# document

id: 721481575cb2c27259014d383891923c
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 15576
content:
"ENSO events are often synchronized to the seasonal cycle in the \r\nobservations, as the associated SST anomalies tend to peak in \r\nboreal winter (November to January) and be at their weakest in the \r\nboreal spring (March to April) (Harrison and Larkin, 1998; Larkin \r\nand Harrison, 2002). The majority of CMIP5 and CMIP6 models \r\nbroadly reproduce the seasonality of ENSO SST variability in the \r\ncentral equatorial Pacific (Taschetto et al., 2014; Abellan et al., 2017; \r\nGrose et al., 2020; Planton et al., 2021) (Figure  3.37). However, \r\nCMIP5 models, while displaying an improvement on CMIP3 models, \r\nappear to under-represent the magnitude of the seasonal variance \r\nmodulation (Bellenger et al., 2014). This under-representation of \r\nseasonal variance modulation continues in CMIP6 models, which \r\ndisplay no statistically significant difference in this behaviour when \r\ncompared to CMIP5 models (Planton et al., 2021) (Figure 3.37)."


id: 2a3944de1b9677c25e448f46c295a046
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 4068
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"The continuum of El  Nino events are typically stratified into two \r\ntypes (often termed 'flavours'), Central Pacific and East Pacific, where \r\nthe name denotes the location of the events' largest SST anomalies \r\n(Annex IV.2.3; Capotondi et al., 2015). As discussed in Section 2.4.2, \r\nthe different types of events tend to produce distinct teleconnections \r\nand climatic impacts (e.g., Taschetto et al., 2020). The characteristics of \r\nEl Nino events of these two flavours in CMIP5 were generally comparable \r\nto the observations (Taschetto et al., 2014). CMIP6 models, however, \r\ndisplay a statistically significant improvement in the representation of \r\nthis ENSO event-to-event SST anomaly diversity when compared with \r\nCMIP5 models (Planton et al., 2021). In addition to this ENSO event \r\ndiversity, the short observational record also displays an increase in "


id: 2dbd584382e031c27d20aa8e613d1bb0
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 4636
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"observational (L'Heureux et al., 2013) and paleo-climatic reconstruction \r\nuncertainties preclude firm conclusions being made about the \r\nlong-term changes in the occurrence of different El Nino event types. \r\nInitial analysis with a selected number of CMIP3 models suggested \r\nthat there may be a forced component to this recent prominence of "


id: 695b9cad1661e168de20913f2ea45630
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 14173
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"the number of the Central Pacific-type events in recent decades (Ashok \r\net al., 2007; McPhaden et al., 2011), which has also been identified \r\nas unusual in the context of the last 500-800 years based on recent \r\npaleo-climatic reconstructions (Section 2.4.2; Y. Liu et al., 2017; Freund \r\net al., 2019). However, the short observational record combined with "


id: 3ea93cb27fa56883d6386e36fddfb02
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 7242
content:
"Vijayeta and Dommenget, 2018; Bayr et al., 2019; Planton et al., \r\n2021). For both CMIP3 and CMIP5, diagnostics of ENSO event growth \r\nappear to show that the models, while producing ENSO variability \r\nthat is qualitatively similar to that observed, do not represent the \r\nbalance of the underlying dynamics well. The atmospheric Bjerknes \r\nfeedback is too weak in the majority of models, while the surface heat \r\nflux feedback is also too weak in the majority of models. The former \r\nrestricts event growth, while the latter restricts event damping, \r\nwhich when combined allow most models to produce variability in a \r\nrange that is consistent with the observations (Bellenger et al., 2014; \r\nS.T.  Kim et al., 2014; Vijayeta and Dommenget, 2018; Bayr et al., \r\n2019). Analysis of ENSO representation in a subset of CMIP6 models \r\nby Planton et al. (2021) suggests that these issues remain."


id: c05fbbb495a0badf75cc6b4956aa97de
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 27826
content:
"Chapter 2 reports low confidence that the apparent change from East \r\nPacific- to Central Pacific-type El  Nino events that occurred in the \r\nlast 20-30 years was representative of a long term change. While \r\nsome climate models do suggest external forcing may affect the \r\nEl Nino event type, most climate models suggest that what has been \r\nobserved is well within the range of natural variability. Thus, there is \r\nlow confidence that anthropogenic forcing has had an influence on \r\nthe observed changes in El Nino event type."


id: 49bf812503ca19016d7d96ce6e7728de
created_at: 2023-03-16 14:00:40
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content:
"The Indian Ocean Basin (IOB) and Dipole (IOD) modes are the two \r\nleading modes of interannual SST variability over the tropical Indian \r\nOcean, featuring basin-wide warming/cooling and an east-west \r\ndipole of SST anomalies, respectively (Annex IV.2.4). The IOD mode \r\nis anchored to boreal summer to autumn by the air-sea feedback, \r\nand often develops in concert with ENSO. Driven by matured ENSO \r\nevents, the IOB mode peaks in boreal spring and often persists \r\ninto the subsequent summer. Similar patterns of Indian Ocean SST \r\nvariability also dominate its decadal and longer time scale variability \r\n(Han et al., 2014b)."


id: 69460e26651a99897abc48fa3248256c
created_at: 2023-03-16 14:00:40
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vector_id: 14155
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"AR5 concluded that models show high and medium performance \r\nin reproducing the IOB and IOD modes, respectively (medium \r\nconfidence), with difficulty in reproducing the persistence of the \r\nIOB and the pattern and magnitude of the IOD (Flato et al., 2013). \r\nThere was low confidence that changes in the IOD were detectable or \r\nattributable to human influence (Bindoff et al., 2013)."


id: b1c4550ed490ba73675ba96a623bccdf
created_at: 2023-03-16 14:00:40
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content:
"After linear detrending, Pacific Decadal Variability (PDV; Annex IV.2.6; \r\nSection 3.7.6) has been suggested as a driver of decadal to \r\nmulti-decadal variations in the IOB mode (Dong et al., 2016). However, \r\ncorrelation between the PDV and a decadal IOB index, defined from \r\nlinearly detrended SST, changed from positive to negative during the \r\n1980s (Han et al., 2014a). The increase in anthropogenic forcing and \r\nrecovery from the eruptions of El Chichon in 1982 and Pinatubo in \r\n1991 may have overwhelmed the PDV influence, and explain this \r\nchange (Dong and McPhaden, 2017; L. Zhang et al., 2018a). However, \r\nthe low statistical degrees of freedom hamper clear detection of \r\nhuman influence in this correlation change."


id: 7bc905e437e9958d630b0a6dbedffe49
created_at: 2023-03-16 14:00:40
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"To summarize, there is medium confidence that changes in the \r\ninterannual IOD variability in the late 20th century inferred from \r\nobservations and proxy records are within the range of internal \r\nvariability. There is no evidence of anthropogenic influence on the \r\ninterannual IOB. On decadal- to multi-decadal time scales, there is \r\nlow confidence that human influence has caused a reversal of the \r\ncorrelation between PDV and decadal variations in the IOB mode. "


id: 2e8a2c2f4fac3a6f5bcf9c6fc0ea0bc2
created_at: 2023-03-16 14:00:40
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"The low confidence in this assessment is due to the short observational \r\nrecord, limited number of models used for the attribution, lack of model \r\nevaluation of the decadal IOB mode, and uncertainty in the contribution \r\nfrom volcanic aerosols. Nevertheless, CMIP5 models have medium \r\noverall performance in reproducing both the interannual IOB and \r\nIOD modes, with an apparently good performance in reproducing the \r\nIOB magnitude arising from compensation of biases in the formation \r\nprocess, and overly high IOD magnitude due to the mean state bias \r\n(high confidence). There is no clear improvement in the simulation of \r\nthe IOD from CMIP5 to CMIP6 models, though there is only medium\r\nconfidence in this assessment, since only a subset of CMIP6 models \r\nhave been examined. There is no evidence for performance changes \r\nin simulating the IOB from CMIP5 to CMIP6 models."


id: 880e805755e0b1a500e68a2e0e9502e6
created_at: 2023-03-16 14:00:40
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"The Atlantic Zonal Mode (AZM), often referred to as the Atlantic \r\nEquatorial Mode or Atlantic Nino, and the Atlantic Meridional Mode \r\n(AMM) are the two leading basin-wide patterns of interannual to \r\ndecadal variability in the tropical Atlantic. Akin to ENSO in the Pacific, \r\nthe term Atlantic Nino is broadly used to refer to years when the \r\nSSTs in the tropical eastern Atlantic basin along the cold tongue are \r\nsignificantly warmer than the climatological average. The AMM is \r\ncharacterized by anomalous cross-equatorial gradients in SST. Both \r\nmodes are associated with altered strength of the Inter-tropical \r\nConvergence Zone (ITCZ) and/or latitudinal shifts in the ITCZ, \r\nwhich locally affect African and American monsoon systems and \r\nremotely affect tropical Pacific and Indian Ocean variability through \r\ninter-basins teleconnections. A detailed description of both AZM and \r\nAMM, as well as their associated teleconnection over land, is given \r\nin Annex IV.2.5"


id: 634f8c00de96948f5a03b1508abd64a2
created_at: 2023-03-16 14:00:40
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"There are some recent indications that increasing model resolution \r\nboth vertically and horizontally, in the ocean and atmospheric \r\ncomponent (Richter, 2015; Small et al., 2015; Harlass et al., 2018), \r\ncould partly alleviate some tropical Atlantic biases in mean state \r\n(Section 3.5.1.2.2), seasonality, interannual- to decadal-variability \r\nand associated teleconnectivity over land, such as with the West \r\nAfrican monsoon (Steinig et al., 2018). Results from CMIP6 tend to \r\nconfirm that increasing resolution is not the unique way to address \r\nthe biases in the tropical Atlantic (Richter and Tokinaga, 2020). For \r\ninstance, the inclusion of a stochastic physics scheme has a nearly \r\nequivalent effect in the improvement of the mean number and the \r\nstrength distribution of tropical Atlantic cyclones (Vidale et al., 2021)."


id: 9c35007bb58cd228a425a206c9f390af
created_at: 2023-03-16 14:00:40
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"AR5 mentioned the considerable difficulty in simulating both \r\nAtlantic Nino and AMM despite some improvements in CMIP5 for \r\nsome models (Flato et al., 2013). Severe biases in mean state and \r\nvariance for both SST and atmospheric dynamics including rainfall \r\n(e.g.,  a  double ITCZ) as well as teleconnections were reported. \r\nThe AR5 highlighted the complexity of the tropical Atlantic biases, \r\nwhich were explained by multiple factors both in the ocean \r\nand atmosphere."


id: 38b1dc3bf066fcee740fa1bc55d5f849
created_at: 2023-03-16 14:00:40
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"Since AR5, further analysis of the major persistent biases in models \r\nhas been reported (Xu et al., 2014; Jouanno et al., 2017; Y. Yang \r\net al., 2017; Dippe et al., 2018; Lubbecke et al., 2018; Voldoire \r\net al., 2019a). Errors in equatorial and basin wide trade winds, \r\ncloud cover and ocean vertical mixing and dynamics both locally \r\nand in remote subtropical upwelling regions, key thermodynamic \r\nocean-atmosphere feedbacks, and tropical land-atmosphere \r\ninteraction have been shown to be detrimental to the representation \r\nof both the Atlantic Nino and AMM leading to poor teleconnectivity \r\nover land (Rodriguez-Fonseca et al., 2015; Wainwright et al., 2019) \r\nand between tropical basins (Ott et al., 2015)."


id: fcb7991da387f5f4a5ccd3d490c05204
created_at: 2023-03-16 14:00:40
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"Despite some improvements (Richter et al., 2014; Nnamchi et al., \r\n2015), biases in the mean state are so large that the mean east-west \r\ntemperature gradient at the equator along the thermocline remains "


id: be8dc3b032fbec0500536d5379b10dca
created_at: 2023-03-16 14:00:40
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vector_id: 27534
content:
"In summary, based on CMIP5 and CMIP6 results, there is no robust \r\nevidence that the observed changes in either the Atlantic Nino or \r\nAMM modes and associated teleconnections over the second half of \r\nthe 20th century are beyond the range of internal variability or have \r\nbeen influenced by natural or anthropogenic forcing. Considering \r\nthe physical processes responsible for model biases in these modes, \r\nincreasing resolution in both ocean and atmosphere components may \r\nbe an opportunity for progress in the simulation of the tropical Atlantic \r\nchanges as evidenced by some individual model studies (Roberts et al., \r\n2018), but this needs confirmation from a multi-model perspective."


id: bc7ffae5d38340942fb444a498930602
created_at: 2023-03-16 14:00:40
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"Pacific Decadal Variability (PDV) is the generic term for the modes of \r\nvariability in the Pacific Ocean that vary on decadal to inter-decadal \r\ntime scales. PDV and its related teleconnections encompass the \r\nPacific Decadal Oscillation (PDO; Mantua et al., 1997; Zhang et al., \r\n1997; Mantua and Hare, 2002), and an anomalous SST pattern in \r\nthe North Pacific, as well as a broader structure associated with \r\nPacific-wide SSTs termed the Inter-decadal Pacific Oscillation (IPO; \r\nPower et al., 1999; Folland et al., 2002; Henley et al., 2015). Since the \r\nPDO and IPO indices are highly correlated, this section assesses them \r\ntogether as the PDV (Annex IV.2.6)."


id: 5099c29142bfe97fd3487d4dba6e35e3
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"AR5 mentioned an overall limited level of evidence for both CMIP3 \r\nand CMIP5 evaluation of the Pacific modes at inter-decadal time \r\nscales, leading to low confidence statements about the models' \r\nperformance in reproducing PDV (Flato et al., 2013) and similarly \r\nlow confidence in the attribution of observed PDV changes to human \r\ninfluence (Bindoff et al., 2013)."


id: 812c2b106b5619c776ac6d66c145c993
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"Biases in the PDV temporal properties and amplitude are present in \r\nCMIP5 (Cheung et al., 2017; Henley, 2017). While model evaluation \r\nis severely hampered by short observational records and incomplete "


id: 5b27f3211814a95bc6b4cf12b3bb7e9a
created_at: 2023-03-16 14:00:40
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"the externally-driven to total PDV is up to only 15% in a multi-model \r\nlarge ensemble of historical simulations. These findings support an \r\nassessment that PDV is mostly driven by internal variability since \r\nthe pre-industrial era. The sensitivity of ensemble-mean PDV trends \r\nto the ensemble size (Oudar et al., 2018), and the dominance of the \r\nensemble spread over the ensemble mean in the 60-year trend of \r\nthe equatorial Pacific zonal SST gradient in large ensemble simulations \r\n(Watanabe et al., 2021), also support this statement."


id: 9f180f6eb09e42b087942c00c2382a9c
created_at: 2023-03-16 14:00:40
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"processes affect PDV phase shifts, are uncertain. Other modelling \r\nstudies find that anthropogenic aerosols can influence the PDV (Verma \r\net al., 2019; Amiri-Farahani et al., 2020; Dow et al., 2020). It is however \r\nunclear whether and how much those forcings contributed to the \r\nobserved variations of PDV. In CMIP6 models, the temporal correlation \r\nof the multi-model ensemble mean PDV index with its observational \r\ncounterpart is insignificant and negligible (Figure  3.39f), suggesting \r\nthat any externally-driven component in historical PDV variations was \r\nweak. Lastly, the multi-model ensemble mean computed from CMIP6 \r\nhistorical simulations shows slightly stronger variation than the CMIP5 \r\ncounterpart, suggesting a greater simulated influence from external \r\nforcings in CMIP6. Still, the fraction of the forced signal to the total \r\nPDV is very low (Figure  3.39f), in contrast to AMV (Section 3.7.7). \r\nConsistently, Liguori et al. (2020) estimate that the variance fraction of "


id: 1e43a4833cfb47ff60b997caadd399fa
created_at: 2023-03-16 14:00:40
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"In CMIP5 last millennium simulations, there is no consistency in \r\ntemporal variations of PDV across the ensemble (Fleming and \r\nAnchukaitis, 2016). This supports the notion that PDV is internal \r\nin nature. However, this issue remains difficult to assess because \r\npaleoclimate reconstructions of PDV have too poor a level of "


id: ccb10ab6f3a37ba01d7ae52c7128ae6d
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 29741
content:
"Atlantic Multi-decadal Variability (AMV) refers to a climate mode \r\nrepresenting basin-wide multi-decadal fluctuations in surface \r\ntemperatures in the North Atlantic (Figure 3.40a,f), with teleconnections \r\nparticularly pronounced over the adjacent continents and the Arctic. \r\nThe AMV phenomenon is usually assessed through SST anomalies \r\naveraged over the entire North Atlantic basin, hereafter the AMV \r\nindex, but it is associated with many physical processes including \r\nthree-dimensional ocean circulation, such as AMOC fluctuations \r\n(Section 3.5.4.1), gyre adjustments, and salt and heat transport in the \r\nentire North Atlantic and subarctic Atlantic basins. The AMV, together \r\nwith the  PDV, has been shown to have modulated GSAT on multi\u0002decadal time scales since pre-industrial times (Cross-Chapter Box 3.1; \r\nT. Wu et al., 2019a; Li et al., 2020). A detailed description of the AMV as \r\nwell as its associated teleconnection over land is given in Annex IV.2.7."


id: 41f3c79af562c955c5055f3963a87f3a
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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"AR5 assessed, based on climate models, that the AMV was primarily \r\ninternally-driven alongside some contribution from external forcings \r\n(mainly anthropogenic aerosols) over the late 20th century (Bindoff et \r\nal., 2013; Flato et al., 2013). But AR5 also concluded that models show \r\nmedium performance in reproducing the observed AMV, with difficulties \r\nin simulating the time scale, the spatial structure and the coherency \r\nbetween all the physical processes involved (Flato et al., 2013)."


id: 16b2150e29b7fa86de7e3fd5510f348b
created_at: 2023-03-16 14:00:40
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"Climate models analysed since AR5 continue to simulate AMV-like \r\nvariability as part of their internal variability. This statement is mostly \r\nbased on CMIP5 pre-industrial control and historical simulations \r\n(Wouters et al., 2012; Schmith et al., 2014; Menary et al., 2015; \r\nRuprich-Robert and Cassou, 2015; Brown et al., 2016b; Chen et al., \r\n2016; Kim et al., 2018a) and is also true for the CMIP6 models \r\n(Menary et al., 2018; Voldoire et al., 2019b). Models also continue \r\nto support links to a wide array of remote climate influences through "


id: b8efc3862d78dc786a5da66b81ed6f5
created_at: 2023-03-16 14:00:40
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"the lagged response to increased AMOC due to strong anthropogenic \r\naerosol forcing over 1955-1985 (Menary et al., 2020) in combination \r\nwith the rapid response through surface flux processes to declining \r\naerosol forcing and increasing greenhouse gas influence since then. \r\nHowever, natural forcings may have also played a significant role. For \r\ninstance, volcanic forcing has been shown to contribute in part to \r\nthe cold phases of the AMV-related SST anomalies observed in the \r\n20th century (Terray, 2012; Bellucci et al., 2017; Swingedouw et al., \r\n2017; Birkel et al., 2018). Over the last millennium, natural forcings \r\nincluding major volcanic eruptions and fluctuations in solar activity \r\nare thought to have driven a larger fraction of the multi-decadal \r\nvariations in the AMV than in the industrial era, with some interplay \r\nwith internal processes (Ottera et al., 2010; Knudsen et al., 2014; \r\nMoffa-Sanchez et al., 2014; J. Wang et al., 2017; Malik et al., 2018; "


id: a0b314160111e0f53f99fa3d388c512
created_at: 2023-03-16 14:00:40
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"There is additional evidence since AR5 that external forcing has been \r\nplaying an important role in shaping the timing and intensity of \r\nthe observed AMV since pre-industrial times (Bellomo et al., 2018; \r\nAndrews et al., 2020). The time synchronisation between observed \r\nand multi-model mean AMV SST indices is significant in both CMIP5 \r\nand CMIP6 historical simulations, while the explained variance of \r\nthe forced response in CMIP6 appears stronger (Figure 3.40d-f). The \r\ncompetition between greenhouse gas warming and anthropogenic \r\nsulphate aerosol cooling has been proposed to be particularly \r\nimportant over the latter half of the 20th century (Booth et al., 2012; \r\nSteinman et al., 2015; Murphy et al., 2017; Undorf et al., 2018a; \r\nHaustein et al., 2019). The latest observed AMV shift from the cold \r\nto the warm phase in the mid-1990s at the surface ocean is well \r\ncaptured in the CMIP6 forced component and may be associated with "


id: c02732f7615817ae9a7a7ed7e259c32a
created_at: 2023-03-16 14:00:40
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content:
"Mann et al., 2021), but other studies question the role of natural \r\nforcings over this period (Zanchettin et al., 2014; Lapointe et al., \r\n2020)."


id: bb94ae5bfd576696ddc7b7c204722d46
created_at: 2023-03-16 14:00:40
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"anthropogenic influence. So far, this chapter has focused on examining \r\nindividual aspects of the climate system in separate sections. The results \r\npresented in Sections 3.3 to 3.7 substantially strengthen our assessment \r\nof the role of human influence on climate since pre-industrial times. \r\nIn this section we look across the whole climate system to assess to \r\nwhat extent a physically consistent picture of human induced change \r\nemerges across the climate system (Figure 3.41)."


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"The observed global surface air temperature warming of 0.9degC to \r\n1.2degC in 2010-2019 is much greater than can be explained by internal \r\nvariability (likely -0.2degC to +0.2degC) or natural forcings (likely -0.1degC \r\nto +0.1degC) alone, but consistent with the assessed anthropogenic \r\nwarming (likely 0.8degC to 1.3degC; Section 3.3.1.1). It is very likely\r\nthat human influence is the main driver of warming over land \r\n(Section 3.3.1.1). Moreover, the atmosphere as a whole has warmed \r\n(Table 7.1), and it is very likely that human-induced greenhouse gas \r\nincreases were the main driver of tropospheric warming since 1979 \r\n(Section 3.3.1.2). It is virtually certain that greenhouse gas forcing \r\nwas the main driver of the observed changes in hot and cold extremes \r\nover land at the global scale (Cross-Chapter Box 3.2)."


id: 678be4018fb3082e3b303a5b460eeca5
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 13879
content:
"As might be expected from a warming atmosphere, moisture in the \r\ntroposphere has increased and precipitation patterns have changed. \r\nHuman influence has likely contributed to the observed changes in \r\nhumidity and precipitation (Section 3.3.2). It is likely that human \r\ninfluence, in particular due to greenhouse gas forcing, is the main \r\ndriver of the observed intensification of heavy precipitation in global \r\nland regions during recent decades (Cross-Chapter Box  3.2). The \r\npattern of ocean salinity changes indicate that fresh regions are \r\nbecoming fresher and that salty regions are becoming saltier as \r\na result of changes in ocean-atmosphere fluxes through evaporation \r\nand precipitation (high confidence) making it extremely likely\r\nthat human influence has contributed to observed near-surface \r\nand subsurface salinity changes since the mid-20th century \r\n(Section  3.5.2.2). Taken together, this evidence indicates a human \r\ninfluence on the water cycle."


id: 7ecbdb767c3335738acd68a95164016b
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 17512
content:
"It is very likely that human influence was the main driver of Arctic \r\nsea ice loss since the late 1970s (Section 3.4.1.1), and very likely that \r\nit contributed to the observed reductions in Northern Hemisphere \r\nspringtime snow cover since 1950 (Section 3.4.2). Human influence \r\nwas very likely the main driver of the recent global, near-universal \r\nretreat of glaciers and it is very likely that it contributed to the \r\nobserved surface melting of the Greenland Ice Sheet over the past \r\ntwo decades (Section 3.4.3.2.1). It is extremely likely that human \r\ninfluence was the main driver of the ocean heat content increase \r\nobserved since the 1970s (Section 3.5.1.3), and very likely that \r\nhuman influence was the main driver of the observed GMSL rise since \r\nat least 1970 (Section 3.5.3.2)."


id: 55339b68abf6f97b3b5f155cc2fe46ac
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 10865
content:
"The AR5 concluded that human influence on the climate system is \r\nclear (IPCC, 2013), based on observed increasing greenhouse gas \r\nconcentrations in the atmosphere, positive radiative forcing, observed \r\nwarming, and physical understanding of the climate system. The AR5 \r\nalso assessed that it was virtually certain that internal variability alone \r\ncould not account for observed warming since 1951 (Bindoff et al., \r\n2013). Evidence has grown since AR5 that observed changes since \r\nthe 1950s in many parts of the climate system are attributable to "


id: 3f28ac6e514fec4da7fb103e6afcae50
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 7326
content:
"Combining the evidence from across the climate system \r\n(Sections 3.3-3.7) increases the level of confidence in the attribution \r\nof observed climate change to human influence and reduces the \r\nuncertainties associated with assessments based on a single variable. \r\nFrom this combined evidence, it is unequivocal that human influence \r\nhas warmed the atmosphere, ocean and land components of the \r\nglobal climate system, taken together."


id: 112fa4b21cdb0b1aec40e690322a4a7e
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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index: climate_gpt_only_giec
vector_id: 181
content:
"Figure 3.41 | Summary figure showing simulated and observed changes in key large-scale indicators of climate change across the climate system, \r\nfor continental, ocean basin and larger scales. Black lines show observations, brown lines and shading show the multi-model mean and 5th-95th percentile ranges for \r\nCMIP6 historical simulations including anthropogenic and natural forcing, and blue lines and shading show corresponding ensemble means and 5th-95th percentile ranges \r\nfor CMIP6 natural-only simulations. Temperature time series are as in Figure 3.9, but with smoothing using a low pass filter. Precipitation time series are as in Figure 3.15 and \r\nocean heat content as in Figure 3.26. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."


id: df30763730cd5ef8983120c377c3b5ae
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 32616
content:
"scoring, indicating that observational uncertainties may be substantial \r\ncompared to model errors. Conversely, similar scores against different \r\nobservational datasets would suggest model biases may be larger than \r\nthe observational uncertainty."


id: 4726a3f2eb33225c64535569da8ee532
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 8575
content:
"Similar to the assessment of multivariate attribution of climate \r\nchange in the previous section, this section covers the performance \r\nof the models across different variables (Sections 3.8.2.1) and \r\ndifferent classes of models (Section 3.8.2.2). Here the focus is on \r\na system-wide assessment using integrative measures of model \r\nperformance that characterize model performance using multiple \r\ndiagnostic fields derived from multi-model ensembles."


id: efc4a7ea10a645d3de49f7ad774da826
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 35230
content:
"The purpose of this section is to use multivariate analyses to address \r\nhow well models simulate present-day and historical climate. For \r\nevery diagnostic field considered, model performance is compared \r\nto one or multiple observational references, and the quality of the \r\nsimulation is expressed as a single number, for example a correlation \r\ncoefficient or a root mean square difference versus the observational \r\nreference. By simultaneously assessing different performance indices, \r\nmodel improvements can be quantified, similarities in behaviour \r\nbetween different models become apparent, and dependencies \r\nbetween various indices become evident (Gleckler et al., 2008; \r\nWaugh and Eyring, 2008)."


id: c0dd7eeaac4526744dff33a4282b24f6
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 27913
content:
"The type of multi-variate analysis of models presented in AR5 remains \r\ncritical to building confidence for example in projections of climate \r\nchange. It is expanded here to the previous-generation CMIP3 and \r\npresent-generation CMIP6 models and also to more variables and \r\nmore climate domains, covering land and ocean as well as sea ice. \r\nThe multi-variate evaluation of these three generations of models \r\nis performed relative to the observational datasets listed in Annex I, \r\nTable AI.1. For many of these datasets, a rigorous characterization of the \r\nobservational uncertainty is not available, see discussion in Chapter 2. \r\nHere, as much as possible, multiple independent observational datasets \r\nare used. Disagreements among them would cause differences in model "


id: a0a89357df12e25412414e52304ddd66
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 22821
content:
"Regarding model performance for the ocean and the cryosphere \r\n(Figure  3.42b), it is apparent that for many models there are \r\nsubstantial differences between the scores for Arctic and Antarctic \r\nsea ice concentration. This might suggest that it is not sea ice physics \r\ndirectly that is driving such differences in performance but rather \r\nother influences, such as differences in geography, the role of large \r\nice shelves (which are absent in the Arctic), or large-scale ocean \r\ndynamics. As for atmospheric variables, progress is evident also \r\nacross all four ocean and ten land variables from CMIP5 to CMIP6."


id: 8190685eeae4f9c92bf0f369578545fb
created_at: 2023-03-16 14:00:40
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content:
"2 | Relative space-tim\r\n CMIP6 for 16 atmosphe\r\nrmance than the median \r\ndatasets are from top to \r\n, AIRS/ERA5, ERA5/NCEP \r\nSST, HadISST, ERA-Interim\r\n4.0/. Further details on d"


id: efc0de04811ee14e7062e155614264d1
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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index: climate_gpt_only_giec
vector_id: 35225
content:
"In summary, CMIP6 models perform generally better for a basket of \r\nvariables covering mean historical climate across the atmosphere, \r\nocean, and land domains than previous-generation and older models \r\n(high confidence). Earth System models characterized by additional \r\nbiogeochemical feedbacks often perform at least as well as related \r\nmore constrained, lower-complexity models lacking these feedbacks \r\n(medium confidence). In many cases, the models score similarly \r\nagainst both observational references, indicating that model errors \r\nare usually larger than observational uncertainties (high confidence). \r\nMoreover, synthesizing across Sections 3.3-3.7, we assess that the \r\nCMIP6 multi-model mean captures most aspects of observed climate \r\nchange well (high confidence)."


id: 4c93dfdd20b59dbf606ebfa98005d73f
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 9473
content:
"precipitation and northward wind at 850 hPa, remain substantial \r\nrelative to mean model errors (see grey dots in Figure 3.43)."


id: d3d8a6ddb48e6938c6ab5fd9145aaa88
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 30851
content:
"AR5 found that reconstructions and simulations of past climates both \r\nshow similar responses in terms of large-scale patterns of climate \r\nchange, such as polar amplification (Flato et al., 2013; Masson\u0002Delmotte et al., 2013). However, for several regional signals (e.g., \r\nthe north-south temperature gradient in Europe and regional \r\nprecipitation changes), the magnitude of change seen in the proxies \r\nrelative to the pre-industrial period was underestimated by the models. \r\nWhen benchmarking CMIP5/PMIP3 models against reconstructions \r\nof the mid-Holocene and LGM, AR5 found only a slight improvement \r\ncompared with earlier model versions across a range of variables. For "


id: e82b5dbeba1f407748c6cb110545fdad
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 34035
content:
"resulted in new model simulations for these time periods (Brierley \r\net al., 2020; Haywood et al., 2020; Kageyama et al., 2021a; Lunt et al., \r\n2021; Otto-Bliesner et al., 2021). These time periods span an assessed \r\ntemperature range of 20degC (Section 2.3.1.1), and for all periods the \r\nPMIP4 multi-model ensemble mean is within 0.5degC of the assessed \r\nrange of GSAT (Figure 3.44a). Those time periods for which the multi\u0002model ensemble mean is outside the assessed range of GSAT, the \r\nmid-Holocene and the Last Interglacial, are primarily forced by orbital \r\nchanges not greenhouse gas forcing, and as a result the forcing as \r\nwell as the assessed and modelled response are relatively close to \r\nzero in the global annual mean. During these periods, climate change \r\ntherefore is a consequence of more poorly understood Earth System "


id: dfe0ecf20ecb578d00758cdb14fc5961
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 32719
content:
"the Last Interglacial, it was noted that the magnitude of observed \r\nannual mean warming in the Northern Hemisphere was only reached \r\nin summer in the models. For the mid-Pliocene Warm Period, it was \r\nnoted that both proxies and models showed a polar amplification of \r\ntemperature compared with the pre-industrial period, but a formal \r\nmodel evaluation was not carried out."


id: 3d181f1c8fafebc807e582a5e9892fad
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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index: climate_gpt_only_giec
vector_id: 6976
content:
"Since AR5, new simulation protocols have been developed in PMIP4 \r\n(Kageyama et al., 2018), which are further described for the mid\u0002Holocene and the Last Interglacial by Otto-Bliesner et al. (2017), for \r\nthe LGM by Kageyama et al. (2017), for the Pliocene by Haywood \r\net al. (2016), and for the early Eocene by Lunt et al. (2017). These have "


id: 8ec03cb3a405eece7a98ce0917b19e6
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 19983
content:
"feedbacks acting on the response to orbital differences versus the \r\npresent-day, affecting the seasonality of insolation."


id: fae78e37ec9d40e38c6f1e1b6cbf80b8
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 36996
content:
"and strong deepening of the AMOC, as was the case for the PMIP3 \r\nmodels (Muglia and Schmittner, 2015; Sherriff-Tadano et al., 2018). \r\nOnly one model (CESM1.2) shows a shoaling of LGM AMOC which \r\nis consistent with reconstructions (Marzocchi and Jansen, 2017; \r\nSherriff-Tadano et al., 2018)."


id: c70c9d2495cc1604e4a41a37ab7ff6ac
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 28899
content:
"17 PMIP4 models completed Last Interglacial simulations (Otto-Bliesner \r\net al., 2021). The comparison to reconstructions is generally good, \r\nexcept for some discrepancies, such as for upwelling systems in \r\nthe South East Atlantic or discrepancies which may result from \r\nlocal melting of remnant ice sheets absent in the Last Interglacial \r\nsimulation protocol. All models simulate a decrease in Arctic sea ice \r\nin summer, commensurate with increased summer insolation, while \r\nsome models even simulate a large or complete loss (Guarino et al., \r\n2020; Kageyama et al., 2021b). Sea ice reconstructions for the central \r\nArctic are, however, too uncertain to evaluate this behaviour. The \r\nLast Interglacial simulations indicate a  clear relationship between \r\nsimulated sea ice loss and model responses to increased greenhouse \r\ngas forcing (Kageyama et al., 2021b; Otto-Bliesner et al., 2021)."


id: 1cc199417071af017b1cd50d290f72a4
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 1975
content:
"Overall, the PMIP multi-model means agree very well (within 0.5degC \r\nof the assessed range) with GSAT reconstructed from proxies \r\nacross multiple time periods, spanning a range from 6degC colder \r\nthan pre-industrial (Last Glacial Maximum) to 14degC warmer than \r\npre-industrial (Early Eocene Climate Optimum) (high confidence). \r\nDuring the orbitally-forced mid-Holocene, the CMIP6 multi-model \r\nmean captures the sign of the regional changes in temperature \r\nand precipitation in most regions assessed, and there have \r\nbeen some regional improvements compared to AR5 (medium \r\nconfidence). The limited number of CMIP6 simulations of the \r\nLGM hinders model evaluation of the multi-model mean, but for \r\nboth LGM and mid-Holocene, models tend to underestimate the \r\nmagnitude of large changes (high confidence). Some long-standing \r\nmodel-data discrepancies, such as a dry bias in North Africa in the \r\nmid-Holocene, have not improved in CMIP6 compared with PMIP3 \r\n(high confidence)."


id: 1d52f7292c914c0b8406b5218982f054
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 2057
content:
"Based on new scientific insights and newly available observations, \r\nmany improvements have been made to models from CMIP5 to \r\nCMIP6, including changes in the representation of physics of the \r\natmosphere, ocean, sea ice, and land surface. In many cases, changes \r\nin the detailed representation of cloud and aerosol processes have \r\nbeen implemented. The new generation of CMIP6 climate models \r\nalso features increases in spatial resolution, as well as inclusion of \r\nadditional Earth system processes and new components (see further \r\ndetails in Section 1.5.3.1 and in Tables AII.5 and AII.6). Such changes \r\nto model physics and resolution are often designed to improve the \r\nfitness-for-purpose of a model such as for projecting regional aspects \r\nof climate (Section 10.3) or to more fully represent feedbacks to make \r\nthe models more fit for long-term climate projections affected for \r\nexample by carbon cycle feedbacks (see also Section 1.5.3.1)."


id: f4920c8866e80e462d21004109006f5e
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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index: climate_gpt_only_giec
vector_id: 35984
content:
"many of the coupled HighResMIP models (Haarsma et al., 2016) use \r\nthe NEMO ocean model, affecting model diversity, and the range and \r\ndegree of process realism (e.g., for aerosols, atmospheric chemistry \r\nand other Earth System components). This section particularly \r\nexplores the influence of model resolution and of complexity on \r\nmodel performance (see also Section 8.5.1)."


id: 7b17cb2d6a86e0b16bef2f9188912a11
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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index: climate_gpt_only_giec
vector_id: 16963
content:
"A key advance in CMIP6 compared to CMIP5 is the presence of \r\nhigh-resolution models that have participated in HighResMIP. \r\nResolution alone can significantly affect a model's performance, \r\nwith some effects propagating to the global scale. Recent studies \r\nhave shown that enhancing the horizontal resolution of models \r\nis seen to significantly affect aspects of large-scale circulation \r\nas well as improve the simulation of small-scale processes and \r\nextremes when compared to CMIP3 and CMIP5 models (see also \r\nSection 11.4.3; Haarsma et al., 2016), with some models approaching \r\n10 km resolution in the atmosphere (Kodama et al., 2021) or ocean \r\n(Caldwell et al., 2019; Gutjahr et al., 2019; Roberts et al., 2019; Chang \r\net al., 2020; Semmler et al., 2020)."


id: 4793d926826659b051347978f4839b70
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 8650
content:
"Additionally, the representation of surface and deeper ocean mean \r\ntemperature is improved in models with higher horizontal resolution \r\n(Sections 3.5.1.1 and 3.5.1.2) with systematic improvements in \r\ncoupled tropical Atlantic sea surface temperature and precipitation \r\nbiases at higher resolutions (Roberts et al., 2019, single model; \r\nVanniere et al., 2019, multi-model), the North Atlantic cold bias (Bock \r\net al., 2020, multi-model; Roberts et al., 2018, 2019; Caldwell et al., \r\n2019; all single models) as well as deep-ocean biases (Small et al., \r\n2014; Griffies et al., 2015; Caldwell et al., 2019; Gutjahr et al., 2019; \r\nRoberts et al., 2019; Chang et al., 2020, all single model studies). \r\nAtlantic ocean transports (heat and volume) are also generally \r\nimproved compared to observations (Grist et al., 2018; Caldwell et al., \r\n2019; Docquier et al., 2019; Roberts et al., 2019, 2020c; Chang et al., "


id: 1e7f868e87a4665b880e9c7d62bb5a9
created_at: 2023-03-16 14:00:40
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vector_id: 2239
content:
"Graphic developers: Bettina K. Gier (Germany), Birgit Hassler \r\n(Germany), Soufiane Karmouche (Germany, Morocco), Seungmok Paik \r\n(Republic of Korea), Min-Gyu Seong (Republic of Korea)"


id: 6b8c7a0f48733153a4df0a4a8654fb6f
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 14539
content:
"The dominant role of humans in driving recent climate change is clear. This conclusion is based on a synthesis of \r\ninformation from multiple lines of evidence, including direct observations of recent changes in Earth's climate; \r\nanalyses of tree rings, ice cores, and other long-term records documenting how the climate has changed in the \r\npast; and computer simulations based on the fundamental physics that governs the climate system."


id: 9130110488af032fa10563aa3ff91064
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 20380
content:
"Climate is influenced by a range of factors. There are two main natural drivers of variations in climate on time \r\nscales of decades to centuries. The first is variations in the sun's activity, which alter the amount of incoming \r\nenergy from the sun. The second is large volcanic eruptions, which increase the number of small particles (aerosols) \r\nin the upper atmosphere that reflect sunlight and cool the surface-an effect that can last for several years \r\n(see also FAQ 3.2). The main human drivers of climate change are increases in the atmospheric concentrations \r\nof greenhouse gases and of aerosols from burning fossil fuels, land use and other sources. The greenhouse \r\ngases trap infrared radiation near the surface, warming the climate. Aerosols, like those produced naturally \r\nby volcanoes, on average cool the climate by increasing the reflection of sunlight. Multiple lines of evidence \r\ndemonstrate that human drivers are the main cause of recent climate change."


id: f28809d1e30b4ace04ff72f19c7acdc5
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 35684
content:
"The current rates of increase of the concentration of the major greenhouse gases (carbon dioxide, methane \r\nand nitrous oxide) are unprecedented over at least the last 800,000 years. Several lines of evidence clearly show \r\nthat these increases are the results of human activities. The basic physics underlying the warming effect of \r\ngreenhouse gases on the climate has been understood for more than a century, and our current understanding \r\nhas been used to develop the latest generation climate models (see FAQ 3.3). Like weather forecasting models, \r\nclimate models represent the state of the atmosphere on a grid and simulate its evolution over time based on \r\nphysical principles. They include a representation of the ocean, sea ice and the main processes important in \r\ndriving climate and climate change."


id: 55dfdfe2f596c1516b319e8733f58bb
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 10991
content:
"Moreover, the dominant effect of human activities is apparent not only in the warming of global surface \r\ntemperature, but also in the pattern of warming in the lower atmosphere and cooling in the stratosphere, \r\nwarming of the ocean, melting of sea ice, and many other observed changes. An additional line of evidence for \r\nthe role of humans in driving climate change comes from comparing the rate of warming observed over recent \r\ndecades with that which occurred prior to human influence on climate. Evidence from tree rings and other \r\npaleoclimate records shows that the rate of increase of global surface temperature observed over the past fifty \r\nyears exceeded that which occurred in any previous 50-year period over the past 2000 years (see FAQ 2.1)."


id: 453f8cfdb4a2c9a9caf1de6dc8517307
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 8323
content:
"Taken together, this evidence shows that humans are the dominant cause of observed global warming over \r\nrecent decades."


id: 72a6ba8ad62fe2e4a555f276951e89a8
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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index: climate_gpt_only_giec
vector_id: 15654
content:
"FAQ 3.1, Figure 1 | Observed warming (1850-2019) is only reproduced in simulations including human influence. Global surface temperature \r\nchanges in observations, compared to climate model simulations of the response to all human and natural forcings (grey band), greenhouse gases only \r\n(red band), aerosols and other human drivers only (blue band) and natural forcings only (green band). Solid coloured lines show the multi-model mean, \r\nand coloured bands show the 5-95% range of individual simulations."


id: ce4a5e11e9b96a66a1b9be1381e77451
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content:
"Natural variability refers to variations in climate that are caused by processes other than human influence. \r\nIt includes variability that is internally generated within the climate system and variability that is driven by \r\nnatural external factors. Natural variability is a major cause of year-to-year changes in global surface climate and \r\ncan play a prominent role in trends over multiple years or even decades. But the influence of natural variability \r\nis typically small when considering trends over periods of multiple decades or longer. When estimated over the \r\nentire historical period (1850-2020), the contribution of natural variability to global surface warming of -0.23degC \r\nto +0.23degC is small compared to the warming of about 1.1degC observed during the same period, which has been \r\nalmost entirely attributed to the human influence."


id: 264860d65f29d218e74342742c34dd9c
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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content:
"To understand how much of observed recent climate change has been caused by natural variability (a process \r\nreferred to as attribution), scientists use climate model simulations. When only natural factors are used to force \r\nclimate models, the resulting simulations show variations in climate on a wide range of time scales in response to \r\nvolcanic eruptions, variations in solar activity, and internal natural variability. However, the influence of natural \r\nclimate variability typically decreases as the time period gets longer, such that it only has mild effects on multi\u0002decadal and longer trends (FAQ 3.2, Figure 1)."


id: 8378d2896fc2e534e97ad8463b2e2116
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 18218
content:
"Consequently, over periods of a couple of decades or less, natural climate variability can dominate the human\u0002induced surface warming trend - leading to periods with stronger or weaker warming, and sometimes even \r\ncooling (FAQ 3.2, Figure 1, left and centre). Over longer periods, however, the effect of natural variability is \r\nrelatively small (FAQ 3.2, Figure 1, right). For instance, over the entire historical period (1850-2019), natural \r\nvariability is estimated to have caused between -0.23degC and +0.23degC of the observed surface warming of \r\nabout 1.1degC. This means that the bulk of the warming has been almost entirely attributed to human activities, \r\nparticularly emissions of greenhouse gases (FAQ 3.1)."


id: e42855bec22ae6f6e5eb7861b9867252
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 33377
content:
"Another way to picture natural variability and human influence is to think of a person walking a dog. The path \r\nof the walker represents the human-induced warming, while their dog represents natural variability. Looking at \r\nglobal surface temperature changes over short periods is akin to focusing on the dog. The dog sometimes moves \r\nahead of the owner and other times behind. This is similar to natural variability that can weaken or amplify \r\nwarming on the short term. In both cases it is difficult to predict where the dog will be or how the climate will \r\nevolve in the near future. However, if we pull back and focus on the slow steady steps of the owner, the path of \r\nthe dog is much clearer and more predictable, as it follows the path of its owner. Similarly, human influence on \r\nthe climate is much clearer over longer time periods."


id: 9e1466a1e8c728b1db3e69b63bf225a6
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 22393
content:
"Natural variability can alter global temperature over short time scales (1 year to ~2 decades) but it has a minimal \r\ninfluence on longer time scales. Since 1850, natural variability ( ) has caused between -0.23degC and 0.23degC \r\nof global temperature change, compared to the warming of about 1.1degC observed ( ) over that period."


id: a7a196feeba656a05e4c599c87decdde
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 23881
content:
"FAQ 3.2, Figure 1 | Annual (left), decadal (middle) and multi-decadal (right) variations in average global surface temperature. The thick \r\nblack line is an estimate of the human contribution to temperature changes, based on climate models, whereas the green lines show the combined effect \r\nof natural variations and human-induced warming, different shadings of green represent different simulations, which can be viewed as showing a range of \r\npotential pasts. The influence of natural variability is shown by the green bars, and it decreases on longer time scales. The data is sourced from the CESM1 \r\nlarge ensemble."


id: 2e2ee799d5b45a6bc3e8c9684ec8fd6
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 4707
content:
"Yes, climate models have improved and continue to do so, becoming better at capturing complex and small\u0002scale processes and at simulating present-day mean climate conditions. This improvement can be measured by \r\ncomparing climate simulations against historical observations. Both the current and previous generations of \r\nmodels show that increases in greenhouse gases cause global warming. While past warming is well simulated by \r\nthe new generation models as a group, some individual models simulate past warming that is either below or \r\nabove what is observed. The information about how well models simulate past warming, as well as other insights \r\nfrom observations and theory, are used to refine this Report's projections of global warming."


id: 1ff93f69c12fd87715f0eb828dbe4525
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 2473
content:
"Climate models are important tools for understanding past, present and future climate change. They are \r\nsophisticated computer programs that are based on fundamental laws of physics of the atmosphere, ocean, \r\nice, and land. Climate models perform their calculations on a three-dimensional grid made of small bricks \r\nor 'gridcells' of about 100 km across. Processes that occur on scales smaller than the model grid cells (such \r\nas the transformation of cloud moisture into rain) are treated in a simplified way. This simplification is done \r\ndifferently in different models. Some models include more processes and complexity than others; some represent \r\nprocesses in finer detail (smaller grid cells) than others. Hence the simulated climate and climate change vary \r\nbetween models."


id: 2beee29107b6c896a2b5f3234208bd93
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 4337
content:
"Scientists evaluate the performance of climate models by comparing historical climate model simulations to \r\nobservations. This evaluation includes comparison of large-scale averages as well as more detailed regional and \r\nseasonal variations. There are two important aspects to consider: (i) how models perform individually and (ii) \r\nhow they perform as a group. The average of many models often compares better against observations than any \r\nindividual model, since errors in representing detailed processes tend to cancel each other out in multi-model \r\naverages."


id: 8a08bca4a25231d771e171e27ea9d678
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 19245
content:
"As an example, FAQ 3.3 Figure 1 compares simulations from the three most recent generations of models (available \r\naround 2008, 2013 and 2021) with observations of three climate variables. It shows the correlation between \r\nsimulated and observed patterns, where a value of 1 represents perfect agreement. Many individual models of \r\nthe new generation perform significantly better, as indicated by values closer to 1. As a group, each generation \r\nout-performs the previous generation: the multi-model average (shown by the longer lines) is progressively closer \r\nto 1. The vertical extent of the colored bars indicates the range of model performance across each group. The \r\ntop of the bar moves up with each generation, indicating improved performance of the best performing models \r\nfrom one generation to the next. In the case of precipitation, the performance of the worst performing models \r\nis similar in the two most recent model generations, increasing the spread across models."


id: 381e8faded4ed427c465b661e12ade39
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 6200
content:
"Developments in the latest generation of climate models, including new and better representation of physical, \r\nchemical and biological processes, as well as higher resolution, have improved the simulation of many aspects \r\nof the Earth system. These simulations, along with the evaluation of the ability of the models to simulate past \r\nwarming as well as the updated assessment of the temperature response to a doubling of CO2 in the atmosphere, \r\nare used to estimate the range of future global warming (FAQ 7.3)."


id: fdd48c4f816c2f85786b901228cc27a7
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 37453
content:
"FAQ 3.3, Figure  1 | Pattern correlations between models and observations of three different variables: surface air temperature, \r\nprecipitation and sea level pressure. Results are shown for the three most recent generations of models, from the Coupled Model Intercomparison \r\nProject (CMIP): CMIP3 (orange), CMIP5 (blue) and CMIP6 (purple). Individual model results are shown as short lines, along with the corresponding ensemble \r\naverage (long line). For the correlations the yearly averages of the models are compared with the reference observations for the period 1980-1999, with \r\n1 representing perfect similarity between the models and observations. CMIP3 simulations performed in 2004-2008 were assessed in the IPCC Fourth \r\nAssessment, CMIP5 simulations performed in 2011-2013 were assessed in the IPCC Fifth Assessment, and CMIP6 simulations performed in 2018-2021 \r\nare assessed in this Report."


id: 31ebaa1079cd833155642a67dab942e1
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 5268
content:
"Govindasamy Bala (India/United States of America), Long Cao (China), Susanna Corti (Italy), \r\nJohn P. Dunne (United States of America), Francois Engelbrecht (South Africa), Erich Fischer \r\n(Switzerland), John C. Fyfe (Canada), Christopher Jones (United Kingdom), Amanda Maycock \r\n(United Kingdom), Joseph Mutemi (Kenya), Ousmane Ndiaye (Senegal), Swapna Panickal (India), \r\nTianjun Zhou (China)"


id: 3cf50ea43404febc8919d967ebdb1bdc
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 6953
content:
"Lee, J.-Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, \r\nJ. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near\u0002Term Information. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the \r\nSixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. \r\nPirani, S.L.  Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, \r\nJ.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, \r\nCambridge, United Kingdom and New York, NY, USA, pp. 553-672, doi:10.1017/9781009157896.006."


id: fc3d33d5be18a5a4ffdee6e1a2bd0a7
created_at: 2023-03-16 14:00:40
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content_type: text
index: climate_gpt_only_giec
vector_id: 37191
content:
"of linear extrapolation, the SR1.5 estimate of when 1.5degC global \r\nwarming is crossed is close to the central estimate reported here. \r\nIt is more likely than not that under SSP1-1.9, GSAT relative to \r\n1850-1900 will remain below 1.6degC throughout the 21st  century, \r\nimplying a potential temporary overshoot of 1.5degC global warming \r\nof no more than 0.1degC. If climate sensitivity lies near the lower end of \r\nthe assessed very likely range, crossing the 1.5degC warming threshold \r\nis avoided in scenarios SSP1-1.9 and SSP1-2.6 (medium confidence). \r\n{2.3.1, Cross-Chapter Box 2.3, 3.3.1, 4.3.4, Box 4.1, 7.5}"


id: 28c10bc574d6cc6234a1643278494f55
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 3841
content:
"This chapter assesses simulations of future global climate change, \r\nspanning time horizons from the near term (2021-2040), mid-term \r\n(2041-2060), and long term (2081-2100) out to the year 2300. \r\nChanges are assessed relative to both the recent past (1995-2014) \r\nand the 1850-1900 approximation to the pre-industrial period."


id: 48e01a7be7bd267dd9d06a274693b6b6
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 8846
content:
"By 2030, GSAT in any individual year could exceed 1.5degC \r\nrelative to 1850-1900 with a  likelihood between 40% \r\nand 60%, across the scenarios considered here (medium \r\nconfidence). Uncertainty in near-term projections of annual GSAT \r\narises in roughly equal measure from natural internal variability and \r\nmodel uncertainty (high  confidence). By contrast, near-term annual \r\nGSAT levels depend less on the scenario chosen, consistent with the \r\nIPCC Fifth Assessment Report (AR5) assessment. Forecasts initialized \r\nfrom recent observations simulate annual GSAT changes for the \r\nperiod 2019-2028 relative to the recent past that are consistent with \r\nthe assessed very likely range (high confidence). {4.4.1, Box 4.1}"


id: bd014655823801f5ba36298a336b98a9
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 27298
content:
"Compared to the recent past (1995-2014), GSAT averaged over \r\nthe period 2081-2100 is very likely to be higher by 0.2degC-1.0degC \r\nin the low-emissions scenario SSP1-1.9 and by 2.4degC-4.8degC \r\nin the high-emissions scenario SSP5-8.5. For the scenarios \r\nSSP1-2.6, SSP2-4.5, and SSP3-7.0, the corresponding very likely\r\nranges are 0.5degC-1.5degC, 1.2degC-2.6degC, and 2.0degC-3.7degC, respectively. \r\nThe uncertainty ranges for the period 2081-2100 continue to be \r\ndominated by the uncertainty in ECS and TCR (very high confidence). \r\nEmissions-driven simulations for SSP5-8.5 show that carbon-cycle \r\nuncertainty is too small to change the assessment of GSAT projections \r\n(high confidence). {4.3.1, 4.3.4, 4.6.2, 7.5}"


id: b4b0d3f2b6d6e9d302a08d029921c86b
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 25957
content:
"Assessed future change in GSAT is, for the first time in an \r\nIPCC report, explicitly constructed by combining scenario\u0002based projections with observational constraints based on \r\npast simulated warming, as well as an updated assessment \r\nof equilibrium climate sensitivity (ECS) and transient \r\nclimate response (TCR). Climate forecasts initialized using recent \r\nobservations have also been used for the period 2019-2028. \r\nThe inclusion of additional lines of evidence has reduced the assessed \r\nuncertainty ranges for each scenario. {4.3.1, 4.3.4, 4.4.1, 7.5}"


id: 7d95656f8d219b034e4600457484a5e5
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 17332
content:
"The CMIP6 models project a  wider range of GSAT change \r\nthan the assessed range (high confidence); furthermore, \r\nthe CMIP6 GSAT increase tends to be larger than in CMIP5 \r\n(very high confidence). About half of the increase in simulated \r\nwarming has occurred because higher climate sensitivity is more \r\nprevalent in CMIP6 than in CMIP5; the other half arises from higher \r\nERF in nominally comparable scenarios (e.g., RCP8.5 and SSP5-8.5; \r\nmedium confidence). In SSP1-2.6 and SSP2-4.5, ERF changes also \r\nexplain about half of the changes in the range of warming (medium \r\nconfidence). For SSP5-8.5, higher climate sensitivity is the primary \r\nreason behind the upper end of the warming being higher than in \r\nCMIP5 (medium confidence). {4.3.1, 4.3.4, 4.6.2, 7.5.6}"


id: 2a3fead183bda3e00fc414a89467154d
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 4071
content:
"While high-warming storylines - those associated with GSAT \r\nlevels above the upper bound of the assessed very likely range - \r\nare by definition extremely unlikely, they cannot be ruled out. \r\nFor SSP1-2.6, such a high-warming storyline implies long-term \r\n(2081-2100) warming well above, rather than well below, 2degC \r\n(high confidence). Irrespective of scenario, high-warming storylines \r\nimply changes in many aspects of the climate system that exceed the \r\npatterns associated with the central estimate of GSAT changes by up \r\nto more than 50% (high confidence). {4.3.4, 4.8}"


id: 4e140b732ab329796f0bad40200e2b3f
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 9707
content:
"It is virtually certain that the average surface warming will \r\ncontinue to be higher over land than over the ocean and that \r\nthe surface warming in the Arctic will continue to be more \r\npronounced than the global average over the 21st  century. \r\nOn average, the surface is expected to warm faster over land \r\nthan over the ocean by a  factor of 1.5 (likely range 1.4 to 1.7). \r\nThe warming pattern likely varies across seasons, with northern high \r\nlatitudes warming more during boreal winter than summer (medium \r\nconfidence). Regions with increasing or decreasing year-to-year \r\nvariability of seasonal mean temperatures will likely increase in their \r\nspatial extent. {4.3.1, 4.5.1, 7.4.4}"


id: 4e3523fe7b6e2f7a065c559f82f3d51b
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 9730
content:
"In response to greenhouse gas-induced warming, it is likely that \r\nglobal land monsoon precipitation will increase, particularly \r\nin the Northern Hemisphere, although Northern Hemisphere \r\nmonsoon circulation will likely weaken. In the long term \r\n(2081-2100), monsoon rainfall change will feature a  north-south \r\nasymmetry characterized by a  greater increase in the Northern \r\nHemisphere than in the Southern Hemisphere and an east-west \r\nasymmetry characterized by an increase in Asian-African monsoon \r\nregions and a  decrease in the North American monsoon  region \r\n(medium confidence). Near-term changes in global monsoon \r\nprecipitation and circulation are uncertain due to model uncertainty \r\nand internal variability such as Atlantic Multi-decadal Variability \r\nand Pacific Decadal Variability (medium  confidence). {4.4.1, 4.5.1, \r\n8.4.1, 10.6.3}"


id: eac8f9b00034adf0450043f7c749db0f
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 34444
content:
"It is very likely that long-term lower-tropospheric warming \r\nwill be larger in the Arctic than in the global mean. It is very \r\nlikely that global mean stratospheric cooling will be larger by the \r\nend of the 21st century in a pathway with higher atmospheric CO2\r\nconcentrations. It is likely that tropical upper tropospheric warming \r\nwill be larger than at the tropical surface, but with an uncertain \r\nmagnitude owing to the effects of natural internal variability and \r\nuncertainty in the response of the climate system to anthropogenic \r\nforcing. {4.5.1, 3.3.1.2}"


id: 4048159a1ddefef11c20da3558cb1bf5
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 7493
content:
"It is likely that at least one large volcanic eruption will occur \r\nduring the 21st century. Such an eruption would reduce GSAT \r\nfor several years, decrease global mean land precipitation, alter \r\nmonsoon circulation, modify extreme precipitation, and change \r\nthe profile of many regional climatic impact-drivers. A  low\u0002likelihood, high-impact outcome would be several large eruptions \r\nthat would greatly alter the 21st century climate trajectory compared \r\nto SSP-based Earth system model projections. {Cross-Chapter Box 4.1}"


id: 1d368c0822d687bc612f4a1a711d690a
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 2035
content:
"Annual global land precipitation will increase over the \r\n21st  century as GSAT increases (high confidence). The likely\r\nrange of change in globally averaged annual land precipitation \r\nduring 2081-2100 relative to 1995-2014 is -0.2 to +4.7% \r\nin the low-emissions scenario SSP1-1.9 and 0.9-12.9% in \r\nthe high-emissions scenario SSP5-8.5, based on all available \r\nCMIP6 models. The corresponding likely ranges are 0.0-6.6% in \r\nSSP1-2.6, 1.5-8.3% in SSP2-4.5, and 0.5-9.6% in SSP3-7.0. {4.3.1, \r\n4.5.1, 4.6.1, 8.4.1}"


id: c38bc64be237c7cf728f5eabc5322a27
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 28336
content:
"In the near term, the forced change in Southern Annular \r\nMode in austral summer is likely to be weaker than observed \r\nduring the late 20th century under all five SSPs assessed. This \r\nis because of the opposing influence in the near- to mid-term from \r\nstratospheric ozone recovery and increases in other greenhouse gases \r\non the Southern Hemisphere summertime mid-latitude circulation \r\n(high confidence). In the near term, forced changes in the Southern \r\nAnnular Mode in austral summer are therefore likely to be smaller \r\nthan changes due to natural internal variability. {4.3.3, 4.4.3}"


id: c112c16b24007d8bdb18ca033640ab0a
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 27952
content:
"Precipitation change will exhibit substantial regional \r\ndifferences and seasonal contrast as GSAT increases over the \r\n21st century (high confidence). As warming increases, a larger land \r\narea will experience statistically significant increases or decreases \r\nin precipitation (medium confidence). Precipitation will very likely\r\nincrease over high latitudes and the tropical oceans, and likely increase \r\nin large parts of the monsoon region, but likely decrease over large \r\nparts of the subtropics in response to greenhouse gas-induced \r\nwarming. Interannual variability of precipitation over many land \r\nregions will increase with global warming (medium confidence). \r\n{4.5.1, 4.6.1, 8.4.1}"


id: b5b34511ac92a23398dc0d10d44b38f6
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 26116
content:
"In the long term, the Southern Hemisphere mid-latitude jet \r\nis likely to shift  poleward and strengthen under  SSP5-8.5 \r\nrelative to 1995-2014. This is likely to be accompanied by an \r\nincrease in the Southern Annular Mode index in all seasons relative \r\nto 1995-2014. For SSP1-2.6, CMIP6 models project no robust change \r\nin the Southern Annular Mode index in the long term. It is likely that \r\nwind speeds associated with extratropical cyclones will strengthen \r\nin the Southern Hemisphere storm track for SSP5-8.5. {4.5.1, 4.5.3}"


id: 23dfcf2a3510d327e4a4b1f1eee283d6
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 3086
content:
"The CMIP6 multi-model ensemble projects a  long-term \r\nincrease in the boreal wintertime Northern Annular Mode \r\nindex under the high-emissions scenarios of SSP3-7.0 and \r\nSSP5-8.5, but regional changes may deviate from a simple shift \r\nin the mid-latitude circulation. Substantial uncertainty and thus \r\nlow confidence remain in projecting regional changes in Northern \r\nHemisphere jet streams and storm tracks, especially for the North \r\nAtlantic basin in winter; this is due to large natural internal variability, \r\nthe competing effects of projected upper- and lower-tropospheric \r\ntemperature gradient changes, and new evidence of weaknesses in \r\nsimulating past variations in North Atlantic atmospheric circulation "


id: 8d9d1738115032a273b441f32bea865a
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updated_at: 2023-03-16 14:00:40
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vector_id: 19808
content:
"Near-term projected changes in precipitation are uncertain, \r\nmainly because of natural internal variability, model \r\nuncertainty, and uncertainty in natural and anthropogenic \r\naerosol forcing (medium confidence).  In the near term, no \r\ndiscernible differences in precipitation changes are projected \r\nbetween different SSPs (high confidence). The anthropogenic aerosol \r\nforcing decreases in most scenarios, contributing to increases in \r\nGSAT (medium confidence) and global mean land precipitation \r\n(low confidence). {4.3.1, 4.4.1, 4.4.4, 8.5}"


id: f9409785a4d7e0f2c8ab9dea77bf1faa
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 36723
content:
"on seasonal-to-decadal time scales. One exception is the expected \r\ndecrease in frequency of atmospheric blocking events over Greenland \r\nand the North Pacific in boreal winter in SSP3-7.0 and SSP5-8.5 \r\nscenarios (medium confidence). {4.5.1}"


id: 2d2e81ec6568663f2a9b156284555c15
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 4552
content:
"If strong mitigation is applied from 2020 onward as reflected \r\nin SSP1-1.9, its effect on 20-year trends in GSAT would likely\r\nemerge during the near term (2021-2040), measured against \r\nan assumed non-mitigation scenario such as SSP3-7.0 and \r\nSSP5-8.5. However, the response of many other climate \r\nquantities to mitigation would be largely masked by internal \r\nvariability during the near term, especially on the regional scale \r\n(high confidence). The mitigation benefits for these quantities would \r\nemerge only later during the 21st century (high confidence). During \r\nthe near term, a small fraction of the surface can show cooling under \r\nall scenarios assessed here, so near-term cooling at any given location \r\nis fully consistent with GSAT increase (high confidence). Events \r\nof reduced and increased GSAT trends at decadal time scales will \r\ncontinue to occur in the 21st century but will not affect the centennial \r\nwarming (very high confidence). {4.6.3, Cross-Chapter Box 3.1}"


id: f769d72c2267430ee08baf96be861c30
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 36439
content:
"Near-term predictions and projections of the sub-polar branch \r\nof the Atlantic Multi-decadal Variability (AMV) on the decadal \r\ntime scale have improved in CMP6 models compared to CMIP5 \r\n(high confidence). This is likely to be related to a more accurate \r\nresponse to natural forcing in CMIP6 models. Initialization contributes \r\nto the reduction of uncertainty and to predicting subpolar sea surface \r\ntemperature. AMV influences on the nearby regions can be predicted \r\nover lead times of 5-8 years (medium confidence). {4.4.3}"


id: 7a7a224d501120860919073181bd0bde
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 16841
content:
"It is virtually certain that the El Nino-Southern Oscillation \r\n(ENSO) will remain the dominant mode of interannual \r\nvariability in a warmer world. There is no model consensus for \r\na systematic change in intensity of ENSO sea surface temperature \r\nvariability over the 21st century in any of the SSP scenarios assessed \r\n(medium confidence). However, it is very likely that ENSO rainfall \r\nvariability, used for defining extreme El Ninos and La Ninas, will \r\nincrease significantly, regardless of amplitude changes in ENSO \r\nSST variability, by the second half of the 21st century in scenarios \r\nSSP2-4.5, SSP3-7.0, and SSP5-8.5. {4.3.3, 4.5.3, 8.4.2}"


id: d2c01fc54397e461371ca9a5b7032302
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 30679
content:
"Because of the near-linear relationship between cumulative \r\ncarbon emissions and GSAT change, the cooling or avoided \r\nwarming from carbon dioxide removal (CDR) is proportional \r\nto the cumulative amount of CO2 removed by CDR \r\n(high  confidence). The climate system response to net negative \r\nCO2 emissions is expected to be delayed by years to centuries. \r\nNet negative CO2 emissions due to CDR will not reverse some \r\nclimate change, such as sea level rise, at least for several centuries \r\n(high confidence). The climate effect of a sudden and sustained CDR \r\ntermination would depend on the amount of CDR-induced cooling \r\nprior to termination and the rate of background CO2 emissions at the \r\ntime of termination (high confidence). {4.6.3, 5.5, 5.6}"


id: 95e514c16f21623ea7ceb1c6b927e6d2
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 21086
content:
"Under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, it is \r\nlikely that the Arctic Ocean in September, the month of annual \r\nminimum sea ice area, will become practically ice-free (sea \r\nice area less than 1 million km2) averaged over 2081-2100 \r\nand all available simulations. Arctic sea ice area in March, the \r\nmonth of annual maximum sea ice area, also decreases in the future \r\nunder each of the considered scenarios, but to a much lesser degree \r\n(in percentage terms) than in September (high confidence). {4.3.2}"


id: 8d88f507f7685c53243900222ef9f05f
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 19792
content:
"Solar radiation modification (SRM) could offset some of the \r\neffects of anthropogenic warming on global and regional climate, \r\nbut there would be substantial residual and overcompensating \r\nclimate change at the regional scale and seasonal time \r\nscale (high  confidence), and there is low confidence in our \r\nunderstanding of the climate response to SRM, specifically \r\nat the regional scale. Since AR5, understanding of the global and \r\nregional climate response to SRM has improved, due to modelling work \r\nwith more sophisticated treatment of aerosol-based SRM options and \r\nstratospheric processes. Improved modelling suggests that multiple \r\nclimate goals could be met simultaneously. A sudden and sustained \r\ntermination of SRM in a  high-emissions scenario such as SSP5-8.5 \r\nwould cause a  rapid climate change (high  confidence). However, \r\na gradual phase-out of SRM combined with emissions reductions and \r\nCDR would more likely than not avoid larger rates of warming. {4.6.3}"


id: d1f7c36340de43865264938557d3b09a
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 30562
content:
"Under the five scenarios assessed, it is virtually certain\r\nthat global mean sea level (GMSL) will continue to rise \r\nthrough the 21st century. For the period 2081-2100 relative to \r\n1995-2014, GMSL is likely to rise by 0.46-0.74 m under SSP3-7.0 \r\nand by 0.30-0.54 m under SSP1-2.6 (medium confidence). For the \r\nassessment of change in GMSL, the contribution from land-ice melt \r\nhas been added offline to the CMIP6-simulated contributions from \r\nthermal expansion. {4.3.2. 9.6}"


id: 8c756a8d60147dcc39f35fa30f80fcb
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 19616
content:
"It is very likely that the cumulative uptake of carbon by \r\nthe ocean and by land will increase through to the end of the \r\n21st century. Carbon uptake by land shows greater increases but \r\nwith greater uncertainties than for ocean carbon uptake. The fraction \r\nof emissions absorbed by land and ocean sinks will be smaller \r\nunder high emissions scenarios than under low emissions scenarios \r\n(high confidence). Ocean surface pH will decrease steadily through \r\nthe 21st  century, except for SSP1-1.9 and SSP1-2.6 where values \r\ndecrease until around 2070 and then increase slightly to 2100 \r\n(high confidence). {4.3.2, 5.4}"


id: 6520b5c4d17c5897a75438011c37aa5f
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 13477
content:
"Earth system modelling experiments since AR5 confirm that the \r\nzero CO2 emissions commitment (the additional rise in GSAT \r\nafter all CO2 emissions cease) is small (likely less than 0.3degC in \r\nmagnitude) on decadal time scales, but that it may be positive \r\nor negative. There is low confidence in the sign of the zero CO2\r\nemissions commitment. Consistent with SR1.5, the central estimate is "


id: 34b186368b9248a8557378d523f3d022
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 5692
content:
"taken as zero for assessments of remaining carbon budgets for global \r\nwarming levels of 1.5degC or 2degC. {4.7.2, 5.5.2}."


id: 42ee9fc5e0875ecafb8d2f20295e96dd
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 7924
content:
"Overshooting specific global warming levels such as 2degC \r\nhas effects on the climate system that persist beyond 2100 \r\n(medium confidence). Under one scenario including a  peak and \r\ndecline in atmospheric CO2 concentration (SSP5-3.4-OS), some \r\nclimate metrics such as GSAT begin to decline but do not fully reverse \r\nby 2100 to levels prior to the CO2 peak (medium confidence). GMSL \r\ncontinues to rise in all models up to 2100 despite a reduction in CO2\r\nto 2040 levels. {4.6.3, 4.7.1, 4.7.2}"


id: bfef8520679c9bbc7517faf99a75ca54
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 27753
content:
"Using extended scenarios beyond 2100, projections show likely\r\nwarming by 2300, relative to 1850-1900, of 1.0degC-2.2degC for \r\nSSP1-2.6 and 6.6degC-14.1degC for SSP5-8.5. By 2300, warming under \r\nthe SSP5-3.4-OS overshoot scenario decreases from a peak around \r\nyear 2060 to a level very similar to SSP1-2.6. Precipitation over land \r\ncontinues to increase strongly under SSP5-8.5. GSAT projected for the \r\nend of the 23rd century under SSP2-4.5 (likely 2.3degC-4.6degC higher \r\nthan over the period 1850-1900) has not been experienced since the \r\nmid-Pliocene, about 3 million years ago. GSAT projected for the end \r\nof the 23rd century under SSP5-8.5 (likely 6.6degC-14.1degC higher than \r\nover the period 1850-1900) overlaps with the range estimated for \r\nthe Miocene Climatic Optimum (5degC-10degC higher) and Early Eocene \r\nClimatic Optimum (10degC-18degC higher), about 15 and 50 million \r\nyears ago, respectively (medium confidence). {2.3.1.1, 4.7.1}"


id: 1e22c49d1e14f68e52b1f3943c6422d9
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 2179
content:
"This chapter assesses simulations of future climate change, covering \r\nboth near-term and long-term global changes. The chapter assesses \r\nsimulations of physical indicators of global climate change, such as \r\nglobal surface air temperature (GSAT), global land precipitation, \r\nArctic sea ice area (SIA), and global mean sea level (GMSL). \r\nFurthermore, the chapter covers indices and patterns of properties \r\nand circulation not only for mean fields but also for modes of \r\nvariability that have global significance. The choice of quantities to \r\nbe assessed is summarized in Cross-Chapter Box 2.2 and comprises \r\na subset of the quantities covered in Chapters 2 and 3. This chapter \r\nprovides consistent coverage from near-term to long-term global \r\nchanges and provides the global reference for the later chapters \r\ncovering important processes and regional change."


id: 72220a67781796120584196e45f6000d
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 15584
content:
"Essential input to the simulations assessed here is provided by future \r\nscenarios of concentrations or anthropogenic emissions of radiatively \r\nactive substances; the scenarios represent possible sets of decisions \r\nby humanity, without any assessment that one set of decisions is \r\nmore probable to occur than any other set (Section  1.6). As in \r\nprevious assessment reports, these scenarios are used for projections \r\nof future climate using global atmosphere-ocean general circulation \r\nmodels (AOGCMs) and Earth system models (ESMs; Section 1.5.3); \r\nthe latter include representation of various biogeochemical cycles \r\nsuch as the carbon cycle, the sulphur cycle, or ozone (e.g.,  Flato, \r\n2011; Flato et al., 2013). This chapter thus provides a comprehensive \r\nassessment of the future global climate response to different future \r\nanthropogenic perturbations to the climate system."


id: 30bd580925b6ca88ffc32fad30d91ac8
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 5090
content:
"The uncertainty assessment in this chapter builds on one particularly \r\nnoteworthy advance since the IPCC Fifth Assessment Report (AR5). \r\nInternal variability, which constitutes irreducible uncertainty over \r\nmuch of the time horizon considered here (Hawkins et  al., 2016; \r\nMarotzke, 2019), can be better estimated in models even under \r\na  changing climate through the use of large initial-condition \r\nensembles (Kay et  al., 2015). For many climate quantities and \r\ncompared to the forced climate change signal, internal variability is \r\ndominant in any individual realization - including the one that will \r\nunfold in reality - in the near term (Kirtman et al., 2013; Marotzke \r\nand Forster, 2015), is substantial in the mid-term, and is still \r\nrecognizable in the long term in many quantities (Deser et al., 2012a; \r\nMarotzke and Forster, 2015). This chapter will use the strengthened \r\ninformation on internal variability throughout."


id: 5f2bcaafd44f8b0bb22e8e44a3f68ba7
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 12534
content:
"The expanded treatment of uncertainty allows this chapter a more \r\ncomprehensive assessment of the benefits from mitigation than in \r\nprevious IPCC reports, as well as the climate response to carbon \r\ndioxide removal (CDR) and solar radiation modification (SRM), and \r\nhow to detect them against the backdrop of internal variability. \r\nImportant advances have been made in the detection and attribution \r\nof mitigation, CDR, and SRM (Burger and Cubasch, 2015; Lo et al., \r\n2016; Ciavarella et  al., 2017); exploring the 'time of emergence' \r\n(ToE; see Annex VII: Glossary) of responses to assumed emissions \r\nreductions (Tebaldi and Friedlingstein, 2013; Samset et  al., 2020) \r\nand the attribution of decadal events to forcing changes that \r\nreflect emissions reductions (Marotzke, 2019; Spring et  al., 2020; \r\nMcKenna et al., 2021)."


id: 1f805eaaa471582502882e7f2f7a8454
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 2387
content:
"The question of the potential crossing of thresholds relative to global \r\ntemperature goals (Geden and Loeschel, 2017) is intimately related \r\nto the benefits of mitigation; a prerequisite is an assessment of how \r\nrobustly magnitudes of warming can be defined (Millar et al., 2017). \r\nThis chapter provides an update to the IPCC Special Report on Global \r\nWarming of 1.5degC (SR1.5, IPCC, 2018a) and constitutes a reference \r\npoint for later chapters and AR6 WGIII on the effects of mitigation, \r\nincluding a robust uncertainty assessment."


id: 41a606d6262a15d630ac3c7a19ff10c8
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 7727
content:
"A central element of this chapter is a  comprehensive assessment \r\nof the sources of uncertainty of future projections (Section  1.4.3). \r\nUncertainty can be broken down into scenario uncertainty, model \r\nuncertainty involving model biases, uncertainty in simulated effective \r\nradiative forcing and model response, and the uncertainty arising "


id: ce4e87b7668bcc9fa3c318f2e6581ba
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 29988
content:
"Section 4.4 synthesizes information from initialized predictions and \r\nnon-initialized projections for the near-term change."


id: 11ef9a1ac75cd64d2836f01670700c26
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 321
content:
"Section  4.5 then covers mid-term and long-term climate change, \r\ndefined here as the periods 2041-2060 and 2081-2100, respectively, \r\nagain relative to the period 1995-2014. The mid-term period is thus \r\nchosen as the twenty-year period following the short-term period \r\nand straddling the mid-century point, year 2050; it is during the \r\nmid-term that differences between scenarios are expected to emerge \r\nagainst internal variability. The long-term period is defined, as in \r\nAR5, as the 20-year period at the end of the century. Section 4.5 \r\nassesses the same set of indicators as Section  4.4, as well as \r\nchanges in internal variability and in large-scale patterns, both of \r\nwhich are expected to emerge in the mid- to long-term. The chapter \r\nsub-division according to time slices (near term, mid-term, and long \r\nterm) is thus to a large extent motivated by the different roles that \r\ninternal variability plays in each period, compared to the expected \r\nforced climate-change signal."


id: 2ce3702a68d468afc460f85c235acf1e
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 4499
content:
"Section  4.6 assesses the climate implications of climate policies, as \r\nsimulated with climate models. First, Section 4.6 assesses patterns of \r\nclimate change expected for various levels of GSAT rise including 1.5degC, \r\n2degC, 3degC, and 4degC, compared to the approximation to the pre-industrial \r\nperiod 1850-1900 to facilitate immediate connection to SR1.5 and \r\nthe temperature goals specified in the Paris Agreement (UNFCCC, \r\n2016). Section 4.6 continues with climate goals, overshoot, and path\u0002dependence, as well as the climate response to mitigation, CDR, and \r\nSRM. Section 4.6 also covers the consistency between RCPs and SSPs."


id: bdc23b001b6d036701b383f595948628
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 27414
content:
"Section  4.4 covers near-term climate change, defined here as the \r\nperiod 2021-2040 and taken relative to the period 1995-2014. \r\nSection  4.4 focuses on global and large-scale climate indicators, \r\nincluding precipitation and circulation indices and selected modes \r\nof variability (see Cross-Chapter Box  2.2 and Annex IV: Modes  of \r\nVariability), as well as on the spatial distribution of warming. \r\nThe  potential roles of short-lived climate forcers (SLCFs) and \r\nvolcanic eruptions on near-term climate change are also discussed. "


id: f7afc2bd5e5ee9c96aca511134ff668d
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 36486
content:
"primarily under ScenarioMIP (O'Neill et  al., 2016). This  allows us to \r\nassess both dimensions of integration across scenarios (Section 4.3) \r\nand global warming levels (Section  4.6) as discussed in Chapter  1 \r\n(Section 1.6). Other MIPs also target future scenarios with a focus on \r\nspecific processes or feedbacks and are summarized in Table 4.1."


id: 47e5c9417992f1c28f179c4356e5092f
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 8680
content:
"Section  4.7 assesses very long-term changes in selected global \r\nclimate indicators, from 2100 to 2300. Section  4.7 continues with \r\nclimate-change commitment and the potential for irreversibility and \r\nabrupt climate change. The chapter concludes with Section 4.8 on \r\nthe potential for low-likelihood, high-impact storylines, followed by \r\nanswers to three frequently asked questions (FAQs)."


id: d8f1969e06b947ceb50c22a6ef3086f9
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 31641
content:
"Multi-model ensembles provide the central focus of projection \r\nassessment. While single-model experiments have great value for \r\nexploring new results and theories, multi-model ensembles additionally \r\nunderpin the assessment of the robustness, reproducibility, and \r\nuncertainty attributable to model internal structure and processes \r\nvariability (Section  4.2.5; Hawkins and Sutton, 2009). Techniques \r\nunderlying the combination of evaluation and weighting that are \r\napplied in this chapter are synthesized in Box 4.1."


id: eb8ccb3fe94fc51dd8148e1f156feb50
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 34561
content:
"Climate model simulations can be performed in either 'concentration\u0002driven' or 'emissions-driven' configurations reflecting whether the \r\nCO2 concentration is prescribed to follow a pre-defined pathway or \r\nis simulated by the Earth system models in response to prescribed \r\nemissions of CO2 (Box 6.4, Ciais et al., 2013). The majority of CMIP6 \r\nexperiments are conducted in concentration-driven configurations in \r\norder to enable models without a  fully interactive carbon cycle to \r\nperform them, and throughout most of this chapter we present results \r\nfrom those simulations unless otherwise stated. Concentrations of \r\nother greenhouse gases are always prescribed. However, the SSP5-8.5 \r\nscenario has also been performed in emissions-driven configuration \r\n('esm-ssp585') by 10 ESMs, and in Section  4.3.1.1 we assess the \r\nimpact on simulated climate over the 21st century."


id: cda9d7c4ddaa0a87dcaac79398339260
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 29884
content:
"Internal variability complicates the identification of forced climate \r\nsignals, especially when considering regional climate signals over \r\nshort time scales (up to a few decades), such as local trends over the \r\nsatellite era (Hawkins and Sutton, 2009; Deser et al., 2012a; Xie et al., \r\n2015; Lovenduski et  al., 2016; Suarez-Gutierrez et  al., 2017). Large \r\ninitial-condition ensembles, where the same model is run repeatedly \r\nunder identical forcing but with initial conditions varied through small \r\nperturbations or by sampling different times of a pre-industrial control "


id: 4e10fcc87b366f6f94c548e0172dc9ae
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 9704
content:
"run, have substantially grown in their use since AR5 (Deser  et  al., \r\n2012a; Kay et al., 2015; Rodgers et al., 2015; Hedemann et al., 2017; \r\nStolpe et al., 2018; Maher et al., 2019). Such large ensembles have \r\nshown potential to quantify  uncertainty due to internal variability \r\n(Hawkins et al., 2016; McCusker et al., 2016; Sigmond and Fyfe, 2016; \r\nLehner et al., 2017; McKinnon et al., 2017; Marotzke, 2019) and thereby \r\nextract the forced signal from the internal variability, which can be \r\ncalibrated against observational data to improve the reliability of \r\nprobabilistic climate projections over the near and mid-term (O'Reilly \r\net al., 2020). Moreover, they allow the investigation of forced changes \r\nin internal variability (e.g., Maher et al., 2018). A key assumption is \r\nthat a given model skilfully represents internal variability; structural \r\nuncertainty is not accounted for."


id: d4e4bd3ae7356a1f5474a53141a43b2f
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 31022
content:
"sustainable pathways, SSP2-4.5 for middle-of-the-road, SSP3-7.0 \r\nfor regional rivalry, and SSP5-8.5 for fossil fuel-rich development. This \r\nchapter focuses its assessment on these, plus the SSP1-1.9 scenario, \r\nwhich is directly relevant to the assessment of the 1.5degC Paris \r\nAgreement goal. Further, this chapter discusses these scenarios and \r\ntheir extensions past 2100 in the context of the very long-term climate \r\nchange in Section  4.7.1. Projections of short-lived climate forcers \r\n(SLCFs) are assessed in more detail in Chapter 6 (Section 6.7)."


id: 74c361104bf4b801834ce6799f9b0fbb
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 15965
content:
"A complementary approach that represents structural uncertainty in \r\na given model is stochastic physics (Berner et al., 2017). The approach has \r\nproven useful in representing structural uncertainty on seasonal climate \r\ntime scales (Weisheimer et  al., 2014; Batte and Doblas-Reyes, 2015; \r\nMacLachlan et al., 2015). Stochastic physics can markedly improve the \r\ninternal variability in a given model (Dawson and Palmer, 2015; Wang \r\net al., 2016; Christensen et al., 2017; Davini et al., 2017; Watson et al., \r\n2017; Strommen et al., 2018; Yang et al., 2019). Stochastic physics can \r\nalso correct long-standing mean-state biases (Sanchez-Gomez et  al., \r\n2016) and can influence the predicted climate sensitivity (Christensen \r\nand Berner, 2019; Strommen et al., 2019; Meccia et al., 2020)."


id: c26d18b8eff6663261198a235d4a307d
created_at: 2023-03-16 14:00:40
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vector_id: 28174
content:
"Perturbed-physics ensembles (Murphy et al., 2004) are also used to \r\nsystematically account for parameter uncertainty in a given model. \r\nUncertain model parameters are identified and ranges in their \r\nvalues selected that conform to emergent observational constraints \r\n(see Section 1.5.4.2). These parameters are then changed between \r\nensemble members to sample the effect of parameter uncertainty on \r\nclimate (Piani et al., 2005; Sexton et al., 2012; Johnson et al., 2018; \r\nRegayre et  al., 2018). It is possible to weight ensemble members \r\naccording to some performance metric or emergent constraint \r\n(e.g., Fasullo et al., 2015; Section 1.5.4.7) to improve the ensemble \r\ndistribution (Box 4.1)."


id: 7b06e24970ae626c53b9d0e0d1f3b372
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 16947
content:
"ScenarioMIP simulations include advances in techniques to better \r\nharmonize with historical forcings relative to CMIP5. For example, \r\nprojected changes in the solar cycle include long-term modulation \r\nrather than a repeating solar cycle (Matthes et al., 2017). Background \r\nnatural aerosols are ramped down to an average historical level used \r\nin the control simulation by 2025 and background volcanic forcing \r\nis ramped up from the value at the end of the historical simulation \r\nperiod (2015) over 10 years to the same constant value prescribed \r\nfor the pre-industrial control (piControl) simulations in the DECK, and \r\nthen kept fixed - both changes are intended to avoid inconsistent \r\nmodel treatment of unknowable natural forcing to affect the \r\nnear-term projected warming."


id: b6013e38cd45b299c2a62ab33e544d19
created_at: 2023-03-16 14:00:40
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vector_id: 26164
content:
"The AR5 drew heavily on four main scenarios, known as Representative \r\nConcentration Pathways (RCPs: Meinshausen et al., 2011; van Vuuren \r\net  al., 2011), and simulation results from CMIP5 (Section  4.2.1; \r\nTaylor et al., 2012). The RCPs were labelled by the approximate radiative \r\nforcing reached at the year 2100, going from 2.6, 4.5, 6.0 to 8.5 W m-2."


id: 1acfd3b7b7648a8ad4bff66ef47a4d80
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 1689
content:
"This chapter draws on model simulations from CMIP6 (Eyring \r\net  al.,  2016) using a  new range of scenarios based on Shared \r\nSocio-economic Pathways (SSPs; O'Neill et al., 2016). The set of SSPs is \r\ndescribed in detail in Chapter 1 (Section 1.6) and recognizes that global \r\nradiative forcing levels can be achieved by different pathways of CO2, \r\nnon-CO2 greenhouse gases (GHGs), aerosols (Amann et al., 2013; Rao \r\net al., 2017) and land use; the set of SSPs therefore establishes a matrix \r\nof global forcing levels and socio-economic storylines. ScenarioMIP \r\n(O'Neill et  al., 2016) identifies four priority (tier-1) scenarios that \r\nparticipating modelling groups are asked to perform, SSP1-2.6 for "


id: f97ba526b5623a5f4e8d3c359658e303
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 36761
content:
"2017; Nadiga et al., 2019), and non-linearity can reduce forecast skill \r\n(Chikamoto et al., 2019). As yet, neither of the initialization strategies \r\nhas been clearly shown to be superior (Hazeleger et  al., 2013; \r\nMagnusson et al., 2013; Smith et al., 2013a; Marotzke et al., 2016), \r\nalthough such comparisons may be sensitive to the model, region, \r\nand details of the initialization and forecast assessment procedures \r\nconsidered (Polkova et al., 2014; Bellucci et al., 2015b)."


id: ddc64eecbd3d97e37999dd3187d54745
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 32389
content:
"This subsection describes the three main sources of near-term \r\ninformation used in Chapter 4. These are (i) the projections from the \r\nCMIP6 multi-model ensemble introduced in Section  4.2.1 (Eyring \r\net  al., 2016; O'Neill et  al., 2016); (ii) observationally constrained \r\nprojections (Gillett et  al., 2013; Stott et  al., 2013); and (iii) the \r\ninitialized predictions contributed to CMIP6 from the Decadal \r\nClimate Prediction Project (DCPP; Boer et al., 2016). The projections \r\nunder (i) and the observational constraints under (ii) are used for \r\nall time horizons considered in this chapter, whereas the initialized \r\npredictions under (iii) are relevant only in the near term."


id: b0348e3a7edc8ae591d546aa84e9fbb7
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 25245
content:
"Observationally constrained projections (Gillett et  al., 2013, 2021; \r\nShiogama et al., 2016; Ribes et al., 2021) use detection and attribution \r\nmethods to attempt to reach consistency between observations and \r\nmodels and thus provide improved projections of near-term change. \r\nNotable advances have been made since AR5, for example the ability \r\nto observationally constrain estimates of Arctic sea ice loss for global \r\nwarming of 1.5degC, 2.0degC, and 3.0degC above pre-industrial levels \r\n(Screen and Williamson, 2017; Jahn, 2018; Screen, 2018; Sigmond \r\net  al., 2018). There is high confidence that these approaches can \r\nreduce the uncertainties involved in such estimates."


id: 45111f7e7a2662afd1072b4dbd8d1c34
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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content:
"The AR5 was the first IPCC report to assess decadal climate predictions \r\ninitialized from the observed climate state (Kirtman et al., 2013), and \r\nassessed with high confidence that these predictions exhibit positive \r\nskill for near-term average surface temperature information, globally \r\nand over large regions, for up to ten years. Substantially more experience \r\nin producing initialized decadal predictions has been gained since AR5; \r\nthe remainder of this subsection assesses the advances made."


id: fedd28fb6b4dfff7cff0b2ebfb216a52
created_at: 2023-03-16 14:00:40
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vector_id: 37645
content:
"Because the 'memory' that potentially enables prediction of multi-year \r\nto decadal internal variability resides mainly in the ocean, some systems \r\ninitialize the ocean state only (e.g., Muller et al., 2012; Yeager et al., \r\n2018), whereas others incorporate observed information in the initial \r\natmospheric states (e.g., Pohlmann et al., 2013; Knight et al., 2014) or \r\nother non-oceanic drivers that provide further sources of predictability \r\n(Alessandri et al., 2014; Weiss et al., 2014; Bellucci et al., 2015a)."


id: 38ae9132ad3e8e8ec6ddad1295ef1207
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 6288
content:
"Pasternack et  al., 2018), as well as more general dependences of \r\ndrift on initial conditions (Fuckar et al., 2014; Pasternack et al., 2018; \r\nNadiga et al., 2019)."


id: e73060964665fd1621b2263667a37d1
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 33871
content:
"and salinity potentially affecting surface climate can be predicted \r\nup to several years in advance (Robson et  al., 2012; Hermanson \r\net al., 2014), although such assessments remain challenging due to \r\nincomplete knowledge of the state of the ocean during the hindcast \r\nevaluation periods (Menary and Hermanson, 2018). A  substantial \r\nimprovement of the sub-polar gyre SST prediction is found in CMIP6 \r\nmodels, which is attributed to a  more accurate response to the \r\nAMOC-related delayed response to volcanic eruptions (Section 4.4.3; \r\nBorchert et al., 2021). A significant improvement GSAT prediction skill \r\nis also found over some land regions including East Asia (Monerie \r\net al., 2018), Eurasia (Wu et al., 2019), Europe (Muller et al., 2012; \r\nD.M. Smith et al., 2019) and the Middle East (D.M. Smith et al., 2019)."


id: de5b3e17e3d4e526183534a22f5bc975
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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content:
"Many skill measures exist that describe different aspects of the \r\ncorrespondence between predicted and observed conditions, and no \r\nsingle metric should be considered exclusively. Important aspects of \r\nforecast performance captured by different skill measures include: (i) the \r\nability to predict the sign and phases of the main modes of decadal \r\nvariability and their regional fingerprint through teleconnections; \r\n(ii) the typical magnitude of differences between predicted and observed \r\nvalues, forecast reliability and resolution (Corti et  al., 2012); and \r\n(iii) whether the forecast ensemble appropriately represents uncertainty \r\nin the predictions. A framework for skill assessment that encompasses \r\neach of these aspects of forecast quality has been proposed (Goddard \r\net al., 2013). A new, process-based method to assess forecast skill in \r\ndecadal predictions is to analyse how well a  specific mechanism is \r\nrepresented at each lead time (Mohino et al., 2016)."


id: ba1a46809f725d6d6c8b0a8dab94ec90
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 26821
content:
"One additional aspect of forecast quality assessment is that estimated \r\nskill can be degraded by errors in observational datasets used for \r\nverification, in addition to errors in the predictions (Massonnet \r\net al., 2016; Ferro, 2017; Karspeck et al., 2017; Juricke et al., 2018). \r\nThis suggests that skill may tend to be underestimated, particularly \r\nfor climate variables whose observational uncertainties are relatively \r\nlarge, and that the predictions themselves may prove useful for \r\nassessing the quality of observational datasets (Massonnet, 2019)."


id: 5d605bce39c14dd5bfeaefecf96023c
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 12216
content:
"Evidence is accumulating that additional properties of the Earth \r\nsystem relating to ocean variability may be skilfully predicted on \r\nmulti-annual time scales. These include levels of Atlantic hurricane \r\nactivity (Smith et al., 2010; Caron et al., 2017), winter sea ice in the \r\nArctic (Onarheim et al., 2015; Dai et al., 2020), drought and wildfire \r\n(Chikamoto et al., 2017; Paxian et al., 2019; Solaraju-Murali et al., \r\n2019), and variations in the ocean carbon cycle including CO2 uptake \r\n(H. Li et al., 2016, 2019; Lovenduski et al., 2019; Fransner et al., 2020) \r\nand chlorophyll (Park et al., 2019)."


id: b636257cdf42d3f09b287620df457b01
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 26197
content:
"In summary, despite challenges (Cassou et al., 2018), there is high \r\nconfidence that initialized predictions contribute information to \r\nnear-term climate change for some regions over multi-annual \r\nto  decadal time scales. Furthermore, there are indications that \r\ninitialized predictions can constrain near-term projections (Befort \r\net  al., 2020). The clearest improvements through initialization \r\nare seen in the North Atlantic and related phenomena such as \r\nhurricane frequency, Sahel and European rainfall. By contrast, there \r\nis medium or low confidence that uncertainty is reduced for other \r\nclimate variables."


id: 51b3f70723303256a5ac6f99481e93a1
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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content:
"Initialized predictions of near-surface temperature are particularly \r\nskilful over the North Atlantic, a region of high potential and realized \r\npredictability (Keenlyside et  al., 2008; Pohlmann et  al., 2009; Boer \r\net al., 2013; Yeager and Robson, 2017). Much of this predictability is \r\nassociated with the North Atlantic subpolar gyre (Wouters et al., 2013), \r\nwhere skill in predicting ocean conditions is typically high (Hazeleger \r\net al., 2013; Brune and Baehr, 2020) and shifts in ocean temperature "


id: 8627aed6a01271a24227d7b646e164e9
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 18648
content:
"a  large fast-adjustment component. Even in such cases, pattern \r\nscaling still works for the slow response component, but a correction \r\nfor the forcing-dependent fast adjustment would be necessary to \r\napply pattern scaling to the total climate change signal. In a multi\u0002model setting, it has been shown that temperature change patterns \r\nconform better to pattern scaling approximation than precipitation \r\npatterns (Tebaldi and Arblaster, 2014)."


id: dbdffd9f0c1f14455eb543d941b6e1b7
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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index: climate_gpt_only_giec
vector_id: 32109
content:
"Herger et al. (2015) have explored the use of multiple predictors for \r\nthe spatial pattern of change at a given degree of global warming, \r\nfollowing the approach of Joshi et al. (2013) that explored the role \r\nof the land-sea warming ratio as a second predictor. They found that \r\nthe land-sea warming contrast changes in a  non-linear way with \r\nGSAT, and that it approximates the role of the rate of global warming \r\nin determining regional patterns of climate change. The inclusion \r\nof the land-sea warming contrast as the second predictor provides \r\nthe largest improvement over the traditional technique. However, as \r\npointed out by Herger et  al. (2015), multiple-predictor approaches \r\nstill cannot detect non-linearities (or internal variability), such as the \r\napparent dependence of spatial temperature variability in the mid- to \r\nhigh latitudes on GSAT (e.g., Fischer and Knutti, 2014; Screen, 2014)."


id: 974aaae5e81bcb2a6f082836c4f0e313
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 21324
content:
"Further developments of the AR5 approach have since explored the \r\nrole of aerosols in modifying regional climate responses at a specific \r\ndegree of global warming and also the effect of different GCMs and \r\nscenarios on the scaled spatial patterns (Frieler et al., 2012; Levy et al., \r\n2013). Furthermore, the modified forcing-response framework (Kamae \r\nand Watanabe, 2012, 2013; Sherwood et al., 2015), which decomposes \r\nthe total climate change into fast adjustments and slow response, \r\nidentifies the fast adjustment as forcing-dependent and the slow \r\nresponse as forcing-independent, scaling with the change in GSAT."


id: b983fac91e25436256d77709fce4e599
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 26728
content:
"For precipitation change, there is near-zero fast adjustment for solar \r\nforcing but suppression during the fast-adjustment phase for  CO2\r\nand black-carbon radiative forcing (Andrews et al., 2009; Bala et al., \r\n2010; Cao et al., 2015). By contrast, the slow response in precipitation \r\nchange is independent of the forcing. This indicates that pattern \r\nscaling is not expected to work well for climate variables that have "


id: bd72524c40eeeafc5fbfd36144804cea
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 27374
content:
"are thus constructed using the time-shift approach, thereby \r\naccounting for various non-linearities and internal variability that \r\ninfluence the projected climate change signal. This implies a reliance \r\non large ensemble sizes to quantify the role of uncertainties in \r\nregional responses to different degrees of global warming. The \r\nassessment in Section  4.6.1 also explores how the rate of global \r\nwarming (as represented by different SSPs), aerosol effects, and \r\ntransient as opposed to stabilization scenarios influence the spatial \r\nvariations in climate change at specific levels of global warming."


id: 70402a884aa24730147ba79cbaebc5f
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 15279
content:
"That said, fitness-for-purpose of the climate models used for long-term \r\nprojections is fundamentally difficult to ascertain and remains an \r\nepistemological challenge (Parker, 2009; Frisch, 2015; Baumberger et al., \r\n2017). Some literature exists comparing previous IPCC projections to \r\nwhat has unfolded over the subsequent decades (Cubasch et al., 2013), \r\nand recent work has confirmed that climate models since around 1970 \r\nhave projected global surface warming in reasonable agreement with \r\nobservations once the difference between assumed and actual forcing \r\nhas been taken into account (Hausfather et al., 2020). However, the \r\nlong-term perspective to the end of the 21st century or even out to \r\n2300 takes us beyond what can be observed in time for a standard \r\nevaluation of model projections, and in this sense the assessment of \r\nlong-term projections will remain fundamentally limited."


id: 62ddc7d283a7d15efddc26bf702f0548
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 13124
content:
"The spread across individual runs within a  multi-model ensemble \r\nrepresents the response to a  combination of different sources of \r\nuncertainties (Section 1.4.3), specifically: scenario uncertainties, climate \r\nresponse uncertainties (also referred to as model uncertainties) \r\nrelated to parametric and other structural uncertainties in the \r\nmodel representation of the climate system, and internal variability \r\n(e.g., Hawkins and Sutton, 2009; Kirtman et al., 2013). While the nature \r\nof these uncertainties was introduced in Section 1.4.3, this subsection \r\nassesses methods to disentangle different sources of uncertainties and \r\nquantify their contributions to the overall ensemble spread."


id: f39fe47d010a91e81f3639d4dcb59b2
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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content:
"The relative magnitude of model uncertainty and internal variability \r\ndepends on the time horizon of the projection, location, spatial \r\nand temporal aggregation, variable, and signal strength (Rowell, \r\n2012; Fischer et  al., 2013; Deser et  al., 2014; Saffioti et  al., 2017; \r\nKirchmeier-Young et al., 2019). New literature published after AR5 \r\nsystematically discusses the role of different sources of uncertainty "


id: ffd896c20e5c4592580d8e0f5b5d50ce
created_at: 2023-03-16 14:00:40
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content:
"and shows that the relative contribution of internal variability is larger \r\nfor short than for long projection horizons (Marotzke and Forster, \r\n2015; Lehner et al., 2020; Maher et al., 2021), larger for high latitudes \r\nthan for low latitudes, larger for land than for ocean variables, larger \r\nat station level than for continental or global means, larger for annual \r\nmaxima/minima than for multi-decadal means,  larger for dynamic \r\nquantities (and, by implication, precipitation) than for temperature \r\n(Fischer et al., 2014)."


id: b48047a4702e5f8741b59d85828ff440
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 25933
content:
"The method introduced by Hawkins and Sutton (2009) and applied to \r\nGSAT projections reveals that by the end of the 21st century, the fraction \r\ncontribution of the climate model response uncertainty to the  total \r\nuncertainty is larger in CMIP6 than in CMIP5 whereas the relative \r\ncontribution of scenario uncertainty is smaller (Lehner et  al., 2020). \r\nThis is the case even when sub-selecting pathways and scenarios that \r\nare most similar in CMIP5 and CMIP6, that is, the range from RCP2.6 \r\nto RCP8.5 vs SSP1-2.6 to SSP5-8-5, respectively (Lehner et al., 2020). \r\nThe larger range of response uncertainty is further consistent with the \r\nlarger range of TCR and GSAT warming for a comparable pathway in \r\nCMIP6 than CMIP5 (Forster et al., 2020; Tokarska et al., 2020)."


id: fe2d36d70aa613e087135c5519039199
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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content:
"Some uncertainties are not, or only partially accounted for in the CMIP6 \r\nexperiments, such as uncertainties in natural forcings from solar \r\nand volcanic forcings, long-term Earth system feedbacks including \r\nland-ice feedbacks, groundwater feedbacks (Smerdon,  2017) or \r\nsome long-term carbon-cycle feedbacks (Fischer et al., 2018). Where \r\nappropriate, this chapter uses results from non-CMIP ESMs or EMICs \r\nto assess the role of these feedbacks. Still other uncertainties - such \r\nas further pandemics, nuclear holocaust, global natural disaster \r\nsuch  as tsunami or asteroid impact, or fundamental technological \r\nchange such as fusion - are not accounted for at all."


id: 3e6038300ab9a97b28d9d771d32369d4
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 7184
content:
"The AR5 used a pragmatic approach to quantify the uncertainty in CMIP5 GSAT projections (Collins et al., 2013). The multi-model \r\nensemble was constructed by picking one realization per model per scenario. For most quantities, the 5-95% ensemble range was \r\nused to characterize the uncertainty, but the 5-95% ensemble range was interpreted as the 17-83% (likely) uncertainty range. \r\nThe uncertainty was thus explicitly assumed to contain sources not represented by the model range. While straightforward and clearly \r\ncommunicated, this approach had several drawbacks."


id: a1e5bfbb903d4e687affcd8878eea6b
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 23035
content:
"Another important consideration concerns the potential weighting of model contributions to an ensemble, based on model \r\nindependence, model performance during the historical period, or both. Such model weighting (in fact, model selection) was performed \r\nin AR5 for projections of Arctic sea ice (Collins et al., 2013), but that particular application has subsequently been shown by Notz \r\n(2015) to be contaminated by internal variability, making the resulting weighting questionable (Stroeve and Notz, 2015). For a general \r\ncautionary note, see Weigel et al. (2010). Approaches that take into account internal variability and model independence have been \r\nproposed since AR5 (Knutti et al., 2017; Boe, 2018; Abramowitz et al., 2019; Brunner et al., 2020)."


id: 122ed5e053ca4d0678b24dd838004d54
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 364
content:
"There are hence good reasons for basing an assessment of future global climate on lines of evidence in addition to the projection \r\nsimulations. However, despite some progress, no universal, robust method for weighting a multi-model projection ensemble is available, \r\nand expert judgement must be included, as it did for AR5, in the assessment of the projections. The default in this chapter follows \r\nthe AR5 approach for GSAT (Collins et al., 2013) and interprets the CMIP6 5-95% ensemble range as the likely uncertainty range."


id: fe9a1425ebca50aabc5bd4c17fec62fc
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vector_id: 37595
content:
"Additional lines of evidence enter the assessment particularly for the most important indicator of global climate change, GSAT. The CMIP6 \r\nensemble generally shows larger projected warming by the end of the 21st century, relative to the average over the period 1995-2014, \r\nthan the CMIP5 ensemble (Section 4.3.1). The warming has increased in part because of models with higher ECS in CMIP6, compared to \r\nCMIP5 (high confidence) (e.g., Meehl et al., 2020; Tokarska et al., 2020; Zelinka et al., 2020; J. Zhu et al., 2020), and in part because of \r\nhigher ERF in CMIP6 than in CMIP5 (e.g., Tebaldi et al., 2021, Section 4.6.2). Because change in several other important climate quantities \r\nscales with change in GSAT (Section 4.2.4), bringing in additional lines of evidence is particularly important for the GSAT assessment. "


id: 7887d03c9d7d18060879e1e7ccab986c
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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content:
". Assessed best estimates, likely, and very likely ranges of ECS and TCR, from process understanding, warming in the instrumental record, \r\npaleoclimates, and emergent constraints (Tables 7.13 and 7.14, and Section 7.5). The ECS and TCR ranges are converted into GSAT \r\nranges using as an emulator a two-layer energy balance model (EBM, e.g., Held et al., 2010) that is driven by the effective radiative "


id: 337938fae00b4203101647d05fed522b
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 5516
content:
"forcing (ERF) assessed in Chapter 7 (Cross-Chapter Box 7.1). Assuming for the ERF resulting from a doubling of the CO2 concentration, \r\nF2 x CO2 = 4.0 W m-2 (close to the best estimate of 3.93 W m-2, Section 7.3), and using the so-called zero-layer approximation to \r\nthe EBM (e.g., Marotzke and Forster, 2015; Jimenez-de-la-Cuesta and Mauritsen, 2019) permits a one-to-one translation of any \r\npair of ECS and TCR into a pair of climate feedback parameter a and ocean heat uptake coefficient ke, using the simple equations \r\na = -F2 x CO2 ECS-1 and ke = F2 x CO2 TCR-1 - F2 x CO2 ECS-1 (e.g., Jimenez-de-la-Cuesta and Mauritsen, 2019; see Chapter 7 for \r\na detailed discussion). The results are displayed in Box 4.1, Figure 1 and are used in the synthesis GSAT assessment in Section 4.3.4."


id: 5fe9f370261068e156cc46306f04832f
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 12673
content:
"Box  4.1, Figure  1 shows annual mean GSAT simulated by CMIP6 models for both the historical period and forced \r\nby scenario SSP2-4.5 until 2100, combined with various characterizations of uncertainty. First, internal variability is \r\nestimated with the 50-member ensemble simulated with CanESM5. The 5-95% ensemble range for annual mean GSAT \r\nin CanESM5 is slightly below 0.4degC; in other CMIP6 large ensembles this range is about 0.5degC (MIROC6, IPSL-CM6A) \r\nand slightly above 0.6degC (S-LENS/EC-Earth3). The CMIP5 large ensemble MPI-GE shows a  range of slightly below 0.5degC \r\n(Bengtsson and Hodges, 2019), in reasonable agreement with observed variability (Maher et  al., 2019). There is thus \r\nhigh confidence in the CMIP6-simulated level of internal variability in annual mean GSAT, as displayed in Box  4.1, Figure  1."


id: da6dba012a4c4bd5d44b8077bfd655f
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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index: climate_gpt_only_giec
vector_id: 31894
content:
"Second, Section 7.5 very likely ECS and TCR ranges are converted into GSAT ranges with the EBM as an emulator using, in this \r\nexample, SSP2-4.5 radiative forcing information. Because the ECS and TCR assessments in Section 7.5 are based on multiple lines \r\nof evidence and the EBM physics are well understood, there is likewise high confidence in the EBM-emulated warming. Third, the \r\ninitialized-forecast ensembles from eight CMIP6 DCPP models are shown in the inset, for the period 2019-2028. During this period, \r\nthe initialized forecasts are consistent, within internal variability, with the EBM-emulated range, further adding to the high confidence\r\nin the assessed-GSAT range."


id: a1c9cd085d61889bc5229a62cc399544
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 23019
content:
"The constrained range of GSAT change is useful for quantifying uncertainties in changes of other climate quanties that scale well \r\nwith GSAT change, such as September Arctic sea ice area, global mean precipitation, and many climate extremes (Cross-Chapter \r\nBox 11.1). However, there are also quantities that do not scale linearly with GSAT change, such as global mean land precipitation, \r\natmospheric circulation, AMOC, and modes of variability, especially ENSO SST variability. Because we do not have robust scientific \r\nevidence to constrain changes in other quantities, uncertainty quantification for their changes is based on CMIP6 projections and \r\nexpert judgement. For the assessment for changes in GMSL, the contribution from land-ice melt has been added offline to the CMIP6 \r\nsimulated contributions from thermal expansion, consistent with Chapter 9 (Section 9.6)."


id: fe6c9b9099aa7f103f3da4a4ae611c30
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 37570
content:
"From the CMIP6 multi-model ensemble we consider historical \r\nsimulations with observed external forcings to 2014 and extensions \r\nto 2100 based on the five high-priority scenarios. We use the first \r\nrealization ('r1') contributed by each modelling group. In tabular form, \r\nwe show ensemble-mean changes and uncertainties for the near-term \r\n(2021-2040), mid-term (2041-2060), and the long-term (2081-2100), \r\nrelative to present-day (1995-2014) and the approximation to \r\npre-industrial (1850-1900). Changes in precipitation over land \r\nnear 1.5degC, 2.0degC, 3.0degC, and 4.0degC of global warming relative to \r\n1850-1900 are also assessed."


id: 281101e61d956ec5e29c6e7518d89001
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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content:
"This section assesses the latest simulations of representative indicators \r\nof global climate change presented as time series and tabulated values \r\nover the 21st century and across the main realms of the global climate \r\nsystem. In the atmospheric realm (Section 4.3.1), we assess simulations \r\nof GSAT (Figure  4.2a) and global land precipitation (Figure  4.2b). \r\nAcross the cryospheric, oceanic, and biospheric realms (Section 4.3.2), \r\nwe assess simulations of Arctic SIA (Figure 4.2c), GMSL (Figure 4.2d), \r\nthe AMOC, ocean and land carbon uptake, and pH. In Section 4.3.3 we \r\nassess simulations of several indices of climate variability, namely, the \r\nindices of the NAM, SAM, and ENSO. Finally, Section 4.3.4 assesses \r\nfuture GSAT change based on the CMIP6 ensemble in combination \r\nwith other lines of evidence. An assessment of projected changes in \r\nrelated global extreme indices can be found in Chapter 11."


id: 78741447555bb48f0dba34fb2ce257f2
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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content:
"1850-1900 for RCP4.5, RCP6.0 and RCP8.5 (high  confidence) and \r\nare likely to exceed 2degC above 1850-1900 for RCP6.0 and RCP8.5 \r\n(high confidence). Global surface temperature changes above 2degC \r\nunder RCP2.6 were deemed unlikely (medium confidence)."


id: 70c898cdad9786d1bf125ea7bb37fee9
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 15371
content:
"The AR5 assessed from CMIP5 simulations and other lines of \r\nevidence that GSAT will continue to rise over the 21st  century if \r\ngreenhouse gas (GHG) concentrations continue increasing (Collins \r\net al., 2013). The AR5 concluded that GSAT for 2081-2100, relative to \r\n1986-2005 will likely be in the 5-95% range of 0.3degC-1.7degC under \r\nRCP2.6 and 2.6degC-4.8degC under RCP8.5. The corresponding ranges for \r\nthe intermediate emissions scenarios with emissions peaking around \r\n2040 (RCP4.5) and 2060 (RCP6.0) are 1.1degC-2.6degC and 1.4degC-3.1degC, \r\nrespectively. The AR5 further assessed that GSAT averaged over \r\nthe period 2081-2100 are projected to likely exceed 1.5degC above "


id: 896037f117331e33e9ada5a3549a0819
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 19154
content:
"Here, for continuity's sake, we assess the CMIP6 simulations of GSAT \r\nin a fashion similar to the AR5 assessment of the CMIP5 simulations. \r\nFrom these, we compute anomalies relative to 1995-2014 and display \r\nthe evolution of ensemble means and 5-95% ranges (Figure  4.2). \r\nWe also use the ensemble mean GSAT difference between 1850-1900 \r\nand 1995-2014, 0.82degC, to provide an estimate of the changes since \r\n1850-1900 (Figure 4.2, right axis). Finally, we tabulate the ensemble \r\nmean changes between 1995-2014 and 2021-2040, 2041-2060, \r\nand 2081-2100 respectively (Figure 4.2)."


id: 80885db26b580cd062bed4a6802dbd1a
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 17745
content:
"Unlike AR5, our focus here is on land rather than global precipitation \r\nbecause land precipitation has greater societal relevance. These are \r\ndisplayed as percent changes relative to 1995-2014 (Figure  4.2b). \r\nBased on these results, we conclude that global land precipitation \r\nis larger during the period 2081-2100 than during the period \r\n1995-2014, under all scenarios considered here (high confidence) \r\n(Table  4.3). Global land precipitation for 2081-2100, relative to \r\n1995-2014, shows a 5-95% range of -0.2 to +4.7% under SSP1-1.9 \r\nand 0.9-12.9% under SSP5-8.5, respectively. The corresponding ranges \r\nunder the other emissions scenarios are 0.0-6.6% (SSP1-2.6), 1.5-\r\n8.3% (SSP2-4.5), and 0.5-9.6% (SSP3-7.0). A detailed assessment of \r\nhydrological sensitivity, or change in precipitation per degree warming, \r\ncan be found in Chapter 8 (Section 8.2.1)."


id: 7f3e426c572177861c15062bdd49ed3b
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 17572
content:
"For scenarios where unanimity across all of the model simulations \r\nthat GSAT change relative to 1850-1900 rises above 1.5degC (SSP2-4.5, \r\nSSP3-7.0, or SSP5-8.5), the ensemble-mean change in global land \r\nprecipitation from 1850-1900 until the time of exceedance is on \r\naverage about 1.6%. For scenarios with unanimous global warming \r\nabove 2.0degC (SSP3-7.0, or SSP5-8.5) and 3.0degC (SSP5-8.5), the \r\nensemble-mean increase in global land precipitation for those \r\nmodels that do exceed 2.0degC and 3.0degC is on average about 2.6% \r\nand 4.9%, respectively. On average under SSP1-1.9 and SSP1-2.6, \r\nthe global land precipitation change for simulations where \r\nglobal warming exceeds 1.5degC and 2.0degC will be about 1.9% and \r\n3.0%, respectively."


id: d08d4dab58b2b5294d8dc8e6f7f20071
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 30336
content:
"The AR5 assessed from CMIP5 projections that global mean \r\nprecipitation over the 21st  century will increase by more than \r\n0.05 mm day-1 (about 2% of global precipitation) and 0.15 mm day-1\r\n(about 5% of global precipitation) under the RCP2.6 and RCP8.5 \r\nscenarios, respectively (Collins et  al., 2013). These changes are \r\ngenerally in line with those from the CMIP6 simulations following \r\nSSP1-2.6 and SSP5-8.5 (Table 4.3)."


id: 5241ac0ce5e4dfc8af7fcb1c000eb0
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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content:
"With regards to the model selection in AR5, model evaluation studies \r\nhave since identified shortcomings of the CMIP5 models to match \r\nthe observed distribution of sea ice thickness in the Arctic (Stroeve \r\net al., 2014; Shu et al., 2015) and the observed evolution of albedo \r\non seasonal scales (Koenigk et  al., 2014). It was also found that \r\nmany models' deviation from observed sea ice cover climatology \r\ncannot be explained by internal variability, whereas the models' \r\ndeviation from observed sea ice cover trend (over the satellite \r\nperiod) can often be explained by internal variability (Olonscheck \r\nand Notz, 2017). This hinders a  selection of models according to \r\ntheir simulated trends, which additionally has been shown to only \r\nhave a  weak effect on the magnitude of simulated future trends \r\n(Stroeve and Notz, 2015)."


id: 2345ce0b47e481167bdd665c53e26e58
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 2999
content:
"Based on results from the CMIP6 models, we conclude that on \r\naverage the Arctic will become practically ice-free in September \r\nby the end of the 21st  century under SSP2-4.5, SSP3-7.0, and \r\nSSP5-8.5 (high confidence) (Figure 4.2c and Table 4.4). Also, in the \r\nCMIP6 models, Arctic SIA in March decreases in the future, but to \r\na much lesser degree, in percentage terms, than in September (high \r\nconfidence) (Table  4.4). A  more detailed assessment of projected \r\nArctic and also Antarctic sea ice change can be obtained in Chapter 9 \r\n(Section 9.3.1)."


id: a08ccb9f80e4137d68cbfe6a93270dbf
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 22794
content:
"that the probability of the Arctic becoming practically ice free at the \r\nend of the 21st century is significantly higher for 2degC warming than \r\nfor 1.5degC warming above 1850-1900 levels (high confidence)."


id: 2e471b8c3bfe662a110e3091af6802dd
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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content:
"The AR5 assessed from CMIP5 process-based simulations that \r\nthe rate of GMSL rise during the 21st century will very likely exceed \r\nthe rate  observed during 1971-2010 for all RCP scenarios due to \r\nincreases in ocean warming and loss of mass from glaciers and ice \r\nsheets (Church et  al., 2013). Further, AR5 concluded that for the \r\nperiod 2081-2100, compared to 1986-2005, GMSL rise is likely\r\n(medium confidence) to be in the 5-95% range of projections \r\nfrom process-based models, which give 0.26-0.55 m for RCP2.6, "


id: bc2f0456769e9d76ad22cee5d719eac1
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 27182
content:
"MIP6 Arctic sea ice area for selected months, time periods, and across five SSPs. Displayed are the multi-model averages across the individual models \r\nheses, the 5-95% ranges. The number of models used in these calculations are shown in Figure 4.2c."


id: 11ce19ef7e86aca8e3724e52a35f1060
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 297
content:
"0.32-0.63 m for RCP4.5, 0.33-0.63 m for RCP6.0, and 0.45-0.82 m \r\nfor RCP8.5. For RCP8.5, the rise by 2100 is 0.52-0.98 m with a rate \r\nduring 2081-2100 of 8-16 mm yr-1."


id: 60c3ff57de07b0a02f7406fa53f70f13
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 12795
content:
"There have been substantial modelling advances since AR5, with \r\nmost sea level projections corresponding to one of three categories: \r\n(i)  central-range projections, combining scenario-conditional \r\nprobability distributions for the different contributions to estimate \r\na  central range under different scenarios; (ii) probabilistic \r\nprojections, which explicitly consider outcomes for a  wide range \r\nof likelihoods, including low-likelihood, high-impact outcomes; and \r\n(iii)  semi-empirical projections, based on statistical relationships \r\nbetween past GMSL changes and climate variables, which \r\nnow calibrate individual contributions and are consistent with \r\nphysical-model based estimates (Section 9.6.3)."


id: 17e624293c32078f92ea6d51853d0b90
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 1253
content:
"Based on the assessment of the latest modelling information \r\n(Figure 4.2d and Section 9.6.3), we conclude that under the SSP3-7.0, \r\nthe likely range of GMSL change averaged over 2081-2100 relative \r\nto 1995-2014 is 0.46-0.74 m. Under SSP1-2.6, the likely range \r\nover the long-term is 0.30-0.54 m. Further, in SSP2-4.5, SSP3-7.0, \r\nand SSP5-8.5, the rise in GMSL is projected to accelerate over the \r\n21st  century. A  detailed assessment of the processes contributing \r\nto these projected rises and accelerations in GMSL, together \r\nwith a comparison to AR5 and SROCC, can be found in Chapter 9 \r\n(Section 9.6.3). Projected changes in the thermosteric component of \r\nGMSL beyond 2300 are assessed in Section 4.7.1."


id: 9e4b384006b2d98a5cee5d5acbd4b833
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 22429
content:
"Based on a  large initial condition ensemble of simulations with \r\na  CMIP5 model (CanESM2) with emissions scenarios leading to \r\nstabilization of global warming of 1.5degC, 2.0degC, or 3.0degC relative \r\nto 1850-1900, AMOC continues to decline for 5-10 years after \r\nGSAT is effectively stabilized at the given GWL (Sigmond et  al., \r\n2020). This is followed by a  recovery of AMOC strength for about \r\nthe next 150 years to a level that is approximately independent of \r\nthe considered stabilization scenario. These results are replicated in \r\nsimulations in a CMIP6 model (CanESM5) with emissions cessation \r\nafter diagnosed CO2 emissions reach 750 Gt, 1000 Gt, or 1500 Gt. \r\nThese emissions levels lead to global warming stabilization at 1.5degC, \r\n2.0degC, or 3.0degC relative to 1850-1900. In summary, in these model \r\nsimulations the AMOC recovers over several centuries after the \r\ncessation of CO2 emissions (medium confidence)."


id: e0e0b734ed95cb4a944a6752635a7aa4
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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vector_id: 32879
content:
"In summary, it is virtually certain that under any one of the assessed \r\nSSPs, there will be continued rise in GMSL through the 21st century."


id: 1164ed1cc943864513b8c82c93cf6413
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 217
content:
"The AR5 assessed from CMIP5 simulations that the Atlantic Meridional \r\nOverturning Circulation (AMOC) will very likely weaken over the \r\n21st century, and the projected weakening of the AMOC is consistent \r\nwith CMIP5 projections of an increase of high-latitude temperature \r\nand high-latitude precipitation, with both effects causing the surface \r\nwaters at high latitudes to become less dense and therefore more \r\nstable (Collins et al., 2013)."


id: a9fee1077ee7607c354c9882156dc241
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
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vector_id: 24281
content:
"Based on CMIP6 models, we find that over the 21st century, AMOC \r\nstrength, relative to 1995-2014, shows a multi-model mean decrease \r\nin each of the SSP scenarios but with a  large spread across the \r\nindividual simulations (Figure 4.6). We also note that the magnitude \r\nof the ensemble-mean strength decrease is approximately scenario \r\nindependent up to about 2060 (Weijer et al., 2020). A more detailed \r\nassessment of these projected AMOC changes, and the mechanisms \r\ninvolved, can be found in Chapter 9 (Section 9.2.3)."


id: a35dc8cb241f1e2e62a84c51d63018e9
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 23264
content:
"The AR5 concluded with very high confidence that ocean carbon \r\nuptake of anthropogenic CO2 will continue under all RCPs through the \r\n21st century, with higher uptake corresponding to higher concentration \r\npathways. The future evolution of the land carbon uptake was assessed \r\nto be much more uncertain than for ocean carbon uptake, with a majority \r\nof CMIP5 models projecting a continued cumulative carbon uptake."


id: 7b9e9bbba752c24ca8e8e61bd0a13644
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 17045
content:
"In summary, we assess from the CMIP6 models that AMOC weakening \r\nover the 21st century is very likely; the rate of weakening is approximately \r\nindependent of the emissions scenario (high confidence)."


id: 7a8c9a7f2988f1c38e87018798e315cd
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 16852
content:
"Based on results from the CMIP6 models, we conclude that the flux \r\nof carbon from the atmosphere into the ocean increases continually \r\nthrough most of 21st  century in the two highest emissions \r\nand decreases continually under the other emissions scenarios \r\n(Figure 4.7a). The flux of carbon from the atmosphere to land shows \r\na similar 21st century behaviour across the scenarios but with much \r\nhigher year-to-year variation than ocean carbon flux (Figure 4.7b). \r\nA  more in-depth assessment and discussion of the mechanism \r\ninvolved can be found in Chapter 5 (Section 5.4.5)."


id: 1ac7352fe2ba98490185a3c208e2d468
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 1683
content:
"In summary, we assess that the cumulative uptake of carbon by the \r\nocean and by land will increase through the 21st century irrespective \r\nof the considered emissions scenarios except SSP1-1.9 (very \r\nhigh confidence)."


id: 43b522db25f7ea8b4c6b9a7840afbda9
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 8063
content:
"The AR5 assessed from CMIP5 simulations that it is virtually certain\r\nthat increasing storage of carbon by the ocean under all four RCPs \r\nthrough to 2100 will increase ocean acidification in the future (Ciais \r\net al., 2013). Specifically, AR5 reported that CMIP5 models project \r\nincreased ocean acidification globally to 2100 under all RCPs, \r\nand that the corresponding model mean and model spread in the \r\ndecrease in surface ocean pH from 1986-2005 to 2081-2100 would \r\nbe 0.065 (0.06-0.07) for RCP2.6, 0.145 (0.14-0.15) for RCP4.5, \r\n0.203 (0.20-0.21) for RCP6.0 and 0.31 (0.30-0.32) for RCP8.5."


id: 4e453e6e725852bd416646b509fe6623
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
index: climate_gpt_only_giec
vector_id: 9740
content:
"Based on results from the CMIP6 models we conclude that, except \r\nfor the lower-emissions scenarios SSP1-1.9 and SSP1-2.6, ocean \r\nsurface pH decreases monotonically through the 21st  century \r\n(high confidence) (Figure 4.8)."


id: b7618e3cf0447a78f22c861a24924d8e
created_at: 2023-03-16 14:00:40
updated_at: 2023-03-16 14:00:40
content_type: text
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"characterize decadal variability (Chiodo et  al., 2019). A  realistic \r\nsimulation of the stratosphere and SST variability in the tropics and \r\nnorthern extratropics are important for a model to realistically capture \r\nthe observed NAM variability. Despite some evidence from climate \r\nmodel studies that anthropogenic forcings influence the NAM, there \r\nis limited evidence for a significant role for anthropogenic forcings in \r\ndriving the observed multi-decadal variations of the NAM over the \r\ninstrumental period (Section 3.7.1)."


id: 2a61d2dfeddab320daef40fa11afd688
created_at: 2023-03-16 14:00:40
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content:
"The AR5 assessed from CMIP5 simulations that the future boreal \r\nwintertime NAM is very likely to exhibit large natural variations and \r\ntrends of similar magnitude to that observed in the past and is likely to \r\nbecome slightly more positive in the future (Collins et al., 2013). Based \r\non CMIP6 model results displayed in Figure  4.9a, we conclude that \r\nthe boreal wintertime surface NAM is more positive by the end of the \r\n21st century under SSP3-7.0 and SSP5-8.5 (high confidence). For these \r\nhigh emissions scenarios, the 5-95% range of NAM index anomalies \r\naveraged from 2081-2100 are 0.3-3.8 hPa and 0.32-5.2  hPa, \r\nrespectively. On the other hand, under neither of the lowest emissions \r\nscenarios, SSP1-1.9 and SSP1-2.6, does the NAM show a  robust \r\nchange, by the end of the 21st century (high confidence)."


id: dee8699cd01db001be8c9816130727bf
created_at: 2023-03-16 14:00:41
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content:
"Significant progress has been made since AR5 in understanding the \r\nphysical mechanisms responsible for changes in the NAM, although \r\nuncertainties remain. It is now clear from the literature that the \r\nNAM response, and the closely-related response of the mid-latitude \r\nstorm tracks, to anthropogenic forcing in CMIP5-era climate models \r\nis determined by a  'tug-of-war' between two opposing processes \r\n(Harvey et al., 2014; Shaw et al., 2016; Screen et al., 2018a): (i) Arctic \r\namplification (Sections 4.5.1.1 and 7.4.4.1), which decreases the \r\nlow-level meridional temperature gradient, reduces baroclinicity on \r\nthe poleward flank of the eddy-driven jet, and shifts the storm tracks \r\nequatorward and leading to a negative NAM (see Box 10.1; Harvey et al., \r\n2015; Hoskins and Woollings, 2015; Peings et al., 2017; Screen et al., \r\n2018a); and (ii) enhanced warming in the tropical upper-troposphere, "


id: fe555163d93fa0f3430dfa09d80690f7
created_at: 2023-03-16 14:00:41
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content:
"We first summarize the assessment of past NAM changes and their \r\nattribution from Chapters 2 and 3 to put into context the future \r\nprojections described here. Strong positive trends for the NAM/NAO \r\nindices were observed since 1960, which have weakened since the \r\n1990s (high confidence) (Section  2.4.1.1). The NAO variability in \r\nthe instrumental record was likely not unusual in the millennial and \r\nmulti-centennial context (Section 2.4.1.1). Climate models simulate \r\nthe gross features of the NAM with reasonable fidelity, including \r\nits interannual variability, but models tend to systematically \r\nunderestimate the amount of multi-decadal variability of the NAM \r\nand jet stream compared to observations (Section  3.7.1; J. Wang \r\net  al., 2017b; Bracegirdle et  al., 2018; Simpson et  al., 2018), with \r\nthe caveat of the observational record being relatively short to "


id: cb804ae4f807c4e4042688b2b3f3c9e9
created_at: 2023-03-16 14:00:41
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content:
"ozone changes on the SAM, climate models must include a realistic \r\nrepresentation of ozone variations (Section 3.7.2). In models that do \r\nnot explicitly represent stratospheric ozone chemistry, which includes \r\nthe majority of the CMIP6 model ensemble, an ozone dataset is \r\nprescribed. To properly capture the effects of ozone depletion and \r\nrecovery on the stratosphere and surface climate, the prescribed \r\nozone dataset must realistically capture observed stratospheric ozone \r\ntrends with sufficiently high temporal resolution (Neely et al., 2014; \r\nYoung et al., 2014). The CMIP6 experiment protocol recommended \r\nthe use of a prescribed 4-D monthly mean ozone concentration field \r\nfor models without stratospheric chemistry (Eyring et al., 2016)."


id: cba9473d6c01bf0d799b6c608ecb744a
created_at: 2023-03-16 14:00:41
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vector_id: 29585
content:
"due to GHG increases and associated water vapour and lapse rate \r\nfeedbacks, which increases the upper-level meridional temperature \r\ngradient and causes a poleward shift of the storm tracks and a positive\r\nNAM (Harvey et al., 2014; Vallis et al., 2015; Shaw, 2019). The large \r\ndiversity in projected NAM changes in CMIP5 multi-model ensemble \r\n(Gillett and Fyfe, 2013) appears to be at least partly explained by the \r\nrelative importance of these two mechanisms in particular models \r\n(Harvey et al., 2014, 2015; Vallis et al., 2015; McCusker et al., 2017; \r\nOudar et  al., 2017). Models that produce larger Arctic amplification \r\nalso tend to produce larger equatorward shifts of the mid-latitude jets \r\nand associated negative NAM responses (Barnes and Polvani, 2015; \r\nHarvey et al., 2015; Zappa and Shepherd, 2017; McKenna et al., 2018; \r\nScreen et al., 2018a; Zappa et al., 2018)."


id: 1d2466df82e42b63876eec6d2881e5d0
created_at: 2023-03-16 14:00:41
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content:
"The AR5 assessed that the positive trend in the austral summer/\r\nautumn SAM observed since 1970 (see Section  2.4.1.2) is likely to \r\nweaken considerably as stratospheric ozone recovers through the \r\nmid-21st  century, while in other seasons the SAM changes depend \r\non the emissions scenario, with a larger increase in SAM for higher \r\nemissions scenarios. In CMIP6 models, the austral summer SAM \r\nis more positive by the end of the 21st century under SSP3-7.0 and \r\nSSP5-8.5 (Figure  4.9b). On the other hand, under SSP1-1.9 and \r\nSSP1-2.6, the SAM is projected to be less positive, especially under \r\nSSP1-1.9 where the 5-95% ranges of anomalies relative to 1995-2014 \r\nare -3.1 to 0.0 hPa averaged from 2081-2100. In summary, under \r\nthe highest emissions scenarios in the CMIP6 models, the SAM in \r\nthe austral summer becomes more positive through the 21st century \r\n(high confidence)."


id: a955b8556a6068ba34da81f8f1986da1
created_at: 2023-03-16 14:00:41
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content:
"Another area of progress is new understanding the role of cloud \r\nradiative effects in shaping the mid-latitude circulation response \r\nto anthropogenic forcing. Through their non-uniform distribution of \r\nradiative heating, cloud changes can modify meridional temperature \r\ngradients and alter mid-latitude circulation and the annular modes in \r\nboth hemispheres (Ceppi et al., 2014; Voigt and Shaw, 2015, 2016; \r\nCeppi and Hartmann, 2016; Ceppi and Shepherd, 2017; Lipat et al., \r\n2018; Albern et al., 2019; Voigt et al., 2019). In addition to the effects \r\nof changing upper and lower tropospheric temperature gradients on \r\nthe NAM, progress has been made since AR5 in understanding the \r\neffect of simulated changes in the strength of the stratospheric polar \r\nvortex on winter NAM projections (Manzini et al., 2014; Zappa and \r\nShepherd, 2017; Simpson et al., 2018)."


id: 93775c650db84aa46f2b80482e918c81
created_at: 2023-03-16 14:00:41
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content:
"The El Nino-Southern Oscillation (ENSO) is the most dominant mode \r\nof variability on interannual time scales and also the dominant source \r\nof seasonal climate predictability (Box  11.3 and Annex IV, \r\nSection AIV.2.3; Timmermann et  al., 2018). The AR5 assessed from \r\nCMIP5 simulations that ENSO variability will very likely remain the \r\ndominant mode of interannual climate variability in the future, and \r\nthat associated ENSO precipitation variability on regional scales is \r\nlikely to intensify (Christensen et al., 2013). However, they assessed \r\nthere was low confidence in projected changes in ENSO variability in \r\nthe 21st century due to a strong component of internal variability."


id: ff00c1fc00b230a213d8b9042375e4e1
created_at: 2023-03-16 14:00:41
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content:
"Over the instrumental period, there has been a  robust positive \r\ntrend in the SAM index, particularly since 1970 (high confidence) \r\n(Section  2.4.1.2). There is medium confidence that the recent \r\ntrend in the SAM is unprecedented in the past several centuries \r\n(Section 2.4.1.2). There is high confidence that stratospheric ozone \r\ndepletion and GHG increases have contributed to the positive SAM \r\ntrend during the late 20th century, with ozone depletion dominating \r\nin austral summer, following the peak of the Antarctic ozone hole \r\nin September-October, and GHG increases dominating in other \r\nseasons  (Section  3.7.2). To capture the effects of stratospheric "


id: 8f3a303e6d4ae5ccb9d1d9406359d082
created_at: 2023-03-16 14:00:41
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"Averaged over the period 2081-2100, GSAT is very likely to be higher \r\nthan in the recent past (1995-2014) by 0.3degC-0.9degC in the low\u0002emissions scenario SSP1-1.9 and by 2.6degC-4.7degC in the high-emission \r\nscenario SSP5-8.5. For the scenarios SSP1-2.6, SSP2-4.5, and SSP3-7.0, \r\nthe corresponding very likely ranges are 0.6degC-1.4degC, 1.3degC-2.5degC, \r\nand 2.0degC-3.8degC, respectively (Figure  4.11 and Table  4.5). Because \r\nthe different approaches for estimating long-term GSAT change \r\nproduce consistent results (Figure  4.11), there is high confidence \r\nin this assessment. These ranges of the long-term projected GSAT \r\nchange generally correspond to AR5 ranges for related scenarios but \r\nthe likelihood is increased to very likely ranges, in contrast to the likely\r\nranges in AR5. Over the mid-term period 2041-2060, the very \r\nlikely GSAT ranges of SSP1-1.9 and SSP5-8.5 are almost completely \r\ndistinct (high confidence) (Table 4.5; see also Section 4.3.1)."


id: c537a0b081b2d06f5ec1b843febf1617
created_at: 2023-03-16 14:00:41
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"CMIP6 models project a wider range of GSAT change than the assessed \r\nrange (high confidence) (Section 4.3.1). The CMIP6 models with a higher \r\nclimate sensitivity simulate warming rates higher than assessed \r\nvery likely here (Section 4.3.1); these rates are very unlikely but not \r\nimpossible to occur and hence cannot be excluded. The implications of \r\nthese very unlikely warming rates for patterns of surface temperature \r\nand precipitation change are assessed in Section 4.8."


id: b154ccdb2b74c666113d44e865d3c46c
created_at: 2023-03-16 14:00:41
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"It is therefore very likely that the amplitude of ENSO rainfall variability \r\nwill intensify in response to global warming over the 21st century \r\nalthough there is no robust consensus from CMIP6 climate models \r\nfor a systematic change in amplitude of ENSO SST variability even in \r\nthe high-emissions scenarios of SSP3-7.0 and SSP5-8.5."


id: 2eb0d834c97610566b7b8ce77c2806ee
created_at: 2023-03-16 14:00:41
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"For the near term, initialized decadal forecasts constitute another \r\nline of evidence over the period 2019-2028 (Box 4.1). The forecasts \r\nare consistent with the assessed GSAT very likely range (Box  4.1, \r\nFigure 1), strengthening the confidence in the near-term assessment."


id: 7741bdcf9bb68c26b6aeb79a8ae4496c
created_at: 2023-03-16 14:00:41
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"GSAT change is assessed using multiple lines of evidence \r\nincluding the CMIP6 projection simulations out to year 2100. The \r\nassessment combines CMIP6 projections driven by SSP scenarios \r\nwith observational constraints on simulated past warming (Box 4.1 "


id: 5ad7041d770c80a04fb9697b113ff26d
created_at: 2023-03-16 14:00:41
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"The assessed ranges of GSAT change can be converted to change \r\nrelative to mean GSAT over the period 1850-1900 for a consistent \r\ncomparison with AR5 (IPCC, 2013) and SR1.5 (IPCC, 2018a). "


id: 8230fd9bd27b094b158c0660003fd265
created_at: 2023-03-16 14:00:41
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content:
"The uncertainty in GSAT relative to 1850-1900 includes the very \r\nlikely ranges of assessed GSAT change relative to 1995-2014 \r\n(depending on scenario and period, between 0.5degC and 2.4degC; \r\nFigure  4.11d and Table  4.5), the uncertainty in historical GSAT \r\nchange from the mean over 1850-1900 to 1995-2014 (about 0.3degC; "


id: f288973e2b5ff664ade4c143adaefe50
created_at: 2023-03-16 14:00:41
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content:
"GSAT was warmer in 1995-2014 (recent past) than 1850-1900 by \r\n0.85 [0.67 to 0.98] degC. GSAT diagnosed for 1986-2005 (AR5 recent \r\npast) relative to 1850-1900 is 0.08degC higher than was diagnosed \r\nin AR5, due to methodological and dataset updates (Cross-Chapter \r\nBox 2.3, Table 1)."


id: 3b23d4ea3715c84e7bce770e2af75394
created_at: 2023-03-16 14:00:41
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"recent observations simulate GSAT changes for the period 2019-2028 \r\nrelative to the recent past that are consistent with the assessed \r\nvery likely range in annual mean GSAT (high confidence) (Box  4.1, \r\nFigure 1, and Table 4.5). Because annual mean GSAT shows a higher \r\nlevel of internal variability than the 20-year mean, individual years \r\nare expected to cross the 1.5degC earlier than the assessed GSAT does. \r\nFor example, Smith et al. (2018) apply a multi-model decadal-forecast \r\nensemble to assess the likelihood that global warming of 1.5degC higher \r\nthan over 1850-1900 will be temporarily exceeded in the near future."


id: 481d3e6fa891a40accacd3ba2ed58c5a
created_at: 2023-03-16 14:00:41
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"This section assesses changes in large-scale climate over the period \r\n2021-2040 and includes information from both projections and \r\ninitialized decadal predictions. The structure is similar to Section 4.3. \r\nUnless noted otherwise, the assessment assumes that there will be \r\nno major volcanic eruption in the near term. The climatic effects of \r\nvolcanic eruptions are assessed in Section 4.4.4 and Cross-Chapter \r\nBox 4.1; Section 4.4.4 also assesses the climate effects of short-lived \r\nclimate forcers."


id: c5864d85671ba28f03bdbdb9240a4733
created_at: 2023-03-16 14:00:41
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content:
"When we repeat the uncertainty quantification for GSAT as in \r\nSection  4.3.4 but with the corresponding higher level of internal \r\nvariability for annual instead of 20-year averages added in quadrature, \r\nwe can estimate the likelihood that an individual year would \r\ncross the GSAT 1.5degC threshold. By 2030, GSAT in any individual \r\nyear could exceed 1.5degC relative to 1850-1900 with a  likelihood \r\nbetween 40 and 60 percent, across the scenarios considered here \r\n(medium confidence)."


id: e7d0f48a94e0d95e6df2cfe38b278c06
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"The AR5 assessed that it is likely that GSAT will increase in the \r\nrange 0.3degC-0.7degC over the period 2016-2035 relative to \r\n1986-2005 (medium confidence), and that there were not large \r\ndifferences in the GSAT change among different RCPs in this period \r\n(Kirtman et  al., 2013). The AR5 further assessed that it is more \r\nlikely than not that the mean GSAT for the period 2016-2035 will \r\nbe more than 1degC above the mean for 1850-1900, and it is very \r\nunlikely that it will be more than 1.5degC above the 1850-1900 mean \r\n(medium confidence). It was shown that in the period 2016-2035, \r\ndifferences in GSAT across RCP scenarios for a single climate model \r\nare typically smaller than differences between climate models under \r\na single RCP scenario, indicating that model structural uncertainty \r\nis larger than scenario uncertainty over that period (Hawkins and \r\nSutton, 2009)."


id: 5cef17317a71993c3e87107adda1a9b9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"Consistent with AR5 and earlier assessments, Figure  4.12 shows \r\nfor SSP1-2.6 and SSP3-7.0 that the largest warming occurs at high \r\nlatitudes, particularly in boreal winter in the Arctic (Section 4.5.1.1), \r\nand larger warming over land than over the ocean (Section 4.5.1.1). In \r\nboth scenarios, the increase in seasonal mean surface temperatures \r\nover many NH land regions exceeds 1degC relative to 1995-2014. In the \r\nnear term, the two scenarios show surface temperature changes that \r\nare similar in magnitude. The trajectories for well-mixed GHGs, and as \r\na consequence the effective radiative forcing, in the scenarios have \r\nnot yet diverged that much (O'Neill et al., 2016; Riahi et al., 2017). \r\nBased on the currently available CMIP6 models, regions that do not \r\nshow robust warming in the near-term include the northern North \r\nAtlantic, parts of India, parts of North America and Eurasia in winter, \r\nand the subtropical eastern Pacific in the Southern Hemisphere."


id: da2652da03698a840515bcf059551e7b
created_at: 2023-03-16 14:00:41
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content:
"Near-term (2021-2040) GSAT changes relative to 1995-2014 \r\nexhibit only minor dependence on SSP scenario, consistent with \r\nAR5 (Table 4.5). Averaged over the twenty years of the near term \r\nand across all scenarios, GSAT is very likely to be higher than over \r\n1995-2014 by 0.4degC-1.0degC (Table 4.5), with most of the uncertainty \r\narising from that in ECS and TCR (high confidence) (Section 4.3.4; \r\ne.g.,  Lehner et  al., 2020). The assessed near-term warming is thus \r\nlarger than in AR5 by 0.1degC to 0.2degC. This upward revision has the \r\nsame magnitude as the ad-hoc downward adjustment to near-term \r\nprojected GSAT change that was performed in AR5 (Box  4.1; \r\nKirtman et al., 2013)."


id: fdcd11b72b67aa8aaaf13e4b2e82c7cb
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content:
"The ERF patterns from aerosols and well-mixed GHGs are distinct \r\n(Chapter 7), and warming patterns therefore depend on the precise \r\nmix of forcing agents in the scenarios. The spatial efficacies - the \r\nchange in surface temperature per unit ERF  - for CO2, sulphate \r\nand black carbon aerosols and solar forcing have been recently \r\nevaluated in climate models (Modak et  al., 2016, 2018; Duan \r\net  al., 2018; Modak and Bala, 2019; Richardson et  al., 2019). On \r\naverage, the spatial patterns of near-surface warming are largely \r\nsimilar for different external drivers (Xie et  al., 2013; Richardson \r\net  al., 2019; Samset et  al., 2020), despite the patterns of forcing \r\nbeing different and despite the large spread across different models \r\n(Richardson et al., 2019)."


id: 8bf0b7bbccaf59ebb36d741a32a84622
created_at: 2023-03-16 14:00:41
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content:
"Averaged near-term GSAT is as likely as not at least 1.5degC higher than \r\nduring 1850-1900, across the five SSP scenarios used here (Table 4.5 \r\nand Section 4.3.4). This much higher likelihood of near-term warming \r\nreaching 1.5degC than in AR5 arises both because surface warming has \r\ncontinued since AR5 (the period 1995-2014 was warmer by 0.16degC \r\nthan 1986-2005; Cross-Chapter Box  2.3, Table  1), and because \r\nof methodological and dataset updates (the AR6 assessment of \r\n1986-2005 GSAT change relative to 1850-1900 is 0.08degC higher \r\nthan in the AR5; Cross-Chapter Box 2.3, Table 1)."


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created_at: 2023-03-16 14:00:41
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vector_id: 3263
content:
"Internal variability in near-surface temperature change is large \r\nin many regions, particularly in mid-latitudes and polar regions \r\n(Hawkins and Sutton, 2012). Projections from individual realizations \r\ncan therefore exhibit divergent regional responses in the near-term \r\nin areas where the amplitude of a forced signal is relatively small \r\ncompared to internal variability (Deser et al., 2012b, 2014, 2016)."


id: cf6b320902c3f98f155953600cdc22b4
created_at: 2023-03-16 14:00:41
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vector_id: 30167
content:
"For annual mean GSAT, uncertainty in near-term projections arises \r\nin roughly equal measure from internal variability and model \r\nuncertainty (high confidence) (Box  4.1). Forecasts initialized from "


id: b292a7dd40dbb6e152e48aac3a53c4fc
created_at: 2023-03-16 14:00:41
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content:
"Analyses  of CMIP5, CMIP6, and single-model large-ensemble \r\nsimulations show that for the uncertainty in near-term precipitation \r\nprojections, model uncertainty and internal variability dominate \r\nwhile the scenario uncertainty is very small (Section 8.5; Lehner et al., \r\n2020). Based on large ensembles of climate change experiments, \r\nit was shown that internal variability decreases over time for \r\nboth temperature and precipitation on decadal scales (Zhang and \r\nDelworth, 2018; Tebaldi et  al., 2021). The precipitation projections \r\nfrom CMIP6 models shows larger model uncertainty associated with \r\nthe higher average transient climate response (Lehner et al., 2020)."


id: cbb4a42dd67f51c840f3ac90955a16bf
created_at: 2023-03-16 14:00:41
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vector_id: 29589
content:
"The AR5 assessed that zonal mean precipitation will very likely\r\nincrease in high and some of the mid latitudes and will more likely than \r\nnot decrease in the subtropics. The AR5 further assessed that the \r\nnear-term changes in precipitation are largely uncertain at regional \r\nscales, and much of the non-robustness in near-term projections is \r\nattributable to internal variability and model uncertainty."


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"The mean patterns of seasonal precipitation change in CMIP6 models \r\nare consistent with AR5, increasing at high latitudes, over oceanic \r\nregions, and in wet regions over the tropics; and decreasing in dry \r\nregions including large parts of the subtropics (Figure  4.13). The \r\nmagnitude of projected changes in precipitation in the near term, \r\nespecially on regional scales is small compared to the magnitude \r\nof internal variability (Section  10.4.3; Hawkins and Sutton, 2011, \r\n2016; Hoerling et al., 2011; Deser et al., 2012b; Power et al., 2012). "


id: f970473dad56b77d9194f69a790f67d4
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 36756
content:
"The 'wet get wetter, dry get drier' paradigm, which has been used to \r\nexplain the global precipitation pattern responding to global warming \r\n(Held and Soden, 2006), might not hold, especially over subtropical \r\nland regions (Greve et  al., 2014; Feng and Zhang, 2015; Greve and \r\nSeneviratne, 2015). Over the tropical oceans, precipitation changes are \r\nlargely driven by the pattern of SST changes (He et al., 2018), and in "


id: a16ff516e909c32413b1aee571d13469
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 22957
content:
"term projected changes in precipitation. Precipitation changes in the \r\nnear term show seasonal amplification, precipitation increase in the \r\nrainy season and decrease in the dry season (Fujita et al., 2019)."


id: 260dc2c40ee6ae81714d37619498f53f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 3407
content:
"Consistent with AR5, we conclude that projected changes of seasonal \r\nmean precipitation in the near term will increase at high latitudes. \r\nNear-term projected changes in precipitation are uncertain mainly \r\nbecause of natural internal variability, model uncertainty, and uncertainty \r\nin natural and anthropogenic aerosol forcing (medium confidence)."


id: 1c2036ee172a0b7cc3352a524160208e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 1884
content:
"The global monsoon is a forced response of the coupled atmosphere-\r\nland-ocean system to the annual cycle of solar insolation and \r\ncharacterized by a  seasonal reversal of circulation and a  seasonal \r\nalternation of dry and wet conditions (Section 8.3.2, Figure 8.11 and \r\nAnnex V). The global monsoon concept helps to dissect the mechanisms \r\nand controlling factors of monsoon variability at various temporal\u0002spatial scales (Wang and Ding, 2008; P.X. Wang et al., 2017)."


id: 4a95a051250ef57ad48302c127640701
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 9135
content:
"In AR5, there was no specific assessment on global monsoon changes \r\nin the near term, but information can be derived from CMIP5 \r\nprojections of the spatial patterns of precipitation change. While \r\nthe basic pattern of wet regions, including global monsoon regions, \r\ntending to get wetter and dry regions tending to get drier is apparent, \r\nlarge response uncertainty is evident in the substantial spread in \r\nthe magnitude of projected change from different simulations. \r\nOver the global land monsoon regions, model uncertainty and \r\ninternal variability together explain 99.7% of the fraction of total \r\nvariance (Zhou et al., 2020), near-term projected multi-model mean \r\nprecipitation changes are almost everywhere smaller than the \r\nestimated standard deviation of internal variability (Figure 4.13)."


id: 372197487a15e3ed7f259eb05a5199a0
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 6058
content:
"Based on results from CMIP6 models, we conclude that Arctic SIA will \r\ndecrease in September in the near term (Figure 4.15, high confidence). \r\nIn the case of 10-year trends ending in the near term, 79% of the \r\nsimulations considered across all the core SSPs project decreasing \r\nArctic sea ice area in September. Due to less of an influence from \r\ninternal variability, this number rises to 98% in the case of 30-year \r\ntrends. A more detailed assessment of near-term Arctic sea ice changes \r\ncan be found in Chapter 9 (Section 9.3.1). A detailed assessment of \r\nAntarctic sea ice changes is in Chapter 9 (Section 9.3.2)."


id: 56239458e2d7016e987fd1974cd7d881
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 11043
content:
"In summary, we assess that near-term changes in global monsoon \r\nprecipitation and circulation will be affected by the combined effects \r\nof model uncertainty and internal variability, such as AMV and PDV, \r\nwhich together are larger than the forced signal (medium confidence)."


id: 6d864c1c44f52b965d697617f2929d6d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 14840
content:
"the near term are generally increasing, but these are unlikely to be \r\ndetected given a large component of terrestrial variability combined \r\nwith model uncertainty. A more detailed assessment is in Chapter 5 \r\n(Section 5.2.1)."


id: 2324db330269759f019d98ed53c67823
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 2973
content:
"In summary, it is likely that ocean carbon flux will increase in the near \r\nterm under the higher emissions scenarios, while a large component \r\nof terrestrial variability makes it is unlikely that an increase in land \r\ncarbon flux will be detected over this period."


id: 69ff3a820d8fd2770c02ab9d19ce5de3
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"This subsection assesses the near-term evolution of the large-scale \r\nmodes of climate variability. Assessment of the physical mechanisms \r\nand the individual feedbacks involved in the future change of each \r\nmode and their role on future regional climate variability are provided \r\nin Sections 8.4.2, 9.2.3 and 10.1.3, and Cross-Chapter Box 10.1."


id: 448094151718904a0990ba2c52175212
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 8204
content:
"The AR5 assessed from CMIP5 simulations that there is only \r\nmedium confidence in near-term projections of a northward shift \r\nof NH storm track and westerlies, and an associated increase in \r\nthe NAM index, because of the large response uncertainty and \r\nthe potentially large influence of internal variability. A tendency in \r\nthe near term towards a slightly more positive NAM in the three \r\nhighest emissions scenarios during boreal fall, winter, and spring is \r\napparent in Figure 4.17a. However, in general the projected near\u0002term multi-model mean change in the NAM is small in magnitude \r\ncompared to the inter-model and/or multi-realization variability \r\nwithin the ensemble (Figure  4.17a; Deser et  al., 2012b, 2017; \r\nBarnes and Polvani, 2015)."


id: bf5db3fbeb50750ab0a069c430c528b9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 27661
content:
"Ocean carbon flux is both a  key feature of the physical ocean in \r\nmitigating the rise of atmospheric CO2 and a driver of changes in the \r\nocean biosphere, including changes in ocean acidity. Based on results \r\nfrom CMIP6 models, we conclude that SSP2-4.5, SSP3-7.0, and \r\nSSP5-8.5 all clearly lead to increasing 10-, 20-, and 30-year trends in \r\nocean carbon flux over the near term (high confidence) (Figure 4.16,). \r\nIncreasing trends in ocean carbon flux are less obvious in the lower\u0002emissions scenarios. Ensemble-mean trends in land carbon flux over "


id: ebc33a0b58fc03c35b4a97a59806cd1b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 34596
content:
"Figure 4.16 | CMIP6 trends in ocean and land carbon flux for 10-year, 20-year, and 30-year periods ending in 2021-2040. (a) Ocean carbon flux. (b) Land \r\ncarbon flux. Plotted are the 5-95% ranges across the ensembles of simulations, for five SSPs. The numbers at the top of the plots are the number of model simulations in each \r\nSSP ensemble. Units are PgC yr-1 per decade. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: 5dcfe780c49bcea4d4a064f2fa046428
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 12292
content:
"the Antarctic ozone hole in October to recover by around 2060 (WMO, \r\n2014, 2018; Dhomse et al., 2018). Observational evidence since AR5 \r\nshows the onset of Antarctic ozone hole recovery (Solomon et  al., \r\n2016; WMO, 2018) that has been attributed to a pause in the summer \r\nSAM trend over the past couple of decades (Saggioro and Shepherd, \r\n2019; Banerjee et al., 2020). In austral summer, ozone recovery and \r\nincreasing GHGs will have opposing effects on the SAM over the next \r\nseveral decades (Barnes et al., 2014)."


id: 301ef51f9154fbef7bf682c4d0a35203
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 4995
content:
"The AR5 assessed that it is likely that increases in GHGs and the \r\nprojected recovery of the Antarctic ozone hole will be the principal \r\ndrivers of future SAM trends. Additionally, the positive trend in \r\naustral summer/autumn SAM observed over the past several decades \r\n(Section 2.4.1.2; Chapter 2 in AR5, Hartmann et al., 2013), is likely\r\nto weaken considerably as stratospheric ozone recovers through to \r\nthe  mid-21st  century. The effects of ozone depletion and recovery \r\non the SH circulation primarily occur in austral summer, while GHGs \r\ninfluence the SH circulation year round (Gillett and Fyfe, 2013; Grise \r\nand Polvani, 2014b). Therefore, they are likely to be the dominant driver \r\nof projected circulation changes outside of austral summer (Gillett \r\nand Fyfe, 2013; Barnes et al., 2014; Solomon and Polvani, 2016). Based \r\non current scenarios specifying future atmospheric decline of ozone \r\ndepleting substances (WMO, 2011), chemistry-climate models project "


id: ffd54ea00b5d956e5cb648f64a90b45e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 37792
content:
"CMIP6 models show a  tendency in the near-term towards a  more \r\npositive SAM index especially in the austral winter (June-July-\r\nAugust, JJA; Figure 4.17b). In all seasons, the differences between \r\nthe central estimates of the change in the SAM index for each SSP \r\nare much smaller than the inter-model ensemble spread. The number \r\nof CMIP6 realizations in Figure 4.17b is larger than the suggested \r\nthreshold of five realizations needed to detect a significant near-term \r\nchange in decadal-mean austral winter SAM index for a single CMIP5 "


id: d471d3322dc966a2a4a888454b828efe
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 30944
content:
"Figure 4.17 | CMIP6 Annular Mode index change (hPa) from 1995-2014 to 2021-2040. (a) Northern Annular Mode (NAM); (b) Southern Annular Mode (SAM). \r\nThe NAM is defined as the difference in zonal mean sea level pressure (SLP) at 35degN and 65degN (Li and Wang, 2003) and the SAM as the difference in zonal mean SLP at 40degS \r\nand 65degS (Gong and Wang, 1999). The shadings are the 5-95% ranges across the simulations. The numbers near the top of each panel are the numbers of model simulations \r\nin each SSP ensemble. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: 423920b61590ec07b559c62aeb866eba
created_at: 2023-03-16 14:00:41
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vector_id: 7819
content:
"model (Solomon and Polvani, 2016), and yet the 5-95% intervals on \r\nthe CMIP6 ensemble spread encompass zero for all core SSPs. This \r\nsuggests both internal variability and model uncertainty contribute \r\nto the CMIP6 ensemble spread in near-term SAM index changes. \r\nBased on these results, it is more likely than not that in the near-term \r\nunder all assessed SSP scenarios the SAM index would become more \r\npositive than in present-day in austral autumn, winter and spring."


id: c250ebcfb148e88f6bee1aeb374a2446
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 28157
content:
"lead to a reduction in the amplitude of IOD events, albeit with no \r\nrobust change in IOD frequency (Cai et al., 2014b). There is no robust \r\nevidence yet suggesting a cessation of IOD variability or a significant \r\nchange in the IOB mode in the near-term."


id: 50cfc92c9578d193ae16c32f1d042c4e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 10158
content:
"Interannual variability of the tropical Atlantic can be described in \r\nterms of two main climate modes: the Atlantic equatorial mode \r\nand the Atlantic meridional mode (AMM; Annex IV, Section AIV2.5). \r\nThe  Atlantic equatorial mode, also commonly referred to as the \r\nAtlantic Nino or Atlantic Zonal Mode, is associated with SST \r\nanomalies near the equator, peaking in the eastern basin, while the \r\nAMM is characterized by an inter-hemispheric gradient of SST and \r\nwind anomalies. Both modes are associated with changes in the \r\nITCZ and related winds and exert a strong influence on the climate in \r\nadjacent and remote regions."


id: 2a92f9bd5092285fb94b6699455015ed
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 4123
content:
"An influence of forcing agents other than stratospheric ozone and \r\nGHGs, such as anthropogenic aerosols, on SAM changes over the \r\nhistorical period has been reported in some climate models (Rotstayn, \r\n2013), but the response across a larger set of CMIP5 models is not \r\nrobust (Steptoe et  al., 2016) and depends on how tropospheric \r\ntemperature responds to aerosols (Choi et al., 2019). This gives low \r\nconfidence in the potential influence of anthropogenic aerosols on \r\nthe SAM in the future."


id: 72504eaa9fd3ef976b16c15442e453df
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 15612
content:
"Despite considerable improvements in CMIP5 with respect to CMIP3, \r\nmost CMIP5 models have difficulties in simulating the mean climate \r\nof the tropical Atlantic (Mohino et  al., 2019) and are not able to \r\ncorrectly simulate the main aspects of Tropical Atlantic Variability \r\n(TAV) and associated impacts. This is presumably the main reason \r\nwhy there is a lack of specific studies dealing with near-term changes \r\nin tropical Atlantic modes. Nevertheless, AR5 reported that the ocean \r\nis more predictable than continental areas at the decadal time scale \r\n(Kirtman et al., 2013). In particular, the predictability in the tropical \r\noceans is mainly associated with decadal variations of the external \r\nforcing component. Since the AMV affects the tropical Atlantic, \r\nnear-term variations of the AMV can modulate the equatorial mode \r\nand the AMM as well as associated impacts."


id: fd58f3a55a758e8701c241276697f676
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 37376
content:
"The AR5 assessed that it is very likely that the ENSO will remain \r\nthe dominant mode of interannual variability in the future but did \r\nnot specify its change in near term. A subset of CMIP5 models that \r\nsimulate the ENSO Bjerknes index most realistically show an increase \r\nof ENSO SST amplitude in the near-term future and decline thereafter \r\n(Kim et al., 2014). However, detection of robust near-term changes of \r\nENSO SST variability in response to anthropogenic forcing is difficult \r\nto achieve due to pronounced unforced low-frequency modulations \r\nof ENSO (Wittenberg, 2009; Maher et al., 2018; Wengel et al., 2018). \r\nFigure 4.10 in Section 4.3.3.2, using CMIP6 models, also shows no \r\nrobust change in ENSO SST variability in the near term."


id: 90d21e5f03ab22d7b9840f5f4fc2e671
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 20328
content:
"While there is no strong model consensus on the change in amplitude \r\nof ENSO SST variability, the amplitude of ENSO-associated rainfall \r\nvariability likely increases (Power et al., 2013; Cai et al., 2015). Analysis \r\nof CMIP6 models shows a  slight increasing trend in amplitude of \r\nrainfall variability over Nino 3.4 region in the near term attributable to \r\nmean moisture increase, regardless of changes in ENSO SST variability \r\n(Figure 4.10). However, there are no distinguishable changes in the \r\nrainfall variability among five SSPs with significant model spread in \r\nthe near term. Hence, no robust change in amplitude of ENSO SST \r\nand rainfall variability is expected in the near term although the \r\nrainfall variability slightly increases (medium confidence)."


id: e45bb92fa51fc25b21959fa06133466a
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 33406
content:
"Important modes of interannual climate variability with pronounced \r\nclimate impacts in the Africa-Indo-Pacific areas of the globe are \r\nthe Indian Ocean Dipole (IOD), which is closely related to, and \r\noften coincides with, ENSO phases (Stuecker et al., 2017), and the \r\nIndian Ocean basin (IOB) mode. This is often described as a capacitor \r\neffect in response to ENSO (Xie et al., 2009; Du et al., 2013) and can \r\nfeed back onto ENSO evolution (Cai et al., 2019). IOD and IOB are \r\nextensively described in Annex IV (Section AIV2.4)."


id: 44512afc940e3ee4e39648bbb9c69a65
created_at: 2023-03-16 14:00:41
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vector_id: 8171
content:
"Recent studies have shown that the AMV can modulate not only \r\nthe characteristics of the Atlantic Ninos, but also their inter-basin \r\nteleconnections (Indian and Pacific). In particular, the Atlantic \r\nNino-ENSO relationship is strongest during negative AMV phases \r\n(Martin-Rey et al., 2014; Losada and Rodriguez-Fonseca, 2016) when \r\nequatorial Atlantic SST variability is enhanced (Martin-Rey et al., 2017; \r\nLubbecke et al., 2018)."


id: c21ea0727ca6a630b214c69e01f8fa9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 28123
content:
"The projected climate mean state changes in the tropical Indian \r\nOcean resemble a  positive IOD state, with faster warming in the \r\nwest compared to the east. This mean state change will potentially "


id: a01cb7eaec6e2421176a6561b160fdc5
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 22726
content:
"Based on CMIP5 and available CMIP6 results, we conclude that there \r\nis a lack of studies on the near-term evolution of TAV and associated \r\nteleconnections for a  comprehensive assessment. However, some \r\nstudies show that despite severe model biases there are skilful \r\npredictions in the mean state of tropical Atlantic surface temperature \r\nseveral years ahead (medium confidence), though skill in simulated \r\nvariability has not been assessed yet. Decadal changes in the Atlantic \r\nNino spatial configuration and associated teleconnections might be \r\nmodulated by the AMV, but there is limited evidence and therefore\r\nlow confidence in these results."


id: 5bc3f2fe3e6c21c0c75f258b07f28dd7
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 11948
content:
"The PDV has been shown to influence the pace of global warming \r\n(Cross-Chapter Box 3.1), but the extent to which PDV is externally forced \r\nor internally generated (Mann et al., 2020) remains an open question, \r\nand there is still no robust evidence. Thus, there is low confidence on \r\nhow the PDV will evolve in the near-term (Bordbar et al., 2019)."


id: eaff1be102cb014bb48e22897955c59e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 34477
content:
"Climate variability of the Pacific Ocean on decadal and inter-decadal \r\ntime scales is described in terms of a number of quasi-oscillatory SST \r\npatterns such as the Pacific Decadal Oscillation (PDO; Mantua et al., \r\n1997) and the Inter-decadal Pacific Oscillation (IPO; Folland, 2002), \r\nwhich are referred to as the Pacific Decadal Variability (PDV; Newman \r\net al., 2016). PDV comprises an inter-hemispheric pattern that varies \r\nat decadal to inter-decadal time scales (Figure  3.35). However, \r\nalthough the spatial domains to derive the IPO and PDO indices differ, \r\nand despite uncertainty related to trend removal and time-filtering \r\n(Newman et al., 2016; Tung et al., 2019), the IPO and PDO are highly \r\ncorrelated in time and they will be assessed together as the PDV \r\n(Annex IV, Section AIV.2.6)."


id: d05c7f37bfb050cfc6f9baf036e59fd0
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 30310
content:
"The AR5 assessed that near-term predictions of PDV (then referred \r\nto as PDO or IPO) were largely model dependent (Mochizuki \r\net  al., 2012; van Oldenborgh et  al., 2012), not robust to sampling \r\nof initialization start-dates, overall not statistically significant, \r\nand worse than persistence (Doblas-Reyes et  al., 2013), although \r\nsome studies showed positive skill for PDV (Mochizuki et al., 2010; \r\nChikamoto et  al., 2013). The CMIP5 decadal-prediction ensemble \r\nyielded no prediction skill of SST over the key PDV centres of action \r\nin the Pacific Ocean, both at two-to-five-year and six-to-nine-year \r\nforecast averages (Doblas-Reyes et al., 2013; Guemas et al., 2013; \r\nBoer and Sospedra-Alfonso, 2019)."


id: 526e64b6c67314a62ca881d6caf9edc0
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 10423
content:
"The AR5 assessed with high confidence that initialized predictions can \r\nimprove the skill for temperature over the North Atlantic, in particular \r\nin the sub-polar branch of AMV, compared to the projections, for \r\nthe first five years (see AR5 WGI Figures 11.3 and 11.4). However, \r\nnon-initialized predictions showed positive correlation over the \r\nsame time-range as well, consistent with the notion that part of this \r\nvariability is caused by external forcing (Section 3.7.7)."


id: efffa028697ddd44aec87a459af76474
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 35265
content:
"Since AR5, near-term initialized predictions, both multi-model (Bellucci \r\net al., 2015a; Garcia-Serrano et al., 2015; D.M. Smith et al., 2019) and \r\nsingle-model ensembles (Marotzke et al., 2016; Simpson et al., 2018; \r\nYeager et al., 2018; Hermanson et al., 2020; Bilbao et al., 2021), confirm \r\nsubstantial skill in hindcasting North Atlantic SST anomalies on a time \r\nrange of eight to ten years. On the same time range, Borchert et al. \r\n(2021) show a  substantial improvement in the prediction of the \r\nsubpolar gyre SST (the northern component of the AMV) in CMIP6 \r\nmodels compared to CMIP5, in both initialized and non-initialized \r\nsimulations. The higher skill of CMIP6 models can be attributed to \r\na  more accurate response of SST variations in the subpolar gyre \r\nto  natural forcing, possibly originating from the AMOC-related \r\ndelayed response to volcanic eruptions (Hermanson et al., 2020)."


id: e4905f369060910cea2b9e4495daec48
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vector_id: 33438
content:
"Since AR5, the processes causing the multi-decadal variability in the \r\nPacific Ocean have become better understood (Newman et al., 2016; \r\nHenley, 2017). However, the relative importance of tropical and \r\nextratropical processes underlying PDV remains unclear; although it \r\nseems to be stochastically driven rather than self-excited (Liu, 2012; \r\nLiu and Di Lorenzo, 2018), the South Pacific being a  key region \r\nfor the tropical branch of PDV (Chung et  al., 2019; Liguori and \r\nDi Lorenzo, 2019)."


id: d0755531a68f24e84c380b37c83e663b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 30323
content:
"Because PDV represents not one, but many dynamical processes, it \r\nrepresents a challenge as a target for near-term climate predictions \r\nand projections. The new generation of decadal forecast systems \r\nkeeps showing poor (Shaffrey et al., 2017) to moderate (D.M. Smith \r\net al., 2019) multi-year prediction skill for PDV, although the potential \r\nfor forecasting capabilities is demonstrated in case studies (Meehl \r\nand Teng, 2012; Meehl et al., 2014). For the near-term, a transition of \r\nPDV from the negative phase (1999-2012) towards a positive phase \r\nis predicted in the coming years (2013-2022; Meehl et al., 2016)."


id: cf0a9c438bb44c2bfcf1bd741e7d4f41
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 30108
content:
"Initialization contributes to the reduction of uncertainty and to \r\npredicting subpolar SST amplitude (Borchert et al., 2021). Yet, skill in \r\npredicting the AMV is not always translated into equally successful \r\npredictions of temperature and precipitation over the nearby land \r\nand ocean regions (Langehaug et al., 2017). This might be related to \r\nsystematic model errors in the simulation of the spatial and temporal \r\nstructure of the AMV and too weak associated teleconnections \r\n(Section 3.7.7), and also to the larger noise in regional land variables \r\ncompared to the AMV index. However, AMV predictions can be "


id: d31fb72983c6cf234bcbd28dc33aa7ac
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 30739
content:
"et al., 2017) shows a projected multi-model mean GSAT anomaly \r\nthat is higher by 0.22degC at mid-century (2045-2054) compared \r\nto SSP3-7.0 (Figure 4.18; Allen et al., 2020), but this difference is \r\nsmaller than the inter-model spread of the SSP3-7.0 projections \r\nbased on the CMIP6 models. Note the SSP3-7.0-lowSLCF-highCH4 \r\nexperiment does not perturb methane from SSP3-7.0 concentrations. \r\nA modified SSP3-7.0-lowSLCF-lowCH4 scenario that also includes \r\nmethane mitigation shows a lower GSAT by mid-century compared \r\nto SSP3-7.0 (Allen et al., 2021)."


id: f399a92c3e546b10ce09b0f0765e1c39
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"used as proxies to predict other variables such as precipitation over \r\nWestern Europe and Eurasia and SAT over Mediterranean, Northern \r\nEurope and north-east Asia (Arthun et al., 2018; Borchert et al., 2019; \r\nRuggieri et al., 2021) whose relationship with North Atlantic SST is \r\nrobust in observations, but not well captured in climate models."


id: 6efda96dcacbe51edaf247445da62060
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 15087
content:
"In summary, the confidence in the predictions of AMV and its effects \r\nis medium. However, there is high confidence that the AMV skill \r\nover five-to-eight-year lead times is improved by using initialized \r\npredictions, compared to non-initialized simulations."


id: 7eaf1fbdf9dd1c7d4a821f4011e71cf
created_at: 2023-03-16 14:00:41
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content:
"A model experiment based on the SSP3-7.0 scenario with aerosols, \r\ntheir precursors, and non-methane tropospheric ozone precursors \r\nset to SSP1-1.9 abundances (SSP3-7.0-lowSLCF-highCH4; Collins "


id: 6a009b87e0218fb8c2d2070f6a3df71e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"Table 4.6 | The net effect of SLCFs on GSAT change. Changes in 20-year averaged GSAT relative to 1995-2014 for 2021-2040, 2041-2060, and 2081-2100 for the five \r\nmarker SSP scenarios. Values give the median and, in parentheses, the 5-95% range calculated from a 2237-member ensemble of the two-layer emulator that is driven with \r\nthe ERF projections, including uncertainties, described in Chapter 7 Supplementary Material 7.SM.1.4. The ensemble is constrained to assessed ranges of ECS, TCR, ocean heat \r\ncontent change, GSAT response, and carbon cycle metrics (Section 7.3.5; Chapter 7 Supplementary Material 7.SM.2.2). The GSAT contribution of individual forcer responses \r\nuse the difference between parallel runs of the constrained two-layer model with all anthropogenic forcing and all anthropogenic forcing with the component of interest \r\n(e.g., methane) removed (Chapter 7 Supplementary Material 7.SM.2.3). Values are given to one decimal place."


id: eca601f535f312e51b4a0a4c7b823555
created_at: 2023-03-16 14:00:41
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content:
"In addition to effects on GSAT, SLCFs affect other aspects of the global \r\nclimate system (Section 6.7.2). The additional warming at high northern \r\nlatitudes associated with projected reductions in aerosol emissions \r\nover the 21st century leads to a more rapid reduction in Arctic sea \r\nice extent in the RCP scenarios (Gagne et  al., 2015). Furthermore, \r\nmitigation of non-methane SLCFs in the SSP3-7.0-lowSLCF-highCH4 \r\nscenario causes an increase in global mean precipitation, with larger \r\nregional changes in southern and eastern Asia (Allen et al., 2020)."


id: d3d0fe7d76d710c4302cf23ab9415898
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 30849
content:
"The main uncertainties in climate effects of SLCFs in the future come \r\nfrom: (i) the uncertainty in anthropogenic aerosol ERF (Section 7.3.3); \r\n(ii) uncertainty in the relative emissions of different SLCFs that have \r\nwarming and cooling effects in the current climate (Section 6.2); and \r\n(iii) physical uncertainty including the efficacy of the climate response \r\nto SLCFs compared to long-lived GHGs (Marvel et al., 2016; Richardson \r\net al., 2019). One example of physical uncertainty is that the shortwave \r\nradiative forcing from methane was neglected in previous calculations \r\n(Etminan et al., 2016; Collins et al., 2018), which affects understanding \r\nof present day and future methane ERF (Modak et al., 2018). Another \r\nexample of physical uncertainty is projected changes in lightning\u0002NOx production, which contribute to future ozone radiative forcing \r\n(Banerjee et al., 2014, 2018; Finney et al., 2018)."


id: 932349cb8302fd33d9236de40648dd45
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 20664
content:
"Since AR5, there has been growing progress in understanding the \r\nclimate impacts of volcanic eruptions. Volcanic forcing is regarded as \r\nthe dominant driver of forced variability in preindustrial surface air \r\ntemperature (Schurer et al., 2013, 2014). Large eruptions in the tropics \r\nand high latitudes were primary drivers of interannual-to-decadal "


id: 58a26be54c0cb42de4da60d4c169269e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 11442
content:
"Contributing Authors: Sarah L. Connors (France/United Kingdom), Amanda Maycock (United Kingdom), Peter W. Thorne (Ireland/\r\nUnited Kingdom), Nicolas Bellouin (United Kingdom/France), Ingo Bethke (Norway/Germany), Deliang Chen (Sweden), Annalisa Cherchi \r\n(Italy), Alejandro Di Luca (Australia/Canada/Argentina), Piers Forster (United Kingdom), Nathan P. Gillett (Canada), Darrell S. Kaufmann \r\n(The United States of America), June-Yi Lee (Republic of Korea), Elizaveta Malinina (Canada/Russian Federation), Seung-Ki Min (Republic \r\nof Korea), Johannes Quaas (Germany), Alex C. Ruane (The United States of America), Jean-Baptiste Sallee (France), Sonia I. Seneviratne \r\n(Switzerland), Chris Smith (United Kingdom), Matthew Toohey (Canada, Germany/Canada), Andrew  Turner (United Kingdom), \r\nCunde Xiao (China), Tianjun Zhou (China)"


id: 9e6c191dbca43e3c994f8aaf229278b1
created_at: 2023-03-16 14:00:41
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"Before the industrial period, explosive volcanic eruptions were the largest source of forced climate variability globally on interannual to \r\ncentennial time scales (Section 2.2). While usually omitted from scenarios used for future climate projections, as they are unpredictable, \r\nvolcanic eruptions have the potential to influence future climate on multi-annual to decadal time scales and affect many climatic \r\nimpact drivers (as defined in Sections 12.1 and 12.3). Since AR5, more comprehensive paleo evidence and observations, as well as \r\nimproved modelling have advanced understanding of the climate response to past volcanic eruptions. Building on multiple chapter \r\nassessments, this box synthesizes how volcanic eruptions affect climate and considers implications of possible future events."


id: ff24da7c29931d26e24d1f6146977f30
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"Proxy records show that large volcanic eruptions with effective radiative forcing (ERF) more negative than -1 W m-2 occurred on \r\naverage twice a century throughout the last 2500 years, the most recent being Pinatubo in 1991 (Section 2.2.2). About eight larger \r\neruptions (ERF stronger than -5 W m-2) also occurred during this period (Figure 2.2), notably Tambora about 1815 and Samalas \r\nabout 1257. A Samalas-type eruption may occur one to two times per millennium on average (Newhall et al., 2018). Typically, three \r\nin every four centuries have experienced at least one eruption stronger than -1 W m-2 (Pinatubo or larger). The volcanic aerosol \r\nburden was 14% lower during the 20th century compared to the average of the preceding 24 centuries (Section 2.2.2), whereas the \r\n13th century was among the most volcanically active, with four eruptions exceeding that of Pinatubo-1991 (Sigl et al., 2015)."


id: 54e7dcc39ca92a081710c1926d13cfcf
created_at: 2023-03-16 14:00:41
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content:
"Major eruptions drive a range of climate system responses for several years depending upon whether the eruption occurs in the tropics \r\n(stratospheric aerosol dispersion into both hemispheres) or the extratropics (dispersion into the hemisphere of eruption) owing to the \r\nBrewer-Dobson circulation. The climatic response also depends on the effective injection height, sulphur mass injected, and time of \r\nyear of the eruption (Marshall et al., 2019, 2020). These factors determine the total mass, lifetime and optical properties of volcanic \r\naerosol in the stratosphere and influence the stratospheric aerosol optical depth (SAOD). The ERF from volcanic stratospheric aerosol \r\nis assessed to be -20 +- 5 W m-2 per unit sAOD (Section 7.3.4.6)."


id: 8f0ee033b98d6314bbc9f2eb8c508783
created_at: 2023-03-16 14:00:41
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vector_id: 20047
content:
"Paik et al., 2020; Predybaylo et al., 2020). Volcanic activity could drive short-term (one-to-three-year) positive changes in the annual \r\nSAM index through modulations in the extratropical temperature gradient and wave driving of the polar stratosphere (Yang and Xiao, \r\n2018). In the cryosphere, Arctic sea ice extent increases for years to decades (Gagne et al., 2017a), and modelling indicates that sea \r\nice/ocean feedbacks can prolong cooling long after volcanic aerosols are removed (Miller et al., 2012). On annual time scales, the ocean \r\nbuffers the atmospheric response to volcanic eruptions by storing the cooling in the ocean subsurface, then feeding it back to the \r\natmosphere. Large eruptions affect ocean heat content and thermosteric sea level over decadal-to-centennial scales (Section 9.2.2.1)."


id: b10dadc8fe95c95b2b994cb06d930314
created_at: 2023-03-16 14:00:41
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content:
"It is likely that at least one large eruption will occur during the 21st century. Such an eruption would reduce GSAT for several years, \r\ndecrease global mean land precipitation, alter monsoon circulation, modify extreme precipitation, and change the profile of many \r\nregional climatic impact-drivers. A low-likelihood, high-impact outcome would be several large eruptions that would greatly alter the \r\n21st century climate trajectory compared to SSP-based ESM projections."


id: ac8f9bca3090730f2046f180b9dcb164
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"Cross-Chapter Box  4.1, Figure  1 | Potential impact of volcanic eruption on future global temperature change. CMIP5 projections of possible \r\n21st-century futures under RCP4.5 after a 1257 Samalas magnitude volcanic eruption in 2044, from Bethke et al. (2017). (a) Volcanic ERF of the most volcanically \r\nactive ensemble member, estimated from SAOD. (b) Annual mean global surface air temperature. Ensemble mean (solid) of future projections including volcanoes \r\n(blue) and excluding volcanoes (red) with 5-95% range (shading) and ensemble minima/maxima (dots); evolution of the most volcanically active member (black). \r\nData created using a SMILE approach with NorESM1 in its CMIP5 configuration. See Sections 2.2.2 and 4.4.4 for more details. Further details on data sources and \r\nprocessing are available in the chapter data table (Table 4.SM.1)."


id: 23affafed653a410d800259be6442b4c
created_at: 2023-03-16 14:00:41
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vector_id: 3063
content:
"Patterns of near-surface air temperature changes show widespread \r\nwarming by 2041-2060 and 2081-2100 (Figure  4.19) for all SSPs \r\nrelative to 1995-2014. The area fraction experiencing warming \r\nincreases with the level of global mean warming. As GSAT continues \r\nto increase, it is very likely that by the middle and the end of the \r\n21st century most of the global land and ocean areas will be warmer \r\nthan in 1995-2014 (high confidence, Section 4.3.1.1)."


id: 76d7fda515da8ba4f8831f1c71ab4aa2
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 16273
content:
"This section assesses how the global atmospheric indicators assessed \r\nin Section 4.3 manifest themselves in large-scale spatial patterns of \r\natmospheric change in the mid-term (2041-2060) and long term \r\n(2081-2100). The patterns of change in any given future period \r\nrepresent a combination of unforced internal variability and a forced \r\nresponse including their interaction (Deser et al., 2016). The role of \r\ninternal variability is much larger at the local to regional scale than \r\nin the global mean projections. We here assess multi-model mean \r\npatterns based on CMIP6 models without any weighting or emergent \r\nconstraints. The mean represents an estimate of the forced response \r\nand is a more homogeneous pattern than the 20-year mean change \r\npatterns in any individual model realization (Knutti et al., 2010)."


id: 950b22351144e900e9da151661a1cabc
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 20955
content:
"The multi-model mean temperature change pattern (Figure  4.19) \r\nshows some robust key characteristics that are independent of the \r\ntime horizon and scenario, such as a land-ocean warming contrast, \r\namplified warming over the Arctic region, assessed below, or the \r\ncomparatively small warming or even cooling in the North Atlantic \r\nsubpolar gyre (Section  9.2.1.1). Furthermore, changes in aerosol \r\nconcentrations and land use and land management can have \r\na direct imprint on the regional warming pattern (Bright et al., 2017; "


id: a9b9fa96bb23cad68b8036384da4745
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 24233
content:
"Kasoar et al., 2018). Note that the global average of the pattern \r\nshown in Figure  4.19 corresponds to CMIP6 multi-model mean \r\nGSAT warming (Section 4.3.1) and is thus somewhat warmer than \r\nthe warming pattern consistent with the central estimate of the \r\nGSAT range assessed in Section  4.3.4. Since the regional mean \r\nwarming scales well with global warming levels independent \r\nof the emissions scenario (Section  4.2.4), the key characteristics \r\nof the spatial pattern assessed here are largely independent of the \r\ndifference between CMIP6 multi-model global mean and assessed \r\nglobal GSAT change."


id: bc4952e44d05a1a8cc4562dc622d826e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 27200
content:
"It is very likely that under all SSPs the warming in the Arctic will be \r\nmore pronounced than in the global average over the 21st century. \r\nSince AR5 the understanding of the physical mechanisms driving \r\npolar amplification has improved."


id: 7f003936139934e2eb036c3c0da059c8
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 17536
content:
"The Arctic surface is projected to warm more than the global average \r\nover the 21st century, with annual-average Arctic warming of about \r\n3degC (SSP1-2.6), 10degC (SSP3-7.0) and 12degC in (SSP5-8.5) by 2081-2100 \r\nrelative to 1995-2014 (Figure  4.19). This phenomenon, known as \r\npolar or Arctic amplification, is a ubiquitous feature of the response \r\nto GHG forcing simulated by climate models (Manabe and Wetherald, \r\n1975, 1980; Manabe and Stouffer, 1980; Robock, 1983; Hansen et al., \r\n1984; Manabe et al., 1991; Holland and Bitz, 2003; Winton, 2006; \r\nPithan and Mauritsen, 2014) and has been observed over recent \r\ndecades concurrent with Arctic sea ice loss (Section 2.3.2.1; Serreze \r\nand Barry, 2011). Based on robust scientific understanding and \r\nagreement across multiple lines of evidence (Section 7.4.4.1), there is \r\nhigh confidence that the rate of Arctic surface warming will continue \r\nto exceed the global average over the 21st century."


id: a1547294c90306f8cc27f4b565f088b3
created_at: 2023-03-16 14:00:41
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vector_id: 22942
content:
"over recent and future decades (Section  6.4.3; Gagne et  al., 2015; \r\nAcosta Navarro et al., 2016; Wobus et al., 2016; Wang et al., 2018)."


id: 6f2a02c7e517a358a96a8a6e24f03021
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 15117
content:
"(Dwyer et al., 2012; Donohoe and Battisti, 2013). On the other hand, \r\nover most of the subtropics and mid-latitudinal land regions except for \r\nparts of Asia, models project stronger warming in hemispheric summer \r\nthan winter (Donohoe and Battisti, 2013; Santer et al., 2018), leading \r\nto an amplification of the seasonal cycle. This phenomenon has been \r\nstudied particularly in the case of the amplified summer warming over \r\nthe Mediterranean region (Seager et al., 2014a; Kroner et al., 2017; \r\nBrogli et al., 2019)."


id: a40d17cbe1a40a2141e53b16a68c65b7
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 23367
content:
"It has long been recognized that along with mean temperatures \r\nalso variance and skewness of the temperature distribution may \r\nbe changing (Gregory and Mitchell, 1995; Mearns et  al., 1997). \r\nBy amplifying or dampening changes in the tail of temperature \r\ndistribution such changes are potentially highly relevant to extremes \r\n(Section  11.3.1) and pose a  serious challenge to adaptation \r\nmeasures. Changes in temperature variability can occur from diurnal \r\nto multi-decadal time scales and from the local to the global scale \r\nwith potentially even opposing signals in different seasons and at \r\nthe different spatial scales"


id: ffab39214cfc29696dd600629b7efb97
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 37767
content:
"Changes in GSAT variability are poorly understood. Based on model \r\nexperiments it has been suggested that unforced variability of GSAT \r\ntends to decrease in a warmer world as a result of reduced albedo \r\nvariability in high latitudes resulting from melting snow and sea ice \r\n(Huntingford et al., 2013; Brown et al., 2017), but confidence remains\r\nlow and an observed change has not been detected. An assessment of \r\nchanges in global temperature variability is inherently challenging due \r\nto the interplay of unforced internal variability and forced changes."


id: 88cca1eda682ffad5ed912b5cd9a1d98
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 19058
content:
"The warming pattern shows distinct seasonal characteristics. \r\nThe  majority of models show a  stronger hemispheric winter than \r\nsummer warming over land poleward of about 55degN and 55degS \r\n(Figure 4.20) and thereby a reduced amplitude of the temperature cycle "


id: d4723f952e9ae1dbcd3267cfa7070442
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 30945
content:
"Changes in tropical temperature variability may arise from changes \r\nin the amplitude of ENSO (Section  4.5.3.2). Over the extratropics, \r\nseveral studies have identified robust large-scale patterns of changes "


id: d2d91b2e08cfb909dba158f8abe3c765
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 30694
content:
"In JJA, the multi-model average projects an increase in year-to-year \r\nJJA variability over Central Europe and North America (Figure 4.21). \r\nIn particular an increase in daily to interannual summer temperature \r\nvariability has been projected over central Europe as a result of larger \r\nyear-to-year variability in soil moisture conditions varying between \r\na  wet and dry regime and leading to enhanced land-atmosphere \r\ninteraction (Seneviratne et  al., 2006; Fischer et  al., 2012; Holmes \r\net  al., 2016). Furthermore, the amplified warming in the source \r\nregions of warm-air advection due to land-ocean warming contrast \r\nand amplified Mediterranean warming (Seager et al., 2014a; Brogli \r\net  al., 2019), may lead to disproportionally strong warming of \r\nthe hottest days and summers and thereby increased variability. \r\nEnhanced temperature variability is further projected over some land \r\nregions in the subtropics and tropics (Bathiany et al., 2018)."


id: 5f25436e8154c549af1c30a510a05fd3
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 12525
content:
"In summary, there is medium confidence that continued warming will \r\nregionally lead to increased and decreased year-to-year temperature \r\nvariability in the extratropics and there is medium confidence that \r\nyear-to-year temperature variability will decrease over parts of the \r\nmid- to high- latitudes of the winter hemisphere."


id: ce5e7f80f61561eb7807349777b16227
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 30003
content:
"Section  12.4.3.2 of AR5 assessed that there is high confidence in \r\nthe overall pattern of projected end of 21st  century tropospheric \r\ntemperature change and that it is very likely that some of the largest \r\nwarming will occur in the northern high latitudes. They further \r\nassessed that proportionately larger warming is likely to occur \r\nin the tropical upper troposphere than at the tropical surface, but \r\nwith medium confidence owing to the relatively large observational "


id: 2cc0ff49f902bec52084783cbb1aea3f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 4474
content:
"uncertainties and contradictory analyses regarding model accuracy in \r\nsimulating tropical upper tropospheric temperature trends."


id: 239168070b8d10e29106afe216e3fde6
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 3044
content:
"stratosphere, the CMIP6 models project a weak cooling in the inner \r\ntropics in SSP1-2.6 and a warming at other latitudes (Figure 4.22). \r\nThere is enhanced lower stratospheric warming over the Antarctic \r\npole owing to the effects of ozone hole recovery on polar temperatures \r\n(Maycock, 2016; Solomon et al., 2017). The projected strengthening of \r\nthe Brewer -Dobson circulation in the future (Hardiman et al., 2014) \r\nalso affects stratospheric temperature trends, with adiabatic cooling \r\nat low latitudes and warming in middle and high latitudes (Fu et al., \r\n2015, 2019). In SSP3-7.0, there is widespread cooling across much \r\nof the stratosphere, as expected from the higher GHG emissions, \r\nwith a smaller warming in the Antarctic lower stratosphere. Owing \r\nto the importance of ozone recovery for the radiative balance of \r\nthe stratosphere, future global and local stratospheric temperature \r\ntrends do not scale with projected GSAT change."


id: 9e25a7f62a48ea8dbeb8377d898c2c31
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 22405
content:
"In summary, new results since AR5 do not generally alter the \r\nunderstanding of projected zonal mean atmospheric temperature \r\nchanges. There is high confidence in the overall pattern of projected \r\ntropospheric temperature changes given its robustness across many \r\ngenerations of climate models. It is further very likely that projected \r\nlong-term tropospheric warming will be larger than the global \r\nmean in the Arctic lower troposphere. It is likely that tropical upper \r\ntropospheric warming will be larger than at the tropical surface, \r\nhowever with an uncertain magnitude owing to the potentially large \r\nrole of natural internal variability and differences across models in \r\nthe simulated free tropospheric temperature response to a  given \r\nforcing scenario (Section 3.3.1.2). It is very likely that global mean \r\nstratospheric cooling will be larger by the end of the 21st century \r\nin a pathway with higher atmospheric CO2 concentrations."


id: 31a06c4a031210e0a3ccbb673b9d85be
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 5216
content:
"Projected stratospheric temperature trends are determined by \r\na balance between the major radiative drivers from ozone recovery, \r\nrising CO2 and other greenhouse gases (including stratospheric \r\nwater vapour) (Maycock, 2016), as well as future changes in the \r\nBrewer -Dobson circulation, which can alter the latitudinal pattern of \r\nstratospheric temperature trends (Fu et al., 2015, 2019). In the lower "


id: f409b2fa93959b8a720b9e95bf2e065b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 35905
content:
"land RH are also strongly influenced by evapotranspiration, which is \r\nsuppressed by the drying of soils and plant responses to increasing \r\nCO2 related to stomatal closure under climate change (Byrne and \r\nO'Gorman, 2015; Berg et  al., 2016; Chadwick et  al., 2016; Swann \r\net  al., 2016; Lemordant et  al., 2018). The combination of oceanic \r\nand continental influences can explain the spatially diverse trends \r\nin the near-surface RH over land in the observations for the recent \r\ndecades, with a generally dominant negative trend at the global scale \r\n(Vicente-Serrano et  al., 2018). There is a  strong feedback between \r\nthe near-surface land RH decrease and land-ocean warming contrast \r\nunder future warming projections (Section 4.5.1.1)."


id: 4f38ee20e96b42dc1a6047c69b4cd991
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 9903
content:
"The AR5 contrasted future changes in near-surface relative humidity \r\n(RH) over land and ocean, concluding with medium confidence that \r\nreductions in near-surface RH over many land areas are likely. The \r\ndecrease in near-surface RH over most land areas is associated with \r\nthe larger warming rates over land than over the ocean and is termed \r\nthe last-saturation-temperature constraint, as explained in AR5."


id: 1e0650fc6bcac7b8902bbfa740314f89
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 2158
content:
"Since AR5, significant effort has been devoted to understanding the \r\nmechanisms for the decrease in near-surface land RH under global \r\nwarming, and the relevance of RH changes for the land-sea warming \r\ncontrast and the water cycle. For the near-surface RH decrease over \r\nland, both the moisture transport from the ocean and land-atmosphere \r\nfeedback processes contribute. For changes in specific humidity over \r\nland, the moisture transport from the ocean is dominant while the \r\nrole of evapotranspiration is secondary (Byrne and O'Gorman, 2016; \r\nChadwick et  al., 2016). Nevertheless, the changes in near-surface "


id: b548c7e6e8132b23413e8608acd30be2
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 26065
content:
"Changes in land RH can modulate the response of the water cycle to \r\nglobal warming (Chadwick et al., 2013; Byrne and O'Gorman, 2015). \r\nMost CMIP5 models project higher precipitation associated with higher \r\nnear-surface RH and temperature under climate change (Lambert \r\net al., 2017). Over land, the spatial gradients of fractional changes \r\nin near-surface RH contribute to a  drying tendency in  precipitation "


id: ef71b1271feea11c5cec46611336b0b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 35156
content:
"precipitation (Pendergrass and Hartmann, 2014), leading to a slow \r\nSST-dependent response and a forcing-dependent rapid adjustment. \r\nRapid adjustments account for large regional differences in \r\nhydrological sensitivity across multiple drivers (Samset et al., 2016; \r\nMyhre et  al., 2017). The rapid regional precipitation response to \r\nincreased CO2 is robust across models, implying that the uncertainty \r\nin long-term changes is mainly associated with the response to \r\nSST-mediated feedbacks (Richardson et  al., 2016). Precipitation \r\nresponse to fast adjustments and slow temperature-driven responses \r\nare assessed in detail in Chapter 8 (Section 8.2.1)."


id: 50ee977423db0265ed7426583e5b365f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 10184
content:
"minus evapotranspiration with warming, which partly explains why \r\nthe 'wet gets wetter, dry gets drier' paradigm does not hold over land \r\n(Byrne and O'Gorman, 2015). Terrestrial aridity is projected to increase \r\nover land, as manifested by a decrease in the ratio of precipitation to \r\npotential evapotranspiration, in which the decrease in near-surface \r\nland RH has a  contribution of about 35% in CMIP5 models under \r\ndoubled CO2 forcing (Fu and Feng, 2014). The aridity can be further \r\namplified by the feedbacks of projected drier soils on land surface \r\ntemperature, RH, and precipitation (Berg et al., 2016)."


id: 68d4675308b80ab8a22655d76a782f62
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 14081
content:
"The CMIP6 multi-model ensemble projects general decreases in \r\nnear-surface relative humidity over a  large fraction of land areas, \r\nbut moderate increases over the ocean (Figure 4.23). The projected \r\nchanges depend on emissions scenario and season. Changes in \r\nnear-surface RH under SSP1-2.6 are insignificant compared to \r\nnatural variability. Under SSP3-7.0, during boreal summer, significant \r\ndecreases relative to natural variability are projected in continental \r\nEurope and the Middle East, North America, South America and \r\nSouth Africa."


id: 6a72d361bcdbbc87b30d798376ac9bf7
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 14355
content:
"In summary, there is medium confidence that continued warming will \r\nlead to decreased near-surface relative humidity over a large fraction \r\nof land areas, but moderate increases over the ocean. There is high \r\nconfidence that near-surface relative humidity will decrease over \r\nparts of the tropical and subtropical latitudes over land."


id: a198732901b7b5e5df250da3727b607a
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 22980
content:
"The AR5 assessed that changes in mean precipitation in a warmer \r\nworld will exhibit substantial spatial variation. Also, the contrast of \r\nmean precipitation between dry and wet regions and between dry \r\nand wet seasons will increase over most of globe as temperatures \r\nincrease. The general pattern of change indicates that high latitude \r\nland masses are likely to experience greater amounts of precipitation \r\ndue to the increased specific humidity of the warmer troposphere as \r\nwell as increased transport of water vapour from the tropics by the \r\nend of this century under the RCP8.5 scenario. Many mid-latitude \r\nand subtropical arid and semi-arid regions will likely experience \r\nless precipitation, while many moist mid-latitude regions will likely\r\nexperience more precipitation by the end of this century under the \r\nRCP8.5 scenario."


id: c4c68fd4a084a42d818bafae40dfeb1f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 28532
content:
"Most of the projected changes in precipitation exhibit a  sharp \r\ncontrast between land and ocean (Sections 8.2.1 and 8.4.1). \r\nTemperature-driven intensification of land-mean precipitation during \r\nthe 20th  century has been masked by fast precipitation responses \r\nto anthropogenic sulphate and volcanic forcing (Allen and Ingram, \r\n2002; Richardson et  al., 2018a). Based on the Precipitation Driver \r\nand Response Model Intercomparison Project (PDRMIP), land-mean \r\nprecipitation is expected to increase more rapidly with the projected \r\ndecrease in sulphate forcing and continued warming, contributing to \r\nincreased global mean precipitation (Table 4.3) and will be clearly \r\nobservable by the mid-21st  century based on RCP4.5 and RCP8.5 \r\nscenarios (Richardson et al., 2018a)."


id: cbab70272cafe494f1565a2b0aa79c2
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 29586
content:
"The corresponding increase in annual mean global land precipitation is \r\n3.3% (0-6.6%), in the SSP1-2.6 and 5.8% (0.5-9.6%) in the SSP3-7.0 \r\n(Table  4.3). CMIP6 models show greater increases in precipitation \r\nover land than either globally or over the ocean (high confidence)."


id: 98bbebf4b3c1b40f8e94541c56bba32f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 21548
content:
"Based on the assessment of CMIP6 models, we conclude that it \r\nis very likely that, in the long term, global mean land and global \r\nmean ocean precipitation will increase with increasing GSAT. Annual \r\nmean and global mean precipitation will very likely increase by \r\n1-3% per degC GSAT warming. The patterns of precipitation change \r\nwill exhibit substantial regional differences and seasonal contrast as \r\nGSAT increases over the 21st century (high confidence). Precipitation \r\nwill very likely increase over high latitudes and the tropical ocean \r\nand will likely increase in large parts of the monsoon regions. \r\nHowever, it is likely to decrease over the subtropics, including \r\nMediterranean, southern Africa and south-west Australia, in response \r\nto GHG-induced warming."


id: bdc67ee0fcbee5f3cbaa69ca1a530b08
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 27416
content:
"Consistent with the findings of AR5, a  gradual increase in global \r\nmean precipitation is projected over the 21st  century with an \r\nincrease of approximately 2.9% (1.0-5.2%) under SSP1-2.6 and 4.7% \r\n(2.3-8.2%) under SSP3-7.0 during 2081-2100 relative to 1995-2014. "


id: 385b96f18e59d61dda2d80de4eee45fa
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 6242
content:
"monsoon precipitation will likely increase by 1.3-2.4% per degC GSAT \r\nwarming among the four scenarios considered here. In the  long \r\nterm, the multi-model mean change (5-95% range of the available \r\n41 projections) of global land monsoon precipitation index is 2.9% \r\n(-0.8  to +7.8%), 3.7% (-2.5 to +8.6%), 3.77% (-3.2  to  +8.1%), \r\nand 5.7% (-2.8 to +12.3%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, and \r\nSSP5-8.5, respectively. This enhancement is caused by thermodynamic \r\nresponses due to increased moisture, which is partly offset by \r\ndynamic responses due to a weakened circulation (Chen et al., 2020). \r\nThe patterns of monsoon rainfall change in the mid- to long-term \r\ninclude a north-south asymmetry characterized by greater increase \r\nin the NH than the SH, and an East-West asymmetry characterized \r\nby enhanced Asian-African monsoons and weakened North American \r\nmonsoon (medium confidence) (Lee and Wang, 2014; Mohtadi et al., \r\n2016; Pascale et al., 2017; Wang et al., 2021)."


id: db6f626488de6a70789bbbbd69b47e24
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 32037
content:
"The AR5 assessed changes of the global monsoon in the context \r\nof long-term trends across the 21st  century and the change by \r\n2081-2100. The AR5 showed growing evidence of improved skill \r\nof climate models in reproducing the climatological features of the \r\nglobal monsoon. Taken together with identified model agreement on \r\nfuture changes, the global monsoon precipitation, aggregated over all \r\nregional monsoon regions, is likely to strengthen in the 21st century \r\nwith increases in its area and intensity, while the monsoon circulation \r\nweakens. In all RCP scenarios, the global monsoon area is very likely \r\nto increase, and the global monsoon precipitation intensity is likely to \r\nincrease, resulting in a  very likely increase in the global monsoon \r\ntotal precipitation, by 2081-2100 (Kitoh et al., 2013)."


id: b8074a96c4cd211e2ab17f8a6cc270eb
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 26483
content:
"Based on the assessment of CMIP6 models, we conclude that it is likely\r\nthat, in the mid- to long term, the global land monsoon precipitation \r\nwill increase with GSAT rise despite a weakened monsoon circulation. \r\nThe global land monsoon precipitation will likely increase by 1.3-2.4% \r\nper degC GSAT warming among the four scenarios. Monsoon precipitation \r\nresponses depend on region and emissions scenario (high confidence)."


id: 1aa620cc38eae5ca299a73dfce90ae47
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 1671
content:
"This subsection provides a  global overview of long-term changes \r\nin atmospheric dynamical features that is complementary to the \r\nregional assessment of links to the hydrological cycle in Chapter 8 \r\n(Section 8.4.2), and assessment of the connections to extreme events \r\nin Chapter 11 (Section 11.7.2)."


id: da3f39563ddffe512c3a4b29177db6db
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 31861
content:
"As was found in AR5, several regional sea level pressure features \r\nstand out from the zonal-mean change. Sea level pressure markedly \r\ndecreases in north-eastern North America and north-eastern Asia in \r\nboreal winter. In boreal summer, sea level pressure robustly decreases "


id: 84b89633f4f4f03cbd740f8905db76de
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 18404
content:
"Based on the projections of changes in precipitation from CMIP6 \r\nunder the four SSPs, the global monsoon precipitation is likely to \r\nstrengthen in the 21st century with increases in its intensity, while \r\nNH summer monsoon circulation weakens (Figure 4.14). Global land "


id: f4456998daab183369702377d6c0e5b9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 35939
content:
"in the Mediterranean and the Middle-East, a decrease that has been \r\nlinked to a large-scale heat low forced by the amplified warming of \r\nthe region (Haarsma et al., 2009). It is likely that sea level pressure \r\nwill increase across the south-western North America and Central \r\nAmerica in boreal summer under SSP3-7.0 due to an intensification \r\nof the eastern North Pacific subtropical summer high (Li et al., 2012) \r\nand a weakening of the North American monsoon (Section 4.5.1.5; \r\nPascale et al., 2017; Wang et al., 2020). These changes in circulation \r\nare connected to drying across the eastern subtropical Pacific and \r\nCentral America regions (Figure 4.24)."


id: b2debcda9875c8158ae302026dfe36a9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 25655
content:
"Storm tracks and mid-latitude westerly jets are dynamically related \r\naspects of mid-latitude circulation. The AR5 assessed that a poleward \r\nshift of the SH westerlies and storm track is likely by the end of the \r\n21st  century under RCP8.5 (medium confidence). In contrast, low \r\nconfidence was assessed for the storm-track response in the NH."


id: 43db5d3733e64b8b1a68071d4e125ffc
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 8087
content:
"zonal winds by the end of the century are generally smaller than in the \r\nSH. In boreal winter, there is a weak poleward shift of the NH zonal\u0002mean westerly jet maximum in SSP3-7.0."


id: f1a78ebebbd1d70f0e2c5663beff8747
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 35526
content:
"boreal summer, while the North Pacific jet weakens in this season \r\n(Simpson et al., 2014; Davini and D'Andrea, 2020; Harvey et al., 2020). \r\nThere is a poleward shift in the westerlies in both the North Pacific and \r\nNorth Atlantic in Autumn (Barnes and Polvani, 2013; Simpson et al., \r\n2014). However, the shift of the westerlies is more uncertain in the \r\nother seasons, particularly in the North Atlantic in winter (Simpson \r\net al., 2014; Zappa and Shepherd, 2017). Here, the circulation response \r\nis not well described as a simple shift, since the North Atlantic jet tends \r\nto be squeezed on both its equatorward and poleward flanks, together "


id: 284983fc4ef62bd0c0298e87888baa2a
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 3748
content:
"CMIP5 and CMIP6 models show a  strong seasonal and regional \r\ndependence in the response to climate change of NH westerlies (Barnes \r\nand Polvani, 2013; Grise and Polvani, 2014b; Simpson et al., 2014; Zappa \r\net al., 2015; Harvey et al., 2020; Oudar et al., 2020). CMIP5 projections \r\nindicate a  poleward shift of the westerlies in the North Atlantic in "


id: c8d0ddff09bdc1e71659a7a620f3e472
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 29154
content:
"Figure 4.26 | Long-term change of zonal-mean, zonal wind. Displayed are multi-model mean changes in (left) boreal winter (December-January-February, DJF) and \r\n(right) austral winter (June-July-August, JJA) zonal mean, zonal wind (m s-1) in 2081-2100 for (top) SSP1-2.6 and (bottom) SSP3-7.0 relative to 1995-2014. The 1995-2014 \r\nclimatology is shown in contours with spacing 10 m s-1. Diagonal lines indicate regions where less than 80% of the models agree on the sign of the change and no overlay \r\nwhere at least 80% of the models agree on the sign of the change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: 8d0681e864cb31e7aaaf1b9adad84d60
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 19710
content:
"The storyline approach is grounded in the identification of a set of \r\nphysical predictors of atmospheric circulation change, such as those \r\ndescribed above (Harvey et al., 2014; Manzini et al., 2014; Shepherd \r\net al., 2018), which act together to determine a specific outcome in \r\nthe projected atmospheric circulation change. The consequences \r\nof multi-model spread in the physical predictors of atmospheric \r\ncirculation change can be investigated, conditioned on a  specified \r\nlevel of global warming (see also Section 1.4.4.2 and Box 10.2; Zappa \r\nand Shepherd, 2017; Zappa, 2019; Mindlin et al., 2020)."


id: 96728d817d1e3e849bd240d504d67d7d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 21188
content:
"As stated in AR5, the number of extratropical cyclones (ETC) \r\ncomposing the storm tracks is projected to weakly decline in future \r\nprojections, but by no more than a few percent change. The reduction \r\nis mostly located on the equatorward flank of the storm tracks, which \r\nis associated with the Hadley cell expansion and a poleward shift in \r\nthe mean genesis latitude of ETCs (Tamarin-Brodsky and Kaspi, 2017). \r\nFurthermore, the poleward propagation of individual ETCs is expected \r\nto increase with warming (Graff and LaCasce, 2014; Tamarin-Brodsky \r\nand Kaspi, 2017), thus contributing to a  poleward shift in the \r\nmid-latitude transient-eddy kinetic energy. The increased poleward \r\npropagation results from the strengthening of the upper tropospheric \r\njet and increased cyclone-associated precipitation (Tamarin-Brodsky \r\nand Kaspi, 2017), which are robust aspects of climate change."


id: 6c6b4e269bf191637ec4860ba8f62b58
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 14685
content:
"In the NH boreal winter, CMIP6 models show a  northward shift \r\nof the ETC density in the North Pacific, a  tripolar pattern in the \r\nNorth Atlantic, and a  weakening of the Mediterranean storm \r\ntrack (Figure  4.27a). CMIP6 models show overall low agreement\r\non changes in ETC density in the North Atlantic in boreal winter \r\n(Figure 4.27a). A poleward shift of the storm track is evident in the \r\nSH (Figure 4.27b), particularly in the Indian and Pacific Ocean sectors. \r\nCMIP6 models still feature long-standing biases in the representation \r\nof storm tracks; for example, the winter storm track into Europe is \r\ntoo zonal, though different measures of storm track activity indicate \r\nsome improvements compared to the previous generations of models \r\n(Harvey et al., 2020; Priestley et al., 2020)."


id: feaf704ba399d4f2d3dc4b4cb91845fc
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 37615
content:
"weather conditions, from heat waves in summer to cold spells in \r\nwinter (see Section 11.7.2 for a detailed discussion of these features \r\nand Section 3.3.3.3 for the assessment of blocking biases in models \r\nsimulations). The AR5 assessed with medium confidence that the \r\nfrequency of blocking would not increase under enhanced GHG \r\nconcentrations, while changes in blocking intensity and persistence \r\nremained uncertain."


id: 3b5b9d7f3fe0f9973ad35124d00a04fd
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 6689
content:
"heating to cyclone intensification (Li et al., 2014; Pfahl et al., 2015; \r\nWillison et al., 2015; Michaelis et al., 2017). In conclusion, there is \r\nonly medium confidence in the projected decrease in the frequency \r\nof intense NH ETCs."


id: 2bfbf7d34127f826691da9aa78fced0b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 4349
content:
"In contrast to the Northern Hemisphere, the Southern Hemisphere \r\nshows an increase in the frequency of intense ETCs in CMIP5 models \r\n(Chang, 2017), and there is high confidence that wind speeds \r\nassociated with ETCs are expected to intensify in the SH storm \r\ntrack for high emissions scenarios. These changes in intensity are \r\naccompanied by an overall southward shift of the SH winter storm \r\ntrack (Figure 4.27b) due to the poleward shift in the upper-level jet \r\nand the increase in the meridional SST gradient linked to the slower \r\nwarming of the Southern Ocean (Grieger et al., 2014)."


id: 32f274c950ef492c34e345389e22911f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 5438
content:
"Regardless of dynamical intensity changes, there is high confidence \r\nthat the number of ETCs associated with extreme precipitation \r\nis projected to increase with warming, due to the increased \r\nmoisture-loading capacity of the atmosphere (Section 8.4.2; Yettella \r\nand Kay, 2017; Hawcroft et al., 2018). "


id: d99edfd4d6bb22acd769dfdefacb29d9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 31750
content:
"Blocking is associated with a class of quasi-stationary, high-pressure \r\nweather systems in the middle and high latitudes that disrupt the \r\nprevailing westerly flow. These events can persist for extended \r\nperiods, such as a week or longer, and can cause long-lived extreme "


id: abccf4c76f4d9d7e65298a5575260281
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 24555
content:
"To better understand the uncertainty in future blocking activity, \r\na process-oriented approach has been proposed that aims to link \r\nblocking responses to different features of the global warming \r\npattern. Upper-level tropical warming might be the key factor \r\nleading to a  reduced blocking, because of the strengthening \r\nof zonal winds (Kennedy et  al., 2016). The more controversial \r\ninfluence of near-surface Arctic warming might lead to an increased \r\nblocking frequency (Mori et  al., 2014; Francis and Vavrus, 2015) \r\n(see Chapter 10, Box 10.1)."


id: ea42e9311742aaa3b10213c5bd0c4417
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 34365
content:
"The model-simulated, long-term trend of ocean acidification is assessed \r\nin Section 4.3.2.5 and Chapter 5 (Section 5.3.4.1). It is virtually certain \r\nthat surface ocean acidification will continue in response to the rise \r\nin atmospheric CO2, and continued penetration of anthropogenic \r\nCO2 from the surface to the deep ocean will acidify the ocean interior \r\n(Figure 4.29). By the end of this century, under SSP3-7.0, a pH reduction \r\nof about 0.3 is found at a few hundred metres depth of the global \r\nocean, with stronger acidification in the interior North Atlantic and the \r\nmid- to high-latitude Southern Ocean. At a depth of about 1 km, a pH \r\nreduction of about 0.1 is found."


id: 790c97832ea157291f0661f20eff1b7f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 16597
content:
"Figure 4.28 shows a clear decrease in blocking activity over Greenland \r\nand North Pacific for SSP7.0 and SSP8.5. Models with the largest \r\ndecrease in blocking frequency in boreal winter are those showing \r\nthe smallest frequency bias during the historical period (Davini \r\nand D'Andrea, 2020). In conclusion, there is medium confidence \r\nthat the frequency of atmospheric blocking events over Greenland \r\nand the North Pacific will decrease in boreal winter in the SSP3-7.0 \r\nand SSP5-8.5 scenarios."


id: be81f2575ab9c46b139fa25a999a1cf9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 27529
content:
"Projections of long-term ocean thermal properties are assessed \r\ncomprehensively in Chapter 9 (Sections 9.2.1.1 and 9.2.2.1) and are \r\nnot covered here to avoid unnecessary overlap."


id: 484f6984324dbd6feb6946f88d451b29
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 8751
content:
"more positive by the end of the century except in boreal summer (JJA) \r\nwhen there is no change in the NAM in these simulations. In boreal \r\nwinter (DJF) under SSP5-8.5, the central estimate is an increase in \r\nthe NAM by almost 3 hPa in the long-term compared to 1995-2014. \r\nThis can be compared to a multi-model mean interannual standard \r\ndeviation in the winter NAM index of 3.4 hPa during the period \r\n1850-1900. We conclude with high confidence that in the mid- to \r\nlong-term, the boreal wintertime surface NAM is more positive under \r\nSSP3-7.0 and SSP5-8.5, while under SSP1-1.9 and SSP1-2.6, the NAM \r\ndoes not show any robust change."


id: cae57a463593fdbc7b6af99f5ec79ade
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 29481
content:
"In most seasons, the SAM becomes more positive by the end of \r\nthe century relative to 1995-2014 under SSP2-4.5, SSP3-7.0, and \r\nSSP5-8.5 (Figure 4.30b). Conversely, under SSP1-1.9 and SSP1-2.6, \r\nin most seasons the SAM index does not show a  robust change \r\ncompared to 1995-2014 except in austral summer when it becomes \r\nsignificantly more negative. The greatest change in the SAM occurs \r\nin austral winter, where CMIP6 models show an ensemble-mean \r\nincrease in the SAM index of almost 5 hPa in SSP5-8.5. This can be \r\ncompared to a  multi-model mean interannual standard deviation \r\nin the austral winter SAM index of 4.0 hPa during 1850-1900. In \r\nconclusion, there is high confidence that in high emissions scenarios \r\n(SSP3-7.0 and SSP5-8.5) the SAM becomes more positive in all \r\nseasons, while in the lowest scenario (SSP1-1.9) there is a  robust \r\ndecrease in austral summer."


id: 6d34dd8001a7c5b63a7e559bb523d3a9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 14802
content:
"The AR5 assessed from CMIP5 simulations that the future boreal \r\nwintertime NAM is very likely to exhibit natural variability and \r\nforced trends of similar magnitude to that observed in the historical \r\nperiod and is likely to become slightly more positive in the future. \r\nConsiderable uncertainty is related to physical mechanisms to explain \r\nthe observed and projected changes in the NAM, but NAM trends are \r\nclearly closely connected to projected shifts in the mid-latitude jets \r\nand storm tracks."


id: 2ebcae090d7c68c4c8ba44e3916dc736
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 4794
content:
"NAM projections from climate models analysed since AR5 reveal \r\nbroadly similar results to the late 21st century. CMIP6 models show \r\na  positive ensemble-mean trend in most seasons and the higher \r\nemissions scenarios that is comparable to between-model or between\u0002realization variability (Figure  4.30a). The NAM generally becomes "


id: 1aed8a67f72684d3d333e9313ef0b080
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 1705
content:
"simulate linear ENSO stability realistically exhibit a  decrease in \r\nENSO amplitude by the second half of the 21st century. However, an \r\nincrease of ENSO SST variability has been projected when considering \r\nbiases in ENSO pattern simulation by different models (Zheng et al., \r\n2016; Cai et al., 2018a). This highlights the importance of constraining \r\ntropical Pacific mean state changes in order to enhance confidence in \r\nthe projected response of ENSO."


id: 46e5412fa1f8d4b46cc720fa57a90198
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 8537
content:
"The AR5 assessed that it is very likely that the El Nino-Southern \r\nOscillation (ENSO) will remain the dominant mode of interannual \r\nvariability in the future. Moreover, due to increased moisture \r\navailability, the associated precipitation variability on regional scales \r\nwas assessed to likely intensify. An eastward shift in the patterns of \r\ntemperature and precipitation variations in the North Pacific and North \r\nAmerica related to El Nino and La Nina teleconnections was projected \r\nwith medium confidence. The stability of teleconnections to other \r\nregional implications including those in Central and South America, \r\nthe Caribbean, parts of Africa, most of Asia, Australia and most Pacific \r\nIslands were assessed to be uncertain (Christensen et al., 2013)."


id: eb15baec8b15a8668d5f6fae6e5f459c
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 34488
content:
"There is also no robust consensus on changes in ENSO diversity. \r\nSeveral studies suggest that an increase in Eastern Pacific (EP)-ENSO \r\nevents tends to be projected particularly in the models with an \r\nEl Nino-like warming (Zheng et al., 2016; Cai et al., 2018a; Fredriksen \r\net al., 2020). However, Freund et al. (2020) suggested that models \r\nwith a  El Nino-like mean warming show a  tendency toward more \r\nCentral Pacific (CP) events but fewer EP events compared to models \r\nwith an La Nina-like warming in both CMIP5 and CMIP6 models."


id: d138318a61041da9da51c23448ceb04c
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 30457
content:
"Even though there is low agreement in simulated changes in ENSO \r\nSST variability, the majority of models project an increase in \r\namplitude of ENSO rainfall variability attributable to the increase \r\nin mean SST and moisture in CMIP5 (Power et al., 2013; Watanabe \r\net  al., 2014; Huang and Xie, 2015) and CMIP6 (Yun et  al., 2021). \r\nIt is likely that extreme El Nino events, accompanied by the eastern \r\nequatorial Pacific rainfall exceeding the 5 mm day-1 rainfall threshold, \r\nwill increase in intensity (Cai et al., 2014a, 2017). However, it has \r\nalso been suggested that historical model biases over the equatorial \r\nPacific cold tongue in CMIP5 may lead to the greater precipitation \r\nmean change and amplification of extreme ENSO-associated rainfall \r\nin CMIP5 (Stevenson et al., 2021)."


id: 236cd805b330bfeec28cd5005ffb0be4
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 3026
content:
"There is limited intermodel agreement on future changes in ENSO \r\nteleconnections largely depending on changes in the mean state and \r\nchanges in ENSO properties (Yeh et al., 2018). Many CMIP5 and CMIP6 \r\nmodels project that the centres of the extratropical teleconnection over \r\nNorth Pacific and North America will shift eastward in association with \r\nan eastward shift in tropical convective anomalies (Yeh et al., 2018; \r\nFredriksen et al., 2020). There is an indication that tropical cyclones "


id: fe31090452395f03a549e7d8491de15f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 37522
content:
"will become more frequent during future El Nino events (and less \r\nfrequent during future La Nina events) by the end of the 21st century \r\n(Chand et  al., 2017), thus contributing to the projected increase in \r\nENSO-associated hydro-climate impacts."


id: 415b9b1c608ac8887c0cb9013f650439
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 7681
content:
"The AR5 assessed that there is low confidence in projected changes \r\nof the Tropical Atlantic Variability (TAV) because of the general failure \r\nof climate models to simulate main aspects of this variability such \r\nas the northward displaced ITCZ. The models that best represent the \r\nAtlantic meridional mode (AMM) show a weakening for future climate \r\nconditions. However, model biases in representation of Altantic Ninos \r\nstrongly limit an assessment of future changes."


id: f7df06b5c1b93dd6c72879b8fefb244c
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 36518
content:
"While CMIP6 models show no robust change in ENSO SST amplitude \r\nin the mid- and long-term period across all four SSPs, a  robust \r\nincrease in ENSO rainfall amplitude is found particularly in SSP2-4.5, \r\nSSP3-7.0, and SSP5-8.5 (Figure 4.10). The changes in ENSO rainfall \r\namplitude in the long-term future (2081-2100) relative to the recent \r\npast (1995-2014) are statistically significant at the 95% confidence."


id: 3e851a49a5942e7dbc94886bb9875983
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 7222
content:
"Long-term changes in TAVs and associated teleconnections are \r\nexpected as a result of global warming, but large uncertainties exist \r\ndue to the models' systematic underestimation of the connection \r\nbetween PDV and Indo-Pacific SST variations (Lubbecke et al., 2018; \r\nCai et al., 2019). Observational analyses show large discrepancies in \r\nSST and trade wind strength (Servain et al., 2014; Mohino and Losada, \r\n2015). Single-model sensitivity experiments show that Atlantic Nino \r\ncharacteristics at the end of 21st  century remain consistent with \r\nthose of the 20th century, though changes in the climatological SSTs \r\ncan lead to changes in the associated teleconnections (Mohino and \r\nLosada, 2015)."


id: ddeb7a630bc87a1ddb776bbfe4b0ca75
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 32411
content:
"To conclude, the forced change in ENSO SST variability is highly \r\nuncertain in CMIP5 and CMIP6 models (medium confidence). However, \r\nit is very likely that ENSO-related rainfall variability will increase \r\nsignificantly regardless of ENSO amplitude changes in the mid- and \r\nlong-term future. It is likely that the pattern of ENSO teleconnection \r\nover the North Pacific and North America will shift eastward."


id: a76f2a9cbdefde090c67a775a95d002f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 23848
content:
"The weakening of the AMOC expected from global warming \r\n(see  Section  4.3.2.3) has been suggested to have an influence on \r\nthe mean background state of tropical-Atlantic surface conditions, \r\nthereby enhancing equatorial Atlantic variability and resulting in \r\na stronger tropical Atlantic-ENSO teleconnection (see Section 3.7.5 \r\nfor a detailed discussion; Svendsen et al., 2014). A recent multi-model \r\nstudy, based on CMIP5, concluded that the TAV-Pacific teleconnection \r\nwill weaken under global warming due to the increased thermal \r\nstability of the atmosphere (F. Jia et al., 2019). However, there is still \r\na clear lack of model studies, and hence no robust evidence on the \r\nlong-term evolution of TAV and associated teleconnections."


id: 4d17f5d17dd15616b3e792027201caf1
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 9554
content:
"The AR5 assessed that there is low confidence in projections of \r\nfuture changes in Pacific decadal variability (PDV) due to the inability \r\nof CMIP5 models to represent the connection between PDV and \r\nIndo-Pacific SST variations. Because the PDV appears to encompass \r\nthe combined effects of different dynamical processes operating \r\nat different time scales, representation of PDV in climate models \r\nremains a challenge (Section 3.7.6) and its long-term evolution under \r\nclimate change uncertain."


id: 9c6bcd180dcda3608ec1c33a82c0fda2
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 22165
content:
"However, it was also found that the frequency of extreme positive \r\nIOD events, which exhibit the largest climate impacts, might increase \r\nby a  factor of about three under the CMIP5 RCP8.5 scenario \r\n(Cai et al., 2014b). Partially consistent with the above result, a more \r\nrecent study by Cai et al. (2021), based on CMIP5 RCP8.5 and CMIP6 \r\nSSP5-8.5 simulations, shows a  robust increased SST variability of \r\nlarge positive IOD events, but a  decreased variability of moderate \r\nIOD events. An approximate doubling of these extreme positive IOD \r\nevents was still found for global warming of 1.5degC warming above \r\npre-industrial levels, without a  projected decline thereafter (Cai \r\net al., 2018b). These results depend, however, on the realism of the \r\nprojected mean state change in the Indian Ocean (G. Li et al., 2016)."


id: be0e43cb26be6b24a0dee2c33fd67654
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 27456
content:
"In addition to uncertainty from the future evolution of the mechanisms \r\nthat determined the PDV, it is also unclear how the background state \r\nin the Pacific Ocean will change due to time-varying radiative forcing, \r\nand how this change will interact with variability at interannual and \r\nlow-frequency time scales (Fedorov et  al., 2020). Recent research \r\nsuggests that the PDV will have a  weaker amplitude and higher \r\nfrequency with global warming (Zhang and Delworth, 2016; Xu and \r\nHu, 2017; Geng et al., 2019). The former appears to be associated \r\nwith a decrease in SST variability and the meridional gradient over \r\nthe Kuroshio-Oyashio region, with a reduction in North Pacific wind \r\nstress and meandering of the subpolar/subtropical gyre interplay \r\n(Zhang and Delworth, 2016). The latter is hypothesized to rely on "


id: 69a284290371f490e6ff6f8d0bee8d96
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 14218
content:
"To conclude, the forced change in IOD in mid- and long-term \r\nfuture remains uncertain due to limited lines of evidence and its \r\ndependence on model mean biases. However, there is low confidence\r\nthat the frequency of extreme positive IOD events will increase under \r\nthe high-emissions scenario of SSP5-8.5."


id: 3c6769f431dbc88e31701003be5f116f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 6865
content:
"the enhanced ocean stratification and shallower mixed layers of \r\na  warmer climate, which would increase the phase speed of the \r\nwestward-propagating oceanic waves, hence shortening the decadal \r\nto inter-decadal component (Goodman and Marshall, 1999; Zhang \r\nand Delworth, 2016; Xu and Hu, 2017). The weakening of the PDV \r\nin a  warmer climate may reduce the internal variability of global \r\nmean surface temperature, to which PDV seems associated (Zhang \r\net al., 1997; Kosaka and Xie, 2016; Geng et al., 2019). Thus, a weaker \r\nand higher frequency PDV could reduce the contribution of internal \r\nvariability to the GSAT trend and eventually lead to a  reduced \r\nprobability of surface-warming hiatus events."


id: f2f18b341aa7e107d104fb5774f0dcd1
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 35745
content:
"This subsection provides an assessment of changes in climate \r\nat 1.5degC, 2degC, 3degC, and 4degC of global warming relative to the \r\nperiod 1850-1900 (Section  1.6.2), in particular a  discussion of \r\nthe regional patterns of change in temperature (Section  4.6.1.1), \r\nprecipitation (Section 4.6.1.2), and aspects of atmospheric circulation \r\n(Section  4.6.1.3). An assessment of changes in extreme weather \r\nevents as a function of different levels of global warming is provided \r\nin Chapter  11, while corresponding analyses of regional climate \r\nchange are provided in Chapter  12 and in the Atlas. This section \r\nbuilds upon assessments from AR5 (Bindoff et al., 2013; Christensen \r\net al., 2013; Collins et al., 2013; Hartmann et al., 2013) and SR1.5 \r\n(SR1.5; Hoegh-Guldberg et  al., 2018), as well as new literature \r\nrelated to projections of climate at 1.5degC, 2degC, and higher levels of \r\nglobal warming above pre-industrial levels."


id: 7ba7c30c5a9a42f4a2a4be7ca999bc37
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 17053
content:
"In summary, based on CMIP5, there is medium confidence that \r\na weaker and higher frequency PDV is expected under global warming."


id: d14d2b55020f02e1beb3f352871c239b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 30465
content:
"Based on paleoclimate reconstructions and model simulations, AR5 \r\nassessed that AMV is unlikely to change its behaviour in the future. \r\nHowever, AMV fluctuations over the coming decades are likely\r\nto influence regional climate, enhancing or offsetting some of the \r\neffects of global warming."


id: 45dcd6a4c7f8a69ef98391ecca16b7a6
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 8379
content:
"Recent proxy-derived reconstructions of AMV-related signals show \r\npersistent multi-decadal variability over the last three centuries \r\n(Kilbourne et al., 2014; Svendsen et al., 2014; Moore et al., 2017), \r\nup to the last millennium (Chylek et  al., 2011; Zhou et  al., 2016; \r\nJ. Wang et  al., 2017b) and beyond (Knudsen et  al., 2011). This \r\nimplies that in the past AMV properties were little affected by large \r\nclimatic excursions."


id: 3c84c13515b7f5122ca8d21f1ca9e18e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 6889
content:
"In conclusion, on the basis of paleoclimate reconstructions and \r\nCMIP5 model simulations, there is low confidence that the AMV \r\nis not expected to change in the future."


id: 44f30f186571b23d41d38cfd81dc2c98
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 8276
content:
"sections. These include overshoot versus stabilization scenarios and \r\nlimitations of pattern scaling for strong mitigation and stabilization \r\nscenarios (Tebaldi and Arblaster, 2014). At least for the case of annual \r\nmean temperature and precipitation, strong evidence exists that even \r\nfor strong mitigation and stabilization scenarios, patterns of change \r\nat lower levels of warming scale similarly to those reconstructed \r\nfrom transient simulations using either standard pattern-correlation \r\nor time-shift methodologies (Tebaldi and Knutti, 2018)."


id: 7d9cf64761d4622612516aa07440682
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 17336
content:
"Pattern scaling performance based on scenario experiments is \r\ngenerally better for near-surface temperature than for precipitation \r\n(Ishizaki et  al., 2013). For precipitation, rapid adjustments due to \r\ndifferent forcing agents must be accounted for (Richardson et  al., \r\n2016). Possible non-linear responses to different forcing levels are \r\nalso important (Good et al., 2015, 2016). Pattern scaling does not \r\nwork as well at high forcing levels (Osborn et al., 2018). It is also \r\nimportant to distinguish the forced response from internal variability \r\nwhen comparing similar warming levels (Suarez-Gutierrez et  al., \r\n2018). The purpose of this section is not to repeat the analysis for \r\nall the variables considered in Sections 4.4 and 4.5, but rather to \r\nshow a selected number of key variables that are important from the \r\nperspective of understanding the response of the physical climate \r\nsystem to different levels of warming."


id: 21d7c5fab2682f977632d04fef197e7
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vector_id: 2763
content:
"While global mean precipitation increases as GSAT rises with the \r\nvery likely range of 1-3% per 1degC (high confidence) (Sections 8.2.1 \r\nand 8.4.1), patterns of precipitation change do not scale as \r\nlinearly with GSAT increase. Nevertheless, common features of \r\nprecipitation change in the multi-model mean across scenarios \r\nstill exist for different levels of global warming (Figure  4.32). \r\nPrecipitation will very likely increase in the high latitudes and \r\nover tropical regions, and will likely increase in large parts of the \r\nmonsoon region, but are likely to decrease over the subtropical \r\nregions, including the Mediterranean, southern Africa, parts of \r\nAustralia and South America at all four levels of global warming. \r\nThe increases and decreases in precipitation will amplify at higher \r\nlevels of global warming (high confidence) (Figure 4.32). Changes \r\nin extreme precipitation events under different levels of global \r\nwarming are assessed in Chapter 11."


id: ef90a333fe146679d85833dba855f41e
created_at: 2023-03-16 14:00:41
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vector_id: 35186
content:
"than over land (Figure 4.33). Precipitation variability in most climate \r\nmodels increases over the global land area in response to warming \r\n(Pendergrass et al., 2017)."


id: 8c96a20b9b7c3d8b9db908ba2a1afb3
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 19640
content:
"In summary, based on the assessment of CMIP6 models, there is high \r\nconfidence that global mean precipitation will increase with increase \r\nin global mean surface temperature. Precipitation will very likely\r\nincrease in the high latitudes and over tropical regions, likely increase \r\nin large parts of the monsoon region, but will likely decrease over \r\nthe subtropical regions. There is high confidence that increases and \r\ndecreases in precipitation will amplify over higher levels of global \r\nwarming. As warming increases, there is medium confidence that \r\na larger land area will experience statistically significant increases or \r\ndecreases in precipitation."


id: 2794cf4fdf221384ad8011a7e0fbfff7
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 3634
content:
"The AR5 reported that the application of pattern scaling to extract \r\ninformation on variables other than surface temperature and \r\nprecipitation was relatively unexplored. Since AR5, new studies \r\nhave examined the relationship between projections of mid-latitude \r\natmospheric circulation and GSAT both in terms of interpreting spread \r\nin responses across the CMIP5 multi-model ensemble (Grise and \r\nPolvani, 2014a, 2016) and to investigate variations in the circulation \r\nresponse as a  function of GSAT change over time within a  given \r\nforcing experiment (Grise and Polvani, 2017; Ceppi et al., 2018)."


id: ec90977e0b0fda5bc99dd1eb2b20826f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 34697
content:
"At a  fixed time horizon, the CMIP5 multi-model spread in GSAT \r\nexplains only a small fraction of the spread in the shift of the Northern \r\nHemisphere mid-latitude circulation due to an abrupt quadrupling in \r\nCO2 (Grise and Polvani, 2016). The fraction of model spread explained \r\nby GSAT in the shift of the Southern Hemisphere circulation is larger, \r\nbut still fairly small (Grise and Polvani, 2014a, 2016). At a fixed time \r\nhorizon and for a  given emissions scenario, CMIP5 multi-model \r\nspread in storm track shifts, and the closely related mid-latitude jets, \r\ncan be better explained by multi-model spread in lower and upper \r\nlevel meridional temperature gradients than by GSAT (Harvey et al., \r\n2014; Grise and Polvani, 2016)."


id: 78e805a4454cc1a8ae9f2416caa82684
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 16577
content:
"simulations. In particular, the differences of regional monsoon \r\nchanges among model setups are dominated by strategy choices \r\nsuch as transient versus quasi-equilibrium set-up, prescription of SST, \r\nand treatment of aerosols (Zhang and Zhou, 2021)."


id: faa4a183b4475a4e1fe7f5af84da41d3
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 36956
content:
"The global and land area fractions with significant precipitation \r\nchanges with global warming are shown in Figure 4.33. It is virtually \r\ncertain that average warming will be higher over land. As warming \r\nincreases, a larger global and land area will experience statistically \r\nsignificant increases or decreases in precipitation (medium confidence). \r\nThe increase of the area fraction with significant precipitation increase \r\nis larger over land than over the ocean, but the increase of the area \r\nfraction with significant precipitation decrease is larger over the ocean "


id: 921ad8b3e0e3b8803c123b0b9c304f16
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 20511
content:
"of warming therefore confounds the effects of ozone recovery and \r\nGHG increases (Ceppi et al., 2018). Given these various limitations, \r\nwe do not apply epoch analysis to assess mid-latitude atmospheric \r\ncirculation changes and related annular modes of variability."


id: c894e2d6d13b3feb88a0bc3a791bb916
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 29123
content:
"Many scenarios aiming at limiting warming by 2100 to 1.5degC involve \r\novershoot - ERF temporarily exceeds a certain level before peaking \r\nand declining again (Annex VII: Glossary). To quantify the implications \r\nof any such overshoot, this subsection assesses reversibility of \r\nclimate due to temporary overshoot of GSAT levels during the \r\n21st century, and implications for the use of carbon budgets. It also \r\nassesses differences in climate outcomes under different pathways, \r\nwith a focus on comparing the SSPs used in CMIP6 with the RCPs \r\nused in CMIP5."


id: 246c70b4e7a137782c1397d33412ff1
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 3174
content:
"4.6.2.2 Consistency Between Shared Socio-economic \r\nPathways and Representative Concentration Pathways"


id: 14cc5ab392086cc36d99e7ae8f6e20cc
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 781
content:
"tipping points are not well understood, but the higher the warming \r\nlevel and the longer the duration of overshoot, the greater the risk of \r\nunexpected changes (Section 4.7.2). Non-reversal of the hydrological \r\ncycle has also been found in some studies with an increase in global \r\nprecipitation following CO2 decrease being attributed to a build-up of \r\nocean heat (Wu et al., 2010), and to a fast atmospheric adjustment to \r\nCO2 radiative forcing (Cao et al., 2011)."


id: 360dccc884067fd7e3eb2eb9e65cff19
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 5907
content:
"As CMIP5 and CMIP6 employed different scenario sets (RCPs and SSPs, \r\nrespectively; see Section 1.6.1.1 and Cross-Chapter Box 1.4), we assess \r\nhow much of the differences in projections are due to the scenario \r\nchange and how much due to model changes. The CMIP6-simulated \r\nGSAT increases tend to be larger than in CMIP5, for nominally \r\ncomparable scenarios (Section 4.3.1; Tebaldi et al., 2021)."


id: bc977d602bf570db818322f6f1bf221d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 27251
content:
"In summary, there is medium confidence that about half of the warming \r\nincrease in CMIP6 compared to CMIP5 is due to higher  climate \r\nsensitivity in CMIP6 models; the other half arises from higher ERF in \r\nnominally comparable scenarios (e.g., RCP8.5 and SSP5-8.5)."


id: df74898cdcf41dd10da4c65a2573ccd6
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 32659
content:
"Climate change can be also offset by solar radiation modification \r\n(SRM) measures that modify the Earth's radiation budget to reduce \r\nglobal warming (see Glossary). CDR and SRM approaches have been \r\ntogether referred to as 'geoengineering' or 'climate engineering' \r\nin the literature (The Royal Society, 2009; NRC, 2015a, b; Schafer \r\net  al., 2015). However, following SR1.5 (de Coninck et  al., 2018), \r\nthese terms are inconsistently used in the literature, so that CDR \r\nand SRM are explicitly differentiated here. SRM contrasts with \r\nclimate change mitigation because it introduces a  'mask' to the \r\nclimate  change  problem by altering the Earth's radiation budget, \r\nrather than attempting to address the root cause of the problem, \r\nwhich is the increase in GHGs in the atmosphere."


id: c585a2c35af0efce845b6247d7394292
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 28654
content:
"Section 4.6.3.1 assesses the emergence of the climate response to \r\nmitigation, which is reflected by the difference between high- and \r\nlow-emissions scenarios. Section  4.6.3.2 then assesses the climate \r\nresponse to mitigation through CDR options, usually assumed \r\nagainst the background of some emissions scenario; note that the \r\nCDR options themselves are assessed in Chapter 5 (Section 5.6.2). \r\nSection 4.6.3.3 assesses the climate system response to SRM options. \r\nThe biogeochemical implications of CDR and SRM are assessed in \r\nChapter 5 (Sections 5.6.2 and 5.6.3, respectively). The importance of \r\nCDR for reaching net zero or negative CO2 emissions in mitigation \r\npathways is assessed in the AR6 WGIII report (Chapters 3, 4, 6, 7 \r\nand 12). The risks for and impacts on human and natural systems \r\ndue to SRM are assessed in the AR6 WGII report (Chapter 16), and \r\nthe international governance issues related to SRM and CDR are \r\nassessed in the AR6 WGIII report (Chapter 14)."


id: f60148e3433576c3eb8fdc7586306367
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 36217
content:
"Reducing GHG emissions will eventually slow and limit the degree of \r\nclimate change relative to high-emissions scenarios such as SSP5-8.5 \r\n(very high confidence). Even when CO2 emissions are reduced, \r\nhowever, atmospheric CO2 concentrations continue to increase \r\nas long as emissions exceed removal by sinks (Millar et al., 2017). \r\nSurface warming would likewise initially continue under scenarios of \r\ndecreasing emissions, resulting in a substantial lag between a peak \r\nin CO2 emissions and peak warming (high confidence) (Ricke and \r\nCaldeira, 2014; Zickfeld and Herrington, 2015). The lag between \r\npeak emissions and warming depends on the emissions history \r\nprior to the peak and also on the rate of the subsequent emissions \r\nreductions (Matthews, 2010; Ricke and Caldeira, 2014; Zickfeld and \r\nHerrington, 2015)."


id: cf068595b5e98195d9c4584acde5a712
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 30105
content:
"Most strong-mitigation scenarios assume - in addition to emissions \r\nreductions  - some form of carbon dioxide removal (CDR). \r\nAnthropogenic activities that remove CO2 from the atmosphere and \r\ndurably store it in geological, terrestrial, or ocean reservoirs, or in \r\nproducts (see Glossary). The SR1.5 (Rogelj et  al., 2018b) assessed \r\nthat all pathways that limit warming to 1.5degC by 2100 with no or \r\nlimited overshoot use CDR. In the SSP class of scenarios, SSP1-1.9 \r\nis characterized by a rapid decline of net CO2 emissions to zero by \r\n2050 and net negative CO2 emissions in the second half of this \r\ncentury (O'Neill et al., 2016; Rogelj et al., 2018a), implying the use of \r\nCDR. The term 'net CO2 emissions' refers to the difference between \r\nanthropogenic CO2 emissions and removal by CDR options, and 'net \r\nnegative CO2 emissions' imply a scenario where CO2 removal exceeds \r\nemissions (van Vuuren et  al., 2011, 2016). The terms 'negative "


id: 97a4c0a4b947e3dbc7428b40169813c3
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 21384
content:
"In addition to the lag between peak emissions and peak warming, \r\nthe climate response to reduced emissions would be overlain by \r\ninternal variability, which can amplify or attenuate the forced \r\nresponse. The resulting masking of differences between scenarios is \r\nillustrated in Figure 4.36 for GSAT trends over 2021-2040 (Maher \r\net al., 2020). The overall trends conform to expectations in that most \r\nsimulations show warming almost everywhere, especially under \r\nscenario RCP8.5 (Figure 4.36 bottom row). But any individual grid \r\npoint can in principle show no warming or even cooling, even under "


id: b075c380424fd4f1838377fe06326bf3
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 25285
content:
"RCP 8.5, over the near term (Figure 4.36, middle row). The magnitude \r\nof pointwise maximum and minimum temperature trends can be as \r\nlarge as 0.5degC per year (Figure 4.36, top and middle rows), exceeding \r\npossible trends in the global mean by one order of magnitude. While \r\nit is only a small fraction of the surface that simultaneously can show \r\ncooling, cooling at any given location is fully consistent with globally \r\naveraged surface warming over the near term (high confidence, since \r\nthe findings of Maher et al. (2020) are consistent across six different \r\nlarge initial-condition ensembles)."


id: afd647e8104b3d28026959d7211bfcbc
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 25187
content:
"possible to observe what the climate would have been without these \r\nmeasures, and any statement about emergence of the response to \r\nmitigation is contingent upon the assumed strength of mitigation in \r\nrelation to an assumed ('counterfactual') no-mitigation scenario. Still, \r\nthere is high agreement on the emergence of the climate response \r\nto mitigation across a number of independent studies using different \r\nmodels and different statistical approaches."


id: cfed4133ff0327e402823b63635e7e0e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 30237
content:
"Among global quantities, emergence of the response to differing CO2\r\nemissions - representing differences between low- and high-emissions \r\nscenarios - is first expected to arise in global mean CO2 concentrations, \r\nabout 10 years after emissions pathways have started diverging (high \r\nconfidence) (Tebaldi and Friedlingstein, 2013; Peters et  al., 2017; \r\nSchwartzman and Keeling, 2020; Spring et al., 2020). In these studies, \r\nemergence is generally defined as the time at which the global mean "


id: 7c6f6a1a5c586a51b6d70500c9603b95
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 17176
content:
"An important development since AR5 has been the quantification \r\nof when the climate response to mitigation can be expected to \r\nemerge from the background noise of internal variability (illustrated \r\nin Figure 4.36; see Section 1.4.2.2 and Glossary). A basic ambiguity \r\narises because once mitigation measures are in place, it is no longer "


id: eac3d2154f893ef0a026d81b943dcfe8
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 34439
content:
"Figure 4.36 | Masking of climate response to mitigation by internal variability in the near term. Near-term (2021-2040) pointwise maximum (top row) and \r\npointwise minimum (middle row) surface air temperature trends in the large initial-condition ensemble from MPI (left and centre columns), and CESM (right-hand \r\ncolumn) models in the RCP2.6 (left-hand column) and RCP8.5 scenarios (centre and right columns). The percentage of ensemble members with a warming trend in \r\nthe near term is shown in the bottom panels. Figure modified from Maher et al. (2020). Further details on data sources and processing are available in the chapter data table \r\n(Table 4.SM.1)."


id: f0595fdd920595445c8afacbb4a71794
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 35315
content:
"concentration first differs between mitigation and non-mitigation \r\nscenarios by more than two standard deviations of internal variability, \r\nalthough there are some methodological differences."


id: 27533548b836534ac61331322bcfbcc6
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 3588
content:
"Attribution to emissions reductions, for the case of RCP2.6 relative \r\nto RCP4.5, is not substantially more likely for 2021-2035 trends \r\nin upper-2000 m OHC than for GSAT (Marotzke, 2019), although \r\nOHC change is thought to be less susceptible to internal variability. \r\nFurthermore, Marotzke (2019) found only around 10% likelihood \r\nof mitigation-benefit emergence during 2021-2035 for change \r\nin AMOC and September Arctic sea ice area. Tebaldi and Wehner \r\n(2018) showed that the differences in temperature extremes \r\nbetween RCP4.5 and RCP8.5 over all land areas become statistically \r\nsignificant by 2050. The seemingly contrasting result of Ciavarella \r\net  al. (2017) that mitigation benefits arise earlier for climate \r\nextremes poses no contradiction, because Ciavarella et  al. (2017) \r\ndid not look at emergence as defined here but at the extremes of \r\na distribution, which differ between scenarios already at a time when \r\nthe distributions are still largely overlapping."


id: aea7f179dc0fc08da696124d71ec9851
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 25007
content:
"In summary, if strong mitigation is applied from 2020 onward as \r\nreflected in SSP1-1.9, its effect on 20-year trends in GSAT would \r\nlikely emerge during the near term, measured against an assumed \r\nnon-mitigation scenario such as SSP3-7.0 and SSP5-8.5. However, \r\nthe response of many other climate quantities to mitigation would \r\nbe largely masked by internal variability during the near term, \r\nespecially on the regional scale (high confidence). The mitigation \r\nbenefits for these quantities would emerge only later during the \r\n21st century (high confidence). During the near term, a small fraction \r\nof the surface can show cooling under all scenarios assessed here, \r\nso near-term cooling at any given location is fully consistent with \r\nglobally averaged surface warming (high confidence)."


id: b79d7d9e3e3be8c44fcd39b677affb2a
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 26413
content:
"CDR options include afforestation, soil carbon sequestration, bioenergy \r\nwith carbon capture and storage (BECCS), wet land restoration, ocean \r\nfertilization, ocean alkalinisation, enhanced terrestrial weathering \r\nand direct air capture and storage (see Section 5.6.2 and Table 5.9 for \r\na more complete discussion). Chapter 8 (Section 8.4.3) assesses the \r\nimplications of CDR for water cycle changes. The potential of different \r\nCDR options in terms of the amount of CO2 removed per year from the \r\natmosphere, costs, co-benefits and side effects of the CDR approaches \r\nare assessed in SR1.5 (de Coninck et al., 2018), the AR6 WGIII Report \r\n(see Chapters 7 and 12), and in several review papers (Fuss et  al., \r\n2018; Lawrence et al., 2018; Nemet et al., 2018). In the literature, CDR \r\noptions are also referred to as 'negative CO2 emissions technologies'."


id: 535e8599fde4b0692dda1a6ca6d32b6d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 10586
content:
"Emergence of mitigation benefits has been studied much less \r\nfor quantities other than globally and annually averaged CO2\r\nconcentration and surface temperature. Boreal-winter temperatures \r\nare more challenging for emergence, due to larger variability in \r\nboreal winter and adding a  decade to the time of emergence, \r\nwhereas emergence times for boreal-summer averages are similar \r\nto the annual temperature averages (Tebaldi and Friedlingstein, \r\n2013). Emergence happens later at the regional scale, with a median \r\ntime of emergence of 30-45 years after emissions paths separate in \r\nRCP2.6 relative to RCP4.5 and RCP8.5; a stricter requirement of 95% \r\nconfidence level instead of median induces a delay of several decades, \r\nbringing time of emergence toward the end of the 21st century at \r\nregional scales (Tebaldi and Friedlingstein, 2013)."


id: f7a46a56a7566cef3fd54da26b58e12f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 36474
content:
"Deployment of CDR will lead to a reduction in atmospheric CO2 levels \r\nonly if uptake by sinks exceeds net CO2 emissions. Hence, there could \r\nbe a  substantial delay between the initiation of CDR and net CO2\r\nemissions turning negative (van Vuuren et al., 2016), and the time to \r\nreach net negative CO2 emissions and the evolution of atmospheric \r\nCO2 and climate thereafter would depend on the combined pathways \r\nof anthropogenic CO2 emissions, CDR, and natural sinks. The cooling \r\n(or avoided warming) due to CDR would be proportional to the \r\ncumulative amount of CO2 removed from the atmosphere by CDR \r\n(Tokarska and Zickfeld, 2015; Zickfeld et al., 2016), as implied by the \r\nnear-linear relationship between cumulative carbon emissions and \r\nGSAT change (Section 5.5)."


id: 948032394c7e6f369034824c41c56670
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 20875
content:
"CDR termination would depend on the amount CO2 removed by CDR \r\nprior to termination and the rate of background CO2 emissions at the \r\ntime of termination. See also Chapter 5, Table 5.9, which summarizes \r\nthe termination effects of individual CDR options."


id: adebb5f521d195cbbae538fac5715273
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 24900
content:
"portfolio of SRM options (Table 4.7) for consistency with AR5 (Boucher \r\net al., 2013) and SR1.5 (de Coninck et al., 2018). Other approaches \r\nsuch as injection of sulphate aerosols into the Arctic troposphere \r\nand sea ice albedo enhancements for moderating regional warming \r\nhave also been suggested (MacCracken, 2016; Field et  al., 2018). \r\nAs noted in SR1.5 (de Coninck et al., 2018), SRM is only considered as \r\na potential supplement to deep mitigation, for example in overshoot \r\nscenarios (MacMartin et al., 2018)."


id: 5e11887c3faa59290cf0488aef077a79
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 12329
content:
"In summary, there is high confidence that, due to the near-linear \r\nrelationship between cumulative carbon emissions and GSAT change, \r\ncooling or avoided warming due to a  CDR option would depend \r\non the cumulative amount of CO2 removed by that CDR option. \r\nThe climate system response to the deployment of CDR is expected \r\nto be delayed by years (e.g.,  in temperature, precipitation, sea ice \r\nextent) to centuries (e.g.,  sea level and AMOC) (high confidence). \r\nThe climate response to a  sudden and sustained CDR termination \r\nwould depend on the amount of CDR-induced cooling prior to \r\ntermination and the rate of background CO2 emissions at the time of \r\ntermination (high confidence)."


id: ec1a257a32e543917c2ba99acace4632
created_at: 2023-03-16 14:00:41
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vector_id: 34643
content:
"Most SRM approaches, including stratospheric aerosol injection (SAI), \r\nmarine cloud brightening (MCB), and surface albedo enhancements \r\n(Table 4.7), aim to cool the Earth by deflecting more solar radiation \r\nto space. Although cirrus cloud thinning (CCT) aims to cool the planet \r\nby increasing the longwave emission to space, it is included in the "


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content:
"To offset the same amount of CO2-induced GSAT increase, \r\ndifferent levels of ERF are required for different methods of SRM \r\n(Schmidt et al., 2012; Chiodo and Polvani, 2016; Modak et al., 2016; \r\nDuan et  al.,  2018; Russotto and Ackerman, 2018; Krishnamohan \r\net al., 2019; Zhao et al., 2021)."


id: 33d5f7b43091f398c97d08375348d685
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 5567
content:
"The SR1.5 (de Coninck et  al., 2018) assessed SRM in terms of its \r\npotential to limit warming to below 1.5degC in temporary overshoot \r\nscenarios and the associated impacts. It concluded that SAI could \r\nlimit warming to below 1.5degC but that the climate response to SAI is \r\nuncertain and varies across climate models. Overall, the assessment \r\nconcluded that the combined uncertainties related to SRM approaches, \r\nincluding technological maturity, limited physical understanding of the \r\nresponse to SRM, potential impacts, and challenges of governance, \r\nconstrain potential deployment of SRM in the near future."


id: e0446211d09677f411adbe0068e8e26d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 32777
content:
"As assessed in AR5 (Boucher et al., 2013), abruptly introducing SRM to \r\nfully offset global warming reduces temperature toward 1850-1900 \r\nvalues with an e-folding time of only about five years (Matthews and \r\nCaldeira, 2007). A more realistic approach would be a slow ramp-up \r\nof SRM to offset further warming (MacCracken, 2016; Tilmes et al., \r\n2016). Modelling studies have consistently shown that SRM has the \r\npotential to offset some effects of increasing GHGs on global and \r\nregional climate, including the melting of Arctic sea ice (Berdahl et al., \r\n2014; Moore et al., 2014) and mountain glaciers (Zhao et al., 2017), \r\nweakening of Atlantic meridional overturning circulation (AMOC; Cao \r\net al., 2016; Hong et al., 2017; Tilmes et al., 2020), changes in extremes \r\nof temperature and precipitation (Curry et al., 2014; Ji et al., 2018; \r\nMuthyala et  al., 2018), and changes in frequency and intensity of \r\ntropical cyclone (Moore et al., 2015; Jones et al., 2017)."


id: 2292147950b7ad3e6b918ff9f0e05dbd
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 2892
content:
"The AR5 assessed SRM modelling mainly based on idealized \r\nsimulations that used solar constant reductions. Since then, more \r\nin-depth investigations into specific SRM approaches have been \r\nconducted with more sophisticated treatment of aerosol-cloud-\r\nradiative interactions and stratospheric dynamics and chemistry \r\nunderlying SAI, MCB, and CCT. Another major development since AR5 \r\nis the investigation into whether multiple climate policy goals may be \r\nmet by optimally designed SRM strategies, including large-ensemble \r\nSAI simulations using multiple injection locations. There are large \r\nuncertainties in important SRM-related processes such as aerosol \r\nmicrophysics and aerosol-cloud-radiation interaction and hence the \r\nlevel of understanding is low."


id: 5f18e6da8a281c2c10ef606457ea72ae
created_at: 2023-03-16 14:00:41
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vector_id: 12513
content:
"As assessed in SR1.5 (de Coninck et al., 2018), most of the knowledge \r\nabout SRM is based on idealized model simulations and some \r\nnatural analogues. In addition to single-model studies, more results \r\nfrom the coordinated modelling work of Geoengineering Model \r\nIntercomparison Project (GeoMIP) have become available. GeoMIP \r\nwas initiated at the time of AR5 (Kravitz et al., 2011, 2013a) and is \r\nnow in its second phase under the framework of CMIP6 (GEOMIP6, \r\nKravitz et al., 2015). However, studies based on GeoMIP6 data are \r\ncurrently limited and hence the assessment on climate response to \r\nSRM here is derived mostly from GeoMIP literature together with \r\nstudies with single models."


id: c14f5fa1d2bbe272c7229c1a30611926
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 28000
content:
"For the same amount of global mean cooling achieved, the pattern \r\nof climate response would depend on SRM characteristics (Niemeier \r\net al., 2013; Duan et al., 2018; Muri et al., 2018). This is illustrated \r\nin Figure 4.38 for temperature and precipitation change relative to \r\na  high-CO2 world for scenarios of CO2 reduction, solar irradiance \r\nreduction, SAI, and MCB. The pattern differences for different \r\nmethods are much larger for precipitation than for temperature. The \r\npattern of climate change resulting from SRM is also different from \r\nthat resulting from CO2 reduction (Figure 4.38). It is virtually certain\r\nthat SRM approaches would not be able to precisely offset the GHG\u0002induced anthropogenic climate change at global and regional scales."


id: c81fd51068bb9f72621f2def9919a35b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 29068
content:
"Simple calculations and climate modelling studies show that about \r\n2% extra solar irradiance reflected away from Earth or a  one \r\npercentage point increase in planetary albedo (0.31 to 0.32) would \r\nsuffice to offset global mean warming from a doubling of the CO2 \r\nconcentration (The Royal Society, 2009; Kravitz et al., 2013a, 2021). "


id: e526bf8a401f8accaecf70c4efbcdca6
created_at: 2023-03-16 14:00:41
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vector_id: 33546
content:
"Because of different sensitivity of precipitation change to CO2 and \r\nsolar forcings (Myhre et  al., 2017), if shortwave-based SRM is used "


id: ae9897bf3fb70a6685aa4915b07d5b4e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 24997
content:
"to fully offset GHG-induced global mean warming, there would \r\nbe a  overcompensation of GHG-induced increase in global mean \r\nprecipitation (Kravitz et  al., 2013a; Tilmes et  al., 2013; Irvine et  al., \r\n2016). Further, regional SRM approaches such as aerosol injections \r\ninto the Arctic stratosphere are likely to remotely influence on tropical \r\nmonsoon precipitation by shifting the mean position of ITCZ (Nalam \r\net al., 2018). However, the shift could be avoided by simultaneously \r\ncooling the southern hemisphere (MacCracken et  al., 2013; Kravitz \r\net al., 2016; Nalam et al., 2018). The SRM response of precipitation \r\nminus evapotranspiration (P-E) is found to be smaller than that \r\nof precipitation because of reduction in both precipitation and \r\nevapotranspiration (Tilmes et al., 2013; Nalam et al., 2018; Irvine et al., \r\n2019). Thus, global mean soil moisture could be effectively maintained, \r\nthough with significant regional variability (Cheng et al., 2019)."


id: b0a5030f9d907a5f115406a4cff2c699
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 25312
content:
"The Geoengineering Large Ensemble Project (GLENS) has investigated \r\nachieving multiple climate policy goals by adjusting the rate of \r\nstratospheric SO2 injection at four different latitudes. GSAT, the \r\ninter-hemispheric temperature difference, and the equator-to-pole \r\ntemperature gradient could be maintained simultaneously at the \r\nyear-2020 level under RCP 8.5 (Tilmes et al., 2018a). The possibility of \r\nusing SAI to simultaneously stabilize non-temperature metrics such \r\nas tropical precipitation and Arctic sea ice extent is also explored (Lee \r\net al., 2020). Furthermore, the potential of achieving multiple climate \r\npolicy goals by combining two SRM approaches is also examined \r\nin a few modelling studies, with low confidence in the outcome of \r\ncombining various approaches and the related climate response \r\n(Boucher et al., 2017; Cao et al., 2017)."


id: 886bf1ef369098368e47c58acd5d9370
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 18992
content:
"The cooling potential of SAI using sulphate aerosols depends on \r\nmany factors (Visioni et al., 2017) including the amount of injection \r\n(Niemeier and Timmreck, 2015), aerosol microphysics (Krishnamohan \r\net al., 2020), the spatial and temporal pattern of injection (Tilmes et al., \r\n2017), response of stratospheric dynamics and chemistry (Richter \r\nJadwiga et al., 2018), and aerosol effect on cirrus clouds (Visioni et al., \r\n2018). A negative radiative forcing of a few W m-2 (ranging from one "


id: 53c7ec43c525a93917a11a1b46535582
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 10649
content:
"the climate response to MCB is as uncertain. Results from global \r\nclimate models are subject to large uncertainty because of different \r\ntreatment of cloud microphysics and inadequate representation of \r\nsub-grid aerosol and cloud processes (Alterskjaer and Kristjansson, \r\n2013; Stuart et al., 2013; Connolly et al., 2014; Stjern et al., 2018). \r\nSea salt deposition over land (Muri et al., 2015) and the effect of sea \r\nsalt emission on atmospheric chemistry (Horowitz et al., 2020) are \r\nsome of the potential side effects of MCB."


id: 62ba52bead477b94b1b1f81f382e26c3
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 13101
content:
"Modelling studies suggest that MCB has the potential to achieve \r\na negative forcing of about 1 to 5 W m-2, depending on the deployment \r\narea and strategies of cloud seeding (Hill and Ming, 2012; Partanen \r\net al., 2012; Alterskjaer et al., 2013; Ahlm et al., 2017; Stjern et al., \r\n2018). Regional applications of MCB has also been suggested for \r\noffsetting severe impacts from tropical cyclones whose genesis is \r\nassociated with higher SST (MacCracken, 2016; Latham et al., 2014) \r\nand for protecting coral reefs from higher SST (Latham et al., 2013). \r\nHowever, such regional approaches also involve large uncertainties \r\nin the magnitude of the responses and consequences."


id: 611a045fc9f85908ce2bbabe8c020f3f
created_at: 2023-03-16 14:00:41
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vector_id: 12849
content:
"Relative to the high-GHG climate and for the same amount of global \r\ncooling, CCT is simulated to cause an increase in global precipitation \r\ncompared to shortwave-based SRM options such as SAI and MCB \r\n(Duan et al., 2018; Muri et al., 2018) because of the opposing effects of \r\nCCT and increased CO2 on outgoing longwave radiation (Kristjansson \r\net  al., 2015; Jackson et  al., 2016). Combining SAI and CCT has \r\nsuggested that GHG-induced changes in global mean temperature \r\nand precipitation can be simultaneously offset (Cao et al., 2017), but \r\nthere is low confidence in the applicability of this result to the real \r\nworld owing to the large uncertainty in simulating aerosol forcing \r\nand the complex cirrus microphysical processes."


id: f00fc455ac6f383c6dbda9df7ac5fb11
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 35277
content:
"Several modelling studies suggest that the direct scattering effect by \r\ninjected particles might also play an important role in the cooling effect \r\nof MCB, but the relative contribution of aerosol-cloud and aerosol-\r\ncloud-radiation effect is uncertain (Partanen et  al., 2012; Kravitz \r\net al., 2013b; Ahlm et al., 2017). Relative to the high-GHG climate, \r\nit is likely that MCB would increase precipitation over tropical land \r\ndue to the inhomogeneous forcing pattern of MCB over ocean and \r\nland (medium confidence) (Bala et al., 2011; Alterskjaer et al., 2013; \r\nNiemeier et al., 2013; Ahlm et al., 2017; Muri et al., 2018; Stjern et al., \r\n2018). Because of the high level of uncertainty associated with cloud \r\nmicrophysics and aerosol-cloud-radiation interaction (Section 7.3), "


id: 5c7ded96a955a0d268817f51e97f3c9
created_at: 2023-03-16 14:00:41
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content_type: text
index: climate_gpt_only_giec
vector_id: 12068
content:
"Surface-based albedo modification could, in principle, achieve \r\na negative radiative forcing of a few W m-2 by enhancing the albedo \r\nof the ocean surface (Gabriel et  al., 2017; Kravitz et  al., 2018). \r\nHowever, the technology does not exist today to increase ocean \r\nalbedo at large scale. An increase in crop albedo or roof albedo in \r\nurban areas could help to reduce warming in densely populated and \r\nimportant agricultural regions, but the effect would be limited to \r\nlocal scales and ineffective at counteracting global warming (Crook \r\net al., 2015; Zhang et al., 2016). Large changes in desert albedo could \r\nin principle result in substantial global cooling, but would severely \r\nalter the hydrological cycle (Crook et al., 2015)."


id: 38d147998dfd0b20c311f1a698ba25e9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 6309
content:
"In addition to above-mentioned SRM methods, a  number of local \r\nintervention methods have been proposed to limit the loss of \r\ncryosphere, such as applying reflective materials over sea ice (Field \r\net  al., 2018), pumping seawater on top of the ice surface (Desch \r\net  al., 2017; Zampieri and Goessling, 2019), depositing a  massive \r\namount of snow over ice sheets (Feldmann et al., 2019), and blocking \r\nwarm seawater from reaching glaciers (J.C. Moore et  al., 2018). \r\nThe  stabilization of ice sheets through local intervention methods \r\nwould reduce sea level commitment (Section 9.6.3.5). However, these \r\nmethods are subject to large uncertainty concerning their feasibility \r\nand effectiveness, and their effects would be largely localized."


id: c0ba51627fb7feba2abd99026b391884
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 27887
content:
"Modelling studies have consistently shown that SRM has the potential \r\nto offset some effect of increasing GHGs on global and regional \r\nclimate (high confidence), but there would be substantial residual or \r\novercompensating climate change at the regional scale and seasonal \r\ntime scale (high confidence). Large uncertainties associated with \r\naerosol-cloud-radiation interactions persist in our understanding \r\nof climate response to aerosol-based SRM options. For the same \r\namount of global mean cooling, different SRM options would cause \r\ndifferent patterns of climate change (medium confidence). Modelling \r\nstudies suggest that it is conceptually possible to achieve multiple \r\nclimate policy goals by optimally designed SRM strategies."


id: 19bc9d37c231bbadf0550f433c279c90
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 1521
content:
"The effect of SRM options on global temperature and precipitation \r\nresponse would be detectable after one or two decades, which is \r\nsimilar to the time scale for the detection of strong mitigation. There \r\nis high confidence that a sudden and sustained termination of a high \r\nlevel of SRM against a high-GHG background would cause a rapid \r\nincrease in temperature at a rate that far exceeds that projected for \r\nclimate change without SRM. However, a gradual phase-out of SRM \r\ncombined with mitigation and CDR would more likely than not avoid \r\nlarge rates of warming."


id: ffacf717e28abfe6d608318ebdb23fa3
created_at: 2023-03-16 14:00:41
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vector_id: 37768
content:
"This section assesses changes in climate beyond 2100. An advance \r\nsince AR5 is the availability of ESM results for scenarios beyond 2100 \r\nand for much longer stabilisation simulations compared with analysis \r\npredominantly based on Earth system models of intermediate \r\ncomplexity (EMICs) at the time of AR5 (e.g., Eby et al., 2013; Zickfeld \r\net al., 2013). Long-term commitment of sea level rise due to thermal \r\nexpansion and ice-sheet loss is assessed in Chapter 9 (Section 9.6.3.5 \r\nand Figure  9.30). Here we assess projections of GSAT, global "


id: cbe4bfdfafa1c686626f0f24bbb06aca
created_at: 2023-03-16 14:00:41
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vector_id: 29616
content:
"precipitation, and Arctic sea ice. Uncertainties relating to potential \r\nlong-term changes in AMOC are treated in Section 9.2.3.1.\r\n* "


id: 62e301279f271b638e5442c6e27421e4
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 13130
content:
"On multi-century time scales it is common to explore changes that \r\nare due to long-term commitment. Here we differentiate between:"


id: 8185457596a5ed68dc92f942a4038716
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 17908
content:
"The zero emissions commitment (ZEC) is the climate change \r\ncommitment that would result, in terms of projected GSAT, from \r\nsetting carbon dioxide (CO2) emissions to zero. It is determined by both \r\ninertia in physical climate system components (ocean, cryosphere, \r\nland surface) and carbon cycle inertia (see Annex VII). In its widest \r\nsense it refers to emissions of all compounds including greenhouses \r\ngases, aerosols and their pre-cursors. A specific sub-category of zero \r\nemissions commitment is the zero CO2 emissions commitment, which \r\nrefers to the climate system response to a cessation of anthropogenic \r\nCO2 emissions excluding the impact of non-CO2 forcers. Assessment \r\nof remaining carbon budgets requires an assessment of zero CO2\r\nemissions commitment as well as of the transient climate response \r\nto cumulative carbon emissions (TCRE; Section 5.5.2)."


id: c8915643f93e34c0e529bd9ffeca0cbd
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"There is an offset of continued warming following cessation \r\nof emissions by continued CO2 removal by natural sinks (high \r\nconfidence) (e.g.,  Matthews and Caldeira, 2008; Solomon et  al., \r\n2009; Joos et  al., 2013; Ricke and Caldeira, 2014). Some models \r\ncontinue warming by up to 0.5degC after emissions cease at 2degC of \r\nwarming (Frolicher et al., 2014; Frolicher and Paynter, 2015; Williams \r\net al., 2017), while others simulate little to no additional warming \r\n(Nohara et  al., 2015). In SR1.5, the available evidence indicated \r\nthat past CO2 emissions do not commit to substantial further "


id: bd7e4511176110e58dffe04d4476856a
created_at: 2023-03-16 14:00:41
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content:
"Table 4.8 | The 20-year average GSAT change 50 years after the cessation \r\nof emissions (ZEC50). Displayed are ZEC50 estimated from eleven ESMs (top) and \r\nnine EMICs (bottom)."


id: 2a2b1637b952348af4b785c479ec743f
created_at: 2023-03-16 14:00:41
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content:
"(neither ECS nor TCR; MacDougall et al., 2020). It is therefore likely\r\nthat the absolute magnitude of ZEC50 is less than 0.3degC, but we \r\nassess low confidence in the sign of ZEC on 50-year time scales. This \r\nis small compared with natural variability in GSAT."


id: b05417982412cad8eb487be1464b765b
created_at: 2023-03-16 14:00:41
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content:
"warming (Allen et al., 2018). A ZEC close to zero was thus applied \r\nfor the computation of the remaining carbon budget (Rogelj et al., \r\n2018b). However, the  available literature consisted of simulations \r\nfrom a  small number of models using a  variety of experimental \r\ndesigns, with some simulations showing a  complex evolution of \r\ntemperature following cessation of emissions (e.g., Frolicher et al., \r\n2014; Frolicher and Paynter, 2015; Williams et al., 2017)."


id: 7d8683febdef7baf015a56152cd620bc
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 17321
content:
"Changes in climate at 2300 have impacts and commitments \r\nbeyond this timeframe (high confidence). Sea level rise may \r\nexceed 2 m on millennial time scales even when warming is \r\nlimited to 1.5degC-2degC, and tens of metres for higher warming levels \r\n(Table 9.10). Randerson et al. (2015) showed increasing importance \r\non carbon cycle feedbacks of slow ocean processes, Mahowald \r\net  al. (2017) showed the long-lasting legacy of land-use effects \r\nand J.K. Moore et al. (2018) show how changes in Southern Ocean \r\nwinds affect nutrients and marine productivity well beyond 2300. \r\nClark et al. (2016) show that physical and biogeochemical impacts \r\nof 21st century emissions have a potential committed legacy of at \r\nleast 10,000 years."


id: f9314f25d863116809429dcca75acf27
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 36718
content:
"time scales. High latitude warming led to longer growing seasons \r\nand increased vegetation growth in the CESM1 model (Liptak \r\net al., 2017), and Burke et al. (2017) found that carbon release from \r\npermafrost areas susceptible to this warming may amplify future \r\nclimate change by up to 17% by 2300."


id: 2e462ace2649198aeb6d167855b39cd5
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
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content:
"Too few CMIP6 models performed the extension simulations to allow \r\na  robust assessment of GSAT projection, and some of those which \r\ndid had higher than average climate sensitivity values. Therefore, we \r\nbase our assessment of GSAT projections (Table 4.9) on the MAGICC7 \r\nemulator calibrated against assessed GSAT to 2100 (Section  4.3.4, \r\nCross-Chapter Box 7.1). Because the emulator approach has not been \r\nevaluated in depth up to 2300 in the same way as it has up to 2100 \r\n(Cross-Chapter Box 7.1) we account for possible additional uncertainty \r\nby assessing the 5-95% range from MAGICC as likely instead of very \r\nlikely. It is therefore likely that GSAT will exceed 2degC above that of \r\nthe period 1850-1900 at the year 2300 in the extended SSP scenarios \r\nSSP2-4.5, SSP3-7.0 and SSP5-8.5  (Figure  4.40). For  SSP1-2.6 and "


id: 198ef6417e6f387b2a53b274208a6aa
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 1496
content:
"Table 4.9 | Change of global surface air temperature at 2300. Displayed are \r\nthe median and 5-95% range of GSAT change at 2300 relative to 1850-1900 for the \r\nsix scenarios used with MAGICC7."


id: 9d4a69d693826d866d3d4f487284aa9b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 22274
content:
"CMIP5 extension simulations, minimum (September) Arctic sea ice \r\narea began to recover for most models under RCP2.6 out to 2300, \r\nwhile RCP4.5 and RCP8.5 extensions became ice-free in September \r\n(Hezel et al., 2014; Bathiany et al., 2016). They also found increasingly \r\nstrong winter responses under continued warming such that under \r\nthe RCP8.5 extension, the Arctic became ice-free nearly year-round \r\nby 2300. Consistent with the assessment in Section  9.3.1.1 that \r\nArctic sea ice area is correlated with GSAT, CMIP6 projections to 2300 \r\nshow partial sea ice recovery by 2300 in SSP1-2.6 in line with GSAT \r\n(Figure 4.40), with one model (MRI-ESM2-0) showing near complete \r\nrecovery to present-day values. SSP1-2.6 and SSP5-3.4-OS are not \r\ndistinguishable in behaviour of Arctic sea ice in these models after \r\n2100. SSP5-8.5 remains ice-free in September up to 2300."


id: 114ad0534d5e695387aaa038554d40e9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 202
content:
"SSP1-1.9, mean warming at 2300 is 1.5degC and 0.9degC respectively. \r\nGSAT differences between SSP5-3.4-overshoot and SSP1-2.6 peak \r\nduring the 21st century but decline to less than about 0.25degC after \r\n2150 (medium confidence)."


id: c0b9602cfed7ccd51753381b2efda946
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 27885
content:
"To place the temperature projections for the end of the 23rd century \r\ninto the context of paleo temperatures, GSAT under SSP2-4.5 (likely\r\n2.3degC-4.6degC higher than over the period 1850-1900) has not been \r\nexperienced since the Mid Pliocene, about three million years ago. \r\nGSAT projected for the end of the 23rd  century under SSP5-8.5 \r\n(likely  6.6degC-14.1degC higher than over the period 1850-1900) \r\noverlaps with the range estimated for the Miocene Climatic Optimum \r\n(5degC-10degC higher) and Early Eocene Climatic Optimum (10degC-18degC \r\nhigher), about 15 and 50 million years ago, respectively (medium \r\nconfidence) (Chapter 2)."


id: 73dae7cd9a4af598d6deb39043a43cff
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 15843
content:
"Abrupt and irreversible changes in the climate system are assessed \r\nacross multiple chapters in AR6. This section provides a cross-chapter \r\nsynthesis of these assessments as an update to Table  12.4 in AR5 \r\nand Table 6.1 in SROCC. Understanding of abrupt climate change and \r\nirreversibility has advanced considerably since AR5 with many of the \r\nprojected changes in proposed Tipping Elements having grown more \r\nconfident (Table 4.10). Many aspects of the physical climate changes \r\ninduced by GHG warming previously demonstrated to be reversible \r\nin a single model have been confirmed in multiple models (Boucher \r\net al., 2012; Tokarska and Zickfeld, 2015) with others such as sea level \r\nrise or terrestrial ecosystems confirmed to continue to respond on long \r\ntime scales (Clark et al., 2016; Zickfeld et al., 2017; Pugh et al., 2018)."


id: bd7c8687e0a2a00ed3287c0a0509ef4a
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 27379
content:
"Chapter  9 assesses with high confidence that on decadal and \r\nlonger time scales, Arctic summer sea ice area will remain highly \r\ncorrelated with global mean temperature until the summer sea ice \r\nhas vanished (Section  9.3.1.1). This means that Arctic sea ice will \r\ncontinue to decline in scenarios of continued warming but will begin \r\nto recover in scenarios where GSAT begins to decrease. Under the "


id: 2e53dd739648b14b82bd1279cd6eb82a
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 4729
content:
"The Carbon Dioxide Removal Model Intercomparison Project \r\n(CDR-MIP; Keller et  al., 2018) comprises a  set of 1% ramp-up, \r\nramp-down simulations aimed at establishing a  multi-model "


id: c652496d5bf997a298f3c5af55b610ab
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 28779
content:
"They found that some degree of irreversible loss of the West Antarctic \r\nIce Sheet (WAIS) may have already begun, that tropical forests are \r\nadversely affected by drought, and rapid development of aragonite \r\nundersaturation at high latitudes affecting calcifying organisms."


id: 8e8bf520b4dd7750ecebda50b5cbbeaa
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 19947
content:
"New since AR5 is the fundamental recognition in SRCCL and in \r\nthis Report (Chapter 5) that projected changes in forests strongly \r\ndepend on the human disturbance and that tropical forest dieback \r\nin the absence of disturbance is largely driven by the increased \r\npotential for drought, while that in boreal forests includes both \r\nthermal and hydrological factors (Drijfhout et al., 2015). For some \r\nproposed tipping elements, the role of seasonal change has become \r\nbetter understood. For example, the lack of a tipping point in the \r\nreduction of summer Arctic sea ice area (Stroeve and Notz, 2015) \r\nhas been further substantiated. The role of abrupt change at the "


id: a9cfb694a9fe7f8f6dea42c826367c17
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 24248
content:
"Table 4.10 | Cross-chapter assessment updating AR5 and SROCC of components in the Earth system that have been proposed as susceptible to tipping \r\npoints/abrupt change, irreversibility, projected 21st century change, and overall change in assessment from previous IPCC reports. Also provided are \r\nconfidence levels and, in parentheses, the main section(s) of this Report in which proposed tipping elements are assessed."


id: f500d241468d0ecb01e5e8491e70ac3f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 36059
content:
"However,  low-warming storylines are not specifically assessed in \r\nthis section to focus on storylines associated with highest risks. This \r\nsection further focuses on storylines of high and very high global \r\nwarming levels along with their manifestation in global patterns \r\nof temperature and precipitation changes. However, this does not \r\naccount for the largest potential changes at regional levels, which \r\nwould require taking into account storylines of regional changes \r\ndependent on changes in atmospheric circulation, land-atmosphere \r\ninteractions, and regional to local feedbacks."


id: 3ec18cf67fae5e904071ccaaca85e67
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 7253
content:
"Previous IPCC assessments have primarily assessed the projected \r\nlikely range of changes (e.g., Collins et al., 2013; see also Box 1.1). \r\nThe focus on the likely range partly results from the design of model \r\nintercomparison projects that are not targeted to systematically \r\nassess the upper and lower bounds of projections, which in principle \r\nwould require a  systematic sampling of structural and parametric \r\nmodel uncertainties. The upper and lower bounds of model \r\nprojections may further be sensitive to the missing representation \r\nof processes and to deep uncertainties about aspects of the climate \r\nsystem (Section 1.2.3.1)."


id: 19eaecae94b662a7faa5349a6ac6925e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 1546
content:
"However, a  comprehensive risk assessment requires taking into \r\naccount high potential levels of warming whose likelihood is low, \r\nbut potential impacts on society and ecosystems are high (Xu and \r\nRamanathan, 2017; Sutton, 2018). Climate-related risks have been \r\nargued to increase with increasing levels of global warming even if \r\ntheir likelihood decreases (O'Neill et al., 2017). Thus, it has recently \r\nbeen argued that an assessment that is too narrowly focused on \r\nthe likely range potentially ignores the changes in the physical \r\nclimate system associated with the highest risks (Sutton, 2018; \r\nsee Section 1.4.4.1)."


id: 93ddec5bcdf5e166c712ff772d59ce0
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 20772
content:
"Given that the CMIP experiments can be considered ensembles of \r\nopportunity that are not designed for probabilistic assessments, \r\nalternative approaches such as physically plausible high-impact \r\nscenarios (Sutton, 2018) or storylines have been suggested to \r\ninvestigate the tail of the distribution (Lenderink et al., 2014; Zappa \r\nand Shepherd, 2017; Kjellstrom et al., 2018; Shepherd et al., 2018; \r\nsee Section  1.4.4). Such storylines informed by a  combination of \r\nprocess understanding, model evidence, and paleo information can \r\nbe used for risk assessment and adaptation planning to test how \r\nwell adaptation strategies would cope if the impacts of climate \r\nchange were more severe than suggested by the likely model range \r\n(Section  1.4.4). Note that by definition the lower bound of the \r\nlikely model range (Box  4.1) is equally likely as the upper bound. "


id: a1461e50ffefbe8ce7542e37d3b119c1
created_at: 2023-03-16 14:00:41
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content:
"For the high-emissions scenarios SSP3-7.0 and SSP5-8.5, \r\na  high-warming storyline is associated with wide-spread warming \r\nthat exceeds the already high best-estimate warming by another \r\n35-50%. For SSP5-8.5, this corresponds to a warming of 1degC-3degC in \r\naddition to the best estimate over most land regions, which implies \r\nmore than 6degC relative to 1995-2014 over most extratropical land \r\nregions and Amazonia. Over large parts of the Arctic, annual mean \r\ntemperatures increase by more than 10degC relative to 1995-2014 \r\nin such a  high-warming storyline under SSP5-8.5. The two lines \r\nof evidence yield more consistent patterns for SSP5-8.5 than for \r\nSSP1-2.6, but there are substantial differences concerning whether \r\nthe strongest warming above the best estimate occurs over the \r\ntropics or extratropical land regions."


id: e26972ee01b56d479695a030e9f298f1
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 33126
content:
"High-warming storylines are very likely also associated with \r\nsubstantial changes in the hydrological cycle due to strong \r\nthermodynamic changes, which can be amplified or offset by \r\ndynamical changes (Emori and Brown, 2005; Seager et  al., 2014b; \r\nChavaillaz et al., 2016; Kroner et al., 2017; Chen et al., 2019). Here \r\nthe assessment of the hydrological cycle in high-warming storylines \r\nis limited to changes in annual mean precipitation, but changes in \r\nseasonal mean precipitation can be even stronger due to enhanced \r\nseasonality in many regions (Box 8.2)."


id: 41e842a388da8ad99213ae839ba92f4b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 7768
content:
"The high-warming storyline shown in Figure  4.42b,c does not \r\ncorrespond to an upper or lower estimate of annual precipitation \r\nincrease and decrease over individual locations, which in many \r\nregions may differ in the sign of the response (Figure  4.42e,f) \r\ndue to differences in the model response and internal variability \r\n(Madsen et  al., 2017). Figure  4.42e,f illustrates upper and lower \r\nlocal estimates corresponding to the 5-95% model range of local \r\nuncertainties as opposed to the global-warming storylines. Note, \r\nhowever, that Figure  4.42e,f does not show a  physically plausible \r\nglobal precipitation response pattern, because information at the \r\ndifferent grid points is taken from different model simulations."


id: d595597c4538f32ca91e7ed2e3d549cd
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 31123
content:
"Quantifying precipitation changes associated with high-warming \r\nstorylines is challenging since models show the largest changes \r\nin precipitation over different regions (Sections 4.5.1 and 4.6.1). \r\nIn some areas, models project opposing signals in different seasons \r\nor a  combination of decreasing mean and increasing extreme \r\nprecipitation (Kendon et  al., 2014; Ban et  al., 2015; Giorgi et  al., \r\n2016; Pendergrass et al., 2017). Models with the most pronounced \r\nGSAT warming are not necessarily associated with the strongest \r\nprecipitation response in all regions, in part due to projected \r\nchanges in atmospheric dynamics (Madsen et al., 2017; Zappa and \r\nShepherd, 2017; Li et al., 2018)."


id: f551cee58f71a697a715efa7284b1cb9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
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vector_id: 36116
content:
"Again, the manifestation of changes in the hydrological cycle for \r\na  high-warming storyline is not limited to precipitation, but would \r\nsubstantially affect other variables such as soil moisture, runoff, \r\natmospheric humidity, and evapotranspiration. The changes are also \r\nnot limited to annual mean precipitation but may be stronger or weaker \r\nfor individual seasons and for precipitation extremes and dry spells."


id: 2cb41d31c143d300ad02605007e2341b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 4461
content:
"While this assessment is limited to temperature and precipitation, \r\nsuch a  high-warming storyline would manifest itself also in other \r\nclimate variables (Sanderson et  al., 2011) assessed in this chapter \r\nsuch as Arctic sea ice, atmospheric circulation changes, and sea \r\nlevel rise (Ramanathan and Feng, 2008; Xu and Ramanathan, 2017; \r\nSteffen et al., 2018)."


id: b999ee8b8ab7f454338648bc3d594671
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 26737
content:
"In summary, while high-warming storylines - those associated with \r\nglobal warming levels above the upper bound of the assessed very \r\nlikely range  - are by definition extremely unlikely, they cannot be \r\nruled out. For SSP1-2.6, such a  high-warming storyline implies \r\nwarming well above rather than well below 2degC (high confidence). \r\nIrrespective of scenario, high-warming storylines imply changes \r\nin many aspects of the climate system that exceed the patterns \r\nassociated with the best estimate of GSAT changes by up to more \r\nthan 50% (high confidence)."


id: f5252db4c21196ffc17e1b1b8aec4db4
created_at: 2023-03-16 14:00:41
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content:
"The authors are deeply grateful to the WGI Bureau and Technical \r\nSupport Unit for their support throughout the writing process. \r\nSpecial thanks go to Anna Pirani, Sarah Connors, Clotilde Pean, Robin \r\nMatthews and Elisabeth  Lonnoy for their organizational support. \r\nSpecial thanks go to Michael Sigmond, Sophie Berger, Paolo Davini, \r\nMelissa Gomis and Tom Maycock for their contributions to figures \r\nand FAQs. We acknowledge outstanding contributions from our two \r\nchapter scientists, Sebastian Milinski and Kyung-Sook Yun. We thank \r\nZiming Chen for contributing the analysis of CMIP6 models. Finally, \r\nwe would like to thank all reviewers for their constructive comments."


id: f9841faf1bc2f02e6e201395c77f3379
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"The parts of the climate system that have shown clear increasing or decreasing trends in recent decades will \r\ncontinue these trends for at least the next twenty years. Examples include changes in global surface temperature, \r\nArctic sea ice cover, and global average sea level. However, over a period as short as twenty years, these trends are \r\nsubstantially influenced by natural climate variability, which can either amplify or attenuate the trend expected \r\nfrom the further increase in greenhouse gas concentrations."


id: c9c139fc1cf9f81cff0aabc16964b20
created_at: 2023-03-16 14:00:41
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content:
"Twenty years are a long time by human standards but a short time from a climate point of view. Emissions of \r\ngreenhouse gases will continue over the next twenty years, as assumed in all the scenarios considered in this \r\nReport, albeit with varying rates. These emissions will further increase concentrations of greenhouse gases in \r\nthe atmosphere (see FAQ 4.2), leading to continued trends in global surface warming and other parts of the \r\nclimate system, including Arctic sea ice and global average sea level (see FAQ 9.2). FAQ 4.1, Figure 1 shows that \r\nboth global surface temperature rise and the shrinking of sea ice in the Arctic will continue, with little difference \r\nbetween high- and low-emissions scenarios over the next 20 years (that is, between the red and blue lines)."


id: b70be72334352caf15ff30d194269248
created_at: 2023-03-16 14:00:41
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content_type: text
index: climate_gpt_only_giec
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content:
"Locally, the effect of natural variability would be much larger still. Simulations (not shown here) indicate that, \r\nlocally, a cooling trend over the next twenty years cannot be ruled out, even under the high-emissions scenario - \r\nat a small number of locations on Earth, but these might lie anywhere. Globally, though, temperatures would \r\nrise under all scenarios."


id: 64353bfa61861a2dd6b6282d21180071
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 13336
content:
"In summary, while the direction of future change is clear for the two important climate quantities shown here - \r\nthe global surface temperature and the Arctic sea ice area in September - the magnitude of the change is much \r\nless clear because of natural variability."


id: 5292bd4ce7421dda146d6db885466011
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 10451
content:
"Current climatic trends will continue in the next 2 decades \r\nbut their exact magnitude cannot be predicted, because of \r\nnatural variability."


id: 89685d1f1762162ed1a3b6d943312fad
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"The effects of substantial reductions in carbon dioxide emissions would not be apparent immediately, and \r\nthe time required to detect the effects would depend on the scale and pace of emissions reductions. Under the \r\nlower-emissions scenarios considered in this Report, the increase in atmospheric carbon dioxide concentrations \r\nwould slow visibly after about five to ten years, while the slowing down of global surface warming would be \r\ndetectable after about twenty to thirty years. The effects on regional precipitation trends would only become \r\napparent after several decades."


id: 1a5c35f9a4be0899cec0e35c14550d15
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 1616
content:
"Reducing emissions of carbon dioxide (CO2) - the most important greenhouse gas emitted by human activities - \r\nwould slow down the rate of increase in atmospheric CO2 concentration. However, concentrations would only \r\nbegin to decrease when net emissions approach zero, that is, when most or all of the CO2 emitted into the \r\natmosphere each year is removed by natural and human processes (see FAQ 5.1 and FAQ 5.3). This delay between \r\na peak in emissions and a decrease in concentration is a manifestation of the very long lifetime of CO2 in the \r\natmosphere; part of the CO2 emitted by humans remains in the atmosphere for centuries to millennia."


id: d988d78602b75510864fd9034e99c1ea
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 31740
content:
"Reducing the rate of increase in CO2 concentration would slow down global surface warming within a decade. \r\nBut this reduction in the rate of warming would initially be masked by natural climate variability and might \r\nnot be detected for a few decades (see FAQ 1.2, FAQ 3.2 and FAQ 4.1). Detecting whether surface warming has \r\nindeed slowed down would thus be difficult in the years right after emissions reductions begin."


id: dd5cfa94b3989010ae2082c417f113a1
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"FAQ 4.2, Figure 1 shows that the atmospheric CO2 concentrations differ noticeably between the two scenarios \r\nabout five to ten years after the emissions have begun to diverge in year 2015. In contrast, the difference in \r\nglobal surface temperatures between the two scenarios does not become apparent until later - about two to \r\nthree decades after the emissions histories have begun to diverge in this example. This time would be longer if \r\nemissions were reduced more slowly than in the low-emissions scenario illustrated here and shorter in the case \r\nof stronger reductions. Detection would take longer for regional quantities and for precipitation changes, which \r\nvary more strongly from natural causes. For instance, even in the low-emissions scenario, the effect of reduced \r\nCO2 emissions would not become visible in regional precipitation until late in the 21st century."


id: 4cb48f0940cafbaabe4690caa0a57396
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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content:
"In summary, it is only after a few decades of reducing CO2 emissions that we would clearly see global temperatures \r\nstarting to stabilize. By contrast, short-term reductions in CO2 emissions, such as during the COVID-19 pandemic, \r\ndo not have detectable effects on either CO2 concentration or global temperature. Only sustained emissions \r\nreductions over decades would have a widespread effect across the climate system."


id: e56b25a071fb9564b7c5d0da74c30a8f
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 33597
content:
"Sustained reduction in carbon dioxide (CO2) emissions \r\nwould become apparent in atmospheric concentration \r\nafter 5-10 years and in the temperature after 20-30 years."


id: 8e39cc391614d17f6f3aef5aa26b9a50
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 19906
content:
"As the planet warms, climate change does not unfold uniformly across the globe, but some patterns of regional \r\nchange show clear, direct and consistent relationships to increases in global surface temperature. The Arctic \r\nwarms more than other regions, land areas warm more than the ocean surface, and the Northern Hemisphere \r\nmore than the Southern Hemisphere. Precipitation increases over high latitudes, tropics and large parts of \r\nthe monsoon regions, but decreases over the subtropics. For cases like these, we can infer the direction and \r\nmagnitude of some regional changes - particularly temperature and precipitation changes - for any given level \r\nof global warming."


id: b6514be1e92bec9667955b867654331e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 26209
content:
"The intensity of climate change will depend on the level of global warming. It is possible to identify certain \r\npatterns of regional climate change that occur consistently, but increase in amplitude, across increasing levels of \r\nglobal warming. Such robust spatial patterns of climate change are largely independent of the specific scenario \r\n(and pathway in time) that results in a given level of global warming. That is, as long as different scenarios result \r\nin the same global warming level, irrespective of the time when this level is attained in each scenario, we can \r\ninfer the patterns of regional change that would result from this warming. When patterns of changes are robust, \r\nregional consequences can be assessed for all levels of global warming, for all future time periods, and for all \r\nscenarios. Temperature and precipitation show such robust patterns of changes that are particularly striking."


id: cb89a552561e09a087ff44448fd72413
created_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 29571
content:
"The high latitudes of the Northern Hemisphere are projected to warm the most, by two to four times the level \r\nof global warming - a phenomenon referred to as Arctic amplification (FAQ 4.3, Figure 1, left). Several processes \r\ncontribute to this high rate of warming, including increases in the absorption of solar radiation due to the loss \r\nof reflective sea ice and snow in a warmer world. In the Southern Hemisphere, Antarctica is projected to warm \r\nfaster than the mid-latitude Southern Ocean, but the Southern Hemisphere high latitudes are projected to \r\nwarm at a reduced amplitude compared to the level of global warming (FAQ 4.3, Figure 1, left). An important \r\nreason for the relatively slower warming of the Southern Hemisphere high latitudes is the upwelling of Antarctic \r\ndeep waters that drives a large surface heat uptake in the Southern Ocean."


id: 9fbccdb82e176b7d4691ab7a993955ba
created_at: 2023-03-16 14:00:41
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vector_id: 22672
content:
"The warming is generally stronger over land than over the ocean, and in the Northern Hemisphere compared to \r\nthe Southern Hemisphere, and with less warming over the central subpolar North Atlantic and the southernmost \r\nPacific. The differences are the result of several factors, including differences in how land and ocean areas \r\nabsorb and retain heat, the fact that there is more land area in the Northern Hemisphere than in the Southern \r\nHemisphere, and the influence of ocean circulation. In the Southern Hemisphere, robust patterns of relatively \r\nhigh warming are projected for subtropical South America, southern Africa, and Australia. The relatively strong \r\nwarming in subtropical southern Africa arises from strong interactions between soil moisture and temperature \r\nand from increased solar radiation as a consequence of enhanced subsidence."


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vector_id: 567
content:
"Precipitation changes are also proportional to the level of global warming (FAQ 4.3, Figure 1, right), although \r\nuncertainties are larger than for the temperature change. In the high latitudes of both the Southern and Northern \r\nHemispheres, increases in precipitation are expected as the planet continues to warm, with larger changes \r\nexpected at higher levels of global warming (FAQ 4.3, Figure 1, right). The same holds true for the projected \r\nprecipitation increases over the tropics and large parts of the monsoon regions. General drying is expected \r\nover the subtropical regions, particularly over the Mediterranean, southern Africa and parts of Australia, South \r\nAmerica, and south-west North America, as well as over the subtropical Atlantic and parts of the subtropical \r\nIndian and Pacific Oceans. Increases in precipitation over the tropics and decreases over the subtropics amplify \r\nwith higher levels of global warming."


id: bf9bb24c7b2162a7429227fe9a6d675d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 27705
content:
"Some regions that are already dry and warm, such as southern Africa and the Mediterranean, are expected to \r\nbecome progressively drier and drastically warmer at higher levels of global warming."


id: edf8767537895f720063f9621299c2fe
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 34916
content:
"In summary, climate change will not affect all the parts of the globe evenly. Rather, distinct regional patterns of \r\ntemperature and precipitation change can be identified, and these changes are projected to amplify as the level \r\nof global warming increases."


id: ac303a6f7dea1477efe923292d6e7bd
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 24625
content:
"FAQ 4.3, Figure 1 | Regional changes in temperature (left) and precipitation (right) are proportional to the level of global warming, \r\nirrespective of the scenario through which the level of global warming is reached. Surface warming and precipitation change are shown relative \r\nto the 1850-1900 climate, and for time periods over which the globally averaged surface warming is 1.5degC (top) and 3degC (bottom), respectively. Changes \r\npresented here are based on 31 CMIP6 models using the high-emissions scenario SSP3-7.0."


id: d2d43730cfe80870904748da9ffc1f8d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 30690
content:
"Marcos H.  Costa (Brazil), Leticia Cotrim da Cunha (Brazil), Peter M.  Cox (United Kingdom), \r\nAlexey V. Eliseev (Russian Federation), Stephanie Henson (United Kingdom), Masao Ishii (Japan), \r\nSamuel Jaccard (Switzerland), Charles Koven (United States of America), Annalea Lohila (Finland), \r\nPrabir K.  Patra (Japan/India), Shilong Piao (China), Joeri Rogelj (United Kingdom/Belgium), \r\nStephen Syampungani (Zambia), Sonke Zaehle (Germany), Kirsten Zickfeld (Canada/Germany)"


id: e976fb8a1132d1e6883e2e65db4e182e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 34239
content:
"Jean-Baptiste  Sallee  (France), Marielle Saunois (France), Edward  A.G.  Schuur (United States \r\nof America), Sonia I. Seneviratne (Switzerland), Ann Stavert (Australia), Parvadha Suntharalingam \r\n(United Kingdom/United States of America), Kaoru Tachiiri (Japan), Jens Terhaar (Switzerland/\r\nGermany), Rona Thompson (Norway, Luxembourg/New Zealand), Hanqin Tian (United States \r\nof America), Jocelyn Turnbull (New Zealand), Sergio M. Vicente-Serrano (Spain), Xuhui Wang \r\n(China), Rik Wanninkhof (United States of America), Philip Williamson (United Kingdom)"


id: b621f245d49531cb052d4d2590801328
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 26183
content:
"Canadell, J.G., P.M.S. Monteiro, M.H. Costa, L. Cotrim da Cunha, P.M. Cox, A.V. Eliseev, S. Henson, M. Ishii, S. Jaccard, \r\nC. Koven, A. Lohila, P.K. Patra, S. Piao, J. Rogelj, S. Syampungani, S. Zaehle, and K. Zickfeld, 2021: Global Carbon and \r\nother Biogeochemical Cycles and Feedbacks. In Climate Change 2021: The Physical Science Basis. Contribution of \r\nWorking Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, \r\nV., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, \r\nE. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, \r\nCambridge, United Kingdom and New York, NY, USA, pp. 673-816, doi:10.1017/9781009157896.007."


id: d0f7b8327992a4292cea8a0a016e6af8
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 30410
content:
"The CO2 emitted from human activities during the decade \r\nof 2010-2019 (decadal average 10.9 +-  0.9  PgC yr-1) was \r\ndistributed between three Earth system components: 46% \r\naccumulated in the atmosphere (5.1 +- 0.02 PgC yr-1), 23% was \r\ntaken up by the ocean (2.5 +- 0.6 PgC yr-1) and 31% was stored \r\nby vegetation in terrestrial ecosystems (3.4 +-  0.9  PgC yr-1) \r\n(high confidence). Of the total anthropogenic CO2 emissions, the \r\ncombustion of fossil fuels was responsible for 81-91%, with \r\nthe  remainder being the net CO2 flux from land-use change and \r\nland management (e.g., deforestation, degradation, regrowth after \r\nagricultural abandonment, and peat drainage). {5.2.1.2, 5.2.1.5; \r\nTable 5.1; Figures 5.5, 5.7, 5.12}"


id: 18b35a9e4063c3e3659154334a457c57
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 1383
content:
"Over the past six decades, the average fraction \r\nof  anthropogenic CO2 emissions that has accumulated in \r\nthe atmosphere (referred to as the airborne fraction) has \r\nremained nearly constant at approximately 44%. The ocean and \r\nland sinks of CO2 have continued to grow over the past six decades \r\nin response to increasing anthropogenic CO2 emissions (high \r\nconfidence). Interannual and decadal variability of the regional and \r\nglobal ocean and land sinks indicate that these sinks are sensitive to \r\nclimate conditions and therefore to climate change (high confidence). \r\n{5.2.1.1, 5.2.1.2, 5.2.1.3, 5.2.1.4; Figures 5.7, 5.8, 5.10}"


id: 49d551fd8fc08263e9c4bb265865fa1d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 9015
content:
"Recent observations show that ocean carbon processes are \r\nstarting to change in response to the growing ocean sink, \r\nand these changes are expected to contribute significantly to \r\nfuture weakening of the ocean sink under medium- to high\u0002emissions scenarios. However, the effects of these changes are not \r\nyet reflected in a weakening trend of the contemporary (1960-2019) \r\nocean sink (high confidence). {5.1.2, 5.2.1.3, 5.3.2.1; Figures  5.8, \r\n5.20; Cross-Chapter Box 5.3}"


id: c610561cfa35946c148369ef37685e6b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 28740
content:
"Atmospheric concentration of CH4 grew at an average rate \r\nof 7.6 +- 2.7 ppb yr-1 for the last decade (2010-2019), with \r\na faster growth of 9.3 +- 2.4 ppb yr-1 over the last six years \r\n(2014-2019) (high confidence). The multi-decadal growth trend \r\nin atmospheric CH4 is dominated by anthropogenic activities (high \r\nconfidence), and the growth since 2007 is largely driven by emissions \r\nfrom both fossil fuels and agriculture (dominated by livestock) \r\n(medium confidence). The interannual variability is dominated by \r\nEl Nino-Southern Oscillation cycles, during which biomass burning \r\nand wetland emissions, as well as loss by reaction with tropospheric \r\nhydroxyl radical (OH) play an important role. {5.2.2; Figures  5.13, \r\n5.14; Table 5.2; Cross-Chapter Box 5.2}"


id: 7c9e6098720a8f617abc96418a29e2d5
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 17204
content:
"It is unequivocal that the increase of CO2, CH4, and N2O in \r\nthe atmosphere over the industrial era is the result of human \r\nactivities (very high confidence). This assessment is based on \r\nmultiple lines of evidence including atmospheric gradients, isotopes, \r\nand inventory data. During the last measured decade, global average \r\nannual anthropogenic emissions of CO2, CH4, and N2O, reached the \r\nhighest levels in human history at 10.9 +- 0.9 petagrams of carbon \r\nper year (PgC yr-1, 2010-2019), 335-383 teragrams of methane per \r\nyear (TgCH4 yr-1, 2008-2017), and 4.2-11.4 teragrams of nitrogen \r\nper year (TgN yr-1, 2007-2016), respectively (high confidence). \r\n{5.2.1, 5.2.2, 5.2.3, 5.2.4; Figures 5.6, 5.13, 5.15}."


id: 1d647fd1ed555f131ec289bd7492747d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 2070
content:
"Atmospheric concentration of N2O grew at an average \r\nrate of 0.85 +-  0.03 ppb yr-1 between 1995 and 2019, with \r\na further increase to 0.95 +- 0.04 ppb yr-1 in the most recent \r\ndecade (2010-2019). This increase is dominated by anthropogenic \r\nemissions, which have increased by 30% between the 1980s and the \r\nmost recent observational decade (2007-2016) (high confidence). \r\nIncreased use of nitrogen fertilizer and manure contributed to about \r\ntwo-thirds of the increase during the 1980-2016 period, with the \r\nfossil fuels/industry, biomass burning, and wastewater accounting for \r\nmuch of the rest (high confidence). {5.2.3; Figures 5.15, 5.16, 5.17}"


id: 3516bcab82c62ee67c4c32075ba77c60
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 5761
content:
"expected to increase land carbon accumulation in the high latitudes \r\n(not including permafrost) and also to lead to a counteracting loss of \r\nland carbon in the tropics (medium confidence, Figure 5.25). More \r\nthan half of the latest CMIP6 ESMs include nutrient limitations on \r\nthe  carbon cycle, but these models still project increasing tropical \r\nland carbon (medium confidence) and increasing global land carbon \r\n(high confidence) through the 21st century. {5.4.1, 5.4.3, 5.4.5; \r\nFigure 5.27; Cross-Chapter Box 5.1}"


id: 585082dc6dda14d828b78ade62cbe508
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 11401
content:
"Future trajectories of the ocean CO2 sink are strongly \r\nemissions-scenario dependent (high confidence). Emissions \r\nscenarios SSP4-6.0 and SSP5-8.5 lead to warming of the surface ocean \r\nand large reductions of the buffering capacity, which will slow the \r\ngrowth of the ocean sink after 2050. Scenario SSP1-2.6 limits further \r\nreductions in buffering capacity and warming, and the ocean sink \r\nweakens in response to the declining rate of increasing atmospheric \r\nCO2. There is low confidence in how changes in the biological pump \r\nwill influence the magnitude and direction of the ocean carbon \r\nfeedback. {5.4.2, 5.4.4, Cross-Chapter Box 5.3}"


id: e064287482a1b5604f9ab3282ec459a1
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 32806
content:
"Ocean deoxygenation is projected to continue to increase with \r\nocean warming (high confidence). Earth system models (ESMs) \r\nproject a 32-71% greater subsurface (100-600 m) oxygen decline, \r\ndepending on the scenario, than reported in the Special Report on \r\nthe Ocean and Cryosphere (SROCC) for the period 2080-2099. This is \r\nattributed to the effect of larger surface warming in CMIP6 models, \r\nwhich increases ocean stratification and reduces ventilation (medium \r\nconfidence). There is low confidence in the projected reduction of \r\noceanic N2O emissions under high-emissions scenarios because \r\nof greater oxygen losses simulated in ESMs in CMIP6, uncertainties \r\nin the process of oceanic N2O emissions, and a  limited number of \r\nmodelling studies available. {5.3.3.2; 7.5}"


id: 9ffab32fd8085895792c14a92726ebff
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 22708
content:
"Oceanic and terrestrial carbon sinks are projected to continue \r\nto grow with increasing atmospheric concentrations of CO2, \r\nbut the fraction of emissions taken up by land and ocean is \r\nexpected to decline as the CO2 concentration increases (high \r\nconfidence). ESMs suggest approximately equal global land and \r\nocean carbon uptake for each of the SSP scenarios. However, the range \r\nof model projections is much larger for the land carbon sink. Despite \r\nthe wide range of model responses, uncertainty in atmospheric CO2\r\nby 2100 is dominated by future anthropogenic emissions rather than \r\nuncertainties related to carbon-climate feedbacks (high confidence). \r\n{5.4.5; Figure 5.25, 5.26}"


id: 6d43bdbd234756ade833d419b8bc7215
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 14812
content:
"Thawing terrestrial permafrost will lead to carbon release \r\n(high confidence), but there is low confidence in the timing, \r\nmagnitude and the relative roles of CO2 versus CH4 as feedback \r\nprocesses. CO2 release from permafrost is projected to be 3-41 PgC \r\nper 1degC of global warming by 2100, based on an ensemble of models. \r\nHowever, the incomplete representation of important processes such \r\nas abrupt thaw, combined with weak observational constraints, \r\nonly allow low confidence in both the magnitude of these estimates \r\nand in how linearly proportional this feedback is to the amount of \r\nglobal warming. It is very unlikely that gas clathrates in terrestrial \r\nand subsea permafrost will lead to a detectable departure from the \r\nemissions trajectory during this century. {5.4.9; Box 5.1}"


id: fa360e8f01cfe9300df2fddc173d29df
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 36881
content:
"Increases in atmospheric CO2 lead to increases in land carbon \r\nstorage through CO2 fertilization of photosynthesis and \r\nincreased water use efficiency (high confidence). However, \r\nthe overall change in land carbon also depends on land-use change \r\nand on the response of vegetation and soil to continued warming and \r\nchanges in the water cycle, including increased droughts in some \r\nregions that will diminish the sink capacity. Climate change alone is "


id: 84785987ab9d5015b4da22d10afa55ce
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 18371
content:
"The net response of natural CH4 and N2O sources to future \r\nwarming will be increased emissions (medium confidence). \r\nKey processes include increased CH4 emissions from wetlands and \r\npermafrost thaw, as well as increased soil N2O emissions in a warmer \r\nclimate, while ocean N2O emissions are projected to decline at "


id: 75fd329943e6e48d47ebdfd16c044cb2
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 16161
content:
"centennial time scale. The magnitude of the responses of each \r\nindividual process and how linearly proportional these feedbacks \r\nare to the amount of global warming is known with low confidence \r\ndue to incomplete representation of important processes in models \r\ncombined with weak observational constraints. Models project that, \r\nover the 21st century, the combined feedback of 0.02-0.09 W m-2 degC-1\r\nis comparable to the effect of a  CO2 release of 5-18 petagrams \r\nof carbon equivalent per degC (PgCeq degC-1) (low confidence). {5.4.7, \r\n5.4.8; Figure 5.29}"


id: 36869d3a0d31e8af5718f82b9bf04245
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 5968
content:
"improvements, resulting in larger, yet consistent estimates. {5.5.2, \r\n5.6; Figure 5.31; Table 5.8}"


id: 39269f5a9e06fe4314faf2416669883b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 6368
content:
"The response of biogeochemical cycles to the anthropogenic \r\nperturbation can be abrupt at regional scales, and irreversible \r\non decadal to century time scales (high confidence). The \r\nprobability of crossing uncertain regional thresholds (e.g.,  high \r\nseverity fires, forest dieback) increases with climate change \r\n(high confidence). Possible abrupt changes and tipping points in \r\nbiogeochemical cycles lead to additional uncertainty in 21st century \r\nGHG concentrations, but these are very likely to be smaller than the \r\nuncertainty associated with future anthropogenic emissions (high \r\nconfidence). {5.4.9}"


id: 8f67e5ea1d4c9d44e91564e8e62a5777
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 20113
content:
"Land- and ocean-based carbon dioxide removal (CDR) methods \r\nhave the potential to sequester CO2 from the atmosphere, \r\nbut the benefits of this removal would be partially offset by \r\nCO2 release from land and ocean carbon stores (very high \r\nconfidence). The fraction of CO2 removed that remains out of the \r\natmosphere, a measure of CDR effectiveness, decreases slightly with \r\nincreasing amount of removal (medium confidence) and decreases \r\nstrongly if CDR is applied at lower CO2 concentrations (medium \r\nconfidence). {5.6.2.1; Figures 5.32, 5.33, 5.34}"


id: 93eda91bb3f1ec1cb744d315c6b53449
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 20780
content:
"The century-scale climate-carbon cycle response to a  CO2\r\nremoval from the atmosphere is not always equal and opposite \r\nto the response to a CO2 emission (medium confidence). For \r\nsimultaneously cumulative CO2 emissions and removals of greater \r\nthan or equal to 100 PgC, CO2 emissions are 4 +- 3% more effective \r\nat raising atmospheric CO2 than CO2 removals are at lowering \r\natmospheric CO2. The asymmetry originates from state-dependencies \r\nand non-linearities in carbon cycle processes and implies that an extra \r\namount of CDR is required to compensate for a positive emission of \r\na given magnitude to attain the same change in atmospheric CO2. \r\nThe net effect of this asymmetry on the global surface temperature \r\nis poorly constrained due to low agreement between models (low \r\nconfidence). {5.6.2.1; Figure 5.35}"


id: 810171edd8c0e156b23468204b9c9531
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 17819
content:
"Mitigation requirements over this century for limiting \r\nmaximum warming to specific levels can be quantified using \r\na  carbon budget that relates cumulative CO2 emissions to \r\nglobal mean temperature increase (high confidence). For the \r\nperiod 1850-2019, a total of 655 +- 65 PgC (2390 +- 240 GtCO2, likely\r\nrange) of anthropogenic CO2 has been emitted. Remaining carbon \r\nbudgets (starting from 1 January 2020) for limiting warming to 1.5degC, \r\n1.7degC, and 2.0degC are 140 PgC (500 GtCO2), 230 PgC (850 GtCO2) and \r\n370  PgC (1350  GtCO2), respectively, based on the 50th percentile \r\nof TCRE. For the 67th percentile, the respective values are 110 PgC \r\n(400 GtCO2), 190 PgC (700 GtCO2) and 310 PgC (1150 GtCO2). These \r\nremaining carbon budgets may vary by an estimated +-  60  PgC \r\n(220 GtCO2) depending on how successfully future non-CO2 emissions \r\ncan be reduced. Since AR5 and the Special Report on Global Warming \r\nof 1.5degC (SR1.5), estimates have undergone methodological "


id: d01071ace7109c3bee5a2da865dc68d1
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 30267
content:
"Wide-ranging side effects of CDR methods have been \r\nidentified that can either weaken or strengthen the carbon \r\nsequestration and cooling potential of these methods and \r\naffect the achievement of sustainable development goals \r\n(high confidence). Biophysical and biogeochemical side effects \r\nof CDR methods are associated with changes in surface albedo, "


id: b79f66b1db47af1f25ac9aa0b36f8d35
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 26414
content:
"Solar radiation modification (SRM) would increase the global \r\nland and ocean CO2 sinks (medium confidence) but would not \r\nstop CO2 from increasing in the atmosphere, thus exacerbating \r\nocean acidification under continued anthropogenic emissions \r\n(high confidence). SRM acts to cool the planet relative to \r\nunmitigated climate change, which would increase the land sink by \r\nreducing plant and soil respiration and slow the reduction of ocean \r\ncarbon uptake due to warming (medium confidence). SRM would not \r\ncounteract or stop ocean acidification (high confidence). The sudden \r\nand sustained termination of SRM would rapidly increase global \r\nwarming, with the return of positive and negative effects on the \r\ncarbon sinks (very high confidence). {4.6.3; 5.6.3}"


id: 248c6f83dbdc7c219e6cef933238508e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 3195
content:
"2020). This emphasizes the central role of terrestrial and ocean CO2\r\nsinks in regulating its atmospheric concentration (Ballantyne et al., \r\n2012; W. Li et al., 2016; Le Quere et al., 2018a; Ciais et al., 2019; \r\nGruber et al., 2019b; Friedlingstein et al., 2020)."


id: f194f60b3ea4a3fc1044778000bc3ff
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 35514
content:
"The physical and biogeochemical controls of greenhouse gases \r\n(GHGs) is a  central motivation for this chapter, which identifies \r\nbiogeochemical feedbacks that have led or could lead to a  future \r\nacceleration, slowdown or abrupt transitions in the rate of GHG \r\naccumulation in the atmosphere, and therefore of climate change. \r\nA  characterization of the trends and feedbacks lead to improved \r\nquantification for the remaining carbon budgets for climate \r\nstabilization, and the responses of the carbon cycle to atmospheric \r\ncarbon dioxide removal (CDR), which is embedded in many of the \r\nmitigation scenarios, to achieve the goals of the Paris Agreement."


id: a3427013a2f0a46dc5edd4b845f8e74c
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 23252
content:
"The chapter covers three dominant GHGs in the human perturbation \r\nof the Earth's radiation budget for which high-quality records exist: \r\nCO2, CH4 and N2O (Figure 5.1)."


id: dd9099d250da69fae8adf1cbf1cb5f3e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 32361
content:
"Section 5.1 (this section) provides the time context on how unique \r\ncurrent and future scenarios of GHGs atmospheric concentrations \r\nand growth rates are in the Earth's history. It also introduces the \r\nmain processes involved in carbon-climate feedbacks, followed by \r\nan assessment of what can be learned from the paleo record towards \r\na  better understanding of contemporary and future GHGs-climate \r\ndynamics and their response to different mitigation trajectories."


id: b6f6e212af3e14220172fcd8805ada3b
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 26317
content:
"Section  5.2 covers the state of the carbon cycle and other \r\nbiogeochemical cycles, and global budgets of CO2, CH4 and N2O for the \r\nindustrial era (since 1750). The section emphasizes the last 60-year \r\nperiod for which high-resolution observations are available and the \r\nmost recent decade for comprehensive GHG budgets. Significant \r\nadvances have taken place since the IPCC Fifth Assessment Report \r\n(AR5), particularly in constraining the annual-to-decadal variability \r\nof the ocean and land carbon sources and sinks, and in revealing the \r\nsensitivity of carbon pools to current and future climate changes. There \r\nhas been an important increase in modelling capability of the three \r\nGHGs, for land and oceans, atmospheric and ocean observations, and "


id: 207f26fe1d267d80670a2b1502c3d8a3
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 2561
content:
"remote sensing products that have enabled researchers to constrain \r\nthe causes of the observed trends and variability."


id: 3626ed276eec8157feb504462dadc026
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 5921
content:
"Section 5.3 builds on the Special Report on the Ocean and Cryosphere \r\n(SROCC) covering the change in ocean acidification due to oceanic \r\nCO2 uptake across the paleo, historical periods and future projections \r\nusing Coupled Model Intercomparison Project Phase 6  (CMIP6), \r\nwith consequences for marine life (assessed in the Sixth Assessment \r\nReport Working Group II, AR6 WGII) and biogeochemical cycles. The \r\nsection also assesses changes in deoxygenation of the oceans due to \r\nwarming, increased stratification of the surface ocean, and slowing \r\nof the meridional overturning circulation."


id: c5a441e00a091426ea816a0bee333b57
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 28674
content:
"Section  5.4 covers the future projections of biogeochemical cycles \r\nand their feedbacks to the climate system fully utilizing the database \r\nof the concentration-driven CMIP6. Since AR5, Earth system models \r\n(ESMs) have made progress towards including more complex carbon \r\ncycle and associated biogeochemical processes that enable exploring \r\na  range of possible future carbon-climate feedbacks and their \r\ninfluences on the climate system. The section addresses uncertainties \r\nand limits of our models to predict future dynamics for GHG emissions \r\ntrajectories, as well as new understanding on processes involved in \r\ncarbon-climate feedbacks and the possibility for rapid and abrupt \r\nchanges brought by non-linear dynamics."


id: e193c2ca65eeabaaad5f6e674988e021
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 32981
content:
"Section  5.5 covers the development of the total and remaining \r\ncarbon budgets to climate stabilization targets and the associated \r\ntransient climate response to cumulative CO2 emissions. The section \r\nshows the progress made since AR5 (IPCC, 2013a) and the Special \r\nReport on Global Warming of 1.5degC (IPCC, 2018), particularly on \r\nkey components required to estimate the remaining carbon budget, \r\nincluding the transient response to cumulative emissions of CO2, the \r\nzero emissions commitment, the projected non-CO2 warming, and \r\nthe unrepresented Earth system feedbacks."


id: 5262d5ef87dfcff144d55bf42a7c1f07
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 10417
content:
"Section  5.6 assesses the impacts of CDR and solar radiation \r\nmodification for the purpose of climate change mitigation on the \r\nglobal carbon cycle, building from the assessment in the IPCC Special \r\nReport on Climate Change and Land (SRCCL). It includes an overview \r\nof the major CDR options and potential collateral biogeochemical \r\neffects beyond the intended climate change mitigation strategies. \r\nThe potential capacity to deliver atmospheric reductions and the \r\nsocio-economic feasibility of such options are assessed in detail in \r\nAR6 working group III (WGIII)."


id: e06b151f7a91e9c3fd7a8d762d81315a
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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vector_id: 32807
content:
"Finally, Section 5.7 highlights the knowledge gaps as limits to the \r\nassessment. The assessment would have been strengthened had \r\nthose gaps not existed."


id: 3cfba93b09aa7cca50b0a8f12aae9ced
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 6955
content:
"Positive and negative climate and carbon feedbacks involve: (i) fast \r\nprocesses on land and oceans at time scales from minutes to years, \r\nsuch as photosynthesis, soil respiration, net primary production, \r\nshallow ocean physics and air-sea fluxes; and (ii) slower processes \r\ntaking from decades to millennia, such as changing ocean buffering \r\ncapacity, ocean ventilation, vegetation dynamics, permafrost \r\nchanges, peat formation and decomposition (Figure 5.2; Ciais et al., \r\n2013; Forzieri et al., 2017; Williams et al., 2019). Depending on the \r\nparticular combination of driver process and response dynamics, \r\nthey behave as positive or negative feedbacks that amplify or "


id: 6344606368ab0163197aa78fe5a75bc6
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 13184
content:
"dampen the magnitude and rates of climate change, respectively \r\n(Cox et al., 2000; Friedlingstein et al., 2003, 2006; Hauck and Volker, \r\n2015; Williams et al., 2019); red and turquoise arrows in Figure 5.2 \r\nand Table 5.1)."


id: 5d273af4aa097bea26358a239e4ffd91
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 12171
content:
"global warming, which is used as the basis to estimate the remaining \r\ncarbon budget (Section  5.5; MacDougall and Friedlingstein, \r\n2015; MacDougall, 2017; Bronselaer and Zanna, 2020; Jones and \r\nFriedlingstein, 2020). There is still low confidence on the relative \r\nroles and importance of the ocean and terrestrial carbon processes \r\non TCRE variability and uncertainty on centennial time scales \r\n(MacDougall, 2016; MacDougall et al., 2017; N.L.  Williams et al., \r\n2017; Katavouta et al., 2018, 2019; Jones and Friedlingstein, 2020) \r\n(Sections 5.5.1.1, 5.5.1.2)."


id: e4cd3fa887dcc25d48f28905a3d03469
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 33477
content:
"Carbon cycle feedbacks co-exist with climate (heat and moisture) \r\nfeedbacks (Cross-Chapter Boxes 5.1 and 5.3), which together drive \r\ncontemporary (Section 5.2) and future (Section 5.4) carbon-climate \r\nfeedbacks (Williams et al., 2019). The excess heat generated by \r\nradiative forcing from increasing concentration of atmospheric \r\nCO2 and other GHGs is mostly taken up by the ocean (>90%) and \r\nthe residual balance partitioned between atmospheric, terrestrial \r\nand ice melting (Cross-Chapter Box 9.2; Frolicher et al., 2015). The \r\ncombined effect of these two large-scale negative feedbacks of CO2\r\nand heat are reflected in the TCRE (Section 5.5 and Cross-Chapter \r\nBox  5.3), which points to a  quasi-linear and quasi-emission-path \r\nindependent relationship between cumulative emissions of CO2 and "


id: e451156f7720e888d1c6b62864b66a17
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 33398
content:
"The combined effects of climate and CO2 concentration feedbacks \r\non the global carbon cycle are projected by ESMs to modify both the \r\nprocesses and natural reservoirs of carbon on a regional and global \r\nscale that may result in positive feedbacks (red arrows in Figure 5.2), \r\nwhich could weaken the major terrestrial and ocean sinks and disrupt \r\nthe airborne fraction and TCRE under medium- to high-emissions \r\nscenarios (Section 5.4.5 and Figure 5.25)."


id: 16b0802332aeeb2223cf09fa75cf967a
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 1088
content:
"Paleoclimatic proxy records extend beyond the variability of recent \r\ndecadal climate oscillations and thus provide an independent \r\nperspective on feedbacks between climate and carbon cycle \r\ndynamics. According to reconstructions, these past changes were \r\nslower than the current anthropogenic ones, so they cannot provide \r\nan unequivocal comparison. Nonetheless, they can help appraise \r\nsensitivities and point towards potentially dominant mechanisms \r\nof change (Tierney et al., 2020) on (sub)centennial to (multi)\r\nmillennial time scales."


id: c9705407e0ab4333deb2a2e525bf77c3
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 29253
content:
"Quantifying past changes in the rate of CO2 accumulation in the \r\natmosphere based on reconstructions using marine sediment \r\nproxies is complex as age model uncertainties, assumptions and \r\nshortcomings underlying proxy applications and sedimentary \r\nprocesses conspire to alter and confound rate estimates (Ajayi et al., \r\n2020). Differential sediment mixing and bioturbation contribute \r\nto smooth and attenuate proxy records (Hupp and Kelly, 2020), \r\nthereby tending to underestimate maximum rates of change (Kemp \r\net al., 2015). Considering the extent to which uncertainties can affect \r\nsediment-based rate estimates, and notwithstanding recent effort \r\nin minimizing their inherent contribution, there is generally low to \r\nmedium confidence in quantifying rates of change on a time scale less \r\nthan a decade back thousands of years, and less than a millennium \r\nback millions of years in the past based on marine sediments."


id: 4e40c4086ed44de544da924a955c867d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 9737
content:
"The AR5 (WGI, Chapter 5) concluded with medium confidence that \r\natmospheric CO2 concentrations reached 350-450  ppm during \r\nthe mid-Pliocene (3.3-3.0  million years ago (Ma)), and possibly \r\n1000  ppm during the Early Eocene (52-48  Ma). The AR5 (WGI, \r\nChapter 5) also concluded with very high confidence that the current \r\nrates of increases in CO2, CH4 and N2O atmospheric concentrations \r\nwere unprecedented with respect to the ice core record covering the \r\nlast deglacial transition (LDT, 18-11 ka) and with medium confidence\r\nthat the rate of change of the reconstructed GHG rise was also \r\nunprecedented compared to the lower resolution of the records of \r\nthe past 800 kyr."


id: dd24db9b83ab2a23f6c7ca130d0b695e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 32289
content:
"In the past, atmospheric CO2 concentrations reached much higher \r\nlevels than present day (Cross-Chapter Box  2.1 and Figure  5.3). \r\nIn  particular, the Paleocene-Eocene thermal maximum (PETM), \r\n55.9-55.7 Ma (Figure 5.3), provides some level of comparison with \r\nthe current and projected anthropogenic increase in CO2 emissions \r\n(Chapter 2). Atmospheric CO2 concentrations increased from about \r\n900 to around 2000 ppm in 3-20 kyr as a result of geological carbon "


id: 3be46570218429ab03c796fba1efee81
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 6786
content:
"ice cores represent the only climatic archive from which past GHG \r\nconcentrations can be directly measured. Major GHGs, CH4, N2O and \r\nCO2 generally co-vary on orbital time scales (Loulergue et al., 2008; \r\nLuthi et al., 2008; Schilt et al., 2010b; Chapter 2), with consistently \r\nhigher atmospheric concentrations during warm intervals of the past, \r\npointing to a  strong sensitivity to climate (Figure  5.4). Modelling \r\nwork suggests that the carbon cycle contributed to globalise \r\nand amplify changes in orbital forcing, which are pacing glacial-\r\ninterglacial climate oscillations (Ganopolski and Brovkin, 2017), \r\nwith ocean biogeochemistry and physics, terrestrial vegetation, \r\npeatland, permafrost and exchanges with the lithosphere including \r\nchemical weathering, volcanic activity, sediment burial and marine \r\ncalcium carbonate compensation all playing a role in modulating the \r\nconcentration of atmospheric GHGs."


id: 9f11e84b407cea7621967fae43a8e471
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 22567
content:
"Since AR5, the number of ice core records and the temporal resolution \r\nof their data for the last 800 kyr have improved, in particular for the \r\nlast 60  kyr. Additionally, the advent of isotopic measurements on \r\nGHGs extracted from air trapped in ice, allows for more robust source \r\napportionments and inventory assessments. Therefore, the ensuing \r\ndiscussion focuses on these two specific aspects."


id: b8745f7ad3d006cdf79b0476f37d1d6d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 26549
content:
"The last 50 Myr (50  million years) have been characterized by \r\na  gradual decline in atmospheric CO2 levels at a  rate of about \r\n16 ppm Myr-1 (Figure 5.3; Foster et al., 2017; Gutjahr et al., 2017). \r\nThe exact cause of this long-term change in CO2 remains uncertain, \r\nbut may be related to an imbalance between long-term sources of \r\nCO2 (volcanic outgassing) and long-term sinks (organic carbon burial \r\nand silicate weathering)."


id: c1f71a1ad4b637152bbc3c4afd269266
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 28095
content:
"Major pre-industrial sources of CH4 comprise wetlands (including \r\nsubglacial environments) and biomass burning (Bock et al., 2010, \r\n2017; Lamarche-Gagnon et al., 2019; Kleinen et al., 2020). Pre\u0002industrial atmospheric N2O concentrations were regulated by \r\nmicrobial production in marine and terrestrial environments and \r\nby photochemical removal in the stratosphere (Schilt et al., 2014; \r\nBattaglia and Joos, 2018b; H.  Fischer et al., 2019). Pre-industrial \r\natmospheric CO2 concentrations were largely regulated by exchange \r\nwith exogenic terrestrial and ocean carbon reservoirs. The imbalance \r\nbetween geological sources and sinks in the ocean-atmosphere-land \r\nbiosphere system additionally plays an important role in modulating \r\nthe air-sea partitioning of the active carbon inventory on multi\u0002millennial time scales (Cartapanis et al., 2018)."


id: 23e0cc47e99cb619c5aaffc4d603b6a8
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 3087
content:
"The most recent time interval when atmospheric CO2 concentration \r\nwas as high as 1000  ppm (i.e.,  similar to the end of 21st century \r\nprojection for the high-end emissions scenario RCP8.5) was around \r\n33.5 Ma, prior to the Eocene-Oligocene transition (Y.G. Zhang et al., \r\n2013; Anagnostou et al., 2016). Atmospheric CO2 levels then reached \r\na critical threshold (1000-750 ppm; DeConto et al., 2008) to allow \r\nfor the development of permanent regional ice sheets on Antarctica, \r\nassociated with changes in Southern Ocean hydrography, which \r\nwould have increased deep ocean CO2 storage (Leutert et al., 2020)."


id: b5fb6cc0c7b7867c2650a9e8976e325e
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
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index: climate_gpt_only_giec
vector_id: 26161
content:
"Model-based estimates indicate that wetland CH4 emissions were \r\nreduced by 24-40% during the Last Glacial Maximum (LGM) when \r\ncompared to pre-industrial, while CH4 emissions related to biomass \r\nburning (wildfires) decreased by 35-75% (Valdes et al., 2005; Hopcroft \r\net al., 2017; Kleinen et al., 2020). N2O emissions decreased by about \r\n30% during the LGM based on data-constrained model estimates \r\n(Schilt et al., 2014; H.  Fischer et al., 2019) owing to a  combination \r\nof a  weaker hydrological cycle and a  generally better ventilated \r\nintermediate depth ocean relative to present, reducing (de)nitrification \r\nprocesses (Galbraith et al., 2013; H. Fischer et al., 2019)."


id: 98da6f433788c830ff50194d8056384d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 21566
content:
"The most recent interval characterized by atmospheric CO2 levels \r\nsimilar to modern (i.e.,  360-420  ppm) was the mid-Pliocene Warm \r\nPeriod (MPWP, 3.3-3.0 Ma; Martinez-Boti et al., 2015; de la Vega et al., \r\n2020) (Chapter 2). The relatively high atmospheric CO2 concentration \r\nduring the MPWP are related to vigorous ocean circulation and \r\na rather inefficient marine biological carbon pump (Burls et al., 2017), \r\nwhich would have reduced deep ocean carbon storage. After the \r\nMPWP, atmospheric CO2 concentrations declined gradually at a rate \r\nof 30 ppm Myr-1 (Figure 5.3; de la Vega et al., 2020), as an increase in \r\nocean stratification led to enhanced ocean carbon storage, allowing for \r\nmajor, sustained advances in Northern Hemisphere ice sheets, 2.7 Ma \r\n(Sigman et al., 2004; DeConto et al., 2008)."


id: 46f455f42753ba51b8c8de63da3a5c6d
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 8548
content:
"During past ice ages, generally colder and drier climate conditions \r\ncontributed to a  substantial decline of the land biosphere carbon \r\ninventory, in particular in boreal peatlands (-300  PgC; Treat et al., \r\n2019). Estimates assessing the glacial decrease in the global terrestrial \r\nbiosphere carbon stock vary between -300 and -600 PgC (Ciais et al., \r\n2012; Peterson et al., 2014; Menviel et al., 2017; Kleinen et al., 2020), \r\npossibly -850 PgC when accounting for ocean-sediment interactions \r\nand burial (Jeltsch-Thommes et al., 2019), a considerable contraction \r\nwhen compared to the modern land biosphere stock. The large range \r\nof estimates reflects a  yet limited understanding of how carbon "


id: f081cc4988ebe7e0c5ff6b8586c2d640
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 35339
content:
"The Antarctic ice core record covering the past 800  kyr provides \r\nan important archive to explore the carbon-climate feedbacks \r\nprior to anthropogenic perturbations (Brovkin et al., 2016). Polar "


id: 1e13b426a8684227a28c523b287800e9
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 2167
content:
"and N2O emissions were reduced under glacial climate conditions. \r\nMultiple lines of evidence indicate with high confidence that \r\nenhanced storage of remineralized CO2 in the ocean interior, owing \r\nto a combination of synergistic mechanisms, was sufficient to balance \r\nthe removal of carbon from the atmosphere and the terrestrial \r\nbiosphere reservoirs combined during the last ice age."


id: 91570f179b32ff6967021c53fe7c4006
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 20395
content:
"Vegetation regrowth and increased precipitation in wetland \r\nregions associated with the mid-deglacial Northern Hemisphere \r\nwarming (referred to as the Bolling/Allerod (B/A) warm interval, \r\n14.7-12.7 ka), in particular in the (sub)tropics, accounts for large \r\nincreases in both CH4 and N2O emissions to the atmosphere \r\n(Baumgartner et al., 2014; Schilt et al., 2014; Bock et al., 2017; \r\nH. Fischer et al., 2019). Specifically, changes in CH4 sources were \r\nsteered by variations in vegetation productivity, source size area, \r\ntemperatures and precipitation as modulated by insolation, local \r\nsea level changes and monsoon intensity (Bock et al., 2017; \r\nKleinen et al., 2020). Changes in the CH4 atmospheric sink term \r\nprobably only played a secondary role in modulating atmospheric \r\nCH4 inventories across the LDT (Hopcroft et al., 2017; Kleinen et al., "


id: fab6809c09531fc1d87b802440ebdfae
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 36968
content:
"2020) Geological emissions, related to the destabilization of fossil \r\n(radiocarbon-dead) CH4 sources buried in continental margins \r\nas a  result of sudden warming, appear small (Bock et al., 2017; \r\nPetrenko et al., 2017; Dyonisius et al., 2020). Stable isotope analysis \r\non N2O extracted from Antarctic and Greenland ice reveal that \r\nmarine and terrestrial emissions increased by 0.7 +-  0.3 and 1.7 \r\n+-  0.3 TgN, respectively, across the LDT (H.  Fischer et al., 2019). \r\nDuring abrupt Northern Hemisphere warmings, terrestrial emissions \r\nresponded rapidly to the northward displacement of the Intertropical \r\nConvergence Zone (ITCZ) associated with the resumption of the \r\nAtlantic meridional overturning circulation (AMOC; H. Fischer et al., \r\n2019). About 90% of these step increases occurred rapidly, possibly \r\nin less than 200 years (H. Fischer et al., 2019). In contrast, marine \r\nemissions increased more gradually, modulated by global ocean \r\ncirculation reorganization."


id: 908931a1f1464bea38bd73927b42f460
created_at: 2023-03-16 14:00:41
updated_at: 2023-03-16 14:00:41
content_type: text
index: climate_gpt_only_giec
vector_id: 20278
content:
"Atmospheric CO2 led reconstructed global average temperature by \r\nseveral centuries (Shakun et al., 2012), corroborating the importance \r\nof CO2 as an amplifier of orbitally driven warming. During the LDT, \r\nthe phasing between Antarctic air temperature and atmospheric \r\nGHG concentration changes was nearly synchronous, yet variable, \r\nowing to the complex nature of the mechanisms modulating the \r\nglobal carbon cycle (Chowdhry Beeman et al., 2019). Mean ocean \r\ntemperature reconstructions, based on noble gas extracted from \r\nAntarctic ice are closely correlated with Antarctic air temperature \r\nand pCO2 records, emphasizing the role the Southern Ocean is \r\nplaying in modulating global climate variability (Bereiter et al., \r\n2018; Baggenstos et al., 2019)."


