Editing IPCC:AR6/WGI/Chapter-3 (section)

===== 3.3.1.2.2 Stratospheric temperature =====

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The AR5 concluded that the CMIP5 models simulated a generally realistic evolution of lower-stratospheric temperatures ( [[#Bindoff--2013|Bindoff et al., 2013]] ; [[#Flato--2013|Flato et al., 2013]] ), which was better than that of the CMIP3 models, in part because they generally include time-varying ozone concentrations, unlike many of the CMIP3 models. Nonetheless, it was noted that there was a tendency for the simulations to underestimate stratospheric cooling compared to observations. [[#Bindoff--2013|Bindoff et al. (2013)]] concluded that it was ''very likely'' that anthropogenic forcing, dominated by stratospheric ozone depletion by chemical reactions involving trace species known as ozone-depleting substances (ODS), had contributed to the cooling of the lower stratosphere since 1979. Increased greenhouse gases cause near-surface warming but cooling of stratospheric temperatures.

For the lower stratosphere, a debate has been ongoing since AR5 between studies finding that models underestimate the cooling of stratospheric temperature ( [[#Santer--2017b|Santer et al., 2017b]] ), in part because of underestimated stratospheric ozone depletion ( [[#Eyring--2013|Eyring et al., 2013]] ; [[#Young--2013|Young et al., 2013]] ), and studies finding that lower stratospheric temperature trends are within the range of observed trends ( [[#Young--2013|Young et al., 2013]] ; [[#Maycock--2018|Maycock et al., 2018]] ). Different observational data and different time periods explain the different conclusions. [[#Aquila--2016|Aquila et al. (2016)]] used forced chemistry-climate models with prescribed SST to investigate the influence of different forcings on global stratospheric temperature changes. They found that in the lower stratosphere, the simulated cooling trend due to increasing greenhouse gases was roughly constant over the satellite era, while changes in ODS concentrations amplified that stratospheric cooling trend during the era of increasing ozone depletion up until the mid-1990s, with a flattening of the temperature trend over the subsequent period over which stratospheric ozone has stabilized ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.5.2|Section 2.2.5.2]] ). [[#Mitchell--2020|Mitchell et al. (2020)]] showed that while models simulate realistic trends in tropical lower-stratospheric temperature over the whole 1979–2014 period when compared with radiosonde data, they tend to overestimate the cooling trend over the ozone depletion era (1979–1997) and underestimate it over the ozone stabilization era (1998–2014; Figure 3.10b,c). They speculate that those disagreements are due to poor representations of stratospheric ozone forcing.

Upper stratospheric temperature changes were not assessed in the context of attribution or model evaluation in AR5, but this is an area where there has been considerable progress over recent years ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.2.1|Section 2.3.1.2.1]] ). Simulated temperature changes in chemistry-climate models show good consistency with the reprocessed dataset from NOAA STAR but are less consistent with the revised UK Met Office record ( [[#Karpechko--2018|Karpechko et al., 2018]] ). The latter still shows stronger cooling than simulated in chemistry-climate models ( [[#Maycock--2018|Maycock et al., 2018]] ). Reanalyses, which assimilate AMSU and SSU datasets, indicate an upper-stratospheric cooling from 1979 to 2009 of about 3°C at 5 hPa and 4°C at 1 hPa that agrees well with the cooling in simulations with prescribed SST and using CMIP5 forcings ( [[#Simmons--2014|Simmons et al., 2014]] ). [[#Mitchell--2016|Mitchell (2016)]] used regularized optimal fingerprinting techniques to carry out an attribution analysis of annual mid- to upper-stratospheric temperature in response to external forcings. They found that anthropogenic forcing has caused a cooling of approximately 2°C–3°C in the upper stratosphere over the period of 1979–2015, with greenhouse gases contributing two thirds of this change and ozone depletion contributing one third. They found a large upper-stratospheric temperature change in response to volcanic forcing (0.4°C–0.6°C for Mount Pinatubo) but that change is still smaller than the lower-stratospheric signal. [[#Aquila--2016|Aquila et al. (2016)]] found that the cooling of the middle and upper stratosphere after 1979 is mainly due to changes in greenhouse gas concentrations. Volcanic eruptions and the solar cycle were found not to affect long-term stratospheric temperature trends but to have short-term influences.

In summary, based on the latest updates to satellite observations of stratospheric temperature, we assess that simulated and observed trends in global mean temperature through the depth of the stratosphere are more consistent than based on previous datasets, but some differences remain ( ''medium confidence'' ). Studies published since AR5 increase our confidence in the simulated stratospheric temperature response to greenhouse gas and ozone changes, and support an assessment that it is ''extremely likely'' that stratospheric ozone depletion due to ozone-depleting substances was the main driver of the cooling of the lower stratosphere between 1979 and the mid-1990s, as expected from physical understanding. Similarly, revised observations and new studies support an assessment that it is ''extremely likely'' that anthropogenic forcing, both from increases in greenhouse gas concentrations and depletion of stratospheric ozone due to ozone-depleting substances, was the main driver of upper-stratospheric cooling since 1979.

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'''Cross-Chapter Box 3.1 | Global Surface Warming Over the Early 21st Century'''

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'''Contributors:''' Christophe Cassou (France), Yu Kosaka (Japan), John C. Fyfe (Canada), Nathan P. Gillett (Canada), Ed Hawkins (United Kingdom), Blair Trewin (Australia)

The AR5 found that the rate of global mean surface temperature (GMST) increase inferred from observations over the 1998–2012 period was lower than the rate of increase over the 1951–2012 period, and lower than the ensemble mean increase in historical simulations from CMIP5 climate models extended by Representative Concentration Pathway (RCP) scenario simulations beyond 2005

( [[#Flato--2013|Flato et al., 2013]] ). This apparent slowdown of surface global warming compared to the 62-year rate was assessed with ''medium confidence'' to have been caused in roughly equal measure by a cooling contribution from internal variability and a reduced trend in external forcing (particularly associated with solar and volcanic forcing) in AR5 based on expert judgement ( [[#Flato--2013|Flato et al., 2013]] ). In AR5 it was assessed that almost all CMIP5 simulations did not reproduce the observed slower warming, and that there was ''medium confidence'' that the trend difference from the CMIP5 ensemble mean was to a substantial degree caused by internal variability with possible contributions from forcing error and model response uncertainty. This Cross-Chapter Box assesses new findings from observational products and statistical and physical models on trends over the 1998–2012 period considered in AR5.

'''Updated observational and reanalyses datasets and comparison with model simulations'''
Since AR5, there have been version updates and new releases of most observational GMST datasets (Cross-Chapter Box 2.3). All the updated products now available consistently find stronger positive trends for 1998–2012 than those assessed in AR5 ( [[#Cowtan--2014|Cowtan and Way, 2014]] ; [[#Karl--2015|Karl et al., 2015]] ; [[#Hausfather--2017|Hausfather et al., 2017]] ; [[#Medhaug--2017|Medhaug et al., 2017]] ; [[#Simmons--2017|Simmons et al., 2017]] ; [[#Risbey--2018|Risbey et al., 2018]] ). [[#Simmons--2017|Simmons et al. (2017)]] reported that the 1998–2012 GMST trends in the updated observational and reanalysis datasets available at that time ranged from 0.06°C to 0.14°C per decade, compared with the 0.05°C per decade on average reported in AR5, while the latest data products reported in [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] Table 2.4 show GMST or global mean near-surface air temperature (GSAT) trends over that period ranging from 0.12°C to 0.14°C per decade. The lowest trend in [[#Simmons--2017|Simmons et al. (2017)]] is from HadCRUT4, now superseded by HadCRUT5, which shows a trend of 0.12°C per decade. The upward revision is mainly due to improved sea surface temperature (SST) datasets and infilling of surface temperature in locations with missing records in observational products, mainly in the Arctic (see Cross-Chapter Box 2.3 for details).

With these updates, all the observed trends assessed here lie within the 10th–90th percentile range of the simulated trends in the CMIP5 and CMIP6 simulations (Cross-Chapter Box 3.1, Figure 1a). This result is insensitive to whether model GSAT (based on surface air 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 whether or not masking with the observational data coverage is applied. Therefore, the observed 1998–2012 trend is consistent with both the CMIP5 or CMIP6 multi-model ensemble of trends over the same period ( ''high confidence'' ).

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'''Cross-Chapter Box 3.1, Figure 1 | 15-year trends of global surface temperature for 1998–2012 and 2012–2026. (a, b)''' GSAT and GMST trends for 1998–2012 '''(a)''' and 2012–2026 '''(b)''' . Histograms are based on GSAT in historical simulations of CMIP6 (red shading, extended by SSP2-4.5) and CMIP5 (grey shading; extended by RCP4.5). Filled and open diamonds at the top represent multi-model ensemble means of GSAT and GMST trends, respectively. Diagonal lines show histograms of HadCRUT5.0.1.0. Triangles at the top of (a) represent GMST trends from Berkeley Earth, GISTEMP, [[#Kadow--2020|Kadow et al. (2020)]] and NOAAGlobalTemp-Interim, and the GSAT trend from ERA5. Selected CMIP6 members whose 1998–2012 trends are lower than the HadCRUT5.0.1.0 mean trend are indicated by purple shading (a) and (b). In (a), model GMST and GSAT, and ERA5 GSAT are masked to match HadCRUT data coverage. '''(c–d)''' Trend maps of annual near-surface temperature for 1998–2012 based on HadCRUT5.0.1.0 mean '''(c),''' and composited surface air temperature trends of subsampled CMIP6 simulations '''(d)''' ''with GSAT trends in the purple shaded'' area in (a). In (c), cross marks indicate trends that are not significant at the 10% level based on t-tests with serial correlation taken into account. The ensemble size used for each of the histograms and the trend composite is indicated at the top right of each of the panels (a, b, d). Model ensemble members are weighted with the inverse of the ensemble size of the same model, so that each model is equally weighted. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

'''Internal variability'''
All the observation-based GMST and GSAT trends are lower than the multi-model mean GMST and GSAT trends of both CMIP5 and CMIP6 for 1998–2012 (Cross-Chapter Box 3.1, Figure 1a). This suggests a possible cooling contribution from internal variability during this period. This is supported by initialized decadal hindcasts, which account for the phase of the multi-decadal modes of variability (Sections [[#_idTextAnchor002|3.7.6]] and [[#_idTextAnchor003|3.7.7]] ), and which reproduce observed global mean SST and GSAT trends better than uninitialized historical simulations ( [[#Guemas--2013|Guemas et al., 2013]] ; [[#Meehl--2014|Meehl et al., 2014]] ).

Studies since AR5 identify Pacific Decadal Variability (PDV) as the leading mode of variability associated with unforced decadal GSAT fluctuations, with additional influence from Atlantic Multi-decadal Variability (Annex IV.2.6, IV.2.7; [[#Brown--2015|Brown et al., 2015]] ; [[#Dai--2015|Dai et al., 2015]] ; [[#Steinman--2015|Steinman et al., 2015]] ; [[#Pasini--2017|Pasini et al., 2017]] ). PDV transitioned from positive (El Niño-like) to negative (La Niña-like) phases during the slow warming period (Figure 3.39f and Cross-Chapter Box 3.1, Figure 1c). Model ensemble members that capture the observed slower decadal warming under transient forcing, and time segments of model simulations that show decadal GSAT decreases under fixed radiative forcing, also feature negative PDV trends (Cross-Chapter Box 3.1, Figure 1d; [[#Meehl--2011|Meehl et al., 2011]] , 2013, 2014; [[#Maher--2014|Maher et al., 2014]] ; [[#Middlemas--2016|Middlemas and Clement, 2016]] ), suggesting the influence of PDV. This is confirmed by statistical models with the PDV-GSAT relationship estimated from observations and model simulations ( [[#Schmidt--2014|Schmidt et al., 2014]] ; [[#Meehl--2016b|Meehl et al., 2016b]] ; [[#Hu--2017|Hu and Fedorov, 2017]] ), selected ensemble members and time segments from model simulations where PDV by chance evolves in phase with observations over the slow warming period ( [[#Huber--2014|Huber and Knutti, 2014]] ; [[#Risbey--2014|Risbey et al., 2014]] ), and coupled model experiments in which PDV evolution is constrained to follow the observations ( [[#Kosaka--2013|Kosaka and Xie, 2013]] , 2016; [[#England--2014|England et al., 2014]] ; [[#Watanabe--2014|Watanabe et al., 2014]] ; [[#Delworth--2015|Delworth et al., 2015]] ). Part of the PDV trend may have been driven by anthropogenic aerosols ( [[#Smith--2016|Smith et al., 2016]] ); however, this result is model-dependent, and internally-driven PDV dominates the forced PDV signal in the CMIP6 multi-model ensemble ( [[#3.7.6|Section 3.7.6]] ). It is also notable that there is large uncertainty in the magnitude of the PDV influence on GSAT across models ( [[#Deser--2017a|Deser et al., 2017a]] ; C.-Y. [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ) and among the studies cited above. In addition to PDV, contributions to the reduced warming trend from wintertime Northern Hemisphere atmospheric internal variability, particularly associated with a trend towards the negative phase of the Northern Annular Mode/North Atlantic Oscillation (Annex IV.2.1; [[#Guan--2015|Guan et al., 2015]] ; [[#Saffioti--2015|Saffioti et al., 2015]] ; [[#Iles--2017|Iles and Hegerl, 2017]] ) or the Cold Ocean–Warm Land (COWL) pattern ( [[#Molteni--2017|Molteni et al., 2017]] ; [[#Yang--2020|Yang et al., 2020]] ) have been suggested, leading to regional continental cooling over a large part of Eurasia and North America (Cross-Chapter Box 3.1, Figure 1c; [[#Li--2015|C. Li et al., 2015]] ; [[#Deser--2017a|Deser et al., 2017a]] ; [[#Gan--2019|Gan et al., 2019]] ).

Such internally-driven variation of decadal GSAT trends is not unique to the 1998–2012 period ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.2.1|Section 1.4.2.1]] ; [[#Lovejoy--2014|Lovejoy, 2014]] ; [[#Roberts--2015|Roberts et al., 2015]] ; [[#Dai--2019|Dai and Bloecker, 2019]] ). Due to the nature of internal variability, surface temperature changes over the 1998–2012 period are regionally- and seasonally-varying (Cross-Chapter Box 3.1, Figure 1c; [[#Trenberth--2014|Trenberth et al., 2014]] ; [[#Zang--2019|Zang et al., 2019]] ). Further, there was no slowdown in the increasing occurrence of hot extremes over land ( [[#Kamae--2014|Kamae et al., 2014]] ; [[#Seneviratne--2014|Seneviratne et al., 2014]] ; [[#Imada--2017|Imada et al., 2017]] ). Thus, the internally-driven slowdown of GSAT increase does not correspond to slowdown of warming everywhere on the Earth’s surface.

'''Updated forcing'''
CMIP5 historical simulations driven by observed forcing variations ended in 2005 and were extended with RCP scenario simulations for model-observation comparisons beyond that date. Post AR5 studies based on updated external forcing show that while no net effect of updated anthropogenic aerosols is found on GSAT trends ( [[#Murphy--2013|Murphy, 2013]] ; [[#Gettelman--2015|Gettelman et al., 2015]] ; [[#Oudar--2018|Oudar et al., 2018]] ), natural forcing by moderate volcanic eruptions in the 21st century ( [[#Haywood--2014|Haywood et al., 2014]] ; [[#Ridley--2014|Ridley et al., 2014]] ; [[#Santer--2014|Santer et al., 2014]] ) and a prolonged solar irradiance minimum around 2009 compared to the normal 11-year cycle ( [[#Lean--2018|Lean, 2018]] ) yield a negative contribution to radiative forcing, which was missing in CMIP5 (Figure 2.2). This explains part of the difference between observed and CMIP5 trends, as shown based on EMIC simulations ( [[#Huber--2014|Huber and Knutti, 2014]] ; [[#Ridley--2014|Ridley et al., 2014]] ), statistical and mathematical models ( [[#Schmidt--2014|Schmidt et al., 2014]] ; [[#Lean--2018|Lean, 2018]] ), and process-based climate models ( [[#Santer--2014|Santer et al., 2014]] ). However, in a single climate model study by [[#Thorne--2015|Thorne et al. (2015)]] , updating most forcings (greenhouse gas concentrations, solar irradiance, and volcanic and anthropogenic aerosols) available when the study was done made no significant difference to the 1998–2012 GMST trend from that obtained with original CMIP5 forcing. Potential underestimation of volcanic (negative) forcing may have played a role ( [[#Outten--2015|Outten et al., 2015]] ). In the multi-model ensemble mean, the 1998–2012 GMST trends are almost equal in CMIP5 and CMIP6 (Cross-Chapter Box 3.1, Figure 1a), suggesting compensation by a higher transient climate response and equilibrium climate sensitivity in CMIP6 than CMIP5 (Section 7.5.6). To summarize, while there is ''medium confidence'' that natural forcing that was missing in CMIP5 contributed to the difference of observed and simulated GMST trends, ''confidence'' remains ''low'' in the quantitative contribution of net forcing updates.

'''Energy budget and heat redistribution'''
The early 21st century slower warming was observed in atmospheric temperatures, but the heat capacity of the atmosphere is very small compared to that of the ocean. Although there is noticeable uncertainty among observational products (H. [[#Su--2017|]] [[#Su--2017|Su et al., 2017]] ) and observation quality changes through time, global ocean heat content continued to increase during the slower surface warming period ( ''very high confidence'' ), at a rate consistent with CMIP5 and CMIP6 historical simulations (Sections 2.3.3.1, [[#_idTextAnchor001|3.5.1.3]] and 7.2.2.2). There is ''high confidence'' that the Earth’s energy imbalance was larger in the 2000s than in the 1985–1999 period (Section 7.2.2.1), consistent with accelerating ocean heat uptake in the past two decades (Section [[#_idTextAnchor001|3.5.1.3]] ). Internal decadal variability is mainly associated with redistribution of heat within the climate system (X.H. [[#Yan--2016|]] [[#Yan--2016|]] [[#Yan--2016|Yan et al., 2016]] ; [[#Drijfhout--2018|Drijfhout, 2018]] ) while associated top of the atmosphere radiation anomalies are weak ( [[#Palmer--2014|Palmer and McNeall, 2014]] ). Heat redistribution in the top 350 m of the Indian and Pacific Oceans has been found to be the main contributor to reduced surface warming during the slower surface warming period ( [[#Lee--2015|Lee et al., 2015]] ; [[#Nieves--2015|Nieves et al., 2015]] ; F. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ), consistent with the simulated signature of PDV ( [[#England--2014|England et al., 2014]] ; [[#Maher--2018a|Maher et al., 2018a]] ; [[#Gastineau--2019|Gastineau et al., 2019]] ). Below 700 m, enhanced heat uptake over the slower surface warming period was observed mainly in the North Atlantic and Southern Ocean ( [[#Chen--2014|Chen and Tung, 2014]] ), though whether this was a response to forcing or a unique signature of the slow GMST warming has been questioned (W. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ).

'''Summary and implications'''
With updated observation-based GMST datasets and forcing, improved analysis methods, new modelling evidence and deeper understanding of mechanisms, there is ''very'' ''high confidence'' that the slower GMST and GSAT increase inferred from observations in the 1998–2012 period was a temporary event induced by internal and naturally-forced variability that partly offset the anthropogenic warming trend over this period. Nonetheless, the heating of the climate system continued during this period, as reflected in the continued warming of the global ocean ( ''very high confidence'' ) and in the continued rise of hot extremes over land ( ''medium confidence'' ). Considering all the sources of uncertainties, it is impossible to robustly identify a single cause of the early 2000s slowdown ( [[#Hedemann--2017|Hedemann et al., 2017]] ; [[#Power--2017|Power et al., 2017]] ); rather, it should be interpreted as due to a combination of several factors ( [[#Huber--2014|Huber and Knutti, 2014]] ; [[#Schmidt--2014|Schmidt et al., 2014]] ; [[#Medhaug--2017|Medhaug et al., 2017]] ).

A major El Niño event in 2014–2016 led to three consecutive years of record annual GMST with unusually strong heat release from the North-western Pacific Ocean ( [[#Yin--2018|Yin et al., 2018]] ), which marked the end of the slower warming period ( [[#Hu--2017|Hu and Fedorov, 2017]] ; J. [[#Su--2017|]] [[#Su--2017|Su et al., 2017]] ; [[#Cha--2018|Cha et al., 2018]] ). The past five-year period (2016–2020) is the hottest five-year period in the instrumental record up to 2020 ( ''high confidence'' ). This rapid warming was accompanied by a PDV shift toward its positive phase (J. [[#Su--2017|]] [[#Su--2017|Su et al., 2017]] ; [[#Cha--2018|Cha et al., 2018]] ). A higher rate of warming following the 1998–2012 period is consistent with the predictions in AR5 Box 9.2 ( [[#Flato--2013|Flato et al., 2013]] ) and with a statistical prediction system (Sévellec and [[#Drijfhout--2018|Drijfhout, 2018]] ). Initialized decadal predictions show higher GMST trends in the early 2020s compared to uninitialized simulations ( [[#Thoma--2015|Thoma et al., 2015]] ; [[#Meehl--2016a|Meehl et al., 2016a]] ).

While some recent studies find that internal decadal GSAT variability may become weaker under GSAT warming, associated in part with reduced amplitude PDV ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.3.5|Section 4.5.3.5]] ; [[#Brown--2017|Brown et al., 2017]] ), the weakening is small under a realistic range of warming. A large volcanic eruption would temporarily cool GSAT (Cross-Chapter Box 4.1). Thus, there is ''very high confidence'' that reduced and increased GMST and GSAT trends at decadal time scales will continue to occur in the 21st century ( [[#Meehl--2013|Meehl et al., 2013]] ; [[#Roberts--2015|Roberts et al., 2015]] ; [[#Medhaug--2016|Medhaug and Drange, 2016]] ). However, such internal or volcanically forced decadal variations in GSAT trend have little effect on centennial warming ( [[#England--2015|England et al., 2015]] ; Cross-Chapter Box 4.1).

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