# document

id: d0f08e9a9cf018a3ae99906295dd13bb
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 30405
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" In contrast, Junod and Christy (2020) find NMAT trends which are 8-17% larger than those for SST in the ERSSTv4 \r\nand HadISST datasets for the period 1900 to 2010, but 11-15% smaller than the SST trends for the same datasets from 1979 to \r\n2010. However, ERSSTv4 uses NMAT data as a basis for homogeneity adjustment so is not fully independent. Kent and Kennedy \r\n(2021) note sensitivity to methodological choices in comparisons but find that NMAT is warming more slowly than SST products \r\nover most periods considered. Rubino et al. (2020) exploit tropical Pacific moored buoy arrays, available since the early 1980s, and \r\nfind differences in NMAT and SST anomalies, which are sensitive to the choice of period and show spatio-temporal ENSO-related \r\n(Annex IV) signals in the differences. "


id: 4456787af1385547e7d54fc60ead6daa
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"CMIP historical simulations and projections agree that GSAT increases faster than GMST, the reverse of what is indicated by many \r\nmarine observations. Several studies approximate the approach used to derive GMST from observations by blending SST over open \r\nocean and SAT over land and sea ice from model output (Cowtan et al., 2015; Richardson et al., 2018; Beusch et al., 2020; Gillett \r\net al., 2021). Cowtan et al. found that trends in GSAT are of the order of 9% larger than for GMST in CMIP5, based on data from \r\n1850-2100 (historical + RCP8.5), if anomalies are blended and sea ice is allowed to vary over time (Cowtan et al., 2015). Broadly \r\nconsistent numbers are found for both CMIP5 and CMIP6, across a range of SSP and RCP scenarios and time periods (Richardson \r\net al., 2018; Beusch et al., 2020; Gillett et al., 2021)"


id: b85ab921eb51a6cbb9a9181d713a4c24
created_at: 2023-03-16 14:01:57
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" Blending monthly anomalies and allowing sea ice to vary, the change in GSAT \r\nfor 2010-2019 relative to 1850-1900 is 2-8% larger than spatially-complete GMST in CMIP6 historical and SSP2-4.5 simulations \r\n(Gillett et al., 2021), and 6-12% larger in CMIP5 historical and RCP2.6 and 8.5 simulations for 2007-2016 relative to 1861-1880 \r\n(Richardson et al., 2018). However, a true like-for-like comparison to observational products is challenging because methodological \r\nchoices have a large impact on the relationship between modelled GMST and GSAT and none of these studies fully reproduces the \r\nmethods used to derive estimates of GMST in recent observational datasets, which use various ways to infill areas lacking in situ \r\nobservations (Jones, 2020). "


id: 78852a71dac0e86b58570929689e952f
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"There is a significant issue in areas where sea ice melts or grows, where the quantity used in observational-based GMST estimates \r\nswitches between air temperature and sea surface temperature. This primarily affects analyses combining SAT anomalies over \r\nland and ice with SST anomalies over ocean. In areas where sea ice has recently melted, the climatological value changes from an \r\nair-temperature based estimate to an SST estimate based upon the freezing point of seawater (-1.8degC). This switch in climatology to, \r\nin general, a warmer climatology, leads to a bias towards reduced warming in anomalies compared with analyses based on absolute \r\ntemperatures. Richardson et al. (2018) found this underestimation to amount to approximately 3% of observed warming in historical \r\nmodel simulations. Given the projected future sea ice losses, the effect will grow in future (low confidence), with potential effects of \r\nthe order of 0"


id: 9165c22792e5cbc01d24f07a148e2ffa
created_at: 2023-03-16 14:01:57
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"1degC in the second half of the 21st century under high warming scenarios, although with some uncertainty arising from \r\nthe large spread of sea ice loss in model projections (Tokarska et al., 2019). "


id: 8b3260e331b1da7e6603bd9327440602
created_at: 2023-03-16 14:01:57
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"The AR5 assessed estimate for historical warming between 1850-1900 and 1986-2005 is 0.61 [0.55 to 0.67] degC. The equivalent in \r\nAR6 is 0.69 [0.54 to 0.79] degC, and the 0.08 [-0.01 to 0.12] degC difference is an estimate of the contribution of changes in observational \r\nunderstanding alone (Cross-Chapter Box 2.3, Table 1). The exact value of this contribution depends upon the metric being compared \r\n(GMST/GSAT, the method used to calculate a trend or change between two periods, the exact reference period used), with the \r\nbest estimates (with the exception of the SR1.5 GSAT estimate) falling between 0.07degC and 0.12degC. The choice of 1850-1900 to \r\n1986-2005 as the basis is due to the widespread use of this period across AR5 and SR1.5 in several contexts. The AR6-assessed \r\nGMST warming between 1850-1900 and 2011-2020 is 1.09 [0.95 to 1.20] degC. An AR5-equivalent assessment using this estimated \r\ndifference in observational understanding is thus 1.01 [0.94 to 1.08] degC"


id: 796636146c773aaa555e9bb30b327145
created_at: 2023-03-16 14:01:57
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" These updates and improvements in observational datasets \r\naffect other quantities that derive from the assessment of GSAT warming, including estimates of remaining carbon budgets and \r\nestimates of crossing times of 1.5degC and 2degC of global warming (see Cross Chapter Box 2.3, Table 1)."


id: e77a2de5ebcbd072d0efbe6a917f6123
created_at: 2023-03-16 14:01:57
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"The updated estimate of historical warming is one contribution to the revised time of projected crossing of the threshold of 1.5degC \r\nglobal warming in comparison with SR1.5, but is not the only reason for this update. The AR6 assessment of future change in GSAT \r\n(Table 4.5) results in the following threshold-crossing times, based on 20-year moving averages. The threshold-crossing time is defined \r\nas the midpoint of the first 20-year period during which the average GSAT exceeds the threshold. During the near term (2021-2040), \r\na 1.5degC GSAT increase relative to the average over the period 1850-1900 is very likely to occur in scenario SSP5-8.5, likely to occur in \r\nscenarios SSP2-4.5 and SSP3-7.0, and more likely than not to occur in scenarios SSP1-1.9 and SSP1-2.6. In all scenarios assessed here \r\nexcept SSP5-8.5, the central estimate of crossing the 1.5degC global warming level lies in the early 2030s. This is in the early part of the \r\nlikely range (2030-2052) assessed in SR1"


id: 4661d74d435876e63edba55c3de74a67
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"5, which assumed continuation of the then-reported warming rate; this estimated rate has \r\nbeen confirmed in AR6 (Section 3.3.1). Roughly half of this difference arises from the higher diagnosed historical warming in AR6. The \r\nother half arises because, for central estimates of climate sensitivity, most scenarios show stronger warming over the near term than \r\nwas assessed as 'current' in SR1.5 (medium confidence). When considering scenarios similar to SSP1-1.9 instead of linear extrapolation, \r\nthe SR1.5 estimate of when 1.5degC global warming is crossed is close to the central estimate reported here (SR1.5, Table 2.SM.12). "


id: 5a7615c2d55f69c3893c16ec5070ddb2
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"Implications for assessment of emissions scenarios and remaining carbon budgets\r\nTo estimate the global warming implications of emissions scenarios, AR5 and SR1.5 combined estimates of observed GMST changes \r\nfrom 1850-1900 to 1986-2005 (Cross-Chapter Box 2.3, Table 1) with GSAT projections of subsequent warming. AR6 undertakes three \r\nchanges to this approach. First, the AR6 assessment of improved observational records is used. Second, the recent past baseline period \r\nis updated from 1986-2005 to 1995-2014, and, third, historical estimates are expressed in GSAT instead of GMST for consistency \r\nof historical estimates with future projections. The updated estimates of warming to date in AR6 lead to higher estimates of future \r\nwarming, all else being equal. The temperature classification of emissions scenarios in the WGIII report adopts the definition of \r\ntemperature classes as introduced in SR1"


id: be2305084e7492554f5872114423d0c9
created_at: 2023-03-16 14:01:57
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"5, and assigns emissions scenarios to these classes based on their AR6 assessed GSAT \r\noutcomes (Cross-Chapter Box 7.1; WGIII Annex C.II.2.4). "


id: c587c6925a43a2fdbded90b1a6df53a9
created_at: 2023-03-16 14:01:57
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"In both AR5 and SR1.5, remaining carbon budgets were expressed as a function of GSAT warming, while also highlighting the \r\nimplications of using historical warming estimates expressed in GMST. The AR5 reported total carbon budgets for GSAT warming \r\nrelative to 1861-1880. The AR5 Synthesis Report (SYR) also includes remaining carbon budget estimates based on AR5 WGIII \r\nscenario projections that use the method for AR5 scenario projections described above. The SR1.5 integrated several methodological \r\nadvancements to estimate remaining carbon budgets and reported budgets for additional GSAT warming since the 2006-2015 period, \r\nestimating, following the application of an adjustment (Richardson et al., 2016, Table 1.1, SR1.5) to GMST, that 0.97degC (+- 0.12degC) of \r\nGSAT warming occurred historically between 1850-1900 and 2006-2015. The AR6 assessment, above, leads to an estimate of 0.94degC \r\nof warming between 1850-1900 and 2006-2015"


id: 2fd8fc5c0619596653005b39db915556
created_at: 2023-03-16 14:01:57
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" All other factors considered equal, the AR6 estimate thus implies that 0.03degC \r\nmore warming is considered for remaining carbon budgets compared to SR1.5. Combining this 0.03degC value with the SR1.5 transient "


id: 9a7f762804b93f0eaaf5527ff380f60f
created_at: 2023-03-16 14:01:57
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"The assessment of global warming to date now being larger than previously assessed has no consequence on the assessment of past \r\nclimate impacts, nor does it generally imply that projected climate impacts are now expected to occur earlier. The implications are \r\nmainly that the level of warming associated with a particular impact has been revised. This has very limited practical implications \r\nfor the assessment of the benefits of limiting global warming to specific levels, as well as for the urgency of adaptation action. For \r\nexample, impacts that occurred in the period 1986-2005 were previously associated with a GMST increase of 0.61degC relative to \r\n1850-1900, relative to AR5 estimates. These impacts are now instead associated with a GMST increase of 0.69degC, relative to the \r\nassessment in this Report. The impacts themselves have not changed. Similarly, the impacts previously associated with a GMST or \r\nGSAT increase of 1"


id: 330a34d9ec6455467b3e324bae45a602
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"5degC will now generally be associated with a slightly different global warming level. This is because projections of \r\nfuture warming and its impacts relative to 1850-1900 are normally made by adding projected warming from a recent past baseline \r\nto an estimate of the observed warming from 1850-1900, as in AR5 and SR1.5. "


id: 89f23b6a1170e8b33b6a916392c6bae6
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"Most of the previously projected impacts and risks associated with global warming of 1.5degC have therefore not changed and are \r\nstill associated with the same level of future warming (0.89degC) relative to 1986-2005. With this warming now estimated as 0.08degC \r\nlarger than in AR5, the future impacts previously associated with 1.5degC warming are now associated with 1.58degC warming. Similarly, \r\nthe impacts now associated with 1.5degC warming would have previously been associated with 1.42degC warming. There are exceptions \r\nwhere impacts studies have used a baseline earlier than 1986-2005 (e.g., King et al., 2017), for which the new estimate of the \r\nhistorical warming would mean an earlier occurrence of the projected impacts. However, even in these cases, the ostensible difference \r\nin impacts associated with a 0.08degC difference in global mean temperature will be small in comparison with the uncertainties"


id: 87f4b6ce8f6e3cd2c88b29879b17876d
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" There \r\nare also substantial uncertainties in regional climate changes and the magnitude of climate impact-drivers projected to occur with \r\nglobal warming of 1.5degC (Betts et al., 2018; Seneviratne et al., 2018). Furthermore, the time of reaching global warming of 1.5degC is \r\nsubject to uncertainties of approximately +-10 years associated with uncertainties in climate sensitivity, and +-3 to 4 years associated \r\nwith the different SSP forcing scenarios (Section 4.3.4, Table 4.5, and see discussion above). "


id: e3362ab8c5da0cc7d976715216fe2a4
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"For SST analyses, three products - HadSST4 (1850-present, Kennedy \r\net  al., 2019), ERSSTv5 (1850-present, Huang et  al., 2017) and \r\nCOBE SST2 (1880-present, (Hirahara et al., 2014) - now have bias \r\nadjustments applied throughout the record. The new SST datasets \r\naccount for two major issues previously identified in AR5: that \r\nglobally averaged buoy SSTs are about 0.12degC cooler than ship\u0002based SSTs (Kennedy et al., 2011; Huang et al., 2015), and that SSTs \r\nfrom ship engine room intakes may have biases for individual ships \r\ndepending upon the sensor set-up (Kent and Kaplan, 2006) but have \r\nan overall warm bias when globally aggregated (Kennedy et al., 2019). \r\nThe first issue primarily affects data since 1990, when buoys began to \r\nincreasingly contribute to the observation network (Woodruff et al., \r\n2011), and the second issue has its largest effect from the 1940s \r\nto the 1970s. From the standpoint of uncertainty, ERSSTv4 (W. Liu \r\net  al., 2015; Huang et  al"


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", 2016) and subsequent versions (Huang \r\net al., 2017), and HadSST4 have estimates presented as ensembles \r\nthat sample parametric uncertainty. Comparisons  between these "


id: 8b0e998e8f56f0c116ec97cd1eb15b7e
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"A number of recent studies also corroborate important components of \r\nthe SST record (Hausfather et al., 2017; Kent et al., 2017; Cowtan et al., \r\n2018; Kennedy et al., 2019). In particular, ATSR SST satellite retrievals \r\n(Merchant et al., 2012; Berry et al., 2018), the near-surface records \r\nfrom hydrographical profiles (Gouretski et  al., 2012; Huang et  al., \r\n2018), and coastal observations (Cowtan et al., 2018) have all been \r\nshown to be broadly consistent with the homogenized SST analyses. \r\nHausfather et al. (2017) also confirmed the new estimate of the rate \r\nof warming seen in ERSSTv4 since the late 1990s through comparison \r\nwith independent SST data sources such as Argo floats and satellite \r\nretrievals. Nevertheless, dataset differences remain in the mid-20th \r\ncentury when there were major, poorly-documented, changes in \r\ninstrumentation and observational practices (Kent et  al"


id: 4774be8ef7ddb993ad7e381ad9b61f71
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", 2017), \r\nparticularly during World War II, when ship observations were limited \r\nand disproportionately originated from US naval sources (Thompson \r\net al., 2008). Kennedy et al. (2019) also identify differences between \r\nthe new HadSST4 dataset and other SST datasets in the 1980s and \r\n1990s, indicating that some level of structural uncertainty remains \r\nduring this period, whilst Chan et al. (2019) and Davis et al. (2019) \r\ndocument residual uncertainties in the early and later 20th century \r\nrecords respectively. "


id: 453922f100bb91cfa568abff91877e8d
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"Historically, SST has been used as a basis for global temperature \r\nassessment on the premise that the less variable SST data provides \r\na better estimate of marine temperature changes than marine \r\nair temperature (MAT) (Kent and Kennedy, 2021). However, MAT \r\nproducts are used to adjust SST biases in the NOAA SST product \r\nbecause they are assessed to be more homogeneous (Huang \r\net  al., 2017). Observational datasets exist for night-marine air \r\ntemperature (NMAT) (e.g., Cornes et al., 2020; Junod and Christy, \r\n2020; Rayner et al., 2020) and there are methods to adjust daytime \r\nMATs (Berry et al., 2004), but there is to date no regularly updated \r\ndataset which combines MAT with temperatures over land. MAT \r\ndatasets are more sparse in recent decades than SST datasets as \r\nmarine datasets have become increasingly dependent on drifting \r\nbuoys (Centurioni et al"


id: 38224ed2d8247ec5440c940981ea7bf9
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", 2019) which generally measure SST but not \r\nMAT, and there are almost no recent winter MAT data south of 40degS \r\n(Swart et  al., 2019). However, the situation reverses in the 19th \r\ncentury with a greater prevalence of MAT than SST measurements \r\navailable in the ICOADS data repository (Freeman et  al., 2017, \r\n2019; Kent and Kennedy, 2021)."


id: 90cd77700ce40735e1fd35afe10a8d6a
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"At the time of AR5 a limitation of conventional datasets was the lack \r\nof coverage, especially in high latitudes, which although recognized \r\nas an issue (Simmons et al., 2010) had not been addressed in most \r\nproducts. Interpolation involves the statistical imputation of values \r\nacross regions with limited data and can add both systematic and \r\nrandom uncertainties (Lenssen et al., 2019). Cowtan and Way (2014) \r\napplied a kriging-based method to extend existing datasets to polar \r\nregions, while Kadow et al. (2020) used an artificial intelligence-based \r\nmethod, and Vaccaro et  al. (2021) used gaussian random Markov \r\nfields, for the same purpose, although only Kadow et al. (2020) uses \r\nthe most recent generation of datasets as its base. The Berkeley Earth \r\nmerged product (Rohde and Hausfather, 2020), HadCRUT5 (Morice \r\net al., 2021) and NOAA GlobalTemp-Interim (Vose et al"


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", 2021) all \r\ninclude interpolation over reasonable distances across data sparse \r\nregions which results in quasi-global estimates from the late 1950s \r\nwhen continuous Antarctic observations commenced. Interpolated \r\ndatasets with substantial coverage of high latitudes show generally \r\nstronger warming of GMST than those with limited data in polar \r\nregions (Vose et al., 2021), and their strong warming at high northern \r\nlatitudes is consistent with independent estimates from reanalyses \r\n(Simmons et al., 2017; Lenssen et al., 2019) and satellites (Cowtan \r\nand Way, 2014). Given the spatial scales of surface temperature "


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created_at: 2023-03-16 14:01:57
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"Temperature trends in RO products are most consistent with each other \r\nand with other observations between 8 km and 25 km (Ho et al., 2012; \r\nSteiner et al., 2013, 2020a). The uncertainty increases above 25 km for \r\nthe early RO period, for which data are based on the single-satellite \r\nCHAMP mission, but data at higher altitudes become more reliable \r\nfor later missions based on advanced receivers (Steiner et al., 2020a), \r\nalong with the application of corrections for ionospheric effects \r\n(Danzer et al., 2020). The uncertainty due to the changing number of \r\nobservations is reduced by correcting for the sampling uncertainty in \r\nRO climatological fields (e.g., Scherllin-Pirscher et al., 2011). For AIRS, \r\nthus far, stability of the instrument has been constrained to less than \r\n0.03degC per decade for selected window channels in a comparison to \r\nSSTs measured by ocean buoys (Aumann et al., 2019)"


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" Trends were \r\ninter-compared with trends in RO data and reanalysis data to assess \r\nsystematic uncertainties (Leroy et al., 2018)."


id: a425ecd05f21407c0588ba61dc64dfca
created_at: 2023-03-16 14:01:57
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"There have been updated radiosonde estimates from the University \r\nof Vienna (RAOBCORE and RICH; Haimberger et al., 2012) and a new \r\ndataset from the State University of New York (UAHRD, Zhou et al., \r\n2020). There are new versions of AMSU products from the University \r\nof Alabama in Huntsville (UAHv6.0; Spencer et al., 2017) and Remote \r\nSensing Systems (RSSv4.0; Mears and Wentz, 2017). These updates \r\nhave led to convergence in the lower stratosphere layer (Maycock \r\net  al., 2018); in particular, the move to UAHv6.0 has addressed \r\nhomogeneity issues identified by Seidel et al. (2016), although residual \r\ndifferences remain (Christy et  al., 2018). Reanalyses products had \r\nidentified limitations near the 300 hPa level where the contribution \r\nof aircraft observations has increased rapidly in recent years (Dee \r\net al., 2011; Gelaro et al., 2017), leading to identified biases (Dee and \r\nUppala, 2009), that have been addressed in ERA5 (Hersbach et al., \r\n2020)"


id: 3bc2e71d7f9369bcacd68e1b67ac2715
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" Modern reanalyses are generally well aligned with radiosonde \r\nand satellite observations in the middle and lower troposphere and \r\nlower stratosphere. A new operational mid- and upper-stratospheric \r\ndataset (STAR) has been developed by Zou and Qian (2016), merging \r\nthe previous 1979-2006 SSU dataset (Zou et  al., 2014) with a \r\ndataset from 1998 onwards drawn from relevant AMSU channels \r\n(Wang and Zou, 2014). Further stratospheric satellite-based datasets \r\nfrom various combinations of satellites have been developed by \r\nMcLandress et al. (2015) and Randel et al. (2016)."


id: 37bb56c8022d23e8ff94b11a8dc726d1
created_at: 2023-03-16 14:01:57
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vector_id: 6144
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"Warming has continued in the lower troposphere according to all \r\nradiosonde, reanalyses and satellite datasets, with a rate over \r\n1980-2019 similar to surface warming rates (Table 2.5; c.f. Table 2.4). \r\nRadiosonde-based products generally show greater warming rates \r\nfor 1980-2019 than satellite-based products and reanalyses. They \r\nalso extend further back to the 1950s and trends since quasi-global \r\ncoverage around 1960 also show warming (Table 2.5). Trends in RO \r\nand AIRS data, supported by radiosonde datasets, exhibit a warming \r\ntrend in most of the mid- to upper- troposphere at all non-polar \r\nlatitudes over 2002-2019. These also exhibit faster warming rates \r\nin the tropics in the upper troposphere than those observed at or \r\nnear the surface (Figure 2.12); with the lowermost stratosphere also \r\nwarming while above it is cooling"


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" There is some spread between \r\ndifferent data types in the tropics near the 15km level, although \r\nthese differences are reduced to near zero if a subset of radiosonde \r\ndata, using only high-quality instruments, is used (Steiner et  al., \r\n2020b). AMSU tropical middle troposphere data also show that \r\nwarming rates are near or above those in the lower troposphere, but \r\nthey are measuring much broader layers which greatly complicates \r\ninterpretation (Steiner et al., 2020b). "


id: 929967da09a22c51a0d1158e38583bad
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 20585
content:
"New assessments of free-atmosphere temperature are available \r\nthrough radio occultation (RO) and Atmospheric Infrared Sounder \r\n(AIRS) products which begin in the early 2000s (Section 1.5.1.1). Global \r\nNavigation Satellite System (GNSS)-RO datasets have been compared \r\nagainst AMSU data records, finding almost identical trends (Khaykin \r\net al., 2017). Comparison of RO with collocated radiosondes, Vaisala \r\nRS90/92 and GCOS Reference Upper Air Network data (RS92-GDP; \r\nDirksen et  al., 2014), show very good correspondence with global \r\nannual mean differences of less than 0.2degC in the upper troposphere \r\nand lower stratosphere. Radiosonde daytime radiation biases were \r\nidentified at higher altitudes (Ladstadter et al., 2015; Ho et al., 2017). \r\nThe stability of RO makes this data a useful comparator for AMSU \r\n(Chen and Zou, 2014) and radiosondes (Ho et al., 2017; Tradowsky \r\net al"


id: fc8a18662240cda2fae5da59516f1fb5
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 37247
content:
", 2017), as well as anchoring post-2006 reanalyses datasets and \r\nimproving their consistency in the lower and middle stratosphere (Long \r\net al., 2017; Ho et al., 2020). The effective vertical resolution of RO \r\nmeasurements in the upper troposphere and lower stratosphere was \r\nfound to be up to 100 m at the tropical tropopause (Zeng et al., 2019a), \r\nwhich is favourable for resolving atmospheric variability (Scherllin\u0002Pirscher et  al., 2012; Wilhelmsen et  al., 2018; Stocker  et  al.,  2019). "


id: d8046096d5fecf6dc292b04db6e4a8b2
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 31514
content:
"Substantial limitations exist in reconstructing the global hydrological \r\ncycle prior to the Quaternary, particularly during the Eocene, due to \r\nthe lack of high-resolution proxy records and their sparsity. Spatial \r\nheterogeneity complicates identification of wetting and drying \r\nsignals during the PETM and the EECO, with paleo data and model \r\nsimulations suggesting an intensified global hydrological cycle \r\n(Carmichael et al., 2016, 2017; Hyland et al., 2017; West et al., 2020), \r\nin particular an increased specific humidity (Winnick et  al., 2015; \r\nvan Dijk et al., 2020). Conditions wetter than present were inferred for \r\nthe MPWP (Cross Chapter Box 2.4), with intensified Asian monsoons \r\n(An et al., 2015) but with nevertheless drier conditions over tropical \r\nand subtropical SH locations (Pontes et  al., 2020)"


id: 6f454415e6bce2b225bca0e210bef0f0
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 15136
content:
" A new global \r\nreconstruction of hydroclimate proxies for the LIG points to stronger \r\nboreal precipitation compared to 1850-1900 over high latitudes \r\nand especially over monsoon areas, with a  more heterogeneous \r\nsignal for the SH (Scussolini et al., 2019). This heterogeneity is also \r\npresent in the tropics, characterized by large zonal differences in \r\nprecipitation change due to the variations in the intensity of Walker \r\ncirculation (Section 2.3.1.4.1). Available records indicate reduced \r\nglobal vegetation cover and abundant atmospheric dust deposition \r\nduring the LGM (increased aridity), particularly over the tropics and \r\nhigh latitudes (Lamy et  al., 2014; Ujvari et  al., 2017). This agrees \r\nwith models and moisture-sensitive proxies, suggesting an overall \r\ndecrease in global precipitation during the LGM relative to recent \r\ndecades, albeit with regional-scale heterogeneity (Cao et al., 2019)"


id: 291876828d1e8b1047602775910750a3
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 3894
content:
" \r\nDespite lower global precipitation amounts, research since AR5 has \r\nidentified a wetting of mid-latitudes during the LGM (Putnam and \r\nBroecker, 2017; Lowry and Morrill, 2018; Morrill et al., 2018), thereby \r\ncomplicating the characterization of the LGM as a  relatively 'dry' \r\nperiod. Low evaporation rates and increased top-soil moisture during \r\nthe LGM may have contributed to elevated levels of large closed\u0002basin lakes located in the 30deg-45deg latitudinal belts (Putnam and \r\nBroecker, 2017; Scheff et al., 2017), such as the south-west United \r\nStates (e.g., Ibarra et al., 2018), southern Australia (Petherick et al., \r\n2013; Fitzsimmons et al., 2015; Sniderman et al., 2019) and Patagonia \r\n(e.g., Quade and Kaplan, 2017). "


id: e136e6f29bbf57c766cc963ef32f66a5
created_at: 2023-03-16 14:01:57
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vector_id: 32933
content:
"For the CE, new proxy records have led to the creation of continental \r\ndrought atlases (Cook et al., 2015; Palmer et al., 2015; Stahle et al., 2016; \r\nMorales et al., 2020) and millennial reanalyses (Steiger et al., 2018; \r\nTardif et al., 2019). These reconstructions highlighted the occurrence \r\nof multi-decadal regional mega-droughts in the NH before 1600 CE, \r\nparticularly during 800-1200 CE, with a predominance of wet periods \r\nafter 1700 CE (Cook et al., 2015; Rodysill et al., 2018; Shuman et al., \r\n2018). In the SH, much of South America and the African tropics \r\nexperienced a reduction of precipitation during 900-1200 CE and \r\na wetting peak during 1500-1800 CE (Tierney et al., 2015; Nash et al., \r\n2016; Fletcher et al., 2018; Luning et al., 2018; Campos et al., 2019), \r\nwith an opposite pattern in southern subtropical Africa (Woodborne \r\net al., 2015; Luning et al., 2018)"


id: 315c6fdf30d88733ce81ed165ac67a9c
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 5184
content:
" Large multi-decadal variability was \r\ndocumented over Australia and New Zealand during the 800-1300 CE \r\nperiod, followed by a well-defined wet period during 1500-1800 CE \r\n(Barr et al., 2014; Evans et al., 2019). "


id: 55b5a867628998f9dadd5cdd05585d
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 10965
content:
"Since 1973, increases in specific humidity have been widespread \r\nand significant across the majority of the land and ocean regions \r\nwhere observations are available (Figure 2.13a). In contrast, trends \r\nin relative humidity show distinct spatial patterns with generally \r\nincreasing trends over the higher latitudes and the tropics and \r\ngenerally decreasing trends over the sub-tropics and mid-latitudes, \r\nparticularly over land areas (Figure 2.13c). Near-surface specific \r\nhumidity over the oceans has increased since the 1970s according to \r\nseveral in-situ, satellite and reanalysis data records (Kent et al., 2014; \r\nRobertson et al., 2020; Willett et al., 2020). According to the HadISDH \r\nproduct, increases in specific humidity and decreases in relative \r\nhumidity are significant particularly over the NH mid-latitudes \r\n(Figure 2.13a,c). Poor data coverage over the SH south of 20degS does \r\nnot allow for the robust assessment of trends"


id: 65bf30f589bed8b61b201b7cd5df9dd2
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 13588
content:
" Sources of uncertainty \r\ninclude the initial measurement accuracy, homogenization over land, \r\nobservational height at ships and instrument bias adjustment over \r\nocean, and sparse spatio-temporal sampling (Prytherch et al., 2015; \r\nRoberts et al., 2019; Willett et al., 2020)."


id: 95f7c1fa37ccd5fd998d597a0ad3ec1e
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 21098
content:
"Since the late 1970s a range of satellite missions permit a quasi-global \r\nassessment of TCWV. Several satellite products provide water vapour \r\nretrievals based upon distinct spectral domains, in addition to products \r\nfrom radiosondes, reanalyses and GNSS radio occultation. The GEWEX \r\nWater Vapour Assessment (G-VAP) provided an intercomparison of \r\nseveral TCWV data records, with global coverage but limited timespan \r\n(Schroder et al., 2018). The various global products generally exhibit \r\na positive trend since 1979 (Figure 2.14; Allan et al., 2014; Mieruch \r\net al., 2014; Schroder et al., 2016; J. Wang et al., 2016), most evident \r\nover the tropics (Gu and Adler, 2013; Chen and Liu, 2016; Mears et al., \r\n2018; Wang and Liu, 2020; Salamalikis et  al., 2021)"


id: 9b61c1e37842bcb328e1553e7fc94ba9
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 21995
content:
" The existence \r\nof apparent breakpoints in several products, which are generally \r\ncoincident with changes in the observing system, lead to trend \r\nestimates that are not in line with theoretical expectations imposed by \r\nthe Clausius-Clapeyron relationship (Schroder et al., 2019), although \r\nother factors such as regional moisture divergence/convergence could "


id: af982b68e2950ad1067d0825d048465b
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 25142
content:
"Recent trends (1980-2019) for GPCC V2020, CRU TS 4.04 and GPCP \r\nV2.3 show significant increases in land precipitation over tropical \r\nAfrica, the eastern portions of Europe and North America, central Asia \r\nand the Maritime Continent (Figure 2.14d-f). Significant decreases \r\nare observed over central South America, western North America, \r\nnorthern Africa and the Middle East. A detailed assessment of the \r\nrecent regional precipitation trends using the same datasets can \r\nbe found in the Atlas. Global trends for 1980-2019 show a general \r\nincrease in annual precipitation over land, which is particularly marked \r\nfor CRU TS 4.04 and GHCNv4 (Table 2.6). These changes have been \r\naccompanied by a strengthening of precipitation seasonality over \r\ntropical land areas, although with broad spread between different \r\nsatellite-based (GPCP, MSWEP_V1.2, PERSIANN-CDR) and in situ \r\ngridded datasets (GPCC, CRU TS; Chou et al., 2013; Li et al., 2016; \r\nTan et al., 2020)"


id: 6837899d1f623d31844d4ecc31eb4a2d
created_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 13980
content:
" Increasing trends since 1980, in contrast to longer\u0002term declining trends since 1901, are particularly evident over much \r\nof Africa, while more widespread negative trends were observed over \r\nmuch of southern South America in the more recent period (Atlas 7.2; \r\nKnutson and Zeng, 2018). A faster recent increase in precipitation \r\nover global land is inferred comparing the precipitation trends over \r\n1960-2019 with 1980-2019 (Table 2.6). Over the global ocean, the \r\ncomparison between precipitation datasets is compromised by the \r\ndifferent measurement periods, as well as the spatial coverage of the \r\navailable products (Adler et al., 2017; Nguyen et al., 2018; Jaber and \r\nAbu-Allaban, 2020; Nogueira, 2020), limiting the ability to assess the \r\nsign and magnitude of precipitation trends. The GPCPv2.3 database \r\n(Adler et al., 2017, 2018) exhibits an increase of 2"


id: f1a3f201941b48eaae375444d0a5f840
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 35522
content:
"94 mm yr-1 per \r\ndecade over 1980-2019, principally due to the trends over the Indian \r\nocean and in the tropical western Pacific (Figure 2.15f). The regional \r\npatterns of recent trends are consistent with the documented increase \r\nin precipitation over tropical wet regions and the decrease over \r\ndry areas, estimated through GPCP v2.2 data (Liu and Allan, 2013; \r\nTrammell et al., 2015; Kao et al., 2017; Polson and Hegerl, 2017)."


id: ca062c1df400ab398b7b59cb577f699a
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 29340
content:
"In situ precipitation records over land extend back for centuries in \r\na few locations, and to the early to mid-20th century quasi-globally. \r\nDatasets differ in their input data, completeness of records, period \r\ncovered, and the gridding procedures applied, which, given spatial \r\nclustering and the small spatial scales of precipitation, results in \r\ndifferences in global and regional estimates of precipitation changes \r\n(Q. Sun et al., 2018; Nogueira, 2020). The spatial variability of observed \r\nlong-term trends (1901-2019) based on GPCC V2020 and CRU \r\nTS 4.04 (Figure 2.15a,b) indicates significant increases in precipitation \r\nmainly over eastern North America, northern Eurasia, southern South \r\nAmerica and north-western Australia. Decreases are strongest across \r\ntropical western and equatorial Africa and southern Asia. The temporal \r\nevolution of global annual land precipitation anomalies exhibits \r\nlittle consistency between GPCC V2020, CRU TS 4"


id: dc8a72ef628278eca0a4959f9cb82655
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 32204
content:
"04 and GHCNv4 \r\ndatasets, especially prior to 1950, that is associated with limitations \r\nin data coverage (Figure 2.15c; Wu et al., 2013; Shen et al., 2014; Gu \r\nand Adler, 2015). These disagreements between datasets prior to the \r\n1950s result in differences in trend estimates over global land (Table \r\n2.6). A qualitative consistency in decadal and interdecadal variations \r\nbetween the products is only observed since the 1950s, with primarily \r\npositive land precipitation anomalies during the 1950s, 1970s and \r\nduring 2000 to 2019 (Figure 2.15c). "


id: 5a7bfffc2d637c05b3a561c8f5143e3b
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 11749
content:
"Several satellite-based precipitation datasets improve the \r\nrepresentation of the spatio-temporal changes since the late 20th \r\ncentury. Some of these are based exclusively on satellite data \r\n(e.g., CMORPH, Joyce et al., 2004; GSMaP, Okamoto et al., 2005), with \r\nothers being combinations of in situ observations, reanalyses and \r\nsatellite retrievals (e.g., CMAP, Xie and Arkin, 1997; TRMM 3B43 V7, \r\nHuffman et al., 2007 ; PERSIANN-CDR, Ashouri et al., 2015; CHIRPS, \r\nFunk et al., 2015; GPCP V2.3, Adler et al., 2018). These can be affected \r\nby systematic and random uncertainties due to inhomogeneities \r\nin the satellite-derived precipitation and station data and the \r\nuncertainties of blending algorithms (Hegerl et al., 2015; Q. Sun et al., \r\n2018). The spatial coverage of these products is near-global, with \r\navailable estimations formally covering 60degS-60degN with decreasing \r\nquality from low to high latitudes, depending on the sensors and \r\nalgorithms used (Hu et al., 2019)"


id: ae3f6324610556c2f5f6f0cdd3a29292
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index: climate_gpt_only_giec
vector_id: 24945
content:
" A detailed description of the most \r\nrelevant satellite products is provided in section 10.2.1.1."


id: 716cbfbd7cd0018af9f3a8c8fc24d796
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 15481
content:
"precipitation exceeds evaporation (P-E >0) for all the reanalysis \r\nproducts (Figure 2.16c), with decreasing trends in P-E for ERA5 and \r\nJRA-55 and increasing trends for MERRA-2 and CFSR. The P-E over \r\nthe global ocean is negative (evaporation exceeding precipitation) \r\nfor most reanalyses (Figure 2.16d), with declining trends in ERA5 \r\nand MERRA-2 dominated by trends in evaporation (Bosilovich et al., \r\n2017; Hersbach et al., 2020) (Figure 2.16d). The recent increase in \r\nocean evaporation was also documented for several reanalyses (Craig \r\net al., 2017) and in satellite data (Andersson et al., 2011; Robertson \r\net  al., 2014), although with considerable differences between \r\navailable estimates (Chandanpurkar et al., 2017; L. Yu et al., 2020). \r\nAn alternative indirect approach to estimate P-E changes is based on \r\nnear-surface ocean salinity (Section 2.3.3.2), which is partially driven \r\nby the freshwater flux at the ocean surface"


id: a4f1019438874ba00871f9f6a32c1e86
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 23520
content:
" The near-surface salinity \r\ntrends are more spatially coherent compared to those revealed by \r\nP-E estimates from reanalyses, with an intensification of the water \r\ncycle over oceans, especially in subtropical regions (Durack et  al., \r\n2012; Skliris et al., 2014; L. Yu et al., 2020). However, the precise rate \r\nof water cycle intensification implied by salinity trends is sensitive to \r\nmethodological choices (e.g., Skliris et al., 2016; Zika et al., 2018)."


id: 43e8526e80f42179dc6b196bb08c0cb6
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 8096
content:
"Paleo reconstructions of rainfall and trade winds extending over the \r\nlast 100 kyr show an intensification of the NH HC concurrently with \r\na weakening of the SH HC and a southward shift of the inter tropical \r\nconvergence zone (ITCZ) during Heinrich stadials (Deplazes et  al., \r\n2013; McGee et al., 2018; Strikis et al., 2018; Wendt et al., 2019). An \r\nintensification of the HC associated with conditions similar to La Nina \r\n(northward migrations of both the ITCZ and the SH westerlies) was \r\nfound in reconstructions for the MH (McGee et al., 2014; Mollier-Vogel \r\net  al., 2019). Changes in insolation from the mid to late Holocene \r\nfavoured a southward migration in the position of the ITCZ and \r\nthe descending branch of the HC in the NH, approaching its current \r\nwidth and position (Wirth et al., 2013; Thatcher et al., 2020)"


id: 1fb1ab5ef7fee44e70a31bbe989a94f
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 2419
content:
" Tree ring \r\nchronologies from the NH mid-latitudes over the last 800 years show \r\nthat the northern edge of the HC tended to migrate southward during \r\npositive phases of ENSO and PDV, with northward shifts during negative \r\nphases (Alfaro-Sanchez et al., 2018). Between 1400 and 1850 CE the \r\nHC over both hemispheres and the ITCZ were displaced southward, \r\nconsistent with occurrence of drought conditions in several NH regions \r\n(Wirth et  al., 2013; Burn and Palmer, 2014; Lechleitner et  al., 2017; \r\nAlfaro-Sanchez et al., 2018; Flores-Aqueveque et al., 2020). Moreover, \r\nseveral proxy records showed not only inter-hemispheric shifts in the \r\nITCZ but a contraction of the tropical belt during 1400-1850 CE, which \r\nfollowed an expansion during 950-1250 CE (Denniston et al., 2016; \r\nGriffiths et al., 2016)."


id: 5f185732fa4c767c1f0ad4d4b6cef644
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 12511
content:
"Human intervention on river discharge linked to increases in \r\nevapotranspiration and some reduction of intra-annual streamflow \r\nvariability (Jaramillo and Destouni, 2015; Chai et  al., 2020) might \r\naffect the detection of trends in extreme daily streamflow events \r\n(Do et al., 2017; Gudmundsson et al., 2019). However, these activities \r\nhave a minor impact on annual streamflow compared to climate \r\nvariations (Dai et  al., 2009; Alkama et  al., 2013). Available global \r\nstudies post-1950 generally concur that there have been more rivers \r\nexperiencing decreases than increases in runoff (Do et  al., 2017; \r\nSu et al., 2018; Gudmundsson et al., 2019; X. Shi et al., 2019). Most \r\nof the rivers have not experienced statistically significant changes \r\nin streamflow, and when globally aggregated there is no significant \r\nchange (Dai and Zhao, 2017)"


id: e1f69fdccbaaf8f0bcfdc02acd29dfc4
created_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 33050
content:
" Global streamflow variability is strongly \r\nmodulated by ENSO and PDV, with below-normal global streamflow \r\nas a response to El Nino events and vice-versa during La  Nina \r\nepisodes (Dai, 2016; Liang et al., 2016; Kim, 2019). The response of \r\nstreamflow to changes in precipitation associated with ENSO and \r\nPDV has heterogeneous regional patterns at subcontinental scales \r\n(Section  8.3.2.9.1). No significant trends are found for reanalysis\u0002based discharge estimates over 1993 to 2015 (Chandanpurkar \r\net  al., 2017). Uncertainties in global streamflow trends arise \r\npredominantly from changes in instrumentation, gauge restoration, \r\nrecalibration of rating curves, flow regulation or channel engineering \r\n(Alkama et al., 2011; Gudmundsson et al., 2018; Ghiggi et al., 2019). "


id: 7b827199449d40d8b1d765a1e9fb9f5b
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 17023
content:
"Since AR5 several studies based upon a range of metrics and \r\ndifferent reanalyses products have suggested that the annual \r\nmean HC extent has shifted poleward at an approximate rate \r\nof 0.1deg-0.5deg latitude per decade over the last about 40 years \r\n(Allen  and Kovilakam, 2017; Davis and Birner, 2017; Grise et  al., \r\n2018; Staten et al., 2018, 2020; Studholme and Gulev, 2018; Grise \r\nand Davis, 2020). The observed widening of the annual mean HC, \r\nrevealed by a variety of metrics, is primarily due to poleward shift \r\nof the Northern Hemisphere HC. There have been stronger upward \r\ntrends in the NH extent of HC after 1992 (Figure 2.17a). The \r\nestimated magnitude of the recent changes based on modern-era \r\nreanalyses is not as large as that in AR5, due to apparent biases \r\nin older-generation reanalyses (Grise et al., 2019). Moreover, large \r\ninterannual variability leads to uncertainties in estimates of long\u0002term changes (Nguyen et al., 2013; Garfinkel et al"


id: 465d15acdbc72cde49423397f7115f22
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 8456
content:
", 2015b; Seviour \r\net al., 2018; Staten et al., 2018), particularly for the NH given its \r\nzonal asymmetries (Staten et al., 2020; Wang et al., 2020). These \r\nlarge-scale features of the HC based on reanalyses agree with \r\nestimates revealed from the Integrated Global Radiosonde Archive \r\n(IGRA) during 1979-2012 (Lucas and Nguyen, 2015; Mathew \r\net al., 2016). Recent trends based on reanalyses indicate a larger \r\nseasonal widening in the HC for summer and autumn in each \r\nhemisphere, although the magnitude of changes in HC extent is \r\nstrongly dependent on dataset and metrics used (Grise et al., 2018; \r\nY. Hu et al., 2018; Staten et al., 2018). The shifts in the HC position \r\nwere accompanied by a narrowing ITCZ over the Atlantic and Pacific \r\nbasins, with no significant change in its location and increases in \r\nthe precipitation intensity (Byrne et al., 2018)."


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vector_id: 21447
content:
"over the last 30-40 years (e.g., Hu et al., 2013; L'Heureux et al., 2013; \r\nYim et al., 2017). Based on estimation of changes in mid-tropospheric \r\nvelocity from changes in observed cloud cover, Bellomo and Clement \r\n(2015) suggest a weakening and eastward shift of the WC over \r\n1920-2010, however the robustness of this signal is questionable due \r\nto high uncertainty in the ship-reported cloud data used before 1954. \r\nUsing centennial-scale 20CR reanalysis Tseng et  al. (2019) showed \r\nthat the vertical westerly wind shear over the western Pacific does \r\nnot indicate any long-term change during 1900-1980, but shows \r\na marked increase since the 1980s that is not present in ERA-Interim \r\nand JRA-55, again calling into question the ability of centennial\u0002scale reanalyses to capture tropical circulation changes. Recent \r\nstrengthening together with a westward shift of the WC (Bayr et al"


id: 4058f99c0ebdd64fa213788ac8eaee16
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 7509
content:
", \r\n2014; Ma and Zhou, 2016) was identified across several reanalysis \r\nproducts and observational datasets, and using different metrics for \r\nquantifying WC. Nevertheless, satellite observations of precipitation \r\nand analyses of upper tropospheric humidity suggest substantially \r\nweaker strengthening of the WC than implied by reanalyses \r\n(Chung et al., 2019). This recent strengthening in the WC is associated \r\nwith enhanced precipitation in the tropical western Pacific, anomalous \r\nwesterlies in the upper troposphere, strengthened downwelling in the \r\ncentral and eastern tropical Pacific, and anomalous surface easterlies \r\nin the western and central tropical Pacific (Dong and Lu, 2013; \r\nMcGregor et al., 2014; Choi et al., 2016)"


id: 4d6519c6f46eff468dc1ebb6ba04856c
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 9610
content:
" Positive trends in sea level \r\npressure over the eastern Pacific and concurrent negative trends over \r\nthe Indonesian region result in a pattern implying a shift towards a La \r\nNina-like WC regime, with strengthening of the Pacific Trade Winds \r\nmainly over 1979-2012 (L'Heureux et al., 2013; England et al., 2014; \r\nSohn et al., 2016; Zhao and Allen, 2019). Seasonal assessment of the \r\nWC showed significant changes in the vertical westerly wind shear \r\nover the Pacific during the austral summer and autumn implying \r\na strengthening (Clem et al., 2017)."


id: c5a2affeceb61e5ba59369ac4d88b6ad
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 28673
content:
"An intensification of the NH monsoons in the early to mid-Holocene \r\nwith increased precipitation and regional expansions of rainfall areas \r\nidentified through a variety of proxy records is shown by Biasutti \r\net  al. (2018) and P.X. Wang et  al. (2017). The response for the SH \r\nmonsoons during this period indicates a weakening in both summer \r\nand winter precipitation (P.X. Wang et al., 2014, 2017; Sachs et al., \r\n2018). A decline in GM precipitation and a retraction of the northern \r\nfringes of monsoon areas was inferred from the mid-Holocene \r\nonwards, with some regions experiencing wetter conditions during \r\nthe mid to late Holocene compared with present and a strengthening \r\nof the SH monsoons (P.X. Wang et al., 2014, 2017; Sachs et al., 2018). \r\nFor the CE, GM reconstructions exhibit inter-hemispheric contrast \r\nduring the period 950-1250 CE, with intensified NH monsoons and \r\nweakened SH monsoons, and the opposite pattern during 1400-1850 \r\nCE (P.X. Wang et al., 2014; An et al"


id: d112680dd89fa104b669d7467608c715
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 30434
content:
"Direct observations highlight that the GM land precipitation, \r\nparticularly over the NH, experienced a slight increase from 1900 \r\nthrough the early 1950s, followed by an overall decrease from the \r\n1950s to the 1980s, and then an increase to present (Kitoh et al., \r\n2013; B. Wang et  al., 2018, 2021; X. Huang et  al., 2019b). This \r\nhighlights the existence of multi-decadal variations in the NH \r\nmonsoon circulation patterns and precipitation intensity (Wang et al., \r\n2013; P.X. Wang et al., 2014, 2017; Monerie et al., 2019). An overall \r\nincrease in monsoon precipitation during extended boreal summer \r\n(JJAS) over the NH since 1979 is revealed by GPCP (Deng et  al., \r\n2018; Han et al., 2019) and CMAP for 1980-2010 (Jiang et al., 2016). \r\nSH summer monsoon behaviour is dominated by strong interannual \r\nvariability and large regional differences (Kitoh et al., 2013; Lin et al., \r\n2014; Jiang et  al., 2016; Kamae et  al., 2017; Deng et  al., 2018; \r\nHan et al"


id: 628b8466f3494f9a8fdab57b9bdf2d5f
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 13072
content:
", 2019), with no significant trends reported by GPCP and \r\nCMAP (Deng et al., 2018). Uncertainty predominantly arises from the \r\nobserved increase in tropical precipitation seasonality (Feng et  al., \r\n2013) and the estimation of GM precipitation over the ocean areas, \r\nleading to a large apparent spread across datasets (Kitoh et al., 2013; \r\nKamae et al., 2017). "


id: 1e05dbb7abc4528a6f939a89c04f480c
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 2157
content:
"Multiple reanalyses and radiosonde observations show an increasing \r\nnumber of extratropical cyclones over the NH since the 1950s \r\n(Chang  and Yau, 2016; X.L. Wang et  al., 2016). The positive trends \r\nare generally consistent among reanalyses since 1979, though with \r\nconsiderable spread (Tilinina et  al., 2013; X.L. Wang et  al., 2016). \r\nIn  recent decades the number of deep extratropical cyclones has \r\nincreased over the SH (Section 8.3.2.8.1 and Figure 8.12; Reboita et al., \r\n2015; X.L. Wang et  al., 2016), while the number of deep cyclones \r\nhas decreased in the NH in both winter and summer (Neu et  al., \r\n2013; Coumou et al., 2015; Chang et al., 2016; J. Wang et al., 2017a; \r\nGertler and O'Gorman, 2019). The regional changes for different \r\nintensity extratropical cyclones are assessed in Section 8.3.2.8.1. \r\nThe  assessment of trends is complicated by strong interannual \r\nto decadal variability, sensitivity to dataset choice and resolution \r\n(Tilinina et al., 2013; Lucas et al"


id: 8bb1a1b9ea60536ff581580ddf145bd0
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 19500
content:
", 2014; X.L. Wang et al., 2016; Pepler \r\net al., 2018; Rohrer et al., 2018) and cyclone identification/tracking \r\nmethods (Neu et al., 2013; Grieger et al., 2018). Thus there is overall \r\nlow confidence for recent changes in global extratropical storm tracks. "


id: 923be85446d548364cbc6d7e5c4d2a87
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 20530
content:
"Proxy records consistent with modelling results imply a southward \r\nshift of the storm tracks over the North Atlantic during the LGM \r\n(Raible et al., 2021). A variety of proxies are available for the changes \r\nin the position of the extratropical jets/westerlies during the Holocene. \r\nRecent syntheses of moisture-sensitive proxy records indicate \r\ndrier-than-present conditions over mid-latitudes of western North \r\nAmerica (Hermann et  al., 2018; Liefert and Shuman, 2020) during \r\nthe MH, which together with a weakened Aleutian Low (Bailey et al., \r\n2018) implies that the winter North Pacific jetstream was shifted \r\nnorthward. A synthesis of lines of evidence from the SH indicates \r\nthat the westerly winds were stronger over 14-5 ka, followed by \r\nregional asymmetry after 5 ka (Fletcher and Moreno, 2012). There \r\nis no consensus on the shifts of the SH westerlies with some studies \r\nimplying poleward migrations (Lamy et al., 2010; Voigt et al., 2015; \r\nTurney et al"


id: f7cd366ab14d2e6989725957eec1722b
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 36507
content:
", 2017; Anderson et al., 2018) and others suggesting an \r\nequatorward shift (Kaplan et al., 2016) in the MH."


id: 391ec5d6b58118fb543e8191c980bee8
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 6361
content:
"A consistent poleward shift of the tropospheric extratropical jets \r\nsince 1979 is reported by multiple reanalyses (Figure 2.18; Davis and \r\nRosenlof, 2012; Davis and Birner, 2013; Pena-Ortiz et al., 2013; Manney \r\nand Hegglin, 2018), and radiosonde winds (Allen et al., 2012). This \r\nis generally consistent with the previously reported shifts retrieved \r\nfrom satellite temperature observations (Fu and Lin, 2011; Davis and \r\nRosenlof, 2012). After the 1960s the magnitude of meridional shifts \r\nin extratropical jets over both the North Atlantic and North Pacific \r\nin August is enhanced compared to multi-century variability (Trouet \r\net  al., 2018). Despite some regional differences (Woollings et  al., \r\n2014; Norris et al., 2016; J. Wang et al., 2017a; Xue and Zhang, 2017; \r\nMa and Zhang, 2018; Melamed-Turkish et al., 2018), overall poleward \r\ndeflection of storm tracks in boreal winter over both the North Atlantic \r\nand the North Pacific was identified during 1979-2010 (Tilinina et al"


id: 5f2943db48f8b69c7d0eda35d097a3e3
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 12530
content:
", \r\n2013). Over the SH extra-tropics there is a similarly robust poleward \r\nshift in the polar jet since 1979 (Pena-Ortiz et al., 2013; Manney and \r\nHegglin, 2018; WMO, 2018), although after 2000 the December-\r\nJanuary-February (DJF) tendency to poleward shift of the SH jet \r\nstream position ceased (Banerjee et al., 2020). The general poleward \r\nmovement in midlatitude jet streams (Lucas et al., 2014) is consistent \r\nwith the expansion of the tropical circulation (Section  2.3.1.4.1). \r\nThe changes of extratropical jets and westerlies are also related to the \r\nannular modes of variability (Section 2.4 and Annex IV)."


id: e2cf019b2ae97806608d5caad9b5aabe
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 33185
content:
"increasing global marine wind speeds over 1979-2015, though \r\nflattening since 2000, whereas MERRA-2 agrees until 1998, but then \r\nexhibits increased variability and an overall decrease in the last two \r\ndecades (Azorin-Molina et al., 2019). This agrees with estimates by \r\nSharmar et al. (2021) showing upward ocean wind trends from 1979 \r\nto 2000 which are consistent in ERA-Interim, ERA5 and MERRA-2, \r\nbut disagree with CFSR trends for the same period. Over 2000-2019 \r\nall reanalyses show diverging tendencies. An updated multiplatform \r\nsatellite database (comprising data from altimeters, radiometers, and \r\nscatterometers) from 1985-2018 shows small increases in mean wind \r\nspeed over the global ocean, with the largest increase observed in the \r\nSouthern Ocean (Young and Ribal, 2019), consistent with signals in \r\nERA-Interim, ERA5 and MERRA-2 (Sharmar et al., 2021)"


id: 4b0d9491bb79fc6a8de4455a7d1f299d
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 9228
content:
" Overall, most \r\nproducts suggest positive trends over the Southern Ocean, western \r\nNorth Atlantic and the tropical eastern Pacific since the early 1980s. "


id: ac6773447a5279027f2d129e9a08606a
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 24661
content:
"Sudden stratospheric warming (SSW), a phenomenon of rapid \r\nstratospheric air temperature increases (sometimes by more than \r\n50degC in 1-2 days), is tightly associated with the reversal of upper \r\nstratospheric zonal winds, and a resulting collapse or substantial \r\nweakening of the stratospheric polar vortex (Butler et  al., 2015; \r\nButler and Gerber, 2018) and on average occurs approximately \r\n6 times per decade in the NH winter (Charlton et al., 2007; Butler \r\net  al., 2015). The SSW record from all modern reanalyses is very \r\nconsistent. There is a higher occurrence of major midwinter SSWs \r\nin the 1980s and 2000s with no SSW events during 1990-1997 \r\n(Reichler et al., 2012; Butler et al., 2015). An assessment of multi\u0002decadal variability and change in SSW events is sensitive to both \r\nchosen metric and methods (Palmeiro et al., 2015)"


id: 6d4f45eb105861d11a42d58a2328405d
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 14816
content:
" Due to the lack of \r\nassimilation of upper air data, the centennial-scale reanalyses do not \r\ncapture SSW events, even for the most recent decades (Butler et al., \r\n2015, 2017) and hence cannot inform on earlier behaviour. There \r\nhas been considerably less study of trends in the SH stratosphere \r\npolar vortex strength despite the interest in the ozone hole and the \r\npotential impact of the SH stratosphere polar vortex strength on it. \r\nThe occurrence of SSW events in the SH is not as frequent as in the \r\nNH, with only 3 documented events in the last 40 years (Shen et al., \r\n2020)."


id: cc3b0b3d2dfdc17fb80b8dedfa77bd1c
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 29675
content:
"The northern stratospheric polar vortex has varied intra-seasonally \r\nand with altitude during recent decades. Multiple reanalysis and \r\nradiosonde datasets show that the midwinter lower stratospheric \r\ngeopotential height (150 hPa) over the polar region north of 60degN \r\nhas increased significantly since the early 1980s (Bohlinger et al., \r\n2014; Garfinkel et al., 2017). This signal extends to the middle and \r\nupper stratosphere. In January-February zonal winds north of 60degN \r\nat 10 hPa have been weakening (Kim et al., 2014; Kretschmer et al., \r\n2018). Daily atmospheric circulation patterns over the northern \r\npolar stratosphere exhibit a decreasing frequency of strong vortex \r\nevents and commensurate increase in more-persistent weak events, \r\nwhich largely explains the observed significant weakening of the \r\nvortex during 1979-2015 (Kretschmer et  al., 2018). The northern \r\npolar vortex has weakened in early winter but strengthened during \r\nlate winter (Bohlinger et al"


id: e0e8783828058228323fcd91e43af00a
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 32885
content:
", 2014; Garfinkel et al., 2015a, 2017; Ivy \r\net al., 2016; Seviour, 2017; Kretschmer et al., 2018). In the middle \r\nand upper stratosphere, a strengthening trend of the northern \r\npolar vortex during DJF has occurred since 1998, contrasting the \r\nweakening trend beforehand (D. Hu et al., 2018). The position of the \r\npolar vortex also has long-term variations, exhibiting a persistent \r\nshift toward Northern Siberia and away from North America in \r\nFebruary over the period 1979-2015 (Zhang et al., 2016; J. Zhang \r\net  al., 2018). Multiple measures show similar location changes \r\n(Seviour, 2017). "


id: 766090cbc98c9b887c5d706c1f36323e
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 16215
content:
"There has been a continuing decline in SIE and Arctic sea ice area \r\n(SIA) in recent years (Figure 2.20a). To reduce grid-geometry \r\nassociated biases and uncertainties (Notz, 2014; Ivanova et al., 2016; \r\nMeier and Stewart, 2019) SIA is used in addition to, or instead of SIE \r\nherein (see also section 9.3.1). A record-low Arctic SIA since the start \r\nof the satellite era (1979) occurred in September 2012 (Figure 2.20a). \r\nDecadal SIA means based on the average of three different satellite \r\nproducts decreased from 6.23 to 3.76 million km2 for September and \r\n14.52 to 13.42 million km2 for March SIA (Figure 2.20a). Initial SIA \r\ndata for 2020 (OSISAF) are within the range of these recent decadal \r\nmeans or slightly below (Figure 2.20a). SIA has declined since 1979 \r\nacross the seasonal cycle (Figure 9.13). Most of this decline in SIA has \r\noccurred after 2000, and is superimposed by substantial interannual \r\nvariability"


id: a41a335bea769a47829e717a74549974
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 23379
content:
" The sharp decline in Arctic summer SIA coincides with \r\nearlier surface melt onset (Mortin et  al., 2016; Bliss et  al., 2017), \r\nlater freeze-up, and thus a longer ice retreat and open water period \r\n(Stammerjohn et al., 2012; Parkinson, 2014; Peng et al., 2018)."


id: d054d0831d7019c98dca864eb2676a39
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 30308
content:
"of different platforms show for the central and western Arctic \r\nOcean (Arctic Ocean north of Canada and Alaska) negative trends \r\nsince the mid-1970s (Lindsay and Schweiger, 2015; Kwok, 2018), \r\nwith a particularly rapid decline during the 2000s, which coincided \r\nwith a large loss of multiyear sea ice. Direct observations from 2004 \r\nand 2017 indicate a decrease of modal ice thickness in the Arctic \r\nOcean north of Greenland by 0.75 m, but with little thinning between \r\n2014 and 2017 (Haas et al., 2017). This agrees with data based on \r\nsatellite altimetry and airborne observations, showing no discernible \r\nthickness trend since 2010 (Kwok and Cunningham, 2015; Kwok, \r\n2018; Kwok and Kacimi, 2018; see Figure 2.21). However, sea-ice \r\nthickness derived from airborne and spaceborne data is still subject \r\nto uncertainties imposed by snow loading. For radar altimeters, \r\ninsufficient penetration of radar signal into the snowpack results in \r\noverestimation of ice thickness (e.g"


id: aafe6748a7740d43a9f45bb3e500e8f1
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 24423
content:
", Ricker et al., 2015; King et al., \r\n2018; Nandan et al., 2020). Negative trends in ice thickness since the \r\n1990s are also reported from the Fram Strait in the Greenland Sea, \r\nand north of Svalbard (E. Hansen et al., 2013; Renner et al., 2014; \r\nKing et al., 2018; Rosel et al., 2018; Spreen et al., 2020). Thickness \r\ndata collected in the Fram Strait originate from ice exported from \r\nthe interior of the Arctic Basin and are representative of a larger \r\ngeographical area upstream in the transpolar drift. A reduction of \r\nsurvival rates of sea ice exported from the Siberian shelves by 15% \r\nper decade has interrupted the transpolar drift and affected the long\u0002range transport of sea ice (Krumpen et al., 2019). The thinner and on \r\naverage younger ice has less resistance to dynamic forcing, resulting \r\nin a more dynamic ice cover (Hakkinen et  al., 2008; Spreen et  al., \r\n2011; Vihma et al., 2012; Kwok et al., 2013)."


id: 29df9539785063e1d29d2402d09c25ff
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 4019
content:
"More recent sea ice reconstructions are based on diverse sources \r\nincluding whaling records (de La Mare, 1997, 2009; Cotte and Guinet, \r\n2007), old ship logbooks (Ackley et  al., 2003; Edinburgh and Day, \r\n2016), and ice core records (Curran et al., 2003; Abram et al., 2010; \r\nSinclair et  al., 2014), amongst other methods (e.g.,  Murphy et  al., \r\n2014). These reconstructions, in combination with recent satellite\u0002based observations indicate: (i) a decrease in summer SIE across all \r\nAntarctic sectors since the early- to mid-20th century; (ii) a decrease \r\nin winter SIE in the East Antarctic and Amundsen-Bellingshausen \r\nSeas sectors starting in the 1960s; and (iii) small fluctuations in \r\nwinter SIE in the Weddell Sea over the 20th century (Hobbs et al., \r\n2016a, b). There are also ice-core indications that the pronounced \r\nRoss Sea increase dates back to the mid-1960s (Sinclair et al., 2014; \r\nThomas and Abram, 2016)"


id: b69af2f8d993c39c2d9d2b029588fafc
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 26248
content:
" While there is reasonable broad-scale \r\nconcurrence across these estimates, the uncertainties are large, there \r\nis considerable interannual variability, and reconstructions require \r\nfurther validation (Hobbs et  al., 2016a, b). New reconstructions \r\n(Thomas et  al., 2019) from Antarctic land ice cores show that SIE \r\nin the Ross Sea had increased between 1900 and 1990, while the \r\nBellingshausen Sea had experienced a decline in SIE; this dipole \r\npattern is consistent with satellite-based observations from 1979 \r\nto 2019 (Parkinson, 2019), but the recent rate of change then has \r\nbeen larger. Records of Antarctic SIE for the late 19th and early \r\n20th centuries (Edinburgh and Day, 2016), show SIE comparable \r\nwith the satellite era, although with marked spatial heterogeneity \r\n(e.g., Thomas et al., 2019)."


id: b7fb9df348481230d9e759e84236b60e
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 26478
content:
"Analysis of the combined in situ observations (Brown, 2002) and the \r\nmulti-observation product (Mudryk et al. 2020) indicates that since \r\n1922, April SCE in the NH has declined by 0.29 million km2 per decade, \r\nwith significant interannual variability (Figure 2.22) and regional \r\ndifferences (Section 9.5.3.1). The limited pre-satellite era data does \r\nnot allow for a similar assessment for the entire spring-summer \r\nperiod. Assessment of SCE trends in the NH since 1978 indicates that \r\nfor the October to February period there is substantial uncertainty \r\nin trends with the sign dependent on the observational product. \r\nAnalysis using the NOAA Climate Data Record shows an increase \r\nin October to February SCE (Hernandez-Henriquez et  al., 2015; \r\nKunkel et al., 2016) while analyses based on satellite borne optical \r\nsensors (Hori et  al., 2017) or multi-observation products (Mudryk \r\net al., 2020) show a negative trend for all seasons (Section 9.5.3.1 \r\nand Figure 9.23)"


id: 2894fd53a6d1cf9748b9a78e638529bd
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 3806
content:
" The greatest declines in SCE have occurred during \r\nboreal spring and summer, although the estimated magnitude is \r\ndataset dependent (Rupp et al., 2013; Estilow et al., 2015; Bokhorst \r\net al., 2016; Thackeray et al., 2016; Connolly et al., 2019). "


id: f47dd60143b383e9e794453ee0044948
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 35971
content:
"Early Nimbus satellite visible and infrared imagery from the 1960s \r\n(Meier et  al., 2013; Gallaher et  al., 2014) indicate higher overall \r\nSIE compared to 1979-2013 (Hobbs et  al., 2016a, b), but with \r\nlarge uncertainties and poorly quantified biases (NA SEM, 2017). \r\nThe continuous satellite passive-microwave record shows that there \r\nwas a modest increase in overall Antarctic SIA of 2.5% +- 0.2% per \r\ndecade (1 standard error over 1979-2015; Comiso et  al., 2017). \r\nFor overall ice coverage and for this period, positive long-term trends \r\nwere most pronounced during austral autumn advance (Maksym, \r\n2019), being moderate in summer and winter, and lowest in spring \r\n(Holland, 2014; Turner et al., 2015; Hobbs et al., 2016a, b; Comiso \r\net al., 2017). Since 2014, overall Antarctic SIE (and SIA) has exhibited \r\nmajor fluctuations from record-high to record-low satellite era \r\nextents (Massonnet et al., 2015; Reid and Massom, 2015; Reid et al., \r\n2015; Comiso et  al"


id: 5b4683cc9a0ffe261f17c19e21097e8e
created_at: 2023-03-16 14:01:57
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vector_id: 11866
content:
", 2017; Parkinson, 2019). After setting record\u0002high extents each September from 2012 through 2014, Antarctic SIE \r\n(and SIA) dipped rapidly in mid-2016 and remained predominantly \r\nbelow average through 2019 (Reid et al., 2020). For the most recent \r\ndecade of observations (2010-2019), the decadal means of three \r\nSIA products (Figure 2.20b) were 2.17 million km2 for February and \r\n15.75  million km2 for September, respectively. The corresponding \r\nlevels for the means for the first decade of recordings (1979-1988) \r\nwere 2.04 million km2 for February and 15.39 million km2 for \r\nSeptember indicating little overall change. Initial SIA data for 2020 \r\n(OSISAF) show SIA for September above, and for February slightly \r\nbelow the recent decadal means (Figure 2.20b). The 2020 September \r\nlevel (OSISAF) remains below the levels observed over 2012-2014."


id: d12b7d9172f485250ef97def51b4c8db
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 30445
content:
"Two recent global reviews on glaciers over the Holocene (Solomina \r\net al., 2015) and the past 2 kyr (Solomina et al., 2016) summarize \r\nthe chronologies of respectively 189 and 275 glaciers. The former \r\nshows that glaciers retreated during the LDT and retracted to their \r\nminimum extent between 8 ka and 6 ka. Except for some glaciers \r\nin the SH and tropics, glaciers expanded thereafter, reaching their \r\nmaximum extent beyond their present-day margins during the mid\u000215th to late 19th centuries CE. With few exceptions, glacier margins \r\nworldwide have retreated since the 19th century, with the rate of \r\nretreat and its global character since the late 20th century being \r\nunusual in the context of the Holocene (Solomina et  al.,  2016, \r\nFigure 2.23a). However, the areal extents of modern glaciers \r\nin most places in the NH are still larger than those of the early \r\nand/or middle Holocene (Solomina et al., 2015)"


id: 7bfcd00a50e9f204141b2558653ea366
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 17109
content:
" When considering \r\nHolocene and present glaciers extents, it is important to account \r\nfor the relatively long adjustment time of glaciers (often referred to \r\nas response time; Section 9.5.1.3); the majority of modern glaciers \r\nare currently out of equilibrium with current climate, even without \r\nfurther global warming (Mernild et al., 2013; Christian et al., 2018; \r\nMarzeion et  al., 2018; Zekollari et  al., 2020). The size of glaciers \r\nduring other periods warmer than the Early to Mid-Holocene, such \r\nas the MPWP and LIG, is largely unknown because the deposits \r\nmarking previous extents were in almost all cases over-ridden \r\nby later glaciations. For Arctic glaciers, different regional studies \r\nconsistently indicate that in many places glaciers are now smaller \r\nthan they have been in millennia (Lowell et al., 2013; Miller et al., \r\n2013, 2017; Harning et al., 2016, 2018; Schweinsberg et al., 2017, \r\n2018; Pendleton et al., 2019)."


id: 7466e3d4e3a098e81ffa8b0abfc31aee
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 15906
content:
"New glacier outline (RGI Consortium, 2017) and glacier mass \r\ncompilations (Zemp et al., 2019, 2020; Ciraci et al., 2020; Hugonnet \r\net  al., 2021) improve, refine and update the quantification of \r\nglacier areal and mass changes based on observations from in situ \r\nand remote sensing data. Observations between the 1960s and \r\n2019 indicate that mass loss has increased over recent decades \r\n(Figure 2.23b). The overall global glacier mass loss rate has increased \r\nfrom 240  +- 9 Gt yr -1 over 2000-2009 to 290 +- 10 Gt yr -1 over \r\n2010-2019 (Hugonnet et al., 2021), confirming that the last decade \r\nexhibits the most negative glacier mass balance since the beginning \r\nof the observational record"


id: 193b0e18df8646d656eb3449f2f9aff
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 1459
content:
" Observations are in general consistent \r\nwith trends revealed by global glacier mass change modelling for \r\nalmost the entire 20th century (1901-1990) implying an estimated \r\nmass loss (without uncharted glaciers (Parkes and Marzeion, 2018) \r\nand excluding peripheral glaciers of Greenland and Antarctica) of very \r\nlikely 210 +- 90 Gt yr -1 and very likely 170 +- 80 Gt yr -1 for the period \r\n1971-2019 (Marzeion et al., 2015; Section 9.5.1 and Table 9.5)."


id: f8ff692cc265e42a5813391546af6b79
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 36685
content:
"time with extended periods when it was predominantly deglaciated \r\n(Bierman et al., 2016; Schaefer et al., 2016). Geological evidence and \r\nmodelling studies suggest periods of glacial intensification during the \r\nPliocene at 4.9 Myr, 4.0 Myr, 3.6 Myr and 3.3 Myr (De Schepper et al., \r\n2014; Bierman et al., 2016; Bachem et al., 2017). Retreat of the GrIS \r\noccurred during the MPWP and GrIS extent was reduced compared \r\nto today with some studies suggesting that the ice sheet was limited \r\nto the highest elevations (De Schepper et al., 2014; Koenig et al., 2015; \r\nHaywood et al., 2016; Blake-Mizen et al., 2019). There is apparent \r\nglacial intensification following the MPWP, 2.75-2.72 Myr (Nielsen \r\nand Kuijpers, 2013; De Schepper et  al., 2014; Blake-Mizen et  al., \r\n2019; Knutz et al., 2019). Several studies agree that during the LIG \r\nthe total GrIS extent was likely less than present day (Section 9.4.1, \r\nFigure 9.17) with the total mass loss ranging from 0.3-6"


id: a20077dbe8813dbb069c6f8965e3c890
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 23054
content:
"2 m sea \r\nlevel equivalent (SLE), although timing and magnitude of this mass \r\nloss are not well constrained (Helsen et al., 2013; Stone et al., 2013; \r\nVasskog et al., 2015; Goelzer et al., 2016; Sinclair et al., 2016; Yau \r\net al., 2016; Clark et al., 2020). During the LGM, the GrIS reached \r\na peak ice volume greater than present (2-5 m SLE), as revealed by \r\nthe limited number of available geological records (Simpson et al., \r\n2009; Lecavalier et al., 2014; Batchelor et al., 2019)."


id: fa20feebdb521ebe319321998a84c9b3
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 36871
content:
"Geological evidence has been used to reconstruct Holocene glacial \r\nfluctuations of the ice sheet margin and lowerings of its surface, \r\nwhich occurred at different times in different places, as recently \r\nreviewed by Noble et al. (2020). In West Antarctica, marine sediments \r\nbelow the ice sheet (Kingslake et al., 2018) corroborate a previous \r\nglacial isostatic adjustment modelling study (Bradley et al., 2015), \r\nwhich suggests that ice had retreated behind the present grounding \r\nline prior to about 10 ka, and then readvanced. Geophysical imaging \r\nindicates a readvance in this area around 6 +- 2 ka (Wearing and \r\nKingslake, 2019). Other studies from the region conclude that ice\u0002sheet retreat and thinning was fastest from 9 to 8 ka (Johnson \r\net al., 2014; McKay et al., 2016; Spector et al., 2017), or millennia \r\nlater, during the MH (Hein et al., 2016; Johnson et al., 2019), with \r\nindications of a subsequent readvance (Venturelli et al., 2020)"


id: 4deded048f6c7a4d23591ca4121ac9be
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 9684
content:
" In \r\nEast Antarctica, rapid ice-sheet thinning occurred between around \r\n9 and 5 ka (Jones et  al., 2015), consistent with previous work \r\nindicating that the ice sheet in many regions was at or close to \r\nits current position by 5 ka (Bentley et  al., 2014). Overall, during \r\nthe MH, the AIS was retreating, but remained more extensive than \r\npresent, while some parts of the ice sheet might have been smaller \r\nthan now (low confidence). "


id: 6dd44310e41635aa6aa3482318074303
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 14893
content:
"Improved estimates of surface mass balance (SMB) in Antarctica \r\nfrom 67  ice core records do not show any substantial changes in \r\naccumulation rates over most of Antarctica since 1200 CE (Frezzotti \r\net al., 2013). The SMB growth rate in Antarctica is estimated to be \r\n7.0 +- 0.1 Gt per decade between 1800 and 2010 and 14.0 +- 1.8 Gt \r\nper decade since 1900 (Thomas et  al., 2017). For the period \r\n1979-2000, an insignificant Antarctic-wide negative SMB trend has \r\nbeen estimated (Medley and Thomas, 2019). The Antarctic Ice Sheet \r\nlost 2670 [1800 to 3540] Gt (SLE 7.4 [5.0 to 9.8] mm) of ice between \r\n1992 and 2020. The rate of ice-sheet (including peripheral glaciers) \r\nmass loss rose from 0 [-36 to +40] Gt yr-1 (SLE  0.0 [-0.10 to \r\n0.11]  mm  yr-1) in 1901-1990 to 192 [145 to 239]  Gt yr-1 (SLE \r\n0.54 [0.47 to 0.61] mm yr -1) for 2006-2018 (Section 9.4.2, Figure 2.24, \r\nand Table 9.5). Within quantified uncertainties, this estimate agrees \r\nwith other recent estimates (Rignot et al"


id: 4f8f8f97c2744ad09007530b8be1e253
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 9949
content:
", 2019; B. Smith et al., 2020; \r\nVelicogna et al., 2020). There is therefore very high confidence that \r\nthe AIS has been losing mass over 1992-2020 (Section 9.4.2.1 and \r\nFigure 2.24). Major contributions to recent AIS changes arise from \r\nWest Antarctica and Wilkes Land in East Antarctica (Rignot et  al., \r\n2019). For the East Antarctic most studies suggest that the mass "


id: 79564331cfe493caefdbc250f9ed229e
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 16656
content:
"Permafrost occurrence during the Pliocene has been inferred from \r\npollen in lake sediments in NE Arctic Russia and permafrost-vegetation \r\nrelationships which indicate that permafrost was absent during the \r\nMPWP in this region (Brigham-Grette et al., 2013; Herzschuh et al., \r\n2016). Analysis of speleothem records in Siberian caves, indicates \r\nthat permafrost was absent in the current continuous permafrost \r\nzone at 60degN at the start of the 1.5 Ma record, with aggradation \r\noccurring around 0.4 Ma (Vaks et al., 2020). There are indications of \r\nextensive permafrost thaw during subsequent interglacials especially \r\nfurther south in the current permafrost zone (Vaks et  al., 2013). \r\nReconstruction of permafrost distribution during the LGM indicates \r\nthat permafrost was more extensive in exposed areas (Vandenberghe \r\net al., 2014). In non-glaciated areas of the North American Arctic there \r\nis permafrost that survived the LIG (French and Millar, 2014)"


id: 87173aa0babf5208daaecdcbf5f5bf48
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 18786
content:
" Trends \r\nand timing of permafrost aggradation and thaw over the last 6 kyr in \r\npeatlands of the NH were recently summarized (Hiemstra, 2018; Treat \r\nand Jones, 2018). Three multi-century periods (ending 1000 Before the \r\nCommon Era (BCE), 500 CE and 1850 CE) of permafrost aggradation, \r\nassociated with neoglaciation periods are inferred resulting in more \r\nextensive permafrost in peatlands of the present-day discontinuous \r\npermafrost zone, which reached a peak approximately 250 years ago, \r\nwith thawing occurring concurrently with post 1850 warming (Treat \r\nand Jones, 2018). Although permafrost persists in peatlands at the \r\nsouthern extent of the permafrost zone where it was absent prior \r\nto 3 ka, there has been thawing since the 1960s (James et al., 2013; \r\nB.M. Jones et al., 2016; Holloway and Lewkowicz, 2020)."


id: 67b984aa2df351544b858c81e358e383
created_at: 2023-03-16 14:01:57
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vector_id: 13910
content:
"Assessment of trends in ALT is complicated by considerable ALT \r\ninterannual variability. For example, in north-western North America \r\nduring the extreme warm year of 1998, ALT was greater than in \r\nprior years. Although ALT decreased over the following few years, \r\nit has generally increased again since the late 2000s (Duchesne \r\net  al., 2015; Romanovsky et  al., 2017b, 2020). However, at some \r\nsites there has been little change in ALT due to ground subsidence \r\nthat accompanies thaw of ice-rich permafrost (Streletskiy et  al., \r\n2017; O'Neill et al., 2019). In the European and Russian Arctic there \r\nhas been a broad-scale increase in ALT during the 21st century \r\n(Streletskiy et al., 2015; Romanovsky et al., 2020). In high elevation \r\nareas in Europe and Asia, increases in ALT have occurred since the \r\nmid-1990s (Y. Liu et al., 2017; Cao et al., 2018; Noetzli et al., 2019, \r\n2020; Zhao et al., 2020)"


id: f5c53cc17b5a9887fa9faed60bd151fa
created_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 36184
content:
" Limited and shorter records for Antarctica \r\nshow marked interannual variability and no apparent trend with \r\nALT being relatively stable or decreasing at some sites since 2006 \r\n(Hrbacek et al., 2018)."


id: 16c310cc29a37b5793d1b372e0af437c
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 1101
content:
"a short period of rapid warming at the end of the Younger Dryas \r\n(12.75-11.55 ka) were rates comparable to those observed since \r\nthe 1970s (Bereiter et al., 2018; Shackleton et al., 2019). Ice cores \r\nimply a small decrease in the global mean ocean temperature during \r\nthe early Holocene (<0.4degC) (Bereiter et al., 2018; Baggenstos et al., \r\n2019). Sediment cores from the equatorial Pacific and Atlantic Ocean \r\n(0-1000 m) indicate a stronger regional cooling (compared to mean \r\nocean temperature) of 1.0degC +- 0.7degC to 1.8degC +- 0.4degC from the early/\r\nmid-Holocene to ca.1750 CE (Rosenthal et al., 2013, 2017; Morley \r\net al., 2014; Kalansky et al., 2015). Sediment cores from the western \r\nequatorial Pacific suggest 0.8degC +- 0.1degC higher temperatures in \r\nthe upper 700 m of the ocean during 950-1100 CE compared to \r\n1400-1750 CE"


id: 2733dbd76296daafa2f42341ea9c8b54
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 3565
content:
" These changes are consistent with a global estimate \r\nderived from combined surface and subsurface ocean temperature \r\nproxy records (PAGES 2k Consortium, 2013; McGregor et al., 2015). \r\nA combined study of model and observational data further confirmed \r\nthese results, treating temperature as a passive tracer (Gebbie and \r\nHuybers, 2019) and addressing the role of circulation dynamics \r\n(Scheen and Stocker, 2020). Collectively, the proxy records indicate \r\na global OHC decrease of about 400 +- 70 ZJ (about 170 +- 100 ZJ \r\nin the Pacific) in the upper 700 m between 950-1100 CE and \r\n1400-1750 CE, and also suggest that the deep Pacific is still adjusting \r\nto this cooling (Rosenthal et al., 2013), partially offsetting the global \r\nincrease since 1750 CE (Gebbie and Huybers, 2019; Gebbie, 2021)."


id: 49ecef8ea4dbd7f9b359bc6649fe5970
created_at: 2023-03-16 14:01:57
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vector_id: 9035
content:
"For the instrumental era, since AR5 and SROCC, new and updated \r\nOHC and ThSL observation-based analyses (Johnson et  al., 2020; \r\nvon Schuckmann et al., 2020) enhance an existing large ensemble \r\nof direct and indirect OHC estimates (Figure 2.26), although some \r\nrely to varying degrees upon information from ocean-climate models. \r\nDirect estimates benefit from improved: bias adjustments (e.g., Cheng \r\net  al., 2018; Leahy et  al., 2018; Palmer et  al., 2018; Ribeiro et  al., \r\n2018; B. Wang et  al., 2018; Bagnell and DeVries, 2020; Gouretski \r\nand Cheng, 2020); interpolation methods (Kuusela and Stein, 2018; \r\nSu et  al., 2020); and characterization of sources of uncertainty \r\n(e.g.,  Good, 2017; Wunsch, 2018; Allison et  al., 2019; Garry et  al., \r\n2019; Meyssignac et al., 2019; Palmer et al., 2021), including those \r\noriginating from forced and intrinsic ocean variability (Penduff et al., \r\n2018)"


id: bfe50c227bd22c8a41c5713b557133b3
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 27748
content:
" After 2006 direct OHC estimates for the upper 2000 m layer \r\nbenefit from the near-global ARGO array with its superior coverage \r\nover 60degS-60degN (Roemmich et al., 2019). Indirect estimates include \r\nOHC and ThSL series inferred from satellite altimetry and gravimetry \r\nsince 2003 (Meyssignac et al., 2019), the passive uptake of OHC (ThSL) \r\nat centennial timescales inferred from observed SST anomalies, and \r\ntime-invariant circulation processes from an ocean state estimation \r\n(e.g., Zanna et al., 2019). Resplandy et al. (2019) estimate the rate \r\nof global OHC uptake over 1991-2016 from changes in atmospheric \r\ncomposition and physical relationships based on CMIP5 model \r\nsimulations. The uncertainties are broader than from direct estimates \r\nbut the estimate is qualitatively consistent."


id: 8dc07f4b60dccf85399923d6207b2439
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 19830
content:
"Table 2.7 | Rates of global ocean heat content (OHC) and global mean thermosteric sea level (ThSL) change for four depth integrations over different \r\nperiods. For the period up to 1971, the assessment for all depth layers is based on Zanna et al. (2019). From 1971 onwards, consistent with AR5, Domingues et al. (2008, updated) \r\nis the central estimate for 0-700 m along with uncertainty from a five-member ensemble (Domingues et al., 2008, updated; Levitus et al., 2012; Good et al., 2013; Cheng et al., \r\n2017; Ishii et al., 2017), following the approach of Palmer et al. (2021). Similarly, Ishii et al. (2017) is the central estimate for 700-2000 m with uncertainty based on a 3-member \r\nensemble (Levitus et al., 2012; Cheng et al., 2017; Ishii et al., 2017). For depths below 2000 m, both central estimate and uncertainty are from Purkey and Johnson (2010, \r\nupdated). In cases when OHC estimates do not have a ThSL counterpart (e.g., Good et al., 2013; Cheng et al"


id: ac3b3b9478bb999337a8f0b04d141138
created_at: 2023-03-16 14:01:57
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vector_id: 24633
content:
", 2017), OHC was converted into ThSL using the average linear \r\nregression coefficients for 0-700 m and 700-2000 m from all available ensemble members. For consistency with the energy and sea-level budgets presented in Chapters 7 and \r\n9, reported rates are based on the difference between the first and last annual mean value in each period (Palmer et al., 2021, Box 7.2, Cross-Chapter Box 9.1). N/A indicates \r\nnot applicable. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)."


id: 64a961c3d0febed30f56574ab5687829
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 13413
content:
"Prior to the instrumental record, reconstructions of near-surface \r\nsalinity change are accomplished by combining isotopic and \r\nelemental proxy data from microfossil plankton shells and skeletons \r\npreserved in deep-sea sediments. These data highlight changes in \r\nthe salinity contrast between the Pacific and Atlantic oceans during \r\npast glacials (Broecker, 1989; Keigwin and Cook, 2007; Costa et al., \r\n2018) and for repeated episodes of increased subtropical salinity \r\n(Schmidt et al., 2004, 2006) and subpolar freshening (Cortijo et al., \r\n1997; Thornalley et  al., 2011) in the North Atlantic ocean. These \r\nepisodes were associated with disruptions to the large-scale deep \r\nocean circulation (Buizert et  al., 2015; Henry et  al., 2016; Lynch\u0002Stieglitz, 2017)"


id: 5c94adfdc977fe70145c9ce002b7e4e8
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 12082
content:
" Further quantification of paleo salinity changes \r\nis complicated by incomplete understanding of proxy-salinity \r\nrelationships and the relative influence of atmospheric and ocean \r\nprocesses across regions and paleo periods (Rohling, 2007; LeGrande \r\nand Schmidt, 2011; Holloway et al., 2016; Conroy et al., 2017)."


id: 4c8a2cd6b1456e9a826b4e00feb7d746
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 9462
content:
"The 1950-2019 trends reveal near-surface freshening of the northern \r\nand western Warm (and fresh) Pool of the Pacific and increased \r\nsalinity maxima in the subtropical Atlantic, strengthening the inter\u0002basin contrast (Figure 2.27a). There are indications that the subpolar \r\nfreshening and subtropical salinification of the Atlantic ocean may \r\nextend back to at least 1896 (Friedman et  al., 2017). Over recent \r\ndecades, new observations from Argo floats and ocean reanalyses \r\nprovide general support that changes in the global patterns of near\u0002surface salinity contrast are broadly associated with an intensification \r\nof the hydrological cycle (Sections 2.3.1.3.5 and 8.3.1.1). However, \r\nthis assessment is complicated by changing observational techniques \r\n(Section 1.5"


id: 23509a1effb1ea311676ab11874cf41b
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 3009
content:
"1), temporally and spatially inhomogeneous sampling \r\nand uncertainties in interpolation algorithms and the substantial \r\ninfluence of modes of natural variabiltity and ocean circulation \r\nprocesses over interannual timescales (Skliris et  al., 2014; Durack, \r\n2015; Grist et al., 2016; Aretxabaleta et al., 2017; Vinogradova and \r\nPonte, 2017; Liu et al., 2020). Following AR5, based on the updated \r\nanalysis from Durack and Wijffels (2010) which infills in situ gaps \r\nto recover large-scale patterns the mean salinity contrast between \r\nhigh- and low- near-surface salinity regions increased by 0.14 \r\n[0.07 to 0.20] from 1950 to 2019."


id: 478d8eab3a7fc7c38d02411d81fd843b
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 8643
content:
"The AR5 concluded based on proxy and instrumental data that the \r\nrate of global mean sea level (GMSL) rise since the mid-19th century \r\nwas larger than the mean rate during the previous two millennia \r\n(high confidence). The SROCC reported with high confidence that \r\nGMSL increases were 1.5 [1.1 to 1.9] mm yr-1 for 1902-2010 \r\n(with an acceleration rate between -0.002 and +0.019 mm yr -2), \r\n2.1  [1.8  to  2.3] mm yr-1 for 1970-2015, 3.2 [2.8 to 3.5] mm yr-1\r\nfor 1993-2015 and 3.6 [3.1 to 4.1] mm yr -1 for 2006-2015. AR5 \r\nreported that GMSL during the LIG was, over several thousand years, \r\nbetween 5 and 10 m higher than 1985-2004 (medium confidence) \r\nwhereas SROCC concluded it was virtually certain that GMSL \r\nexceeded current levels (high confidence), and reached a peak that \r\nwas likely 6-9 m higher than today, but did not exceed 10 m (medium \r\nconfidence)"


id: f9296f27a00d551b7090bcb9234ca52e
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content:
" The AR5 concluded with high confidence that there were \r\ntwo intra-LIG GMSL peaks and that the millennial-scale rate during \r\nthese periods exceeded 2 mm yr -1. The AR5 had high confidence that \r\nGMSL during the MPWP did not exceed 20 m above present. Based \r\non new understanding, SROCC placed the upper bound at 25 m but \r\nwith low confidence."


id: 7ae170373b7ed123d85ae3f6b7ff929e
created_at: 2023-03-16 14:01:57
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content:
"Recent studies have highlighted uncertainties in estimates of GMSL \r\nduring the LIG, including the extent of GMSL variability (Capron et al., \r\n2019). Vertical land motions (Austermann et al., 2017) are starting to \r\nbe considered quantitatively (e.g., Stephenson et al., 2019), but are \r\nstill bounded by large uncertainties. The distribution and thickness \r\nof pre-LIG ice sheets (Dendy et al., 2017; Rohling et al., 2017) and \r\nisostasy driven by sediment loading since the LIG (Pico, 2020) \r\nadd further uncertainty. In light of these recent studies and previous \r\nassessments, there is medium confidence that peak GMSL during the \r\nLIG was likely between 5 and 10 m higher than modern. Relative \r\nsea-level estimates from some sites (e.g., Bahamas and Seychelles) \r\nreport ephemeral, metre-scale fluctuations (Vyverberg et al., 2018). \r\nDifferent generations of LIG reef growth at other sites (e.g"


id: bbe2b7b9b513965c85283477915f5e25
created_at: 2023-03-16 14:01:57
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content:
", Yucatan \r\nPeninsula, Western Australia) suggest the occurrence of sudden \r\naccelerations in GMSL change (Blanchon et al., 2009; O'Leary et al., \r\n2013). However, other sites (e.g.,  South Australia, Mediterranean), \r\nindicate that LIG sea level was substantially stable (T.-Y. Pan et al., \r\n2018; Polyak et  al., 2018). In addition, there are uncertainties \r\nin the interpretation of local relative sea level from some GMSL \r\nreconstructions (Barlow et  al., 2018). Therefore, low confidence is \r\nassigned to any GMSL rate of change estimated within the LIG."


id: e00da632955df845f3bb6a909d7b36f3
created_at: 2023-03-16 14:01:57
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vector_id: 32742
content:
"Since SROCC, two new tide gauge reconstructions of 20th century \r\nGMSL change have been published, although both rely upon CMIP \r\nmodels to varying degrees (Figure 2.28). Frederikse et al. (2020) used \r\na 'virtual station' method and a probabilistic framework to estimate \r\nGMSL change and its uncertainties since 1900. Dangendorf et al. (2019) \r\ncombined a Kalman Smoother (Hay et al., 2015) with Reduced Space \r\nOptimal Interpolation (Church and White, 2011; Ray and Douglas, \r\n2011) in an effort to better represent both the long-term GMSL change \r\nwhile preserving information on sea-level variability. In  addition, \r\nnew ensemble-based methods for quantifying GMSL change have \r\nbeen presented that account for both structural and parametric \r\nuncertainty (Palmer et al., 2021). Altimeter time series of GMSL change \r\n(Figure 2.28) have been extended to 2019/2020 but bias adjustments \r\n(Watson et al., 2015; Beckley et al., 2017; Dieng et al., 2017; Ablain \r\net al., 2019; Legeais et al"


id: 291ebc5b1d0b6700d6d0b171f1ba3705
created_at: 2023-03-16 14:01:57
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vector_id: 3901
content:
"On multi-millennial timescales, proxy evidence indicates that the \r\nAMOC varied repeatedly in strength and vertical structure. During \r\nthe last glacial period, particularly around the LGM, AMOC was \r\nestimated to be shallower than present, although there is continued \r\ndebate about the magnitude of the shoaling (Lynch-Stieglitz et al., \r\n2007; Gebbie, 2014), and whether this change was associated with \r\na weaker overturning (Ritz et al., 2013; Menviel et al., 2017; Muglia \r\net al., 2018). There are indications that substantial variations in AMOC \r\nwere associated with abrupt climate changes during the glacial \r\nintervals, including Dansgaard-Oeschger and Heinrich events (14-\r\n70 ka) (McManus et al., 2004; Bohm et al., 2015; Henry et al., 2016; \r\nLynch-Stieglitz, 2017). During these millennial-scale oscillations, \r\nweakened AMOC was associated with dramatic cooling in the NH \r\nand warming in the SH (Buizert et al., 2015; Henry et al"


id: 6e510e104117e932f365e32eb02f1d7f
created_at: 2023-03-16 14:01:57
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content:
", 2016), while \r\nhemispheric changes of opposite sign accompanied strengthened \r\nAMOC. After the final demise of the Laurentide ice sheet about 8 \r\nka, the mean overall strength of AMOC has been relatively stable \r\nthroughout the rest of the Holocene compared to the preceding 100 \r\nkyr (Hoffmann et al., 2018; Lippold et al., 2019). There are however \r\nindications of episodic variations in AMOC during the Holocene \r\n(Bianchi and McCave, 1999; Oppo et  al., 2003; Thornalley et  al., \r\n2013; Ayache et al., 2018), and past interglacial intervals (Galaasen \r\net  al., 2014, 2020; Hayes et  al., 2014; Mokeddem et  al., 2014; H. \r\nHuang et al., 2020). Over the last 3 kyr, there are indications that \r\nAMOC variability was potentially linked to decreasing production of \r\nLabrador Sea Water (LSW), one of the water masses contributing to \r\nAMOC (Alonso-Garcia et  al., 2017; Moffa-Sanchez and Hall, 2017; \r\nMoffa-Sanchez et al., 2019)."


id: 337eef31fe03c478bfa742a68e2c7f6e
created_at: 2023-03-16 14:01:57
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vector_id: 5521
content:
"Based on the ensemble approach of Palmer et  al. (2021) and an \r\nupdated WCRP Global Sea Level Budget Group (2018) assessment \r\n(Figure 2.28) GMSL rose at a rate of 1.32 [0.58 to 2.06] mm yr -1 for the \r\nperiod 1901-1971, increasing to 1.87 [0.82 to 2.92] mm yr -1 between \r\n1971 and 2006, and further increasing to 3.69 [3.21 to 4.17] mm yr -1\r\nfor 2006-2018 (high confidence). The average rate for 1901-2018 \r\nwas 1.73 [1.28 to 2.17] mm yr-1 with a total rise of 0.20 [0.15 to \r\n0.25] m (Table 9.5). The acceleration rate very likely is 0.094 [0.082 to \r\n0.115] mm yr-2 for 1993-2018 (WCRP Global Sea Level Budget Group, \r\n2018, updated), consistent with other estimates (Watson et al., 2015; \r\nX. Chen et al., 2017; Nerem et al., 2018; WCRP Global Sea Level Budget \r\nGroup, 2018; Ablain et al., 2019; Legeais et al., 2020)"


id: 92f625b8ef7ac5f54da1f8d4cf4ef632
created_at: 2023-03-16 14:01:57
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vector_id: 20640
content:
" For the period \r\n1902-2010 the updated tide gauge reconstructions published since \r\nSROCC also show a robust acceleration over the 20th century and \r\nthe ensemble estimate of Palmer et al. (2021) gives a value of 0.0053 \r\n[0.0042 to 0.0073] mm yr-2, based on an unweighted quadratic fit."


id: 738bcef3d5e288e4d6ffb500a89ab7b3
created_at: 2023-03-16 14:01:57
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vector_id: 15802
content:
"Since the 1980s, multiple lines of observational evidence for AMOC \r\nchange exist. Ship-based hydrographic estimates of AMOC as \r\nfar back as the 1980s show no overall decline in AMOC strength \r\n(Fu  et  al., 2020; Worthington et  al., 2021). Direct indications from \r\nin-situ observations report a -2.5 +- 1.4 Sv change between 1993 and \r\n2010 across the OVIDE section, superimposed on large interannual to \r\ndecadal variability (Mercier et al., 2015). At 41degN and 26degN, a decline \r\nof -3.1 +- 3.2 Sv per decade and -2.5 +- 2.1 Sv per decade respectively \r\nhas been reported over 2004-2016 (Baringer et  al., 2018; Smeed \r\net al., 2018). However, Moat et al. (2020) report an increase in AMOC \r\nstrength at 26degN over 2009-2018. Recent time series of moored \r\nobservations at 11oS (Hummels et  al., 2015), 34degS (Meinen et  al., \r\n2018; Kersale et al., 2020), and between 57 and 60oN (Lozier et al., \r\n2019) are currently too short to permit robust conclusions about \r\nchanges"


id: 540722225e9b21a8e6e70c402a8942b8
created_at: 2023-03-16 14:01:57
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content:
" The directly observed AMOC weakening since 2004, while \r\nsignificant, is over too short a period to assess whether it is part of \r\na longer term trend or dominated by decadal-scale internal variability \r\n(Smeed et al., 2014; Collins et al., 2019; Moat et al., 2020). Notably an \r\nincrease and subsequent decline in the 1990s is present in estimates \r\nof AMOC and associated heat transport constructed from reanalyses \r\nor auxiliary data (Section 9.2.3.1; Frajka-Williams, 2015; Jackson \r\net al., 2016; Trenberth and Fasullo, 2017; Jackson and Wood, 2020)."


id: 7300242d767dca6df05c8f82912345b4
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 15706
content:
"The intensity of the Kuroshio current system in the north-west Pacific \r\nvaried in conjunction with the glaciation cycles over the last 1 Myr, \r\nwith some limited glacial-interglacial variability in position (Jian et al., \r\n2000; Gallagher et al., 2015). The Agulhas current has strengthened \r\nsubstantially during the warming associated with deglaciations \r\nof the past 1 Myr (Peeters et  al., 2004; Bard and Rickaby, 2009; \r\nMartinez-Mendez et  al., 2010; Marino et  al., 2013; Ballalai et  al., \r\n2019). According to sediment core analyses, the Agulhas leakage \r\nvaried by about 10 Sv during major climatic transitions over the past \r\n640 kyr (Caley et al., 2014). Available data suggests that there was \r\nrelatively little change in the net flow of the ACC during the LGM, \r\nwith no consensus on the sign of changes (McCave et al., 2013; Lamy \r\net al., 2015; Lynch-Stieglitz et al"


id: 3b6fc7939b63c4530edc9a36856779fa
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content:
", 2016), except at one location at \r\nthe northern edge of the Drake Passage where a 40% decrease of \r\ntransport had been reported (Lamy et al., 2015). Longer time series \r\nfrom the northern entrance to Drake Passage suggest a consistent \r\ntransport variability of 6-16% through glacial climate cycles, with \r\nhigher current speeds during interglacial times and reduced current \r\nspeeds during glacial intervals (Toyos et al., 2020). Inferred variability \r\nin the size and strength of the North Atlantic subpolar gyre was \r\nsubstantial, and included rapid changes on millennial time scales \r\nduring both interglacial and glacial intervals over the last 150 kyr \r\n(Born and Levermann, 2010; Mokeddem et al., 2014; Irvali et al., 2016; \r\nMokeddem and McManus, 2016). North Atlantic - Arctic exchange \r\nhas also varied in the past, with indications of an increasing inflow of \r\nAtlantic waters into the Arctic during the late Holocene (Slubowska \r\net al"


id: 2fe810f1b87293aadd0961dd5cd46b9e
created_at: 2023-03-16 14:01:57
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content:
", 2005) with an acceleration to the recent inflow that is now the \r\nlargest of the past 2 kyr (Spielhagen et al., 2011)."


id: a431c9ce3be5b8208308e704d5e87547
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 23387
content:
"Understanding of changes in surface pH at paleo time-scales has \r\nincreased since AR5 (Clarkson et  al., 2015; Foster and Rae, 2016; \r\nZeebe et al., 2016; Gutjahr et al., 2017; Sosdian et al., 2018; Henehan \r\net al., 2019; Anagnostou et al., 2020; Harper et al., 2020; Muller et al., \r\n2020). Over the last 65 million years there have been several intervals \r\nwhen the pH of surface waters varied concurrently with climate \r\nchange such as during the PETM, EECO, and MCO (Figure  2.29a \r\nand Section 5.3.1.1). However, only during the PETM is the change \r\nsufficiently well-constrained to allow for a direct comparison with \r\nrecent and current trends (Kirtland Turner, 2018). This event was \r\nassociated with profound perturbations of the global carbon cycle, \r\nocean warming, deoxygenation and a surface ocean pH decrease \r\nlikely ranging from 0.15 to 0.30 units (Penman et al., 2014; Gutjahr \r\net al., 2017; Babila et al"


id: 5d9a06815fc224ab8b4f2e6ba81307fa
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vector_id: 12250
content:
", 2018) - a rate that was likely at least an \r\norder of magnitude slower than today (Cui et al., 2011; Bowen et al., \r\n2015; Frieling et al., 2016; Zeebe et al., 2016; Gutjahr et al., 2017; \r\nKirtland Turner, 2018; Gingerich, 2019)."


id: d6ac45d44d5fa9d0a12c546a7c395c1e
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vector_id: 31280
content:
"Paleo evidence suggests that surface ocean pH has gradually \r\nincreased over the last 50 Myr (Anagnostou et  al., 2016, 2020; \r\nSosdian et al., 2018) (Figure 2.29a). Global mean surface pH values \r\nas low as observed during recent decades are uncommon in the \r\nlast 2 Myr (Figure 2.29b) (Martinez-Boti et al., 2015a; Chalk et al., \r\n2017; Dyez et  al., 2018; Sosdian et  al., 2018), and have not been \r\nexperienced in at least the last 25 kyr (Figure 2.29c; Palmer and \r\nPearson, 2003; Foster, 2008; Palmer et al., 2010; Henehan et al., 2013; \r\nKirschke et al., 2013; Martinez-Boti et al., 2015a; Naik et al., 2015; \r\nEzat et al., 2017; Gray et al., 2018; Shao et al., 2019). The magnitude \r\nof pH change during the Pleistocene glacial-interglacial cycles was \r\n0.1-0.15 pH units  - similar to recent changes in the modern era \r\n(Figure 2.29c and Section 5.3.1.2; Honisch et al., 2009; Chalk et al., \r\n2017; Shao et al., 2019)"


id: 4cb1bfef3dec8e9a968a58b4d7d7d47f
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content:
" Maximum rates of pH change during the \r\nLDT, inferred from changes in atmospheric CO2 recorded in ice cores \r\n(Marcott et al., 2014) and the established relationships between pH \r\nand CO2 changes and the boron isotope proxy (Hain et al., 2018), \r\nreached -0.02 pH units per century at about 11.7 ka, about 14.8 ka \r\nand about 16.3 ka, as previously sequestered CO2 was transferred \r\nfrom the ocean interior to the subsurface ocean (Martinez-Boti et al., \r\n2015a; Jaccard et al., 2016; Rae et al., 2018)."


id: 7e5331a9037ab0e7332d00ec2c9e608
created_at: 2023-03-16 14:01:57
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content:
"Since the 1980s, the global ocean has experienced a decline in \r\nsurface pH of 0.016 +- 0.006 pH units per decade based on indirect \r\npH products (Figure 2.29d; Lauvset et  al., 2015; Hurd et  al., 2018; \r\nIPCC,  2019; Gehlen et  al., 2020) that agrees with the decline of \r\n0.017-0.025  pH units per decade assessed in SROCC from direct \r\ntime-series measurements of pH. Section 5.3.2.2 assesses a decline \r\nthat ranges from 0.01 to 0.026 pH units per decade for the tropical and \r\nsubtropical open ocean areas, and 0.003-0.026 pH units per decade \r\nfor the polar and subpolar open ocean regions by using time series \r\nand ship-based datasets from the surface ocean CO2 measurement \r\nnetwork (Bakker et al., 2016; Gehlen et al., 2020; Gregor and Gruber, \r\n2021). There is general consensus that global surface ocean pH trends \r\nover the past two decades have exceeded the natural background \r\nvariability (Lauvset et  al., 2015; Bindoff et  al., 2019; Gehlen et  al., \r\n2020)"


id: fd23f818b4a21e474a2135466a246660
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" However, for some areas sparse data coverage, and large \r\nyear-to-year variations hinders the detection of long-term surface \r\nocean pH trends; for example in the Southern Ocean (Lauvset et al., \r\n2015; Bindoff et al., 2019) and in the Arctic Ocean (Lauvset et al., 2015; \r\nBindoff et al., 2019; Meredith et al., 2019)."


id: b143756e72844be198fdbfd4d60fc83c
created_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 25394
content:
"Since AR5 evidence for changes in oxygen content based on new \r\nproxy reconstructions has increased (Hoogakker et  al., 2015, 2018; \r\nGottschalk et al., 2016, 2020; Anderson et al., 2019). Paleo records \r\npoint to past periods of reduced oceanic oxygen levels during the late \r\nPermian (about 250 Ma), the Jurassic and the Cretaceous (180-100 \r\nMa), alongside associated global scale disturbances of the global \r\ncarbon cycle. Emerging studies for the Cenozoic Era suggest more \r\nstable ocean oxygenation conditions throughout the interval on \r\nmillion-year time scales (X. Wang et al., 2016). Sedimentary proxy data \r\nindicate, however, that the seemingly stable Cenozoic was punctuated \r\nby transient, widespread deoxygenation during the PETM (Dickson \r\net al., 2012; Winguth et al., 2012; Remmelzwaal et al., 2019), with \r\nparts of the ocean reaching anoxic levels (Section 5.3.1.1; Yao et al., \r\n2018)"


id: d25e84f9f6e4737b23dcdefe17acfba7
created_at: 2023-03-16 14:01:57
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content:
" Since the LGM, there was an overall emergence and expansion \r\nof low-oxygen waters into the ocean intermediate depths as a result \r\nof rapid warming and a reorganization of the global overturning \r\ncirculation (Galbraith and Jaccard, 2015). The maximum expansion \r\nof oxygen-depleted waters during the LDT occurred coincidently with \r\nrapid warming in the NH at 14.7-12.9 ka (Jaccard and Galbraith, \r\n2012; Moffitt et al., 2015; Hoogakker et al., 2018). Deep (>1500 m) \r\nocean oxygen levels increased by 100-150 umol kg-1 since the LGM, \r\nreaching modern oxygen levels at about 10 ka (Section 5.3.1.2; \r\nHoogakker et al., 2015; Gottschalk et al., 2016; Anderson et al., 2019). "


id: fbd447cc939b9f1fe99a987fabc67bc7
created_at: 2023-03-16 14:01:57
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content:
"Subsurface (100-400 m) oxygen in the California Current system is \r\nestimated to have declined by 24 +- 2 mmol kg-1 (1.0 mmol kg-1 per \r\ndecade) between 1993 and 2018, a rate similar to the global upper \r\n1000 m average (Bindoff et  al., 2019). In some locations, however, \r\nthe magnitude of oxygen loss substantially exceeds global averages \r\n(Queste et al., 2018; Bronselaer et al., 2020; Cummins and Ross, 2020; \r\nStramma et  al., 2020). For example, a decline in oxygen content of \r\n11.7 +- 3.5% in the upper 4000 m, including a decline of 20.4 +- 7.2% \r\nin the upper 1550 m, is reported in the North Pacific over the period \r\n1958-2018 (Cummins and Ross, 2020) (equivalent to 2.3 mmol kg-1 per \r\ndecade in the upper 1550 m and 2.0 mmol kg-1 decade- 1 throughout the \r\n4000 m water column)"


id: 5e5552f5c7ca2595be5edb148ee35ae9
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" In some regions of the Southern Ocean south \r\nof 65degS oxygen in the upper 2000 m has declined by 60 mmol kg-1\r\n(~52  mmol kg-1 per decade) based on comparisons of 2014-2019 \r\nand 1985-2005 observations (Bronselaer et  al., 2020). Within some \r\nOMZs regions of the Indian ocean, oxygen has declined from 6-12 to \r\n<2 mmol kg-1 between the 1960s and 2015-2016 (Bristow et al., 2017; \r\nAl-Said et al., 2018; Queste et al., 2018)."


id: 68a508d97519c091e4986738acfc9377
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content:
"Since AR5 WGII, analysis of a longer time series of ocean-colour data \r\n(1998-2012) has shown (Aiken et al., 2017) that the expansion of \r\nthe low nutrient part of the North Atlantic oligotrophic gyre was \r\nsignificant, at 0.27 x 106 km2 per decade, but that the rate was \r\nmuch lower than that reported earlier by Polovina et  al. (2008). \r\nFurthermore, Aiken et al. (2017) reported no significant trend in the \r\noligotrophic area of the South Atlantic Gyre. With the time series \r\nextended to 2016, von Schuckmann et al. (2018) reported that since \r\n2007, there was a general decreasing trend in the areas of the North \r\nand South Pacific oligotrophic Gyres, while the North and South \r\nAtlantic oligotrophic Gyres remained stable, with little change in \r\narea, consistent with Aiken et al. (2017)"


id: 3f96570e956eec2a96df7e997535673d
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content:
" The changing sign of trends \r\nin the areal extent of the oligotrophic gyres with increase in the \r\nlength of the time series raises the possibility that these changes \r\narise from interannual to multi-decadal variability. The time series \r\nof ocean-colour data is too short to discern any trend that might be \r\nsuperimposed on such variability. "


id: 50d824ff2ad1e3d00955a60946515cf2
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content:
"Similarly, there is limited consistent and long-term information on \r\nlarge-scale distributions of marine organisms at higher trophic levels. \r\nBut there are increased indications since AR5 and SROCC that the \r\ndistributions of various higher trophic-level organisms are shifting \r\nboth polewards and to deeper levels (Edwards et al., 2016; Haug et al., \r\n2017; Atkinson et al., 2019; Lenoir et al., 2020; Pinsky et al., 2020), \r\nmostly consistent with changes in temperature. However observations \r\nalso show a smaller set of counter-intuitive migrations towards \r\nwarmer and shallower waters, which could be related to changes \r\nin phenology and in larval transport by currents (Fuchs et al., 2020). \r\nThere are also strengthening indications of greater representation by \r\nspecies with warm-water affinity in marine communities, consistent \r\nwith expectations under observed warming (Burrows et  al., 2019)"


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" \r\nThere are indications that pre-1850 CE plankton communities are \r\ndifferent from their modern counterparts globally (Jonkers et al., 2019). \r\nIndicators of geographical distributions of species (mostly from coastal \r\nwaters) suggest that the rates at which some species are leaving or \r\narriving at an ecosystem are variable, leading to changes in community \r\ncomposition (Blowes et al., 2019), with likely greater representation of \r\nwarm-water species in some locations (Burrows et al., 2019). "


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content:
"Recent model-based results with assimilation of satellite data (Gregg \r\nand Rousseaux, 2019), show global annual mean marine primary \r\nproduction of around 38 (+-1.13) PgC yr -1 over 1998-2015. This new \r\nresult lies towards the low end of values reported in earlier, satellite\u0002based, studies (range 36.5-67 PgC yr -1, reported in Sathyendranath \r\net  al. (2020)). Reconciling the results of Gregg and Rousseaux \r\n(2019) with earlier satellite-based studies leads to a mean of \r\n47 (+-7.8) PgC yr-1. There is a strong correlation between interannual \r\nregional variability in marine primary production and climate \r\nvariability (Racault et al., 2017b; Gregg and Rousseaux, 2019). The \r\nincrease in primary production in the Arctic has been associated \r\nwith retreating sea ice and with increases in nutrient supply and \r\nchlorophyll concentration (Lewis et al., 2020). Gregg and Rousseaux \r\n(2019) reported a decreasing trend in marine primary production, of \r\n-0.8 PgC (-2.1%) per decade globally"


id: 5eda661c3b3a6772a38ee7a668739b10
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 12464
content:
" There is low confidence in this \r\ntrend because of the small number of studies and the short length of \r\nthe time series (<20 years)."


id: 2c440c7691133ba4e0b307c15d839b5a
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 4393
content:
"Phenological links between multiple components of an ecosystem \r\nhave to be maintained intact, to retain system integrity. Since all \r\nhigher pelagic organisms depend on phytoplankton for their food, \r\neither directly or indirectly, a match favours survival, and a mismatch \r\nis antagonistic to survival. Match represents synchronicity in the \r\nphenological events of both prey and predator. There are indications \r\nfrom ocean-colour data used in conjunction with fisheries data that \r\nthe survival rate of various larger marine organisms depends on \r\nphenological metrics related to the seasonality of phytoplankton \r\ngrowth. Such links have been demonstrated, for example, for \r\nhaddock (Melanogrammus aeglefinus) in the North-west Atlantic \r\n(Platt et  al., 2003); northern shrimp in the North Atlantic (Koeller \r\net al., 2009; Ouellet et al., 2011); sardine (Sardinella aurita) off the \r\nIvory coast (Kassi et  al"


id: 8dd23a2526f9fc31fc454d89700ea3d7
created_at: 2023-03-16 14:01:57
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vector_id: 19839
content:
", 2018); cod (Gadus morhua) and haddock \r\n(Melanogrammus aeglefinus) larvae in the North-West Atlantic \r\n(Trzcinski et al., 2013); and oil sardine (Sardinella longiceps) off the \r\nsouth-west coast of India. Borstad et al. (2011) showed that fledgling \r\nproduction rate of rhinoceros auklets (Cerorhinca monocerata) \r\non a  remote island in coastal north-eastern Pacific was related to \r\nseasonal values of chlorophyll-a biomass in the vicinity of the island. "


id: 3863f9bef9b0d8f446b7dd59c3a22934
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 6247
content:
"Changes in the length of the photosynthetically active growing \r\nseason (derived from the Normalized Difference Vegetation Index \r\n(NDVI)) are also evident over many land areas since the early 1980s. \r\nIncreases of about 2.0 days per decade are apparent north of 45degN \r\nsince the early 1980s (centred over mid-latitude Eurasia and north\u0002eastern North America), with indications of a reversal to a decline \r\nin season length starting in the early 2000s (Barichivich et al., 2013; \r\nZhao et al., 2015; Garonna et al., 2016; Q. Liu et al., 2016; Park et al., \r\n2016). Satellite-based records suggest that most NH regions have \r\nexperienced both an earlier start and a later end to the growing \r\nseason, a finding supported by ground-based data (Piao et al., 2020). \r\nA number of studies also capture increases in growing season length \r\nover the Canadian Arctic (W. Chen et al., 2016), Fennoscandia (Hogda \r\net  al., 2013), most of Europe (Garonna et  al"


id: 1a79f04cca148b5f447bb0cf96b5f74
created_at: 2023-03-16 14:01:57
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vector_id: 1649
content:
"Binney et al., 2017), the northern treeline being about 17deg to 23deg \r\nlatitude south of its present-day location in most areas. During the \r\nLDT, pervasive ecosystem transformations occurred in response to \r\nwarming and other climatic changes (Nolan et  al., 2018; Fordham \r\net al., 2020). By the MH, North Africa had experienced a widespread \r\nconversion from grasslands to desert (Hoelzmann et al., 1998; Prentice \r\net  al., 2000; Sha et  al., 2019), and the northernmost treeline had \r\nshifted poleward again to about 1deg to 3deg latitude north of its current \r\nlocation (MacDonald et al., 2000; Binney et al., 2009; Williams et al., \r\n2011). Over the past half century, there has been an increase in the \r\nspatial synchrony of annual tree growth across all continents that is \r\nunprecedented during the past millennium (Manzanedo et al., 2020). \r\nElevated rates of vegetation change in the Holocene are consistent \r\nwith climate variability (Shuman et al"


id: a810dcd7c472bbce21e764f54b6155ba
created_at: 2023-03-16 14:01:57
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vector_id: 23955
content:
", 2019), intensified human land \r\nuse (Fyfe et al., 2015; Marquer et al., 2017), and resulting increased \r\necosystem novelty (Finsinger et al., 2017; K.D. Burke et al., 2019)."


id: 284f0754b4f53fe125f7e4eb65c730ed
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 3753
content:
"Long-term ecological records capture extensive range shifts during \r\nthe 20th and early 21st centuries (Lenoir and Svenning, 2015; \r\nPecl  et  al., 2017). Research has been most extensive for North \r\nAmerica and western Eurasia, with fewer studies for central Africa, \r\neastern Asia, South America, Greenland, and Antarctica (Lenoir and \r\nSvenning, 2015). Most documented changes are toward cooler \r\nconditions  - that is, poleward and upslope (Lenoir et  al., 2008; \r\nHarsch et  al., 2009; Elmendorf et  al., 2015; Parmesan and Hanley, \r\n2015; Evans and Brown, 2017). Notably, a large, quasi-global \r\nanalysis (Chen et  al., 2011) estimated that many insect, bird, and \r\nplant species had shifted by 17 (+-3) km per decade toward higher \r\nlatitudes and 11 (+-2) m per decade toward higher elevations since \r\nthe mid-20th century, with changes in both the leading and trailing \r\nedges of species ranges (Rumpf et al., 2018)"


id: d63d72a9366a3bdf395ed899bc58e92e
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 31220
content:
" Over the past century, \r\nlong-term ecological surveys also show that species turnover (i.e., the \r\ntotal number of gains and losses of species within an area) has \r\nsignificantly increased across a broad array of ecosystems (Dornelas \r\net al., 2014, 2019), including undisturbed montane areas worldwide \r\n(Gibson-Reinemer et al., 2015). Despite global losses to biodiversity, \r\nhowever, most local assemblages have experienced a change in \r\nbiodiversity rather than a systematic loss (Pimm et al., 2014). With \r\nincreased species turnover, the novelty of contemporary communities \r\nrelative to historical baselines has risen (Hobbs et al., 2009; Radeloff \r\net  al., 2015) due to greater spatial homogenization, mixtures of \r\nexotic and native species, altered disturbance regimes, and legacies \r\nof current or historic land use (Olden and Rooney, 2006; Schulte \r\net al., 2007; Thompson et al., 2013; Goring et al., 2016)"


id: 28d473f3f724c94dc161026c72488150
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 3857
content:
" In general, \r\nterrestrial species have had lower rates of turnover than marine \r\nspecies (Dornelas et al., 2018; Blowes et al., 2019)."


id: 2368698feaf69a9d8947d31de5bb8f05
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 3022
content:
"changes in specific vegetation types, such as a  7% rise in global \r\ntree cover for 1982-2016 (Song et al., 2018) and an expansion of \r\nshrub extent in the Arctic tundra over 1982-2017 (Myers-Smith \r\net al., 2020). The increased greening is largely consistent with CO2\r\nfertilization at the global scale, with other changes being noteworthy \r\nat the regional level (Piao et al., 2020); examples include agricultural \r\nintensification in China and India (X. Chen et al., 2019; Gao et al., \r\n2019) and temperature increases in the northern high latitudes \r\n(Kong et al., 2017; Keenan and Riley, 2018) and in other areas such \r\nas the Loess Plateau in central China (Y. Wang et al., 2018). Notably, \r\nsome areas (such as parts of Amazonia, central Asia, and the Congo \r\nbasin) have experienced browning (i.e.,  decreases in green leaf \r\narea and/or mass) (Hoogakker et al., 2015; Gottschalk et al., 2016; \r\nAnderson et  al., 2019)"


id: d1010c354317625ea23c9f5ab4676f1f
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 30417
content:
" Because rates of browning have exceeded \r\nrates of greening in some regions since the late 1990s, the increase in \r\nglobal greening has been somewhat slower in the last two decades \r\n(T.-Y. Pan et al., 2018). "


id: 43d3d14ce4f1be52d5055db1b1bc3912
created_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 8084
content:
"Consistent with species range shifts, SRCCL noted that there have \r\nbeen changes in the geographical distribution of climate zones. \r\nPoleward shifts in temperate and continental climates are evident \r\nacross the globe over 1950-2010, with decreases in the area \r\n(and increases in the average elevation) of polar climates (Chan and \r\nWu, 2015). Zonal changes towards higher latitudes in winter plant \r\nhardiness regions are apparent since the 1970s over the central \r\nand eastern USA, with elevational changes also being important \r\nin the western USA (Daly et  al., 2012). A clear northward shift in \r\nwinter plant hardiness zones is detectable across western Canada \r\nsince 1930, with somewhat lesser changes in the south-eastern part \r\nof the country (McKenney et  al., 2014). A northward migration of \r\nagro-climate zones is also evident over Europe since the mid-1970s \r\n(Ceglar et al., 2019)"


id: eef716524134525023d7f8e2f282eaea
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 35071
content:
" In addition, a shift toward more arid climate \r\nzones is apparent in some areas, such as the Asian monsoon region \r\n(Son and Bae, 2015) as well as parts of South America and Africa \r\n(Spinoni et al., 2015)."


id: 938e5d57efd765b7acea6b0af3599049
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 20729
content:
"Global-scale linear trends differ substantially across products for the \r\nsame periods and trend metrics used (Jiang et al., 2017). Several factors \r\ncontribute to this large span in estimated changes. Remotely sensed \r\nvegetation products vary in their spatial and temporal completeness as \r\nwell as resolution and are sensitive to contamination from atmospheric \r\ncomposition, clouds, snow cover, and anisotropy, as well as orbital \r\nchanges and sensor degradations (de Jong et  al., 2012; Zhu et  al., \r\n2016; Jiang et al., 2017; Xiao et al., 2017; N. Pan et al., 2018). Ground\u0002based measurements suitable for calibration and validation are scarce \r\nbefore 2000 (Xiao et al., 2017), and the recalibration of satellite records \r\n(e.g., as in from MODIS Collection 5 to 6) can affect trends (Piao et al., \r\n2020)"


id: 296769e47f839fa5bcb487649dd0614c
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 3954
content:
" It is possible that the increase in greenness over 2000-2015 \r\nis larger than the increase in gross primary production (based on flux \r\ntower measurements and MODIS Collection 6 data) (L. Zhang et al., \r\n2018). Land use changes and altered disturbance regimes (e.g., floods, \r\nfires, diseases) may mask large-scale signals (Franklin et  al., 2016). \r\nIn addition, there is a plethora of models for the identification of "


id: 6668008486174b70497df2a1eda27734
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 13702
content:
"Vegetation index data derived from AVHRR and MODIS depicts \r\nincreases in aspects of vegetation greenness (i.e.,  green leaf area \r\nand/or mass) over the past four decades (Piao et  al., 2020). NDVI \r\nincreased globally from the early 1980s through the early 2010s \r\n(Y.  Liu et  al., 2015a). N. Pan et  al. (2018) found NDVI increases \r\nover about 70% of the Earth's vegetated surface through 2013, \r\nand Osborne et  al. (2018) noted strong upward changes in NDVI \r\nin the circumpolar Arctic through 2016. Globally integrated Leaf \r\nArea Index (LAI) also rose from the early 1980s through at least \r\nthe early 2010s (Zhu et al., 2016; Forzieri et al., 2017; Jiang et al., \r\n2017; Xiao et  al., 2017) and probably through near-present; for \r\nexample, C. Chen et  al. (2019) documented an LAI increase over \r\none-third of the global vegetated area from 2000-2017"


id: 7823437113cee2c442cf0a1f3dc7ae5d
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 16467
content:
" Although \r\nless frequently analysed for temporal trends, Fraction of Absorbed \r\nPhotosynthetically Active Radiation (FAPAR) likewise increased \r\nover many global land areas (particularly China, India, and eastern \r\nEurope) in the past two decades (Figure 2.33; Forkel et  al., 2014; \r\nGobron, 2018; Keenan and Riley, 2018). There are also documented "


id: bd316c9715836644519f0c81c8db6503
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 27331
content:
"The Pliocene Epoch is one of the best-documented examples of a warmer world during which the slow responding components of \r\nthe climate system were approximately in balance with concentrations of atmospheric CO2, similar to present (e.g., Haywood et al., \r\n2016). It provides a means to constrain Earth's equilibrium climate sensitivity (Section 7.5.3) and to assess climate model simulations \r\n(Section 7.4.4.1.2). During the Pliocene, continental configurations were similar to present (Cross-Chapter Box 2.4 Figure 1a), and \r\nmany plant and animal species living then also exist today. These similarities increase reliability of paleo-environmental reconstructions \r\ncompared with those for older geological periods. Within the well-studied mid-Pliocene Warm Period (MPWP, also called the \r\nmid-Piacenzian Warm Period, 3.3-3.0 Ma), the interglacial period KM5c (3.212-3"


id: c554dcc660edd8971586131eda7f0f00
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 28628
content:
"187 Ma) has become a focus of research because \r\nits orbital configuration, and therefore insolation forcing, was similar to present (global mean insolation = -0.022 W m-2 relative to \r\nmodern; Haywood et al., 2013), allowing for the climatic state associated with relatively high atmospheric CO2 to be assessed with \r\nfewer confounding variables."


id: a3b18f69b428d52394baae798984ec41
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 23318
content:
"During the KM5c interglacial, atmospheric CO2 concentration was typically between 360 and 420 ppm (Section 2.2.3.1). New climate \r\nsimulations of this interval from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) show a multi-model mean global \r\nsurface air temperature of 3.2 [2.1 to 4.8] degC warmer than control simulations (Cross-Chapter Box 2.4, Figure 1a; Haywood et al., 2020). \r\nThis is consistent with proxy evidence for the broader MPWP, which indicates that global mean surface temperature was 2.5degC-4.0degC \r\nhigher than 1850-1900 (Section 2.3.1.1.1). Global mean sea level was between 5 and 25 m higher than present (Section 2.3.3.3). \r\nGeological evidence (Section 2.3.2.4) and ice-sheet modelling (Section 9.6.2) indicate that both the Antarctic and Greenland Ice \r\nSheets were substantially smaller than present (Cross-Chapter Box 2.4, Figure 1c). Attribution of sea level highstands to particular \r\nice-sheet sources (Section 9.6"


id: db2091950068b4bb6fbb17f514944fd7
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 31990
content:
"2) is challenging (DeConto and Pollard, 2016; Golledge, 2020), but improving (Berends et al., 2019; \r\nGrant et al., 2019). "


id: ffce48aab61b7ed36d4743d2164b9711
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 37787
content:
"The latitudinal temperature gradient during the MPWP was reduced relative to present-day and the consistency between proxy and \r\nmodelled temperatures has improved since AR5 (Section 7.4.4.1.2). Northern high latitude (>60degN) SSTs were up to 7degC higher than \r\n1850-1900 (Bachem et al., 2016; McClymont et al., 2020; Sanchez-Montes et al., 2020), and terrestrial biomes were displaced poleward \r\n(e.g., Dowsett et al., 2019) (Cross-Chapter Box 2.4, Figure 1b). Arctic tundra regions currently underlain by permafrost were warm \r\nenough to support boreal forests, which shifted northward by approximately 250 km in Siberia, and up to 2000 km in the Canadian \r\nArctic Archipelago (Salzmann et al., 2013; Fletcher et al., 2017). The shift caused high-latitude surface albedo changes, which further \r\namplified the Pliocene global warming (Zhang and Jiang, 2014)"


id: c934ad3614b931de887a23c0cadafe79
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 29218
content:
" Vegetation changes in north-east Siberia indicate that MPWP summer \r\ntemperatures were up to 6degC higher than present day (Brigham-Grette et al., 2013). Farther south, modern boreal forest regions in \r\nRussia and eastern North America were covered with temperate forests and grasslands, whereas highly diverse, warm-temperate \r\nforests with subtropical taxa were widespread in central and eastern Europe (Cross-Chapter Box 2.4, Figure 1). While seasonal sea ice \r\nwas present in the North Atlantic and Arctic oceans, its winter extent was reduced relative to present (Knies et al., 2014; Clotten et al., \r\n2018), and some models suggest that the Arctic was sea ice free during the summer (Howell et al., 2016; Feng et al., 2020). "


id: 19123753a1c9191fa0d45693627424ae
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 1438
content:
"The average longitudinal temperature gradient in the tropical Pacific was weaker during the Pliocene than during 1850-1900 \r\n(Section 7.4.4.2.2). Changes in Pacific SSTs and SST gradients had far-reaching impacts on regional climates through atmospheric \r\nteleconnections, affecting rainfall patterns in western North America (Burls and Fedorov, 2017; Ibarra et al., 2018). The reduced zonal \r\nSST gradient has led to suggestions that the Pliocene Pacific experienced a 'permanent El Nino' state (Molnar and Cane, 2002; Fedorov, \r\n2006). However, there is no direct geological evidence, nor support from climate models, that ENSO variability collapsed during the \r\nPliocene. Although not located in the centre-of-action region for ENSO, Pliocene corals show temperature variability over 3-7 year \r\ntimescales (Watanabe et  al., 2011). In addition, a multi-model intercomparison indicates that ENSO existed, albeit with reduced \r\nvariability (Brierley, 2015)"


id: 5f3fbc1d1b3003c7f0177089cd20a676
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 12554
content:
"Vegetation reconstructions for the late Pliocene indicate regionally wetter conditions resulting in an expansion of tropical savannas \r\nand woodlands in Africa and Australia at the expense of deserts (Cross-Chapter Box 2.4, Figure 1b). PlioMIP2 climate models \r\ngenerally simulate higher rates of mean annual precipitation in the tropics and high latitudes, and a decrease in the subtropics, with \r\na multi-model mean global increase of 0.19 [0.13-0.32] mm day-1 relative to control simulations (Cross-Chapter Box 2.4, Figure 1a; \r\nHaywood et al., 2020). Both simulations and limited proxy evidence indicate stronger monsoons in northern Africa, Asia, and northern \r\nAustralia relative to present, but trends are uncertain in other monsoon regions (X. Li et al., 2018; Yang et al., 2018; X. Huang et al., \r\n2019a; R. Zhang et al., 2019). There is thus medium confidence that monsoon systems were stronger during the Pliocene"


id: a3dbe81cf4621ba20bc5efe9239ab3fd
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 23342
content:
" Simulations \r\nof MPWP climate show that global tropical cyclone intensity and duration increased during the MPWP (Yan et al., 2016); however, \r\nthere is low confidence in this result because inter-model variability is high."


id: e9039cb18df282cc649d948eb7c02f4f
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 34179
content:
"Cross-Chapter Box 2.4, Figure 1 | Climate indicators of the mid-Pliocene Warm Period (MPWP; 3.3-3.0 million years ago, Ma) from models \r\nand proxy data. (a) Simulated surface air temperature (left) and precipitation rate anomaly (right) anomaly (relative to 1850-1900) from the Pliocene Model \r\nIntercomparison Project Phase 2 multi-model mean, including CMIP6 (n = 4) and non-CMIP6 (n = 12) models. Symbols represent site-level proxy-based estimates of \r\nsea-surface temperature for KM5c (n = 32), and terrestrial temperature (n = 8) and precipitation rate for the MPWP (n = 8). (b) Distribution of terrestrial biomes was \r\nconsiderably different during the Piacenzian Stage (3.6-2.6 Ma) (upper) compared with present-day (lower)"


id: a26d296da1c65102e909efb90a65e3de
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 23119
content:
" Biome distributions simulated with a model (BIOME4) \r\nin which Pliocene biome classifications are based on 208 locations, with model-predicted biomes filling spatial gaps, and the present day, with the model adjusted \r\nfor CO2 concentration of 324 parts per million (ppm). (c) Ice-sheet extent predicted using modelled climate forcing and showing where multiple models consistently \r\npredict the former presence or absence of ice on Greenland (n = 8 total) and Antarctica (n = 10 total). Further details on data sources and processing are available \r\nin the chapter data table (Table 2.SM.1)."


id: 4522a6bda1936b97d5be838bf4fa2f90
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 8299
content:
"winter NAM and NAO since the late 1990s (Pinto and Raible, 2012; \r\nHanna et  al., 2018). This is particularly evident in December NAO \r\n(Hanna et al., 2015) and NAM (Overland and Wang, 2015) indices, and \r\nis not unusual on multi-decadal time scales (Woollings et al., 2018a). \r\nSince the 1990s, a statistically significant summer NAO decline was \r\nreported, which is, to a lesser extent, also evident in the winter NAO, \r\nlinked to an enhanced blocking activity over Greenland (Hanna et al., \r\n2015, 2016; Wachowicz et al., 2021). However, this was moderated by \r\npersistent positive NAO values since 2015 (Annex IV.2.1). Based on \r\nobservations and reanalysis datasets, multi-decadal variations were \r\nfound for the NAM patterns: the Atlantic centre remained unchanged \r\nthroughout 1920-2010, whereas the Pacific centre was stronger \r\nduring 1920-1959 and 1986-2010 and weaker during 1960-1985 \r\n(Gong et  al., 2018)"


id: dba7aa0fc10dd6bc5c8df980d8ed544a
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 32079
content:
" Multidecadal changes were also observed in \r\nthe position of the centres of action of the NAO (Moore et al., 2013; \r\nZuo et al., 2016). "


id: 3a0234e67bd12a04b95a25a399f1ae7c
created_at: 2023-03-16 14:01:57
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content:
"New multi-millennial-timescale NAO reconstructions are derived \r\nfrom marine and lake sediments, speleothems, tree rings and ice \r\ncores (Ortega et  al., 2015; Faust et  al., 2016; Sjolte et  al., 2018). \r\nNAO variability over the past 8 kyr suggests the presence of \r\na  significant 1.5  kyr periodicity (Darby et  al., 2012). Positive NAO \r\nconditions dominated during the MH, while the prevailing NAO sign \r\nduring the early and late Holocene was negative according to most \r\nreconstructions (Olsen  et  al., 2012; Ait Brahim et  al., 2019; Rothe \r\net  al., 2019; Hernandez et  al., 2020). For the CE, reconstructions \r\ndeveloped since AR5 indicate no dominant NAO phase during \r\n1000-1300 CE (Jones et al., 2014; Baker et al., 2015; Ortega et al., \r\n2015; Lasher and Axford, 2019; Hernandez et al., 2020), with either \r\nnegative (Baker et al., 2015; Faust et al., 2016; Mellado-Cano et al., \r\n2019) or a more variable phase of the NAO (Jones et al., 2014; Ortega \r\net al., 2015; Sjolte et al"


id: f0241f5702d2aef7db2e11521a28d506
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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content:
", 2018; Cook et al., 2019) between 1400 and \r\n1850 CE. Several instrument-based NAO reconstructions extending \r\nback to the 17th and 18th centuries highlight the presence of multi\u0002decadal variations in the NAO phases (Cornes et al., 2013; Cropper \r\net al., 2015), although these studies have limitations considering the \r\nseasonality of the centres of action and the locations of the stations \r\nused. Recent reconstructions of the large scale sea level pressure field \r\nyield more robust NAO analysis, showing a persistently negative NAO \r\nphase from the 1820s to the 1870s, with positive values dominating \r\nduring the beginning of the 20th century followed by a declining \r\ntrend over 1920-1970, with a recovery thereafter to a period of \r\nconsistently high values between 1970 and the early 1990s (Delaygue \r\net al., 2019; Mellado-Cano et al., 2019). Based on the evaluation of \r\nseveral NAO reconstructions for recent centuries, Hernandez et  al"


id: 6714affa09a89041bdd8862d046ed9a6
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 13808
content:
" \r\n(2020) highlighted that the strong positive NAO phases of the 1990s \r\nand early 21st century were not unusual. "


id: a0959c55063b98aa05bd1c9d7d4fafc
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 22802
content:
"Several studies have attempted to reconstruct the evolution of the \r\nSAM during the Holocene using proxies of the position and strength \r\nof the SH zonal winds, although with no clear consensus regarding \r\nthe timing and phase of the SAM (Hernandez et al., 2020). The early \r\nHolocene was dominated by SAM positive phases (Moreno et  al., \r\n2018; Reynhout et  al., 2019), consistent with increasing westerly \r\nwind strength (Lamy et al., 2010), with some reconstructions showing \r\nsignificant centennial and millennial variability but no consistent trend \r\nafter 5 ka (Hernandez et  al., 2020). For the CE, enhanced westerly \r\nwinds occurred over 0-1000 CE, as reflected in increased burning \r\nactivity in Patagonia (Turney et al., 2016a) and tree ring records from \r\nsouthern New Zealand (Turney et al., 2016b) imply a predominantly \r\npositive SAM phase"


id: 6ea78155c9ce138cdb2ee35800c37ced
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 15033
content:
" Pollen records and lake sediments from Tasmania, \r\nsouthern mainland Australia, New Zealand and southern South \r\nAmerica, inferred the period of 1000 to 1400 CE to be characterized \r\nby anomalously dry conditions south of 40degS, implying a positive \r\nSAM (Moreno et al., 2014; Fletcher et al., 2018; Evans et al., 2019; \r\nMatley et al., 2020). Nevertheless, proxy reconstructions of the SAM \r\nbased on temperature-sensitive records from tree rings, ice cores, \r\nlake sediments and corals spanning the mid-to-polar latitudes show \r\nalternating positive and negative phases (Figure 2.35)."


id: 7dfb3137d2ef86682d10559adcd3b9c2
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 17394
content:
"Before the mid-1950s, SAM indices derived from station-based \r\ndatasets, and centennial reanalyses show pronounced interannual \r\nand decadal variability but no significant trends, with low correlation \r\nbetween SAM indices due to the diversity across different datasets \r\nand sensitivity to the definition used for the index calculation \r\n(Barrucand et  al., 2018; Schneider and Fogt, 2018; J. Lee et  al., \r\n2019). Various SAM indices exhibit significant positive trends \r\nsince the 1950s, particularly during austral summer and autumn \r\n(Barrucand et al., 2018; Schneider and Fogt, 2018; J. Lee et al., 2019), \r\nunprecedented for austral summer over the last 150 years (J.M. Jones \r\net al., 2016; Fogt and Marshall, 2020). This indicates a strengthening \r\nof the surface westerly winds around Antarctica, related to both the \r\nposition and intensity of the subpolar jet in the SH (Section 2.3.1.4.3; \r\nIvy et al., 2017; IPCC, 2019)"


id: ab63ac43e4bacf909dbbab61f18e9faf
created_at: 2023-03-16 14:01:58
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vector_id: 24483
content:
"Manucharyan and Fedorov (2014) found that ENSO-like variability \r\nhas been present, at least sporadically, during epochs of millions \r\nof years (including the MPWP; Cross-Chapter Box 2.4), with proxy \r\nrecords indicating that this was the case even when cross-Pacific \r\nSST gradients were much weaker than present. There is substantial \r\ndisagreement between proxy records for ENSO activity during \r\nthe early Holocene (Zhang et  al., 2014; White et  al., 2018), and \r\nfor ENSO activity and mean state at the LGM (Leduc et  al., 2009; \r\nKoutavas and Joanides, 2012; Sadekov et al., 2013; Ford et al., 2015, \r\n2018; Zhu  et  al., 2017; Tierney et  al., 2020). A number of studies \r\n(Cobb  et  al., 2013; H.  McGregor et  al., 2013; Carre et  al., 2014; \r\nEmile-Geay et  al., 2016; Thompson et  al., 2017; Tian et  al., 2017; \r\nWhite et al., 2018; Grothe et al"


id: 7c65d4eddf3af5dd741264741fd2f16a
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 17171
content:
", 2019) have found that ENSO was \r\nsubstantially weaker than at present at various times in the mid\u0002Holocene within the period from 6 to 3 ka, with stronger decreases \r\nin variability revealed by remote proxies than by those close to the \r\ncore region of ENSO activity. However, Karamperidou et al. (2015) \r\nfind that weakening in ENSO-related variability in eastern Pacific \r\nproxies does not necessarily correspond to weakening in central \r\nPacific proxies. Barrett et  al. (2018) concluded that multi-proxy \r\nreconstructions are more efficient at identifying eastern Pacific than \r\ncentral Pacific events. This suggests that a weakening of proxy-based \r\nsignals may indicate an along-equatorial shift in ENSO activity rather \r\nthan a weakening of ENSO during some periods. Following the period \r\nof weak ENSO variability in the mid-Holocene, a number of studies \r\nfind an increase in ENSO activity which, depending upon the study, \r\ncommences between 4"


id: afe80a6b3729f4ef9439a8d09af8f2c5
created_at: 2023-03-16 14:01:58
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vector_id: 25200
content:
"4 and 3 ka (Koutavas and Joanides, 2012; \r\nCobb et al., 2013; Zhang et al., 2014; S. Chen et al., 2016; Emile-Geay \r\net al., 2016; Thompson et al., 2017; Du et al., 2021). "


id: 1a00b8d36cf5892b8dfcebeb0e25893a
created_at: 2023-03-16 14:01:58
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vector_id: 1556
content:
"ERSSTv5, addressed known SST biases in ERSSTv3 in the equatorial \r\nPacific which affected the derived mean state and amplitude of indices \r\nbased on that dataset (Huang et al., 2015). During the instrumental \r\nperiod, there is no robust indication of any significant century-scale \r\ntrend in the east-west SST gradient across the equatorial Pacific \r\nOcean, with periods when gradients have been stronger and weaker \r\nthan the long-term average on decadal timescales, associated \r\nwith a predominance of La Nina or El Nino events respectively. The \r\nfrequency of El Nino and La Nina events is also subject to considerable \r\ndecadal variability (e.g.,  Hu et  al., 2013) but with no indication of \r\na long-term signal in the frequency of events. The ENSO amplitude \r\nsince 1950 has increased relative to the 1910-1950 period, as \r\nconfirmed by independent proxy records (e.g.,  Gergis and Fowler, \r\n2009), the Southern Oscillation Index (SOI) (Braganza et al., 2009) and \r\nSSTs (e.g"


id: 3ace9900c61ba27d471ede8d295fa1c4
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 6620
content:
", Ohba, 2013; Yu and Kim, 2013), although there is a spread \r\nbetween different proxy and instrumental sources as to the magnitude \r\nof that increase (Figure 2.36). The El Nino events of 1982-1983, \r\n1997-1998 and 2015-2016 had the strongest anomalies in the Nino \r\n3.4 SST index since 1950. Their predominance was less evident from \r\nindices based on de-trended data such as the Oceanic Nino Index (ONI) \r\n(which still ranked them as the three strongest events since 1950, but \r\nonly by a small margin), and in the SOI. B. Huang et al. (2019a) also \r\nnote that analyses based upon buoy and Argo data, which are only \r\navailable since the 1990s, are more capable of resolving strong events \r\nthan analyses which do not include such data."


id: 6bde8b6cfaf174a4f1b2c6024f9c6955
created_at: 2023-03-16 14:01:58
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content:
"Prior to the 1950s, SST observations in the tropical Pacific were much \r\nsparser and hence uncertainties in Nino indices are much larger \r\n(B. Huang et al., 2020). SOI data and some newer SST-based studies \r\nshow high ENSO amplitude, comparable to the post-1950 period, in \r\nthe period from the mid-late 19th century to about 1910, but proxy \r\nindicators generally indicate that the late 19th and early 20th century \r\nwere less active than the late 20th century (Figure 2.36). Yu and Kim's \r\n(2013) implementation of the ONI found a number of events with the \r\nONI above 1.5degC between 1888 and 1905, then no such events until \r\n1972, whilst the SOI indicates comparable or stronger events to the \r\nthree strongest post-1950 events in 1896 and 1905. Giese and Ray \r\n(2011) also found a number of such events between 1890 and 1920 \r\nin the SODA ocean reanalysis, corroborated further by B. Huang et al. \r\n(2020) and Vaccaro et  al"


id: 118c6187eb089a0d43e840ef36d3bb3d
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content:
" (2021), who found that the strength of \r\nthe 1877-1878 event was comparable with that of the 1982-1983, \r\n1997-1998 and 2015-2016 events. There have also been a number of \r\nstrong La Nina events (e.g., 1973-1974, 1975-1976 and 2010-2011), \r\nwith few clear analogues in the 1920-1970 period; the proxy-based \r\nanalysis of McGregor et  al. (2010) indicates that the mid-1970s \r\nLa Nina period was also extreme in a multi-centennial context. There \r\nis no indication that the frequency of high-amplitude events since the \r\n1970s reflects a long-term trend which can be separated from multi\u0002decadal variability, given apparent presence of several high-amplitude \r\nevents in the late 19th and early 20th centuries, and the relatively \r\nlarge uncertainty in pre-1950 SST data in the tropical Pacific region. "


id: b720aecc37c2c444873e03da147783ac
created_at: 2023-03-16 14:01:58
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vector_id: 26346
content:
"There is no robust indication of any changes in ENSO teleconnections \r\nover multi-centennial timescales (Hernandez et al., 2020) despite multi\u0002decadal variability. Shi and Wang (2018) found that teleconnections \r\nwith the broader Asian summer monsoon, including the Indian and \r\nthe East Asian monsoon, were generally stable since the 17th century \r\nduring the developing phase of the monsoon, and showed substantial \r\ndecadal variability, but no clear trend, during the decaying phase. \r\nThey also found that the weakening of teleconnections between the \r\nIndian summer monsoon and ENSO in recent decades had numerous \r\nprecedents over the last few centuries. Rasanen et  al. (2016) also \r\nfound substantial decadal variability, but little trend, in the strength \r\nof the relationship between ENSO and monsoon precipitation in \r\nSouth East Asia between 1650 and 2000. Datwyler et  al"


id: 596c77426140ceae1380c537bd3b13d2
created_at: 2023-03-16 14:01:58
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vector_id: 11561
content:
" (2019) \r\nidentified a number of multi-decadal periods with apparently changed \r\nteleconnections at times over the last 400 years. "


id: 33ffe1bd67e86e1c5836a0bf860f9f6d
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 5598
content:
"For the LGM, enhanced equatorial Indian Ocean productivity \r\nin marine sediment records was associated with strengthened \r\nwesterly jets, in line with a shallower central-western Indian Ocean \r\nthermocline and stronger negative IOD events (Punyu et al., 2014). \r\nThe LGM Indian Ocean basin was substantially modified by the \r\nexposure of the tropical shelves (DiNezio et  al., 2018) and this \r\nhas been associated with an Indian Ocean 'El Nino' (Thirumalai \r\net  al., 2019). Wurtzel et  al. (2018) contend that during the LDT, \r\nincluding the Younger Dryas event, Indian Ocean precipitation did \r\nnot mirror the zonal asymmetry observed in Indian Ocean SSTs \r\nin the Holocene based upon a speleothem record from Sumatra, \r\nbut instead reflected shifts in moisture transport pathways and \r\nsources. Using Seychelles corals, representing the western pole \r\nof the IOD, spanning from the MH to present, Zinke et al"


id: a7caecc887344120f6b8e44cc6000dc5
created_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 23913
content:
" (2014) \r\nidentified changes in seasonality, with the lowest seasonal SST \r\nrange in the MH and then again around 2 ka, while the largest \r\nseasonal range occurred around 4.6 ka and then again during \r\nthe near-present (1990-2003). Reconstructions from fossil corals \r\nfor the eastern Indian Ocean point to stronger negative IOD SST \r\nanomalies due to the enhanced upwelling and cooling driven by \r\na stronger monsoon with enhanced anomalous easterly winds in \r\nthe eastern Indian Ocean during the MH (Abram et  al., 2020b). \r\nNiedermeyer et al. (2014) from the analysis of stable isotopes in \r\nterrestrial plant waxes, suggest that the period 6.5 ka to 4.5 ka was \r\ncharacterized by an anomalously positive IOD mean state. During \r\nvarious parts of the Holocene, periods of a mean positive IOD-like \r\nstate were associated with increased IOD variability, including \r\nevents that exceed the magnitude of the strongest events during \r\nthe instrumental period (Abram et al., 2020a)."


id: 845e4bb9cafc1edab14e4df242b5efe7
created_at: 2023-03-16 14:01:58
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content:
"In the instrumental period, teleconnections associated with ENSO \r\nare well known to vary on decadal to multi-decadal timescales \r\n(e.g., He et al., 2013; Lee and Ha, 2015; Ashcroft et al., 2016; Jin et al., \r\n2016; Wang et al., 2019). Yun and Timmermann (2018) found that \r\ndecadal variations in teleconnections between ENSO and the \r\nIndian monsoon did not extend beyond what would be expected \r\nfrom a  stochastic process. Many observed decadal changes in \r\nteleconnections in the instrumental period are consistent with a shift \r\nto more central Pacific El Nino events (Evtushevsky et  al., 2018; \r\nYeh et al., 2018; Yu and Sun, 2018; Zhao and Wang, 2019). Effects \r\nof the PDV (Kwon et  al., 2013; S. Wang et  al., 2014; Dong  et  al., \r\n2018) and the AMV (Kayano et  al., 2019) can also modulate \r\nENSO teleconnections, and affect the frequency of CP versus EP \r\nevents (Ashok et al., 2007)"


id: 8e264dc3678bbaaeb8a048852aae12ec
created_at: 2023-03-16 14:01:58
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content:
" Chiodi and Harrison (2015) found that \r\nteleconnections over the most recent decades are broadly consistent \r\nwith those over the last 100 years. Variability in teleconnections can \r\nalso occur on timescales longer than characteristic PDV timescales \r\n(e.g., Gallant et al., 2013). "


id: 54e82d375efbf17a74a342b97a17923f
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content:
"The existence of the PDV in the centuries prior to the instrumental \r\nperiod is evidenced by a variety of proxy records based on tree rings \r\n(Biondi et al., 2001; D'Arrigo and Ummenhofer, 2015), corals (Felis \r\net al., 2010; Deng et al., 2013; Linsley et al., 2015) and sediments \r\n(Lapointe et al., 2017; O'Mara et al., 2019). There is little coherence \r\nbetween the various paleo-proxy indices prior to the instrumental \r\nrecord, and neither these nor the instrumental records provide \r\nindications of a clearly defined spectral peak (Chen and Wallace, 2015; \r\nM. Newman et al., 2016; Henley, 2017; L. Zhang et al., 2018; Buckley \r\net al., 2019). For instance, spectral analysis from millennia length PDV \r\nreconstructions shows spectral peaks at multi-decadal, centennial \r\nand bi-centennial time scales (Beaufort and Grelaud, 2017), while \r\nonly multi-decadal oscillations can be detected in the shorter (less \r\nthan 400 years into the past) paleoclimate reconstructions"


id: 2be2e74b04870f4870ca6f7cb9b8fdf5
created_at: 2023-03-16 14:01:58
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content:
" A variety \r\nof proxies suggest a shift in the PDV from the early-mid Holocene, \r\nwhich was characterized by a persistently negative phase of the PDO \r\n(i.e., weak Aleutian Low), to the late Holocene, and more variable and \r\nmore positive PDO (i.e., strong Aleutian Low) conditions. This shift at \r\naround 4.5 ka is also evident in the PDO periodicities, changing from \r\nbidecadal and pentadecadal variability in the early Holocene to only \r\npentadecadal periodicities in the late Holocene (Hernandez et  al., \r\n2020). Several proxy records indicate that the strengthening in the \r\nAleutian Low inferred since the late 17th century is unprecedented \r\nover the last millennium (Z. Liu et al., 2017; Osterberg et al., 2017; \r\nWinski et al., 2017), in line with an increase in PDV low-frequency \r\nvariability (Williams et al., 2017; Hernandez et al., 2020)."


id: 6861728c7abd50d0cbc3219805da4c1d
created_at: 2023-03-16 14:01:58
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vector_id: 14008
content:
"During the observational period the AZM and AMM (Figure 2.37) are \r\nrelated to the AMV largely controlling the interhemispheric gradient \r\nof the SST at decadal to multi-decadal timescales (Tokinaga and \r\nXie, 2011; Polo et  al., 2013; Svendsen et  al., 2014a; Li et  al., 2015; \r\nLubbecke et  al., 2018). The AZM interannual variability is enhanced \r\n(Lubbecke et  al., 2018; Foltz et  al., 2019), and is more strongly \r\nrelated to ENSO, during the negative phase of the AMV (Martin-Rey \r\net al., 2014, 2018; Polo et al., 2015; Nnamchi et al., 2020). The AZM \r\ndisplayed a persistent weakened variability over the 1960-2009 period \r\nassociated with declined cold tongue upwelling (Tokinaga and Xie, \r\n2011), which became pronounced since 2000 (Prigent et al., 2020a, \r\nb). Despite these multi-decadal fluctuations, there is limited evidence\r\nfor any sustained change in the AMM (Chang et  al., 2011; Martin\u0002Rey et al., 2018) and AZM (Martin-Rey et al., 2018; Nnamchi et al"


id: 668fa60d1873f87d89184ca85cf01fdd
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content:
", \r\n2020) during the instrumental period. The AZM and AMM interact \r\non interannual timescales (Servain et al., 1999; Foltz and McPhaden, \r\n2010; Pottapinjara et al., 2019) leading in 2009 to extremes of both \r\nmodes in which the negative phase of the AMM (Foltz et al., 2012; \r\nBurmeister et al., 2016) preceded an equatorial cold tongue cold event \r\nthat was unprecedented in the prior 30 years (Foltz and McPhaden, \r\n2010; Burmeister et al., 2016)."


id: 63f0afe20a7ddfac9e1f8c23c7bf9450
created_at: 2023-03-16 14:01:58
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vector_id: 13290
content:
"It's warming rapidly. Over the past 2 million years, Earth's climate has fluctuated between relatively warm \r\ninterglacial periods and cooler glacial periods, when ice sheets grew over vast areas of the northern continents. \r\nIntervals of rapid warming coincided with the collapse of major ice sheets, heralding interglacial periods such as \r\nthe present Holocene Epoch, which began about 12,000 years ago. During the shift from the last glacial period \r\nto the current interglacial, the total temperature increase was about 5degC. That change took about 5000 years, \r\nwith a maximum warming rate of about 1.5degC per thousand years, although the transition was not smooth. In \r\ncontrast, Earth's surface has warmed approximately 1.1degC since 1850-1900. However, even the best reconstruction \r\nof global surface temperature during the last deglacial period is too coarsely resolved for direct comparison with \r\na period as short as the past 150 years"


id: 30e45acc8995d6aa5243dff1b71232cd
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content:
" But for the past 2000 years, we have higher-resolution records that show \r\nthat the rate of global warming during the last 50 years has exceeded the rate of any other 50-year period."


id: bb4caf8fa12922904c8d4b62a16056d5
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vector_id: 27015
content:
"Lisa Bock (Germany), Elizaveta Malinina (Canada/Russian Federation), Gudfinna Adalgeirsdottir \r\n(Iceland), Jonathan L. Bamber (United Kingdom), Chris Brierley (United Kingdom), Lee de Mora \r\n(United Kingdom), John P. Dunne (United States of America), John C. Fyfe (Canada), Peter J. Gleckler \r\n(United States of America), Peter Greve (Austria/Germany), Lukas Gudmundsson (Switzerland/\r\nGermany, Iceland), Karsten Haustein (United Kingdom, Germany/Germany), Ed  Hawkins \r\n(United Kingdom), Benjamin J. Henley (Australia), Marika M. Holland (United States of America), \r\nChris Huntingford (United Kingdom), Colin Jones (United Kingdom), Masa Kageyama (France), \r\nRemi Kazeroni (Germany/France), Yeon-Hee Kim (Republic of Korea), Charles Koven (United States \r\nof America), Gerhard Krinner (France/Germany, France), Eunice Lo (United  Kingdom/China), \r\nDaniel J. Lunt (United Kingdom), Sophie Nowicki (United States of America/France, United States \r\nof America), Adam S"


id: c0669eb9aa0a6f31eee6c00a45765edb
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 27830
content:
" Phillips (United States of America), Valeriu Predoi (United Kingdom), \r\nKrishnan Raghavan (India), Malcolm J. Roberts (United Kingdom), Jon Robson (United Kingdom), \r\nLucas Ruiz (Argentina), Jean-Baptiste Sallee (France), Benjamin D. Santer (United States \r\nof America), Andrew P. Schurer (United Kingdom), Jessica Tierney (United States of America), \r\nBlair  Trewin (Australia), Katja Weigel (Germany), Xuebin Zhang (Canada), Anni Zhao \r\n(United Kingdom/China), Tianjun Zhou (China)"


id: ad573c066e1450de7dbadcbd17d38af3
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 24811
content:
"The likely range of human-induced warming in global-mean \r\nsurface air temperature (GSAT) in 2010-2019 relative to \r\n1850-1900 is 0.8degC-1.3degC, encompassing the observed warming \r\nof 0.9degC-1.2degC, while the change attributable to natural forcings \r\nis only -0.1degC to +0.1degC. The best estimate of human-induced \r\nwarming is 1.07degC. Warming can now be attributed since 1850-1900, \r\ninstead of since 1951 as done in AR5, thanks to a better understanding \r\nof uncertainties and because observed warming is larger. The likely\r\nranges for human-induced GSAT and global mean surface temperature \r\n(GMST) warming are equal (medium confidence). Attributing \r\nobserved warming to specific anthropogenic forcings remains more \r\nuncertain. Over the same period, forcing from greenhouse gases1\r\nlikely increased GSAT by 1.0degC-2.0degC, while other anthropogenic \r\nforcings including aerosols likely decreased GSAT by 0.0degC-0.8degC"


id: 1f7f18926fca246c296bdfa3257dda92
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 2386
content:
" It is \r\nvery likely that human-induced greenhouse gas increases were the \r\nmain driver2 of tropospheric warming since comprehensive satellite \r\nobservations started in 1979, and extremely likely that human-induced \r\nstratospheric ozone depletion was the main driver of cooling in the \r\nlower stratosphere between 1979 and the mid-1990s. {3.3.1}"


id: 449859ce3aa34518a846c3d43b192393
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 8218
content:
"The evidence for human influence on recent climate change \r\nstrengthened from the IPCC Second Assessment Report to the IPCC Fifth \r\nAssessment Report, and is now even stronger in this assessment. The \r\nIPCC Second Assessment Report (SAR, 1995) concluded 'the balance \r\nof evidence suggests that there is a discernible human influence on \r\nglobal climate'. In subsequent assessments (TAR, 2001; AR4, 2007; and \r\nAR5, 2013), the evidence for human influence on the climate system \r\nwas found to have progressively strengthened. The AR5 concluded \r\nthat human influence on the climate system is  clear, evident from \r\nincreasing greenhouse gas concentrations in the atmosphere, positive \r\nradiative forcing, observed warming, and physical understanding of \r\nthe climate system. This chapter updates the assessment of human \r\ninfluence on the climate system for large-scale indicators of climate \r\nchange, synthesizing information from paleo records, observations and \r\nclimate models"


id: 158429957b02ba0615b2d488ea36fe9c
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 901
content:
" It also provides the primary evaluation of large-scale \r\nindicators of climate change in this Report, complemented by fitness\u0002for-purpose evaluation in subsequent chapters."


id: ecb384a3c41f0da006320864084cbdd1
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 34715
content:
"The CMIP6 model ensemble reproduces the observed \r\nhistorical global surface temperature trend and variability \r\nwith biases small enough to support detection and attribution \r\nof human-induced warming (very high confidence). The CMIP6 \r\nhistorical simulations assessed in this report have an ensemble mean \r\nglobal surface temperature change within 0.2degC of the observations \r\nover most of the historical period, and observed warming is within the \r\n5-95% range of the CMIP6 ensemble. However, some CMIP6 models \r\nsimulate a warming that is either above or below the assessed 5-95% \r\nrange of observed warming. CMIP6 models broadly reproduce surface \r\ntemperature variations over the past millennium, including the cooling \r\nthat follows periods of intense volcanism (medium confidence). For \r\nupper air temperature, there is medium confidence that most CMIP5 \r\nand CMIP6 models overestimate observed warming in the upper \r\ntropical troposphere by at least 0"


id: 3643d7f5b34c0deb5eb49a1fb5615c1d
created_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 5932
content:
"1degC per decade over the period \r\n1979 to 2014. The latest updates to satellite-derived estimates of \r\nstratospheric temperature have resulted in decreased differences \r\nbetween simulated and observed changes of global mean temperature \r\nthrough the depth of the stratosphere (medium confidence). {3.3.1}"


id: e22e7cd3a5290724ed7b8501d61a427e
created_at: 2023-03-16 14:01:58
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content:
"For most large-scale indicators of climate change, the \r\nsimulated recent mean climate from the latest generation \r\nCoupled Model Intercomparison Project Phase 6 (CMIP6) \r\nclimate models underpinning this assessment has improved \r\ncompared to the Coupled Model Intercomparison Project \r\nPhase 5 (CMIP5) models assessed in AR5 (high confidence).\r\nSome differences from observations remain, for example in regional \r\nprecipitation patterns. High-resolution models exhibit reduced \r\nbiases in some but not all aspects of surface and ocean climate \r\n(medium confidence), and most Earth system models, which include \r\nbiogeochemical feedbacks, perform as well as their lower-complexity \r\ncounterparts (medium confidence). The multi-model mean captures \r\nmost aspects of observed climate change well (high confidence)"


id: 1854dc6f11c199e75634bea6e3636fc3
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 1321
content:
" The \r\nmulti-model mean captures the proxy-reconstructed global-mean \r\nsurface air temperature (GSAT) change during past high- and low-CO2\r\nclimates (high confidence) and the correct sign of temperature and \r\nprecipitation change in most assessed regions in the mid-Holocene \r\n(medium confidence). The simulation of paleoclimates on continental \r\nscales has improved compared to AR5 (medium confidence), but \r\nmodels often underestimate large temperature and precipitation \r\ndifferences relative to the present day (high confidence). {3.8.2}"


id: 3530967ce1c61c64dcbc2dea2fa85ee8
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 5783
content:
"It is likely that human influence has contributed to observed \r\nlarge-scale precipitation changes since the mid-20th century.\r\nNew attribution studies strengthen previous findings of a detectable \r\nincrease in Northern Hemisphere mid- to high-latitude land \r\nprecipitation (high confidence). Human influence has contributed to \r\nstrengthening the zonal mean precipitation contrast between the wet \r\ntropics and dry subtropics (medium confidence). Yet, anthropogenic \r\naerosols contributed to decreasing global land summer monsoon \r\nprecipitation from the 1950s to 1980s (medium confidence). There \r\nis also medium confidence that human influence has contributed \r\nto high-latitude increases and mid-latitude decreases in Southern \r\nHemisphere summertime precipitation since 1979 associated with \r\nthe trend of the Southern Annular Mode toward its positive phase"


id: 19afa33653895d8c275580b9dca7ca27
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 1512
content:
" \r\nDespite improvements, models still have deficiencies in simulating \r\nprecipitation patterns, particularly over the tropical ocean (high \r\nconfidence). {3.3.2, 3.3.3, 3.5.2}"


id: f5ae9a3a135537cf27bae6a125be064f
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 36169
content:
"This chapter assesses the evidence for human influence on \r\nobserved large-scale indicators of climate change that are described \r\nin Cross-Chapter Box  2.2 and assessed in Chapter  2. It takes \r\nadvantage of the longer period of record now available in many \r\nobservational datasets. The assessment of the human-induced \r\ncontribution to observed climate change requires an estimate of the \r\nexpected response to human influence, as well as an estimate of the \r\nexpected climate evolution due to natural forcings and an estimate of \r\nvariability internal to the climate system (internal climate variability). \r\nFor this we need high quality models, primarily climate and Earth \r\nsystem models. Since AR5, a new set of coordinated model results \r\nfrom the World Climate Research Programme (WCRP) Coupled Model \r\nIntercomparison Project Phase 6 (CMIP6; Eyring et al., 2016a) has \r\nbecome available"


id: eb778cac5bf9bbf5b7508671950b339f
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 34544
content:
" Together with updated observations of large-scale \r\nindicators of climate change (Chapter  2), CMIP simulations are \r\na key resource for assessing human influence on the climate system. \r\nPre-industrial control and historical simulations are of most relevance \r\nfor model evaluation and assessment of internal variability, and these \r\nsimulations are evaluated to assess fitness-for-purpose for attribution, \r\nwhich is the focus of this chapter (see also Section 1.5.4). This chapter \r\nprovides the primary evaluation of large-scale indicators of climate \r\nchange in this Report, and is complemented by other fitness-for\u0002purpose evaluations in subsequent chapters. CMIP6 also includes an \r\nextensive set of idealized and single forcing experiments for attribution "


id: 1109ac16d2d6c2c5d53b1e3c70c35bd9
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 159
content:
"The evidence of human influence on the climate system has \r\nstrengthened progressively over the course of the previous five IPCC \r\nassessments, from the Second Assessment Report that concluded 'the \r\nbalance of evidence suggests a discernible human influence on climate' \r\nthrough to the Fifth Assessment Report (AR5) which concluded that 'it \r\nis extremely likely that human influence caused more than half of the \r\nobserved increase in global mean surface temperature (GMST) from \r\n1951 to 2010' (see also Sections 1.3.4 and 3.3.1.1). The AR5 concluded \r\nthat climate models had been developed and improved since the \r\nFourth Assessment Report (AR4) and were able to reproduce many \r\nfeatures of observed climate. Nonetheless, several systematic biases \r\nwere identified (Flato et al., 2013). This chapter additionally builds on \r\nthe assessment of attribution of global temperatures contained in the \r\nIPCC Special Report on Global Warming of 1.5degC (SR1"


id: 387f8d5c9dc1f84f5994f882d1da2b43
created_at: 2023-03-16 14:01:58
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vector_id: 6268
content:
"In this chapter, we use the Earth System Model Evaluation Tool \r\n(ESMValTool, Eyring et al., 2020; Lauer et al., 2020; Righi et al., 2020) \r\nand the NCAR Climate Variability Diagnostic Package (CVDP, Phillips \r\net al., 2014) that is included in the ESMValTool to produce most of \r\nthe figures. This ensures traceability of the results and provides an \r\nadditional level of quality control. The ESMValTool code to produce \r\nthe figures in this chapter was released as open source software at \r\nthe time of the publication of this Report (see details in the Chapter \r\nData Table, Table SM.3.1). Figures in this chapter are produced either \r\nusing one ensemble member from each model, or using all available \r\nensemble members and weighting each simulation by 1/(NMi), where \r\nN is the number of models and Mi is the ensemble size of the ith \r\nmodel, prior to calculating means and percentiles"


id: b6f22858d2b58b6ef934515f77d9faa2
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 26310
content:
" Both approaches \r\nensure that each model used is given equal weight in the figures, and \r\ndetails on which approach is used are provided in the figure captions."


id: 4327d2ed15ccb67e1e136140a76a55d6
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 7965
content:
"Whereas in previous IPCC assessment reports the comparison of \r\nsimulated and observed climate change was done separately in a \r\nmodel evaluation chapter and a chapter on detection and attribution, \r\nin AR6 these comparisons are integrated together. This has the \r\nadvantage of allowing a single discussion of the full set of explanations \r\nfor any inconsistency in simulated and observed climate change, \r\nincluding missing forcings, errors in the simulated response to forcings, \r\nand observational errors, as well as an assessment of the application \r\nof detection and attribution techniques to model evaluation. Where \r\nsimulated and observed changes are consistent, this can be interpreted \r\nboth as supporting attribution statements, and as giving confidence in \r\nsimulated future change in the variable concerned (see also Box 4.1)"


id: 65d9e0a8cfbc5ba0e39fa2cb01db3baf
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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content:
" \r\nHowever, if a model's simulation of historical climate change has \r\nbeen tuned to agree with observations, or if the models used in an \r\nattribution study have been selected or weighted on the basis of the \r\nrealism of their simulated climate response, this information would \r\nneed to be considered in the assessment and any attribution results \r\ncorrespondingly tempered. An integrated discussion of evaluation and \r\nattribution supports such a robust and transparent assessment."


id: f1ffe24f89b56b16ce12aa8ad61d1188
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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content:
"This chapter starts with a brief description of methods for detection \r\nand attribution of observed changes in Section 3.2, which builds on \r\nthe more general introduction to attribution approaches in the Cross\u0002Working Group Box on Attribution in Chapter 1. In this chapter we \r\nassess the detection of anthropogenic influence on climate on large \r\nspatial scales and long temporal scales, a concept related to, but \r\ndistinct from, that of the emergence of anthropogenically-induced \r\nclimate change from the range of internal variability on local scales \r\nand shorter time scales (Section 1.4.2.2). The following sections \r\naddress the climate system component by component, in  each \r\ncase assessing human influence and evaluating climate  models' \r\nsimulations of the relevant aspects of climate and climate \r\nchange"


id: 82efe714278b007a89ab535e446100ec
created_at: 2023-03-16 14:01:58
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vector_id: 18125
content:
" This chapter assesses the evaluation and attribution of global, \r\nhemispheric, continental and ocean basin-scale indicators of climate \r\nchange in the atmosphere and at the Earth's surface (Section 3.3), \r\ncryosphere (Section 3.4), ocean (Section 3.5), and biosphere (Section \r\n3.6), and the evaluation and attribution of modes of variability \r\n(Section  3.7), the period of slower warming in the early 21st century \r\n(Cross-Chapter Box 3.1) and large-scale changes in extremes (Cross\u0002Chapter Box 3.2). Model evaluation and attribution on sub-continental \r\nscales are not covered here, since these are assessed in the Atlas and \r\nin Chapter 10, and extreme event attribution is not covered since it is \r\nassessed in Chapter 11. Section 3.8 assesses multivariate attribution \r\nand integrative measures of model performance based on multiple \r\nvariables, as well as process representation in different classes of \r\nmodels. The chapter structure is summarized in Figure 3.1."


id: de1ad4b51a5d6882a697d8ba14e5c60b
created_at: 2023-03-16 14:01:58
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vector_id: 32441
content:
"Regression-based methods, also known as fingerprinting methods, \r\nhave been widely used for detection of climate change and attribution \r\nof the change to different external drivers. Initially, these methods were \r\napplied to detect changes in global surface temperature, and were \r\nthen extended to other climate variables at different time and spatial \r\nscales (e.g., Hegerl et al., 1996; Hasselmann, 1997; Allen and Tett, \r\n1999; Gillett et al., 2003b; Zhang et al., 2007; Min et al., 2008a, 2011). \r\nThese approaches are based on multivariate linear regression and \r\nassume that the observed change consists of a linear combination of \r\nexternally forced signals plus internal variability, which generally holds \r\nfor large-scale variables (Hegerl and Zwiers, 2011). The regressors \r\nare the expected space-time response patterns to different climate \r\nforcings (fingerprints), and the residuals represent internal variability"


id: 604fe37d871722d6d7fcd06b4d2f11b6
created_at: 2023-03-16 14:01:58
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vector_id: 12727
content:
" \r\nFingerprints are usually estimated from climate model simulations \r\nfollowing spatial and temporal averaging. A regression coefficient \r\nwhich is significantly greater than zero implies that a detectable \r\nchange is identified in the observations. When the confidence interval \r\nof the regression coefficient includes unity and is inconsistent with \r\nzero, the magnitude of the model simulated fingerprints is assessed \r\nto be consistent with the observations, implying that the observed \r\nchanges can be attributed in part to a particular forcing. Variants \r\nof linear regression have been used to address uncertainty in the \r\nfingerprints due to internal variability (Allen and Stott, 2003) as well \r\nas structural model uncertainty (Huntingford et al., 2006)."


id: 87136467cd8d46fa11b7106f48d216e
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 18782
content:
"procedure requires an estimate of the inverse covariance matrix of \r\nthe internal variability, and some approaches have been proposed \r\nfor more reliable estimation of this (Ribes et al., 2009). A signal can \r\nbe spuriously detected due to too-small noise, and hence simulated \r\ninternal variability needs to be evaluated with care. Model-simulated \r\nvariability is typically checked through comparing modelled variance \r\nfrom unforced simulations with the observed residual variance using \r\na standard residual consistency test (Allen and Tett, 1999), or an \r\nimproved one (Ribes and Terray, 2013). Imbers et al. (2014) tested \r\nthe sensitivity of detection and attribution results to different \r\nrepresentations of internal variability associated with short-memory \r\nand long-memory processes"


id: 8a26389958b6739e62f493b506a53f71
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 19265
content:
" Their results supported the robustness \r\nof previous detection and attribution statements for the global mean \r\ntemperature change but they also recommended the use of a wider \r\nvariety of robustness tests."


id: 4a4b83a07da48724868d26df8cbfd218
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 9094
content:
"additivity assumption and likelihood maximization, which estimates \r\nclimate model uncertainty based on an ensemble of opportunity and \r\ntests whether observations are inconsistent with internal variability \r\nand consistent with the expected response from climate models. The \r\nmethod was further developed by Ribes et al. (2021), who applied \r\nit to narrow the uncertainty range in the estimated human-induced \r\nwarming. Hannart and Naveau (2018), on the other hand, extended \r\nthe application of standard causal theory (Pearl, 2009) to the context \r\nof detection and attribution by converting a time series into an event, \r\nand calculating the probability of causation, an approach which \r\nmaximizes the causal evidence associated with the forcing. On the \r\nother hand, Schurer et al. (2018) employed a Bayesian framework \r\nto explicitly consider climate modelling uncertainty in the optimal \r\nregression method"


id: 570cc45969a4893f136bc3d6739de7d0
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 11177
content:
" Application of these approaches to attribution of \r\nlarge-scale temperature changes supports a dominant anthropogenic \r\ncontribution to the observed global warming."


id: 6a6d5436424dfdd446f8cdbce0c622c8
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 14352
content:
"Some recent studies focused on the improved estimation of the \r\nscaling factor (regression coefficient) and its confidence interval. \r\nHannart et al. (2014) described an inference procedure for scaling \r\nfactors which avoids making the assumption that model error and \r\ninternal variability have the same covariance structure. An integrated \r\napproach to optimal fingerprinting was further suggested in which all \r\nuncertainty sources (i.e., observational error, model error, and internal \r\nvariability) are treated in one statistical model without a preliminary \r\ndimension reduction step (Hannart, 2016). Katzfuss et al. (2017) \r\nintroduced a similar integrated approach based on a  Bayesian \r\nmodel averaging. On the other hand, DelSole et al. (2019) suggested \r\na bootstrap method to better estimate the confidence intervals of \r\nscaling factors even in a weak-signal regime"


id: b5bd459466876286e40e86590f8b68ac
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 26122
content:
" It is notable that some \r\nstudies do not optimize fingerprints, as uncertainty in the covariance \r\nintroduces a further layer of complexity, but results in only a limited \r\nimprovement in detection (Polson and Hegerl, 2017)."


id: 75f120a654dc092ed5a38e0d06ed606
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 16148
content:
"Another fingerprinting approach uses pattern similarity between \r\nobservations and fingerprints, in which the leading empirical \r\northogonal function obtained from the time-evolving multi-model \r\nforced simulation is usually defined as a fingerprint (e.g., Santer \r\net al., 2013; Marvel et al., 2019; Bonfils et al., 2020). Observations and \r\nmodel simulations are then projected onto the fingerprint to measure \r\nthe degree of spatial pattern similarity with the expected physical \r\nresponse to a given forcing. This projection provides the signal time \r\nseries, which is in turn tested against internal variability, as estimated \r\nfrom long control simulations. As a way to extend this pattern-based \r\napproach to a high-dimensional detection variable at daily time \r\nscales, Sippel et al. (2019, 2020) proposed using the relationship \r\npattern with a global climate change metric as a fingerprint"


id: a64da10f33b26b69b97058f416edc22a
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 23699
content:
" To solve \r\nthe high-dimensional regression problem which makes regression \r\ncoefficients not well constrained, they incorporated a statistical \r\nlearning technique based on a regularized linear regression, which \r\noptimizes a global warming signal by giving lower weight to regions \r\nwith large internal variability."


id: 91888e36f9b896f218b592d4cba228f9
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 20437
content:
"LGM simulation by the Community Earth System Model Version 2.1 \r\n(CESM2.1) is an exception related to the high equilibrium climate \r\nsensitivity (ECS) of that model (Section 7.5.6; Kageyama et al., 2021a; \r\nZhu et al., 2021). Figure 3.2a illustrates the wide range of simulated \r\nglobal LGM temperature responses in both ensembles. CMIP6 models \r\ntend to underestimate the cooling over land, but agree better with \r\noceanic reconstructions. For the mid-Holocene, the regional biases \r\nfound in CMIP5 simulations are similar to those in pre-industrial and \r\nhistorical simulations (Harrison et al., 2015; Ackerley et al., 2017), \r\nsuggesting common causes. CMIP5 models underestimate Arctic \r\nwarming in the mid-Holocene (Yoshimori and Suzuki, 2019). CMIP6 \r\nmodels simulate a mid-latitude, subtropical, and tropical cooling \r\ncompared to the pre-industrial period, whereas temperature proxies \r\nindicate a warming (see Section 2.3.1.1.2; Brierley et al., 2020; \r\nKaufman et al"


id: 7fcedf7fe0c26621bd198191e7f50549
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vector_id: 17652
content:
", 2020), although accounting for seasonal effects in the \r\nproxies may reduce the discrepancy (Bova et al., 2021). Over the past \r\nmillennium, reconstructed and simulated temperature anomalies, \r\ninternal variability, and forced response agree well over Northern \r\nHemisphere continents, but those statistics disagree strongly in \r\nthe Southern Hemisphere, where models seem to overestimate \r\nthe response (PAGES 2k-PMIP3 group, 2015). That disagreement is \r\npartly explained by the lower quality of the reconstructions in the \r\nSouthern Hemisphere, but model and/or forcing errors may also \r\ncontribute (Neukom et al., 2018). Figure  3.2b shows that land/sea \r\nwarming contrast behaves coherently in model simulations across \r\nmultiple periods, with a slight non-linearity in land warming due to \r\na smaller contribution of snow cover to temperature response in \r\nwarmer climates. A multivariate assessment of paleoclimate model \r\nsimulations is carried out in Section 3.8.2."


id: f6d3b81677d37cff878305ab50e5ab1c
created_at: 2023-03-16 14:01:58
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content:
"Reconstructions of past temperature from paleoclimate proxies \r\n(Section 2.3.1.1 and Cross-Chapter Box  2.1) have been used to \r\nevaluate modelled past climate temperature change patterns. The \r\nAR5 found that CMIP5 (Taylor et al., 2012) models were able to \r\nreproduce the large-scale patterns of temperature during the Last \r\nGlacial Maximum (LGM) (Flato et al., 2013) and simulated a polar \r\namplification broadly consistent with reconstructions for warm \r\n(Pliocene and Eocene) and cold (LGM) periods (Masson-Delmotte \r\net al., 2013). Since AR5, a better understanding of temperature \r\nproxies and their uncertainties and in some cases the forcing applied \r\nto model simulations has led to better agreement between models \r\nand reconstructions over a wide range of past climates. For the \r\nPliocene and Eocene warm periods, understanding of uncertainties in \r\ntemperature proxies (Hollis et al., 2019; McClymont et al"


id: 10ca56564b87446c30347a92f02cec87
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content:
", 2020) and \r\nthe boundary conditions used in climate simulations (Haywood et al., \r\n2016; Lunt et al., 2017) has improved, and some models now agree \r\nbetter with temperature proxies for these time periods compared \r\nto models assessed in AR5 (Sections 7.4.4.1.2, 7.4.4.2.2 and Cross\u0002Chapter Box 2.4; Zhu et al., 2019; Haywood et al., 2020; Lunt et al., \r\n2021). For the Last Interglacial (LIG), improved temporal resolution of \r\ntemperature proxies (Capron et al., 2017) and better appreciation of \r\nthe importance of freshwater forcing (Stone et al., 2016) have clarified \r\nthe reasons behind apparent model-data inconsistencies. Regional \r\nLIG temperature responses simulated by CMIP6 are within the \r\nuncertainty ranges of reconstructed temperature responses, except \r\nin regions where unresolved changes in regional ocean circulation, \r\nmeltwater, or vegetation changes may cause model mismatches \r\n(Otto-Bliesner et al., 2021)"


id: acd982ea4586f21987f56dfb6420401c
created_at: 2023-03-16 14:01:58
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content:
" For the LGM, the CMIP5 and CMIP6 \r\nensembles compare similarly to new sea surface temperature (SST) \r\nand surface air temperature (SAT) proxy reconstructions (Figure 3.2a; \r\nCleator et al., 2020; Tierney et al., 2020b). The very cold CMIP6 "


id: a6404d576eae6cf328a02f0fe974d966
created_at: 2023-03-16 14:01:58
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content:
"For the historical period, AR5 assessed with very high confidence\r\nthat CMIP5 models reproduced observed large-scale mean surface \r\ntemperature patterns, although errors of several degrees appear in \r\nelevated regions, like the Himalayas and Antarctica, near the edge \r\nof the sea ice in the North Atlantic, and in upwelling regions. This \r\nassessment is updated here for the CMIP6 simulations. Figure  3.3 \r\nshows the annual mean surface air temperature at 2 m for the \r\nCMIP5 and CMIP6 multi-model means, both compared to the fifth \r\ngeneration European Centre for Medium-Range Weather Forecasts \r\n(ECMWF) atmospheric reanalysis (ERA5; Section 1.5.2) for the \r\nperiod 1995-2014. The distribution of biases is similar in CMIP5 and \r\nCMIP6 models, as already noted by several studies (Crueger et al., \r\n2018; Gainusa-Bogdan et al., 2018; Kuhlbrodt et al., 2018; Lauer \r\net al., 2018). Arctic temperature biases seem more widespread in \r\nboth ensembles than assessed at the time of AR5"


id: 7035bb4f4e369e1f123868c503d34a1c
created_at: 2023-03-16 14:01:58
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vector_id: 15269
content:
" The fundamental \r\ncauses of temperature biases remain elusive, with errors in clouds \r\n(Lauer et al., 2018), ocean circulation (Kuhlbrodt et al., 2018), winds \r\n(Lauer et al., 2018), and surface energy budget (Hourdin et al., 2015; \r\nSeferian et al., 2016; Gainusa-Bogdan et al., 2018) being frequently \r\ncited candidates. Increasing horizontal resolution shows promise for \r\ndecreasing long-standing biases in surface temperature over large \r\nregions (Bock et al., 2020). Panels e and f of Figure 3.3 show that \r\nbiases in the mean High-Resolution Model Intercomparison Project \r\n(HighResMIP, Haarsma et al., 2016) models (see also Table AII.6) are \r\nsmaller than those in the mean of the corresponding lower-resolution \r\nversions of the same models simulating the same period (see also \r\nSection 3.8.2.2). However, the bias reduction is modest (Palmer and "


id: 87481d066e3584f3893f8279799eae9c
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 18820
content:
"7.3.3, and Figure 6.8; Andrews et al., 2020; Dittus et al., 2020; Flynn \r\nand Mauritsen, 2020). Papalexiou et al. (2020), Tokarska et al. (2020) \r\nand Stolpe et al. (2021) all report that CMIP6 models on average \r\noverestimate warming from the 1970s or 1980s to the 2010s, \r\nalthough quantitative conclusions depend on which observational \r\ndataset is compared against (see also Table 2.4). However, Figure 3.4, \r\nwhich includes a larger number of models than available to those \r\nstudies, indicates that the CMIP6 multi-model mean tracks observed \r\nwarming better than the CMIP5 multi-model mean after the year \r\n2000. The CMIP6 multi-model mean GSAT warming between 1850-\r\n1900 and 2010-2019 and associated 5-95% range is 1.09 [0.66 \r\nto 1.64]  degC. Cross-Chapter Box  2.3 assessed GSAT warming over \r\nthe same period at 1.06 [0.88 to 1.21] degC. So some CMIP6 models \r\nsimulate a warming that is smaller than the assessed observed range, \r\nand other CMIP6 models simulate a warming that is larger"


id: 4ff20754fe6c129ce6b55075b296fd52
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 10017
content:
" That \r\noverestimated warming may be an early symptom of overestimated \r\nECS in some CMIP6 models (Section 7.5.6; Meehl et al., 2020; Schlund \r\net al., 2020), and has implications for projections of GSAT changes \r\n(Chapter 4; Liang et al., 2020; Nijsse et al., 2020; Tokarska et al., 2020; \r\nRibes et al., 2021). In some models, a large ECS and a strong aerosol \r\nforcing lead to too large a mid-20th century cooling followed by \r\noverestimated warming rates in the late 20th century when aerosol \r\nemissions decrease (Golaz et al., 2019; Flynn and Mauritsen, 2020). \r\nTemperature biases are driven by both model physics and prescribed \r\nforcing, which is a challenge for model development."


id: c500ec08b67df143362af5d369a6a060
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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content:
"Stevens, 2019). In addition, the biases of the limited number of models \r\nparticipating in HighResMIP are not entirely representative of overall \r\nCMIP6 biases, especially in the Southern Ocean, as  indicated by \r\ncomparing panels b and f of Figure 3.3.\r\nThe AR5 assessed with very high confidence that models reproduce \r\nthe general history of the increase in global-scale annual mean \r\nsurface temperature since the year 1850, although AR5 also reported \r\nthat an observed reduction in the rate of warming over the period \r\n1998-2012 was not reproduced by the models (Cross-Chapter \r\nBox 3.1; Flato et al., 2013). Figure 3.2c and Figure 3.4 show time series \r\nof anomalies in annually and globally averaged surface temperature \r\nsimulated by CMIP5 and CMIP6 models for the past millennium \r\nand the period 1850 to 2020, respectively, with the baseline set \r\nto 1850-1900 (see Section 1.4.1). As also indicated by Figure 3"


id: 77e76cc1b05e495e08997e464ae97be4
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vector_id: 16424
content:
"4, \r\nthe spread in simulated absolute temperatures is large (Palmer and \r\nStevens, 2019). However, the discussion is based on temperature \r\nanomaly time series instead of absolute temperatures because our \r\nfocus is on evaluation of the simulation of climate change in these \r\nmodels, and also because anomalies are more uniformly distributed \r\nand are more easily deseasonalized to isolate long-term trends (see \r\nSection 1.4.1). CMIP6 models broadly reproduce surface temperature \r\nvariations over the past millennium, including the cooling that follows \r\nperiods of intense volcanism (medium confidence) (Figure  3.2c). \r\nSimulated GMST anomalies are well within the uncertainty range \r\nof temperature reconstructions (medium confidence) since about \r\nthe year 1300, except for some short periods immediately following \r\nlarge volcanic eruptions, for which simulations driven by different \r\nforcing datasets disagree (Figure 3.2c)"


id: a8bc98fb743a9f77a68f671863e33372
created_at: 2023-03-16 14:01:58
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content:
" Before the year 1300, larger \r\ndisagreements between models and temperature reconstructions \r\nare expected because forcing and temperature reconstructions are \r\nincreasingly uncertain further back in time, but specific causes have \r\nnot been identified conclusively (Ljungqvist et al., 2019; PAGES 2k \r\nConsortium, 2019) (medium confidence). For the historical period, \r\nresults for CMIP6 shown in Figure 3.4 suggest that the qualitative \r\nhistory of surface temperature increase is well reproduced, including \r\nthe increase in warming rates beginning in the 1960s and the \r\ntemporary cooling that follows large volcanic eruptions."


id: e4835f748888848c14ea82f4f3745537
created_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 33431
content:
"Chylek et al. (2020) argue that CMIP5 models overestimate the \r\ntemperature response to volcanic eruptions. Lehner et al. (2016), \r\nRypdal (2018) and Stolpe et al. (2021) point instead to missed \r\ncompensating effects on surface temperature change associated \r\nwith internal variability in the El Nino-Southern Oscillation (ENSO) \r\nor the Atlantic Multi-decadal Oscillation (AMO). An alternative view \r\nsees those ENSO and AMO responses as expressions of changes in \r\nclimate feedbacks driven by the geographical pattern of SST changes \r\n(Andrews et al., 2018). At least one model is able to reproduce such \r\npattern effects (Gregory and Andrews, 2016). Errors in the volcanic \r\nforcing prescribed in simulations, including for CMIP6 (Rieger et al., \r\n2020), also introduce differences with the observed temperature \r\nresponse, independently of the quality of the model physics"


id: f9ee192c4f4ed503d272894055ed8be3
created_at: 2023-03-16 14:01:58
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content:
" In \r\naddition, comparisons of the modelled temperature  response \r\nto large eruptions over the past millennium to temperature \r\nreconstructions based on tree rings show a much better agreement \r\n(Lucke et al., 2019; F. Zhu et al., 2020) than comparisons to the annual, \r\nmulti-temperature proxy reconstructions shown in Figure 3.2c. These \r\nconsiderations, and Figures 3.2c and 3.4, suggest that CMIP6 models \r\ndo not systematically overestimate the cooling that follows large \r\nvolcanic eruptions (see also Cross-Chapter Box 4.1)."


id: 518b70990c43d69d3d58f49a3df9897c
created_at: 2023-03-16 14:01:58
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vector_id: 10282
content:
"Although virtually all CMIP6 modelling groups report improvements \r\nin their model's ability to simulate current climate compared to the \r\nCMIP5 version (Gettelman et al., 2019; Golaz et al., 2019; Mauritsen \r\net al., 2019; Swart et al., 2019; Voldoire et al., 2019b; T. Wu et al., \r\n2019b; Bock et al., 2020; Boucher et al., 2020; Dunne et al., 2020), it \r\ndoes not necessarily follow that the simulation of temperature trends \r\nis also improved (Bock et al., 2020; Fasullo et al., 2020). The CMIP6 \r\nmulti-model ensemble encompasses observed warming and the \r\nmulti-model mean tracks those observations within 0.2degC over most \r\nof the historical period. Figure 3.4 confirms the findings of Papalexiou \r\net al. (2020), who highlighted based on 29 CMIP6 models that most \r\nmodels replicate the period of slow warming  between 1942 and \r\n1975 and the late twentieth century warming (1975-2014)"


id: 602ea31823b5738bffd12fef3106641d
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vector_id: 12705
content:
" The \r\nCMIP6 multi-model mean is cooler over the period 1980-2000 than \r\nboth observations and CMIP5 (Figure  3.4; Bock et al., 2020; Flynn \r\nand Mauritsen, 2020; Gillett et al., 2021). Biases of several tenths \r\nof a degree in some CMIP6 models over that period may be due \r\nto an overestimate in aerosol radiative forcing (Sections 6.3.5 and "


id: bef4255a2cf60d2132e378bb51ae872b
created_at: 2023-03-16 14:01:58
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vector_id: 27598
content:
"Figure 3.3 | Annual mean near-surface (2 m) air temperature (degC) for the period 1995-2014. (a) Multi-model (ensemble) mean constructed with one realization of \r\nthe CMIP6 historical experiment from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the climatology of the fifth \r\ngeneration European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5). (c) Multi-model mean of the root mean square \r\nerror calculated over all months separately and averaged, with respect to the climatology from ERA5. (d) Multi-model mean bias defined as the difference between the CMIP6 \r\nmulti-model mean and the climatology from ERA5. The difference between the multi-model mean of (e) high-resolution and (f) low-resolution simulations of four HighResMIP \r\nmodels and the climatology from ERA5 is also shown"


id: bbb9251fb55fce8fa6dfcfb97d78d84a
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content:
" Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where >=66% of \r\nmodels show change greater than the variability threshold and >=80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, \r\nwhere <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where >=66% of models show change greater \r\nthan the variability threshold and <80% of all models agree on sign of change. For more information on the advanced approach, please refer to Cross-Chapter Box Atlas.1. \r\nDots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is not lower in at least three out of four models than in the corresponding \r\nlow-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."


id: 8c52015eed0ef685d68c8822af32755b
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content:
"temperature warming over the early 21st century (see Cross-Chapter \r\nBox 3.1). Evidence coming mostly from paleo studies is mixed on \r\nwhether CMIP5 models underestimate decadal and multi-decadal \r\nvariability in global mean temperature. Schurer et al. (2013) found \r\ngood agreement between internal variability derived from paleo \r\nreconstructions, estimated as the fraction of variance that is not \r\nexplained by forced responses, and modelled variability, although \r\nthe subset of CMIP5 models they used may have been associated \r\nwith larger variability than the full CMIP5 ensemble. PAGES 2k \r\nConsortium (2019) found that the largest 51-year trends in both \r\nreconstructions of global mean temperature and fully forced climate \r\nsimulations over the period 850 to 1850 were almost identical. Zhu \r\net al. (2019) showed agreement in the modelled and reconstructed \r\ntemporal spectrum of global surface temperatures on annual to \r\nmulti-millennial time scales"


id: 80cc43493e8aac521bd2c955ef316a4b
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content:
" However, they suggest that decadal- to \r\ncentennial variability is partly forced by slow orbital changes that \r\npredate the last millennium. This is consistent with Gebbie and \r\nHuybers (2019), who showed that the deep ocean has been out \r\nof equilibrium over that period. Laepple and Huybers (2014) found \r\ngood agreement between modelled and proxy-derived decadal \r\nocean temperature variability, but underestimates of variance by \r\nmodels by at least a factor of ten at centennial time scales because \r\nmodels underestimate the difference between the warm and cold \r\nperiods of the last millennium. Parsons et al. (2020) found that some \r\nCMIP6 models exhibit much higher multi-decadal variability in GSAT \r\nthan CMIP5 models, with indications that variability in these models "


id: 903f9bf2e80936dded078c616fe324e3
created_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 20230
content:
"in the conclusions from detection and attribution. The AR5 found \r\nthat CMIP5 models simulate realistic variability in global-mean \r\nsurface temperature on decadal time scales, with variability on \r\nmulti-decadal time scales being more difficult to evaluate because \r\nof the short observational record (Flato et al., 2013). Since AR5, new \r\nwork has characterized the contributions of variability in different \r\nocean areas to SST variability, with tropical modes of variability like \r\nENSO dominant on time scales of five to ten years, while longer time \r\nscales see the variance maxima move poleward to the North Atlantic, \r\nNorth Pacific, and Southern oceans (Monselesan et al., 2015). There \r\nmay, however, be sizeable, two-way interdependencies between \r\nENSO and sea surface temperature variability in different basins \r\n(Kumar et al., 2014; Cai et al., 2019), and ENSO's influence on global \r\nsurface temperature variability may not be confined only to decadal \r\ntime scales (Triacca et al"


id: 50635695fa336f9f24d37c58f54c47f6
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vector_id: 10094
content:
", 2014). Studies based on large ensembles \r\nof 20th and 21st century climate change simulations confirm that \r\ninternal variability has a substantial influence on global warming \r\ntrends over periods shorter than 30-40 years (Kay et al., 2015; Dai \r\nand Bloecker, 2019). Although the equatorial Pacific seems to be the \r\nmain source of internal variability on decadal time scales, Brown \r\net al. (2016a) linked diversity in modelled oceanic convection, sea \r\nice, and energy budget in high-latitude regions to overall diversity in \r\nmodelled internal variability."


id: 6f4444c89e6810ed72a24994ba25b6e
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index: climate_gpt_only_giec
vector_id: 15135
content:
"Looking at periods preceding the instrumental record, AR5 assessed \r\nwith high confidence that the 20th century annual mean surface \r\ntemperature warming reversed a 5000-year cooling trend in Northern \r\nHemisphere mid- to high latitudes caused by orbital forcing, and \r\nattributed the reversal to anthropogenic forcing with high confidence \r\n(see also Section 2.3.1.1). Since AR5, the combined response to solar, \r\nvolcanic and greenhouse gas forcing was detected in all Northern \r\nHemisphere continents (PAGES 2k-PMIP3 group, 2015) over the \r\nperiod 864 to 1840. In contrast, the effect of those forcings was not \r\ndetectable in the Southern Hemisphere (Neukom et al., 2018). Global \r\nand Northern Hemisphere temperature changes from reconstructions \r\nover this period have been attributed mostly to volcanic forcing \r\n(Schurer et al., 2014; McGregor et al., 2015; Otto-Bliesner et al., 2016; \r\nPAGES 2k Consortium, 2019; Buntgen et al"


id: 7abf74114297e00f53894239f4c5834e
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content:
", 2020), with a smaller \r\nrole for changes in greenhouse gas forcing, and solar forcing playing \r\na minor role (Schurer et al., 2014; PAGES 2k Consortium, 2019)."


id: 38932d2f061b964e2efc56f4395fd313
created_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
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content:
"The previous paragraph took an ensemble-mean view of model \r\nperformance, but individual models disagree on unforced variability. \r\nFigure 3.6 illustrates the large differences in GSAT variability in unforced \r\nCMIP6 pre-industrial control simulations, following the method of \r\nParsons et al. (2020). Surface temperatures in pre-industrial conditions \r\nare especially variable in the ten models highlighted in Figure 3.6a, \r\nand some models substantially exceed the variability seen in CMIP5 \r\nmodels (Parsons et al., 2020). Figure 3.6b shows that the distribution \r\nof warming trends simulated by CMIP6 models in historical simulations \r\nis clearly distinct from that simulated in unforced pre-industrial control \r\nsimulations. Still, the unforced variability of the five most variable \r\nmodels approaches half that observed over the historical period \r\nunder anthropogenically forced conditions (Figure 3.6c; Parsons et al., \r\n2020; Ribes et al., 2021)"


id: 79360973a79b726c75addb0c0afc5114
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content:
" For  the Centre National de la Recherche \r\nMeteorologique (CNRM) models, which are among the most variable, \r\nthe large, low-frequency variability is attributed to strong simulated \r\nAtlantic Multi-decadal Variability (Seferian et al., 2019; Voldoire et al., \r\n2019b), which is difficult to rule out because of the short observational \r\nrecord (Section 3.7.7; Cassou et al., 2018). But, importantly, patterns \r\nof temperature variability simulated by even the most variable models \r\ndiffer from the pattern of forced temperature change (Parsons et al., \r\n2020). Taken together, this discussion and Figures  3.2, 3.5 and 3.6 \r\nindicate that the statistics of internal variability in models compare \r\nwell in most cases to observational estimates and temperature proxy \r\nreconstructions, though some CMIP6 models appear to have higher \r\nmulti-decadal variability than CMIP5 models or proxy reconstructions"


id: 1eac2c071ce79ba7af1b80f9959626aa
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content:
" \r\nWhen used in attribution studies, models with overestimated \r\nvariability would increase estimated uncertainties and make results \r\nstatistically conservative."


id: 5856f46035e7ed1b2135388df45a132a
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 11406
content:
"Focusing now on warming over the historical period, AR5 assessed \r\nthat it was extremely likely that human influence was the dominant \r\ncause of the observed warming since the mid-20th century, and \r\nthat it was virtually certain that warming over the same period \r\ncould not be explained by internal variability alone. Since AR5 many \r\nnew attribution studies of changes in global surface temperature \r\nhave focused on methodological advances (see also Section 3.2). \r\nThose advances include better accounting for observational and \r\nmodel uncertainties, and internal variability (Ribes and Terray, 2013; \r\nHannart, 2016; Ribes et al., 2017; Schurer et al., 2018); formulating \r\nthe attribution problem in a counterfactual framework (Hannart and \r\nNaveau, 2018); and reducing the dependence of the attribution on \r\nuncertainties in climate sensitivity and forcing (Otto et al., 2015; \r\nHaustein et al., 2017, 2019)"


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vector_id: 21490
content:
" Studies now account for uncertainties \r\nin the statistics of internal variability, either explicitly (Hannart, 2016; \r\nHannart and Naveau, 2018; Ribes et al., 2021) or implicitly (Ribes and \r\nTerray, 2013; Schurer et al., 2018; Gillett et al., 2021), thus addressing \r\nconcerns about over-confident attribution conclusions. Accounting for \r\nobservational uncertainty increases the range of warming attributable \r\nto greenhouse gases by only 10 to 30% (Jones and Kennedy, 2017; \r\nSchurer et al., 2018). While some attribution studies estimate \r\nattributable changes in globally-complete GSAT (Schurer et al., 2018; \r\nGillett et al., 2021; Ribes et al., 2021), others attribute changes in \r\nobservational GMST, but this makes little difference to attribution \r\nconclusions (Schurer et al., 2018). Moreover, based on a synthesis of \r\nobservational and modelling evidence, Cross-Chapter Box 2"


id: 7cfbf3482a5508cea0cc31459cd6084c
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 17257
content:
"with anthropogenic aerosols cooling temperatures throughout the \r\n20th century, especially between 1950 and 1980 when the estimated\r\nrange of aerosol cooling was about 0.1degC to 0.5degC. Jones et al. (2016) \r\nattributed a warming of 0.87 to 1.22degC per century over the period \r\n1906 to 2005 to greenhouse gases, partially offset by a cooling of \r\n-0.54degC to -0.22degC per century attributed to aerosols. But they also \r\nfound that detection of the greenhouse gas or the aerosol signal \r\noften fails, because of uncertainties in modelled patterns of change \r\nand internal variability. That point is illustrated by Figure 3.7, which \r\nshows two- and three-way fingerprinting regression coefficients for 13 \r\nCMIP6 models and the corresponding attributable warming ranges, \r\nderived using HadCRUT4 (Gillett et al., 2021). Regression coefficients \r\nwith an uncertainty range that includes zero mean that detection has \r\nfailed"


id: e6e851808b935a8f1a8074cb3d8a9053
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 33816
content:
" Models with regression coefficients significantly less than one \r\nsignificantly overpredict the temperature response to the corresponding \r\nforcing. Conversely, models with regression coefficients significantly \r\ngreater than one underpredict the response to these forcings. While \r\nestimates of warming attributable to anthropogenic influence derived \r\nusing individual models are generally consistent, estimates of warming "


id: 7965dfba5c9d4c214f6a5d128e2e3156
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 16670
content:
"this is the earliest base period used in attribution literature, and \r\nthis is a reference period used in IPCC SR1.5 and earlier reports. \r\nIt should, however, be noted that Cross-Chapter Box  1.2 assesses \r\nwith medium confidence that there was an anthropogenic warming \r\nwith a likely range of 0.0degC-0.2degC between 1750 and 1850-1900. \r\nFigure  3.8 also shows the GSAT changes directly simulated in \r\nresponse to these forcings in thirteen CMIP6 models. In spite of their \r\ndifferent methodologies and input datasets, the three attribution \r\napproaches yield very similar results, with the anthropogenic \r\nattributable warming range encompassing observed warming, and \r\nthe natural attributable warming being close to zero"


id: 7ffa8a31525940ddf471d3100408a8e9
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 17680
content:
" The warming \r\ndriven by greenhouse gas increases is offset in part by cooling due \r\nto other anthropogenic forcing agents, mostly aerosols, although \r\nuncertainties in these contributions are larger than the uncertainty in \r\nthe net anthropogenic warming, as discussed above. Estimates based \r\non physical understanding of forcing and ECS made by Chapter 7 are \r\nclose to estimates from attribution studies, despite being the products "


id: 21e4a15c853964d419e7a5984b5b6d18
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 2775
content:
"correct sign of the inter-hemispheric contrast when forced with all \r\nforcings but underestimate its magnitude. Figure 3.9 shows global \r\nsurface temperature change in CMIP6 all-forcing and natural-only \r\nsimulations globally, averaged over continents, and separately over \r\nland and ocean surfaces. All-forcing simulations encompass observed \r\ntemperature changes for all regions, while natural-only simulations \r\nfail to do so in recent decades except in Antarctica, based on the \r\nannual means shown. As stated above, warming results from \r\na partial offset of greenhouse gas warming by aerosol cooling. That \r\noffset is stronger over land than ocean. Regionally, models show \r\na large range of possible temperature responses to greenhouse gas \r\nand aerosol forcing, which complicates single-forcing attribution. \r\nA more detailed discussion of regional attribution can be found in \r\nSection 10.4"


id: 6237a0a89405db695808df57878ce11a
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 13007
content:
" Over global land surfaces, Chan and Wu (2015) used \r\nCMIP5 simulations to attribute a warming trend of 0.3 (2.5%-97.5% \r\nconfidence interval: 0.2-0.36) degC per decade to anthropogenic \r\nforcing, with natural forcing only contributing 0.05 (0.02-0.06) degC \r\nper decade. Accounting for unsampled sources of uncertainty and the \r\navailability of only a single study, their result suggests that it is very \r\nlikely that human influence is the main driver of warming over land."


id: f255ee226a9e1ab947d7a6c4bb9f66d1
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 35654
content:
"The IPCC SR1.5 best estimate and likely range of anthropogenic \r\nattributable GMST warming was 1.0 +- 0.2degC in 2017 with respect \r\nto the period 1850-1900. Here, the best estimate is expressed \r\nin terms of GSAT and is calculated as the average of the three \r\nestimates shown in Figure 3.9, yielding a value of 1.07degC. Ranges \r\nfor attributable GSAT warming are derived by finding the smallest \r\nranges with a precision of 0.1degC which span all of the 5-95% ranges \r\nfrom the attribution studies shown in Figure 3.9"


id: e5e3a2b537b57b62e59116962c00444c
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 33666
content:
" These ranges are \r\nthen assessed as likely rather than very likely because the studies \r\nmay underestimate the importance of the structural limitations of \r\nclimate models, which probably do not represent all possible sources \r\nof internal variability; use too simple climate models, which may \r\nunderestimate the role of internal variability; or underestimate model \r\nuncertainty, especially when using model ensembles of limited size \r\nand inter-dependent models, for example through common errors in \r\nforcings across models, as discussed above. This leads to a likely range \r\nfor anthropogenic attributable warming in 2010-2019 relative to \r\n1850-1900 of 0.8 to 1.3degC in terms of GSAT. This range encompasses \r\nthe best estimate and very likely range of observed GSAT warming of \r\n1.06 [0.88 to 1.21] degC over the same period (Cross-Chapter Box 2.3)"


id: ea24a4afc72463f0d9aad37425c92f70
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 34340
content:
" \r\nThere is medium confidence that the best estimate and likely ranges \r\nof attributable warming expressed in terms of GMST are equal to \r\nthose for GSAT (Cross-Chapter Box 2.3). Repeating the process for \r\nother time periods leads to the best estimates and likely ranges listed \r\nin Table 3.1. GSAT change attributable to natural forcings is -0.1 to \r\n+0.1degC. The likely range of GSAT warming attributable to greenhouse \r\ngases is assessed in the same way to be 1.0 to 2.0degC while the GSAT \r\nchange attributable to aerosols, ozone and land-use change is -0.8 \r\nto 0.0degC. Progress in attribution techniques allows the important \r\nadvance of attributing observed surface temperature warming since \r\n1850-1900, instead of since 1951 as was done in AR5."


id: d5eec2b3ccf42fc4e1f48eb8bcba415e
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 31174
content:
"In summary, since the publication of AR5, new literature has \r\nemerged that better accounts for methodological and climate model \r\nuncertainties in attribution studies (Ribes et al., 2017; Hannart and \r\nNaveau, 2018) and that concludes that anthropogenic warming \r\nis approximately equal to observed warming over the 1951-2010 \r\nperiod. The IPCC SR1.5 reached the same conclusion for 2017 relative \r\nto 1850-1900 based on anthropogenic warming and associated \r\nuncertainties calculated using the method of Haustein et al. (2017). \r\nMoreover, the improved understanding of the causes of the apparent \r\nslowdown in warming over the beginning of the 21st century and \r\nthe difference in simulated and observed warming trends over this \r\nperiod (Cross-Chapter Box 3.1) further improve our confidence in the \r\nassessment of the dominant anthropogenic contribution to observed \r\nwarming"


id: 1655e4fdbdda1138bd3eb7c2af9f15e1
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 1039
content:
" In deriving our assessments, these considerations are \r\nbalanced against new literature that raises questions about the ability \r\nof some models to simulate variability in surface temperatures over \r\na  range of time scales (Laepple and Huybers, 2014; Parsons et al., \r\n2017; Friedman et al., 2020), and the finding that some CMIP6 models \r\nexhibit substantially higher multi-decadal internal variability than \r\nthat seen in CMIP5, which remains to be fully understood (Parsons \r\net al., 2020; Ribes et al., 2021). Further, uncertainties in simulated \r\naerosol-cloud interactions are still large (Section 7.3.3.2.2), resulting \r\nin very diverse spatial responses of different climate models to "


id: d59f8176a0ee80bd828249fcc8a34fd5
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 31128
content:
"The IPCC AR5 assessed the likely range of the contribution of internal \r\nvariability to GMST warming to be -0.1 to +0.1degC over the period \r\n1951-2010. Since then, several studies have downplayed the \r\ncontribution of internal modes of variability to global temperature \r\nvariability, often by arguing for a forced component to those internal \r\nmodes (Mann et al., 2014; Folland et al., 2018; Haustein et al., \r\n2019; Liguori et al., 2020). Haustein et al. (2017) found a 5-95% \r\nconfidence interval of -0.09degC to +0.12degC for the contribution of \r\ninternal variability to warming between 1850-1879 and 2017. \r\nRibes et al. (2021) imply a contribution of internal variability of \r\n-0.02degC +- 0.16degC to warming between 2010-2019 and 1850-1900, \r\nassuming independence between errors in the observations and in \r\nthe estimate of the forced response"


id: 46bcb58661d4b6572a0c978b72024dcd
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 8516
content:
" Based on these studies, but \r\nallowing for unsampled sources of error, we assess the likely range \r\nof the contribution of internal variability to GSAT warming between \r\n2010-2019 and 1850-1900 to be -0.2degC to +0.2degC."


id: c52e1afab0567089ff84849f4167906d
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 28602
content:
"Several studies since AR5 have continued to demonstrate an \r\ninconsistency between simulated and observed temperature trends \r\nin the tropical troposphere, with models simulating more warming \r\nthan observations (Mitchell et al., 2013, 2020; Santer et al., 2017a, b; \r\nMcKitrick and Christy, 2018; Po-Chedley et al., 2021). Santer et al. \r\n(2017b) used updated and improved satellite retrievals to investigate \r\nmodel performance in simulating the tropical mid- to upper\u0002troposphere trends, and removed the influence of stratospheric cooling \r\nby regression. These factors were found to reduce the size of the \r\ndiscrepancy in mid- to upper-tropospheric temperature trends between \r\nmodels and observations over the satellite era, but a discrepancy \r\nremained. Santer et al"


id: a980b3fe44b9d3bee844040da8909774
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 24201
content:
" (2017a) found that during the late 20th century, \r\nthe discrepancies between simulated and satellite-derived mid- to \r\nupper-tropospheric temperature trends were consistent with internal \r\nvariability, while during most of the early 21st century, simulated \r\ntropospheric warming was significantly larger than observed, which \r\nthey related to systematic deficiencies in some of the external forcings \r\nused after year 2000 in the CMIP5 models. However, in CMIP6, \r\ndifferences between simulated and observed upper-tropospheric \r\ntemperature trends persist despite updated forcing estimates (Mitchell \r\net al., 2020). Figure 3.10 shows that CMIP6 models forced by combined \r\nanthropogenic and natural forcings overestimate temperature trends \r\ncompared to radiosonde data (Haimberger et al., 2012) throughout \r\nthe tropical troposphere (Mitchell et al., 2020)"


id: 3feca7b69862ce70678b55e75fb8daec
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 7444
content:
" Over the 1979-2014 \r\nperiod, models are more consistent with observations in the lower \r\ntroposphere, and least consistent in the upper troposphere around \r\n200 hPa, where biases exceed 0.1degC per decade. Several studies using \r\nCMIP6 models suggest that differences in climate sensitivity may \r\nbe an important factor contributing to the discrepancy between the \r\nsimulated and observed tropospheric temperature trends (McKitrick \r\nand Christy, 2020; Po-Chedley et al., 2021), though it is difficult to \r\ndeconvolve the influence of climate sensitivity, changes in aerosol \r\nforcing and internal variability in contributing to tropospheric warming \r\nbiases (Po-Chedley et al., 2021). Another study found that the absence \r\nof a hypothesized negative tropical cloud feedback could explain half \r\nof the upper troposphere warming bias in one model (Mauritsen and \r\nStevens, 2015)."


id: 58317e31882dfe136c3e9452863db46b
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 11381
content:
"The IPCC SR1.5 gave a likely range for the human-induced warming rate \r\nof 0.1degC to 0.3degC per decade in 2017, with a best estimate of 0.2degC per \r\ndecade (Allen et al., 2018). Table 3.1 lists the estimates of attributable \r\nanthropogenic warming rate over the period 2010-2019 based on the \r\nthree studies that underpin the assessment of GSAT warming (Haustein \r\net al., 2017; Gillett et al., 2021; Ribes et al., 2021). Estimates from \r\nHaustein et al. (2017), based on observed warming, and Ribes et al. \r\n(2021), based on CMIP6 simulations constrained by observed warming, \r\nare in good agreement. The Gillett et al. (2021) estimate, also based \r\non CMIP6 models, corresponds to a larger anthropogenic attributable \r\nwarming rate, because of a smaller warming rate attributed to natural \r\nforcing than in Ribes et al. (2021). This disagreement does not support \r\na decrease in uncertainty compared to the SR1.5 assessment"


id: de730171a726b40c54b47588b61737e4
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 32498
content:
" So the \r\nrange for anthropogenic attributable surface temperature warming \r\nrate of 0.1degC to 0.3degC per decade is again assessed to be likely, with a \r\nbest estimate of 0.2degC per decade."


id: a810fd80ccfd1a57fb5a385411eb7535
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 23956
content:
"The AR5 assessed as likely that anthropogenic forcings, dominated \r\nby greenhouse gases, contributed to the warming of the troposphere \r\nsince 1961 (Bindoff et al., 2013). Since then, there has been further \r\nprogress in detecting and attributing tropospheric temperature \r\nchanges. Mitchell et al. (2020) used CMIP6 models to find that the \r\nmain driver of tropospheric temperature changes is greenhouse gases. \r\nPrevious detection of the anthropogenic influence on tropospheric \r\nwarming may have overestimated uncertainties: Pallotta and Santer \r\n(2020) found that CMIP5 climate models overestimate the observed \r\nnatural variability in global mean tropospheric temperature on time \r\nscales of 5-20 years. Nevertheless, Santer et al. (2019) found that \r\nstochastic uncertainty is greater for tropospheric warming than \r\nstratospheric cooling because of larger noise and slower recovery time \r\nfrom the Mount Pinatubo eruption in the troposphere"


id: 2b30eca935bf2f0bc4214a2cc7fe93f3
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 4232
content:
" The detection \r\ntime of the anthropogenic signal in the tropospheric warming can be \r\naffected by both the model climate sensitivity and the model response \r\nto aerosol forcing. Volcanic forcing is also important, as models that \r\ndo not consider the influence of volcanic eruptions in the early 21st \r\ncentury overestimate the observed tropospheric warming since 1998 \r\n(Santer et al., 2014). Changes in the amplitude of the seasonal cycle \r\nof tropospheric temperatures have also been attributed to human \r\ninfluence. Santer et al. (2018) found that satellite data and climate "


id: 15015aaf494568b0db5f5b08967d33d7
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 807
content:
"In summary, studies continue to find that CMIP5 and CMIP6 \r\nmodel simulations warm more than observations in the tropical \r\nmid- and upper-troposphere over the 1979-2014 period (Mitchell \r\net al., 2013, 2020; Santer et al., 2017a, b; Suarez-Gutierrez et al., \r\n2017; McKitrick and Christy, 2018), and that overestimated surface \r\nwarming is partially responsible (Mitchell et al., 2013; Po-Chedley \r\net al., 2021). Some studies point to forcing errors in the CMIP5 \r\nsimulations in the early 21st century as a possible contributor \r\n(Mitchell et al., 2013; Sherwood and Nishant, 2015; Santer et al., \r\n2017a), but CMIP6 simulations use updated forcing estimates yet \r\ngenerally still warm more than observations. Although accounting \r\nfor internal variability and residual observational errors can reconcile \r\nmodels with observations to some extent (Suarez-Gutierrez et al., \r\n2017; Mitchell et al"


id: ea213f2e2a3c853e63d562ab5e2341ce
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 34337
content:
", 2020), some studies suggest that climate \r\nsensitivity also plays a role (Mauritsen and Stevens, 2015; McKitrick \r\nand Christy, 2020; Po-Chedley et al., 2021). Hence, we assess with \r\nmedium confidence that CMIP5 and CMIP6 models continue to \r\noverestimate observed warming in the upper tropical troposphere \r\nover the 1979-2014 period by at least 0.1degC per decade, in part "


id: 95fdf29d867b7407882fcb24d79fced7
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 21103
content:
"Upper stratospheric temperature changes were not assessed in \r\nthe context of attribution or model evaluation in AR5, but this is \r\nan area where there has been considerable progress over recent \r\nyears (Section  2.3.1.2.1). Simulated temperature changes in \r\nchemistry-climate models show good consistency with the reprocessed \r\ndataset from NOAA STAR but are less consistent with the revised \r\nUK Met Office record (Karpechko et al., 2018). The latter still shows \r\nstronger cooling than simulated in chemistry-climate models (Maycock \r\net al., 2018). Reanalyses, which assimilate AMSU and SSU datasets, \r\nindicate an upper-stratospheric cooling from 1979 to 2009 of about \r\n3degC at 5 hPa and 4degC at 1 hPa that agrees well with the cooling in \r\nsimulations with prescribed SST and using CMIP5 forcings (Simmons \r\net al., 2014)"


id: 7b0a64d760c7c803a5b20a59c583d3c0
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 16954
content:
" Mitchell (2016) used regularized optimal fingerprinting \r\ntechniques to carry out an attribution analysis of annual mid- to upper\u0002stratospheric temperature in response to external forcings. They found \r\nthat anthropogenic forcing has caused a cooling of approximately \r\n2degC-3degC in the upper stratosphere over the period of 1979-2015, with \r\ngreenhouse gases contributing two thirds of this change and ozone \r\ndepletion contributing one third. They found a large upper-stratospheric \r\ntemperature change in response to volcanic forcing (0.4degC-0.6degC \r\nfor Mount Pinatubo) but that change is still smaller than the lower\u0002stratospheric signal. Aquila et al. (2016) found that the cooling of the \r\nmiddle and upper stratosphere after 1979 is mainly due to changes in \r\ngreenhouse gas concentrations. Volcanic eruptions and the solar cycle \r\nwere found not to affect long-term stratospheric temperature trends \r\nbut to have short-term influences."


id: 51a3ca445abbed3dc58cca55ef2a1eca
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 10304
content:
"For the lower stratosphere, a debate has been ongoing since AR5 \r\nbetween studies finding that models underestimate the cooling \r\nof stratospheric temperature (Santer et al., 2017b), in part because of \r\nunderestimated stratospheric ozone depletion (Eyring et al., 2013; \r\nYoung et al., 2013), and studies finding that lower stratospheric \r\ntemperature trends are within the range of observed trends (Young \r\net al., 2013; Maycock et al., 2018). Different observational data and \r\ndifferent time periods explain the different conclusions. Aquila et al. \r\n(2016) used forced chemistry-climate models with prescribed SST to \r\ninvestigate the influence of different forcings on global stratospheric \r\ntemperature changes"


id: 30c51b07e4d834c8d091baa87b2b599b
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 5094
content:
" They found that in the lower stratosphere, \r\nthe simulated cooling trend due to increasing greenhouse gases \r\nwas roughly constant over the satellite era, while changes in ODS \r\nconcentrations amplified that stratospheric cooling trend during \r\nthe era of increasing ozone depletion up until the mid-1990s, with \r\na  flattening of the temperature trend over the subsequent period \r\nover which stratospheric ozone has stabilized (Section 2.2.5.2). \r\nMitchell et al. (2020) showed that while models simulate realistic \r\ntrends in tropical lower-stratospheric temperature over the whole \r\n1979-2014 period when compared with radiosonde data, they \r\ntend to overestimate the cooling trend over the ozone depletion era "


id: 1970cd5e1bd3fc0714eb5d1321a61d8b
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 1485
content:
"In summary, based on the latest updates to satellite observations of \r\nstratospheric temperature, we assess that simulated and observed \r\ntrends in global mean temperature through the depth of the \r\nstratosphere are more consistent than based on previous datasets, \r\nbut some differences remain (medium confidence). Studies published \r\nsince AR5 increase our confidence in the simulated stratospheric \r\ntemperature response to greenhouse gas and ozone changes, and \r\nsupport an assessment that it is extremely likely that stratospheric \r\nozone depletion due to ozone-depleting substances was the main \r\ndriver of the cooling of the lower stratosphere between 1979 and \r\nthe mid-1990s, as expected from physical understanding"


id: 259b32722fc24f9860772445f4273199
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 3345
content:
" Similarly, \r\nrevised observations and new studies support an assessment that it \r\nis extremely likely that anthropogenic forcing, both from increases in \r\ngreenhouse gas concentrations and depletion of stratospheric ozone \r\ndue to ozone-depleting substances, was the main driver of upper\u0002stratospheric cooling since 1979."


id: 7afa940b0cc848a2d46c6a415624268f
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 16939
content:
"Updated observational and reanalyses datasets and comparison with model simulations\r\nSince AR5, there have been version updates and new releases of most observational GMST datasets (Cross-Chapter Box 2.3). All the \r\nupdated products now available consistently find stronger positive trends for 1998-2012 than those assessed in AR5 (Cowtan and Way, \r\n2014; Karl et al., 2015; Hausfather et al., 2017; Medhaug et al., 2017; Simmons et al., 2017; Risbey et al., 2018). Simmons et al. (2017) \r\nreported that the 1998-2012 GMST trends in the updated observational and reanalysis datasets available at that time ranged from \r\n0.06degC to 0.14degC per decade, compared with the 0.05degC per decade on average reported in AR5, while the latest data products reported \r\nin Chapter 2 Table 2.4 show GMST or global mean near-surface air temperature (GSAT) trends over that period ranging from 0.12degC to \r\n0.14degC per decade. The lowest trend in Simmons et al"


id: b511b4a704827cbb6f30fb54a5aeb1e1
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" (2017) is from HadCRUT4, now superseded by HadCRUT5, which shows a trend \r\nof 0.12degC per decade. The upward revision is mainly due to improved sea surface temperature (SST) datasets and infilling of surface \r\ntemperature in locations with missing records in observational products, mainly in the Arctic (see Cross-Chapter Box 2.3 for details)."


id: 2130dcff6b5ba4078984045d6fffaa26
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 2658
content:
"Studies since AR5 identify Pacific Decadal Variability (PDV) as the leading mode of variability associated with unforced decadal GSAT \r\nfluctuations, with additional influence from Atlantic Multi-decadal Variability (Annex IV.2.6, IV.2.7; Brown et al., 2015; Dai et al., 2015; \r\nSteinman et al., 2015; Pasini et al., 2017). PDV transitioned from positive (El Nino-like) to negative (La Nina-like) phases during the \r\nslow warming period (Figure 3.39f and Cross-Chapter Box 3.1, Figure 1c). Model ensemble members that capture the observed slower \r\ndecadal warming under transient forcing, and time segments of model simulations that show decadal GSAT decreases under fixed \r\nradiative forcing, also feature negative PDV trends (Cross-Chapter Box 3.1, Figure 1d; Meehl et al., 2011, 2013, 2014; Maher et al., \r\n2014; Middlemas and Clement, 2016), suggesting the influence of PDV"


id: 77b609683f42233747eb73b3ab82eec5
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 16393
content:
" This is confirmed by statistical models with the PDV-GSAT \r\nrelationship estimated from observations and model simulations (Schmidt et al., 2014; Meehl et al., 2016b; Hu and Fedorov, 2017), \r\nselected ensemble members and time segments from model simulations where PDV by chance evolves in phase with observations \r\nover the slow warming period (Huber and Knutti, 2014; Risbey et al., 2014), and coupled model experiments in which PDV evolution is \r\nconstrained to follow the observations (Kosaka and Xie, 2013, 2016; England et al., 2014; Watanabe et al., 2014; Delworth et al., 2015). \r\nPart of the PDV trend may have been driven by anthropogenic aerosols (Smith et al., 2016); however, this result is model-dependent, \r\nand internally-driven PDV dominates the forced PDV signal in the CMIP6 multi-model ensemble (Section 3.7.6). It is also notable \r\nthat there is large uncertainty in the magnitude of the PDV influence on GSAT across models (Deser et al., 2017a; C.-Y. Wang et al"


id: f476c0cd52b9b00e79494c36724beb8c
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 35964
content:
", \r\n2017) and among the studies cited above. In addition to PDV, contributions to the reduced warming trend from wintertime Northern \r\nHemisphere atmospheric internal variability, particularly associated with a trend towards the negative phase of the Northern Annular \r\nMode/North Atlantic Oscillation (Annex IV.2.1; Guan et al., 2015; Saffioti et al., 2015; Iles and Hegerl, 2017) or the Cold Ocean-Warm \r\nLand (COWL) pattern (Molteni et al., 2017; Yang et al., 2020) have been suggested, leading to regional continental cooling over a large \r\npart of Eurasia and North America (Cross-Chapter Box 3.1, Figure 1c; C. Li et al., 2015; Deser et al., 2017a; Gan et al., 2019)."


id: dd3e74543338323ae37fafbed6e59f
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 32304
content:
"CMIP5 historical simulations driven by observed forcing variations ended in 2005 and were extended with RCP scenario simulations \r\nfor model-observation comparisons beyond that date. Post AR5 studies based on updated external forcing show that while no \r\nnet effect of updated anthropogenic aerosols is found on GSAT trends (Murphy, 2013; Gettelman et al., 2015; Oudar et al., 2018), \r\nnatural forcing by moderate volcanic eruptions in the 21st century (Haywood et al., 2014; Ridley et al., 2014; Santer et al., 2014) \r\nand a prolonged solar irradiance minimum around 2009 compared to the normal 11-year cycle (Lean, 2018) yield a negative \r\ncontribution to radiative forcing, which was missing in CMIP5 (Figure 2.2). This explains part of the difference between observed \r\nand CMIP5 trends, as shown based on EMIC simulations (Huber and Knutti, 2014; Ridley et al., 2014), statistical and mathematical \r\nmodels (Schmidt et al., 2014; Lean, 2018), and process-based climate models (Santer et al"


id: f3a10bc73458b260fee4f96a93a79843
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 35843
content:
", 2014). However, in a single climate \r\nmodel study by Thorne et al. (2015), updating most forcings (greenhouse gas concentrations, solar irradiance, and volcanic and \r\nanthropogenic aerosols) available when the study was done made no significant difference to the 1998-2012 GMST trend from that \r\nobtained with original CMIP5 forcing. Potential underestimation of volcanic (negative) forcing may have played a role (Outten et al., \r\n2015). In the multi-model ensemble mean, the 1998-2012 GMST trends are almost equal in CMIP5 and CMIP6 (Cross-Chapter \r\nBox 3.1, Figure 1a), suggesting compensation by a higher transient climate response and equilibrium climate sensitivity in CMIP6 \r\nthan CMIP5 (Section 7.5.6). To summarize, while there is medium confidence that natural forcing that was missing in CMIP5 \r\ncontributed to the difference of observed and simulated GMST trends, confidence remains low in the quantitative contribution of \r\nnet forcing updates."


id: f7a4e0aaf0d7a5477f89238c400bdf3b
created_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 36476
content:
"The early 21st century slower warming was observed in atmospheric temperatures, but the heat capacity of the atmosphere is very \r\nsmall compared to that of the ocean. Although there is noticeable uncertainty among observational products (H. Su et al., 2017) and \r\nobservation quality changes through time, global ocean heat content continued to increase during the slower surface warming period \r\n(very high confidence), at a rate consistent with CMIP5 and CMIP6 historical simulations (Sections 2.3.3.1, 3.5.1.3 and 7.2.2.2). \r\nThere is high confidence that the Earth's energy imbalance was larger in the 2000s than in the 1985-1999 period (Section 7.2.2.1), \r\nconsistent with accelerating ocean heat uptake in the past two decades (Section 3.5.1.3). Internal decadal variability is mainly associated \r\nwith redistribution of heat within the climate system (X.H. Yan et al., 2016; Drijfhout, 2018) while associated top of the atmosphere \r\nradiation anomalies are weak (Palmer and McNeall, 2014)"


id: 60574e0f98253367e1ddf52cc5cdf1d4
created_at: 2023-03-16 14:01:58
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vector_id: 12733
content:
" Heat redistribution in the top 350 m of the Indian and Pacific Oceans has \r\nbeen found to be the main contributor to reduced surface warming during the slower surface warming period (Lee et al., 2015; Nieves \r\net al., 2015; F. Liu et al., 2016), consistent with the simulated signature of PDV (England et al., 2014; Maher et al., 2018a; Gastineau \r\net al., 2019). Below 700 m, enhanced heat uptake over the slower surface warming period was observed mainly in the North Atlantic and \r\nSouthern Ocean (Chen and Tung, 2014), though whether this was a response to forcing or a unique signature of the slow GMST warming \r\nhas been questioned (W. Liu et al., 2016)."


id: c5554520057cc5264e01b35d8673857
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 28629
content:
"sedimentary paleorecord spanning the last millennium (Tierney et al., \r\n2015). This may be a signature of anthropogenic forcing but cannot \r\nyet be distinguished from natural variability (Hoell et al., 2017; Philip \r\net al., 2018). Likewise, tree rings indicate that the 2012-2014 drought \r\nin the south-western United States was exceptionally severe in the \r\ncontext of natural variability over the last millennium, and may have \r\nbeen exacerbated by the contribution of anthropogenic temperature \r\nrise (medium confidence) (Griffin and Anchukaitis, 2014; Williams \r\net al., 2015). Furthermore, Williams et al. (2020) used a combination \r\nof hydrological modelling and tree-ring reconstructions to show that \r\nthe period from 2000 to 2018 was the driest 19-year span in south\u0002western North America since the late 1500s"


id: ddc642efc790d664e44ffb20e85798c5
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 32388
content:
" Nonetheless, tree rings \r\nalso indicate the presence of prolonged megadroughts in western \r\nNorth America throughout the last millennium that were more severe \r\nthan 20th and 21st century events (high confidence) (Cook et al., 2004, \r\n2010, 2015). These were associated with internal variability (Coats \r\net al., 2016; Cook et al., 2016b) and indicate that large-magnitude \r\nchanges in the water cycle may occur irrespective of anthropogenic \r\ninfluence (see also McKitrick and Christy, 2019)."


id: 15188707c16eb22ea5c6479f8d366378
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 827
content:
"mid-Holocene tended to be underestimated (Flato et al., 2013). \r\nFurther analysis of CMIP5 models confirmed these results but \r\nhas also revealed systematic offsets from the paleoclimate record \r\n(DiNezio and Tierney, 2013; Hargreaves and Annan, 2014; Harrison \r\net al., 2014, 2015; Bartlein et al., 2017; Scheff et al., 2017; Tierney \r\net al., 2017). Harrison et al. (2014) concluded that CMIP5 models \r\ndo not perform better in simulating rainfall during the LGM and \r\nmid-Holocene than earlier model versions despite higher resolution \r\nand complexity. However, prescribing changes in vegetation and dust \r\nwas found to improve the match to the paleoclimate record (Pausata \r\net al., 2016; Tierney et al., 2017) suggesting that vegetation feedbacks \r\nin the CMIP5 models may be too weak (low confidence) (Hopcroft \r\net al., 2017). Brierley et al"


id: 2814bb044688cb6d6d6299738790c2d1
created_at: 2023-03-16 14:01:58
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vector_id: 3714
content:
" (2020) compared the latitudinal gradient \r\nof annual precipitation changes in the European-African sector \r\nsimulated by CMIP6 models for the mid-Holocene with pollen-based \r\nreconstructions and showed that models generally reproduce the \r\ndirection of changes seen in the reconstructions (Figure 3.11). They \r\ndo not show a robust signal in area averaged rainfall over most \r\nEuropean regions where quantitative reconstructions exist, which "


id: 611b1597c0e9fe8daf1d014687310b0d
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 12851
content:
"of the troposphere, and found that the simulated human fingerprint \r\npattern was detectable at the 5% level by 2002 in water vapour \r\nsatellite data (from 1988 to 2006). The observed changes matched the \r\nhistorical simulations forced by greenhouse gas changes and other \r\nanthropogenic forcings, and not those due to natural variability alone. \r\nThen, Santer et al. (2009) repeated this study with CMIP5 models, \r\nand found that the detection and attribution conclusions were not \r\nsensitive to model quality. These results demonstrate that the human \r\nfingerprint is governed by robust and basic physical processes, such \r\nas the water vapour feedback. Finally, Chung et al. (2014) extended \r\nthis line of research by focusing on the global-mean water vapour \r\ncontent in the upper troposphere"


id: d4a80e29c07a706301d6d54a33ac4abe
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 30983
content:
" Using satellite-based observations \r\nand sets of CMIP5 climate simulations run under various climate\u0002forcing options, they showed that the observed moistening trend of the \r\nupper troposphere over the 1979-2005 period could not be explained \r\nby internal variability alone, but is attributable to a combination of \r\nanthropogenic and natural forcings. This increase in water vapour is \r\naccompanied by a reduction in mid-tropospheric relative humidity \r\nand clouds in the subtropics and mid-latitude in both models and \r\nobservations related to changes in the Hadley cell (Section 3.3.3.1.1; \r\nLau and Kim, 2015)."


id: 944aba6b5b310bff7d4980ef763c2be0
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 20841
content:
"Dunn et al. (2017) confirmed earlier findings that global mean surface \r\nrelative humidity increased between 1973 and 2000, followed by \r\na steep decline (also reported in Willett et al., 2014) until 2013, and \r\nspecific humidity correspondingly increased and then remained \r\napproximately constant (see also Section 2.3.1.3.2), with none of the \r\nCMIP5 models capturing this behaviour. They noted biases in the mean \r\nstate of the  CMIP5 models' surface relative humidity (and ascribed \r\nthe failure to the representation of land surface processes and their \r\nresponse to CO2 forcing), concluding that these biases preclude any \r\ndetection and attribution assessment. On the other hand, Byrne and \r\nO'Gorman (2018) showed that the positive trend in specific humidity \r\ncontinued in recent years and can be detected over land and ocean \r\nfrom 1979 to 2016"


id: 771736c32e5a164e43d8bf70f11a4cab
created_at: 2023-03-16 14:01:58
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vector_id: 16303
content:
" Moreover, they provided a theory suggesting that \r\nthe increase in annual surface temperature and specific humidity as well \r\nas the decrease in relative humidity observed over land are linked to \r\nwarming over the neighbouring ocean. They also pointed out that the \r\nnegative trend in relative humidity over land regions is quite uncertain \r\nand requires further investigation. A recent study has also identified \r\nan anthropogenically-driven decrease in relative humidity over the \r\nNorthern Hemisphere mid-latitude continents in summer between 1979 \r\nand 2014, which was underestimated by CMIP5 models (Douville and \r\nPlazzotta, 2017). Furthermore, in a modelling study Douville et al. (2020) \r\nshowed that this decrease in boreal summer relative humidity over mid\u0002latitudes is related not only to global ocean warming, but also to the \r\nphysiological effect of CO2 on plants in the land surface model."


id: 6ddba5895cc6dfbebd1fd4f2195ca48
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 14899
content:
"Water vapour is the most important natural greenhouse gas and its \r\namount is expected to increase in a global warming context leading \r\nto further warming. Particularly important are changes in the upper \r\ntroposphere because there water vapour regulates the strength of \r\nthe water-vapour feedback (Section 7.4.2.2). CMIP5 models have \r\nbeen shown to have a wet bias in the tropical upper troposphere \r\nand a dry bias in the lower troposphere, with the former bias and \r\nmodel spread being larger than the latter (Jiang et al., 2012; Tian \r\net al., 2013). Tian et al. (2013) also showed that in comparison to the \r\nAIRS specific humidity, CMIP5 models have the well-known double \r\nInter-tropical Convergence Zone (ITCZ) bias in the troposphere from \r\n1000 hPa to 300 hPa, especially in the tropical Pacific"


id: 4447493148be2107bec73c271f86f533
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 8164
content:
" Water vapour \r\nbiases in models are dominated by errors in relative humidity \r\nthroughout the troposphere, which are in turn closely related to \r\nerrors in large scale circulation; temperature errors dominate near \r\nthe tropopause (Takahashi et al., 2016). Section 7.4.2.2 discusses \r\nthis topic in more detail for CMIP6 models. However, Schroder et al. \r\n(2019) show that the majority of well-established water vapour \r\nrecords are affected by inhomogeneity issues and thus should \r\nbe used with caution (see also Section 2.3.1.3.3). A comparison \r\nof trends in column water vapour path for 1998-2019 in satellite \r\ndata, a reanalysis, CMIP5 and CMIP6 simulations averaged over \r\nthe near-global ocean reveals that while on average model trends \r\nare higher than those in observations and a reanalysis, the latter lie \r\nwithin the multi-model range (Figure 3.12)."


id: 75722a23edfd87842f010dbd6d30bdf5
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 16064
content:
"between increasing heavy precipitation and decreasing light to \r\nmoderate rainfall (Thackeray et al., 2018b), a  characteristic found \r\nin the observational record (Gu and Adler, 2018). Regional model \r\nperformance is further assessed in Chapter  8 and the Atlas, while \r\nprecipitation extremes are considered in Chapter 11.\r\nThe simulation of annual mean rainfall patterns in the CMIP6 models\r\nreveals minor improvements compared to those in CMIP5 \r\nmodels (Figure 3.13). The persistent biases include the double ITCZ in \r\nthe tropical Pacific (seen as bands of excessive rainfall on both sides \r\nof the equatorial Pacific in Figure 3.13b,d) and the southward-shifted \r\nITCZ in the equatorial Atlantic, which have been linked to the meridional \r\npattern of SST bias (S. Zhou et al., 2020) and the reduced sensitivity of \r\nprecipitation to local SST (Good et al., 2021)"


id: c2dd43296a2e0b10ae1d846d5a9de864
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 28238
content:
" Tian and Dong (2020) also \r\nfound that all three generations of CMIP models share similar systematic \r\nannual mean precipitation errors in the tropics, but that the double \r\nITCZ bias is slightly reduced in CMIP6 models in comparison to CMIP3 \r\nand CMIP5 models. They also found some improvement in the overly \r\nintense Indian ocean ITCZ and the too dry South American continent \r\nexcept over the Andes. Fiedler et al. (2020) identified improvements in \r\nthe tropical mean spatial correlations and root mean square error of \r\nthe climatology as well as in the day-to-day variability, but found little \r\nchange across CMIP phases in the double ITCZ bias and diurnal cycle. \r\nThe CMIP6 models reproduce better the domain and intensity of the \r\nglobal monsoon (see Section 3.3.3.2). Moreover, CMIP6 models better \r\nrepresent the storm tracks (Priestley et al., 2020; also Section 3.3.3"


id: 408c4cc357f14df55de7c06d07ed3025
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 7534
content:
"3), \r\nthereby reducing the precipitation biases in the North Atlantic and \r\nmid-latitudes of the Southern Hemisphere (Figure 3.13b,d). As a result, \r\npattern correlations between simulated and observed annual mean \r\nprecipitation range between 0.80 and 0.92 for CMIP6 models, compared \r\nto a range of 0.79 to 0.88 for CMIP5 (Bock et al., 2020). This relative \r\nimprovement may be related to increased model resolution, as found \r\nwhen comparing biases in the mean of the HighResMIP models with \r\nthe mean of the corresponding lower-resolution versions of the same \r\nmodels (see Figure 3.13e,f), particularly in the tropics and extratropical \r\nstorm tracks. Consistent with this, a recent study using several coupled \r\nmodels showed that increasing the atmospheric resolution leads to \r\na strong decrease in the precipitation bias in the tropical Atlantic ITCZ \r\n(see further discussion in Section 3.8.2.2; Vanniere et al., 2019)"


id: 219411a9f5cca758c08a58816f793a2d
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 2709
content:
" Based \r\non these results we assess that despite some improvements, CMIP6 \r\nmodels still have deficiencies in simulating precipitation patterns, \r\nparticularly over the tropical ocean (high confidence)."


id: bb0c4183a8cc45b248e6098ed9ee65ab
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 26973
content:
"Since AR5, X. Li et al. (2016b) found that CMIP5 models simulate the \r\nlarge scale patterns of annual mean land precipitation and seasonality \r\nwell, as well as reproducing qualitatively the observed zonal mean \r\nland precipitation trends for the period 1948-2005: models capture \r\nthe drying trends in the tropics and at 45degS and the wetting trend in \r\nthe Northern Hemisphere mid- to high latitudes, but the amplitudes \r\nof the changes are much smaller than observed. Land precipitation \r\nwas found to show enhanced seasonality in observations (Chou \r\net al., 2013), qualitatively consistent with the simulated response to \r\nanthropogenic forcing (Dwyer et al., 2014). However, models do not \r\nappear to reproduce the zonal mean trends in the magnitude of the \r\nseasonal cycle over the period 1948-2005, nor the two-dimensional \r\ndistributions of trends of annual precipitation and seasonality over \r\nland, but differences may be explainable by internal variability \r\n(X. Li et al., 2016b)"


id: 44632ec6fbe56c5253283dba50ba4151
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 8181
content:
" However, observed trends in seasonality depend \r\non data set used (X. Li et al., 2016b; Marvel et al., 2017), and Marvel \r\net al. (2017) found that observed changes in the annual cycle phase \r\nare consistent with model estimates of forced changes. These \r\nphase changes are mainly characterized by earlier onset of the wet \r\nseason on the equatorward flanks of the extratropical storm tracks, \r\nparticularly in the Southern Hemisphere. Box 8.2 assesses regional \r\nchanges in water cycle seasonality."


id: 1fa395d02cc1b25e0e4ec249522b89d
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 2411
content:
"Figure 3.13 | Annual-mean precipitation rate (mm day-1) for the period 1995-2014. (a) Multi-model (ensemble) mean constructed with one realization of the CMIP6 \r\nhistorical experiment from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the precipitation analysis from the \r\nGlobal Precipitation Climatology Project (GPCP) version 2.3 (Adler et al., 2003). (c) Multi-model mean of the root mean square error calculated over all months separately and \r\naveraged with respect to the precipitation analysis from GPCP version 2.3. (d) Multi-model mean bias, calculated as the difference between the CMIP6 multi-model mean and \r\nthe precipitation analysis from GPCP version 2.3. Also shown is the multi-model mean bias as the difference between the multi-model mean of (e) high resolution and (f) low\u0002resolution simulations of four HighResMIP models and the precipitation analyses from GPCP version 2.3"


id: f571a692c58bac3ac4fbe7e5f5336a9
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 36138
content:
" Uncertainty is represented using the advanced approach. No overlay \r\nindicates regions with robust signal, where >=66% of models show change greater than the variability threshold and >=80% of all models agree on sign of change; diagonal lines \r\nindicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting \r\nsignal, where >=66% of models show change greater than the variability threshold and <80% of all models agree on the sign of change. For more information on the advanced \r\napproach, please refer to the Cross-Chapter Box Atlas.1. Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is not lower in at least \r\nthree out of four models than in the corresponding low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."


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vector_id: 16627
content:
"For the Southern Hemisphere extratropics, Solman and Orlanski (2016) \r\nfound that the observed summertime rainfall increase over high \r\nlatitudes and decrease over mid-latitudes over the period 1979-2010 \r\nare quasi-zonally symmetric and related to changes in eddy activity. The \r\nlatter were in turn found to be associated with the poleward shift of \r\nthe westerlies due mostly to ozone depletion. Positive rainfall trends \r\nin the subtropics, particularly over south-eastern South America (see \r\nalso Section 10.4.2.2) and northern and central Australia, have been also \r\nattributed to stratospheric ozone depletion (Kang et al., 2011; Gonzalez \r\net al., 2014) and greenhouse gases (Vera and Diaz, 2015; Saurral et al., \r\n2019). During austral winter, wetting at high latitudes and drying at \r\nmid-latitudes are not zonally homogenous, due to both changes in eddy \r\nactivity and increased lower troposphere humidity"


id: 5afb514e3de8eedf2b34c69b765105e0
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 11830
content:
" Solman and Orlanski \r\n(2016) associated these climate changes with increases in greenhouse \r\ngas concentration levels. Recently, Blazquez and Solman (2017) have \r\nshown that CMIP5 models represent very well the dynamical forcing \r\nand the frequency of frontal precipitation in the Southern Hemisphere \r\nwinter extratropics, but the amount of precipitation due to fronts is \r\noverestimated. Chapters 10 and 11 validate in more detail the simulation \r\nof fronts in climate models (Sections 10.3.3.4.4 and 11.7.2.3)."


id: 990bacadda1eacdfc183e98bade7a7c6
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 21596
content:
"Section 2.3.1.3.6 assesses that there have not been significant trends \r\nin global average streamflow over the last century, though regional \r\ntrends have been observed, driven in part by internal variability. Only \r\na limited number of studies have systematically compared observed \r\nstreamflow trends at continental to global scales with changes \r\nsimulated by global circulation models in a detection and attribution \r\nsetting. H. Yang et al. (2017) did not find a significant correlation \r\nbetween observed runoff changes and changes simulated in CMIP5 \r\nmodels in most grid cells, consistent with the assessment that observed \r\nchanges are dominated by internal variability. In  a  pan-European \r\nassessment, Gudmundsson et al. (2017) attributed the spatio\u0002temporal pattern of decreasing streamflow in southern Europe and \r\nincreasing streamflow in northern Europe to anthropogenic climate \r\nchange, but also concluded that additional effects of human water \r\nwithdrawals could not be excluded"


id: a85004e5620417d0cbbdb777aa6e0bd2
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 24010
content:
" Focussing on continental runoff \r\nbetween 1958 and 2004, Alkama et al. (2013) found a significant \r\nchange only when using reconstructed data over all rivers, and a \r\nlarge uncertainty in the estimate of the global streamflow trend due \r\nto opposing changes over different continents. Gedney et al. (2014) \r\ndetected the influence of aerosols on streamflow in North America \r\nand Europe, with aerosols having driven an increase in streamflow \r\ndue to reduced evaporation (see Section 8.3.1.5 for details on \r\nprocesses). There is also evidence for a detectable anthropogenic \r\ncontribution toward earlier winter-spring streamflows in the north\u0002central US (Kam et al., 2018) and in western Canada (Najafi et al., \r\n2017). From a model evaluation perspective, Sheffield et al. (2013) \r\nreported that CMIP5 models reproduce spatial variations in runoff in \r\nNorth America well, though they tend to underestimate it."


id: 7a29478a7d5ccab9e70f5d55a5a452cc
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 16791
content:
"Overall, studies published since AR5 provide further evidence of an \r\nanthropogenic influence on precipitation, and therefore we now assess \r\nthat it is likely that human influence has contributed to large-scale \r\nprecipitation changes observed since the mid-20th century. New \r\nattribution studies strengthen previous findings of a detectable increase \r\nin mid to high latitude land precipitation over the Northern Hemisphere \r\n(high confidence). There is medium confidence that human influence \r\nhas contributed to a strengthening of the zonal mean wet tropics-dry \r\nsubtropics contrast, and that tropical rainfall changes follow the wet\u0002gets-wetter, dry-gets-drier paradigm. There is also medium confidence\r\nthat ozone depletion has increased precipitation over the southern high \r\nlatitudes and decreased it over southern mid-latitudes during austral \r\nsummer"


id: 65a1db387abe5906966af1773babba90
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 13567
content:
" Owing to observational uncertainties and inconsistent results \r\nbetween studies, we conclude that there is low confidence in the \r\nattribution of changes in the seasonality of precipitation."


id: 2223ff8e2a9a03aac6dc7c213dc9803e
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 2819
content:
"Chapter 11 assesses that there is high confidence that climate models can reproduce the mean state and overall warming of temperature \r\nextremes observed globally and in most regions, although the magnitude of the trends may differ, and the ability of models to capture \r\nobserved trends in temperature-related extremes depends on the metric evaluated, the way indices are calculated, and the time \r\nperiods and spatial scales considered (Section 11.3.3). There has been widespread evidence of human influence on various aspects of \r\ntemperature extremes, at global, continental, and regional scales. This includes attribution to human influence of observed changes \r\nin intensity, frequency, and duration and other relevant characteristics at global and continental scales (Section 11.3.4). The left-hand \r\npanel of Cross-Chapter Box 3"


id: 404d04faf6df68b81727a86605bf243d
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 7495
content:
"2, Figure 1 clearly shows that long-term changes in the global mean annual maximum daily maximum \r\ntemperature can be reproduced by both CMIP5 and CMIP6 models forced with the combined effect of natural and anthropogenic \r\nforcings, but cannot be reproduced by simulations under natural forcing alone. Consistent with the assessment for global mean \r\ntemperature (Section 3.3.1), aerosol changes are found to have offset part of the greenhouse gas induced increase in hot extremes \r\nglobally and over most continents over the 1951-2015 period (Hu et al., 2020; Seong et al., 2021), though greenhouse gas and aerosol \r\ninfluences are less clearly separable in observed changes in cold extremes."


id: 51d4a8b9aa3ede8237fb686beddfc631
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 10334
content:
"An important piece of evidence supporting the SREX and AR5 assessment that there is medium confidence that anthropogenic forcing \r\nhas contributed to a global scale intensification of heavy precipitation during the second half of the 20th century is the evidence \r\nfor anthropogenic influence on other aspects of the global hydrological cycle. The most significant aspect of that is the increase in \r\natmospheric moisture content associated with warming which should, in general, lead to enhanced extreme precipitation, particularly \r\nassociated with enhanced convergence in tropical and extratropical cyclones (Sections 8.2.3.2 and 11.4.1). Such a connection is \r\nsupported by the fact that annual maximum one-day precipitation increases with global mean temperature at a rate similar to the \r\nincrease in the moisture holding capacity in response to warming, both in observations and in model simulations"


id: dd90355992fc526c99a2ec4742c0b1e3
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 32359
content:
" Additionally, models \r\nproject an increase in extreme precipitation across global land regions even in areas in which total annual or seasonal precipitation \r\nis projected to decrease."


id: e3f0a7039cf584adf1f4f424efc0df4c
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 33339
content:
"The overall performance of CMIP6 models in simulating extreme precipitation intensity and frequency is similar to that of CMIP5 models \r\n(high confidence), and there is high confidence in the ability of models to capture the large-scale spatial distribution of precipitation \r\nextremes over land (Section 11.4.3). Evidence of human influence on extreme precipitation has become stronger since AR5. Considering \r\nchanges in precipitation intensity averaged over all wet days, there is high confidence that daily mean precipitation intensities have \r\nincreased since the mid-20th century in a majority of land regions, including Europe, North America and Asia, and it is likely that \r\nsuch an increase is mainly due to anthropogenic emissions of greenhouse gases (Sections 8.3.1.3 and 11.4.4). Section 11.4"


id: 89a2531c918576c0bcf2e1c908e1628f
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 19189
content:
"4 also \r\nfinds a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation \r\nthan expected by chance, which can only be explained when anthropogenic greenhouse gas forcing is considered. The right-hand "


id: 980abb0a244f268152872630916c2064
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 21431
content:
"CMIP5 models reproduce the mean state of the Walker circulation \r\nwith reasonable fidelity, evidenced by the spatial pattern correlations \r\nof equatorial zonal mass stream function between models and \r\nobservations being larger than 0.88 (Ma and Zhou, 2016). CMIP5 \r\nhistorical simulations on average simulate a significant weakening \r\nof the Pacific Walker circulation over the 20th century (DiNezio et al., \r\n2013; Sandeep et al., 2014; Kociuba and Power, 2015), which is \r\nalso seen in CMIP6 (Figure 3.16d). This weakening is accompanied \r\nby a reduction of convective activity over the Maritime Continent \r\nand an enhancement over the central equatorial Pacific (DiNezio \r\net al., 2013; Sandeep et al., 2014; Kociuba and Power, 2015). \r\nIn the CMIP6 simulations, greenhouse gas forcing induces this \r\nweakening (Figure 3.16d), which is consistent with theories based \r\non radiative-convective equilibrium (Vecchi et al"


id: e784194da6491fc433d4df09ebca6770
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 33924
content:
", 2006; Vecchi and \r\nSoden, 2007) and thermodynamic air-sea coupling (Xie et al., 2010), \r\nbut inconsistent with a theory highlighting the ocean dynamical \r\neffect which suggests a strengthening in response to greenhouse \r\ngas increases (Clement et al., 1996; Seager et al., 2019; see also \r\nSection 7.4.4.2.1). Seager et al. (2019) attributed this inconsistency \r\nto equatorial Pacific SST biases in the models (Section  3.5.1.2.1). \r\nHowever, observational and reanalysis datasets disagree on the sign \r\nof trends in the Walker Circulation strength over the 1901-2010 \r\nperiod (Figure 3.16d), and Section 2.3.1.4.1 assesses low confidence\r\nin observed long-term Walker Circulation trends. The observational \r\nuncertainty remains high in the trends since the 1950s (Tokinaga \r\net al., 2012; L'Heureux et al., 2013), though both CMIP5 and CMIP6 \r\nhistorical simulations span trends of all but one observational data \r\nset (Figure  3.16e)"


id: 5bb5e0b74c940c38535da80dfb78d42d
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 11939
content:
" For this period, external influence simulated \r\nin CMIP6 is insignificant due to a partial compensation of forced \r\nresponses to greenhouse gases and aerosols and large internal \r\ndecadal variability (Figure  3.16e). It is notable that while AMIP \r\nsimulations on average show strengthening over both the periods, "


id: 70f9034292d854f4568eacb976191195
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 15407
content:
"Figure 3.16 | Model evaluation and attribution of changes in Hadley cell extent and Walker circulation strength. (a-c) Trends in subtropical edge latitude of the \r\nHadley cells in (a) the Northern Hemisphere for 1980-2014 annual means and (b, c) Southern Hemisphere for (b) 1980-2014 annual means and (c) 1980/81-1999/2000 \r\nDecember-January-February means. Positive values indicate northward shifts. (d-f) Trends in the Pacific Walker circulation strength for (d) 1901-2010, (e) 1951-2010 and \r\n(f) 1980-2014. Positive values indicate strengthening. Based on CMIP5 historical (extended with RCP4.5), CMIP6 historical, AMIP, pre-industrial control, and single forcing \r\nsimulations along with HadSLP2 and reanalyses. Pre-industrial control simulations are divided into non-overlapping segments of the same length as the other simulations"


id: 76053fdefd4262efd55e8d5a01fe4468
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 16170
content:
" White \r\nboxes and whiskers represent means, interquartile ranges and 5th and 95th percentiles, calculated after weighting individual members with the inverse of the ensemble size of \r\nthe same model, so that individual models are equally weighted (Section 3.2). The filled boxes represent the 5-95% confidence interval on the multi-model mean trends of the \r\nmodels with at least three ensemble members, with dots indicating the ensemble means of individual models. The edge latitude of the Hadley cell is where the surface zonal \r\nwind velocity changes sign from negative to positive, as described in the Appendix of Grise et al. (2018). The Pacific Walker circulation strength is evaluated as the annual mean \r\ndifference of sea level pressure between 5degS-5degN, 160degW-80degW and 5degS-5degN, 80degE-160degE. Further details on data sources and processing are available in the chapter data \r\ntable (Table 3.SM.1)."


id: de752fd44ff9a59f235bce119046c980
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 32500
content:
"Paleoclimate proxy evidence shows that the global monsoon has \r\nvaried with orbital forcing and greenhouse gases (Section 2.3.1.4.2; \r\nMohtadi et al., 2016; Seth et al., 2019). These large-magnitude \r\nintensifications and weakenings in the global monsoon involved \r\nin some cases orders-of-magnitude changes in precipitation locally \r\n(Harrison et al., 2014; Tierney et al., 2017). Paleoclimate modelling \r\nand limited data from past climate states with high CO2 suggest \r\nthat precipitation intensifies in the monsoon domain under elevated \r\ngreenhouse gases, providing context for present and future trends \r\n(Passey et al., 2009; Haywood et al., 2013; Zhang et al., 2013b). In \r\nmodel simulations of the mid-Pliocene, when globally averaged \r\ntemperature was higher than present day, precipitation was larger \r\nin West African, South Asian and East Asian monsoons than under \r\npre-industrial conditions, consistent with proxy evidence (Zhang \r\net al., 2015; Sun et al"


id: cbccc2c0faec9b4f029efd811d9a9327
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 29605
content:
", 2016, 2018; Corvec and Fletcher, 2017; \r\nX. Li et al., 2018). Prescott et al. (2019) and R. Zhang et al. (2019) \r\nfind an important role for orbital forcing and CO2 in the mid-Pliocene \r\nmonsoon expansion and intensification. Models are also able to \r\ncapture interhemispherically contrasting monsoon changes in the \r\nLast Interglacial in response to orbital forcing and greenhouse gases, \r\nwith wetter West African and Asian monsoons and a drier South \r\nAmerican monsoon as seen in proxies (Govin et al., 2014; Gierz \r\net al., 2017; Pedersen et al., 2017). In overall agreement with proxy \r\nevidence, a model with transient forcing simulates wetting and drying \r\nrespectively of the Southern and Northern Hemisphere monsoons \r\nduring the last deglaciation, with an important contribution from \r\nAtlantic Meridional Overturning Circulation (AMOC) slowdown \r\n(Otto-Bliesner et al., 2014; Mohtadi et al., 2016)."


id: da9717d11778e9913d4ace5f2cf3a1a1
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 31918
content:
"It is likely that human influence has contributed to the poleward \r\nexpansion of the zonal mean Hadley cell in the Southern Hemisphere \r\nsince the 1980s. This assessment is supported by studies since AR5, \r\nwhich consistently find human influence from greenhouse gas \r\nincreases on the expansion, with additional influence from ozone \r\ndepletion in austral summer. For the strong ozone depletion period \r\nof 1981-2000, human influence is detectable in the summertime \r\npoleward expansion in the Southern Hemisphere (medium \r\nconfidence). By contrast, there is medium confidence that the \r\nexpansion of the zonal mean Hadley cell in the Northern Hemisphere \r\nis within the range of internal variability, with contributions from \r\nPDV and other internal variability"


id: 9ffe13b5ccd4f7b1f8d416ebabdb703
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 22713
content:
" The causes of the observed \r\nstrengthening of the Pacific Walker circulation over the 1980-2014 \r\nperiod are not well understood, since the observed strengthening \r\ntrend is outside the range of variability simulated in the coupled \r\nmodels (medium confidence). Large observational uncertainty, lack \r\nof understanding of the mechanism underlying the poleward Hadley \r\ncell expansion, and contradicting theories on the greenhouse gas \r\ninfluence and uncertainty in the aerosol influence on the Walker \r\ncirculation strength, limit confidence in these assessments."


id: 302e13ef9c8db1afd15399406d9eaf84
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 5006
content:
"Figure 3.17 | Model evaluation of global monsoon domain, intensity, and circulation. (a, b) Climatological summer-winter range of precipitation rate, scaled by \r\nannual mean precipitation rate (shading) and 850 hPa wind velocity (arrows) based on (a) GPCP and ERA5 and (b) a multi-model ensemble mean of CMIP6 historical simulations \r\nfor 1979-2014. The region enclosed by red lines is the monsoon domain based on the definition by Wang and Ding (2008). (c, d) Five-year running mean anomalies of (c) global \r\nland monsoon precipitation index defined as the percentage anomaly of the summertime precipitation rate averaged over the monsoon regions over land, relative to its average \r\nfor 1979-2014 (the period indicated by light grey shading) and (d) the tropical monsoon circulation index defined as the vertical shear of zonal winds between 850 and 200 \r\nhPa levels averaged over 0deg-20degN, from 120degW eastward to 120degE in Northern Hemisphere summer (Wang et al"


id: 6bbd4b7b99389cc8fd1d55159350ca83
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 14579
content:
", 2013; m s-1) in CMIP5 historical and RCP4.5 simulations, \r\nand CMIP6 historical and AMIP simulations. Summer and winter are defined for individual hemispheres: May to September is defined as Northern Hemisphere summer and \r\nSouthern Hemisphere winter, and November to March is defined as Northern Hemisphere winter and Summer Hemisphere summer. The numbers of models and simulations \r\nare given in the legend. The multi-model ensemble mean and percentiles are calculated after weighting individual ensemble members with the inverse of the ensemble size of \r\nthe same model, so that individual models are equally weighted irrespective of ensemble size. Further details on data sources and processing are available in the chapter data \r\ntable (Table 3.SM.1)."


id: 98a69f5eb760e6cb4d03fe7dd21e9c06
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 21533
content:
"summer monsoon precipitation (Figure 3.17c). However, the 1960s \r\npeak in the Northern Hemisphere summer monsoon circulation \r\nis outside the 5th-95th percentile range of CMIP5 and CMIP6 \r\nhistorical simulations for two out of three reanalyses (Figure 3.17d). \r\nModelling studies show that greenhouse gas increases act to \r\nenhance Northern Hemisphere summer monsoon precipitation \r\nintensity (Liu et al., 2012; Polson et al., 2014; Chai et al., 2018; L. \r\nZhang et al., 2018b). Since the mid-20th century, however, modelling \r\nstudies show that this effect was overwhelmed by the influence of \r\nanthropogenic aerosols in CMIP5 (Polson et al., 2014; Guo et al., \r\n2015; Y. Zhang et al., 2018; Giannini and Kaplan, 2019) and in \r\nCMIP6 (T. Zhou et al., 2020). Weakening of the monsoon circulation \r\nand reduction of moisture availability are important in this aerosol \r\ninfluence (T. Zhou et al., 2020)"


id: 76b373b27ff4a3a79d7b7efd431a1fb4
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 16256
content:
" Besides these human influences, \r\nthe global monsoon is sensitive to internal variability and natural \r\nforcing including ENSO and volcanic aerosols on interannual \r\ntime scales and PDV and AMV on decadal to multi-decadal time \r\nscales (Wang et al., 2013, 2018; F. Liu et al., 2016; Jiang and Zhou, \r\n2019; Zuo et al., 2019); though AMV in the 20th century may \r\nhave been partly driven by aerosols, see Section 3.7.7. Indeed, \r\nAMIP simulations better reproduce the observed multi-decadal \r\nvariations of the global monsoon precipitation and circulation \r\n(Figure  3.17c,d). Y.  Zhang et al. (2018) find that the multi-model \r\nensemble mean trend of global land monsoon precipitation in \r\nhistorical simulations, dominated by anthropogenic aerosol forcing \r\ncontributions, emerges out of the 90% range of internally-driven \r\ntrends in pre-industrial control simulations. However, it should \r\nbe noted that CMIP5 models tend to under-represent the PDV \r\nmagnitude (Section 3.7"


id: 6148457ec2b15b772bd7c3994d398ec2
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 12879
content:
"6), suggesting potential overconfidence \r\nin the detection of the forced signal. An observed enhancement \r\nin global summer monsoon precipitation since the 1980s is \r\naccompanied by an intensification of the Northern Hemisphere \r\nsummer monsoon circulation (Figure  3.17c,d). These trends \r\nappear to be at the extreme of the range of the CMIP6 historical \r\nsimulation ensemble but are well captured by AMIP simulations \r\n(Figure 3.17c,d). While the precipitation increase is consistent with \r\ngreenhouse gas forcing, the circulation intensification is opposite \r\nto the simulated response to greenhouse gas forcing, and these \r\nenhancements have been attributed to PDV and AMV (Wang et al., \r\n2013; Kamae et al., 2017)."


id: 4e4a0d16b55a0e6f2ad6aeb6ffe58673
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 9746
content:
"Since AR5, new research using CMIP5 and CMIP6 models has confirmed \r\nthat increasing the model resolution improves the simulation of \r\ncyclones and blocking in all seasons albeit with some exceptions and \r\ncaveats (Zappa et al., 2013; Davini et al., 2017; Schiemann et al., 2017, \r\n2020; Davini and D'Andrea, 2020; Priestley et al., 2020). New research \r\nalso finds that model performance with respect to the simulation \r\nof cyclones and that of blocking events are correlated (Zappa et al., \r\n2014), suggesting biases in both are aspects of the same underlying \r\nproblems in models (Figure  3.18). In the North Pacific basin the \r\nannual mean blocking frequency is now well simulated compared to \r\nearlier evaluations, but substantial errors in the blocking frequency \r\nremain in the Euro-Atlantic sector (Figure 3.18; Dunn-Sigouin and \r\nSon, 2013; Davini and D'Andrea, 2016, 2020; Mitchell et al., 2017; \r\nWoollings et al., 2018b)"


id: ce830a48c13442195c8d59c9fd329a68
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 30030
content:
" While there is a resolution dependence \r\nin the size of this bias, even at very high resolution blocking in the \r\nEuro-Atlantic sector remains underestimated (Schiemann et al., 2017), \r\nand there is evidence of a compensation of errors as the resolution is \r\nincreased (Davini et al., 2017). Davini and D'Andrea (2020) show that \r\nwhile the simulation of blocking improves with increasing resolution \r\nin CMIP3, CMIP5, and CMIP6 models, other factors contribute to \r\nbiases, particularly to the underestimation of Euro-Atlantic blocking \r\n(Schiemann et al., 2020). The persistence of blocking events, typically "


id: 13ddef970b1906f96e6c9155e57d200
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 629
content:
"In summary, while greenhouse gas increases acted to enhance the global \r\nland monsoon precipitation over the 20th century (medium confidence), \r\nconsistent with projected future enhancement (Section 4.5.1.5), this \r\ntendency was overwhelmed by anthropogenic aerosols from the 1950s \r\nto the 1980s, which contributed to weakening of global land summer \r\nmonsoon precipitation intensity for this period (medium confidence). \r\nThere is medium confidence that the intensification of global monsoon \r\nprecipitation and Northern Hemisphere summer monsoon circulation \r\nsince the 1980s is dominated by internal variability. These assessments \r\nare supported respectively by multi-model detection and attribution \r\nstudies which find an important role for anthropogenic aerosols \r\nin the weakening trend, and studies that identify a role for AMV \r\nand PDV in inducing the Northern Hemisphere summer monsoon \r\ncirculation enhancement since the 1980s"


id: 1107d86f609c0179c0fa7292e065b952
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 156
content:
"For the Southern Hemisphere (not considered in AR5), Priestley \r\net al. (2020) find considerable improvement in the placement of \r\nthe Southern Ocean storm track during summer (December to \r\nFebruary) in CMIP6 models versus CMIP5, consistent with a more \r\nrealistic annual mean surface wind maximum latitude in CMIP6 \r\nthan in CMIP5 (Goyal et al., 2021). Relative to CMIP5, both low- and \r\nhigh-resolution CMIP6 models have increased track densities south \r\nof about 55degS and decreased track densities between about 40degS \r\nand 55degS, in better agreement with observations than CMIP5 models \r\n(Parsons et al., 2016; Patterson et al., 2019). CMIP5 models and high\u0002resolution CMIP6 models simulate a storm track that is positioned \r\ntoo far equatorward, although the bias is smaller in the high\u0002resolution models. By contrast, the low-resolution CMIP6 models \r\nsimulate a storm track that is slightly too far poleward on average \r\n(Priestley et al., 2020)"


id: 5c88653cf2c88400c0548edf0969b3db
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 12076
content:
" In winter (June to August), the biases found in \r\nCMIP5 are only slightly improved in CMIP6, with models continuing \r\nto underestimate the broad maximum cyclone track density in the \r\nsouth-eastern Indian Ocean and overestimate the minimum density \r\nin the south-western South Pacific (Priestley et al., 2020)."


id: 21cdc31323a628d0040473df725974e0
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 2752
content:
"Section 2.3.1.4.3 assesses that the extratropical jets and cyclone \r\ntracks have likely shifted poleward in both hemispheres since the \r\n1980s with marked seasonality in trends (medium confidence). For \r\nthe Southern Hemisphere, studies using CMIP5 and other models \r\nimply that both ozone depletion and increasing greenhouse gases \r\nhave caused substantial atmospheric circulation change since \r\nthe 1960s when concentrations of ozone-depleting substances \r\nstarted to increase (Eyring et al., 2013; Iglesias-Suarez et al., \r\n2016; Karpechko et al., 2018; Son et al., 2018). In particular, ozone \r\ndepletion, during austral summer, has been linked to a poleward \r\nshift of the westerly jet and Southern Hemisphere circulation zones \r\nand a southward expansion of the tropics (Kang et al., 2011), which \r\nis associated with a strengthening trend of the Southern Annular \r\nMode (SAM; Section 3.7.2)"


id: 3e37e4c96211e7c5b0327817d43cd9f7
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 7162
content:
" This has been well reproduced by \r\nclimate models with prescribed historical ozone concentration or \r\ninteractive ozone chemistry (Gerber and Son, 2014; Son et al., 2018; \r\nFigure 3.19)."


id: 8446e0912baa917c4e152e00eeba2915
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 18348
content:
"The AR5 concluded that 'anthropogenic forcings are very likely to have \r\ncontributed to Arctic sea ice loss since 1979' (Bindoff et al., 2013), based \r\non studies showing that models can reproduce the observed decline only \r\nwhen including anthropogenic forcings, and formal attribution studies. \r\nSince the beginning of the modern satellite era in 1979, Northern \r\nHemisphere sea ice extent has exhibited significant declines in all \r\nmonths with the largest reduction in September (see Section 2.3.2.1.1, \r\nand Figures 3.20 and 3.21 for more details on observed changes). The \r\nrecent Arctic sea ice loss during summer is unprecedented since 1850 \r\n(high confidence), but as in AR5 and SROCC there remains only medium \r\nconfidence that the recent reduction is unique during at least the past \r\n1000 years due to sparse observations (Sections 2.3.2.1.1 and 9.3.1.1). \r\nCMIP5 models also simulate Northern Hemisphere sea ice loss over the \r\nsatellite era but with large differences among models (e.g"


id: 4ac4d12b23b496050c65865ff23625ca
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 9181
content:
", Massonnet \r\net al., 2012; Stroeve et al., 2012). The envelope of simulated ice loss \r\nacross model simulations encompasses the observed change, although \r\nobservations fall near the low end of the CMIP5 and CMIP6 distributions \r\nof trends (Figure 3.20). CMIP6 models on average better capture the \r\nobserved Arctic sea ice decline, albeit with large inter-model spread. Notz \r\net al. (2020) found that CMIP6 models better reproduce the sensitivity \r\nof Arctic sea ice area to CO2 emissions and global warming than earlier \r\nCMIP models although the models' underestimation of this sensitivity \r\nremains. Davy and Outten (2020) also found that CMIP6 models can \r\nsimulate the seasonal cycle of Arctic sea ice extent and volume better \r\nthan CMIP5 models. For the assessment of physical processes associated \r\nwith changes in Arctic sea ice, see Section 9.3.1.1."


id: 61da9fafe0f7a5b9583066c6209c169d
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 12960
content:
"Seviour et al. (2016) found that stratosphere-resolving CMIP5 \r\nmodels, on average, reproduce the observed frequency of vortex \r\nsplits (one form of SSWs) but with a wide range of model-specific \r\nbiases. Models that produce a better mean state of the polar vortex \r\nalso tend to produce a more realistic SSW frequency (Seviour \r\net al., 2016). The mean sea level pressure anomalies occurring in \r\nCMIP5 model simulations when an SSW is underway, however, \r\ndiffer substantially from those in reanalyses (Seviour et al., \r\n2016). Unlike stratosphere-resolving models, models with limited \r\nstratospheric resolution, which make up more than half of the \r\nCMIP5 ensemble, underestimate the frequency of SSWs (Osprey \r\net al., 2013; J. Kim et al., 2017)"


id: 95f5248e7b7c5f3201b4f3fb6314b584
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 21096
content:
" Taguchi (2017) found a general \r\nunderestimation in CMIP5 models of the frequency of 'major' SSWs \r\n(which are associated with a break-up of the polar vortex), an \r\naspect of an under-representation in those models of dynamical \r\nvariability in the stratosphere. Wu and Reichler (2020) found that \r\nfiner vertical resolution in the stratosphere and a model top above \r\nthe stratopause tend to be associated with a more realistic SSW \r\nfrequency in CMIP5 and CMIP6 models."


id: 6245e96c09a6c80890453630d3df022e
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 13016
content:
"Since AR5, there have been several new detection and attribution \r\nstudies on Arctic sea ice. While the attribution literature has mostly used \r\nsea ice extent (SIE), it is closely proportional to sea ice area (SIA; Notz, \r\n2014), which is assessed in Chapters 2 and 9 and shown in Figures 3.20 \r\nand 3.21. Kirchmeier-Young et al. (2017) compared the observed time \r\nseries of the September SIE over the period 1979-2012 with those from \r\ndifferent large ensemble simulations which provide a robust sampling \r\nof internal climate variability (CanESM2, CESM1, and CMIP5) using an \r\noptimal fingerprinting technique. They detected anthropogenic signals \r\nwhich were separable from the response to natural forcing due to solar \r\nirradiance variations and volcanic aerosol, supporting previous findings \r\n(Figure 3.21; Min et al., 2008b; Kay et al., 2011; Notz and Marotzke, \r\n2012; Notz and Stroeve, 2016)"


id: f3b2bf21e5ea8055c1370ae6bf76ebb3
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 35854
content:
" Using selected CMIP5 models and \r\nthree independently derived sets of observations, Mueller et al. (2018) \r\ndetected fingerprints from greenhouse gases, natural, and other \r\nanthropogenic forcings simultaneously in the September Arctic SIE over "


id: d75d3e43d869e25403d415a26e565b2
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 31402
content:
"forced and unforced simulations using multiple climate models and \r\nfound that internal variability explains about 40% of the observed \r\nSeptember sea ice melting trend, supporting previous studies (Stroeve \r\net al., 2012). Based on the large ensembles of CESM1 and CanESM2, \r\nthe September Arctic sea ice extent variance first increases and then \r\ndecreases as SIE declines from its pre-industrial value (Kirchmeier\u0002Young et al., 2017; Mueller et al., 2018) consistent with previous work \r\n(Goosse et al., 2009), but neither study found a strong sensitivity of \r\ndetection and attribution results to the change in variability. Further \r\nwork has indicated that internally-driven summer atmospheric \r\ncirculation trends with enhanced atmospheric ridges over Greenland \r\nand the Arctic Ocean, which project on the negative phase of the \r\nNorth Atlantic Oscillation (Section 3.7.1), play an important role \r\nin the observed Arctic sea ice loss (Hanna et al., 2015; Ding et al., \r\n2017)"


id: f2f31b61b8068df31551b4be2f6dc9c0
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 35747
content:
" A fingerprint analysis using the CESM large ensemble suggests \r\nthat this internal variability accounts for 40-50% of the observed \r\nSeptember Arctic sea ice decline (Ding et al., 2019; England et al., \r\n2019). Internally-generated decadal tropical variability and associated \r\natmospheric teleconnections were suggested to have contributed to \r\nthe changing atmospheric circulation in the Arctic and the associated \r\nrapid sea ice decline from 2000 to 2014 (Meehl et al., 2018)."


id: 535f83ec72f4d956b90385c4b6cba25c
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 10587
content:
"and declines in the Bellingshausen and Amundsen Seas (Hobbs et al., \r\n2016). Observed regional trends have been particularly large in austral \r\nautumn (see Section 2.3.2.1.2, and also Section 9.3.2.1 for more details \r\nof regional changes and related physical processes). Starting in austral \r\nspring of 2016, the ice extent decreased strongly (Turner et al., 2017) and \r\nhas since remained anomalously low (Figure 3.21 and Figure 2.20). This \r\ndecrease has been associated with anomalous atmospheric conditions \r\nassociated with teleconnections from warming in the eastern Indian \r\nOcean and a negative Southern Annular Mode (Chenoli et al., 2017; \r\nStuecker et al., 2017; Schlosser et al., 2018; Meehl et al., 2019; Purich \r\nand England, 2019; G. Wang et al., 2019). A decadal-scale warming of \r\nthe near-surface ocean that resulted from strengthened westerlies may \r\nalso have contributed to and helped to sustain the sea ice loss (Meehl \r\net al., 2019)"


id: b9ef267986a6daa0594e2409682c3634
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 26795
content:
" Before satellites and on even longer time scales, very \r\nlimited observational data and proxy coverage leads to low confidence \r\nin all aspects of Antarctic sea ice (Sections 2.3.2.1.2 and 9.3.2.1)."


id: 95ebc14cfea187cba54e87e628403940
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 21092
content:
"CMIP5 climate models generally simulate Antarctic sea ice loss over \r\nthe satellite era since 1979 (Mahlstein et al., 2013; Turner et al., 2013) \r\nin contrast to the observed change, and CMIP6 models also simulate \r\nAntarctic ice loss (Roach et al., 2020; Figure 3.20 and 3.21). A number \r\nof studies have suggested that this discrepancy may be in part due \r\nto the role of internal variability in the observed change (Mahlstein \r\net al., 2013; Polvani and Smith, 2013; Zunz et al., 2013; Meehl et al., \r\n2016c; Turner et al., 2016), including teleconnections associated with \r\ntropical Pacific variability (Meehl et al., 2016c) and changing surface \r\nconditions resulting from multi-decadal ocean circulation variations \r\n(Singh et al., 2019). However, when the spatial pattern is considered, \r\ntrends in the summer and autumn (from 1979-2005) appear outside \r\nthe range of internal variability (Hobbs et al., 2015)"


id: b87eb4cfb931ab543828938cfbf3ca5a
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 26555
content:
" This suggests \r\nthat the models may exhibit an unrealistic simulation of the Antarctic \r\nsea ice forced response or the internal variability of the system. \r\nDiscrepancies among the models in simulated sea ice variability \r\n(Zunz et al., 2013), the sea ice climatological state (Roach et al., \r\n2018), upper ocean temperature trends (Schneider and Deser, 2018), \r\nSouthern Hemisphere westerly wind trends (Purich et al., 2016), or \r\nthe sea ice response to Southern Annular Mode variations (Ferreira \r\net al., 2014; Holland et al., 2017; Kostov et al., 2017; Landrum et al., \r\n2017) may all play some role in explaining these differences with the \r\nobserved trends. Increased fresh water fluxes caused by mass loss of \r\nthe Antarctic Ice Sheet (either by melting at the front of ice shelves \r\nor via iceberg calving) have been suggested as a possible mechanism \r\ndriving the multi-decadal Antarctic sea ice expansion (Bintanja et al., \r\n2015; Pauling et al"


id: 9b94d61477f0ebf45a2cd2c0cbbe8efa
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 22036
content:
", 2016) but there is a lack of consensus on this \r\nmechanism's impacts (Pauling et al., 2017). A recent study based on \r\na decadal prediction system suggests that initializing the state of \r\nthe Antarctic Bottom Water cell allows the system to reproduce the \r\nobserved Antarctic sea ice increase (Zhang et al., 2017), consistent \r\nwith the suggestion that multi-decadal variability associated with \r\nvariations in deep convection has contributed to the observed \r\nincrease in Antarctic sea ice since 1979 (Latif et al., 2013; Zhang \r\net al., 2017; L. Zhang et al., 2019) (see also Section 9.3.2.1).\r\nThere have been several studies that aimed to identify causes of the \r\nobserved Antarctic SIE changes. Gagne et al. (2015) assessed \r\nthe consistency of observed and simulated changes in Antarctic SIE for \r\nan extended period using recovered satellite-based estimates, and found "


id: adf6a52f95665d902d92957e04cf0d
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 24907
content:
"that the observed trends since the mid-1960s are not inconsistent \r\nwith model simulated trends. Studies based on the satellite period \r\nalso indicate that the observed trends are largely within the range of \r\nsimulated internal variability (Hobbs et al., 2016). A few distinct factors \r\nthat led to the weak signal-to-noise ratio in Antarctic SIE trends have \r\nbeen further identified, which include large multi-decadal variability \r\n(Monselesan et al., 2015), the short observational record (e.g., Abram \r\net al., 2013), and the limited model performance at representing \r\nthe complex Antarctic climate system as discussed above (Bintanja \r\net al., 2013; Uotila et al., 2014)"


id: 6f4bf97cd90d7d3c98357486be6cd303
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 15139
content:
" The short period of comprehensive \r\nsatellite observations, beginning in 1979, makes it challenging to set \r\nthe observed increase between 1979 and 2015, or the subsequent \r\ndecrease, in a long-term context, and to assess whether the difference \r\nin trend between observations and models, which mostly simulate \r\nlong-term decreases, is systematic or a rare expression of internal \r\nvariability on decadal to multi-decadal time scales."


id: 13b14e4b0e6e59d38e89e8d238a4f14a
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 600
content:
"The AR5 noted the strong linear correlation between Northern \r\nHemisphere snow cover extent (SCE) and annual-mean surface air \r\ntemperature in CMIP5 models. It was assessed as likely that there \r\nhad been an anthropogenic contribution to observed reductions in \r\nNorthern Hemisphere snow cover since 1970 (Bindoff et al., 2013). \r\nThe AR5 assessed that CMIP5 models reproduced key features of \r\nobserved snow cover well, including the seasonal cycle of snow cover \r\nover northern regions of Eurasia and North America, but had more \r\ndifficulties in more southern regions with intermittent snow cover. \r\nThe AR5 also found that CMIP5 models underestimated the observed \r\nreduction in spring snow cover over this period (Figure  3.22; see \r\nalso Brutel-Vuilmet et al., 2013; Thackeray et al., 2016; Santolaria\u0002Otin and Zolina, 2020). This behaviour has been linked to how \r\nthe snow-albedo feedback is represented in models (Thackeray \r\net al., 2018a)"


id: 645ab7f4ef6978ce9a000276067bf2b6
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 13358
content:
" The CMIP5 multi-model ensemble has been shown to \r\nrepresent the snow-albedo feedback more realistically than CMIP3, \r\nalthough models from some individual modelling centres have not \r\nimproved or have even got worse (Thackeray et al., 2018a). There is \r\nstill a systematic overestimation of the albedo of boreal forest covered \r\nby snow (Thackeray et al., 2015; Y. Li et al., 2016). Consequently, the \r\nsnow albedo feedback might have been overestimated by CMIP5 \r\nmodels (Section 9.5.3; Xiao et al., 2017)."


id: 7b285c6a9954584ba60ee62f26fc2f86
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 16967
content:
"Figure 3.22 | Time series of Northern Hemisphere March-April mean snow \r\ncover extent (SCE) from observations, CMIP5 and CMIP6 simulations. The \r\nobservations (grey lines) are updated Brown-NOAA (Brown and Robinson, 2011), \r\nMudryk et al. (2020), and GLDAS2. CMIP5 (top) and CMIP6 (bottom) simulations \r\nof the response to natural plus anthropogenic forcing are shown in brown, natural \r\nforcing only in green, and the pre-industrial control simulation range is presented in \r\nblue. Five-year mean anomalies are shown for the 1923-2017 period with the x-axis \r\nrepresenting the centre years of each five-year mean. CMIP5 all forcing simulations \r\nare extended by using RCP4.5 scenario simulations after 2005 while CMIP6 all forcing \r\nsimulations are extended by using SSP2-4.5 scenario simulations after 2014"


id: 960e834088a52706e8d34db584bd53f3
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 21116
content:
" Shading \r\nindicates 5th-95th percentile ranges for CMIP5 and CMIP6 all and natural forcings \r\nsimulations, and solid lines are ensemble means, based on all available ensemble \r\nmembers with equal weight given to each model (Section 3.2). The blue vertical bar \r\nindicates the mean 5th-95th percentile range of pre-industrial control simulation \r\nanomalies, based on non-overlapping segments. The numbers in brackets indicate the \r\nnumber of models used. Anomalies are relative to the average over 1971-2000. For \r\nmodels, SCE is restricted to grid cells with land fraction >=50%. Greenland is excluded \r\nfrom the total area summation. Figure is modified from Paik and Min (2020), their \r\nFigure 1. Further details on data sources and processing are available in the chapter \r\ndata table (Table 3.SM.1)."


id: c1518e199b0c908e7739202346933f2c
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 28001
content:
"glaciers (Shannon et al., 2019). Moreover, standalone glacier models \r\nhave now been systematically compared in GlacierMIP (Hock et al., \r\n2019a; Marzeion et al., 2020). The Antarctic and Greenland Ice Sheets \r\nwere absent in global climate models that pre-date CMIP6 (Eyring \r\net al., 2016a), however some preliminary analyses that used results \r\nfrom CMIP5 to drive standalone ice-sheet models were included \r\nin AR5 (Church et al., 2013a). For the first time in CMIP, the latest \r\nCMIP6 phase includes a coordinated effort to simulate temporally \r\nevolving ice sheets within the Ice Sheet Model Intercomparison \r\nProject (ISMIP6; Box  9.3; Nowicki et al., 2016). Our understanding \r\nof aspects of the global water storage contained in glaciers and ice \r\nsheets, and their contribution to sea-level rise, has improved since \r\nAR5 and SROCC (Hock et al., 2019b; Meredith et al"


id: 1aaf3a3fcf96a263557c76a3bc5e2653
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 1675
content:
", 2019) both in \r\nmodels and observations (see assessment of observations and model \r\nevaluation for the Greenland Ice Sheet in Sections 2.3.2.4.1 and 9.4.1; \r\nAntarctica in Sections 2.3.2.4.2 and 9.4.2; and glaciers in Sections \r\n2.3.2.3 and 9.5.1)."


id: 2f3e8e623fa3e8f2cde9c73de6959029
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 4851
content:
"In summary, it is very likely that anthropogenic influence contributed \r\nto the observed reductions in Northern Hemisphere springtime snow \r\ncover since 1950. CMIP6 models better represent the seasonality \r\nand geographical distribution of snow cover than CMIP5 simulations \r\n(high confidence). Both CMIP5 and CMIP6 models simulate strong \r\ndeclines in spring SCE during recent years, in general agreement \r\nwith observations, causing the multi-model mean decreasing trend \r\nin spring SCE to now better agree with observations than in earlier \r\nevaluations. Evidence has yet to emerge that interactions between \r\nvegetation and snow, found problematic in CMIP5, have improved in \r\nCMIP6 models (Section 9.5.3). Such deficiencies in the representation \r\nof snow in climate models mean there is medium confidence in the \r\nsimulation of snow cover over the northern continents in CMIP6 model \r\nsimulations. The models consistently link snow extent to surface air \r\ntemperature (Figure 9.24)"


id: c3831ab8a536e037d25105913101b49c
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 28328
content:
" With warming of near-surface air linked to \r\nanthropogenic influence, and particularly to greenhouse gas increases \r\n(Section 3.3.1.1), this provides additional evidence that reductions in \r\nsnow cover are also caused by human activity."


id: cb588d05355ee41fdfa5b7fe65a01dae
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 29543
content:
"While Chapter  9 (Sections 9.4 and 9.5) discusses process \r\nunderstanding for glaciers and ice sheets, as well as evaluation of \r\nglobal and regional-scale glacier and ice-sheet models, our focus \r\nhere is on the attribution of large-scale changes in glaciers and ice \r\nsheets. Land ice in the form of glaciers has been included in CMIP \r\nclimate and Earth system models as components of the land surface \r\nmodels for many years. However, their representation is simplified \r\nand is omitted altogether in the less complex modelling systems. In \r\nCMIP3 (Meehl et al., 2007) and CMIP5 (Taylor et al., 2012) land ice \r\narea fraction, a component of land surface models, was defined as \r\na time-independent quantity, and in most model configurations was \r\npreset at the simulation initialization as a permanent land feature. \r\nIn CMIP6 considerable progress has been made in improving and \r\nevaluating the representation of modelled land ice"


id: 9771921578ab69707921f98176ef2b51
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 21355
content:
"A regional study considering 85 Northern Hemisphere glacier systems \r\nconcluded that there is a discernible human influence on glacier mass \r\nbalance, with glacier model simulations driven by CMIP5 historical \r\nand greenhouse gas-only simulations showing a glacier mass loss, \r\nwhereas those driven by natural-only forced simulations showed a net \r\nglacier growth (Hirabayashi et al., 2016). In addition, a study of the \r\nrole of climate change in glacier retreat using a simple mass-balance \r\nmodel for 37 glaciers worldwide, concluded that observed length \r\nchanges would not have occurred without anthropogenic climate \r\nchange, with observed length variations exceeding those associated \r\nwith internal variability by several standard deviations in many \r\ncases (Roe et al., 2017). Roe et al. (2021) used the same model to \r\nestimate that at least 85% of cumulative glacier mass loss since \r\n1850 is attributable to anthropogenic influence. While Marzeion \r\net al"


id: 1410043eefb15029e8fa7da056bee3ee
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
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vector_id: 666
content:
"(Sasgen et al., 2020; Tedesco and Fettweis, 2020). These results \r\nsupport the finding that increased surface melting is associated with \r\nwarming, although atmospheric circulation anomalies, including \r\nthe summer North Atlantic Oscillation (NAO) and variations in \r\nsnowfall play an important role in driving interannual variations \r\n(Section 9.4.1.1; Sasgen et al., 2020; Tedesco and Fettweis, 2020). \r\nFurther, a coupled ice-sheet-climate model study found emergence \r\nof decreased surface mass balance prior to the present day in coastal \r\nlocations in Greenland, which dominate the integrated surface mass \r\nbalance (Fyke et al., 2014), suggesting that observed variations \r\nin surface mass balance in these regions might be expected to be \r\ndistinguishable from internal variability"


id: 8ca7d27e5d0c737bf036a9bb71e12316
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 19648
content:
" A CMIP6 simulation of the \r\nhistorical period showed stable Greenland surface mass balance up \r\nto the 1990s, after which it declined due to increased melt and runoff, \r\nconsistent with a downscaled reanalysis (van Kampenhout et al., \r\n2020). Further, all experts surveyed in a structured expert judgement \r\nexercise examining the causes of the increase in mass loss from \r\nthe Greenland Ice Sheet over the last two decades (Bamber et al., \r\n2019) concluded that external forcing was responsible for at least \r\n50% of the mass loss. A comparison of Greenland Ice Sheet mass \r\nloss trends from observations and AR5 model projections for the \r\nperiod 2007-2017 found that the magnitude of the observed surface \r\nmass balance trends was at the top of the AR5 assessed range, \r\nwhile mass loss due to changing ice dynamics was near the centre \r\nof the AR5 range (Slater et al., 2020), providing further evidence of \r\nconsistent anthropogenically-forced mass loss trends in models and \r\nobservations."


id: 7c54a60a3e3cc22333e0780a7cd67045
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 17160
content:
"AR5 assessed that there was low confidence in attributing the causes \r\nof the observed mass loss from the Antarctic Ice Sheet since 1993 \r\n(Bindoff et al., 2013). The SROCC assessed that there is medium \r\nagreement but limited evidence of anthropogenic forcing of Antarctic \r\nmass balance through both surface mass balance and glacier \r\ndynamics. It further assessed that Antarctic ice loss is dominated by \r\nacceleration, retreat and rapid thinning of the major West Antarctic \r\nIce Sheet outlet glaciers (very high confidence), driven by melting \r\nof ice shelves by warm ocean waters (high confidence). Based on \r\nupdated observations, Chapter  2 assesses that there is very high \r\nconfidence that the Antarctic Ice Sheet lost mass between 1992 and \r\n2017, and that there is medium confidence that this mass loss has \r\naccelerated. Models of Antarctic Ice Sheet evolution are evaluated \r\nin detail in Section 9.4.2"


id: bbf516c979ff4c73cc4e01cc532eed58
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 27135
content:
"2, which assesses that there is medium \r\nconfidence in many ice-sheet processes in Antarctic Ice Sheet models, \r\nbut low confidence in the ocean forcing affecting basal melt rates. \r\nCMIP5 and CMIP6 models perform similarly in their simulation of \r\nAntarctic surface mass balance (Section 9.4.2.2, Gorte et al., 2020). "


id: 1b3628a1bb8fb58490770c07829b9168
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 1746
content:
"Detection and attribution studies of change in the Greenland Ice \r\nSheet remain challenging (Kjeldsen et al., 2015; Bamber et al., \r\n2019). This is in part due to the short observational record (Shepherd \r\net al., 2012, 2018, 2020; Bamber et al., 2018; Cazenave et al., 2018; \r\nMouginot et al., 2019; Rignot et al., 2019) and the challenges this \r\nposes to the evaluation of modelling efforts (Section 9.4.1.2). The \r\nlatter require not only dynamic ice-sheet models, but also appropriate \r\natmospheric and oceanic conditions to use as a boundary forcing \r\nto drive the models (Nowicki and Seroussi, 2018; Barthel et al., \r\n2020). Nonetheless, new literature since AR5 finds that ice-sheet \r\nmass balance calculations using reanalysis-driven regional model \r\nsimulations of surface mass balance are found to agree well with \r\nthe observed decrease in ice-sheet mass over the past twenty years \r\n(Fettweis et al., 2020; Sasgen et al"


id: edb84e4533f5546a417cbc993cd92f8f
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 34863
content:
", 2020; Tedesco and Fettweis, \r\n2020), consistent with earlier studies (Flato et al., 2013). These \r\nstudies also show that the exceptional melt events observed in 2012 \r\nand 2019 were associated with exceptional atmospheric conditions "


id: 73b82b3a79bb9128f51cb353145e727a
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 15824
content:
"Ice discharge around the West Antarctic Ice Sheet is strongly \r\ninfluenced by variability in basal melt (Jenkins et al., 2018; Hoffman \r\net al., 2019), in particular at decadal and longer time scales (Snow \r\net al., 2017). Basal melt rate variability can be induced by wind-driven \r\nocean current changes, which may partly be of anthropogenic origin \r\nvia greenhouse gas forcing (Holland et al., 2019). Moreover, ice \r\ndischarge losses from the Antarctic Ice Sheet over the 2007-2017 \r\nperiod are close to the centre of the model-based range projected \r\nin AR5 (Slater et al., 2020). However, expert opinion differs as to \r\nwhether recent Antarctic ice loss from the West Antarctic Ice Sheet \r\nhas been driven primarily by external forcing or by internal variability, \r\nand there is no consensus (Bamber et al., 2019)"


id: 2428441a3565ff6e186a7eb848f0cf9
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 3133
content:
" Anthropogenic \r\ninfluence on the Antarctic surface mass balance, which is expected \r\nto partially compensate for ice discharge losses through increases in \r\nsnowfall, is currently masked by strong natural variability (Previdi and \r\nPolvani, 2016; Bodart and Bingham, 2019), and observations suggest \r\nthat it has been close to zero over recent years (see further discussion \r\nin Section 9.4.2.1; Slater et al., 2020)."


id: 416cc857483b0f889257a7cd1feee693
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 7693
content:
"In CMIP3 and CMIP5 models, large SST biases were found in the \r\nmid- and high latitudes (Flato et al., 2013). In CMIP6, the Northern \r\nHemisphere mid-latitude surface temperature biases appear to be \r\nmarginally improved in the multi-model mean when contrasted to \r\nCMIP5 despite large biases remaining in a few models (Figures 3.23a \r\nand 3.24). There is a decreased spread of the zonal mean SST error \r\nbetween 50degN and 30degS, relative to CMIP5 (Figure  3.24a). On the \r\nother hand, the Southern Ocean's warm surface temperature bias \r\nremains (Figure 3.23a; Beadling et al., 2020), and is on average larger \r\nin CMIP6 than in CMIP5 models (Figures 3.23a and 3.24). This warm \r\nbias is often associated with persistent overlying atmospheric cloud \r\nbiases (Hyder et al., 2018)"


id: ac4f1a3b6e5a2258d0124ff5249eba02
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 24647
content:
" Several other large biases also appear \r\nto remain largely unresolved in CMIP6, particularly warm biases in \r\nexcess of 1degC along the equatorial eastern continental boundaries of \r\nthe tropical Atlantic and Pacific Oceans (Figure 3.23a)."


id: 4aa41296636b8e74590ef5ba6f9b4fb3
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 9149
content:
"In CMIP5, mean state biases in the tropical Pacific Ocean including the \r\nexcessive equatorial cold tongue, erroneous mean thermocline depth \r\nand slope along the equator remained but were improved relative \r\nto CMIP3 (Flato et al., 2013). Misrepresentation of the interaction \r\nbetween the atmosphere and ocean via the Bjerknes feedback and \r\nvertical mixing parameterizations, and a bias in winds were among \r\nthe suggested reasons for the persistent biases (Li et al., 2014; Zhu \r\nand Zhang, 2018). Moving to CMIP6, a reduction of the cold bias in \r\nthe equatorial cold tongue in the central Pacific is found on average \r\nin the CMIP6 models (Figure  3.24b; Grose et al., 2020; Planton \r\net al., 2021), however, this reduced bias is not statistically significant \r\nwhen considered across the multi-model ensemble (Planton et al., \r\n2021)"


id: 935017598079697f22e554f3274117f0
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 20692
content:
" It is also noteworthy that the longitude of the 28degC isotherm \r\nis closer to observed in CMIP6 than in CMIP5, with a coincident \r\nreduction in the CMIP6 inter-model standard deviation (Grose et al., \r\n2020). The latter result implies that there is an improvement in the \r\nrepresentation of the tropical Pacific mean state in CMIP6 models. \r\nComparison of biases in individual HighResMIP models with biases \r\nin lower resolution versions of the same models indicates that there \r\nis no consistent improvement in SST biases in most of the equatorial \r\nPacific with resolution (Figure 3.3e; Bock et al., 2020)."


id: 39952b804f3f6fd58875ba92c8f6b487
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 6437
content:
"Fundamental features such as the mean zonal SST gradient in the \r\ntropical Atlantic were not reproduced in CMIP5 models. Studies \r\nhave proposed that weaker than observed alongshore winds, \r\nunderestimation of stratocumulus clouds, coarse model resolution, and \r\ninsufficient oceanic cooling due to a deeper thermocline depth and \r\nweak vertical velocities at the base of the mixed layer in the eastern \r\nbasin, underpinned these tropical Atlantic SST gradient biases (Hourdin \r\net al., 2015; Richter, 2015). The SST gradient biases still remain in \r\nCMIP6. On average the cold bias in the western part of the basin is \r\nreduced while the warm bias in the eastern part has slightly increased \r\n(Figure 3.24b,c; Richter and Tokinaga, 2020). Several CMIP6 models, \r\nhowever, display large reductions in biases of the zonal SST gradient, \r\nsuch that the eastern equatorial Atlantic warm SST bias and associated \r\nwesterly wind biases are mostly eliminated in these models (Richter \r\nand Tokinaga, 2020)"


id: 4b7733f10917286affe339bf414cd51
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 9288
content:
" The high resolution (HighResMIP) CMIP6 models \r\nshow a better representation of the zonal SST gradient (Figure 3.24b,c), \r\nbut some lower resolution models also perform well, suggesting that \r\nresolution is not the only factor responsible for biases in Tropical \r\nAtlantic SST (Richter and Tokinaga, 2020)."


id: 67734ac51c7f1db311413f38738231eb
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 13863
content:
"Figure 3.26 | Global ocean heat content in CMIP6 simulations and observations. Time series of observed (black) and simulated (red) global ocean heat content \r\nanomalies with respect to 1995-2014 for the full ocean depth (left-hand panel); upper layer: 0-700 m (top right-hand panel); intermediate layer: 700-2000 m (middle \r\nright-hand panel); and the abyssal ocean: >2000 m (bottom right-hand panel). The best estimate observations (black solid line) for the period of 1971-2018, along \r\nwith very likely ranges (black shading) are from Section 2.3.3.1. For the models (1860-2014), ensemble members from 15 CMIP6 models are used to calculate the multi-model \r\nmean values (red solid line) after averaging across simulations for each independent model. The very likely ranges in the simulations are shown in red shading"


id: ff8e5832ca1c205c1cb258151c94bd95
created_at: 2023-03-16 14:01:58
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vector_id: 37751
content:
" Simulation drift \r\nhas been removed from all CMIP6 historical runs using a contemporaneous portion of the linear fit to each corresponding pre-industrial control run (Gleckler et al., 2012). Units \r\nare zettajoules (ZJ; 1021 joule). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."


id: f28fbeac59a556b0bf4179950c8823cd
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 35690
content:
"the global intermediate ocean (700-2000 m) warmed substantially \r\nfrom 1971 to the present. Further, ocean layer warming contributions \r\nare reported as 61% (0-700 m), 31% (700-2000 m) and 8% \r\n(>2000 m) for the 1971 to 2018 period (Table  2.7). CMIP5 model \r\nsimulations replicate this partitioning fairly well for the industrial-era \r\n(1865 to 2017) throughout the upper (0-700 m, 65%), intermediate \r\n(700-2000 m, 20%) and deep (>2000 m, 15%) layers (Gleckler et al., \r\n2016; Durack et al., 2018). The corresponding warming percentages \r\nfor the multi-model mean of a subset of CMIP6 simulations over the \r\n1850-2014 period are 58% for the upper, 21% for the intermediate, \r\nand 22% for the deep-ocean layers (Figure 3.26). These results are \r\nconsistent with SROCC which assessed that it is virtually certain that \r\nboth the upper and intermediate ocean warmed from 2004 to 2016, \r\nwith an increased rate of warming since 1993 (Bindoff et al., 2019)"


id: 61980fedecbaa1bc3838727320ad615f
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 12923
content:
" \r\nThe spatial distribution of these changes for different ocean depths \r\nis assessed in Section 9.2.2.1."


id: b311e0eddd3d256285155fea07b59ad2
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 25689
content:
"While ocean assessments have primarily focused on temperature \r\nchanges, improved observational salinity products since the early \r\n2000s have supported more assessment of long-term ocean salinity \r\nchange and variability from AR4 (Bindoff et al., 2007) to AR5 across \r\nboth models and observations (Flato et al., 2013; Rhein et al., 2013). \r\nThe AR5 assessed that it was very likely that anthropogenic forcings \r\nhave made a discernible contribution to surface and subsurface \r\nocean salinity changes since the 1960s. The SROCC augmented these \r\ninsights, noting that observed high latitude freshening and warming \r\nhave very likely made the surface ocean less dense with a stratification \r\nincrease of between 2.18% and 2.42% from 1970 to 2017 (Bindoff \r\net al., 2019). A recent observational analysis has expanded on these \r\nassessments, suggesting a very marked summertime density contrast \r\nenhancement across the mixed layer base of 6.2-11"


id: 24c02a1d2a0572f2888f5af3cced2b36
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 3223
content:
"6% per decade, \r\ndriven by changes in temperature and salinity, which is more than six \r\ntimes larger than previous estimates (Sallee et al., 2021). An idealized \r\nocean modelling study suggests that the enhanced stratification can \r\naccount for a third of the salinity enhancement signal since 1990 \r\n(Zika et al., 2018). Thus, there has been an expansion of observed \r\nglobal- and basin-scale salinity change assessment literature since \r\nAR5, with many new studies reproducing the key patterns of long\u0002term salinity change reported in AR5 (Rhein et al., 2013), and \r\nlinking these through modelling studies to coincident changes in \r\nevaporation-precipitation patterns at the ocean surface (Sections \r\n2.3.1.3, 3.3.2, 8.2.2.1 and 9.2.2)."


id: b3c860b3d90fc10418efc42824d85cc
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 25800
content:
"Since AR5, the attribution of ocean heat content increases to \r\nanthropogenic forcing has been further supported by more detection \r\nand attribution studies. These studies have shown that contributions \r\nfrom natural forcing alone cannot explain the observed changes in \r\nocean heat content in either the upper or intermediate ocean layers, \r\nand a response to anthropogenic forcing is clearly detectable in ocean \r\nheat content (Gleckler et al., 2016; Bilbao et al., 2019; Tokarska et al., \r\n2019). Moreover, a response to greenhouse gas forcing is detectable \r\nindependently of the response to other anthropogenic forcings \r\n(Bilbao et al., 2019; Tokarska et al., 2019), which has offset part of \r\nthe greenhouse gas induced warming. Further evidence is provided \r\nby the agreement between observed and simulated changes in \r\nglobal thermal expansion associated with the ocean heat content \r\nincrease when both natural and anthropogenic forcings are included \r\nin the simulations (Section 3.5.3"


id: 2e1d0a6449d85694fa14c844d1a73da2
created_at: 2023-03-16 14:01:58
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vector_id: 4697
content:
"2), though internal variability plays a \r\nlarger role in driving basin-scale thermosteric sea level trends (Bilbao \r\net al., 2015). Over the Southern Ocean, warming is detectable over \r\nthe late 20th century and is largely attributable to greenhouse gases \r\n(Swart et al., 2018; Hobbs et al., 2021), while other anthropogenic \r\nforcings such as ozone depletion have been shown to mitigate the \r\nwarming in some of the CMIP5 simulations (Swart et al., 2018; Hobbs \r\net al., 2021). The use of the mean temperature above a fixed isotherm \r\nrather than fixed depth further strengthens a robust detection of the \r\nanthropogenic response in the upper ocean (Weller et al., 2016), and \r\nbetter accounting for internal variability in the upper ocean (Rathore \r\net al., 2020), helps explain reported hemispheric asymmetry in ocean \r\nheat content change (Durack et al., 2014b)."


id: d760bba5b07586187cba53ab389dc6c4
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 31403
content:
"Considering the bulk of evidence, it is extremely likely that human \r\ninfluence has contributed to observed near-surface and subsurface \r\nsalinity changes across the globe since the mid-20th century. All \r\navailable multi-decadal assessments have confirmed that the \r\nassociated pattern of change corresponds to fresh regions becoming \r\nfresher and salty regions becoming saltier (high confidence). CMIP5 \r\nand CMIP6 models are only able to reproduce these patterns \r\nin simulations that include greenhouse gas increases (medium \r\nconfidence). Changes to the coincident atmospheric water cycle and \r\nocean-atmosphere fluxes (evaporation and precipitation) are the \r\nprimary drivers of the basin-scale observed salinity changes (high \r\nconfidence). This result is supported by all available observational \r\nassessments, along with a growing number of climate modelling \r\nstudies targeted at assessing ocean and water cycle changes"


id: d8a8b00c15807d5c67228362fe69b344
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 31599
content:
" \r\nThe basin-scale changes are consistent across models and intensify \r\non centennial scales from the historical period through to the \r\nprojections of future climate (high confidence)."


id: e5aa0258b4a09a6dfb35b0794c1ad66f
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 33639
content:
"In an attribution study of the sea-level contributions of glaciers, \r\nMarzeion et al. (2014) found that between 1991 and 2010, the \r\nanthropogenic fraction of global glacier mass loss was 69 +- 24% (see \r\nalso Section 3.4.3.1). Slangen et al. (2016) considered all quantifiable \r\ncomponents of the GMSL budget and showed that anthropogenically \r\nforced changes account for 69 +- 31% of the observed sea level rise \r\nover the period 1970 to 2005, whereas natural forcings combined \r\nwith internal variability have a much smaller effect - only contributing \r\n9 +- 18% of the change over the same period. These studies indicate \r\nthat about 70% of the combined change in glaciers, ice-sheet surface \r\nmass balance and thermal expansion since 1970 can be attributed \r\nto anthropogenic forcing, and that this percentage has increased \r\nover the course of the 20th century. Detection studies on GMSL \r\nchange in the 20th century (Becker et al., 2014; Dangendorf et al"


id: 3e69879e9e57f2c4142b841818e21370
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 7194
content:
", \r\n2015) found that observed total GMSL change in the 20th century \r\nwas inconsistent with internal variability. Dangendorf et al. (2015) \r\ndetermined that for 1900 to 2011 at least 45% of GMSL change is \r\nhuman-induced. A study that developed a semi-empirical model to \r\nlink sea-level change to observed GMST change concluded that at \r\nleast 41% of the 20th century sea-level rise would not have happened \r\nin the absence of the century's increasing GMST and that there was "


id: 65f5c3f0498c81ac4d520d0fe0e15fc8
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 13632
content:
"general features of the AMOC mean state reasonably well, but there is \r\na large spread in the latitude and depth of the maximum overturning, \r\nand the maximum AMOC strength (Figure 3.30a).\r\nThe short length of the observed time-series (RAPID has measured the \r\nAMOC since 2004), sparse observations, observational uncertainties \r\n(Sinha et al., 2018), as well as significant observed variability on \r\ninterannual and longer time scales, makes comparison with modelled \r\nAMOC variability challenging. RAPID observations show that the \r\noverturning at 26degN was 2.9 Sv weaker in the multi-year average of \r\n2008-2012 relative to 2004-2008 and 2.5 Sv weaker in 2012-2017 \r\nrelative to 2004-2008 (Smeed et al., 2014, 2018) (see also Section \r\n2.3.3.4.1). As expected, this weakening was accompanied by \r\na  significant reduction in northward heat transport (Bryden et al., \r\n2020)"


id: 651808e08f75ce1be5db3786dcac4055
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 13469
content:
" CMIP5 and CMIP6 models produce a forced weakening of the \r\nAMOC over the 2012-2017 period relative to 2004-2008, but at \r\n26degN the multi-model mean response is substantially weaker than \r\nthe observed AMOC decline over the same period. The discrepancy \r\nbetween the modelled multi-model mean (i.e., the forced response) \r\nand the RAPID observed AMOC changes has led studies to suggest \r\nthat the observed weakening over 2004-2017 is largely due to \r\ninternal variability (Yan et al., 2018). However, comparison of \r\nobserved RAPID AMOC variability with modelled variability also \r\nreveals that most CMIP5 models appear to underestimate the \r\ninterannual and decadal time scale AMOC variability (Roberts et al., \r\n2014; Yan et al., 2018), and, although the overall variance is larger \r\nin CMIP6 than in CMIP5, similar results are found analysing the \r\nCMIP6 models (Figure 3.30b,c)"


id: d6364d03347319bd4148d8b4909c96f9
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vector_id: 31217
content:
" It is currently unknown why most \r\nmodels underestimate this AMOC variability, or whether they are \r\nunderestimating the internal or externally forced components. \r\nThis underestimation of AMOC variability may also have potential \r\nimplications for detection and attribution, the relationship between \r\nAMOC and AMV (see Section 3.7.7), and near-term predictions. \r\nThere is also emerging evidence, based on analysis of freshwater \r\ntransports, that the AMOC in CMIP5-era models is too stable, largely \r\ndue to systematic biases in ocean salinity (W. Liu et al., 2017; \r\nMecking et al., 2017). Such a  systematic bias may potentially be \r\nlinked with the underestimation of both simulated AMOC internal \r\nvariability through eddy-mean flow interactions that are poorly \r\nrepresented in standard CMIP-class model resolution (Leroux et al., \r\n2018), and externally forced change."


id: e8b71f8811d24d519ca046f0242a3a71
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 34127
content:
"Despite the additional six years or so of observations since AR5, the \r\nevaluation of the AMOC in models continues to be severely hampered \r\nby  the geographically sparse and temporally short observational \r\nrecord. The longest continuous observational estimates of the AMOC \r\nare based on measurements taken at 26degN by the RAPID-MOCHA \r\narray (Smeed et al., 2018). Basic evaluation of the AMOC at 26degN \r\nshows that the CMIP5 and CMIP6 multi-model mean overturning \r\nstrength is comparable with RAPID (Reintges et al., 2017; Weijer et al., \r\n2020), but the model range is large (12-29 sverdrups (Sv)) for CMIP5 \r\n(Zhang and Wang, 2013); and 10-31 Sv for CMIP6 (Weijer et al., 2020) \r\n(Figure 3.30a). It is noted that deviations of AMOC strength in CMIP5 \r\nmodels have been related to global-scale sea surface temperature \r\nbiases (C. Wang et al., 2014). Both coupled and ocean-only models \r\nalso underestimate the depth of the AMOC cell (Danabasoglu et al., \r\n2014; Weijer et al., 2020; Figure 3"


id: a79ca4b7e1c38fce3b62544719c3b39b
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 23879
content:
"30a). Paleo-climatic evidence has \r\nalso raised questions regarding the accuracy of the representation of \r\nthe strength and depth of the modelled AMOC during past periods \r\n(Otto-Bliesner et al., 2007; Muglia and Schmittner, 2015). Overall, \r\nhowever, both the CMIP5 and CMIP6 model ensembles simulate the "


id: f4f81acd007bf5770958d6d1236c60f1
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 36051
content:
"As reported in Section 2.3.3.4.1, estimates of AMOC since at least \r\n1950, which are generated from observed surface temperatures \r\nor sea surface height, suggest the AMOC weakened through the \r\n20th century (low confidence) (Ezer et al., 2013; Caesar et al., 2018). \r\nOver the same period, the CMIP5 multi-model mean showed no \r\nsignificant net forced response in AMOC (Cheng et al., 2013). However, \r\na significant forced change is simulated in the CMIP6 multi-model \r\nmean, where a clear increase of the AMOC is seen over the 1940-1985 \r\nperiod (Figure 3.30e; Menary et al., 2020). Although there is general \r\nagreement that the influence of greenhouse gases acts to a weaken \r\nthe modelled AMOC (Delworth and Dixon, 2006; Caesar et al., 2018), \r\nchanges in solar, volcanic and anthropogenic aerosol emissions can \r\nlead to temporary changes in AMOC on decadal- to multi-decadal \r\ntime scales (Delworth and Dixon, 2006; Menary et al., 2013; Menary \r\nand Scaife, 2014; Swingedouw et al"


id: 1ae196c420568abf00b255a80fed673e
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 1701
content:
"Despite notable improvements of CMIP6 models compared to CMIP5 \r\nmodels, inherent limitations in the representation of important \r\nprocesses at play in the Southern Ocean's horizontal and vertical \r\ncirculation remain (Section 9.2.3.2). For instance, Southern Ocean \r\nmesoscale eddies are largely parameterized in the current generation \r\nof climate models and, despite their small spatial scales, they are \r\na key element for establishing the ACC and upper overturning cell, as \r\nwell as for their future evolution under changing atmospheric forcing \r\n(Kuhlbrodt et al., 2012; Downes and Hogg, 2013; Gent, 2016; Downes \r\net al., 2018; Poulsen et al., 2018). The absence of ice-sheet coupling \r\nin the CMIP6 model suite is another important limitation, as basal \r\nmeltwater and calving can influence the circulation, particularly the \r\nlower cell of the Southern Ocean (Bronselaer et al., 2018; Golledge \r\net al., 2019; Lago and England, 2019; Jeong et al., 2020; Moorman \r\net al., 2020)"


id: 483e3d5630a9680cbbb246ede9bb3ffa
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 8737
content:
" We note that early development of global climate \r\nmodels with interactive ice-shelf cavities has begun and is showing \r\npotential to be developed (Jeong et al., 2020)."


id: b7b91cb0f0aae1e5e686022619aa86
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 26440
content:
"The modelled strength of the ACC clearly improved from CMIP3, in \r\nwhich the models tended to underestimate the strength of the ACC, \r\nto CMIP5 (Meijers et al., 2012). This improvement in the realism of \r\nACC strength continues from CMIP5 to CMIP6, with the modelled ACC \r\nstrength converging toward the magnitude of observed estimates of \r\nnet flow through the Drake Passage (Beadling et al., 2020). There is, \r\nhowever, a small number of models that still display an ACC that \r\nis much weaker than that observed, while several models also display \r\nmuch more pronounced ACC decadal variability than that observed \r\n(Beadling et al., 2020). The increased realism of the ACC was at least \r\npartly related to noted improvements in all metrics of the Southern \r\nOcean's surface wind stress forcing (Beadling et al., 2020)"


id: a3b9559ad35548643682cbdea187be99
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 23322
content:
" The most \r\nnotable wind stress forcing improvements were found in the strength \r\nand the latitudinal position of the zonally-averaged westerly wind \r\nstress maximum (Beadling et al., 2020; Bracegirdle et al., 2020). \r\nWhile the two-cell structure of the overturning circulation appears \r\nto be well captured by CMIP5 models (Sallee et al., 2013; Russell \r\net al., 2018), they tend to underestimate the intensity of the lower \r\ncell overturning, and overestimate the intensity of the upper cell "


id: 6baf7424624c683a20c119accab34ae2
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 14570
content:
"The AR5 did not make attribution statements on changes in global \r\ncarbon sinks. The IPCC Special Report on Climate Change and Land \r\n(SRCCL) assessed with high confidence that global vegetation \r\nphotosynthetic activity has increased over the last 2-3 decades \r\n(Jia et al., 2019). That increase was attributed to direct land use \r\nand management changes, as well as to CO2 fertilization, nitrogen \r\ndeposition, increased diffuse radiation and climate change (high \r\nconfidence). The AR5 assessed with high confidence that CMIP5 \r\nEarth System Models (ESMs) simulate the global mean land and \r\nocean carbon sinks within the range of observation-based estimates \r\n(Flato et al., 2013). The IPCC SRCCL, however, noted the remaining \r\nshortcomings of carbon cycle schemes in ESMs (Jia et al., 2019), \r\nwhich for example do not properly incorporate thermal responses of \r\nrespiration and photosynthesis, and frequently omit representations \r\nof permafrost thaw (Comyn-Platt et al"


id: 198bcdc2ca816976d101c3fed0e4ee2c
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 1495
content:
", 2018), the nitrogen cycle \r\n(R.Q. Thomas et al., 2015) and its influence on vegetation dynamics \r\n(Jeffers et al., 2015), the phosphorus cycle (Fleischer et al., 2019), and \r\naccurate implications of carbon store changes for a range of land use "


id: f579eb4bab5550e37994a4f09f37191
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 36145
content:
"constrain specific parts of the terrestrial carbon cycle such as plant \r\nrespiration (Huntingford et al., 2017). Figure 3.31 compares historical \r\nemissions-driven CMIP6 simulations of global mean atmospheric CO2\r\nconcentration and net ocean and land carbon fluxes to the assessed \r\nCO2 concentration and fluxes from the Global Carbon Project \r\n(Friedlingstein et al., 2019). For 2014, the CMIP6 models simulate \r\na range of CO2 concentrations centred around the observed value of \r\n397 ppmv, with a range of 381 to 412 ppmv. GSAT anomalies simulated \r\nover the historical period are very similar in models that simulate or \r\nprescribe changes in atmospheric CO2 concentrations (Figures 3.31b \r\nand 3.4a). Most models simulate realistic temporal evolution of the \r\nglobal net ocean and land carbon fluxes, although model spread is \r\nlarger over land (Figure 3.31c,d; see also Sections 3.6.2 and 5.4.5.2, \r\nand Figure  5.24)"


id: 24236eac7193232596f2c6c2c9102653
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 3127
content:
" Although literature published soon after AR5 \r\nhighlighted the importance of representing nitrogen limitation on \r\nplant growth (Peng and Dan, 2015; R.Q. Thomas et al., 2015), more \r\nrecent studies note that models without nitrogen limitation can still \r\nbe consistent with the latest estimates of historical carbon cycle \r\nchanges (Arora et al., 2020; Meyerholt et al., 2020). Uncertainties \r\nin the photosynthetic response to atmospheric CO2 concentrations \r\nat global scales, shifts in carbon allocation and turnover, land-use \r\nchange (Hoffman et al., 2014; Wieder et al., 2019), and water \r\nlimitation are also important influences on land carbon fluxes."


id: 9a054a885221ab71a152f3ea32df96b
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 21759
content:
"This section considers three main large-scale indicators of climate \r\nchange relevant to the terrestrial carbon cycle: atmospheric \r\nCO2 concentration, atmosphere-land CO2 fluxes, and leaf area \r\nindex. These indicators were chosen because they have been \r\nthe target of attribution studies. Other indicators, like land use \r\nand management, and wildfires, relate to human influence \r\nbut are discussed in Chapter  5. Chapter  7 discusses energetic \r\nconsequences of changes in the terrestrial carbon cycle in \r\nSection 7.4.2.5.2. CMIP5 and CMIP6 ESMs are most often run \r\nwith prescribed observed historical changes in atmospheric \r\nCO2 concentration and diagnose CO2 emissions consistent \r\nwith these"


id: 8d736eff5514ba3194973b42d22877b7
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 19780
content:
" Such calculations require that the models simulate \r\nrealistic changes in the terrestrial carbon cycle over the historical \r\nperiod, as changes to land carbon stores will influence the size \r\nof CO2 emissions consistent with prescribed CO2 pathways, and \r\nassociated remaining carbon budgets (Section 5.5). Such testing of \r\nexisting models is needed while also recognising there are process \r\nrepresentations still requiring inclusion."


id: 23b7c18984e6af13261139554cc77dbb
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 3068
content:
"importance of CO2 fertilization is decreased by drought stress, but \r\nalso found only a small contribution from agricultural and land use \r\nchanges. However, many global models do not include nitrogen \r\nfertilization, changes to crop cultivars or irrigation effects, with the \r\nlatter associated with deficiencies in simulated terrestrial water \r\ncycling (H. Yang et al., 2018). All these factors influence the capability \r\nof models to simulate accurately the seasonal cycle in atmosphere\u0002land CO2 exchanges. Model comparisons to the atmospheric CO2\r\nconcentration record for Barrow, Alaska, suggest that models \r\nunderestimate current levels of carbon fixation (Winkler et al., \r\n2019) and have deficiencies in their phenological representation of \r\ngreenness levels, particularly for autumn (Z. Li et al., 2018). Based on \r\nthese studies and noting the uncertainty in the processes ultimately \r\ndriving changes in atmospheric CO2 seasonal cycles (Section 5.2.1"


id: d86076d5686ffd3714b7833871e42877
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 31567
content:
"4), \r\nwe assess with medium confidence that fertilization by anthropogenic \r\nincreases in atmospheric CO2 concentrations is the main driver of the \r\nincrease in the amplitude of the seasonal cycle of atmospheric CO2."


id: 77903f50627010faf0c5e226382534f5
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 16364
content:
"All models and observational estimates agree that interannual \r\nvariability in net CO2 uptake is much larger over land than over \r\nthe ocean. Studies demonstrate that regional variations in both \r\nthe trends and the yearly strength of the terrestrial carbon sink are \r\nconsiderable. Land carbon uptake is dominated by the extratropical \r\nnorthern latitudes (see also Section 5.4.5.3 and Figure  5.25; Ciais \r\net al., 2019) because the tropics may have become a net source of \r\ncarbon (Baccini et al., 2017). At local to regional scales, the dominant \r\ndriver of yearly sink strength variations is water availability, but at \r\ncontinental to global scales, temperature anomalies are the dominant \r\ndriver (Section 5.2.1.4.2; Jung et al., 2017). The major role of levels \r\nof water stored in the ground in influencing land-atmosphere CO2\r\nexchange has also been confirmed through simultaneous analysis \r\nof satellite gravimetry and atmospheric CO2 levels (Humphrey et al., \r\n2018)"


id: d127ae3b2430401b07959341df63b1a3
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 30441
content:
" When considered globally, simulated land and ocean carbon \r\nsinks fall within the range of observation-based estimates with high \r\nconfidence. But there is also high confidence that that apparent \r\nsuccess arises for the wrong reasons, as models underestimate the \r\nNorthern Hemisphere carbon sink, as discussed in Section 5.4.5.3."


id: 1a1c2dfb0914b635675e0baf54d6fc82
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 1570
content:
"Detection and attribution methods have been applied to leaf area \r\nindex, which represents 'greenness' and general photosynthetic \r\nproductivity (see Section 2.3.4.3). Nitrogen deposition and land \r\ncover change trends remain small compared to variability, so \r\nattributing changes in leaf area index to those processes is difficult. \r\nUsing three satellite products and ten land models, Zhu et al. (2016) \r\nfound increases in leaf area index (greening) over 25-50% of global \r\nvegetated areas, and they attributed 70% of this greening to CO2\r\nfertilization, although they found that land use change can dominate \r\nregionally. This is consistent with the attribution study of observed \r\ngreening of Mao et al. (2016), and with Mao et al. (2013) who found \r\nthat CO2 fertilization was the dominant cause of enhanced vegetation \r\ngrowth, with latitudinal changes in leaf area index explained by the \r\nlarger land surface warming in the Northern Hemisphere"


id: 8067364c50ef1e0c686a57c91704e232
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 17730
content:
" These \r\nconclusions are also consistent with those of Zhu et al. (2017), \r\nwho found a dominant role for CO2 fertilization in driving leaf area \r\nindex changes in an attribution study in which land models were \r\nfirst weighted by performance. However, Chen et al. (2019) has \r\nchallenged these results by showing that greening in India and China \r\nwas driven by land-use change."


id: ba636d4aa9b02af79e9dbfd35a1f4a5a
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 26856
content:
"The seasonal cycle in atmospheric CO2, which is driven by the \r\ndrawdown of carbon by photosynthesis on land during the summer \r\nand release by respiration during the winter, has increased in \r\namplitude since the start of systematic monitoring (Figure 3.32; see \r\nalso Section 2.3.4.1). This trend, which is larger at higher latitudes of \r\nthe Northern Hemisphere, was first reported by Keeling et al. (1996) \r\nand has continued. Changes in vegetation productivity have also \r\nbeen observed, as well as longer growing seasons (Park et al., 2016). \r\nHowever, a slow down of the increasing trend has been noted, linked to \r\na slow down of both vegetation greening and growing-season length \r\nincreases (Buermann et al., 2018; Z. Li et al., 2018; K. Wang et al., \r\n2020). Figure 3.32 shows that CMIP6 terrestrial carbon cycle models \r\npartially capture the increasing amplitude of the seasonal cycle of the \r\nland carbon sink, also seen in observational reconstructions"


id: ebc59e88596ff5183af8c6f182cb080f
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 34599
content:
" However, \r\nthe identification of the human influence that contributes most \r\nstrongly to these changes in the seasonal cycle is debated."


id: 273eecef35b8bc7ba59f90cc9d6c9ad4
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 3574
content:
"Proposed causes of the trend in the amplitude of the seasonal cycle \r\nof CO2, and its amplification at higher latitudes, include increases in \r\nthe summer productivity and/or increases in the magnitude of winter \r\nrespiration of northern ecosystems (Barichivich et al., 2013; Graven \r\net al., 2013; Forkel et al., 2016; Wenzel et al., 2016), increases in \r\nproductivity throughout the Northern Hemisphere by CO2 fertilization, \r\nand increases in the productivity of agricultural crops in northern \r\nmid-latitudes (Gray et al., 2014; Zeng et al., 2014). Recent studies \r\nhave attempted to quantify the different contributions by comparing \r\natmospheric CO2 observations with ensembles of land surface \r\nmodel simulations. Piao et al. (2017) found that CO2 fertilization of \r\nphotosynthesis is the main driver of the increase in the amplitude \r\nof the seasonal cycle of atmospheric CO2 but noted that climate \r\nchange drives the latitudinal differences in that increase. North of \r\n40degN, Bastos et al"


id: 48760a22c76783051e2dc84e945c5a35
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 8772
content:
" (2019) also found CO2 fertilization to be the most \r\nlikely driver, with warming at northern high latitudes contributing \r\na decrease in amplitude, in contrast to earlier conclusions (Graven \r\net al., 2013; Forkel et al., 2016), and agricultural and land use \r\nchanges making only a small contribution. For temperate regions \r\nof the Northern Hemisphere, K. Wang et al. (2020) found that the "


id: da36a885c38982bdb5ae1320808b7b47
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 31854
content:
"The AR5 concluded that oxygen concentrations have decreased in \r\nthe open ocean since 1960 and such decreases can be attributed \r\nin part to human influence with medium confidence. The decrease \r\nin ocean oxygen content in the upper 1000 m, between 1970 and \r\n2010, is further confirmed in SROCC (medium confidence), with \r\nthe oxygen minimum zone expanding in volume (see also Section \r\n5.3.3.2). Observed oxygen declines over the last several decades \r\n(Stendardo and Gruber, 2012; Stramma et al., 2012; Schmidtko et al., \r\n2017) match model estimates in the surface ocean (Oschlies et al., \r\n2017) but are much larger than model derived estimates in the \r\ninterior (Bopp et al., 2013; Cocco et al., 2013). Some of this difference \r\nhas been interpreted as due to a lack of representation of coastal \r\neutrophication in these models (Breitburg et al., 2018), but much of \r\nit remains unexplained"


id: 6c7120b49911949ea6451387238beb10
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 14686
content:
" This disparity is particularly apparent in the \r\neastern Pacific oxygen minimum zone, where some CMIP5 models \r\nshowed increasing trends whereas observations show a strong \r\ndecrease (Cabre et al., 2015). However, proxy reconstructions suggest \r\nthat over the last century the ocean may have in fact undergone \r\nincreases in oxygen in the most oxygen poor regions (Deutsch et al., \r\n2014). As discussed in Section 5.3.1, ocean oxygen went through \r\nwide oscillations on multi-centennial time scales through the last \r\ndeglaciation, with abrupt warming resulting in loss of oxygen in \r\nsubsurface waters of the North Pacific (Praetorius et al., 2015). The \r\nglobal upper ocean oxygen inventory is negatively correlated with \r\nocean heat content with a regression coefficient comparable to that \r\nfound in ocean models (Ito et al., 2017)"


id: ea34715c494cbd2c0eff46ed7baecb46
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 34352
content:
"As one of the most commonly observed surface parameters, the \r\npartial pressure of CO2 has been the topic of considerable detection \r\nand attribution work. In North Atlantic subtropical and equatorial \r\nbiomes, warming has been shown to be a significant and persistent \r\ncontributor to the observed increase in the partial pressure of \r\nCO2 since the mid-2000s with long-term warming leading to \r\na reduction in ocean carbon uptake (Fay and McKinley, 2013), and \r\nwith both the partial pressure of CO2 and associated carbon uptake \r\ndemonstrating strong predictability as a function of interannual to \r\ndecadal climate state (H. Li et al., 2016; Li and Ilyina, 2018). In \r\nthe Southern Ocean however, detection and attribution of surface \r\ntrends in the partial pressure of CO2 has proven more elusive and \r\ndependent on methodology, with some studies suggesting that \r\nSouthern Ocean carbon uptake slowed from about 1990 to 2006 \r\nand subsequently strengthened from 2007 to 2010 (Lovenduski \r\net al"


id: 3e21d8823c47115dda2cd53e3ec0b5cc
created_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 7142
content:
", 2008; Fay et al., 2014; Ritter et al., 2017). Other studies \r\nhave suggested that poor representation of the seasonal cycle in \r\nthe Southern Ocean may confound the models' ability to represent \r\nchanges in the partial pressure of CO2 in the Southern Ocean (Nevison \r\net al., 2016; Mongwe et al., 2018)."


id: e04eafaf24c119292eef224407cabd9a
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 32788
content:
"Ocean acidification is one of the most detectible metrics of \r\nenvironmental change and was well covered in AR5, in which it \r\nwas assessed that the uptake of anthropogenic CO2 had very likely\r\nresulted in acidification of surface waters (Bindoff et al., 2013). \r\nSince then, observations and simulations of multi-decadal trends in \r\nsurface carbon chemistry have increased in robustness. The evidence \r\non ocean pH decline had further strengthened in SROCC with good \r\nagreement found between CMIP5 models and observations and an \r\nassessment that the ocean was continuing to acidify in response to \r\nongoing carbon uptake (Bindoff et al., 2019). An observed decrease \r\nin global surface open ocean pH is assessed in Section 2.3.3.5 to \r\nbe virtually certain to have occurred with a rate of 0.003-0.026 per \r\ndecade for the past 40 years. The ocean acidification has occurred not \r\nonly in the surface layer but also in the interior of the ocean (Sections \r\n2.3.3.5 and 5.3.3)"


id: 7248d64f8fff712aab34ce082b7a2bd
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 15607
content:
" Rates have been observed to be between -0.015 \r\nand -0.020 per decade in mode and intermediate waters of the North \r\nAtlantic through the combined effect of increased anthropogenic and \r\nremineralized carbon (Rios et al., 2015) and acidification has been \r\nobserved down to 3000 m in the deep water formation regions "


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vector_id: 14387
content:
"New studies since AR5 continue to find that CMIP5 models reproduce \r\nthe spatial structure and magnitude of the NAM reasonably well (Lee \r\nand Black, 2013; Zuo et al., 2013; Davini and Cagnazzo, 2014; Ying \r\net al., 2014; Ning and Bradley, 2016; Deser et al., 2017b; Gong et al., \r\n2017) although the North Pacific SLP anomalies remain generally too \r\nstrong (Zuo et al., 2013; Gong et al., 2017) and the subtropical North \r\nAtlantic lobe of SLP anomalies conversely too weak (Ning and Bradley, \r\n2016) in many models. Such overall biases noted in both CMIP3 \r\nand CMIP5 (Davini and Cagnazzo, 2014) persist in CMIP6 historical \r\nsimulations, even though the multi-model multi-member ensemble \r\nmean spatial correlation between modelled and observed NAM is \r\nslightly higher (Figure 3.33a,d,g). Regarding the NAO, the majority \r\nof CMIP5 models very successfully simulate its spatial structure (Lee \r\net al"


id: 85a0ff37471c359feac1af8532ae7fa2
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vector_id: 18562
content:
", 2019) and its associations with extratropical jet, storm track \r\nand blocking variations over a broad North-Atlantic/Europe domain \r\n(Davini and Cagnazzo, 2014) and over land through teleconnections \r\n(Volpi et al., 2020). The good performance of the models is confirmed \r\nin CMIP6 with a marginal improvement of the averaged observation\u0002model spatial correlation (Figure  3.33b,e,h) and better skill based \r\non other evaluation metrics (Fasullo et al., 2020). The slight \r\nunderestimation of the SLP anomalies related to the NAO centres \r\nof actions over the Azores and Greenland-Iceland-Norwegian Seas \r\nremain unchanged compared to CMIP5."


id: 3b81c1e7894f7173513027168cd984da
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 6712
content:
"CMIP5 models with a model top within the stratosphere seriously \r\nunderestimate the amplitude of the variability of the wintertime \r\nNAM expression in the stratosphere, in contrast to CMIP5 models \r\nwhich extend well above the stratopause (Lee and Black, 2015). \r\nHowever, even in the latter models, the stratospheric NAM events, \r\nand their downward influence on the troposphere, are insufficiently \r\npersistent (Charlton-Perez et al., 2013; Lee and Black, 2015). \r\nIncreased vertical resolution does not show any significant added \r\nvalue in reproducing the structure and magnitude of the tropospheric \r\nNAM (Lee and Black, 2013) nor in the NAO predictability as assessed \r\nin a seasonal prediction context with a multi-model approach (Butler \r\net al., 2016)"


id: fa11e24a7578936f7a656d53ece4adc5
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 36857
content:
" On the other hand, there is mounting evidence that \r\na correct representation of the Quasi Biennal Oscillation, extratropical \r\nstratospheric dynamics (the polar vortex and sudden stratospheric \r\nwarmings), and related troposphere-stratosphere coupling, as well as \r\ntheir interplay with ENSO, are important for NAO/NAM timing (Scaife \r\net al., 2016; Karpechko et al., 2017; Domeisen, 2019; Domeisen et al., \r\n2019), in spite of underestimated troposphere-stratosphere coupling \r\nfound in models compared to observations (O'Reilly et al., 2019b)."


id: 29b2113c20f3ced184f5a6d30cb122fc
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 3992
content:
"not statistically significant, the sign of the multi-model mean forced \r\nresponse due to natural forcing is consistent with the observed \r\nreduction of solar activity since the 1980s (Section 2.2.1) whose \r\ninfluence would have favoured the negative phase of wintertime \r\nNAM/NAO based on the fingerprint of the nearly periodical 11-year \r\nsolar cycle extracted from models (Scaife et al., 2013; Andrews et al., \r\n2015; Thieblemont et al., 2015) or observations (Gray et al., 2016; \r\nLudecke et al., 2020). But such an NAO response to solar forcing \r\nremains highly uncertain and controversial, being contradicted by \r\nlonger proxy records over the last millennium (Sjolte et al., 2018) \r\nand modelling evidence (Gillett and Fyfe, 2013; Chiodo et al., \r\n2019). For all seasons and for all individual forcings, uncertainties \r\nremain in the estimation of the forced response in the NAM trend as \r\nevidenced by considerable model spread (Figure 3"


id: 76d82571fd41ca623e7b2396ad678612
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 16274
content:
"34a) and because \r\nthe simulated forced component has small amplitude compared \r\nto internal variability."


id: 46e60886100cc03bd411366c2883b95d
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 8538
content:
"Despite new efforts since AR5 to reconstruct the NAO beyond the \r\ninstrumental record, it is still very challenging to assess the role \r\nof external forcings in the apparent multi-decadal to centennial \r\nvariability present throughout the last millennium. Large uncertainties \r\nremain in the reconstructed NAO index that are sensitive to the \r\ntypes of proxies and statistical methods (Trouet et al., 2012; Ortega \r\net al., 2015; Anchukaitis et al., 2019; Cook et al., 2019; Hernandez \r\net al., 2020; Michel et al., 2020) and reconstructed NAO variations \r\nare often not reproduced using pseudo-proxy approaches in models \r\n(Lehner et al., 2012; Landrum et al., 2013). At low frequency, it \r\nremains challenging to evaluate if the observed or reconstructed \r\nsignal corresponds to an actual change in the NAO intraseasonal to \r\ninterannual intrinsic properties or rather to a change in the mean \r\nbackground atmospheric circulation changes projecting on a specific \r\nphase of the mode"


id: 951a93b64ae64fe74a8fb94c33dbf7bb
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 20964
content:
" Consequently, conflicting results emerge in the \r\nattribution of reconstructed long-term variations in the NAO to solar \r\nforcing, whose influence thus remains controversial (Gomez-Navarro \r\nand Zorita, 2013; Moffa-Sanchez et al., 2014; Ortega et al., 2015; \r\nAit Brahim et al., 2018; Sjolte et al., 2018; Xu et al., 2018). Influences \r\nfrom major volcanic eruptions appear to be more robust (Ortega et al., \r\n2015; Swingedouw et al., 2017) even if some modelling experiments \r\nquestion the amplitude of the response, which mostly projects on \r\nthe positive phase of the NAM/NAO (Bittner et al., 2016). The \r\nforced response is dependent on the strength, seasonal timing and \r\nlocation of the eruption but may also depend on the mean climate \r\nbackground state (Zanchettin et al., 2013) and/or the phases of the \r\nmain modes of decadal variability such as the AMV (Section 3.7.7; \r\nMenegoz et al., 2018)."


id: 5707040d0f48149dc146726ec00b1c34
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 11171
content:
"Additional research has shown that CMIP5 models reproduce \r\nthe spatial structure of the SAM well, but tend to overestimate its \r\nvariability in austral summer at interannual time scales, although this \r\nvariability is within the observational uncertainty (Figure  3.33c,f,i; \r\nZheng et al., 2013; Schenzinger and Osprey, 2015). This is related \r\nto the models' tendency to simulate slightly more persistent SAM \r\nanomalies in summer compared to reanalyses (Schenzinger and \r\nOsprey, 2015; Bracegirdle et al., 2020). This may be due in part to \r\ntoo weak a  negative feedback from tropospheric planetary waves \r\n(Simpson et al., 2013). CMIP6 models show improved performance \r\nin reproducing the spatial structure and interannual variance of the \r\nSAM in summer based on Lee et al. (2019) diagnostics (Figure 3.33i), \r\nwith a better match of its trend with reanalyses over 1979-2014 \r\n(Figure 3"


id: 2b48907cc3b4fcdc9abd8e124f5a7bb8
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 4247
content:
"33l), more realistic persistence and improved positioning of \r\nthe westerly jet, which in CMIP5 models on average is located too far \r\nequatorward (Bracegirdle et al., 2020; Grose et al., 2020). In CMIP5, it \r\nis also found that models which extend above the stratopause tend \r\nto simulate stronger summertime trends in the late 20th century \r\nthan their counterparts with tops within the stratosphere (Rea \r\net al., 2018; Son et al., 2018), though other differences between \r\nthese sets of models, such as additional physical processes operating \r\nin the stratosphere or interactive ozone chemistry, may have also \r\naffected these results (Gillett et al., 2003a; Sigmond et al., 2008; Rea \r\net al., 2018). At the surface, Ogawa et al. (2015) demonstrate with an \r\natmospheric model the importance of sharp mid-latitude SST gradients \r\nfor stratospheric ozone depletion to affect the SAM in summer"


id: c2d4df76f6d40087328ec0bfad229891
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 28233
content:
" These \r\nstudies imply that the well resolved stratosphere combined with finer \r\nocean horizontal resolution has contributed to the stronger simulated \r\ntrends in CMIP6 than in CMIP5."


id: 4608edafab7eff5ac1877b2def5b385d
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 8403
content:
"to internal variability could be even lower (Figure 3.34b). Although \r\npaleo-reconstructions of the SAM index are uncertain and vary in \r\nterms of long-term trends (Section 2.4.1.2), new reconstructions \r\nshow that the 60-year summertime SAM trend since the mid-20th \r\ncentury is outside the 5th-95th percentile range of the trends in the \r\npre-industrial variability, which matches the trend range of CMIP5 \r\npre-industrial control simulations well (Datwyler et al., 2018).\r\nIn general agreement with AR5, new research continues to indicate \r\nthat both stratospheric ozone depletion and increasing greenhouse \r\ngases have contributed to the trend of the SAM during austral summer \r\ntoward its positive phase in recent decades (Solomon and Polvani, \r\n2016), with the ozone depletion influence dominating (Gerber and \r\nSon, 2014; Son et al., 2018)"


id: b80f9158f2bb74bda0cb783e3d1163cc
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 26489
content:
" In CMIP6 historical simulations there are \r\nsignificant positive SAM trends over the 1979-2019 period in austral \r\nsummer, although the contribution from ozone forcing evaluated \r\nwith the four available models is not significant (Figure 3.34b). Three \r\nof these models share the same standard prescribed ozone forcing \r\nand produce significantly positive SAM trends over an extended \r\nperiod (1957-2019). The fourth model, MRI-ESM2-0, has the option \r\nof interactive ozone chemistry. Its ozone-only experiment is forced \r\nby prescribed ozone derived from its own historical simulations \r\nand produces a negative SAM trend associated with weak ozone \r\ndepletion (Morgenstern et al., 2020). Morgenstern et al. (2014) and \r\nMorgenstern (2021) find an indirect influence of greenhouse gases \r\non the SAM via induced ozone changes in coupled chemistry-climate \r\nsimulations, which differ from the prescribed ozone simulations shown \r\nin Figure 3.34b"


id: 841f652645c52cec27b9cfb0bcff7e87
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
content_type: text
index: climate_gpt_only_giec
vector_id: 18323
content:
" Since about 1997, the effective abundance of ozone\u0002depleting halogen has been decreasing in the stratosphere (WMO, \r\n2018), leading to a stabilization or even a reversal of stratospheric \r\nozone depletion (Sections 2.2.5.2 and 6.3.2.2). The ozone stabilization \r\nand slight recovery since about 2000 may have caused a pause in the \r\nsummertime SAM trend (Figure 3.34c; Saggioro and Shepherd, 2019; \r\nBanerjee et al., 2020), although some influence from internal variability \r\ncannot be ruled out. While some studies find an anthropogenic aerosol \r\ninfluence on the summertime SAM (Gillett et al., 2013; Rotstayn, \r\n2013), recent studies with larger multi-model ensembles find that \r\nthis effect is not robust (Steptoe et al., 2016; Choi et al., 2019), \r\nconsistent with CMIP6 single forcing ensembles (Figure 3.34). In the \r\nCMIP5 simulations, volcanic stratospheric aerosol has a  significant \r\nweakening effect on the SAM in autumn and winter (Cross-Chapter \r\nBox  4"


id: 2613f993f80a323eb8f258a131e5e8a4
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 3410
content:
"1; Gillett and Fyfe, 2013), but there is no evidence that this \r\neffect leads to a significant multi-decadal trend since the late 20th \r\ncentury. Beyond external forcing, Fogt et al. (2017) show a significant \r\nassociation of tropical SST variability with the summertime SAM \r\ntrend since the mid-20th century in agreement with Lim et al. (2016), \r\nwho, however, demonstrate that such a teleconnection between the \r\nsummertime SAM and El Nino-Southern Oscillation (Annex IV.2.3), \r\nfound in observations, is missing in many CMIP5 models."


id: f9fd9aeb9521e6d73fe1fb14ac69269b
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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index: climate_gpt_only_giec
vector_id: 36841
content:
"Observations show strong multi-decadal modulation of ENSO variance \r\nthroughout the 20th century, with the most recent period displaying \r\nlarger variability while the mid-century displayed relatively low ENSO \r\nvariability (Figure 2.36; Li et al., 2013; McGregor et al., 2013; Hope \r\net al., 2017). As assessed in Section 2.4.2, ENSO amplitude since 1950 \r\nis higher than over the pre-industrial period from 1850 as far back \r\nas 1400 (medium confidence), but there is low confidence that it is \r\nhigher than the variability over periods prior to 1400. This reported \r\nvariance increase suggests that external forcing plays a role in the \r\nENSO variance changes (Hope et al., 2017). However, large ensembles \r\nof single model or multiple model simulations do not find strong trends \r\nin ENSO variability over the historical period, suggesting that external \r\nforcing has not yet modulated ENSO variability with a magnitude that \r\nexceeds the range of internal variability (Hope et al"


id: 556ebec3d685553d4a05edb650e037b2
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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vector_id: 10913
content:
", 2017; Maher et al., \r\n2018b; Stevenson et al., 2019). This is consistent with the Chapter 2 \r\nassessment that there is no clear evidence for a recent sustained shift \r\nin ENSO beyond the range of variability on decadal to millennial time \r\nscales (Section 2.4.2). CMIP5 and CMIP6 models show a decrease in \r\nENSO variance in the mid-Holocene (Brown et al., 2020), though not to \r\nthe extent seen in paleo-proxy records (Emile-Geay et al., 2016). This \r\nsuggests that both modelled and observed ENSO respond to changes \r\nin external forcing, but not necessarily in the same manner."


