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

=== 3.3.1 Temperature ===

<div id="h2-7-siblings" class="h2-siblings"></div>

<div id="3.3.1.1" class="h3-container"></div>

<span id="surface-temperature"></span>
==== 3.3.1.1 Surface Temperature ====

<div id="h3-3-siblings" class="h3-siblings"></div>

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.

<div id="3.3.1.1.1" class="h4-container"></div>

<span id="model-evaluation"></span>
===== 3.3.1.1.1 Model evaluation =====

<div id="h4-1-siblings" class="h4-siblings"></div>

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]] .

<div id="_idContainer009" class="_idGenObjectStyleOverride-1"></div>

[[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.

<div id="_idContainer012" class="Basic-Text-Frame"></div>

[[File:9ae9d81ddeca5734e2e7187cf097a96b IPCC_AR6_WGI_Figure_3_3.png]]

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.

<div id="_idContainer014" class="Basic-Text-Frame"></div>

[[File:72fcbc98740a23baf81355209bdf9ca3 IPCC_AR6_WGI_Figure_3_4.png]]

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]] ).

<div id="_idContainer016" class="Basic-Text-Frame"></div>

[[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.

<div id="_idContainer018" class="Basic-Text-Frame"></div>

[[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.

<div id="3.3.1.1.2" class="h4-container"></div>

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

<div id="h4-2-siblings" class="h4-siblings"></div>

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]] ).

<div id="_idContainer020" class="Basic-Text-Frame"></div>

[[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.

<div id="_idContainer022" class="_idGenObjectStyleOverride-1"></div>

[[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.

<div id="_idContainer024" class="Basic-Text-Frame"></div>

[[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.

<div id="_idContainer025"></div>

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.

<div id="3.3.1.2" class="h3-container"></div>

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

<div id="h3-4-siblings" class="h3-siblings"></div>

( [[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.

<div id="3.3.1.2.1" class="h4-container"></div>

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

<div id="h4-3-siblings" class="h4-siblings"></div>

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]] ).

<div id="_idContainer027" class="Basic-Text-Frame"></div>

[[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.

<div id="3.3.1.2.2" class="h4-container"></div>

<span id="stratospheric-temperature"></span>
===== 3.3.1.2.2 Stratospheric temperature =====

<div id="h4-4-siblings" class="h4-siblings"></div>

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.

<div id="cross-chapter-box-3.1" class="h2-container box-container"></div>

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

<div id="h2-8-siblings" class="h2-siblings"></div>

'''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'' ).

<div id="_idContainer028" class="Body-copy_Boxes_Blue-Boxes_•-Box-subhead-H1---no-space-below"></div>

[[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).

<div id="3.3.2" class="h2-container"></div>

<span id="precipitation-humidity-and-streamflow"></span>