# document

id: 5e4341495a709ce5b15ca048f599ea92
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13871
content:
"to a half of this difference was due to an overestimate of the SST trend \r\nduring this period (Flato et al., 2013). Since AR5, additional studies \r\nbased on CMIP5 and CMIP6 models show that this warming bias in \r\ntropospheric temperatures remains. Recent studies have investigated \r\nthe role of observational uncertainty, the model response to external \r\nforcings, the influence of the time period considered, and the role \r\nof biases in SST trends in contributing to this bias."


id: e954e995bf6f1d1190bd7ba7bf88a53e
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38408
content:
"Chapter 2 assessed that the troposphere has warmed since at least \r\nthe 1950s, that it is virtually certain that the stratosphere has cooled, \r\nand that there is medium confidence that the upper troposphere \r\nin the tropics has warmed faster than the near-surface since at \r\nleast 2001 (Section 2.3.1.2). The AR5 assessed that anthropogenic \r\nforcings, dominated by greenhouse gases, likely contributed to the \r\nwarming of the troposphere since 1961 and that anthropogenic \r\nforcings, dominated by the depletion of the ozone layer due to \r\nozone-depleting substances, very likely contributed to the cooling \r\nof the lower stratosphere since 1979. Since AR5, understanding \r\nof observational uncertainties in the radiosonde and satellite data \r\nhas improved with more available data and longer coverage, and \r\ndifferences between models and observations in the tropical \r\natmosphere have been investigated further."


id: 70664fc89168e517a248d03c1ae4fc9f
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17035
content:
"Mitchell et al. (2013) and Mitchell et al. (2020) found a smaller \r\ndiscrepancy in tropical tropospheric temperature trends in models \r\nforced with observed SSTs (see also Figure  3.10a), and CMIP5 \r\nmodels and observations were found to be consistent below 150 hPa \r\nwhen viewed in terms of the ratio of temperature trends aloft to \r\nthose at the surface (Mitchell et al., 2013). Flannaghan et al. (2014) \r\nand Tuel (2019) showed that most of the tropospheric temperature \r\ntrend difference between CMIP5 models and the satellite-based \r\nobservations over the 1970-2018 period is due to respective \r\ndifferences in SST warming trends in regions of deep convection, \r\nand Po-Chedley et al. (2021) showed that CMIP6 models with a more "


id: 1dd427d46529b6906c270d9eb79d86
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2435
content:
"The AR5 assessed with low confidence that most, though not \r\nall, CMIP3 (Meehl et al., 2007) and CMIP5 (Taylor et al., 2012) \r\nmodels overestimated the observed warming trend in the tropical \r\ntroposphere during the satellite period 1979-2012, and that a third "


id: d51840638d3d5a2d185b684ee5cae8aa
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34813
content:
"realistic SST simulation in the central and eastern Pacific show \r\na better performance than other models. Though systematic biases \r\nstill remain, this indicates that the bias in tropospheric temperature \r\nwarming in models is in part linked to surface temperature warming \r\nbiases, especially in the lower troposphere."


id: ae51c9814f9d23177c178e903947ecf2
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 27900
content:
"(1979-1997) and underestimate it over the ozone stabilization era \r\n(1998-2014; Figure 3.10b,c). They speculate that those disagreements \r\nare due to poor representations of stratospheric ozone forcing."


id: 62bf1e839089c64333b395f4ead1c1a7
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14667
content:
"models driven by anthropogenic forcing show consistent amplitude \r\nincreases at mid-latitudes in both hemispheres, amplitude decreases \r\nat high latitudes in the Southern Hemisphere, and small changes in \r\nthe tropics."


id: 14a61931827a0b1964f6d7163dd68c2b
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 857
content:
"In summary, these studies confirm the dominant role of human activities \r\nin tropospheric temperature trends. We therefore assess that it is very \r\nlikely that anthropogenic forcing, dominated by greenhouse gases, \r\nwas the main driver of the warming of the troposphere since 1979."


id: 513ce4a4fb6ca86ba73f7f7deee67482
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11573
content:
"The AR5 concluded that the CMIP5 models simulated a generally \r\nrealistic evolution of lower-stratospheric temperatures (Bindoff et al., \r\n2013; Flato et al., 2013), which was better than that of the CMIP3 \r\nmodels, in part because they generally include time-varying ozone \r\nconcentrations, unlike many of the CMIP3 models. Nonetheless, it was \r\nnoted that there was a tendency for the simulations to underestimate \r\nstratospheric cooling compared to observations. Bindoff et al. \r\n(2013) concluded that it was very likely that anthropogenic forcing, \r\ndominated by stratospheric ozone depletion by chemical reactions \r\ninvolving trace species known as ozone-depleting substances (ODS), \r\nhad contributed to the cooling of the lower stratosphere since 1979. \r\nIncreased greenhouse gases cause near-surface warming but cooling \r\nof stratospheric temperatures."


id: 6d51693c862f1e12c58e987d6876b94
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 16511
content:
"Contributors: Christophe Cassou (France), Yu Kosaka (Japan), John C. Fyfe (Canada), Nathan P. Gillett (Canada), Ed Hawkins \r\n(United Kingdom), Blair Trewin (Australia)"


id: 7774dc20cfe2a757ed733c5a0a706be0
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18220
content:
"(Flato et al., 2013). This apparent slowdown of surface global warming compared to the 62-year rate was assessed with medium \r\nconfidence to have been caused in roughly equal measure by a cooling contribution from internal variability and a reduced trend in \r\nexternal forcing (particularly associated with solar and volcanic forcing) in AR5 based on expert judgement (Flato et al., 2013). In AR5 \r\nit was assessed that almost all CMIP5 simulations did not reproduce the observed slower warming, and that there was medium \r\nconfidence that the trend difference from the CMIP5 ensemble mean was to a substantial degree caused by internal variability \r\nwith possible contributions from forcing error and model response uncertainty. This Cross-Chapter Box assesses new findings from \r\nobservational products and statistical and physical models on trends over the 1998-2012 period considered in AR5."


id: 7106ddec21c48bf1ea1e27f3da1bf13
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17147
content:
"With these updates, all the observed trends assessed here lie within the 10th-90th percentile range of the simulated trends in the \r\nCMIP5 and CMIP6 simulations (Cross-Chapter Box 3.1, Figure 1a). This result is insensitive to whether model GSAT (based on surface \r\nair temperature) or GMST (based on a blend of surface air temperature over land and sea ice and SST over open ocean) is used, and to \r\nwhether or not masking with the observational data coverage is applied. Therefore, the observed 1998-2012 trend is consistent with \r\nboth the CMIP5 or CMIP6 multi-model ensemble of trends over the same period (high confidence)."


id: 7de99db0054c73644fef712da91a2d19
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19332
content:
"All the observation-based GMST and GSAT trends are lower than the multi-model mean GMST and GSAT trends of both CMIP5 and \r\nCMIP6 for 1998-2012 (Cross-Chapter Box 3.1, Figure 1a). This suggests a possible cooling contribution from internal variability during \r\nthis period. This is supported by initialized decadal hindcasts, which account for the phase of the multi-decadal modes of variability \r\n(Sections 3.7.6 and 3.7.7), and which reproduce observed global mean SST and GSAT trends better than uninitialized historical \r\nsimulations (Guemas et al., 2013; Meehl et al., 2014)."


id: 16930a2e22c974e072e3cc8d751a65d6
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 1186
content:
"Such internally-driven variation of decadal GSAT trends is not unique to the 1998-2012 period (Section 1.4.2.1; Lovejoy, 2014; Roberts \r\net al., 2015; Dai and Bloecker, 2019). Due to the nature of internal variability, surface temperature changes over the 1998-2012 period "


id: fd5fa1108d6ba7a933d4e77ab6e706e9
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 41932
content:
"are regionally- and seasonally-varying (Cross-Chapter Box 3.1, Figure 1c; Trenberth et al., 2014; Zang et al., 2019). Further, there was no \r\nslowdown in the increasing occurrence of hot extremes over land (Kamae et al., 2014; Seneviratne et al., 2014; Imada et al., 2017). Thus, \r\nthe internally-driven slowdown of GSAT increase does not correspond to slowdown of warming everywhere on the Earth's surface."


id: e0461b28ca84674ab501809e5c5e12ac
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36707
content:
"With updated observation-based GMST datasets and forcing, improved analysis methods, new modelling evidence and deeper \r\nunderstanding of mechanisms, there is very high confidence that the slower GMST and GSAT increase inferred from observations in \r\nthe 1998-2012 period was a temporary event induced by internal and naturally-forced variability that partly offset the anthropogenic \r\nwarming trend over this period. Nonetheless, the heating of the climate system continued during this period, as reflected in the \r\ncontinued warming of the global ocean (very high confidence) and in the continued rise of hot extremes over land (medium confidence). \r\nConsidering all the sources of uncertainties, it is impossible to robustly identify a single cause of the early 2000s slowdown (Hedemann \r\net al., 2017; Power et al., 2017); rather, it should be interpreted as due to a combination of several factors (Huber and Knutti, 2014; \r\nSchmidt et al., 2014; Medhaug et al., 2017)."


id: b72a143a294367f31e603a6bc8fefd05
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29437
content:
"A major El Nino event in 2014-2016 led to three consecutive years of record annual GMST with unusually strong heat release from \r\nthe North-western Pacific Ocean (Yin et al., 2018), which marked the end of the slower warming period (Hu and Fedorov, 2017; J. Su \r\net al., 2017; Cha et al., 2018). The past five-year period (2016-2020) is the hottest five-year period in the instrumental record up to \r\n2020 (high confidence). This rapid warming was accompanied by a PDV shift toward its positive phase (J. Su et al., 2017; Cha et al., \r\n2018). A higher rate of warming following the 1998-2012 period is consistent with the predictions in AR5 Box 9.2 (Flato et al., 2013) \r\nand with a statistical prediction system (Sevellec and Drijfhout, 2018). Initialized decadal predictions show higher GMST trends in the \r\nearly 2020s compared to uninitialized simulations (Thoma et al., 2015; Meehl et al., 2016a)."


id: a9f84f3912c880b28dcbfe1709051f76
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 27125
content:
"While some recent studies find that internal decadal GSAT variability may become weaker under GSAT warming, associated in part \r\nwith reduced amplitude PDV (Section 4.5.3.5; Brown et al., 2017), the weakening is small under a realistic range of warming. A \r\nlarge volcanic eruption would temporarily cool GSAT (Cross-Chapter Box 4.1). Thus, there is very high confidence that reduced and \r\nincreased GMST and GSAT trends at decadal time scales will continue to occur in the 21st century (Meehl et al., 2013; Roberts et al., \r\n2015; Medhaug and Drange, 2016). However, such internal or volcanically forced decadal variations in GSAT trend have little effect on \r\ncentennial warming (England et al., 2015; Cross-Chapter Box 4.1)."


id: 57cb52729f6bdfd5f530a5abb76cb253
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 12706
content:
"A fact hindering detection and attribution studies in precipitation \r\nand other hydrological variables is the large internal variability of \r\nthese fields relative to the anthropogenic signal. This low signal-to\u0002noise ratio hinders the emergence of the anthropogenic signal from \r\nnatural variability. Moreover, the sign of the change depends on \r\nlocation and time of the year. Paleoclimate records provide valuable \r\ncontext for observed trends in the 20th and 21st century and assist \r\nwith the attribution of these trends to human influence (see also \r\nSection 2.3.1.3.1). By nature, hydrological proxy data represent \r\nregional conditions, but taken together can represent large-scale \r\npatterns. As an example of how paleorecords have helped assessing \r\nthe origin of changes, we consider some, mainly subtropical, regions \r\nwhich have experienced systematic drying in recent decades (see also \r\nSection 8.3.1.3). Paleoclimate simulations of monsoons are assessed \r\nin Section 3.3.3.2."


id: bc58e90c8c9da97fb9af55c97072f767
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 30383
content:
"Records of tree ring width have provided evidence that recent \r\nprolonged dry spells in the Levant and Chile are unprecedented in the \r\nlast millennium (high confidence) (Cook et al., 2016a; Garreaud et al., \r\n2017). East Africa has also been drying in recent decades (Rowell et al., \r\n2015; Hoell et al., 2017), a trend that is unusual in the context of the "


id: 5a87d2d8a3d623a993667afc86f7bc67
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13182
content:
"Paleoclimate records also allow for model evaluation under \r\nconditions different from present-day. The AR5 concluded that \r\nmodels can successfully reproduce to first-order patterns of past \r\nprecipitation changes during the Last Glacial Maximum (LGM) and \r\nmid-Holocene, though simulated precipitation changes during the "


id: b3415ae38781d9fcfcb4525bc6e1c2d7
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 28777
content:
"is not incompatible with reconstructions. Over the Sahara/Sahel and \r\nWest Africa regions, where reconstructions suggest positive anomalies \r\nduring the mid-Holocene, both CMIP5 and CMIP6 models also \r\nsimulate a rainfall increase, but it is much weaker (see also Section \r\n3.3.3.2). Overall, however, large discrepancies remain between \r\nsimulations and reconstructions."


id: a4dd9e47a3d91cd8b6f1525b1c60d854
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 26228
content:
"Liu et al. (2018) evaluated the soil moisture changes that occurred \r\nduring the LGM and concluded that the multi-model median from \r\nCMIP5 is consistent with available paleo-records in some regions, \r\nbut not in others. CMIP5 models accurately reproduce an increase \r\nin moisture in the western United States, related to an intensified \r\nwinter storm track and decreased evaporative demand (Oster et al., \r\n2015; Ibarra et al., 2018; Lora, 2018). On the other hand, CMIP5 \r\nmodels show a wide variety of responses in the tropical Indo-Pacific \r\nregion, with only a few matching the pattern of change inferred from \r\nthe paleoclimate record (DiNezio and Tierney, 2013; DiNezio et al., \r\n2018). The variable response across models is related to the effect \r\nof the exposure of the tropical shelves during glacial times, which "


id: 39af76a8696a29f9420f60da964cfa27
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7361
content:
"Figure 3.12 | Total column water vapour trends (% per decade) for the period 1988-2019 averaged over the near-global oceans (50degS-50degN). The figure \r\nshows satellite data (RSS) and ERA5.1 reanalysis, as well as CMIP5 (blue) and CMIP6 (red) historical simulations. All available ensemble members were used (see Section 3.2). \r\nFits to the model trend probability distributions were performed with kernel density estimation. Figure is updated from Santer et al. (2007). Further details on data sources and \r\nprocessing are available in the chapter data table (Table 3.SM.1)."


id: b53f22ca5f6a018d38ae5103c16d966f
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29124
content:
"variously intensifies or weakens convection in the rising branch of \r\nthe Walker cell, depending on model parameterization (DiNezio \r\net al., 2011). For the Last Interglacial, CMIP6 models reproduce \r\nthe proxy-based increased precipitation relative to pre-industrial in the \r\nNorth African, South Asian and North American regions, but not in \r\nAustralia (Scussolini et al., 2019)."


id: 123e80fe2641c79cec0673fb2a0db3a8
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 405
content:
"In summary, there is medium confidence that CMIP5 and CMIP6 \r\nmodels can reproduce broad aspects of precipitation changes during \r\npaleo reference periods, but large discrepancies remain. Further \r\nassessment of model performance and comparison between CMIP5 \r\nand CMIP6 during past climates can be found in Section 3.8.2.1."


id: 2891c1b94d8099fec5c13b88c8e612c8
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4347
content:
"The AR5 concluded that an anthropogenic contribution to increases \r\nin specific humidity is found with medium confidence at and near the \r\nsurface. A levelling off of atmospheric water vapour over land in \r\nthe last two decades that needed better understanding, and remaining \r\nobservational uncertainties, precluded a more confident assessment \r\n(Bindoff et al., 2013). Sections 4.5.1.3 and 8.3.1.4 show that there \r\nhave been significant advances in the understanding of the processes \r\ncontrolling land surface humidity. In particular, there has been a focus \r\non the role of oceanic moisture transport and land-atmosphere \r\nfeedbacks in explaining the observed trends in relative humidity."


id: a9de85c5472e8e2947f4106c95bf4da0
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 27108
content:
"In summary, we assess that it is likely that human influence has \r\ncontributed to moistening in the upper troposphere since 1979. \r\nAlso, there is medium confidence that human influence contributed \r\nto a global increase in annual surface specific humidity, and medium \r\nconfidence that it contributed to a decrease in surface relative humidity \r\nover mid-latitude Northern Hemisphere continents during summertime."


id: 66b98461e3927e9df6ce9579ae43f42c
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 15331
content:
"The detection and attribution of tropospheric water vapour changes \r\ncan be traced back to Santer et al. (2007), who used estimates of \r\natmospheric water vapour from the satellite-based Special Sensor \r\nMicrowave Imager (SSM/I) and from CMIP3 historical climate \r\nsimulations. They provided evidence of human-induced moistening "


id: ab2346842b7b816d3c787f0e24dc5a54
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 27336
content:
"AR5 concluded that there was medium confidence that human \r\ninfluence had contributed to large-scale precipitation changes over \r\nland since 1950, including an increase in the Northern Hemisphere \r\nmid- to high latitudes. Moreover, AR5 concluded that observational \r\nuncertainties and challenges in precipitation modelling precluded \r\na more confident assessment (Bindoff et al., 2013). Overall, it found \r\nthat large-scale features of mean precipitation in CMIP5 models were \r\nin modest agreement with observations, but there were systematic \r\nerrors in the tropics (Flato et al., 2013)."


id: b948a34e9582ab2bbd4a6b31d695fec2
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29804
content:
"The CMIP5 models have also been shown to adequately simulate \r\nthe mean and interannual variability of the global monsoon \r\n(Section 3.3.3.2), but maintain the double ITCZ bias in the equatorial \r\nPacific (Lee and Wang, 2014; Tian, 2015; Ni and Hsu, 2018). Despite \r\nthe ITCZ bias, CMIP5 models have been used to detect in reanalysis \r\na southward shift in the ITCZ prior to 1975, followed by a northward \r\nshift in the ITCZ after 1975, in response to forced changes in \r\ninter-hemispheric temperature contrast (Sections 3.3.1.1 and 8.3.2.1, \r\nand Figure 8.11; Bonfils et al., 2020; Friedman et al., 2020). CMIP5 \r\nmodels perform better than CMIP3 models, in particular regarding the \r\nglobal monsoon domain and intensity (Lee and Wang, 2014)."


id: 5c225eb44e99d7491893566bb72a0410
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13465
content:
"Recent studies comparing observations and CMIP5 simulations have \r\nshown that tropical volcanic eruptions induce a significant reduction \r\nin global precipitation, particularly over the wet tropics, including the \r\nglobal monsoon regions (Iles and Hegerl, 2014; Paik and Min, 2017; \r\nPaik et al., 2020a). Reconstructions and modelling studies also suggest \r\na distinct remote influence of volcanic forcing such that large volcanoes \r\nerupting in one hemisphere can enhance monsoon precipitation in the \r\nother hemisphere (F. Liu et al., 2016; Zuo et al., 2019). The climatic effect \r\nof volcanic eruptions is further assessed in Cross-Chapter Box 4.1."


id: 58bf907edd3957173417a4984f7b3725
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 12856
content:
"In observations at time scales less than a day intermittent rainfall \r\nfluctuations dominate variability, but CMIP5 models systematically \r\nunderestimate them (Covey et al., 2018). Moreover, as noted in \r\nprevious generation models, CMIP5 models produce rainfall too early \r\nin the day (Covey et al., 2016). Also, models overpredict precipitation \r\nfrequency but have weaker intensity, although comparison with \r\nobserved datasets is complex as there are large differences in intensity \r\namong them (Herold et al., 2016; Pendergrass and Deser, 2017; \r\nTrenberth et al., 2017). Regarding trends in precipitation intensity, \r\nmodels have also been shown to reproduce the compensation "


id: 1cae40703f6a89090b490e25395bad84
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2220
content:
"An intensification of the wet-dry zonal mean patterns, consisting of \r\nthe wet tropical and mid-latitude bands becoming wetter, and the dry \r\nsubtopics becoming drier is expected in response to greenhouse gas \r\nand ozone changes (Section 8.2.2.2). However, detecting these changes "


id: 47eca0ff3844cd3ca4ecd77b90b0db17
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9864
content:
"is complicated by model errors in locating the main features of rainfall \r\npatterns. To deal with this issue, Marvel and Bonfils (2013) identified \r\nin each CMIP5 historical simulation the latitudinal peaks and troughs \r\nof the rainfall latitudinal patterns, measured the amplification and shift \r\nof these patterns in a pattern-based fingerprinting study, and found \r\nthat the simultaneous amplification and shift in zonal precipitation \r\npatterns are detectable in Global Precipitation Climatology Project \r\n(GPCP) observations over the 1979-2012 period. Similarly, Bonfils \r\net al. (2020) found that the intensification of wet-dry zonal patterns \r\nidentified in CMIP5 historical simulations is detectable in reanalyses \r\nover the 1950-2014 period (see also Figure 8.11)."


id: 765ba0a51292e3afc3012a07d6034440
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18046
content:
"wet-gets-wetter, dry-gets-drier paradigm over tropical land areas as \r\nwell. Other studies suggest that this paradigm does not necessarily hold \r\nover dry regions where moisture is limited (see also Section 8.2.2.1; \r\nGreve et al., 2014; Kumar et al., 2015). Polson and Hegerl (2017) \r\nexplained this discrepancy by taking into account the seasonal and \r\ninterannual movement of the regions (Allan, 2014). A follow-up study \r\nusing CMIP6 models also found that the observed strengthening \r\ncontrast of precipitation over wet and dry regions was detectable, \r\nalthough the increase was significantly larger in observations than in \r\nthe multi-model mean. The change was attributed to a combination \r\nof anthropogenic and natural forcings, with anthropogenic forcings \r\ndetectable in multi-signal analyses (Figure 3.14; Schurer et al., 2020)."


id: a6cfd068d93cb11a449123d2f39e26af
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 26561
content:
"Based on long-term island precipitation records, Polson et al. (2016) \r\nidentified significant increases in precipitation in the tropics and \r\ndecreases in the subtropics, which are consistent with those simulated \r\nby the CMIP5 models. Moreover, results from Polson and Hegerl (2017) \r\ngive support to an intensification of the water cycle according to the "


id: aa5b23b5c74e2f36e4441f6562d02cd
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 27200
content:
"Global land precipitation has likely increased since the middle of the \r\n20th century (medium confidence), while there is low confidence\r\nin trends in land data prior to 1950 and over the ocean during the \r\nsatellite era due to disagreement between datasets (Section 2.3.1.3.4). "


id: 64eead38d41c9b1bc96aaef64d17491a
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 15032
content:
"Figure  3.15a shows the time evolution of the global mean land \r\nprecipitation since 1950, as well as the trend during the period. Adler \r\net al. (2017) found no significant trend in the global mean precipitation \r\nduring the satellite era, consistent with model simulations (Wu \r\net al., 2013) and physical understanding of the energy budget (Section \r\n8.2.1). This has been suggested to be due to the negative effect of \r\nanthropogenic sulphate aerosol that opposed the positive influence of \r\nrising global mean temperatures due to greenhouse gases (Salzmann, \r\n2016; Richardson et al., 2018). The precipitation change expected from \r\nocean warming is also partly offset by the fast atmospheric adjustment \r\nto increasing greenhouse gases (Section 8.2.1). Over the ocean, the \r\nnegligible trend may be due to the cancelling effects of CO2 and \r\naerosols (Richardson et al., 2018)."


id: 79fc02a2950d7d0d35f2c152ddcaa8a3
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18660
content:
"observed and simulated trends found that detectable anthropogenic \r\nincreasing trends have occurred prominently over many mid- to \r\nhigh-latitude regions of the Northern Hemisphere and subtropics \r\nof the Southern Hemisphere. The observed trends in many cases are \r\nsignificantly stronger than modelled in the CMIP5 historical runs for \r\nthe 1901-2010 period (though not for 1951-2010), which may be \r\ndue to disagreement between observed datasets (Section 2.3.1.3.4), \r\nand/or suggest possible deficiencies in models."


id: 70c33122ccaa0656964f3a039566c32a
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17099
content:
"The observed precipitation increase in the Northern Hemisphere high \r\nlatitudes over the period 1966-2005 was attributed to anthropogenic \r\nforcing by a study using CMIP5 models (Wan et al., 2015) supporting \r\nthe AR5 assessment. Initial results from CMIP6 also support the role \r\nof anthropogenic forcing in the precipitation increase observed in \r\nNorthern Hemisphere high latitudes (see Figure 3.15c): the observed \r\npositive trend detected for the band 60degN-90degN can only be "


id: d790a4f1c37fafcef0a42aee236fad32
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35229
content:
"reproduced when anthropogenic forcing is included, although models \r\ntend to simulate overall a larger positive trend. A similar positive trend, \r\nbut less significant, is also detected between 30degN-60degN, while in the \r\nsouthern mid-latitudes no trend is simulated (see Figure 3.15d, f)."


id: c697ffdb18b4b52a89b386e337d71154
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 32243
content:
"the timing of peak streamflow, and the qualitative evaluation of \r\nstudies reporting on observed and simulated trends, AR5 concluded \r\nthat there is medium confidence that anthropogenic influence on \r\nclimate has affected streamflow in some middle and high latitude \r\nregions (Bindoff et al., 2013). The AR5 also noted that observational \r\nuncertainties are large and that often only a limited number of \r\nmodels were considered."


id: 4ad856696a6ee42b855a5d96b5963f0e
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 10409
content:
"Over the ocean, observations show coherent large-scale patterns of \r\nfresh ocean regions becoming fresher and salty ocean regions saltier \r\nacross the globe, which has been related through modelling studies \r\nto changes in precipitation minus evaporation and is consistent with \r\nthe wet-gets-wetter, dry-gets-drier paradigm (see Sections 3.5.2.2 \r\nand 8.2.2.1; Durack et al., 2012, 2013; Skliris et al., 2014; Durack, \r\n2015; Hegerl et al., 2015; Levang and Schmitt, 2015; Zika et al., 2015; \r\nGrist et al., 2016; Cheng et al., 2020)."


id: 832b4add6c12cf0e2961b2bd3e720e3d
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 20200
content:
"Recently, Gudmundsson et al. (2021) performed a global detection \r\nand attribution study on streamflow and found that some regions are \r\ndrying and others are wetting. Moreover, the simulated streamflow \r\ntrends are consistent with observations only if externally forced \r\nclimate change is considered, and the simulated effects of water and \r\nland management cannot reproduce the observed trends. The effects \r\nof volcanic eruptions in driving reduced streamflow have also been \r\ndetected in the wet tropics (Iles and Hegerl, 2015; Zuo et al., 2019)."


id: 5290277a450617bec71e296b7908c0b4
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11808
content:
"In summary, there is medium confidence that anthropogenic climate \r\nchange has altered local and regional streamflow in various parts \r\nof the world and that the associated global-scale trend pattern is \r\ninconsistent with internal variability. Moreover, human interventions \r\nand water withdrawals, while affecting streamflow, cannot explain \r\nthe observed spatio-temporal trends (medium confidence)."


id: fb21858ce9af1c73a5cebab75dcbc8ec
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 41557
content:
"Streamflow is to-date the only variable of the terrestrial water \r\ncycle with enough in-situ observations to allow for detection and \r\nattribution analysis at continental to global scales. Based on evidence \r\nfrom a few formal detection and attribution studies, particularly on "


id: e6f8316e5db0cbd9310fe7049fd2a09e
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 37921
content:
"Cross-Chapter Box 3.2 | Human Influence on Large-scale Changes in Temperature \r\nand Precipitation Extremes"


id: 6b214ca163223e9a82c9f9b858e7451c
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 16143
content:
"Contributors: Nathan P. Gillett (Canada), Seung-Ki Min (Republic of Korea), Krishnan Raghavan (India), Ying Sun (China), \r\nXuebin Zhang (Canada)"


id: 32179da5d90967aa9c7b346a1ef72d1e
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 6072
content:
"Understanding how temperature and precipitation extremes have changed at large scales and the causes of these changes is an \r\nimportant part of our overall assessment of human influence on the climate system. Chapter 11 assesses changes in extremes and \r\ntheir causes, while this Cross-Chapter Box summarizes relevant assessments and supporting evidence in Chapters 8 and 11 and relates \r\nchanges in extremes to mean changes on global and continental scales."


id: 88730d81ad9edcd3d738942c1a7716b5
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 21163
content:
"One important aspect of various indicators of temperature extremes is their connection to mean temperature at local, regional and \r\nglobal scales. For example, the highest daily temperature in a summer is often highly correlated with the summer mean temperature. \r\nModel projections show that changes in temperature extremes are often closely related to shifts in mean temperature (Seneviratne \r\net al., 2016; Kharin et al., 2018). It is thus no surprise that changes in temperature extremes are consistent with warming mean \r\ntemperature, with warming leading to more hot extremes and fewer cold extremes. Given the attribution of mean warming to \r\nhuman influence (Section 3.3.1), and the connection between changes in mean and extreme temperatures, it is to be expected that \r\nanthropogenic forcing has also influenced temperature extremes."


id: 541e5d7be78893bd4567ed82416be104
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 12075
content:
"Chapter 11 assesses that it is virtually certain that human-induced greenhouse gas forcing is the main contributor to the observed \r\nincrease in the likelihood and severity of hot extremes and the observed decrease in the likelihood and severity of cold extremes on \r\nglobal scales, and very likely that this applies on most continents."


id: e6da2899181371cadd4bdb37f57325a1
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 37905
content:
"panel of Cross-Chapter Box 3.2, Figure 1 demonstrates the consistency between changes in global average annual maximum daily \r\nprecipitation in the observations and model simulations under combined anthropogenic and natural forcing, and inconsistency with \r\nsimulations under natural forcing alone. While there is more evidence in the literature to quantify the net anthropogenic influence on \r\nextreme precipitation than the influence of individual forcing components, a dominant contribution of greenhouse gas forcing to the \r\nlong-term intensification of extreme precipitation on global and continental scales has recently been quantified separately from the \r\ninfluence of anthropogenic aerosol and natural forcings (Dong et al., 2020; Paik et al., 2020b)."


id: b10c0f614e547a6ac090f1b20e64508c
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 28379
content:
"Chapter 11 assesses that it is likely that human influence, in particular due to greenhouse gas forcing, is the main driver of the \r\nobserved intensification of heavy precipitation in global land regions during recent decades (Section 11.4.4)."


id: 144c822e8799995113de733505618ca2
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 788
content:
"range in the Northern Hemisphere (Quan et al., 2018; Grise et al., \r\n2019), whereas the trend in the annual-mean Southern Hemisphere \r\nedge is outside the 5th-95th percentile range of internal variability \r\nin CMIP6 in three out of the four reanalyses (Figure 3.16b). For the \r\nSouthern Hemisphere summer when the simulated human influence \r\nis strongest, the 1981-2000 trend in three out of the four reanalyses \r\nfalls outside the 5th-95th percentile range of internal variability \r\n(Figure 3.16c; L. Tao et al., 2016; Grise et al., 2018, 2019)."


id: 2ce78595a524c3bc70a0797a0a573183
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5171
content:
"The tropical tropospheric circulation features meridional and zonal \r\noverturning circulations, called Hadley and Walker circulations. In the \r\nzonal mean, the downwelling branch of the Hadley circulation cell \r\nis located in the subtropics and is often used as an indicator of the \r\nmeridional extent of the tropics. In the equatorial zonal-vertical \r\nsection, the major rising branch of the Walker circulation is located \r\nover the Maritime continent with secondary ascending regions over \r\nnorthern South America and Africa. The zonal component of the \r\nsurface trade winds over most of the equatorial Pacific and Atlantic \r\nis associated with the Walker circulation. This section assesses the \r\nzonal-mean Hadley cell extent and the Pacific Walker circulation \r\nstrength. Regional and water cycle aspects of these circulations are \r\nassessed in more detail in Section 8.3.2."


id: a3b15a9ba0045f28bae4a41cb52b59f
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 26003
content:
"In CMIP5 simulations, greenhouse gas increases and, in austral summer, \r\nstratospheric ozone depletion, contribute to the Southern Hemisphere \r\nexpansion (Gerber and Son, 2014; Nguyen et al., 2015; L. Tao et al., \r\n2016; Y.H. Kim et al., 2017), but the ozone influence is not significant \r\nin available CMIP6 simulations (Figure 3.16b-c). Since the 2000s, the \r\nstabilization or slight recovery of stratospheric ozone (Section 2.2.5.2) \r\nis consistent with the smaller observed trends (Banerjee et al., 2020). \r\nWhile many CMIP5 models under-represent the magnitude of the PDV, \r\nimplying potential overconfidence on the detection of human influence \r\non the Hadley cell expansion, this is less the case for the CMIP6 models \r\n(Section 3.7.6). However, the mechanism underlying the Hadley cell \r\nexpansion remains unclear (Staten et al., 2018, 2020), precluding \r\na process-based validation of the simulated human influence."


id: d8c3ec1e387aeac4935f9b38baa70ce2
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35432
content:
"AR5 found medium confidence that the depletion of stratospheric \r\nozone had contributed to Hadley cell widening in the Southern \r\nHemisphere in austral summer (Bindoff et al., 2013). It also noted \r\nthat in contrast to a simulated weakening in response to greenhouse \r\ngas forcing, the Walker circulation had actually strengthened since \r\nthe early 1990s, precluding any detection of human influence."


id: 4a44cefcbdf302aa617b4cd008c2614b
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 10307
content:
"Grise et al. (2019) found that a metric based on surface zonal winds, \r\nwhich are well constrained by surface observations, best compares \r\nreanalyses with CMIP5 models. With this method and new reanalysis \r\nproducts, the CMIP5 historical simulations exhibit comparable mean \r\nstates and variability of the subtropical edge latitude of the Hadley \r\ncells to those observed (Grise et al., 2019)."


id: 6bfedd61ebaa01ed24aff2386f72a328
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 16303
content:
"Chapter 2 assesses that there has very likely been a widening of the \r\nHadley circulation since the 1980s (Section 2.3.1.4.1). The CMIP5 \r\n(Davis and Birner, 2017; Grise et al., 2018) and CMIP6 (Grise and \r\nDavis, 2020) historical simulation ensembles span the observed \r\ntrends of the zonal-mean Hadley cell edges since the 1980s \r\n(Figure 3.16a-c). Studies based on CMIP5 models find a contribution \r\nfrom human influence to the observed widening trend, especially in \r\nthe Southern Hemisphere (Gerber and Son, 2014; Staten et al., 2018, \r\n2020; Grise et al., 2019; Jebri et al., 2020), which is confirmed based \r\non CMIP6 (Figure 3.16b,c; Grise and Davis, 2020)."


id: a37975d3ad834e3d7f2bcd463269be8c
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25966
content:
"In the annual mean, internal variability, including Pacific Decadal \r\nVariability (PDV; Annex IV.2.6), contributed to the observed \r\nzonal-mean Hadley cell expansion since 1980 comparably with \r\nhuman influence (Allen et al., 2014; Allen and Kovilakam, 2017; \r\nMantsis et al., 2017; Amaya et al., 2018; Grise et al., 2018). Indeed, \r\nthe ensemble-mean expansion in historical simulations is significantly \r\nweaker than in most of the reanalyses shown in Figure  3.16a-c, \r\nwhile the Atmospheric Model Intercomparison Project (AMIP) \r\nsimulations forced by observed SSTs (Figure 3.16a-c) show stronger \r\ntrends than historical coupled simulations on average (Nguyen et al., \r\n2015; Davis and Birner, 2017; Grise et al., 2018). The human-induced \r\nchange has not yet clearly emerged out of the internal variability "


id: 73d247318a9ee914b4d3e2595aba61c2
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17630
content:
"those simulations are forced by one reconstruction of SST, which \r\nitself is subject to uncertainty before the 1970s (Deser et al., 2010; \r\nTokinaga et al., 2012)."


id: 95a65dd6dba9c13f02ffa69383740e81
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 23521
content:
"Trends since the 1980s in in-situ and satellite observations and \r\nreanalyses  exhibit strengthening of the Pacific Walker circulation \r\nand SST gradient (Section 2.3.1.4.1 and Figure 3.16f; L'Heureux et al., \r\n2013; Boisseson et al., 2014; England et al., 2014; Kociuba and \r\nPower, 2015; Ma and Zhou, 2016). AMIP simulations reproduce this \r\nstrengthening (Figure 3.16d; Boisseson et al., 2014; Ma and Zhou, 2016), \r\nindicating a dominant role of SST changes. However, all reanalysis \r\ntrends lie outside the 5-95% range of simulated CMIP6 historical \r\nWalker circulation trends over this period (Figure 3.16f), consistent with \r\nCMIP5 results (England et al., 2014; Kociuba and Power, 2015). This \r\nmay be in part caused by the underestimation of the PDV magnitude \r\nespecially in CMIP5 models (Section 3.7.6; Kociuba and Power, 2015; \r\nChung et al., 2019), but also suggests a potential error in simulating the "


id: 90433aebd0c01375826c30b0d536e45d
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22582
content:
"Observational SST products indicate that the equatorial zonal SST \r\ngradient from the western to the eastern equatorial Pacific has \r\nstrengthened since 1870 (Section 7.4.4.2.1). While CMIP5 historical \r\nsimulations on average simulate a weakening, large ensemble \r\nsimulations span the observed strengthening since the 1950s \r\n(Watanabe et al., 2021) suggesting an important contribution from \r\ninternal variability. Coats and Karnauskas (2017) also find that the \r\nanthropogenic influence on the SST gradient is yet to emerge out of \r\ninternal variability even on centennial time scales."


id: 978bd4da89a81b18ff74d90c6ada234b
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 23878
content:
"et al., 2000), momentum and energy budgets (Biasutti et al., 2018; \r\nGeen et al., 2020). Assessments of regional monsoon changes are \r\nmade in Sections 8.3.2.4, 10.4.2.1 and 10.6.3."


id: 4a1e68f84b457967468b050174e1c9fd
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 10280
content:
"forced changes of the Walker circulation. Specifically, anthropogenic \r\nand volcanic aerosol changes over this period may have driven \r\na strengthening (DiNezio et al., 2013; Takahashi and Watanabe, 2016; \r\nHua et al., 2018). This aerosol influence may be indirect via Atlantic \r\nMulti-decadal Variability (AMV; Annex IV.2.7) through inter-basin \r\nteleconnections (McGregor et al., 2014; Chikamoto et al., 2016; \r\nKucharski et al., 2016; X. Li et al., 2016a; Ruprich-Robert et al., 2017), \r\nwhich may be underestimated in models due to SST biases in the \r\nequatorial Atlantic (Section 3.5.1.2.2; McGregor et al., 2018). Note \r\nalso the large uncertainty in aerosol influence on the Walker circulation \r\n(Kuntz and Schrag, 2016; Hua et al., 2018; Oudar et al., 2018), which is \r\nalso seen in CMIP6 (Figure 3.16f)."


id: 83f3751c450a60aff17343857c39a389
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 20366
content:
"AR5 assessed that CMIP5 models simulated monsoons better than \r\nCMIP3 models but that biases remained in domains and intensity \r\n(high confidence) (Flato et al., 2013). There were no detection and \r\nattribution assessment statements on the decreasing trend of global \r\nmonsoon precipitation over land from the 1950s to the 1980s or \r\nthe increasing trend of global monsoon precipitation afterwards. \r\nIn the paleoclimate context, it was determined with high confidence\r\nthat orbital forcing produces strong interhemispheric rainfall variability \r\nevident in multiple types of proxies (Masson-Delmotte et al., 2013)."


id: a60204cea7c0d7f58d67df3d4ca72e5b
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 26429
content:
"Paleoclimate data from the Pliocene epoch suggest that there \r\nwas a reduction in the zonal SST gradient in the tropical Pacific \r\nunder a similar CO2 concentration as today (Section 7.4.4.2.2 and \r\nCross-Chapter Box 2.4). Tierney et al. (2019) found that this weaker \r\ngradient compared to pre-industrial, which suggests a weaker Walker \r\ncirculation, is captured by climate models under Pliocene CO2 levels, \r\nin agreement with the CMIP6 response to greenhouse gas forcing \r\n(Figure 3.16d), though the magnitude of this effect varies strongly \r\nbetween models (Corvec and Fletcher, 2017)."


id: 9e9f2b6919a600380a2569b3a3a74f3f
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25137
content:
"During the mid-Holocene, global monsoons were stronger especially \r\nin the Northern Hemisphere with an expansion of the West African \r\nmonsoon domain in response to orbital forcing (Biasutti et al., 2018; \r\nSection 2.3.1.4.2). Simulations of the mid-Holocene with CMIP5 \r\nand CMIP6 models qualitatively capture the stronger Northern \r\nHemisphere monsoon (Jiang et al., 2015; Brierley et al., 2020), mainly \r\ndriven by atmospheric circulation changes (D'Agostino et al., 2019). \r\nHowever, the models underestimate the monsoon expansion found \r\nin proxy reconstructions (Perez-Sanz et al., 2014; Harrison et al., \r\n2015; Tierney et al., 2017), which may be linked to mean biases in \r\nthe monsoon domain (Brierley et al., 2020) and may be improved \r\nby imposing vegetation and dust changes (Pausata et al., 2016). The \r\nmodels simulate the weaker Southern Hemisphere monsoon during "


id: 28e6fe05d8f342bffb7ad912ff7ae714
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4415
content:
"Monsoons are seasonal transitions of regimes in atmospheric \r\ncirculation and precipitation with the annual cycle of solar insolation, \r\nin association with redistribution of moist static energy (Wang and \r\nDing, 2008; P.X. Wang et al., 2014; Biasutti et al., 2018). The global \r\nmonsoon can be defined to encompasses all monsoon systems based \r\non precipitation contrast in the solstice seasons (Wang and Ding, \r\n2008; Figure 3.17). All regional monsoons are intimately connected \r\nto the global tropical atmospheric overturning by mass (Trenberth "


id: 8c16e61943c181dfe3ae1bb6bd848464
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 21819
content:
"the mid-Holocene (D'Agostino et al., 2020), consistent with proxy \r\nevidence (Section 2.3.1.4.2). These studies indicate that models \r\ncan qualitatively reproduce past global monsoon changes seen in \r\nproxies, though issues remain in quantitatively reproducing proxy \r\nobservations. Studies of last millennium simulations show that \r\nsimulated global monsoon precipitation increases with global mean \r\ntemperature, while changes in monsoon circulation and hemispheric \r\nmonsoon precipitation depend on forcing sources (Liu et al., 2012; \r\nChai et al., 2018). Compared to greenhouse gas and solar variations, \r\nvolcanic forcing is more effective in changing the global monsoon \r\nprecipitation over the last millennium (Chai et al., 2018)."


id: a22887fb415f4991d77e2c0a3c6a542f
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25765
content:
"the timings of onset and withdrawal of the Southern Hemisphere \r\nmonsoon. Notable inter-model differences were identified in \r\nCMIP5, with the multi-model ensemble mean outperforming \r\nindividual models (Lee and Wang, 2014). Common biases were \r\nidentified across CMIP5 models in moist static energy and upper\u0002tropospheric temperature associated with the South Asian summer \r\nmonsoon, which may arise from overly smoothed model topography \r\n(Boos and Hurley, 2012). However, in atmospheric models with \r\nincreasing resolution approaching 20 km, improvements in monsoon \r\nprecipitation are not universal across regions and models, and overall \r\nimprovements are unclear (Johnson et al., 2016; Ogata et al., 2017; \r\nL. Zhang et al., 2018b)."


id: d64bb41d845be5bd3adbe45ea637c318
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35013
content:
"Reproducing monsoons in terms of domain, precipitation amount, \r\nand timings of onset and retreat over the historical period also \r\nremains difficult. While CMIP5 historical simulations broadly capture \r\nglobal monsoon domains and intensity based on summer and winter \r\nprecipitation differences, they underestimate the extent and intensity \r\nof East Asian and North American monsoons while overestimating \r\nthem over the tropical western North Pacific (Lee and Wang, 2014; \r\nM. Yan et al., 2016). B. Wang et al. (2020) reported that CMIP6 \r\nmodels simulate the global monsoon domain and precipitation better \r\n(Figure 3.17a,b), albeit with biases in annual mean precipitation and "


id: 3aa4d02fc3bea1aef190bb001bc37a73
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7523
content:
"In instrumental records, global summer monsoon precipitation \r\nintensity (measured by summer precipitation averaged over the \r\nmonsoon domain) decreased from the 1950s to 1980s, followed \r\nby an increase (Section 2.3.1.4.2 and Figure 3.17c), arising mainly \r\nfrom variations in Northern Hemispheric land monsoons. A CMIP5 \r\nmulti-model study by Y. Zhang et al. (2018) found that observed \r\n1951-2004 trends of the global and Northern Hemisphere summer \r\nland monsoon precipitation intensity are well captured by historical \r\nsimulations, and CMIP6 models show similar results for global land "


id: 5e45a117b5bb7036f16ea815f12f323c
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13875
content:
"there is high confidence that orbital forcing contributed to higher \r\nNorthern Hemisphere monsoon precipitation in the mid-Pliocene and \r\nmid-Holocene than pre-industrial. While CMIP5 models can capture \r\nthe domain and precipitation intensity of the global monsoon, biases \r\nremain in their regional representations, and they are unsuccessful \r\nin quantitatively reproducing changes in paleo reconstructions (high \r\nconfidence). CMIP6 models reproduce the domain and precipitation \r\nintensity of the global monsoon observed over the instrumental \r\nperiod better than CMIP5 models (medium confidence). However, \r\nCMIP5 and CMIP6 models fail to fully capture the variations of the \r\nNorthern Hemisphere summer monsoon circulation (Figure 3.17d), but \r\nthere is low confidence in this assessment due to a lack of evidence in \r\nthe literature."


id: 7ec53628c7ceb1b4daa080251b6f6a38
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19457
content:
"Extratropical jets are wind maxima in the upper troposphere which \r\nare often associated with storms, blocking, and weather extremes. \r\nBlocking refers to long-lived, stationary high-pressure systems that \r\nare often associated with a poleward displacement of the jet, causing \r\ncold spells in winter and heatwaves in summer (e.g., Sousa et al., \r\n2018). Sections 2.3.1.4.3, 8.3.2.7, and 11.7.2 discuss these features \r\nin more detail."


id: e7a20c8973e4175fbeb0bd5ee723af5f
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38058
content:
"AR5 concluded that models were able to capture the general \r\ncharacteristics of extratropical cyclones and storm tracks, although \r\nit also noted that most models underestimated cyclone intensity, \r\nthat biases in cyclone frequency were linked to biases in sea surface \r\ntemperatures, and that resolution can play a significant role in the \r\nquality of the simulation of storms (Flato et al., 2013). Similarly, AR5 \r\nfound with high confidence that simulation of blocking was improved \r\nwith increases in resolution. The AR5 did not specifically assess changes \r\nin Southern Hemisphere storm track characteristics or blocking."


id: a17f72fdd2d03c6cd7e02b9b94f14de
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25628
content:
"underestimated, has not improved from CMIP5 to CMIP6 (Schiemann \r\net al., 2020). Section 10.3.3.3 discusses the implications of the biases \r\ndiscussed here for regional climate."


id: 6de9b75fed8f33f68b7a09d8431e7ce
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 16598
content:
"summer do Patterson et al. (2019) find a systematic, multi-model \r\nunderestimation of the blocking frequency in and around the Tasman \r\nSea. The blocking frequency is anticorrelated with the amplitude \r\nof the SAM. Ozone depletion, through stratosphere-troposphere \r\ncoupling, may have caused an increase in the blocking frequency in \r\nthe South Atlantic sector (Dennison et al., 2016); this finding requires \r\nconfirmation using a multi-model approach."


id: 98122eb699208be9fe1caabb8f1c8877
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 23965
content:
"For the North Pacific storm track CMIP6 simulations exhibit large \r\nremaining underestimations of cyclone frequencies during summer \r\n(June to August), which for the low-resolution models have essentially \r\nremained unchanged versus CMIP5, and there is only a small resolution \r\ndependence of this bias (Priestley et al., 2020). During winter (December \r\nto February), both CMIP5 and CMIP6 models tend to place the North \r\nPacific storm track too far equatorward (M. Yang et al., 2018; Priestley \r\net al., 2020), leading to an overestimation of cyclones between 30degN \r\nand 40degN in the Pacific and an underestimation to the north of \r\nthis. Both low- and high-resolution models show this pattern, but \r\nlow-resolution models generally simulate fewer cyclones throughout \r\nthe North Pacific (Priestley et al., 2020)."


id: 7057c6494c8793a0f7a3d030f6d9e653
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17021
content:
"In addition to inadequate resolution, blocking and storm track biases \r\nin both hemispheres also result from mean state biases, in particular, \r\nbiases related to the parameterization of orographic effects and to \r\nthe misrepresentation of the Gulf Stream SST front (Anstey et al., \r\n2013; Berckmans et al., 2013; Davini and D'Andrea, 2016; O'Reilly \r\net al., 2016a; Pithan et al., 2016; Schiemann et al., 2017). Nonetheless \r\noverall SST biases have been suggested to have only a weak relevance \r\nto blocking (Davini and D'Andrea, 2016)."


id: c0515ecb0f164df77c260cffb5ab01d7
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31138
content:
"Section 2.3.1.4.3 assesses that the total number of extratropical \r\ncyclones has likely increased since the 1980s in the Northern \r\nHemisphere (low confidence), but with fewer deep cyclones \r\nparticularly in summer. This observed reduction in cyclone activity \r\nby about 4% per decade in the Northern Hemisphere in summer \r\n(Chang et al., 2016; Section 2.3.1.4.3) may be associated with \r\nhuman-induced warming. CMIP5 historical simulations generally \r\nreproduce a reduction but underestimate its magnitude (Chang \r\net al., 2016). Furthermore, feedback mechanisms associated with \r\nclouds may be responsible for substantial inter-model spread (Chang \r\net al., 2016; Voigt and Shaw, 2016). In boreal winter, recent studies \r\nhave suggested a potential influence of the rapid Arctic warming on \r\nobserved intensification of Northern Hemisphere storm track activity \r\nin the past few decades, while other studies question this possibility \r\n(Cross-Chapter Box 10.1)."


id: 491e0ef09199139bdb4fb2c75dadf001
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 10088
content:
"In winter, the North Atlantic storm track remains displaced to the \r\nsouth and east in many models (Harvey et al., 2020), leading to \r\nunderestimation of cyclone frequencies near the North American coast \r\nand overestimation in the eastern North Atlantic. Higher-resolution \r\nCMIP6 models perform slightly better in this regard than low\u0002resolution models. In summer (June to August), cyclone frequencies \r\nthroughout the extratropical North Atlantic, which were substantially \r\nunderestimated in CMIP5, have improved in CMIP6 high-resolution \r\nmodels. In low-resolution CMIP6 models, the problem is essentially \r\nunchanged (Priestley et al., 2020); this is associated with generally \r\nunderestimated variability of sea level pressure in CMIP models \r\n(Harvey et al., 2020)."


id: 8206f4123a3902a87d2be2125230a130
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19996
content:
"There is only one contiguous blocking region in the Southern \r\nHemisphere, with the blocking frequency maximizing in the South \r\nPacific and minimizing in the southern Indian Ocean regions (Parsons \r\net al., 2016; Patterson et al., 2019). CMIP5 simulations agree relatively \r\nwell with ERA-Interim in this region regarding the distribution of \r\nblocking events (Parsons et al., 2016). Individual models exhibit \r\nconsiderable biases in the blocking frequency; however only in austral "


id: b1fd9e0a39e380600e8a770a1b406c0b
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 28538
content:
"In summary, there is low confidence that an observed decrease in \r\nthe frequency of Northern Hemisphere summertime extratropical \r\ncyclones is linked to anthropogenic influence. In the Southern \r\nHemisphere, there is high confidence that human influence, in the \r\nform of ozone depletion, has contributed to the observed poleward \r\nshift of the jet in austral summer, while confidence is low for "


id: d5ff029cd871b3291bdf514a82576f3e
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34967
content:
"Figure 3.18 | Instantaneous Northern-Hemisphere blocking frequency (% of days) in the extended northern winter season (December-January-February-\r\nMarch - DJFM) for the years 1979-2000. Results are shown for the ERA5 reanalysis (black), CMIP5 (blue) and CMIP6 (red) models. Coloured lines show multi-model means and \r\nshaded ranges show corresponding 5-95% ranges constructed with one realization from each model. Figure is adapted from Davini and D'Andrea (2020), their Figure 12 and \r\nfollowing the D'Andrea et al. (1998) definition of blocking. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."


id: 7d63eb00ede4544b6fcfea542f23a525
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19230
content:
"human influence on historical blocking activity. The low confidence\r\nstatements are due to the limited number of studies available. The \r\nshift of the Southern Hemisphere jet is correlated with modulations \r\nof the SAM (Section 3.7.2). There is medium confidence in model \r\nperformance regarding the simulation of the extratropical jets, storm \r\ntrack and blocking activity, with increased resolution sometimes \r\ncorresponding to better performance, but important shortcomings \r\nremain, particularly for the Euro-Atlantic sector of the Northern \r\nHemisphere. Nonetheless, synthesizing across Sections 3.3.3.1-\r\n3.3.3.3, there is high confidence that CMIP6 models capture the \r\ngeneral characteristics of the tropospheric large-scale circulation."


id: 18c356d20c2860d31d665934d14bfa9e
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 1577
content:
"In summary, an anthropogenic influence on the frequency or other \r\naspects of SSWs has not yet been robustly detected. There is low \r\nconfidence in the ability of models to simulate any such trends over \r\nthe historical period because of large natural interannual variability \r\nand also due to substantial common biases in the simulated mean \r\nstate affecting the simulated frequency of SSWs."


id: 3c96b42502bc2691ff31075cf56d3b44
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7851
content:
"Sudden stratospheric warmings (SSWs) are stratospheric weather \r\nevents associated with anomalously high temperatures at high \r\nlatitudes persisting from days to weeks. Section 2.3.1.4.5 discusses \r\nthe definition and observational aspects of SSWs. SSWs are often \r\nassociated with anomalous weather conditions, for example, winter \r\ncold spells, in the lower atmosphere (e.g., Butler et al., 2015; Baldwin \r\net al., 2021)."


id: 5e59878934fa7d39bfa0093a50c7f46
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13892
content:
"Some studies find an increase in the frequency of SSWs under \r\nincreasing greenhouse gases (e.g., Schimanke et al., 2013; Young \r\net al., 2013; J. Kim et al., 2017). However, this behaviour is not \r\nrobust across ensembles of chemistry-climate models (Mitchell et al., \r\n2012; Ayarzaguena et al., 2018; Rao and Garfinkel, 2021). There is \r\nan absence of studies specifically focusing on simulated trends in \r\nSSWs during recent decades, and the short record and substantial \r\ndecadal variability yields low confidence in any observed trends in \r\nthe occurrence of SSW events in the Northern Hemisphere winter \r\n(Section 2.3.1.4.5). Such an absence of a trend and large variability \r\nwould also be consistent with a recent reconstruction of SSWs \r\nextending back to 1850, based on sea level pressure observations \r\n(Domeisen, 2019), although this time series has limitations as it is not \r\nbased on direct observations of SSWs."


id: 798c0aa9b6bf32ce4ca765ddcc509ab9
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18588
content:
"and Eisenman, 2017). Sea ice thermodynamic considerations \r\nindicate that the magnitude of sea ice variability and loss depends \r\non ice thickness (Bitz, 2008; Massonnet et al., 2018) and  hence \r\nthe climatology simulated by different models may influence their \r\nsimulated sea ice trends (medium confidence), as indicated by the \r\nregression lines in Figure 3.20."


id: f7cf8b9449e99655aa10071e6fa595f
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 40973
content:
"the period 1953-2012. They further showed that about a quarter of the \r\ngreenhouse gas induced decrease in SIE has been offset by an increase \r\ndue to other anthropogenic forcing (mainly aerosols). Similarly, Gagne \r\net al. (2017b) suggested that the observed increase in Arctic sea \r\nice concentration over the 1950-1975 period was primarily due to \r\nthe cooling contribution of anthropogenic aerosol forcing based on \r\nsingle model simulations. Gagne et al. (2017a) identified a detectable \r\nincrease in Arctic SIE in response to volcanic eruptions using CMIP5 \r\nmodels and four observational datasets. Polvani et al. (2020) suggested \r\nthat ozone depleting substances played a substantial role in the Arctic \r\nsea ice loss over the 1955-2005 period."


id: 6ae5e79b3c7d4d08733d68e3a1da7fc6
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 16091
content:
"An important consideration in comparing Arctic sea ice loss in \r\nmodels and observations is the role of internal variability (medium \r\nconfidence). Using ensemble simulations from a single model, \r\nKay et al. (2011) suggested that internal variability could account \r\nfor about half of the observed September ice loss. More recently, \r\nlarge ensemble simulations have been performed with many more \r\nensemble members (Kay et al., 2015). These enable a more robust \r\ncharacterization of internal variability in the presence of forced \r\nanthropogenic change. Using such large ensembles, some studies \r\ndiscussed the influence of internal variability on Arctic sea ice trends \r\n(Swart et al., 2015). Song et al. (2016) also compared the trends in the "


id: edf5919e219a4017f2a35db98776b63
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 39236
content:
"Differences in sea ice loss among the models (Figure  3.20) have \r\nbeen attributed to a number of factors (see also Section 9.3.1.1). \r\nThese factors include the late 20th century simulated sea ice state \r\n(Massonnet et al., 2012), the magnitude of changing ocean heat \r\ntransport (Mahlstein and Knutti, 2011), and the rate of global warming \r\n(e.g., Gregory et al., 2002; Mahlstein and Knutti, 2012; Rosenblum "


id: 51c7625351129685f81e226926bbeac6
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11673
content:
"Figure 3.20 | Mean (x-axis) and trend (y-axis) of Arctic sea ice area (SIA) in September (left) and Antarctic SIA in February (right) for 1979-2017 from \r\nCMIP5 (upper) and CMIP6 (lower) models. All individual models (ensemble means) and the multi-model mean values are compared with the observations (OSISAF, NASA \r\nTeam, and Bootstrap; see Figure 9.13). Solid line indicates a linear regression slope with corresponding correlation coefficient (r) and p-value provided. Note the different scales \r\nused on the y-axis for Arctic and Antarctic SIA. Results remain essentially the same when using sea ice extent (SIE; not shown). Further details on data sources and processing \r\nare available in the chapter data table (Table 3.SM.1)."


id: 605c295a175e96bd751d723e2713ac83
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14261
content:
"Some recent studies evaluated the human contribution to recent \r\nrecord minimum SIE events in the Arctic. Analysing CMIP5 simulations, \r\nZhang and Knutson (2013) found that the observed 2012 record low \r\nin September Arctic SIE is inconsistent with internal climate variability \r\nalone. Based on several large ensembles, Kirchmeier-Young et al. \r\n(2017) concluded that the observed 2012 SIE minimum cannot be \r\nreproduced in a simulation excluding human influence. Fuckar et al. \r\n(2016) showed that climate change contributed to the record low \r\nMarch Arctic SIE in 2015, which was accompanied by the record \r\nminimum SIE in the Sea of Okhotsk (Paik et al., 2017)."


id: d7b8d42ff6a027d716d2e2b4a8536cef
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35260
content:
"Based on the new attribution studies since AR5, we conclude that it \r\nis very likely that anthropogenic forcing mainly due to greenhouse \r\ngas increases was the main driver of Arctic sea ice loss since \r\n1979. Increases in anthropogenic aerosols have offset part of the \r\ngreenhouse gas induced Arctic sea ice loss since the 1950s (medium \r\nconfidence). Despite large differences in the mean sea ice state in \r\nthe Arctic, Arctic sea ice loss is captured by all CMIP5 and CMIP6 \r\nmodels. Nonetheless, large inter-model differences in the Arctic sea \r\nice decline remain, limiting our ability to quantify forced changes and \r\ninternal variability contributions."


id: c561562fadfc4ec98f0f8577d5e23f2c
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 32034
content:
"AR5 concluded that 'there is low confidence in the attribution of the \r\nobserved increase in Antarctic SIE since 1979' (Bindoff et al., 2013) due \r\nto the limited understanding of the external forcing contribution as well \r\nas the role of internal variability. Based on a difference between the \r\nfirst and last decades, Antarctic sea ice cover exhibited a small increase \r\nin summer and winter over the 1979-2017 period (Section 2.3.2.1.2, \r\nand Figures 3.20 and 3.21). However, these changes are not statistically \r\nsignificant and starting in late 2016, anomalously low sea ice area \r\nhas been observed (Section 2.3.2.1.2). The mean hemispheric sea ice \r\nchanges result from much larger, but partially compensating, regional \r\nchanges with increases in the western Ross Sea and Weddell Sea "


id: 72158f9a8bfebf71b5606b791925f489
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17318
content:
"GSAT and SCE also exists in CMIP6 (Mudryk et al., 2020). Like CMIP5, \r\nthe CMIP6 models capture the negative trend in spring snow cover \r\nthat has occurred in recent decades (Figure  3.22). However, the \r\nmedian CMIP6 model now produces slightly stronger post-1981 \r\ndeclines in the March to April mean SCE than the CMIP5 median \r\n(Mudryk et al., 2020). Until about 1980, the models produce a \r\ngenerally stable March to April SCE, but after that a substantial \r\ndecline, reaching a loss of about 2 x 106 km2 in 2012-2017 relative \r\nto the 1971-2000 average. Compared to earlier studies which found \r\nthat models underestimate observed trends for the 1979-2005 \r\nperiod (Brutel-Vuilmet et al., 2013), both CMIP5 and CMIP6 models "


id: e1bdc97765b44a484ce5db2fa4867f1
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36992
content:
"In conclusion, the observed small increase in Antarctic sea ice extent \r\nduring the satellite era is not generally captured by global climate \r\nmodels, and there is low confidence in attributing the causes of \r\nthe change."


id: 1c9e349bb85c24005e422e8e9c7c2247
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2212
content:
"Seasonal snow cover is a defining climate feature of the northern \r\ncontinents. It is therefore of considerable interest that climate \r\nmodels correctly simulate this feature. It is discussed in more detail in \r\nSection 9.5.3, and observational aspects of snow cover are assessed \r\nin Section 2.3.2.2."


id: 94799a424e3afcaa8c17839d68f0cb9d
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 23328
content:
"CMIP6 models improve on CMIP5 models in producing slightly \r\nincreased SCE versus CMIP5, correcting the low bias in CMIP5 \r\n(Mudryk et al., 2020). The linear relationship noted above between "


id: 99e14fb6f5788776c9a01ec23f3ef35a
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 24301
content:
"show improved agreement with the observations over the period to \r\n2017 (Figure 3.22). One remaining concern is a failure of most CMIP6 \r\nmodels to correctly represent the relationship between snow cover \r\nextent and snow mass, reflecting too slow seasonal increases and \r\ndecreases of SCE in the models (Mudryk et al., 2020)."


id: 4743e829a421986357ae25da529b06bd
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9743
content:
"Several CMIP5 and CMIP6 based studies have consistently \r\nattributed the observed Northern Hemisphere spring SCE changes \r\n(Section 2.3.2.2) to anthropogenic influences (Rupp et al., 2013; Najafi \r\net al., 2016; Paik and Min, 2020), with the observed changes being \r\nfound to be inconsistent with natural variability alone. Similarly, spring \r\nsnow thickness (Snow Water Equivalent) changes on the scale of the \r\nNorthern Hemisphere have been attributed to greenhouse gas forcing \r\n(Jeong et al., 2017). Using individual forcing simulations from multiple \r\nCMIP6 models, Paik and Min (2020) detected greenhouse gas influence \r\nin the observed decrease of early spring SCE between 1925 and 2019, \r\nwhich was found to be separable from the responses to other forcings."


id: 897bc419d02a8bc2d14c6300518e5aab
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 21363
content:
"Glaciers are defined as perennial surface land ice masses \r\nindependent of the Antarctic and Greenland Ice Sheets (Sections 9.5 \r\nand 2.3.2.3). The AR5 assessed that anthropogenic influence had \r\nlikely contributed to the retreat of glaciers observed since the 1960s \r\n(Bindoff et al., 2013), based on a high level of scientific understanding \r\nand robust estimates of observed mass loss, internal variability, and \r\nglacier response to climatic drivers. The SROCC (Hock et al., 2019b) \r\nconcluded that atmospheric warming was very likely the primary \r\ndriver of glacier recession."


id: 3da9cc5ae0b53248935cea11677bc52
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 8016
content:
"Simulations of glacier mass changes under climate change rely on \r\nglacier models driven by climate model output, often in collaborative \r\nresearch efforts such as GlacierMIP (Hock et al., 2019a; Marzeion \r\net al., 2020). The GlacierMIP project is a systematic coordinated \r\nmodelling effort designed to further understanding of glacier loss \r\nusing global models. While the low resolution and remaining biases \r\nof climate model-derived boundary forcing data is a limitation, the \r\nrelease of the Randolph Glacier Inventory (Pfeffer et al., 2014; RGI \r\nConsortium, 2017) has supported more sophisticated, systematic and \r\ncomprehensive modelling of glaciers worldwide (Hock et al., 2019a)."


id: 79c96c275692a49e89e31ab6608c5786
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18633
content:
"naturally-forced simulations exhibited a substantial negative mass \r\nbalance, which Roe et al. (2021) argued is unrealistic. Moreover, \r\nMarzeion et al. (2014) estimated that anthropogenic influence \r\ncontributed 69 +- 24% of glacier mass loss for the period 1991 to \r\n2010, consistent with a progressively increasing fraction of mass loss \r\nattributable to anthropogenic influence found by Roe et al. (2021)."


id: 91a5de53e077556f1920d185384820c1
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22852
content:
"In summary, considering together the SROCC assessment that \r\natmospheric warming was very likely the primary driver of glacier \r\nrecession, the results of Roe et al. (2017, 2021) and our assessment of \r\nthe dominant role of anthropogenic influence in driving atmospheric \r\nwarming (Section 3.3.1), we conclude that human influence is very \r\nlikely the main driver of the near-universal retreat of glaciers globally \r\nsince the 1990s."


id: 4125f7d8827152a82331299d370db047
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 8642
content:
"The AR5 assessed that it is likely that anthropogenic forcing \r\ncontributed to the surface melting of the Greenland Ice Sheet since \r\n1993 (Bindoff et al., 2013). The SROCC did not directly assess the \r\nattribution of Greenland Ice Sheet change to anthropogenic forcing, \r\nbut it did assess with medium confidence that summer melting of the \r\nGreenland Ice Sheet has increased to a level unprecedented over at \r\nleast the last 350 years, which is two-to-fivefold the pre-industrial \r\nlevel (see also Trusel et al., 2018)."


id: 2f83032f66d16c373271f49dd44c9216
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5621
content:
"Section 2.3.2.4.1 assesses that Greenland Ice Sheet mass loss began \r\nin the latter half of the 19th century and that the rate of loss has \r\nincreased substantially since the turn of the 21st century (high \r\nconfidence), and also notes that integration of proxy evidence and \r\nmodelling indicates that the last time the rate of mass loss was similar \r\nto the 20th century rate was during the early Holocene. Models of \r\nGreenland Ice Sheet evolution are evaluated in detail in Section \r\n9.4.1.2, which assesses that there is overall medium confidence in \r\nthese models. Model evaluation of surface mass balance changes \r\nover the Greenland Ice Sheet, including regional aspects, is also \r\nassessed in Atlas.11.2.3."


id: a9e022c34b42b9d680303e835864b38f
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 27109
content:
"Drawing together the evidence from the continued and strengthened \r\nobserved mass loss, the agreement between anthropogenically \r\nforced climate simulations and observations, and historical and paleo \r\nevidence for the unusualness of the observed rate of surface melting \r\nand mass loss, we assess that it is very likely that human influence has \r\ncontributed to the observed surface melting of the Greenland Ice Sheet \r\nover the past two decades, and that there is medium confidence in an \r\nanthropogenic contribution to recent overall mass loss from Greenland."


id: 29f6c039461f38711933ef6a784a22e6
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4605
content:
"coverage. Observed changes to ocean temperature (Section 2.3.3.1), \r\nsalinity (Section 2.3.3.2), sea level (Section 2.3.3.3) and ocean \r\ncirculation (Section 2.3.3.4) are reported in Chapter 2.  A more \r\nprocess-based assessment of ocean changes, alongside the \r\nassessment of variability and changes in ocean properties with \r\nspatial scales smaller than ocean basins, is presented in Chapter 9."


id: 1a10ca1eb4a89b774d38e1c327542a28
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 1788
content:
"Model evaluation of surface mass balance over the Antarctic Ice \r\nSheet, including regional aspects, is also assessed in Atlas.11.1.3."


id: ca442439a8a382b05f14006d3ca0b36b
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 32929
content:
"Ocean temperature and ocean heat content are key physical variables \r\nconsidered for climate model evaluation and are primary indicators \r\nof a changing ocean climate. This section assesses the performance \r\nof climate models in representing the mean state ocean temperature \r\nand heat content (Section 3.5.1.1), with a particular focus on the \r\ntropical oceans given the importance of air-sea coupling in these \r\nareas (Section 3.5.1.2). This is followed by an assessment of detection \r\nand attribution studies of changes in ocean temperature and heat \r\ncontent (Section 3.5.1.3). Changes in global surface temperature are \r\nassessed in Section 3.3.1.1."


id: 292b07043ccae1c29b5f729f324e230d
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4454
content:
"Overall, there is medium agreement but limited evidence of \r\nanthropogenic influence on Antarctic mass balance through changes \r\nin ice discharge."


id: 1c1eb5c23fc0a15aa12af225d9988ff
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2127
content:
"The global ocean plays an important role in the climate system, as it \r\nis responsible for transporting and storing large amounts of heat \r\n(Sections 3.5.1 and 9.2.2.1), freshwater (Sections 3.5.2 and 9.2.2.2) \r\nand carbon (Sections 3.6.2 and 5.2.1.3) that are exchanged with the \r\natmosphere. Therefore, accurate ocean simulation in climate models \r\nis essential for the realistic representation of the climatic response \r\nto anthropogenic warming, including rates of warming, sea level \r\nrise and carbon uptake, and the representation of coupled modes of \r\nclimate variability."


id: 30529cc088d41d3ca582eaf6427bb8b0
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5754
content:
"Ocean model development has advanced considerably since AR5 \r\n(Section 1.5.3.1). Ongoing model developments since AR5 have \r\nfocused on improving the realism of the simulated ocean in coupled \r\nmodels, with horizontal resolutions increasing to 10-100 km (from \r\nabout 200 km in CMIP5), and increased vertical resolutions in \r\nmany modelling systems of 0-1 m for near-surface levels (from the \r\nhighest resolution of 10 m in CMIP5). These developments are aimed \r\nat improving the representation of the diurnal cycle and coupling \r\nto the atmosphere (e.g., Bernie et al., 2005, 2007, 2008). General \r\nimprovements to simulated ocean fidelity in response to increasing \r\nresolution are expected (Hewitt et al., 2017), and the effects of model \r\nresolution on the fidelity of ocean models are discussed in more \r\ndetail in Sections 9.2.2 and 9.2.4."


id: fdb39c614fcf4f6e8f770021d10c81b2
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 41995
content:
"Overall, the simulated and observed trends in SST patterns are \r\ngenerally consistent for the historical period (Olonscheck et al., \r\n2020). The CMIP6 models generally represent the observed pattern of \r\ntrends better than the CMIP5 models, and observed trends fall within \r\nthe range of simulated trends over a larger area for CMIP6 models \r\nthan for CMIP5 models (Olonscheck et al., 2020)."


id: a22b0cff2d1cae9379c4f278d7474a23
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25768
content:
"The CMIP5 multi-model mean zonally averaged subsurface ocean \r\ntemperature showed warm biases between 200 m and 2000 m \r\n(mid-depth) over most latitudes, with exceptions in the Southern \r\nOcean (>60degS, 100-2000 m) and upper (0-400 m) Arctic Ocean. Cold \r\nbiases were simulated near the surface (0-200 m) at most latitudes \r\n(Flato et al., 2013). CMIP6 biases are broadly consistent with those \r\nreported in CMIP5 for the near-surface (<200 m) and mid-depth \r\n(200-2000 m) ocean (Voldoire et al., 2019b; Beadling et al., 2020; \r\nY. Zhu et al., 2020). The warm bias begins between 100 and 400 m \r\ndepth in all three basins, however, it is most prominent in the Atlantic "


id: 3d75583d934a9dcc22308eaa1f35fdd
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7988
content:
"Figure 3.24 | Biases in zonal mean and equatorial sea surface temperature \r\n(SST) in CMIP5 and CMIP6 models. CMIP6 (red), CMIP5 (blue) and HighResMIP \r\n(green) multi-model mean (a) zonally averaged SST bias; (b) equatorial SST bias; \r\nand (c) equatorial SST compared to observed mean SST (black line) for 1979-1999. \r\nThe inter-model 5th and 95th percentiles are depicted by the respective shaded \r\nrange. Model climatologies are derived from the 1979-1999 mean of the historical \r\nsimulations, using one simulation per model. The Hadley Centre Sea Ice and Sea Surface \r\nTemperature version 1 (HadISST) (Rayner et al., 2003) observational climatology for \r\n1979-1999 is used as the reference for the error calculation in (a) and (b); and for \r\nobservations in (c). Further details on data sources and processing are available in the \r\nchapter data table (Table 3.SM.1)."


id: a3b135f4397a7502730d212bf0488adc
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 26002
content:
"Ocean, with a maximum magnitude in the equatorial latitudes, as \r\nin CMIP5 (Figure  3.25). In the Pacific, the large warm biases are \r\nmostly seen in the subtropical regions (30degN-60degN and 30degS-60degS). \r\nThe cool near surface tropical bias is most prominent in the Pacific \r\nOcean and also present in the Atlantic, with a smaller magnitude \r\n(Figure 3.25). Relative to CMIP5, the most prominent difference is an \r\nincrease to the mid-depth (300-2000 m) warm bias in CMIP6 and a \r\nchange in sign of the bias from cold to warm for the Southern Ocean \r\nmid-depth (>60degS) from CMIP5 to CMIP6 (Figure 3.25). Compared \r\nto CMIP3 and CMIP5, there is improved agreement between most \r\nCMIP6 models and observations in their representation of the \r\nzonal mean temperature of the upper 100 m of the Southern Ocean \r\n(Beadling et al., 2020)."


id: e9066f163e45c4d5c3c804e8e2d2fdf4
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38351
content:
"Focusing on the deep ocean (>2000 m), the CMIP6 ensemble mean \r\nshows a prominent and consistent warm bias (Figure 3.25), in all basins \r\nexcept the equatorial and northern Pacific, which contrasts to a cold \r\nbias seen in CMIP5 (Flato et al., 2013). We note that while an updated \r\nobservational temperature dataset is used in this assessment (WOA09 \r\nwas used in AR5, while WOA18, 1981-2010 is used in AR6), the deep\u0002ocean warm bias remains and is approaching double the magnitude \r\n(about 0.5degC) of the equivalent CMIP5 multi-model mean bias, a feature \r\nwhich is particularly prominent in the Atlantic and southern Indian "


id: a21e98f40e54cd07b0c7aea0a613d948
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25757
content:
"eddy transport, and reducing temperature drifts in the deeper ocean \r\n(Griffies et al., 2015; von Storch et al., 2016). For some models, the mean \r\nabsolute error in ocean temperature below 500 m is smaller in the high \r\nresolution version compared to the low resolution version, particularly \r\nin eddy-active regions such as the North Atlantic (Rackow et al., 2019). \r\nIncreasing the horizontal resolution of individual climate models often \r\nleads to an overall decrease in the surface temperature biases over \r\nregions where they persisted through earlier CMIP generations, such \r\nas the central and western equatorial Pacific, as well as the North \r\nand tropical Atlantic (Figure 3.3e; Roberts et al., 2019; Hewitt et al., \r\n2020). Despite this, as a group the four HighResMIP models included \r\nin Figures 3.3e and 3.24 do not on average show smaller SST biases \r\nthan the CMIP6 multi-model mean, demonstrating the importance of \r\nfactors other than resolution in contributing to SST biases."


id: b60c1b25e62d9dcb148686180945f2b7
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29252
content:
"Oceans. Increased horizontal resolution as well as the choice of the \r\nvertical coordinate are reported to partly improve these biases in some \r\nmodels (Adcroft et al., 2019; Rackow et al., 2019; Hewitt et al., 2020)."


id: dc74d92ff5c2daa077eabf3733e3bdbc
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36052
content:
"Since AR5, there has been growing evidence that the representation \r\nof mean surface and deeper ocean temperatures in coupled climate \r\nmodels can be improved by increasing the horizontal resolution both \r\nin the ocean and the atmosphere (e.g., Small et al., 2014; Hewitt et al., \r\n2016; Iovino et al., 2016; Roberts et al., 2019). At an ocean resolution \r\nof around 1deg, which is typical of CMIP6 models, some processes are \r\nparameterized rather than explicitly resolved, leading to a compromise \r\nin their dynamical representation. An increase in the model resolution \r\nallows for processes to be explicitly resolved, and can for example, \r\nenhance the simulation of eddies, thus improving simulated vertical "


id: 5a8aa4ca6ee911bf9f66aefa6fe43a2c
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13185
content:
"In summary, there is little improvement in the multi-model mean \r\nsea surface and zonal mean ocean temperatures from CMIP5 to \r\nCMIP6 (medium confidence). Nevertheless, the CMIP6 models show \r\na somewhat more realistic pattern of SST trends (low confidence)."


id: 86485998f388290532137aefadfe6649
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 20780
content:
"a large spread in the thermocline depth, particularly in the equatorial \r\npart of the basin (Saji et al., 2006; Fathrio et al., 2017b), which has \r\nbeen linked to the parameterization of the vertical mixing and the wind \r\nstructure, leading to a misrepresentation of the ventilation process in \r\nsome models (Schott et al., 2009; Richter, 2015; Shikha and Valsala, \r\n2018). A common problem with the CMIP5 models is therefore a warm \r\nbias in the subsurface, mainly at depths around the thermocline, which \r\nis also apparent in the CMIP6 models (Figure 3.25g)."


id: 530a95b5a65b5ea40be1ee4a7670d356
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11870
content:
"In the CMIP6 multi-model mean, the western tropical Indian Ocean \r\nshows a slightly larger warm bias compared to CMIP5 (Figure 3.24 b,c), \r\nwhich in part could be related to excessive supply of warm water \r\nfrom the Red Sea (Grose et al., 2020; Semmler et al., 2020). The \r\nHighResMIP models show decreases in SST bias across the Indian \r\nOcean with increasing resolution (Figure  3.3e; Bock et al., 2020), \r\nthough as a group the SST biases in the HighResMIP models are no \r\nsmaller than those of the full CMIP6 ensemble."


id: 494aa41affa4c910d09684983c053b41
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 10114
content:
"In summary, the structure and magnitude of multi-model mean ocean \r\ntemperature biases have not changed substantially between CMIP5 \r\nand CMIP6 (medium confidence). Although biases remain in the latest \r\ngeneration models, the broad consistency between the observed and \r\nsimulated basin-scale ocean properties suggests that CMIP5 and CMIP6 \r\nmodels are appropriate tools for investigating ocean temperature \r\nand ocean heat content responses to forcing. This also provides \r\nhigh confidence in the utility of CMIP-class models for detection and \r\nattribution studies, for both ocean heat content (Section 3.5.1.3) and \r\nthermosteric sea level applications (Section 3.5.3.2)."


id: cb50fa7828599bcc85e2bb0be555fc31
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33101
content:
"The ocean plays an important role as the Earth's primary energy store. \r\nThe AR5 and SROCC assessed that the ocean accounted for more than \r\n90% of the Earth's energy change since the 1970s (Rhein et al., 2013; \r\nBindoff et al., 2019). These assessments are consistent with recent \r\nstudies assessed in Section 7.2 and Cross-Chapter Box  9.1, which \r\nfind that 91% of the observed change in Earth's total energy from \r\n1971 to 2018 was stored in the ocean (von Schuckmann et al., 2020). \r\nThe AR5 concluded that anthropogenic forcing has very likely made \r\na substantial contribution to ocean warming above 700 m, whereas \r\nbelow 700  m, limited measurements restricted the assessment of \r\nocean heat content changes in AR5 and prevented a robust comparison \r\nbetween observations and models (Bindoff et al., 2013)."


id: ecd1460ac524d081026e24ff5f9a8522
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 39030
content:
"With the recent increase in ocean sampling by Argo to 2000 m \r\n(Roemmich et al., 2015; Riser et al., 2016; von Schuckmann et al., \r\n2016) and the resulting improvements in estimates of ocean heat \r\ncontent (Abraham et al., 2013; Balmaseda et al., 2013; Durack et al., \r\n2014b; Cheng et al., 2017; von Schuckmann et al., 2020), a more \r\nquantitative assessment of the global ocean heat content changes \r\nthat extends into the intermediate ocean (700-2000 m) over the \r\nmore recent period (from 2005 to the present; Durack et al., 2018) \r\ncan be performed. Observed ocean heat content changes are \r\ndiscussed in Section 2.3.3.1, where it is reported that it is virtually \r\ncertain that the global upper ocean (0-700 m) and very likely that "


id: f22b8503283e3a5d9dd80191ac153fa6
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 39998
content:
"The tropical Indian Ocean mean state is reasonably well simulated both \r\nin CMIP5 and CMIP6 (Figure 3.24b,c). However, CMIP5 models show "


id: a3addc6544b1c8474e4a09f783f9ccc0
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 26000
content:
"et al., 2019; Tokarska et al., 2019). Temporary (<10 years) surface \r\nand subsurface cooling during and after large volcanic eruptions \r\nis also captured in the upper-ocean, and global mean ocean heat \r\ncontent (Balmaseda et al., 2013). The ocean heat content increase is \r\nalso reflected in the corresponding ocean thermal expansion which is \r\na leading contributor to global mean sea level rise (Sections 3.5.3.2 \r\nand 9.2.4, and Box 9.1)."


id: 8601a8a35dabbabcd88681df189c4948
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 20717
content:
"For the period 1971-2014, the rate of ocean heat uptake for the \r\nglobal ocean in the CMIP6 models is about 6.43 [2.08-8.66] \r\nZJ yr-1, with the upper, intermediate and deeper layers respectively \r\naccounting for 68%, 16% and 16% of the full depth global heat \r\nuptake (Figure  3.26). Overall, the simulated ocean heat content \r\nchanges are consistent with the updated and improved observational \r\nanalyses, within the very likely uncertainty range defined for each \r\n(see also Section 2.3.3.1, Table 2.7; Domingues et al., 2008; Purkey \r\nand Johnson, 2010; Levitus et al., 2012; Good et al., 2013; Cheng \r\net al., 2017; Ishii et al., 2017; Zanna et al., 2019) as well as with the \r\nocean components of total Earth heating assessed in Section 7.2.2.2, \r\nTable  7.1. Nevertheless, large uncertainties remain, particularly in \r\nthe deeper layers due to the poor temporal and spatial sampling \r\ncoverage, particularly in the Atlantic, Southern and Indian Oceans "


id: eb74aab8ac34711dca7da0de52af3895
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38780
content:
"The multi-model means of both CMIP5 and CMIP6 historical \r\nsimulations forced with time varying natural and anthropogenic \r\nforcing shows robust increases in ocean heat content in the upper \r\n(0-700 m) and intermediate (700-2000 m) ocean (high confidence) \r\n(Figure 3.26; Cheng et al., 2016, 2019; Gleckler et al., 2016; Bilbao "


id: 603187348c5783e954989c3a0d780907
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14227
content:
"(Garry et al., 2019). The very likely ranges of the simulated trends \r\nfor the full ocean depth and below 2000 m fall within the very \r\nlikely range of observed uptake during the last two decades. In the \r\nintermediate layer, the multi-model ensemble mean mostly stays \r\nabove the observed 5th-95th percentile range before the year \r\n2000, and below that range after 2000. For the upper ocean, some \r\nindividual model realizations show a reduced ocean heat content \r\nincrease during the 1970s and 1980s, which is then compensated \r\nby a greater warming than the observations from the early 1990s. \r\nThese discrepancies have been linked with a temporary increase in \r\nthe Southern Ocean deep water formation rate, as well as with the \r\nmodels' strong aerosol cooling effects and high equilibrium climate \r\nsensitivity (see also Section 7.5.6 and Box 7.2; Andrews et al., 2019, \r\n2020; Golaz et al., 2019; Dunne et al., 2020; Winton et al., 2020)."


id: ef7a0267cd1c7508cd81d2734309da63
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 39508
content:
"deeper ocean (very high confidence). Updated observations, like \r\nmodel simulations, show that warming extends throughout the \r\nentire water column (high confidence)."


id: 7f4b075429bbd99fa1e98970a518bf8f
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19548
content:
"Nevertheless, simulations show that the rate of ocean heat uptake \r\nhas doubled in the past few decades, when contrasted to the rate \r\nover the complete 20th century (Figure 3.26), with over a third of \r\nthe accumulated heat stored below 700 m (Cheng et al., 2016, 2019; \r\nGleckler et al., 2016; Durack et al., 2018). The Southern Ocean shows \r\nthe strongest ocean heat uptake that penetrates to deeper layers \r\n(Section 9.2.3.2), whereas ocean heat content increases in the Pacific \r\nand Indian Oceans largely occur in the upper layers (Bilbao et al., \r\n2019)."


id: 5edd2a8f30c9fba919396dad80e7424b
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13983
content:
"Unlike SSTs, simulated sea surface salinity (SSS) does not provide \r\na direct feedback to the atmosphere. However, some recent work \r\nhas identified indirect radiative feedbacks through sea-salt aerosol \r\ninteractions (Ayash et al., 2008; Amiri-Farahani et al., 2019; Z. Wang \r\net al., 2019) that can act to strengthen tropical cyclones, and increase \r\nprecipitation (Balaguru et al., 2012, 2016; Grodsky et al., 2012; Reul \r\net al., 2014; Jiang et al., 2019). The absence of a direct feedback is one \r\nof the primary reasons why salinity simulation is difficult to constrain \r\nin ocean modelling systems, and why deviations from the observed \r\nnear-surface salinity mean state between models and observations \r\nare often apparent (Durack et al., 2012; Shi et al., 2017)."


id: 1d3929789bb3029d9ff27cb42d9a2fad
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2321
content:
"When compared to the routine assessment of simulated SST, \r\nsimulated SSS has not received the same research attention at \r\nglobal- to basin-scales. For CMIP3, there was reasonable agreement \r\nbetween the basin-scale patterns of salinity, with a comparatively \r\nfresher Pacific when contrasted to the salty Atlantic, and basin \r\nsalinity maxima features aligning well with the corresponding \r\natmospheric evaporation minus precipitation field (Durack et al., \r\n2012). Similar features are also reproduced in CMIP5 along with \r\nrealistic variability in the upper layers, but less variability than \r\nobservations at 300 m and deeper, especially in the poorly sampled "


id: 99783d89451c7698afbb9b0f51a2e779
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 24227
content:
"In summary, there is strong evidence for an improved understanding \r\nof the observed global ocean heat content increase. It is extremely \r\nlikely that human influence was the main driver of the ocean heat \r\ncontent increase observed since the 1970s, which extends into the "


id: 384ca0c0f8b5eab3fcbc7dc8d5649aa
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7112
content:
"Antarctic region (Pierce et al., 2012). In a regional study, only \r\nconsidering the Indian Ocean, CMIP5 SSS was assessed and it \r\nwas shown that model biases were primarily linked to biases in \r\nthe precipitation field, with ocean circulation biases playing a \r\nsecondary role (Fathrio et al., 2017a). The sea surface salinity bias \r\nin CMIP6 models is shown in Figure 3.23b."


id: 63b955b4b8ba9eb3b14fef554608935b
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14830
content:
"AR5 concluded that it was very likely that anthropogenic forcings \r\nhad made a discernible contribution to surface and subsurface ocean \r\nsalinity changes since the 1960s (Bindoff et al., 2013; Rhein et al., \r\n2013). It highlighted that the spatial patterns of salinity trends, and \r\nthe mean fields of salinity and evaporation minus precipitation are all \r\nsimilar, with an enhancement to Atlantic Ocean salinity and freshening \r\nin the Pacific and Southern Oceans. Since AR5 all subsequent work \r\non assessing observed and modelled salinity changes has confirmed \r\nthese results."


id: 24113283b46dd44ded051f2b4b28b7fa
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 3527
content:
"For the first time in AR5, alongside global zonal mean temperature, \r\nglobal zonal mean salinity bias with depth was assessed for \r\nthe CMIP5 models. This showed a strong upper ocean (<300 m) \r\nnegative salinity (fresh) bias of order 0.3 PSS-78, with a tendency \r\ntoward a positive salinity (salty) bias (<0.25 PSS-78) in the Northern \r\nHemisphere intermediate layers (200-3000 m) (Flato et al., 2013). \r\nThese biases are also present in CMIP6, albeit with slightly smaller \r\nmagnitudes (Figure  3.25). Here we expand the global zonal mean \r\nbias assessment to consider the three independent ocean basins \r\nindividually, which allows for an assessment as to which basin \r\nbiases are dominating the global zonal mean. The basin with the \r\nmost pronounced biases is the Atlantic, with a strong upper ocean \r\n(<300 m) fresh bias, of order 0.3 PSS-78 just like the global zonal \r\nmean, and a marked subsurface salinity bias that exceeds 0.5 PSS-78 \r\nin equatorial waters between 400-1000 m."


id: 109412f50447e6d7c75bce778d44b2a3
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 101
content:
"Considerable changes to observed broad- or basin-scale ocean \r\nnear-surface salinity fields have been reported (see Section 2.3.3.2), \r\nand these have been linked to changes in the evaporation minus \r\nprecipitation patterns at the ocean surface through model simulations, \r\ntypically expressing a pattern of change where climatological mean \r\nfresh regions become fresher and corresponding salty regions \r\nbecoming saltier (Durack et al., 2012, 2013; Zika et al., 2015; Lago \r\net al., 2016; Skliris et al., 2016, 2018; Cheng et al., 2020), also broadly \r\npresent in the CMIP6 multi-model mean (Figure 3.27). At basin-scales, "


id: 2788ec51b20e03c20bda8f6cf95e7ba1
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4132
content:
"The Pacific Ocean shares the strongest similarity to the global bias, \r\nwith a similar upper ocean (<300 m) fresh bias. Lower magnitude \r\npositive salinity biases (about 0.3 PSS-78) are also present in both \r\nhemispheres between 200 and 3000 m, and deeper in the Southern \r\nHemisphere (Figure 3.25). The Indian Ocean shows similar features \r\nto the Southern Hemisphere Pacific, with a marked upper ocean \r\n(<500 m) fresh bias of order 0.3 PSS-78, and a strong near-surface \r\npositive bias of order 0.4 PSS-78 associated with the Arabian Sea \r\n(Figure 3.25)."


id: dc0337b2ef7d5665f80f40af4924b101
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35987
content:
"For the Southern Ocean in CMIP5, considerable fresh biases \r\nexist through the water column, and are most pronounced in the \r\nventilated layers representing the subtropical mode and intermediate \r\nwater masses (Sallee et al., 2013). A fresh bias in upper and \r\nintermediate layers of comparable magnitude is also seen in CMIP6 \r\n(Figure 3.25). The structure of the biases in the CMIP6 multi-model \r\nmean (which averages across many simulations with differing \r\nsubsurface geographies and differing Southern Ocean salinity \r\nbiases (Beadling et al., 2020)) is similar to that evident in the CMIP5 \r\nmulti-model mean, but with slightly smaller magnitudes. The Arctic \r\nOcean also on average exhibits a surface-enhanced fresh bias in the \r\nupper ocean (Figure 3.25), which is much larger than its Southern \r\nHemisphere counterpart."


id: 450140b05e4673d9fbcf6bc31c31951a
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9369
content:
"In summary, the structure of the salinity biases in the multi-model \r\nmean has not changed substantially between CMIP5 and CMIP6 \r\n(medium confidence), though there is limited evidence that the \r\nmagnitude of subsurface biases has been reduced. Biases are \r\nsufficiently small to provide confidence in the utility of CMIP-class \r\nmodels for detection and attribution of ocean salinity."


id: 4b2db27731f2b5a52f771c3c428b70f8
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 10473
content:
"Figure 3.27 | Maps of multi-decadal salinity trends for the near-surface \r\nocean. Units are Practical Salinity Scale 1978 [PSS-78] per decade. (Top) The best \r\nestimate (Section 2.3.3.2) observed trend (1950-2019, Durack and Wijffels, 2010). \r\n(Bottom) Simulated trend from the CMIP6 historical experiment multi-model mean \r\n(1950-2014). Black contours show the climatological mean salinity in increments of \r\n0.5 PSS-78 (thick lines 1 PSS-78). Further details on data sources and processing are \r\navailable in the chapter data table (Table 3.SM.1)."


id: dde96f56d4c8b8e0ccac4f39d1ca9608
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36303
content:
"the depth-integrated effect of mean salinity changes as captured in \r\nhalosteric sea level for the top 0 to 2000 m has also been assessed \r\nbased on observational products, and these results mirror near-surface \r\npatterns in the CMIP5 and CMIP6 models, with most areas that are \r\nbecoming fresher at the surface exhibiting increases in halosteric sea \r\nlevel, and areas becoming saltier exhibiting decreases (Durack et al., \r\n2014a; Figure  3.28). Further investigations using observations and \r\nmodels together have tied the long-term patterns of surface and \r\nsubsurface salinity changes to coincident changes to the evaporation "


id: 5220c9ab85f986af93e7eb048ec7b8d0
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11720
content:
"minus precipitation field over the ocean (Durack et al., 2012, 2013; \r\nDurack, 2015; Levang and Schmitt, 2015; Zika et al., 2015, 2018; Grist \r\net al., 2016; Lago et al., 2016; Cheng et al., 2020), however the rate of \r\nthese changes through time continues to be an active area of active \r\nresearch (Skliris et al., 2014; Zika et al., 2015, 2018; Cheng et al., \r\n2020; Sallee et al., 2021)."


id: 9bebe4e915e259504f7fa1c159d45ed0
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 24683
content:
"Climate change detection and attribution studies have considered \r\nsalinity, with the first of these assessed in AR5 (Bindoff et al., 2013). Since "


id: e177668e659d33d59986cceddce78a08
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36936
content:
"change have focused on thermosteric sea level changes (i.e., thermal \r\nexpansion due to warming) and ocean dynamic sea level change, \r\nboth of which are simulated in the CMIP5-generation of models. \r\nThe improved agreement between modelled thermal expansion and \r\nobserved estimates during the historical period led the SROCC to \r\nassess a high confidence level in the simulated thermal expansion \r\nusing climate models and high confidence in their ability to project \r\nfuture thermal expansion."


id: d736951826f7d000988d651ab4d2d8c1
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35173
content:
"that time, the positive detection conclusions (Stott et al., 2008; Pierce \r\net al., 2012; Terray et al., 2012) have been supported by a number of \r\nmore recent and independent assessments which have reproduced the \r\nmulti-decadal basin-scale patterns of change in observations and models \r\n(Figures 3.27 and 3.28; Durack et al., 2014a; Durack, 2015; Levang and \r\nSchmitt, 2015; Skliris et al., 2016). Observed depth-integrated basin \r\nresponses, contrasting the Pacific and Atlantic basins (freshening Pacific \r\nand enhanced salinity Atlantic) were also shown to be replicated in most \r\nhistorical (natural and anthropogenically forced) simulations, with \r\nthis basin contrast absent in CMIP5 and CMIP6 natural-only simulations \r\nthat exclude anthropogenic forcing (Durack et al., 2014a; Figure 3.28)."


id: f168cc3e57dc0eb21269d15eb6999128
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 39858
content:
"Since CMIP5 models do not include all necessary components of sea \r\nlevel change, this gap has been bridged by using offline models (for \r\nglacier melt and ice-sheet surface mass balance) driven by reanalyses \r\nand model output. Some studies have used offline mass inputs to \r\naccount for dynamic ice-sheet and terrestrial contributions. Slangen \r\net al. (2017) and Meyssignac et al. (2017) suggested including \r\ncorrections to several contributions to sea level changes including to \r\nthe Greenland surface mass balance and glacier contributions, based \r\non differences between CMIP5-driven model results and reanalysis\u0002driven results. This helps close the gap between models and \r\nobservations for the 20th century globally, as well as providing better \r\nagreement with tide gauge observations in terms of interannual and \r\nmulti-decadal variability at the regional scale."


id: 863882c880aeb2c4e974f687d41cc1b3
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 20766
content:
"While observational sparsity considerably limits quantification of \r\nregional changes, a recent study by Friedman et al. (2017) assessed \r\nsalinity changes in the Atlantic Ocean from 1896 to 2013 and \r\nconfirmed the pattern of mid-to-low latitude enhanced salinity \r\nand  high latitude North Atlantic freshening over this period exists \r\neven after accounting for the effects of the NAO and AMO."


id: 5eb4676c9d5be3e0f639e6932b818231
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13949
content:
"In CMIP6, ice sheets (see Sections 3.4.3.2 and 9.4) are included for \r\nthe first time in ISMIP6 (Nowicki et al., 2016). There is also scope \r\nfor  new insights into terrestrial water contributions from land \r\nsurface (and sub-surface) modelling in the Land Surface, Snow and \r\nSoil moisture Model Intercomparison Project (LS3MIP; van den Hurk \r\net al., 2016). In parallel, the GlacierMIP project (Hock et al., 2019a; \r\nMarzeion et al., 2020; see Sections 3.4.3.1 and 9.5) is also underway, \r\nand has provided more quantitative guidance and a comprehensive \r\nassessment of the uncertainties and best estimates of the current and \r\nfuture contributions of glaciers to the sea level budget."


id: b591972674327c173db24671b09af4a5
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29172
content:
"The SROCC concluded with high confidence that the dominant \r\ncause of GMSL rise since 1970 is anthropogenic forcing. Prior to \r\nthat, AR5 had concluded that it is very likely that there has been \r\na  substantial contribution from anthropogenic forcings to GMSL \r\nrise since the 1970s. Since AR5, several studies have identified \r\na human contribution to observed sea level change resulting from \r\na warming climate as manifest in thermosteric sea level change and \r\nthe contribution from melting glaciers and ice sheets."


id: 2019c4a01c9dfe418fae74783b09b9d8
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2868
content:
"In keeping with the scope of this chapter, this section addresses \r\nglobal and basin-scale sea level changes, whereas regional and local \r\nsea level changes are assessed in Section 9.6. In AR5, the observed \r\nsea level budget was closed by considering all contributing factors \r\nincluding ocean warming, mass contributions from terrestrial \r\nstorage, glaciers, and the Antarctic and Greenland ice sheets \r\n(Church et al., 2013b). The SROCC found that the observed global \r\nmean sea level (GMSL) rise is consistent within uncertainties with \r\nthe sum of the estimated observed contributions for 1993-2015 \r\nand 2006-2015."


id: 359d16ed896092ff504acb3739cc1731
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 6687
content:
"For the global mean thermosteric sea level change, Slangen et al. \r\n(2014) showed the importance of anthropogenic forcings (combined \r\ngreenhouse gas and aerosol forcings) for explaining the magnitude \r\nof the observed changes between 1957 and 2005, considering the \r\nfull depth of the ocean and natural forcings in order to capture the \r\nvariability (see also Figure 3.29). Over the 1950-2005 period, Marcos \r\nand Amores (2014) found that human influence explains 87% of \r\nthe 0-700 m global thermosteric sea level rise. Both thermosteric \r\nand regional dynamic patterns of sea level change in individual \r\nforcing experiments from CMIP5 were considered by Slangen et al. \r\n(2015) who showed that responses to anthropogenic forcings are \r\nsignificantly different from both internal variability and inter-model "


id: ae8052eb4ae04d3cfbcffe7e3134130b
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 27936
content:
"The current generation of climate models do not fully resolve many \r\nof the components required to close the observed sea level budget, \r\nsuch as glaciers, ice sheets and land water storage (see Section 9.6 \r\nand Box 9.1). Consequently, most CMIP-based analyses of sea level "


id: c374b290482f78f42e3bb56484114c09
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31665
content:
"a 95% probability that by 1970 GMSL was higher than that which \r\nwould have occurred in the absence of increasing GMST (Kopp et al., \r\n2016). Richter et al. (2020) compared modelled sea level change \r\nwith the satellite altimeter observations from 1993 to 2015; a period \r\nshort enough that internal variability can dominate the spatial \r\npattern of change. They found that when GMSL is not removed, \r\nmodel simulated zonally averaged sea level trends are consistent \r\nwith altimeter observations globally as well as in each ocean basin \r\nand much larger than might be expected from internal variability. \r\nUsing spatial correlation, Fasullo and Nerem (2018) showed that the \r\nsatellite altimeter trend pattern is already detectable."


id: e0e7aabb0e74f3a717654132ed99b53c
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36832
content:
"differences and that although greenhouse gas and anthropogenic \r\naerosol forcings produce opposite GMSL responses, there are \r\ndifferences in the response on regional scales. Based on these \r\nstudies, we conclude that it is very likely that anthropogenic forcing \r\nwas the main driver of the observed global mean thermosteric sea \r\nlevel change since 1970."


id: ffc09dfcf2fe5ce361decaf8c4ceba38
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42387
content:
"We note that current detection and attribution studies do not \r\nyet include all processes that are important for sea-level change \r\n(see Section 9.6). However, based on the body of literature available, \r\nwe conclude that the main driver of the observed GMSL rise since \r\nat least 1971 is very likely anthropogenic forcing. The assessed \r\nperiod starts in 1971 for consistency with observations assessed in \r\nCross-Chapter Box 9.1."


id: 555eb7ff2d9f33ca2a5cde3e35bebd80
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 12297
content:
"Circulation of the ocean, whether it be wind or density driven, plays \r\na prominent role in the heat and freshwater transport of the Earth \r\nsystem (Buckley and Marshall, 2016). Thus, its accurate representation \r\nis crucial for the realistic representation of water mass properties, and \r\nreplication of observed changes driven by atmosphere-land-ocean \r\ncoupling. Here, we assess the ability of CMIP models to reproduce \r\nthe observed large-scale ocean circulation, along with assessment \r\nof the detection and attribution of any anthropogenically-driven \r\nchanges. We also note that the process-based understanding of these \r\ncirculation changes and circulation changes occurring at smaller \r\nscales is assessed in Section 9.2.3."


id: 795a952c049477e85bcfe300b9c4d1e4
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18545
content:
"The Atlantic Meridional Overturning Circulation (AMOC) represents \r\na large-scale flow of warm salty water northward at the surface \r\nand a return flow of colder water southward at depth. As such, \r\nits mean state plays an important role in transporting heat in the \r\nclimate system, while its variability can act to redistribute heat (see \r\nSections 2.3.3.4.1 and 9.2.3.1 for more details). Paleo-climatic and \r\nmodel evidence suggest that changes in AMOC strength have played \r\na prominent role in past transitions between warm and cool climatic \r\nphases (e.g., Dansgaard et al., 1993; Ritz et al., 2013)."


id: 8dd3fa9470bef58477d2eba136da0150
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22141
content:
"The AR5 concluded that while climate models suggested that an \r\nAMOC slowdown would occur in response to anthropogenic forcing, \r\nthe short direct observational AMOC record precluded it from being \r\nused to support this model finding. Chapter  2 reports with high \r\nconfidence, a weakening of the AMOC was observed in the mid-2000s \r\nto the mid-2010s, while again also noting that the observational \r\nrecord was too short to determine whether this is a significant \r\ntrend or a manifestation of decadal and multi-decadal variability \r\n(Section  2.3.3.4.1). Indirect evidence of AMOC weakening since at \r\nleast the 1950s is also presented, but confidence in this longer-term \r\ndecrease was low (Section 2.3.3.4.1)."


id: 47832a0172107c515f8b5846e1a08768
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9794
content:
"simulations (Robson et al., 2012; Danabasoglu et al., 2016), which \r\nshow AMOC changes that are broadly consistent with the CMIP6 \r\nresponse, appear to disagree with observational estimates of AMOC \r\nover the historical period (Ezer et al., 2013; Caesar et al., 2018). It is \r\nnoted, however, that the relatively short length of the forced ocean \r\nsimulations and ocean reanalysis precludes a comparable assessment \r\nof 20th century trends. Furthermore, despite the similar AMOC \r\nevolution seen in forced ocean model simulations and the CMIP6 \r\nmodels, it is unclear whether the same underlying mechanisms are \r\nresponsible for the changes."


id: 5ac1dcf255dae9d8fca71d62b15b8bda
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13220
content:
"As  such, the simulated net forced response in AMOC is a balance \r\nbetween the different forcing factors (Section 9.2.3.1; Delworth and \r\nDixon, 2006; Menary et al., 2020). The differing AMOC response of \r\nCMIP5 and CMIP6 models during the historical period has been \r\nassociated with stronger aerosol effective radiative forcing in the \r\nCMIP6 models (Menary et al., 2020), such that the aerosol-induced \r\nAMOC increase during the 1940-1985 period overcomes the \r\ngreenhouse gas induced decline (Figure  3.30e). However, models \r\nsimulate a range of anthropogenic aerosol effective radiative forcing \r\nand a range of historical AMOC trends in CMIP6 (Menary et al., 2020) \r\nand there remains considerable uncertainty over the realism of the \r\nCMIP6 AMOC response during the 20th century (Figure 3.30d-f) due \r\nto disagreement among the differing lines of evidence. For example, \r\nocean reanalysis (Jackson et al., 2019) and forced ocean model "


id: e9e6b2fffd4efb85a5d4ddc2290cf4eb
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38501
content:
"overturning (Sallee et al., 2013). As the lower overturning cell is \r\nclosely related to Antarctic Bottom Water formation and deep \r\nconvection, both fields also display substantial errors in CMIP5 \r\nmodels (Heuze et al., 2013, 2015). CMIP6 climate models show \r\nclear improvements compared to CMIP5 in their representation of \r\nAntarctic Bottom Water, which suggests an improved representation \r\nof the lower overturning cell (Heuze, 2021)."


id: 8d4665213310ae495db5e06ddb409d53
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22040
content:
"to have occurred with high confidence in Section 2.3.3.4.1, as \r\nthe changes are outside of the range of modelled AMOC trends \r\n(regardless of whether they are forced or internally generated) in \r\nmost models. Thus, we have low confidence that anthropogenic \r\nforcing has influenced the observed changes in AMOC strength \r\nin the post-2004 period. In addition, there remains considerable \r\nuncertainty over the realism of the CMIP6 AMOC response during \r\nthe 20th century due to disagreement among the differing lines \r\nof observational and modelled evidence (i.e., historical AMOC \r\nestimates, ocean reanalysis, forced ocean simulations and \r\nhistorical CMIP6 simulations). Thus, we have low confidence that \r\nanthropogenic forcing has had a significant influence on changes \r\nin AMOC strength during the 1860-2014 period."


id: c098855840e81ebc6f5b0b4e4f77fbfd
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31185
content:
"The Southern Ocean circulation provides the principal connections \r\nbetween the world's major ocean basins through the circulation of \r\nthe Antarctic Circumpolar Current (ACC), while also largely controlling \r\nthe connection between the deep and upper layers of the global \r\nocean circulation, through its upper and lower overturning cells."


id: 50e7faf275f6b7fb35be1359ea18c464
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11507
content:
"The assessment of observations presented in Sections 2.3.3.4.2 \r\nand 9.2.3.2 reports that there is no evidence of an ACC transport \r\nchange, and it is unlikely that the mean meridional position of the \r\nACC has moved southward in recent decades (Sections 2.3.3.4.2 \r\nand 9.2.3.2). This is despite observations of surface wind displaying \r\nan intensification and southward shift (Section 2.4.1.2). There \r\nis low confidence in an observed intensification of upper ocean \r\noverturning in the Southern Ocean and there is medium confidence\r\nfor a slowdown of the Antarctic Bottom Water circulation and \r\ncommensurate Antarctic Bottom Water volume decrease since the \r\n1990s (Section 9.2.3.2). Section 9.2.3.2 presents new evidence, since \r\nSROCC, which assessed with medium confidence that the lower \r\ncell can episodically increase as a response to climatic anomalies, \r\ntemporally counteracting the forced tendency for reduced bottom \r\nwater formation."


id: 6ba3fd34d7daf0d9ea8c90a33000e76d
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 16243
content:
"In summary, while there have been improvements across successive \r\nCMIP phases (from CMIP3 to CMIP6) in the representation of \r\nthe Southern Ocean circulation, such that the mean zonal and \r\noverturning circulations of the Southern Ocean are now broadly \r\nreproduced, substantial observational uncertainty and climate \r\nmodel challenges preclude attribution of Southern Ocean circulation \r\nchanges (high confidence)."


id: f5fb1db95eef2bc343b79a277a4d8433
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 40632
content:
"and land management options (Erb et al., 2018; Harper et al., 2018) \r\n(see Sections 5.2.1.4.1 and 5.4, Figure 5.24 and Table 5.4 for details)."


id: 80fa626027f9dc05452cc615363af4a2
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19831
content:
"Leaf area index increases attributed to CO2 fertilization are due \r\nto a direct raised physiological response. However, for drylands, \r\nCO2-induced stomatal closure may act to conserve soil moisture and \r\nthereby indirectly drive higher photosynthesis through higher water \r\nuse efficiency (Lu et al., 2016). In models with nitrogen deposition, \r\nthere is evidence that this simulated effect also influences leaf area \r\nindex trends, however, because of a lack of literature based on \r\nlarge-scale land simulations including both nutrient limitation and \r\ncrop intensification, it is not yet possible to make an attribution \r\nstatement about their individual roles in leaf area index changes."


id: 7ac7de72a9d45b48d717ee9d86552714
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18801
content:
"In summary, Earth system models simulate globally averaged land \r\ncarbon sinks within the range of observation-based estimates \r\n(high confidence), but global-scale agreement masks large \r\nregional disagreements. Based on new studies that attribute \r\nchanges in atmospheric CO2 seasonal cycle to CO2 fertilization, \r\nalbeit counteracted by other factors, combined with the medium \r\nconfidence that models represent the processes driving changes in "


id: e8719cc830766c1a9b6dd13e8059e912
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38232
content:
"carbon fluxes and surface chlorophyll concentrations show moderate \r\nimprovements between CMIP5 and CMIP6. Similar improvements are \r\nseen in the representation of subsurface oxygen concentrations in \r\nmost ocean basins, while the representation of surface macronutrient \r\nconcentrations in CMIP6 is shown to have improved with respect \r\nto silicic acid but declined slightly with respect to nitrate. Model \r\nrepresentation of the micronutrient iron has not improved substantially \r\nsince CMIP5, but many more models are capable of representing iron. \r\nIn addition, a comparison of the carbon concentration and carbon \r\nclimate feedbacks shows no significant change between CMIP5 and \r\nCMIP6 (Arora et al., 2020)."


id: 63cbc31c931d9dd6bcfdb566eb229a79
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14849
content:
"Since AR5, research has also focused on the detection and attribution \r\nof regional patterns in ocean biogeochemical change relating to \r\ninterior deoxygenation, air-sea CO2 flux, and ocean carbon uptake and \r\nassociated acidification. Characterization of flux variability requires \r\nunderstanding of the suite of physical and biological processes \r\nincluding transport, heat fluxes, interior ventilation, biological \r\nproduction and gas exchange which can have very different controls \r\non seasonal versus interannual time scales in both the North Pacific \r\n(Ayers and Lozier, 2012) and North Atlantic (Breeden and McKinley, \r\n2016). In the Southern Ocean, models have difficulty reproducing \r\nthe observed seasonal cycle and interannual variability, making \r\nattribution particularly challenging (Lovenduski et al., 2016; Mongwe \r\net al., 2016, 2018)."


id: 9271b4b765be09fefc0e2792386b28e4
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22984
content:
"the seasonal cycle, we assess that there is medium confidence that \r\nCO2 fertilization is the main driver of the increase in the amplitude of \r\nthe seasonal cycle of atmospheric CO2. Based on available literature, \r\nCO2 fertilization has been the main driver of the observed greening \r\ntrend, but there is only low confidence in this assessment because \r\nof ongoing debate about the relative roles of CO2 fertilization, high \r\nlatitude warming, and land management, and the low number of \r\nmodels that represent the whole suite of processes involved."


id: 196a72de2066a811f7f6ee1d58d9b1a9
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 1686
content:
"Since CMIP5, there has been a general increase in ocean horizontal and \r\nvertical grid resolution in ocean model components (Arora et al., 2020; \r\nSeferian et al., 2020). The latter of these developments is particularly \r\nsignificant for projections of ocean stressors as it directly affects the \r\nrepresentation of stratification. Updates in the representation of ocean \r\nbiogeochemical processes between CMIP5 and CMIP6 have typically \r\ninvolved an increase in model complexity. Specific developments have \r\nbeen the more widespread inclusion of micronutrients, such as iron, \r\nvariable stoichiometric ratios, more detailed representation of lower \r\ntrophic levels including bacteria and the cycling and sinking of organic \r\nmatter. CMIP6 biogeochemical model performance is generally an \r\nimprovement on that of the parent CMIP5 generation of models \r\n(Seferian et al., 2020). The global representation of present-day air-sea "


id: 7a6b57bda457cb1419c3b7fe29e59fdf
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18733
content:
"(Perez et al., 2018). There has also been considerable improvement in \r\ndetection and attribution of anthropogenic CO2 versus eutrophication\u0002based acidification in coastal waters (Wallace et al., 2014)."


id: a674367e27410e265761153260b2eed7
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 26505
content:
"deoxygenation. The spatial distribution of the ocean deoxygenation \r\nin the interior of the ocean as well as over coastal areas is further \r\nassessed in Section 5.3."


id: e705407226767dbaac874145d23ecff5
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 37939
content:
"The increased evidence in recent studies supports an assessment that \r\nit is virtually certain that the uptake of anthropogenic CO2 was the \r\nmain driver of the observed acidification of the global surface open \r\nocean. The observed increase in acidification over the North Atlantic \r\nsubtropical and equatorial regions since 2000 is likely associated \r\nin part with an increase in ocean temperature, a response which \r\ncorresponds to the expected weakening of the ocean carbon sink with \r\nwarming. Due to strong internal variability, systematic changes in \r\ncarbon uptake in response to climate warming have not been observed \r\nin most other ocean basins at present. We further assess, consistent \r\nwith AR5 and SROCC, that deoxygenation in the upper ocean is due \r\nin part to anthropogenic forcing, with medium confidence. There is \r\nhigh confidence that Earth system models simulate a realistic time \r\nevolution of the global mean ocean carbon sink."


id: 92b45429081b2acfe8fdf54479ca4fc
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 23025
content:
"This section assesses model evaluation and attribution of changes \r\nin the modes of climate variability listed in Cross-Chapter Box 2.2, \r\nTable 2. The structure of the modes is described in Annex IV, observed \r\nchanges in the modes and associated teleconnections are assessed \r\nin Section 2.4, and the role of the modes in shaping regional climate \r\nis assessed in Section 10.1.3.2."


id: c733ee17fd0f2da859bd64d645953476
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 32364
content:
"Section 5.2.1.3 assesses that both observational reconstructions \r\nbased on the partial pressure of CO2 and ocean biogeochemical models \r\nshow a quasi-linear increase in the ocean sink of anthropogenic CO2\r\nfrom 1.0 +- 0.3 PgC yr-1 to 2.5 +- 0.6 PgC yr -1 between 1960-1969 and \r\n2010-2019 in response to global CO2 emissions (high confidence). \r\nDuring the 1990s, the global net flux of CO2 into the ocean is \r\nestimated to have weakened to 0.8  +-  0.5  PgC yr -1 while in 2000 \r\nand thereafter, it is estimated to have strengthened considerably \r\nto rates of 2.0 +- 0.5 PgC yr -1, associated with changes in SST, the \r\nsurface concentration of dissolved inorganic carbon and alkalinity, \r\nand decadal variations in atmospheric forcing (Landschutzer et al., \r\n2016, see also Section 5.2)."


id: 783f0e3b6288313025227baf8f517c28
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18366
content:
"The Northern Annular Mode (NAM; also known as the Arctic \r\nOscillation) is an oscillation of atmospheric mass between the Arctic \r\nand northern mid-latitudes, analogous to the Southern Annular Mode \r\n(SAM; Section 3.7.2). It is the leading mode of variability of sea-level \r\npressure in the northern extratropics but also has a clear fingerprint \r\nthrough the troposphere up to the lower stratosphere, with maximum \r\nexpression in boreal winter (Kidston et al., 2015). The North Atlantic \r\nOscillation (NAO) can be interpreted as the regional expression of the \r\nNAM and captures most of the related variance in the troposphere over \r\na broad North Atlantic/Europe domain. Indices measuring the state of \r\nthe NAO correlate highly with those of the NAM, and teleconnection \r\npatterns for both modes are rather similar (Feldstein and Franzke, \r\n2006). A detailed description of the NAM and the NAO as well as \r\ntheir associated teleconnection over land is given in Annex IV.2.1."


id: 37d42271f7646fb686cb8754d0308027
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7031
content:
"AR5 found that while models simulated correctly most of the \r\nspatial properties of the NAM, substantial inter-model differences \r\nremained in the details of the associated teleconnection patterns \r\nover land (Flato et al., 2013). The AR5 reported that most models \r\ndid not reproduce the observed positive trend of the NAO/NAM \r\nindices during the second half of the 20th century. It was unclear \r\nto what extent this failure reflected model shortcomings and/or if \r\nthe observed trend could be simply related to pronounced internal "


id: 4ee86f507e0c4713d40a6674c4331f2e
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11163
content:
"climate variability. The AR5 accordingly did not make an attribution \r\nassessment for the NAO/NAM."


id: ee2740dce8946fcba9acf733bb0700fa
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 39271
content:
"of trends in the CMIP6 historical simulations, and the observed NAM \r\ntrends over the same period lie above the 90th percentile. There is \r\na tendency for the CMIP5 models to systematically underestimate \r\nthe level of multi-decadal versus interannual variability of the winter \r\nNAO and jet stream compared to observations (X. Wang et al., 2017; \r\nBracegirdle et al., 2018; Simpson et al., 2018). Results from CMIP6 \r\n(Figure 3.33j,k) and over the 1958-2019 period (Figure 3.34a) confirm \r\nthis conclusion and seriously question the ability of the models to \r\nsimulate long-term fluctuations of the NAO/NAM, independently of \r\nits forced or internal origins."


id: 6978d1da549b9bc7e4748b951acd3ab2
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 15824
content:
"Dedicated SST-forced stand-alone atmospheric model experiments \r\n(AMIP) suggest that ocean forcing appears to play a role in decadal \r\nvariability of the NAO and associated fluctuations in the strength \r\nof the jet (Woollings et al., 2015). In particular, Atlantic and Indian \r\nOcean SST anomalies (Fletcher and Cassou, 2015; Baker et al., 2019; \r\nDouville et al., 2019; Dhame et al., 2020) may have contributed \r\nto the long-term positive trend of the winter NAO/NAM over the \r\n20th  century, but there is only low confidence in such a causal \r\nrelationship because of the limitation of the imposed SST approach \r\nin AMIP and the uncertainties in observed SST trends among datasets \r\nused as forcing of the atmospheric model. The representation of \r\nthe NAM and NAO spatial structure is slightly improved in AMIP \r\nensembles (Figure 3.33g,h), which also produce slightly larger trends \r\nthan the historical simulations for the NAO, but not for the NAM."


id: 68992da51c992998ed6593d3731fc70b
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 15671
content:
"When calculated over the most recent two decades, the wintertime \r\nNAM/NAO trend is weakly negative since the mid-1990s (Hanna \r\net al., 2015). Recent studies based on observations (Gastineau and \r\nFrankignoul, 2015) and dedicated modelling experiments (Davini \r\net al., 2015; Peings and Magnusdottir, 2016) suggest that the recent \r\ndominance of negative NAM/NAO could be partly related to the latest \r\nshift of the Atlantic Multi-decadal Variability (AMV) to a warm phase \r\n(Sections 2.4.4 and 3.7.7). Some recent modelling studies also find \r\nthat the Arctic sea ice decline might be partly responsible for more \r\nrecurrent negative NAM/NAO (Peings and Magnusdottir, 2013; B.M. \r\nKim et al., 2014; Nakamura et al., 2015), while other studies do not \r\nrobustly identify such responses in models (see also Cross-Chapter \r\nBox 10.1)."


id: 4fa3ed93a6242ffc4bda469782611271
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11298
content:
"In contrast to winter, the observed trend of the NAO index over \r\n1958-2014 is overall negative in summer and is associated with \r\nmore recurrent blocking conditions over Greenland, in particular \r\nsince the mid-1990s, thus contributing to the acceleration of melting \r\nof the Arctic sea ice (Section 3.4.1.1) and Greenland Ice Sheet \r\n(Section 3.4.3.2; Fettweis et al., 2013; Hanna et al., 2015; Ding et al., \r\n2017). The origin of the negative trend of the summer NAO has \r\nnot been clearly identified, and is hypothesized to be the result of \r\ncombined influences (Lim et al., 2019), though trends in summertime \r\nNAO should also be interpreted with caution because of the presence \r\nof strong multi-decadal variability. The recent observed negative NAO \r\nprevalence and related blocking over Greenland is not present in any \r\nof the CMIP5 models (Hanna et al., 2018)."


id: c963cb4630d1c62d9a9d59d556743fbd
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 32757
content:
"The observed trend of the NAM and NAO indices is positive in \r\nwinter when calculated from the 1960s (Section 2.4.1.1) but it \r\nincludes large multi-decadal variability, which means that the \r\nnature of the trend should be interpreted with caution (Gillett et al., \r\n2013). The multi-model multi-member ensemble mean of the trend \r\nestimated from historical simulations over that period is very close \r\nto zero for both CMIP5 and CMIP6 (Figures 3.33j,k and 3.34a). Even \r\nif one cannot rule out that 1958-2014 was an exceptional period \r\nof variability, the observational estimates of the wintertime NAO \r\ntrend lie outside the 5th-95th percentile range of the distribution "


id: 90209aaed1d9cedb70e00556f88aa3a6
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22568
content:
"Regarding the influence of external forcings since pre-industrial \r\ntimes, AR5 noted that CMIP5 models tend to show an increase in the \r\nNAM in response to greenhouse gas increases (Bindoff et al., 2013). "


id: 309663efb0e5dabe0994bba010dfe238
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5801
content:
"Based on the CMIP5 historical ensemble, Gillett and Fyfe (2013) \r\nhowever showed that such a trend is not significant in all seasons. \r\nA multi-model assessment of eight CMIP5 models found a NAM \r\nincrease in response to greenhouse gases, but no robust influence of \r\naerosol changes (Gillett et al., 2013). As for ozone depletion, there is \r\nno robust detectable influence on long-term trends of the NAO/NAM \r\n(Karpechko et al., 2018) in contrast to the SAM (Section 3.7.2), but \r\nthere are indications that extreme Arctic ozone depletion events and \r\ntheir surface expression are linked to an anomalously strong NAM \r\nepisodes (Calvo et al., 2015; Ivy et al., 2017). However, the direction \r\nof causality here is not clear."


id: e37b803d4de356f77519f661f7991e9f
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 37318
content:
"Conclusions on external forcing influences on the NAM are supported \r\nby CMIP6 results based on single forcing ensembles (Figure 3.34a). \r\nPositive trends are found in historical simulations over 1958-2019 \r\nin boreal winter and are mainly driven by greenhouse gas increases. \r\nNo significant trends are simulated in response to anthropogenic \r\naerosols, stratospheric ozone or natural forcing. Albeit weak and "


id: edbc18aaf0f8ae0a1eb7c8aef527a5a5
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 39182
content:
"The Southern Annular Mode (SAM) consists of a meridional \r\nredistribution of atmospheric mass around Antarctica (Figure 3.33c,f), \r\nassociated with a meridional shift of the jet and surface westerlies \r\nover the Southern Ocean. SAM indices are variously defined as the \r\ndifference in zonal-mean sea level pressure or geopotential height \r\nbetween middle and high latitudes or via a principal-component \r\nanalysis (Annex IV.2.2). Observational aspects of the SAM are \r\nassessed in Section 2.4.1.2."


id: 166be329541beab3dca7eb1e33d87d8d
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 1164
content:
"AR5 assessed that CMIP5 models have medium performance in \r\nreproducing the SAM with biases in pattern (Flato et al., 2013). It \r\nalso concluded that the trend of the SAM toward its positive phase in \r\naustral summer since the mid-20th century is likely to be due in part \r\nto stratospheric ozone depletion, and there was medium confidence \r\nthat greenhouse gases have also played a role (Bindoff et al., 2013). \r\nBased on proxy reconstructions, AR5 found with medium confidence \r\nthat the positive SAM trend since 1950 was anomalous compared \r\nto the last 400 years (Masson-Delmotte et al., 2013)."


id: 7f2556f252860c797e29c66d46b87d0b
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19522
content:
"Finally, there is some evidence of an apparent signal-to-noise \r\nproblem referred to as 'paradox' in seasonal and decadal hindcasts \r\nof the NAO over the period 1979-2018 (Scaife and Smith, 2018), \r\nwhich suggests that the NAO response to external forcing, SST or \r\nsea ice anomalies could be too weak in models. The weakness of the \r\nsignal has been related to troposphere-stratosphere coupling which \r\nis too intermittent (O'Reilly et al., 2019b) and to chronic model biases \r\nin the persistence of NAO/NAM daily regimes, which is critically \r\nunderestimated in coupled models (Strommen and Palmer, 2019; \r\nZhang and Kirtman, 2019), and which does not exhibit significant \r\nimprovement when model resolution is increased (Fabiano et al., \r\n2020). Note, however, that the apparent signal-to-noise problem may \r\nbe dependent on the period analysed over the 20th century, which \r\nquestions its interpretation as a general characteristic of coupled \r\nmodels (Weisheimer et al., 2020)."


id: 1147aef9fd4e3faa3920f07e3bdb3207
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 235
content:
"In summary, CMIP5 and CMIP6 models are skilful in simulating the \r\nspatial features and the variance of the NAM/NAO and associated \r\nteleconnections (high confidence). There is limited evidence for \r\na significant role for anthropogenic forcings in driving the observed \r\nmulti-decadal variations of the NAM/NAO from the mid 20th century. \r\nConfidence in attribution is low: (i) because there is a large spread in the \r\nmodelled forced responses which is overwhelmed anyway by internal \r\nvariability; (ii) because of the apparent signal-to-noise problem; and (iii) \r\nbecause of the chronic inability of models to produce a range of trends \r\nwhich encompasses the observed estimates over the last 60 years."


id: b7b5f1a86e01f46ebab0db947eeeb99c
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29538
content:
"CMIP6 historical simulations capture the observed positive trend of \r\nthe summertime SAM when calculated from the 1970s to the 2010s \r\n(Figure 3.34b). J.L. Thomas et al. (2015) found that the chance of the \r\nobserved 1980-2004 trend occurring only due to internal variability is \r\nless than 10% in many of the CMIP5 models, and results from CMIP6 \r\nmodels suggest that the chance of the 1979-2019 trend being due "


id: 447ab2adbae3dfe1cfa13567cf9a1428
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9275
content:
"such as variations in the imposed insolation may be too weak, whether \r\nmodels are insufficiently sensitive to such variations, or whether internal \r\nvariability including that associated with tropical Pacific variability \r\nis under-represented (Abram et al., 2014). The explanation could be \r\na combination of all these factors. However, despite the aforementioned \r\nlimitations of the reconstructions, Section 2.4.1.2 assesses that \r\nthe recent positive trend in the SAM is likely unprecedented in at \r\nleast the past millennium (medium confidence). CMIP5 and CMIP6 \r\nlast-millennium simulations only capture the present anomalous state \r\nduring the final decades of the simulations which are dominated by \r\nhuman influence; this state is also outside the range of simulated \r\nvariability characteristic of pre-industrial times."


id: 98447ac24581681aa6e31e99a0a3d6b5
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 24001
content:
"In summary, it is very likely that anthropogenic forcings have \r\ncontributed to the observed trend of the summer SAM toward its \r\npositive phase since the 1970s. This assessment is supported by \r\nfurther model studies that confirm the human influence on the \r\nsummertime SAM with improved models since AR5. While ozone \r\ndepletion contributed to the trend from the 1970s to the 1990s \r\n(medium confidence), its influence has been small since 2000, \r\nleading to a weaker summertime SAM trend over 2000-2019 \r\n(medium confidence). Climate models reproduce the spatial structure \r\nof the summertime SAM observed since the late 1970s well (high \r\nconfidence). CMIP6 models reproduce the spatiotemporal features \r\nand recent multi-decadal trend of the summertime SAM better than \r\nCMIP5 models (medium confidence). However, there is a large spread \r\nin the intensity of the SAM response to ozone and greenhouse gas \r\nchanges in both CMIP5 and CMIP6 models (high confidence), which "


id: 9136400481c72ae0e201e1c0f3a238c6
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22757
content:
"On longer time scales, last millennium experiments from CMIP5 \r\nmodels fail to capture multicentennial variability evident in the \r\nreconstructions for the pre-industrial era (Abram et al., 2014; Datwyler \r\net al., 2018), which is also the case in those from available CMIP6 \r\nmodels (Figure  3.35). However, there is large uncertainty among \r\nreconstructions (Section  2.4.1.2). It is therefore unclear whether this \r\ndisagreement reflects this observational uncertainty, whether forcings "


id: 16e855b4d33afdbf71ff0df5f7b82e17
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 1249
content:
"limits the confidence in the assessment of the ozone contribution \r\nto the observed trends. CMIP5 and CMIP6 models do not capture \r\nmulticentennial variability of the SAM found in proxy reconstructions \r\n(low confidence). This confidence level reflects that it is unclear \r\nwhether this is due to a model or an observational shortcoming."


id: a8fb41c45178b7f9ae6265a940bd2173
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 26937
content:
"The El  Nino-Southern Oscillation (ENSO), which is generated via \r\nseasonally modulated interactions between the tropical Pacific ocean \r\nand atmosphere, influences severe weather, rainfall, river flow and \r\nagricultural production over large parts of the world (McPhaden \r\net al., 2006). In fact, the remote climate influence of ENSO is so large \r\nthat knowledge of its current phase and forecasts of its future phase \r\nlargely underpin many seasonal rainfall and temperature forecasts \r\nworldwide (Annex IV.2.3)."


id: 85f96ef39151a0d6941bd0a471db60b1
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 20713
content:
"AR5 noted that there have been clear improvements in the simulation of \r\nENSO through previous generations of CMIP models (Flato et al., 2013), \r\nsuch that many CMIP5 models displayed behaviour that was qualitatively \r\nsimilar to that of the observed ENSO (Guilyardi et al., 2012). However, \r\nsystematic errors were identified in the models' representation of the \r\ntropical Pacific mean state and aspects of their interannual variability \r\nthat affect quantitative comparisons. The AR5 assessment of ENSO \r\nconcluded that the considerable observed inter-decadal modulations \r\nin ENSO amplitude and spatial pattern were largely consistent with \r\nunforced model simulations. Thus, there was low confidence in the role \r\nof a human-induced influence in these (Bindoff et al., 2013)."


id: 627e587ff67bd7a296be4c9455ac769
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14615
content:
"Observed ENSO amplitude, which is measured by the standard \r\ndeviation of SST anomalies in a central equatorial Pacific region often \r\nreferred to as the Nino 3.4 region, along with the lifecycle of events, are \r\nboth reasonably well reproduced by most CMIP5 and CMIP6 models \r\n(Figure 3.36; Bellenger et al., 2014; Planton et al., 2021). The average \r\nCMIP5 model ENSO amplitude is slightly lower than that observed, \r\nwhile the average CMIP6 model ENSO amplitude is slightly higher than \r\nobserved (Figure 3.36). The ENSO amplitude of the individual models, \r\nhowever, is highly variable across CMIP5 and CMIP6 models with many \r\ndisplaying either more or less variability than observed (Stevenson, \r\n2012; Grose et al., 2020; Planton et al., 2021)."


id: 721481575cb2c27259014d383891923c
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17317
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4640
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5337
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 15803
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 8213
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31145
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 10208
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 15788
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 28487
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18990
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5468
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 21087
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14770
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 24738
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7180
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 41824
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 30787
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 30409
content:
"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
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11455
content:
"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
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19860
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13286
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25211
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2523
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33338
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 8795
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 1203
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29745
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25478
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31102
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 30222
content:
"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)."


id: d6d126393bc9b1c27f575b34c2366b7c
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35104
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 15485
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19460
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 12264
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 8299
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 217
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36520
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9719
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 39573
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31228
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25474
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19920
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 39567
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 10738
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34609
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38174
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36636
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7930
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22304
content:
"feedbacks acting on the response to orbital differences versus the \r\npresent-day, affecting the seasonality of insolation."


id: fae78e37ec9d40e38c6f1e1b6cbf80b8
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 41526
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 32335
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2242
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2339
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 40403
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18856
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9813
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2569
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 16223
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22753
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 40066
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 12376
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9408
content:
"Taken together, this evidence shows that humans are the dominant cause of observed global warming over \r\nrecent decades."


id: 72a6ba8ad62fe2e4a555f276951e89a8
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17413
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
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33628
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 3905
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 20268
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 37444
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25047
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 26704
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5398
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2840
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4981
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 21458
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7076
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42017
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 6036
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7904
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 41742
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4385
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 10037
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 30497
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29040
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19265
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4646
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11003
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11028
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38664
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 8499
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2315
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31679
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31271
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29193
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 3498
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
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22107
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 41215
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5232
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 40904
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18740
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34428
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 23569
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22089
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34290
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 21897
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 15057
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 6510
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 8968
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31056
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2500
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17325
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5829
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14048
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2751
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 8739
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33633
content:
"Section 4.4 synthesizes information from initialized predictions and \r\nnon-initialized projections for the near-term change."


id: 11ef9a1ac75cd64d2836f01670700c26
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 342
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5167
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 30646
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 40949
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9859
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35461
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38808
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33515
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 10997
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34772
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17780
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31489
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18848
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29243
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 1915
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 41258
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36280
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 28235
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9376
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42237
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7178
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 37973
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36379
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29965
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13716
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29283
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11660
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 20753
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35969
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 23815
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29849
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 30590
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17006
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14692
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 40236
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42408
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29002
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42084
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 8152
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25712
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 392
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
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42168
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18405
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 6306
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14189
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35729
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25685
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42138
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4249
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18394
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17103
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 21345
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19740
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19540
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34039
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11746
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 3409
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25455
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5416
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 30349
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 324
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14321
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 1409
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25081
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36828
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 255
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 27130
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25949
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18956
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18756
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 1913
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9142
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11042
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29474
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4680
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 36477
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42119
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33130
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33156
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2301
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 27014
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 23148
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42263
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22401
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 32002
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 28428
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5492
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18185
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13232
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 20034
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 40068
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7591
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9903
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 32056
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38090
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13615
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35676
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
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42012
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 21787
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)."


id: 24fd3cf44f80b33953dae9216b2e3b99
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 3708
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33844
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 28656
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33163
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."


id: 978ca2500a8963e768520804c339be7e
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 23879
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 41250
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25617
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 3869
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2130
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 10359
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 6917
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 12430
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 16534
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 3379
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 15913
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9282
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 30936
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38844
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13799
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5727
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42405
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34695
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 8852
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31471
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11491
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 4719
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 17355
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 41927
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22686
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 37472
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 9258
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31449
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25382
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 13403
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38704
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34014
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11783
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 39603
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
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 37498
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34027
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33769
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34486
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 40230
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 16800
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19445
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 15916
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 38992
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34606
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 23091
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 12839
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25096
content:
"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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42293
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 12208
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 22368
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 28381
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 27597
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 3474
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 18121
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 23426
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 27081
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 30372
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 19494
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25603
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 16827
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 26051
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 42374
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 21230
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34696
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 34448
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 14042
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33648
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5140
content:
"uncertainties and contradictory analyses regarding model accuracy in \r\nsimulating tropical upper tropospheric temperature trends."


id: 239168070b8d10e29106afe216e3fde6
created_at: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 3458
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25056
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 5979
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 40307
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11224
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 2481
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29129
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 39500
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 11521
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 15704
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 16007
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 25648
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 31934
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 33160
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 24088
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 30648
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 7125
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35881
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 29591
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 1890
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 35697
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 20492
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: 2023-04-19 13:21:34
updated_at: 2023-04-19 13:21:34
content_type: text
index: climate_gpt_v2
vector_id: 40345
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)."


