We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO 2 , characterized by an effective sensitivity S. This evidence includes feedback process understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density function (PDF) for S given all the evidence, including tests of robustness to difficult-to-quantify uncertainties and different priors. The 66% range is 2.6-3.9 K for our Baseline calculation and remains within 2.3-4.5 K under the robustness tests; corresponding 5-95% ranges are 2.3-4.7 K, bounded by 2.0-5.7 K (although such high-confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S, in particular using comprehensive models and process understanding to address limitations in the traditional forcing-feedback paradigm for interpreting past changes. Plain Language Summary Earth's global "climate sensitivity" is a fundamental quantitative measure of the susceptibility of Earth's climate to human influence. A landmark report in 1979 concluded that it probably lies between 1.5°C and 4.5°C per doubling of atmospheric carbon dioxide, assuming that other influences on climate remain unchanged. In the 40 years since, it has appeared difficult to reduce this uncertainty range. In this report we thoroughly assess all lines of evidence including some new developments. We find that a large volume of consistent evidence now points to a more confident view of a climate sensitivity near the middle or upper part of this range. In particular, it now appears extremely unlikely that the climate sensitivity could be low enough to avoid substantial climate change (well in excess of 2°C warming) under a high-emission future scenario. We remain unable to rule out that the sensitivity could be above 4.5°C per doubling of carbon dioxide levels, although this is not likely. Continued ©2020. American Geophysical Union. All Rights Reserved.

Global radiative feedbacks have been found to vary in global climate model (GCM) simulations. Atmospheric GCMs (AGCMs) driven with historical patterns of sea surface temperatures (SSTs) and sea ice concentrations produce radiative feedbacks that trend toward more negative values, implying low climate sensitivity, over recent decades. Freely evolving coupled GCMs driven by increasing CO2 produce radiative feedbacks that trend toward more positive values, implying increasing climate sensitivity, in the future. While this time variation in feedbacks has been linked to evolving SST patterns, the role of particular regions has not been quantified. Here, a Green’s function is derived from a suite of simulations within an AGCM (NCAR’s CAM4), allowing an attribution of global feedback changes to surface warming in each region. The results highlight the radiative response to surface warming in ascent regions of the western tropical Pacific as the dominant control on global radiative feedback changes. Historical warming from the 1950s to 2000s preferentially occurred in the western Pacific, yielding a strong global outgoing radiative response at the top of the atmosphere (TOA) and thus a strongly negative global feedback. Long-term warming in coupled GCMs occurs preferentially in tropical descent regions and in high latitudes, where surface warming yields small global TOA radiation change but large global surface air temperature change, and thus a less-negative global feedback. These results illuminate the importance of determining mechanisms of warm pool warming for understanding how feedbacks have varied historically and will evolve in the future.

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Radiative feedbacks depend on the spatial patterns of sea surface temperature (SST) and thus can change over time as SST patterns evolve—the so-called pattern effect. This study investigates intermodel differences in the magnitude of the pattern effect and how these differences contribute to the spread in effective equilibrium climate sensitivity (ECS) within CMIP5 and CMIP6 models. Effective ECS in CMIP5 estimated from 150-yr-long abrupt4×CO2 simulations is on average 10% higher than that estimated from the early portion (first 50 years) of those simulations, which serves as an analog for historical warming; this difference is reduced to 7% on average in CMIP6. The (negative) net radiative feedback weakens over the course of the abrupt4×CO2 simulations in the vast majority of CMIP5 and CMIP6 models, but this weakening is less dramatic on average in CMIP6. For both ensembles, the total variance in the effective ECS is found to be dominated by the spread in radiative response on fast time scales, rather than the spread in feedback changes. Using Green’s functions derived from two AGCMs shows that the spread in feedbacks on fast time scales may be primarily due to differences in atmospheric model physics, whereas the spread in feedback evolution is primarily governed by differences in SST patterns. Intermodel spread in feedback evolution is well explained by differences in the relative warming in the west Pacific warm-pool regions for the CMIP5 models, but this relation fails to explain differences across the CMIP6 models, suggesting that a stronger sensitivity of extratropical clouds to surface warming may also contribute to feedback changes in CMIP6.

Estimates of climate sensitivity from models and observations are reconciled by accounting for slowly responding climate mode.

We compare top‐of‐atmosphere (TOA) radiative fluxes observed by the Clouds and the Earth's Radiant Energy System (CERES) and simulated by seven general circulation models forced with observed sea‐surface temperature (SST) and sea‐ice boundary conditions. In response to increased SSTs along the equator and over the eastern Pacific (EP) following the so‐called global warming “hiatus” of the early 21st century, simulated TOA flux changes are remarkably similar to CERES. Both show outgoing shortwave and longwave TOA flux changes that largely cancel over the west and central tropical Pacific, and large reductions in shortwave flux for EP low‐cloud regions. A model's ability to represent changes in the relationship between global mean net TOA flux and surface temperature depends upon how well it represents shortwave flux changes in low‐cloud regions, with most showing too little sensitivity to EP SST changes, suggesting a “pattern effect” that may be too weak compared to observations.

Earth's climatological pattern of sea-surface temperature (SST) plays a key role in shaping the large-scale atmospheric circulation and regional climate. In particular, the relative warmth of the Warm Pool in the western Indo-Pacific compared to the Cold Tongue in the eastern equatorial Pacific drives the Walker circulation in the tropical atmosphere, which through its impact on the upper tropospheric divergence in the Warm Pool generates large-scale atmospheric Rossby waves that propagate into higher latitudes and impact climate around the globe (Bjerknes, 1969;Sardeshmukh & Hoskins, 1988). This is part of a two-way coupling between the tropical atmosphere and ocean; the Walker circulation also helps shape the climatological SST pattern by driving upwelling of cold waters in the Cold Tongue and ocean heat-flux convergence in the Warm Pool (Bjerknes, 1969;Neelin et al., 1998).In response to anthropogenic greenhouse gas forcing, climate models generally show weakening of the Walker circulation (Vecchi et al., 2006) and enhanced warming in the eastern equatorial Pacific (Meehl & Washington, 1996). In contrast, SST observations show enhanced warming in the Indo-Pacific Warm Pool and weak cooling in the eastern equatorial Pacific over the 20th century (

Attribution and prediction of global and regional warming requires a better understanding of the magnitude and spatial characteristics of internal global mean surface air temperature (GMST) variability. We examine interdecadal GMST variability in Coupled Modeling Intercomparison Projects, Phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6) preindustrial control (piControl), last millennium, and historical simulations and in observational data. We find that several CMIP6 simulations show more GMST interdecadal variability than the previous generations of model simulations. Nonetheless, we find that 100-year trends in CMIP6 piControl simulations never exceed the maximum observed warming trend. Furthermore, interdecadal GMST variability in the unforced piControl simulations is associated with regional variability in the high latitudes and the east Pacific, whereas interdecadal GMST variability in instrumental data and in historical simulations with external forcing is more globally coherent and is associated with variability in tropical deep convective regions.Plain Language Summary Ongoing and future global and regional warming will progress as a combination of internal climate variability and forced climate change. Understanding the magnitude and spatial patterns associated with internal climate variability is an important aspect of being able to predict when, where, and how climate change will be felt around the globe. Here, we show that the latest climate model simulations, which will be used in the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 6 (AR6), simulate a large range in magnitudes of internal global mean temperature variability. Although there are large unforced global temperature trends in some models, we find that even the most variable models never generate unforced global temperature trends equal to the recently observed global warming trends forced by greenhouse gas emissions. We examine the regions associated with internal climate variability and forced climate change in climate model simulations and find that only forced simulations show a pattern of warming consistent with instrumental data. PARSONS ET AL.1 of 11 400 years of nine Coupled Model Intercomparison Project, Phase 6 (CMIP6) preindustrial control (piControl) simulations. Stippling denotes local geographic regions where the global versus local relationship exceeds the noise threshold and local variability leads global variability at interdecadal timescales (section 2.3). Note that these panels show a subselection of CMIP6 models, including the three most, three median, and three least variable models as measured by standard deviation of GMST. Figure S2 shows coherence from all 39 CMIP6 piControl simulations.