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

== 3.3 Human Influence on the Atmosphere and Surface ==

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=== 3.3.1 Temperature ===

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==== 3.3.1.1 Surface Temperature ====

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Surface temperature change is the aspect of climate in which the climate research community has had most confidence over past IPCC assessment reports. This confidence comes from the availability of longer observational records compared to other indicators, a large response to anthropogenic forcing compared to variability in the global mean, and a strong theoretical understanding of the key thermodynamics driving its changes ( [[#Collins--2010|Collins et al., 2010]] ; [[#Shepherd--2014|Shepherd, 2014]] ). The AR5 assessed that it was ''extremely likely'' that human activities had caused more than half of the observed increase in global mean surface temperature from 1951 to 2010, and ''virtually certain'' that internal variability alone could not account for the observed global warming since 1951 ( [[#Bindoff--2013|Bindoff et al., 2013]] ). The AR5 also assessed with ''very high confidence'' that climate models reproduce the general features of the global-scale annual mean surface temperature increase over 1850–2011 and with ''high confidence'' that models reproduce global and Northern Hemisphere temperature variability on a wide range of time scales ( [[#Flato--2013|Flato et al., 2013]] ). This section assesses the performance of the new generation CMIP6 models (see Table AII.5) in simulating the patterns, trends, and variability of surface temperature, and the evidence from detection and attribution studies of human influence on large-scale changes in surface temperature.

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===== 3.3.1.1.1 Model evaluation =====

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To be fit for detecting and attributing human influence on globally-averaged surface temperatures, climate models need to represent, based on physical principles, both the response of surface temperature to external forcings and the internal variability in surface temperature over various time scales. This section assesses the performance of those aspects in the latest generation CMIP6 climate models. See ( [[#3.8|Section 3.8]] for evaluation at continental scales, [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] for model evaluation in the context of regional climate information, and the [[IPCC:Wg1:Chapter:Atlas|Atlas]] for region-by-region assessments of model performance.

Reconstructions of past temperature from paleoclimate proxies ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1|Section 2.3.1.1]] and Cross-Chapter Box 2.1) have been used to evaluate modelled past climate temperature change patterns. The AR5 found that CMIP5 ( [[#Taylor--2012|Taylor et al., 2012]] ) models were able to reproduce the large-scale patterns of temperature during the Last Glacial Maximum (LGM) ( [[#Flato--2013|Flato et al., 2013]] ) and simulated a polar amplification broadly consistent with reconstructions for warm (Pliocene and Eocene) and cold (LGM) periods ( [[#Masson-Delmotte--2013|Masson-Delmotte et al., 2013]] ). Since AR5, a better understanding of temperature proxies and their uncertainties and in some cases the forcing applied to model simulations has led to better agreement between models and reconstructions over a wide range of past climates. For the Pliocene and Eocene warm periods, understanding of uncertainties in temperature proxies ( [[#Hollis--2019|Hollis et al., 2019]] ; [[#McClymont--2020|McClymont et al., 2020]] ) and the boundary conditions used in climate simulations ( [[#Haywood--2016|Haywood et al., 2016]] ; [[#Lunt--2017|Lunt et al., 2017]] ) has improved, and some models now agree better with temperature proxies for these time periods compared to models assessed in AR5 (Sections 7.4.4.1.2, 7.4.4.2.2 and Cross-Chapter Box 2.4; [[#Zhu--2019|Zhu et al., 2019]] ; [[#Haywood--2020|Haywood et al., 2020]] ; [[#Lunt--2021|Lunt et al., 2021]] ). For the Last Interglacial (LIG), improved temporal resolution of temperature proxies ( [[#Capron--2017|Capron et al., 2017]] ) and better appreciation of the importance of freshwater forcing ( [[#Stone--2016|Stone et al., 2016]] ) have clarified the reasons behind apparent model-data inconsistencies. Regional LIG temperature responses simulated by CMIP6 are within the uncertainty ranges of reconstructed temperature responses, except in regions where unresolved changes in regional ocean circulation, meltwater, or vegetation changes may cause model mismatches ( [[#Otto-Bliesner--2021|Otto-Bliesner et al., 2021]] ). For the LGM, the CMIP5 and CMIP6 ensembles compare similarly to new sea surface temperature (SST) and surface air temperature (SAT) proxy reconstructions (Figure 3.2a; [[#Cleator--2020|Cleator et al., 2020]] ; [[#Tierney--2020b|Tierney et al., 2020b]] ). The very cold CMIP6 LGM simulation by the Community Earth System Model Version 2.1 (CESM2.1) is an exception related to the high equilibrium climate sensitivity (ECS) of that model (Section 7.5.6; [[#Kageyama--2021a|Kageyama et al., 2021a]] ; [[#Zhu--2021|Zhu et al., 2021]] ). Figure 3.2a illustrates the wide range of simulated global LGM temperature responses in both ensembles. CMIP6 models tend to underestimate the cooling over land, but agree better with oceanic reconstructions. For the mid-Holocene, the regional biases found in CMIP5 simulations are similar to those in pre-industrial and historical simulations ( [[#Harrison--2015|Harrison et al., 2015]] ; [[#Ackerley--2017|Ackerley et al., 2017]] ), suggesting common causes. CMIP5 models underestimate Arctic warming in the mid-Holocene ( [[#Yoshimori--2019|Yoshimori and Suzuki, 2019]] ). CMIP6 models simulate a mid-latitude, subtropical, and tropical cooling compared to the pre-industrial period, whereas temperature proxies indicate a warming (see [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1.2|Section 2.3.1.1.2]] ; [[#Brierley--2020|Brierley et al., 2020]] ; [[#Kaufman--2020|Kaufman et al., 2020]] ), although accounting for seasonal effects in the proxies may reduce the discrepancy ( [[#Bova--2021|Bova et al., 2021]] ). Over the past millennium, reconstructed and simulated temperature anomalies, internal variability, and forced response agree well over Northern Hemisphere continents, but those statistics disagree strongly in the Southern Hemisphere, where models seem to overestimate the response ( [[#PAGES%202k-PMIP3%20group--2015|PAGES 2k-PMIP3 group, 2015]] ). That disagreement is partly explained by the lower quality of the reconstructions in the Southern Hemisphere, but model and/or forcing errors may also contribute ( [[#Neukom--2018|Neukom et al., 2018]] ). Figure 3.2b shows that land/sea warming contrast behaves coherently in model simulations across multiple periods, with a slight non-linearity in land warming due to a smaller contribution of snow cover to temperature response in warmer climates. A multivariate assessment of paleoclimate model simulations is carried out in [[#3.8.2|Section 3.8.2]] .

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[[File:1ce2530ae198a3957bfa3dca50d72667 IPCC_AR6_WGI_Figure_3_2.png]]

Figure 3.2 | Changes in surface temperature for different paleoclimates. '''(a)''' Comparison of reconstructed and modelled surface temperature anomalies for the Last Glacial Maximum over land and ocean in the Tropics (30°N–30°S). Land-based reconstructions are from [[#Cleator--2020|Cleator et al. (2020)]] . Ocean-based reconstructions are from [[#Tierney--2020b|Tierney et al. (2020b)]] . Model anomalies are calculated as the difference between Last Glacial Maximum and pre-industrial control simulations of the PMIP3 and PMIP4 ensembles, sampled at the reconstruction data points. '''(b)''' Land–sea contrast in global mean surface temperature change for different paleoclimates. Small symbols show individual model simulations from the CMIP5 and CMIP6 ensembles. Large symbols show ensemble means and assessed values. '''(c)''' Upper panel shows time series of volcanic radiative forcing, in W m <sup>−2</sup> , as used in the CMIP5 ( [[#Gao--2008|Gao et al., 2008]] ; [[#Crowley--2013|Crowley and Unterman, 2013]] ; see also [[#Schmidt--2011|Schmidt et al., 2011]] ) and CMIP6 (850 CE to 1900 CE from [[#Toohey--2017|Toohey and Sigl (2017)]] , 1850–2015 from [[#Luo--2018|Luo (2018)]] ). The forcing was calculated from the stratospheric aerosol optical depth at 550 nm shown in Figure 2.2. Lower panel shows time series of global mean surface temperature anomalies, in °C, with respect to 1850–1900 for the CMIP5 and CMIP6 past1000 simulations and their historical continuation simulations. Simulations are coloured according to the volcanic radiative forcing dataset they used. The median reconstruction of temperature from [[#PAGES%202k%20Consortium--2019|PAGES 2k Consortium (2019)]] is shown in black, the 5–95% confidence interval is shown by grey lines and the grey envelopes show the 1st, 5th, 15th, 25th, 35th, 45th, 55th, 65th, 75th, 85th, 95th, and 99th percentiles. All data in both panels are band-passed filtered, where frequencies longer than 20 years have been retained. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

For the historical period, AR5 assessed with ''very high confidence'' that CMIP5 models reproduced observed large-scale mean surface temperature patterns, although errors of several degrees appear in elevated regions, like the Himalayas and Antarctica, near the edge of the sea ice in the North Atlantic, and in upwelling regions. This assessment is updated here for the CMIP6 simulations. Figure 3.3 shows the annual mean surface air temperature at 2 m for the CMIP5 and CMIP6 multi-model means, both compared to the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5; [[IPCC:Wg1:Chapter:Chapter-1#1.5.2|Section 1.5.2]] ) for the period 1995–2014. The distribution of biases is similar in CMIP5 and CMIP6 models, as already noted by several studies ( [[#Crueger--2018|Crueger et al., 2018]] ; [[#Găinuşă-Bogdan--2018|Găinuşă-Bogdan et al., 2018]] ; [[#Kuhlbrodt--2018|Kuhlbrodt et al., 2018]] ; [[#Lauer--2018|Lauer et al., 2018]] ). Arctic temperature biases seem more widespread in both ensembles than assessed at the time of AR5. The fundamental causes of temperature biases remain elusive, with errors in clouds ( [[#Lauer--2018|Lauer et al., 2018]] ), ocean circulation ( [[#Kuhlbrodt--2018|Kuhlbrodt et al., 2018]] ), winds ( [[#Lauer--2018|Lauer et al., 2018]] ), and surface energy budget ( [[#Hourdin--2015|Hourdin et al., 2015]] ; [[#Séférian--2016|Séférian et al., 2016]] ; [[#Găinuşă-Bogdan--2018|Găinuşă-Bogdan et al., 2018]] ) being frequently cited candidates. Increasing horizontal resolution shows promise for decreasing long-standing biases in surface temperature over large regions ( [[#Bock--2020|Bock et al., 2020]] ). Panels e and f of Figure 3.3 show that biases in the mean High-Resolution Model Intercomparison Project (HighResMIP, [[#Haarsma--2016|Haarsma et al., 2016]] ) models (see also Table AII.6) are smaller than those in the mean of the corresponding lower-resolution versions of the same models simulating the same period (see also ( [[#3.8.2.2|Section 3.8.2.2]] ). However, the bias reduction is modest ( [[#Palmer--2019|Palmer and Stevens, 2019]] ). In addition, the biases of the limited number of models participating in HighResMIP are not entirely representative of overall CMIP6 biases, especially in the Southern Ocean, as indicated by comparing panels b and f of Figure 3.3.

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Figure 3.3 '''|''' '''Annual mean near-surface (2 m) air temperature (°C) for the period 1995–2014. (a)''' Multi-model (ensemble) mean constructed with one realization of the CMIP6 historical experiment from each model. '''(b)''' Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the climatology of the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5). '''(c)''' Multi-model mean of the root mean square error calculated over all months separately and averaged, with respect to the climatology from ERA5. '''(d)''' Multi-model mean bias defined as the difference between the CMIP6 multi-model mean and the climatology from ERA5. The difference between the multi-model mean of '''(e)''' high-resolution and '''(f)''' low-resolution simulations of four HighResMIP models and the climatology from ERA5 is also shown. Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where &lt;66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and &lt;80% of all models agree on sign of change. For more information on the advanced approach, please refer to Cross-Chapter Box Atlas.1. Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is not lower in at least three out of four models than in the corresponding low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

The AR5 assessed with ''very high confidence'' that models reproduce the general history of the increase in global-scale annual mean surface temperature since the year 1850, although AR5 also reported that an observed reduction in the rate of warming over the period 1998–2012 was not reproduced by the models (Cross-Chapter Box 3.1; [[#Flato--2013|Flato et al., 2013]] ). Figure 3.2c and Figure 3.4 show time series of anomalies in annually and globally averaged surface temperature simulated by CMIP5 and CMIP6 models for the past millennium and the period 1850 to 2020, respectively, with the baseline set to 1850–1900 (see [[IPCC:Wg1:Chapter:Chapter-1#1.4.1|Section 1.4.1]] ). As also indicated by Figure 3.4, the spread in simulated absolute temperatures is large ( [[#Palmer--2019|Palmer and Stevens, 2019]] ). However, the discussion is based on temperature anomaly time series instead of absolute temperatures because our focus is on evaluation of the simulation of climate change in these models, and also because anomalies are more uniformly distributed and are more easily deseasonalized to isolate long-term trends (see [[IPCC:Wg1:Chapter:Chapter-1#1.4.1|Section 1.4.1]] ). CMIP6 models broadly reproduce surface temperature variations over the past millennium, including the cooling that follows periods of intense volcanism ( ''medium confidence'' ) (Figure 3.2c). Simulated GMST anomalies are well within the uncertainty range of temperature reconstructions ( ''medium confidence'' ) since about the year 1300, except for some short periods immediately following large volcanic eruptions, for which simulations driven by different forcing datasets disagree (Figure 3.2c). Before the year 1300, larger disagreements between models and temperature reconstructions are expected because forcing and temperature reconstructions are increasingly uncertain further back in time, but specific causes have not been identified conclusively ( [[#Ljungqvist--2019|Ljungqvist et al., 2019]] ; [[#PAGES%202k%20Consortium--2019|PAGES 2k Consortium, 2019]] ) ( ''medium confidence'' ). For the historical period, results for CMIP6 shown in Figure 3.4 suggest that the qualitative history of surface temperature increase is well reproduced, including the increase in warming rates beginning in the 1960s and the temporary cooling that follows large volcanic eruptions.

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Figure 3.4 | '''Observed and simulated time series of the anomalies in annual and global mean surface air temperature (GSAT).''' All anomalies are differences from the 1850–1900 time-mean of each individual time series. The reference period 1850–1900 is indicated by grey shading. '''(a)''' Single simulations from CMIP6 models (thin lines) and the multi-model mean (thick red line). Observational data (thick black lines) are from the Met Office Hadley Centre/Climatic Research Unit dataset (HadCRUT5), and are blended surface temperature (2 m air temperature over land and sea surface temperature over the ocean). All models have been subsampled using the HadCRUT5 observational data mask. Vertical lines indicate large historical volcanic eruptions. CMIP6 models which are marked with an asterisk are either tuned to reproduce observed warming directly, or indirectly by tuning equilibrium climate sensitivity. Inset: GSAT for each model over the reference period, not masked to any observations. '''(b)''' Multi-model means of CMIP5 (blue line) and CMIP6 (red line) ensembles and associated 5th to 95th percentile ranges (shaded regions). Observational data are HadCRUT5, Berkeley Earth, National Oceanic and Atmospheric Administration NOAAGlobalTemp-Interim and [[#Kadow--2020|Kadow et al. (2020)]] . Masking was done as in (a). CMIP6 historical simulations were extended with SSP2-4.5 simulations for the period 2015–2020 and CMIP5 simulations were extended with RCP4.5 simulations for the period 2006–2020. All available ensemble members were used (see [[#3.2|Section 3.2]] ). The multi-model means and percentiles were calculated solely from simulations available for the whole time span (1850–2020). Figure is updated from [[#Bock--2020|Bock et al. (2020)]] , their Figures 1 and 2. CC BY 4.0 [https://unfccc.int/resource/docs/2017/cop23/eng/l13.pdf https://creativecommons.org/licenses/by/4.0/] . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Although virtually all CMIP6 modelling groups report improvements in their model’s ability to simulate current climate compared to the CMIP5 version ( [[#Gettelman--2019|Gettelman et al., 2019]] ; [[#Golaz--2019|Golaz et al., 2019]] ; [[#Mauritsen--2019|Mauritsen et al., 2019]] ; [[#Swart--2019|Swart et al., 2019]] ; [[#Voldoire--2019b|Voldoire et al., 2019b]] ; T. [[#Wu--2019|Wu et al., 2019]] b; [[#Bock--2020|Bock et al., 2020]] ; [[#Boucher--2020|Boucher et al., 2020]] ; [[#Dunne--2020|Dunne et al., 2020]] ), it does not necessarily follow that the simulation of temperature trends is also improved ( [[#Bock--2020|Bock et al., 2020]] ; [[#Fasullo--2020|Fasullo et al., 2020]] ). The CMIP6 multi-model ensemble encompasses observed warming and the multi-model mean tracks those observations within 0.2°C over most of the historical period. Figure 3.4 confirms the findings of [[#Papalexiou--2020|Papalexiou et al. (2020)]] , who highlighted based on 29 CMIP6 models that most models replicate the period of slow warming between 1942 and 1975 and the late twentieth century warming (1975–2014). The CMIP6 multi-model mean is cooler over the period 1980–2000 than both observations and CMIP5 (Figure 3.4; [[#Bock--2020|Bock et al., 2020]] ; [[#Flynn--2020|Flynn and Mauritsen, 2020]] ; [[#Gillett--2021|Gillett et al., 2021]] ). Biases of several tenths of a degree in some CMIP6 models over that period may be due to an overestimate in aerosol radiative forcing (Sections 6.3.5 and 7.3.3, and Figure 6.8; [[#Andrews--2020|Andrews et al., 2020]] ; [[#Dittus--2020|Dittus et al., 2020]] ; [[#Flynn--2020|Flynn and Mauritsen, 2020]] ). [[#Papalexiou--2020|Papalexiou et al. (2020)]] , [[#Tokarska--2020|Tokarska et al. (2020)]] and [[#Stolpe--2021|Stolpe et al. (2021)]] all report that CMIP6 models on average overestimate warming from the 1970s or 1980s to the 2010s, although quantitative conclusions depend on which observational dataset is compared against (see also Table 2.4). However, Figure 3.4, which includes a larger number of models than available to those studies, indicates that the CMIP6 multi-model mean tracks observed warming better than the CMIP5 multi-model mean after the year 2000. The CMIP6 multi-model mean GSAT warming between 1850–1900 and 2010–2019 and associated 5–95% range is 1.09 [0.66 to 1.64] °C. Cross-Chapter Box 2.3 assessed GSAT warming over the same period at 1.06 [0.88 to 1.21] °C. So some CMIP6 models simulate a warming that is smaller than the assessed observed range, and other CMIP6 models simulate a warming that is larger. That overestimated warming may be an early symptom of overestimated ECS in some CMIP6 models (Section 7.5.6; [[#Meehl--2020|Meehl et al., 2020]] ; [[#Schlund--2020|Schlund et al., 2020]] ), and has implications for projections of GSAT changes (Chapter 4; [[#Liang--2020|Liang et al., 2020]] ; [[#Nijsse--2020|Nijsse et al., 2020]] ; [[#Tokarska--2020|Tokarska et al., 2020]] ; [[#Ribes--2021|Ribes et al., 2021]] ). In some models, a large ECS and a strong aerosol forcing lead to too large a mid-20th century cooling followed by overestimated warming rates in the late 20th century when aerosol emissions decrease ( [[#Golaz--2019|Golaz et al., 2019]] ; [[#Flynn--2020|Flynn and Mauritsen, 2020]] ). Temperature biases are driven by both model physics and prescribed forcing, which is a challenge for model development.

[[#Chylek--2020|Chylek et al. (2020)]] argue that CMIP5 models overestimate the temperature response to volcanic eruptions. [[#Lehner--2016|Lehner et al. (2016)]] , [[#Rypdal--2018|Rypdal (2018)]] and [[#Stolpe--2021|Stolpe et al. (2021)]] point instead to missed compensating effects on surface temperature change associated with internal variability in the El Niño–Southern Oscillation (ENSO) or the Atlantic Multi-decadal Oscillation (AMO). An alternative view sees those ENSO and AMO responses as expressions of changes in climate feedbacks driven by the geographical pattern of SST changes ( [[#Andrews--2018|Andrews et al., 2018]] ). At least one model is able to reproduce such pattern effects ( [[#Gregory--2016|Gregory and Andrews, 2016]] ). Errors in the volcanic forcing prescribed in simulations, including for CMIP6 ( [[#Rieger--2020|Rieger et al., 2020]] ), also introduce differences with the observed temperature response, independently of the quality of the model physics. In addition, comparisons of the modelled temperature response to large eruptions over the past millennium to temperature reconstructions based on tree rings show a much better agreement ( [[#Lücke--2019|Lücke et al., 2019]] ; F. [[#Zhu--2020|]] [[#Zhu--2020|Zhu et al., 2020]] ) than comparisons to the annual, multi-temperature proxy reconstructions shown in Figure 3.2c. These considerations, and Figures 3.2c and 3.4, suggest that CMIP6 models do not systematically overestimate the cooling that follows large volcanic eruptions (see also Cross-Chapter Box 4.1).

When interpreting model simulations of historical temperature change, it is important to keep in mind that some models are tuned towards representing the observed trend in global mean surface temperature over the historical period ( [[#Hourdin--2017|Hourdin et al., 2017]] ). In Figure 3.4 the CMIP6 models that are documented to have been tuned to reproduce observed warming, typically by tuning aerosol forcing or factors that influence the model’s ECS, are marked with an asterisk. Such tuning of a model can strongly impact its temperature projections ( [[#Mauritsen--2020|Mauritsen and Roeckner, 2020]] ). However, [[#Bock--2020|Bock et al. (2020)]] reported that there is no statistically significant difference in multi-model mean GSAT between the models that had been tuned based on observed warming compared to those which had not. Moreover, only two of thirteen models used for the Detection and Attribution Model Intercomparison Project (DAMIP) simulations on which CMIP6 attribution studies are based were tuned towards historical warming ( [[#Bock--2020|Bock et al., 2020]] ; [[#Gillett--2021|Gillett et al., 2021]] ). Further, tuning is done on globally averaged quantities, so does not substantially change the spatio-temporal pattern of response on which many regression-based attribution studies are based ( [[#Bock--2020|Bock et al., 2020]] ). Therefore, we assess with ''high confidence'' that the tuning of a small number of CMIP6 models to observed warming has not substantially influenced attribution results assessed in this chapter.

The reliance of detection and attribution studies on climate models (see [[#3.2|Section 3.2]] ) requires that those models simulate realistic statistics of internal variability on multi-decadal time scales. An incorrect estimate of variability in models would affect confidence in the conclusions from detection and attribution. The AR5 found that CMIP5 models simulate realistic variability in global-mean surface temperature on decadal time scales, with variability on multi-decadal time scales being more difficult to evaluate because of the short observational record ( [[#Flato--2013|Flato et al., 2013]] ). Since AR5, new work has characterized the contributions of variability in different ocean areas to SST variability, with tropical modes of variability like ENSO dominant on time scales of five to ten years, while longer time scales see the variance maxima move poleward to the North Atlantic, North Pacific, and Southern oceans ( [[#Monselesan--2015|Monselesan et al., 2015]] ). There may, however, be sizeable, two-way interdependencies between ENSO and sea surface temperature variability in different basins ( [[#Kumar--2014|Kumar et al., 2014]] ; [[#Cai--2019|Cai et al., 2019]] ), and ENSO’s influence on global surface temperature variability may not be confined only to decadal time scales ( [[#Triacca--2014|Triacca et al., 2014]] ). Studies based on large ensembles of 20th and 21st century climate change simulations confirm that internal variability has a substantial influence on global warming trends over periods shorter than 30–40 years ( [[#Kay--2015|Kay et al., 2015]] ; [[#Dai--2019|Dai and Bloecker, 2019]] ). Although the equatorial Pacific seems to be the main source of internal variability on decadal time scales, [[#Brown--2016a|Brown et al. (2016a)]] linked diversity in modelled oceanic convection, sea ice, and energy budget in high-latitude regions to overall diversity in modelled internal variability.

Interest in internal variability since the publication of AR5 stems in part from its importance in understanding the slower global surface temperature warming over the early 21st century (see Cross-Chapter Box 3.1). Evidence coming mostly from paleo studies is mixed on whether CMIP5 models underestimate decadal and multi-decadal variability in global mean temperature. [[#Schurer--2013|Schurer et al. (2013)]] found good agreement between internal variability derived from paleo reconstructions, estimated as the fraction of variance that is not explained by forced responses, and modelled variability, although the subset of CMIP5 models they used may have been associated with larger variability than the full CMIP5 ensemble. [[#PAGES%202k%20Consortium--2019|PAGES 2k Consortium (2019)]] found that the largest 51-year trends in both reconstructions of global mean temperature and fully forced climate simulations over the period 850 to 1850 were almost identical. [[#Zhu--2019|Zhu et al. (2019)]] showed agreement in the modelled and reconstructed temporal spectrum of global surface temperatures on annual to multi-millennial time scales. However, they suggest that decadal- to centennial variability is partly forced by slow orbital changes that predate the last millennium. This is consistent with [[#Gebbie--2019|Gebbie and Huybers (2019)]] , who showed that the deep ocean has been out of equilibrium over that period. [[#Laepple--2014|Laepple and Huybers (2014)]] found good agreement between modelled and proxy-derived decadal ocean temperature variability, but underestimates of variance by models by at least a factor of ten at centennial time scales because models underestimate the difference between the warm and cold periods of the last millennium. [[#Parsons--2020|Parsons et al. (2020)]] found that some CMIP6 models exhibit much higher multi-decadal variability in GSAT than CMIP5 models, with indications that variability in these models is also higher than that from proxy reconstructions. CMIP6 models may not share the underestimation by CMIP5 models of variability in decadal to multi-decadal modes of variability, such as Pacific Decadal Variability ( [[#3.7.6|Section 3.7.6]] ; [[#England--2014|England et al., 2014]] ; [[#Thompson--2014|Thompson et al., 2014]] ; [[#Schurer--2015|Schurer et al., 2015]] ) and Atlantic Multi-decadal Variability (AMV), which may be partly forced, (see [[#3.7.7|Section 3.7.7]] ) but this assessment is limited by the small number of available studies. For the Southern Hemisphere, [[#Hegerl--2018|Hegerl et al. (2018)]] found an instance of internal variability in the early 20th century larger than that modelled, but indicated that could be an observational issue. [[#Friedman--2020|Friedman et al. (2020)]] found biases in interhemispheric SST contrast in some models that may be consistent with underestimated cooling after early-20th century eruptions or underestimated Pacific Decadal Variability, but could also be due to an imperfect separation between internal variability and forced signal in the observations. Figure 3.2c, updated from [[#PAGES%202k%20Consortium--2019|PAGES 2k Consortium (2019)]] , compares modelled temperatures to reconstructions over the last millennium. It indicates that models reproduce the observed variability well, at least for the time scales between 20 and 50 years that paleo reconstructions typically resolve and that the figure represents. In summary, decadal GMST variability simulated in CMIP6 models spans the range of residual decadal variability in large-scale reconstructions ( ''medium evidence'' , ''low agreement'' ).

In addition, new literature suggests that anthropogenic forcing itself may locally increase or decrease variability in surface temperatures ( [[#Screen--2014|Screen et al., 2014]] ; [[#Qian--2015|Qian and Zhang, 2015]] ; [[#Brown--2017|Brown et al., 2017]] ; [[#Park--2018|Park et al., 2018]] ; [[#Santer--2018|Santer et al., 2018]] ; [[#Weller--2020|Weller et al., 2020]] ). These studies imply limitations in the use of pre-industrial control simulations to quantify the role of unforced variability over the historical period. Some recent attribution studies ( [[#Gillett--2021|Gillett et al., 2021]] ; [[#Ribes--2021|Ribes et al., 2021]] ) have estimated variability from ensembles of forced simulations instead, which would be expected to resolve any such changes in variability.

Figure 3.5 shows the standard deviation of zonal-mean surface temperature in CMIP6 pre-industrial control simulations and observed temperature datasets. Results are consistent with those based on CMIP5 models, which showed the largest model spread where variability is also large, in the tropics and mid- to high latitudes ( [[#Flato--2013|Flato et al., 2013]] ). Modelled variability is within a factor two of observed variability over most of the globe. The apparent overestimation of high latitude variability in models compared to observations may be due to interpolation and infilling over data sparse high latitude regions in the observational products shown here ( [[#Jones--2016|Jones, 2016]] ).

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[[File:89c130003ac057ad307bb43aaf0ac219 IPCC_AR6_WGI_Figure_3_5.png]]

Figure 3.5 | '''The standard deviation of annually averaged zonal-mean near-surface air temperature.''' This is shown for four detrended observed temperature datasets (HadCRUT5, Berkeley Earth, NOAAGlobalTemp-Interim and [[#Kadow--2020|Kadow et al. (2020)]] , for the years 1995-2014) and 59 CMIP6 pre-industrial control simulations (one ensemble member per model, 65 years) (after [[#Jones--2013|Jones et al., 2013]] ). For line colours see the legend of Figure 3.4. Additionally, the multi-model mean (red) and standard deviation (grey shading) are shown. Observational and model datasets were detrended by removing the least-squares quadratic trend. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

The previous paragraph took an ensemble-mean view of model performance, but individual models disagree on unforced variability. Figure 3.6 illustrates the large differences in GSAT variability in unforced CMIP6 pre-industrial control simulations, following the method of [[#Parsons--2020|Parsons et al. (2020)]] . Surface temperatures in pre-industrial conditions are especially variable in the ten models highlighted in Figure 3.6a, and some models substantially exceed the variability seen in CMIP5 models ( [[#Parsons--2020|Parsons et al., 2020]] ). Figure 3.6b shows that the distribution of warming trends simulated by CMIP6 models in historical simulations is clearly distinct from that simulated in unforced pre-industrial control simulations. Still, the unforced variability of the five most variable models approaches half that observed over the historical period under anthropogenically forced conditions (Figure 3.6c; [[#Parsons--2020|Parsons et al., 2020]] ; [[#Ribes--2021|Ribes et al., 2021]] ). For the Centre National de la Recherche Météorologique (CNRM) models, which are among the most variable, the large, low-frequency variability is attributed to strong simulated Atlantic Multi-decadal Variability ( [[#Séférian--2019|Séférian et al., 2019]] ; [[#Voldoire--2019b|Voldoire et al., 2019b]] ), which is difficult to rule out because of the short observational record ( [[#3.7.7|Section 3.7.7]] ; [[#Cassou--2018|Cassou et al., 2018]] ). But, importantly, patterns of temperature variability simulated by even the most variable models differ from the pattern of forced temperature change ( [[#Parsons--2020|Parsons et al., 2020]] ). Taken together, this discussion and Figures 3.2, 3.5 and 3.6 indicate that the statistics of internal variability in models compare well in most cases to observational estimates and temperature proxy reconstructions, though some CMIP6 models appear to have higher multi-decadal variability than CMIP5 models or proxy reconstructions. When used in attribution studies, models with overestimated variability would increase estimated uncertainties and make results statistically conservative.

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[[File:ff55ffd4ceaf6c8cdb4215341f9645a0 IPCC_AR6_WGI_Figure_3_6.png]]

'''Figure 3.6 | Simulated internal variability of global surface air temperature (GSAT) versus observed changes. (a)''' Time series of five-year running mean GSAT anomalies in 45 CMIP6 pre-industrial control (unforced) simulations. The 10 most variable models in terms of five-year running mean GSAT are coloured according to the legend on Figure 3.4. '''(b)''' Histograms of GSAT changes in CMIP6 historical simulations (extended by using SSP2-4.5 simulations) from 1850–1900 to 2010–2019 are shown by pink shading in (c), and GSAT changes between the average of the first 51 years and the average of the last 20 years of 170-year overlapping segments of the pre-industrial control simulations shown in (a) are shown by blue shading. GMST changes in observational datasets for the same period are indicated by black vertical lines. '''(c)''' Observed GMST anomaly time series relative to the 1850–1900 average. Black lines represent the five-year running means while grey lines show unfiltered annual time series. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

In summary, there is ''high confidence'' that CMIP6 models reproduce observed large-scale mean surface temperature patterns and internal variability as well as their CMIP5 predecessors, but with little evidence for reduced biases. CMIP6 models also reproduce historical GSAT changes similarly to their CMIP5 counterparts ( ''medium confidence'' ). However, in spite of model imperfections, there is ''very high confidence'' that biases in surface temperature trends and variability simulated by the CMIP5 and CMIP6 ensembles are small enough to support detection and attribution of human-induced warming.

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<span id="detection-and-attribution"></span>
===== 3.3.1.1.2 Detection and attribution =====

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Looking at periods preceding the instrumental record, AR5 assessed with ''high confidence'' that the 20th century annual mean surface temperature warming reversed a 5000-year cooling trend in Northern Hemisphere mid- to high latitudes caused by orbital forcing, and attributed the reversal to anthropogenic forcing with ''high confidence'' (see also ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1|Section 2.3.1.1]] ). Since AR5, the combined response to solar, volcanic and greenhouse gas forcing was detected in all Northern Hemisphere continents ( [[#PAGES%202k-PMIP3%20group--2015|PAGES 2k-PMIP3 group, 2015]] ) over the period 864 to 1840. In contrast, the effect of those forcings was not detectable in the Southern Hemisphere ( [[#Neukom--2018|Neukom et al., 2018]] ). Global and Northern Hemisphere temperature changes from reconstructions over this period have been attributed mostly to volcanic forcing ( [[#Schurer--2014|Schurer et al., 2014]] ; [[#McGregor--2015|McGregor et al., 2015]] ; [[#Otto-Bliesner--2016|Otto-Bliesner et al., 2016]] ; [[#PAGES%202k%20Consortium--2019|PAGES 2k Consortium, 2019]] ; [[#Büntgen--2020|Büntgen et al., 2020]] ), with a smaller role for changes in greenhouse gas forcing, and solar forcing playing a minor role ( [[#Schurer--2014|Schurer et al., 2014]] ; [[#PAGES%202k%20Consortium--2019|PAGES 2k Consortium, 2019]] ).

Focusing now on warming over the historical period, AR5 assessed that it was ''extremely likely'' that human influence was the dominant cause of the observed warming since the mid-20th century, and that it was ''virtually certain'' that warming over the same period could not be explained by internal variability alone. Since AR5 many new attribution studies of changes in global surface temperature have focused on methodological advances (see also ( [[#3.2|Section 3.2]] ). Those advances include better accounting for observational and model uncertainties, and internal variability ( [[#Ribes--2013|Ribes and Terray, 2013]] ; [[#Hannart--2016|Hannart, 2016]] ; [[#Ribes--2017|Ribes et al., 2017]] ; [[#Schurer--2018|Schurer et al., 2018]] ); formulating the attribution problem in a counterfactual framework ( [[#Hannart--2018|Hannart and Naveau, 2018]] ); and reducing the dependence of the attribution on uncertainties in climate sensitivity and forcing ( [[#Otto--2015|Otto et al., 2015]] ; [[#Haustein--2017|Haustein et al., 2017]] , 2019). Studies now account for uncertainties in the statistics of internal variability, either explicitly ( [[#Hannart--2016|Hannart, 2016]] ; [[#Hannart--2018|Hannart and Naveau, 2018]] ; [[#Ribes--2021|Ribes et al., 2021]] ) or implicitly ( [[#Ribes--2013|Ribes and Terray, 2013]] ; [[#Schurer--2018|Schurer et al., 2018]] ; [[#Gillett--2021|Gillett et al., 2021]] ), thus addressing concerns about over-confident attribution conclusions. Accounting for observational uncertainty increases the range of warming attributable to greenhouse gases by only 10 to 30% ( [[#Jones--2017|Jones and Kennedy, 2017]] ; [[#Schurer--2018|Schurer et al., 2018]] ). While some attribution studies estimate attributable changes in globally-complete GSAT ( [[#Schurer--2018|Schurer et al., 2018]] ; [[#Gillett--2021|Gillett et al., 2021]] ; [[#Ribes--2021|Ribes et al., 2021]] ), others attribute changes in observational GMST, but this makes little difference to attribution conclusions ( [[#Schurer--2018|Schurer et al., 2018]] ). Moreover, based on a synthesis of observational and modelling evidence, Cross-Chapter Box 2.3 assesses that the current best estimate of the scaling factor between GMST and GSAT is one, and therefore attribution studies of GMST and GSAT are here treated together in deriving assessed warming ranges. Studies also increasingly validate their multi-model approaches using imperfect model tests ( [[#Schurer--2018|Schurer et al., 2018]] ; [[#Gillett--2021|Gillett et al., 2021]] ; [[#Ribes--2021|Ribes et al., 2021]] ). Alternative techniques, based purely on statistical or econometric approaches, without the need for climate modelling, have also been applied ( [[#Estrada--2013|Estrada et al., 2013]] ; [[#Stern--2014|Stern and Kaufmann, 2014]] ; [[#Dergiades--2016|Dergiades et al., 2016]] ) and match the results of physically-based methods. The larger range of attribution techniques and improvements to those techniques increase confidence in the results compared to AR5.

In contrast, studies published since AR5 indicate that closely constraining the separate contributions of greenhouse gas changes and aerosol changes to observed temperature changes remains challenging. Nonetheless, attribution of warming to greenhouse gas forcing has been found as early as the end of the 19th century ( [[#Schurer--2014|Schurer et al., 2014]] ; [[#Owens--2017|Owens et al., 2017]] ; [[#PAGES%202k%20Consortium--2019|PAGES 2k Consortium, 2019]] ). [[#Hegerl--2019|Hegerl et al. (2019)]] found that volcanism cooled global temperatures by about 0.1°C between 1870 and 1910, then a lack of volcanic activity warmed temperatures by about 0.1°C between 1910 and 1950, with anthropogenic aerosols cooling temperatures throughout the 20th century, especially between 1950 and 1980 when the estimated range of aerosol cooling was about 0.1°C to 0.5°C. [[#Jones--2016|Jones et al. (2016)]] attributed a warming of 0.87 to 1.22°C per century over the period 1906 to 2005 to greenhouse gases, partially offset by a cooling of −0.54°C to −0.22°C per century attributed to aerosols. But they also found that detection of the greenhouse gas or the aerosol signal often fails, because of uncertainties in modelled patterns of change and internal variability. That point is illustrated by Figure 3.7, which shows two- and three-way fingerprinting regression coefficients for 13 CMIP6 models and the corresponding attributable warming ranges, derived using HadCRUT4 ( [[#Gillett--2021|Gillett et al., 2021]] ). Regression coefficients with an uncertainty range that includes zero mean that detection has failed. Models with regression coefficients significantly less than one significantly overpredict the temperature response to the corresponding forcing. Conversely, models with regression coefficients significantly greater than one underpredict the response to these forcings. While estimates of warming attributable to anthropogenic influence derived using individual models are generally consistent, estimates of warming attributable to greenhouse gases and aerosols separately based on individual models are not all consistent, and detection of the aerosol influence fails more often than that of greenhouse gases. Hence, results of recent studies emphasize the need to use multi-model means to better constrain estimates of GSAT changes attributable to greenhouse gas and aerosol forcing ( [[#Schurer--2018|Schurer et al., 2018]] ; [[#Gillett--2021|Gillett et al., 2021]] ; [[#Ribes--2021|Ribes et al., 2021]] ).

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[[File:d3a10075e7a486c6d96c99e82c0e505c IPCC_AR6_WGI_Figure_3_7.png]]

Figure 3.7 | '''Regression coefficients and corresponding attributable warming estimates for individual CMIP6 models.''' Upper panels show regression coefficients based on a two-way regression '''(left)''' and three-way regression '''(right)''' , of observed five-year mean, globally averaged, masked and blended surface temperature (HadCRUT4) onto individual model response patterns, and a multi-model mean, labelled ‘Multi’. Anthropogenic, natural, greenhouse gas, and other anthropogenic (aerosols, ozone, land-use change) regression coefficients are shown. Regression coefficients are the scaling factors by which the model responses must be multiplied to best match observations. Regression coefficients consistent with one indicate a consistent magnitude response in observations and models, and regression coefficients significantly greater than zero indicate a detectable response to the forcing concerned. Lower panels show corresponding observationally-constrained estimates of attributable warming in globally-complete GSAT for the period 2010–2019, relative to 1850–1900, and the horizontal black line shows an estimate of observed warming in GSAT for this period. Figure is adapted from [[#Gillett--2021|Gillett et al. (2021)]] , their Extended Data Figure 3. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Figure 3.8 compares attributable changes in globally complete GSAT for the period 2010–2019 relative to 1850–1900 from three detection and attribution studies, two of which use CMIP6 multi-model means ( [[#Gillett--2021|Gillett et al., 2021]] ; [[#Ribes--2021|Ribes et al., 2021]] ), and an estimate based on assessed effective radiative forcing and transient and equilibrium climate sensitivity (see Section 7.3.5.3). The reference period 1850–1900 is used to assess attributable temperature changes because this is when the earliest gridded surface temperature records start, this is when the CMIP6 historical simulations start, this is the earliest base period used in attribution literature, and this is a reference period used in IPCC SR1.5 and earlier reports. It should, however, be noted that Cross-Chapter Box 1.2 assesses with ''medium confidence'' that there was an anthropogenic warming with a ''likely'' range of 0.0°C–0.2°C between 1750 and 1850–1900. Figure 3.8 also shows the GSAT changes directly simulated in response to these forcings in thirteen CMIP6 models. In spite of their different methodologies and input datasets, the three attribution approaches yield very similar results, with the anthropogenic attributable warming range encompassing observed warming, and the natural attributable warming being close to zero. The warming driven by greenhouse gas increases is offset in part by cooling due to other anthropogenic forcing agents, mostly aerosols, although uncertainties in these contributions are larger than the uncertainty in the net anthropogenic warming, as discussed above. Estimates based on physical understanding of forcing and ECS made by ( [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] are close to estimates from attribution studies, despite being the products of a different approach. This agreement enhances confidence in the magnitude and causes of attributable surface temperature warming.

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[[File:b5453f34bf66d2a892da5bef7f33d5e4 IPCC_AR6_WGI_Figure_3_8.png]]

'''Figure 3.8 | Assessed contributions to observed warming, and supporting lines of evidence.''' Shaded bands show assessed ''likely'' ranges of temperature change in GSAT, 2010–2019 relative to 1850–1900, attributable to net human influence, well-mixed greenhouse gases, other human forcings (aerosols, ozone, and land-use change), natural forcings, and internal variability, and the 5–95% range of observed warming. Bars show 5–95% ranges based on (left to right) [[#Haustein--2017|Haustein et al. (2017)]] , [[#Gillett--2021|Gillett et al. (2021)]] and [[#Ribes--2021|Ribes et al. (2021)]] , and crosses show the associated best estimates. No 5–95% ranges were provided for the [[#Haustein--2017|Haustein et al. (2017)]] greenhouse gas or other human forcings contributions. The [[#Ribes--2021|Ribes et al. (2021)]] results were updated using a revised natural forcing time series, and the [[#Haustein--2017|Haustein et al. (2017)]] results were updated using HadCRUT5. The ( [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] best estimates and ranges were derived using assessed forcing time series and a two-layer energy balance model as described in Section 7.3.5.3. Coloured symbols show the simulated responses to the forcings concerned in each of the models indicated. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

The AR5 found ''high confidence'' for a major role for anthropogenic forcing in driving warming over each of the inhabited continents, except for Africa where they found only ''medium confidence'' because of limited data availability ( [[#Bindoff--2013|Bindoff et al., 2013]] ). At the hemispheric scale, [[#Friedman--2020|Friedman et al. (2020)]] and [[#Bonfils--2020|Bonfils et al. (2020)]] detected an anthropogenically forced response of inter-hemispheric contrast in surface temperature change, which has a complex time evolution but shows the Northern Hemisphere cooling relative to the Southern Hemisphere until around 1975 but then warming after that. [[#Bonfils--2020|Bonfils et al. (2020)]] attribute the Northern Hemisphere reversal to a combination of reduced aerosol forcing and greenhouse gas induced warming of Northern Hemisphere land masses. [[#Friedman--2020|Friedman et al. (2020)]] found that CMIP5 models simulate the correct sign of the inter-hemispheric contrast when forced with all forcings but underestimate its magnitude. Figure 3.9 shows global surface temperature change in CMIP6 all-forcing and natural-only simulations globally, averaged over continents, and separately over land and ocean surfaces. All-forcing simulations encompass observed temperature changes for all regions, while natural-only simulations fail to do so in recent decades except in Antarctica, based on the annual means shown. As stated above, warming results from a partial offset of greenhouse gas warming by aerosol cooling. That offset is stronger over land than ocean. Regionally, models show a large range of possible temperature responses to greenhouse gas and aerosol forcing, which complicates single-forcing attribution. A more detailed discussion of regional attribution can be found in Section 10.4. Over global land surfaces, [[#Chan--2015|Chan and Wu (2015)]] used CMIP5 simulations to attribute a warming trend of 0.3 (2.5%–97.5% confidence interval: 0.2–0.36) °C per decade to anthropogenic forcing, with natural forcing only contributing 0.05 (0.02–0.06) °C per decade. Accounting for unsampled sources of uncertainty and the availability of only a single study, their result suggests that it is ''very likely'' that human influence is the main driver of warming over land.

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[[File:85198fe800e9fd13ec9c5f9973af10cb IPCC_AR6_WGI_Figure_3_9.png]]

'''Figure 3.9 | Global, land, ocean and continental annual mean near-surface air temperatures anomalies in CMIP6 models and observations.''' Time series are shown for CMIP6 historical anthropogenic and natural (brown), natural-only (green), greenhouse gas only (grey) and aerosol only (blue) simulations (thick lines show multi-model means and shaded regions show the 5th to 95th percentile ranges) and for HadCRUT5 (black). All models have been subsampled using the HadCRUT5 observational data mask. Temperature anomalies are shown relative to 1950–2010 for Antarctica and relative to 1850–1900 for other continents. CMIP6 historical simulations are extended using the SSP2-4.5 scenario simulations. All available ensemble members were used (see [[#3.2|Section 3.2]] ). Regions are defined by [[#Iturbide--2020|Iturbide et al. (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

In summary, since the publication of AR5, new literature has emerged that better accounts for methodological and climate model uncertainties in attribution studies ( [[#Ribes--2017|Ribes et al., 2017]] ; [[#Hannart--2018|Hannart and Naveau, 2018]] ) and that concludes that anthropogenic warming is approximately equal to observed warming over the 1951–2010 period. The IPCC SR1.5 reached the same conclusion for 2017 relative to 1850–1900 based on anthropogenic warming and associated uncertainties calculated using the method of [[#Haustein--2017|Haustein et al. (2017)]] . Moreover, the improved understanding of the causes of the apparent slowdown in warming over the beginning of the 21st century and the difference in simulated and observed warming trends over this period (Cross-Chapter Box 3.1) further improve our confidence in the assessment of the dominant anthropogenic contribution to observed warming. In deriving our assessments, these considerations are balanced against new literature that raises questions about the ability of some models to simulate variability in surface temperatures over a range of time scales ( [[#Laepple--2014|Laepple and Huybers, 2014]] ; [[#Parsons--2017|Parsons et al., 2017]] ; [[#Friedman--2020|Friedman et al., 2020]] ), and the finding that some CMIP6 models exhibit substantially higher multi-decadal internal variability than that seen in CMIP5, which remains to be fully understood ( [[#Parsons--2020|Parsons et al., 2020]] ; [[#Ribes--2021|Ribes et al., 2021]] ). Further, uncertainties in simulated aerosol-cloud interactions are still large (Section 7.3.3.2.2), resulting in very diverse spatial responses of different climate models to aerosol forcing, and inter-model differences in the historical global mean temperature evolution and in diagnosed cooling attributable to aerosols (Figure 3.8). Moreover, like previous generations of coupled model simulations, historical and single forcing CMIP6 simulations follow a common experimental design ( [[#Eyring--2016a|Eyring et al., 2016a]] ; [[#Gillett--2016|Gillett et al., 2016]] ) and are thus all driven by the same common set of forcings, even though these forcings are uncertain. Hence, forcing uncertainty is not directly accounted for in most of the attribution and model evaluation studies assessed here, although this limitation can to some extent be addressed by comparing with previous generation multi-model ensembles or individual model studies using different sets of forcings.

The IPCC SR1.5 best estimate and ''likely'' range of anthropogenic attributable GMST warming was 1.0 ± 0.2°C in 2017 with respect to the period 1850–1900. Here, the best estimate is expressed in terms of GSAT and is calculated as the average of the three estimates shown in Figure 3.9, yielding a value of 1.07°C. Ranges for attributable GSAT warming are derived by finding the smallest ranges with a precision of 0.1°C which span all of the 5–95% ranges from the attribution studies shown in Figure 3.9. These ranges are then assessed as ''likely'' rather than ''very likely'' because the studies may underestimate the importance of the structural limitations of climate models, which probably do not represent all possible sources of internal variability; use too simple climate models, which may underestimate the role of internal variability; or underestimate model uncertainty, especially when using model ensembles of limited size and inter-dependent models, for example through common errors in forcings across models, as discussed above. This leads to a ''likely'' range for anthropogenic attributable warming in 2010–2019 relative to 1850–1900 of 0.8 to 1.3°C in terms of GSAT. This range encompasses the best estimate and ''very likely'' range of observed GSAT warming of 1.06 [0.88 to 1.21] °C over the same period (Cross-Chapter Box 2.3). There is ''medium confidence'' that the best estimate and ''likely'' ranges of attributable warming expressed in terms of GMST are equal to those for GSAT (Cross-Chapter Box 2.3). Repeating the process for other time periods leads to the best estimates and ''likely'' ranges listed in Table 3.1. GSAT change attributable to natural forcings is −0.1 to +0.1°C. The ''likely'' range of GSAT warming attributable to greenhouse gases is assessed in the same way to be 1.0 to 2.0°C while the GSAT change attributable to aerosols, ozone and land-use change is −0.8 to 0.0°C. Progress in attribution techniques allows the important advance of attributing observed surface temperature warming since 1850–1900, instead of since 1951 as was done in AR5.

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Table 3.1 | '''Estimates of warming in GSAT attributable to human influence for different periods in °C, all relative to the 185''' '''0''' '''–1900 base period.''' Uncertainty ranges are 5–95% ranges for individual studies and ''likely'' ranges for the assessment. The results shown in the table use the methods described in the three studies indicated, but applied to additional periods and the warming trend. [[#Ribes--2021|Ribes et al. (2021)]] results were updated using a corrected natural forcing time series, and [[#Haustein--2017|Haustein et al. (2017)]] results were updated to use HadCRUT5.

{| class="wikitable"
|-
|
| 1986–2005

| 1995–2014

| 2006–2015

| 2010–2019

| Warming Rate 2010–2019

|-
| [[#Ribes--2021|Ribes et al. (2021)]]

| 0.65 (0.52 to 0.77)

| 0.82 (0.69 to 0.94)

| 0.94 (0.8 to 1.08)

| 1.03 (0.89 to 1.17)

| 0.23 (0.18 to 0.29)

|-
| [[#Gillett--2021|Gillett et al. (2021)]]

| 0.63 (0.32 to 0.94)

| 0.84 (0.63 to 1.06)

| 0.98 (0.74 to 1.22)

| 1.11 (0.92 to 1.30)

| 0.35 (0.30 to 0.41)

|-
| [[#Haustein--2017|Haustein et al. (2017)]]

| 0.73 (0.58 to 0.82)

| 0.88 (0.75 to 0.98)

| 0.98 (0.87 to 1.10)

| 1.06 (0.94 to 1.22)

| 0.23 (0.19 to 0.35)

|-
| Assessment

| 0.68 (0.3 to 1.0)

| 0.85 (0.6 to 1.1)

| 0.97 (0.7 to 1.3)

| 1.07 (0.8 to 1.3)

| 0.2 (0.1 to 0.3)

|}

The IPCC AR5 assessed the ''likely'' range of the contribution of internal variability to GMST warming to be −0.1 to +0.1°C over the period 1951–2010. Since then, several studies have downplayed the contribution of internal modes of variability to global temperature variability, often by arguing for a forced component to those internal modes ( [[#Mann--2014|Mann et al., 2014]] ; [[#Folland--2018|Folland et al., 2018]] ; [[#Haustein--2019|Haustein et al., 2019]] ; [[#Liguori--2020|Liguori et al., 2020]] ). [[#Haustein--2017|Haustein et al. (2017)]] found a 5–95% confidence interval of −0.09°C to +0.12°C for the contribution of internal variability to warming between 1850–1879 and 2017. [[#Ribes--2021|Ribes et al. (2021)]] imply a contribution of internal variability of −0.02°C ± 0.16°C to warming between 2010–2019 and 1850–1900, assuming independence between errors in the observations and in the estimate of the forced response. Based on these studies, but allowing for unsampled sources of error, we assess the ''likely'' range of the contribution of internal variability to GSAT warming between 2010–2019 and 1850–1900 to be −0.2°C to +0.2°C.

The IPCC SR1.5 gave a ''likely'' range for the human-induced warming rate of 0.1°C to 0.3°C per decade in 2017, with a best estimate of 0.2°C per decade ( [[#Allen--2018|Allen et al., 2018]] ). Table 3.1 lists the estimates of attributable anthropogenic warming rate over the period 2010–2019 based on the three studies that underpin the assessment of GSAT warming ( [[#Haustein--2017|Haustein et al., 2017]] ; [[#Gillett--2021|Gillett et al., 2021]] ; [[#Ribes--2021|Ribes et al., 2021]] ). Estimates from [[#Haustein--2017|Haustein et al. (2017)]] , based on observed warming, and [[#Ribes--2021|Ribes et al. (2021)]] , based on CMIP6 simulations constrained by observed warming, are in good agreement. The [[#Gillett--2021|Gillett et al. (2021)]] estimate, also based on CMIP6 models, corresponds to a larger anthropogenic attributable warming rate, because of a smaller warming rate attributed to natural forcing than in [[#Ribes--2021|Ribes et al. (2021)]] . This disagreement does not support a decrease in uncertainty compared to the SR1.5 assessment. So the range for anthropogenic attributable surface temperature warming rate of 0.1°C to 0.3°C per decade is again assessed to be ''likely'' , with a best estimate of 0.2°C per decade.

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<span id="upper-air-temperature"></span>
==== 3.3.1.2 Upper-air Temperature ====

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( [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] assessed that the troposphere has warmed since at least the 1950s, that it is ''virtually certain'' that the stratosphere has cooled, and that there is ''medium confidence'' that the upper troposphere in the tropics has warmed faster than the near-surface since at least 2001 ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.2|Section 2.3.1.2]] ). The AR5 assessed that anthropogenic forcings, dominated by greenhouse gases, ''likely'' contributed to the warming of the troposphere since 1961 and that anthropogenic forcings, dominated by the depletion of the ozone layer due to ozone-depleting substances, ''very likely'' contributed to the cooling of the lower stratosphere since 1979. Since AR5, understanding of observational uncertainties in the radiosonde and satellite data has improved with more available data and longer coverage, and differences between models and observations in the tropical atmosphere have been investigated further.

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<span id="tropospheric-temperature"></span>
===== 3.3.1.2.1 Tropospheric temperature =====

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The AR5 assessed with ''low confidence'' that most, though not all, CMIP3 ( [[#Meehl--2007|Meehl et al., 2007]] ) and CMIP5 ( [[#Taylor--2012|Taylor et al., 2012]] ) models overestimated the observed warming trend in the tropical troposphere during the satellite period 1979–2012, and that a third to a half of this difference was due to an overestimate of the SST trend during this period ( [[#Flato--2013|Flato et al., 2013]] ). Since AR5, additional studies based on CMIP5 and CMIP6 models show that this warming bias in tropospheric temperatures remains. Recent studies have investigated the role of observational uncertainty, the model response to external forcings, the influence of the time period considered, and the role of biases in SST trends in contributing to this bias.

Several studies since AR5 have continued to demonstrate an inconsistency between simulated and observed temperature trends in the tropical troposphere, with models simulating more warming than observations ( [[#Mitchell--2013|Mitchell et al., 2013]] , 2020; [[#Santer--2017a|Santer et al., 2017a]] , b; [[#McKitrick--2018|McKitrick and Christy, 2018]] ; [[#Po-Chedley--2021|Po-Chedley et al., 2021]] ). [[#Santer--2017b|Santer et al. (2017b)]] used updated and improved satellite retrievals to investigate model performance in simulating the tropical mid- to upper-troposphere trends, and removed the influence of stratospheric cooling by regression. These factors were found to reduce the size of the discrepancy in mid- to upper-tropospheric temperature trends between models and observations over the satellite era, but a discrepancy remained. [[#Santer--2017a|Santer et al. (2017a)]] found that during the late 20th century, the discrepancies between simulated and satellite-derived mid- to upper-tropospheric temperature trends were consistent with internal variability, while during most of the early 21st century, simulated tropospheric warming was significantly larger than observed, which they related to systematic deficiencies in some of the external forcings used after year 2000 in the CMIP5 models. However, in CMIP6, differences between simulated and observed upper-tropospheric temperature trends persist despite updated forcing estimates ( [[#Mitchell--2020|Mitchell et al., 2020]] ). Figure 3.10 shows that CMIP6 models forced by combined anthropogenic and natural forcings overestimate temperature trends compared to radiosonde data ( [[#Haimberger--2012|Haimberger et al., 2012]] ) throughout the tropical troposphere ( [[#Mitchell--2020|Mitchell et al., 2020]] ). Over the 1979–2014 period, models are more consistent with observations in the lower troposphere, and least consistent in the upper troposphere around 200 hPa, where biases exceed 0.1°C per decade. Several studies using CMIP6 models suggest that differences in climate sensitivity may be an important factor contributing to the discrepancy between the simulated and observed tropospheric temperature trends ( [[#McKitrick--2020|McKitrick and Christy, 2020]] ; [[#Po-Chedley--2021|Po-Chedley et al., 2021]] ), though it is difficult to deconvolve the influence of climate sensitivity, changes in aerosol forcing and internal variability in contributing to tropospheric warming biases ( [[#Po-Chedley--2021|Po-Chedley et al., 2021]] ). Another study found that the absence of a hypothesized negative tropical cloud feedback could explain half of the upper troposphere warming bias in one model ( [[#Mauritsen--2015|Mauritsen and Stevens, 2015]] ).

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[[File:f84a8ba815752cf82c4431be8ae09afe IPCC_AR6_WGI_Figure_3_10.png]]

'''Figure 3.10 | Observed and simulated tropical mean temperature trends through the atmosphere.''' Vertical profiles of temperature trends in the tropics (20°S–20°N) for three periods: '''(a)''' 1979–2014, '''(b)''' 1979–1997 (ozone depletion era) and '''(c)''' 1998–2014 (ozone stabilization era). The black lines show trends in the Radiosonde Innovation Composite Homogenization (RICH) 1.7 (long dashed) and Radiosonde Observation Correction using Reanalysis (RAOBCORE) 1.7 (dashed) radiosonde datasets ( [[#Haimberger--2012|Haimberger et al., 2012]] ), and in the ERA5/5.1 reanalysis (solid). Grey envelopes are centred on the RICH 1.7 trends, but show the uncertainty based on 32 RICH-observations members of version 1.5.1 of the dataset, which used version 1.7.3 of the RICH software but with the parameters of version 1.5.1. ERA5 was used as reference for calculating the adjustments between 2010 and 2019, and ERA-Interim was used for the years before that. Red lines show trends in CMIP6 historical simulations from one realization of each of 60 models. Blue lines show trends in 46 CMIP6 models that used prescribed, rather than simulated, sea surface temperatures (SSTs). Figure is adapted from [[#Mitchell--2020|Mitchell et al. (2020)]] , their Figure 1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

[[#Mitchell--2013|Mitchell et al. (2013)]] and [[#Mitchell--2020|Mitchell et al. (2020)]] found a smaller discrepancy in tropical tropospheric temperature trends in models forced with observed SSTs (see also Figure 3.10a), and CMIP5 models and observations were found to be consistent below 150 hPa when viewed in terms of the ratio of temperature trends aloft to those at the surface ( [[#Mitchell--2013|Mitchell et al., 2013]] ). [[#Flannaghan--2014|Flannaghan et al. (2014)]] and [[#Tuel--2019|Tuel (2019)]] showed that most of the tropospheric temperature trend difference between CMIP5 models and the satellite-based observations over the 1970–2018 period is due to respective differences in SST warming trends in regions of deep convection, and [[#Po-Chedley--2021|Po-Chedley et al. (2021)]] showed that CMIP6 models with a more realistic SST simulation in the central and eastern Pacific show a better performance than other models. Though systematic biases still remain, this indicates that the bias in tropospheric temperature warming in models is in part linked to surface temperature warming biases, especially in the lower troposphere.

In summary, studies continue to find that CMIP5 and CMIP6 model simulations warm more than observations in the tropical mid- and upper-troposphere over the 1979–2014 period ( [[#Mitchell--2013|Mitchell et al., 2013]] , 2020; [[#Santer--2017a|Santer et al., 2017a]] , b; [[#Suárez-Gutiérrez--2017|Suárez-Gutiérrez et al., 2017]] ; [[#McKitrick--2018|McKitrick and Christy, 2018]] ), and that overestimated surface warming is partially responsible ( [[#Mitchell--2013|Mitchell et al., 2013]] ; [[#Po-Chedley--2021|Po-Chedley et al., 2021]] ). Some studies point to forcing errors in the CMIP5 simulations in the early 21st century as a possible contributor ( [[#Mitchell--2013|Mitchell et al., 2013]] ; [[#Sherwood--2015|Sherwood and Nishant, 2015]] ; [[#Santer--2017a|Santer et al., 2017a]] ), but CMIP6 simulations use updated forcing estimates yet generally still warm more than observations. Although accounting for internal variability and residual observational errors can reconcile models with observations to some extent ( [[#Suárez-Gutiérrez--2017|Suárez-Gutiérrez et al., 2017]] ; [[#Mitchell--2020|Mitchell et al., 2020]] ), some studies suggest that climate sensitivity also plays a role ( [[#Mauritsen--2015|Mauritsen and Stevens, 2015]] ; [[#McKitrick--2020|McKitrick and Christy, 2020]] ; [[#Po-Chedley--2021|Po-Chedley et al., 2021]] ). Hence, we assess with ''medium confidence'' that CMIP5 and CMIP6 models continue to overestimate observed warming in the upper tropical troposphere over the 1979–2014 period by at least 0.1°C per decade, in part because of an overestimate of the tropical SST trend pattern over this period.

The AR5 assessed as ''likely'' that anthropogenic forcings, dominated by greenhouse gases, contributed to the warming of the troposphere since 1961 ( [[#Bindoff--2013|Bindoff et al., 2013]] ). Since then, there has been further progress in detecting and attributing tropospheric temperature changes. [[#Mitchell--2020|Mitchell et al. (2020)]] used CMIP6 models to find that the main driver of tropospheric temperature changes is greenhouse gases. Previous detection of the anthropogenic influence on tropospheric warming may have overestimated uncertainties: [[#Pallotta--2020|Pallotta and Santer (2020)]] found that CMIP5 climate models overestimate the observed natural variability in global mean tropospheric temperature on time scales of 5–20 years. Nevertheless, [[#Santer--2019|Santer et al. (2019)]] found that stochastic uncertainty is greater for tropospheric warming than stratospheric cooling because of larger noise and slower recovery time from the Mount Pinatubo eruption in the troposphere. The detection time of the anthropogenic signal in the tropospheric warming can be affected by both the model climate sensitivity and the model response to aerosol forcing. Volcanic forcing is also important, as models that do not consider the influence of volcanic eruptions in the early 21st century overestimate the observed tropospheric warming since 1998 ( [[#Santer--2014|Santer et al., 2014]] ). Changes in the amplitude of the seasonal cycle of tropospheric temperatures have also been attributed to human influence. [[#Santer--2018|Santer et al. (2018)]] found that satellite data and climate models driven by anthropogenic forcing show consistent amplitude increases at mid-latitudes in both hemispheres, amplitude decreases at high latitudes in the Southern Hemisphere, and small changes in the tropics.

In summary, these studies confirm the dominant role of human activities in tropospheric temperature trends. We therefore assess that it is ''very likely'' that anthropogenic forcing, dominated by greenhouse gases, was the main driver of the warming of the troposphere since 1979.

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<span id="stratospheric-temperature"></span>
===== 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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[[File:f47669ecd7c06b9027c182aba74543b2 IPCC_AR6_WGI_CCBox_3_1_Figure_1.png]]
'''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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<span id="precipitation-humidity-and-streamflow"></span>
=== 3.3.2 Precipitation, Humidity and Streamflow ===

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<span id="paleoclimate-context"></span>
==== 3.3.2.1 Paleoclimate Context ====

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A fact hindering detection and attribution studies in precipitation and other hydrological variables is the large internal variability of these fields relative to the anthropogenic signal. This low signal-to-noise ratio hinders the emergence of the anthropogenic signal from natural variability. Moreover, the sign of the change depends on location and time of the year. Paleoclimate records provide valuable context for observed trends in the 20th and 21st century and assist with the attribution of these trends to human influence (see also ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.1|Section 2.3.1.3.1]] ). By nature, hydrological proxy data represent regional conditions, but taken together can represent large-scale patterns. As an example of how paleorecords have helped assessing the origin of changes, we consider some, mainly subtropical, regions which have experienced systematic drying in recent decades (see also Section 8.3.1.3). Paleoclimate simulations of monsoons are assessed in [[#3.3.3.2|Section 3.3.3.2]] .

Records of tree ring width have provided evidence that recent prolonged dry spells in the Levant and Chile are unprecedented in the last millennium ( ''high confidence'' ) ( [[#Cook--2016a|Cook et al., 2016a]] ; [[#Garreaud--2017|Garreaud et al., 2017]] ). East Africa has also been drying in recent decades ( [[#Rowell--2015|Rowell et al., 2015]] ; [[#Hoell--2017|Hoell et al., 2017]] ), a trend that is unusual in the context of the sedimentary paleorecord spanning the last millennium ( [[#Tierney--2015|Tierney et al., 2015]] ). This may be a signature of anthropogenic forcing but cannot yet be distinguished from natural variability ( [[#Hoell--2017|Hoell et al., 2017]] ; [[#Philip--2018|Philip et al., 2018]] ). Likewise, tree rings indicate that the 2012–2014 drought in the south-western United States was exceptionally severe in the context of natural variability over the last millennium, and may have been exacerbated by the contribution of anthropogenic temperature rise ( ''medium confidence'' ) ( [[#Griffin--2014|Griffin and Anchukaitis, 2014]] ; [[#Williams--2015|Williams et al., 2015]] ). Furthermore, [[#Williams--2020|Williams et al. (2020)]] used a combination of hydrological modelling and tree-ring reconstructions to show that the period from 2000 to 2018 was the driest 19-year span in south-western North America since the late 1500s. Nonetheless, tree rings also indicate the presence of prolonged megadroughts in western North America throughout the last millennium that were more severe than 20th and 21st century events ( ''high confidence'' ) ( [[#Cook--2004|Cook et al., 2004]] , 2010, 2015). These were associated with internal variability ( [[#Coats--2016|Coats et al., 2016]] ; [[#Cook--2016b|Cook et al., 2016b]] ) and indicate that large-magnitude changes in the water cycle may occur irrespective of anthropogenic influence (see also [[#McKitrick--2019|McKitrick and Christy, 2019]] ).

Paleoclimate records also allow for model evaluation under conditions different from present-day. The AR5 concluded that models can successfully reproduce to first-order patterns of past precipitation changes during the Last Glacial Maximum (LGM) and mid-Holocene, though simulated precipitation changes during the mid-Holocene tended to be underestimated ( [[#Flato--2013|Flato et al., 2013]] ). Further analysis of CMIP5 models confirmed these results but has also revealed systematic offsets from the paleoclimate record ( [[#DiNezio--2013|DiNezio and Tierney, 2013]] ; [[#Hargreaves--2014|Hargreaves and Annan, 2014]] ; [[#Harrison--2014|Harrison et al., 2014]] , 2015; [[#Bartlein--2017|Bartlein et al., 2017]] ; [[#Scheff--2017|Scheff et al., 2017]] ; [[#Tierney--2017|Tierney et al., 2017]] ). [[#Harrison--2014|Harrison et al. (2014)]] concluded that CMIP5 models do not perform better in simulating rainfall during the LGM and mid-Holocene than earlier model versions despite higher resolution and complexity. However, prescribing changes in vegetation and dust was found to improve the match to the paleoclimate record ( [[#Pausata--2016|Pausata et al., 2016]] ; [[#Tierney--2017|Tierney et al., 2017]] ) suggesting that vegetation feedbacks in the CMIP5 models may be too weak ( ''low confidence'' ) ( [[#Hopcroft--2017|Hopcroft et al., 2017]] ). [[#Brierley--2020|Brierley et al. (2020)]] compared the latitudinal gradient of annual precipitation changes in the European–African sector simulated by CMIP6 models for the mid-Holocene with pollen-based reconstructions and showed that models generally reproduce the direction of changes seen in the reconstructions (Figure 3.11). They do not show a robust signal in area averaged rainfall over most European regions where quantitative reconstructions exist, which is not incompatible with reconstructions. Over the Sahara/Sahel and West Africa regions, where reconstructions suggest positive anomalies during the mid-Holocene, both CMIP5 and CMIP6 models also simulate a rainfall increase, but it is much weaker (see also ( [[#3.3.3.2|Section 3.3.3.2]] ). Overall, however, large discrepancies remain between simulations and reconstructions.

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[[File:33efc5a39a5a4f520a1db9ebb7ebbd01 IPCC_AR6_WGI_Figure_3_11.png]]

Figure 3.11 | '''Comparison between simulated annual precipitation changes and pollen-based reconstructions in the mid-Holocene (6000 years ago).''' The area-averaged changes relative to the pre-industrial control simulations over five regions ( [[#Iturbide--2020|Iturbide et al., 2020]] ) as simulated by CMIP6 models (individually identifiable, one ensemble member per model) and CMIP5 models (blue) are shown, stretching from the tropics to high-latitudes. All regions contain multiple quantitative reconstructions of changes relative to present day; their interquartile range are shown by boxes and with whiskers for their full range excluding outliers. Figure is adapted from [[#Brierley--2020|Brierley et al. (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

[[#Liu--2018|Liu et al. (2018)]] evaluated the soil moisture changes that occurred during the LGM and concluded that the multi-model median from CMIP5 is consistent with available paleo-records in some regions, but not in others. CMIP5 models accurately reproduce an increase in moisture in the western United States, related to an intensified winter storm track and decreased evaporative demand ( [[#Oster--2015|Oster et al., 2015]] ; [[#Ibarra--2018|Ibarra et al., 2018]] ; [[#Lora--2018|Lora, 2018]] ). On the other hand, CMIP5 models show a wide variety of responses in the tropical Indo-Pacific region, with only a few matching the pattern of change inferred from the paleoclimate record ( [[#DiNezio--2013|DiNezio and Tierney, 2013]] ; [[#DiNezio--2018|DiNezio et al., 2018]] ). The variable response across models is related to the effect of the exposure of the tropical shelves during glacial times, which variously intensifies or weakens convection in the rising branch of the Walker cell, depending on model parameterization ( [[#DiNezio--2011|DiNezio et al., 2011]] ). For the Last Interglacial, CMIP6 models reproduce the proxy-based increased precipitation relative to pre-industrial in the North African, South Asian and North American regions, but not in Australia ( [[#Scussolini--2019|Scussolini et al., 2019]] ).

In summary, there is ''medium confidence'' that CMIP5 and CMIP6 models can reproduce broad aspects of precipitation changes during paleo reference periods, but large discrepancies remain. Further assessment of model performance and comparison between CMIP5 and CMIP6 during past climates can be found in [[#3.8.2.1|Section 3.8.2.1]] .

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<span id="atmospheric-water-vapour"></span>
==== 3.3.2.2 Atmospheric Water Vapour ====

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The AR5 concluded that an anthropogenic contribution to increases in specific humidity is found with ''medium confidence'' at and near the surface. A levelling off of atmospheric water vapour over land in the last two decades that needed better understanding, and remaining observational uncertainties, precluded a more confident assessment ( [[#Bindoff--2013|Bindoff et al., 2013]] ). Sections 4.5.1.3 and 8.3.1.4 show that there have been significant advances in the understanding of the processes controlling land surface humidity. In particular, there has been a focus on the role of oceanic moisture transport and land-atmosphere feedbacks in explaining the observed trends in relative humidity.

Water vapour is the most important natural greenhouse gas and its amount is expected to increase in a global warming context leading to further warming. Particularly important are changes in the upper troposphere because there water vapour regulates the strength of the water-vapour feedback (Section 7.4.2.2). CMIP5 models have been shown to have a wet bias in the tropical upper troposphere and a dry bias in the lower troposphere, with the former bias and model spread being larger than the latter ( [[#Jiang--2012|Jiang et al., 2012]] ; [[#Tian--2013|Tian et al., 2013]] ). [[#Tian--2013|Tian et al. (2013)]] also showed that in comparison to the AIRS specific humidity, CMIP5 models have the well-known double Inter-tropical Convergence Zone (ITCZ) bias in the troposphere from 1000 hPa to 300 hPa, especially in the tropical Pacific. Water vapour biases in models are dominated by errors in relative humidity throughout the troposphere, which are in turn closely related to errors in large scale circulation; temperature errors dominate near the tropopause ( [[#Takahashi--2016|Takahashi et al., 2016]] ). Section 7.4.2.2 discusses this topic in more detail for CMIP6 models. However, [[#Schröder--2019|Schröder et al. (2019)]] show that the majority of well-established water vapour records are affected by inhomogeneity issues and thus should be used with caution (see also ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.3|Section 2.3.1.3.3]] ). A comparison of trends in column water vapour path for 1998–2019 in satellite data, a reanalysis, CMIP5 and CMIP6 simulations averaged over the near-global ocean reveals that while on average model trends are higher than those in observations and a reanalysis, the latter lie within the multi-model range (Figure 3.12).

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[[File:dc96a534bd92c51c5bc548b5a349cacd IPCC_AR6_WGI_Figure_3_12.png]]

Figure 3.12 | '''Total column water vapour trends (% per decade) for the period 1988–2019 averaged over the near-global oceans (50°S–50°N).''' The figure shows satellite data (RSS) and ERA5.1 reanalysis, as well as CMIP5 (blue) and CMIP6 (red) historical simulations. All available ensemble members were used (see [[#3.2|Section 3.2]] ). Fits to the model trend probability distributions were performed with kernel density estimation. Figure is updated from [[#Santer--2007|Santer et al. (2007)]] . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

The detection and attribution of tropospheric water vapour changes can be traced back to [[#Santer--2007|Santer et al. (2007)]] , who used estimates of atmospheric water vapour from the satellite-based Special Sensor Microwave Imager (SSM/I) and from CMIP3 historical climate simulations. They provided evidence of human-induced moistening of the troposphere, and found that the simulated human fingerprint pattern was detectable at the 5% level by 2002 in water vapour satellite data (from 1988 to 2006). The observed changes matched the historical simulations forced by greenhouse gas changes and other anthropogenic forcings, and not those due to natural variability alone. Then, [[#Santer--2009|Santer et al. (2009)]] repeated this study with CMIP5 models, and found that the detection and attribution conclusions were not sensitive to model quality. These results demonstrate that the human fingerprint is governed by robust and basic physical processes, such as the water vapour feedback. Finally, [[#Chung--2014|Chung et al. (2014)]] extended this line of research by focusing on the global-mean water vapour content in the upper troposphere. Using satellite-based observations and sets of CMIP5 climate simulations run under various climate-forcing options, they showed that the observed moistening trend of the upper troposphere over the 1979–2005 period could not be explained by internal variability alone, but is attributable to a combination of anthropogenic and natural forcings. This increase in water vapour is accompanied by a reduction in mid-tropospheric relative humidity and clouds in the subtropics and mid-latitude in both models and observations related to changes in the Hadley cell ( [[#3.3.3.1.1|Section 3.3.3.1.1]] ; [[#Lau--2015|Lau and Kim, 2015]] ).

[[#Dunn--2017|Dunn et al. (2017)]] confirmed earlier findings that global mean surface relative humidity increased between 1973 and 2000, followed by a steep decline (also reported in [[#Willett--2014|Willett et al., 2014]] ) until 2013, and specific humidity correspondingly increased and then remained approximately constant (see also ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.2|Section 2.3.1.3.2]] ), with none of the CMIP5 models capturing this behaviour. They noted biases in the mean state of the CMIP5 models’ surface relative humidity (and ascribed the failure to the representation of land surface processes and their response to CO <sub>2</sub> forcing), concluding that these biases preclude any detection and attribution assessment. On the other hand, [[#Byrne--2018|Byrne and O’Gorman (2018)]] showed that the positive trend in specific humidity continued in recent years and can be detected over land and ocean from 1979 to 2016. Moreover, they provided a theory suggesting that the increase in annual surface temperature and specific humidity as well as the decrease in relative humidity observed over land are linked to warming over the neighbouring ocean. They also pointed out that the negative trend in relative humidity over land regions is quite uncertain and requires further investigation. A recent study has also identified an anthropogenically-driven decrease in relative humidity over the Northern Hemisphere mid-latitude continents in summer between 1979 and 2014, which was underestimated by CMIP5 models ( [[#Douville--2017|Douville and Plazzotta, 2017]] ). Furthermore, in a modelling study [[#Douville--2020|Douville et al. (2020)]] showed that this decrease in boreal summer relative humidity over mid-latitudes is related not only to global ocean warming, but also to the physiological effect of CO <sub>2</sub> on plants in the land surface model.

In summary, we assess that it is ''likely'' that human influence has contributed to moistening in the upper troposphere since 1979. Also, there ''is medium confidence'' that human influence contributed to a global increase in annual surface specific humidity, and ''medium confidence'' that it contributed to a decrease in surface relative humidity over mid-latitude Northern Hemisphere continents during summertime.

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<span id="precipitation"></span>
==== 3.3.2.3 Precipitation ====

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AR5 concluded that there was ''medium confidence'' that human influence had contributed to large-scale precipitation changes over land since 1950, including an increase in the Northern Hemisphere mid- to high latitudes. Moreover, AR5 concluded that observational uncertainties and challenges in precipitation modelling precluded a more confident assessment ( [[#Bindoff--2013|Bindoff et al., 2013]] ). Overall, it found that large-scale features of mean precipitation in CMIP5 models were in modest agreement with observations, but there were systematic errors in the tropics ( [[#Flato--2013|Flato et al., 2013]] ).

Since AR5, X. [[#Li--2016b|Li et al. (2016b)]] found that CMIP5 models simulate the large scale patterns of annual mean land precipitation and seasonality well, as well as reproducing qualitatively the observed zonal mean land precipitation trends for the period 1948–2005: models capture the drying trends in the tropics and at 45°S and the wetting trend in the Northern Hemisphere mid- to high latitudes, but the amplitudes of the changes are much smaller than observed. Land precipitation was found to show enhanced seasonality in observations ( [[#Chou--2013|Chou et al., 2013]] ), qualitatively consistent with the simulated response to anthropogenic forcing ( [[#Dwyer--2014|Dwyer et al., 2014]] ). However, models do not appear to reproduce the zonal mean trends in the magnitude of the seasonal cycle over the period 1948–2005, nor the two-dimensional distributions of trends of annual precipitation and seasonality over land, but differences may be explainable by internal variability (X. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] b). However, observed trends in seasonality depend on data set used (X. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] b; [[#Marvel--2017|Marvel et al., 2017]] ), and [[#Marvel--2017|Marvel et al. (2017)]] found that observed changes in the annual cycle phase are consistent with model estimates of forced changes. These phase changes are mainly characterized by earlier onset of the wet season on the equatorward flanks of the extratropical storm tracks, particularly in the Southern Hemisphere. Box 8.2 assesses regional changes in water cycle seasonality.

The CMIP5 models have also been shown to adequately simulate the mean and interannual variability of the global monsoon ( [[#3.3.3.2|Section 3.3.3.2]] ), but maintain the double ITCZ bias in the equatorial Pacific ( [[#Lee--2014|Lee and Wang, 2014]] ; [[#Tian--2015|Tian, 2015]] ; [[#Ni--2018|Ni and Hsu, 2018]] ). Despite the ITCZ bias, CMIP5 models have been used to detect in reanalysis a southward shift in the ITCZ prior to 1975, followed by a northward shift in the ITCZ after 1975, in response to forced changes in inter-hemispheric temperature contrast (Sections 3.3.1.1 and 8.3.2.1, and Figure 8.11; [[#Bonfils--2020|Bonfils et al., 2020]] ; [[#Friedman--2020|Friedman et al., 2020]] ). CMIP5 models perform better than CMIP3 models, in particular regarding the global monsoon domain and intensity ( [[#Lee--2014|Lee and Wang, 2014]] ).

In observations at time scales less than a day intermittent rainfall fluctuations dominate variability, but CMIP5 models systematically underestimate them ( [[#Covey--2018|Covey et al., 2018]] ). Moreover, as noted in previous generation models, CMIP5 models produce rainfall too early in the day ( [[#Covey--2016|Covey et al., 2016]] ). Also, models overpredict precipitation frequency but have weaker intensity, although comparison with observed datasets is complex as there are large differences in intensity among them ( [[#Herold--2016|Herold et al., 2016]] ; [[#Pendergrass--2017|Pendergrass and Deser, 2017]] ; [[#Trenberth--2017|Trenberth et al., 2017]] ). Regarding trends in precipitation intensity, models have also been shown to reproduce the compensation between increasing heavy precipitation and decreasing light to moderate rainfall ( [[#Thackeray--2018b|Thackeray et al., 2018b]] ), a characteristic found in the observational record ( [[#Gu--2018|Gu and Adler, 2018]] ). Regional model performance is further assessed in [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] and the Atlas, while precipitation extremes are considered in Chapter 11.

The simulation of annual mean rainfall patterns in the CMIP6 models reveals minor improvements compared to those in CMIP5 models (Figure 3.13). The persistent biases include the double ITCZ in the tropical Pacific (seen as bands of excessive rainfall on both sides of the equatorial Pacific in Figure 3.13b,d) and the southward-shifted ITCZ in the equatorial Atlantic, which have been linked to the meridional pattern of SST bias (S. [[#Zhou--2020|]] [[#Zhou--2020|Zhou et al., 2020]] ) and the reduced sensitivity of precipitation to local SST ( [[#Good--2021|Good et al., 2021]] ). [[#Tian--2020|Tian and Dong (2020)]] also found that all three generations of CMIP models share similar systematic annual mean precipitation errors in the tropics, but that the double ITCZ bias is slightly reduced in CMIP6 models in comparison to CMIP3 and CMIP5 models. They also found some improvement in the overly intense Indian ocean ITCZ and the too dry South American continent except over the Andes. [[#Fiedler--2020|Fiedler et al. (2020)]] identified improvements in the tropical mean spatial correlations and root mean square error of the climatology as well as in the day-to-day variability, but found little change across CMIP phases in the double ITCZ bias and diurnal cycle. The CMIP6 models reproduce better the domain and intensity of the global monsoon (see [[#3.3.3.2|Section 3.3.3.2]] ). Moreover, CMIP6 models better represent the storm tracks ( [[#Priestley--2020|Priestley et al., 2020]] ; also ( [[#3.3.3.3|Section 3.3.3.3]] ), thereby reducing the precipitation biases in the North Atlantic and mid-latitudes of the Southern Hemisphere (Figure 3.13b,d). As a result, pattern correlations between simulated and observed annual mean precipitation range between 0.80 and 0.92 for CMIP6 models, compared to a range of 0.79 to 0.88 for CMIP5 ( [[#Bock--2020|Bock et al., 2020]] ). This relative improvement may be related to increased model resolution, as found when comparing biases in the mean of the HighResMIP models with the mean of the corresponding lower-resolution versions of the same models (see Figure 3.13e,f), particularly in the tropics and extratropical storm tracks. Consistent with this, a recent study using several coupled models showed that increasing the atmospheric resolution leads to a strong decrease in the precipitation bias in the tropical Atlantic ITCZ (see further discussion in [[#3.8.2.2|Section 3.8.2.2]] ; [[#Vannière--2019|Vannière et al., 2019]] ). Based on these results we assess that despite some improvements, CMIP6 models still have deficiencies in simulating precipitation patterns, particularly over the tropical ocean ( ''high confidence'' ).

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[[File:0cec6193260a8480eb69f4fad725ce73 IPCC_AR6_WGI_Figure_3_13.png]]

Figure 3.13 | '''Annual-mean precipitation rate (mm day''' '''–1''' ''') for the period 1995–2014. (a)''' Multi-model (ensemble) mean constructed with one realization of the CMIP6 historical experiment from each model. '''(b)''' Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the precipitation analysis from the Global Precipitation Climatology Project (GPCP) version 2.3 ( [[#Adler--2003|Adler et al., 2003]] ). '''(c)''' Multi-model mean of the root mean square error calculated over all months separately and averaged with respect to the precipitation analysis from GPCP version 2.3. '''(d)''' Multi-model mean bias, calculated as the difference between the CMIP6 multi-model mean and the precipitation analysis from GPCP version 2.3. Also shown is the multi-model mean bias as the difference between the multi-model mean of '''(e)''' high resolution and '''(f)''' low-resolution simulations of four HighResMIP models and the precipitation analyses from GPCP version 2.3. Uncertainty is represented using the advanced approach. No overlay indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where &lt;66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and &lt;80% of all models agree on the sign of change. For more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1. Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is not lower in at least three out of four models than in the corresponding low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Recent studies comparing observations and CMIP5 simulations have shown that tropical volcanic eruptions induce a significant reduction in global precipitation, particularly over the wet tropics, including the global monsoon regions ( [[#Iles--2014|Iles and Hegerl, 2014]] ; [[#Paik--2017|Paik and Min, 2017]] ; [[#Paik--2020a|Paik et al., 2020a]] ). Reconstructions and modelling studies also suggest a distinct remote influence of volcanic forcing such that large volcanoes erupting in one hemisphere can enhance monsoon precipitation in the other hemisphere (F. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ; [[#Zuo--2019|Zuo et al., 2019]] ). The climatic effect of volcanic eruptions is further assessed in Cross-Chapter Box 4.1.

An intensification of the wet–dry zonal mean patterns, consisting of the wet tropical and mid-latitude bands becoming wetter, and the dry subtopics becoming drier is expected in response to greenhouse gas and ozone changes (Section 8.2.2.2). However, detecting these changes is complicated by model errors in locating the main features of rainfall patterns. To deal with this issue, [[#Marvel--2013|Marvel and Bonfils (2013)]] identified in each CMIP5 historical simulation the latitudinal peaks and troughs of the rainfall latitudinal patterns, measured the amplification and shift of these patterns in a pattern-based fingerprinting study, and found that the simultaneous amplification and shift in zonal precipitation patterns are detectable in Global Precipitation Climatology Project (GPCP) observations over the 1979–2012 period. Similarly, [[#Bonfils--2020|Bonfils et al. (2020)]] found that the intensification of wet–dry zonal patterns identified in CMIP5 historical simulations is detectable in reanalyses over the 1950–2014 period (see also Figure 8.11).

Based on long-term island precipitation records, [[#Polson--2016|Polson et al. (2016)]] identified significant increases in precipitation in the tropics and decreases in the subtropics, which are consistent with those simulated by the CMIP5 models. Moreover, results from [[#Polson--2017|Polson and Hegerl (2017)]] give support to an intensification of the water cycle according to the wet-gets-wetter, dry-gets-drier paradigm over tropical land areas as well. Other studies suggest that this paradigm does not necessarily hold over dry regions where moisture is limited (see also Section 8.2.2.1; [[#Greve--2014|Greve et al., 2014]] ; [[#Kumar--2015|Kumar et al., 2015]] ). [[#Polson--2017|Polson and Hegerl (2017)]] explained this discrepancy by taking into account the seasonal and interannual movement of the regions ( [[#Allan--2014|Allan, 2014]] ). A follow-up study using CMIP6 models also found that the observed strengthening contrast of precipitation over wet and dry regions was detectable, although the increase was significantly larger in observations than in the multi-model mean. The change was attributed to a combination of anthropogenic and natural forcings, with anthropogenic forcings detectable in multi-signal analyses (Figure 3.14; [[#Schurer--2020|Schurer et al., 2020]] ).

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[[File:8946db0b175de9fa41f10aaefd9df3d4 IPCC_AR6_WGI_Figure_3_14.png]]

Figure 3.14 | '''Wet (a) and dry (b) region tropical mean (30°S–30°N) annual precipitation anomalies.''' Observed data are shown with black lines (GPCP), ERA5 reanalysis is shown in grey, single model simulations are shown with light blue/red lines (CMIP6), and multi-model mean results are shown with dark blue/red lines (CMIP6). Wet and dry region annual anomalies are calculated as the running mean over 12 months relative to a 1988–2020 base period. The regions are defined as the wettest third and driest third of the surface area, calculated for the observations and for each model separately for each season (following [[#Polson--2017|Polson and Hegerl, 2017]] ). Scaling factors '''(c, d)''' are calculated for the combination of the wet and dry region mean, where the observations, reanalysis and all the model simulations are first standardized using the mean standard deviation of the pre-industrial control simulations. Two total least squares regression methods are used: noise in variables (following [[#Polson--2017|Polson and Hegerl, 2017]] ) which estimates a best estimate and a 5–95% confidence interval using the pre-industrial controls (circle and thick green line) and the pre-industrial controls with double the variance (thin green line); and a bootstrap method ( [[#DelSole--2019|DelSole et al., 2019]] ) (5–95% confidence interval shown with a purple line and best estimate with a circle). Panel (c) shows results for GPCP and panel (d) for ERA5. Figure is adapted from [[#Schurer--2020|Schurer et al. (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Global land precipitation has ''likely'' increased since the middle of the 20th century ( ''medium confidence'' ), while there is ''low confidence'' in trends in land data prior to 1950 and over the ocean during the satellite era due to disagreement between datasets ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.4|Section 2.3.1.3.4]] ). Figure 3.15a shows the time evolution of the global mean land precipitation since 1950, as well as the trend during the period. [[#Adler--2017|Adler et al. (2017)]] found no significant trend in the global mean precipitation during the satellite era, consistent with model simulations ( [[#Wu--2013|Wu et al., 2013]] ) and physical understanding of the energy budget (Section 8.2.1). This has been suggested to be due to the negative effect of anthropogenic sulphate aerosol that opposed the positive influence of rising global mean temperatures due to greenhouse gases ( [[#Salzmann--2016|Salzmann, 2016]] ; [[#Richardson--2018|Richardson et al., 2018]] ). The precipitation change expected from ocean warming is also partly offset by the fast atmospheric adjustment to increasing greenhouse gases (Section 8.2.1). Over the ocean, the negligible trend may be due to the cancelling effects of CO <sub>2</sub> and aerosols ( [[#Richardson--2018|Richardson et al., 2018]] ).

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[[File:6965d17c0b5e1e5bdf038a871bbd85eb IPCC_AR6_WGI_Figure_3_15.png]]

Figure 3.15 | '''Observed and simulated time series of anomalies in zonal average annual mean precipitation. (a), (c–f)''' Evolution of global and zonal average annual mean precipitation (mm day <sup>–1</sup> ) over areas of land where there are observations, expressed relative to the base period of 1961–1990, simulated by CMIP6 models (one ensemble member per model) forced with both anthropogenic and natural forcings (brown) and natural forcings only (green). Multi-model means are shown in thick solid lines and shading shows the 5–95% confidence interval of the individual model simulations. The data is smoothed using a low pass filter. Observations from three different datasets are included: gridded values derived from Global Historical Climatology Network (GHCN version 2) station data, updated from [[#Zhang--2007|Zhang et al. (2007)]] , data from the Global Precipitation Climatology Product (GPCP L3 version 2.3, [[#Adler--2003|Adler et al. (2003)]] ) and from the Climate Research Unit (CRU TS4.02, [[#Harris--2014|Harris et al. (2014)]] ). Also plotted are boxplots showing interquartile and 5–95% ranges of simulated trends over the period for simulations forced with both anthropogenic and natural forcings (brown) and natural forcings only (blue). Observed trends for each observational product are shown as horizontal lines. Panel (b) shows annual mean precipitation rate (mm day <sup>–1</sup> ) of GHCN version 2 for the years 1950–2014 over land areas used to compute the plots. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

A gridpoint based analysis of annual precipitation trends over land regions since 1901 ( [[#Knutson--2018|Knutson and Zeng, 2018]] ) comparing observed and simulated trends found that detectable anthropogenic increasing trends have occurred prominently over many mid- to high-latitude regions of the Northern Hemisphere and subtropics of the Southern Hemisphere. The observed trends in many cases are significantly stronger than modelled in the CMIP5 historical runs for the 1901–2010 period (though not for 1951–2010), which may be due to disagreement between observed datasets ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.4|Section 2.3.1.3.4]] ), and/or suggest possible deficiencies in models.

The observed precipitation increase in the Northern Hemisphere high latitudes over the period 1966–2005 was attributed to anthropogenic forcing by a study using CMIP5 models ( [[#Wan--2015|Wan et al., 2015]] ) supporting the AR5 assessment. Initial results from CMIP6 also support the role of anthropogenic forcing in the precipitation increase observed in Northern Hemisphere high latitudes (see Figure 3.15c): the observed positive trend detected for the band 60°N–90°N can only be reproduced when anthropogenic forcing is included, although models tend to simulate overall a larger positive trend. A similar positive trend, but less significant, is also detected between 30°N–60°N, while in the southern mid-latitudes no trend is simulated (see Figure 3.15d, f).

For the Southern Hemisphere extratropics, [[#Solman--2016|Solman and Orlanski (2016)]] found that the observed summertime rainfall increase over high latitudes and decrease over mid-latitudes over the period 1979–2010 are quasi-zonally symmetric and related to changes in eddy activity. The latter were in turn found to be associated with the poleward shift of the westerlies due mostly to ozone depletion. Positive rainfall trends in the subtropics, particularly over south-eastern South America (see also Section 10.4.2.2) and northern and central Australia, have been also attributed to stratospheric ozone depletion ( [[#Kang--2011|Kang et al., 2011]] ; [[#Gonzalez--2014|Gonzalez et al., 2014]] ) and greenhouse gases ( [[#Vera--2015|Vera and Díaz, 2015]] ; [[#Saurral--2019|Saurral et al., 2019]] ). During austral winter, wetting at high latitudes and drying at mid-latitudes are not zonally homogenous, due to both changes in eddy activity and increased lower troposphere humidity. [[#Solman--2016|Solman and Orlanski (2016)]] associated these climate changes with increases in greenhouse gas concentration levels. Recently, [[#Blazquez--2017|Blazquez and Solman (2017)]] have shown that CMIP5 models represent very well the dynamical forcing and the frequency of frontal precipitation in the Southern Hemisphere winter extratropics, but the amount of precipitation due to fronts is overestimated. Chapters 10 and 11 validate in more detail the simulation of fronts in climate models (Sections 10.3.3.4.4 and 11.7.2.3).

Over the ocean, observations show coherent large-scale patterns of fresh ocean regions becoming fresher and salty ocean regions saltier across the globe, which has been related through modelling studies to changes in precipitation minus evaporation and is consistent with the wet-gets-wetter, dry-gets-drier paradigm (see Sections 3.5.2.2 and 8.2.2.1; [[#Durack--2012|Durack et al., 2012]] , 2013; [[#Skliris--2014|Skliris et al., 2014]] ; [[#Durack--2015|Durack, 2015]] ; [[#Hegerl--2015|Hegerl et al., 2015]] ; [[#Levang--2015|Levang and Schmitt, 2015]] ; [[#Zika--2015|Zika et al., 2015]] ; [[#Grist--2016|Grist et al., 2016]] ; [[#Cheng--2020|Cheng et al., 2020]] ).

Overall, studies published since AR5 provide further evidence of an anthropogenic influence on precipitation, and therefore we now assess that it is ''likely'' that human influence has contributed to large-scale precipitation changes observed since the mid-20th century. New attribution studies strengthen previous findings of a detectable increase in mid to high latitude land precipitation over the Northern Hemisphere ( ''high confidence'' ). There is ''medium confidence'' that human influence has contributed to a strengthening of the zonal mean wet tropics-dry subtropics contrast, and that tropical rainfall changes follow the wet-gets-wetter, dry-gets-drier paradigm. There is also ''medium confidence'' that ozone depletion has increased precipitation over the southern high latitudes and decreased it over southern mid-latitudes during austral summer. Owing to observational uncertainties and inconsistent results between studies, we conclude that there is ''low confidence'' in the attribution of changes in the seasonality of precipitation.

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<span id="streamflow"></span>
==== 3.3.2.4 Streamflow ====

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Streamflow is to-date the only variable of the terrestrial water cycle with enough in-situ observations to allow for detection and attribution analysis at continental to global scales. Based on evidence from a few formal detection and attribution studies, particularly on the timing of peak streamflow, and the qualitative evaluation of studies reporting on observed and simulated trends, AR5 concluded that there is ''medium confidence'' that anthropogenic influence on climate has affected streamflow in some middle and high latitude regions ( [[#Bindoff--2013|Bindoff et al., 2013]] ). The AR5 also noted that observational uncertainties are large and that often only a limited number of models were considered.

( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.6|Section 2.3.1.3.6]] assesses that there have not been significant trends in global average streamflow over the last century, though regional trends have been observed, driven in part by internal variability. Only a limited number of studies have systematically compared observed streamflow trends at continental to global scales with changes simulated by global circulation models in a detection and attribution setting. H. [[#Yang--2017|]] [[#Yang--2017|Yang et al. (2017)]] did not find a significant correlation between observed runoff changes and changes simulated in CMIP5 models in most grid cells, consistent with the assessment that observed changes are dominated by internal variability. In a pan-European assessment, [[#Gudmundsson--2017|Gudmundsson et al. (2017)]] attributed the spatio-temporal pattern of decreasing streamflow in southern Europe and increasing streamflow in northern Europe to anthropogenic climate change, but also concluded that additional effects of human water withdrawals could not be excluded. Focussing on continental runoff between 1958 and 2004, [[#Alkama--2013|Alkama et al. (2013)]] found a significant change only when using reconstructed data over all rivers, and a large uncertainty in the estimate of the global streamflow trend due to opposing changes over different continents. [[#Gedney--2014|Gedney et al. (2014)]] detected the influence of aerosols on streamflow in North America and Europe, with aerosols having driven an increase in streamflow due to reduced evaporation (see Section 8.3.1.5 for details on processes). There is also evidence for a detectable anthropogenic contribution toward earlier winter-spring streamflows in the north-central US ( [[#Kam--2018|Kam et al., 2018]] ) and in western Canada ( [[#Najafi--2017|Najafi et al., 2017]] ). From a model evaluation perspective, [[#Sheffield--2013|Sheffield et al. (2013)]] reported that CMIP5 models reproduce spatial variations in runoff in North America well, though they tend to underestimate it.

Recently, [[#Gudmundsson--2021|Gudmundsson et al. (2021)]] performed a global detection and attribution study on streamflow and found that some regions are drying and others are wetting. Moreover, the simulated streamflow trends are consistent with observations only if externally forced climate change is considered, and the simulated effects of water and land management cannot reproduce the observed trends. The effects of volcanic eruptions in driving reduced streamflow have also been detected in the wet tropics ( [[#Iles--2015|Iles and Hegerl, 2015]] ; [[#Zuo--2019|Zuo et al., 2019]] ).

In summary, there is ''medium confidence'' that anthropogenic climate change has altered local and regional streamflow in various parts of the world and that the associated global-scale trend pattern is inconsistent with internal variability. Moreover, human interventions and water withdrawals, while affecting streamflow, cannot explain the observed spatio-temporal trends ( ''medium confidence'' ).

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'''Cross-Chapter Box 3.2 | Human Influence on Large-scale Changes in Temperature and Precipitation Extremes'''

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'''Contributors:''' Nathan P. Gillett (Canada), Seung-Ki Min (Republic of Korea), Krishnan Raghavan (India), Ying Sun (China), Xuebin Zhang (Canada)

Understanding how temperature and precipitation extremes have changed at large scales and the causes of these changes is an important part of our overall assessment of human influence on the climate system. [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses changes in extremes and their causes, while this Cross-Chapter Box summarizes relevant assessments and supporting evidence in Chapters 8 and 11 and relates changes in extremes to mean changes on global and continental scales.

'''Attribution of temperature extremes'''
One important aspect of various indicators of temperature extremes is their connection to mean temperature at local, regional and global scales. For example, the highest daily temperature in a summer is often highly correlated with the summer mean temperature. Model projections show that changes in temperature extremes are often closely related to shifts in mean temperature ( [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Kharin--2018|Kharin et al., 2018]] ). It is thus no surprise that changes in temperature extremes are consistent with warming mean temperature, with warming leading to more hot extremes and fewer cold extremes. Given the attribution of mean warming to human influence ( [[#3.3.1|Section 3.3.1]] ), and the connection between changes in mean and extreme temperatures, it is to be expected that anthropogenic forcing has also influenced temperature extremes.

( [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses that there is ''high confidence'' that climate models can reproduce the mean state and overall warming of temperature extremes observed globally and in most regions, although the magnitude of the trends may differ, and the ability of models to capture observed trends in temperature-related extremes depends on the metric evaluated, the way indices are calculated, and the time periods and spatial scales considered (Section 11.3.3). There has been widespread evidence of human influence on various aspects of temperature extremes, at global, continental, and regional scales. This includes attribution to human influence of observed changes in intensity, frequency, and duration and other relevant characteristics at global and continental scales (Section 11.3.4). The left-hand panel of Cross-Chapter Box 3.2, Figure 1 clearly shows that long-term changes in the global mean annual maximum daily maximum temperature can be reproduced by both CMIP5 and CMIP6 models forced with the combined effect of natural and anthropogenic forcings, but cannot be reproduced by simulations under natural forcing alone. Consistent with the assessment for global mean temperature ( [[#3.3.1|Section 3.3.1]] ), aerosol changes are found to have offset part of the greenhouse gas induced increase in hot extremes globally and over most continents over the 1951–2015 period ( [[#Hu--2020|Hu et al., 2020]] ; [[#Seong--2021|Seong et al., 2021]] ), though greenhouse gas and aerosol influences are less clearly separable in observed changes in cold extremes.

( [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses that it is ''virtually certain'' that human-induced greenhouse gas forcing is the main contributor to the observed increase in the likelihood and severity of hot extremes and the observed decrease in the likelihood and severity of cold extremes on global scales, and ''very likely'' that this applies on most continents.

'''Attribution of precipitation extremes'''
An important piece of evidence supporting the SREX and AR5 assessment that there is ''medium confidence'' that anthropogenic forcing has contributed to a global scale intensification of heavy precipitation during the second half of the 20th century is the evidence for anthropogenic influence on other aspects of the global hydrological cycle. The most significant aspect of that is the increase in atmospheric moisture content associated with warming which should, in general, lead to enhanced extreme precipitation, particularly associated with enhanced convergence in tropical and extratropical cyclones (Sections 8.2.3.2 and 11.4.1). Such a connection is supported by the fact that annual maximum one-day precipitation increases with global mean temperature at a rate similar to the increase in the moisture holding capacity in response to warming, both in observations and in model simulations. Additionally, models project an increase in extreme precipitation across global land regions even in areas in which total annual or seasonal precipitation is projected to decrease.

The overall performance of CMIP6 models in simulating extreme precipitation intensity and frequency is similar to that of CMIP5 models ( ''high confidence'' ), and there is ''high confidence'' in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land (Section 11.4.3). Evidence of human influence on extreme precipitation has become stronger since AR5. Considering changes in precipitation intensity averaged over all wet days, there is ''high confidence'' that daily mean precipitation intensities have increased since the mid-20th century in a majority of land regions, including Europe, North America and Asia, and it is ''likely'' that such an increase is mainly due to anthropogenic emissions of greenhouse gases (Sections 8.3.1.3 and 11.4.4). Section 11.4.4 also finds a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, which can only be explained when anthropogenic greenhouse gas forcing is considered. The right-hand

Cross-Chapter Box 3.2

panel of Cross-Chapter Box 3.2, Figure 1 demonstrates the consistency between changes in global average annual maximum daily precipitation in the observations and model simulations under combined anthropogenic and natural forcing, and inconsistency with simulations under natural forcing alone. While there is more evidence in the literature to quantify the net anthropogenic influence on extreme precipitation than the influence of individual forcing components, a dominant contribution of greenhouse gas forcing to the long-term intensification of extreme precipitation on global and continental scales has recently been quantified separately from the influence of anthropogenic aerosol and natural forcings ( [[#Dong--2020|Dong et al., 2020]] ; [[#Paik--2020b|Paik et al., 2020b]] ).

[[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses that it is ''likely'' that human influence, in particular due to greenhouse gas forcing, is the main driver of the observed intensification of heavy precipitation in global land regions during recent decades (Section 11.4.4).

[[File:0e05877b182295b6f9fbd194fdc03b74 IPCC_AR6_WGI_CCBox_3_2_Figure_1.png]]
'''Cross-Chapter Box 3.2, Figure 1 | Comparison of observed and simulated changes in global mean temperature and precipitation extremes.''' Time series of globally averaged five-year mean anomalies of the annual maximum daily maximum temperature (TXx in °C) and annual maximum 1-day precipitation (Rx1day as standardized probability index in %) between 1953 and 2017 from the HadEX3 observations and the CMIP5 and CMIP6 multi-model ensembles with natural and human forcing '''(top)''' and natural forcing only '''(bottom)''' . For CMIP5, historical simulations for 1953–2005 are combined with corresponding RCP4.5 scenario runs for 2006–2017. For CMIP6, historical simulations for 1953–2014 are combined with SSP2-4.5 scenario simulations for 2015–2017. Numbers in brackets represent the number of models used. The time-fixed observational mask has been applied to model data throughout the whole period. Grid cells with more than 70% of data available between 1953 and 2017 plus data for at least three years between 2013 and 2017 are used. Coloured lines indicate multi-model means, while shading represents 5th–95th percentile ranges, based on all available ensemble members with equal weight given to each model ( [[#3.2|Section 3.2]] ). Anomalies are relative to 1961–1990 means. Figure is updated from [[#Seong--2021|Seong et al. (2021)]] , their Figure 3 and [[#Paik--2020b|Paik et al. (2020b)]] , their Figure 3. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

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<span id="atmospheric-circulation"></span>
=== 3.3.3 Atmospheric Circulation ===

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<span id="the-hadley-and-walker-circulations"></span>
==== 3.3.3.1 The Hadley and Walker Circulations ====

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The tropical tropospheric circulation features meridional and zonal overturning circulations, called Hadley and Walker circulations. In the zonal mean, the downwelling branch of the Hadley circulation cell is located in the subtropics and is often used as an indicator of the meridional extent of the tropics. In the equatorial zonal-vertical section, the major rising branch of the Walker circulation is located over the Maritime continent with secondary ascending regions over northern South America and Africa. The zonal component of the surface trade winds over most of the equatorial Pacific and Atlantic is associated with the Walker circulation. This section assesses the zonal-mean Hadley cell extent and the Pacific Walker circulation strength. Regional and water cycle aspects of these circulations are assessed in more detail in Section 8.3.2.

AR5 found ''medium confidence'' that the depletion of stratospheric ozone had contributed to Hadley cell widening in the Southern Hemisphere in austral summer ( [[#Bindoff--2013|Bindoff et al., 2013]] ). It also noted that in contrast to a simulated weakening in response to greenhouse gas forcing, the Walker circulation had actually strengthened since the early 1990s, precluding any detection of human influence.

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<span id="hadley-cell-extent"></span>
===== 3.3.3.1.1 Hadley cell extent =====

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[[#Grise--2019|Grise et al. (2019)]] found that a metric based on surface zonal winds, which are well constrained by surface observations, best compares reanalyses with CMIP5 models. With this method and new reanalysis products, the CMIP5 historical simulations exhibit comparable mean states and variability of the subtropical edge latitude of the Hadley cells to those observed ( [[#Grise--2019|Grise et al., 2019]] ).

( [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] assesses that there has ''very likely'' been a widening of the Hadley circulation since the 1980s ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.1|Section 2.3.1.4.1]] ). The CMIP5 ( [[#Davis--2017|Davis and Birner, 2017]] ; [[#Grise--2018|Grise et al., 2018]] ) and CMIP6 ( [[#Grise--2020|Grise and Davis, 2020]] ) historical simulation ensembles span the observed trends of the zonal-mean Hadley cell edges since the 1980s (Figure 3.16a–c). Studies based on CMIP5 models find a contribution from human influence to the observed widening trend, especially in the Southern Hemisphere ( [[#Gerber--2014|Gerber and Son, 2014]] ; [[#Staten--2018|Staten et al., 2018]] , 2020; [[#Grise--2019|Grise et al., 2019]] ; [[#Jebri--2020|Jebri et al., 2020]] ), which is confirmed based on CMIP6 (Figure 3.16b,c; [[#Grise--2020|Grise and Davis, 2020]] ).

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[[File:a1a79ce037eeab534e19613ebdd8b703 IPCC_AR6_WGI_Figure_3_16.png]]

Figure 3.16 | '''Model evaluation and attribution of changes in Hadley cell extent and Walker circulation strength. (a–c)''' Trends in subtropical edge latitude of the Hadley cells in '''(a)''' the Northern Hemisphere for 1980–2014 annual means and '''(b, c)''' Southern Hemisphere for '''(b)''' 1980–2014 annual means and '''(c)''' 1980/81–1999/2000 December–January–February means. Positive values indicate northward shifts. '''(d–f)''' Trends in the Pacific Walker circulation strength for '''(d)''' 1901–2010, '''(e)''' 1951–2010 and '''(f)''' 1980–2014. Positive values indicate strengthening. Based on CMIP5 historical (extended with RCP4.5), CMIP6 historical, AMIP, pre-industrial control, and single forcing simulations along with HadSLP2 and reanalyses. Pre-industrial control simulations are divided into non-overlapping segments of the same length as the other simulations. White boxes and whiskers represent means, interquartile ranges and 5th and 95th percentiles, calculated after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted ( [[#3.2|Section 3.2]] ). The filled boxes represent the 5–95% confidence interval on the multi-model mean trends of the models with at least three ensemble members, with dots indicating the ensemble means of individual models. The edge latitude of the Hadley cell is where the surface zonal wind velocity changes sign from negative to positive, as described in the Appendix of [[#Grise--2018|Grise et al. (2018)]] . The Pacific Walker circulation strength is evaluated as the annual mean difference of sea level pressure between 5°S–5°N, 160°W–80°W and 5°S–5°N, 80°E–160°E. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

In the annual mean, internal variability, including Pacific Decadal Variability (PDV; Annex IV.2.6), contributed to the observed zonal-mean Hadley cell expansion since 1980 comparably with human influence ( [[#Allen--2014|Allen et al., 2014]] ; [[#Allen--2017|Allen and Kovilakam, 2017]] ; [[#Mantsis--2017|Mantsis et al., 2017]] ; [[#Amaya--2018|Amaya et al., 2018]] ; [[#Grise--2018|Grise et al., 2018]] ). Indeed, the ensemble-mean expansion in historical simulations is significantly weaker than in most of the reanalyses shown in Figure 3.16a–c, while the Atmospheric Model Intercomparison Project (AMIP) simulations forced by observed SSTs (Figure 3.16a–c) show stronger trends than historical coupled simulations on average ( [[#Nguyen--2015|Nguyen et al., 2015]] ; [[#Davis--2017|Davis and Birner, 2017]] ; [[#Grise--2018|Grise et al., 2018]] ). The human-induced change has not yet clearly emerged out of the internal variability range in the Northern Hemisphere ( [[#Quan--2018|Quan et al., 2018]] ; [[#Grise--2019|Grise et al., 2019]] ), whereas the trend in the annual-mean Southern Hemisphere edge is outside the 5th–95th percentile range of internal variability in CMIP6 in three out of the four reanalyses (Figure 3.16b). For the Southern Hemisphere summer when the simulated human influence is strongest, the 1981–2000 trend in three out of the four reanalyses falls outside the 5th–95th percentile range of internal variability (Figure 3.16c; L. [[#Tao--2016|]] [[#Tao--2016|Tao et al., 2016]] ; [[#Grise--2018|Grise et al., 2018]] , 2019).

In CMIP5 simulations, greenhouse gas increases and, in austral summer, stratospheric ozone depletion, contribute to the Southern Hemisphere expansion ( [[#Gerber--2014|Gerber and Son, 2014]] ; [[#Nguyen--2015|Nguyen et al., 2015]] ; L. [[#Tao--2016|]] [[#Tao--2016|Tao et al., 2016]] ; Y.H. [[#Kim--2017|]] [[#Kim--2017|Kim et al., 2017]] ), but the ozone influence is not significant in available CMIP6 simulations (Figure 3.16b–c). Since the 2000s, the stabilization or slight recovery of stratospheric ozone ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.5.2|Section 2.2.5.2]] ) is consistent with the smaller observed trends ( [[#Banerjee--2020|Banerjee et al., 2020]] ). While many CMIP5 models under-represent the magnitude of the PDV, implying potential overconfidence on the detection of human influence on the Hadley cell expansion, this is less the case for the CMIP6 models ( [[#3.7.6|Section 3.7.6]] ). However, the mechanism underlying the Hadley cell expansion remains unclear ( [[#Staten--2018|Staten et al., 2018]] , 2020), precluding a process-based validation of the simulated human influence.

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<span id="walker-circulation-strength"></span>
===== 3.3.3.1.2 Walker circulation strength =====

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CMIP5 models reproduce the mean state of the Walker circulation with reasonable fidelity, evidenced by the spatial pattern correlations of equatorial zonal mass stream function between models and observations being larger than 0.88 ( [[#Ma--2016|Ma and Zhou, 2016]] ). CMIP5 historical simulations on average simulate a significant weakening of the Pacific Walker circulation over the 20th century ( [[#DiNezio--2013|DiNezio et al., 2013]] ; [[#Sandeep--2014|Sandeep et al., 2014]] ; [[#Kociuba--2015|Kociuba and Power, 2015]] ), which is also seen in CMIP6 (Figure 3.16d). This weakening is accompanied by a reduction of convective activity over the Maritime Continent and an enhancement over the central equatorial Pacific ( [[#DiNezio--2013|DiNezio et al., 2013]] ; [[#Sandeep--2014|Sandeep et al., 2014]] ; [[#Kociuba--2015|Kociuba and Power, 2015]] ). In the CMIP6 simulations, greenhouse gas forcing induces this weakening (Figure 3.16d), which is consistent with theories based on radiative-convective equilibrium ( [[#Vecchi--2006|Vecchi et al., 2006]] ; [[#Vecchi--2007|Vecchi and Soden, 2007]] ) and thermodynamic air-sea coupling ( [[#Xie--2010|Xie et al., 2010]] ), but inconsistent with a theory highlighting the ocean dynamical effect which suggests a strengthening in response to greenhouse gas increases ( [[#Clement--1996|Clement et al., 1996]] ; [[#Seager--2019|Seager et al., 2019]] ; see also Section 7.4.4.2.1). [[#Seager--2019|Seager et al. (2019)]] attributed this inconsistency to equatorial Pacific SST biases in the models ( [[#3.5.1.2.1|Section 3.5.1.2.1]] ). However, observational and reanalysis datasets disagree on the sign of trends in the Walker Circulation strength over the 1901–2010 period (Figure 3.16d), and [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.1|Section 2.3.1.4.1]] assesses ''low confidence'' in observed long-term Walker Circulation trends. The observational uncertainty remains high in the trends since the 1950s ( [[#Tokinaga--2012|Tokinaga et al., 2012]] ; [[#L’Heureux--2013|L’Heureux et al., 2013]] ), though both CMIP5 and CMIP6 historical simulations span trends of all but one observational data set (Figure 3.16e). For this period, external influence simulated in CMIP6 is insignificant due to a partial compensation of forced responses to greenhouse gases and aerosols and large internal decadal variability (Figure 3.16e). It is notable that while AMIP simulations on average show strengthening over both the periods, those simulations are forced by one reconstruction of SST, which itself is subject to uncertainty before the 1970s ( [[#Deser--2010|Deser et al., 2010]] ; [[#Tokinaga--2012|Tokinaga et al., 2012]] ).

Observational SST products indicate that the equatorial zonal SST gradient from the western to the eastern equatorial Pacific has strengthened since 1870 (Section 7.4.4.2.1). While CMIP5 historical simulations on average simulate a weakening, large ensemble simulations span the observed strengthening since the 1950s ( [[#Watanabe--2021|Watanabe et al., 2021]] ) suggesting an important contribution from internal variability. [[#Coats--2017|Coats and Karnauskas (2017)]] also find that the anthropogenic influence on the SST gradient is yet to emerge out of internal variability even on centennial time scales.

Trends since the 1980s in in-situ and satellite observations and reanalyses exhibit strengthening of the Pacific Walker circulation and SST gradient ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.1|Section 2.3.1.4.1]] and Figure 3.16f; L’Heureux et al., 2013; [[#Boisséson--2014|Boisséson et al., 2014]] ; [[#England--2014|England et al., 2014]] ; [[#Kociuba--2015|Kociuba and Power, 2015]] ; [[#Ma--2016|Ma and Zhou, 2016]] ). AMIP simulations reproduce this strengthening (Figure 3.16d; [[#Boisséson--2014|Boisséson et al., 2014]] ; [[#Ma--2016|Ma and Zhou, 2016]] ), indicating a dominant role of SST changes. However, all reanalysis trends lie outside the 5–95% range of simulated CMIP6 historical Walker circulation trends over this period (Figure 3.16f), consistent with CMIP5 results ( [[#England--2014|England et al., 2014]] ; [[#Kociuba--2015|Kociuba and Power, 2015]] ). This may be in part caused by the underestimation of the PDV magnitude especially in CMIP5 models (Section [[#_idTextAnchor002|3.7.6]] ; [[#Kociuba--2015|Kociuba and Power, 2015]] ; [[#Chung--2019|Chung et al., 2019]] ), but also suggests a potential error in simulating the forced changes of the Walker circulation. Specifically, anthropogenic and volcanic aerosol changes over this period may have driven a strengthening ( [[#DiNezio--2013|DiNezio et al., 2013]] ; [[#Takahashi--2016|Takahashi and Watanabe, 2016]] ; [[#Hua--2018|Hua et al., 2018]] ). This aerosol influence may be indirect via Atlantic Multi-decadal Variability (AMV; Annex IV.2.7) through inter-basin teleconnections ( [[#McGregor--2014|McGregor et al., 2014]] ; [[#Chikamoto--2016|Chikamoto et al., 2016]] ; [[#Kucharski--2016|Kucharski et al., 2016]] ; X. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] a; [[#Ruprich-Robert--2017|Ruprich-Robert et al., 2017]] ), which may be underestimated in models due to SST biases in the equatorial Atlantic ( [[#3.5.1.2.2|Section 3.5.1.2.2]] ; [[#McGregor--2018|McGregor et al., 2018]] ). Note also the large uncertainty in aerosol influence on the Walker circulation ( [[#Kuntz--2016|Kuntz and Schrag, 2016]] ; [[#Hua--2018|Hua et al., 2018]] ; [[#Oudar--2018|Oudar et al., 2018]] ), which is also seen in CMIP6 (Figure 3.16f).

Paleoclimate data from the Pliocene epoch suggest that there was a reduction in the zonal SST gradient in the tropical Pacific under a similar CO <sub>2</sub> concentration as today (Section 7.4.4.2.2 and Cross-Chapter Box 2.4). [[#Tierney--2019|Tierney et al. (2019)]] found that this weaker gradient compared to pre-industrial, which suggests a weaker Walker circulation, is captured by climate models under Pliocene CO <sub>2</sub> levels, in agreement with the CMIP6 response to greenhouse gas forcing (Figure 3.16d), though the magnitude of this effect varies strongly between models ( [[#Corvec--2017|Corvec and Fletcher, 2017]] ).

<div id="3.3.3.1.3" class="h4-container"></div>

<span id="summary"></span>
===== 3.3.3.1.3 Summary =====

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It is ''likely'' that human influence has contributed to the poleward expansion of the zonal mean Hadley cell in the Southern Hemisphere since the 1980s. This assessment is supported by studies since AR5, which consistently find human influence from greenhouse gas increases on the expansion, with additional influence from ozone depletion in austral summer. For the strong ozone depletion period of 1981–2000, human influence is detectable in the summertime poleward expansion in the Southern Hemisphere ( ''medium confidence'' ). By contrast, there is ''medium confidence'' that the expansion of the zonal mean Hadley cell in the Northern Hemisphere is within the range of internal variability, with contributions from PDV and other internal variability. The causes of the observed strengthening of the Pacific Walker circulation over the 1980–2014 period are not well understood, since the observed strengthening trend is outside the range of variability simulated in the coupled models ( ''medium confidence'' ). Large observational uncertainty, lack of understanding of the mechanism underlying the poleward Hadley cell expansion, and contradicting theories on the greenhouse gas influence and uncertainty in the aerosol influence on the Walker circulation strength, limit confidence in these assessments.

<div id="3.3.3.2" class="h3-container"></div>

<span id="global-monsoon"></span>
==== 3.3.3.2 Global Monsoon ====

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Monsoons are seasonal transitions of regimes in atmospheric circulation and precipitation with the annual cycle of solar insolation, in association with redistribution of moist static energy ( [[#Wang--2008|Wang and Ding, 2008]] ; P.X. [[#Wang--2014|]] [[#Wang--2014|Wang et al., 2014]] ; [[#Biasutti--2018|Biasutti et al., 2018]] ). The global monsoon can be defined to encompasses all monsoon systems based on precipitation contrast in the solstice seasons ( [[#Wang--2008|Wang and Ding, 2008]] ; Figure 3.17). All regional monsoons are intimately connected to the global tropical atmospheric overturning by mass ( [[#Trenberth--2000|Trenberth et al., 2000]] ), momentum and energy budgets ( [[#Biasutti--2018|Biasutti et al., 2018]] ; [[#Geen--2020|Geen et al., 2020]] ). Assessments of regional monsoon changes are made in Sections 8.3.2.4, 10.4.2.1 and 10.6.3.

<div id="_idContainer043" class="•-2-columns"></div>

[[File:fdafa2a0d46675b23309ca966f9b3f3a IPCC_AR6_WGI_Figure_3_17.png]]

Figure 3.17 | '''Model evaluation of global monsoon domain, intensity, and circulation. (a, b)''' Climatological summer-winter range of precipitation rate, scaled by annual mean precipitation rate (shading) and 850 hPa wind velocity (arrows) based on (a) GPCP and ERA5 and (b) a multi-model ensemble mean of CMIP6 historical simulations for 1979–2014. The region enclosed by red lines is the monsoon domain based on the definition by [[#Wang--2008|Wang and Ding (2008)]] . '''(c, d)''' Five-year running mean anomalies of (c) global land monsoon precipitation index defined as the percentage anomaly of the summertime precipitation rate averaged over the monsoon regions over land, relative to its average for 1979–2014 (the period indicated by light grey shading) and (d) the tropical monsoon circulation index defined as the vertical shear of zonal winds between 850 and 200 hPa levels averaged over 0°–20°N, from 120°W eastward to 120°E in Northern Hemisphere summer ( [[#Wang--2013|Wang et al., 2013]] ; m s <sup>–1</sup> ) in CMIP5 historical and RCP4.5 simulations, and CMIP6 historical and AMIP simulations. Summer and winter are defined for individual hemispheres: May to September is defined as Northern Hemisphere summer and Southern Hemisphere winter, and November to March is defined as Northern Hemisphere winter and Summer Hemisphere summer. The numbers of models and simulations are given in the legend. The multi-model ensemble mean and percentiles are calculated after weighting individual ensemble members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of ensemble size. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

AR5 assessed that CMIP5 models simulated monsoons better than CMIP3 models but that biases remained in domains and intensity ( ''high confidence'' ) ( [[#Flato--2013|Flato et al., 2013]] ). There were no detection and attribution assessment statements on the decreasing trend of global monsoon precipitation over land from the 1950s to the 1980s or the increasing trend of global monsoon precipitation afterwards. In the paleoclimate context, it was determined with ''high confidence'' that orbital forcing produces strong interhemispheric rainfall variability evident in multiple types of proxies ( [[#Masson-Delmotte--2013|Masson-Delmotte et al., 2013]] ).

Paleoclimate proxy evidence shows that the global monsoon has varied with orbital forcing and greenhouse gases ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] ; [[#Mohtadi--2016|Mohtadi et al., 2016]] ; [[#Seth--2019|Seth et al., 2019]] ). These large-magnitude intensifications and weakenings in the global monsoon involved in some cases orders-of-magnitude changes in precipitation locally ( [[#Harrison--2014|Harrison et al., 2014]] ; [[#Tierney--2017|Tierney et al., 2017]] ). Paleoclimate modelling and limited data from past climate states with high CO <sub>2</sub> suggest that precipitation intensifies in the monsoon domain under elevated greenhouse gases, providing context for present and future trends ( [[#Passey--2009|Passey et al., 2009]] ; [[#Haywood--2013|Haywood et al., 2013]] ; [[#Zhang--2013b|Zhang et al., 2013b]] ). In model simulations of the mid-Pliocene, when globally averaged temperature was higher than present day, precipitation was larger in West African, South Asian and East Asian monsoons than under pre-industrial conditions, consistent with proxy evidence ( [[#Zhang--2015|Zhang et al., 2015]] ; [[#Sun--2016|Sun et al., 2016]] , 2018; [[#Corvec--2017|Corvec and Fletcher, 2017]] ; X. [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ). [[#Prescott--2019|Prescott et al. (2019)]] and R. [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al. (2019)]] find an important role for orbital forcing and CO <sub>2</sub> in the mid-Pliocene monsoon expansion and intensification. Models are also able to capture interhemispherically contrasting monsoon changes in the Last Interglacial in response to orbital forcing and greenhouse gases, with wetter West African and Asian monsoons and a drier South American monsoon as seen in proxies ( [[#Govin--2014|Govin et al., 2014]] ; [[#Gierz--2017|Gierz et al., 2017]] ; [[#Pedersen--2017|Pedersen et al., 2017]] ). In overall agreement with proxy evidence, a model with transient forcing simulates wetting and drying respectively of the Southern and Northern Hemisphere monsoons during the last deglaciation, with an important contribution from Atlantic Meridional Overturning Circulation (AMOC) slowdown ( [[#Otto-Bliesner--2014|Otto-Bliesner et al., 2014]] ; [[#Mohtadi--2016|Mohtadi et al., 2016]] ).

During the mid-Holocene, global monsoons were stronger especially in the Northern Hemisphere with an expansion of the West African monsoon domain in response to orbital forcing ( [[#Biasutti--2018|Biasutti et al., 2018]] ; [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] ). Simulations of the mid-Holocene with CMIP5 and CMIP6 models qualitatively capture the stronger Northern Hemisphere monsoon ( [[#Jiang--2015|Jiang et al., 2015]] ; [[#Brierley--2020|Brierley et al., 2020]] ), mainly driven by atmospheric circulation changes ( [[#D’Agostino--2019|D’Agostino et al., 2019]] ). However, the models underestimate the monsoon expansion found in proxy reconstructions ( [[#Perez-Sanz--2014|Perez-Sanz et al., 2014]] ; [[#Harrison--2015|Harrison et al., 2015]] ; [[#Tierney--2017|Tierney et al., 2017]] ), which may be linked to mean biases in the monsoon domain ( [[#Brierley--2020|Brierley et al., 2020]] ) and may be improved by imposing vegetation and dust changes ( [[#Pausata--2016|Pausata et al., 2016]] ). The models simulate the weaker Southern Hemisphere monsoon during the mid-Holocene ( [[#D’Agostino--2020|D’Agostino et al., 2020]] ), consistent with proxy evidence ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] ). These studies indicate that models can qualitatively reproduce past global monsoon changes seen in proxies, though issues remain in quantitatively reproducing proxy observations. Studies of last millennium simulations show that simulated global monsoon precipitation increases with global mean temperature, while changes in monsoon circulation and hemispheric monsoon precipitation depend on forcing sources ( [[#Liu--2012|Liu et al., 2012]] ; [[#Chai--2018|Chai et al., 2018]] ). Compared to greenhouse gas and solar variations, volcanic forcing is more effective in changing the global monsoon precipitation over the last millennium ( [[#Chai--2018|Chai et al., 2018]] ).

Reproducing monsoons in terms of domain, precipitation amount, and timings of onset and retreat over the historical period also remains difficult. While CMIP5 historical simulations broadly capture global monsoon domains and intensity based on summer and winter precipitation differences, they underestimate the extent and intensity of East Asian and North American monsoons while overestimating them over the tropical western North Pacific ( [[#Lee--2014|Lee and Wang, 2014]] ; M. [[#Yan--2016|]] [[#Yan--2016|]] [[#Yan--2016|Yan et al., 2016]] ). [[#Wang--2020|]] [[#Wang--2020|B. Wang et al. (2020)]] reported that CMIP6 models simulate the global monsoon domain and precipitation better (Figure 3.17a,b), albeit with biases in annual mean precipitation and the timings of onset and withdrawal of the Southern Hemisphere monsoon. Notable inter-model differences were identified in CMIP5, with the multi-model ensemble mean outperforming individual models ( [[#Lee--2014|Lee and Wang, 2014]] ). Common biases were identified across CMIP5 models in moist static energy and upper-tropospheric temperature associated with the South Asian summer monsoon, which may arise from overly smoothed model topography ( [[#Boos--2012|Boos and Hurley, 2012]] ). However, in atmospheric models with increasing resolution approaching 20 km, improvements in monsoon precipitation are not universal across regions and models, and overall improvements are unclear ( [[#Johnson--2016|Johnson et al., 2016]] ; [[#Ogata--2017|Ogata et al., 2017]] ; L. [[#Zhang--2018b|]] [[#Zhang--2018|Zhang et al., 2018]] b ) ''.''

In instrumental records, global summer monsoon precipitation intensity (measured by summer precipitation averaged over the monsoon domain) decreased from the 1950s to 1980s, followed by an increase ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] and Figure 3.17c), arising mainly from variations in Northern Hemispheric land monsoons. A CMIP5 multi-model study by Y. [[#Zhang--2018|Zhang et al. (2018)]] found that observed 1951–2004 trends of the global and Northern Hemisphere summer land monsoon precipitation intensity are well captured by historical simulations, and CMIP6 models show similar results for global land summer monsoon precipitation (Figure 3.17c). However, the 1960s peak in the Northern Hemisphere summer monsoon circulation is outside the 5th–95th percentile range of CMIP5 and CMIP6 historical simulations for two out of three reanalyses (Figure 3.17d). Modelling studies show that greenhouse gas increases act to enhance Northern Hemisphere summer monsoon precipitation intensity ( [[#Liu--2012|Liu et al., 2012]] ; [[#Polson--2014|Polson et al., 2014]] ; [[#Chai--2018|Chai et al., 2018]] ; L. [[#Zhang--2018b|]] [[#Zhang--2018|Zhang et al., 2018]] b ). Since the mid-20th century, however, modelling studies show that this effect was overwhelmed by the influence of anthropogenic aerosols in CMIP5 ( [[#Polson--2014|Polson et al., 2014]] ; [[#Guo--2015|Guo et al., 2015]] ; Y. [[#Zhang--2018|Zhang et al., 2018]] ; [[#Giannini--2019|Giannini and Kaplan, 2019]] ) and in CMIP6 (T. [[#Zhou--2020|]] [[#Zhou--2020|Zhou et al., 2020]] ). Weakening of the monsoon circulation and reduction of moisture availability are important in this aerosol influence (T. [[#Zhou--2020|]] [[#Zhou--2020|Zhou et al., 2020]] ). Besides these human influences, the global monsoon is sensitive to internal variability and natural forcing including ENSO and volcanic aerosols on interannual time scales and PDV and AMV on decadal to multi-decadal time scales ( [[#Wang--2013|Wang et al., 2013]] , 2018; F. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ; [[#Jiang--2019|Jiang and Zhou, 2019]] ; [[#Zuo--2019|Zuo et al., 2019]] ); though AMV in the 20th century may have been partly driven by aerosols, see [[#3.7.7|Section 3.7.7]] . Indeed, AMIP simulations better reproduce the observed multi-decadal variations of the global monsoon precipitation and circulation (Figure 3.17c,d). Y. [[#Zhang--2018|Zhang et al. (2018)]] find that the multi-model ensemble mean trend of global land monsoon precipitation in historical simulations, dominated by anthropogenic aerosol forcing contributions, emerges out of the 90% range of internally-driven trends in pre-industrial control simulations. However, it should be noted that CMIP5 models tend to under-represent the PDV magnitude ( [[#3.7.6|Section 3.7.6]] ), suggesting potential overconfidence in the detection of the forced signal. An observed enhancement in global summer monsoon precipitation since the 1980s is accompanied by an intensification of the Northern Hemisphere summer monsoon circulation (Figure 3.17c,d). These trends appear to be at the extreme of the range of the CMIP6 historical simulation ensemble but are well captured by AMIP simulations (Figure 3.17c,d). While the precipitation increase is consistent with greenhouse gas forcing, the circulation intensification is opposite to the simulated response to greenhouse gas forcing, and these enhancements have been attributed to PDV and AMV ( [[#Wang--2013|Wang et al., 2013]] ; [[#Kamae--2017|Kamae et al., 2017]] ).

In summary, while greenhouse gas increases acted to enhance the global land monsoon precipitation over the 20th century ( ''medium confidence'' ), consistent with projected future enhancement ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.5|Section 4.5.1.5]] ), this tendency was overwhelmed by anthropogenic aerosols from the 1950s to the 1980s, which contributed to weakening of global land summer monsoon precipitation intensity for this period ( ''medium confidence'' ). There is ''medium confidence'' that the intensification of global monsoon precipitation and Northern Hemisphere summer monsoon circulation since the 1980s is dominated by internal variability. These assessments are supported respectively by multi-model detection and attribution studies which find an important role for anthropogenic aerosols in the weakening trend, and studies that identify a role for AMV and PDV in inducing the Northern Hemisphere summer monsoon circulation enhancement since the 1980s. Supported by multi-model simulations that are qualitatively consistent with proxy evidence, there is ''high confidence'' that orbital forcing contributed to higher Northern Hemisphere monsoon precipitation in the mid-Pliocene and mid-Holocene than pre-industrial. While CMIP5 models can capture the domain and precipitation intensity of the global monsoon, biases remain in their regional representations, and they are unsuccessful in quantitatively reproducing changes in paleo reconstructions ( ''high confidence'' ). CMIP6 models reproduce the domain and precipitation intensity of the global monsoon observed over the instrumental period better than CMIP5 models ( ''medium confidence'' ). However, CMIP5 and CMIP6 models fail to fully capture the variations of the Northern Hemisphere summer monsoon circulation (Figure 3.17d), but there is ''low confidence'' in this assessment due to a lack of evidence in the literature.

<div id="3.3.3.3" class="h3-container"></div>

<span id="extratropical-jets-storm-tracks-and-blocking"></span>
==== 3.3.3.3 Extratropical Jets, Storm Tracks and Blocking ====

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Extratropical jets are wind maxima in the upper troposphere which are often associated with storms, blocking, and weather extremes. Blocking refers to long-lived, stationary high-pressure systems that are often associated with a poleward displacement of the jet, causing cold spells in winter and heatwaves in summer (e.g., [[#Sousa--2018|Sousa et al., 2018]] ). Sections 2.3.1.4.3, 8.3.2.7, and 11.7.2 discuss these features in more detail.

AR5 concluded that models were able to capture the general characteristics of extratropical cyclones and storm tracks, although it also noted that most models underestimated cyclone intensity, that biases in cyclone frequency were linked to biases in sea surface temperatures, and that resolution can play a significant role in the quality of the simulation of storms ( [[#Flato--2013|Flato et al., 2013]] ). Similarly, AR5 found with ''high confidence'' that simulation of blocking was improved with increases in resolution. The AR5 did not specifically assess changes in Southern Hemisphere storm track characteristics or blocking.

Since AR5, new research using CMIP5 and CMIP6 models has confirmed that increasing the model resolution improves the simulation of cyclones and blocking in all seasons albeit with some exceptions and caveats ( [[#Zappa--2013|Zappa et al., 2013]] ; [[#Davini--2017|Davini et al., 2017]] ; [[#Schiemann--2017|Schiemann et al., 2017]] , 2020; [[#Davini--2020|Davini and D’Andrea, 2020]] ; [[#Priestley--2020|Priestley et al., 2020]] ). New research also finds that model performance with respect to the simulation of cyclones and that of blocking events are correlated ( [[#Zappa--2014|Zappa et al., 2014]] ), suggesting biases in both are aspects of the same underlying problems in models (Figure 3.18). In the North Pacific basin the annual mean blocking frequency is now well simulated compared to earlier evaluations, but substantial errors in the blocking frequency remain in the Euro-Atlantic sector (Figure 3.18; [[#Dunn-Sigouin--2013|Dunn-Sigouin and Son, 2013]] ; [[#Davini--2016|Davini and D’Andrea, 2016]] , 2020; [[#Mitchell--2017|Mitchell et al., 2017]] ; [[#Woollings--2018b|Woollings et al., 2018b]] ). While there is a resolution dependence in the size of this bias, even at very high resolution blocking in the Euro-Atlantic sector remains underestimated ( [[#Schiemann--2017|Schiemann et al., 2017]] ), and there is evidence of a compensation of errors as the resolution is increased ( [[#Davini--2017|Davini et al., 2017]] ). [[#Davini--2020|Davini and D’Andrea (2020)]] show that while the simulation of blocking improves with increasing resolution in CMIP3, CMIP5, and CMIP6 models, other factors contribute to biases, particularly to the underestimation of Euro-Atlantic blocking ( [[#Schiemann--2020|Schiemann et al., 2020]] ). The persistence of blocking events, typically underestimated, has not improved from CMIP5 to CMIP6 ( [[#Schiemann--2020|Schiemann et al., 2020]] ). Section 10.3.3.3 discusses the implications of the biases discussed here for regional climate.

<div id="_idContainer045" class="•-2-columns"></div>

[[File:f46b15e7e781d75ae9cdfbea3c54a0c5 IPCC_AR6_WGI_Figure_3_18.png]]

Figure 3.18 | '''Instantaneous Northern-Hemisphere blocking frequency (% of days) in the extended northern winter season (December–January''' '''–''' '''February–March – DJFM) for the years 197''' '''9–''' '''2000.''' Results are shown for the ERA5 reanalysis (black), CMIP5 (blue) and CMIP6 (red) models. Coloured lines show multi-model means and shaded ranges show corresponding 5–95% ranges constructed with one realization from each model. Figure is adapted from [[#Davini--2020|Davini and D’Andrea (2020)]] , their Figure 12 and following the [[#D’Andrea--1998|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).

For the North Pacific storm track CMIP6 simulations exhibit large remaining underestimations of cyclone frequencies during summer (June to August), which for the low-resolution models have essentially remained unchanged versus CMIP5, and there is only a small resolution dependence of this bias ( [[#Priestley--2020|Priestley et al., 2020]] ). During winter (December to February), both CMIP5 and CMIP6 models tend to place the North Pacific storm track too far equatorward (M. [[#Yang--2018|]] [[#Yang--2018|Yang et al., 2018]] ; [[#Priestley--2020|Priestley et al., 2020]] ), leading to an overestimation of cyclones between 30°N and 40°N in the Pacific and an underestimation to the north of this. Both low- and high-resolution models show this pattern, but low-resolution models generally simulate fewer cyclones throughout the North Pacific ( [[#Priestley--2020|Priestley et al., 2020]] ).

In winter, the North Atlantic storm track remains displaced to the south and east in many models ( [[#Harvey--2020|Harvey et al., 2020]] ), leading to underestimation of cyclone frequencies near the North American coast and overestimation in the eastern North Atlantic. Higher-resolution CMIP6 models perform slightly better in this regard than low-resolution models. In summer (June to August), cyclone frequencies throughout the extratropical North Atlantic, which were substantially underestimated in CMIP5, have improved in CMIP6 high-resolution models. In low-resolution CMIP6 models, the problem is essentially unchanged ( [[#Priestley--2020|Priestley et al., 2020]] ); this is associated with generally underestimated variability of sea level pressure in CMIP models ( [[#Harvey--2020|Harvey et al., 2020]] ).

For the Southern Hemisphere (not considered in AR5), [[#Priestley--2020|Priestley et al. (2020)]] find considerable improvement in the placement of the Southern Ocean storm track during summer (December to February) in CMIP6 models versus CMIP5, consistent with a more realistic annual mean surface wind maximum latitude in CMIP6 than in CMIP5 ( [[#Goyal--2021|Goyal et al., 2021]] ). Relative to CMIP5, both low- and high-resolution CMIP6 models have increased track densities south of about 55°S and decreased track densities between about 40°S and 55°S, in better agreement with observations than CMIP5 models ( [[#Parsons--2016|Parsons et al., 2016]] ; [[#Patterson--2019|Patterson et al., 2019]] ). CMIP5 models and high-resolution CMIP6 models simulate a storm track that is positioned too far equatorward, although the bias is smaller in the high-resolution models. By contrast, the low-resolution CMIP6 models simulate a storm track that is slightly too far poleward on average ( [[#Priestley--2020|Priestley et al., 2020]] ). In winter (June to August), the biases found in CMIP5 are only slightly improved in CMIP6, with models continuing to underestimate the broad maximum cyclone track density in the south-eastern Indian Ocean and overestimate the minimum density in the south-western South Pacific ( [[#Priestley--2020|Priestley et al., 2020]] ).

There is only one contiguous blocking region in the Southern Hemisphere, with the blocking frequency maximizing in the South Pacific and minimizing in the southern Indian Ocean regions ( [[#Parsons--2016|Parsons et al., 2016]] ; [[#Patterson--2019|Patterson et al., 2019]] ). CMIP5 simulations agree relatively well with ERA-Interim in this region regarding the distribution of blocking events ( [[#Parsons--2016|Parsons et al., 2016]] ). Individual models exhibit considerable biases in the blocking frequency; however only in austral summer do [[#Patterson--2019|Patterson et al. (2019)]] find a systematic, multi-model underestimation of the blocking frequency in and around the Tasman Sea. The blocking frequency is anticorrelated with the amplitude of the SAM. Ozone depletion, through stratosphere-troposphere coupling, may have caused an increase in the blocking frequency in the South Atlantic sector ( [[#Dennison--2016|Dennison et al., 2016]] ); this finding requires confirmation using a multi-model approach.

In addition to inadequate resolution, blocking and storm track biases in both hemispheres also result from mean state biases, in particular, biases related to the parameterization of orographic effects and to the misrepresentation of the Gulf Stream SST front ( [[#Anstey--2013|Anstey et al., 2013]] ; [[#Berckmans--2013|Berckmans et al., 2013]] ; [[#Davini--2016|Davini and D’Andrea, 2016]] ; [[#O’Reilly--2016a|O’Reilly et al., 2016a]] ; [[#Pithan--2016|Pithan et al., 2016]] ; [[#Schiemann--2017|Schiemann et al., 2017]] ). Nonetheless overall SST biases have been suggested to have only a weak relevance to blocking ( [[#Davini--2016|Davini and D’Andrea, 2016]] ).

( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.3|Section 2.3.1.4.3]] assesses that the total number of extratropical cyclones has ''likely'' increased since the 1980s in the Northern Hemisphere ( ''low confidence'' ), but with fewer deep cyclones particularly in summer. This observed reduction in cyclone activity by about 4% per decade in the Northern Hemisphere in summer ( [[#Chang--2016|Chang et al., 2016]] ; [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.3|Section 2.3.1.4.3]] ) may be associated with human-induced warming. CMIP5 historical simulations generally reproduce a reduction but underestimate its magnitude ( [[#Chang--2016|Chang et al., 2016]] ). Furthermore, feedback mechanisms associated with clouds may be responsible for substantial inter-model spread ( [[#Chang--2016|Chang et al., 2016]] ; [[#Voigt--2016|Voigt and Shaw, 2016]] ). In boreal winter, recent studies have suggested a potential influence of the rapid Arctic warming on observed intensification of Northern Hemisphere storm track activity in the past few decades, while other studies question this possibility (Cross-Chapter Box 10.1).

( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.3|Section 2.3.1.4.3]] assesses that the extratropical jets and cyclone tracks have ''likely'' shifted poleward in both hemispheres since the 1980s with marked seasonality in trends ( ''medium confidence'' ). For the Southern Hemisphere, studies using CMIP5 and other models imply that both ozone depletion and increasing greenhouse gases have caused substantial atmospheric circulation change since the 1960s when concentrations of ozone-depleting substances started to increase ( [[#Eyring--2013|Eyring et al., 2013]] ; [[#Iglesias-Suarez--2016|Iglesias-Suarez et al., 2016]] ; [[#Karpechko--2018|Karpechko et al., 2018]] ; [[#Son--2018|Son et al., 2018]] ). In particular, ozone depletion, during austral summer, has been linked to a poleward shift of the westerly jet and Southern Hemisphere circulation zones and a southward expansion of the tropics ( [[#Kang--2011|Kang et al., 2011]] ), which is associated with a strengthening trend of the Southern Annular Mode (SAM; [[#3.7.2|Section 3.7.2]] ). This has been well reproduced by climate models with prescribed historical ozone concentration or interactive ozone chemistry ( [[#Gerber--2014|Gerber and Son, 2014]] ; [[#Son--2018|Son et al., 2018]] ; Figure 3.19).

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[[File:adf164c94327a53bfeee634def663a37 IPCC_AR6_WGI_Figure_3_19.png]]

Figure 3.19 | '''Long-term mean (thin black contours) and linear trend (colour) of zonal mean December–January–February zonal winds from 1985 to 2014 in the Southern Hemisphere.''' The figure shows '''(a)''' ERA5 and '''(b)''' the CMIP6 multi-model mean (58 CMIP6 models). The solid contours show positive (westerly) and zero long-term mean zonal wind, and the dashed contours show negative (easterly) long-term mean zonal wind. Only one ensemble member per model is included. Figure is modified from [[#Eyring--2013|Eyring et al. (2013)]] , their Figure 12. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

In summary, there is ''low confidence'' that an observed decrease in the frequency of Northern Hemisphere summertime extratropical cyclones is linked to anthropogenic influence. In the Southern Hemisphere, there is ''high confidence'' that human influence, in the form of ozone depletion, has contributed to the observed poleward shift of the jet in austral summer, while ''confidence'' is ''low'' for human influence on historical blocking activity. The ''low confidence'' statements are due to the limited number of studies available. The shift of the Southern Hemisphere jet is correlated with modulations of the SAM ( [[#3.7.2|Section 3.7.2]] ). There is ''medium confidence'' in model performance regarding the simulation of the extratropical jets, storm track and blocking activity, with increased resolution sometimes corresponding to better performance, but important shortcomings remain, particularly for the Euro-Atlantic sector of the Northern Hemisphere. Nonetheless, synthesizing across Sections 3.3.3.1–3.3.3.3, there is ''high confidence'' that CMIP6 models capture the general characteristics of the tropospheric large-scale circulation.

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==== 3.3.3.4 Sudden Stratospheric Warming Activity ====

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Sudden stratospheric warmings (SSWs) are stratospheric weather events associated with anomalously high temperatures at high latitudes persisting from days to weeks. [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.5|Section 2.3.1.4.5]] discusses the definition and observational aspects of SSWs. SSWs are often associated with anomalous weather conditions, for example, winter cold spells, in the lower atmosphere (e.g., [[#Butler--2015|Butler et al., 2015]] ; [[#Baldwin--2021|Baldwin et al., 2021]] ).

[[#Seviour--2016|Seviour et al. (2016)]] found that stratosphere-resolving CMIP5 models, on average, reproduce the observed frequency of vortex splits (one form of SSWs) but with a wide range of model-specific biases. Models that produce a better mean state of the polar vortex also tend to produce a more realistic SSW frequency ( [[#Seviour--2016|Seviour et al., 2016]] ). The mean sea level pressure anomalies occurring in CMIP5 model simulations when an SSW is underway, however, differ substantially from those in reanalyses ( [[#Seviour--2016|Seviour et al., 2016]] ). Unlike stratosphere-resolving models, models with limited stratospheric resolution, which make up more than half of the CMIP5 ensemble, underestimate the frequency of SSWs ( [[#Osprey--2013|Osprey et al., 2013]] ; J. [[#Kim--2017|]] [[#Kim--2017|Kim et al., 2017]] ). [[#Taguchi--2017|Taguchi (2017)]] found a general underestimation in CMIP5 models of the frequency of ‘major’ SSWs (which are associated with a break-up of the polar vortex), an aspect of an under-representation in those models of dynamical variability in the stratosphere. [[#Wu--2020|Wu and Reichler (2020)]] found that finer vertical resolution in the stratosphere and a model top above the stratopause tend to be associated with a more realistic SSW frequency in CMIP5 and CMIP6 models.

Some studies find an increase in the frequency of SSWs under increasing greenhouse gases (e.g., [[#Schimanke--2013|Schimanke et al., 2013]] ; [[#Young--2013|Young et al., 2013]] ; J. [[#Kim--2017|]] [[#Kim--2017|Kim et al., 2017]] ). However, this behaviour is not robust across ensembles of chemistry-climate models ( [[#Mitchell--2012|Mitchell et al., 2012]] ; [[#Ayarzagüena--2018|Ayarzagüena et al., 2018]] ; [[#Rao--2021|Rao and Garfinkel, 2021]] ). There is an absence of studies specifically focusing on simulated trends in SSWs during recent decades, and the short record and substantial decadal variability yields ''low confidence'' in any observed trends in the occurrence of SSW events in the Northern Hemisphere winter ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.5|Section 2.3.1.4.5]] ). Such an absence of a trend and large variability would also be consistent with a recent reconstruction of SSWs extending back to 1850, based on sea level pressure observations ( [[#Domeisen--2019|Domeisen, 2019]] ), although this time series has limitations as it is not based on direct observations of SSWs.

In summary, an anthropogenic influence on the frequency or other aspects of SSWs has not yet been robustly detected. There is ''low confidence'' in the ability of models to simulate any such trends over the historical period because of large natural interannual variability and also due to substantial common biases in the simulated mean state affecting the simulated frequency of SSWs.

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