glaciers on \r\nthe Antarctic Peninsula and in Patagonia during the Holocene were \r\nstrongly affected by the southern westerly winds, sea ice extent, \r\nand ocean circulation (Garcia et al., 2020). Recent studies indicate \r\nthat explosive volcanism can drive glacier advances (Solomina et al., \r\n2015, 2016; Schweinsberg et  al., 2018; Bronnimann et  al., 2019). \r\nIn summary, on millennial time scales over the Holocene, there is \r\nhigh confidence that orbital forcing drove hemispheric-scale glacier \r\nvariations, but new studies provide a nuanced picture of responses to \r\na variety of regional-scale forcings."


id: 74ecb120a9b3fc186cfcc37bcf818ad0
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 15990
content:
"In contrast to the global glacier mass decline (Figure 9.21, Table 9.5, and \r\nSupplementary Material 9.SM.2), a few glaciers have gained mass or \r\nadvanced due to internal glacier dynamics or locally restricted climatic \r\ncauses. The SROCC discusses the 'Karakoram anomaly' (centred on the \r\nwestern Kunlun range (at about 80degE, 35degN), but also covering part \r\nof the Pamir and Karakoram ranges), where glaciers have been close \r\nto balance since at least the 1970s, and had a slightly positive mass \r\nbalance since the 2000s. Since SROCC, new evidence suggests that this \r\nanomaly is related to a combination of low-temperature sensitivity of \r\ndebris-covered glaciers, a decrease of summer air temperatures (Cross\u0002Chapter Box 10.3), and an increase in snowfall, possibly caused by \r\nincreases in evapotranspiration from irrigated agriculture (Bonekamp \r\net al., 2019; de Kok et al., 2020; Farinotti et al., 2020; Shean et al., \r\n2020)"


id: f8859ce96986c8e2f1322dacb3f53104
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 36611
content:
" However, a recent geodetic mass balance estimate suggests \r\nsubstantially increased thinning rates of High Mountain Asian glaciers \r\nafter about 2010 (Hugonnet et al., 2021). There is limited evidence to \r\nassess whether the Karakoram anomaly will persist in coming decades \r\nbut, due to the projected increase in air temperature throughout the \r\nregion, its long-term persistence is unlikely (high confidence) (Cross\u0002Chapter Box  10.3; Kraaijenbrink et  al., 2017; de Kok et  al., 2020; \r\nFarinotti et al., 2020)."


id: 1e1ea0b06dd70f9da86ff44bb14f67e4
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 2178
content:
"had a  significant effect on observed long-term glacier changes. \r\nSeveral studies have shown melt increases due to the deposition \r\nof light-absorbing particles (Schmale et  al., 2017; Wittmann et  al., \r\n2017; Sigl et al., 2018; Di Mauro et al., 2019, 2020; Magalhaes et al., \r\n2019; Constantin et al., 2020). Conversely, increasingly thick debris \r\ncover (>2-5 cm) on retreating glaciers can slow down glacier melt \r\n(Pratap et al., 2015; Brun et al., 2016). Although debris covers only \r\nabout 4-7% of the total glacier area globally (Scherler et al., 2018; \r\nHerreid and Pellicciotti, 2020), many glaciers are heavily debris\u0002covered in their lower reaches, especially in High Mountain Asia, the \r\nCaucasus, the European Alps, Southern Andes and Alaska, resulting \r\nin different responses to warming than similar clean-ice glaciers"


id: 8478c9c6a0faf5f81aefdbcaa9a4b5a6
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 18372
content:
" A \r\nshift in regional meteorological conditions, driven by the location and \r\nstrength of the upper level zonal wind, has been found to have forced \r\nrecent high mass loss rates in Western North America (Menounos \r\net al., 2019). High geothermal heat flux areas underneath glaciers and \r\nhigh energy dissipation in the flow of water and ice causes additional \r\nmass loss of the glaciers in Iceland (Johannesson et  al., 2020), \r\naccounting for 20% of the mass loss since 1994 (Adalgeirsdottir \r\net al. 2020). Glacier lake volume in front of retreating glaciers, has \r\nincreased globally by around 48% between 1990 and 2018 (Shugar \r\net al., 2020), which can increase both subaqueous melt and calving. \r\nIn summary, there is high confidence that non-climatic drivers have \r\nand will continue to modulate the first-order temperature response \r\nof glaciers in some regions."


id: a28c76e4a83283f7af5c2d401182dfa0
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 23136
content:
"Secondary processes such as debris-cover thickening (e.g.,  Herreid \r\nand Pellicciotti, 2020), albedo changes due to light-absorbing \r\nparticles (e.g., Magalhaes et al., 2019; Williamson et al., 2019), trends \r\nof refreezing and water storage in firn (e.g., Ochwat et al., 2021), \r\ndynamic instabilities such as surges (e.g., Thogersen et  al., 2019) \r\nor glacier collapse (e.g.,  Kaab et  al., 2018), are not represented \r\nin global glacier models, resulting in both underestimated and \r\noverestimated sensitivity to warming that is currently not possible to \r\nquantify. Furthermore, challenges for future projections are caused \r\nby the low-resolution and high-spatial variability at sub-grid scale \r\nof the precipitation amount provided by general circulation models \r\n(GCMs), which requires downscaling to the spatial scale of a glacier \r\n(Maussion et al., 2019; Zekollari et al., 2019; Marzeion et al., 2020)"


id: 4d83ee76f60ab2b7d5a2ad7547a1079
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 9628
content:
" \r\nIn summary, in agreement with SROCC, progress in global scale \r\nglacier modelling efforts allows medium confidence in the capability \r\nof current-generation glacier models to simulate the magnitude and \r\ntiming of glacier mass changes as a response to climatic forcing."


id: 4ab7cafe9a550f81e1548cab5a6052ef
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 9162
content:
"The AR5 (Vaughan et  al., 2013) and SROCC (Hock et  al., 2019b) \r\nstated with high confidence that the world's glaciers are presently \r\nin imbalance due to the warming of recent decades. The observed \r\nretreat of glaciers is only a partial response to the already realized \r\nwarming (Christian et al., 2018), and they are committed to losing \r\nconsiderable mass in the future, even without further change in \r\nair temperature (Mernild et al., 2013; Trussel et al., 2013; Zekollari \r\nand Huybrechts, 2015; Huss and Fischer, 2016; Marzeion et  al., \r\n2018; Jouvet and Huss, 2019). One model estimates that 36 +- 8 % \r\nof global glacier mass is already committed to be lost due to \r\npast greenhouse gas emissions (Marzeion et  al., 2018). Although \r\naccumulation and ablation instantly determine the SMB, the glacier \r\ngeometries adjust to changed atmospheric conditions over a longer \r\ntime (Zekollari et  al., 2020)"


id: 99c748762e817037dc65712ca3c8ea73
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 21717
content:
" The adjustment time, often referred \r\nto as the response time, is variable from one glacier to another, \r\ndepending on the glacier geometry (thickness and steepness), SMB \r\nand gradient (e.g., Johannesson et al., 1989; Harrison et al., 2001; \r\nLuthi, 2009; Zekollari et al., 2020). Response time is variable: years \r\nfor smaller and steeper glaciers (Beedle et  al., 2009; Luthi and \r\nBauder, 2010; Rabatel et al., 2013), up to tens or hundreds of years \r\nfor larger and gentle-sloped glaciers (e.g., Burgess and Sharp, 2004; "


id: 647432ff0bdc0d8304d27e9741afa9d7
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 13370
content:
"mass loss compared to the RCP forced simulations, although with \r\nfewer global glacier models. To enable the glacier contribution to \r\nfuture sea level rise to be estimated under the full range of SSP \r\nscenarios (Section  9.6.3.3), the GlacierMIP results are emulated \r\nusing a Gaussian process model (Box 9.3 and Table 9.4; Edwards \r\net al., 2021). The emulated projections show a narrower range than \r\nthe roughly equivalent RCP projections, which may be explained \r\nby not accounting for covariance in the regional uncertainties \r\n(Marzeion et al., 2020) and by the fact that the emulator caps sea \r\nlevel contribution for each region at the volume above floatation \r\nestimated by Farinotti et al. (2019) (Table 9.SM.2). Comparison of \r\nsimulated and emulated regional sea level contributions support this \r\nexplanation. Rates of change and post-2100 sea level projections \r\nare estimated with the AR5 parametric fit (Supplementary \r\nMaterial 9.SM.4.5; Church et al"


id: 260bba391004ed7ccca3ba8488ea0859
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 3406
content:
", 2013b) applied to the GlacierMIP \r\nresults (Marzeion et al., 2020), and these are also shown in Table 9.4 \r\nfor comparison."


id: 8a894e50168ad71d510bd9c6be5c1174
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 19333
content:
"The SROCC assessed that global glacier mass loss by 2100, relative \r\nto 2015 will be 18 [likely range 11 to 25] % for scenario RCP2.6 \r\nand 36 [likely range 26 to 47] % for RCP8.5, and that many glaciers \r\nwill disappear regardless of the emissions scenario (very high \r\nconfidence). Since SROCC, new results from Marzeion et al. (2020) \r\nhave been published (Box 9.3, Figure 9.21 and Table 9.4, including \r\nperipheral glaciers in Greenland and Antarctica). Glaciers will lose \r\n29,000 [9000 to 49,000] Gt and 58,000 [28,000 to 88,000] Gt \r\nover the period 2015-2100 for RCP2.6 and RCP8.5, respectively \r\n(medium confidence), which represents 18 [5 to 31] % and 36 [16 to \r\n56] % of their early 21st  century mass, respectively (Table  9.4). \r\nWithin  uncertainties, these agree with SROCC estimates, although \r\nwith a slightly smaller mass loss due to the inclusion of models with \r\nlower sensitivity to changing climate conditions (Marzeion et  al., \r\n2020)"


id: 6ac9c873798af2bd03b54f73307d2fe3
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 14413
content:
" The greatest source of uncertainty in glacier mass loss until \r\nthe middle of the 21st century is the disagreement between glacier \r\nmodels, with emissions scenario becoming the dominant cause of \r\nuncertainty by the end of the 21st century (Marzeion et al., 2020)."


id: e241c0790544c06368a9de8d50537091
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 33095
content:
"Permafrost changes mostly refer to changes in extent, temperature \r\nand active layer thickness (ALT). The SROCC (Hock et  al., 2019b; \r\nMeredith et  al., 2019) reported with very high confidence that \r\nrecord high permafrost temperatures at the depth of the zero \r\nannual amplitude (the depth about 10-20 m below the surface \r\nwhere the seasonal soil temperature cycle vanishes) were attained \r\nin recent decades in the Northern circumpolar permafrost region, \r\nhigh confidence that permafrost has warmed over recent decades in \r\nmany mountain ranges, and overall very high confidence that global \r\nwarming over the last decades has led to widespread permafrost \r\nwarming. As reported in SROCC, the global (polar and mountain) \r\npermafrost temperature has increased at 0.29degC  +-  0.12degC near \r\nthe depth of zero annual amplitude between 2007 and 2016 \r\n(Biskaborn et  al., 2019). Stronger warming has been observed in \r\nthe continuous permafrost zone (0.39degC +- 0"


id: 1b031d83a60f50e333b8c5e15b1bc553
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 1721
content:
"15degC) compared to \r\nthe discontinuous zone (0.20degC +- 0.10degC), consistent with the fact \r\nthat, near the melting point, a large amount of energy is required \r\nfor melting the ice (Figure 9.22), and because of the reduced effect \r\nof Arctic amplification in more southerly locations (Romanovsky \r\net  al., 2017). This is consistent with longer-term Arctic trends \r\nfrom deep boreholes shown in Figure 2.22. Mountain permafrost \r\ntemperature trends are heterogeneous, reflecting variations in local \r\nconditions such as topography, surface type, soil texture and snow \r\ncover, but again, generally weaker warming rates are observed \r\nin warmer permafrost at temperatures close to 0degC, particularly \r\nwhen ice content is high (e.g., Mollaret et al., 2019; Noetzli et al., \r\n2019; PERMOS, 2019). In summary, strong variability in recent \r\npermafrost temperature trends is linked to local conditions, \r\nregionally varying temperature trends, and the thermal state of \r\npermafrost itself"


id: 71a577a5099a8bc5ee746c090c8b5d6f
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 15513
content:
" However, as discussed in Section  2.3.2.5, there \r\nis overall high confidence in the observed increases in permafrost \r\ntemperature over the past three to four decades throughout the \r\npermafrost regions."


id: 34e7d7b6d9932caad3dbbc57d66f56ab
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 5731
content:
"In AR5 and SROCC, glacier mass loss beyond 2100 was calculated \r\nusing a parametric fit to available model simulations. In section 9.6.3.5, \r\nthat same parametric fit is applied to Marzeion et al. (2020) projections, \r\nresulting in complete glacier mass loss at year 2300 under SSP5-8.5 \r\nand 40-100% mass loss under SSP1-2.6. Clark et al. (2016) simulate \r\nglacier mass evolution, not including glaciers peripheral to the Antarctic \r\nIce Sheet (AIS), for different warming levels for the next 10,000 years. \r\nThere is limited evidence and low confidence that, at sustained \r\nwarming levels between 1.5 and 2degC, about 50-60% of glacier mass \r\nwill remain, predominantly in the polar regions. At sustained warming \r\nlevels between 2 and 3degC, about 50-60% of glacier mass outside \r\nAntarctica will be lost and, at sustained warming levels, between 3 and \r\n5degC, 60-75% of glacier mass outside Antarctica will disappear. Based \r\non Marzeion et al"


id: a447554f8d39398a600167eb6108feab
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 23401
content:
" (2020), there is medium confidence that nearly all \r\nglacier mass in low latitudes, Central Europe, the Caucasus, western \r\nCanada and the USA, North Asia, Scandinavia and New Zealand will \r\ndisappear at this high warming level."


id: eac9ed618f7832b087df7bdbe1ca831d
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 34445
content:
"As stated in AR5 (Flato et al., 2013), coupled models contributing to \r\nCMIP5 showed large inter-model variability of permafrost extent due \r\nto deficiencies in reproducing surface characteristics and processes \r\n(Koven et  al., 2013), particularly thermal properties of the ground \r\nand snow. These deficiencies led SROCC (Meredith et al., 2019) to \r\nexpress only medium confidence in the models' capacity to correctly \r\nproject the magnitude of future permafrost changes, in spite of high \r\nconfidence in the models' projection of a general thaw depth increase \r\nand a substantial loss of shallow permafrost. The SROCC further noted \r\nthat several types of physical 'pulse' disturbances, in particular fire \r\nand thermokarst formation, are usually not represented in coupled \r\nclimate models. This has been discussed in detail in SROCC, which \r\nassessed that there is high confidence that permafrost degradation \r\nthrough fire (Jones et  al., 2015; Gibson et  al"


id: 700482fc85526d372b8bf11cc148695e
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 15240
content:
", 2018) is currently \r\noccurring faster in some well-studied regions than during the first \r\nhalf of the 20th century, and medium confidence that thermokarst \r\nformation, to which about 20% of the northern permafrost region \r\nis vulnerable (Olefeldt et  al., 2016), can lead to faster large-scale \r\npermafrost degradation in response to climate change."


id: 854831f5671ee7e713763ef3ff943ed1
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 18497
content:
"Since SROCC, dedicated modelling of the evolution of ice- and \r\norganic-rich permafrost in the north-east Siberian lowlands (Nitzbon \r\net al., 2020) has shown that not representing thermokarst-inducing \r\nprocesses in ice-rich terrain leads to a  systematic underestimation \r\nof the rapidity and magnitude of permafrost thaw. Simplified \r\ninventory-based modelling (Turetsky et  al., 2020) points towards \r\nsimilar conclusions. Although these pulse disturbances still need to \r\nbe represented in CMIP-type models, there have been many new \r\ndevelopments to that type of model since CMIP5 and AR5. Soil \r\nfreezing and its thermal and hydrological effects are now included in \r\na large number of land-surface modules that are part of the CMIP6 \r\nensemble (S. Chadburn et al., 2015; Hagemann et al., 2016; Cuntz \r\nand Haverd, 2018; Guimberteau et al., 2018; Yokohata et al., 2020), \r\nsometimes allowing for the effects of excess ice (Lee et al., 2014)"


id: 365585d387d6f0d1d5ad1028d04cf530
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 5941
content:
" \r\nImproved representation of snow insulation in models has led \r\nto more realistic simulated permafrost extents (e.g.,  Paquin and \r\nSushama, 2015). In a post-CMIP5 ensemble of land-surface models \r\ndriven by observed meteorological conditions (McGuire et al., 2016), \r\ninter-model spread was substantially reduced when the ensemble \r\nwas restricted to models that appropriately represented the effect of \r\nsnow insulation on the underlying soil (W. Wang et al., 2016). More \r\ndetailed descriptions of high-latitude vegetation characteristics, \r\nvegetation dynamics, and snow-vegetation interactions have been \r\nincluded in several models since AR5 (S.E. Chadburn et  al., 2015; \r\nPorada et al., 2016; Druel et al., 2017)."


id: e9dbff04dc5462e0c0a64dfcbbe66976
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 34290
content:
"Based on multiple studies, there is medium confidence that \r\nwidespread retreat of coastal permafrost is accelerating in the \r\nArctic (Gunther et al., 2015; Cunliffe et al., 2019; Isaev et al., 2019). \r\nThere is also consistent evidence of complete permafrost thaw in \r\nareas of discontinuous and sporadic permafrost since about 1980, \r\nbut this evidence is geographically scattered (Camill, 2005; Kirpotin \r\net al., 2011; James et al., 2013; B.M. Jones et al., 2016; Borge et al., \r\n2017; Chasmer and Hopkinson, 2017; Gibson et al., 2018). In spite \r\nof increasing evidence of landscape changes from site studies and \r\nremote sensing, quantifying permafrost extent change remains \r\nchallenging because it is a subsurface phenomenon that cannot be \r\nobserved directly (Jorgenson and Grosse, 2016; Trofaier et al., 2017). \r\nA modelling study for the Qinghai-Tibet Plateau between the 1960s \r\nand the 2000s (Ran et al"


id: 235690cbfb862f2c81beb93241cc4cbd
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 3013
content:
", 2018) suggests transition from permafrost \r\nto seasonally frozen ground over an area of more than 400,000 km2. \r\nIn summary, there is medium confidence that complete permafrost \r\nthaw in recent decades is a common phenomenon in discontinuous \r\nand sporadic permafrost regions. In addition, paleoclimatic evidence \r\npresented in Section  2.3.2.5 confirms a  long-term sensitivity of \r\npermafrost extent to climatic variations, although an analysis \r\nof North American speleothem records over the last two glacial cycles \r\nindicates that this apparent high sensitivity could be a consequence \r\nof regional-scale variability (Batchelor et al., 2019)."


id: 762990fce018fa580775eb68d63d101f
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 16192
content:
"In spite of the aforementioned structural improvements to many \r\nmodels, the simulated current permafrost extent from available \r\nCMIP6 models shows no substantial improvement with respect to \r\nCMIP5 (see Figure 9.22a). The extent of the region where permafrost \r\nis simulated within the top 15 m in the Northern Hemisphere for the \r\n1979-1998 period is characterized by very large scatter in the coupled \r\nCMIP5 and CMIP6 historical simulations compared to estimates \r\nof the present permafrost extent based on multiple observational \r\nlines of evidence (Zhang et al., 1999) and models based on satellite \r\nobservations and reanalyses (Gruber, 2012; Obu et al., 2019). Outliers \r\nwith very low simulated permafrost extent are models that have only \r\na very shallow soil column (leading to an underestimate of thermal \r\ninertia at depth) and do not take into account soil water phase \r\nchanges"


id: b71a3c1b675c228d509785e48cdb14fc
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 26338
content:
" These inadequacies lead to an overestimate of seasonal \r\nthaw depth, exceeding the total thickness of the models' soil \r\ncolumns (Burke et al., 2020). Excessive simulated permafrost extent \r\ncan in several cases be traced to insufficient thermal insulation by the \r\nwinter snow cover (Burke et al., 2020)."


id: 535dd3582dda932e3056aec4b99b6d27
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 10585
content:
"A strong transient temperature sensitivity of the volume of \r\nperennially frozen soil in the top 3 m below the surface is consistently \r\nsuggested by the available CMIP6 models (Figure  9.22b). Relative \r\nto the current volume, the transient sensitivity of the modelled \r\npermafrost volume in the top 3 m to GSAT changes (with respect \r\nto the 1995-2014 average and up to +3degC change, that is, about up \r\nto +4degC with respect to pre-industrial levels) is about 25 +- 5 % degC-1\r\n(Burke et  al., 2020), but there is only medium confidence in this \r\nvalue and 1 standard deviation uncertainty range because of the \r\nmodel deficiencies discussed in 9.5.2.2. It is important to note that \r\npermafrost loss will not be limited to the top 3 m, with delayed \r\nresponse of deeper permafrost. The simulated transient temperature \r\nsensitivity of permafrost volume is slightly stronger in the SSP1-2"


id: ffa86129934f4f4856917392898199dc
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 37766
content:
"6 \r\nscenario than in other SSPs because subsurface temperature lag \r\nincreases with higher atmospheric warming rates, particularly when \r\nground ice melting induces additional delays."


id: 850aa80eaa49e56d91042c478d7f86
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 18453
content:
"The AR5 (Vaughan et  al., 2013) reported that NH SCE in June \r\nvery likely decreased by 11.7 [8.8 to 14.6] % per decade over the \r\n1967-2012 period, exceeding the absolute and relative reductions \r\nobserved in March and April. The AR5 further reported very high \r\nconfidence that NH March and April SCE decreased over the 90 years \r\nafter 1922. The SROCC only assessed snow cover changes for the \r\nArctic and mountain areas. For the Arctic (north of 60degN), SROCC \r\n(Meredith et al., 2019) expressed high confidence in SCE decreases \r\nof -3.5 +- 1.9% per decade in May and -13.4 +- 5.4% per decade in \r\nJune, based on a combination of multiple datasets (Mudryk et al., \r\n2017). Concerning mountain snow cover, SROCC (Hock et al., 2019b) \r\nreported with high confidence that mountain snow cover (both in \r\nterms of SCE and maximum SWE) has generally declined since the \r\nmiddle of the 20th century at lower elevations"


id: 1ba6638576281f0852b74578f5e84f85
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 1810
content:
" At higher elevations, \r\nSROCC reported medium confidence in generally insignificant \r\nsnow cover trends (where these were available). The large-scale \r\nassessment provided in Section 2.3.2.2 of this Report reports very \r\nhigh confidence in substantial reductions of NH SCE (particularly in \r\nspring) since 1978, and states that there is limited evidence that this \r\ndecline extends back to the early 20th century."


id: a11a0a23d2c25bae63a49fc0a3422f39
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 22901
content:
"Hemispheric reconstructions with simple snow models and in situ \r\nobservations have extended a  pre-satellite record to precede the \r\nsatellite record and extend back to 1922 (Brown and Robinson, \r\n2011), putting the satellite era in historical context. This study, also \r\nassessed in AR5, suggests an increase in North American spring \r\n(March-April) SCE from 1915 to about 1950, followed by a decrease \r\nof the same total magnitude afterwards. In Eurasia, a negative trend \r\nin April is visible over the entire 1922-2010 period of record, while \r\nin March, a step decrease at about 1985 separates two periods with \r\ninsignificant trends. Overall, combining March and April, consistency \r\nbetween the continental trends since 1950, and agreement in sign \r\nwith the NOAA satellite record since 1967, provides high confidence\r\nin Northern Hemisphere spring snow cover decrease since about \r\n1950. Analysis of paleoclimate records (Pederson et  al., 2011; \r\nBelmecheri et al"


id: b73c2895f71515f46d46944cf8dedcf0
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 26362
content:
", 2016) suggests that recent snowpack reductions \r\nin western North America are exceptional on a millennial time scale \r\n(medium confidence)."


id: 27e4a31ad781438370bca1c808cc49c5
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 3688
content:
"Since AR5, the number of global or hemispheric-scale gridded \r\nSWE products has substantially increased. A  validation and \r\nintercomparison (Mortimer et al., 2020) of datasets - derived from: \r\n(i) reanalysis-based products; (ii) a combined surface observation - \r\npassive  microwave remote sensing product; and (iii) stand-alone \r\npassive microwave products - has led to better understanding of the \r\nstrengths and limitations of each. These gridded products consistently \r\nidentify negative trends in maximum pre-melt SWE across the \r\n1981-2016 period over Eurasia and North America (Figure 9.23c,d; \r\nMudryk et al., 2020). To further constrain SWE uncertainty, Pulliainen \r\net  al. (2020) implemented a  bias correction based on snow course \r\nobservations which yielded a current best estimate for the average \r\n1980-2018 March SWE over NH non-alpine land north of 40degN of 2938 \r\n[likely range 2846-3062] Gt. Using this method, the bias-corrected \r\nGlobSnow v3.0 dataset suggests a 4"


id: b290c28c3642fa08b16594b67e3912b4
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 25615
content:
"6 Gt yr-1 decrease of March SWE \r\nover this 39-year period across North America, and a negligible trend \r\nacross Eurasia. These SWE trends are consistent with the continental \r\nSCE trends over this period, as assessed above, but strong regional \r\nand temporal variability only allows medium confidence in the signs \r\nand magnitudes of these trends. However, there is high confidence in \r\na general decline of NH spring SWE since 1981 (Section 2.3.2.2). In the \r\nlonger term (see also Section  2.3.2.2), annual maximum SD trends "


id: 6f3be5c15cbde7f38dafeffbf53d7203
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 15124
content:
"Since AR5, progress in the observation, description and understanding \r\nof snow microstructure (Kinar and Pomeroy, 2015; Calonne et  al., \r\n2017) and its links to physical (thermal and radiative) properties (Lowe \r\net al., 2013; Calonne et al., 2014) has prompted efforts to represent \r\nphysical properties as a function of the evolving snow microstructure in \r\nmodels (Carmagnola et al., 2014; Calonne et al., 2015). However, even \r\nstate-of-the-art snow models intended for meteorological and climate \r\napplications still struggle to correctly represent the time evolution of \r\nthe snow thermal properties, particularly of cold and dry tundra snow \r\n(Domine et al., 2016). Moreover, most, if not all, CMIP6 climate models \r\ndo not explicitly represent the darkening of snow by deposition of black \r\ncarbon and other light-absorbing aerosol species known to influence \r\nsnow melt rates (Section 7.3.4.3). Regardless of these shortcomings, \r\nsnow modules of climate models continue to be improved"


id: d10ff7d73a2669d35fcae335528f71c1
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 30430
content:
" Recent \r\nprogress includes the incorporation of multiple energy balances \r\nwithin the canopy and between sub-grid tiles with different snow \r\nheights (Aas et al., 2017; Boone et al., 2017) and inclusion of advanced \r\nspecific snow models in coupled climate models (Niwano et al., 2018; \r\nVoldoire et  al., 2019), opening the prospect of future progress in \r\nquantifying snow-related feedbacks in a  changing climate. Recently \r\ndeveloped multi-physics snow models (Essery, 2015; Lafaysse et al., \r\n2017), which are able to emulate the behaviour of a large number of \r\nmodels in a broad range of climates, allow model shortcomings and \r\nkey parameter uncertainties, for example, concerning snow masking by \r\nvegetation or snow thermal conductivity, to be identified. Guidance for \r\nfuture model improvement can be provided by improved diagnostics, \r\nsuch as a concise metric of snow insulation (A.G. Slater et al"


id: 6c54ea15d34abbe44e9ff10c2ee3db97
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 14668
content:
", 2017), \r\nwhich builds on an observed relation between effective seasonal mean \r\nSD and the dampening of winter season temperature decrease within \r\nthe soil, and allows an efficient quantification of inaccuracies in the \r\nsimulated snow insulation effect."


id: fd750899d07ed786f1f35dbc4fcce909
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 37391
content:
"There is high confidence that large inter-model variations in the \r\nsnow-cover sensitivity to temperature can largely be explained by \r\ninaccuracies in the simulated snow-albedo feedback (Qu and Hall, \r\n2014); a multi-model sub-ensemble of CMIP5 models that simulate \r\na  correct magnitude of this feedback presents a  40%  reduced \r\nspread in the projected 21st  century Northern Hemisphere land \r\nwarming trend (Thackeray and Fletcher, 2016). Errors of the \r\nsimulated feedback strength were linked to: (i) systematic positive \r\nalbedo biases over the boreal forest belt, mostly due to unrealistic \r\ntreatment of vegetation masking (Thackeray and Fletcher, 2016); \r\n(ii) inaccurate prescribed tree cover fraction and inappropriate \r\nparametrization of leaf area index in some models (Loranty et al., \r\n2014; L. Wang et al., 2016); and (iii) low spatial resolution leading \r\nto inaccuracies in the strength of the simulated snow albedo \r\nfeedback in mountainous regions (Letcher and Minder, 2015)"


id: 40431969b701e82a328972ab6f301f0
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 7487
content:
" \r\nAlthough the representation of snow-albedo feedback improved \r\nin many CMIP5 models over CMIP3, some models deteriorated \r\n(Thackeray et al., 2018)."


id: ed5feaaa4efea9f06748f996bb0ec624
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 34821
content:
"The SROCC (Oppenheimer et al., 2019) discussed the development \r\nand application of new statistical methodologies for reconstructing \r\nglobal mean sea level (GMSL) from tide gauge data over the \r\n20th  century (Box  9.1). Based on an ensemble of tide gauge \r\nreconstructions, SROCC assessed an average rate of GMSL rise \r\nof 1.38  [0.81 to 1.95, very likely range]  mm yr-1 for the period \r\n1901-1990. Since SROCC, two new GMSL reconstructions have \r\nbeen published (Dangendorf et  al., 2019; Frederikse et  al., 2020b) \r\nand are included in an updated ensemble estimate of GMSL change \r\n(Section 2.3.3.3; Palmer et al., 2021). Based on these updated data and \r\nmethods, the GMSL change over the (pre-satellite) period 1901-1990 \r\nis assessed to be 0.12 [0.07 to 0.17, very likely range] m with an \r\naverage rate of 1.35 [0.78 to 1.92, very likely range] mm yr -1 (high \r\nconfidence) (Table  9.5; Section  2.3.3.3) in agreement with SROCC \r\nassessment"


id: a30ee957b772efcd0c4ba6162cfc7cae
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 23219
content:
" Both this assessment and SROCC have substantially \r\nlarger uncertainties than the AR5 assessment, which was based on \r\na single tide gauge reconstruction and did not account for structural \r\nuncertainty (see Palmer et al., 2021 for a discussion)."


id: f317a78b966a3b1006b42db95c51708c
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 35761
content:
"The SROCC (Oppenheimer et  al., 2019) concluded that GMSL \r\nincreased at a rate of 3.16 [2.79 to 3.53, very likely range] mm yr -1\r\nin the period 1993-2015 (the satellite altimetry era), and a  rate \r\nof 3.58  [3.10 to 4.06, very likely range]  mm yr -1 in the period \r\n2006-2015  - the Gravity Recovery and Climate Experiment \r\n(GRACE)/Argo data era (high confidence). An updated assessment \r\nfor the periods 1993-2018 and 2006-2018 yields values of \r\n3.25  [2.88 to 3.61] and 3.69 [3.21 to 4.17]  mm yr -1 (high \r\nconfidence) (Table  9.5), with the slightly larger central estimates \r\nconsistent with the observed acceleration in GMSL rise since the \r\nlate 1960s (Dangendorf et al., 2019), given the longer assessment \r\nperiods. Based on the GMSL assessed time series presented in \r\nSection 2.3.3.3, GMSL acceleration is estimated as 0.075 [0.066 to \r\n0.080] mm yr -2 for 1971-2018 and 0.094 [0.082-0.115] mm yr -2\r\nfor 1993-2018 (high confidence)"


id: d1a82536d79c701734c9600f8b6b822c
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 30517
content:
" For the common period of \r\n1993-2010, the assessed rate of GMSL rise based on tide gauge \r\nreconstructions (3.19 [1.18 to 5.20]  mm  yr -1) is consistent \r\nwith the assessment based on satellite altimetry (2.77  [2.26 to \r\n3.28] mm yr -1), within the estimated uncertainties."


id: 4dfa3afe749074b6d4078cc281cc015a
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 9689
content:
"The sea level budget in SROCC included the anthropogenic \r\ncontribution of land-water storage (LWS; Box  9.1) change from \r\na  single estimate (Wada, 2016). Since SROCC, two studies have \r\ncombined estimates of natural LWS change with anthropogenic LWS \r\nchanges from reservoir impoundment and groundwater depletion \r\n(Caceres et  al., 2020; Frederikse et  al., 2020b). For Caceres et  al. \r\n(2020), zero change is assumed for the period 1901-1948, since \r\ntheir LWS change estimates are not available before 1948. Given the \r\nlarge year-to-year changes associated with hydrological variability, \r\nthe assessed changes in LWS (Table 9.5) are based on linear trends \r\nfor each period, following Palmer et al. (2021). Structural uncertainty \r\nis estimated from the standard deviation of the trends across the \r\ntwo studies, and parametric uncertainty is estimated based on the \r\nMonte Carlo simulations of Frederikse et  al. (2020b)"


id: 22647f604844ea280f9519489f0c2965
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 2859
content:
" These two \r\nsources of uncertainty are combined in quadrature, and the assessed \r\ncentral estimate is taken as the average of the ensemble mean \r\ntrends. Compared to SROCC-assessed LWS trend of -0.12  mm yr-1\r\nfor the period 1901-1990, the updated assessment leads to a more \r\nnegative trend of -0.16 [-0.35 to 0.04] mm yr-1, although the two \r\nare consistent within the estimated uncertainties. Previous studies \r\nand SROCC have highlighted the large uncertainty in estimates \r\nof LWS change over the 20th  century (Gregory et  al., 2013), and \r\ntherefore SROCC assessment of low confidence in the estimated LWS \r\ncontribution to GMSL change is maintained."


id: 4861e1c40535417e5ed8174c79b4ee04
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 8762
content:
"For both periods in the satellite era  - that is, 1993-2018 and \r\n2006-2018 - the sum of contributions is consistent with the total \r\nobserved GMSL change (high confidence) (Table 9.5). However, the \r\nlatter period, which is characterized by improved data quality and \r\ncoverage associated with satellite and Argo observations, shows \r\nmuch closer agreement in the central estimates. The marginal \r\nsea level budget closure for the period 1993-2018 may indicate \r\nunderestimated uncertainty, which may be structural as well as \r\nparametric. The sea level budget assessments across the various \r\nperiods in Table 9.5 demonstrate that the acceleration in GMSL rise \r\n(Section 2.3.3.3) since the late 1960s is mostly the result of increased \r\nice-sheet mass loss. However, all contributions to GMSL rise show \r\ntheir largest rate during 2006-2018, with the ice sheets accounting \r\nfor 27% of the total change during this period"


id: 20d59f00d1e865e1803642fb5115f89b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 2601
content:
" Because of the \r\nincreased ice-sheet mass loss, the total loss of land ice (glaciers and \r\nice sheets) was the largest contributor to GMSL rise over the period \r\n2006-2018 (high confidence)."


id: 1acb9dae4681a579a5c23054023fccf
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 1686
content:
"Since SROCC, a  new ocean heat content reconstruction \r\n(Section 2.3.3.1; Zanna et al., 2019) has allowed global thermosteric \r\nsea level change to be estimated over the 20th century. As a result, \r\nthe sea level budget for the 20th century can now be assessed for the \r\nfirst time. For the periods 1901-1990 and 1901-2018, the assessed \r\nvery likely range for the sum of components is found to be consistent \r\nwith the assessed very likely range of observed GMSL change \r\n(medium confidence), in agreement with Frederikse et  al. (2020b; \r\nTable 9.5). This represents a major step forward in the understanding \r\nof observed GMSL change over the 20th century, which is dominated \r\nby glacier (52%) and Greenland Ice Sheet mass loss (29%) and \r\nthe effect of ocean thermal expansion (32%), with a  negative \r\ncontribution from the LWS change (-14%)"


id: d7aba54fdff0115fcbf94151d074cd53
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 31459
content:
" While the combined mass \r\nloss for Greenland and glaciers is consistent with SROCC, updates \r\nin the underlying datasets lead to differences in partitioning of the \r\nmass loss."


id: a301637c86d26b9cb92814a636a3ae8d
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 23211
content:
"Hamlington et al., 2020b). There was intensified sea level rise during \r\nthe 1990s and 2000s, with 10-year trends exceeding 20 mm yr-1 in the \r\nwestern tropical Pacific Ocean, while sea level trends were negative \r\non the North American west coast. During the 2010s, the situation \r\nreversed, with western Pacific sea level falling at more than 10 mm yr-1\r\n(Hamlington et al., 2020b). For the Atlantic Ocean, SROCC described \r\nregional sea level variability as being driven primarily by wind and heat \r\nflux variations associated with the North Atlantic Oscillation (NAO) and \r\nheat transport changes associated with Atlantic Meridional Overturning \r\nCirculation (AMOC) variability. During periods of subpolar North \r\nAtlantic warming, winds along the European coast are predominantly \r\nfrom the south and may communicate steric anomalies onto the \r\ncontinental shelf, driving regional sea level rise, with the reverse during \r\nperiods of cooling (Chafik et al., 2019)"


id: 57a9d496425753560e64aad5bffd3bdc
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 11291
content:
" High rates of sea level rise in the \r\nNorth Indian Ocean are accompanied by a weakening summer South \r\nAsian monsoon circulation (Swapna et al., 2017)."


id: 2e111453470466d4d095b642dddc83e3
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 4687
content:
"The simulated ocean dynamic and thermosteric response to external \r\nforcings during 1861-2005 is only larger than simulated internal \r\nvariability in the Southern Ocean and North Pacific on a  1deg grid \r\n(Slangen et al., 2015). However, on spatial scales exceeding 2000 km, \r\na  detectable signal is revealed in the last 45 years in 63% of the \r\nglobal ocean area (Richter et al., 2017). The thermosteric change in \r\nthe upper 700 m in the period 1970-2005 shows similar observed \r\nand simulated forced geographical patterns, and anthropogenic \r\nforcing accounts for part (North Atlantic, 65%) or all (tropical Pacific, \r\nSouthern Ocean) of the observed regional mean (Marcos and Amores, \r\n2014). The influences of greenhouse gases and anthropogenic \r\naerosols can be partially distinguished by considering geographical \r\nor vertical ocean temperature variations (Slangen et al., 2015; Bilbao \r\net al., 2019; Fasullo et al., 2020)"


id: 8f3cc9c66ff6a79f31ae08f4b804ef47
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 20079
content:
" Zonal-mean forced ocean dynamic \r\nsea level change alone is not detectable but, using spatial correlation, \r\nthe global geographical pattern during the altimeter period is \r\ndetectable in sea level trends (Fasullo and Nerem, 2018). This pattern \r\nmay already or will soon be detectable in individual years, based \r\non an analysis of CMIP5 climate model simulations (Bilbao et  al., \r\n2015). Anthropogenic forcing, dominated by greenhouse gases, \r\nhas strengthened the meridional sea level gradient in the Southern \r\nOcean since the 1960s (Slangen et  al., 2015; Bilbao et  al., 2019; \r\nFasullo et al., 2020). New evidence finds that observed zonal-mean \r\ntotal sea level trends during 1993-2018 in all basins are inconsistent \r\nwith unforced variability alone, but are consistent with the modelled \r\nresponse to external forcing (Richter et al., 2020)."


id: 5b3321a543518ec0b3f2538cc185a3ee
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 11855
content:
"The SROCC estimated regional sea level changes from combinations \r\nof the various contributions to sea level change from CMIP5 climate \r\nmodel outputs, allowing comparison with satellite altimeter and \r\ntide gauge observations. Closure of the regional sea level budget is \r\ncomplicated by the fact that regional sea level variability is larger \r\nthan GMSL variability. Also, there are more processes that need to \r\nbe considered, such as vertical land movement and ocean dynamical \r\nchanges (Box  9.1). A  number of observation-based studies have \r\nfocused on specific areas, such as the Mediterranean (Garcia et al., \r\n2006), the South China Sea (Feng et al., 2012), the east coast of the \r\nUSA (Frederikse et al., 2017; Piecuch et al., 2018), the North Atlantic \r\nbasin (Kleinherenbrink et al., 2016) and the north-western European \r\ncontinental shelf seas (Frederikse et  al., 2016)"


id: e25a7e72275c001e9f87b0470d0c0ed0
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 33117
content:
" Studies using tide \r\ngauge data and observation-based estimates of the contributions \r\nfind that, while local agreement is not yet possible, the observational \r\nsea level budget can be closed on a  basin scale (Slangen et  al., \r\n2014b; Frederikse et al., 2016, 2018, 2020b). A budget analysis for \r\nthe GRACE era found that the budget closes in some, but not all, \r\ncoastal regions: substantial parts of the sea level change signal in \r\nthe North Atlantic could not be explained by steric or barystatic \r\nchanges (Rietbroek et  al., 2016). This is in agreement with other \r\nwork comparing climate model estimates to 20th-century tide \r\ngauge observations (Meyssignac et  al., 2017), where the majority \r\nof local spatial variability is determined by the ocean dynamic \r\ncomponent. Vertical land movement is another major cause of local \r\nspatial variability in sea level change and, for instance, relevant for \r\noceanic islands (Forbes et al., 2013; Martinez-Asensio et al., 2019)"


id: c2eb86ea8801df84936048f3af79a8d8
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 28245
content:
" \r\nIn summary, the regional sea level budget, using either observations \r\nor models, can currently only be closed on basin scales (medium \r\nconfidence), with large uncertainties remaining on smaller scales."


id: d9f344b849620b7f08ca5dd48c89f069
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 31812
content:
"concur that MIS 11 was an extremely long interglacial that exhibited \r\npositive annual at 0.5degC +- 1.6 degC (Irvali et al., 2020) and summer \r\nat 2.1degC-3.4  degC (Robinson et  al., 2017) temperature anomalies \r\n(de Wet et al., 2016). The GMSL was 6-13 m above present (medium \r\nconfidence) (Section 2.3.3.3). The Greenland Ice Sheet lost 4.5-6 m \r\n(Reyes et al., 2014) or about 6.1 m (3.9-7 m, 95% confidence) sea \r\nlevel equivalent (SLE) by about 7  kyr after peak summer warmth \r\n(Robinson et al., 2017), with marine-based ice from AIS (Blackburn \r\net al., 2020) contributing 6.4-8.8 m SLE at this time (Mas e Braga \r\net al., 2021). Agreement between GMSL and ice-sheet reconstructions \r\ngives high confidence in identifying a  high sensitivity of both ice \r\nsheets to the protracted duration of thermal forcing, even at low \r\nwarming levels (Reyes et al., 2014; Robinson et al., 2017; Irvali et al., \r\n2020; Mas e Braga et al., 2021)"


id: f18e5499e313f6671f8c996c715548c2
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 35505
content:
" Modelled mean mass loss rates for \r\nthe Greenland Ice Sheet of 0.4 m kyr -1 during MIS  11 (Robinson \r\net  al., 2017) are indistinguishable from recent mass loss rates \r\naveraged over 1992-2018 (Section 9.4.1.1). In summary, geological \r\nreconstructions and numerical simulations consistently show that \r\npast warming levels of <2degC (GMST) are sufficient to trigger multi\u0002metre mass loss from both the Greenland and Antarctic ice sheets \r\nif maintained for millennia (high confidence), in agreement with \r\nSROCC findings for comparable warming levels during MIS 5e, the \r\nLast Interglacial."


id: c9b201cdfd695c5108aa0a3e2e1b3315
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 29288
content:
"The MPWP climate had substantial polar amplification, up to 8degC \r\nabove pre-industrial levels in Arctic Russia (Section 7.4.4.1; Fischer \r\net  al., 2018). Ice-sheet model simulations indicate that Northern \r\nHemisphere glaciation was limited to high-elevation regions in \r\neastern and southern Greenland (medium confidence) (Figure 9.17; \r\nDe Schepper et  al., 2014; Yan et  al., 2014; Koenig et  al., 2015; \r\nDowsett et al., 2016; Berends et al., 2019) with Northern Hemisphere \r\nglaciation only becoming more widespread from the (cooler) late \r\nPliocene (Bachem et al., 2017; Blake-Mizen et al., 2019; Knutz et al., \r\n2019; Sanchez-Montes et al., 2020). Southern Hemisphere glaciation \r\nwas characterized by an Antarctic Ice Sheet (AIS) reduced in volume \r\nfrom the present (medium confidence) (Figure 9.18; Dowsett et al., \r\n2016; Berends et al., 2019; Grant et al., 2019; Miller et al., 2020) with \r\nmountain ice fields in the Andes of South America (De Schepper et al., \r\n2014)"


id: 130adecfac39d88eca0bd1d58480166
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 511
content:
" Ice-sheet models are inconsistent in the magnitude of the sea \r\nlevel contribution from Antarctica (DeConto and Pollard, 2016; Yan \r\net al., 2016; Golledge et al., 2017b; Berends et al., 2019; DeConto \r\net al., 2021) but near-field sedimentological reconstructions support \r\nprecessionally modulated and eccentricity-paced multi-metre sea \r\nlevel contributions from the Wilkes Subglacial Basin over 3-5 kyr \r\n(Patterson et al., 2014; Bertram et al., 2018). In summary, under a past \r\nwarming level of around 2.5degC-4degC, ice sheets in both hemispheres \r\nwere reduced in extent compared to present (high confidence). \r\nProxy-based evidence (Section  2.3.3.3) combined with numerical \r\nmodelling indicates that, on millennial time scales, the GMSL \r\ncontribution arising from ice sheets was >5 m (high confidence) \r\nor >10 m (medium confidence) (Figures 9.17 and 9.18; Moucha and \r\nRuetenik, 2017; Berends et al., 2019; Dumitru et al., 2019)."


id: 3a6ffcad6098f40fc070c14175b0ed7b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 6560
content:
"Since AR5, information has improved about the LIG, when GMST \r\nwas about 0.5degC-1.5degC above 1850-1900 (medium confidence) \r\n(Section 2.3.1.1). The LIG had higher summer insolation than present \r\nand polar amplified sea surface and surface air temperatures that \r\nreached >1degC-4degC and >3degC-11 degC in the Arctic respectively (Landais \r\net al., 2016; Capron et al., 2017; Fischer et al., 2018). Mean annual and \r\nmaximum summer ocean temperatures peaked early (129-125 ka) \r\nin the interglacial period, reaching 1.1 +- 0.3 degC above the modern \r\nglobal mean (Shackleton et  al., 2020) with summer anomalies of \r\n2.5degC-3.5 degC in the Southern Ocean (Bianchi and Gersonde, 2002) \r\nand spatially variable timing (Chadwick et al., 2020). It is virtually \r\ncertain that GMSL was higher than today, likely by 5-10 m (medium \r\nconfidence) (Section  2.3.3.3). Global mean thermal expansion \r\npeaked at about 0.9 +- 0"


id: 61dd567fb64ddb3c18d219ee4a65fbb3
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 12962
content:
"3 m early in the LIG (about 129 ka), declining \r\nto modern levels by about 127 ka (Shackleton et al., 2020). With no \r\nmore than 0.3 +- 0.1 m of GMSL rise from glaciers (Section 9.5.1), at \r\nmost 1.0 +- 0.3 m of the GMSL rise originated from sources other than \r\nthe polar ice sheets."


id: bd2267eb12fc307dca292b595fb5e6f9
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 27324
content:
"During Meltwater pulse 1A (MWP-1A), GMSL very likely (medium \r\nconfidence) rose by 8-15 m (Liu et al., 2016). Consistent with AR5, the \r\ndrivers of this rapid rise remain ambiguous. The spatial patterns of RSL \r\nchange over this interval are inadequately observed to constrain the \r\nrelative contributions of the North American and Antarctic ice sheets (Liu \r\net al., 2016). Modelling studies of the North American Ice Sheet permit \r\na 3-6 m (Gregoire et al., 2016) or 6-9 m contribution over the duration \r\nof MWP-1A (Tarasov et al., 2012). Sedimentological evidence (Weber \r\net al., 2014; Bart et al., 2018) provides near-field evidence for an Antarctic \r\ncontribution, consistent with modelling studies (Golledge et al., 2014; \r\nStuhne and Peltier, 2015), but does not constrain the magnitude of the \r\ncontribution. A recent statistical analysis of Norwegian Sea and Arctic \r\nOcean sediments suggests a 3-7 m contribution from the Eurasian Ice \r\nSheet (Brendryen et al"


id: fc781331f7a5a2db886052ccebe2ec00
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 37229
content:
", 2020), a possibility not considered in AR5 or \r\nthe meta-analysis of Liu et al. (2016). In summary, MWP-1A appears \r\nto have been driven by a combination of melt in North America (high \r\nconfidence), Eurasia (low confidence), and Antarctica (low confidence), \r\nbut the budget is not closed."


id: dd71ae453e5f07669f57e4f27da41b23
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 32340
content:
"Far-field GMSL records suggest that the AIS contributed to LIG sea \r\nlevel from 129.5-125 ka (Figure 9.18) but direct evidence is sparse. \r\nThinning of part of the WAIS is interpreted from a 130-80 ka hiatus in \r\nthe Patriot Hills horizontal ice core record (Turney et al., 2020). Marine \r\nsediment records suggest a dynamic response of the Wilkes Subglacial \r\nBasin (WSB) of the EAIS during this period, indicating a response time \r\nscale of 1000-2500 yr (Wilson et al., 2018), consistent with modelling \r\nstudies (Mengel and Levermann, 2014; Golledge et  al., 2017b; \r\nSutter et al., 2020). Isotopic changes in the Talos Dome ice core are \r\ninconsistent with local surface lowering, limiting retreat to 0.4-0.8 m \r\nSLE from this sector (Sutter et al., 2020). Ice-sheet models forced with \r\nunmodified atmosphere-ocean models (Goelzer et  al., 2016; Clark \r\net al., 2020) simulate 3-4.4 m SLE mass loss, primarily from the WAIS, \r\nwith no retreat in WSB (e.g., Figure 9.18)"


id: 7149ec400a60516e10adac5fd286b601
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 15461
content:
" Models forced with proxy\u0002based or ad hoc LIG ocean temperature anomalies (DeConto and \r\nPollard, 2016; Sutter et al., 2016) indicate collapse of West Antarctica \r\nunder 2degC-3degC ocean forcing yielding 3-7.5 m sea level contribution, \r\nbut modest or no retreat in the WSB. Based on limited evidence and \r\nlimited agreement between models, there is low confidence in both \r\nthe magnitude and timing of LIG mass loss from the AIS."


id: 85d91a116cafbd8ca052be8d9af48cb1
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 18602
content:
"Around half (50-60 m) of the GMSL rise since the LGM occurred \r\nduring the early Holocene at a sustained rate of about 15 m kyr-1 from \r\naround 11.4-8.2 ka (Lambeck et  al., 2014), possibly punctuated by \r\nabrupt meltwater pulses (Smith et al., 2011; Carlson and Clark, 2012; \r\nTornqvist and Hijma, 2012; Harrison et  al., 2019). An abrupt (about \r\n1.1 m) sea level rise around 8.2 ka was associated with drainage of the \r\npro-glacial Agassiz and Ojibway lakes, attributed to accelerated melt \r\nfrom collapsing Laurentide Ice Sheet ice saddles (Matero et al., 2017). \r\nThe Laurentide Ice Sheet provided the greatest contribution (27 m) to \r\nearly Holocene GMSL (Peltier et al., 2015; Roy and Peltier, 2017), the \r\nScandinavian Ice Sheet contributed about 2 m from the beginning of \r\nthe Holocene until its demise by around 10.5 ka, (Cuzzone et al., 2016), \r\nwhile the Barents Sea Ice Sheet contributed a  small but unknown \r\namount (Patton et al., 2015, 2017; Auriac et al., 2016)"


id: 50901e8b4045011f98527f4081d49af
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 10121
content:
" The Greenland \r\nIce Sheet contributed about 4 m, consistent with ice thinning rates \r\ninferred from the Camp Century ice core (Lecavalier et  al., 2017; \r\nMcFarlin et  al., 2018). Recent estimates of Antarctic contributions \r\nduring the early Holocene vary considerably from about 1.2 m to \r\n8.5 m (Whitehouse et al., 2012; Ivins et al., 2013; Argus et al., 2014; \r\nBriggs et al., 2014; Golledge et al., 2014; Pollard et al., 2016; Roy and \r\nPeltier, 2017; Albrecht et al., 2020). In summary, the early Holocene was \r\ncharacterized by steadily rising GMSL as global ice sheets continued \r\nto retreat from their LGM extents. This steady rise was punctuated by \r\nabrupt pulses during episodes of rapid meltwater discharge."


id: da5660ec39ba871c77c0e84a13b13e0f
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 31875
content:
"and extent, there is medium confidence that minima were reached \r\nat different times in different areas during the period 8-3  ka  BP \r\n(Larsen et  al., 2015; Young and Briner, 2015; Briner et  al., 2016). \r\nGeochronological and numerical modelling studies indicate that it \r\nis likely (medium confidence) that the period of smaller-than-present \r\nice extent in all sectors of Greenland persisted for at least 2000 \r\nto 3000 years (Larsen et  al., 2015; Young and Briner, 2015; Briner \r\net al., 2016; Nielsen et al., 2018). Based on ice-sheet modelling and \r\ncarbon-14 (14C) dating (Kingslake et al., 2018) suggested that West \r\nAntarctic grounding lines retreated prior to around 10 ka BP, followed \r\nby a  readvance. Other studies from the same region conclude \r\nthat retreat was fastest from 9-8 ka BP (Spector et  al., 2017), or \r\nfrom 7.5-4.8  ka  BP (Venturelli et  al., 2020). Marine geological \r\nevidence indicates open marine conditions east of Ross Island by \r\n8.6 +- 0.2 ka BP (McKay et al"


id: ecd1ecd9ffae059a9e205f82d072e408
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 34739
content:
", 2016). In the western Weddell Sea, \r\nJohnson et al. (2019) reported rapid glacier thinning from 7.5-6 ka BP. \r\nHein et al. (2016) concluded that the fastest thinning further south \r\ntook place from 6.5-3.5 ka BP, potentially contributing 1.4-2 m to \r\nGMSL. Geophysical data indicate stabilization or readvance in this \r\narea around 6 +- 2 ka BP (Wearing and Kingslake, 2019). In coastal \r\nDronning Maud Land (East Antarctica) rapid thinning occurred \r\n9-5 ka BP (Kawamata et al., 2020), whereas glaciers in the Northern \r\nAntarctic Peninsula receded during the period 11-8 ka BP and \r\nreadvanced to their maximal extents by 7-4 ka BP (Kaplan et  al., \r\n2020). In summary, higher-than-pre-industrial GMST during the \r\nmid-Holocene coincided with recession of the Greenland Ice Sheet \r\nto a  smaller-than-present extent (high confidence)"


id: a3d9703b909b189be60d19556ef579db
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 23339
content:
" Multiple lines \r\nof evidence give high confidence that thinning or retreat in parts \r\nof Antarctica during  the Holocene  took place at different times in \r\ndifferent places. However, limited data means there is only low \r\nconfidence in whether or not the ice sheet as a whole was smaller \r\nthan present during the mid-Holocene."


id: 12ff486568e0e1a1d4a333c4d005f6a4
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 496
content:
"Sections 9.6.3.3 and 9.6.3.4 present likely ranges of the new global \r\nmean sea levels, incorporating only processes in whose projections \r\nthere is at least medium confidence, consistent with headline \r\nprojections in AR5 and SROCC. As emphasized by SROCC, there is \r\na  substantial likelihood that sea level rise will be outside the likely\r\nrange. As described in Box 1.1, since the definition of 'likely' refers to \r\nat least 66% probability, there may be as much as a 34% probability \r\nthat the processes in which there is at least medium confidence will \r\ngenerate outcomes outside the likely range. Furthermore, additional \r\nprocesses in which there is low confidence (Section 9.4.2.4; Box 9.4) \r\nmay also contribute to sea level change. The presentation of likely sea \r\nlevel change (Tables 9.8-9.9 and in Figures 9.27, 9.29) is therefore \r\naccompanied by a low confidence range intended to reflect potential \r\ncontributions from additional processes under high-emissions \r\nscenarios"


id: 59c7cbde82530c50848eeeb9531279c7
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 11629
content:
" The low confidence range incorporates ice-sheet projections \r\nbased on Structured Expert Judgement (SEJ)  - that is, a  formal, \r\ncalibrated method of combining quantified expert assessments that \r\nincorporates all potential processes  - and projections from an AIS \r\nmodel that includes the marine ice cliff instability (a specific uncertain \r\nprocess not generally included in ice-sheet models; Section 9.4.2.4)."


id: 8eb70b076df8d8bd67e9ec852c337a9e
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 19979
content:
"The AR5 (Church et al., 2013b) generated GMSL projections for the RCPs \r\nby combining information from CMIP5 climate models with glacier \r\nand ice-sheet surface mass balance (SMB) models and assessments \r\nof projected ice-sheet dynamic and land-water storage contributions \r\n(Section 9.6.3.2). The SROCC (Oppenheimer et al., 2019) updated AR5 \r\nprojections based on a revised assessment of the AIS contribution to \r\nGMSL rise. The AR5 and SROCC employ a  baseline period of 1986 \r\nto 2005, which is updated in this Report to a baseline period of 1995 \r\nto 2014 (Section  1.4.1). Between these two periods, GMSL rose by \r\n3 cm, and this correction is applied to projections from previous reports \r\nto allow comparison (Table  9.8). Accounting for this shift, SROCC \r\nconcludes that, with medium confidence, GMSL will rise between \r\n0.40 (0.26-0.56, likely range) m (RCP2.6) and 0.81 (0.58-1.07 m, likely\r\nrange) m (RCP8.5) by 2100 relative to 1995-2014"


id: a5507ee145910827dc63097433b18574
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 23574
content:
" The AR5 and SROCC \r\nGMSL projections for the 2007-2018 period have been shown to be \r\nconsistent with observed trends in GMSL and regional weighted mean \r\ntide gauges (J. Wang et al., 2021). "


id: 64fe86c84754caef551750b259eef59b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 13459
content:
"The combination of tide gauge observations and geological \r\nreconstructions indicates that a sustained increase of GMSL began \r\nbetween 1820-1860 and led to a 20th-century GMSL rise that was \r\nvery likely (high confidence) faster than in any preceding century in \r\nthe last 3000 years (Section 2.3.3.3). At a regional level, tide gauge \r\nand geological data from the North Atlantic and Australasia show \r\ninflections in RSL trends between 1895-1935, with an increase of \r\n0.8 to 2.5  mm yr -1 across the inflection (Gehrels and Woodworth, \r\n2013). A statistical meta-analysis of globally distributed geological \r\nand tide gauge data (Kopp et al., 2016) found that, in all 20 examined \r\nregions with geological records stretching back at least 2000 years, \r\nthe rate of RSL rise in the 20th century was greater than the local \r\naverage over 0-1700 CE"


id: 5d1eeb8bdc620e58b0f3816ec5715315
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 12168
content:
" In four of the 20 regions, all in the North \r\nAtlantic (Connecticut, New Jersey, North Carolina, and Iceland), the \r\n19th  century rate was also greater than the 0-1700 CE average \r\n(90% confidence interval). In summary, rates of RSL rise exceeding \r\nthe pre-industrial background rate of rise are apparent in parts of the \r\nNorth Atlantic in the 19th century (medium confidence), and in most \r\nof the world in the 20th century (high confidence)."


id: 8356bb7177e3b38630656bbd3a5ff552
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 18188
content:
"the updated projections to AR5 (Church et al., 2013b) and SROCC \r\n(Oppenheimer et  al., 2019) (Tables 9.7 and 9.8). Since there is no \r\nsingle model that can directly compute all of the contributions \r\nto sea level change (Box  9.1), the contributions to sea level are \r\ncomputed separately and then combined (Tables 9.8 and 9.9). For \r\nconsistency with global surface air temperature (GSAT) projections \r\n(Section 4.3.1.1), and assessment of equilibrium climate sensitivity \r\n(ECS) and transient climate response (TCR; Section 7.5), temperature\u0002dependent projections (thermal expansion, ice sheets, glaciers) are \r\nforced by GSAT projections from a two-layer energy budget emulator \r\n(Smith et  al., 2018) that is calibrated to be consistent with the \r\nassessment of ECS and TCR (Box 7.1, Supplementary Material 7.SM.2)"


id: f6ef008e1aaa0a1ec2995abe776955b2
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 36360
content:
" \r\nThroughout, likely ranges are assessed based on the combination \r\nof uncertainty in the GSAT distribution and uncertainty in the \r\nrelationships between GSAT and changes to individual components. \r\nIn general, 17th-83rd percentile results, incorporating both GSAT \r\nand sea level process uncertainty, are interpreted as likely ranges. \r\nThis is distinct from the approach used by AR5, which interpreted \r\nthe 5th-95th percentile range of CMIP5 projections, and therefore \r\nof GMSL projections driven by them, as likely ranges. The shift in \r\ninterpretation is consistent with the use of the emulator for GSAT \r\n(Box 4.1, Cross-Chapter Box 7.1). Very likely ranges are not assessed \r\nbecause of the potential for processes in whose projections there \r\nis currently low confidence to substantially augment total projected \r\nGMSL change."


id: 1c1393f7054dc2ff3b4901b1b2e03f14
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 1878
content:
"For processes whose projections we have at least medium \r\nconfidence in, the updated projections use emulated Ice Sheet \r\nModel Intercomparison Project for CMIP6 (ISMIP6) projections of the \r\nGreenland Ice Sheet (Section 9.4.1.3; Figure 9.17; Tables 9.2 and 9.7; \r\nBox 9.3). Since the ISMIP6 emulator does not account for temporal \r\ncorrelation, a parametric fit to the ISMIP6 results is used to calculate \r\nrates of change (Supplementary Material 9.SM.4.4). For projections \r\nbeyond 2100 (when the ISMIP6 simulations end), the polynomial fit \r\nis extrapolated based on two alternate approaches: (i) an assumption \r\nof constant rates of mass change after 2100; and (ii) for SSP1-2.6 \r\nand SSP5-8.5, a quadratic function of time extending to 2300 based \r\non the multi-model assessment of contributions under RCP2.6 and \r\nRCP8.5 at 2300 (Section  9.4.1.4)"


id: e935f0687e905a3acf86fc2b8fa50162
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 34211
content:
" Differences between the two \r\napproaches are small up to 2150, and since the latter approach is not \r\navailable for all scenarios, only the former (constant rates) is used \r\nfor time series projections up to 2150. Both approaches are used for \r\nexamining uncertainty in the timing of different levels of GMSL rise \r\nand to inform projections for the year 2300 (Section  9.4.1.4). For \r\n2100, the ISMIP6 emulator yields the likely contribution from the \r\nGreenland Ice Sheet shown in Table 9.2 and Figure 9.17, representing \r\na slight narrowing from AR5 projections."


id: 905a80cf53010f7c1b2406e8a5c79d0f
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 20243
content:
"For the Antarctic Ice Sheet (AIS), AR5 applied a temperature-based \r\nscaling approach for SMB and a quadratic function of time, calibrated \r\nto a multi-model assessment, for dynamic contributions. The SROCC \r\nused a new assessment based on the results of five process-based \r\nstudies (Section  9.4.2.5). For processes in whose projections we \r\nhave at least medium confidence, the likely range projections \r\nfor the AIS are based on: (i) the emulated ISMIP6 ensemble; and \r\n(ii) the  LARMIP-2 ensemble, augmented with AR5 parametric \r\nAntarctic SMB model. The GMSL projections are produced with both \r\ndistributions and combined in a  'p-box' (Kriegler and Held, 2005; \r\nLe Cozannet et  al., 2017), which represents the upper and lower \r\nbounds of the distribution (Section 9.4.2.5, Box 9.3 and Table 9.3)"


id: c6dd48fe9201b9cad3a4a9cc60223823
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 28869
content:
" \r\nA  likely range is then identified, spanning the lower of the two \r\n17th percentile projections and the higher of the two 83rd percentile \r\nprojections,5 with the median taken as the mean of the medians of \r\nthe two projections. Since the ISMIP6 emulator does not account \r\nfor temporal correlation, the AR5 parametric AIS model is substituted \r\nfor the emulator in the p-box for rates of change. As AR5 projections \r\nare modestly lower than those from the ISMIP6 emulator, this \r\nsubstitution modestly broadens the likely range at the low end for \r\nprojections of rate and changes beyond 2100. For projections beyond \r\n2100 (when the ISMIP6 and LARMIP-2 simulations end), the AIS \r\nsimulations are extrapolated using the same two approaches as the \r\nGreenland Ice Sheet (GrIS) projections (Section  9.4.1.4). The likely\r\nranges to 2100 are consistent with SROCC (Table 9.8)."


id: 2dcadc549252a97b423bf1c9ec534ca9
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 4643
content:
"In AR5 and SROCC, global mean thermosteric sea level rise \r\nwas derived from the 21 members of the CMIP5 ensemble that \r\nprovided the required variables (Section  9.2.4.1). The AR5 and \r\nSROCC removed drift estimated based on a  pointwise polynomial \r\nfit to pre-industrial control simulations. They extended projections \r\nto scenarios not provided by the models by calculating the heat \r\ncontent of the climate system from GMST and net radiative flux, \r\nand converting this to global mean thermosteric sea level rise \r\nusing each model's diagnosed expansion efficiency coefficient. The \r\nAR5 and SROCC derived the associated uncertainties by assuming \r\na normal distribution, with the 5th-95th percentile CMIP5 ensemble \r\ninterpreted as the likely range. In this Report, global mean thermosteric \r\nsea level rise is derived from a  two-layer energy budget emulator \r\nconsistent with the assessment of ECS and TCR (Section  9.2.4.1; \r\nSupplementary Material 9.SM.4.2 and 9.SM.4.3)"


id: 29e00f044c42a0cabb6c1028edd03494
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 4020
content:
" Despite the change \r\nin methodology, this leads to a  likely global mean thermosteric \r\ncontribution (17th-83rd percentile) between 1995-2014 and 2100 \r\nthat represents a minimal change from AR5 and SROCC (Table 9.8)."


id: ac962b49b2ee3c1f4a03c7a9d6c33e0c
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 24701
content:
"Gravitational, rotational, and deformational (GRD) effects (Box 9.1) \r\nlead to distinct variations in the RSL change pattern, which are similar \r\nacross a range of benchmarked GRD solvers (Martinec et al., 2018; \r\nPalmer et al., 2020). There is high confidence in the understanding \r\nof GRD processes. RSL rise associated with GRD is very likely to be \r\nlargest in the Pacific, due to the combined effects of projected GrIS, \r\nAIS and glacier mass loss (high confidence) (e.g., Kopp et al., 2014; \r\nSlangen et  al., 2014b; Larour et  al., 2017; Mitrovica et  al., 2018). \r\nThe GRD effect associated with mass loss from an ice sheet is sensitive \r\nto the spatial distribution of that mass loss. For example, the GRD \r\ncontribution to RSL rise in Australia will be larger for Antarctic mass \r\nloss sourced fromthe Antarctic Peninsula than for Antarctic mass loss \r\nsourced fromThwaites Glacier"


id: b59c5379ccf9d5b2293916945365e1ed
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 26104
content:
" In parts of north-eastern North \r\nAmerica and north-western Europe, GRD effects associated with \r\nmass loss from southern Greenland will lead to an RSL fall, whereas \r\nmass loss from northern Greenland will lead to an RSL rise (high \r\nconfidence) (Figure 9.26; Larour et al., 2017; Mitrovica et al., 2018). \r\nThe AR5 and SROCC computed RSL patterns using a gravitationally \r\nself-consistent GRD solver given the amounts, locations and timing \r\nof the projected barystatic sea level changes driven by glaciers, ice \r\nsheets and LWS (Church et al., 2013b). A similar GRD solver is used \r\nin the updated projections (following Slangen et  al., 2014b). The \r\nEarth model used is based on the Preliminary reference Earth model \r\n(PREM: Dziewonski and Anderson, 1981), and is elastic, compressible \r\nand radially stratified."


id: f2e35c11e3713108ba19396255960d36
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 35739
content:
"In the updated projections, a statistical relationship is applied, linking \r\nhistorical and future SSP global population to dam impoundment and \r\ngroundwater extraction (Rahmstorf et al., 2012; Kopp et al., 2014). \r\nThe population/groundwater depletion relationship is calibrated \r\nbased on the same studies used in AR5 (Konikow, 2011; Wada et al., \r\n2012), reduced by about 20% to account for water retained on land \r\n(Wada et al., 2016). The population/dam impoundment relationship \r\nis calibrated based on Chao et al. (2008). However, while historically \r\ndam impoundment has been declining with population, recent \r\nliterature shows that planned dam construction considerably \r\nexceeds the historical trend (Zarfl et al., 2015; Hawley et al., 2020). \r\nOver 2020-2040, the impoundment contribution to GMSL rise \r\nbased on past trends would be about -0.1 mm yr -1, compared to \r\nabout -0.5 mm yr -1 if all currently planned dams are built (Hawley \r\net al"


id: a94e29d32a47b1a13d005ad902e15934
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 24166
content:
", 2020) and the statistical projection is therefore augmented \r\nby an additional -0.4 to 0.0 mm yr -1 over 2020-2040 to account \r\nfor the possible effects of planned dam construction. As in AR5 and \r\nSROCC, climatically driven changes to land-water storage (LWS) \r\nhave not been included in published sea level projections, as they \r\nare not well quantified (e.g., Jensen et al., 2019) or are considered \r\nnegligible (e.g.,  permafrost, Section  9.5.2). This approach yields \r\na likely global-mean land-water storage contribution (Figure 9.27, \r\nTable  9.8) that is slightly lower and narrower than the AR5 and \r\nSROCC likely ranges. Since the projections are explicitly population \r\ndriven, these projections also exhibit a weak scenario dependence, \r\nwith a contribution around 0.01 m higher under SSP3 than under \r\nother scenarios."


id: ce3f23ea778f21e8706144cf78a96d11
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 29969
content:
"Glacial Isostatic Adjustment (GIA) leads to vertical land motion \r\n(VLM; see Box 9.1) and changes in sea surface height, both of which \r\ncontribute to RSL change. GIA uncertainty is caused by uncertainty \r\nin the rheological structure of the solid Earth, which drives the \r\nlonger-term viscous Earth deformation, as well as uncertainty in \r\nthe modelled global ice history (e.g., Whitehouse, 2018). In AR5 and \r\nSROCC, GIA contributions to RSL change were calculated using a sea \r\nlevel equation solver with an ice-sheet history taken as the mean \r\nof the ICE5G (Peltier et al., 2015) and ANU (Lambeck et al., 2014) \r\nice-sheet models. Since AR5, new global models are emerging that \r\nmore rigorously treat ice and Earth structure uncertainty (Caron \r\net al., 2018). However, there is also a growing recognition that lateral \r\nvariations in Earth structure limit the utility of global models that treat \r\nthe solid Earth as though it were laterally uniform (Love et al., 2016; \r\nHuang et al., 2019; T"


id: ad1d081c4bd3e75d42bb933ea2f8f276
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 24779
content:
"In AR5 and SROCC, the ocean dynamic sea level contribution to RSL \r\nprojections was derived from the CMIP5 ensemble, after removing the \r\ndrift estimate based on pre-industrial control simulations. This Report \r\nuses updated simulations from the CMIP6 ensemble (Section 9.2.4.2; \r\nSupplementary Material 9.SM.4.2) to project the ocean dynamic \r\nsea level contribution to RSL change (Section 9.2.4.2; Figure 9.26). \r\nTo produce ocean dynamic sea level projections consistent with the \r\nglobal mean thermosteric projections from the two-layer energy \r\nbudget emulator, we follow the approach of Kopp et  al. (2014), \r\nemploying a  correlation between global-mean thermosteric sea \r\nlevel change and ocean dynamic sea level derived from the CMIP6 \r\nensemble (Supplementary Material 9.SM.4.3)"


id: 3bcdd1c52264b4257daccb2bbabbfb09
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 6767
content:
"constant over the projected period. In areas where rapid subsidence \r\noccurs in a cluster of tide gauges (e.g., the western Gulf of Mexico), \r\nthe associated rates are interpolated between the tide gauges. \r\nIn areas where the available tide gauges exhibit large, tectonically \r\ndriven VLM that changes considerably in rate over short distances \r\n(e.g., Alaska and the Bering Strait), a sizable uncertainty propagates \r\ninto the RSL projections (Figure 9.26). Rates of RSL rise are likely to \r\nbe underestimated due to subsidence in shallow strata that are not \r\nrecorded by tide gauges (Keogh and Tornqvist, 2019) and in some \r\nlocations may therefore be minimum values, especially if anomalously \r\nhigh subsidence rates associated with fluid extraction are also \r\nconsidered (e.g.,  Minderhoud et  al., 2017). Therefore, depending \r\non location, there is low to medium confidence in the GIA and VLM \r\nprojections employed in this Report"


id: 180054e9f0b8715f3e6c04c9ec6e428b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 1266
content:
"VLM rates comparable to or greater than rates of GMSL change. \r\nComplete global projections of these processes are not available \r\nbecause of the small spatial scales, the sensitivity of subsidence to \r\nlocal human activities, and the stochasticity of tectonics (Woppelmann \r\nand Marcos, 2016; Oppenheimer et al., 2019). Therefore, integrated \r\nRSL projections to date have either included only the component of \r\nVLM associated with GIA (as in AR5 and SROCC), or used a constant \r\nlong-term background rate of change (including both GIA and other \r\nlong-term drivers of VLM) estimated from historical tide gauge trends \r\n(e.g.,  Kopp et  al., 2014). The updated projections use the second \r\napproach and extrapolate the field of long-term background rates \r\nof RSL change, including long-term VLM derived from tide gauges, \r\nto global coverage using a  spatio-temporal statistical approach \r\n(Supplementary Material 9.SM.4.6; Kopp et al., 2014)"


id: 7f39df1bc4b49a74edce39c77dccaf34
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 17569
content:
"Since AR5, a small number of modelling studies have examined the \r\nreversibility of the multimillennial sea level commitment under carbon \r\ndioxide (CO2) removal, solar radiation modification or local ice shelf \r\nengineering. The slow response of the deep ocean to forcing leads \r\nto global-mean thermosteric sea level fall occurring long afterward, \r\neven if CO2 levels are restored after a transient increase: global mean \r\nthermosteric sea level rise takes more than a millennium to reverse \r\n(Ehlert and Zickfeld, 2018). Rapid reversion to pre-industrial CO2\r\nconcentrations has been found to be ineffective at fostering regrowth \r\nof the AIS (DeConto et al., 2021) but may reduce the multimillennial \r\nsea level commitment (DeConto and Pollard, 2016). Altering \r\nsub-ice-shelf bathymetry (Wolovick and Moore, 2018) or triggering \r\nice shelf advance through massive snow deposition (Feldmann et al., \r\n2019) might interrupt marine ice sheet instability (Section 9.4.2"


id: dd00d5411703aa6451360f39efb21c69
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 32267
content:
"4) \r\nand thus reduce sea level commitment. A reversion to pre-industrial \r\nGreenland Ice Sheet temperatures with solar radiation modification \r\nis projected to stop mass loss in Greenland but leads to minimal \r\nregrowth (Applegate and Keller, 2015). Based on limited evidence, \r\ncarbon dioxide removal, solar radiation modification, and local \r\nice-shelf engineering may be effective at reducing the yet-to-be\u0002realized sea level commitment, but ineffective at reversing GMSL rise \r\n(low confidence)."


id: 340cb75e60a91d4a0136d24054791d25
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 5605
content:
"In Antarctica, high warming might lead to floating ice shelves starting to break up earlier than expected due to processes not yet \r\naccounted for in ice-sheet models or in current climate models used to force ice-sheet projections. Such processes include hydrofracturing \r\ndriven by surface meltwater, and increase in ocean thermal forcing driven by ocean circulation changes (Sections 9.2.2.3, 9.2.3.2 and \r\n9.4.2.3; Hellmer et al., 2012, 2017; Silvano et al., 2018; Hazel and Stewart, 2020). In particular, the Thwaites and Pine Island Glacier \r\nice shelves could potentially disintegrate this century, which might trigger MICI before 2100 (DeConto and Pollard, 2016; DeConto \r\net al., 2021). MISI could potentially develop earlier and faster than simulated by the majority of models if fast flowing ice streams \r\nfollow plastic, instead of currently assumed more viscous, sliding laws (Sun et al., 2020)"


id: ea024c365d7ff6af20de18fbc63e043f
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 34312
content:
" Oceanic feedbacks could drive high-end sea \r\nlevel rise by changes in the meltwater-driven overturning circulation in ice cavities that cause additional melting (Jeong et al., 2020); \r\nby a warming of the ocean water in contact with the ice shelves due to increased stratification and thus reduced vertical mixing \r\n(Sections 9.2.2.3 and 9.2.3.2; Golledge et al., 2019; Moorman et al., 2020; Sadai et al., 2020); or by an increase in sea ice cover due \r\nto increased ocean stratification (Section 9.3.2.1), which could reduce the amount of warm, moist air that reaches the continent, \r\nand limit the mass gain from snowfall over the ice sheet (Sadai et al., 2020)."


id: 7bc4e4c2efb765afe8a2e740149692b3
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 17073
content:
"Identifying the potential drivers of a high-end sea level rise allows identification of sites and observables that can provide early \r\nwarnings of a much faster sea level rise than the likely range of this and previous reports. One potential site for such monitoring is \r\nThwaites Glacier, which is melting faster in some places and slower in others than models simulate. At this glacier, the effect of tides \r\nand channelling of warm water flows on the melting is evident (Milillo et al., 2019), making the floating ice shelf potentially vulnerable \r\nto breakup from hydrofracturing, driven by surface meltwater, much earlier than expected. In addition, the glacier is retreating towards \r\na zone with deeper bedrock, which at its present rate of retreat would be reached in 30 years (Yu et al., 2019). Thwaites Glacier is \r\ntherefore a strong candidate to experience large-scale MISI and/or MICI (Golledge et al., 2019; DeConto et al"


id: ffa779fe9993b14a498b9184b3b49ce0
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 37764
content:
", 2021), making it the \r\nideal site for monitoring early warning signals of accelerated sea level rise from Antarctica. Such signals could possibly be observed \r\nwithin the next few decades (Scambos et al., 2017)."


id: fde3654c80549c74a15d36076f0a32d1
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 37464
content:
"deployments of in situ measurements in the very dynamic surf \r\nzone means that long-term records of ETWL or ECWL are limited to \r\na few sites; tidal gauges are typically located in sheltered locations \r\n(e.g.,  harbours) where wave contributions are absent (Lambert \r\net al., 2020). Consequently, trends in wave contributions to ESL are \r\ntypically derived from trends in wave conditions observed offshore. \r\nOn the basis of satellite altimeter observations, SROCC reported \r\nincreasing extreme wave heights in the Southern and North Atlantic \r\noceans of around 1.0 and 0.8 cm yr -1, respectively, over the period \r\n1985-2018 (medium confidence). The SROCC (Collins et al., 2019) \r\nalso identified sea ice loss in the Arctic as leading to increased wave \r\nheights over the period 1992-2014 (medium confidence). Since \r\nSROCC, the satellite wave record has been shown to be sensitive \r\nto alternate processing techniques, leading to important differences \r\nin reported trends (Timmermans et  al"


id: e569d514c3f8d0bdb4ef158f02ad5971
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 33594
content:
", 2020). The most common \r\nobservation platforms for surface waves over the past 30 years are \r\nin situ buoys. However, evolving biases associated with changing \r\ninstrument type, configuration and sampling methodology introduce \r\nartificial trends (e.g.,  Gemmrich et  al., 2011; Timmermans et  al., \r\n2020). Accurate metadata is required to address these issues, and, \r\nwhile available locally, are only beginning to be globally coordinated \r\n(Centurioni et  al., 2019). Wave reanalysis and hindcast products \r\nhave also been used to investigate total water level at global scale \r\n(Melet et  al., 2018; Reguero et  al., 2019). Their applicability for \r\ntrend analysis is limited by inhomogeneous data for assimilation \r\n(Stopa et al., 2019), but they inform relationships between seasonal, \r\ninterannual to inter-decadal variability of climate indices and wind\u0002wave characteristics (A.G. Marshall et al., 2015, 2018; Kumar et al., \r\n2016; Stopa et al., 2016)"


id: 1a442fbb9956418dda6ce0d718139a4b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 1600
content:
" To summarize, satellite era trends in wave \r\nheights of order 0.5 cm yr -1 have been reported, most pronounced in \r\nthe Southern Ocean. However, sensitivity of processing techniques, \r\ninadequate spatial distribution of observations, and homogeneity \r\nissues in available records limit confidence in reported trends \r\n(medium confidence)."


id: e41e675281bd0cfcc4879d3f5deb4438
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 33372
content:
"The SROCC (Oppenheimer et al., 2019) reported variations in storm \r\nsurge not related to changes in RSL, and concluded with high \r\nconfidence that consideration of localized storm surge processes was \r\nessential to monitor trends in ESL. SL events driven by storm surge \r\nare a response to tropical and extratropical cyclones. While historical \r\ntrends in extra-tropical cyclones are less clear (Section  11.7.2.1), \r\nthere is mounting evidence for an increasing proportion of stronger \r\ntropical cyclones globally, with an associated poleward migration \r\n(Section 11.7.1.2). These changes are captured in the ESL record, for \r\nexample, via increasing intensity and poleward shift in the location of \r\ntyphoon-driven storm surges reported across 64 years (1950-2013) \r\nin the western North Pacific (Oey and Chou, 2016)"


id: fd5717a3dd731a2c564079bc027311ac
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 37372
content:
" Along the \r\neast coast of the USA, there has been an increase in frequency of \r\nESL events due to tropical cyclone changes since 1923 that can \r\nbe statistically linked to changes in global average temperature \r\n(Grinsted et al., 2013), and the signal is projected to emerge around \r\n2030 (Lee et al., 2017). At century and longer time scales, geological \r\nproxies such as overwash deposits in coastal lagoons or sinkholes can \r\nbe used to reconstruct past changes in storm activity (e.g., Brandon \r\net  al., 2013; Lin et  al., 2014) and put recent events into historical \r\nperspective (e.g.,  Brandon et  al., 2015). However, there is low \r\nconfidence in the current ability to quantitatively compare geological \r\nproxies with gauge data"


id: 9d7deec6bba2b627d9b61b62abb4f33e
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 22310
content:
" Historical storm surge activity is being \r\nincreasingly assessed with use of hydrodynamic model simulations \r\nand data-driven global reconstructions to supplement tide gauge \r\nobservations to investigate historical changes at centennial to \r\nmillennial time scales (e.g., Ji et al., 2020; Muis et al., 2020; Tadesse \r\net al., 2020). Large regional variations and limited observational data \r\nlead to low confidence in observed trends in the surge contribution \r\nto increasing ESL."


id: df8b75c8869a74edace36857b08e00d5
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 32670
content:
"Only a  few studies have attempted to quantify the role of \r\nanthropogenic climate change in ESL events (e.g., Mori et al., 2014; \r\nTakayabu et al., 2015; Turki et al., 2019). Detection and attribution of \r\nthe human influence on climatic changes in surges, and waves remains \r\na  challenge (Ceres et  al., 2017), with limited evidence to suggest \r\nin some instances  - for example, poleward migration of tropical \r\ncyclones in the Western North Pacific (Section  11.7.1.2), changes \r\nin surges and waves can be attributed to anthropogenic climate \r\nchange (low confidence). With RSL change being considered the \r\nprimary driver of observed tidal changes, there is medium confidence\r\nthat these changes can be attributed to human influence. The close \r\nrelationship between local ESL and long-term RSL change, combined \r\nwith the robust attribution of GMSL change (Section 9.6.1"


id: 9441e82a6a70d5231a360d3c8a44af2
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 20836
content:
"4), implies \r\nthat observed global changes in ESL can be attributed, at least in \r\npart, to human-caused climate change (medium confidence), but \r\nreconciling regional variation in these changes is not yet possible \r\n(Section 9.6.1.4)."


id: caf2c35746ec159861322ad3ae389a0
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 29494
content:
"There are two distinct methods used to project future ESL changes: \r\n(i) The static, or mean sea level offset, approach employs historical \r\ndistributions of tidal, surge and wave components and adjusts future \r\nESL distributions for mean RSL rise; (ii) The dynamic approach employs \r\nhydrodynamic and/or wave models forced with atmospheric fields \r\nderived from general circulation models (GCMs) to project changes \r\nin tidal, storm surge and wave distributions, which are then combined \r\nwith RSL projections to project future ESLs; and (iii) The  dynamic \r\napproach is computationally expensive. Use of the dynamic approach \r\non large spatial or global scales has only recently been successful \r\nto project 21st-century changes in ETWL (Vousdoukas et al., 2017, \r\n2018) and ECWL (Melet et  al., 2020). Kirezci et  al"


id: 6d6e2783908977e71a61e96bfb97427a
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 14826
content:
" (2020) assume \r\nstationarity in global wave and storm surge simulations to assess \r\nprojected 21st-century changes in episodic coastal ETWL-driven \r\nflooding under global sea level rise scenarios."


id: b006da3feb9d865ab43c5195ef7a0e81
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 25224
content:
"In the dynamic approach, the low resolution of the forcing fields \r\narising from GCMs limits the ability to resolve historical and future \r\nchanges in tropical and extra-tropical storm frequency and intensity, \r\nand resolution of local geography and morphology limit ability to \r\nrepresent ECWL (Box 9.1). Not all relevant processes - such as river \r\ndischarge - are included in the dynamic models, and ESL events are \r\ntypically a  combination of multiple contributing processes, which \r\nare often not independent (Jevrejeva et  al., 2019). In both static \r\nand dynamical approaches, global assessment of the performance \r\nof modelled storm surge and wave contributions to ESL is limited \r\nby poor coverage of observations (limited to tide gauges for \r\nESWL, Muis et al., 2020), and unavailable for the wave dependent \r\nETWL and ECWL estimates (Vitousek et al., 2017; Vousdoukas et al., \r\n2018; Kirezci et al., 2020; Lambert et al., 2020; Melet et al., 2020)"


id: 76dbe55c16e37d51316565f8d0e17f9b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 16277
content:
" \r\nIn studies to date, individual models are used to simulate different \r\ncontributions to ESL, non-linear interactions are not well captured, \r\nand uncertainties associated with downscaling methodology are \r\npoorly resolved, leading to low confidence in available ESL projections \r\nthat include these modelled wave and surge contributions."


id: 9e29d6b4a9c9160bdc93b712a08d8735
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 22407
content:
"The SROCC (Oppenheimer et al., 2019) presents projections of ESL \r\nderived using a  static approach. Such projections often quantify \r\nchanges in ESL event frequency, expressed as 'frequency amplification \r\nfactors' (Hunter, 2010, 2012). Like RSL projections, frequency \r\namplification factors increase under higher-emissions scenarios, \r\nand differences between scenarios increase over time. The  SROCC \r\nconcludes that even small to moderate changes in mean RSL can \r\nlead to hundred- to thousand-fold increases in the frequencies \r\nwith which certain thresholds are exceeded  - for example, what \r\nis currently a  1-in-100-year ESL height (1% annual probability or \r\n0.01 expected annual events) will be expected once or even multiple \r\ntimes per year in future at many locations (Figure 9.32). The SROCC \r\nshowed that currently rare ESL events (e.g., with an average return \r\nperiod of 100 years) will occur annually or more frequently at most \r\navailable locations for RCP4"


id: c2c8396a1c99f2047a567ce878013b20
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 28227
content:
"5 by the end of the century (high \r\nconfidence). Results from these assessments are sensitive to the type \r\nof ESL probability distribution assumed (Buchanan et al., 2016; Wahl \r\net al., 2017), as well as the magnitude and uncertainty of projected \r\nRSL change (Slangen et al., 2017; Wahl et al., 2017; Frederikse et al., \r\n2020a). Frequency amplification factors tend to be largest in tropical \r\nregions due in part to higher RSL rise projections, but primarily to \r\nthe relative rarity of high ESLs in areas with little historical exposure \r\nto tropical or extratropical cyclones. Alternative representation of \r\nchanges in ESL, such as presenting changes in exceedances per year \r\n(Sweet and Park, 2014), are subject to similar sensitivities, and lead \r\nto medium confidence in projected changes of event frequency using \r\nthese methods."


id: 3fb6c198a5b387712125a4c6296d1791
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 7411
content:
"Since SROCC, a quantitative assessment of a community ensemble \r\nof global wind-wave projections (Morim et al., 2019) found robust \r\nprojected changes of around 5-10% (positive or negative, depending \r\non region) in annual mean significant wave height, mean wave \r\nperiod, and/or mean wave directions along about 52% of the world's \r\ncoastline that exceed internal climate variability under RCP8.5 by \r\n2100. Continued retreat of sea ice cover in the Arctic will lead to \r\nmore energetic wind-wave conditions (Casas-Prat and Wang, 2020). \r\nWave climate modelling methods introduce up to around 50% of \r\nthe ensemble variance in mean wave climate projections (Morim \r\net  al., 2019). GCMs do not typically resolve the higher-resolution \r\ntropical and extratropical storm features required to accurately \r\ndetermine the contribution of extreme waves to ESLs and individual \r\nstudies have sought to improve resolution to address these issues \r\n(e.g., Timmermans et al., 2017)"


id: e7070d3bdc1f316be14c2b80eeb6d3b8
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 33840
content:
" To date, projections of wave height \r\nextremes have been constrained to single wave model configurations \r\n(e.g., Timmermans et  al., 2017; Meucci et  al., 2020). In summary, \r\nthere is medium confidence in projections of changes in mean wave \r\nclimate but low confidence in the projected changes in extreme \r\nwave conditions due to limited evidence."


id: 3fccad903fc71873ce9f6b1b6799ba1b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 7421
content:
"catchments are steep and cause high rainfall near the coast, such as \r\nin south-west UK (Svensson and Jones, 2004). The compound effect \r\nof storm surge and rainfall contributes greater projected flood risk \r\nthan climate-induced amplification (Hsiao et al., 2021). However, at \r\nother locations, co-occurrence was unimportant because streamflow \r\ntiming did not coincide with the coastal peak storm surge (Hudson \r\nRiver, Orton et al., 2012; Rhine delta, Klerk et al., 2015). The SROCC \r\n(Oppenheimer et  al., 2019) detailed the complexity of interactions \r\nin deltaic environments. Direct increases in flooding driven by \r\nincreasing RSL and storm surge, rain, or correlations between these \r\nflood-drivers (e.g.,  Moftakhari et  al., 2017; Orton et  al., 2020) are \r\nexpected to be further accompanied by increases in flooding due to \r\nsubsidence (vertical land movement) and sedimentation (RSL-driven \r\nblockage of river flows)"


id: ee01ba020040c56d4c7bf85e9d8700a4
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 34925
content:
" The probability of concurrent surge, wave \r\nand precipitation events has been projected to increase by more \r\nthan 25% by 2100 compared to present, with high northern latitudes \r\ndisplaying compound flooding becoming more than 2.5 times as \r\nfrequent, and weakening in the subtropics (Bevacqua et al., 2020). \r\nHowever, the number of studies on compound events is still limited \r\nand so there is low confidence in understanding the extent by which \r\ncompound events of surge with rain will change in response to RSL \r\nrise and climate change."


id: 3009cdee613e143133ec1f65e4f5122f
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 4986
content:
"Correlations between changes in sea level-forced (mean sea level \r\nand tidal) and atmospherically-forced drivers (ocean surface waves \r\nand surges) of ESLs have only been considered in a  few studies, \r\nalthough high surge and high waves co-occur along a  majority of \r\nthe world's coastlines (Marcos et  al., 2019). Along the east coast \r\nfo the USA, ocean dynamic sea level change and change in power \r\ndissipation index (a proxy for North Atlantic tropical cyclone activity) \r\nare correlated across CMIP5 GCMs, resulting in an increase in ESLs \r\nrelative to analyses assuming independence of these changes \r\n(Little et al., 2015). In the Irish Sea, dynamically coupled wave-tide \r\nmodelling results in high water wave heights up to 20% higher than \r\nin an uncoupled analysis (Lewis et al., 2019)"


id: d431e839ecf69e26e173d173deca64f5
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 30907
content:
" In the German Bight, \r\nRSL rise relaxes the breaking criterion of nearshore waves (assuming \r\nno geomorphological response), allowing larger waves to propagate \r\ncloser to shore, leading to increased wave runup (Arns et al., 2017). \r\nIn south-western Australia, the influence of projected SLR was found \r\nto exceed the influence of projected changes in forcing winds on wave \r\ncharacteristics at the coast (Wandres et al., 2017). Thus, projections of \r\nESL that do not consider correlations between and among sea level \r\nforced and atmospherically forced drivers can differ strongly from \r\ncoupled projections (medium confidence). "


id: d74c8a92b81ee55730e3865b07bbf794
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 31386
content:
"Humans are changing the climate and there are mechanisms that amplify the warming in the polar regions \r\n(Arctic and Antarctic). The Arctic is already warming faster than anywhere else (see FAQ 4.3). This is significant \r\nbecause these colder high latitudes are home to our two remaining ice sheets: Antarctica and Greenland. Ice \r\nsheets are huge reservoirs of frozen freshwater, built up by tens of thousands of years of snowfall. If they were \r\nto completely melt, the water released would raise global sea level by about 65 m. Understanding how these ice \r\nsheets are affected by warming of nearby ocean and atmosphere is therefore critically important. The Greenland \r\nand Antarctic ice sheets are already slowly responding to recent changes in climate, but it takes a long time \r\nfor these huge masses of ice to adjust to changes in global temperature. That means that the full effects of \r\na warming climate may take hundreds or thousands of years to play out"


id: f9d5bcf9cbfed6494679c9d8f708afa2
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 36815
content:
" An important question is whether these \r\nchanges can eventually be reversed, once levels of greenhouse gases in the atmosphere are stabilized or reduced \r\nby humans and natural processes. Records from the past can help us answer this question."


id: 30d1a08a80a59e9f464c5dcfb50855ff
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 5104
content:
"For at least the last 800,000 years, the Earth has followed cycles of gradual cooling followed by rapid warming \r\ncaused by natural processes. During cooling phases, more and more ocean water is gradually deposited as \r\nsnowfall, causing ice sheets to grow and sea level to slowly decrease. During warming phases, the ice sheets \r\nmelt more quickly, resulting in more rapid rises in sea level (FAQ 9.1, Figure 1). Ice sheets build up very slowly \r\nbecause growth relies on the steady accumulation of falling snow that eventually compacts into ice. As the \r\nclimate cools, areas that can accumulate snow expand, reflecting back more sunlight that otherwise would keep \r\nthe Earth warmer. This means that, once started, glacial climates develop rapidly. However, as the climate cools, \r\nthe amount of moisture that the air can hold tends to decrease"


id: 58e147731cc3fe56cdf8532ad7d161eb
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 11476
content:
" As a result, even though glaciations begin quite \r\nquickly, it takes tens of thousands of years for ice sheets to grow to a point where they are in balance with the \r\ncolder climate."


id: ea1d3cff4a96bc0c8233ae394953b179
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 34334
content:
"Ice sheets retreat more quickly than they grow because of processes that, once triggered, drive self-reinforcing \r\nice loss. For ice sheets that are mostly resting on bedrock above sea level - like the Greenland Ice Sheet - the main \r\nself-reinforcing loop that affects them is the 'elevation-mass balance feedback' (FAQ 9.1, Figure 1, right). In this \r\nsituation, the altitude of the ice-sheet surface decreases as it melts, exposing the sheet to warmer air. The lowered \r\nsurface then melts even more, lowering it faster still, until eventually the whole ice sheet disappears. In places \r\nwhere the ice sheet rests instead on bedrock that is below sea level, and which also deepens inland, including \r\nmany parts of the Antarctic Ice Sheet, an important process called 'marine ice sheet instability' is thought to drive \r\nrapid retreat (FAQ 9.1, Figure 1, left). This happens when the part of the ice sheet that is surrounded by sea water \r\nmelts"


id: 89e720c77a9a6eb7ef17840c85614d3c
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 19225
content:
" That leads to additional thinning, which in turn accelerates the motion of the glaciers that feed into these \r\nareas. As the ice sheet flows more quickly into the ocean, more melting takes place, leading to more thinning \r\nand even faster flow that brings ever-more glacier ice into the ocean, ultimately driving rapid deglaciation of \r\nwhole ice-sheet drainage basins."


id: da156f0a20f5dae33cf2d0d239fd3fa7
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 31838
content:
"In many places, local sea level change will be larger or smaller than the global average. From year to year and \r\nplace to place, changes in ocean circulation and wind can lead to local sea level change. In regions where large \r\nice sheets, such as the Fennoscandian in Eurasia and the Laurentide and Cordilleran in North America, covered \r\nthe land during the last ice age, the land is still slowly rising up now that the extra weight of the ice sheets is \r\ngone. This local recovery is compensating for global sea level rise in these regions and can even lead to local \r\ndecrease in sea level. In regions just beyond where the former ice sheets reached and the Earth bulged upwards, \r\nthe land is now falling and, as a result, local sea level rise is faster than the global rate"


id: 64469efa9dd59d1549305570d59aa9a8
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 13346
content:
" In many regions within \r\nlow-lying delta regions (such as New Orleans and the Ganges-Brahmaputra delta), the land is rapidly subsiding \r\n(sinking) because of human activities such as building dams or groundwater and fossil fuel extraction. Further, \r\nwhen an ice sheet melts, it has less gravitational pull on the ocean water nearby. This reduction in gravitational \r\nattraction causes sea level to fall close to the (now less-massive) ice sheet while causing sea level to rise farther \r\naway. Melt from a polar ice sheet therefore raises sea level most in the opposite hemisphere or in low latitudes - \r\namounting to tens of centimetres difference in rise between regions by 2100."


id: bbd56183d0786e2faa69e02a99bfe9c3
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 27112
content:
"Gudfina Adalgeirsdottir (Iceland), Bhupesh  Adhikary (Nepal), Muhammad Adnan (Pakistan), \r\nBodo Ahrens (Germany), Muhammad  Amjad (Pakistan), Paola  A.  Arias (Colombia), Farooq \r\nMohamed Azam (India), Segolene  Berthou (United Kingdom/France), Melissa  S.  Bukovsky \r\n(United States of America), Alex J. Cannon (Canada), Ana Casanueva (Spain), Annalisa Cherchi \r\n(Italy), Erika Coppola (Italy), Faye Abigail Cruz (Philippines), Joseph D. Daron (United Kingdom), \r\nMarie-Estelle Demory (Switzerland/France, Switzerland), Claudine Dereczynski (Brazil), Alejandro \r\nDi Luca (Australia, Canada/Argentina), Leandro  B.  Diaz (Argentina), Herve  Douville (France), \r\nSergio Henrique Faria (Spain/Brazil), Baylor Fox-Kemper (United States of America), Shin Fukui \r\n(Japan), Laura Gallardo (Chile), Subimal  Ghosh (India), Nathan P. Gillett (Canada), Melissa \r\nI. Gomis (France/Switzerland), Hugues  Goosse (Belgium), Irina  V"


id: df778a7df319447bb04f8619b9a2ee7d
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 32660
content:
" Gorodetskaya (Portugal/\r\nBelgium, Russian Federation), Michael Grose (Australia), Jose Manual Gutierrez (Spain), Pandora \r\nHope (Australia), Akm  Saiful  Islam (Bangladesh), Christopher D. Jack (South Africa), Richard \r\nG. Jones (United Kingdom), Martin W. Jury (Spain/Austria), Asif Khan (Pakistan), Akio  Kitoh \r\n(Japan), Svitlana Krakovska (Ukraine), Gerhard  Krinner (France/Germany, France), Hiroyuki \r\nKusaka (Japan), Stefan Lange (Germany), Flavio Lehner (United States of America/Switzerland), \r\nChristopher Lennard (South Africa), Jian Li (China), Fei Liu (China), Martin Menegoz (France), \r\nThanh  Ngo-Duc (Vietnam), Dirk  Notz (Germany), Friederike  Otto (United Kingdom/Germany), \r\nWendy Parker (United States of America), Carlos  Perez  Garcia-Pando (Spain), Izidine  Pinto \r\n(South Africa/Mozambique), Jan  Polcher (France/Germany), Krishnan  Raghavan (India), \r\nRoshanka  Ranasinghe (The Netherlands/Sri  Lanka, Australia), Ingo  Richter (Japan/Germany), \r\nAlex C"


id: ff875b97fcc07ff147aaecdf2ed8ca2e
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 37746
content:
" Ruane (United States of America), Lucas Ruiz (Argentina), Sajjad Saeed (Belgium, Italy/\r\nPakistan), Ramiro I. Saurral (Argentina), Reinhard K.H. Schiemann (United Kingdom/Germany), \r\nSonia I. Seneviratne (Switzerland), Chris  Shaw (United  Kingdom), Theodore  G.  Shepherd "


id: d93131b6a6bd28647a9f5dbe22c8ab99
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 31679
content:
"Although climate change is a global phenomenon, its manifestations \r\nand consequences are different in different regions, and therefore \r\nclimate information on spatial scales ranging from sub-continental to \r\nlocal is used for impact and risk assessments. Chapter 10 assesses \r\nthe foundations of how regional climate information is distilled \r\nfrom multiple, sometimes contrasting, lines of evidence. Starting \r\nfrom the assessment of global-scale observations in Chapter  2, \r\nChapter 10 assesses the challenges and requirements associated with \r\nobservations relevant at the regional scale. Chapter 10 also assesses \r\nthe fitness of modelling tools available for attributing and projecting \r\nanthropogenic climate change in a regional context starting from the \r\nmethodologies assessed in Chapters 3 and 4"


id: 904dd877ff75a1eaf6fbefd72ac151fd
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 20237
content:
" Regional climate change \r\nis the result of the interplay between regional responses to both \r\nnatural forcings and human influence (considered in Chapters 2, 5, 6 \r\nand 7), responses to large-scale climate phenomena characterizing \r\ninternal variability (considered in Chapters 1-9), and processes and \r\nfeedbacks of a regional nature."


id: 6e0b7244322b7e8ca251db1b11521cbb
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 14926
content:
"Global and regional climate models are important sources \r\nof climate information at the regional scale. Global models by \r\nthemselves provide a useful line of evidence for the construction of \r\nregional climate information through the attribution or projection \r\nof forced changes or the quantification of the role of the internal \r\nvariability (high confidence). Dynamical downscaling using regional \r\nclimate models adds value in representing many regional weather and \r\nclimate phenomena, especially over regions of complex orography or \r\nwith heterogeneous surface characteristics (very high confidence). \r\nIncreasing climate model resolution improves some aspects of \r\nmodel performance (high confidence). Some local-scale phenomena \r\nsuch as land-sea breezes and mountain wind systems can only be \r\nrealistically represented by simulations at a  resolution of the order \r\nof 10 km or finer  (high confidence)"


id: 61d13091f7e45a8864bffef768cd5626
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 12956
content:
" Simulations at kilometre-scale \r\nresolution add value in particular to the representation of convection, \r\nsub-daily precipitation extremes (high confidence) and soil-moisture-\r\nprecipitation feedbacks (medium confidence). Sensitivity experiments \r\naid the understanding of regional processes and can provide additional \r\nuser-relevant information. {10.3.3, 10.4, 10.5, 10.6} "


id: ac64eb270d2cffe30b62142c25c3082
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 24657
content:
"Different types of climate model ensembles allow for the \r\nassessment of regional climate projection uncertainties, \r\nalthough ensemble spread is not a  full measure of the \r\nuncertainty (very high confidence). Multi-model ensembles \r\nenable the assessment of regional climate response uncertainty \r\n(very  high confidence). Discarding models that fundamentally \r\nmisrepresent processes relevant for a  given purpose improves the \r\nfitness of multi-model ensembles for generating regional climate \r\ninformation (high confidence). At the regional scale, multi-model \r\nmean and ensemble spread are not sufficient to characterize \r\nlow-likelihood, high-impact changes or situations where different \r\nmodels simulate substantially different or even opposing changes \r\n(high confidence). In such cases, storylines aid the interpretation \r\nof projection uncertainties"


id: e46b133213b71326a7a6c67c9c116c79
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 33417
content:
" Since AR5, the availability of multiple \r\nsingle-model initial-condition large ensembles (SMILEs) allows for \r\na more robust separation of model uncertainty and internal variability \r\nin regional-scale projections and provides a  more comprehensive \r\nspectrum of possible changes associated with internal variability \r\n(high confidence). {10.3.4} "


id: 2d95fac1b20cc6e25645d4d518d743a3
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 4611
content:
"The performance of global and regional climate models \r\nand their fitness for future projections depend on their \r\nrepresentation of relevant processes, forcings and drivers \r\nand on the specific context. Improving global model performance \r\nfor regional scales is fundamental for increasing their usefulness \r\nas regional information sources. It is also key for improving the \r\nboundary conditions for dynamical downscaling and the input for \r\nstatistical approaches, in particular when regional climate change \r\nis strongly influenced by large-scale circulation changes. Increasing \r\nresolution per se does not solve all performance limitations. \r\nIncluding the relevant forcings (e.g.,  aerosols, land-use change \r\nand stratospheric ozone concentrations) and representing the \r\nrelevant feedbacks (e.g"


id: f5e16a8aa9ac2a3b28aea61fc5b5f88
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 36199
content:
",  snow-albedo, soil-moisture-temperature, \r\nsoil-moisture-precipitation) in global and regional models is \r\na prerequisite for reproducing historical regional trends and ensuring \r\nfitness for future projections (high confidence). The sign of projected \r\nregional changes of variables such as precipitation and wind speed \r\nis in some cases only simulated in a trustworthy manner if relevant \r\nregional processes are represented (medium confidence). {10.3.3, \r\n10.4.1, 10.4.2, 10.6.2, Cross-Chapter Box 10.2} "


id: f152bfa2b8ce9f4607748bf953b5ced9
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 35469
content:
"While human influence has contributed to multi-decadal mean \r\nprecipitation changes in several regions, internal variability \r\ncan delay emergence of the anthropogenic signal in long-term \r\nprecipitation changes in many land regions (high confidence).\r\nMultiple attribution approaches, including optimal fingerprinting, \r\ngrid-point detection, pattern recognition and dynamical adjustment \r\nmethods, as well as multi-model, single-forcing large ensembles and \r\nmulti-centennial paleoclimate records, support the contribution of \r\nhuman influence to several regional multi-decadal mean precipitation \r\nchanges  (high confidence). At regional scale, internal variability \r\nis stronger and uncertainties in observations, models and human \r\ninfluence are all larger than at the global scale, precluding a robust \r\nassessment of the relative contributions of greenhouse gases, \r\nstratospheric ozone, different aerosol species and land-use/land-cover \r\nchanges"


id: 927b5b00591c32b381e3386daed52143
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 20566
content:
" Multiple lines of evidence, combining multi-model ensemble \r\nglobal projections with those coming from SMILEs, show that internal \r\nvariability is largely contributing to the delayed or absent emergence \r\nof the anthropogenic signal in long-term regional mean precipitation \r\nchanges (high confidence). {10.4.1, 10.4.2, 10.4.3, 10.6.3, 10.6.4}"


id: 2dde9251cb36d55243f1c1f3325aec52
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 4652
content:
"The performance of statistical downscaling, bias adjustment \r\nand weather generators in climate change applications \r\ndepends on the specific model and on the dynamical climate \r\nmodel driving it. Knowledge is still limited about suitable predictors \r\nfor statistical downscaling of regional climate change, particularly for \r\nprecipitation. Bias adjustment cannot overcome all consequences \r\nof unresolved or strongly misrepresented physical processes, \r\nsuch as large-scale circulation biases or local feedbacks, and may \r\ninstead introduce other biases and implausible climate change \r\nsignals (medium confidence). Using bias adjustment as a  method \r\nfor statistical downscaling, particularly for coarse-resolution global \r\nmodels, may lead to substantial misrepresentations of regional \r\nclimate and climate change (medium confidence). Instead, dynamical \r\ndownscaling may resolve relevant local processes prior to bias \r\nadjustment, thereby improving the representation of regional \r\nchanges"


id: 2937ff0c0522e0868fb6a1385e1095ac
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 3918
content:
" The performance of statistical approaches and their fitness \r\nfor future projections depends on predictors and change factors \r\ntaken from the driving dynamical models (high confidence). {10.3.3, \r\nCross-Chapter Box 10.2}"


id: c53911f147f1410ff5273c9e0d0f3050
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 28611
content:
"Regional climate change refers to a change in climate in a given region \r\n(Section 10.1.2.1) identified by changes in the mean or higher moments \r\nof the probability distribution of a climate variable and persisting for \r\na  few decades or longer. It can also refer to a  change in temporal \r\nproperties such as persistence and frequency of occurrence of weather \r\nand climate extreme events. Regional climate change may be caused by \r\nnatural internal processes such as atmospheric internal variability and \r\nlocal climate response to low-frequency modes of climate variability \r\n(Technical Annex IV), as well as by changes in external forcings such as \r\nmodulations of the solar cycle, orbital forcing, volcanic eruptions, and \r\npersistent anthropogenic changes in the composition of the atmosphere \r\nor in land use and land cover (Cross-Chapter Box 3.2; IPCC, 2018a), \r\nin addition to the interactions and feedbacks between them"


id: 8583598b7134c8b0d502e2c5952f766a
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 18538
content:
" Process \r\ninteraction in space is pervasive, which means that small spatial scales \r\noften have an influence on the larger scales (Palmer, 2013; Sandu \r\net al., 2016). Depending on the context, a region may refer to a large \r\narea such as a monsoon region, but may also be confined to smaller \r\nareas such as coastlines, mountain ranges or human settlements like \r\ncities. Users (understood as anyone incorporating climate information \r\ninto their activity) often request climate information for these range of \r\nscales since their operating and adaptation decision scales range from \r\nthe local to the sub-continental level."


id: 2ce97aaf843936922ce28dfb416d9c65
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 4504
content:
"(Vaittinada Ayar et al., 2016; Zhang et al., 2017; L. Cai et al., 2018). \r\nFor simulations that use CMIP3 boundary conditions other periods \r\nhave been used. As a consequence, these regional simulations mix \r\nfor the recent period historical simulations with projections. The \r\nmismatch needs to be considered when assessing results obtained \r\nfrom both global and regional models in the context of the climate \r\ninformation distillation process, or when linking the regional chapters \r\nto the assessments performed in previous chapters. The choice \r\nof baseline provides a  source of uncertainty for the assessment \r\nof climate impacts (e.g., for the response of bird species in Africa; \r\nBaker et al., 2016). Besides, a range of different baselines may need \r\nto be considered to satisfy a variety of users, since this choice affects \r\nthe perceived result (Dobor and Hlasny, 2019)"


id: 32764d171a0cd4939e4db7e7651389f
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 5353
content:
" The influence of the \r\ndifferent baseline periods can be explored using the Interactive Atlas \r\nwhere different baselines are available, for example, 1986-2005 \r\n(according to AR5), 1995-2014 (this Report), and both 1961-1990 \r\nand 1981-2010 (WMO)."


id: c25b98eecb5785976d1fa87289ac552f
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 28165
content:
"One way of overcoming the baseline uncertainty is to define the \r\nresults for a given model based on specific global mean temperature \r\nchanges from the pre-industrial period (e.g.,  Sylla et  al., 2018 for \r\nWest Africa; Kjellstrom et al., 2018 for Europe; Taylor et al., 2018 for \r\nthe Caribbean; Montroull et al., 2018 for South America). The specific \r\nglobal mean temperature is known as global warming level (GWL; \r\nSections 1.6.2 and 10.6.4, and Cross-Chapter Box  11.1). The GWL \r\nis a useful dimension of integration because important changes in \r\nregional climate, including many types of extremes, scale quasi\u0002linearly with the GWLs, often independently of the underlying \r\nemissions scenarios (e.g., Hoegh-Guldberg et al., 2018; Beusch et al., \r\n2020; Seneviratne and Hauser, 2020), always taking into account \r\ncaveats described in Cross-Chapter Box 11.1"


id: 1472ada16ddf23f5b2c7ced2cd7857c8
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 725
content:
" In addition, GWLs allow \r\na  separated analysis of the global and regional climate responses \r\nassociated with a  warming level (Section  10.6.4; Seneviratne and \r\nHauser, 2020). The choice of global temperature goal in the context \r\nof the 2015 Paris Agreement means that there is an increasing desire \r\nfor the regional climate information to be expressed as a function \r\nof GWLs."


id: 57f0e22d0c20ed67e313daf8df27340
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 11340
content:
"Variability in regional climate arises from natural and anthropogenic \r\nforcings, internal variability including the local expression of large-scale \r\nremote drivers (also known as teleconnections), and the feedbacks \r\nbetween them. Due to the many possible drivers of variability and \r\nchange (Figure  10.3), quantifying the interplay between internal \r\nmodes of decadal variability and any externally forced component \r\nis crucial in attempts to attribute causes of regional climate changes \r\n(e.g., Hoell et al., 2017; Nath et al., 2018). A regional climate signal \r\ncould arise purely due to some anthropogenic influence or conversely, \r\nentirely due to internal variability, but it is most likely the result of \r\na combination of both (Section 10.4). This section briefly introduces \r\nthese sources of regional variability and should be read along with \r\ncorresponding sections in Chapters 3, 6 and 7. Section 10.3 assesses \r\ntheir representation in climate models, Section 10"


id: ffb7493388a3d7c50dbb1e595aab09c6
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 37776
content:
"Natural aerosols include mineral dust, volcanic aerosol and sea salt. \r\nThe feedback processes between climate and mineral dust as well \r\nas sea salt are assessed in Section 6.4, while the volcanic aerosol \r\nis dealt with in Cross-Chapter Box  4.1. Mineral dust created by \r\nwind erosion of arid and semi-arid surfaces dominates the aerosol \r\nload over some areas. The major sources of contemporary dust are \r\nlocated in the arid topographic basins of northern Africa, Middle \r\nEast, Central and south-west Asia, the Indian subcontinent, and East \r\nAsia (Prospero et al., 2002; Ginoux et al., 2012) and emissions are \r\ncontrolled by changes in surface winds, precipitation, and vegetation \r\n(Ridley et al., 2014; W. Wang et al., 2015; DeFlorio et al., 2016; Evan \r\net al., 2016; Pu and Ginoux, 2018). Dust both scatters and absorbs \r\nradiation and serves as a nuclei of warm and cold clouds (Chapter 6)"


id: d96a18a45b1c5fc1e60553fde77a8696
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 31716
content:
" \r\nThe surface direct radiative effect is likely negative over land and \r\nocean, especially when the assumed solar absorption by dust is \r\nlarge (Miller et al., 2014; Strong et al., 2015). Surface temperature \r\nand precipitation adjust to the direct radiative effect with both sign \r\nand magnitude depending on the dust absorptive properties. Dust \r\noften cools the surface,  but in regions such as the Sahara surface \r\nair temperature increases as the shortwave absorption by dust is \r\nincreased, leading to increases of surface temperature over the major \r\nreflective dust sources (Miller et al., 2014; Solmon et al., 2015; Strong \r\net al., 2015; Jin et al., 2016; Sharma and Miller, 2017)."


id: 87ccfa8a790857b352171640f14a0fd8
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 18892
content:
"Variations in solar forcing (Section  2.2.1) could influence regional \r\nclimate through its modulation of circulation patterns, although this \r\nresearch field is still hampered by large observational and modelling \r\nuncertainties. The 11-year solar cycle has been suggested to affect the \r\nleading atmospheric circulation modes of the North Atlantic region \r\nin model-based studies (Gray et al., 2013; Thieblemont et al., 2015; \r\nSjolte et al., 2018). In particular the solar cycle has been suggested as \r\nan important source of near-term predictability of the North Atlantic \r\nOscillation (NAO; Kushnir et  al., 2019), while other studies have \r\nnot found evidence for links between the solar cycle and NAO in \r\nobservational records (Ortega et al., 2015; Sjolte et al., 2018; Chiodo \r\net al., 2019). On centennial time scales, solar fluctuations were found \r\nto be correlated with the Eastern Atlantic Pattern (Sjolte et al., 2018)"


id: 664acb351e22a7c094c94afff3182d34
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 13683
content:
" \r\nPossible influences on winter circulation and temperature over \r\nEurasia (Chen et al., 2015) and North America (Liu et al., 2014; Li and \r\nXiao, 2018) have also been identified."


id: 7e3cf6dc2685ff169382ef4867009def
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 17425
content:
"Volcanic eruptions load the atmosphere with large amounts of sulphur, \r\nwhich is transformed through chemical reactions and micro-physics \r\nprocesses into sulphate aerosols (Cross-Chapter Box 4.1; Stoffel et al., \r\n2015; LeGrande et al., 2016). If the plume reaches the stratosphere, \r\nsulphate aerosols can remain there for months or years (about two to \r\nthree for large eruptions) and can then be transported to other areas by \r\nthe Brewer-Dobson circulation. If the eruption occurs in the tropics, its \r\nplume is dispersed across the Earth in a few years, while if the eruption \r\noccurs in the high latitudes, aerosols mainly remain in the same \r\nhemisphere (Pausata et  al., 2015). The global temperature response \r\nobserved after the last five major eruptions of the last two centuries \r\nis estimated to be around -0.2degC (Swingedouw et  al., 2017), in \r\nassociation with a general decrease of precipitation (Iles and Hegerl, \r\n2017)"


id: 8f18727d1f06794254f3fed44e0f4b6b
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 20053
content:
" Nevertheless, the statistical significance of the regional response \r\nremains difficult to evaluate over the historical era (Bittner et al., 2016; \r\nSwingedouw et al., 2017) due to the small sampling of large volcanic \r\neruptions over this period and the fact that the signal is superimposed \r\nupon relatively large internal variability (Gao and Gao, 2018; Dogar \r\nand Sato, 2019). Evidence from paleoclimate observations is therefore \r\ncrucial to obtain a  sufficient signal-to-noise ratio (Sigl  et  al.,  2015). "


id: f291a0515073fe66cd85353f3ace9e12
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 35692
content:
"Due to the large ocean heat capacity and their long temporal scales, \r\nmulti-annual to multi-decadal modes of ocean variability such as \r\nthe Pacific Decadal Variability (PDV; Newman et al., 2016) and the \r\nAtlantic Multi-decadal Variability (AMV; Buckley and Marshall, 2016) \r\nare key drivers of regional climate change. In the case of the AMV \r\nboth natural (volcanic) and anthropogenic (aerosol) external forcings \r\nare thought to be involved in its timing and intensity (Section 3.7.7). \r\nThese modes not only affect nearby regions but also remote parts \r\nof the globe through atmospheric teleconnections (Meehl et  al., \r\n2013; Dong and Dai, 2015) and can act to modulate the influence of \r\nnatural and anthropogenic forcings (Davini et al., 2015; Ghosh et al., \r\n2017; Menegoz et  al., 2018b). The dynamics of the ocean modes \r\nis simultaneously affected by other modes of variability spanning \r\nthe full range of spatial and temporal scales due to non-linear \r\ninteractions (Figure 10"


id: 3297f45ec2591be8f44b7c607878fca0
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 5380
content:
"3; Kucharski et al., 2010; Dong et al., 2018).\r\nThis mutual interdependence can result in changing characteristics of \r\nthe connection over time (Gallant et al., 2013; Brands, 2017; Dong and \r\nMcPhaden, 2017), and of their regional climate impact (Martin-Gomez \r\nand Barreiro, 2016, 2017). As with atmospheric modes of variability, \r\nthe regional influence of ocean modes of variability on regional \r\nclimates can be seasonally dependent (Haarsma et al., 2015)."


id: b9f8768b2af6d516b01290f451a745c1
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 26802
content:
"Regional climate information is synthesized from different lines of \r\nevidence from a  number of sources (Sections 10.2-10.4) taking \r\ninto account the context of a  user vulnerable to climate variability \r\nand change at regional scales (Baztan et  al., 2017) and the values \r\nof all relevant actors (Corner et  al., 2014; Bessette et  al., 2017) in \r\na process called distillation (Section 10.5). Distillation, understood as \r\nthe process  of synthesizing information about climate change from \r\ndifferent lines of evidence obtained from a  variety of sources and \r\ntaking into account the user context and the values of all relevant \r\nactors, allows the connection of global climate change to the local \r\nand regional scales, where adaptation responses and policy decisions \r\ntake place. Climate information is translated into the user context in \r\na co-production process that introduces further user-relevant elements \r\nleading to user-relevant climate information (Figure  10"


id: d95f6246752532201ffe5bfe1fd90dda
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 31712
content:
"1; Pettenger, \r\n2016; Verrax, 2017) for a specific demand like, for instance, guiding \r\nclimate-resilient development (Kruk et al., 2017; Parker and Lusk, 2019)."


id: fac43ef77f2a24a078409f687b9fb8
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 36975
content:
"Chapter 11 assesses observed, attributed and projected changes in \r\nweather and climate extremes, provides a mechanistic understanding \r\non how changes in extremes are related to human-induced climate \r\nchange and provides regional, continental and global-scale \r\nassessments on changes in extremes, including compound events. \r\nChapter  12 identifies elements of the climate system relevant for \r\nsectoral impacts referred to as climatic impact-drivers (CIDs), assesses \r\npast and future evolutions of sector-relevant CIDs for each AR6 \r\nregion, synthesizes such evolutions for different time periods and by \r\nGWL, and assesses how CIDs are used in climate services. The Atlas \r\nassesses observed, attributed and projected changes in mean climate, \r\nperforms a comparison of CMIP5, CMIP6 and CORDEX simulations, \r\nevaluates downscaling performance and assesses approaches to \r\ncommunicate climate information"


id: f3d1e728517407eef5a081a4cd497bf4
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 35874
content:
" The Interactive Atlas facilitates \r\nthe exploration of datasets assessed in all chapters through a wide \r\nrange of maps, graphs and tables generated in an interactive manner. \r\nThis allows for the comparison of changes at warming levels and \r\nscenario/time-period combinations, display of  indices for extremes \r\nand CIDs, and serves all chapters in the report to facilitate synthesis \r\ninformation and support the Technical Summary and the Summary \r\nfor Policymakers."


id: f552b353f717f0ebb81d05cf6c13acc6
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 36118
content:
"Working Group II (WGII) Chapter 21 (Hewitson et al., 2014b) addressed the regional climate change context from the perspective of \r\nimpacts, vulnerability and adaptation. This chapter emphasized that a good understanding of decision-making contexts is essential \r\nto define the type and scale of information required from physical climate. Further, the chapter identified that the regional climate \r\ninformation was limited by the paucity of comprehensive observations and their analysis along with the different levels of confidence \r\nin projections (high confidence). Notably, at the time of AR5, many studies still relied on global datasets, models, and assessment \r\nmethods to inform regional decisions, which were not considered as effective as tailored regional approaches"


id: 5c0c18debeaac7408318b6060c5dd683
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 11988
content:
" The regional scale \r\nwas not defined but instead it was emphasized that climate change responses play out on a range of scales, and the relevance and \r\nlimitations of information differ strongly from global to local scales, and from one region to another."


id: 1c0b3008811187229248f7fdfceee532
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 1874
content:
"In this box, the possible influences of the Arctic warming on the lower latitudes are assessed. This linkage was also the topic of Box 3.2 \r\nof the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019b). It is a topic that has been strongly \r\ndebated (Ogawa et al., 2018; K. Wang et al., 2018). Separate hypotheses have emerged for winter and summer that describe possible \r\nmechanisms of how the Arctic can influence the weather and climate at lower latitudes. They involve changes in the polar vortex, \r\nstorm tracks, jet stream, planetary waves, stratosphere-troposphere coupling, and eddy-mean flow interactions, thereby affecting the \r\nmid-latitude atmospheric circulation, and the frequency, intensity, duration, seasonality and spatial extent of extremes and climatic \r\nimpact-drivers like cold spells, heatwaves, and floods (Cross-Chapter Box 10.1, Figure 1)"


id: 97ffa957dd0a830dc1a4a15915e54c60
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 21424
content:
" However, we note that a decrease in the \r\nintensity of cold extremes has been observed in the Northern Hemisphere mid-latitudes in winter since 1950 (Section 11.3.2; van \r\nOldenborgh et al., 2019). Since SROCC, new literature has appeared, and the mechanisms and their criticisms are assessed here as an \r\nupdate and extension to the SROCC box."


id: a7915987959188109007a519706f7d3d
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 23870
content:
"It has been proposed that Arctic amplification, by reducing the equator-pole temperature contrast, could result in a weaker and \r\nmore meandering jet with Rossby waves of larger amplitude (Francis et al., 2017; Zhang and Luo, 2020). This may cause weather \r\nsystems to travel eastward more slowly and thus, all other things being equal, Arctic amplification could lead to more persistent \r\nweather patterns over the mid-latitudes (Francis and Vavrus, 2012). The persistent large meandering flow may increase the likelihood \r\nof connected patterns of temperature and precipitation climatic impact-drivers because they frequently occur when atmospheric \r\ncirculation patterns are persistent, which tends to occur with a strong meridional wind component. Another possible consequence of \r\nArctic warming is on the NAO/AO that shows a negative trend over the 1990s and early 2000s (Robson et al"


id: e6efaa5e1eb650bbcc0bb5f66f384923
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 33819
content:
", 2016; Iles and Hegerl, \r\n2017), and has been linked to the reduction of sea ice in the Barents and Kara seas, and the increase in Eurasian snow cover (Cohen \r\net al., 2012; Nakamura et al., 2015; Yang et al., 2016). During negative NAO/AO the storm tracks shift equatorward and winters are \r\npredominantly more severe across northern Eurasia and the eastern United States, but relatively mild in the Arctic. This temperature \r\npattern is sometimes referred to as the 'warm Arctic-cold continents (WACC)' pattern (Chen et al., 2018). However, L. Sun et al. (2016) \r\nnoticed that the WACC is a manifestation of natural variability. Enhanced sea ice loss in the Barents-Kara Sea has also been related \r\nto a weakening of the stratospheric polar vortex (Kretschmer et al., 2020) and its increased variability (Kretschmer et al., 2016) that \r\nwould induce a negative NAO/AO (Kim et al., 2014), the WACC pattern (Kim et al"


id: c004ad6d6a7e0cdf43ab4699de24a9c0
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 27762
content:
", 2014), and an increase in cold air outbreaks (CAO) \r\nin mid-latitudes (Kretschmer et al., 2018). Arctic warming might also increase Eurasian snow cover in autumn caused by the moister \r\nair that is advected into Eurasia from the Arctic with reduced sea ice cover (Cohen et al., 2014; Jaiser et al., 2016), although Peings \r\n(2019) suggests a possible influence of Ural blockings on both the autumn snow cover and the early winter polar stratosphere. The \r\ncirculation changes over the Ural-Siberian region are also suggested to provide a link between Barents-Kara sea ice and the NAO \r\n(Santolaria-Otin et al., 2021)."


id: 2a8e05d4e8808c76ffa7fb85227426df
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 4119
content:
"As in winter, Arctic summer warming may result in a weakening of the westerly jet and mid-latitude storm tracks, as suggested for \r\nthe recent period of Arctic warming (Coumou et al., 2015; Petrie et al., 2015; Chang et al., 2016). Additional proposed consequences \r\nare a southward shift of the jet (Butler et al., 2010) and a double jet structure associated with an increase of the land-ocean thermal \r\ngradient at the coastal boundary (Coumou et al., 2018). It is hypothesized that weaker jets, diminished meridional temperature \r\ncontrast, and reduced baroclinicity might induce a larger amplitude in stationary wave response to stationary forcings (Zappa et al., \r\n2011; Petoukhov et al., 2013; Hoskins and Woollings, 2015; Coumou et al., 2018; Mann et al., 2018; R. Zhang et al., 2020), and \r\nalso that a double jet structure would favour wave resonance (Kornhuber et al., 2017; Mann et al., 2017)"


id: 4b9b2bc05d4cd9cfb1399ed4362fae4
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 9314
content:
" Some studies suggest \r\nthat this is corroborated by an observed increase of quasi-stationary waves (Di Capua and Coumou, 2016; Vavrus et al., 2017; \r\nCoumou et al., 2018)."


id: 24c2da21151c2fbb08c3b319cac0f8ea
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 3225
content:
"often criticized for the lack of statistical significance and the inability to disentangle cause and effect (Barnes, 2013; Barnes and \r\nPolvani, 2013; Screen and Simmonds, 2013; Barnes et al., 2014; Hassanzadeh et al., 2014; Barnes and Screen, 2015; Sorokina et al., \r\n2016; Douville et al., 2017; Gastineau et al., 2017; Blackport and Screen, 2020a; Oudar et al., 2020; Riboldi et al., 2020). The role of the \r\nBarents-Kara sea ice loss is challenged by Blackport et al. (2019) who find a minimal influence of reduced sea ice on severe mid-latitude \r\nwinters, and by Warner et al. (2020) who suggest that the apparent winter NAO response to the Barents-Kara sea ice variability is \r\nmainly an artefact of the Aleutian Low internal variability and of the co-variability between sea ice and the Aleutian Low originating \r\nfrom tropical-extratropical teleconnections. Also Gong et al. (2020) do not find a link between Rossby wave propagation into the \r\nmid-latitudes and Arctic sea ice loss"


id: 5e924ef46f985fad4d872c37098bc9a7
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 12412
content:
" Mori et al. (2019) argue that models underestimate the influence of the Barents-Kara Sea ice \r\nloss on the atmosphere, which is disputed by Screen and Blackport (2019). Other studies have stressed the importance of atmospheric \r\nvariability as a driver of Arctic variability (Lee, 2014; Woods and Caballero, 2016; Praetorius et al., 2018; Olonscheck et al., 2019). \r\nAnalysing observed key variables of mid-latitude climate for 1980-2020, Blackport and Screen (2020b) and Riboldi et al. (2020) argue \r\nthat the Arctic influence on mid-latitudes is small compared to other aspects of climate variability, and that observed periods of strong \r\ncorrelation are an artefact of internal variability or intermittency (Kolstad and Screen, 2019; Siew et al., 2020; Warner et al., 2020)."


id: 690cc8160bc326f3d79561ed9c6f72eb
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 14121
content:
"Summarizing, different hypotheses have been developed about the influence of recent Arctic warming on the mid-latitudes in both \r\nwinter and summer. Although some of the proposed mechanisms seem to be supported by various studies, the underlying mechanisms \r\nand relative strength compared to internal climate variability have been questioned. A recent review (Cohen et al., 2020) states that \r\ndivergent conclusions between model and observational studies, and also between different model studies, continue to obfuscate \r\na clear understanding of how Arctic warming is influencing mid-latitude weather. In this context, Shepherd (2016b) stresses the need \r\nfor collaboration between scientists with different viewpoints for further understanding that could be achieved by carefully designed, \r\nmulti-investigator, coordinated, multi-model simulations, data analyses and diagnostics (Overland et al., 2016). In agreement with \r\nBox 3"


id: e193fd1fd9b029c051d779893dcd2f06
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 32982
content:
"2 of SROCC, there is low to medium confidence in the exact role and quantitative effect of historical Arctic warming and sea ice \r\nloss on mid-latitude atmospheric variability."


id: 12605b214e007aa02130a219be247616
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 393
content:
"Regarding future climate, it is important to note that mid-latitude variability is also affected by many drivers other than the Arctic \r\nchanges and that those drivers as well as the linkages to mid-latitude variability might change in a warmer world. The AMV, PDV, \r\nENSO (see Annex IV), upper tropospheric tropical heating, polar stratospheric vortex, and land surface processes associated with soil \r\nmoisture (Miralles et al., 2014; Hauser et al., 2016) and snow cover (Nakamura et al., 2019; Sato and Nakamura, 2019) are a few \r\nexamples. A considerable body of literature has shown that changes to the NAO/AO on seasonal and climate change time scales \r\ncan be driven by variations in the wavelength and amplitude of Rossby waves, mainly of tropical origin (Fletcher and Kushner, 2011; \r\nCattiaux and Cassou, 2013; Ding et al., 2014; Goss et al., 2016)"


id: ecd30944b03d658d76684013abbb89d5
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 34740
content:
" The influence of future Arctic warming on mid-latitude circulation \r\nis difficult to disentangle from the effect of such a plethora of drivers (Blackport and Kushner, 2017; F. Li et al., 2018). One of the \r\nconsequences of climate change is a poleward shift of the jet induced by the tropical warming (Barnes and Polvani, 2013), which is \r\nless obvious in winter especially over the North Atlantic (Peings et al., 2018; Oudar et al., 2020), and the increase of the meridional \r\ntemperature gradient in the upper troposphere, which increases storm track activity (Barnes and Screen, 2015; Parding et al., 2019). \r\nAlthough climate models indicate that future Arctic warming and the associated equator-pole temperature gradient decrease could \r\naffect mid-latitude climate and variability (Haarsma et al., 2013a; McCusker et al., 2017; Zappa et al., 2018), and even the tropics and \r\nsubtropics (Deser et al., 2015; Cvijanovic et al., 2017; K. Wang et al., 2018; England et al"


id: 366238878ec6ccd4fa905ad7237ad420
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 5949
content:
", 2020; Kennel and Yulaeva, 2020), they do \r\nnot reveal a strong influence on extreme weather (Woollings et al., 2014)."


id: 6bb7a202a6aace22507a35dfa4c65c7f
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 14572
content:
"Surface or in situ observations can come from a variety of networks: \r\nclimate reference networks, mesoscale weather and supersite \r\nobservation networks, citizen science networks, among others, all \r\nwith their strengths and weaknesses (McPherson, 2013; Thorne et al., \r\n2018). Supersite observatories are surface and atmospheric boundary \r\nlayer observing networks that measure a large number of atmospheric \r\nand soil variables at least hourly over a  decade or more, ideally \r\nlocated in rural areas (Ackerman and Stokes, 2003; Haeffelin et al., \r\n2005; Xie et al., 2010; Chiriaco et al., 2018). Adequate calibration of \r\ninstruments, quality control and homogenization are essential in these \r\nsites. They produce valuable data needed to diagnose processes and \r\nchanges in regional and local climate. Many climate datasets have \r\nbeen developed from in situ station observations, at different spatial \r\nscales and temporal frequencies (Annex I: Observational Products)"


id: 26ed41dff0cf14843658edbd14bfeb4a
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 3516
content:
" \r\nThese include sub-daily (Dumitrescu et al., 2016; Blenkinsop et al., \r\n2017), daily (Chen et  al., 2008; Camera et  al., 2014; Journee \r\net al., 2015; Funk et al., 2015; Aalto et al., 2016; Beck et al., 2017a, b; \r\nSchneider et al., 2017) or monthly time scales (Cuervo-Robayo et al., \r\n2014; Aryee et al., 2018). Sub-daily data is useful for estimating storm \r\nsurge (Mori et al., 2014) or river discharge (Shrestha et al., 2015), \r\ndaily data for carbon-stock dynamics (Haga et al., 2020) or tourism \r\n(Watanabe et  al., 2018), and monthly data for beach morphology \r\n(Bennett et al., 2019)."


id: 53a6a0fba1a820d25456cfa744677129
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 10628
content:
"Availability of active sensors on LEO satellites enables measurement \r\nof microphysical properties of aerosol, cloud and precipitation, which \r\ncan advance regional climate studies and process evaluation studies \r\nto improve regional climate models (high confidence). An example is \r\nthe polar-orbiting 'afternoon-train' satellite constellation (known as \r\nthe A-train), incorporating Aqua, CALIPSO, Cloudsat, PARASOL, Glory \r\nand Aura satellites. Vertical profiling observations from Cloudsat \r\n(with a W-band cloud radar) and CALIPSO (with a cloud lidar) led \r\nto considerable advances in measurements of cloud microphysics \r\n(Stephens et al., 2018). Precipitation and its extremes are essential \r\nconcerns of regional climate studies. The GPM (65degN-65degS, \r\n2014-present) and the preceding TRMM (36.5degN-36"


id: 7eae16445a8d2867037ee988c1607d8
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 17494
content:
"5degS, \r\n1997-2015) with Ku-/Ka-band precipitation radars have provided \r\nthree-dimensional measurements of precipitation with about 5 km \r\nresolution and sub-daily sampling (Skofronick-Jackson et al., 2017). \r\nTheir non-sun-synchronous observation works to cross-calibrate the \r\nconstellation satellites to produce global high-resolution mapped \r\nproducts of precipitation, such as Integrated Multi-satellitE Retrievals \r\nfor GPM (IMERG; Huffman et  al., 2007) and the Global Satellite \r\nMapping of Precipitation (GSMaP; Kubota et al., 2007), with hourly \r\nsampling at about 11 km resolution. The CPC MORPHing technique \r\n(CMORPH) has provided 30 min interval global precipitation with \r\nabout 8 km coverage since 2002 (Joyce et al., 2004). Precipitation \r\nestimations from Remotely Sensed Information using Artificial Neural \r\nNetworks (PERSIANN) is a  sub-daily to daily rainfall product that \r\ncovers 50degS to 50degN globally with 25 km resolution from 2000 to \r\nthe present (Nguyen et al"


id: 4f7a02e6d95c83563b6ab954b9a42348
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 9939
content:
", 2019), and is used for semi-global-scale \r\nprecipitation coverage (Benestad, 2018). TRMM/GPM observations \r\nhave enabled estimates to be obtained for global four-dimensional \r\nconvective heating (Shige et al., 2009; Tao et al., 2016; Takayabu and \r\nTao, 2020)."


id: 297e1233a612c5e51b6f942c43672fb9
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 3964
content:
"The use of these data has enhanced our understanding of precipitation \r\nprocesses at regional scale (high confidence), such as diurnal cycles \r\nin a large river valley (H. Chen et al., 2012), and in coastal (Hassim \r\net al., 2016; Yokoi et al., 2017) and mountainous regions (Hirose et al., \r\n2017). Three-dimensional observations revealed the contrasts in \r\nregional characteristics of rainfall extremes in monsoon regions and \r\ncontinental dry regions (Sohn et al., 2013; Hamada and Takayabu, \r\n2018). Satellite measurements are also used to evaluate climate \r\nmodel performance, as well as to develop new parametrizations. As \r\na demonstration of the utility of these products in studying model bias, \r\na subtropical cumulus congestus regime has been identified that may \r\nbe implicated in the unrealistic double Inter-tropical Convergence \r\nZone (ITCZ) found in some climate models (Takayabu et al., 2010; \r\nHirota et al., 2011, 2014)"


id: 1966bbbcb1b919b849064d10f3aac12c
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 1480
content:
" Another example is a parametrization of \r\na land surface model that was developed specifically for a certain soil \r\ntype. By assimilating satellite brightness temperature observations \r\nwith their LDAS-UT scheme, Yang et al. (2007) successfully optimized \r\na land surface model for the Tibetan Plateau."


id: 8cd850efa109f07d0c6135c79c4647ce
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 19677
content:
"Reanalyses incorporate an increasing volume of observations from \r\na growing number of sources over time, which sometimes presents \r\na  difficulty for trend analysis. However, regional reanalyses are \r\nvaluable for regional climate assessments, since they can employ \r\nhigh-resolution model simulations due to their limited spatial domain. \r\nTheir accuracy is also better than global reanalyses since they are \r\noften developed over regions with a high density of observational \r\ndata (sometimes not freely available for all regions) to be assimilated \r\ninto the model (e.g., Yamada et al., 2012). Regional reanalyses can \r\nassimilate locally dense and high-frequency observations, such as \r\nfrom local observation networks (Mahmood et al., 2018; Su et al., \r\n2019) and radar precipitation (Wahl et al., 2017) in addition to the \r\nobservations assimilated by global reanalyses. In some regional \r\nreanalyses, satellite-derived high-resolution sea ice (Bromwich \r\net al"


id: 42b42a86163b49d6ab6e62e9e9ef34f8
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 7886
content:
", 2016, 2018) and sea surface temperature (Su et al., 2019) are \r\nalso applied as lower boundary conditions. The periods of regional \r\nreanalyses are limited by the availability of the observations for \r\nassimilation and by the global reanalyses needed as lateral boundary \r\nconditions. Most regional reanalyses cover the past 10 to 30 years. \r\nThere are also regional reanalysis activities that use conventional \r\nobservations only, which produce consistent datasets over 60 years \r\nto capture precipitation trends, extremes and changes (Fukui et al., \r\n2018). Existing regional reanalyses cover North America (Mesinger \r\net  al., 2006), Europe (Dahlgren et  al., 2016; Jermey and Renshaw, \r\n2016; Kaspar et al., 2020), the Arctic (Bromwich et al., 2016, 2018), \r\nSouth Asia (Mahmood et al., 2018), and Australia (Su et al., 2019). \r\nA project for regional reanalysis covering Japan has also started (Fukui \r\net al"


id: d79f84af2a833e5680e2c27cb4b6af9f
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 31449
content:
", 2018), where grid spacing is between 5 and 32 km, although \r\ncumulus parametrizations are still needed to compute sub-grid \r\nscale cumulus convection. Recently, reanalyses using convection\u0002permitting regional models have been published (e.g., Wahl et  al., \r\n2017, for central Europe)."


id: 35b7da55eae02bf83a6459db8f4609e4
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 5859
content:
"For application at a  regional scale, it is important to consider \r\nvariations in the spatiotemporal resolution of the satellite products. \r\nA simple concatenation of data in time can show artificial jumps that \r\nare artefacts of changes in calibration and processing algorithms, or \r\nrelated to satellite orbital stability or changing performance of the \r\ninstruments (Wielicki et al., 2013; Barrett et al., 2014). Recalibration and \r\ncross-calibration are then prerequisites for obtaining homogeneous \r\ntime series of measurements across different or successive satellites \r\nthat can then be used to produce long series that are valid as climate \r\ndata records (Kanemaru et al., 2017; Merchant et al., 2017). Scale \r\nrepresentativeness is also an issue in utilizing soil observations \r\n(Taylor et  al., 2012, 2013). Although a  variety of technologies to \r\nmeasure soil moisture at the point scale exist (Dobriyal et al., 2012), \r\nits spatial representativeness is less than 1 m2 (Ochsner et al"


id: 36cd7bd0313d8bdd5cb6665a00ca064c
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 6010
content:
", 2013; \r\nL. Liu et al., 2016). Therefore, to be able to use in situ soil moisture for \r\nvalidating coarser-scale data from satellites or models, networks of \r\npoint-scale measurements are used (Crow et al., 2015; Polcher et al., \r\n2016). Smaller networks are typically of the size of a single climate \r\nmodel gridcell or a  satellite pixel and are suitable for monitoring \r\nwatersheds, while small numbers of those representing larger areas \r\n(>100 km2) are emerging (Ochsner et al., 2013)."


id: b19656f996e94317d08072055a021c15
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 25439
content:
"The regional reanalyses represent the frequencies of extremes and \r\nthe distributions of precipitation, surface air temperature, and surface \r\nwind better than global reanalyses (high confidence). This is due to the \r\nuse of high-resolution regional climate models (RCMs), as indicated \r\nby different regional climate modelling studies (Mesinger et al., 2006; \r\nBollmeyer et al., 2015; Bromwich et al., 2016, 2018; Dahlgren et al., \r\n2016; Jermey and Renshaw, 2016; Fukui et al., 2018; Su et al., 2019). \r\nRegional reanalyses, however, retain uncertainties due to deficiencies \r\nin the physical parametrization used in RCMs and by the use of \r\nrelatively simple data assimilation algorithms (Bromwich et al., 2016; \r\nJermey and Renshaw, 2016; Su et  al., 2019). Regional reanalyses \r\ncan provide estimates that are more consistent with observations \r\nthan dynamical downscaling approaches, due to the assimilation \r\nof additional local observations (high confidence) (Bollmeyer et al"


id: a170389b092c666b97a87647b4b5b606
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 22958
content:
"Homogenization aims to make data spatially and temporally \r\n'homogeneous'. Changes in a  homogeneous time series are solely \r\ndue to large-scale climatic changes (whether forced or due to internal \r\nvariability). Station data are influenced by factors that act at regional \r\nscales, from the mesoscale and local scale down to the microscale \r\n(WMO, 2019). Station time series contain inhomogeneities such as \r\nartificial jumps or trends, which hamper assessments of regional long\u0002term trends. Typical reasons for this are the urbanization of a station's \r\nsurroundings, which can lead to warming (Hamdi, 2010; Hansen et al., \r\n2010; Adachi et al., 2012; Jones, 2016; Y. Sun et al., 2016), or relocations \r\noutside of the urban area, which could lead to cooling (Tuomenvirta, \r\n2001; Yan et  al., 2010; Xu et  al., 2013; Dienst et  al., 2017, 2019)"


id: a79067797ff23c4f964eeb78eba9b14a
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 23869
content:
" \r\nAnother potential source of inhomogeneity is a change in measurement \r\nmethods that affect most instruments of an observational network \r\nover a limited time span, such as the transition to Stevenson screens \r\n(Parker, 1994; Bohm et al., 2010; Brunet et al., 2011; Auchmann and \r\nBronnimann, 2012) or to automatic weather stations (WMO, 2017b)."


id: 309ea89adc4efeca8f6a7c87319a8107
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 5071
content:
"The usefulness of any observational dataset is conditioned by \r\nthe availability and outcome of a  quality control (QC) process. \r\nThe objective of the QC is to verify that data are representative \r\nof the measured variable and to what degree the value could be \r\ncontaminated by unrelated or conflicting factors (WMO, 2017a). \r\nData quality assessment is key for ensuring that the data are credible \r\nand to establish trusted relationships between the data provider and \r\nthe users (Nightingale et al., 2019). QC is performed for all relevant \r\nglobal climate datasets (e.g., Menne et al., 2018). For instance, QC \r\ninforms users that old reanalysis datasets can be inconsistent in the \r\nlong term because they assimilated inhomogeneous observations \r\nover the reanalyses period (Kobayashi et al., 2015)"


id: 1625ddd0d8e38aee27e763a925e9a2ff
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 1009
content:
" As a consequence, \r\nthe evaluation against independent observations suggests that \r\nreanalyses should not be automatically regarded as climate\u0002quality products for monitoring long-term trends at the regional \r\nlevel (Manzanas et al., 2014; Torralba et al., 2017). QC needs to be \r\nsystematically carried out by the institutions responsible for handling \r\nthe data (e.g., Cao et al., 2016b)."


id: 798f7eed07f372bb2b7c4d00fe8a26b1
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 16697
content:
"Kidd et  al. (2017) made some assumptions about GPCC-available \r\ngauges and indicated that only 1.6% of Earth's surface lies within \r\n10  km of a  rain gauge, and many areas of the world are beyond \r\n100  km from the nearest rain gauge. Data scarcity is especially \r\ncritical over Africa (Nikulin et  al., 2012; Dike et  al., 2018) but the \r\napparent data scarcity could be due to reasons other than actual \r\npaucity of data, as stated earlier. For instance, over South Africa, \r\nthe number of weather stations collecting daily temperature used \r\nin the fourth version of the Climatic Research Unit Temperature \r\ndataset (CRUTEM4, Osborn and Jones, 2014) has significantly \r\ndeclined since 1980 (Archer et  al., 2018). Although CRUTEM4 has \r\nnow been replaced by CRUTEM5 (Osborn et  al., 2021) it has yet \r\nto take advantage of the significant international efforts to curate \r\nand make available improved global holdings (Rennie et al"


id: 8b81677eb79e1bfd1ca8b23c6bee079e
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 19469
content:
", 2014) \r\nwhich increased the global available station count for monthly mean \r\ntemperatures. This includes additional stations from many African \r\ncountries. The apparent decline in stations since the 1980s could also \r\nbe due to countries not contributing their data to the SYNOP/CLIMAT \r\nnetworks for reasons other than having non-operational stations."


id: 151055566e920d0653892a37dde203cc
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 821
content:
"Second, in addition to systematic errors, homogenized data also \r\nsuffer from random errors, introduced by the homogenization process. \r\nThese errors are largest at the station level but are also present in \r\nnetwork-averaged signals (Lindau and Venema, 2018b). These errors \r\nare determined by the break time series, as well as the noise series \r\nand the performance of the homogenization method, are spatially \r\ncorrelated, and have an impact on activities such as interpolation and \r\nstatistical post-processing of climate simulations (Section 10.2.3.1). \r\nThird, the above discussion pertains to the homogenization of \r\nmonthly and annual means. Homogenization of daily variability \r\naround the mean is more difficult. For daily data, specific correction \r\nmethods are used (Della-Marta and Wanner, 2006; Mestre et  al., \r\n2011; Trewin, 2013; C. Zhou et al"


id: 53906dfe3f3226effd66e440bbc34f18
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 10616
content:
", 2021) that are able to improve the \r\nhomogeneity of test cases, although recent independent validation \r\nefforts were not able to show much improvement (Chimani et al., \r\n2018). The difference with homogenization methods of monthly \r\nand annual means may stem from assumptions on the nature of \r\ninhomogeneities for daily data, which are not yet well understood \r\n(Chimani et al., 2018)."


id: dc6a4d339abd4d244c7a5efa020830d8
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 32180
content:
"Even in Europe, precipitation station density in the widely used E-OBS \r\ngridded dataset varies largely in space and time across regions (Prein \r\nand Gobiet, 2017). This variability is partly due to the reluctance of \r\nsome data owners to share their data with an international effort. \r\nRegardless of the reason, low station density is a major source of \r\nuncertainty (Isotta et al., 2015). Kirchengast et al. (2014) and O and \r\nFoelsche (2019) found that at least 2 to 5 (12) stations are required for \r\ncapturing the area-averaged precipitation amount of heavy summer \r\nprecipitation events on a daily (hourly) basis with a normalized root\u0002mean-square error of less than 20%. Like the E-OBS dataset, gridded \r\ndaily temperature and precipitation datasets are being developed \r\nfor other regions of the world. Examples include south-east Asia \r\n(SA-OBS, Van den Besselaar et al., 2017), and Latin America and West \r\nAfrica (ICA&D, Van den Besselaar et al., 2015)"


id: 5ce1f2f328e0d1670fa67e3afcef17a3
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 12130
content:
" Despite the uneven \r\ndistribution of stations in space and time, the value in these initiatives \r\nis illustrated by the large number of studies in which the data product \r\nis used. This is the case, for instance, in the work of Condom et al. \r\n(2020) over the Andes, a region with prominent data scarcity, and \r\nthe African Monsoon Multidisciplinary Analyses project over West \r\nAfrica (AMMA; e.g.,  Lebel and Ali, 2009). There have been efforts \r\nto reduce data scarcity through initiatives such as the International \r\nSurface Temperature Initiative (ISTI, Thorne et al., 2011), GHCND, and \r\nthe Expanding Met Office Hadley Centre ISD with quality-controlled, \r\nsub-daily station data from 1931 (HadISD, Dunn et al., 2016)."


id: 74631e912d2be62d3cb4c12f6c9c83e1
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 15903
content:
"being the dominant factor (Herrera et al., 2019). Uncertainty due to \r\ninterpolation is typically small for temperature but substantial for \r\nprecipitation and its derivatives, such as drought indices (Chubb et al., \r\n2015; Hellwig et al., 2018). The largest uncertainties typically occur \r\nin sparsely sampled mountain areas (Section 10.2.2.5). Interpolation \r\ngenerally give rise to smoothing effects, such as low variability of \r\nthe derived dataset with respect to the in situ observations (Chen \r\net al., 2019). As a result, the effective resolution of gridded data is \r\ntypically much lower than its nominal resolution. For instance, a 5 km \r\ngridded precipitation dataset for the European Alps has an effective \r\nresolution of about 10 to 25 km (Isotta et al., 2014). In an example \r\nfor precipitation in Spain, the effective resolution converged to the \r\nnominal resolution only when at least 6 to 7 stations were inside \r\nthe gridcell (Herrera et al., 2019)"


id: ffd64582942bca7675b12dc7497530c8
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 37794
content:
" To account for the smoothing errors, \r\nnew stochastic ensemble observation datasets have been introduced \r\n(Von Clarmann, 2014)."


id: 69854b2f963076611053aa4d9281f68e
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 14197
content:
"Spatiotemporal variability of meteorological parameters observed over \r\nmountainous areas is often large, indicating strong control exerted by \r\nlocal topography on meteorological parameters (Gultepe et al., 2014). \r\nDifficult access, harsh climatic conditions as well as instrumental \r\nissues make meteorological measurements extremely challenging \r\nat higher elevations (Azam et  al., 2018; Beniston et  al., 2018). \r\nMeasurements of wind speed, temperature, relative humidity and \r\nradiative fluxes are critical for climate model evaluation, but difficult \r\nto handle due to their point-scale representativeness and small-scale \r\nspatiotemporal variability over mountainous terrain, and often need \r\nadjustment (Gultepe, 2015). High-altitude (>3000 metres) permanent \r\nmeteorological stations are limited and current knowledge is mainly \r\nbased on valley-bottom or low-elevation meteorological stations (Qin \r\net  al., 2009; Lawrimore et  al., 2011; Gultepe, 2015; Condom et  al"


id: d9e6182b0ded2a5e4542d11be8b03438
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 31803
content:
", \r\n2020), which, generally do not represent the higher elevation climate \r\n(Immerzeel et al., 2015; Shea et al., 2015)."


id: 2131dd11857061f5472adbb6587ad511
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 2659
content:
"Measuring precipitation amounts, especially of solid precipitation, in \r\nmountainous areas is particularly challenging due to the presence \r\nof orographic barriers, strong vertical and horizontal precipitation \r\nrate variability, and the difficulty in finding representative sites for \r\nprecipitation measurements (Barry, 2012). However, the precipitation \r\namounts can be indirectly estimated by the observed point mass \r\nbalances at glacier accumulation areas representing net snow \r\naccumulation (Haimberger et  al., 2012; Immerzeel et  al., 2015; \r\nSakai et al., 2015; Azam et al., 2018). There is very high confidence\r\nthat precipitation measurements, especially solid precipitation, in \r\nmountainous areas are strongly affected by the gauge location and \r\nsetup. Precipitation measurements are also affected by the type of \r\nmeasurement method, presence/absence of shielding, presence/\r\nabsence of a heating system and operating meteorological conditions \r\n(Nitu et al., 2018)"


id: ab6a285e777904e090d6b4be5d7a2aa5
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 24486
content:
" Solid precipitation measurements may have errors \r\nranging from 20% to 50%, largely due to under-catch in windy, icing \r\nand riming conditions (Rasmussen et al., 2012), and therefore require \r\ncorrections by applying transfer functions developed mainly from \r\ncollected wind speed and temperature data (Kochendorfer et  al., \r\n2017). The latest Solid Precipitation Intercomparison Experiment \r\n(SPICE) report recommends measurements of wind speed, wind "


id: cac664dc759cc04d35547587a5e4239d
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 29465
content:
"Recent advances in remote-sensing methods provide an alternative, \r\nbut they also have limitations over mountainous areas. Different \r\nversions of the Tropical Rainfall Measuring Mission (TRMM) \r\nproducts were found to perform differently over mountainous \r\nareas (Zulkafli et  al., 2014). Orographic heavy rainfall associated \r\nwith Typhoon Morakot in 2009 was severely underestimated in all \r\nmicrowave products including TRMM 3B42 (Shige et al., 2013). The \r\nunderestimation has been mitigated in the Global Satellite Mapping \r\nof Precipitation (GSMaP) product by considering the orographic \r\neffects (Shige et al., 2013). Studies have suggested a high accuracy of \r\npassive optical satellite (e.g., MODIS, Landsat) snow products under \r\nclear skies when compared with the field observations. However, \r\ncloud masking and sub-pixel cloud heterogeneity in these snow-cover \r\nproducts considerably restrict their applications (Kahn et al., 2011; \r\nBrun et al., 2015; Tang et al"


id: e7e476f38f6c3df9a7e1d8fefee1d047
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 33979
content:
", 2017; Stillinger et al., 2019). Gridded \r\ndatasets (e.g.,  CRU, GPCC Full Data Product, GPCC Monitoring \r\nProduct, ERA-Interim, ERA5, ERA5-land, MERRA-2, MERRA-2 bias \r\nadjusted, PERSIANN-CDR) are of paramount importance, yet they \r\noften lack enough in situ observations to improve the temporal \r\nand spatial distribution of meteorological parameters over complex \r\nmountain terrain (Zandler et al., 2019)."


id: 4d2c494a11880f9b7fd0cbc388176ebf
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 9569
content:
"Statistical downscaling, bias adjustment and weather generators are \r\npost-processing methods used to derive climate information from \r\nclimate simulations. They all require observational data for calibration \r\nas well as evaluation (Section  10.3.3.1). Typically, the so-called \r\nperfect prognosis methods use quasi-observations for the predictors \r\n(i.e.,  reanalyses) and actual observations for the predictands (the \r\nsurface variables of interest). By contrast, bias adjustment methods \r\nuse observations only for the predictands. Weather generators typically \r\nrequire only observed predictands, although some are conditioned \r\non observed predictors as well. Very often these methods are based \r\non daily data, because of user needs, but also because of the limited \r\navailability of sub-daily observations and the limited ability of climate \r\nmodels to realistically simulate sub-daily weather (Iizumi et  al., \r\n2012)"


id: 4b6e01d31951d95b433635c2b3398cc3
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 9282
content:
" Some methods are calibrated on the monthly scale, but some \r\nof the generated time series are then further disaggregated to the \r\ndaily scale (e.g., Thober et al., 2014). A few methods, mainly weather \r\ngenerators, represent sub-daily weather (Mezghani and Hingray, \r\n2009; Kaczmarska et al., 2014). Many methods simulate temperature \r\nand precipitation only, although some also represent wind, radiation \r\nand other variables. The limited availability of high quality and long \r\nobservational records typically restricts these applications to a  few \r\ncases (Verfaillie et al., 2017; Pryor and Hahmann, 2019). Overall, there \r\nis high confidence that limited availability of station observations, \r\nincluding variables beyond temperature and precipitation as well as \r\nsub-daily data, limit the use of statistical modelling of regional climate."


id: d715618795028fa03cf0af986765c2fb
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 31350
content:
"Beyond climate monitoring, the quality and availability of multiple \r\nobservational reference datasets play a  central role in model \r\nevaluation. In fact, when using observations for model evaluation, \r\nthere are multiple examples where inter-observational uncertainty is \r\nas large as the inter-model variability. This has been shown for various \r\naspects of the Indian monsoon (Section 10.6.3; Collins et al., 2013a) \r\nand for precipitation uncertainties over Africa (Section 10.6.4; Nikulin \r\net al., 2012; Sylla et al., 2013; Dosio et al., 2015; Bador et al., 2020) \r\nand Europe (Prein and Gobiet, 2017). Kotlarski et al. (2019) compared \r\nthree high-resolution observational temperature and precipitation \r\ndatasets (E-OBS, a compilation of national/regional high-resolution \r\ngridded datasets, and the EURO4M-MESAN 0.22deg reanalysis based on \r\na high-resolution limited-area model) with five EURO-CORDEX RCMs \r\ndriven by ERA-Interim"


id: e79df43cd47c904ea9daf5e91c0075cf
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 33939
content:
" Generally, the differences between RCMs are \r\nlarger than those between observation datasets, but for individual \r\nregions and performance metrics, observational uncertainty can \r\ndominate. They also showed that the choice of reference dataset \r\ncan have an influence on the RCM performance score. Over the high \r\nmountain Asia region and East Asia, differences among gridded \r\nprecipitation datasets can generate significant uncertainties in \r\nderiving precipitation characteristics (J. Kim et al., 2015; Kim and Park, \r\n2016; Guo et al., 2017). Over western North America, observational \r\nuncertainty induces differences in multi-decadal precipitation \r\ntrends (Lehner et al., 2018). Taking a very different perspective, the \r\nagreement between model simulations may be used to estimate \r\nthe uncertainty and quality of observations (Massonnet et al., 2016)"


id: 70a5c47c8a443fdec0eae27dfddc7b6a
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 15342
content:
" \r\nThere is high confidence that an ensemble of multiple observational \r\nreferences at a regional scale is fundamental for model performance \r\nassessment. The uncertainties vary according to region, season, and \r\nstatistical properties (Cross-Chapter Box 10.2)."


id: d36fff964a15a80b05d90d74f2eaefaa
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 30787
content:
"Paleo-reanalyses are enabling a new range of applications and have \r\nalready provided useful information on seasonal-to-multi-decadal \r\nclimate variability over past millennia. They are useful tools to study \r\nthe co-variance between variables at interannual-to-centennial time \r\nscales and at regional to global spatial scales. In particular, they have \r\nhighlighted the processes that can be responsible for changes in \r\ncontinental hydrology at multi-decadal time scales (Franke et al., 2017; \r\nKlein and Goosse, 2018; Steiger et al., 2018). Paleo-reanalyses have \r\nconfirmed a large contribution of internal variability in past changes \r\nat regional scale during the pre-industrial period, superimposed on \r\na weak common signal due to forcing changes (Goosse et al., 2012) \r\nand the absence of a globally coherent warm period in the common \r\nera before the recent warming (Neukom et al., 2019)"


id: 503b7133a04bed7e01e55de6126b2143
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 10062
content:
" Reconstructions \r\nof the atmospheric state obtained in the reanalysis also provide robust \r\nevidence of a local enhancement of warming or cooling conditions \r\ndue to changes in atmospheric circulation, such as for the warm \r\nconditions in some European regions around 950-1250 CE, the cooling \r\nobserved in 1809/1810, or the cold and rainy 1816 summer in Europe \r\n(Cross-Chapter Box 4.1; Goosse et al., 2012; Hakim et al., 2016; Franke \r\net al., 2017; Schurer et al., 2019)."


id: 64b0e691f2ff5145f0c0d402f74752a2
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 13416
content:
"Gutowski  Jr.  et  al., 2016) provides ensembles of high-resolution \r\nhistorical (starting as early as 1950) and future climate projections for \r\nvarious regions. RCMs in CORDEX typically have a horizontal resolution \r\nbetween 10 and 50 km. But much finer spatial resolution is required \r\nto fully resolve deep convection, an important cause of precipitation \r\nin much of the world. Therefore, an emerging strand in dynamical \r\ndownscaling employs simulations at convection permitting scales, \r\nat horizontal resolutions of a few kilometres, where deep-convection \r\nparametrizations can be switched off, approximately simulating deep \r\nconvection (Prein et  al., 2015; Stratton et  al., 2018; Coppola et  al., \r\n2020). A recent study indicates that switching off the deep-convection \r\nparametrization may be beneficial also in simulations performed at \r\ncoarser resolutions (Vergara-Temprado et  al., 2020)"


id: 439a15def6e895c06da1833fc04f9c7a
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 8042
content:
" Alternatively, \r\nsome RCMs make use of scale-aware parametrizations that are able \r\nto adapt to increasing resolution without switching off the convection \r\nscheme (Hamdi et  al., 2012; De Troch et  al., 2013; Plant and Yano, \r\n2015; Giot et al., 2016; Termonia et al., 2018; Yano et al., 2018)."


id: 7899365fc27fbb4ad8df021986e3b5a5
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 16536
content:
"RCMs have often consisted of atmospheric and land components \r\nthat do not include all possible Earth system processes and therefore \r\nneglect important processes such as air-sea coupling (in standard \r\nRCMs sea surface temperatures, SSTs, are prescribed from global \r\nmodel simulations or reanalyses) or the chemistry of aerosol-cloud \r\ninteraction (aerosols prescribed with a  climatology), which may \r\ninfluence regional climate projections. Therefore, some RCMs have \r\nbeen extended by coupling to additional components like interactive \r\noceans, sometimes with sea ice (Kjellstrom et al., 2005; Somot et al., \r\n2008; Van Pham et al., 2014; Sein et al., 2015; Ruti et al., 2016; Zou \r\nand Zhou, 2016a; Zou et  al., 2017; Samanta et  al., 2018), rivers \r\n(Sevault et al., 2014; Lee et al., 2015; Di Sante et al., 2019), glaciers \r\n(Kotlarski et al., 2010), and aerosols (Zakey et al., 2006; Zubler et al., \r\n2011; Nabat et al., 2015)"


id: b964a93653f255870914dcb76c9b3fe1
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 26712
content:
" The coupling of these components allows \r\nfor the investigation of additional climate processes such as regional \r\nsea level change (Adloff et al., 2018), ocean-land interactions (Lima \r\net al., 2019; Soares et al., 2019a), or the impact of high-frequency \r\nocean-atmosphere coupling on the climatology of Mediterranean \r\ncyclones (Flaounas et al., 2018)."


id: 9912a95ec3a1586aab9713f0dda1a46d
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 21602
content:
"Regional climate models (RCMs) are dynamical models similar \r\nto global models that are applied over a  limited area, but with \r\na  horizontal resolution higher than that of standard global \r\nmodels. They are the basis for dynamical downscaling to produce \r\nsub-continental climate information (e.g., Chapters 11, 12 and Atlas) \r\nbut are also often used for process understanding. At lateral and, if \r\napplicable, lower boundaries, RCMs take their values from a driving \r\ndataset, which could be a global model or a reanalysis. RCMs are \r\ntypically one-way nested: they do not feed back into the driving \r\nmodel, although two-way nested global model-RCM simulations \r\nhave been performed that examine regional influence on large-scale \r\nclimate, potentially improving it (Lorenz and Jacob, 2005; Harris \r\nand Lin, 2013; Junquas et al., 2016). Spectral nudging (Kida et al., \r\n1991; Waldron et al., 1996; von Storch et al"


id: aec71aa6d8643821d86651420e9f217
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 25027
content:
", 2000; Kanamaru and \r\nKanamitsu, 2007) can increase consistency with the driving model, \r\nwhereby selected variables, such as the wind field, are forced to \r\nclosely follow a prescribed large-scale field over a specified range \r\nof spatial scales. RCMs can inherit biases from the driving global \r\nmodel in addition to producing biases themselves (Hall, 2014; \r\nHong and Kanamitsu, 2014; Dosio et  al., 2015; Takayabu et  al., \r\n2016). The consistency between the circulation features simulated \r\nby the RCM and those inherited through the boundary conditions \r\ndepends on (i) the relative importance of the large-scale forcing \r\ncompared to local-scale phenomena, and (ii) the size of the RCM \r\ndomain (e.g.,  Diaconescu and Laprise, 2013). Large domains also \r\nallow the RCM to generate much of its own internally generated \r\nunforced variability (Nikiema et  al., 2017, and references therein; \r\nSanchez-Gomez and Somot, 2018)."


id: 54cbdbf30c8bae59e8e38bfaf6737163
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 10793
content:
"An alternative or addition to dynamical downscaling is the use of \r\nstatistical approaches to generate regional projections. In AR5 these \r\nmethods were collectively referred to as statistical downscaling, \r\nbut their performance assessment has received little attention. \r\nA major conclusion was that a wide range of different methods exist \r\nand a  general assessment of their performance is difficult (Flato \r\net  al., 2014). Since AR5, several initiatives have been launched to \r\nimprove the understanding of statistical approaches such as VALUE \r\n(Validating and Integrating Downscaling Methods for Climate \r\nChange Research, now merged into the EURO-CORDEX activities; \r\nMaraun et  al., 2015), STaRMIP (Statistical Regionalization Models \r\nIntercomparisons and Hydrological Impacts Project; Vaittinada Ayar \r\net  al., 2016) and BADJAM (Bias ADJustment of climate scenarios \r\nfor Agricultural Model applications; Galmarini et  al., 2019)"


id: 7eda963b66dbbbac4032194119b79bd3
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 17520
content:
" The \r\nperformance of different implementations of these approaches will \r\nbe assessed in Section 10.3.3.7."


id: ed3a12dcd00fb1d9f791acf5f77ff2d3
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 34802
content:
"extends univariate methods, which adjust statistics of individual \r\nvariables separately, to joint adjustment of multiple variables \r\nsimultaneously. Implementations remove biases in (i) specific \r\nmeasures of multivariate dependence, like correlation structure, via \r\nlinear transformations (Bardossy and Pegram, 2012; Cannon, 2016), \r\nor, more flexibly, (ii) the full multivariate distribution via non-linear \r\ntransformations (Vrac and Friederichs, 2015; Dekens et  al., 2017; \r\nCannon, 2018; Vrac, 2018; Robin et al., 2019). Other research strands \r\nfocus on the explicit separation of bias adjustment and downscaling \r\n(Section  10.3.1.3.5), or the integration of process understanding \r\n(Maraun et  al., 2017), such as by conditioning the adjustment on \r\nthe occurrence of relevant phenomena (Addor et al., 2016; Verfaillie \r\net al., 2017; Manzanas and Gutierrez, 2019)"


id: c72121164be0897bbc45eaa1a59bb1e
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 28914
content:
" Some authors suggest \r\nto mitigate the influence of large-scale temperature or circulation \r\nbiases by performing a bias adjustment of the driving fields prior to \r\ndynamical downscaling (Colette et al., 2012; Hernandez-Diaz et al., \r\n2013, 2019). Issues that may arise when using bias adjustment are \r\ndiscussed in Cross-Chapter Box 10.2."


id: 9731fb6c6accaf2489346980dd976958
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 21300
content:
"Equivalent simulations can be conducted for individual events, \r\nthereby allowing for very high resolution. With counterfactual past \r\nclimate conditions, such simulations can be used for conditional event \r\nattribution (Trenberth et  al., 2015; Chapter  11), using hypothetical \r\nfuture conditions to generate physical climate storylines of how \r\nspecific events may manifest in a  warmer climate. The approach \r\nhas been employed to study extreme events that require very high \r\nresolution simulations such as tropical cyclones (Lackmann, 2015; \r\nTakayabu et al., 2015; Lau et al., 2016; Kanada et al., 2017a; Gutmann \r\net  al., 2018; Patricola and Wehner, 2018; J. Chen et  al., 2020) or \r\nconvective precipitation events (Pall et al., 2017; Hibino et al., 2018). \r\nThe range of possible events is broader and has included Korean \r\nheatwaves (Kim et al., 2018) and monsoon onset in West Africa (Lawal \r\net  al., 2016)"


