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

==== 3.3.1.1 Surface Temperature ====

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

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

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

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

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[[File:1ce2530ae198a3957bfa3dca50d72667 IPCC_AR6_WGI_Figure_3_2.png]]

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

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

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[[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.

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

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

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

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

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

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

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

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

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[[File:89c130003ac057ad307bb43aaf0ac219 IPCC_AR6_WGI_Figure_3_5.png]]

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

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

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[[File:ff55ffd4ceaf6c8cdb4215341f9645a0 IPCC_AR6_WGI_Figure_3_6.png]]

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

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

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===== 3.3.1.1.2 Detection and attribution =====

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

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

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

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[[File:d3a10075e7a486c6d96c99e82c0e505c IPCC_AR6_WGI_Figure_3_7.png]]

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

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

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[[File:b5453f34bf66d2a892da5bef7f33d5e4 IPCC_AR6_WGI_Figure_3_8.png]]

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

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

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[[File:85198fe800e9fd13ec9c5f9973af10cb IPCC_AR6_WGI_Figure_3_9.png]]

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

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

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

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

{| class="wikitable"
|-
|
| 1986–2005

| 1995–2014

| 2006–2015

| 2010–2019

| Warming Rate 2010–2019

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

| 0.65 (0.52 to 0.77)

| 0.82 (0.69 to 0.94)

| 0.94 (0.8 to 1.08)

| 1.03 (0.89 to 1.17)

| 0.23 (0.18 to 0.29)

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

| 0.63 (0.32 to 0.94)

| 0.84 (0.63 to 1.06)

| 0.98 (0.74 to 1.22)

| 1.11 (0.92 to 1.30)

| 0.35 (0.30 to 0.41)

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

| 0.73 (0.58 to 0.82)

| 0.88 (0.75 to 0.98)

| 0.98 (0.87 to 1.10)

| 1.06 (0.94 to 1.22)

| 0.23 (0.19 to 0.35)

|-
| Assessment

| 0.68 (0.3 to 1.0)

| 0.85 (0.6 to 1.1)

| 0.97 (0.7 to 1.3)

| 1.07 (0.8 to 1.3)

| 0.2 (0.1 to 0.3)

|}

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

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

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