# document

id: 37bbcac5c7f4c4c625f5b39162b21e0d
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: 6145
content:
"Most CMIP5 and CMIP6 models are found to represent the general \r\nstructure of observed SST anomalies during ENSO events well (Kim \r\nand Yu, 2012; Taschetto et al., 2014; Brown et al., 2020; Grose et al., \r\n2020). However, the majority of CMIP5 models display SST anomalies \r\nthat: i) extend too far to the west (Taschetto et al., 2014; Capotondi \r\net al., 2015); and ii) have meridional widths that are too narrow (Zhang \r\nand Jin, 2012) compared to the observations. CMIP6 models display \r\na statistically significant improvement in the longitudinal representation \r\nof ENSO SST anomalies relative to CMIP5 models (Planton et al., 2021), \r\nhowever, systematic biases in the zonal extent and meridional width \r\nremain in CMIP6 models (Fasullo et al., 2020; Planton et al., 2021)"


id: 7b8b7616cfa3b6da1c8851fbb63e50e
created_at: 2023-03-16 14:01:58
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" The \r\nENSO phase asymmetry, where observed strong El Nino events are larger \r\nand have a shorter duration than strong La Nina events (Ohba and Ueda, \r\n2009; Frauen and Dommenget, 2010), is also under-represented in both \r\nCMIP5 and CMIP6 models (Zhang and Sun, 2014; Planton et al., 2021). \r\nIn this instance, both CMIP5 and CMIP6 models typically display El Nino \r\nevents that have a longer duration than those observed, La Nina events \r\nthat have a similar duration to those observed, and there is very little \r\nasymmetry in the duration of El Nino and La Nina phases (Figure 3.36). \r\nRoberts et al. (2018) find an improvement in amplitude asymmetry \r\nin a HighResMIP model, but the under-representation remains."


id: c101b8c6ce24e6e48e1c59c4e91fa108
created_at: 2023-03-16 14:01:58
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vector_id: 27940
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"Central Pacific-type events (Yeh et al., 2009), but analysis since then \r\nsuggests that this behaviour is (i) consistent with that expected from \r\ninternal variability (Newman et al., 2011); and (ii) not apparent across \r\nthe full CMIP5 ensemble of historical simulations (Taschetto et al., \r\n2014). Analysis of single-model large ensembles suggests that changes \r\nto ENSO event type in response to historical radiative forcing are not \r\nsignificant (e.g., Stevenson et al., 2019). These same results, however, \r\nalso suggest that multiple forcings can have significant influences \r\non ENSO type and that the net response will depend on the accurate \r\nrepresentation of the balance of these forcings (Stevenson et al., 2019).\r\nThe climatic effects of ENSO outside the tropical Pacific largely arise \r\nthrough atmospheric teleconnections that are induced by ENSO-driven \r\nchanges in deep tropical atmospheric convection and heating (Yeh \r\net al., 2018)"


id: 33ff97a0003efabfab218d8218644dbe
created_at: 2023-03-16 14:01:58
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vector_id: 5597
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" The teleconnections to higher latitudes are forced by \r\nwaves that propagate into the extratropics (Hoskins and Karoly, \r\n1981) and respectively excite the Pacific-North American pattern \r\n(Horel and Wallace, 1981) and Pacific-South American pattern (Karoly, \r\n1989; Irving and Simmonds, 2016) in the Northern and Southern \r\nHemispheres. Given the influence of these teleconnections on climate \r\nand extremes around the globe, it is important to understand how \r\nwell they are reproduced in CMIP models. What has also become clear \r\nis that spatial correlations of ENSO's teleconnections calculated over \r\nrelatively short periods (<100 years) may not be the most effective \r\nway to assess these relationships (Langenbrunner and Neelin, 2013; \r\nPerry et al., 2020). This is because the spatial patterns are significantly \r\naffected by internal atmospheric variability on relatively short time \r\nscales (<100 years; Batehup et al., 2015; Perry et al., 2020)"


id: 3eb8519d43febccb7fe87df08babfcae
created_at: 2023-03-16 14:01:58
updated_at: 2023-03-16 14:01:58
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" However, \r\nlooking at simplified metrics like the agreement in the sign of the \r\nteleconnections (Langenbrunner and Neelin, 2013), regional average \r\nteleconnection strength over land (Perry et al., 2020), or a combination \r\nof both (Power and Delage, 2018) provides a more robust depiction \r\nof the teleconnection representation. Examining sign agreement for \r\nthe teleconnection patterns, ensembles of CMIP5 AMIP simulations \r\ndisplay broad spatial regions with high sign agreement with the \r\nobservations, suggesting that the model ensemble is producing \r\nuseful information regarding the teleconnected precipitation signal \r\n(Langenbrunner and Neelin, 2013). Looking at regional averages of \r\nCMIP5 historical simulations, Power and Delage (2018) show that \r\nthe average coupled model teleconnection pattern reproduces the \r\nsign of the observed teleconnections in the majority of the 25 regions \r\nanalysed"


id: c128e46a5a9358271bbcc042a5bc92a7
created_at: 2023-03-16 14:01:58
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" The sign agreement between the observed teleconnection \r\nand the multi-model mean teleconnection remains strong in CMIP6 \r\n(18 out of 20 displayed regions; Figure 3.38), and the observed DJF \r\n(December-January-February) teleconnection strength falls within \r\nthe modelled range in all of the displayed regions for temperature \r\nand precipitation. Note, however, that while there is broad agreement \r\nin ENSO teleconnections between CMIP6 models and observations \r\nduring DJF (e.g., Fasullo et al., 2020), there are regions and seasons \r\nwhere the modelled teleconnection strength is outside the observed \r\nrange (Chen et al., 2020).\r\nMost CMIP5 and CMIP6 models exhibit ENSO behaviour during the \r\nhistorical period that, to first order, is qualitatively similar to that of \r\nthe observed ENSO. Many studies are now delving deeper into the \r\nmodels to understand if they are accurately producing the dynamics \r\ndriving ENSO and its initiation (Jin et al., 2006; Bellenger et al., 2014; "


id: c9a5f65c967e4e37b7e7c68c8a73c73a
created_at: 2023-03-16 14:01:58
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"To conclude, ENSO representation in CMIP5 models displayed \r\na significant improvement from the representation of ENSO variability \r\nin CMIP3 models, which displayed much more intermodel spread in \r\nstandard deviation, and stronger biennial periodicity (Guilyardi et al., \r\n2012; Flato et al., 2013). In general, there has been no large step \r\nchange in the representation of ENSO between CMIP5 and CMIP6, \r\nhowever, CMIP6 models appear to better represent some key ENSO \r\ncharacteristics (e.g., Brown et al., 2020; Planton et al., 2021). The \r\ninstrumental record and paleo-proxy evidence through the Holocene \r\nall suggest that ENSO can display considerable modulations in \r\namplitude, pattern and period (see also Section 2.4.2). For the \r\nperiod since 1850, there is no clear evidence for a sustained shift \r\nin ENSO index beyond the range of internal variability"


id: 2331c4b1643699d109964d269b7793dc
created_at: 2023-03-16 14:01:58
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" However, \r\npaleo-proxy evidence indicates with medium confidence that ENSO \r\nvariability since 1950 is greater than at any time between 1400 and \r\n1850 (Section 2.4.2). Coupled models display large changes of ENSO \r\nbehaviour in the absence of external forcing changes, and little-to-no \r\nvariance sensitivity to historical anthropogenic forcing. Thus, there is \r\nlow confidence that anthropogenic forcing has led to the changes of \r\nENSO variability inferred from paleo-proxy evidence."


id: e50552d0b3763ba08e3fb304e2e3c51e
created_at: 2023-03-16 14:01:58
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"NAO/NAM in winter 2019-2020 assessed over the instrumental \r\nera has been accordingly linked to the record IOD event of autumn \r\n2019 (Hardiman et al., 2020), which has been associated with the \r\ndevastating record fire season in Australia (Wang and Cai, 2020).\r\nThe observed Indian Ocean basin-average SST increase on multi-decadal \r\nand centennial time scales is well represented by CMIP5 historical \r\nsimulations, and has been attributed to the effects of greenhouse gases \r\noffset in part by the effects of anthropogenic aerosols mainly through \r\naerosol-cloud interactions (Dong and Zhou, 2014; Dong et al., 2014b). \r\nThe observed SST trend is larger in the western than eastern tropical \r\nIndian Ocean, which leads to an apparent upward trend of the IOD \r\nindex, but this trend is statistically insignificant (Section 2.4.3)"


id: c0f7c6e7c0059febdf8e92e17628168f
created_at: 2023-03-16 14:01:58
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" CMIP5 \r\nmodels capture this warming pattern, which may be associated with \r\nWalker circulation weakening over the Indian Ocean due to greenhouse \r\ngas forcing (Dong and Zhou, 2014). However, strong internal decadal \r\nIOD-like variability and observational uncertainty preclude attribution \r\n(Cai et al., 2013; Han et al., 2014b; Gopika et al., 2020). Such a positive \r\nIOD-like change in equatorial zonal SST gradient suggests an increase \r\nin the frequency of extreme positive events (Cai et al., 2014) and \r\nskewness (Cowan et al., 2015) of the IOD mode. While there is some \r\nevidence of an increase in frequency of positive IOD events during the \r\nsecond half of the 20th century, the current level of IOD variability is \r\nnot unprecedented in a proxy reconstruction for the last millennium \r\n(Section 2.4.3; Abram et al., 2020)"


id: f4743019f18f7bd7f27a3f986f6d6c94
created_at: 2023-03-16 14:01:58
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" Besides, the IOD magnitude in \r\nthe late 20th century is not significantly different between CMIP5 \r\nsimulations forced by historical and natural-only forcings, though \r\nthis conclusion is based on only five selected ensemble members \r\nthat realistically reproduce statistical features of the IOD (Blau and \r\nHa, 2020). While selected CMIP5 models show weakening (Thielke \r\nand Molg, 2019) and seasonality changes (Blau and Ha, 2020) \r\nin IOD-induced rainfall anomalies in tropical eastern Africa, no \r\ncomparison with observational records has been made. Likewise, \r\nwhile a strengthening tendency of the ENSO-IOB mode correlation \r\nand resultant intensification of the IOB mode are found in historical or \r\nfuture simulations in selected CMIP5 models (Hu et al., 2014; Tao et al., \r\n2015), such a change has not been detected in observational records."


id: 678d97cd4d8ea3e4ae09dd7e731638cf
created_at: 2023-03-16 14:01:58
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"Since AR5, CMIP5 model representation of these modes has been \r\nanalysed in detail, finding that most of the models qualitatively \r\nreproduce the spatial and seasonal features of the IOB and IOD modes \r\n(Chu et al., 2014; Liu et al., 2014; W. Tao et al., 2016). Improvements in \r\nsimulating the IOB mode since CMIP3 have been identified in reduced \r\nmulti-model mean biases and inter-model spread (W. Tao et al., 2016). \r\nCMIP5 models overall capture the transition from the IOD to IOB \r\nmodes during an ENSO event (W. Tao et al., 2016). The IOB mode is \r\nforced in part through a cross-equatorial wind-evaporation-SST \r\nfeedback triggered by ENSO-forced anomalous ocean Rossby waves \r\nthat propagate to the shallow climatological thermocline dome in the \r\ntropical south-western Indian Ocean (Du et al., 2009). Consistently, \r\nmodels with a deeper climatological thermocline dome produce \r\na weaker and less persistent IOB mode (G. Li et al., 2015a; Zheng et al., \r\n2016)"


id: 4d2e7e481ef4aeccc8f8289f3e22e481
created_at: 2023-03-16 14:01:58
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" The deep thermocline bias remains in the ensemble mean of \r\nCMIP5 models due to a common surface easterly wind bias over the \r\nequatorial Indian Ocean (Lee et al., 2013) associated with weaker South \r\nAsian summer monsoon circulation (G. Li et al., 2015b). However, the \r\ninfluence of this systematic bias may be compensated by other biases, \r\nresulting in a realistic IOB magnitude (W. Tao et al., 2016). Halder et al. \r\n(2021) found that CMIP6 models reproduce the IOB mode reasonably \r\nwell, but did not evaluate the progress since CMIP5."


id: b326178df705ba3aae55c948674a759a
created_at: 2023-03-16 14:01:58
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"By contrast, the IOD magnitude is overestimated by CMIP5 models on \r\naverage, though with noticeable improvements from CMIP3 models \r\n(Liu et al., 2014). The overestimation of the IOD magnitude remains \r\nin most of 34 CMIP6 models examined in McKenna et al. (2020) with \r\nworsening on average in July and August. A too steep climatological \r\nthermocline slope along the equator due to the surface easterly wind \r\nbias in boreal summer and autumn contributes to this IOD magnitude \r\nbias through an excessively strong Bjerknes feedback in CMIP5 (Liu \r\net al., 2014; G. Li et al., 2015b; Hirons and Turner, 2018). The surface \r\neasterly bias and associated east-west SST gradient bias are not \r\nimproved in CMIP6 (Long et al., 2020; Section 3.5.1.2.3), suggesting \r\nthat the thermocline bias also remains. McKenna et al. (2020) \r\nadditionally find degradation in the positive-negative asymmetry \r\nof the IOD but an improvement in IOD frequency in a subset of \r\nCMIP6 models compared to CMIP5"


id: db64c7855099eff98eb063ff0e49626b
created_at: 2023-03-16 14:01:58
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" In terms of teleconnections, the \r\nequatorial surface easterly wind bias also affects the IOD-associated \r\nmoisture transport anomalies toward tropical eastern Africa \r\n(Hirons and Turner, 2018) where the IOD is associated with strong \r\nprecipitation anomalies in boreal autumn (Annex IV.2.4). CMIP5 \r\nand CMIP6 models capture the IOD teleconnection to Southern and \r\nCentral Australian precipitation although it is weaker on average than \r\nobserved, with no clear improvements from CMIP5 to CMIP6 (Grose \r\net al., 2020). Strong IOD events could also influence the Northern \r\nHemisphere extratropical circulation in winter and in particular the \r\nNAM (Section 3.7.1), based on interference between forced Rossby \r\nwaves emerging from the Indian Ocean and climatological stationary \r\nwaves (Fletcher and Cassou, 2015). The record positive phase of the "


id: d952d5e564100a4bcfd1dbf8d299de1e
created_at: 2023-03-16 14:01:58
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"opposite to observed in two thirds of the CMIP5 models \r\n(Section 3.5.1.2.2), which clearly affects the simulation of the Atlantic \r\nNino and associated dynamics (Munoz et al., 2012; Ding et al., 2015; \r\nDeppenmeier et al., 2016). The interhemispheric SST gradient is also \r\nsystematically underestimated in models, with a too cold mean state \r\nin the northern part of the tropical Atlantic ocean and too warm \r\nconditions in the South Atlantic basin. The seasonality is poorly \r\nreproduced and the wind-SST coupling is weaker than observed \r\nso that altogether, and despite AMM-like variability in 20th century \r\nclimate simulations, AMM is not the dominant Atlantic mode in all \r\nCMIP5 models (Liu et al., 2013; Amaya et al., 2017). These biases in \r\nmean state translate into biases in modelling the mean ITCZ (Flato \r\net al., 2013)"


id: eadc6372f5f39e6aea6047fa659b0a
created_at: 2023-03-16 14:01:58
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" Similar biases were found in experiments using CMIP5 \r\nmodels but with different climate background states, such as Last \r\nGlacial Maximum, mid-Holocene and future scenario simulations \r\n(Brierley and Wainer, 2018). Analyses of CMIP6 show encouraging \r\nresults in the representation of Atlantic Nino and AMM modes of \r\nvariability in terms of amplitude and seasonality. Some models now \r\ndisplay reduced biases in the spatial structure of the modes and related \r\nexplained variance but persistent errors still remain on average in the \r\ntiming of the modes and in the coupled nature of the modes, that is, \r\nthe strength of the link between ocean (SST, mixed layer depth) and \r\natmospheric (wind) anomalies (Richter and Tokinaga, 2020), as well \r\nas in the Atlantic Ocean equatorial east-west temperature gradient \r\n(Section 3.5.1.2.2, Figure 3.24)."


id: 694205d7423e84ab2ef8a28adf4186f
created_at: 2023-03-16 14:01:58
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"Section 2.4.4 assess that there is low confidence in any sustained \r\nchanges to the AZM and AMM variability in instrumental \r\nobservations. Moreover, any attribution of possible human influence \r\non the Atlantic modes and associated teleconnections is limited \r\nby the poor fidelity of CMIP5 and CMIP6 models in reproducing \r\nthe mean tropical Atlantic climate, its seasonality and variability, \r\ndespite hints of some improvement in CMIP6, as well as other \r\nsources of uncertainties related to limited process understanding \r\nin the observations (Foltz et al., 2019), the response of the tropical \r\nAtlantic climate to anthropogenic aerosol forcing (Booth et al., \r\n2012; Zhang et al., 2013a) and the presence of strong multi-decadal \r\nfluctuations related to AMV (Section 3.7.7) and cross-tropical basin \r\ninteractions (Martin-Rey et al., 2018; Cai et al., 2019)"


id: f85dc454e09aa82367395aad593ff788
created_at: 2023-03-16 14:01:58
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" The fact that \r\nmost models poorly represent the climatology and variability of the \r\ntropical Atlantic combined with the short observational record makes \r\nit difficult to place the recent observed changes in the context of \r\ninternal multi-annual variability versus anthropogenic forcing."


id: 4aef3a8beb3eaabe7ed62a30ba5fd4ab
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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"observational coverage before satellite measurements started, the \r\nduration of PDV phases appears to be shorter in coupled models \r\nthan in observations, and correspondingly the ratio of decadal to \r\ninterannual variance is underestimated (Figure 3.39e,f; Henley et al., \r\n2017). This apparent bias may be associated with overly biennial \r\nbehaviour of Pacific trade wind variability and related ENSO activity, \r\nleaving too weak variability on decadal time scales (Kociuba and \r\nPower, 2015). ENSO influence on the extratropical North Pacific \r\nOcean at decadal time scales is also very diverse among both CMIP3 \r\nand CMIP5 models, being controlled by multiple factors (Nidheesh \r\net al., 2017). In terms of amplitude, the variance of the PDV index \r\nafter decadal filtering is significantly weaker in the concatenated \r\nCMIP5 ensemble than the three observational estimates used in \r\nFigure  3.39e (p <0.1 with an F-test)"


id: 59324c3b120cf42350a67ebb4df3d2c0
created_at: 2023-03-16 14:01:59
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" Consequently, the observed \r\nPDV fluctuations over the historical period often lie in the tails of the \r\nmodel distributions (Figure 3.39e,f). Even if one cannot rule out that \r\nthe observed PDV over the instrumental era represents an exceptional \r\nperiod of variability, it is plausible that the tendency of the CMIP5 \r\nmodels to systematically underestimate the low frequency variance \r\nis due to an incomplete representation of decadal-scale mechanisms \r\nin these models. This situation is slightly improved in CMIP6 historical \r\nsimulations but remains a concern (Fasullo et al., 2020). The results of \r\nMcGregor et al. (2018) suggest that the under-representation of the \r\nvariability stems from Atlantic mean SST biases (Section 3.5.1.2.2) \r\nthrough inter-basin coupling."


id: 4edfac944c5f8770f86e7ef8716d18e5
created_at: 2023-03-16 14:01:59
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"While PDV is primarily understood as an internal mode of variability \r\n(Si and Hu, 2017), there are some indications that anthropogenically \r\ninduced SST changes project onto PDV and have contributed to its \r\npast evolution (Bonfils and Santer, 2011; Dong et al., 2014a; Boo \r\net al., 2015; Xu and Hu, 2018). However, the level of evidence is \r\nlimited because of the difficulty in correctly separating internal versus \r\nexternally forced components of the observed SST variations, and \r\nbecause it is unclear whether the dynamics of the PDV are operative \r\nin this forced SST change pattern. Over the last two to three decades \r\nwhich encompass the period of slower GMST increase (Cross-Chapter \r\nBox 3.1), Smith et al. (2016) found that anthropogenic aerosols have \r\ndriven part of the PDV change toward its negative phase. A similar \r\nresult is shown in Takahashi and Watanabe (2016) who found \r\nintensification of the Pacific Walker circulation in response to aerosol \r\nforcing (Section 3.3.3.1"


id: 4738b4f527d648dac1b979edfc17a83c
created_at: 2023-03-16 14:01:59
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content:
"2). Indeed, CMIP6 models simulate a negative \r\nPDV trend since the 1980s (Figure 3.39f), which is much weaker than \r\ninternal variability. However, a response to anthropogenic aerosols is \r\nnot robustly identified in a large ensemble of a model (Oudar et al., \r\n2018), across CMIP5 models (Hua et al., 2018), or in idealized model \r\nsimulations (Kuntz and Schrag, 2016). Alternatively, inter-basin \r\nteleconnections associated with the warming of the North Atlantic \r\nOcean related to the mid-1990s phase shift of the AMV (McGregor et al., \r\n2014; Chikamoto et al., 2016; Kucharski et al., 2016; X. Li et al., 2016a; \r\nRuprich-Robert et al., 2017), and also warming in the Indian Ocean \r\n(Luo et al., 2012; Mochizuki et al., 2016), could have favoured a PDV \r\ntransition to its negative phase in the 2000s. Considering the possible \r\ninfluence of external forcing on Indian Ocean decadal variability \r\n(Section 3.7.4) and AMV (Section 3.7"


id: af738ea528f487316a5a22e893bae74d
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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"7), any such human influence \r\non PDV would be indirect through changes in these ocean basins, \r\nand then imported to the Pacific via inter-basin coupling. However, \r\nthis human influence on AMV, and how consistently such inter-basin "


id: 8bc6b47444de97b0f02599cfa3095a32
created_at: 2023-03-16 14:01:59
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content:
"The implication of PDV in the observed slowdown of the GMST \r\nwarming rate in the early 2000s (Cross-Chapter Box 3.1) has triggered \r\nconsiderable research on decadal climate variability and predictability \r\nsince AR5 (Meehl et al., 2013, 2016b; England et al., 2014; Dai et al., \r\n2015; Steinman et al., 2015; Kosaka and Xie, 2016; Cassou et al., \r\n2018). Many studies find that the broad spatial characteristics of \r\nPDV are reasonably well represented in unforced climate models \r\n(Newman et al., 2016; Henley, 2017) and in historical simulations \r\nin CMIP5 and CMIP6 (Figure 3.39), although there is sensitivity to \r\nthe methodology used to remove the externally-forced component \r\nof the SST (Bonfils and Santer, 2011; Xu and Hu, 2018). Compared \r\nwith CMIP3 models, CMIP5 models exhibit overall slightly better \r\nperformance in reproducing PDV and associated teleconnections \r\n(Polade et al., 2013; Joshi and Kucharski, 2017), and also smaller \r\ninter-model spread (Lyu et al., 2016)"


id: 5cb88f57a3dbfc304e1062947a5a4c12
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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content:
" CMIP6 models on average show \r\nslightly improved reproduction of the PDV spatial structure than \r\nCMIP5 models (Figure 3.39a-c; Fasullo et al., 2020). SST anomalies in \r\nthe subtropical South Pacific lobe are, however, too weak relative to \r\nthe equatorial and North Pacific lobes in CMIP5 pre-industrial control \r\nand historical simulations (Henley et al., 2017), a bias that remains \r\nin CMIP6 (Figure 3.39b)."


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content:
"atmospheric teleconnections (Martin et al., 2014; Ruprich-Robert \r\net al., 2017, 2018; Monerie et al., 2019; Qasmi et al., 2020; Ruggieri \r\net al., 2021). Even if debate remains (Clement et al., 2015; Cane \r\net al., 2017; Mann et al., 2020), there is now stronger evidence for \r\na crucial role of oceanic dynamics in internal AMV that is primarily \r\nlinked to the AMOC and its interplay with the NAO (Zhang et al., \r\n2013a; Muller et al., 2015; O'Reilly et al., 2016b, 2019a; Delworth \r\net al., 2017; Zhang, 2017; Sun et al., 2019; Kim et al., 2020). However, \r\nconsiderable diversity in the spatio-temporal properties of the \r\nsimulated AMV is found in both pre-industrial control and historical \r\nCMIP5 experiments (Zhang and Wang, 2013; Wills et al., 2019). Such \r\nmodel diversity is presumably associated with the wide range of \r\ncoupled processes associated with AMV (Baker et al., 2017; Woollings \r\net al"


id: e17b557a56752f29c2c71cec7c47a1af
created_at: 2023-03-16 14:01:59
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vector_id: 32968
content:
", 2018a) including large-scale atmospheric teleconnections and \r\nregional feedbacks relating to tropical clouds, Arctic sea ice in the \r\nsubarctic basins and Saharan dust, whose relative importance and \r\ninteractions across time scales are specific to each model (Martin \r\net al., 2014; Brown et al., 2016b)."


id: 79674382e05a1a3fb724996fea1c366b
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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content:
"To conclude, there is high confidence that internal variability has been \r\nthe main driver of the PDV since pre-industrial times, despite some \r\nmodelling evidence for potential external influence. This assessment \r\nis supported by studies based on large ensemble simulations that \r\nfound the dominance of internally-driven PDV, and the CMIP6-based \r\nassessment (Figure  3.39). As such, PDV is an important driver of \r\ndecadal internal climate variability which limits detection of human \r\ninfluence on various aspects of decadal climate change on global \r\nto regional scales (high confidence). Model evaluation of PDV is \r\nhampered by short observational records, spatial incompleteness of \r\nobservations before the satellite observation era, and poor agreement \r\namong paleoclimate reconstructions"


id: 9f594881e6c5e91f8848e4898521270c
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 22612
content:
" Despite the limitations of these \r\nmodel-observation comparisons, CMIP5 models, on average, simulate \r\nbroadly realistic spatial structures of the PDV, but with a clear bias \r\nin the South Pacific (medium confidence). CMIP5 models also very\r\nlikely underestimate PDV magnitude. CMIP6 models tend to show \r\nbetter overall performance in spatial structure and magnitude of \r\nPDV, but there is low confidence in this assessment due to the lack \r\nof literature."


id: 675ee316a50d8bce652f5266a1e4a0b1
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 13854
content:
"Additional studies since AR5 corroborate that CMIP5-era models \r\ntend to underestimate many aspects of observed AMV and its SST \r\nfingerprint. On average, the duration of modelled AMV episodes is \r\ntoo short, the magnitude of AMV is too weak and its basin-wide \r\nSST spatial structure is limited by the poor representation of the \r\nlink between the tropical North Atlantic and the subpolar North \r\nAtlantic/Nordic seas (Martin et al., 2014; Qasmi et al., 2017). Such \r\nmismatches between observed and simulated AMV (Figure 3.40c-e) \r\nhave been associated with intrinsic model biases in both mean state \r\n(Menary et al., 2015; Drews and Greatbatch, 2016) and variability \r\nin the ocean and overlying atmosphere. For instance, compared to \r\navailable observational data CMIP5 models underestimate the ratio \r\nof decadal to interannual variability of the main drivers of AMV, \r\nnamely the AMOC, NAO and related North Atlantic jet variations \r\n(Section 3.7.1; Bracegirdle et al., 2018; Kim et al"


id: 518f699860270d38e705f54eb72a155c
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 10285
content:
", 2018b; Simpson \r\net al., 2018; Yan et al., 2018), which has strong implications for the \r\nsimulated temporal statistics of AMV, AMV-induced teleconnections \r\n(Ault et al., 2012; Menary et al., 2015) and AMV predictability."


id: df7a868fff17009bc2a20667410b60da
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 32663
content:
"The increase of AMV variance in CMIP6 models (stronger magnitude \r\nand longer duration) seems to be explained by the enhanced \r\nvariability in the subpolar North Atlantic SST (Figure 3.40b,c), which \r\nis particularly pronounced in some models, associated with greater \r\nvariability in the AMOC (Section 3.5.4.1; Voldoire et al., 2019a; Boucher \r\net al., 2020) and greater GMST multi-decadal variability (Section 3.3.1 \r\nand Figure 3.40c-f; Voldoire et al., 2019b; Parsons et al., 2020). The \r\ndecadal variance in SST in the subpolar North Atlantic seems now \r\nto be slightly overestimated in CMIP6 compared to observational \r\nestimates, while the AMV-related tropical SST anomalies remain \r\nweaker in line with CMIP5 (Figure 3.40b)"


id: 80ba9c094c385d6c900363c1bb3f17a7
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 17775
content:
" The mechanisms producing \r\nthe tropical-extratropical relationship at decadal time scales \r\nremain poorly understood despite stronger evidence since AR5 for \r\nthe importance of the subpolar gyre SST anomalies in generating \r\ntropical changes through atmospheric teleconnection (Caron \r\net al., 2015; Ruprich-Robert et al., 2017; Kim et al., 2020). Significant \r\ndiscrepancies remain in the simulated AMV spatial pattern when \r\nhistorical simulations are compared to multivariate observations (Yan \r\net al., 2018; Robson et al., 2020)."


id: 68f12d94e10f1cc9e26b2ab120a413e3
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 14103
content:
"Model evaluation of the AMV phenomenon remains difficult because \r\nof short observational records (especially of detailed process-based \r\nobservations), the lack of stationarity in the variance, spatial patterns \r\nand frequency of the AMV assessed from modelled SST (Qasmi et al., \r\n2017), difficulties in estimating the forced signals in both historical \r\nsimulations and observations (Tandon and Kushner, 2015), and \r\nbecause of probable interplay between internally and externally-driven \r\nprocesses (Watanabe and Tatebe, 2019). Furthermore, models \r\nsimulate a large range of historical anthropogenic aerosol forcing \r\n(Smith et al., 2020) and questions often referred to as signal-to-noise \r\nparadox have been raised concerning the models' ability to correctly \r\nsimulate the magnitude of the response of AMV-related atmospheric \r\ncirculation phenomena, such as the NAO (Section 3.7.1), to both \r\ninternally and externally generated changes (Scaife and Smith, 2018)"


id: 5da495e81207da10b6b3c8474c9b9008
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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content:
" \r\nRelated methodological and epistemological uncertainties also call \r\ninto question the relevance of the traditional basin-average SST \r\nindex to assessing the AMV phenomenon (Zanchettin et al., 2014; \r\nFrajka-Williams et al., 2017; Haustein et al., 2019; Wills et al., 2019)."


id: 643188547e937f19d2ef19f4465eee36
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 13332
content:
"To summarize, results from CMIP5 and CMIP6 models together \r\nwith new statistical techniques to evaluate the forced component \r\nof modelled and observed AMV, provide robust evidence that \r\nexternal forcings have modulated AMV over the historical period. \r\nIn particular, anthropogenic and volcanic aerosols are thought to \r\nhave played a role in the timing and intensity of the negative (cold) \r\nphase of AMV recorded from the mid-1960s to mid-1990s and \r\nsubsequent warming (medium confidence). However, there is low \r\nconfidence in the estimated magnitude of the human influence. \r\nThe limited level of confidence is primarily explained by difficulties \r\nin accurately evaluating model performance in simulating AMV"


id: 9f886a59370b2b3ba69447f16ceaea2a
created_at: 2023-03-16 14:01:59
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content:
" \r\nThe evaluation is severely hampered by short instrumental records \r\nbut also, equally importantly, by the lack of detailed and coherent \r\nlong-term process-based observations (for example of the AMOC, \r\naerosol optical depth, surface fluxes and cloud changes), which limit \r\nour process understanding. In addition, studies often rely solely on \r\nsimplistic SST indices that may be hard to interpret (Zhang et al., \r\n2016) and may mask critical physical inconsistencies in simulations \r\nof the AMV compared to observations (Zhang, 2017)."


id: 45fe612638298b813d7c484217fe06b2
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 8398
content:
"An analysis of a basket of 16 atmospheric variables (Figure 3.42a) \r\nassessed across CMIP3, CMIP5, and CMIP6 models but excluding \r\nhigh-resolution models participating in HighResMIP, reveals the \r\nprogress made between these three generations of models (Bock \r\net al., 2020). Progress is evidenced by the increasing prevalence of \r\nblue colours (indicating a performance better than the median) for \r\nthe more recent model versions. Additionally, a few CMIP6 models \r\noutperform the best-performing CMIP5 models. Progress is evident \r\nacross all 16 variables. As noted in AR5, the models typically score \r\nsimilarly against both observational reference datasets, indicating \r\nthat indeed uncertainties in these reference datasets are smaller \r\nthan model biases"


id: 43b8c8eb3a6259bebc245a8c562519ae
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 8065
content:
" Several models and model families perform \r\nbetter compared to observational references than the median, across \r\na majority of the climate variables assessed, and conversely some \r\nother models or model families compare more poorly against these \r\nreference datasets. Such a good correspondence across a range \r\nof diagnostic fields probing different aspects of climate enhances \r\nconfidence that the improved performances reflect progress in the \r\nphysical realism of these simulations. An alternative explanation, \r\nthat progress is due to a cancellation of errors achieved by model \r\ntuning, appears improbable given the large number of diagnostic \r\nfields involved here. However, several instances of poor model \r\nperformance (red colours in Figure  3.42) still exist in the CMIP6 \r\nensemble. Family relationships (i.e.  various degrees of shared \r\nformulations; Knutti et al"


id: 3cc1159601664f2d17d617406ef26e33
created_at: 2023-03-16 14:01:59
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vector_id: 6926
content:
", 2013) between the models are apparent, \r\nfor example, the GISS, GFDL, CESM, CNRM, and HadGEM/UKESM1/\r\nACCESS families score similarly across all atmospheric variables, \r\nboth for the CMIP5 and CMIP6 generations. In the cases of CESM2/\r\nCESM2(WACCM), CNRM-CM6-1/CNRM-ESM2-1, NorCPM1/\r\nNorESM2-LM, and HadGEM3-GC31-LL/UKESM1-0-LL, the high\u0002complexity model scores as well or better than its lower-complexity \r\ncounterpart, indicating that increasing complexity by adding Earth \r\nsystem features, which by removing constraints could be expected to \r\ndegrade a model's performance, does not necessarily do so. Several \r\nhigh climate-sensitivity models (Section 7.5; Meehl et al., 2020), in \r\nparticular CanESM5, CESM2, CESM2-WACCM, HadGEM3-GC31-LL, \r\nand UKESM1-0-LL, score well against the benchmarks. In accordance \r\nwith AR5 and earlier assessments, the multi-model mean, with some \r\nnotable exceptions, is better than any individual model (Annan and \r\nHargreaves, 2011; Rougier, 2016)."


id: 746f62896551ac988d7a0e6322660d8d
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 15911
content:
"AR5 found significant differences between models in the simulation \r\nof mean climate in the CMIP5 ensemble when measured against \r\nmeteorological reanalyses and observations (Flato et al., 2013), see \r\nalso Stouffer et al. (2017). The AR5 determined that for the diagnostic \r\nfields analysed, the models usually compared similarly against two \r\ndifferent reference datasets, suggesting that model errors were \r\ngenerally larger than observational uncertainties or other differences \r\nbetween the observational references. In agreement with previous \r\nassessments, the CMIP5 multi-model mean generally performed \r\nbetter than individual models (Annan and Hargreaves, 2011; Rougier, \r\n2016). The AR5 considered 13 atmospheric fields in its assessment \r\nfor the instrumental period but did not assess multi-variate model \r\nperformance in other climate domains (e.g., ocean, land, and sea ice)"


id: 96a35a503fd276dab7ad242188d1af13
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 21220
content:
" \r\nThe AR5 found only modest improvement regarding the simulation \r\nof climate for two periods of the Earth's history (the Last Glacial \r\nMaximum and the mid-Holocene) between CMIP5 and previous \r\npaleoclimate simulations. Similarly, for the modern period only \r\nmodest, incremental progress was found between CMIP3 and \r\nCMIP5 regarding the simulation of precipitation and radiation. The \r\nrepresentation of clouds also showed improvement, but remained \r\na key challenge in climate modelling (Flato et al., 2013)."


id: 1e7308353fd800c7018a4cc0c00684a0
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 2232
content:
"In addition to the multivariate assessments of simulations of the \r\nrecent historical period, simulations of selected periods of the Earth's \r\nmore distant history can be used to benchmark climate models by \r\nexposing them to climate forcings that are radically different from \r\nthe present and recent past (Harrison et al., 2015, 2016; Kageyama \r\net al., 2018; Tierney et al., 2020a). These time periods provide an out\u0002of-sample test of models because they are not in general used in the \r\nprocess of model development. They encompass a range of climate \r\ndrivers, such as volcanic and solar forcing for the Last Millennium, \r\norbital forcing for the mid-Holocene and Last Interglacial, and changes \r\nin greenhouse gases and ice sheets for the LGM, mid-Pliocene Warm \r\nPeriod, and early Eocene (Sections 2.2 and 2.3). These drivers led to \r\nclimate changes, including in surface temperature (Section 2.3.1.1) \r\nand the hydrological cycle (Section 2.3.1.3"


id: be60d9875ddb02ae7d6ae6f7691c2050
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 27500
content:
"1), which are described \r\nby paleoclimate proxies that have been synthesized to support \r\nevaluations of models on a global and regional scale. However, the \r\nmore sparse, indirect, and regionally incomplete climate information \r\navailable from paleo-archives motivates a different form of the \r\nmultivariate analysis of simulations covering these periods versus the \r\nequivalent for the historical period, as described below."


id: 2b4737ccef7906f3019779e134bc3d72
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 4244
content:
"Using centred pattern correlations (quantifying pattern similarity \r\non a scale of -1 to 1, with 1 expressing perfect similarity and 0 no \r\nrelationship) for selected fields, AR5 documented improvements \r\nbetween CMIP3 and CMIP5 in surface air temperature, outgoing \r\nlongwave radiation, and precipitation (Figure  9.6 of Flato et al., \r\n2013). Little further progress between CMIP3 and CMIP5 was found \r\nfor fields that were already quite well simulated in CMIP3 (such \r\nas surface air temperature and outgoing longwave radiation). For \r\nprecipitation, the spread reduced because the worst-performing \r\nmodels improved. The shortwave cloud radiative effect remained \r\nrelatively poorly simulated with significant inter-model spread \r\n(e.g.,  Calisto et al., 2014). This comparison of centred pattern \r\ncorrelations is designed to help determine the quality of simulation \r\nof different diagnostics relative to each other, and also to examine \r\nprogress between generations of models. Figure  3"


id: a22381920cee55d1897a0d018a8aa7f0
created_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 23071
content:
"43 shows the \r\ncentred pattern correlations for 16 variables for CMIP3, CMIP5 and \r\nCMIP6 models. In the ensemble averages, CMIP6 performs better \r\nthan CMIP5 and CMIP3 for near-surface temperature, precipitation, \r\nmean sea-level pressure, and many other variables. For the variables \r\nshown, the uncertainties in observational datasets, in particular for "


id: aeedba588682c0c7d25f486c71cb62ae
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 25057
content:
"Figure 3.43 | Centred pattern correlations between models and observations for the annual mean climatology over the period 1980-1999. Results are \r\nshown for individual CMIP3 (green), CMIP5 (blue) and CMIP6 (red) models (one ensemble member from each model is used) as short lines, along with the corresponding multi-model \r\nensemble averages (long lines). Correlations are shown between the models and the primary reference observational data set (from left to right: ERA5, GPCP-SG, CERES-EBAF, \r\nCERES-EBAF, CERES-EBAF, CERES-EBAF, JRA-55, ESACCI-SST, ERA5, ERA5, ERA5, ERA5, ERA5, ERA5, AIRS, ERA5). In addition, the correlation between the primary reference and \r\nadditional observational datasets (from left to right: NCEP, GHCN, -, -, -, -, ERA5, HadISST, NCEP, NCEP, NCEP, NCEP, NCEP, NCEP, ERA5, NCEP) are shown (solid grey circles) if \r\navailable"


id: 8a1b952d9dfbd37f8154e67a1e75634f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 19257
content:
" To ensure a fair comparison across a range of model resolutions, the pattern correlations are computed after regridding all datasets to a resolution of 4deg in longitude and \r\n5deg in latitude. Figure is updated and expanded from Bock et al. (2020), their Figure 7 CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. Further details on data sources and \r\nprocessing are available in the chapter data table (Table 3.SM.1)."


id: 430f05a1ecfd35eb25c13db2af3bba05
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 7946
content:
"Figure 3.44 | Multivariate synopsis of paleoclimate model results compared to observational references. Data-model comparisons for (a) GSAT anomalies for \r\nfive PMIP4 periods and for regional features for the (b) mid-Holocene and (c) LGM periods, for PMIP3 and PMIP4 models. The results from CMIP6 models are shown as coloured \r\ndots. In (a) the light orange shading shows the very likely assessed ranges presented in Section 2.3.1.1. In (b) and (c), the regions and variables are defined as follows: North \r\nAmerica (20degN-50degN, 140degW-60degW), Western Europe (35degN-70degN, 10degW-30degE) and West Africa (0deg-30degN, 10degW-30degE); mean temperature of the coldest month (MTCO; \r\ndegC), mean temperature of the warmest month (MTWA; degC), mean annual precipitation (MAP; mm yr -1). In (b) and (c) the ranges shown for the reconstructions (Bartlein et al. \r\n(2011) for mid-Holocene and Cleator et al"


id: d7369950fbbff9f9ce3c3136977ba758
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 31376
content:
" (2020) for LGM) are based on the standard error given at each site: the average and associated standard deviation over each area is \r\nobtained by computing 1000 times the average of randomly drawn values from the Gaussian distributions defined at each site by the reconstruction mean and standard error; \r\nthe light orange colour shows the +-1 standard deviation of these 1000 estimates. The dots on (b) and (c) show the average of the model output for grid points for which there \r\nare reconstructions. The ranges for the model results are based on an ensemble of 1000 averages over 50 years randomly picked in the model output time series for each region \r\nand each variable: the mean +- one standard deviation is plotted for each model. Figure is adapted from Brierley et al. (2020), their Figure S3 for the mid-Holocene; and from \r\nKageyama et al. (2021b), their Figure 12 for the LGM. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."


id: 9104bb1d99ccfa7bffa043d0b0a3a2f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 20356
content:
"Polar amplification in the LGM, mid-Pliocene Warm Period, and \r\nEarly Eocene Climatic Optimum (EECO) simulations is assessed in \r\nSection 7.4.4.1.2. Here we focus on the mid-Holocene and the LGM, \r\nwhich have been a part of AMIP or CMIP through several assessment \r\ncycles, and as such serve as a reference to quantify regional model-data \r\nagreement from one IPCC assessment to another. We compare the \r\nresults from 15 CMIP6 models using the PMIP4 protocol (CMIP6-PMIP4), \r\nwith non-CMIP6 models using the PMIP4 protocol, with PMIP3 models, \r\nand with regional temperature and precipitation changes from proxies \r\nfor the mid-Holocene (Figure  3.44b). For six out of seven variables \r\nshown, the CMIP6 multi-model mean captures the correct sign of the \r\nchange. For five out of seven of them the CMIP6 ensemble mean is \r\nwithin the reconstructed range"


id: ca6eacab3d6515fe8dc75911aef61e2f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 29409
content:
" For the other two variables (changes in \r\nthe mean temperature of the warmest month over North America and \r\nin the mean annual precipitation over West Africa) nearly all PMIP4 \r\nand PMIP3 models are outside the reconstructed range. CMIP6 models \r\nshow regional patterns of temperature changes similar to the PMIP3 \r\nensemble (Brierley et al., 2020), but the slight mid-Holocene cooling in \r\nPMIP4 compared with PMIP3, probably associated with lower imposed \r\nmid-Holocene carbon dioxide concentrations (Otto-Bliesner et al., \r\n2017), improves the regional model performance for summer and \r\nwinter temperatures (Figure 3.44b). However, this cooling also results \r\nin a CMIP6 mid-Holocene GSAT that lies further from the assessed \r\nrange (Figure 3.44a)"


id: 9fb78184bfb8ebcba048465968520cb3
created_at: 2023-03-16 14:01:59
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vector_id: 22667
content:
" All models show an expansion of the monsoon \r\nareas from the pre-industrial to the mid-Holocene simulations in the \r\nNorthern Hemisphere, but this expansion in some cases is only large \r\nenough to cancel out the bias in the pre-industrial control simulations \r\n(Section 3.3.3.2; Brierley et al., 2020). There is a slight improvement in \r\nrepresenting the northward expansion of the West African monsoon \r\nregion in PMIP4 compared with PMIP3 (Figures 3.11 and 3.44b)."


id: ec737980b8c42dad87e53b5caaa848d1
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 34682
content:
"Fourteen simulations of the LGM climate have been produced \r\nfollowing the CMIP6-PMIP4 protocol using 11 models, five of \r\nwhich are from the latest CMIP6 generation. The multi-model-mean \r\nglobal cooling simulated by these models is close to that simulated \r\nby the CMIP5-PMIP3 ensemble, but the range of results is larger. \r\nThe increase in the range is largely due to the inclusion of CESM2 \r\nwhich simulates a much larger cooling than the other PMIP4 models \r\n(Figure  3.44a). This is consistent with its larger climate sensitivity \r\n(see also Section 3.3.1.1; Zhu et al., 2021). The other models on \r\naverage also simulate slightly larger cooling in PMIP4 versus PMIP3 \r\n(Kageyama et al., 2017, 2021a). The PMIP4 multi-model mean is \r\nwithin the range of reconstructed regional averages for four out \r\nof seven regional variables; this is unchanged from PMIP3 but for \r\ndifferent variables (Figure 3.44c)"


id: cb8463c5be044a78880126dcdb84744c
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 29569
content:
" For all fields, the results of many \r\nindividual models are outside the reconstructed range. For two \r\nvariables out of seven (changes in the mean temperature of the \r\nwarmest month and mean annual precipitation over Western Europe) \r\nno model is within the range of the reconstructions. This analysis is \r\nstrengthened compared with the equivalent analysis in AR5 because \r\nit is based on larger and improved reconstructions (Cleator et al., \r\n2020). Most CMIP6-PMIP4 models simulate a slightly stronger AMOC \r\nin the LGM, but no strong deepening of the AMOC (Kageyama et al., \r\n2021a), while most other PMIP4 models simulate a strengthening "


id: d84f6e76b02a8e663c9527e6048cc179
created_at: 2023-03-16 14:01:59
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vector_id: 31558
content:
"2020), as well as some aspects of air-sea interactions (P. Wu et al., \r\n2019, single model; Bellucci et al., 2021, multi-model). However, warm\u0002biased sea surface temperatures in the Southern Ocean are worse in \r\ncomparison to standard resolution CMIP6 models (Bock et al., 2020). \r\nThe AR5 noted problems with the simulation of clouds in this region \r\nwhich were later attributed to a lack of supercooled liquid clouds \r\n(Bodas-Salcedo et al., 2016). Mesoscale ocean processes are critical \r\nto maintaining the Southern Ocean stratification and response to \r\nwind forcing (Marshall and Radko, 2003; Hallberg and Gnanadesikan, \r\n2006), and their explicit representation requires even higher ocean \r\nresolution (Hallberg, 2013). Similarly, atmospheric convection remains \r\nunresolved even in the highest-resolution climate models participating \r\nin HighResMIP"


id: 2647bf4afa9d673a8b70b78005913cc7
created_at: 2023-03-16 14:01:59
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vector_id: 3439
content:
" However, there is also evidence of improvements \r\nin the frequency, distribution and interannual variability of tropical \r\ncyclones in HighResMIP (Roberts et al., 2020a, b), particularly in the \r\nNorthern Hemisphere (see further discussion in Section 11.7.1.3), \r\nand their interaction with the ocean (Scoccimarro et al., 2017, single \r\nmodel), as well as the global moisture budget (Vanniere et al., 2019). \r\nAt higher resolution the track density of tropical cyclones is increased \r\npractically everywhere where tropical cyclones occur. Simulation of \r\nsome climate extremes is shown to be improved at higher resolution \r\nincluding explosively developing extra-tropical cyclones (Vries et al., \r\n2019; Jiaxiang et al., 2020), blocking (Section 3.3.3.3; Fabiano et al., \r\n2020; Schiemann et al., 2020) and European extreme precipitation \r\ndue to a better representation of the North Atlantic storm track (van \r\nHaren et al., 2015) and orographic boundary conditions (Schiemann \r\net al., 2018)."


id: da26dbe272c5cff13af770525d58ec62
created_at: 2023-03-16 14:01:59
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vector_id: 31847
content:
"As discussed in Section 3.3, CMIP6 models reproduce observed \r\nlarge-scale mean surface temperature patterns as well as their \r\nCMIP5 predecessors, but biases in surface temperature in the \r\nmean of HighResMIP models are smaller than those in the mean \r\nof the corresponding standard resolution CMIP6 configurations of \r\nthe same models (Section 3.3.1.1 and Figure  3.3). The extent and \r\ncauses of improvements due to increased horizontal resolutions \r\nin the atmosphere and ocean domains depend on the model \r\n(Kuhlbrodt et al., 2018; Roberts et al., 2018, 2019; Sidorenko et al., \r\n2019), although they typically involve better process representation \r\n(for  example of ocean currents and atmospheric storms) which \r\ncan lead to reduced biases in top of atmosphere radiation and \r\ncloudiness.  Precipitation has likewise improved in CMIP6 versus \r\nCMIP5 models, but biases remain"


id: 587388adbeca4a18c925042d9b3a327
created_at: 2023-03-16 14:01:59
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content:
" The high resolution (<25 km) \r\nclass of models participating in HighResMIP compares regionally \r\nbetter against observations than the standard resolution CMIP6 \r\nmodels (of order 100 km, Figure 3.13; Section 3.3.2), partly because \r\nof an improved representation of orographic (mountain-induced) \r\nprecipitation which constitutes a major fraction of precipitation on \r\nland, but other processes also play an important role (Vanniere et al., \r\n2019). However, there are also large parts of the tropical ocean where \r\nprecipitation in high-resolution models is not improved compared to \r\nstandard resolution CMIP6 models (Vanniere et al., 2019)."


id: 669b68ed9aa1b83bdffd40db543720ed
created_at: 2023-03-16 14:01:59
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vector_id: 13734
content:
"In CMIP6 a number of Earth system models have increased the realism \r\nby which key biogeochemical aspects of the coupled Earth system are \r\nrepresented, affecting, for example, the carbon and nitrogen cycles, \r\naerosols, and atmospheric chemistry (e.g., Cao et al., 2018; Gettelman \r\net al., 2019; Lin et al., 2019; Mauritsen et al., 2019; Seferian et al., \r\n2019; Sellar et al., 2019; Sidorenko et al., 2019; Swart et al., 2019; \r\nDunne et al., 2020; Seland et al., 2020; Wu et al., 2020; Ziehn et al., \r\n2020). In addition to increased process realism, the level of coupling \r\nbetween the physical climate and biogeochemical components of \r\nthe Earth system has also been enhanced in some models (Mulcahy \r\net al., 2020) as well as across different biogeochemical components \r\n(see Section 5.4 for a discussion and Table 5.4 for an overview). For \r\nexample, the nitrogen cycle is now simulated in several ESMs (Zaehle \r\net al., 2015; Davies-Barnard et al., 2020)"


id: b75eeb16f581b1d153da956588bddbfa
created_at: 2023-03-16 14:01:59
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content:
" This advance accounts \r\nfor the fertilization effect nitrogen availability has on vegetation \r\nand carbon uptake, reducing uncertainties in the simulations of \r\nthe carbon uptake responses to physical climate change (Section \r\n3.6.1) and to CO2 increases (Arora et al., 2020), thus improving \r\nconfidence in the simulated airborne fraction of CO2 emissions \r\n(Jones and Friedlingstein, 2020) and better constraining remaining \r\ncarbon budgets (Section 5.5). Such advances also allow investigation \r\nof land-based climate change mitigation options (e.g., through \r\nchanges in land management and associated terrestrial carbon \r\nuptake (Mahowald et al., 2017; Pongratz et al., 2018)) or interactions \r\nbetween different facets of the managed Earth system, such as \r\ninteractions between mitigation efforts targeting climate warming \r\nand air quality (West et al., 2013). A number of developments also \r\nexplicitly target improved simulation of the past."


id: 98a8dbc9d9e915197509da51fe7c78fb
created_at: 2023-03-16 14:01:59
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vector_id: 21535
content:
"Further such ESM developments include: (i) Apart from the nitrogen \r\ncycle, extending terrestrial carbon cycle models to simulate \r\ninteractions between the carbon cycle and other nutrient cycles, such \r\nas phosphorus, that are known to play an important role in limiting \r\nfuture plant uptake of CO2 (Zaehle et al., 2015). (ii) Introducing \r\nexplicit coupling between interactive atmospheric chemistry and \r\naerosol schemes (Gettelman et al., 2019; Sellar et al., 2019), which \r\nhas been shown to affect estimates of historical aerosol radiative \r\nforcing (Karset et al., 2018). Furthermore, interactive treatment \r\nof atmospheric chemistry in a full ESM supports investigation of \r\ninteractions between climate and air quality mitigation efforts, such as \r\nin AerChemMIP (Collins et al., 2017), as well as interactions between \r\nstratospheric ozone recovery and global warming (Morgenstern \r\net al., 2018)"


id: ce07c66d8737a7e6362aba077a81bd36
created_at: 2023-03-16 14:01:59
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content:
" (iii) Coupling between components of Earth system \r\nmodels has been extended to increase their utility for studying future \r\ninteractions across the full Earth system, such as between ocean \r\nbiogeochemistry and cloud-aerosol processes (Mulcahy et al., 2020), \r\nand vegetation and impacts on dust production (Kok et al., 2018), \r\nproduction of secondary organic aerosols (SOA, Zhao et al., 2017) \r\nand Equilibrium Climate Sensitivity (ECS), whereby enhanced CO2\r\nfertilization of land vegetation causes changes in regional surface \r\nalbedo (Andrews et al., 2019). Increased coupling between physical \r\nclimate and biogeochemical processes in a single ESM, along with \r\nan increased number of interactively represented processes, such as \r\npermafrost thaw, vegetation, wildfires and continental ice sheets \r\nincreases our ability to investigate the potential for abrupt and \r\ninteractive changes in the Earth system (see Sections 4.7.3 and 5.4.9, \r\nand Box 5.1). Table 5"


id: 6e0b80e32cfe01482e5c4a05221b4e64
created_at: 2023-03-16 14:01:59
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content:
"In summary, both high-resolution and high-complexity models have \r\nbeen evaluated as part of CMIP6. In comparison with standard \r\nresolution CMIP6 models, higher resolution probed under the \r\nHighResMIP activity (Haarsma et al., 2016) improves aspects of the \r\nsimulation of climate (particularly concerning sea surface temperature) \r\nbut discrepancies remain and there are some regions, such as parts of \r\nthe Southern Ocean, where currently attainable resolution produces \r\ninferior performance (high confidence). Such model behaviour can \r\nindicate deficiencies in model physics that are not simply associated \r\nwith resolution"


id: 7e8d830212af783fcee8cb58d8ee6f35
created_at: 2023-03-16 14:01:59
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content:
" In several cases, high-complexity ESMs that include \r\nadditional interactions between Earth system components and thus \r\nhave potential for additional associated model errors nevertheless \r\nperform as well as their low-complexity counterparts, illustrating that \r\ninteractively simulating these Earth System components as part of \r\nthe climate system is now well established."


id: 5a499baa3f4a3bf785351dd957077544
created_at: 2023-03-16 14:01:59
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vector_id: 11709
content:
"Results consistently show that such climate models can only reproduce the observed warming (black line in \r\nFAQ 3.1, Figure 1) when including the effects of human activities (grey band in FAQ 3.1, Figure 1), in particular \r\nthe increasing concentrations of greenhouse gases. These climate models show a dominant warming effect of \r\ngreenhouse gas increases (red band, which shows the warming effects of greenhouse gases by themselves), \r\nwhich has been partly offset by the cooling effect of increases in atmospheric aerosols (blue band). By contrast, \r\nsimulations that include only natural processes, including internal variability related to El Nino and other similar \r\nvariations, as well as variations in the activity of the sun and emissions from large volcanoes (green band in \r\nFAQ 3.1, Figure 1), are not able to reproduce the observed warming"


id: 6d46feeacc2f938f7873b8de49815ae9
created_at: 2023-03-16 14:01:59
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content:
" The fact that simulations including only \r\nnatural processes show much smaller temperature increases indicates that natural processes alone cannot explain \r\nthe strong rate of warming observed. The observed rate can only be reproduced when human influence is added \r\nto the simulations."


id: 4faf884020143a7ad40d49fcf6fdf620
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 9973
content:
"Paleoclimatic records (indirect measurements of climate that can extend back many thousands of years) and \r\nclimate models all show that global surface temperatures have changed significantly over a wide range of time \r\nscales in the past. One of these reasons is natural variability, which refers to variations in climate that are either \r\ninternally generated within the climate system or externally driven by natural changes. Internal natural variability \r\ncorresponds to a redistribution of energy within the climate system (for example via atmospheric circulation\r\nchanges similar to those that drive the daily weather) and is most clearly observed as regional, rather than \r\nglobal, fluctuations in surface temperature. External natural variability can result from changes in the Earth's \r\norbit, small variations in energy received from the sun, or from major volcanic eruptions"


id: 94ebd2c7cba6a9c404123742008733b2
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 20939
content:
" Although large orbital \r\nchanges are related to global climate changes of the past, they operate on very long time scales (i.e., thousands \r\nof years). As such, they have displayed very little change over the past century and have had very little influence \r\non temperature changes observed over that period. On the other hand, volcanic eruptions can strongly cool the \r\nEarth, but this effect is short-lived and their influence on surface temperatures typically fades within a decade \r\nof the eruption."


id: 3cb17d24748117d929e6ac25e0afab9a
created_at: 2023-03-16 14:01:59
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vector_id: 6913
content:
"Climate modelling started in the 1950s and, over the years, models have become increasingly sophisticated as \r\ncomputing power, observations and our understanding of the climate system have advanced. The models used \r\nin the IPCC First Assessment Report published in 1990 correctly reproduced many aspects of climate (FAQ 1.1). \r\nThe actual evolution of the climate since then has confirmed these early projections, when accounting for the \r\ndifferences between the simulated scenarios and actual emissions. Models continue to improve and get better \r\nand better at simulating the large variety of important processes that affect climate. For example, many models \r\nnow simulate complex interactions between different aspects of the Earth system, such as the uptake of carbon \r\ndioxide by vegetation on land and by the ocean, and the interaction between clouds and air pollutants"


id: 2b695ada39d80f43ac4c3d2ba1218aa8
created_at: 2023-03-16 14:01:59
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vector_id: 4266
content:
" While \r\nsome models are becoming more comprehensive, others are striving to represent processes at higher resolution, \r\nfor example to better represent the vortices and swirls in currents responsible for much of the transport of heat \r\nin the ocean."


id: 14f397aa6fa965facd75fed74e8e7b15
created_at: 2023-03-16 14:01:59
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vector_id: 801
content:
"Sebastian Milinski (Germany), Kyung-Sook Yun (Republic of Korea), Kyle Armour (United  States \r\nof America), Nicolas Bellouin (United Kingdom/France), Ingo Bethke (Norway/Germany), \r\nMichael P. Byrne (United Kingdom /Ireland), Christophe Cassou (France), Deliang Chen (Sweden), \r\nAnnalisa Cherchi (Italy), Hannah M. Christensen (United Kingdom), Sarah L. Connors (France/United \r\nKingdom), Alejandro Di Luca (Australia, Canada/Argentina), Sybren S. Drijfhout (The Netherlands), \r\nChristopher G. Fletcher (Canada/United Kingdom, Canada), Piers Forster (United Kingdom), \r\nJavier  Garcia-Serrano (Spain), Nathan P. Gillett (Canada), Darrell  S.  Kaufmann (United States  of \r\nAmerica), David  P.  Keller (Germany/United States of America), Ben  Kravitz (United States \r\nof America), Hongmei Li (Germany/China), Yongxiao Liang (Canada/China), Andrew H. MacDougall \r\n(Canada), Elizaveta  Malinina (Canada/Russian Federation), Matthew  Menary (France/United \r\nKingdom), William  J"


id: a49819e97c9487b384d687a4b668b9bc
created_at: 2023-03-16 14:01:59
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vector_id: 23460
content:
"  Merryfield (Canada/United States of America), Seung-Ki Min (Republic \r\nof Korea), Zebedee R.J. Nicholls (Australia), Dirk Notz (Germany), Brodie Pearson (United States of \r\nAmerica/United Kingdom), Matthew D. K. Priestley (United Kingdom), Johannes Quaas (Germany), \r\nAurelien Ribes (France), Alex C. Ruane (United States of America), Jean-Baptiste Sallee (France), \r\nEmilia  Sanchez-Gomez (France/Spain), Sonia  I.  Seneviratne (Switzerland), Aimee  B.  A.  Slangen \r\n(The  Netherlands), Chris Smith (United Kingdom), Malte  F.  Stuecker (United States of America/\r\nGermany), Ranjini Swaminathan (United Kingdom/India), Peter W. Thorne (Ireland/ United Kingdom), \r\nKatarzyna  B. Tokarska (Switzerland/Poland), Matthew Toohey (Canada, Germany/Canada), \r\nAndrew Turner (United Kingdom), Danila Volpi (Italy), Cunde Xiao (China), Giuseppe Zappa (Italy)"


id: e8c82a2f2eb63a95e6d881a013ca374c
created_at: 2023-03-16 14:01:59
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vector_id: 34139
content:
"The projections assessed here are mainly based on a new range \r\nof scenarios, the Shared Socio-economic Pathways (SSPs) used \r\nin the Coupled Model Intercomparison Project Phase 6 (CMIP6).\r\nAmong the SSPs, the focus is on the five scenarios SSP1-1.9, SSP1-2.6, \r\nSSP2-4.5, SSP3-7.0, and SSP5-8.5. In the SSP labels, the first number \r\nrefers to the assumed shared socio-economic pathway, and the second \r\nrefers to the approximate global effective radiative forcing (ERF) in \r\n2100. Where appropriate, this chapter also assesses new results from \r\nCMIP5, which used scenarios based on Representative Concentration \r\nPathways (RCPs). Additional lines of evidence enter the assessment, \r\nespecially for change in globally averaged surface air temperature \r\n(GSAT) and global mean sea level (GMSL), while assessment for \r\nchanges in other quantities is mainly based on CMIP6 results. Unless \r\nnoted otherwise, the assessments assume that there will be no major \r\nvolcanic eruption in the 21st century"


id: db5a98a7477bace244289005ac40195
created_at: 2023-03-16 14:01:59
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content:
" {1.6, 4.2.2, 4.3.2, 4.3.4, 4.6.2, \r\nBox 4.1, Cross-Chapter Box 4.1, Cross-Chapter Box 7.1, 9.6}"


id: 9c3fd3ff4e7098f866f7977bf267e0d8
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vector_id: 22135
content:
"In the near term (2021-2040), a 1.5degC increase in the 20-year \r\naverage of GSAT, relative to the average over the period \r\n1850-1900, is very likely to occur in scenario SSP5-8.5, \r\nlikely to occur in scenarios SSP2-4.5 and SSP3-7.0, and more \r\nlikely than not to occur in scenarios SSP1-1.9 and SSP1-2.6.\r\nThe  threshold-crossing time is defined as the midpoint of the  first \r\n20-year period during which the average GSAT exceeds the \r\nthreshold. In all scenarios assessed here except SSP5-8.5, the central \r\nestimate of crossing the 1.5degC threshold lies in the early 2030s. This \r\nis in the early part of the likely range (2030-2052) assessed in the \r\nIPCC Special Report on Global Warming of 1.5degC (SR1.5), which \r\nassumed continuation of the then-current warming rate; this rate has \r\nbeen confirmed in the AR6. Roughly half of this difference between \r\nassessed crossing times arises from a  larger historical warming \r\ndiagnosed in AR6"


id: e6d37865ae9f471c57ef762462a38655
created_at: 2023-03-16 14:01:59
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vector_id: 33799
content:
" The other half arises because for central estimates \r\nof climate sensitivity, most scenarios show stronger warming over \r\nthe near term than was assessed as 'current' in SR1.5 (medium \r\nconfidence). When considering scenarios similar to SSP1-1.9 instead "


id: e6e11b380cfe016ce0c99d1758307921
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 33809
content:
"from internal variability (Cox and Stephenson, 2007; Hawkins and \r\nSutton, 2009). An additional source of projection uncertainty arises \r\nfrom possible future volcanic eruptions and future solar variability. \r\nAssessment of uncertainty relies on multi-model ensembles such as the \r\nCoupled Model Intercomparison Project Phase 6 (CMIP6, Eyring et al., \r\n2016), single-model initial-condition large ensembles (e.g.,  Kay \r\net al., 2015; Deser et al., 2020), and ensembles initialized from the \r\nobserved climate state (decadal predictions, e.g., Smith et al., 2013a; \r\nMeehl et al., 2014; Boer et al., 2016; Marotzke et al., 2016). Ensemble \r\nevaluation methods include assessment of model performance and \r\nindependence (e.g., Knutti et al., 2017; Boe, 2018; Abramowitz et al., \r\n2019); emergent and other observational constraints (e.g., Allen and \r\nIngram, 2002; Hall and Qu, 2006; Cox et al"


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content:
", 2018); and the uncertainty \r\nassessment of equilibrium climate sensitivity and transient climate \r\nresponse in Chapter 7. Ensemble evaluation is assessed in Box 4.1 \r\nthrough the inclusion of lines of evidence in addition to the projection \r\nensembles, including implications for potential model weighting."


id: 1583e47de624ac7dcb005e273b70b5e7
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 900
content:
"Every projection assessment is conditioned on a  particular forcing \r\nscenario. If sufficient evidence is available, a  detailed probabilistic \r\nassessment of a physical climate outcome can be performed for each \r\nscenario separately. By contrast, there is no agreed-upon approach \r\nto assigning probabilities to forcing scenarios, to the point that it has \r\nbeen debated whether such an approach can even exist (e.g., Grubler \r\nand Nakicenovic, 2001; Schneider, 2001, 2002). Although there were \r\nsome recent attempts to ascribe subjective probabilities to scenarios \r\n(e.g.,  Ho et  al., 2019; Hausfather and Peters, 2020), and although \r\n'feasibility' along different dimensions is an important concept in \r\nscenario research (see AR6 WGIII Chapter 3), the scenarios used for \r\nthe model-based projections assessed in this chapter do not come \r\nwith statements about their likelihood of actually unfolding in the \r\nfuture"


id: f571d6fe8a7f360fcf35b37d81c7612d
created_at: 2023-03-16 14:01:59
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vector_id: 36139
content:
" Therefore, it is usually not possible to combine responses to \r\nindividual scenarios into an overall probabilistic statement about \r\nexpected future climate. Exceptions to this limit in the assessment \r\nare possible only under special circumstances, such as for some \r\nstatements about near-term climate changes that are largely \r\nindependent of the scenario chosen (e.g., Section 4.4.1). Beyond this, \r\nno combination of responses to different scenarios can be assessed \r\nin this chapter but may be possible in future assessments."


id: b09c30c9bd16eeec41f04cec7526f4d7
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 25306
content:
"The chapter is organized as follows (Figure  4.1). After Section  4.2 \r\non the methodologies used in the assessment, Section 4.3 assesses \r\nprojected changes in key global climate indicators throughout the \r\n21st  century, relative to the period 1995-2014, which comprises \r\nthe last 20 years of the historical simulations of CMIP6 (Eyring et al., \r\n2016) and hence the most recent past simulated with the observed \r\natmospheric composition. The global climate indicators assessed \r\ninclude GSAT, global land precipitation, Arctic sea ice area (SIA), \r\nglobal mean sea level (GMSL), the Atlantic Meridional Overturning \r\nCirculation (AMOC), global mean ocean surface pH, carbon \r\nuptake by land and ocean, the global monsoon, the Northern and \r\nSouthern Annular Modes (NAM and SAM), and the El Nino-Southern \r\nOscillation (ENSO)"


id: cbbf57fa218673ace2dcedadabca4231
created_at: 2023-03-16 14:01:59
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vector_id: 29598
content:
" Differently from the assessment for changes \r\nin other quantities only based on the range of CMIP6 projections, \r\nadditional lines of evidence enter the assessment for GSAT and GMSL \r\nchange. For most results and figures based on CMIP6, one realization \r\nfrom each model (the first of the uploaded set) is used. Section 4.3 \r\nfinally synthesizes the assessment of GSAT change using multiple \r\nlines of evidence in addition to the CMIP6 projection simulations."


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"Similar to the approach used in AR5 (Flato et al., 2013), the primary \r\nlines of evidence of this chapter are comprehensive climate models \r\n(atmosphere-ocean general circulation models, AOGCMs) and Earth \r\nsystem models (ESMs); ESMs differ from AOGCMs by including \r\nrepresentations of various biogeochemical cycles. We also build \r\non results from ESMs of intermediate complexity (EMICs; Claussen \r\net  al., 2002; Eby et  al., 2013) and other types of models where \r\nappropriate. This chapter focuses on a particular set of coordinated \r\nmulti-model experiments known as model intercomparison projects \r\n(MIPs). These frameworks recommend and document standards for \r\nexperimental design for running AOGCMs and ESMs to minimize \r\nthe chance of differences in results being misinterpreted. CMIP is an \r\nactivity of the World Climate Research Programme (WCRP), and the \r\nlatest phase is CMIP6 (Eyring et al., 2016)"


id: be95a36100617647520414c689b4a5f2
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" To establish robustness of \r\nresults, it is vital to assess the performance of these models in terms \r\nof mean state, variability, and the response to external forcings. \r\nThat evaluation has been undertaken using the CMIP6 'Diagnostic, \r\nEvaluation and Characterization of Klima' (DECK) and historical \r\nsimulations in Chapter 3 of this Report, which concludes that there \r\nis high confidence that the CMIP6 multi-model mean captures most \r\naspects of observed climate change well (Section 3.8.3.1)."


id: 842f035ccc11ed9fa3153d7c17229053
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 18336
content:
"In presenting results and evidence, this chapter tries to be as \r\ncomprehensive as possible. In tables we show multi-model mean \r\nchange and 5-95% range for all five SSPs, while in time series \r\nfigures we show multi-model mean change for all five SSPs but for \r\nclarity 5-95% range only for SSP1-2.6 and SSP3-7.0. Where maps are \r\npresented, due to space restrictions we focus on showing multi-model \r\nmean change for SSP1-2.6 and SSP3-7.0. SSP1-2.6 is preferred over \r\nSSP1-1.9 because the latter has far fewer simulations available. The \r\nhigh-end scenarios RCP8.5 or SSP5-8.5 have recently been argued \r\nto be implausible to unfold (e.g.,  Hausfather and Peters, 2020; see \r\nChapter 3 of the AR6 WGIII). However, where relevant we show results \r\nfor SSP5-8.5, for example to enable backwards compatibility with AR5, \r\nfor comparison between emissions-driven and concentration-driven \r\nsimulations, and because there is greater data availability of daily \r\noutput for SSP5-8.5"


id: 4da0ff84ebf743208f0ae134952e2695
created_at: 2023-03-16 14:01:59
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vector_id: 9646
content:
" When presenting low-likelihood, high-warming \r\nstorylines we also show results from the high-end SSP5-8.5 scenario."


id: f81e2e2fa4c9c1500f5c29f62df2ae59
created_at: 2023-03-16 14:01:59
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vector_id: 36549
content:
"Complete backward comparability between CMIP5 and CMIP6 scenarios \r\ncannot be established for detailed regional assessments, because the \r\nSSP scenarios include regional forcings - especially from land use and \r\naerosols - that are different from the CMIP5 RCPs. Even at a global level, \r\na quantitative comparison is challenging between corresponding SSP and \r\nRCP radiative forcing levels due to differing contributions to the forcing \r\n(Meinshausen et al., 2020) and evidence of differing model responses \r\n(Section 4.6.2.2; Wyser et al., 2020). The RCP scenarios assessed in AR5 \r\nall showed similar, rapid reductions in SLCFs and emissions of SLCF \r\nprecursor species over the 21st century; the CMIP5 projections hence \r\ndid not sample a wide range of possible trajectories for future SLCFs \r\n(Chuwah et al., 2013). The SSP scenarios assessed in the AR6 offer more \r\nscope to explore SLCF pathways as they sample a broader range of air \r\nquality policy options (Gidden et al"


id: f441e9511d274bdc4affd0aaf7288bac
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content:
", 2019) and relationships of CO2 to \r\nnon-CO2 greenhouse gases (Meinshausen et al., 2020). Section 4.6.2.2 \r\nassesses RCP and SSP differences. Other MIPs (see Section 4.2.1) have \r\nbeen designed to explicitly explore some of the implications of the \r\ndifferent socio-economic storylines for a given radiative forcing level."


id: 2f746e450b14db843604b42ce2f14ed5
created_at: 2023-03-16 14:01:59
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vector_id: 4889
content:
"There is also a  wide range of techniques employed to assimilate \r\nobserved information into models in order to generate suitable \r\ninitial conditions (Polkova et  al., 2019). These range in complexity \r\nfrom simple relaxation towards observed time series of sea surface \r\ntemperature (SST) (Mignot et  al., 2016) or wind stress anomalies \r\n(Thoma et al., 2015a, b), to relaxation toward three-dimensional ocean \r\nand sometimes atmospheric state estimates from various sources \r\n(e.g.,  Pohlmann et  al., 2013; Knight et  al., 2014; Dunstone et  al., \r\n2016), or hybrid relaxation combining surface and tri-dimensional \r\nrestoring as function of ocean basins and depth (Sanchez-Gomez \r\net al., 2016), to sophisticated data assimilation methods such as the \r\nensemble Kalman filter (Nadiga et al., 2013; Counillon et al., 2014, \r\n2016; Msadek et al., 2014; Karspeck et al., 2015; Brune et al., 2018; \r\nCassou et  al., 2018; Polkova et  al"


id: 4a9a2e1160e34fa6dc2424cd070d45f7
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vector_id: 9141
content:
", 2019), the four-dimensional \r\nensemble-variational hybrid data assimilation (He et al., 2017, 2020) \r\nand the initialization of sea ice (Guemas et  al., 2016; Kimmritz \r\net  al., 2018). In addition, decadal predictions necessarily consist \r\nof ensembles of forecasts to quantify uncertainty, as discussed in \r\nSection 4.2.1. A common way to generate an ensemble is through \r\nsets of initial conditions containing small variations that lead to \r\ndifferent subsequent climate trajectories. A  variety of methods \r\nand assumptions has been employed to generate and filter initial\u0002condition ensembles for decadal prediction (e.g., Marini et al., 2016; \r\nKadow et al., 2017). As yet, there is no clear consensus as to which \r\ninitialization and ensemble generation techniques are most effective, \r\nand evaluations of their comparative performance within a  single \r\nmodelling framework are needed (Cassou et al., 2018)."


id: 2ea1a5443b0069c61cc824e08885a8e3
created_at: 2023-03-16 14:01:59
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content:
"A consequence of model imperfections and resulting model systematic \r\nerrors is that estimates of these errors must be removed from the \r\nprediction to isolate the predicted climate anomaly and the phase \r\nof the decadal modes of climate variability (Sections 4.4.3.5 and \r\n4.4.3.6, and Annex IV, Sections AIV.2.6 and AIV.2.7). Because of the \r\ntendency for systematic drifts to occur following initialization, bias \r\ncorrections generally depend on time since the start of the forecast, \r\noften referred to as lead time. In practice, the lead-time-dependent \r\nbiases are calculated using ensemble retrospective predictions, \r\nalso known as hindcasts, and recommended basic procedures for \r\nsuch corrections are provided in previous studies (Goddard et  al., \r\n2013; Boer et al., 2016)"


id: 830b7d7c34c593058452f70acd5df6d4
created_at: 2023-03-16 14:01:59
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vector_id: 18146
content:
" The biases are also dynamically corrected \r\nduring hindcasts and predictions by incorporating the multi-year \r\nmonthly mean analysis increments from the initialization into \r\nthe initial condition at each integration step (Wang et al., 2013b). \r\nBesides mean climate as a function of lead time, further aspects of \r\ndecadal predictions may be biased, such as the modes of variability \r\n(see Section 3.7 and Annex IV) upon which drift patterns are projected \r\n(Sanchez-Gomez et al., 2016), and additional correction procedures \r\nhave thus been proposed to remove biases in representing long-term \r\ntrends (Kharin et al., 2012; Kruschke et al., 2016; Balaji et al., 2018; "


id: dd98ec93f09e2aa9781fef7764141d04
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index: climate_gpt_only_giec
vector_id: 32371
content:
"Ocean initialization techniques generally use one of two strategies. \r\nUnder full-field initialization, estimates of observed climate states \r\nare represented directly on the model grid. A potential drawback is \r\nthat predictions initialized using the full-field approach tend to drift \r\ntoward the biased climate preferred by the model (Smith et al., 2013a; \r\nBellucci et al., 2015b; Sanchez-Gomez et al., 2016; Kroger et al., 2018; \r\nNadiga et al., 2019). Such drifts can be as large as, or larger than, \r\nthe climate anomaly being predicted and may therefore obscure \r\npredicted climate anomalies (Kroger et al., 2018) unless corrected for \r\nthrough post-processing. By contrast, anomaly initialization reduces \r\ndrifts by adding observed anomalies (i.e., deviations from mean \r\nclimate) to the mean model climate (Pohlmann et al., 2013; Smith \r\net al., 2013a; Thoma et al., 2015b; Cassou et al"


id: 49a329746129a0e5ad9992be9d003b55
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vector_id: 8974
content:
", 2018), but has the \r\ndisadvantage that the model state is then further from the real world \r\nfrom the start of the prediction. For both approaches, unrealistic \r\nfeatures in the observation data used for initialization may induce \r\nunrealistic transient behavior (Pohlmann et  al., 2017; Teng et  al., "


id: 925aaf6e85e95c819e14fd43aee22993
created_at: 2023-03-16 14:01:59
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vector_id: 20551
content:
"Skill for multi-year to decadal precipitation forecasts is generally much \r\nlower than for temperature, although one exception is Sahel rainfall \r\n(Sheen et al., 2017), due to its dependence on predictable variations \r\nin North Atlantic SST through teleconnections (Annex IV; Martin and \r\nThorncroft, 2014a). Predictive skill on decadal time scales is found for \r\nextratropical storm-tracks and storm density (Kruschke et al., 2016; \r\nSchuster et  al., 2019), atmospheric blocking (Schuster et  al., 2019; \r\nAthanasiadis et al., 2020), the Quasi-Biennial Oscillation (QBO; Scaife \r\net al., 2014; Pohlmann et al., 2019) and over the tropical oceans (tropical \r\ntrans-basin variability; Chikamoto et al., 2015). In addition, decadal \r\npredictions with large ensemble sizes are able to predict multi-annual \r\ntemperature (Peters et al., 2011; Sienz et al., 2016; Borchert et al., 2019; \r\nSospedra-Alfonso and Boer, 2020), precipitation (Yeager et al., 2018; \r\nD.M. Smith et  al"


id: ec761db43e6561f50104ae4a5b9ac80f
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index: climate_gpt_only_giec
vector_id: 34683
content:
", 2019), and atmospheric circulation (Smith et  al., \r\n2020) anomalies over certain land regions, although the ensemble\u0002mean magnitudes are much weaker than observed. This discrepancy \r\nmay be symptomatic of an apparent deficiency in climate models that \r\ncauses some predictable signal, such as that associated to the North \r\nAtlantic Oscillation (NAO; Section AIV.2.1), to be much weaker than \r\nin nature (Eade et al., 2014; Scaife and Smith, 2018; Strommen and \r\nPalmer, 2019; Smith et  al., 2020), while others, such as that linked \r\nto the SAM (Section AIV.2.2), are more consistent with observations \r\n(Byrne et al., 2019)."


id: d6799ef120791f01210899ec66420bda
created_at: 2023-03-16 14:01:59
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content:
"Skill assessments have shown that initialized predictions can \r\nout-perform their uninitialized counterparts (Doblas-Reyes et  al., \r\n2013; Meehl et al., 2014; Bellucci et al., 2015a; D.M. Smith et al., 2018, \r\n2019; Yeager et al., 2018), although such comparisons are sensitive \r\nto the region and variable considered, multi-model predictions are \r\ngenerally more skilful than individual models (Doblas-Reyes et  al., \r\n2013; D.M. Smith et al., 2013b, 2019). Considerable skill, especially for \r\ntemperature, can be attributed to external forcings such as changes \r\nin GHG, aerosol concentrations, and volcanic eruptions. On a global \r\nscale, this contribution to skill has been found to exceed that from \r\nthe prediction of internal variability except in the early stages (about \r\none year for global SST) of the forecast (Corti et al., 2015; Sospedra\u0002Alfonso and Boer, 2020; Bilbao et al"


id: e299bf2d99c6fdc29d726b5a91564602
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vector_id: 33158
content:
", 2021), though idealized potential \r\nskill measures and observations-based studies suggest that improving \r\nthe prediction of internal variability could extend this crossover to \r\nlonger lead times (Boer et  al., 2013; Arthun et  al., 2017). In some \r\ncases, part of the skill arises from the ability of initialized predictions \r\nto capture observed transitions of major modes of climate variability \r\n(Meehl et al., 2016) such as the Pacific Decadal Variability (PDV) and \r\nthe Atlantic Multi-decadal Variability (AMV; see Sections 4.4.3.5 \r\nand 4.4.3.6, and Annex IV, Sections AIV.2.6 and AIV.2.7)."


id: 13ca4fd5ca56e82dcc0724adbf6ff98
created_at: 2023-03-16 14:01:59
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content:
"Projected climate change is typically represented in this chapter \r\nfor specific future periods. One important source of uncertainty in \r\nprojections presented for fixed future epochs (time-slabs/time-slices) \r\nis the underlying scenario used; another is the structural uncertainty \r\nassociated with model climate sensitivity. Presenting projections and \r\nassociated measures of uncertainty for specific periods (see Sections 4.4 \r\nand 4.5) remains the most widely applied methodology towards \r\ninforming climate change impact studies. It is becoming increasingly \r\nimportant from the perspective of climate change and mitigation \r\npolicy, however, to present projections also as a function of the change \r\nin global mean temperature (i.e., global warming levels, GWLs). They \r\nare expressed either in terms of changes of global mean surface \r\ntemperature (GMST) or GSAT (see  Section  1.6.2 and Cross-Chapter \r\nBox 2.3). For example, the IPCC SR1.5 (Hoegh-Guldberg et al"


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content:
", 2018) \r\nassessed the regional patterns of warming and precipitation change for \r\nGMST increase of 1.5degC and 2degC above 1850-1900 levels. Techniques \r\nused to represent the spatial variations in climate at a given GWL are \r\nreferred to as pattern scaling."


id: f7028baca85d2b63138c88051f6d0e6c
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content:
"In the 'traditional' methodology as applied in AR5 (Collins et al., 2013), \r\npatterns of climate change in space are calculated as the product of the \r\nchange in GSAT at a given point in time and a spatial pattern of change \r\nthat is constant over time and the scenario under consideration, and \r\nwhich may or may not depend on a particular climate model (Allen \r\nand Ingram, 2002; Mitchell, 2003; Lambert and Allen, 2009; Andrews \r\nand Forster, 2010; Bony et al., 2013; Lopez et al., 2014). This approach \r\nassumes that external forcing does not affect the internal variability \r\nof the climate system, which may be regarded a stringent assumption \r\nwhen taking into account decadal and multi-decadal variability (Lopez \r\net  al., 2014) and the potential non-linearity of the climate change \r\nsignal. Moreover, pattern scaling is expected to have lower skill for \r\nvariables with large spatial variability (Tebaldi and Arblaster, 2014)"


id: ae79ccafb8dccfb99015141f971701bf
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content:
" \r\nPattern scaling also fails to capture changes in boundaries that move \r\npoleward such as sea ice extent and snow cover (Collins et al., 2013), \r\nand temporal frequency quantities such as frost days that decrease \r\nunder warming but are bounded at zero. Spatial patterns are also \r\nexpected to be different between transient and equilibrium simulations \r\nbecause of the long adjustment time scale of the deep ocean."


id: 4bfd22659109c6f945e47075be8e0cda
created_at: 2023-03-16 14:01:59
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content:
"An alternative to the traditional pattern scaling approach is the \r\ntime-shift method described by Herger et al. (2015) which is applied in \r\nthis chapter (also called the epoch approach; see Section 4.6.1). When \r\napplied to a transient scenario such as SSP5-8.5, a future time-slab is \r\nreferenced to a particular increase in the GSAT (e.g., 1.5degC or 2degC of \r\nglobal warming above pre-industrial levels). The spatial patterns that \r\nresult represent a  direct scaling of the spatial variations of climate \r\nchange at the particular level of global warming. An important \r\nadvantage of this approach is that it ensures physical consistency \r\nbetween the different variables for which changes are presented \r\n(Herger et al., 2015). The internal variability does not have to be scaled \r\nand is consistent with the GSAT change. Furthermore, the time-shift \r\nmethod allows for a partial comparison of how the rate of increase \r\nin GSAT influences the regional spatial patterns of climate change"


id: c45b054cebff5c0b5a2f42f218ace73f
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content:
" \r\nFor example, spatial patterns of change for global warming of 2degC \r\ncan be compared across the SSP2-4.5 and SSP5-8.5 scenarios. Direct \r\ncomparisons can also be obtained between variations in the regional \r\nimpacts of climate change for the case where global warming stabilizes \r\nat, for instance, 1.5degC or 2degC of global warming, as opposed to the case \r\nwhere the GSAT reaches and then exceeds the 1.5degC or 2degC thresholds \r\n(Tebaldi and Knutti, 2018). An important potential caveat is that \r\nforcing mechanisms such as aerosol radiative forcing are represented \r\ndifferently in different models, even for the same SSP. This may imply \r\ndifferent regional aerosol direct and indirect effects, implying different \r\nregional climate change patterns. Hence, it is important to consider the \r\nvariations in the forcing mechanisms responsible for a specific increase \r\nin GSAT towards understanding the uncertainty range associated with \r\nthe variations in regional climate change"


id: 5712feb4c723c7c25791545579dd7cb8
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content:
" A minor practical limitation \r\nof this approach is that stabilization scenarios at 1.5degC or 2degC of global \r\nwarming, such as SSP1-2.6, do not allow for spatial patterns of change \r\nto be calculated from these scenarios at higher levels of global warming, \r\nwhile it is possible in scenarios such as SSP5-8.5 (Herger et al., 2015).\r\nIn this chapter, the spatial patterns of change as a function of GWLs \r\n(defined in terms of the increase in the GSAT relative to 1850-1900) "


id: 9ddc6a65c53f9331a0eeeee199186747
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vector_id: 22356
content:
"As discussed extensively in AR5 (Collins et al., 2013), ensemble spread \r\nin projections performed with different climate models accounts for \r\nonly part of the entire model uncertainty, even when considering the \r\nuncertainty in the radiative forcing in projections (Vial et al., 2013) \r\nand forced response. The AR5 uncertainty characterisation (Kirtman \r\net  al., 2013) followed Hawkins and Sutton (2009) and diagnosed \r\ninternal variability through a high-pass temporal filter. This approach \r\nhas deficiencies particularly if internal variability manifests on the \r\nmulti-decadal time scales (Deser et al., 2012a; Marotzke and Forster, \r\n2015) and is classified as (model) response uncertainty instead of \r\ninternal variability. Single-model initial-condition large ensembles \r\nrevealed that the AR5 approach underestimates the role of internal \r\nvariability uncertainty and overestimates the role of model uncertainty \r\n(Maher et al., 2018; Stolpe et al., 2018; Lehner et al"


id: 667047d7506b7619bc8aee36ba8cc176
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content:
", 2020) particularly \r\nat the local scale while yielding a  reasonable approximation for \r\nuncertainty separation for GSAT (Lehner et al., 2020)."


id: b193b2182baacc0815927af3f4d01a6d
created_at: 2023-03-16 14:01:59
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content:
"The AR5 assessed with very high confidence that climate models \r\nreproduce the general features of the global-scale annual mean surface \r\ntemperature increase over the historical period, including the more \r\nrapid warming in the second half of the 20th century, and the cooling \r\nimmediately following large volcanic eruptions. Furthermore, because \r\nclimate and Earth system models are based on physical principles, they \r\nwere assessed in AR5 to reproduce many important aspects of observed \r\nclimate. Both aspects were argued to contribute to our confidence in the \r\nmodels' suitability for their application in quantitative future predictions \r\nand projections (Flato et al., 2013). This Report assesses (in Section 3.8"


id: 99337ecaa5e864399bb6cd9e0f0a7113
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content:
"2) \r\nwith high confidence that for most large-scale indicators of climate \r\nchange, the recent mean climate simulated by the latest generation \r\nclimate models underpinning this assessment has improved compared \r\nto the models assessed in AR5, and with high confidence that the \r\nmulti-model mean captures most aspects of observed climate change \r\nwell. These assessments form the foundation of applying climate and \r\nEarth system models to the projections assessed in this chapter. Where \r\nappropriate, the assessment of projected changes is accompanied by an \r\nassessment of process understanding and model evaluation."


id: 128f834b84625c22be93b6ed7b72b236
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vector_id: 416
content:
"Single-model initial-condition large ensembles thus represent a crucial \r\nstep towards a cleaner separation of model uncertainty and internal \r\nvariability than available for AR5 (Deser et al., 2014, 2016; Saffioti et al., \r\n2017; Sippel et al., 2019; Milinski et al., 2020; von Trentini et al., 2020; \r\nMaher et al., 2021). Novel approaches have been proposed to further \r\nquantify internal variability in multi-model ensembles (Hingray and Said, \r\n2014; Evin et al., 2019; Hingray et al., 2019). For time horizons beyond \r\nthe limit of decadal predictability (Branstator and Teng, 2010; Meehl \r\net al., 2014; Marotzke et al., 2016), such as in the CMIP6 projections, \r\nthe simulations are starting from random rather than assimilated \r\ninitial conditions"


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content:
" Internal variability constitutes an uncertainty in the \r\nprojection of the climate in a future period of 10 or 20 years that is \r\nirreducible, but can be precisely quantified for individual models using \r\nsufficiently large initial-condition ensembles (Fischer et al., 2013; Deser \r\net al., 2016, 2020; Hawkins et al., 2016; Pendergrass et al., 2017; Luo \r\net al., 2018; Dai and Bloecker, 2019; Maher et al., 2019)."


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vector_id: 32649
content:
"Uncertainties in emissions of greenhouse gases and aerosols that affect \r\nfuture radiative forcings are represented by selected SSP scenarios \r\n(Sections 1.6.1 and 4.2.2). In addition to emission uncertainties, \r\nSSPs represent uncertainties in land use changes (van Vuuren et al., \r\n2011; Ciais et al., 2013; O'Neill et al., 2016; Christensen et al., 2018). \r\nAdditional uncertainty comes from climate carbon-cycle feedbacks \r\nand the residence time of atmospheric constituents, and are at least \r\npartly accounted for in emissions-driven simulations as opposed to \r\nconcentration-driven simulations (Friedlingstein et al., 2014; Hewitt \r\net al., 2016). The climate carbon-cycle feedbacks affect the transient \r\nclimate response to cumulative CO2 emissions (TCRE). Constraining \r\nthis uncertainty is crucial for the assessment of remaining carbon \r\nbudgets consistent with global mean temperature levels (Millar \r\net al., 2017; IPCC, 2018a) and is covered in Chapter 5 of this Report"


id: 7883da8b38d6575430c761534e70d68c
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 16520
content:
" \r\nFinally, there are uncertainties in future solar and volcanic forcing \r\n(Cross-Chapter Box 4.1)."


id: ef4b1a5dc35766fa78b8eb2d41598c96
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 35126
content:
"Most methods for quantifying robustness assume that only one \r\nrealization from each model is applied. There are challenges that \r\narise from having heterogeneous multi-model ensembles with many \r\nmembers for some models and single members for others (Olonscheck \r\nand Notz, 2017; Evin et  al., 2019). Furthermore, the methods that \r\nmap model robustness usually ignore that sharing parametrizations \r\nor entire components across coupled models can lead to substantial \r\nmodel interdependence (Fischer et  al., 2011; Kharin et  al., 2012; \r\nKnutti et al., 2013, 2017; Leduc et al., 2015; Sanderson et al., 2015, \r\n2017; Annan and Hargreaves, 2017; Boe, 2018; Abramowitz et  al., \r\n2019). This may lead to a  biased estimate of model agreement if \r\na substantial fraction of models is interdependent. The methodologies \r\nand results in this literature since AR5 are higher in quality and \r\nclarity"


id: 52ee876b07b64521393ae38c3f43d43b
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 10501
content:
" However, quantifying and accounting for model dependence \r\nin a  robust way remains challenging (Abramowitz et  al.,  2019). \r\nFurthermore, absence of significant mean change in a certain climate \r\nvariable does not imply absence of substantial impact, because \r\nthere may be substantial change in variability, which is typically not \r\nmapped (McSweeney and Jones, 2013)."


id: 3e64ef12c7ceed6fc9abf1bd4a8a509e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 7190
content:
"Chapter  4 uses the advanced approach, taking into account the \r\nsign and significance of the change (Cross-Chapter Box Atlas.1, \r\napproach  C). Where not applicable, such as due to a  lack of the \r\nnecessary model output, the simple method is used taking into account \r\nonly agreement on the sign of the change across the multi-model \r\nensemble (Cross-Chapter Box Atlas.1, approach B). The advanced \r\napproach is similar to the method used in AR5 but isolates conflicting \r\nsignals as proposed in Zappa et  al. (2021). It  uses three mutually \r\nexclusive categories and distinguishes (i) areas with significant change \r\nand high model agreement (no overlay), (ii) areas with no change or no \r\nrobust change (diagonal lines), and (iii) areas with significant change \r\nbut low agreement (crossed lines)"


id: 209af110e599a37b3737d91ed0f7a2ce
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 2574
content:
" Category (i) marks areas where \r\nthe climate change signals likely emerge from internal variability, \r\nwhere two-thirds or more of the models project changes greater than \r\ninternal variability and 80% or more of the models agree on the sign \r\nof the change. Category (ii) marks areas where fewer than two-thirds \r\nof the models project changes greater than internal variability, and \r\ncategory (iii) marks areas with significant but conflicting signals, where \r\ntwo-thirds or more of the models project changes greater than internal \r\nvariability but less than 80% agree on the sign of the change."


id: 4fa2a66898bbdb174f7f2ae3a9a38533
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 9960
content:
"Maps of multi-model mean changes provide an average estimate for \r\nthe forced model climate response to a certain forcing. However, they \r\ndo not include any information on the robustness of the response across \r\nmodels nor on the significance of the change with respect to unforced \r\ninternal variability (Tebaldi et  al., 2011). Models can consistently \r\nshow absence of significant change, in which case they should not be \r\nexpected to agree on the sign of a change (e.g., Tebaldi et al., 2011; \r\nKnutti and Sedlacek, 2013; Fischer et al., 2014). If a multi-model mean \r\nmap of precipitation shows no change, it is unclear whether the models \r\nconsistently project insignificant changes or whether projections span \r\nboth significant increases and significant decreases. Several methods \r\nhave been proposed to distinguish significant conflicting signals from \r\nagreement on no significant change (Tebaldi et al., 2011; Knutti and \r\nSedlacek, 2013; McSweeney and Jones, 2013; Zappa et al., 2021)"


id: ba8fa56a47ccba601a37eca09585d5af
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 26884
content:
" A set \r\nof different methods have been introduced in the literature to display \r\nmodel robustness and to put a climate change signal into the context \r\nof internal variability. Box 12.1 in AR5 provides a detailed assessment \r\nof different methods of mapping model robustness and Cross-Chapter \r\nBox Atlas.1 provides an update of recent proposals including the \r\nmethods used in this Report."


id: 7a83193dfa640ddd4a2e53ff09b5a895
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 16845
content:
"In this chapter variability is defined as , where\r\nis the standard deviation of 20-year means in the pre-industrial \r\ncontrol simulations (see Cross-Chapter Box, Atlas.1). Category (a) \r\nuses a definition very similar to the AR5 method for stippling, except \r\nthat the model signal is compared to its corresponding internal rather \r\nthan the multi-model mean variability, to account for the substantial \r\nmodel differences in pre-industrial internal variability (Parsons et al., \r\n2020). Changes smaller than internal variability can have potential \r\nimpacts particularly if they persist over sustained periods such as \r\nseveral decades. Finally, even when changes do not exceed variability \r\nat the grid point level they may exceed variability if aggregated over \r\ncatchment basins, regions, or continents (Cross-Chapter Box Atlas.1)"


id: 67c50453da3972c2ef227a8b5e251d1f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 13914
content:
" \r\nMaps of mean changes also ignore potential changes in variability \r\naddressed by a  more comprehensive assessment of changes in \r\ntemperature variability (Section  4.5.1) and modes of internal \r\nvariability (Section 4.4.3)."


id: c5ead46423edfd45225093a76a47a17f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 28714
content:
"Simulated GSAT over 1995-2014, relative to 1850-1900 period, \r\nwarms by very similar amounts in the two sets of simulations: 0.82degC \r\n(0.45-1.31) in emissions-driven compared with 0.75degC (0.53-1.09) \r\nin concentration-driven simulations. By the end of the 21st century, \r\nwarming in emissions-driven simulations is very similar: 4.58degC \r\n(3.53-6.70), reflecting the slightly lower CO2 concentration \r\nsimulated by the ESMs compared with warming under the \r\nprescribed CO2 pathway of 4.69degC (3.70-6.77). This difference in \r\nmodel-mean response is more than an order of magnitude smaller \r\nthan the 5-95% spread across model projections. The spread in CO2\r\nconcentration, compared with the prescribed default concentration, \r\nleads to a very small increase by about 0.1degC in the spread of GSAT \r\nprojections, but it is not possible to tell if this is a direct consequence \r\nof the simulation configuration or internal variability of the model \r\nsimulations"


id: f3ab9e0e6a8abc00e164cf382ca34ca9
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 35851
content:
" These differences due to experimental configuration \r\nwould be smaller still under scenarios with lower CO2 levels, and \r\nso we assess that results from concentration-driven and emissions\u0002driven configurations do not affect the assessment of GSAT \r\nprojections (high confidence)."


id: 12df1e49f38d70e30935c8a94a1fc70f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 474
content:
"Studies focusing on the relationship of sea ice extent and changes in \r\nexternal drivers have consistently found a much-reduced likelihood \r\nof a  practically ice-free Arctic Ocean during summer for global \r\nwarming of 1.5degC than for 2.0degC (Screen and Williamson, 2017; \r\nJahn, 2018; Niederdrenk and Notz, 2018; Notz and Stroeve,  2018; \r\nSigmond  et  al.,  2018; Olson et  al., 2019). This is shown here in \r\na  large initial-condition ensemble of observationally constrained \r\nmodel simulations where GSAT are stabilized at 1.5degC, 2.0degC and \r\n3.0degC warming relative to 1850-1900 in the RCP8.5 scenario \r\n(Figure  4.5). Temperature stabilization is achieved by switching \r\noff all the anthropogenic emissions around the time that GSAT \r\nfirst reaches the stabilization thresholds. Simulations have been \r\nobservationally constrained to correct for a model bias in simulated \r\nhistorical September sea ice extent"


id: e53e4358b01dc6998e5ac7046023b60a
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 33565
content:
" In these simulations, Arctic sea \r\nice coverage in September is simulated, on average, to drop below \r\n1 million km2 around 2040, consistent with the AR5 set of assessed \r\nmodels (Sigmond et al., 2018). The individual model simulations, for \r\nwhich there are twenty for each stabilized temperature level, show "


id: 99b38504c050204fff1f98ec6a23588b
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 21703
content:
"The Northern Annular Mode (NAM) is the leading mode of variability \r\nin the NH extratropical atmosphere (Section AIV.2.1). Throughout this \r\nchapter, we use a simple fixed latitude-based NAM index defined as \r\nthe difference in SLP between 35degN and 65degN (Section AIV.2.1; Li and \r\nWang, 2003). The NAM index computed from the latitudinal gradient \r\nin SLP is strongly correlated with variations in the latitudinal position \r\nand strength of the mid-latitude westerly jets, and with the spatial \r\ndistribution of Arctic sea ice (Caian et al., 2018). Projected changes \r\nin the position and strength of the mid-latitude westerly jets, storm \r\ntracks, and atmospheric blocking in both hemispheres are assessed \r\nin Section 4.5.1.6. The AR5 referred to the NAM, and its synonym the \r\nArctic Oscillation (AO), through its regional counterpart, the North \r\nAtlantic Oscillation (NAO). Here, we use the term NAM to refer also \r\nto the AO and NAO (Section AIV.2"


id: 6eb90cdf2ba889ce8b364f59e4f4b02d
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 15046
content:
"The Southern Annular Mode (SAM) is the leading mode of large-scale \r\nextratropical atmospheric variability in the Southern Hemisphere and \r\ninfluences most of the southern extratropics (Annex IV, Section AIV.2.2). \r\nIn its positive phase, the SAM characterizes anomalously low pressure \r\nover the polar cap and high pressure in southern mid-latitudes (Marshall, \r\n2003). While there are some zonal asymmetries to the structure of the \r\nSAM (Section AIV.2.2), it is more symmetric than its NH counterpart \r\n(Fyfe et  al., 1999). Throughout this chapter, we use a  simple fixed \r\nlatitude-based SAM index defined as the difference in zonal mean SLP \r\nbetween 40degS and 65degS (Gong and Wang, 1999; see Section AIV.2.2 for \r\ndiscussion of other SAM indices). Although the SAM is often used as \r\na proxy for the location of the mid-latitude westerly wind belt, trends \r\nin the SAM can reflect a combination of changes in jet position, width, \r\nand strength"


id: 789b731e6ecc382e525cae5bc80a70f5
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 16538
content:
" The changes in the Southern Hemisphere circulation \r\nassociated with the SAM influence surface wind stress (Wang et al., \r\n2014) and hence affect the Southern Ocean."


id: ebb572e19e7ec4f733115e9a78d2db97
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 34585
content:
"Among a range of indices proposed for representing ENSO, we use \r\nthe most prominent one, the Nino 3.4 index, defined as the average \r\nequatorial SST or precipitation across the central equatorial Pacific \r\n(5degS-5degN, 170degW-120degW; Section AIV.2.3). Here, we consider the \r\nevolution of the amplitude of Nino 3.4 index for SST and precipitation \r\nover the 21st  century as projected by CMIP6 models. Analysis of \r\nCMIP6 models shows there is no robust model consensus on the \r\nforced changes in the amplitude of ENSO SST variability even under \r\nthe high-emissions scenarios SSP3-7.0 and SSP5-8.5, but a significant \r\nincreasing trend in the amplitude of ENSO precipitation variability is \r\nprojected across the 21st century in the four SSPs (Figure 4.10). This \r\nis broadly consistent with results from CMIP5 models (Christensen \r\net al., 2013; Power et al., 2013; Cai et al., 2015; Chen et al., 2017; \r\nWengel et al., 2018), recent studies with CMIP6 models (Brown et al"


id: 50feec4252abbb6aac5f5597c87607a9
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 10191
content:
", \r\n2020; Fredriksen et al., 2020; Freund et al., 2020; Yun et al., 2021), \r\nand large initial-condition ensemble experiments (Maher et al., 2018; \r\nZheng et al., 2018; Haszpra et al., 2020)."


id: 79b7e05fd661174cb8c1f68052f6a17b
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 16717
content:
"and Figure 4.11a,b; Brunner et al., 2020; Liang et al., 2020; Nijsse \r\net  al., 2020; Tokarska et  al., 2020; Ribes et  al., 2021), as well \r\nas the AR6-updated assessment of ECS and TCR in Section  7.5. \r\nThe approaches of (Liang et al., 2020; Tokarska et al., 2020; Ribes \r\net al., 2021) have first been extended to all 20-year averaging periods \r\nbetween 2000 and 2100. For each 20-year period, the 5%, 50%, and \r\n95% percentile GSAT values of these three constrained CMIP6 results \r\nare averaged percentile by percentile (Figure 4.11c). Then, an emulator \r\nbased on a two-layer energy balance model (e.g., Held et al., 2010) \r\nis driven by the Chapter 7-derived ERF"


id: c571076ab15f575f59d8dc20297aff2f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 28645
content:
" The emulator parameters are \r\nchosen such that the central estimate, lower bound of the very likely \r\nrange, and upper bound of the very likely range of climate feedback \r\nparameter and ocean heat uptake coefficient take the values that \r\nmap onto the corresponding combination of ECS (3degC, 2degC and \r\n5degC, respectively) and TCR (1.8degC, 1.2degC and 2.4degC, respectively) \r\nof Section 7.5 (see Box 4.1). As a final step, the constrained-CMIP6 \r\nand the emulator-based 5%, 50%, and 95% percentile GSAT values \r\nare averaged percentile by percentile (Figure 4.11c,d and Table 4.5). \r\nConstrained CMIP6 results and the ECS- and TCR-based emulator \r\nthus contribute one-half each to the GSAT assessment. Because the \r\nemulator results and (Ribes et al., 2021) represent the forced response \r\nonly, and averaging over the other two individual estimates (Liang \r\net al., 2020; Tokarska et al"


id: c8c4a9496b5452aa094cf89800a6eac8
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 29149
content:
", 2020) further reduces the contribution \r\nfrom internal variability, the assessed GSAT time series are assumed \r\nto represent purely the forced response."


id: e081712c80ac1e0351e16daf58509f05
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 32822
content:
"the subtropics, precipitation response is driven primarily by the fast \r\nadjustment to CO2 forcing (He and Soden, 2017). In addition to the \r\nresponse to GHG forcing, forcing from natural and anthropogenic \r\naerosols exert impacts on regional patterns of precipitation \r\n(Section  10.3.1; Ramanathan et  al., 2005; Bollasina et  al., 2011; \r\nPolson et al., 2014; Krishnan et al., 2016; L. Liu et al., 2018; Shawki \r\net al., 2018). The large uncertainties in near-term regional precipitation \r\nprojections arise due to the interplay between internal variability and \r\nanthropogenic external forcing (Endo et al., 2018; Wang et al., 2021). \r\nUncertainties in future aerosol emissions scenarios contribute to \r\nuncertainties in regional precipitation projections (Wilcox et al., 2020). \r\nAerosol changes induce a drying in the SH tropical band compensated \r\nby wetter conditions in the NH counterpart (Acosta Navarro et al., 2017)"


id: 710381dcb4e8cc2b058a3ea4440944e3
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 15414
content:
" \r\nThe spatially uneven distribution of the aerosol forcing may also induce \r\nchanges in tropical precipitation caused by shifts in the mean location \r\nof the intertropical convergence zone (ITCZ) (Hwang et al., 2013; Ridley \r\net al., 2015; Voigt et al., 2017). Because of the large uncertainty in the \r\naerosol radiative forcing and the dynamical response to the aerosol \r\nforcing there is medium confidence in the impacts of aerosols on near\u0002"


id: c35844859d6ad597d446b709b0c0cee1
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 28299
content:
"Figure 4.13 | Near-term change of seasonal mean precipitation. Displayed are projected spatial patterns of CMIP6 multi-model mean change (%) in (top) December-January-\r\nFebruary (DJF) and (bottom) June-July-August (JJA) precipitation from SSP1-2.6 and SSP3-7.0 in 2021-2040 relative to 1995-2014. The number of models used is indicated \r\nin the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show \r\na change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change \r\nor no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold"


id: 47dd6d8f54c7d45bfcc12354b50c368f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 8675
content:
" Crossed lines indicate areas of conflicting signals \r\nwhere at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on \r\ndata sources and processing are available in the chapter data table (Table 4.SM.1)."


id: d4c446c24c5e2b559c98517f8ca5abd0
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 30999
content:
"The AR5 assessed that for RCP8.5, Arctic sea ice coverage in September \r\nwill drop below 1 million km2, or become practically ice-free, at some \r\npoint between 2040 and 2060 (Collins et al., 2013). Since AR5, there \r\nhas been substantial progress in understanding the response of \r\nArctic sea ice to near-term changes in external forcing. In particular, \r\nit is very likely that different trajectories of the near-term evolution \r\nof anthropogenic forcing cause distinctly different likelihood ranges \r\nfor very low sea ice coverage to occur over the next two decades \r\n(Notz and Stroeve, 2018). For example, there is an unlikely drop of \r\nSeptember Arctic sea ice coverage to below 1 million km2 before 2040 \r\nfor RCP 2.6, and a likely drop of September Arctic sea ice coverage \r\nto below 1 million km2 before 2040 for RCP 8.5 (medium confidence \r\ngiven the single study). The much higher likelihood of a practically \r\nsea ice free Arctic Ocean during summer before 2040 in RCP8.5 \r\ncompared to RCP2"


id: 581360720258326cc559a4c920060ebb
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 11363
content:
"6 is consistent with related studies assessed in \r\nSROCC that find a substantially increased likelihood of an ice-free \r\nArctic Ocean for 2.0degC compared to 1.5degC mean global warming \r\nrelative to pre-industrial levels (Screen and Williamson, 2017; Jahn, \r\n2018; Niederdrenk and Notz, 2018; Notz and Stroeve, 2018; Sigmond \r\net al., 2018; Olson et al., 2019)."


id: 81af76846f924857186257993f6d1a6e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 17935
content:
"The global land monsoon precipitation index, defined as the area\u0002weighted precipitation rate in the global land monsoon domain, tends \r\nto increase in the near term under all five core SSPs (Figure 4.14a) (Chen \r\net al., 2020), but changes are small compared to the intermodel spread \r\nin the historical period. The Northern Hemisphere summer monsoon \r\ncirculation index, defined as the vertical shear of zonal winds between \r\n850 and 200 hPa averaged in a zone stretching from Mexico eastward \r\nto the Philippines (0deg-20degN, 120degW-120degE), tends to decrease under \r\nfour of the five SSP scenarios (Figure 4.14b), potentially offsetting \r\nmonsoon precipitation increase. Projected changes in the global \r\nmonsoon circulation are also uncertain, because they are influenced \r\nby internal variability such as AMV and PDV (see Section  3.3.3.2) \r\nand structural differences across models. In the near-term, for CMIP6 \r\nprojections (Figure 4"


id: 55f163ff76c04843b39fa87939004e1b
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 11007
content:
"14a), the multi-model mean (5-95% range) of \r\nglobal land monsoon precipitation change is 1.9% (-0.4 to 4.9%), \r\n1.6% (-1.0 to 5.2%), 1.3% (-1.7 to 3.7%), and 1.9% (-0.8 to 5.2%) \r\nunder SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively."


id: 9681ab3dc2b1ec89b0bdb74d3dabda3e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 21200
content:
"On seasonal to interannual time scales, there is new evidence since \r\nAR5 that initialized predictions show lower potential predictability \r\nfor the boreal winter NAO than the correlation skill with respect \r\nto observations (Eade et  al., 2014; Baker et  al., 2018; Scaife and \r\nSmith, 2018; Athanasiadis et  al., 2020). This has been referred to \r\nin the literature as a 'signal-to-noise paradox' and means that very \r\nlarge ensembles of predictions are needed to isolate the predictable \r\ncomponent of the NAO. While the processes that contribute to the \r\npredictability of the winter NAO on seasonal time scales may be \r\ndistinct from the processes that drive multi-decadal trends, there is \r\nemerging evidence that initialized predictions also underrepresent the \r\npredictability of the winter NAO on decadal time scales (D.M. Smith \r\net al., 2019)"


id: aa3655772f004257e637b394acaaa58e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 24317
content:
" Post-processing and aggregation of initialized predictions \r\nmay therefore reveal significant skill for predicting the winter NAO on \r\ndecadal time scales (Smith et al., 2020). Considering these new results \r\nsince AR5, in the near-term it is likely that any anthropogenic forced \r\nsignal in the NAM will be of comparable magnitude or smaller than \r\nnatural internal variability in the NAM (medium confidence)."


id: c1f7200a82819e8b01db52f3a0c5da80
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 28096
content:
"Since AR5, there have been advances in understanding the role of \r\ninternal climate variability for projected near-term SH circulation \r\ntrends (Solomon and Polvani, 2016). A large initial-condition ensemble \r\nfollowing the RCP4.5 emissions scenario shows a monotonic positive \r\nSAM trend in austral winter. In austral summer, the SAM trend over \r\nthe first half of the 21st century is weaker compared to the strongly \r\npositive trend observed and simulated over the late 20th  century. \r\nIn that model, the number of realizations required to identify \r\na  detectable change in decadal mean austral winter SAM index \r\nfrom a year 2000 reference state decreased to below five by around \r\n2025-2030 (Solomon and Polvani, 2016). However, in December-\r\nJanuary-February (DJF) the same criterion is not met until the second \r\nhalf of the 21st century, owing to the near-term opposing effects of \r\nozone recovery and GHGs on the austral-summer SAM"


id: bc527207fb8b898731f0e1885c242013
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 27210
content:
" In austral \r\nsummer, forced changes in the SAM index in the near-term are \r\ntherefore likely to be smaller than changes due to internal variability \r\n(Figure 4.17b; Barnes et al., 2014; Solomon and Polvani, 2016)."


id: befcaf851efd9e878c0326eca1264bdd
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 27602
content:
"There are no specific studies focusing on near-term changes in \r\ntropical Atlantic modes; nevertheless, decadal predictions show \r\nthat although the North Atlantic stands out in most CMIP5 models \r\nas the primary region where skill might be improved because of \r\ninitialization, encouraging results have also been found in the \r\ntropical Atlantic (Meehl et  al., 2014). The effect of initialization in \r\nthe tropical Atlantic is not only visible in surface temperature but also \r\nin the subsurface ocean (Corti et al., 2015). In particular, initialization \r\nimproves the skill via remote ocean conditions in the North Atlantic \r\nsubpolar gyre and tropical Pacific, which influence the tropical \r\nAtlantic through atmospheric teleconnections (Dunstone et al., 2011; \r\nVecchi et  al., 2014; Garcia-Serrano et  al., 2015)"


id: 2d3bf595c660fc56a863739cd990fe5f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 4564
content:
" Improvements of \r\nsome aspects of climate prediction systems (initialization techniques, \r\nlarge ensembles, increasing model resolution) have also led to skill \r\nimprovements over the tropical Atlantic (Pohlmann et  al., 2013; \r\nMonerie et al., 2017; Yeager and Robson, 2017)."


id: b0610c22f2aed1cc8bfa6ba9ed66c0aa
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
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vector_id: 25271
content:
"The Atlantic Multi-decadal Variability (AMV) is a large-scale climate \r\nmode accounting for the main fluctuations in North Atlantic SST on \r\nmulti-decadal time scales (Section AIV.2.7). The AMV influences air \r\ntemperatures and precipitation over adjacent and remote continents, \r\nand its undulations can partially explain the observed variations in \r\nthe NH mean temperatures (Steinman et al., 2015). The origin of this \r\nvariability is still uncertain. Ocean dynamics (e.g.,  changes in the \r\nAMOC), external forcing, and local atmospheric forcing all seem to \r\nplay a role (Menary et al., 2015; Ruprich-Robert and Cassou, 2015; \r\nBrown et al., 2016; Cassou et al., 2018; Wills et al., 2019). Recent \r\nstudies have discussed that the ocean dynamics play an active role \r\nin generating AMV (Oelsmann et al., 2020) and its interplay with the \r\nNAO (Vecchi et  al., 2017; R. Zhang et  al., 2019; Kim et  al"


id: f41a30458b89934c6c7be5f826bf84fc
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 35913
content:
", 2020), \r\nalthough natural and anthropogenic external forcing might be \r\ncrucial in modulating its amplitude and timing (Bellucci et al., 2017; \r\nBellomo et  al., 2018; Andrews et  al., 2020; Borchert et  al., 2021; \r\nMann et al., 2021; see Sections 3.7.7 and AIV.2.7)."


id: 206a4f07ff4a4d3de432063373a26c62
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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content:
"Encouraging results about the prediction of land precipitation linked \r\nto the warm AMV phase (Section 3.7.7 and Annex IV, Figure AIV.2.7) \r\non a  two-to-nine-year time scale are reported in the multi-model \r\nstudy by D.M. Smith et  al. (2019). Positive correlations with \r\nobservations are found in the Sahel, South America, the Maritime \r\nContinent. Analyses from large-ensemble decadal prediction systems \r\nsuch as the community Earth system model decadal prediction large \r\nensemble (CESM-DPLE; Yeager et al., 2018) show an improvement \r\nwith respect to the CMIP5 decadal hindcasts (Martin and Thorncroft, \r\n2014b) in forecasting Sahel precipitation over three to seven years, \r\nwhich is consistent with the current understanding of AMV impact \r\nover Africa (Mohino et al., 2016; D.M. Smith et al., 2019)"


id: ef6800b85388cc85f80deda98077259f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 35149
content:
" CESM-DPLE \r\npredicts drought conditions over the Sahel through 2020, which is \r\ncompatible with a shift towards a negative phase of AMV as a result \r\nof a  weakening of the AMOC, advocated by a  number of studies \r\n(Hermanson et al., 2014; Robson et al., 2014; Yeager et al., 2015)."


id: de84c53ea4733c3a8170d44a2143747d
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 32512
content:
"Building on CMIP6 results for the effects of reducing SLCF \r\nemissions from a baseline of SSP3-7.0, the overall contribution of \r\nSLCFs to GSAT changes in the marker SSPs are now quantified using \r\na simple climate model emulator. For consistency with Section 6.7.2 \r\nand Figure  6.22, the basket of SLCF compounds considered \r\nincludes aerosols, ozone, methane, black carbon on snow and \r\nhydrofluorocarbons (HFCs) with lifetimes of less than 50 years. In \r\nthe five marker SSPs considered, the net effect of SLCFs contributes \r\nto a higher GSAT in the near, mid- and long term (Table 4.6 and \r\nSection  6.7.2). In the SSP1-1.9 and SSP1-2.6 scenarios, SLCFs \r\ncontribute to a higher GSAT by a central estimate of around 0.3degC \r\ncompared to 1995-2014 across the three-time horizons. In the \r\nlong-term, the 0.3C warming due to SLCFs in SSP1-2.6 can be \r\ncompared to the assessed very likely GSAT change for this period of \r\n0.5degC-1.5degC (Section 4.3.4 and Table 4.5). The SSP2-4.5, SSP3-7"


id: 2113edab181b7011bc5a27cafbede212
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 2639
content:
"0 \r\nand SSP5-8.5 scenarios all show a larger SLCF effect on GSAT in \r\nthe long term relative to the near term. In SSP3-7.0, the long\u0002term warming due to SLCFs by 0.7degC can be compared with the \r\nassessed very likely GSAT anomaly for this period of 2.0degC -3.7degC \r\n(Section 4.3.4). In summary, it is very likely that changes in SLCFs \r\ncontribute to an overall warmer GSAT over the near, mid- and long \r\nterm in the five SSP scenarios considered (Table 4.6, Section 6.7.2 \r\nand Figure 6.22)."


id: 869455b6e2437ef014c5dd91be1c8cc4
created_at: 2023-03-16 14:01:59
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content:
"Mitigation of SLCFs affects future climate projections and could \r\nalter the time of emergence of anthropogenic climate change \r\nsignals. The AR5  assessed that emission reductions aimed at \r\ndecreasing local air pollution could have a  near-term warming \r\nimpact on climate (high confidence) (Kirtman et al., 2013). Because \r\nof their shorter lifetimes, reductions in emissions of SLCF species \r\nmainly influence near-term GSAT trends (Chalmers et  al., 2012; \r\nShindell et  al., 2017; Shindell and Smith, 2019), but on decadal \r\ntime scales the near-term response to even very large reductions \r\nin SLCFs may be difficult to detect in the presence of large internal \r\nclimate variability (Samset et  al., 2020). The changes in SLCF \r\nemissions during the COVID-19 pandemic has resulted in a small \r\nnet radiative forcing without a discernible impact on GSAT (Cross\u0002Chapter Box 6.1). SLCF mitigation also leads to a higher GSAT in \r\nthe mid- to long-term (Smith and Mizrahi, 2013; Stohl et al"


id: d0b47ed12c609354f6216c243d6facc0
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content:
", 2015; \r\nHienola et al., 2018) and can influence peak warming during the \r\n21st century (Rogelj et al., 2014; Hienola et al., 2018). This section \r\nfocuses on the total effect of SLCF changes on GSAT projections \r\nin the SSP scenarios. A more detailed breakdown of the separate \r\nclimate effects of SLCF species and precursor species can be found \r\nin Sections 6.7.2 and 6.7.3."


id: 5ef20a1b5b43e3ce548f9fe03b95d512
created_at: 2023-03-16 14:01:59
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content:
"temperature variability in the Northern Hemisphere during the past \r\n2,500 years, with cooling persisting for up to ten years after some \r\nof the largest eruptive episodes (Sigl et al., 2015). Repeated clusters \r\nof volcanic eruptions can induce a net negative radiative forcing that \r\nresults in a centennial- and global-scale cooling trend via a decline \r\nin mixed-layer oceanic heat content (McGregor et  al., 2015). The \r\nresponse to multi-decadal changes in volcanic forcing (representing \r\nclusters of eruptions) shows similar cooling in both simulations and \r\nreconstructions of NH temperature. Volcanic eruptions generally \r\nresult in decreased global precipitation for up to a few years following \r\nthe eruption (Iles and Hegerl, 2014, 2015; Man et  al., 2014), with \r\nclimatologically wet regions drying and climatologically dry regions \r\nwetting (medium confidence), which is opposite to the response \r\nunder global warming (Held and Soden, 2006; Iles et al., 2013; Zuo \r\net  al"


id: 405b587f2dc02075b71b413d1ad0df0e
created_at: 2023-03-16 14:01:59
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vector_id: 7510
content:
", 2019a, b). El Nino-like warming appears after large volcanic \r\neruptions, as seen in both observations (Adams et al., 2003; McGregor \r\net al., 2010; Khodri et al., 2017) and climate model simulations (Ohba \r\net al., 2013; Pausata et al., 2015; Colose et al., 2016; Stevenson et al., \r\n2016; Khodri et al., 2017; Predybaylo et al., 2017; Zuo et al., 2018). \r\nThe large tropical eruptions are coincident with positive Indian Ocean \r\ndipole events (Maher et al., 2015)."


id: ce9e72498ed10a2c3b8d6abda059eb46
created_at: 2023-03-16 14:01:59
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vector_id: 30050
content:
"In AR5, uncertainty due to future volcanic activity was not considered \r\nin the assessment of the CMIP5 21st  century climate projections \r\n(Taylor  et  al., 2012; O'Neill et  al., 2016). Since AR5, there has been \r\nconsiderable progress in quantifying the impacts of volcanic eruptions \r\non decadal climate prediction and longer-term climate projections \r\n(Meehl et al., 2015; Swingedouw et al., 2015, 2017; Timmreck et al., \r\n2016; Bethke et al., 2017; Illing et al., 2018). By exploring 60 possible \r\nvolcanic futures under RCP4.5, it has been demonstrated that the \r\ninclusion of time-varying volcanic forcing may enhance climate \r\nvariability on annual-to-decadal time scales (Bethke et  al., 2017). \r\nConsistent with a  tropospheric cooling response, the change in \r\nensemble spread in the volcanic cases is skewed towards lower GSAT \r\nrelative to the non-volcanic cases (Cross-Chapter Box 4.1, Figure 1)"


id: 7374665b3174fd24728913d8534f9b81
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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content:
" \r\nIn  these simulations with multiple volcanic forcing futures there is: \r\n(i) an increase in the frequency of extremely cold individual years; (ii) an \r\nincreased likelihood of decades with negative GSAT trend (decades \r\nwith negative GSAT trends become 50% more commonplace); \r\n(iii)  later anthropogenic signal emergence (the mean time at which \r\nthe signal of global warming emerges from the noise of natural \r\nclimate variability is delayed almost everywhere) (high confidence); \r\nand (iv) a 10% overall reduction in global land monsoon precipitation \r\nand a 20% overall increase in the ensemble spread (Man et al., 2021)."


id: 8f3628fe7e587f2050f9b2776ee218dd
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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content:
"Another factor that could substantially alter projections in the \r\nnear-term would be the occurrence of a  large explosive volcanic \r\neruption, or even a decadal to multi-decadal sequence of small-to\u0002moderate volcanic eruptions as witnessed over the early 21st century \r\n(Cross-Chapter Box 4.1; Santer et al., 2014). An eruption similar to \r\nthe last large tropical eruption, Mount Pinatubo in the Philippines \r\nin June 1991, is expected to cause substantial Northern Hemisphere \r\n(NH) cooling, peaking between 0.09degC and 0.38degC and lasting for \r\nthree to five years, as indicated by climate model simulations over the \r\npast millennium (e.g., Jungclaus et al., 2010). Phase 3 of Paleoclimate \r\nModelling Intercomparison Project (PMIP3) simulated a  significant \r\nNH cooling in response to individual volcanic events (peaks between \r\n0.1degC and 0.5degC, depending on model, during the first year after \r\nthe eruption) that lasts for three to five years"


id: 41c990e330f9a8a75a0ee1712b2976ab
created_at: 2023-03-16 14:01:59
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vector_id: 7752
content:
" On a regional scale, \r\nthe double volcanic events that occurred in 536 and 540 CE resulted \r\nin a cooling of 2degC (Buntgen et al., 2016; Toohey et al., 2016)."


id: 5a2decad0d9b7de3bbc4264c9d031a69
created_at: 2023-03-16 14:01:59
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vector_id: 11697
content:
"Due to the direct radiative effect of volcanic stratospheric aerosols, large volcanic eruptions lead to an overall decrease of GSAT, which can \r\nextend to multi-decadal or century time scales in the case of clustered volcanism (Section 3.3.1.1; Schurer et al., 2013; McGregor et al., \r\n2015; Sigl et al., 2015; Kobashi et al., 2017; Zambri et al., 2017; Bronnimann et al., 2019; Neukom et al., 2019). Large eruptions also increase \r\nthe frequency of extremely cold individual years and the likelihood of cooling trends occurring in individual decades (Cross-Chapter \r\nBox 3.1 and Section 4.4.4; Paik and Min, 2018). Re-dating of ice core chronologies now confirms that the coldest decades of the past \r\napproximately 2000 years are the outcome of volcanic eruptions (Sigl et al., 2015; Buntgen et al., 2016; Toohey et al., 2016; Neukom et al., \r\n2019). CMIP5 and CMIP6 models reproduce the decreased GSAT that follows periods of intense volcanism"


id: 282f928b39d4a5ebae139ed96d92a746
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content:
" New reconciliations between \r\nsimulations and proxy-based reconstructions of past eruptions have been achieved through better Earth System Model representation \r\nof volcanic plume chemical compositions (Legrande et al., 2016; Marshall et al., 2020; F. Zhu et al., 2020). Yet, remaining disagreements \r\nreflect differences in the volcanic forcing datasets used in the simulations (medium confidence) (Section 3.3.1.1 and Figure 3.2c)."


id: 954c1d7e59f14b375fec630d0506f910
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content:
"Although incomplete, proxy records show large impacts upon contemporary society from eruptions such as 1257 Samalas and 1815 \r\nTambora, the latter resulting in 'the year without a summer' with multiple harvest failures across the Northern Hemisphere (e.g., Raible \r\net al., 2016). Comparing CMIP5 multi-model simulations with observations has improved understanding of the hydrological responses \r\nto 20th century eruptions, particularly global land monsoon drying, and associated uncertainties (Section 3.3.2.3). Global mean land \r\nprecipitation decreases for up to a few years following the eruption, with climatologically wet regions drying and dry regions wetting \r\n(Sections 3.3.2.3 and 4.4.4). Changes in monsoon circulations occur with a general weakening of tropical precipitation (Section 8.5.2.3) \r\nand a decrease in extreme precipitation over global monsoon regions (Section 11.4.4)"


id: a0bc81d37cc38d426302d6d8b110598e
created_at: 2023-03-16 14:01:59
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content:
" Monsoon precipitation in one hemisphere tends \r\nto be enhanced by eruptions occurring in the other hemisphere or reduced if they occur in the same hemisphere (Sections 3.3.2.3 \r\nand 8.5.2.3). Volcanic eruptions have been linked to the onset of El Nino followed by La Nina although this connection remains \r\ncontentious (Adams et al., 2003; Bradley et al., 2003; McGregor et al., 2010; Khodri et al., 2017; F. Liu et al., 2018; Sun et al., 2019; "


id: 222acf8190bb44af3f981c6c42687d8e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 2822
content:
"Given the unpredictability of individual eruptions, volcanic forcing is prescribed as a constant background loading in CMIP6 models \r\n(Eyring et al., 2016). This means the effects of potential large volcanic eruptions are largely absent from model projections, and few studies \r\nhave addressed the potential implications on 21st century warming. One study considered future scenarios with hypothetical volcanic \r\neruptions consistent with levels of Common Era volcanic activity (Bethke et al., 2017) under RCP4.5 and found that climate projections \r\ncould be substantially altered (Cross-Chapter Box 4.1, Figure 1). Although temporary, close to pre-industrial level temperatures could be \r\nexperienced globally for a few years after a 1257 Samalas-sized eruption. Several other key climate indicators are also changed substantially, \r\nconsistent with evidence from past events. Bethke et al. (2017) suggest that an eruption early in the 21st century could delay the timing \r\nof crossing 1"


id: 7bb6900cb244f806d39452fb5cd559c1
created_at: 2023-03-16 14:01:59
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content:
"5degC global warming by several years. Clustered eruptions would have substantial impact upon GSAT evolution throughout \r\nthe century (Cross-Chapter Box 4.1, Figure 1), and could have far-reaching implications, as observed for past eruptions. For near-term \r\nresponse options, decadal prediction models can update 21st-century projections once a volcanic eruption occurs (Timmreck et al., 2016)."


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content:
"Figure 4.19 | Mid- and long-term change of annual mean surface temperature. Displayed are projected spatial patterns of multi-model mean change in annual mean \r\nnear-surface air temperature (degC) in 2041-2060 and 2081-2100 relative to 1995-2014 for (top) SSP1-2.6 and (bottom) SSP3-7.0. The number of models used is indicated in \r\nthe top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show \r\na change greater than the internal-variability threshold (see Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with \r\nno change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold"


id: 86c05940497e117a2c6f0f3f8a29113d
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vector_id: 18734
content:
" Crossed lines indicate areas of \r\nconflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. \r\nFurther details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: e54a6d18361fecc879b047be1ad1d05e
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content:
"It is virtually certain that future average warming will be higher over \r\nland than over the ocean. Section 2.2.1 of SRCCL (G. Jia et al., 2019) \r\nassessed that it is certain that land temperatures have increased \r\nmore than global mean temperatures since the pre-industrial period. \r\nThis so-called land-ocean warming contrast is a striking feature of \r\nobserved trends (Lambert and Chiang, 2007; Byrne and O'Gorman, \r\n2018) and projected changes in surface-air temperature (Sutton \r\net  al., 2007; Joshi and Gregory, 2008; Dong et  al., 2009; Lambert \r\net al., 2011; Drost et al., 2012; Bayr and Dommenget, 2013; Byrne \r\nand O'Gorman, 2013b; Izumi et al., 2013; Joshi et al., 2013). Between \r\n1979 and 2016, average temperature over land increased by 42% \r\nmore than over the ocean (Byrne and O'Gorman, 2018). A  similar \r\nwarming contrast is found in CMIP5 projections though with large \r\ndifferences across models and latitudes (Sutton et al., 2007; Drost \r\net al"


id: acd0f2c307abc995a505f631eb468ee7
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vector_id: 24735
content:
", 2012; Byrne and O'Gorman, 2013b; Joshi et al., 2013), which \r\nis also consistent with paleoclimate evidence (Izumi et  al., 2013; \r\nSchmidt et al., 2014). The ratio of land-to-ocean warming is greater \r\nthan one for almost all regions (high confidence) and is larger for \r\ndry subtropical continents (about 1.5) than for moist regions in the \r\ntropics and mid-latitudes (about 1.2; Byrne and O'Gorman, 2013a). \r\nProjected warming over land and ocean only is shown in Table 4.2 \r\nfor different scenarios, and the global average ratio of land-to-ocean \r\nwarming in CMIP6 is 1.5 with a likely range of 1.4 to 1.7, which is \r\nconsistent with estimates based on CMIP5."


id: 2cdee098cd967b0e3bcace2c58a6c31
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 4494
content:
"A variety of mechanisms contribute to Arctic amplification \r\n(Section 7.4.4.1.1). While surface-albedo feedbacks associated with \r\nthe loss of sea ice and snow have long been known to play important \r\nroles (Arrhenius, 1896; Manabe and Stouffer, 1980; Robock, 1983; \r\nHall, 2004), it is now recognized that temperature (lapse-rate and \r\nPlanck) feedbacks also contribute to Arctic amplification through \r\na less efficient longwave radiative damping to space with warming \r\nat high latitudes (Winton, 2006; Pithan and Mauritsen, 2014; Goosse \r\net al., 2018; Stuecker et al., 2018). Increases in poleward atmospheric \r\nlatent heat transport and oceanic heat transport also contribute to \r\nArctic warming (Holland and Bitz, 2003; Bitz et al., 2006; Lee et al., \r\n2011, 2017; Alexeev and Jackson, 2013; Marshall et al., 2014, 2015; \r\nWoods and Caballero, 2016; Nummelin et al., 2017; Singh et al., 2017; \r\nMerlis and Henry, 2018; Oldenburg et al., 2018; Armour et al., 2019; \r\nBeer et al., 2020)"


id: ec2e806e5685295e448988b97760cab5
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 34655
content:
" Projected reduction in the strength of the AMOC \r\nover the 21st century is expected to reduce Arctic warming, but even \r\na strong AMOC reduction would not eliminate Arctic amplification \r\nentirely (medium confidence) (Liu et  al., 2017; Y. Liu et  al., 2018; \r\nWen et al., 2018)."


id: 6a6c3bfba5eb069f19f909b1fb5aca22
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 14350
content:
"Since AR5, a robust physical understanding of the warming contrast \r\nbeen developed. A simple theory based on atmospheric dynamics and \r\nmoisture transport shows that surface-air temperature and relative \r\nhumidity over land are strongly coupled, and demonstrates that the \r\nwarming contrast occurs because air over land is drier than over \r\nthe ocean (Joshi et al., 2008; Byrne and O'Gorman, 2013a, b, 2018). \r\nThe warming contrast causes land relative humidity to decrease \r\n(Byrne and O'Gorman, 2016, 2018; Chadwick et al., 2016) and this \r\nfeeds back on and strengthens the warming contrast. Differences \r\nin land-relative humidity responses across models are the primary \r\ncause of uncertainty in the land-ocean warming contrast (Byrne \r\nand O'Gorman, 2013b). These land-relative humidity changes are \r\nultimately controlled by moisture transport between the land and \r\nocean boundary layers (Byrne and O'Gorman, 2016; Chadwick et al"


id: cb3f84b4196d4c513e5f379becffc277
created_at: 2023-03-16 14:01:59
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vector_id: 29533
content:
", \r\n2016) and are also sensitive to characteristics of land surfaces that \r\nare challenging to model, including stomatal conductance and soil \r\nmoisture (Berg et al., 2016; Zarakas et al., 2020)."


id: 7543f5601d7b24e30fb4925fd5fefb48
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 16037
content:
"There remains substantial uncertainty in the magnitude of projected \r\nArctic amplification (Smith et  al., 2020), with the Arctic warming \r\nranging from two to four times the global average in models (Holland \r\nand Bitz, 2003; Nummelin et  al., 2017). This uncertainty primarily \r\nstems from different representations of polar surface-albedo, lapse\u0002rate, and cloud feedbacks, and from different projected poleward \r\nenergy transport changes (Holland and Bitz, 2003; Crook et al., 2011; \r\nMahlstein and Knutti, 2011; Pithan and Mauritsen, 2014; Bonan \r\net al., 2018). The magnitude of Arctic amplification may also depend \r\non the mix of radiative forcing agents (Najafi et al., 2015; Sand et al., \r\n2016; Stjern et al., 2019) such as the contribution of ozone depleting \r\nsubstances (Polvani et al., 2020). Tropospheric aerosol emissions tend \r\nto reduce simulated Arctic warming over the middle of the 20th century \r\n(Gagne et al"


id: e0b2517bf2ce2fac2907f39eeafc61b6
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 32856
content:
"Climate models project a weaker polar amplification in the SH than in \r\nthe NH under transient warming (Figure 4.19). Model simulations (Hall, \r\n2004; Danabasoglu and Gent, 2009; Li et al., 2013) and paleoclimate \r\nproxies indicate polar amplification in both hemispheres near \r\nequilibrium, but generally with less warming in the Antarctic than \r\nthe Arctic (Section 7.4.4.1.2). The primary driver of delayed warming \r\nof the southern high latitudes is the upwelling in the Southern Ocean \r\nand associated ocean heat uptake that is then transported away \r\nfrom Antarctica by northward flowing surface waters (Frolicher et al., \r\n2015; Marshall et al., 2015; Armour et al., 2016; W. Liu et al., 2018), \r\nalthough asymmetries in feedbacks between the poles also play \r\na role (Section 7.4.4.1.1)"


id: 3a9beac897d84784eab9b86b5bc2f10c
created_at: 2023-03-16 14:01:59
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vector_id: 6585
content:
" Changes in westerly surface winds over the \r\nSouthern Ocean have the potential to affect the rate of sea-surface \r\nwarming, but there is currently low confidence in even the sign of \r\nthe effect based on a diverse range of climate model responses to \r\nwind changes (Marshall et  al., 2014; Ferreira et  al., 2015; Kostov \r\net al., 2017; Seviour et al., 2019). A substantial increase in freshwater \r\ninput to the ocean from the Antarctic ice sheet could further slow the \r\nemergence of SH polar amplification by cooling the Southern Ocean \r\nsurface (Bronselaer et  al., 2018; Golledge et  al., 2019; Schloesser \r\net al., 2019), but this process is not represented in current climate \r\nmodels which lack dynamic ice sheets. Thus, while there is high \r\nconfidence that the SH high latitudes will warm by more than the \r\ntropics on centennial time scales, there is low confidence that such \r\na feature will emerge this century (Section 7.4.4.1)."


id: 6e6d3d25c665a91a56dcd353c9b07dd2
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 14998
content:
"in variability of annual and particularly seasonal mean temperature, \r\nincluding (i) a reduction in mid- to high-latitude winter temperature \r\nvariability and (ii) an increase in summer temperature variability \r\nover land in the tropics and subtropics (Huntingford et  al., 2013; \r\nHolmes et  al., 2016; see Figure  4.21). The multi-ensemble average \r\nacross seven single-model initial-condition large ensembles projects \r\na consistent reduction in year-to-year December -January-February \r\n(DJF) variability around about 50degN-70degN and June-July-August \r\n(JJA) variability around 55degS-70degS along the edge of the sea ice- and \r\nsnow-covered region (Figure 4.21). There is growing evidence that \r\nyear-to-year and day-to-day temperature variability decreases in \r\nwinter over northern mid- to high-latitudes (Fischer et  al., 2011; \r\nDe Vries et al., 2012; Screen, 2014; Schneider et al., 2015; Holmes \r\net al., 2016; Borodina et al., 2017; Tamarin-Brodsky et al"


id: db893beeb728a04a18d258463a992cc8
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 32056
content:
", 2020) which \r\nimplies that the lowest temperatures rise more than the respective \r\nclimatological mean temperatures (medium confidence). Over the \r\nNH, reduced high-latitude temperature variability is associated \r\nwith disproportionally large warming in source region of cold-air \r\nadvection due to Arctic amplification and land-sea contrast (De Vries \r\net al., 2012; Screen, 2014; Holmes et al., 2016). It has further been \r\nargued that a reduction in snow and sea ice coverage from partly \r\nto completely snow- and ice-free ocean and land surface would \r\nsubstantially reduce cold-season temperature variability (Gregory and \r\nMitchell, 1995; Fischer et al., 2011; Borodina et al., 2017) and lead to \r\na shortening of the cold season and earlier onset of the warm season \r\n(Cassou and Cattiaux, 2016)"


id: 2cb70bb1210576f38cfde3168967a79e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 4463
content:
" Mid-latitudinal winter temperature \r\nvariability is further affected by a complex interplay of a multitude \r\nof processes including potential changes in atmospheric circulation, \r\nbut there is low confidence in the dominant contribution of Arctic \r\nwarming compared to other drivers (Cross-Chapter Box 10.1)."


id: c385540046c6245f81950dedbdbc8cb8
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 28331
content:
"Figure  4.21 | Percentage change in interannual variability of (left) December -January-February (DJF) and (right) June-July-August (JJA) mean \r\ntemperature averaged across seven large initial condition ensembles. Average changes across seven single-model, initial-condition large ensembles are shown for \r\nRCP8.5 in 2081-2100 (and where not available for 2080-2099) relative to 1995-2014. Standard deviations are calculated across all members of the large ensembles for every \r\ngiven year to avoid inflation due to the underlying trend and then averaged across the period. Changes are averaged across the ensembles MPI-GE (100 members, Maher \r\net al., 2019), CanESM2 (50 members, Kirchmeier-Young et al., 2017), NCAR-CESM (30 members, Kay et al., 2015), GFDL-CM3 (20 members, Sun et al., 2018), GFDL-ESM2M \r\n(30 members, Rodgers et al., 2015), CSIRO-Mk3-6-0 (30 members, Jeffrey et al., 2013) and EC-EARTH (16 members, Hazeleger et al., 2010). Also see Deser et al"


id: 643135e6942cf247434c3100ad422673
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 13331
content:
" (2020) for \r\nfurther information on those ensembles. Diagonal lines indicate areas with low model agreement where fewer than 80% of the models agree on the sign of the change, and \r\nno overlay areas with high model agreement where at least 80% of the models agree on the sign of the change. Further details on data sources and processing are available \r\nin the chapter data table (Table 4.SM.1)."


id: 6f8954aaa77fb4a17612e3bd35200368
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 15178
content:
"CMIP6 projections show warming throughout the troposphere by the \r\nend of this century and a mix of warming and cooling in the stratosphere \r\ndepending on the emissions scenario (Figure 4.22). The patterns of \r\ntropospheric temperature change are highly consistent with those \r\nderived from earlier generations of climate models as assessed in \r\nAR5, AR4 and TAR. In SSP1-2.6, the multi-model mean warming \r\nremains below 3degC everywhere in the troposphere except near the \r\nsurface in the Arctic; this is similar to the findings in AR5 based on \r\nCMIP5 models for RCP2.6. In SSP3-7.0, the zonal mean tropospheric \r\nwarming is also largest in the tropical upper troposphere, reaching \r\nmore than 5degC, and near the surface in the Arctic where warming \r\nexceeds 8degC (Figure 4.22). It is likely that the warmer projected GSAT \r\nin the unconstrained CMIP6 model ensemble contributes to larger \r\nwarming in the tropical upper troposphere and in the Arctic lower \r\ntroposphere"


id: 6e46d1fde7ea8505e5ea3a5a10565e93
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 14972
content:
" This assessment is based on the understanding of polar \r\namplification assessed in Chapter  7 (Section  7.4.4.1), and at low \r\nlatitudes is based on the understanding of moist convective processes \r\nas well as the relationship between CMIP5- and CMIP6-simulated \r\nsurface temperatures and tropical upper tropospheric warming over \r\nthe historical period (Section 3.3.1.2)."


id: 369119591ac8be1e379e502077cab988
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 5974
content:
"Figure  4.23 | Long-term changes in seasonal mean relative humidity. Displayed are projected spatial patterns of multi-model mean change (%) in seasonal \r\n(top) December -January-February (DJF) and (bottom) June-July-August (JJA) mean near-surface relative humidity in 2081-2100 relative to 1995-2014, for (left) SSP1-2.6 \r\nand (right) SSP3-7.0. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from \r\ninternal variability, that is, where at least 66% of the models show a change greater than the internal variability threshold (Section 4.2.6) and at least 80% of the models agree \r\non the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the \r\ninternal-variability threshold"


id: 65ee000c5deeb789ba6b2a5a3785828b
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 13625
content:
" Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but \r\nfewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: eb0a77c2112dd41695859e8c18bf9264
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 34484
content:
"The thermodynamic response to global warming is associated with \r\na 'wet get wetter' mechanism, with enhanced moisture flux leading \r\nto subtropical dry regions getting drier and tropical and mid-latitude \r\nwet regions getting wetter (Held and Soden, 2006; Chou et al., 2009). \r\nRecent studies suggest that the dry-get-drier argument does not hold, \r\nespecially over subtropical land regions (Greve et  al., 2014; Feng \r\nand Zhang, 2015; Greve and Seneviratne, 2015). The discrepancy \r\nmay be partly arising due to differences in model climatologies \r\nand by change in the location of wet and dry regions (Polson and \r\nHegerl, 2017). Over the 21st century, significant rate of precipitation \r\nchange is associated with a spatial stabilization and intensification \r\nof moistening and drying patterns (Chavaillaz et  al., 2016). In the \r\ntropics, weakening of circulation leads to a 'wet gets drier, dry gets \r\nwetter' pattern (Chadwick et  al., 2013)"


id: b4f1f8587d502d539ae7e3c55b6bccf3
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 26007
content:
" Climate model agreement \r\nfor precipitation change in the tropics is lower than for other regions \r\n(Knutti and Sedlacek, 2013; McSweeney and Jones, 2013). Sources \r\nof inter-model uncertainty in regional rainfall projections arise from \r\ncirculation changes (Kent et al., 2015; Chadwick, 2016) and spatial \r\nshifts in convection and convergence, associated with SST pattern \r\nchange and land-sea thermal contrast change (Kent et  al., 2015; \r\nChadwick et  al., 2017) with a  secondary contribution from the \r\nresponse to direct CO2 forcing (Chadwick, 2016). Factors governing \r\nchanges in large-scale precipitation patterns are assessed in detail in \r\nSections 8.2.2 and 10.4.1."


id: 665f2067093a2315480ffce4c9aedccb
created_at: 2023-03-16 14:01:59
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vector_id: 13697
content:
"Long-term multi-model mean change in seasonal precipitation \r\n(JJA and DJF) from CMIP6 models (Figure  4.24) shows substantial \r\nregional differences and seasonal contrast. Changes in seasonal \r\nprecipitation under SSP1-2.6 are small compared to internal \r\nvariability. Consistent with the AR5, patterns of precipitation \r\nchange are very likely to increase in the high latitudes especially \r\nduring local winter and over tropical oceans under SSP3-7.0 (high \r\nconfidence). CMIP6 projections show an increase in precipitation \r\nover larger parts of the monsoon regions and decreases in many \r\nsubtropical regions including the Mediterranean, southern Africa \r\nand south-west Australia (medium confidence). The large-scale \r\npatterns of precipitation shown in Figure 4.24 are consistent with the \r\npatterns presented in Section 8.4.1.3. Precipitation changes exhibit \r\nstrong seasonal characteristics (Box 8.2), and, in many regions, the \r\nsign of the precipitation changes varies with season"


id: 7bc9493267771cc29f377225cbc5e137
created_at: 2023-03-16 14:01:59
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vector_id: 17077
content:
" Precipitation \r\nvariability is projected to increase over a majority of global land area, \r\nas assessed in Chapter 8 (Section 8.4.1.3.3), over a wide range of \r\ntime scales in response to warming (Pendergrass et al., 2017)."


id: e8045feb48c1b5b81ceb9426a599a196
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 34000
content:
"Since AR5, progress has been achieved in understanding changes \r\nin patterns and rates of precipitation with GSAT rise. The projected \r\nprecipitation changes can be decomposed into a part that is related \r\nto atmospheric circulation referred to as dynamical component \r\nand a  part related to water vapour changes, the thermodynamic \r\ncomponent. Based on process understanding and modelling (Flaschner \r\net al., 2016; Samset et al., 2016), global mean precipitation will very \r\nlikely increase by 1-3% per degC of GSAT warming (Section  8.2.1). \r\nThe increase in atmospheric water vapour is a robust change under \r\nglobal warming, the sensitivity of global precipitation change to \r\nwarming is smaller (2% per degC) as compared to water vapour change \r\n(7% per degC; Held and Soden, 2006). Global energy balance places \r\na  strong constraint on the global mean precipitation (Allen and \r\nIngram, 2002; Pendergrass and Hartmann, 2014; Myhre et al., 2018; \r\nSiler et  al.,  2019)"


id: a472e47c0a4410529d740a860d3aecab
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 23439
content:
"Figure 4.24 | Long-term change of seasonal mean precipitation. Displayed are projected spatial patterns of multi-model mean change (%) in (top) December-January-\r\nFebruary (DJF) and (bottom) June-July-August (JJA) mean precipitation in 2081-2100 relative to 1995-2014, for (left) SSP1-2.6 and (right) SSP3-7.0. The number of models \r\nused is indicated in the top right of the maps. No map overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at \r\nleast 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal \r\nlines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold"


id: 3a6f2b731719ab65cf72156e54ae553e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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" \r\nCrossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all \r\nmodels agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: 488a8f8e175f0d198cac9183fa2b7c70
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 8785
content:
"Since AR5, there has been progress in understanding physical \r\nmechanisms for the projected changes in global monsoon and \r\nquantifying the sources of uncertainty in projections. The increase in \r\nglobal monsoon precipitation under warming is primarily attributed \r\nto the increase of moisture convergence, which comes mainly from \r\nthe thermodynamic effect due to increasing atmospheric moisture \r\nbut is partly offset by reduced convergence (W. Zhang et  al., \r\n2019; Chen et  al., 2020). The dynamic effect, such as monsoon \r\ncirculation changes, dominates regional differences in the projected \r\nmonsoon  precipitation  changes (Chen et  al., 2020). Specifically, \r\nNH monsoon precipitation will increase more strongly than its \r\nSH counterpart, due to an increase in hemispheric temperature \r\ndifference between the NH and SH, enhancement of the Hadley \r\ncirculation, and atmospheric moistening, countered by stabilization \r\nof the troposphere (Lee and Wang, 2014)"


id: ab5ed685988773a4d8176efa4116d9a6
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 24478
content:
" The seasonality of \r\nglobal monsoon rainfall is projected to be enhanced in response to \r\nwarming, featuring a greater wet-dry season contrast (Lee and Wang \r\n2014; Zhang et al. 2019). In addition, the interannual variability of \r\nglobal monsoon rainfall is projected to intensify mainly over land, \r\nwith a strengthened relationship between global monsoon and ENSO \r\n(Hsu et al., 2013; Wang et al., 2020, 2021)."


id: 2bf54165b131e0203287c9689055f83c
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 4343
content:
"The AR5 assessed that mean sea level pressure is projected to decrease \r\nin high latitudes and to increase in mid-latitudes. Such a pattern is \r\nassociated with a poleward shift in the storm track and an increase \r\nin the annular mode index. This broad pattern is also found in CMIP6 \r\nmodels (Figure 4.25). Under SSP1-2.6, the pattern in sea level pressure \r\nchange resembles that for SSP3-7.0, but the amplitudes are small \r\ncompared to internal variability in 20-year means (Figure 4.25). One \r\nexception is found in the SH mid-latitudes, where pressure robustly \r\nincreases in SSP3-7.0 in both austral summer and winter, but shows \r\nno robust change in SSP1-2.6. This is likely attributable to the larger \r\nGHG forcing in SSP3-7.0 compared to SSP1-2.6, which contributes \r\nto a poleward shift of the SH mid-latitude circulation and becomes \r\nrelatively more important than the effect of ozone recovery which \r\ndrives an equatorward shift in the circulation (see Section 4.5.3"


id: c25261ef51b8364a8c53fdf87718ebd
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 28159
content:
"1 on \r\nthe Southern Annular Mode; Barnes and Polvani, 2013; Barnes et al., \r\n2014; Bracegirdle et al., 2020b). The poleward shift in SH mid-latitude \r\ncirculation in SSP3-7.0 likely contributes to the wetting trend at high \r\nsouthern latitudes (Figure 4.25)."


id: 594df11b4688d6e3732e5a6cec09abfd
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 11539
content:
"For the uncertainty in mean monsoon precipitation projections, the \r\nmodel uncertainty is the dominant contributor throughout the century \r\nand explains more than 70% of the inter-model variance during near \r\nterm, mid-term, and long term. The contribution of internal variability \r\nis only important at the beginning in early decades, while scenario \r\nuncertainty becomes important at the end of the 21st  century. The \r\nsources of uncertainty for the mean and extreme monsoon precipitation \r\nmainly differ in the long-term projection, when the contribution of \r\nscenario uncertainty is comparable to the model uncertainty for extreme \r\nprecipitation (Zhou et al., 2020). Although the magnitude of internal \r\nvariability differs between CMIP5 models and single-model, initial\u0002condition large ensembles, the impact is only evident in the beginning \r\ndecades"


id: b45ffad23bc92515556515caa7c69dfb
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 25895
content:
" For the mid- and long term, the magnitude difference does not \r\nalter that model uncertainty is the dominant source of uncertainty in \r\nthe projections of global land monsoon precipitation (Zhou et al., 2020)."


id: 4b0cb95ae82c912346a0e08de0f6755f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 9227
content:
"Under both SSP1-2.6 and SSP3-7.0 there is a strengthening and lifting \r\nof the subtropical jets in both hemispheres (Figure 4.26), consistent \r\nwith the response to large-scale tropospheric warming found in earlier \r\ngenerations of climate models (Collins et al., 2013). In the SH, GHG \r\nemissions tend to force a poleward shift of the jet, but this is opposed, \r\nparticularly in austral summer, by the stratospheric ozone hole recovery \r\n(Barnes and Polvani, 2013; Barnes et  al., 2014; Bracegirdle et  al., \r\n2020b). Consistent with sea level pressure changes, CMIP6 models \r\nproject a strengthening and poleward shift of the SH jet in austral \r\nsummer and winter under SSP3-7.0, but smaller and non-robust \r\nchanges in SH mid-latitude zonal winds under SSP1-2.6 (Figure 4.26; \r\nsee also Section 4.5.3.1). CMIP6 models show an improved simulation \r\nof the SH jet stream latitude (Bracegirdle et al., 2020a; Curtis et al., \r\n2020)"


id: 8f38ea3fd076451b557bfe00417dac47
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 20075
content:
" This has been linked to a reduction in the projected poleward \r\nshift of the SH jet in austral summer compared to the CMIP5 models \r\n(Curtis et al., 2020; Goyal et al., 2021), although differences in the \r\npattern of SST response may also play a  role (Wood  et  al., 2020). \r\nIn the NH extratropics, the changes in lower-tropospheric zonal-mean "


id: 4c51555e52a9e697b45f09620cba8779
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 9434
content:
"Figure 4.25 | Long-term change of seasonal-mean sea level pressure. Displayed are projected spatial patterns of multi-model mean change in (top) December-January-\r\nFebruary (DJF) and (bottom) June-July-August (JJA) mean sea level pressure (hPa) in 2081-2100 relative to 1995-2014, for (left) SSP1-2.6 and (right) SSP3-7.0. The number \r\nof models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at \r\nleast 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal \r\nlines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold"


id: bec59dd57c627da22e221e1cea7657f3
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 27568
content:
" Crossed \r\nlines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree \r\non the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: ef437f45a2991d95cd0d0c0d6b23132f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 35115
content:
"with an eastward extension into Europe (Li et al., 2018; Peings et al., \r\n2018; Simpson et al., 2019a; Harvey et al., 2020; Oudar et al., 2020). \r\nSimulations indicate that most of the changes in winter storminess \r\nover the Euro-Atlantic region will occur only after exceeding the 1.5degC \r\nwarming level (Barcikowska et al., 2018).\r\nProgress since AR5 has improved understanding of the climate change \r\naspects that can drive these different, and potentially opposite, \r\nresponses in the mid-latitude jets and storm tracks. A poleward shift \r\nof the jets and storm tracks is expected in response to an increase \r\nin the atmospheric stratification and in the upper-tropospheric \r\nequator-to-pole meridional temperature gradient, while it is opposed \r\nby the decrease in the meridional temperature gradient in the \r\nlower troposphere associated with the polar amplification of global \r\nwarming (Harvey et  al., 2014; Shaw et  al., 2016)"


id: f29527388845d1611fb6a96a18ccd4de
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 35696
content:
" Recent analyses \r\nhave identified additional climate aspects that can drive mid-latitude \r\njet changes, including patterns in sea surface warming (Mizuta \r\net al., 2014; Langenbrunner et al., 2015; Ceppi et al., 2018; Wood et al., \r\n2020), land-sea warming contrast (Shaw and Voigt, 2015), loss of sea \r\nice (Deser et al., 2015; Harvey et al., 2015; Screen et al., 2018b; Zappa \r\net al., 2018), and changes in the strength of the stratospheric polar \r\nvortex (Manzini et al., 2014; Grise and Polvani, 2017; Simpson et al., \r\n2018; Ceppi and Shepherd, 2019). From an energetics perspective, \r\nthe uncertainty in the response of the jet streams depends on the \r\nresponse of clouds, their non-spatially uniform radiative feedbacks \r\nshaping the meridional profile of warming (Ceppi et al., 2014; Voigt \r\nand Shaw, 2015, 2016; Ceppi and Hartmann, 2016; Ceppi and \r\nShepherd, 2017; Lipat et  al., 2018; Albern et  al., 2019; Voigt et  al., \r\n2019)"


id: a251176d6a7ce5b341fe746964faac90
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 23105
content:
" Climate models seem to underestimate the forced component \r\nof the year-to-year variability in the atmospheric circulation, \r\nparticularly in the North Atlantic sector (Scaife and Smith, 2018), \r\nwhich suggests some relevant dynamical processes may not be well \r\nrepresented. Whether and how this may affect long-term projections \r\nis unknown. In conclusion, due to the influence from competing \r\ndynamical drivers and the absence of observational evidence, there \r\nis medium confidence in a projected poleward shift of the NH zonal\u0002mean low-level westerlies in autumn and summer and low confidence\r\nin the other seasons. There is also overall low confidence in projected \r\nregional changes in the NH low-level westerlies, particularly for the \r\nNorth Atlantic basin in boreal winter."


id: 6b89549056f5f16554e0a13914b3df1a
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 14536
content:
"Regarding the dynamical intensity of the storm tracks (Section 11.7.2), \r\nthe number of ETCs associated with intense surface wind speeds and \r\nundergoing explosive pressure deepening are projected to strongly \r\ndecrease in the NH winter (Seiler and Zwiers, 2016; Chang, 2018). \r\nThe weakening of surface winds of ETCs in the NH is attributed to \r\nthe reduced low-level baroclinicity from SST and sea ice changes \r\n(Harvey et al., 2014; Seiler and Zwiers, 2016; J. Wang et al., 2017a). \r\nThere are, however, regional exceptions such as in the northern North \r\nPacific, where explosive and intense ETCs are projected to increase \r\nin association with the poleward shift of the jet and increased upper\u0002level baroclinicity (Seiler and Zwiers, 2016). Eddy kinetic energy \r\nand intense cyclone activity are also projected to decrease in the \r\nNH summer in association with a  weakening of the jet (Lehmann \r\net al., 2014; Chang et al., 2016)"


id: de03bf31db9e8fa14e2cfd4f5990416f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 32429
content:
" However, explosive cyclones tend \r\nto be too weak in climate models (Seiler and Zwiers, 2016; Priestley \r\net al., 2020), though this bias seems to be reduced in high-resolution \r\nsimulations (Jiaxiang et al., 2020). Furthermore, models may not fully \r\ncapture the contribution of the future increase in mesoscale latent "


id: a1a0a715fe57bcf5b2f1b9e616a9100
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 22984
content:
"The anthropogenic forced signal in extratropical atmospheric circulation \r\nmay well be small compared to internal variability (Deser et al., 2012b, \r\n2014) and, as assessed in AR5, there is generally low agreement across \r\nmodels in many aspects of regional atmospheric circulation change \r\nparticularly in the NH (Shepherd, 2014). The latter means that, in some \r\nregions, a multi-model average perspective of atmospheric circulation \r\nchange represents a  small residual after averaging over large \r\nintermodel spread. This is in strong contrast to thermodynamic aspects \r\nof climate change, such as surface temperature change, for which \r\nmodel results are generally highly consistent (see, e.g., Figure 4.19). \r\nFurthermore, models share systematic biases in some aspects of \r\nextratropical atmospheric circulation such as mid-latitude jets, which \r\ncan have complex implications for understanding forced changes \r\n(Simpson and Polvani, 2016)"


id: dd8a4ca73d9903e0606becb8319f1ddb
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 32355
content:
" Given these issues, an emerging field \r\nof research since AR5 has focused on the development of 'storylines' \r\nfor regional atmospheric circulation change (Shepherd,  2019). "


id: f1ed129ec826db72e3b4759149c96b9a
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 35583
content:
"The CMIP5 projections suggest that the response of blocking \r\nfrequency to climate change might be quite complex (Dunn-Sigouin \r\net  al., 2013; Masato et  al., 2013). An eastward shift of winter \r\nblocking activity in the NH is indicated (Masato et al., 2013; Kitano \r\nand Yamada, 2016; Lee and Ahn, 2017; Matsueda and Endo, 2017) \r\nwhile during boreal summer, blocking frequency tends to decrease \r\nin mid-latitudes (Matsueda and Endo, 2017), with the exception of \r\nthe eastern Europe-western Russia region (Masato et al., 2013). The \r\nprojected decrease of blocking in boreal summer partially contrasts \r\nwith the observed increase in Greenland blocking (Hanna et al., 2018; \r\nDavini and D'Andrea, 2020). However, as shown in Woollings et al. \r\n(2018), the spatial distribution and the magnitude of the suggested \r\nchanges are sensitive to the blocking detection methods (Schwierz \r\net al., 2004; Barriopedro et al., 2010; Davini et al., 2012)"


id: 6a23079bf4438c335a4c1274a6e6eb19
created_at: 2023-03-16 14:01:59
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vector_id: 14305
content:
" In the SH, \r\nblocking frequency is projected to decrease in the Pacific sector \r\nduring austral spring and summer. However, seasonal and regional \r\nchanges are not totally consistent across the models (Parsons et al., \r\n2016), and, as assessed in Section 3.3.3.3, model biases might affect \r\ntheir response."


id: c18a21e5af887f712d85166eb7ab942b
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 28040
content:
"Projections with CMIP6 ESMs (Kwiatkowski et  al., 2020) show \r\na surface pH decline of -0.16 +- 0.002 (+-1 standard deviation) under \r\nSSP1-2.6 and -0.44 +- 0.005 under SSP5-8.5 from 1870-1899 to \r\n2080-2099. The high-latitude oceans, in particular the Arctic, show \r\ngreater decline in pH and accelerated acidification (Terhaar et  al., \r\n2020). For the same period, model-projected bottom-water pH \r\ndecline is -0.018 +- 0.001 under SSP1-2.6 and -0.030 +- 0.002 under \r\nSSP5-8.5. The projected large-scale surface ocean acidification will be \r\nprimarily determined by the pathway of atmospheric CO2, with weak \r\ndependence on change in climate (high confidence) (Section 5.3.4.1; \r\nHurd et al., 2018). However, for a given atmospheric CO2 scenario, \r\nuncertainty in projected ocean acidification increases with ocean \r\ndepth because of model-simulated differences in ocean circulation \r\nthat transports anthropogenic CO2 from the surface to bottom ocean \r\n(high confidence) (Kwiatkowski et al"


id: 38ca86e38ddb858a33d055e2727ce98c
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 6305
content:
", 2020). For example, projected \r\nsurface pH fully separates between SSPs scenarios before 2050, but \r\nsome overlap across SSPs is still found for projected bottom-water pH \r\nin 2080 (Kwiatkowski et al., 2020)."


id: cb3650000025ef4cdf75078599a7d7b0
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 29530
content:
"The AR5 assessed it is likely that the evolution of the SAM over the \r\n21st century will be primarily determined by the interplay between \r\nthe effects of ozone recovery and changing GHG concentrations and \r\ninfluence the SAM in opposing ways. Owing to the relative effects \r\nof these two drivers, CMIP5 model SAM and Southern Hemisphere \r\ncirculation projections differed markedly across forcing scenarios \r\nand across seasons (Barnes and Polvani, 2013; Barnes et al., 2014). \r\nCMIP5 models simulated a  weak negative SAM trend in austral \r\nsummer for RCP4.5 by the end of the century (F. Zheng et al., 2013), \r\nwhile for RCP8.5 they simulated a weak positive SAM trend in austral \r\nsummer (F. Zheng et al., 2013). A substantial fraction of the spread in \r\nCMIP5 projections of the end of century SH summer jet shift under \r\nRCP8"


id: 98d83bcd550ba7c43bddb241ba6d76c3
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 21562
content:
"5 may be attributable to differences in the simulated change in \r\nbreak-up of the stratospheric polar vortex, with models that produce \r\na later break-up date showing a larger summertime poleward jet shift \r\n(Ceppi and Shepherd, 2019). For RCP2.6, the effect of ozone recovery \r\non the SAM has been found to dominate over that of GHGs in austral \r\nsummer (Eyring et  al., 2013). In austral winter, the poleward shift \r\nof the SH circulation in CMIP5 models, and the associated increase \r\nin the SAM index, tends to be larger, on average, in higher forcing \r\nscenarios though with substantial inter-model spread (Barnes et al., \r\n2014). New research since the AR5 shows that the previous theory \r\nfor the apparent relationship across models between the annual \r\nmean climatological SH jet position and the amplitude of forced SH \r\njet shift (Kidston and Gerber, 2010) does not hold at seasonal time \r\nscales (Simpson and Polvani, 2016)."


id: e6786efbdd3e4796a4af59b676eb9c98
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 33746
content:
"There is no consensus on changes in amplitude of ENSO SST \r\nvariability across CMIP iterations. The main factors driving the \r\ndiversity of ENSO SST amplitude change in climate models are internal \r\nvariability, SST-mean warming pattern, and model systematic biases. \r\nFirst, pronounced low-frequency modulations of ENSO exist even in \r\nunforced control simulations due to internal variability, which leads \r\na large uncertainty in quantifying future ENSO changes (Wittenberg, \r\n2009; Vega-Westhoff and Sriver, 2017; Zheng et al., 2018). Second, \r\nENSO characteristics depend on the climate mean state of the tropical \r\nPacific; however, ENSO can also influence the mean state through \r\nnon-linear processes (Cai et al., 2015; Timmermann et al., 2018)"


id: 2cb5285a5d51fba44de5307775d33eed
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 4462
content:
" The \r\nresponse of the tropical Pacific mean state to anthropogenic forcing \r\nis characterized by a  faster warming on the equator compared to \r\nthe off-equatorial region, a faster warming of the eastern equatorial \r\nPacific compared to the central tropical Pacific (e.g., El Nino-like mean \r\nSST warming, see Section  7.4.4.2), and a  weakening of the Walker \r\ncirculation in most models. Those models with a El Nino-like warming \r\ntend to project a  strengthening of ENSO SST variability whereas \r\nmodels with a La Nina-like warming tend to project a weakening of \r\nvariability (Zheng et al., 2016; Kohyama and Hartmann, 2017; J. Wang \r\net  al., 2017b; Cai et  al., 2018a; Fredriksen et  al., 2020). Third, how \r\nto take model biases into account leads to different ENSO changes. \r\nKim et  al. (2014) suggested that a  subset of CMIP5 models that "


id: f7a748a85f7f6089ef6cd9a7fea72079
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 36478
content:
"In the mid- to long-term, projected climate mean state changes in the \r\ntropical Indian Ocean are expected to resemble a positive IOD state, \r\nwith faster warming in the west compared to the east (Cai et al., 2013; \r\nX.-T. Zheng et al., 2013). However, it was argued that this projected \r\nmean state change could be due to the large mean state biases in \r\nthe simulated current climate and potentially not a realistic outcome \r\n(G. Li et al., 2016). Mean state biases also lead to lack of consensus \r\non projected equatorial Indian Ocean SST variability and equatorial \r\nmodes of climate variability independent of the IOD (DiNezio et al., \r\n2020). If mean state change will indeed resemble a  positive IOD \r\nstate, however, this would lead to a  reduction in the amplitude \r\ndifference between positive and negative IOD events, but with no \r\nrobust change in IOD frequency (Cai et al., 2013)"


id: d2c726ca717264735433c86db1b3ef1d
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 30683
content:
" For a small subset \r\nof CMIP5 models that simulate IOD events best, a  slight increase \r\nin IOD frequency was found under the CMIP5 RCP4.5 scenario \r\n(Chu et al., 2014)."


id: b56f75ad5e7bbf94de0f3534c61ff65b
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 26078
content:
"Several methodologies have been applied to estimate the spatial \r\npatterns of climate change associated with a given level of global \r\nwarming. These include performing model simulations under \r\nstabilisation scenarios designed to achieve a  specific level of \r\nglobal warming, the analysis of epochs identified within transient \r\nsimulations that systematically exceed different thresholds of global \r\nwarming (e.g., Mitchell et al., 2017; Dosio et al., 2018; Hoegh-Guldberg \r\net al., 2018; Kjellstrom et al., 2018), and analysis based on statistical \r\nmethodologies that include empirical scaling relationships (ESR; \r\nSchleussner et al., 2017; Dosio and Fischer, 2018; Seneviratne et al., \r\n2018) and statistical pattern scaling (e.g., Kharin et al., 2018). These \r\ndifferent methodologies are assessed in some detail in Section 4.2.4 \r\n(James et  al"


id: 6afe4238545c4545d92fb407cd6a8309
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 14454
content:
", 2017) and generally provide qualitatively consistent \r\nresults regarding changes in the spatial patterns of temperature and \r\nrainfall means and extremes (see Chapter 11) at different levels of \r\nglobal warming."


id: 5f414f46c9a2cd0334ed6cec9186d706
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 12555
content:
"AMV long-term changes under future warming scenarios have so \r\nfar scarcely been investigated. A  study on the CMIP5 multi-model \r\nsimulations under RCP8.5 scenario by (Villamayor et al., 2018) found \r\nno substantial differences in the simulated SST patterns (and  in \r\nthe related tropical rainfall response) when RCP8.5, historical and \r\npiControl simulations are compared. Such results suggest that the \r\nAMV is not expected to change under global warming. A  more \r\nrecent single-model large ensemble study (Hand et al., 2020) shows \r\na  pronounced change in the AMV pattern under global warming \r\nlinked to a strong reduction of the mean AMOC and its variability. \r\nHowever, since a  superposition of multiple processes controls the \r\nAMV, as extensively discussed in Annex IV (Section AIV.2.7), in \r\nChapter  3 (Section  3.7.7), and in Chapter  9 (Section  9.2.3.1), the \r\nlength of the RCP8"


id: b148304b6dc381be38da6e7da8e1323e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 25396
content:
"5 simulations might be not sufficient to properly \r\nevaluate the respective weight and interplay of internal components \r\nand influences from external forcing on AMV projections."


id: 20c146ca5795b52f77543f7bc5aa38b8
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 2591
content:
"In this subsection, we present the projected patterns of climate change \r\nobtained following the epoch approach (also called the time-shift \r\nmethod, see Section 4.2.4) under the Tier 1 SSPs (SSP1-2.6, SSP2-4.5, \r\nSSP3-7.0 and SSP5-8.5). For each model simulation considered under \r\neach of these SSPs, 20-year moving averages of the global average \r\natmospheric surface temperature are first constructed, then this time \r\nseries is used to detect the first year during when GSAT exceeds the \r\n1.5degC, 2degC, 3degC and 4degC thresholds with respect to the 1850-1900 \r\n(Cross-Chapter Box  11.1). The temperature thresholds are not \r\nexceeded in all the model simulations across the Tier 1 SSPs. That \r\nis, decreasing numbers of simulations are available for the analysis \r\nof patterns of change as the temperature threshold increases"


id: 5a559362fa265855db70b77ffffd642c
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 11722
content:
" For \r\neach simulation within which a  given temperature threshold is \r\nexceeded, a 20-year global climatology is subsequently constructed \r\nto represent that level of global warming, centred on the year for \r\nwhich the threshold was first exceeded. The composite of all such \r\nclimatologies across the Tier 1 SSPs and model simulations constitute \r\nthe spatial patterns of change for a  given temperature threshold. \r\nSome of the complexities of scaling patterns of climate change with \r\ndifferent levels of global warming are also discussed in the following "


id: e0a5a7186d8d5c28793b317c225a64dd
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 32849
content:
"The largest increase in annual mean temperature is found in the \r\nhigh latitudes of the Northern Hemisphere across all levels of \r\nglobal warming (virtually certain) (Figure  4.31). This phenomenon \r\npeaks in the Arctic and is known as Arctic amplification, with the \r\nunderlying physical mechanisms assessed in detail in Section 4.5.1 \r\nand Chapter  7 (Section  7.4.4.1). For the CMIP6 ensemble average \r\nconsidered here, Arctic annual mean temperatures warm by a factor \r\nof 2.3, 2.5, 2.4 and 2.4 for 1.5degC, 2degC, 3degC and 4degC of global warming, \r\nrespectively. That is, Arctic warming scales approximately linearly \r\nwith GSAT. Generally, when Arctic amplification is considered across \r\nindividual models, warming occurs at a factor of two to four times the \r\nglobal level of warming"


id: a4a5bfdbe8efec579133aabeb32539bc
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 23471
content:
" It is unlikely that warming in the Southern \r\nHemisphere high latitudes in the 21st century will exceed the change \r\nin GSAT, or that it will substantially exceed warming in the tropics, \r\nfor GSAT change ranging between 1.5degC and 4degC (Figure 4.31 and \r\nTable  4.2). Correspondingly, there is low confidence of Antarctic \r\namplification occurring under transient, 21st century low mitigation \r\nscenarios (Table 4.2 and Section 7.4.4.1). The Antarctic continent is \r\nprojected to warm at a higher rate than the mid-latitude Southern \r\nOcean, however, at all levels of global warming (Figure  4.31). \r\nThe relevant physical mechanisms that reduce the amplitude of polar \r\namplification over Antarctica compared to the Arctic are assessed in \r\ndetail in Section 4.5.1 and Chapter 7 (Section 7.4.4.1)"


id: 646a478210b2536e40ea8568798d5071
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updated_at: 2023-03-16 14:01:59
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vector_id: 13366
content:
" In the Southern \r\nHemisphere the strongest warming over land is to occur, at any given \r\nlevel of global warming, over the subtropical areas of South America, \r\nsouthern Africa and Australia (high confidence). The relatively \r\nstrong warming in subtropical southern Africa may be attributed to \r\nstrong soil-moisture-temperature coupling and projected increased \r\ndryness under enhanced subsidence (Engelbrecht et al., 2015; Vogel \r\net al., 2017). Across the globe, in the tropics, subtropics, and mid\u0002to high latitudes, temperatures tend to scale linearly with the level \r\nof increase in GSAT and patterns of change are largely scenario \r\nindependent (high confidence)."


id: cdd87d4619a4a8c58c74821d35c3ed9f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 29903
content:
"Global warming of 1.5degC implies higher mean temperatures \r\ncompared to 1850-1900, with generally higher warming over land \r\ncompared to ocean areas (virtually certain) and larger warming in \r\nhigh latitudes compared to low latitudes (Figure 4.31). In addition, \r\nglobal warming of 2degC versus 1.5degC results in robust increases in \r\nthe mean temperatures in almost all locations, both on land and \r\nin the ocean (virtually certain), with subsequent further warming \r\nat almost all locations at higher levels of global warming (virtually \r\ncertain) (Hoegh-Guldberg et al., 2018). For each particular level of \r\nglobal warming, relatively larger mean warming is projected for land \r\nregions (virtually certain) (see Figure 4.31; Christensen et al., 2013; \r\nCollins et al., 2013; Seneviratne et al., 2016). The projected changes \r\nat 1"


id: 6c92314c2d5c0dab0faadbcc8b7b26a
created_at: 2023-03-16 14:01:59
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vector_id: 14703
content:
"5degC and 2degC global warming are consistent with observed \r\nhistorical global trends in temperature and their attribution to \r\nanthropogenic forcing (Chapter 3), as well as with observed changes \r\nunder the recent global warming of 0.5degC (Schleussner et al., 2017; \r\nHoegh-Guldberg et al., 2018). That is, spatial patterns of temperature \r\nchanges associated with the 0.5degC difference in GMST warming \r\nbetween 1991-2010 and 1960-1979 (Schleussner et  al., 2017; \r\nHoegh-Guldberg et al., 2018) are consistent with projected changes \r\nunder 1.5degC and 2degC of global warming."


id: de96ec439aaae90a5d782d8438a35542
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 32520
content:
"The SR1.5 stated low confidence regarding changes in global \r\nmonsoons at 1.5degC versus 2degC of global warming, as well as \r\ndifferences in monsoon responses at 1.5degC versus 2degC. Generally, \r\nstatistically significant changes in regional annual average \r\nprecipitation are expected at a global mean warming of 2.5degC-3degC \r\nor more (Tebaldi et  al., 2015). Over the austral-winter rainfall \r\nregions of south-western South America, South Africa and Australia, \r\nprojected decreases in mean annual rainfall show high agreement\r\nacross models and a  strong climate change signal even under \r\n1.5degC of global warming, with further amplification of the signal \r\nat higher levels of global warming (high confidence) (Mindlin et al., \r\n2020). This is a signal evident in observed rainfall trends over these \r\nregions (Sections 2.3.1.3 and 8.3.1.6 ). Also, over the Asian monsoon \r\nregions, increases in rainfall will occur at 1.5degC and 2degC of global \r\nwarming (Chevuturi et  al., 2018)"


id: 86abc4dc461f8a7251a406c4e5819362
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 18727
content:
" At warming levels of 1.5degC and \r\n2degC, the changes in global monsoons are strongly dependent on \r\nthe modelling strategies used, such as fully coupled transient, fully \r\ncoupled quasi-equilibrium, and atmosphere-only quasi-equilibrium "


id: d4a1c39077c5f1c63000052c48471e0e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 30979
content:
"Figure 4.32 | Projected spatial patterns of change in annual average precipitation (expressed as a percentage change) at different levels of global \r\nwarming. Displayed are (a-d) spatial patterns of change in annual precipitation at 1.5degC, 2degC, 3degC, and 4degC of global warming relative to the period 1850-1900. No map \r\noverlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the \r\ninternal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant \r\nchange, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least \r\n66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change"


id: aa5ba0659a30b90055d01c845c37de41
created_at: 2023-03-16 14:01:59
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vector_id: 24340
content:
" Values were assessed from \r\na 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are available in the \r\nchapter data table (Table 4.SM.1)."


id: 7d883cb0e0e734478a6c35f6cffde0b
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 17322
content:
"In the North Atlantic, North Pacific, and Southern Hemisphere, \r\nthe transient response of the mid-latitude jets to forcing behaves \r\nnon-linearly with GSAT (Grise and Polvani, 2017; Ceppi et al., 2018). \r\nThis is a  consequence of the time-dependence of the relationship \r\nbetween radiative forcing and GSAT and the temporal evolution of \r\nSST patterns (Ceppi et al., 2018), with a potential seasonal component \r\nin the SH associated with polar stratospheric temperature changes \r\n(Grise and Polvani, 2017). Consequently, the epoch approach applied \r\nto a transient simulation of the 21st century will overestimate the \r\nmid-latitude circulation response in a stabilized climate. Dedicated \r\ntime slice experiments simulating stabilized climates are therefore \r\nrequired to assess differences in mid-latitude circulation at given \r\nlevels of global warming (Li et al., 2018)"


id: b23a845f5eea77d7f56e304e35390d65
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 25543
content:
" A further complication in \r\nthe SH is the competing influences of ozone recovery and increasing \r\nGHG concentrations on the austral-summer mid-latitude circulation \r\nduring the first half the 21st  century (Barnes and Polvani, 2013; \r\nBarnes et  al., 2014). Using transient 21st  century experiments to \r\ndiagnose changes in SH mid-latitude circulation at different levels "


id: 3556d867481c39758cb5c23055ad7538
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 5805
content:
"The SR1.5 (IPCC, 2018b) concluded with high confidence that \r\novershoot trajectories 'result in higher impacts and associated \r\nchallenges compared to pathways that limit global warming to \r\n1.5degC with no or limited overshoot'. The degree and duration of \r\novershoot affects the risks and impacts likely to be experienced \r\n(Hoegh-Guldberg et al., 2018) and the emissions pathway required to \r\nachieve it (Akimoto et al., 2018). Consequences relating to ice sheets \r\nand climatic extremes have been found to be greater at 2degC of global \r\nwarming than at 1.5degC (Schleussner et  al., 2016; Hoegh-Guldberg \r\net al., 2018) but even on recovery to lower temperatures, these effects \r\nmay not reverse. Overshoot has been found to lead to irreversible \r\nchanges in thermosteric sea level (Tokarska and Zickfeld, 2015; Palter \r\net al., 2018; Tachiiri et al., 2019), AMOC (Palter et al., 2018), ice sheets, \r\nand permafrost carbon (Sections 4.7.2 and 5.4"


id: 12c56ace044e85bf08f7a5aca8d69997
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 451
content:
"Global temperature is expected to remain approximately constant if \r\nemissions of CO2 were to cease (Section 4.7.1.1), and so reductions \r\nin GSAT are only possible in the event of net negative global CO2\r\nemissions. We assess here results from an overshoot scenario \r\n(SSP5-3.4-OS; O'Neill et al., 2016), which explores the implications \r\nof a peak and decline in forcing during the 21st century. Reversibility \r\nunder more extreme and idealized carbon dioxide removal (CDR) \r\nscenarios is assessed in Section 4.6.3. In SSP5-3.4-OS, CO2 peaks \r\nat 571 ppm in the year 2062 and reverts to 497 ppm by 2100 - \r\napproximately the same level as in 2040. SSP5-3.4-OS has strong \r\nnet negative emissions of CO2, exceeding those in SSP1-2.6 \r\nand SSP1-1.9 from 2070 onwards and reaching -5.5  PgC  yr -1\r\n(-20 GtCO2 yr -1) by 2100. While this causes global mean temperature \r\nto decline, changes in climate have not fully reversed by 2100 under \r\nthis reversal of CO2 concentration (Figure  4.34)"


id: 66c4964a0f1f56eec408cb964edcb32a
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 13764
content:
" Quantities are \r\ncompared for 2081-2100 relative to a 20-year period (2034-2053) \r\nof the same average CO2. Differences between these two periods of \r\nthe same CO2 are: GSAT: 0.28 +- 0.30degC (mean +- standard deviation); \r\nglobal land precipitation: 0.026 +- 0.011 mm day-1; September \r\nArctic sea ice area: -0.32 +- 0.53 million km2; thermosteric sea level: \r\n12 +- 0.8 cm. As assessed in Section  9.3.1.1, Arctic sea ice area \r\nis linearly reversible with GSAT. Although these climate quantities \r\nare not fully reversible, the overshoot scenario results in reduced \r\nclimate change compared with stabilisation or continued increase \r\nin greenhouse gases (Tsutsui et al., 2006; Palter et al., 2018; Tachiiri \r\net al., 2019) (high confidence)."


id: c0db7cd17d40ff0f4ed80f727d2b1b8d
created_at: 2023-03-16 14:01:59
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vector_id: 27909
content:
"The radiative forcing labels on SSP and RCP scenarios is approximate \r\nand enables the multiple climate forcings within the scenario to be \r\ncharacterized by a single number. While the scenarios are similar in \r\nterms of the stratospheric adjusted radiative forcing (Tebaldi et  al., \r\n2021), they differ more in their effective radiative forcing (ERF). The \r\ncombination of component forcings (CO2, non-CO2 greenhouse gases, \r\naerosols) within the scenario also differ (Meinshausen et  al., 2020). \r\nThe ERF levels in the RCP and SSP scenarios have been calculated \r\nby sampling uncertainty in forcing from a  range of different GHG \r\nspecies and aerosols (see 7.SM.1.4 for details). Figure  4.35 shows \r\nthe time evolution and 2081-2100 mean across the families of \r\nscenarios and how this affects projections of GSAT. That the ERFs differ \r\nbetween corresponding SSP and RCP scenarios makes a comparison \r\nbetween  CMIP6 and CMIP5 projections challenging (Tebaldi et  al., \r\n2021). Wyser et  al"


id: e5fe428819cde02c93bb5cce74c0a2a1
created_at: 2023-03-16 14:01:59
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vector_id: 33674
content:
" (2020) find the EC-Earth3-Veg model exhibits \r\nstronger radiative forcing and substantially greater warming under \r\nSSP5-8.5 than RCP8.5, and similar, but smaller additional warmings \r\nfor SSP2-4.5 and SSP1-2.6 compared with RCP4.5 and RCP2.6, \r\nrespectively. In addition to the global response, climate can vary \r\nregionally due to non-CO2 components of forcing (Samset et al., 2016; \r\nRichardson et al., 2018a, b)."


id: 1fed194b64da1b4cc41a3c09eb216e76
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 2460
content:
"Emulators (Cross-Chapter Box 7.1) can be used to aid understanding \r\nof differences between generations of scenarios. The AR5 (Collins \r\net  al., 2013) explored the differences between CMIP3 and CMIP5 \r\n(their Figure  12.40). Here we use an emulator calibrated to AR6 \r\nassessed GSAT ranges, thus eliminating the effect of differences \r\nin the model ensembles, to analyse the differences between \r\nSSP and RCP scenarios. MAGICC7.5 in its WGIII-calibrated setup \r\n(see Cross-Chapter Box 7.1) projects differences in 2081-2100 mean \r\nwarming between the RCP2.6 and SSP1-2.6 scenarios of around \r\n0.2degC, between RCP4.5 and SSP2-4.5 of around 0.3degC and between \r\nRCP8.5 and SSP5-8.5 of around 0.3degC (Figure  4.35b). The SSP \r\nscenarios also have a wider 5-95% range simulated by MAGICC7.5 \r\nexplaining about half of the increased range seen when comparing \r\nCMIP5 and CMIP6 models. Higher climate sensitivity is, though, the \r\nprimary reason behind the upper end of the warming for SSP5-8"


id: 320f4d4409a387f267ef1f0dd335167c
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 5286
content:
"5 \r\nreaching 1.5degC higher than the CMIP5 results. Compared with the \r\ndifferences between the CMIP5 and CMIP6 multi-model ensembles \r\nfor the same scenario pairs (Table A6 in Tebaldi et  al., 2021), the \r\nhigher ERFs of the SSP scenarios contribute approximately half of \r\nthe warmer CMIP6 SSP outcomes (medium confidence)."


id: 9becaf6d76ced77c61f730625e1afb99
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 22088
content:
"The transient climate response to cumulative CO2 emissions, \r\nTCRE, allows climate policy goals to be associated with remaining \r\ncarbon budgets as global temperature increase is near-linear with \r\ncumulative emissions (Section 5.5). Research since AR5 has shown \r\nthat the concept of near-linearity of climate change to cumulative \r\ncarbon emissions holds for measures other than just GSAT, such as \r\nregional climate (Leduc et al., 2016) or extremes (Harrington et al., \r\n2016; Seneviratne et  al., 2016). However, ocean heat and carbon \r\nuptake do exhibit path dependence, leading to deviation from the \r\nTCRE relationship for levels of overshoot above 300 PgC (Zickfeld \r\net al., 2016; Tokarska et al., 2019). Sea level rise, loss of ice sheets, \r\nand permafrost carbon release may not reverse under overshoot \r\nand recovery of GSAT and cumulative emissions (Section 4.7)"


id: 7e83238c902019d5d450fc5d1562d5be
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 17467
content:
" TCRE \r\nremains a valuable concept to assess climate policy goals and how \r\nto achieve them but given the non-reversibility of different climate \r\nmetrics with CO2 and GSAT reductions, it has limitations associated \r\nwith evaluating the climate response under overshoot scenarios and \r\nCO2 removal (medium confidence)."


id: 9a8dc27b77441364868985d4e5ff9d27
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 21848
content:
"Emergence in GSAT would be delayed further, owing to the inertia in \r\nthe climate system. Although not investigating emergence as defined \r\nhere in AR6, Tebaldi et al. (2021) used a 20-year running-mean GSAT \r\nand compared pairwise either model-by-model or between CM IP6 \r\nensemble means from the core set of five scenarios assessed in \r\nthis chapter. Differences by more than 0.1degC showed up in most \r\ncases in the near term,  with only some of the individual models \r\nand the comparisons of the closest scenarios showing a delay until \r\nthe mid-term. Taking internal variability explicitly into account, Tebaldi \r\nand Friedlingstein (2013) and Samset et al. (2020) found emergence \r\nof mitigation benefits in GSAT changes about 25-30 years after \r\nRCP2.6 emissions diverge from the higher-emissions trajectories \r\nin RCP4.5 and RCP8.5"


id: f22c0ede1304fc01c4c6d996c619e04e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 35634
content:
" Consistently, Marotzke (2019) found about \r\none-third likelihood that a trend reduction in GSAT, over the period \r\n2021-2035 relative to 2005-2020, would be attributable to the \r\nemissions reductions implied by the difference between RCP2.6 and \r\nRCP4.5. Emergence of the GSAT response to mitigation of individual \r\nshort-lived climate forcers (SLCFs) would likewise not occur until \r\nseveral decades after emissions trajectories diverge, owing to the \r\nrelatively small influence of individual SLCFs on the total ERF (Samset \r\net al., 2020), see also Section 4.4.4 and Figure 4.18."


id: 648acaa5d5c93a4afdfbcffeba2499fa
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 13384
content:
"In contrast to the earlier studies, emergence in GSAT within the \r\nnear-term has recently been found by McKenna et  al. (2021) who \r\ninvestigated the likelihood that under the SSP scenarios GSAT trends \r\nwill exceed the largest historical observed 20-year trends. They found \r\nthat under scenario SSP1-1.9, the 20-year GSAT trends would likely be \r\nlower than in SSP3-7.0 and SSP5-8.5 within the near term. This earlier \r\ndiagnosed time of emergence compared to Marotzke (2019), while \r\nusing a similar statistical approach, presumably arose because of the \r\nlonger-period trends (20 rather than 15 years) and the larger difference \r\nbetween emissions trajectories considered (medium confidence). \r\nUsing 20-year temperature anomalies relative to 1995-2014 instead \r\nof 20-year trends yielded a low probability of emergence (McKenna \r\net al., 2021), consistent with the AR5 (Collins et al., 2013; Kirtman \r\net al., 2013; Tebaldi and Friedlingstein, 2013; Samset et al., 2020)"


id: b1bc5ddd31d2a3dfc28a1d8dca25a5ed
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 25455
content:
" It is \r\nnot yet understood why GSAT trends appear to show faster emergence \r\nof mitigation benefits, compared to GSAT anomalies."


id: 9297da3710e5b69ae92fa3c0d47c152f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 20584
content:
"Emissions pathways that limit globally averaged warming to 1.5degC \r\nor 2degC by the year 2100 assume the use of CDR approaches in \r\ncombination with emissions reductions to follow net negative CO2\r\nemissions trajectory in the second half of this century. For instance, \r\nin SR1.5, all analysed pathways limiting warming to 1.5degC by 2100 \r\nwith no or limited overshoot include the use of CDR to some extent to \r\noffset anthropogenic CO2 emissions and the median of CO2 removal \r\nacross all scenarios was 730 GtCO2 in the 21st  century (Rickels \r\net al., 2018; Rogelj et al., 2018b). Affordable and environmentally \r\nand socially acceptable CDR options at scale well before 2050 are \r\nan important element of 1.5degC-consistent pathways especially in \r\novershoot scenarios (de Coninck et  al., 2018). The required scale \r\nof removal by CDR can vary from 1-2 GtCO2 yr -1 year from 2050 \r\nonwards to as much as 20 GtCO2 yr -1 (Waisman et  al., 2019)"


id: 4bba484b279eb6fa4eeecf2b57f475dd
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 9329
content:
" \r\nIn the SSP class of scenarios, net CO2 emissions turn negative from \r\naround 2050 in SSP1-1.9 and around 2070 in SSP1-2.6 and in the \r\novershoot scenario SSP5-3.4-OS (O'Neill et  al., 2016). Thus, CDR \r\nwould play a pivotal role in limiting climate warming to 1.5degC or \r\n2degC (Minx et al., 2018). In stark contrast, however, two extensive \r\nreviews (Lawrence et al., 2018; Nemet et al., 2018) conclude that it \r\nis implausible that any CDR technique can be implemented at the \r\nscale needed by 2050."


id: 64379d8753ef6c849b2fcce9744e86a4
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 13338
content:
"Figure  4.37 illustrates the first results from CDRMIP (Keller et  al., \r\n2018). Other studies that use similar (Zickfeld et al., 2016; Schwinger \r\nand Tjiputra, 2018; Jeltsch-Thommes et al., 2020) or other idealized \r\nscenarios (MacDougall, 2013) or more realistic net negative CO2\r\nemissions scenarios such as RCP2.6 (C.D. Jones et al., 2016b) and \r\nscenarios that limit warming to 2degC or less after different levels of \r\novershoot (Tokarska and Zickfeld, 2015) arrive at similar conclusions. \r\nChanges in key climate variables substantially lag behind the decline \r\nin CO2 (Figure 4.37). The precipitation increase at the beginning of \r\nthe ramp-down phase agrees with the increase in precipitation for an \r\nabrupt decline in CO2 (Cao et al., 2011). Notwithstanding a decline in \r\natmospheric CO2, global mean thermosteric sea level would continue \r\nto rise"


id: 71e045edfd1d25756575192bf0f5a73a
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 15545
content:
" When atmospheric CO2 returns to the piControl level, global \r\nmean thermosteric sea level is higher than its value at peak CO2\r\n(Figure 4.37), and it is likely that thermosteric global sea level would \r\nnot return to piControl levels for over 1000 years after atmospheric \r\nCO2 is restored to piControl concentrations (Tokarska and Zickfeld, \r\n2015; Ehlert and Zickfeld, 2018). Therefore, there is high confidence \r\nthat sea level rise will not be reversed by CDR at least for several \r\ncenturies Chapter  9 (Section  9.6.3.5). A  comparison of different \r\nmodels shows recovery of AMOC intensity during net negative CO2\r\nemissions, but the results are model dependent - strengthening with \r\nan overshoot in most models (Jackson et al., 2014) and strengthening \r\nbut not reaching the initial state in some models (Sgubin et al., 2015)"


id: 16e3c39b664df147e4caff6ffd4add
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 1125
content:
" \r\nThe overall lag in response is qualitatively similar to the lagged \r\nclimate system response in the overshoot scenario SSP5-34-OS where \r\nCO2 rises until 2062 and decreases thereafter (Figure 4.34). The lag \r\nin climate response to CDR causes hysteresis between key climate \r\nvariables such as temperature, precipitation, AMOC and sea level, \r\nand atmosphere CO2 with the hysteresis characteristics dependent \r\non the rate of CDR and climate sensitivity (MacDougall, 2013; Jeltsch\u0002Thommes et al., 2020)."


id: 6a0dcdbf6b96aac08621e16a707cadfa
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 14294
content:
"When CDR is applied continuously and at scales as large as currently \r\ndeemed possible, under RCP8.5 as the background scenario, the \r\nwidely discussed CDR options such as afforestation, ocean iron \r\nfertilization and surface ocean alkalinisation are individually \r\nexpected to be relatively ineffective, with limited (8%) warming \r\nreductions relative to the scenario with no CDR option (Keller et al., \r\n2014). Hence, the potential role that CDR will play in lowering \r\nthe temperature in high-emissions scenarios is limited (medium \r\nconfidence). The challenges involved in comparing the climatic \r\neffects of various CDR options has also been recognized in recent \r\nstudies (Sonntag et  al., 2018; Mengis et  al., 2019)"


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content:
" For instance, \r\ndue to compensating processes such as biogeophysical effects of \r\nafforestation (warming from albedo decrease when croplands are \r\nconverted to forests) more carbon is expected to be removed from \r\nthe atmosphere by afforestation than by ocean alkalinisation to \r\nreach the same global mean cooling."


id: 861c688f3506575621fc2e7faca6fdb1
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 18641
content:
"Termination of CDR refers to a sudden and sustained discontinuation \r\nof CDR deployment (see Section  4.6.3.3 for termination effects of \r\nSRM). The literature on the termination effects of CDR is limited, \r\nmostly considering scenarios where CDR implementation is explicit \r\nand does not result in net negative CO2 emissions (Keller et  al., \r\n2014; Gonzalez et  al., 2018). In simulations where CDR is applied \r\non the RCP8.5 scenario at scales as large as currently deemed \r\npossible, the increase in global mean warming rates following CDR \r\ntermination are relatively small in comparison to SRM termination \r\n(Keller et al., 2014). The exception is artificial ocean upwelling where \r\nsurface cooling is mainly caused by bringing cold water from the \r\ndeep ocean; upon termination this causes larger rates of surface \r\nwarming (Oschlies et al., 2010). When background emissions are as \r\nhigh as in RCP8"


id: baa6fee99ce2614176c9e218313e8ead
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 26904
content:
"5, termination of a  large global-scale application \r\nof CDR such as ocean alkalinisation for multiple decades could also \r\nresult in large regional warming rates (up to 0.15degC per year) that \r\nare comparable to those caused by termination of SRM (Gonzalez \r\net al., 2018). In such cases, large amounts of CO2 would be removed \r\nfrom the atmosphere before termination, and termination would \r\ncause a temporal trajectory of atmospheric CO2 that is parallel to the \r\nhigh-emissions scenario but from an atmosphere with much lower \r\nCO2 levels. Because CO2 radiative forcing is a logarithmic function of \r\nCO2 concentration, large regional warming rates are simulated in such \r\nterminations. Thus, there is high confidence that the climate effect of "


id: f77fc14e895950249e1966484c660e3b
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 36450
content:
"The climate response to CDR-caused net negative CO2 emissions \r\nhas been studied in Earth system models by prescribing idealized \r\nramp-down of CO2 concentrations (MacDougall, 2013; Zickfeld \r\net al., 2016; Schwinger and Tjiputra, 2018), CO2 concentrations of \r\nRCP scenarios that have net negative CO2 emissions (C.D. Jones \r\net al., 2016b), and idealized net negative CO2 emissions scenarios \r\n(Tokarska and Zickfeld, 2015). The Carbon Dioxide Removal Model \r\nIntercomparison Project (CDRMIP) uses multiple ESMs to explore \r\nthe climate response, effectiveness of CO2 removal, and challenges \r\nof CDR options (Keller et al., 2018). Idealized CDRMIP simulations \r\nincrease CO2 concentrations at 1% per year from the level in the \r\npre-industrial control run (piControl) to 4xCO2 and subsequently \r\ndecrease at the same rate to the piControl level. This section \r\nassesses the lag in climate response to CDR-caused negative \r\nemissions; climate 'reversibility' is assessed in Section  4.7.2"


id: dedef3fac6a446279ae2a195971c579e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 32562
content:
" \r\nThe  ramp-down phase, though unrealistic, represents the 'net \r\nnegative CO2 emissions' phase."


id: 64c3cf80afc7768a98f420ab7f565d4d
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 13422
content:
"The AR5 assessed the climate response to, as well as risks and side \r\neffects of, several SRM options (Boucher et al., 2013) and concluded \r\nwith high confidence that SRM, if practicable, could substantially \r\noffset a  global temperature rise and partially offset some other \r\nimpacts of global warming, but the compensation for the climate \r\nchange caused by GHGs would be imprecise. The AR5 furthermore \r\nconcluded that models consistently suggest that SRM would generally \r\nreduce climate differences compared to a world with elevated GHG \r\nconcentrations and no SRM; however, there would also be residual \r\nregional differences in climate (e.g., temperature and rainfall) when \r\ncompared to a climate without elevated GHGs"


id: e6bd4660ad010dbf3ad96889d0f525d6
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 33788
content:
" The AR5 concluded \r\nwith high confidence that scaling SRM to substantial levels would \r\ncarry the risk that if the SRM were terminated for any reason, surface \r\ntemperatures would increase rapidly (within a  decade or two) to \r\nvalues consistent with the GHG forcing (Boucher et al., 2013)."


id: 93723ad048383ac877e6551d001b4f21
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 20714
content:
"This subsection assesses the global and large-scale physical climate \r\nsystem response to SRM based on theoretical and modelling studies. \r\nThere is no mature technology today to implement any of the \r\nSRM options assessed here. A short summary of the SRM options, \r\nincluding the proposed mechanism of each SRM approach, radiative \r\nforcing potential, and key climate and environmental effects, is listed \r\nin Table 4.7. Chapter 5 (Section 5.6.3) assesses the biogeochemical \r\nimplications of SRM, Chapter 6 (Section 6.4.6) assesses the potential \r\nERF of the aerosol-based SRM options and Chapter 8 (Section 8.6.3) \r\nassesses the abrupt water cycle changes in response to initiation or \r\ntermination of SRM. The risks to human and natural systems, impacts \r\nof SRM, ethics, and perceptions are assessed in the WGII Report \r\n(Chapter  16). Governance issues associated with SRM research \r\nand deployment are assessed in the WGII and WGIII Reports"


id: b8ffa5a166f571a5391b2e50ca379470
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 26640
content:
" The \r\nassessment of technical feasibility and engineering aspects of SRM is \r\nbeyond the scope of this Report."


id: d97d3238332e86654e0bdaeb6959317c
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 31734
content:
"The climate response to SRM depends greatly on the characteristics of \r\nSRM implementation approaches. There could be substantial residual \r\nor overcompensating climate change at both the global and regional \r\nscales and seasonal time scales (Kravitz et  al., 2014; McCusker \r\net al., 2015; Irvine et al., 2016; Fasullo et al., 2018; Jiang et al., 2019; \r\nGertler et  al., 2020). This is because the climate response to SRM \r\noptions is different from the response to GHG increase (Figure 4.38). \r\nFor  instance, when global mean warming is offset by a  uniform \r\nreduction in incoming sunlight, there is residual warming in the high \r\nlatitudes and overcooling in the tropics (Kravitz et al., 2013a; Kalidindi \r\net al., 2015), and a reduction in tropical mean rainfall (Tilmes et al., \r\n2013). In simulations of stratospheric SO2 injection, SRM diminishes \r\nthe amplitude of the seasonal cycle of temperature at many high\u0002latitude locations, with warmer winters and cooler summers (Jiang \r\net  al"


id: 2a2b5f1f639526df528c8c86837cc40a
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 4059
content:
", 2019). Further, the rates of response could differ between \r\nsurface temperature and slow components in the climate system \r\nsuch as sea level rise (Irvine et  al., 2012; Jones et  al., 2018). SRM \r\nimplemented at a moderate intensity, for example by offsetting half \r\nof the global warming, has the potential to reduce negative effects \r\nsuch as reduced precipitation that are associated with fully offsetting \r\nglobal mean warming (Irvine et al., 2019; Irvine and Keith, 2020)."


id: a6820c46826951c188387a0e8eea0412
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 23725
content:
"to eight W m-2) could be achieved depending on the amount and \r\nlocation of SO2 injected into the stratosphere (Aquila et  al., 2014; \r\nPitari et al., 2014; Niemeier and Timmreck, 2015; Kravitz et al., 2017; \r\nKleinschmitt et al., 2018; Tilmes et al., 2018a). The simulated efficacy \r\nof SAI by emission of SO2 (radiative forcing per mass of injection rate) \r\ngenerally decreases with the increase in injection rate because of the \r\ngrowth of larger particles (about 0.5 microns) through condensation \r\nand coagulation reducing the mass scattering efficiency (Niemeier \r\nand Timmreck, 2015; Kleinschmitt et  al., 2018). However, efficacy \r\nchanges little for total injection rate up to about 25 Tg sulphur \r\nper year when SO2 is injected at multiple locations simultaneously \r\n(Kravitz et  al., 2017; Tilmes et  al., 2018a)"


id: 15ca660d639073f4febabeb5d381c458
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 948
content:
" Differences in model \r\nrepresentation of aerosol microphysics, evolution of particle size, \r\nstratospheric dynamics and chemistry, and aerosol microphysics-\r\nradiation-circulation interactions all contribute to the uncertainty in \r\nsimulated cooling efficiency of SAI. Compared to sulphate aerosols, \r\ninjection of non-sulphate particles would result in different cooling \r\nefficacy, but understanding is limited (Pope et al., 2012; Weisenstein \r\net al., 2015; A.C. Jones et al., 2016)."


id: 1674a0571f35e8b5ec8d9cb509b65caa
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 1060
content:
"Earlier modelling studies focused on the effect of equatorial sulphate \r\ninjection that tends to overcool the tropics and undercool the poles. \r\nCompared to equatorial injection, off-equatorial injection at multiple \r\nlocations shows a  closer resemblance to the baseline climate in \r\nmany aspects, including temperature, precipitation, and sea ice \r\ncoverage (Kravitz et  al., 2019). However, significant regional and \r\nseasonal residual and overcompensating climate change is reported, \r\nincluding regional shifts in precipitation, continued warming of \r\npolar oceans, and shifts in the seasonal cycle of snow depth and \r\nsea ice cover (Fasullo et al., 2018; Jiang et al., 2019; Simpson et al., \r\n2019b). By appropriately adjusting the amount, latitude, altitude, and \r\ntiming of the aerosol injection, modelling studies suggest that SAI is\r\nconceptually able to achieve some desired combination of radiative \r\nforcing and climate response (medium confidence) (MacMartin et al., \r\n2017; Dai et al"


id: f7f6b48ced497b9c08cb6b7c2ee71a7e
created_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 36527
content:
"Most SRM research has focused on stratospheric aerosol injection \r\n(SAI) and most SAI studies have assessed the effects of injection. Most \r\nresearch has focused on stratospheric aerosol injection (SAI): the \r\ninjection of sulphate particles or its precursor gases such as SO2, which \r\nwould then be oxidized to H2SO4. Injection of other types of aerosol \r\nparticles, such as calcite (CaCO3), titanium dioxide (TiO2), aluminium \r\noxide (Al2O3), and engineered nanoparticles has also been proposed \r\n(Keith, 2010; Ferraro et al., 2011; Pope et al., 2012; Weisenstein et al., \r\n2015; A.C. Jones et  al., 2016; Keith et  al., 2016), but are much less \r\nstudied compared to sulphate injection. The natural analogue for \r\nsulphate aerosol injection is major volcanic eruptions (Cross-Chapter \r\nBox  4.1). While volcanic eruptions are not perfect analogues for \r\nSAI (Robock et  al., 2013; Plazzotta et  al., 2018; Duan et  al"


id: d2902e939cd756b197708212a30fbd55
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 30653
content:
", 2019), \r\nstudies on climate impacts of past volcanic eruptions can inform on \r\nthe potential impact of stratospheric sulphate injection. For example, \r\nemergent constraints (Chapters 1 and 5) that relate the climate system \r\nresponse to volcanic eruptions can be used to reduce uncertainty of \r\nthe land surface temperature response to SAI (Plazzotta et al., 2018)."


id: 7aab155758500498d4dc1d855871734a
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 16877
content:
"There is large uncertainty in the stratospheric response to SAI, and \r\nthe change in stratospheric dynamics and chemistry would depend \r\non the amount, size, type, location, and timing of injection. There is \r\nhigh confidence that aerosol-induced stratospheric heating will play \r\nan important role in surface climate change (Simpson et al., 2019b) \r\nby altering the effective radiative forcing (Krishnamohan et al., 2019), \r\nlower stratosphere stability (Ferraro and Griffiths, 2016), quasi-biennial \r\noscillation (QBO) (Aquila et  al., 2014; Niemeier and Schmidt, 2017; \r\nKleinschmitt et  al., 2018), polar vortexes (Visioni et  al., 2020a), and \r\nNorth Atlantic Oscillation (Jones et  al., 2021). Model simulations \r\nindicate stronger polar jets and weaker storm tracks and a poleward \r\nshift of the tropospheric mid-latitude jets in response to stratospheric \r\nsulphate injections in the tropics (Ferraro et al., 2015; Richter Jadwiga \r\net al"


id: cb6df5c9ed2f10e6e13a7f9e0f0a9257
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 29555
content:
", 2018), as the meridional temperature gradient is increased in the \r\nlower stratosphere by the aerosol-induced heating. The aerosol-induced \r\nwarming would also offset some of the GHG-induced stratospheric \r\ncooling. Compared to equatorial injection, off-equatorial injection is \r\nlikely to result in reduced change in stratospheric heating, circulation, \r\nand QBO (Richter Jadwiga et al., 2018; Kravitz et al., 2019). Stratospheric \r\nozone response to sulphate injection is uncertain depending on the \r\namount, altitude, and location of injection (WMO,  2018). It is likely"


id: 56be86d436fb614bb8c5e66721cfc506
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 11121
content:
"that sulphate injection would cause a  reduction in polar column \r\nozone concentration and delay the recovery of Antarctic ozone hole \r\n(Pitari et al., 2014; Richter Jadwiga et al., 2018; Tilmes et al., 2018b), \r\nwhich would have implications for UV radiation and surface ozone \r\n(Pitari et al., 2014; Xia et al., 2017; Richter Jadwiga et al., 2018; Tilmes \r\net al., 2018b). Injection of non-sulphate aerosols is likely to result in less \r\nstratospheric heating and ozone loss (Pope et al., 2012; Weisenstein \r\net  al., 2015; Keith et  al., 2016). One side effect of SAI is increased \r\nsulphate deposition at surface. A  recent modelling study indicates \r\nthat to maintain global temperature at 2020 levels under RCP 8.5, \r\nincreased sulphate deposition from stratospheric sulphate injection \r\ncould be globally balanced by the projected decrease in tropospheric \r\nanthropogenic SO2 emissions, but the spatial distribution of sulphate \r\ndeposition would move from low to high latitudes (Visioni et al"


id: 48e945634704a6829106e45e4461a3f3
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 8856
content:
"Cirrus clouds trap more outgoing thermal radiation than they \r\nreflect incoming solar radiation and thus have an overall warming \r\neffect  on the climate system (Mitchell and Finnegan, 2009). The \r\naim of cirrus cloud thinning (CCT) is to reduce cirrus cloud optical \r\ndepth by increasing the heterogeneous nucleation via seeding cirrus \r\nclouds with an optimal concentration of ice nucleating particles, \r\nwhich might cause larger ice crystals and rapid fallout, resulting in \r\nreduced lifetime and coverage of cirrus clouds (Muri et  al., 2014; \r\nGasparini et al., 2017; Lohmann and Gasparini, 2017; Gruber et al., \r\n2019). CCT  aims to achieve the opposite effect of contrails that \r\nincrease cirrus cover and cause a small positive ERF (Section 7.3)"


id: 5b6d724649fba7ba332254358db92889
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 11884
content:
" \r\nA high-resolution modelling study of CCT over a limited area of the \r\nArctic suggested that cirrus seeding causes a decrease in ice crystal \r\nnumber concentration and a reduction in mixed-phase cloud cover, \r\nboth of which cause a cooling effect (Gruber et al., 2019)."


id: 70402680294ee9fc9136ac58d16c8b90
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 15278
content:
"Marine cloud brightening (MCB) involves injecting small aerosols \r\nsuch as sea salt into the base of marine stratocumulus clouds \r\nwhere the aerosols act as cloud condensation nuclei (CCN). In the \r\nabsence of other changes, an increase in CCN would produce higher \r\ncloud droplet number concentration with reduced droplet sizes, \r\nincreasing cloud albedo. Increased droplet concentration may also \r\nincrease cloud water content and optical thickness, but recent studies \r\nsuggest that liquid water path response to anthropogenic aerosols is \r\nweak due to the competing effects of suppressed precipitation and \r\nenhanced cloud water evaporation (Toll et al., 2019). An analogue \r\nfor MCB are reflective, persistent 'ship tracks' observed after the \r\npassage of a  sea-going vessel emitting combustion aerosols into \r\nsusceptible clouds (Christensen and Stephens, 2011; Chen et  al., \r\n2012; Gryspeerdt et al., 2019). A recent study (Diamond et al"


id: da42323b52253a2ecd673df2e0f49ed
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index: climate_gpt_only_giec
vector_id: 31865
content:
", 2020) \r\nfound a  substantial increase in cloud reflectivity from shipping in \r\nsouth-east Atlantic basin, suggesting that a  regional-scale test of \r\nMCB in stratocumulus-dominated regions could be successful."


id: 80889ce98669141cc770a1788f519fcb
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 17746
content:
"Under present-day climate, cirrus clouds exerts a  net positive \r\nradiative forcing of about 5 W m-2 (Gasparini and Lohmann, 2016; \r\nHong et al., 2016), indicating a maximum cooling potential of the \r\nsame magnitude if all cirrus cloud were removed from the climate \r\nsystem. However, modelling results show a  much smaller cooling \r\neffect of CCT. For the optimal ice nuclei seeding concentration and \r\nglobally non-uniform seeding strategy, a net negative cloud radiative \r\nforcing of about 1 to 2 W m-2 is achieved (Storelvmo and Herger, \r\n2014; Gasparini et  al., 2020). A  few studies find that no seeding \r\nstrategy could achieve a significant cooling effect, owing to complex \r\nmicrophysical mechanisms limiting robust climate responses to \r\ncirrus seeding (Penner et al., 2015; Gasparini and Lohmann, 2016). \r\nA higher than optimal concentration of ice nucleating particles could \r\nalso result in over-seeding that increases rather than decreases cirrus \r\noptical thickness (Storelvmo et  al"


id: 8726a5a08d3bd2d43a28f3f4a677cfb3
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index: climate_gpt_only_giec
vector_id: 18796
content:
", 2013; Gasparini and Lohmann, \r\n2016). Thus, there is low confidence in the cooling effect of CCT, due \r\nto limited understanding of cirrus microphysics, its interaction with \r\naerosols, and the complexity of seeding strategy."


id: 61ae801d31b682c3137a31af0ac36d1f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 12934
content:
"A hypothetical, sudden and sustained termination of SRM in a world \r\nwith high GHG concentrations has been simulated to cause climate \r\nrebound effects such as rapid increase in global temperature, \r\nprecipitation, and sea level, and rapid reduction in sea ice area (Jones \r\net al., 2013; McCusker et al., 2014; Crook et al., 2015; Muri et al., \r\n2018). Model simulations also show reduced precipitation over \r\nland areas in the first few years following termination, indicating \r\ngeneral drying that would exacerbate the effects of rapid warming \r\n(McCusker et al., 2014). A sudden and sustained termination of SRM \r\nis also expected to weaken carbon sinks, accelerating atmospheric \r\nCO2 accumulation and warming (Tjiputra et  al., 2016; Muri et  al., \r\n2018; Plazzotta et al., 2019). A gradual phase-out of SRM combined \r\nwith mitigation and CDR could reduce the large warming rates \r\nfrom sudden SRM termination (MacMartin et  al., 2014; Keith and \r\nMacMartin, 2015; Tilmes et al"


id: f716403ccf6a8fafdfd263a6aa1af94a
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index: climate_gpt_only_giec
vector_id: 36385
content:
", 2016), though this would be limited \r\nby how rapidly emission reductions can be scaled up (Ekholm and \r\nKorhonen, 2016)."


id: 552779636a10c3c55ea6a60baef2373d
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 10857
content:
"Internal variability could mask the response to solar radiation \r\nmodification (SRM)-related forcing in the near term (Section 4.6.3.1). \r\nA detection of the global scale climate system response to stratospheric \r\nsulphate aerosol injection will likely require a  forcing of the size \r\nproduced by the 1991 Mount Pinatubo eruption (Robock et al., 2010). \r\nIn model simulations of where 5 Tg SO2 is injected into the stratosphere \r\ncontinuously (roughly one fourth of the 1991 Pinatubo eruption per \r\nyear) under RCP 4.5, it is shown that, relative to the high-GHG world \r\nwithout SRM, the effect of SRM on global temperature and precipitation \r\nis detectable after one to two decades (Burger and Cubasch, 2015; \r\nLo et al., 2016) which is similar to the time scale for the emergence of \r\nGSAT trends due to strong mitigation (Section 4.6.3.1). The detection \r\ntime is sensitive to detection methods and filtering techniques \r\n(Lo et  al., 2016)"


id: 94d9f1fe8dbae7427327285cddbc4157
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 20931
content:
" An  analysis using GLENS simulation (MacMartin \r\net  al., 2019) compares response in temperature, precipitation, and \r\nprecipitation minus evapotranspiration (P-E) between a climate state \r\nwith GHG-induced 1.5degC global mean temperature change and that \r\nwith the same global mean temperature but under RCP4.5 emissions \r\nand a limited deployment of SO2 injection. It is found that at grid-scale, \r\ndifference in climate response between these two climate states are \r\nnot detectable by the end of this century. However, for higher emissions \r\nscenarios of the RCP8.5 and correspondingly larger SRM deployment \r\nfor maintaining the same global mean temperature change of 1.5degC, \r\nthe regional differences are detectable before the end of the century"


id: 680d8195c0dba14094cc0a1d117aeecb
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 13959
content:
" \r\nIn addition to surface temperature and precipitation, observations of \r\naerosol burden and temperature in the stratosphere via the deployment \r\nof stratospheric aerosol observing system might facilitate the detection \r\nof climate response to SAI."


id: 387db06ca1c162293380b6323f316a7e
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updated_at: 2023-03-16 14:01:59
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vector_id: 6267
content:
"This subsection assesses changes in global climate indices out to \r\n2300 using extensions of the SSP scenarios (Meinshausen et al., 2020) \r\nand literature based on extensions to the RCP scenarios from CMIP5 \r\n(Meinshausen et al., 2011), which differ from the SSPs despite similar \r\nlabelling of global radiative forcing levels (Section 4.6.2). Meinshausen \r\net al. (2020) describe the extensions to the SSP scenarios, which differ \r\nslightly from the ScenarioMIP documentation (O'Neill et al., 2016). \r\nA simplified approach across scenarios reduces emissions such that \r\nafter 2100, land-use CO2 emissions are reduced to zero by 2150; any \r\nnet negative fossil CO2 emissions are reduced to zero by 2200, and \r\npositive fossil CO2 emissions are reduced to zero by 2250. Non-CO2\r\nfossil fuel emissions are also reduced to zero by 2250 while land\u0002use-related non-CO2 emissions are held constant at 2100 levels"


id: cf3875b311942f5349a294d836e6dd0b
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 30140
content:
" The \r\nextensions are created up to the year 2500, but ESM simulations \r\nhave only been requested, as part of the CMIP6 protocol, to run \r\nto 2300. As a  result, unlike the RCP8.5 extension, SSP5-8.5 sees \r\na decline in CO2 concentration after 2250, but the radiative forcing \r\nlevel is similar, reaching approximately 12 W m-2 during most of the \r\nextension. Both SSP1-2.6 and SSP5-3.4-OS decrease radiative forcing \r\nafter 2100. SSP5-3.4-OS is designed to return to the same level of \r\nforcing as SSP1-2.6 during the first half of the 22nd century. Because \r\nrelatively few CMIP6 ESMs have submitted results beyond 2100, \r\nGSAT projections using the MAGICC7 emulator (see Cross-Chapter \r\nBox 7.1) are also shown here."


id: 7cc957474a12074b99c326c779167533
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 17231
content:
"Here we draw on new simulations to provide an assessment of ZEC \r\nusing multiple ESMs (Jones et  al., 2019) and EMICs (MacDougall \r\net al., 2020). Figure 4.39 shows results from 20 models that simulate \r\nthe evolution of CO2 and the GSAT response following cessation of \r\nCO2 emissions for an experiment where 1000 PgC is emitted during \r\na 1% per year CO2 increase. All simulations show a strong reduction in \r\natmospheric CO2 concentration following cessation of CO2 emissions \r\nin agreement with previous studies and basic theory that natural \r\ncarbon sinks will persist. Therefore, there is very high confidence that \r\natmospheric CO2 concentrations would decline for decades if CO2\r\nemissions cease. Temperature evolution in the 100 years following \r\ncessation of emissions varies by model and across time scales, with \r\nsome models showing declining temperature, others having ZEC close \r\nto zero, and others showing continued warming following cessation \r\nof emissions (Figure  4.39)"


id: e43aeb98545504fbb2a699d0554c27d2
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 33386
content:
" The GSAT response depends on the \r\nbalance of carbon sinks and ocean heat uptake (MacDougall et al., \r\n2020). The 20-year average GSAT change 50 years after the cessation \r\nof emissions (ZEC50) is summarized in Table 4.8. The mean value of \r\nZEC50 is -0.079degC, with 5-95% range -0.34degC-0.28degC. There is no \r\nstrong relationship between ZEC50 and modelled climate sensitivity "


id: 8df7e1f4a34c300afefccd2df74664d9
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 19866
content:
"Both CMIP6 and CMIP5 results show that global temperature beyond \r\n2100 is strongly dependent on scenario, and the difference in GSAT \r\nprojections between high- and low-emissions scenarios continues to \r\nincrease (high confidence). Under the extended RCP2.6 (Caesar et al., \r\n2013) and SSP1-2.6 scenarios, where CO2 concentration and radiative \r\nforcing continue to decline beyond 2100, GSAT stabilizes during the \r\n21st century before decreasing and remaining below 2degC until 2300, \r\nexcept in some of the very high climate-sensitivity ESMs, which \r\nproject GSAT to stay above 2degC by 2300 (Figure 4.40). Under RCP8.5, \r\nregional temperature changes above 20degC have been reported in \r\nmultiple models over high-latitude land areas (Caesar et al., 2013; \r\nRanderson et  al., 2015). Non-CO2 forcing and feedbacks remain \r\nimportant by 2300 (high confidence). Randerson et al. (2015) found \r\nthat 1.6degC of warming by 2300 came from non-CO2 forcing alone \r\nin RCP8.5, and Rind et  al"


id: eac09fe347b9b311c93817e83895b0cc
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 34435
content:
"Similar to AR5 and SROCC, AR6 defines an abrupt climate change as \r\na large-scale abrupt change in the climate system that takes place \r\nover a few decades or less, persists (or is anticipated to persist) for at \r\nleast a few decades and causes substantial impacts in human and/or \r\nnatural systems (Glossary). Further, AR6 considers such a perturbed \r\nstate of a  dynamical system as irreversible on a  given time scale, \r\nif the recovery time scale from this state due to natural processes \r\ntakes substantially longer than the time scale of interest (Glossary). \r\nThe AR6 adopts the related definition of a tipping point as a critical \r\nthreshold beyond which a system reorganizes, often abruptly and/\r\nor irreversibly, and a tipping element as a component of the Earth \r\nsystem that is susceptible to a tipping point (Glossary)"


id: b7906dc671b84a035923367dc051b167
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
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vector_id: 26407
content:
" Tipping points \r\nmay involve global or regional climate changes from one stable state \r\nto another stable state or to changes that occur faster than the rate \r\nof change of forcing (Alley et al., 2003) and include shifts from one \r\nequilibrium state to another and other responses of the climate system \r\nto external forcing (Section  1.2.4.2). While reversibility has been \r\ndefined alternatively in the literature with respect to the response \r\nspecifically to idealized CO2 forcing and generally GSAT change, AR6 \r\nconsiders both definitions synonymous, because it has been widely \r\ndemonstrated that the GSAT change is reversible in models with \r\nrespect to CO2 with a several-year lag (Boucher et al., 2012)."


id: 3b5030b985daba9bfbfc91585b45f959
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 6686
content:
"Global land precipitation will continue to increase in line with GSAT \r\nunder high emissions scenarios (medium confidence). Precipitation \r\nchanges over land show larger variability and a  less clear signal \r\nthan global total precipitation. Caesar et  al. (2013) showed that \r\nunder the CMIP5 extension simulations, HadGEM2-ES projected \r\nglobal land precipitation to remain roughly the same in RCP2.6, to \r\nincrease by about 4% in RCP4.5 and to increase by about 7% in \r\nRCP8.5. Their results showed global precipitation increasing linearly \r\nwith temperature while radiative forcing increases, but then more \r\nquickly if forcing is stabilized or reduced. This backs up findings of an \r\nintensification of the hydrological cycle following CO2 decrease which \r\nhas been attributed to a build-up of ocean heat (Wu et al., 2010), and \r\nto a fast atmospheric adjustment to CO2 radiative forcing (Cao et al., \r\n2011). Figure  4"


id: 1fec0e98997548e3496751f2467f9679
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
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vector_id: 2459
content:
"40 shows that global land precipitation increases \r\nin CMIP6 models until 2300 for SSP5-8.5 but stabilizes in SSP1-2.6 \r\nand SSP5-3.4-OS. SSP1-2.6 and SSP5-3.4-OS are not distinguishable \r\nin behaviour of projected global land precipitation after 2100."


id: 8a613cd050c3cbf78c646746d58c9374
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 19309
content:
"assessment of reversibility of Earth system components. Preliminary \r\nresults from CDRMIP are presented in Section  4.6.3. Results from \r\nthe SSP5-3.4-Overshoot scenario and other quantities of climate \r\nchange at the same CO2 level before and after overshoot are \r\nassessed in Section  4.6.2. Forcing reversal is followed by reversal \r\nof ocean surface and land temperature along with land and ocean \r\nprecipitation, snow cover, and Arctic sea ice with a lag of a few years \r\nto decades (Table 4.10). Other tipping elements have much longer \r\ntime scales of reversibility from decades to millennia. Drijfhout et al. \r\n(2015) provided an assessment of 13 regional mechanisms of abrupt \r\nchange, finding abrupt changes in sea ice, oceanic flows, land ice, \r\nand terrestrial ecosystem response, although with little consistency \r\namong the models"


id: 323f773cc1d905dfadc1118da3514a12
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 5317
content:
" The potential for abrupt changes in ice sheets, \r\nthe AMOC, tropical forests, and ecosystem responses to ocean \r\nacidification were also recently reviewed by (Good et  al., 2018). "


id: d325251001d0256a0b84b14ee60c9f17
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
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vector_id: 30744
content:
"edges (Bathiany et al., 2020) has also been clarified, as has been \r\nthe importance of distinguishing summer from winter mechanisms \r\nand associated abruptness, because ice area reduces gradually in \r\nsummer, but not necessarily in winter (Bathiany et al., 2016). For \r\nother tipping elements including AMOC (Section  9.2.3.1), mixed \r\nlayer depth (Section 9.2.1.3), and sea level rise (Section 9.6.3.5), \r\nan increase in the diversity of model structure and sensitivity to \r\nmultiple factors has led to a better understanding of the complexity \r\nof the problem, with some increase in assessed uncertainty and an \r\nassessed deep uncertainty (Glossary) related to projected sea level \r\nrise with global warming levels above 3degC (Section 9.6.3.5). In still \r\nother cases such as Antarctic sea ice (Section 9.3.2) and Southern \r\nOcean Meridional Overturning Circulation (MOC; Section 9.2.3.1), \r\nuncertainty remains high"


id: 3d122046bfb4352ca3821516bad53b4d
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 6970
content:
" Finally, it has also been postulated that \r\nmodels may be prone to being too stable (Valdes, 2011) based on \r\nthe limitations of models as well as other lines of evidence such \r\npaleo-evidence of abrupt events (Dakos et  al., 2008; Klus et  al., \r\n2018; Sime et al., 2019)."


id: fa5ad758c96768ea904ff1143823d40f
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 36905
content:
"This section adopts an approach suggested in Sutton (2018). Since \r\nchanges in temperature and precipitation tend to increase with \r\nthe level of warming (Section 4.6.1), low-likelihood, high-warming \r\nstorylines are here illustrated for a level of warming consistent with \r\nthe upper bound of the assessed very likely range (Section 4.3.4) and \r\nfor a level of warming above the very likely range. ECS and TCR are \r\nthe dominant sources of uncertainty in projections of future warming \r\nunder moderate to strong emissions scenarios (Section 7.5.7)"


id: bbca44bedba30b696e599e6dfd5c0ab4
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 27101
content:
" Thus, \r\na very high level of warming may occur if ECS and TCR are close to \r\nor above the upper bound of the assessed very likely range, which, \r\nto agree with historical trends, would require a  strong historical \r\naerosol cooling and/or strong SST pattern effects, combined with \r\nstrong positive cloud feedback and substantial biases in paleoclimate \r\ntemperature reconstructions, each of which are assessed as either \r\nunlikely or very unlikely, though not ruled out (Section 7.5.5)."


id: 32e3610892c8b70d50b90f40d799001
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
index: climate_gpt_only_giec
vector_id: 5425
content:
"For SSP1-2.6, the warming consistent with the upper bound of the \r\nassessed very likely range corresponds to a  warming of 1.5degC in \r\n2081-2100 relative to 1995-2014 and 2.4degC relative to 1850-1900 \r\n(Section 4.3.4), a warming well above the 2degC warming level even in \r\nSSP1-2.6. Based on different lines of evidence, Figure 4.41 illustrates \r\nby how much such a  low-likelihood, high-warming storyline \r\nexceeds the warming pattern consistent with the assessed best \r\nestimate GSAT warming of 0.9degC relative to 1995-2014. The  first \r\nestimate  (Figure  4.41, second row) is based on the assumption \r\nthat the multi-model mean temperature pattern scales linearly with \r\nglobal mean warming. While linear scaling provides an appropriate \r\napproximation for changes in temperatures patterns at lower levels \r\nof warming (Section 4.2.4), this assumption cannot easily be tested \r\nfor an extrapolation to higher levels of warming. Thus, a  second \r\nestimate (Figure 4"


id: 491e5a5b634413934a9081b0b9b9fcb2
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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index: climate_gpt_only_giec
vector_id: 8893
content:
"41, third row) is based on the average of the five \r\nmodels that simulate a GSAT warming most consistent with the upper \r\nbound of the assessed very likely range (Section 4.3.4 and Box 4.1; \r\nnote some of the models share components). The two estimates \r\nfor the annual mean temperature pattern for a  low-likelihood, \r\nhigh-warming storyline consistently show a  warming pattern that \r\nsubstantially exceeds the best estimate warming pattern in most \r\nregions except around the North Atlantic and the parts of the Arctic. \r\nPattern scaling suggests more than 50% warming above the best \r\nestimate, with 2degC-3degC warming over much of Eurasia and North \r\nAmerica and more than 4degC warming relative to 1995-2014 over the \r\nArctic (Figure 4.41c)"


id: 97446106f0f381ccced91f6b23634a1e
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
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vector_id: 21319
content:
" The other approach based on five models shows \r\nless warming than the best estimate and even larger area of cooling \r\nin the North Atlantic but more warming than the best estimate over \r\nmuch of the tropical Pacific, Atlantic, around Antarctica and other the \r\nland regions (Figure 4.41e)."


id: 1ed052661cc95a9cc16894845371cfbc
created_at: 2023-03-16 14:01:59
updated_at: 2023-03-16 14:01:59
content_type: text
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vector_id: 2291
content:
"Figure 4.41 | High-warming storylines for changes in annual mean temperature. (a, b) Changes in 2081-2100 relative to 1995-2014 consistent with the assessed \r\nbest global surface air temperature (GSAT) estimate (0.9degC and 3.5degC relative to 1995-2014 for SSP1-2.6 and SSP5-8.5, respectively). The CMIP6 multi-model mean is linearly \r\npattern-scaled to the best GSAT estimate. (c-h) Annual mean warming above the best estimate, relative to panels (a) and (b) respectively; note the different colour bar in a high \r\nand very high-warming storyline for 2081-2100. (c, d) Multi-model mean warming pattern scaled to very high GSAT level corresponding to the upper bound of the assessed \r\nvery likely range (4.8degC for SSP5-8.5 and 1.5degC for SSP1-2.6; see Section 4.3.4). (e, f) Average of five models with high GSAT warming nearest to the upper estimate of the very \r\nlikely range (CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR, EC-Earth3 for SSP1-2"


id: fef49a4e4f1afab7388672872308e6c9
created_at: 2023-03-16 14:02:00
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vector_id: 37660
content:
"6 and ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-\r\n1-HRfor SSP5-8.5); (g, h) Average of four and five models, respectively (ACCESS-CM2, HadGEM3-GC31-LL, HadGEM3-GC31-MM, UKESM1-0-LL for SSP1-2.6 and CanESM5, \r\nCanESM5-CanOE, HadGEM3-GC31-LL: HadGEM3-GC31-MM, UKESM1-0-LL for SSP5-8.5) projecting very high GSAT warming exceeding the very likely range. Further details \r\non data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: 1109aec9a9647c0dfca4f5f2d572a7cc
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 160
content:
"Different alternative estimates of changes in annual mean precipitation \r\npatterns consistent with high-warming levels are compared here. \r\nThe first estimate (Figure 4.42b) is based on a linear pattern scaling of \r\nthe multi-model mean precipitation pattern for SSP5-8.5 (Figure 4.42a) \r\nto be consistent with the upper bound of the assessed very likely\r\nGSAT range (see above). This estimate is reasonably consistent with \r\nthe average response of the five models with GSAT warming most \r\nconsistent with the upper bound of the very likely warming range \r\n(Figure 4.42c) except for Australia. Both estimates show about 30-40% \r\nlarger changes in annual mean precipitation than the response pattern \r\nconsistent with the best GSAT estimate. In a high-warming storyline, \r\nwidespread increases of more than 30% occur in many regions north \r\nof 50degN and over parts of the tropics"


id: be77b5f80bd4e1bc354ea5cc59a06f0
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 27515
content:
" Around the Mediterranean and \r\nother parts of the subtropics, a high-warming storyline is associated \r\nwith a  reduction in annual mean precipitation of more than 30% \r\ndepending on the season."


id: 2eda76595aa40ef48be0c3ee062c0554
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 4803
content:
"While individual models project even stronger warming over \r\nextratropical land regions (Figure 4.41 bottom row), their projected \r\nGSAT warming exceeds the assessed very likely 5-95% range and \r\nthus correspond to an extremely unlikely (below 5% likelihood) \r\nstoryline. While all the models consistent with such a storyline tend \r\nto overestimate the observed warming trend over the historical \r\nperiod (Brunner et al., 2020; Liang et al., 2020; Nijsse et al., 2020; \r\nTokarska et al., 2020; Ribes et al., 2021), some of them show a good \r\nrepresentation of several aspects of the present-day climate (Andrews \r\net al., 2019; Sellar et al., 2019; Swart et al., 2019). Such a very high\u0002warming storyline implies widespread warming of more than 1.5degC \r\nand 3degC above the best-estimate warming pattern under SSP1-2.6 \r\nand SSP5-8.5, respectively. Under SSP1-2"


id: 69028d238df238d0fa04c0ff74db23e2
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 14115
content:
"6, this corresponds to \r\nmore than 3degC warming relative to 1995-2014 over land regions in \r\nthe northern mid- to high latitudes and more than 6degC in the Arctic \r\n(Figure 4.41g). Under SSP5-8.5, such a very high-warming storyline \r\nimplies more than 8degC warming over parts of Amazonia and more \r\nthan 6degC over most other tropical land regions (Figure 4.41h)."


id: b364b54b4d6e338b6db6c56e02c9fc75
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 25731
content:
"Both the multi-model mean and the pattern-scaled responses show \r\na smoother pattern than in individual simulations (Tebaldi and Knutti, \r\n2007; Knutti et al., 2010), because the multi-model mean filters out \r\ninternal variability and because model differences in the location \r\nof the largest change tend to cancel. Individual model simulations \r\nshow opposing signs in precipitation change such as over parts of \r\nAustralia, the west coast of North America, parts of West Africa and \r\nIndia (Figure 4.42d), which tend to offset in the multi-model mean \r\nresponse. The spatial probability distribution of precipitation changes \r\nshows that areas of strong precipitation increase or decrease occur \r\nin all models (Figure  4.42g, see also Section  4.6.1). However, due \r\nto the spatial smoothing, the multi-model mean response shows \r\na lower area fraction of drying than most of the individual models \r\n(Tebaldi and Knutti, 2007; Knutti et al., 2010)"


id: 6dc8c241d960638027bbbde5e7ea4359
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 14881
content:
" The five models with \r\nGSAT warming consistent with a high-warming storyline and the two \r\nmodels projecting GSAT warming exceeding the very likely GSAT \r\nwarming range show a  much larger area fraction of drying and \r\nsomewhat larger fraction of strong precipitation increases than the \r\nmulti-model mean (Figure 4.42b-d)."


id: 44d06c265645f4665003cc416d3bae93
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 8246
content:
"Figure 4.42 (continued): (a) Estimates for annual mean precipitation changes in 2081-2100, relative to 1995-2014, consistent with the best global surface air temperature \r\n(GSAT) estimate derived by linearly scaling the CMIP6 multi-model mean changes to a GSAT change of 3.5degC. (b, c) Estimates for annual mean precipitation changes in \r\n2081-2100 relative 1995-2014 in a storyline representing a physically plausible high-global-warming level. (b) Multi-model-mean precipitation scaled to high-global-warming \r\nlevel (corresponding to 4.8degC, the upper bound of the very likely range; see Section 4.3.4). (c) Average of five models with GSAT warming nearest to the high level of \r\nwarming (ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR) (d) Annual mean precipitation changes in four of the five individual model simulations \r\naveraged in (c). (e, f) Local upper estimate (95% quantile across models) and lower estimate (5% quantile across models) at each grid point"


id: c878d40b9e9781b67f48031bee24101b
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 29105
content:
" Information at individual grid \r\npoints comes from different model simulations and illustrates local uncertainty range but should not be interpreted as a pattern. (g) Area fraction of changes in annual mean \r\nprecipitation 2081-2100 relative to 1995-2014 for (i) all CMIP6 model simulations (thin black lines), (ii) models shown in (c) (red lines), and (iii) models showing very high \r\nwarming above the models shown in (c) (dark red lines). The grey range illustrates the 5-95% range across CMIP6 models and the solid black line the area fraction of the \r\nmulti-model mean pattern shown in (a). Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: 9887c01416381fa731f93f58b813ad00
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 21511
content:
"However, these expected trends will be overlain by natural climate variability (see FAQ 3.2). First, a major volcanic \r\neruption might occur, such as the 1991 eruption of Mt. Pinatubo on the Philippines; such an eruption might cause \r\na global surface cooling of a few tenths of a degree Celsius lasting several years. Second, both atmosphere and \r\nocean show variations that occur spontaneously, without any external influence. These variations range from \r\nlocalized weather systems to continent- and ocean-wide patterns and oscillations that change over months, years, \r\nor decades. Over a period of twenty years, natural climate variability strongly influences many climate quantities, \r\nwhen compared to the response to the increase in greenhouse gas concentrations from human activities. The effect \r\nof natural variability is illustrated by the very different trajectories that individual black, red or blue lines can \r\ntake in FAQ 4.1, Figure 1"


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vector_id: 33916
content:
" Whether natural variability would amplify or attenuate the human influence cannot \r\ngenerally be predicted out to twenty years into the future. Natural climate variability over the next twenty years \r\nthus constitutes an uncertainty that at best can be quantified accurately but that cannot be reduced."


id: 28c17dea099377371b7ffd373d3ebb1b
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 3843
content:
"The time needed to detect the effect of emissions reductions is illustrated by comparing low- and high-emissions \r\nscenarios (FAQ 4.2, Figure 1). In the low-emissions scenario (SSP1-2.6), CO2 emissions level off after 2015 and begin \r\nto fall in 2020, while they keep increasing throughout the 21st century in the high-emissions scenario (SSP3-7.0). \r\nThe uncertainty arising from natural internal variability in the climate system is represented by simulating each \r\nscenario ten times with the same climate model but starting from slightly different initial states back in 1850 \r\n(thin lines). For each scenario, the differences between individual simulations are caused entirely by simulated \r\nnatural internal variability. The average of all simulations represents the climate response expected for a given \r\nscenario"


id: 67d6ad4aa23c2e88d0d3ecac75c0938f
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 13923
content:
" The climate history that would actually unfold under each scenario would consist of this expected \r\nresponse combined with the contribution from natural internal variability and the contribution from potential \r\nfuture volcanic eruptions (the latter effect is not represented here)."


id: 4fa2e6960aed1760b7ee57669ad912c4
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 9961
content:
"Georgii A. Alexandrov (Russian Federation), Govindasamy Bala (India/United States of America), \r\nLaurent Bopp (France), Lena Boysen (Germany), Long Cao (China), Naveen Chandra (Japan/\r\nIndia), Philippe Ciais (France), Sergey N.  Denisov (Russian Federation), Frank J.  Dentener (EU, \r\nThe Netherlands), Herve Douville (France), Amanda Fay (United States of America), Piers Forster \r\n(United Kingdom), Baylor Fox-Kemper (United States of America), Pierre  Friedlingstein \r\n(United  Kingdom), Weiwei Fu (United States of America/China), Sabine Fuss (Germany), \r\nVeronique  Garcon (France), Bettina Gier (Germany), Nathan P.  Gillett (Canada), Luke Gregor \r\n(Switzerland/South Africa), Karsten Haustein (United Kingdom/Germany), Vanessa Haverd \r\n(Australia), Jian He (United States of America/China), Helene T.  Hewitt (United Kingdom), \r\nForrest  M.  Hoffman (United States of America), Tatiana Ilyina (Germany), Robert  B"


id: 4c7fbe961501110e640e665c381852b6
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 9454
content:
"  Jackson \r\n(United  States of America), Christopher  Jones (United Kingdom), David P.  Keller (Germany/\r\nUnited  States of America), Lester Kwiatkowski (France/United Kingdom), Robin D.  Lamboll \r\n(United Kingdom/United States of America, United Kingdom), Xin Lan (United States of America/\r\nChina), Charlotte  Laufkotter (Switzerland/Germany), Corinne Le Quere (United  Kingdom), \r\nAndrew  Lenton (Australia), Jared Lewis (Australia/New Zealand), Spencer  Liddicoat \r\n(United  Kingdom), Laura  Lorenzoni (United States of America/Venezuela), Nicole Lovenduski \r\n(United States of  America), Andrew  H.  MacDougall (Canada), Sabine Mathesius (Canada/\r\nGermany), H.  Damon Matthews (Canada), Malte  Meinshausen (Australia/Germany), \r\nIgor I. Mokhov (Russian Federation), Vaishali Naik (United States of America), Zebedee R. J"


id: 2f1fca8df22a7a1571c894f37dd714af
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 4842
content:
"It is unequivocal that the increases in atmospheric carbon dioxide \r\n(CO2), methane (CH4) and nitrous oxide (N2O) since the pre-industrial \r\nperiod are caused by human activities. The accumulation of GHGs in \r\nthe atmosphere is determined by the balance between anthropogenic \r\nemissions, anthropogenic removals, and physical-biogeochemical \r\nsource and sink dynamics on land and in the ocean. This chapter \r\nassesses how physical and biogeochemical processes of the carbon \r\nand nitrogen cycles affect the variability and trends  of GHGs in \r\nthe atmosphere as well as ocean acidification and deoxygenation. \r\nIt identifies physical and biogeochemical feedbacks that have affected \r\n(or could affect) future rates of GHG accumulation in the atmosphere, \r\nand therefore, influence climate change and its impacts"


id: cff03cea06b1ef0056e21fa496d58765
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 30238
content:
" This chapter \r\nalso assesses the remaining carbon budget to limit global warming \r\nwithin various goals, as well as the large-scale consequences of \r\ncarbon dioxide removal (CDR) and solar radiation modification (SRM) \r\non biogeochemical cycles. {Figures 5.1, 5.2}."


id: e6a8be7587690865f90e4a11c570012b
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 33772
content:
"Global mean concentrations for well-mixed GHGs (CO2, \r\nCH4  and  N2O) in 2019 correspond to increases of about \r\n47%, 156%, and 23%, respectively, above the levels in 1750 \r\n(representative of the pre-industrial era) (high confidence).\r\nCurrent atmospheric concentrations of the three GHGs are higher \r\nthan at any point in the last 800,000  years, and in 2019 reached \r\n409.9 parts per million (ppm) of CO2, 1866.3 parts per billion (ppb) \r\nof CH4, and 332.1 ppb of N2O (very high confidence). Current CO2\r\nconcentrations in the atmosphere are also unprecedented in the last \r\n2 million years (high confidence)"


id: 30bf7795398de76c8aaa8f2451888c9f
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 5091
content:
" In the past 60 million years, there \r\nhave been periods in Earth's history when CO2 concentrations were \r\nsignificantly higher than at present, but multiple lines of evidence \r\nshow that the rate at which CO2 has increased in the atmosphere \r\nduring 1900-2019 is at least 10 times faster than at any other \r\ntime during the last 800,000 years (high confidence), and 4-5 times \r\nfaster than during the last 56 million years (low confidence). {5.1.1, \r\n2.2.3; Figures 5.3, 5.4; Cross-Chapter Box 2.1}"


id: 76f9b4cb930f295d4e726e207939ad60
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 16294
content:
"Ocean acidification is strengthening as a result of the ocean \r\ncontinuing to take up CO2 from human-caused emissions (very \r\nhigh confidence). This CO2 uptake is driving changes in seawater \r\nchemistry that result in the decrease of pH and associated reductions \r\nin the saturation state of calcium carbonate, which is a constituent \r\nof skeletons or shells of a variety of marine organisms. These trends of \r\nocean acidification are becoming clearer globally, with a very likely\r\nrate of decrease in pH in the ocean surface layer of 0.016 to 0.020 per \r\ndecade in the subtropics and 0.002 to 0.026 per decade in subpolar \r\nand polar zones since the 1980s. Ocean acidification has spread \r\ndeeper in the ocean, surpassing 2000 m depth in the northern North \r\nAtlantic and in the Southern Ocean"


id: f677e25324ba4f102deb74e98e55ba49
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 36289
content:
" The greater projected pH declines \r\nin Coupled Model Intercomparison Project Phase 6 (CMIP6) models \r\nare primarily a consequence of higher atmospheric CO2 concentrations \r\nin the Shared Socio-economic Pathways (SSPs) scenarios than \r\ntheir Coupled Model Intercomparison Project Phase  5  (CMIP5) \r\nRepresentative Concentration Pathway (RCP) analogues. {5.3.2.2, \r\n5.3.3.1; 5.3.4.1; Figures 5.20, 5.21} "


id: d9df2ba57aa49932055fba7f4ff16219
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 31795
content:
"Beyond 2100, land and ocean may transition from being \r\na  carbon sink to a  source under either very high emissions \r\nor net negative emissions scenarios, but for different reasons. \r\nUnder very high emissions scenarios such as SSP5-8.5, ecosystem \r\ncarbon losses due to warming lead the land to transition from \r\na carbon sink to a source (medium confidence), while the ocean is \r\nexpected to remain a sink (high confidence). For scenarios in which \r\nCO2 concentration stabilizes, land and ocean carbon sinks gradually \r\ntake up less carbon as the increase in atmospheric CO2 slows down. \r\nIn scenarios with moderate net negative CO2 emissions, and CO2\r\nconcentrations declining during the 21st century (e.g., SSP1-2.6), the \r\nland sink transitions to a net source in decades to a few centuries \r\nafter CO2 emissions become net negative, while the ocean remains \r\na sink (low confidence). Under scenarios with large net negative CO2\r\nemissions and rapidly declining CO2 concentrations (e.g., SSP5-3"


id: 2a78860e10bf06156f9f20333b5bb064
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 4111
content:
"4-OS \r\n(overshoot)), both land and ocean switch from a sink to a transient \r\nsource during the overshoot period (medium confidence). {5.4.10, \r\n5.6.2.1.2; Figures 5.30, 5.33}"


id: 89dbfc8259bb4dfd28d49cb89914993
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 19217
content:
"Several factors affect the precise value of remaining carbon \r\nbudgets, including estimates of historical warming, future \r\nemissions from thawing permafrost, and variations in \r\nprojected non-CO2 warming. Remaining carbon budget estimates \r\ncan increase or decrease by 150 PgC (likely range; 150 PgC equals \r\n550 GtCO2) due to uncertainties in the level of historical warming, \r\nand by an additional +-  60  PgC (+-220 GtCO, likely range) due to \r\ngeophysical uncertainties surrounding the climate response to non\u0002CO2 emissions such as CH4, N2O, and aerosols. Permafrost thaw is \r\nincluded in the estimates, together with other feedbacks that are \r\noften not captured by models"


id: 2fe903941bd4efbe1a222ff814f7d28d
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 4965
content:
" Despite the large uncertainties \r\nsurrounding the quantification of the effects of additional Earth \r\nsystem feedback processes, such as emissions from wetlands and \r\npermafrost thaw, these feedbacks represent identified additional \r\namplifying risk factors that scale with additional warming and mostly \r\nincrease the challenge of limiting warming to specific temperature \r\nthresholds. These uncertainties do not change the basic conclusion \r\nthat global CO2 emissions would need to decline to at least net zero \r\nto halt global warming. {5.4, 5.5.2} "


id: 35bbb966f49bcc6e74825085133adee2
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 5863
content:
"There is a  near-linear relationship between cumulative \r\nCO2 emissions and the increase in global mean surface air \r\ntemperature (GSAT) caused by CO2 over the course of this \r\ncentury for global warming levels up to at least 2degC relative to \r\npre-industrial (high confidence). Halting global warming would \r\nthus require global net anthropogenic CO2 emissions to become zero. \r\nThe ratio between cumulative CO2 emissions and the consequent \r\nGSAT increase, which is called the transient climate response to \r\ncumulative emissions of CO2 (TCRE), likely falls in the 1.0degC-2.3degC \r\nper 1000 PgC range. The narrower range compared to the IPCC Fifth \r\nAssessment Report (AR5) is due to a better integration of evidence \r\nacross the science in this assessment"


id: 6eb4ea7aaea52c5ef4dcf58e1f6efba5
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 15042
content:
" Beyond this century, there \r\nis low confidence that the TCRE remains an accurate predictor of \r\ntemperature changes in scenarios of very low or net negative CO2 \r\nemissions because of uncertain Earth system feedbacks that can \r\nresult in further warming or a  path-dependency of warming as \r\na function of cumulative CO2 emissions. {5.4, 5.5.1}"


id: e292e313ffc557ff29fb2795085b5803
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 33155
content:
"the water cycle, emissions of CH4 and N2O, ocean acidification \r\nand marine ecosystem productivity (high confidence). These side \r\neffects and associated Earth system feedbacks can decrease carbon \r\nuptake and/or change local and regional climate, and in turn limit \r\nthe CO2 sequestration and cooling potential of specific CDR methods \r\n(medium confidence). Deployment of CDR, particularly on land, \r\ncan also affect water quality and quantity, food production and \r\nbiodiversity, with consequences for the achievement of related \r\nsustainable development goals (high confidence). These effects \r\nare often highly dependent on local context, management regime, \r\nprior land use, and scale of deployment (high confidence). A wide \r\nrange of co-benefits are obtained with methods that seek to restore \r\nnatural ecosystems or improve soil carbon (high confidence). The \r\nbiogeochemical effects of terminating CDR are expected to be small \r\nfor most CDR methods (medium confidence). {5.6.2.2; Figure 5"


id: 44a7d0f4ead7a9ef6f7d5998921947c4
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 8225
content:
"Changes in the abundance of well-mixed GHGs -  carbon dioxide \r\n(CO2), methane (CH4) and nitrous oxide (N2O) - in the atmosphere \r\nplay a large role in determining the Earth's radiative properties and \r\nits climate in the past, the present and the future (Chapters  2, 4, \r\n6 and 7). Since 1950, the increase in atmospheric GHGs has been the \r\ndominant cause of the human-induced climate change (Section 3.3). \r\nWhile the main driver of changes in atmospheric GHGs over the \r\npast 200 years relates to the direct emissions from human activities, \r\nthe net accumulation of GHGs in the atmosphere is controlled by \r\nbiogeochemical source-sink dynamics of carbon that exchange \r\nbetween multiple reservoirs on land, oceans and atmosphere. \r\nThe combustion of fossil fuels and land-use change for the period \r\n1750-2019 released an estimated 700 +- 75 PgC (1 PgC = 1015 g of \r\ncarbon) into the atmosphere, of which less than half remains in the \r\natmosphere today (Sections  5.2.1.2; 5.2.1"


id: 96db0f79d4eff492f3fca772fb95b2c6
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 21252
content:
"feedbacks through radiative forcing (Figure  5.2) (Friedlingstein \r\net al., 2006; C.D. Jones et al., 2013; Jones and Friedlingstein, 2020). \r\nBiogeochemical feedbacks follow as an outcome of both carbon and \r\nclimate forcing on the physics and the biogeochemical processes of \r\nthe ocean and terrestrial carbon cycles (Figure 5.2) (Katavouta et al., \r\n2018; Williams et al., 2019; Jones and Friedlingstein, 2020). Together, \r\nthese carbon-climate feedbacks can amplify or suppress climate \r\nchange by altering the rate at which CO2 builds up in the atmosphere \r\nthrough changes in the land and ocean sources and sinks (Figure 5.2; \r\nC.D. Jones et al., 2013; Raupach et al., 2014; Williams et al., 2019). \r\nThese changes depend on the, often non-linear, interaction of the \r\ndrivers (CO2 and climate) and processes in the ocean and land as well \r\nas the emissions scenarios (Figure 5.2; Sections 5.4 and 5.6) (Raupach \r\net al., 2014; Schwinger et al., 2014; Williams et al., 2019)"


id: 33de353a080dbae631f1e9668b93dad3
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 5572
content:
" There is \r\nhigh confidence that carbon-climate feedbacks and their century \r\nscale evolution play a critical role in two linked climate metrics that \r\nhave significant climate and policy implications: (i) the fraction of \r\nanthropogenic CO2 emissions that remains in the atmosphere, the \r\nso-called airborne fraction of CO2 (AF; Section  5.2.1.2, Figures  5.2 \r\nand 5.7, and FAQ 5.1); and (ii) the quasi-linear trend characteristic \r\nof the transient temperature response to cumulative CO2 emissions \r\n(TCRE; Section 5.5; MacDougall, 2016; Williams et al., 2016; Jones \r\nand Friedlingstein, 2020) and other GHGs (CH4 and N2O). This chapter \r\nassesses the implications of these issues from the perspective \r\nof carbon cycle processes (Figure  5.2) in Section  5.2 (historical \r\nand contemporary), Section  5.3 (changing carbonate chemistry), \r\nSection  5.4 (future projections), Section  5.5 (remaining carbon \r\nbudget) and Section  5"


id: 4909fd96b6de1b4108a4754ff7572ec1
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 8874
content:
"The airborne fraction is an important constraint for adjustments \r\nin carbon-climate feedbacks and reflects the partitioning of CO2\r\nemissions between reservoirs by the negative feedbacks, which \r\nwere 31% on land and 23% in the ocean for the decade 2010-2019 \r\nand also dominated the historical period (Figure  5.2; Table  5.1) \r\n(Friedlingstein et al., 2020). During the period 1959-2019, the \r\nairborne fraction has largely followed the growth in anthropogenic \r\nCO2 emissions with a  mean of 44% and a  large interannual \r\nvariability (Ballantyne et al., 2012; Ciais et al., 2019; Friedlingstein \r\net al., 2020) (Section  5.2.1.2; Table  5.1). The negative feedback to \r\nCO2 concentrations is associated with its impact on the air-sea and \r\nair-land CO2 exchange through strengthening of partial pressure of \r\nCO2 (pCO2) gradients as well as the internal processes that enhance \r\nuptake"


id: 4bdeff9bad8c610ab63a3186fa9342ab
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 9355
content:
" Two of these key processes are the buffering capacity of the \r\nocean and the CO2 fertilization effect on gross primary production \r\n(Sections 5.4.1-5.4.4)."


id: a22628705bf3cda831d548c36646130e
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 23074
content:
"Figure 5.2 | Key compartments, processes and pathways that govern historical and future CO2 concentrations and carbon-climate feedbacks through \r\nthe coupled Earth system. The anthropogenic CO2 emissions, including land-use change, are partitioned via negative feedbacks (turquoise dotted arrows) between the \r\nocean (23%), the land (31%) and the airborne fraction (46%) of anthropogenic CO2 that sets the changing CO2 concentration in the atmosphere (2010-2019; Table 5.1). This \r\nregulates most of the radiative forcing that creates the heat imbalance that drives the climate feedbacks to the ocean (blue) and land (green). Positive feedbacks (red arrows) \r\nresult from processes in the ocean and on land (red text). Positive feedbacks are influenced by both carbon-concentration and carbon-climate feedbacks simultaneously. \r\nAdditional biosphere processes have been included, but these have an as-yet-uncertain feedback impact (blue-dotted arrows)"


id: f4effbdc5f362fa1cfe3e8bbc3162914
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 36044
content:
" CO2 removal from the atmosphere into the ocean, \r\nland and geological reservoirs, necessary for negative emissions, has been included (grey arrows). Although this schematic is built around CO2 (the dominant greenhouse gas), \r\nsome of the same processes also influence the fluxes of CH4 and N2O and the strength of the positive feedbacks from the terrestrial and ocean systems."


id: aaa3d96e8424613bcbf3a7a916110229
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 24374
content:
"release to the ocean-atmosphere system (Zeebe et al., 2016; Gutjahr \r\net al., 2017; Cui and Schubert, 2018; Kirtland Turner, 2018). There \r\nis low to medium confidence in evaluations of the total amount \r\nof carbon released during the PETM, as proxy data constrained \r\nestimates vary from around 3000 to more than 7000  PgC, with \r\nmethane hydrates, volcanic emissions, terrestrial and/or marine \r\norganic carbon, or some combination thereof, as the probable \r\nsources of carbon (Zeebe et al., 2009; Cui et al., 2011; Gutjahr et al., \r\n2017; Elling et al., 2019; S.M. Jones et al., 2019; Haynes and Honisch, \r\n2020). Methane emissions related to hydrate/permafrost thawing \r\nand fossil carbon oxidation may have acted as positive feedbacks \r\n(Lunt et al., 2011; Armstrong McKay and Lenton, 2018; Lyons et al., \r\n2019), as the inferred increase in atmospheric CO2 can only account \r\nfor approximately half of the reported warming (Zeebe et al., 2009)"


id: 1d060020dcaec86a122934114aafa126
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 2014
content:
" \r\nThe estimated, time-integrated carbon input is broadly similar \r\nto  the RCP8.5 extension scenario, although CO2 emissions rates \r\n(0.3-1.5 Pg yr-1) and by inference the rate of CO2 accumulation in the \r\natmosphere (4-42 ppm per century) during the PETM were at least \r\n4-5 lower than during the modern era (from 1995 to 2014; Table 2.1; \r\nZeebe et al., 2016; Gingerich, 2019)."


id: 4dc81d363fa8c8b59f133b20d4df01c5
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 9667
content:
"cycle dynamics were altered by glacially perturbed nutrient fluxes and \r\nsoil dynamics, as well as largely exposed shelf areas in the tropics as \r\na result of lowered sea level. Recent estimates suggest deep-sea CO2\r\nstorage during the last ice age exceeded modern values by as much \r\nas 750 - 950 PgC (Skinner et al., 2015, 2017; Buchanan et al., 2016; \r\nAnderson et al., 2019; Gottschalk et al., 2020b). A  combination of \r\nincreased CO2 solubility associated with 2-3degC lower mean oceanic \r\ntemperatures (Bereiter et al., 2018), increased the oceanic residence \r\ntime of CO2 (Skinner et al., 2017), altered oceanic alkalinity (Yu \r\net al., 2010; Cartapanis et al., 2018). A generally more efficient marine \r\nbiological carbon pump (BCP; Galbraith and Jaccard, 2015; J. Yu et al., \r\n2019; Galbraith and Skinner, 2020) enhanced the partition CO2 into the \r\nocean interior, (although the relative contribution of each mechanism \r\nremains a matter of debate)"


id: 40ae95a4bdabf72da34a8a0900663cc6
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 7555
content:
" Recent observationally constrained ESM \r\nresults highlight that air-sea disequilibrium amplifies the effect of \r\ncooling and iron fertilization on glacial carbon storage (Khatiwala \r\net al., 2019)."


id: 842cdb3875e6c5be87702701afbcc2cd
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 18333
content:
"The gradual increase in atmospheric CO2 across the LDT was \r\npunctuated by three centennial 10-13 ppm increments, coeval with \r\n100-200 ppb increases in CH4 (Marcott et al., 2014), reminiscent \r\nof similar oscillations reported for the last ice age associated with \r\ntransient warming events (Dansgaard/Oeschger (DO) events; Ahn \r\nand Brook, 2014; Rhodes et al., 2017; Bauska et al., 2018) as well as \r\nprevious deglacial transitions (Nehrbass-Ahles et al., 2020). The rate \r\nof change in atmospheric CO2 accumulation during these transient \r\nevents exceeds the averaged deglacial growth rates by at least 50% \r\n(Table 2.1, Figure 5.4). The early deglacial release of remineralized \r\ncarbon from the ocean abyss coincided with the  resumption of \r\nSouthern Ocean overturning circulation (Skinner  et al., 2010; \r\nSchmitt et al., 2012; Ferrari et al., 2014; Gottschalk et al., 2016, \r\n2020a; Jaccard et al., 2016; Rae et al., 2018; Moy et al"


id: e8df3ec24afcdfc34cd296c3638b1b0d
created_at: 2023-03-16 14:02:00
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vector_id: 34155
content:
", 2019) and \r\nthe concomitant reduction in the global efficiency of the marine \r\nBCP, associated, in part, with dwindling iron fertilization (Hain \r\net al., 2010; Martinez-Garcia et al., 2014; Jaccard et al., 2016) The \r\ntwo subsequent pulses, centred 14.8 and 12.9 ka, are associated \r\nwith enhanced air-sea gas exchange in the Southern Ocean (T. Li \r\net al., 2020), iron fertilization in the South Atlantic and North Pacific \r\n(Lambert et al., 2021) and rapid increase in soil respiration owing \r\nto the resumption of AMOC and associated southward migration \r\nof the ITCZ (Marcott et al., 2014; Bauska et al., 2018). Rapid \r\nwarming of high northern latitudes contributed to thaw permafrost, \r\npossibly liberating labile organic carbon to the atmosphere (Kohler \r\net al., 2014; Crichton et al., 2016; Winterfeld et al., 2018; Meyer \r\net al., 2019)"


id: a3f7c01f2e71c5c3206bee9d91b7bb98
created_at: 2023-03-16 14:02:00
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vector_id: 23353
content:
" Ocean surface pH reconstructions indicate that the \r\nocean was oversaturated with respect to the atmosphere during \r\nthe early, mid-LDT (Martinez-Boti et al., 2015b; Shao et al., 2019; \r\nShuttleworth et al., 2021), suggesting that ocean sources at that \r\ntime may have been larger than terrestrial sources. Over the course \r\nof the LDT, the decrease in Northern Hemisphere permafrost carbon \r\nstocks has been more than compensated by an increase in the \r\ncarbon stocks of mineral soils, peatland and vegetation (Lindgren \r\net al., 2018; Jeltsch-Thommes et al., 2019). The land biosphere was, \r\non average, a  net sink for atmospheric carbon and accumulated \r\nseveral hundred Gt of carbon over the LDT. Detailed investigations \r\nreveal that Antarctic air temperatures, and more generally Southern \r\nHemisphere (30degS-60degS) proxy temperature reconstructions, led \r\nthe rise in pCO2 at the onset of the LDT, 18  ka ago, by several \r\nhundred years (Shakun et al., 2012; Chowdhry Beeman et al., 2019)"


id: e0b11cadb68ab96e82d0125a1c8e9667
created_at: 2023-03-16 14:02:00
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vector_id: 32855
content:
"Atmospheric GHG concentrations were much less variable during \r\nthe pre-industrial Holocene (from 11.7 ka to 1750 CE). Atmospheric \r\nCH4 concentrations decreased at the beginning of the Holocene, \r\nconsistent with a general weakening of boreal sources (Yang et al., \r\n2017; Beck et al., 2018) and further decline during the mid-Holocene \r\nowing to a reduction in Southern Hemisphere emissions concomitant \r\nwith a  southward shift of the ITCZ (Singarayer et al., 2011; Beck \r\net al., 2018). Atmospheric CH4 concentrations increased about 5 ka, \r\nwhich prompted the hypothesis of an early anthropogenic influence \r\nrelated to land-use changes in South East Asia (Ruddiman et al., \r\n2016). However, stable isotope compositions on CH4 extracted from \r\nGreenland and Antarctic ice (Beck et al., 2018) reveal that natural \r\nemissions located in the southern tropics were responsible for the \r\nrise in atmospheric CH4 concentrations, in line with model simulations \r\n(Singarayer et al"


id: d4b851f11f9f934a1c5acdf2b1207fc8
created_at: 2023-03-16 14:02:00
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vector_id: 30993
content:
", 2011) thus disputing the early anthropogenic \r\ninfluence on the global CH4 budget. Atmospheric N2O concentrations \r\nincreased slightly (20 ppb) across the Holocene, associated with \r\na gradual decline in its nitrogen stable isotope composition (H. Fischer \r\net al., 2019). The combined signal is consistent with a small increase \r\nin terrestrial emissions, offset by a  reduction in marine emissions \r\n(Schilt et al., 2010b; H. Fischer et al., 2019)."


id: 956b5730a969bdf41c63584ea43712a4
created_at: 2023-03-16 14:02:00
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vector_id: 21005
content:
"There are two anthropogenic sources of carbon dioxide (CO2): fossil \r\nemissions and net emissions (including removals) resulting from \r\nland-use change and land management (also shown in this chapter \r\nas LULUCF: land use, land-use change, and forestry; in previous \r\nIPCC reports it has been termed forestry and other land use, FOLU). \r\nFossil CO2 emissions include the combustion of the fossil fuels \r\ncoal, oil and gas, covering all sectors of the economy (electricity, \r\ntransport, industrial, and buildings), fossil carbonates such as in \r\ncement manufacturing, and other industrial processes such as the \r\nproduction of chemicals and fertilizers (Figure  5.5a). Fossil CO2\r\nemissions are estimated by combining economic activity data and \r\nemissions factors, with different levels of methodological complexity \r\n(tiers) or approaches (e.g., IPCC Guidelines for National Greenhouse \r\nGas Inventories)"


id: 470799b4be2c32b923a11b720f145d98
created_at: 2023-03-16 14:02:00
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vector_id: 8558
content:
" Several organizations or groups provide estimates \r\nof fossil CO2 emissions, with each dataset having slightly different \r\nsystem boundaries, methods, activity data, and emissions factors \r\n(Andrew, 2020). Datasets cover different time periods, which can \r\ndictate the datasets and methods that are used for a  particular \r\napplication. The data reported here is from an annually updated \r\ndata source that combines multiple sources to maximise temporal \r\ncoverage (Friedlingstein et al., 2020). The uncertainty in global fossil \r\nCO2 emissions is estimated to be +-5% (1 standard deviation)."


id: d72909d4459b9ccdf9895cfee9ffa161
created_at: 2023-03-16 14:02:00
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vector_id: 31365
content:
"The early Holocene decrease in CO2 concentration by about 5 ppm \r\n(Schmitt et al., 2012) has been attributed to post-glacial regrowth \r\nin terrestrial biomass and a gradual increase in peat reservoirs over \r\nthe Holocene, resulting in the sequestration of several hundred PgC \r\n(Yu et al., 2010; Nichols and Peteet, 2019). Peat accumulation \r\nrates in boreal and temperate regions were higher under warmer \r\nsummer conditions in the early to mid-Holocene (Loisel et al., 2014; \r\nStocker et al., 2017). The 20 ppm gradual increase of atmospheric \r\nCO2 starting 7  ka has been attributed to a  decrease in natural \r\nterrestrial biomass due to climate change, carbonate compensation \r\nand enhanced shallow water carbonate deposition (Menviel and \r\nJoos, 2012; Brovkin et al., 2016), consistent with stable carbon \r\nisotope measurements on CO2 extracted from Antarctic ice (Elsig \r\net al., 2009; Schmitt et al., 2012)"


id: 1be2bd3e905dcf4ec83e479bf6921823
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 1850
content:
" These isotopic measurements do \r\nnot support an early anthropogenic influence on atmospheric CO2\r\ndue to land-use change and forest clearing (Ruddiman et al., 2016). \r\nRecent paleoceanographic evidence suggests that remineralized \r\ncarbon outgassing associated with increased Southern Ocean \r\ncirculation and upwelling (Studer et al., 2018), possibly promoted \r\nby stronger Southern Hemisphere westerly winds (Saunders et al., \r\n2018), could have additionally contributed to the late Holocene \r\nincrease in atmospheric CO2 concentrations. However, the role of \r\nthese mechanisms remained insignificant in transient Holocene \r\nESM simulations (Brovkin et al., 2019). Overall, as in AR5 (WGI, \r\nChapter  5), there is medium confidence in the key drivers of \r\nthe  CO2 increase between the early Holocene and the  beginning \r\nof the industrial era, yet there is low confidence in the relative \r\ncontributions of these drivers due to insufficient quantitative \r\nconstraints on particular processes."


id: ece808d51ca57d5f091df0fec214b4cb
created_at: 2023-03-16 14:02:00
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vector_id: 34752
content:
"CO2 emissions from coal use grew at 4.8% yr-1 in the 2000s, but \r\nslowed to 0.4% yr-1 in the 2010s. CO2 emissions from oil use grew \r\nsteadily at 1.1% yr-1 in both the 2000s and 2010s. CO2 emissions \r\nfrom gas use grew at 2.5% yr-1 in the 2000s and 2.4% yr-1 in 2010s, \r\nbut has shown signs of accelerated growth of 3% yr-1 since 2015 \r\n(G.P. Peters et al., 2020). Direct CO2 emissions from carbonates in \r\ncement production are around 4% of total fossil CO2 emissions, \r\nand grew at 5.8% yr-1 in the 2000s but a slower 2.4% yr-1 in the \r\n2010s. The uptake of CO2 in cement infrastructure (carbonation) \r\noffsets about one half of the carbonate emissions from current \r\ncement production (Friedlingstein et al., 2020). These results are \r\nrobust across the different fossil CO2 emissions datasets, despite \r\nminor differences in levels and rates, as expected given the \r\nreported uncertainties (Andrew, 2020)"


id: a2e9943a742306004b9ffb90fc6f27d5
created_at: 2023-03-16 14:02:00
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vector_id: 23197
content:
" During 2020, the COVID-19 \r\npandemic led to a rapid, temporary decline in fossil CO2 emissions, \r\nestimated to be around 7% based on a synthesis of four estimates. \r\n(Cross-Chapter Box  6.1; Forster et al., 2020; Friedlingstein et al., \r\n2020; Le Quere et al., 2020; Z. Liu et al., 2020)."


id: df136b504eee5d673710b43364514c
created_at: 2023-03-16 14:02:00
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vector_id: 32589
content:
"Progress since AR5 and SRCCL (IPCC, 2019a) allows more accurate \r\nestimates of gross and net fluxes due to the availability of more \r\nmodels, model advancement in terms of inclusiveness of land-use \r\npractices, and advanced land-use forcings (Ciais et al., 2013; Klein \r\nGoldewijk et al., 2017; Hurtt et al., 2020). In addition, important \r\nterminological discrepancies were resolved. First, synergistic effects \r\nof land-use change and environmental changes have been identified \r\nas a key reason for the large discrepancies between model estimates \r\nof the LULUCF flux, explaining up to 50% of differences (Pongratz \r\net al., 2014; Stocker and Joos, 2015; Gasser et al., 2020)"


id: 955986a3e3c5ed4b608b9ac894f0d016
created_at: 2023-03-16 14:02:00
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vector_id: 20996
content:
" Another \r\nreason for discrepancies relates to natural fluxes being considered \r\nas part of the LULUCF flux when occurring on managed land in the \r\nUnited Nations Framework Convention on Climate Change (UNFCCC) \r\nnational GHG inventories; these fluxes are considered part of the \r\nnatural terrestrial sink in global vegetation models and excluded in \r\nbookkeeping models (Grassi et al., 2018). LULUCF fluxes following \r\nnational GHG inventories or Food and Agriculture Organization \r\nof the United Nations (FAO) datasets, including recent estimates \r\n(Tubiello et al., 2021), are thus excluded from our global assessment, \r\nbut their comparison against the academic approach is available \r\nelsewhere - at the global scale (Jia et al., 2019) and European level \r\n(Petrescu et al., 2020)."


id: 88eac24431dc4da4f12791d6ef7f6135
created_at: 2023-03-16 14:02:00
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content:
"For the decade 2010-2019, average emissions were estimated at \r\n1.6 +- 0.7 PgC yr-1 (mean +- standard deviation, 1 sigma; Friedlingstein \r\net al., 2020). A likely general upward trend since 1850 is reversed \r\nduring the second part of the 20th century (Figure  5.5b). Trends \r\nsince the 1980s have low confidence because they differ between \r\nestimates, which is related, among other things, to Houghton and \r\nNassikas (2017) using a different land-use forcing than Hansis et al. \r\n(2015) and the DGVMs. Higher emissions estimates are expected \r\nfrom DGVMs run under transient environmental conditions compared \r\nto bookkeeping estimates, because the DGVM estimate includes \r\nthe loss of additional sink capacity"


id: 7b873f2d9069b167efc1042c99476c67
created_at: 2023-03-16 14:02:00
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vector_id: 17029
content:
" Because the transient setup \r\nrequires a reference simulation without land-use change to separate \r\nanthropogenic fluxes from natural land fluxes, LULUCF estimates \r\nby DGVMs include the sink forests that would have developed in \r\nresponse to environmental changes on areas that in reality have been \r\ncleared (Pongratz et al., 2014). The agricultural areas that replaced \r\nthese forests have a reduced residence time of carbon, lacking woody \r\nmaterial, and thus provide a substantially smaller additional sink over \r\ntime (Gitz and Ciais, 2003). The loss of additional sink capacity is \r\ngrowing in particular with atmospheric CO2 and increases DGVM\u0002based LULUCF flux estimates relative to bookkeeping estimates over \r\ntime (Figure 5.5)."


id: 3dbdf66b6e167b20054e2dba4f9574da
created_at: 2023-03-16 14:02:00
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content:
"Atmospheric CO2 concentration measurements in remote locations \r\nbegan in 1957 at the South Pole Observatory (SPO) and in 1958 \r\nat Mauna Loa Observatory (MLO), Hawaii, USA (Keeling, 1960) \r\n(Figure  5.6a). Since then, measurements have been extended to \r\nmultiple locations around the world (Bacastow et al., 1980; Conway \r\net al., 1994; Nakazawa et al., 1997). In addition, high-density global \r\nobservations of total column CO2 measurements by dedicated GHG\u0002observing satellites began in 2009 (Yoshida et al., 2013; O'Dell \r\net al., 2018). Annual mean CO2 growth rates are observed to be \r\n1.56 +- 0.18 ppm yr-1 (average and range from 1 standard deviation \r\nof annual values) over the 61 years of atmospheric measurements \r\n(1959-2019), with the rate of CO2 accumulation almost tripling \r\nfrom an average of 0.82 +-  0.29  ppm yr-1 during the decade of \r\n1960-1969 to 2.39 +- 0.37 ppm yr-1 during the decade of 2010-2019 \r\n(Chapter 2)"


id: 4311313cd6559d16d34d16da8040f7a2
created_at: 2023-03-16 14:02:00
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" The latter agrees well with that derived for total column \r\n(XCO2) measurements by the Greenhouse Gases Observing Satellite \r\n(GOSAT; Figure 5.6b). The interannual oscillations in monthly mean \r\nCO2 growth rates (Figure 5.6b) show a close relationship with the \r\nEl Nino-Southern Oscillation (ENSO) cycle (Figure 5.6b) due to the \r\nENSO-driven changes in terrestrial and ocean CO2 sources and sinks \r\non the Earth's surface (Section 5.2.1.4)."


id: 780acc5e472c896e6a31d224d6b2929d
created_at: 2023-03-16 14:02:00
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vector_id: 16450
content:
"Multiple lines of evidence unequivocally establish the dominant \r\nrole of human activities in the growth of atmospheric CO2. First, the \r\nsystematic increase in the difference between the MLO and SPO \r\nrecords (Figure 5.6a) is caused primarily by the increase in emissions \r\nfrom fossil fuel combustion in industrialized regions that are situated \r\npredominantly in the Northern Hemisphere (Ciais et al., 2019). Second, \r\nmeasurements of the stable carbon isotope in the atmosphere (d13C-\r\nCO2) are more negative over time because CO2 from fossil fuels \r\nextracted from geological storage is depleted in 13C (Figure  5.6c; \r\nRubino et al., 2013; Keeling et al., 2017). Third, measurements of the \r\nd(O2/N2) ratio show a declining trend because for every molecule of \r\ncarbon burned, 1.17 to 1.98 molecules of oxygen (O2) is consumed \r\n(Figure 5.6d; Ishidoya et al., 2012; Keeling and Manning, 2014)"


id: 91cc82e2de15859da89c2abff331bed9
created_at: 2023-03-16 14:02:00
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content:
" These \r\nthree lines of evidence confirm unambiguously that the atmospheric \r\nincrease of CO2 is due to an oxidative process (i.e.,  combustion). \r\nFourth, measurements of radiocarbon (14C-CO2) at sites around the \r\nworld (Levin et al., 2010; Graven et al., 2017; Turnbull et al., 2017) \r\nshow a continued long-term decrease in the 14C/12C ratio. Fossil fuels \r\nare devoid of 14C and therefore fossil fuel-derived CO2 additions \r\ndecrease the atmospheric 14C/12C ratio (Suess, 1955)."


id: ac9fbb33b69f9808250f701325f8ab14
created_at: 2023-03-16 14:02:00
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vector_id: 24708
content:
"More evidence on the pre-industrial LULUCF flux has emerged \r\nsince AR5 in the form of new estimates of cumulative carbon losses \r\nuntil today, and of a better understanding of natural carbon cycle \r\nprocesses over the Holocene (Ciais et al., 2013). Cumulative carbon \r\nlosses by land-use activities since the start of agriculture and forestry \r\n(pre-industrial and industrial era) have been estimated at 116 PgC \r\nbased on global compilations of carbon stocks for soils (Sanderman \r\net al., 2017) with about 70  PgC of this occurring prior to 1750, \r\nand for vegetation as 447  PgC (inner quartiles of 42 calculations: \r\n375-525  PgC) (Erb et al., 2018). Emissions prior to 1750 can be \r\nestimated by subtracting the post-1750 LULUCF flux from Table 5.1 \r\nfrom the combined soil and vegetation losses until today; they would \r\nthen amount to 328 (161-501) PgC assuming error ranges are \r\nindependent"


id: cab02a1aa2dd43cf5d5c194db40afd61
created_at: 2023-03-16 14:02:00
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vector_id: 29449
content:
"Figure 5.6 | Time series of CO2 concentrations and related measurements \r\nin ambient air. (a) Concentration time series and MLO-SPO difference, (b) growth \r\nrates, (c) 14C and 13C isotopes, and (d) O2/N2 ratio. The data for Mauna Loa \r\nObservatory (MLO) and South Pole Observatory (SPO) are taken from the Scripps \r\nInstitution of Oceanography (SIO)/University of California, San Diego (Keeling et al., \r\n2001). The global mean CO2 are taken from National Oceanic and Atmospheric \r\nAdministration (NOAA) cooperative network (as in Chapter 2), and Greenhouse Gases \r\nObserving Satellite (GOSAT) monthly mean XCO2 (mixing ratio) time series are taken \r\nfrom National Institute for Environmental Studies (Yoshida et al., 2013). CO2 growth \r\nrates are calculated as the time derivative of deseasonalized time series (Nakazawa \r\net al., 1997). The D(O2/N2) are expressed in per meg units (= (FF/M) x 106, where \r\nFF = moles of O2 consumed by fossil-fuel burning, M = 3"


id: 80c60363de7786eac0710f765db0dbde
created_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 17785
content:
"706 x 1019, total number \r\nof O2 molecules in the atmosphere (Keeling and Manning, 2014). The 14CO2 time \r\nseries at Barring Head, Wellington, New Zealand (BHD) is taken from GNS Science \r\nand NIWA (Turnbull et al., 2017). The multivariate ENSO index (MEI) is shown as the \r\nshaded background in panel (b); (warmer shade indicates El Nino). Further details on \r\ndata sources and processing are available in the chapter data table (Table 5.SM.6)."


id: 6edd6c93ad379f0ad3c03b4273a2d5d4
created_at: 2023-03-16 14:02:00
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vector_id: 15069
content:
"2014). The sink rate has declined over the past six decades, which \r\nindicates that the combined ocean and land sinks are not growing \r\nas fast as the growth in atmospheric CO2 (Raupach et al., 2014; \r\nBennedsen et al., 2019). Possible explanations for the sink rate decline \r\nare that the land and/or ocean CO2 sinks are no longer responding \r\nlinearly with CO2 concentrations or that anthropogenic emissions \r\nare slower than exponential (Figure  5.7 and Sections  5.2.1.3 and \r\n5.2.1.4; Gloor et al., 2010; Raupach et al., 2014; Bennedsen et al., \r\n2019). In addition, both diagnostics are influenced by major climate \r\nmodes (e.g., ENSO) and volcanic eruptions that contribute to high \r\ninterannual variability (Gloor et al., 2010; Frolicher et al., 2013; \r\nRaupach et al., 2014), suggesting high sensitivity to future climate \r\nchange. Uncertain land-use change fluxes (Section 5.2.1.2) influence \r\nthe robustness of the trends"


id: f9ff254fb461ab02be27815d70f89697
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 36845
content:
" Based on the airborne fraction (AF), \r\nit is concluded with medium confidence that both ocean and land \r\nCO2 sinks have grown consistent with the rising of anthropogenic \r\nemissions. Further research is needed to understand the drivers of \r\nchanges in the CO2 sink rate."


id: 2391cbf82ce004e1636384f5a4879ece
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 3045
content:
"In the first assessment approach, the mean global multi-decadal \r\n(1960-2019) trends in the ocean sink (Socean) for CO2 show a high \r\ndegree of coherence across the nine GOBMs and six pCO2-based \r\nobservational product reconstructions (1987-2018) which, despite \r\na  temporary slowdown (or 'hiatus') in the 1990s, is also quasi\u0002linear over that period (Figure 5.8a; Gregor et al., 2019; Hauck et al., \r\n2020). This coherence between the GOBMs and observations-based \r\nreconstructions (1987-2018; r2=0.85) provides high confidence\r\nthat the ocean sink (Socean in Section  5.2.1.5) evaluated from \r\nGOBMs (1960-2019) grew quasi-linearly from 1.0 +-  0.3  PgC yr-1\r\nto 2.5 +- 0.6 PgC yr-1 between the decades 1960-1969 and 2010-\r\n2019 in response to global CO2 emissions (Figure  5.8a; Table  5.1; \r\nFriedlingstein et al., 2020; Hauck et al., 2020)"


id: afe52760d453c65b5816ce9a62538455
created_at: 2023-03-16 14:02:00
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vector_id: 25198
content:
" The cumulative ocean \r\nCO2 uptake (105  +-  20  PgC) is 23% of total anthropogenic CO2\r\nemissions (450 +- 50 PgC) for the same period (Friedlingstein et al., \r\n2020). Notwithstanding the high confidence in the magnitude of \r\nthe annual to decadal trends for Socean, this assessment is moderated \r\nto medium confidence by the low confidence in the currently \r\ninadequately constrained uncertainties in the pre-industrial land-to\u0002ocean carbon flux, the uncertain magnitude of winter outgassing from \r\nthe Southern Ocean, and the uncertain effect of the ocean surface \r\ncool-skin, the effect of data sparsity, differences between wind \r\nproducts and the uncertain contribution from the changing land-\r\nocean continuum on global and regional fluxes (Jacobson et al., 2007; \r\nResplandy et al., 2018; Roobaert et al., 2018; Bushinsky et al., 2019; \r\nHauck et al., 2020; Watson et al., 2020; Gloege et al., 2021)"


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vector_id: 20466
content:
" However, \r\nboth GOBMs and pCO2-based observational products independently \r\nreveal a slowdown or 'hiatus' of the ocean sink in the 1990s, which \r\nprovides a  valuable constraint for model verification and leads to \r\ngreater confidence in the model outputs (Figure 5.8a; Landschutzer \r\net al., 2016; Gregor et al., 2018; DeVries et al., 2019; Hauck et al., \r\n2020). A number of studies point to the role of the Southern Ocean in \r\nthe global '1990s hiatus' in air-sea CO2 fluxes, but provide different \r\nprocess-based explanations linking ocean temperature, mixing and \r\nmeridional overturning circulation (MOC) responses to variability \r\nin large-scale climate systems, wind stress and volcanic activity, as \r\nwell as the sensitivity of the air-sea CO2 flux to small changes in \r\nthe atmospheric forcing from anthropogenic CO2 (Landschutzer et al., \r\n2016; DeVries et al., 2017; Bronselaer et al., 2018; Gregor et al., 2018; \r\nGruber et al., 2019a; Keppler and Landschutzer, 2019; McKinley et al"


id: aba6909b0a3fd61dc78062fef52fead6
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 24530
content:
"Since AR5 and SROCC, major advances in globally coordinated \r\nocean CO2 observations (Surface Ocean CO2 Atlas, SOCAT; and \r\nGlobal Ocean Data Analysis Project, GLODAP), the harmonization \r\nof ocean and coastal-observation-based products, atmospheric and \r\noceanic inversion models and forced global ocean biogeochemical \r\nmodels (GOBMs) have increased the level of confidence in the \r\nassessment of trends and variability of air-sea fluxes and storage of \r\nCO2 in the ocean during the historical period (1960-2018; see also \r\nSupplementary Materials 5.SM.1; Ciais et al., 2013; Bakker et al., \r\n2016; Landschutzer et al., 2016, 2020; Bindoff et al., 2019; DeVries \r\net al., 2019; Gregor et al., 2019; Gruber et al., 2019a, b; Tohjima et al., \r\n2019; Friedlingstein et al., 2020; Hauck et al., 2020; Olsen et al., \r\n2020)"


id: c810e293ea6decd5ed1f8c092e91a816
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 29056
content:
" A major advance since SROCC is that, for the first time, all six \r\npublished observational product fluxes used in this assessment, are \r\nmade more comparable using a common ocean and sea ice cover \r\narea, integration of climatological coastal fluxes scaled to increasing \r\natmospheric CO2 and an ensemble mean of ocean fluxes calculated \r\nfrom three re-analysis wind products (Supplementary Materials \r\n5.SM.2; Landschutzer et al., 2014, 2020; Rodenbeck et al., 2014; \r\nZeng et al., 2014; Denvil-Sommer et al., 2019; Gregor et al., 2019; \r\nIida et al., 2021). From a process point of view, the ocean uptake \r\nof anthropogenic carbon is a two-step set of abiotic processes that \r\ninvolves the exchange of CO2, first across the air-sea boundary into \r\nthe surface mixed layer, followed by its transport into the ocean \r\ninterior where it is stored for decades to millennia, depending on \r\nthe depth of storage (Gruber et al., 2019b)"


id: 84ea67676206c18ca41e0df068b4925b
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 18432
content:
" Two definitions of air-\r\nsea fluxes of CO2 are used in this assessment for both observational \r\nproducts and models: Socean is the global mean ocean CO2 sink and \r\nFnet denotes the net spatially varying CO2 fluxes (Hauck et al., 2020). \r\nAdjustment of the mean global Fnet for the pre-industrial sea-to-air \r\nCO2 flux associated with land-to-ocean carbon flux term makes Fnet\r\ncomparable to Socean (Jacobson et al., 2007; Resplandy et al., 2018; \r\nHauck et al., 2020).\r\nThere are multiple lines of observational and modelling evidence that \r\nsupport with high confidence the finding that, in the historical period "


id: 3c4accd1aff061c8f3e0aa6cf5cdd49e
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 6851
content:
"Here we provide a third comparative assessment approach depicting \r\nthe spatial coherence of ocean air-sea fluxes and storage rates \r\nof CO2 as well as a quantitative assessment of both fluxes for the \r\nsame period (1994-2007; Figure  5.9). Observation-based pCO2\r\nflux products show that emissions of natural CO2 occur mostly in \r\nthe tropics and high-latitude Southern Ocean, and that the uptake \r\nand storage of anthropogenic CO2 occurs predominantly in the mid\u0002latitudes (Chapter 9, Figure 5.9 and Cross-Chapter Box 5.3). Strong \r\nocean CO2 sink regions are those in the mid-latitudes associated \r\nwith the cooling of poleward flowing subtropical surface waters \r\nas well as equatorward flowing sub-polar surface waters, both of \r\nwhich contribute to the formation of Mode, Intermediate and Deep \r\nwater masses that transport anthropogenic CO2 into the ocean \r\ninterior on time scales of decades to centuries in both hemispheres \r\n(Section 9.2.2.3 and Figure 5.9; DeVries, 2014; Gruber et al"


id: 7ac53118fbcbf0718da075254679dfe9
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 16896
content:
", 2019b; \r\nWu et al., 2019). The mean decadal scale magnitude and uncertainties \r\nof Socean from net air sea fluxes (Fnet) were calculated from an ensemble \r\nof six observational-based product reconstructions (Figure  5.9a) \r\nand the storage rates in the ocean interior derived from multiple \r\nocean interior CO2 datasets (Gruber et al., 2019b; Figure 5.9b). The \r\ncumulative CO2 stored in the ocean interior from 1800 to 2007 has \r\nbeen estimated at 140 +-18 PgC (Gruber et al., 2019b). As reported in \r\nSROCC (Section 5.2.2.3.1; IPCC, 2019b), the net ocean CO2 storage \r\nbetween 1994-2007 was 29 +- 4 PgC, which corresponds to a mean \r\nstorage of 26 +- 5% of anthropogenic CO2 emissions for that period \r\n(Gruber et al., 2019b). The resulting net annual storage rate of \r\nanthropogenic CO2, equivalent to Socean for the period mid-1994 to \r\nmid-2007 is 2.2 +- 0.3 PgC yr-1, which is in very close agreement with \r\nthe top-down air-sea flux estimate of Socean of 2.1 +-  0"


id: 63710feae397efa6d65bd99c45d86c06
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 13218
content:
"5  PgC yr-1\r\nfrom GOBMs and 1.9 +- 0.3PgC yr-1 from pCO2-based observational \r\nproducts with the steady river carbon flux correction of 0.62 PgC yr-1\r\nfor the same time period (Gruber et al., 2019b; Hauck et al., 2020). \r\nThis close agreement between these independent ocean CO2 sink \r\nestimates derived from air-sea fluxes and storage rates in the ocean "


id: 41a3efee1d9464f7612a13140e6ed689
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 7723
content:
"The intent of this assessment is to show how global variability can \r\nbe regionally forced (Gregor et al., 2019; Landschutzer et al., 2019; \r\nHauck et al., 2020). Since AR5 and SROCC, advances in global \r\nocean CO2 flux products, GOBMs and atmospheric inversion models \r\nhave strengthened confidence in the assessment of how ocean \r\nregions influence mean global variability and trends of ocean CO2\r\nair-sea fluxes (Fnet; see Supplementary Materials Figure  5.SM.1; \r\nCiais et al., 2013; Landschutzer et al., 2014, 2015; Rodenbeck et al., \r\n2014; McKinley et al., 2017; Bindoff et al., 2019; Gregor et al., 2019; \r\nFriedlingstein et al., 2020; Hauck et al., 2020). The coherence in the \r\nregional variability of the anomalies in Fnet from three independent \r\nlines of evidence support with high confidence that the non-steady \r\nstate global interannual-decadal variability of Fnet has clear regional \r\ninfluences (Gregor et al., 2019; Landschutzer et al., 2019)"


id: 4f778d85c4321e60d8c6c440374d87f2
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 9936
content:
" The tropical \r\noceans contribute the most to the global mean interannual variability \r\n(Supplementary Materials Figure 5.SM.1d). The high latitude oceans, \r\nparticularly the Southern Ocean, contribute the most to the global\u0002scale decadal variability (Supplementary Materials Figure 5.SM5.1b,c; \r\n(Landschutzer et al., 2016, 2019; Gregor et al., 2019; Gruber et al., \r\n2019a; Hauck et al., 2020). The influence of the Southern Ocean \r\non the global mean decadal variability and the 1990s hiatus is \r\nsupported by the highest regional-global correlation coefficients \r\n(Supplementary Materials Figures  5.SM.1a,c). In contrast, the "


id: d624b04198333bd2bb205c115088e677
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 31202
content:
"Regional decadal-scale anomalies in the variability of ocean CO2\r\nstorage have also emerged, probably associated with changes in the \r\nMOC, which may influence the global variability in Fnet (Chapter 9; \r\nDeVries et al., 2017). In the interior of the Indian and Pacific sectors \r\nof the Southern Ocean, and the North Atlantic, the increase in the \r\nCO2 inventory from 1994 to 2007 was about 20% smaller than \r\nexpected from the atmospheric CO2 increase during the same period \r\nand the anthropogenic CO2 inventory in 1994 (Sabine eta al., 2004; \r\nGruber et al., 2019a). There is medium confidence that the ocean \r\nCO2 inventory strengthened again in the decade 2005-2015 (DeVries \r\net al., 2017). In the North Atlantic, a low rate of anthropogenic CO2\r\nstorage at 1.9 +- 0.4 PgC per decade during the time period of 1989-\r\n2003 increased to 4.4 +-  0.9  PgC per decade during 2003-2014. \r\nThis is associated with changing ventilation patterns driven by the \r\nNorth Atlantic Oscillation (Woosley et al"


id: 6ec23e8563e2b15999fcfe9879bfb301
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 15052
content:
", 2016). In the Pacific sector \r\nof the Southern Ocean, the rate of anthropogenic CO2 storage also \r\nincreased from 8.8 +- 1.1 (1s) PgC per decade during 1995-2005 to \r\n11.7 +- 1.1 PgC per decade during 2005-2015 (Carter et al., 2019). \r\nHowever, in the Subantarctic Mode Water of the Atlantic sector of "


id: cb31bc8160a2bbe0cca84549ba575520
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 29527
content:
"Lower fire emissions of CO2 and enhanced vegetation carbon \r\nuptake due to reduced global burned area have contributed to the \r\nincreasing global net land CO2 sink in the recent decade (Arora \r\nand Melton, 2018; Yin et al., 2020) (low to medium confidence). \r\nSatellite observations reveal a declining trend in global burned area \r\nby about 20% over past two decades (Andela et al., 2017; Earl \r\nand Simmonds, 2018; Forkel et al., 2019), a trend most pronounced \r\nin regions like northern Africa (Forkel et al., 2019; Zubkova et al., \r\n2019; Bowman et al., 2020) and Mediterranean Europe (Turco et al., \r\n2016). However, burned area trends are highly heterogeneous \r\nregionally with increasing trends reported in regions like western \r\nUnited States (Holden et al., 2018; Abatzoglou et al., 2019). Some \r\nregions (e.g.,  Amazon basin and Australia) experienced record\u0002breaking fire events in 2019 and 2020 (e.g.,  Boer et al., 2020), \r\nwhose effects on burned area trends remain to be explored"


id: e5e7c04fe9f3c8bc13a5bb346c8f6993
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 33668
content:
" The \r\nburned area trends were primarily attributed to both human\u0002induced climate change and human activities (Jolly et al., 2015; \r\nAndela et al., 2017; Holden et al., 2018; Turco et al., 2018; Teckentrup \r\net al., 2019; Bowman et al., 2020), as well as changing frequency of \r\nlightning in the boreal region (Veraverbeke et al., 2017). In addition \r\nto changes in the burned area, fire dynamics could affect the trend \r\nin land-atmosphere CO2 exchange indirectly through increasing \r\nconcentration of air pollutants (see Section  6.3.4 for impacts \r\nof ozone and aerosol on the carbon cycle; Yue and Unger, 2018; \r\nLasslop et al., 2019)."


id: ac6d836fdcd7123e5f49028040c52bed
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
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vector_id: 24664
content:
"The global net land CO2 sink is assessed to have grown over the \r\npast six decades (Sarmiento et al., 2010; Ballantyne et al., 2017; Le \r\nQuere et al., 2018b; Ciais et al., 2019; Friedlingstein et al., 2020) \r\n(high confidence). Estimated as residual from the mass balance \r\nbudget of fossil fuel CO2 emissions minus atmospheric CO2 growth \r\nand the ocean CO2 sink, the global net land CO2 sink (including both \r\nland CO2 sink and net land-use change emissions) increased from \r\n0.3 +- 0.6 PgC yr-1 during the 1960s to 1.8 +- 0.8 PgC yr-1 during the \r\n2010s (Friedlingstein et al., 2020). An increasing global net land CO2\r\nsink since the 1980s (Figure 5.10) was consistently suggested both \r\nby atmospheric inversions (e.g., Peylin et al., 2013) and by DGVMs \r\n(e.g.,  Sitch et al., 2015; Friedlingstein et al., 2019). The Northern \r\nHemisphere contributes more to the net increase in the land CO2 sink \r\ncompared to the Southern Hemisphere (Ciais et al"


id: 2f9d67e66b958e862cd9b7326b76859a
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 4912
content:
", 2019), and boreal \r\nand temperate forests probably contribute the most (Tagesson et al., \r\n2020). Attributing an increased net land CO2 sink to finer regional \r\nscales remains challenging, but inversions of satellite-based column \r\nCO2 products that have emerged since AR5 are a promising tool to \r\nfurther constrain regional land-atmosphere CO2 exchange (Ciais \r\net al., 2013; Houweling et al., 2015; Reuter et al., 2017; O'Dell et al., \r\n2018; Palmer et al., 2019)."


id: ede8d13958658fa21186f0016ac53f0d
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 34902
content:
"carbon-nitrogen interactions vary greatly among models, large \r\nuncertainties remain on how nitrogen cycling regulates the \r\nresponse of ecosystem carbon uptake to higher atmospheric CO2\r\n(Walker et al., 2015; Wieder et al., 2019; Davies-Barnard et al., 2020; \r\nMeyerholt et al., 2020; see Section 5.4.1). Fire modules have been \r\nincorporated into 10 of 16 DGVMs contributing to the global carbon \r\nbudget (Le Quere et al., 2018a), and a growing number of models \r\nhave representations of human ignitions and fire suppression \r\nprocesses (Rabin et al., 2017; Teckentrup et al., 2019). There are also \r\ngrowing DGVM developments to include management practices \r\n(Pongratz et al., 2018) and the effects of secondary forest regrowth \r\n(Pugh et al., 2019), though models still under-represent intensively \r\nmanaged ecosystems, such as croplands and managed forests \r\n(Guanter et al., 2014; Thurner et al., 2017)"


id: ca5f72f73e8757902cc64378af3d6be4
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 29403
content:
" Processes that have \r\nnot yet played a significant role in the land CO2 sink of the past \r\ndecades but can grow in importance, include permafrost (Box 5.1) \r\nand peatlands dynamics (Dargie et al., 2017; Gibson et al., 2019), \r\nhave also been incorporated in some DGVMs (Koven et al., 2015b; \r\nBurke et al., 2017a; Guimberteau et al., 2018). Growing numbers \r\nand varieties of Earth observations are being jointly used to drive \r\nand benchmark models, helping to further identify missing key \r\nprocesses or mechanisms that are poorly represented in the current \r\ngeneration of DGVMs (e.g., Collier et al., 2018)."


id: 5955e94f720185b92407e3b1dfefc23f
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 11543
content:
"The AR5 stated that the interannual variability of the atmospheric \r\nCO2 growth rate is dominated by tropical land ecosystems. A set of \r\nnew satellite measurements applied to assess the variability of the \r\ntropical land carbon balance since AR5 (Ciais et al., 2013) confirm \r\nthis statement, including satellite column CO2 measurements, \r\nestimating the recent anomalous land-atmosphere CO2 exchange \r\ninduced by El Nino at continental scale (e.g.,  J.  Liu et al., 2017; \r\nPalmer et al., 2019), and L-band vegetation optical depth, \r\nestimating tropical above-ground biomass carbon stock changes \r\n(Fan et al., 2019). In addition, based on medium evidence and \r\nmedium agreement between studies with DGVMs and atmospheric \r\ninversions, semi-arid ecosystems over the tropical zones have \r\na  larger contribution to interannual variability in global land-\r\natmosphere CO2 exchange than moist tropical forest ecosystems \r\n(low to medium confidence) (Poulter et al., 2014; Ahlstrom et al"


id: cbbdf8390895570bbfe0264c0d1e920c
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 29594
content:
"Figure 5.11 | Interannual variation in detrended anomalies of the net land CO2 sink and land surface air temperature during 1980-2019. Correlation \r\ncoefficients between the net land CO2 sink anomalies and temperature anomalies are show on the right bar plots. The net land CO2 sink is estimated by four atmospheric \r\ninversions (blue) and 15 Dynamic Global Vegetation Models (DGVMs) (green), respectively (Friedlingstein et al., 2020). Solid blue and green lines show model mean detrended \r\nanomalies of the net land CO2 sink. The ensemble mean of DGVMs is bounded by the 1-s inter-model spread in each large latitude band (North 30degN-90degN, Tropics \r\n30degS-30degN, South 90degS-30degS) and the globe. The ensemble mean of atmospheric inversions is bounded by model spread. For each latitudinal band, the anomalies of the net \r\nland CO2 sink and temperature (orange) were obtained by removing the long-term trend and seasonal cycle"


id: 44eac5a10809aa457f4cc08833f2c857
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 8270
content:
" A 12-month running mean was taken to reduce high-frequency \r\nnoise. The bars in the right panels show correlation coefficients between the net land CO2 sink anomalies and temperature anomalies for each region. ** indicates P<0.01; \r\n* indicates P<0.05. The grey shaded area shows the intensity of El Nino-Southern Oscillation (ENSO) as defined by the Nino 3.4 index. Two volcanic eruptions (El Chichon and \r\nMount Pinatubo) are indicated with light blue dashed lines. Temperature data are from the Climatic Research Unit (CRU), University of East Anglia (Harris et al., 2014). Anomalies \r\nwere calculated following Patra et al. (2005), but using a 12-month low-pass filter and detrended to obtain interannual variations. Further details on data sources and processing \r\nare available in the chapter data table (Table 5.SM.6)."


id: b5dd94ce052f91b4f95cd9dce6c27b5b
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 26145
content:
"Multiple lines of evidence suggest that WUE has increased in near proportionality to atmospheric CO2 (high confidence) at a rate \r\ngenerally consistent with Earth system models (ESMs), despite variation in the WUE response to CO2 (De Kauwe et al., 2013; Frank \r\net al., 2015; Keeling et al., 2017; Lavergne et al., 2019; Walker et al., 2021). Both field-scale CO2 enrichment experiments and process \r\nmodels show the effect of physiologically induced water savings, particularly under water-limiting conditions (De Kauwe et al., 2013; \r\nFarrior et al., 2015; Lu et al., 2016; Roy et al., 2016). Plants can also benefit from reduced drought stress due to enhanced CO2\r\nwithout ecosystem-scale water savings (Jiang et al., 2021). To some extent, this increased WUE offsets the effects of enhanced vapour \r\npressure deficit (VPD) on plant transpiration (Bobich et al., 2010; Creese et al., 2014; Jiao et al"


id: 26aaebbc27d3580393189a1fe2578465
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 3484
content:
", 2019), but will have limited effect on \r\nameliorating plant water stress during extreme drought events (Xu et al., 2016; Menezes-Silva et al., 2019; L. Liu et al., 2020), when \r\nleaf stomata are governed primarily by soil moisture (Roy et al., 2016)."


id: 7aa0db1571500759b437f2f308d001b4
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 16867
content:
"Uncertainties remain on the magnitude of sensitivity of the land carbon fluxes to droughts. Global studies indicate stronger control \r\nof soil moisture to variations in satellite proxies of GPP than VPD (Stocker et al., 2019; L. Liu et al., 2020). However, given that VPD \r\nincreases exponentially with atmospheric warming, some studies suggest that VPD in stomatal regulation will become increasingly \r\nmore important under a warmer climate (Novick et al., 2016; Grossiord et al., 2020). It is difficult to isolate the relative contributions of \r\nwarmer temperature, higher VPD and lower soil moisture. This is because land-atmosphere feedbacks cause a simultaneous increase \r\nof plant evaporative demand and of root zone water deficit impairing plant root uptake (Berg et al., 2016). These physiological \r\nresponses can be further compounded by drought legacies (Anderegg et al., 2015), changes in structure and population dynamics due \r\nto forest mortality (McDowell et al"


id: 34591f7e70688423a964aa8d2f3a67ac
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 5651
content:
", 2020), disturbances associated with drought (fire, insects damage; Anderegg et al., 2020) and \r\npossible trade-offs between resistance and resilience (X. Li et al., 2020). Nonetheless, ESMs suggest that increased drought effects \r\nunder very high levels of global warming (about 4degC at the end of the 21st century) contribute to the reduced efficiency of the land \r\nsink (Green et al., 2019)."


id: 69bfd995c22c1217c9a3999f62f92b48
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
content_type: text
index: climate_gpt_only_giec
vector_id: 14238
content:
"What are the limits of carbon dioxide removal from a water cycle perspective?\r\nCarbon dioxide removal (CDR) options based on terrestrial carbon sinks will require the appropriation of significant amounts of \r\nwater at the landscape level. Most mitigation pathways that seek to limit global warming to 1.5degC or less than 2degC require the \r\nremoval of about 30 to 300 GtC from the atmosphere by 2100 (Rogelj et al., 2018b). Bioenergy with carbon capture and storage \r\n(BECCS), and afforestation/reforestation are the dominant CDR options used in climate stabilization scenarios, implying large \r\nrequirements for land and water (Section 5.6; Beringer et al., 2011; Boysen et al., 2017b; Fajardy and Mac Dowell, 2017; Jans et al., \r\n2018; Seferian et al., 2018b; Yamagata et al., 2018; Stenzel et al., 2019). A review of freshwater requirements for irrigating biomass \r\nplantations shows a range between 15 and 1250 km3 per GtC of biomass harvest"


id: 81fa702a3f84e86d95c2bdaf98c73b05
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 17971
content:
" This is equivalent to a water requirement of \r\n99-8250 km3 for the median BECCS deployment of around 3.3 GtC yr-1 (Smith et al., 2016) in <2degC-scenarios (Stenzel et al., \r\n2021), assuming that biomass is converted to electricity, which is substantially less efficient than converting biomass to heat. \r\nThese large ranges are the result of different assumptions about the type of biomass and yield improvements, management, and \r\nland availability. The use of alternative feedstocks, such as wastes, residues and algae, would lead to smaller water requirements \r\n(Smith et al., 2019)."


id: 383cc3ee19415c619ea6bd1fed95f130
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 6218
content:
"Over the past decade (2010-2019), 10.9 +-  0.9  PgC yr-1 were \r\nemitted from human activities, which were distributed between \r\nthree Earth system components: 46% accumulated in the \r\natmosphere (5.1 +- 0.02 PgC yr-1), 23% was taken up by the ocean \r\n(2.5 +- 0.6 PgC yr-1) and 31% was stored by vegetation in terrestrial \r\necosystems (3.4 +-  0.9  PgC yr-1) (Table  5.1). There is a  budget \r\nimbalance of 0.1  PgCyr-1 which is within the uncertainties of the \r\nother terms. Over the industrial era (1750-2019), the total cumulative \r\nCO2 fossil fuel and industry emissions were 445 +- 20 PgC, and the \r\nLULUCF flux (= net land-use change in Figure 5.12) was 240 +- 70 PgC \r\n(medium confidence). The equivalent total emissions (685 +- 75 PgC) \r\nwas distributed between the atmosphere (285  +-  5  PgC), oceans \r\n(170 +- 20 PgC) and land (230 +- 60 PgC; Table 5.1), with a budget \r\nimbalance of 20  PgC.  This budget (Table  5"


id: b30b3604404e40b83c8351344732376c
created_at: 2023-03-16 14:02:00
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content:
"The  CH4 variability in the atmosphere is mainly the result of the \r\nnet balance between the sources and sinks on the Earth's surface \r\nand chemical losses in the atmosphere. Atmospheric transport \r\nevens out the regional CH4 differences between different parts of \r\nthe Earth's atmosphere. The steady-state lifetime is estimated to \r\nbe 9.1 +- 0.9 years (Section 6.3.1 and Table 6.2). About 90% of the \r\nloss of atmospheric CH4 occurs in the troposphere by reaction with \r\nhydroxyl radical (OH), 5% by bacterial soil oxidation, and the rest \r\n5% by chemical reactions with OH, excited state oxygen (O1D), \r\nand atomic chlorine (Cl) in the stratosphere (Saunois et al., 2020). \r\nMethane has large emissions from natural and anthropogenic origins, \r\nbut a clear demarcation of their nature is difficult because of the use \r\nand conversions of the natural ecosystem for human activities"


id: 2456684ed4dde06bdc6aaef16088558b
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" The \r\nlargest natural sources are from wetlands, freshwater and geological \r\nprocess, while the largest anthropogenic emissions are from enteric \r\nfermentation and manure treatment, landfills and waste treatment, \r\nrice cultivation and fossil fuel exploitation (Table 5.2). In the past two \r\ncenturies, CH4 emissions have nearly doubled, predominantly human \r\ndriven since 1900, and persistently exceeded the losses (virtually \r\ncertain), thereby increasing the atmospheric abundance as evidenced \r\nfrom the ice core and firn air measurements (Ferretti et al., 2005; \r\nGhosh et al., 2015)."


id: db034e35198b0c08bef8a89654a36cc3
created_at: 2023-03-16 14:02:00
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content:
"Since the start of direct measurements of CH4 in the atmosphere in \r\nthe 1970s (Figure 5.13), the highest growth rate was observed from \r\n1977 to 1986 at 18 +- 4 ppb yr-1 (multi-year mean and 1 standard \r\ndeviation) (Rice et al., 2016). This rapid CH4 growth followed the \r\ngreen revolution with increased crop production and a fast rate of \r\nindustrialization that caused rapid increases in CH4 emissions from \r\nruminant animals, rice cultivation, landfills, oil and gas industry and \r\ncoal mining (Ferretti et al., 2005; Ghosh et al., 2015; Crippa et al., \r\n2020). Due to increases in oil prices in the early 1980s, emissions \r\nfrom gas flaring declined significantly (Stern and Kaufmann, 1996). \r\nThis explains the first reduction in CH4 growth rates from 1985 to \r\n1990 (Steele et al., 1992; Chandra et al., 2021)"


id: c926b98cb6862c3d3d97825606547ac1
created_at: 2023-03-16 14:02:00
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content:
" Further emissions \r\nreductions occurred following the Mt Pinatubo eruption in 1991 \r\nthat triggered a reduction in CH4 growth rate through a decrease in \r\nwetland emissions driven by lower surface temperatures due to the \r\nlight scattering by aerosols (Banda et al., 2016; Chandra et al., 2021). \r\nIn the late 1990s through to 2006 there was a temporary pause in the \r\nCH4 growth rate, with higher confidence on its causes than in AR5: "


id: e8d11cafcd82f122db65aaaabc289c54
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content:
"2016; Patra et al., 2016; Schaefer et al., 2016; Schwietzke et al., 2016; \r\nTurner et al., 2017; Worden et al., 2017; He et al., 2020); studies \r\ndisagree on the relative contribution of thermogenic, pyrogenic \r\nand biogenic emission processes and variability in tropospheric \r\nOH concentration. The renewed CH4 increase is accompanied by \r\na reversal of d13C trend to more negative values post 2007; opposite \r\nto what occurred in the 200 years prior (Ferretti et al., 2005; Ghosh \r\net al., 2015; Schaefer et al., 2016; Schwietzke et al., 2016; Nisbet et al., \r\n2019), suggesting an increasing contribution from animal farming, \r\nlandfills and waste, and a slower increase in emissions from fossil \r\nfuel exploitation since the early 2000s (Patra et al., 2016; Jackson \r\net al., 2020; Chandra et al., 2021). Atmospheric concentrations of \r\nCH4 reached 1866.3 ppb in 2019 (Figure  5.14)"


id: 29a73334b7bcdbea5d7619e74d934572
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content:
" A  comprehensive \r\nassessment of the CH4 growth rates over the past four decades is \r\npresented in Cross-Chapter Box 5.2."


id: df5a95e6dff14f93f3b62ec3bbcf7141
created_at: 2023-03-16 14:02:00
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content:
"In the agriculture and waste sectors (Table 5.2), livestock production \r\nhas the largest emissions source (109 Tg yr-1 in 2008-2017) \r\ndominated by enteric fermentation by about 90%. Methane is \r\nformed during the storage of manure, when anoxic conditions are \r\ndeveloped (Hristov et al., 2013). Emissions from enteric fermentation \r\nand manure have increased gradually from about 87 Tg yr-1 in \r\n1990-1999 to 109 Tg yr-1 in 2008-2017 mainly due to the increase \r\nin global total animal numbers. Methane production in livestock \r\nrumens (cattle, goats, sheep, water buffalo) are affected by the type, \r\namount and quality of feeds, energy consumption, animal size, health \r\nand growth rate, meat and milk production rate, and temperature \r\n(Broucek, 2014; S.R.O. Williams et al., 2020; SRCCL Section  5.4.3)"


id: 967034b7a2d07fc771a7079b9dda4c40
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" \r\nWaste management and landfills produced 64 Tg yr-1 in 2008-2017, \r\nwith global emissions increasing steadily since the 1970s and, \r\ndespite significant declines in the USA, western Europe and Japan \r\n(Crippa et al., 2020; Hoglund-Isaksson et al., 2020)."


id: 6f01971fa07701a6cab9d5a5e4dd9d29
created_at: 2023-03-16 14:02:00
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content:
"The positive gradient between CH4 at Cape Grim, Australia (41degS) and \r\nTrinidad Head, USA (41degN), and the bigger difference between Trinidad \r\nHead and global mean CH4 compared to that between global mean \r\nCH4 and Cape Grim, strongly suggest that the Northern Hemisphere \r\nis the dominant origin of anthropogenic CH4 emissions (Figure 5.13). \r\nThe loss rate of CH4 in troposphere does not produce a large positive \r\nnorth-south hemispheric gradient in CH4 due to parity in hemispheric \r\nmean OH concentration (Patra et al., 2014), or in the case of greater OH \r\nconcentrations in the northern rather than the Southern Hemisphere \r\nas simulated by the chemistry-climate models (Naik et al., 2013). Coal \r\nmining contributed about 35% of the total CH4 emissions from all \r\nfossil fuel-related sources. Top-down estimates of fossil fuel emissions \r\n(106 Tg yr-1) are smaller than bottom-up estimates (115  Tg yr-1) \r\nduring 2008-2017 (Table  5.2)"


id: 86c0de9e8415d8dd197272e88bbdc20d
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content:
" Inventory-based estimates suggest \r\nthat CH4 emissions from coal mining increased by 17 Tg yr-1 between \r\nthe periods 2002-2006 and 2008-2012, with a dominant contribution \r\nfrom China (Peng et al., 2016; Crippa et al., 2020; Hoglund-Isaksson \r\net al., 2020). Inventory-based estimates suggest that CH4 emissions \r\nfrom coal mining increased by 17 Tg yr-1 between the periods 2002-\r\n2006 and 2008-2012, with a dominant contribution from China (Peng \r\net al., 2016; Crippa et al., 2020; Hoglund-Isaksson et al., 2020). Recent \r\ncountry statistics and detailed inventory-based estimates show that \r\nCH4 emissions from coal mining in China declined between 2012 \r\nand 2016 (Sheng et al., 2019; Gao et al., 2020), while atmospheric\u0002based estimates suggest a  continuation of CH4 emissions growth \r\nbut at a slower rate to the year 2015 (Miller et al., 2019) and 2016 \r\n(Chandra et al., 2021)"


id: ee37dfa4f48f7cd78262503a2246bc65
created_at: 2023-03-16 14:02:00
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vector_id: 34949
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" Emissions from oil and gas extraction and \r\nuse decreased in the 1980s and 1990s, but increased in the 2000s \r\nand 2010s (Dlugokencky et al., 1994; Stern and Kaufmann, 1996; \r\nHowarth, 2019; Crippa et al., 2020). The attribution to multiple CH4\r\nsources using spatially aggregated atmospheric d13C data remained \r\nunderdetermined to infer the global total emissions from the fossil \r\nfuel industry, biomass burning and agriculture (Rice et al., 2016; \r\nSchaefer et al., 2016; Schwietzke et al., 2016; Worden et al., 2017; \r\nThompson et al., 2018)."


id: 5e70f2d012b536c9cf2393e33c9df38c
created_at: 2023-03-16 14:02:00
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content:
"Freshwater wetlands are the single largest global natural source \r\nof CH4 in the atmosphere, accounting for about 26% of the total \r\nCH4 source (robust evidence, medium agreement). Progress has \r\nbeen made since AR5 (Ciais et al., 2013) in better constraining \r\nfreshwater lake and river emissions and reducing double counting \r\nwith wetland emissions. Bottom-up and top-down estimates for \r\n2008-2017 are 149 and 180 Tg yr-1, respectively, with a  top\u0002down uncertainty range of 159-199 Tg yr-1 (Table  5.2). The large \r\nuncertainties stem from challenges in mapping wetland area and \r\ntemporal dynamics to landscape estimates, and in scaling methane \r\nproduction, transport and consumption processes that are measured \r\nwith small chambers or flux towers (Pham-Duc et al., 2017). Both the \r\ntop-down and bottom-up estimates presented in Table 5"


id: d0a93799a2f8037bb190be4c1c9b4b5a
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content:
"2 indicate \r\nlittle increase in wetland CH4 emissions during the last three decades, \r\nwith the new estimates being slightly smaller than in AR5 due to \r\nupdated wetland maps and ecosystem model simulations (Melton \r\net al., 2013; Poulter et al., 2017). Wetland emissions show strong \r\ninterannual variability due to the changes in inundated land area, air \r\ntemperature and microbial activity (Bridgham et al., 2013). Present \r\nterrestrial ecosystem model simulated CH4 emissions variability does \r\nnot produce strong correlation with the El Nino-Southern Oscillation \r\n(ENSO) cycle (Cross-Chapter Box 5.2, Figure 2), although observation \r\nevidence is emerging for lower CH4 emissions during El Ninos and \r\ngreater emissions during La Nina (Pandey et al., 2017)."


id: 1f923f8164fabcc4f5459fdd51f5db9c
created_at: 2023-03-16 14:02:00
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content:
"In AR5, the ocean CH4 emissions were reported together with \r\ngeological emissions, summing up to 54 (33-75) Tg yr-1. Coastal \r\noceans, fjords and mud volcanos are major sources of CH4 in the \r\nmarine environment, but CH4 flux measurements are sparse. Saunois \r\net al. (2020) estimate that the oceanic budget, including biogenic, \r\ngeological and hydrate emissions from coastal and open ocean, is \r\n6 (range 4-10) Tg yr-1 for the 2000s, which is in good agreement with \r\nan air-sea flux measurement-based estimate of 6-12 Tg yr-1 (Weber \r\net al., 2019). When estuaries are included, the total oceanic budget \r\nis 9-22 Tg yr-1, with a mean value of 13 Tg yr-1. A recent synthesis \r\nsuggests that CH4 emissions from shallow coastal ecosystems, \r\nparticularly from mangroves, can be as high as 5-6 Tg yr-1 (Al-Haj \r\nand Fulweiler, 2020). The reservoir emissions, including coastal \r\nwetlands and tidal flats, contribute up to 13 Tg yr-1 (Borges and Abril, \r\n2011; Deemer et al., 2016)"


id: 7fa53a0909174160710e65ba714b684b
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vector_id: 17630
content:
" Methane seepage from the Arctic shelf, \r\npossibly triggered by the loss of geological storage due to warming \r\nand thawing of permafrost and hydrate decomposition, has a wide \r\nestimated range of 0.0-17 Tg yr-1 (Shakhova et al., 2010, 2014, 2017; \r\nBerchet et al., 2016); advanced eddy covariance measurements put \r\nthe best estimate at about 3 Tg yr-1 from the East Siberian Arctic \r\nshelf (Thornton et al., 2020). The current flux is expected to be a mix \r\nof pre-industrial and climate change-driven fluxes, CH4 seepage is \r\nanticipated to increase in a warmer world (Dean et al., 2018)."


id: 4f0ca7d008e3e5bd09b0af347ecaa3b9
created_at: 2023-03-16 14:02:00
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content:
"Inland water (lakes, rivers, streams, ponds, estuaries) emissions are \r\nproportionally the largest source of uncertainty in the CH4 budget. \r\nSince AR5 (Ciais et al., 2013), the inland water CH4 source has been \r\nrevised from 8-73 Tg yr-1 (1980s) to 117-212 Tg yr-1 (2000s) with \r\nthe availability of more observational data and improved areal \r\nestimates (Bastviken et al., 2011; Deemer et al., 2016; Stanley \r\net al., 2016; DelSontro et al., 2018; Saunois et al., 2020). However, \r\nit is difficult to estimate bottom-up CH4 emissions, due to the large \r\nspatial and temporal variation in lake and river CH4 fluxes (Wik et al., \r\n2016; Crawford et al., 2017; Natchimuthu et al., 2017), uncertainties \r\nin their global area (Allen and Pavelsky, 2018), a  relatively small \r\nnumber of observations, and varying measurement methods -  for \r\nexample, those neglecting ebullition, varying upscaling methods, and \r\nlack of appropriate processes (Sanches et al., 2019; Engram et al., \r\n2020; L. Zhang et al"


id: d5694777760a12a075a5b5139b838788
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content:
", 2020). Accordingly, there is no clear accounting \r\nof inland waters in top-down budgets, which is the main reason \r\nfor the large gap in bottom-up and top-down estimates of 'other \r\nsources' in the CH4 budget (Table  5.2). Despite recent progress in \r\nseparating wetlands from inland waters, there is double-counting in "


id: c454031bebaa4093a248409cbba971fb
created_at: 2023-03-16 14:02:00
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vector_id: 28459
content:
"The atmospheric methane (CH4) growth rate has varied widely over the past three decades, and the causes have been extensively \r\nstudied since AR5. The mean growth rate decreased from 15 +- 5 ppb yr-1 in the 1980s to 0.48 +- 3.2 ppb yr-1 during 2000-2006 \r\n(the so-called quasi-equilibrium phase) and returned to an average rate of 7.6 +- 2.7 ppb yr-1 in the past decade (2010-2019) (based \r\non data in Figure 5.14). Atmospheric CH4 grew faster (9.3 +- 2.4 ppb yr-1) over the last six years (2014-2019) - a period with prolonged \r\nEl Nino conditions, which contributed to high CH4 growth rates consistent with behaviour during previous El Nino events (Figure 5.14b). \r\nBecause of large uncertainties in both the emissions and sinks of CH4, it has been challenging to quantify accurately the methane \r\nbudget and ascribe reasons for the growth over 1980-2019"


id: 3759ee7c0626ef713a819fa27609696a
created_at: 2023-03-16 14:02:00
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content:
" In the context of CH4 emissions mitigation, it is critical to understand if \r\nthe changes in growth rates are caused by emissions from human activities or by natural processes responding to changing climate. \r\nIf CH4 continues to grow at rates similar to those observed over the past decade, it will contribute to decadal scale climate change and \r\nhinder the achievement of the long-term temperature goals of the Paris Agreement (Section 7.3.2.2; Nisbet et al., 2019)."


id: c92c5ff9aa74268d55a1704a965c9ab5
created_at: 2023-03-16 14:02:00
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content:
"Cross-Chapter Box 5.2, Figure 1 | Methane sources and sinks for four decades from atmospheric inversions with the budget imbalance (source-\r\nsink; dark blue bars) (plotted on the left y-axis). Top-down analysis from Kirschke et al. (2013); Saunois et al. (2020). The global CH4 concentration seen in the black \r\nline (plotted on the right y-axis), representing National Oceanic and Atmospheric Administration (NOAA) observed global monthly mean atmospheric CH4 in dry-air \r\nmole fractions for 1983-2019 (Chapter 2, Annex V). Natural sources include emissions from natural wetlands, lakes and rivers, geological sources, wild animals, \r\ntermites, wildfires, permafrost soils, and oceans. Anthropogenic sources include emissions from enteric fermentation and manure, landfills, waste and wastewater, rice \r\ncultivation, coal mining, oil and gas industry, biomass and biofuel burning"


id: 166dd767bbf42fb091452bc4695d7bca
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content:
" The top-down total sink is determined from global mass balance that includes chemical \r\nlosses due to reactions with hydroxyl (OH), atomic chlorine (Cl), and excited atomic oxygen (O1D), and oxidation by bacteria in aerobic soils (Table 5.2). Further details \r\non data sources and processing are available in the chapter data table (Table 5.SM.6)."


id: 9ca1a97ab12135d5a0bb51ece99a16ea
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"A few studies emphasize the role of chemical destruction by hydroxyl (OH; the primary sink of methane), in driving changes in \r\nthe growth of atmospheric methane abundance, in particular after 2006 (Rigby et al., 2017; Turner et al., 2017). Studies applying \r\nthree-dimensional atmospheric inversion (McNorton et al., 2018), simple multi-species inversion (Thompson et al., 2018), as well as \r\nempirical methods using a variety of observational constraints based on OH chemistry (Nicely et al., 2018; Patra et al., 2021), do not \r\nfind trends in OH large enough to explain the methane changes post-2006. On the contrary, global chemistry-climate models based \r\non fundamental principles of atmospheric chemistry and known emissions trends of anthropogenic non-methane short-lived climate \r\nforcers simulate an increase in OH over this period (Zhao et al., 2019; Stevenson et al., 2020; see Section 6.2.3)"


id: b5a824f84e3968eecad95369f4403b9b
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content:
" These contrasting lines \r\nof evidence suggest that OH changes may have had a small moderating influence on methane growth since 2007 (low confidence)."


id: 91b969836296cb6eca0663312fc82404
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content:
"Cross-Chapter Box 5.2 Figure 2 shows that modelled wetland emissions anomalies for all regions did not exhibit statistically significant \r\ntrends (high agreement between models, medium evidence). Thus, the inter-decadal difference of total CH4 emissions derived from \r\ninversion models and wetland emissions, arises mainly from anthropogenic activities. The time series of regional emissions suggest \r\nthat progress towards atmospheric CH4 quasi-equilibrium was primarily driven by reductions in anthropogenic (fossil fuel exploitation) \r\nemissions in Europe, Russia and temperate North America over 1988-2000. In the global totals, emissions equalled loss in the early \r\n2000s. The growth since 2007 is driven by increasing agricultural emissions from East Asia (1997-2017), West Asia (2005-2017), \r\nBrazil (1988-2017) and Northern Africa (2005-2017), and fossil fuel exploitations in temperate North America (2010-2017; Lan et al., \r\n2019; Crippa et al., 2020; Hoglund-Isaksson et al"


id: 151aaa0dd15e375acf47f06149126468
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"There is evidence from emissions inventories at country level and regional scale inverse modelling that CH4 growth rate variability \r\nbetween 1988 and 2017 is closely linked to anthropogenic activities (medium agreement). Isotopic composition observations and \r\ninventory data suggest that concurrent emissions changes from both fossil fuels and agriculture are playing roles in the resumed CH4\r\ngrowth since 2007 (high confidence). Shorter-term decadal variability is predominantly driven by the influence of El Nino-Southern \r\nOscillation on emissions from wetlands and biomass burning (Cross-Chapter Box 5.2, Figure 2), and loss due to OH variations (medium \r\nconfidence), but lacking quantitative contribution from each of the sectors"


id: 2a5a6346e958c064dd5e68060b559a12
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content:
" By synthesizing all available information regionally from \r\na priori (bottom-up) emissions, satellite and surface observations, including isotopic information, and inverse modelling (top-down), \r\nthe capacity to track and explain changes in, and drivers of, natural and anthropogenic CH4 regional and global emissions has \r\nimproved since AR5, but fundamental uncertainties related to OH variations remain unchanged."


id: d9081062384e1457166d3dfa4ced674a
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content:
"The tropospheric abundance of N2O was 332.1 +- 0.4 ppb in 2019 \r\n(Figure  5.15), which is 23% higher than pre-industrial levels of \r\n270.1 +- 6.0 ppb (robust evidence, high agreement). Current estimates \r\nare based on atmospheric measurements with high accuracy and \r\ndensity (Francey et al., 2003; Elkins et al., 2018; Prinn et al., 2018), \r\nand pre-industrial estimates are based on multiple ice-core records\r\n(Section 2.2.3.2.3). The average annual tropospheric growth rate was \r\n0.85 +- 0.03 ppb yr-1 during the period 1995 to 2019 (Figure 5.15a). \r\nThe atmospheric growth rate increased by about 20% between \r\nthe decade 2000-2009 and the most recent decade of 2010-2019 \r\n(0.95 +- 0.04 ppb yr-1) (robust evidence, high agreement). The growth \r\nrate in 2010-2019 was also higher than during 1970-2000 (0.6-\r\n0.8 ppb yr-1; Ishijima et al., 2007) and the 30-year period prior to 2011 \r\n(0.73 +- 0.03 ppb yr-1), as reported by AR5. New evidence since AR5 \r\n(WGI, Section 6.4"


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content:
"3) confirms that, in the tropics and subtropics, large \r\ninterannual variations in the atmospheric growth rate are negatively \r\ncorrelated with the multivariate ENSO index (MEI) and associated \r\nanomalies in land and ocean fluxes (Ji et al., 2019; Thompson et al., \r\n2019; S. Yang et al., 2020) (Figure 5.15a)."


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"The AR5 (WGI, Section 6.4.3) and SRCCL (Section 2.3.3) concluded \r\nthat agriculture is the largest anthropogenic source of N2O \r\nemissions. Since SRCCL (2.3.3), a  new synthesis of inventory\u0002based and modelling studies shows that the widespread use of \r\nsynthetic fertilizers and manure on cropland and pasture, manure \r\nmanagement and aquaculture resulted in 3.8 (2.5-5.8) TgN yr-1\r\n(average 2007-2016) (robust evidence, high agreement) (Table 5.3; \r\nWiniwarter et al., 2018; FAO, 2019; Janssens-Maenhout et al., 2019; \r\nTian et al., 2020). Observations from field-measurements (Song et al., \r\n2018), inventories (Wang et al., 2020) and atmospheric inversions \r\n(Thompson et al., 2019) further corroborate the assessment of SRCCL \r\nthat there is a non-linear relationship between N2O emissions and \r\nnitrogen input, implying an increasing fraction of fertilizer lost as \r\nN2O with larger fertilizer excess (medium evidence, high agreement)"


id: 2c6579bad1d115b9d1ab80faf553b46b
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 4416
content:
" \r\nSeveral studies using complementary methods indicate that \r\nagricultural N2O emissions have increased by more than 45% since \r\nthe 1980s (high confidence) (Figure  5.16 and Table  5.3; Davidson, \r\n2009; Winiwarter et al., 2018; Janssens-Maenhout et al., 2019; Tian \r\net al., 2020), mainly due to the increased use of nitrogen fertilizer \r\nand manure. N2O emissions from aquaculture are among the fastest \r\nrising contributors of N2O emissions, but their overall magnitude is \r\nstill small in the overall N2O budget (Tian et al., 2020)."


id: afa2331e2fbea28d922830445731e954
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 25148
content:
"The principal non-agricultural anthropogenic sources of N2O are \r\nindustry, specifically chemical processing, wastewater, and the \r\ncombustion of fossil fuels (Table  5.3). Industrial emissions of N2O \r\nmainly due to nitric and adipic acid production have decreased \r\nin North America and Europe since the widespread installation \r\nof abatement technologies in the 1990s (Perez-Ramirez et al., \r\n2003; Lee et al., 2011; Janssens-Maenhout et al., 2019). There is \r\nstill considerable uncertainty in industrial emissions from other \r\nregions of the world with contrasting trends between inventories \r\n(Thompson et al., 2019). Globally, industrial emissions and emissions \r\nfrom fossil fuel combustion by stationary sources, such as power \r\nplants, as well as smaller emissions from mobile sources (e.g., road \r\ntransport and  aviation) have remained nearly constant between \r\nthe 1980s and 2007-2016 (medium evidence, medium agreement) \r\n(Winiwarter et al., 2018; Janssens-Maenhout et al"


id: 5cfa606b22b861a032f7060d9d7e3d09
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 12148
content:
", 2019; Tian et al., \r\n2020). Wastewater N2O emissions, including those from domestic \r\nand industrial sources, have increased from 0.2 (0.1-0.3) TgN yr-1\r\nto 0.35 (0.2-0.5) TgN yr-1 between the 1980s and 2007-2016 (Tian \r\net al., 2020)."


id: f70ef6faae560a4a7be8ab7e4261677f
created_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 36379
content:
"Soils are the largest natural source of N2O, arising primarily from \r\nnitrogen processing associated with microbial nitrification and \r\ndenitrification (Table 5.3; Butterbach-Bahl et al., 2013; Snider et al., \r\n2015). Under some conditions, soils can also act as a net sink of N2O, \r\nbut this effect is small compared to the overall source (Schlesinger, \r\n2013). Since AR5 (WGI, Section  6.4.3), improved global process\u0002based models (Tian et al., 2019) suggest a present-day source of 6.7 \r\n(5.3-8.1) TgN yr-1 (2007-2016 average), which is consistent with \r\nthe estimate in AR5. Process-based models and inventory-based \r\nmethods show that increased N deposition has enhanced terrestrial \r\nN2O emissions by 0.8 (0.4-1.4 TgN  yr-1) relative to approximately \r\npre-industrial times, and by 0.2 (0.1-0.2) TgN yr-1 between the 1980s \r\nand 2007-2016 (limited evidence, medium agreement) (Figure 5.16; \r\nTian et al., 2019). This estimate is at the high end of the range \r\nreported in AR5 (WGI, Section  6.4"


id: 61449ba85a469ce5e2f9ae87414d0e7e
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 12875
content:
"Atmospheric abundance of GHGs is proportional to their emissions\u0002loss budgets in the Earth's environment. There are multiple metrics \r\nto evaluate the relative importance of different GHGs for the global \r\natmospheric radiation budget and the socio-economic impacts \r\n(Section 7.6). Metrics for weighting emissions are further developed \r\nin AR6 WGIII. Figure 5.19 shows the regional emissions of the three \r\nmain GHGs. For North Asia, Europe, Temperate North America and \r\nWest Asia, the most dominant GHG source is CO2 (high confidence) \r\n(Figure  5.19) while, for East Asia, South Asia, South East Asia, \r\nTropical South America, Temperate North America and Central Africa, \r\nthe source is CH4 (Figure  5.19). The N2O emissions are dominant \r\nin regions with intense use of nitrogen fertilizers in agriculture. \r\nOnly boreal North America showed net sinks of CO2, while close \r\nto flux neutrality is observed for North Asia, Southern Africa, and \r\nAustralasia"


id: 10f726e4de1be6b49cd3ed36262dabe1
created_at: 2023-03-16 14:02:00
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vector_id: 142
content:
" Persistent emissions of CO2 are observed for Tropical \r\nand South America, northern Africa, and South East Asia (medium \r\nconfidence). The medium confidence arises from large uncertainties \r\nin the estimated non-fossil fuel CO2 fluxes over these regions due to \r\nthe lack of high-quality atmospheric measurements."


id: f6c9a5672b8d3027e0bdb7690f2a029a
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 36337
content:
"The surface ocean has absorbed a quarter of all anthropogenic CO2\r\nemissions, mainly through physical-chemical processes (McKinley \r\net al., 2016; Gruber et al., 2019b; Friedlingstein et al., 2020). Once \r\ndissolved in seawater, CO2 reacts with water and forms carbonic acid, \r\nwhich in turn dissociates, leading to a decrease in the concentration \r\nof carbonate (CO3\r\n-2) ions, and increasing both bicarbonate (HCO3-\r\n) \r\nand hydrogen (H+) ion concentration. This process has caused a shift \r\nin the carbonate chemistry towards a  less basic state, commonly \r\nreferred to as 'ocean acidification' (Caldeira and Wickett, 2003; \r\nOrr et al., 2005; Doney et al., 2009). Although the societal concern \r\nregarding ocean acidification is relatively recent (about the last \r\n20  years), the physical-chemical basis for the ocean absorption \r\n(sink) of atmospheric CO2 has been discussed much earlier by Revelle \r\nand Suess (1957)"


id: 87faa1fbe30068f047d66d8919bc9700
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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vector_id: 18920
content:
" The AR5 and SROCC assessments were of robust \r\nevidence that the H+ ion concentration is increasing in the surface \r\nocean, thereby reducing seawater pH (= -log [H+]) (Section 2.3.4.1; Orr \r\net al., 2005; Feely et al., 2009; Ciais et al., 2013; Bindoff et al., 2019), \r\nand there is high confidence that ocean acidification is impacting \r\nmarine organisms (Bindoff et al., 2019)."


id: c5eda35e5a30a6339c9ba092127ef429
created_at: 2023-03-16 14:02:00
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vector_id: 28715
content:
"A limited number of independent proxy records indicate that the \r\nPETM was associated with a  surface ocean pH decline ranging \r\nfrom 0.15 to 0.30 units (Penman et al., 2014; Gutjahr et al., 2017; \r\nBabila et al., 2018). It was also accompanied by a rapid (<10 ka) \r\nshallowing of the carbonate saturation horizon, resulting in \r\nthe widespread dissolution of sedimentary carbonate, followed \r\nby a  gradual (100  kyr) recovery (Zachos et al., 2005; Bralower \r\net al., 2018). The remarkable similarity among sedimentary records \r\nspanning a  wide range of ecosystems suggests with medium \r\nconfidence that the perturbation in the ocean carbonate saturation \r\nwas global (Babila et al., 2018) and directly resulted from elevated \r\natmospheric CO2 levels. The degree of acidification is similar to the \r\n0.4 pH unit decrease projected for the end of the 21st century under \r\nRCP8.5 (Gattuso et al"


id: 3c6953181c4eb174077f736e1f8757a9
created_at: 2023-03-16 14:02:00
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vector_id: 6869
content:
", 2015) and is estimated to have occurred at \r\na rate about one order of magnitude slower than the current rate \r\nof ocean acidification (Zeebe et al., 2016). There is low confidence\r\nin the inferred rates of ocean acidification inherent to the range of \r\nuncertainties affecting rates estimates based on marine sediments \r\n(Section 5.1.2.1)."


id: 35305ba6c926d0753468b6465b52a6dd
created_at: 2023-03-16 14:02:00
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vector_id: 5782
content:
"Ocean oxygen decline, or deoxygenation, is driven by changes in ocean \r\nventilation and solubility (Bindoff et al., 2019). It is virtually certain\r\nthat anthropogenic forcing has made a  substantial contribution to \r\nthe ocean heat content increase over the historical period (Bindoff \r\net al., 2019; IPCC, 2019c; Chapter 9, Section 2.3.3.1), strengthening \r\nupper water column stratification. Ocean warming decreases the \r\nsolubility of dissolved oxygen in seawater, and it contributes to about \r\n15% of the  dissolved oxygen decrease in the oceans according to \r\nestimates based on solubility and the recent SROCC assessment \r\n(medium confidence), especially in sub-surface waters, between \r\n100-600 m depth (Helm et al., 2011; Schmidtko et al., 2017; Breitburg \r\net al., 2018; Oschlies et al., 2018; SROCC, Section 5.3.1). Stratification \r\nreduces the ventilation flux into the ocean interior, contributing to \r\nmost of the remaining ocean deoxygenation (Schmidtko et al., 2017; \r\nBreitburg et al"


id: 9ac3fc9a3e246d96e97f4d2c509fb63
created_at: 2023-03-16 14:02:00
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vector_id: 21881
content:
", 2018; Section  3.6.2). Deoxygenation may enhance \r\nemissions of nitrous oxide, especially from oxygen minimum zones \r\n(OMZs) or hypoxic coastal areas (Breitburg et al., 2018; Oschlies et al., \r\n2018). Since SROCC (Bindoff et al., 2019), CMIP6 model simulation \r\nresults agree with the reported 2% loss (4.8 +- 2.1 Pmoles O2) in total \r\ndissolved oxygen in the upper ocean layer (100-600 m) for the 1970-\r\n2010 period (Helm et al., 2011; Ito et al., 2017; Schmidtko et al., 2017; \r\nKwiatkowski et al., 2020; Section  2.3.4.2). The response of marine \r\norganisms to the coupled effects of ocean warming, acidification and \r\ndeoxygenation occur at different metabolic levels on different groups, \r\nand include respiratory stress and reduction of thermal tolerance \r\n(Gruber, 2011; Bindoff et al., 2019; IPCC, 2019c; Kawahata et al., \r\n2019). An assessment of these effects on marine biota is found in WGII \r\nAR6 Chapter 2."


id: e9429752889a35f46de5457398ba0d38
created_at: 2023-03-16 14:02:00
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vector_id: 34218
content:
"Ocean pH time series are based on the reconstruction of coral \r\nboron isotope ratios (d11B). A majority of coral d11B data have been \r\ngenerated from the western Pacific region with a few records from \r\nthe Atlantic Ocean. Biweekly resolution paleo-pH records show \r\nmonsoonal variation of about 0.5 pH unit in the South China Sea \r\n(Liu et al., 2014). Interannual ocean pH variability in the range of \r\n0.07-0.16 pH unit characterizes southwest Pacific corals that are \r\nattributed to El Nino-Southern Oscillation (ENSO) (H.C. Wu et al., \r\n2018) and river runoff (D'Olivo et al., 2015). Decadal (10-, 22- and 48-\r\nyear) ocean pH variations in the south-west Pacific have been linked \r\nto the Inter-decadal Pacific Oscillation, causing variations of up to \r\n0.30 pH unit in the Great Barrier Reef (Pelejero et al., 2005; Wei et al., \r\n2009) but weaker (about 0.08 pH unit) in the open ocean (H.C. Wu \r\net al., 2018). Decadal variations in the South China Sea pH changes \r\nof 0.10-0"


id: d520ae02b654571e7b52c47381d49e30
created_at: 2023-03-16 14:02:00
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content:
"20 have also been associated with the variation in the East \r\nAsian monsoon (Liu et al., 2014; Wei et al., 2015), as a weakening of \r\nthe Asian winter monsoon leads to sluggish water circulation within \r\nthe reefs, building up localised CO2 concentration in the water due to \r\ncalcification and respiration."


id: 7b0fd43bc3d8c09b6f259b51f131ae1f
created_at: 2023-03-16 14:02:00
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content:
"Since the beginning of the industrial period in the mid-19th century, \r\ncoral d11B-derived ocean pH has decreased by 0.06-0.24 pH unit in \r\nthe South China Sea (Liu et al., 2014; Wei et al., 2015) and 0.12 pH \r\nunit in the south-west Pacific (H.C. Wu et al., 2018). Since the mid\u000220th century, a distinct feature of coral d11B records relates to ocean \r\nacidification trends, albeit having a wide range of values: 0.12-0.40 pH \r\nunit in the Great Barrier Reef (Wei et al., 2009; D'Olivo et al., 2015), \r\n0.05-0.08 pH unit in the north-west Pacific (Shinjo et al., 2013) and \r\n0.04-0.09 pH unit in the Atlantic Ocean (Goodkin et al., 2015; Fowell \r\net al., 2018). Concurrent coral carbon isotopic (d13C) measurements \r\ninfer ocean uptake of anthropogenic CO2 from the combustion of \r\nfossil fuel, based on the lower abundance of 13C in fossil fuel carbon"


id: a976cb7cc683856f6ba82c5863099be3
created_at: 2023-03-16 14:02:00
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vector_id: 24196
content:
" \r\nWestern Pacific coral records show depleted d13C trends since the \r\nlate 19th century that are more prominent since the mid-20th century \r\n(high confidence) (Pelejero et al., 2005; Wei et al., 2009; Shinjo et al., \r\n2013; Liu et al., 2014; Kubota et al., 2017; H.C. Wu et al., 2018)."


id: a92111d5b03985311c6d6e016746553
created_at: 2023-03-16 14:02:00
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vector_id: 24142
content:
"During the LDT, deep ocean ventilation increased as Antarctic Bottom \r\nWater (AABW) (Skinner et al., 2010; Gottschalk et al., 2016; Jaccard \r\net al., 2016) and subsequently the Atlantic meridional overturning \r\ncirculation (McManus et al., 2004; Lippold et al., 2016) resumed, \r\ntransferring previously sequestered remineralized carbon from the \r\nocean interior to the upper ocean, and eventually the atmosphere \r\n(Skinner et al., 2010; Galbraith and Jaccard, 2015; Gottschalk et al., \r\n2016; Ronge et al., 2016, 2020; Sikes et al., 2016; Rae et al., 2018), \r\ncontributing to the deglacial CO2 rise. Intermediate depths lost \r\noxygen as a result of sluggish ventilation and increasing temperatures \r\n(decreasing saturation). As the world emerged from the last Glacial \r\nperiod, OMZs underwent a large volumetric increase at the beginning \r\nof the Bolling-Allerod (B/A), a  northern-hemisphere wide warming \r\nevent, 14.7 ka (Jaccard and Galbraith, 2012; Praetorius et al"


id: fd7517f0e609821fc9e8ea078c46a128
created_at: 2023-03-16 14:02:00
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vector_id: 37392
content:
", 2015) \r\nwith deleterious consequences for benthic ecosystems (e.g., Moffitt \r\net al., 2015). These observations indicate with high confidence that \r\nthe rate of warming, affecting the solubility of oxygen and upper \r\nwater column stratification, coupled with changes in subsurface \r\nocean ventilation, impose a  direct control on the degree of ocean \r\ndeoxygenation, implying a  high sensitivity of ocean oxygen loss \r\nto warming. The expansion of OMZs contributed to a  widespread \r\nincrease in water column (de)nitrification (Galbraith and Kienast, \r\n2013), which contributed substantially to enhanced marine N2O "


id: 70da275b8fe16af6ed783b79884274f7
created_at: 2023-03-16 14:02:00
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vector_id: 15387
content:
"Ono et al., 2019; Bates and Johnson, 2020; Supplementary Material \r\nTable  5.SM.3). These rates are consistent with the rates expected \r\nfrom the transient equilibration with increasing atmospheric CO2\r\nconcentrations, but the variability of rate in decadal time scale has \r\nalso been detected with robust evidence (Ono et al., 2019; Bates \r\nand Johnson, 2020). In the tropical Pacific, its central and eastern \r\nupwelling zones exhibited a faster pH decline of -0.022 to -0.026 pH \r\nunit per decade due to increased upwelling of CO2-rich sub-surface \r\nwaters in addition to anthropogenic CO2 uptake (Sutton et al., 2014; \r\nLauvset et al., 2015). By contrast, warm pools in the western tropical \r\nPacific exhibited slower pH decline of -0.010 to -0.013 pH unit per \r\ndecade (Supplementary Material Table 5.SM.3; Lauvset et al., 2015; \r\nIshii et al., 2020). Observational and modelling studies (Nakano et al., \r\n2015; Ishii et al"


id: b2f410bf5e71d1f79f3fbce03bf012d
created_at: 2023-03-16 14:02:00
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vector_id: 25667
content:
", 2020) consistently suggest that slower acidification \r\nin this region is attributable to the anthropogenic CO2 taken up in the \r\nextratropics around a decade ago and transported to the tropics via \r\nshallow meridional overturning circulations."


id: 3156895df43bb2cd29876601e8454f84
created_at: 2023-03-16 14:02:00
updated_at: 2023-03-16 14:02:00
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index: climate_gpt_only_giec
vector_id: 5180
content:
"Figure 5.20 | Multi-decadal trends of pH (Total Scale) in surface layer at various sites of the oceans and a global distribution of annual mean pH adjusted \r\nto the year 2000. Time-series data of pH are from Dore et al. (2009), Olafsson et al. (2009), Gonzalez-Davila et al. (2010), Bates et al. (2014), Takahashi et al. (2014), \r\nWakita et al. (2017), Merlivat et al. (2018), Ono et al. (2019), and Bates and Johnson (2020). Global distribution of annual mean pH have been evaluated from data of surface \r\nocean pCO2measurements (Bakker et al., 2016; Jiang et al., 2019). Acronyms in panels: KNOT and K2 - Western Pacific subarctic gyre time series; HOT - Hawaii Ocean Time\u0002series; BATS - Bermuda Atlantic Time-series Study; DYFAMED - Dynamics of Atmospheric Fluxes in the Mediterranean Sea; ESTOC - European Station for Time-series in the \r\nOcean Canary Islands; CARIACO - Carbon Retention in a Colored Ocean Time-series"


