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This study leveraged a Lagrangian framework to examine the evolution of stratocumulus clouds under cold and warm advections (CADV and WADV) in the Community Earth System Model 2 (CESM2) against observations. We found that CESM2 simulates a too rapid decline in low‐cloud fraction (LCF) and cloud liquid water path (CLWP) under CADV conditions, while it better aligns closely with observed LCF under WADV conditions but overestimates the increase in CLWP. Employing an explainable machine learning approach, we found that too rapid decreases in LCF and CLWP under CADV conditions are related to overestimated drying effects induced by sea surface temperature, whereas the substantial increase in CLWP under WADV conditions is associated with the overestimated moistening effects due to free‐tropospheric moisture and surface winds. Our findings suggest that overestimated drying effects of sea surface temperature on cloud properties might be one of crucial causes for the high equilibrium climate sensitivity in CESM2. Plain Language Summary: Stratocumulus clouds have extensive coverage over the oceans and modulate the climate system by efficiently reflecting incoming solar radiation back to space. However, their simulations in climate models are challenging due to complex meteorological controls, in which temperature advection is one of the most uncertain controlling factors. To enhance our understanding, we examine the stratocumulus evolution influenced by cold‐advection (CADV) and warm‐advection (WADV) in the midlatitudes in a climate model, CESM2. A too rapid decrease in low‐cloud fraction (LCF) and cloud liquid water path (CLWP) is erroneously simulated under CADV conditions, while an increase in CLWP is substantially overestimated under WADV conditions. Using an explainable machine learning approach, these errors are found to be caused by the amplified drying or moistening effects due to improper treatments of meteorological controls on clouds in CESM2. This study suggests that these misrepresentations of cloud physics in the midlatitudes should be imperatively improved to reduce climate prediction uncertainties. Key Points: Community Earth System Model 2 simulates a too rapid decline in low‐cloud fraction and cloud liquid water path when clouds experience cold‐air advectionIt substantially overestimates increases in cloud liquid water path when clouds experience warm‐air advectionUtilizing an explainable machine learning methodology, the effects of meteorological factors on cloud evolution are assessed [ABSTRACT FROM AUTHOR]

Abstract This study leveraged a Lagrangian framework to examine the evolution of stratocumulus clouds under cold and warm advections (CADV and WADV) in the Community Earth System Model 2 (CESM2) against observations. We found that CESM2 simulates a too rapid decline in low‐cloud fraction (LCF) and cloud liquid water path (CLWP) under CADV conditions, while it better aligns closely with observed LCF under WADV conditions but overestimates the increase in CLWP. Employing an explainable machine learning approach, we found that too rapid decreases in LCF and CLWP under CADV conditions are related to overestimated drying effects induced by sea surface temperature, whereas the substantial increase in CLWP under WADV conditions is associated with the overestimated moistening effects due to free‐tropospheric moisture and surface winds. Our findings suggest that overestimated drying effects of sea surface temperature on cloud properties might be one of crucial causes for the high equilibrium climate sensitivity in CESM2.

Despite decades of effort to constrain equilibrium climate sensitivity (ECS), current best estimates still exhibit a large spread. Past studies have sought to reduce ECS uncertainty through a variety of methods including emergent constraints. One example uses global temperature variability over the past century to constrain ECS. While this method shows promise, it has been criticized for its susceptibility to the influence of anthropogenic forcing and the limited length of the instrumental record used to compute temperature variability. Here, we investigate the emergent relationship between ECS and two metrics of global temperature variability using model simulations and paleoclimate reconstructions over the last millennium (850–1999). We find empirical evidence in support of these emergent relationships. Observational constraints suggest a central ECS estimate of 2.6–2.8 K, consistent with the Intergovernmental Panel on Climate Change's consensus estimate of 3K. Moreover, they suggest ECS "likely" ranges of 1.8–3.3 K and 2.0–3.6 K. Plain Language Summary: Future changes in global‐mean temperatures have substantial implications for climate‐related risks, and global‐mean temperatures will continue to increase in response to carbon emissions (a property known as climate sensitivity). In addition to long‐term, human‐driven warming, the global surface temperature also exhibits short‐term swings due to natural climate variability. Recently, climate model experiments have shown some evidence of a linear relationship between climate sensitivity and climate variability, with potential applications for climate model development and future predictions. We show that this relationship holds when applied to paleoclimate data from the Common Era (850–1999) and use the reconstructed temperature record over this period to estimate climate sensitivity. Our analysis considers a period (∼1,000 years) that is much longer than what has been used in prior studies (∼100 years) and thus paints a fuller picture of past temperature variability. We show that interannual and decadal fluctuations in climate could offer a novel technique for assessing the Earth's sensitivity to external forcing. Our results are consistent with other lines of evidence, increasing confidence in our approach. We hope our findings add value to the existing collection of climate sensitivity estimates and encourage future use of paleoclimate data to verify proposed constraints on climate quantities. Key Points: We use model output and temperature reconstructions from 850 to 1999 to test a proposed emergent constraint on climate sensitivityThe proposed emergent constraint largely holds for this period, which is longer and more stationary than that of prior workOur central estimates of equilibrium climate sensitivity are 2.6–2.8 K, consistent with prior results and the IPCC's consensus estimate [ABSTRACT FROM AUTHOR]

Abstract Despite decades of effort to constrain equilibrium climate sensitivity (ECS), current best estimates still exhibit a large spread. Past studies have sought to reduce ECS uncertainty through a variety of methods including emergent constraints. One example uses global temperature variability over the past century to constrain ECS. While this method shows promise, it has been criticized for its susceptibility to the influence of anthropogenic forcing and the limited length of the instrumental record used to compute temperature variability. Here, we investigate the emergent relationship between ECS and two metrics of global temperature variability using model simulations and paleoclimate reconstructions over the last millennium (850–1999). We find empirical evidence in support of these emergent relationships. Observational constraints suggest a central ECS estimate of 2.5–2.7 K, consistent with the Intergovernmental Panel on Climate Change's consensus estimate of 3K. Moreover, they suggest ECS “likely” ranges of 1.7–3.3 K and 1.9–3.5 K.

Contemporary general circulation models (GCMs) and Earth system models (ESMs) are developed by a large number of modeling groups globally. They use a wide range of representations of physical processes, allowing for structural (code) uncertainty to be partially quantified with multi‐model ensembles (MMEs). Many models in the MMEs of the Coupled Model Intercomparison Project (CMIP) have a common development history due to sharing of code and schemes. This makes their projections statistically dependent and introduces biases in MME statistics. Previous research has focused on model output and code dependence, and model code genealogy of CMIP models has not been fully analyzed. We present a full reconstruction of CMIP3, CMIP5, and CMIP6 code genealogy of 167 atmospheric models, GCMs, and ESMs (of which 114 participated in CMIP) based on the available literature, with a focus on the atmospheric component and atmospheric physics. We identify 12 main model families. We propose family and ancestry weighting methods designed to reduce the effect of model structural dependence in MMEs. We analyze weighted effective climate sensitivity (ECS), climate feedbacks, forcing, and global mean near‐surface air temperature, and how they differ by model family. Models in the same family often have similar climate properties. We show that weighting can partially reconcile differences in ECS and cloud feedbacks between CMIP5 and CMIP6. The results can help in understanding structural dependence between CMIP models, and the proposed ancestry and family weighting methods can be used in MME assessments to ameliorate model structural sampling biases. Plain Language Summary: Contemporary global climate models are developed by a large number of modeling groups internationally. Commonly, projections from multiple models are used together to calculate multi‐model means and quantify uncertainty. Because many of the models share parts of their computer code, algorithms and parametrization schemes, they are not independent. Overrepresented models can cause biases in multi‐model means, and uncertainty may be underestimated if model dependence is not taken into account. We document a full code genealogy of 167 models, of which 114 participated in the Coupled Model Intercomparison Project (CMIP) phases 3, 5, and 6, with a focus on the atmospheric component. We identify 12 main model families. We show that models in the same family often have similar estimates of key climate properties. We propose statistical weighting methods based on the model family and code relationship, and show that they can reconcile some of the difference in results between the two most recent CMIP phases. The weighting methods or a selection of independent models based on the genealogy can be used in model assessment studies to reduce the effects of model dependence. Key Points: We reconstruct a code genealogy of 167 climate models with a focus on the atmospheric component and atmospheric physicsAll models originate from 12 main model families, and models in the same family often have similar climate feedbacks and sensitivityProposed ancestry and family weighting can partly reconcile differences in means between the Coupled Model Intercomparison Project phases [ABSTRACT FROM AUTHOR]

Abstract Equilibrium climate sensitivity (ECS) refers to the total global warming caused by an instantaneous doubling of CO2 from the preindustrial level. It is mainly estimated through the linear fit between the changes in global-mean surface temperature and top-of-atmosphere net radiative flux, due to the high costs of millennial-length simulations for reaching a stable climate. However, the accuracy can be influenced by the response’s nonlinearity and the internal noise, especially when using a limited-length simulation. Here, we propose a new method that derives a new series using an exponential-interval sampling (EIS) method for the original simulation to reduce the noise and estimate the ECS more accurately. Utilizing the millennial-length simulations of LongRunMIP, we prove that the EIS method can effectively reduce the influence of internal variability, and the estimated ECS based on the first 150 years of simulation is closer to the final ECS in the millennial-length simulations than previous estimations with the deviation rate decreased by around 1/3. The ECS in CMIP6 models estimated by the EIS method ranges from 1.93 to 6.78 K, and suggests that the multimodel mean ECS derived from the original series with previous methods could be underestimated.

The Coupled Model Intercomparison Project (phase 6) (CMIP6) global circulation models (GCMs) predict equilibrium climate sensitivity (ECS) values ranging between 1.8 and 5.7 ∘ C. To narrow this range, we group 38 GCMs into low, medium and high ECS subgroups and test their accuracy and precision in hindcasting the mean global surface warming observed from 1980–1990 to 2011–2021 in the ERA5-T2m, HadCRUT5, GISTEMP v4, and NOAAGlobTemp v5 global surface temperature records. We also compare the GCM hindcasts to the satellite-based UAH-MSU v6 lower troposphere global temperature record. We use 143 GCM ensemble averaged simulations under four slightly different forcing conditions, 688 GCM member simulations, and Monte Carlo modeling of the internal variability of the GCMs under three different model accuracy requirements. We found that the medium and high-ECS GCMs run too hot up to over 95% and 97% of cases, respectively. The low ECS GCM group agrees best with the warming values obtained from the surface temperature records, ranging between 0.52 and 0.58 ∘ C. However, when comparing the observed and GCM hindcasted warming on land and ocean regions, the surface-based temperature records appear to exhibit a significant warming bias. Furthermore, if the satellite-based UAH-MSU-lt record is accurate, actual surface warming from 1980 to 2021 may have been around 0.40 ∘ C (or less), that is up to about 30% less than what is reported by the surface-based temperature records. The latter situation implies that even the low-ECS models would have produced excessive warming from 1980 to 2021. These results suggest that the actual ECS may be relatively low, i.e. lower than 3 ∘ C or even less than 2 ∘ C if the 1980–2021 global surface temperature records contain spurious warming, as some alternative studies have already suggested. Therefore, the projected global climate warming over the next few decades could be moderate and probably not particularly alarming. [ABSTRACT FROM AUTHOR]

Global warming is a gradual increase in the Earth's temperature generally due to the greenhouse effect caused by increased levels of carbon dioxide. Global warming has had significant consequences for human life, significantly affecting agricultural production, ecosystems, and water resources. The climate system of the Earth responds to a perturbation to the top of the atmosphere radiative balance through a change in temperature. This imbalance constitutes a radiative forcing of the climate system, and the magnitude of the response is determined by the strength of the forcing and the net radiative feedback. Equilibrium Climate Sensitivity (ECS) is an estimate of the eventual steady-state global warming at double CO2 and Transient Climate Response (TCR) is the mean global warming predicted to occur around the time of doubling CO2 in GCM and ESM runs for which atmospheric CO2 concentration is prescribed to increase at 1% per year. This study aimed to evaluate the performance of Climate Model Intercomparison Project Phase 6 (CMIP6) models by selecting 30 models considering TCR and ECS and to investigate temperature spatial distribution and annual trends in Iran. The performance of these models has been investigated with data from Iran's fifty-one synoptic stations for the historical period (1980-2014) using the Taylor diagram and box plot. The results showed that most CMIP6 models have good performance in simulating spatial temperature patterns. However, in the area-averaged, 73% of the selected models have estimated the temperature of the country as less than the station data. In general, more than 56% of the models showed a correlation higher than 0.5 compared to station data in the area-averaged temperature of Iran. Four models including CanESM5, INM-CM5-0, TaiESM1, and UKESM1-0-LL have shown the highest performance in estimating the temperature in Iran. The area-averaged annual temperature trend, which was examined by the modified Mann-Kendall test, showed that the temperature trend of CMIP6 models is increasing along with the observational data for all models. Most CMIP6 models, however, have simulated higher warming rates in the historical period, which differs from station data. These differences cannot be explained by internal climate variability (ICV), and the equilibrium climate sensitivity of most CMIP6 models has created a greater rate of warming in the models. For example, models such as CanESM5 and UKESM1-0-LL, which showed the highest trend, had the highest ECS and TCR among all. [ABSTRACT FROM AUTHOR]

Warming climate and precipitation changes induce notable shifts in climate zones. In this study, the latest generation of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the previous generation CMIP5 under high‐emission scenarios are used together with observations and applied to the Köppen‐Geiger climate classification. The aim is to shed light on how projected warming and changes in precipitation will influence future climate zones and their associated ecosystems while revealing differences between the two model generations. Compared to CMIP5 models, CMIP6 models exhibit slightly improved performance in replicating the observed Köppen‐Geiger map for the historical (1976–2005) period and similar inter‐model agreement for the future. The models show major changes in climate zones with a range of projections depending on which ensemble subset is used: 37.9%–48.1% of the global land area is projected to change climate zone by the end of the century, with the most pronounced changes expected over Europe (71.4%–88.6%) and North America (51.2%–65.8%). CMIP6 models project a higher rate of areal climate zone change (km2/year) throughout the 21st century, which is mainly driven by their greater global land warming rates. Using a likely equilibrium climate sensitivity subset of CMIP6 models that is consistent with the latest evidence constrains the climate zone shifts, and their projections better match the results of CMIP5 simulations. Although the high warming rates of some CMIP6 models are less credible, the risks associated with them are greater, and they heighten the need for urgent action to preserve terrestrial ecosystems. Plain Language Summary: Köppen‐Geiger climate classification is a tool to map regional climate zones based on a region's temperature and precipitation. In recent decades, increasing temperatures and changing precipitation trends have started to affect the distribution of those climate zones, and more extensive changes are expected throughout the 21st century. In this paper, we assess the observed and expected changes in the global distribution of Köppen‐Geiger climate zones using observations along with the latest and the previous generation of global climate model projections. We find that shifts in climate zones are more pronounced in the latest generation of models due to their questionable greater global warming rate projections. Depending on which subset of model projections is used, up to half of the Earth's land area faces the risk of shifting to a different climate zone by the end of the century, with the greatest changes expected in Europe and North America. The rate of change (affected area per year) is also projected to accelerate through the 21st century, suggesting that vulnerable species and agricultural practices might have less time to adapt to changes in climate zones than previously projected. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibits slightly improved performance in simulating the globally observed historical Köppen‐Geiger climate zones compared to CMIP5By the end of the century, 38%–48% of the global land area is projected to be in a different climate zone than todayThe rate of climate zone change is projected to accelerate, with more pronounced rates in CMIP6 due to considerably higher warming rates [ABSTRACT FROM AUTHOR]

Abstract Contemporary general circulation models (GCMs) and Earth system models (ESMs) are developed by a large number of modeling groups globally. They use a wide range of representations of physical processes, allowing for structural (code) uncertainty to be partially quantified with multi‐model ensembles (MMEs). Many models in the MMEs of the Coupled Model Intercomparison Project (CMIP) have a common development history due to sharing of code and schemes. This makes their projections statistically dependent and introduces biases in MME statistics. Previous research has focused on model output and code dependence, and model code genealogy of CMIP models has not been fully analyzed. We present a full reconstruction of CMIP3, CMIP5, and CMIP6 code genealogy of 167 atmospheric models, GCMs, and ESMs (of which 114 participated in CMIP) based on the available literature, with a focus on the atmospheric component and atmospheric physics. We identify 12 main model families. We propose family and ancestry weighting methods designed to reduce the effect of model structural dependence in MMEs. We analyze weighted effective climate sensitivity (ECS), climate feedbacks, forcing, and global mean near‐surface air temperature, and how they differ by model family. Models in the same family often have similar climate properties. We show that weighting can partially reconcile differences in ECS and cloud feedbacks between CMIP5 and CMIP6. The results can help in understanding structural dependence between CMIP models, and the proposed ancestry and family weighting methods can be used in MME assessments to ameliorate model structural sampling biases.

Climate models project a weakening and expansion of the Hadley circulation (HC) under global warming but with considerable spread in the magnitude of these changes. Here, utilizing models from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), we illustrate how the variance in projected changes in the HC arises from equilibrium climate sensitivity (ECS) uncertainty across models. Models with higher ECS project a greater extent of static stability increase hence larger HC changes. Using the best estimate of ECS with value of 3 K (∼2.5–4.0 K) to constrain the HC projection, we reveal that the constrained projection yields a 15% (11%) decrease in the weakening (poleward shift) of the HC in the Northern (Southern) Hemisphere compared to the multimodel mean under the SSP5-8.5 scenario. The corresponding projection uncertainty is reduced by about 77.4% and 75.6%, respectively. Our results indicate a smaller-than-expected change in the HC in response to increased CO _2 concentrations.

Philosophers argue that many choices in science are influenced by values or have value-implications, ranging from the preference for some research method's qualities to ethical estimation of the consequences of error. Based on the argument that awareness of values in the scientific process is a necessary first step to both avoid bias and attune science best to the needs of society, an analysis of the role of values in the physical climate science production process is provided. Model-based assessment of climate sensitivity is taken as an illustrative example; climate sensitivity is useful here because of its key role in climate science and relevance for policy, by having been the subject of several assessments over the past decades including a recent shift in assessment method, and because it enables insights that apply to numerous other aspects of climate science. It is found that value-judgements are relevant at every step of the model-based assessment process, with a differentiated role of non-epistemic values across the steps, impacting the assessment in various ways. Scrutiny of current philosophical norms for value-management highlights the need for those norms to be re-worked for broader applicability to climate science. Recent development in climate science turning away from direct use of models for climate sensitivity assessment also gives the opportunity to start investigating the role of values in alternative assessment methods, highlighting similarities and differences in terms of the role of values that encourage further study. [ABSTRACT FROM AUTHOR]

Equilibrium climate sensitivity (ECS) refers to the total global warming caused by an instantaneous doubling of CO2 from the preindustrial level. It is mainly estimated through the linear fit between the changes in global-mean surface temperature and top-of-atmosphere net radiative flux, due to the high costs of millennial-length simulations for reaching a stable climate. However, the accuracy can be influenced by the response's nonlinearity and the internal noise, especially when using a limited-length simulation. Here, we propose a new method that derives a new series using an exponential-interval sampling (EIS) method for the original simulation to reduce the noise and estimate the ECS more accurately. Utilizing the millennial-length simulations of LongRunMIP, we prove that the EIS method can effectively reduce the influence of internal variability, and the estimated ECS based on the first 150 years of simulation is closer to the final ECS in the millennial-length simulations than previous estimations with the deviation rate decreased by around 1/3. The ECS in CMIP6 models estimated by the EIS method ranges from 1.93 to 6.78 K, and suggests that the multimodel mean ECS derived from the original series with previous methods could be underestimated. [ABSTRACT FROM AUTHOR]

The Community Earth System Model version 2 (CESM2) simulates a high equilibrium climate sensitivity (ECS > 5°C) and a Last Glacial Maximum (LGM) that is substantially colder than proxy temperatures. In this study, we examine the role of cloud parameterizations in simulating the LGM cooling in CESM2. Through substituting different versions of cloud schemes in the atmosphere model, we attribute the excessive LGM cooling to the new CESM2 schemes of cloud microphysics and ice nucleation. Further exploration suggests that removing an inappropriate limiter on cloud ice number (NoNimax) and decreasing the time‐step size (substepping) in cloud microphysics largely eliminate the excessive LGM cooling. NoNimax produces a more physically consistent treatment of mixed‐phase clouds, which leads to an increase in cloud ice content and a weaker shortwave cloud feedback over mid‐to‐high latitudes and the Southern Hemisphere subtropics. Microphysical substepping further weakens the shortwave cloud feedback. Based on NoNimax and microphysical substepping, we have developed a paleoclimate‐calibrated CESM2 (PaleoCalibr), which simulates well the observed twentieth century warming and spatial characteristics of key cloud and climate variables. PaleoCalibr has a lower ECS (∼4°C) and a 20% weaker aerosol‐cloud interaction than CESM2. PaleoCalibr represents a physically more consistent treatment of cloud microphysics than CESM2 and is a valuable tool in climate change studies, especially when a large climate forcing is involved. Our study highlights the unique value of paleoclimate constraints in informing the cloud parameterizations and ultimately the future climate projection. Plain Language Summary: The Community Earth System Model version 2 (CESM2) shows a much higher equilibrium climate sensitivity (ECS > 5°C) than its predecessor models (≤4°C), which, if true, implies a greater future warming than previously thought and a more severe challenge for climate adaptation and mitigation. It is critical to determine whether the high ECS is realistic and what causes its increase. In a previous study, we suggested that the high ECS is likely unrealistic because CESM2 simulates excessive cooling for an ice age climate—the Last Glacial Maximum (LGM; ∼21,000 years ago). In this study, we investigate which aspects of CESM2 are responsible for the extreme LGM cooling and the high ECS. We find that the simulated LGM climate is very sensitive to treatments of cloud microphysical processes, and that removing an inappropriate limiter on cloud ice number and using a smaller time‐step size in the microphysics largely eliminate the excessive LGM cooling. With these microphysical modifications, CESM2 simulates a much lower ECS (∼4°C) and matches present‐day observations well. Our study suggests that an ECS > 5°C is likely unrealistic and highlights the importance of using past climates to inform and validate the model development including the treatment of clouds. Key Points: Excessive Last Glacial Maximum (LGM) cooling and an ECS > 5°C in Community Earth System Model version 2 are attributed to cloud microphysical processes including ice nucleationA new configuration (PaleoCalibr) is developed that removes an inappropriate cloud‐ice‐number limiter and decreases microphysical timestepPaleoCalibr simulates realistic LGM and modern climates, a lower ECS (3.9°C), and a weaker shortwave cloud feedback [ABSTRACT FROM AUTHOR]

The persistent inter‐model spread in the response of global‐mean surface temperature to increased CO2 (known as the "Equilibrium Climate Sensitivity," or "ECS") is a crucial problem across model generations. This work examines the influence of the models' present‐day atmospheric circulation climatologies, and the accompanying climatological cloud radiative effects, in explaining that spread. We analyze the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and find that they simulate a more poleward, and thus more realistic, edge of the Hadley cell (HC) in the Southern Hemisphere than the CMIP5 models, although the climatological shortwave cloud radiative effects are similar in the two generations of models. A few CMIP5 models with extreme equatorward biases in the HC edge exhibited high ECS due to strong Southern midlatitude shortwave cloud radiative warming in response to climate change, suggesting an ECS dependence on HC position. We find that such constraint no longer holds for the CMIP6 models, due to the absence of models with extreme HC climatologies. In spite of this, however, the CMIP6 models show an increased spread in ECS, with more models in the high ECS range. In addition, an improved representation of the climatological jet dynamics does not lead to a new emergent constraint in the CMIP6 models either. [ABSTRACT FROM AUTHOR]

Abstract The transient climate response to cumulative carbon emissions (TCRE) is a key metric in estimating the remaining carbon budget for given temperature targets. However, the TCRE has a small scenario dependence that can be non-negligible for stringent temperature targets. To investigate the parametric correlations and scenario dependence of the TCRE, the present study uses a 512-member ensemble of an Earth system model of intermediate complexity (EMIC) perturbing 11 physical and biogeochemical parameters under scenarios with steady increases of 0.25%, 0.5%, 1%, 2%, or 4% per annum (ppa) in the atmospheric CO2 concentration (pCO2), or an initial increase of 1% followed by an annual decrease of 1% thereafter. Although a small difference of 5% (on average) in the TCRE is observed between the 1-ppa and 0.5-ppa scenarios, a significant scenario dependence is found for the other scenarios, with a tendency toward large values in gradual or decline-after-a-peak scenarios and small values in rapidly increasing scenarios. For all scenarios, correlation analysis indicates a remarkably large correlation between the equilibrium climate sensitivity (ECS) and the relative change in the TCRE, which is attributed to the longer response time of the high ECS model. However, the correlations of the ECS with the TCRE and its scenario dependence for scenarios with large pCO2 increase rates are slightly smaller, and those of biogeochemical parameters such as plant respiration and the overall pCO2–carbon cycle feedback are larger, than in scenarios with gradual increases. The ratio of the TCREs under the overshooting (i.e., 1-ppa decrease after a 1-ppa increase) and 1-ppa increase only scenarios had a clear positive relation with zero-emission commitments. Considering the scenario dependence of the TCRE, the remaining carbon budget for the 1.5 °C target could be reduced by 17 or 22% (before and after considering the unrepresented Earth system feedback) for the most extreme case (i.e., the 67th percentile when using the 0.25-ppa scenario as compared to the 1-ppa increase scenario). A single ensemble EMIC is also used to indicate that, at least for high ECS (high percentile) cases, the scenario dependence of the TCRE should be considered when estimating the remaining carbon budget.

Abstract The Community Earth System Model version 2 (CESM2) simulates a high equilibrium climate sensitivity (ECS > 5°C) and a Last Glacial Maximum (LGM) that is substantially colder than proxy temperatures. In this study, we examine the role of cloud parameterizations in simulating the LGM cooling in CESM2. Through substituting different versions of cloud schemes in the atmosphere model, we attribute the excessive LGM cooling to the new CESM2 schemes of cloud microphysics and ice nucleation. Further exploration suggests that removing an inappropriate limiter on cloud ice number (NoNimax) and decreasing the time‐step size (substepping) in cloud microphysics largely eliminate the excessive LGM cooling. NoNimax produces a more physically consistent treatment of mixed‐phase clouds, which leads to an increase in cloud ice content and a weaker shortwave cloud feedback over mid‐to‐high latitudes and the Southern Hemisphere subtropics. Microphysical substepping further weakens the shortwave cloud feedback. Based on NoNimax and microphysical substepping, we have developed a paleoclimate‐calibrated CESM2 (PaleoCalibr), which simulates well the observed twentieth century warming and spatial characteristics of key cloud and climate variables. PaleoCalibr has a lower ECS (∼4°C) and a 20% weaker aerosol‐cloud interaction than CESM2. PaleoCalibr represents a physically more consistent treatment of cloud microphysics than CESM2 and is a valuable tool in climate change studies, especially when a large climate forcing is involved. Our study highlights the unique value of paleoclimate constraints in informing the cloud parameterizations and ultimately the future climate projection.

Abstract The persistent inter‐model spread in the response of global‐mean surface temperature to increased CO2 (known as the “Equilibrium Climate Sensitivity,” or “ECS”) is a crucial problem across model generations. This work examines the influence of the models' present‐day atmospheric circulation climatologies, and the accompanying climatological cloud radiative effects, in explaining that spread. We analyze the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and find that they simulate a more poleward, and thus more realistic, edge of the Hadley cell (HC) in the Southern Hemisphere than the CMIP5 models, although the climatological shortwave cloud radiative effects are similar in the two generations of models. A few CMIP5 models with extreme equatorward biases in the HC edge exhibited high ECS due to strong Southern midlatitude shortwave cloud radiative warming in response to climate change, suggesting an ECS dependence on HC position. We find that such constraint no longer holds for the CMIP6 models, due to the absence of models with extreme HC climatologies. In spite of this, however, the CMIP6 models show an increased spread in ECS, with more models in the high ECS range. In addition, an improved representation of the climatological jet dynamics does not lead to a new emergent constraint in the CMIP6 models either.

Recent explorations of the state‐dependence of Earth's equilibrium climate sensitivity (ECS) have revealed a pronounced peak in ECS at a surface temperature of ∼310 K. This ECS peak has been observed in models spanning the model hierarchy, suggesting a robust physical source. Here, we propose an explanation for this ECS peak using a novel spectrally resolved decomposition of clear‐sky longwave feedbacks. We show that the interplay between spectral feedbacks in H2O‐dominated and CO2‐dominated portions of the longwave spectrum, along with moist‐adiabatic amplification of upper‐tropospheric warming, conspire to produce a minimum in the feedback parameter, and a corresponding peak in ECS, at a surface temperature of 310 K. Mechanism‐denial tests highlight three key ingredients for the ECS peak: (1) H2O continuum absorption to quickly close spectral windows at high surface temperature; (2) moist‐adiabatic tropospheric temperatures to enhance upper‐tropospheric warming; and (3) energetically consistent increases of CO2 with surface temperature. Plain Language Summary: Earth's equilibrium climate sensitivity (ECS) is roughly defined as the equilibrium change in surface temperature resulting from a doubling of CO2. It is well‐known that ECS can exhibit a considerable state‐dependence, in that its value depends on both the baseline surface temperature and CO2 concentration. Curiously, recent explorations of the state‐dependence of ECS have revealed the presence of a pronounced peak in ECS at a surface temperature of ∼310 K, with ECS then decreasing at higher surface temperatures and CO2 concentrations. Here, we propose an explanation for this peak in ECS that depends only on clear‐sky longwave feedbacks. Our explanation attributes the peak in ECS to a minimum in the magnitude of the feedback parameter, which occurs as the system transitions between two different methods of reequilibrating to an imposed energy imbalance. At low surface temperature and CO2, Earth reequilibrates to an imposed imbalance by changing the amount of radiation escaping to space through spectral windows where the opacity of H2O is low. At high surface temperatures and CO2 concentrations, these H2O "windows" have closed, and Earth reequilibrates primarily by changing the amount of radiation escaping to space in spectral intervals where CO2 opacity dominates over H2O opacity. Key Points: A simple one‐dimensional climate model exhibits a peak in equilibrium climate sensitivity (ECS) at a surface temperature of around 310 KThis peak in ECS arises from a competition between decreasing emission from the H2O "windows" and increasing emission from CO2 "radiator fins"Moist‐adiabatic warming in the upper troposphere is key for the efficacy of the CO2 radiator fins, and hence for the ECS peak [ABSTRACT FROM AUTHOR]

The upper end of the equilibrium climate sensitivity (ECS) has increased substantially in the latest Coupled Model Intercomparison Projects phase 6 with eight models (as of this writing) reporting an ECS > 5°C. The Community Earth System Model version 2 (CESM2) is one such high‐ECS model. Here we perform paleoclimate simulations of the Last Glacial Maximum (LGM) using CESM2 to examine whether its high ECS is realistic. We find that the simulated LGM global mean temperature decrease exceeds 11°C, greater than both the cooling estimated from proxies and simulated by an earlier model version (CESM1). The large LGM cooling in CESM2 is attributed to a strong shortwave cloud feedback in the newest atmosphere model. Our results indicate that the high ECS of CESM2 is incompatible with LGM constraints and that the projected future warming in CESM2, and models with a similarly high ECS, is thus likely too large. Plain Language Summary: Equilibrium climate sensitivity (ECS) is one of the most important metrics in climate science. It measures the amount of global warming over hundreds of years after a doubling of the atmospheric CO2 concentration. An ECS range of 1.5°C–4.5°C has been consistently supported by climate models over the past 40 years. However, this has changed in the latest generation of climate models with eight (as of this writing) showing an ECS > 5°C. Such a high ECS implies that future warming will be much greater than previously thought for the same amount of greenhouse gas emissions, making it more challenging for human and natural systems to adapt. This study examines whether the ECS in one "high‐ECS" model—Community Earth System Model version 2 (CESM2)—is realistic. Our approach is to perform a paleoclimate simulation of the culmination of the last glacial period, which was colder and had a lower atmospheric CO2 level than today. We find that the magnitude of cooling in the CESM2 simulation is much larger than supported by the observational evidence, indicating the model's ECS is too large. We further find that the high ECS of CESM2 is caused by the treatment of clouds in the model. Key Points: Community Earth System Model version 2 simulates an Last Glacial Maximum (LGM) global temperature at least 5°C colder than the proxy estimate, indicating its equilibrium climate sensitivity is too highThe large LGM cooling is caused by a strong shortwave cloud feedback in the new atmosphere modelThe shortwave cloud feedback in LGM simulations is connected to that in abrupt 4×CO2 simulations from the present‐day climate [ABSTRACT FROM AUTHOR]

One of the most used metrics to gauge the effects of climate change is the equilibrium climate sensitivity, defined as the long‐term (equilibrium) temperature increase resulting from instantaneous doubling of atmospheric CO2. Since global climate models cannot be fully equilibrated in practice, extrapolation techniques are used to estimate the equilibrium state from transient warming simulations. Because of the abundance of climate feedbacks—spanning a wide range of temporal scales—it is hard to extract long‐term behavior from short‐time series; predominantly used techniques are only capable of detecting the single most dominant eigenmode, thus hampering their ability to give accurate long‐term estimates. Here, we present an extension to those methods by incorporating data from multiple observables in a multicomponent linear regression model. This way, not only the dominant but also the next‐dominant eigenmodes of the climate system are captured, leading to better long‐term estimates from short, nonequilibrated time series. Plain Language Summary: Although it is clear that the atmospheric CO2 concentration influences the Earth's climate, it is difficult to quantify its long‐term effects accurately. Scientific efforts in this direction focus on idealized experiments carried out in global climate models. In these experiments, atmospheric CO2 is (instantaneously) doubled, and the long‐term temperature increase this causes is recorded. This resulting temperature increase is called the (equilibrium) climate sensitivity; accurately knowing its value helps to better quantify the effects of different emission scenarios on the future climate. However, it takes a very long time before all processes in a climate model are fully settled—especially in state‐of‐the‐art, more and more detailed models—and, in practice, settling all is simply not feasible. Hence, climate sensitivity needs to be estimated from limited model data. This is particularly difficult as the climate system consists of many processes that behave on vastly different time scales. Here, we present a new estimation technique that is better capable of capturing the very slow processes than conventional techniques, and hence leads to a more accurate quantification of (equilibrium) climate sensitivity. Key Points: A new and improved equilibrium climate sensitivity estimation technique is introduced that is intrinsically multieigenmodalThis new estimation technique better captures long‐term model behavior from short‐term forcing experiments compared to conventional methodsThe method uses multiple observables and can also estimate their equilibrium values, expediting multivariate sensitivity metrics [ABSTRACT FROM AUTHOR]

The transient climate response to cumulative carbon emissions (TCRE) is a key metric in estimating the remaining carbon budget for given temperature targets. However, the TCRE has a small scenario dependence that can be non-negligible for stringent temperature targets. To investigate the parametric correlations and scenario dependence of the TCRE, the present study uses a 512-member ensemble of an Earth system model of intermediate complexity (EMIC) perturbing 11 physical and biogeochemical parameters under scenarios with steady increases of 0.25%, 0.5%, 1%, 2%, or 4% per annum (ppa) in the atmospheric CO2 concentration (pCO2), or an initial increase of 1% followed by an annual decrease of 1% thereafter. Although a small difference of 5% (on average) in the TCRE is observed between the 1-ppa and 0.5-ppa scenarios, a significant scenario dependence is found for the other scenarios, with a tendency toward large values in gradual or decline-after-a-peak scenarios and small values in rapidly increasing scenarios. For all scenarios, correlation analysis indicates a remarkably large correlation between the equilibrium climate sensitivity (ECS) and the relative change in the TCRE, which is attributed to the longer response time of the high ECS model. However, the correlations of the ECS with the TCRE and its scenario dependence for scenarios with large pCO2 increase rates are slightly smaller, and those of biogeochemical parameters such as plant respiration and the overall pCO2–carbon cycle feedback are larger, than in scenarios with gradual increases. The ratio of the TCREs under the overshooting (i.e., 1-ppa decrease after a 1-ppa increase) and 1-ppa increase only scenarios had a clear positive relation with zero-emission commitments. Considering the scenario dependence of the TCRE, the remaining carbon budget for the 1.5 °C target could be reduced by 17 or 22% (before and after considering the unrepresented Earth system feedback) for the most extreme case (i.e., the 67th percentile when using the 0.25-ppa scenario as compared to the 1-ppa increase scenario). A single ensemble EMIC is also used to indicate that, at least for high ECS (high percentile) cases, the scenario dependence of the TCRE should be considered when estimating the remaining carbon budget. [ABSTRACT FROM AUTHOR]

The economic impacts of climate change are highly uncertain. Two of the most important uncertainties are the sensitivity of the climate system and the so-called damage functions, which relate climate change to economic costs and benefits. Despite broad awareness of these uncertainties, it is unclear which of them is most important, especially at the regional level. Here we construct regional damage functions, based on two different global damage functions, and apply them to two climate models with vastly different climate sensitivities. We find that uncertainty in both climate sensitivity and aggregate economic damages per degree of warming are of similar importance for the global economic impact of climate change, with the decrease in global economic productivity ranging between 4% and 24% by the end of the century under a high-emission scenario. At the regional level, however, the effects of climate change can vary even more substantially, depending both on a region’s initial temperature and the amount of warming it experiences, with some regions gaining in productivity and others losing. The ranges of uncertainty are therefore potentially much larger at a regional level. For example, at the end of the century, under a high-emission scenario, we find that India’s productivity decreases between 13% and 57% and Russia’s increases between 24% and 74%, while Germany’s change in productivity ranges from an increase of 8% to a decrease of 4%. Our findings emphasise the importance of including these uncertainties in estimates of future economic impacts, as they are vital for the resulting impacts and thus policy implications.

Stratospheric water vapor (SWV) changes, in response to increasing CO2, as a feedback component may play an important role in the Earth's energy budget. It has drawn extensive studies in the past decade. Here, we calculate the SWV climate feedback using the 150‐year CO2 forcing (1pctCO2) simulations in the CMIP6 ensemble of models. All models robustly show a moistening of the stratosphere, causing a positive radiative feedback to surface warming. We find that the stratospheric moistening rate and the SWV feedback both increase with surface warming. The moistening occurs at a rate of 0.9 ± 0.1 ppmv K−1 and its radiative feedback measured by the fixed dynamical heating method is 0.11 ± 0.02 W m−2 K−1 in the first 50 model years; the moistening rate increases to 1.2 ± 0.2 ppmv K−1 and the feedback increases to 0.16 ± 0.03 W m−2 K−1 in the last 50 model years when the global‐mean surface temperature is 3.3 K warmer. These increases are found to be caused by an amplified rate of tropical tropopause warming with respect to surface warming, which is 0.6 and 1.1 K K−1 for the two 50‐year periods, respectively. We conclude that the SWV feedback is strengthening with surface warming, which can contribute to increasing climate sensitivity in the future under global warming. Plain Language Summary: We study stratospheric water vapor changes under greenhouse warming, using the simulations from the latest state‐of‐the‐art CMIP6 models. Our results show that the stratospheric moistening rate and stratospheric water vapor radiative feedback both increase with climate warming for all the models. These increases are found to be caused by an amplified rate of tropical tropopause warming with respect to surface warming. The strengthening of the stratospheric water vapor feedback can contribute to accelerating simulated global warming in the future. Key Points: The ratio of tropical tropopause warming to surface warming amplifies with time in global warmingThe enhanced tropical tropopause warming results in accelerated increase of stratospheric water vaporThe stratospheric water vapor increase leads to an enhanced stratospheric water vapor feedback [ABSTRACT FROM AUTHOR]

Modeling and paleoclimate proxy‐based studies suggest that equilibrium climate sensitivity (ECS) depends on the background climate state, though the reason is not thoroughly understood. Here we study the state dependence of ECS over a large range of global mean surface temperature (GMST) in the Community Atmosphere Model (CAM) Versions 4, 5, and 6 by varying atmospheric CO2 concentrations. We find a robust increase of ECS with GMST in all three models, albeit at different rates, which is primarily attributed to strengthening of the shortwave cloud feedback (λcld) at both high and low latitudes. Over high latitudes, increasing GMST leads to a reduction in the cloud ice fraction, weakening the (negative) cloud‐phase feedback due to the phase transition of cloud ice to liquid and thereby strengthening λcld. Over low‐latitude regions, increasing GMST strengthens λcld likely through the nonlinear increase in water vapor, which causes low‐cloud thinning through thermodynamic and radiative processes. Plain Language Summary: Equilibrium climate sensitivity (ECS) is defined as the equilibrium increase in global mean temperature as a result of a doubling of atmospheric CO2 concentration. The latest assessment by the Intergovernmental Panel on Climate Change reported a likely ECS range of 1.5–4.5°C. Narrowing the ECS range is of paramount importance for prediction of future warming. Earth's surface has experienced prolonged periods of large magnitude warming in the geological past, which provide important empirical information on ECS. To quantitatively use the paleoclimate information, we need a complete understanding of how ECS may depend on the background climate. In this study, we investigate the physical mechanisms responsible for the state dependence of ECS using three climate models that have distinct model physics. In all three models, we find that ECS grows as the background climate warms; that is, a warmer climate is more sensitive to external forcing. We attribute the increase of ECS to both high‐ and low‐latitude cloud processes. Over high latitudes, cloud ice fraction decreases with global warming, weakening the potential for mixed‐phase clouds to reflect solar radiation and amplifying surface warming. Over low latitudes, global warming enhances the efficiency of processes that make clouds less opaque, again, amplifying surface warming. Key Points: ECS increases with CO2‐induced global warming in CAM6, CAM5, and CAM4 and is primarily attributed to the strengthening of cloud feedbackHigh‐latitude λcld strengthens with warming due to a decrease of cloud ice fraction and a weakening of the negative cloud‐phase feedbackLow‐latitude λcld strengthening is linked to cloud thinning over subsidence regions likely caused by cloud interactions with water vapor [ABSTRACT FROM AUTHOR]

A climate model emulator that mimics an ensemble of state‐of‐the‐art coupled climate models has been used for probabilistic climate projections. To emulate and compare the latest and previous multimodel ensembles, this study establishes a new method to diagnose a set of parameters of effective radiative forcing, feedback, and impulse response functions by fitting a minimal emulator to time series of individual models in response to step‐ and ramp‐shaped CO2 forcing up to a quadrupling concentration level. The diagnosed CO2 forcing is scaled down to a doubling level, leading to an unbiased estimate of equilibrium climate sensitivity. The average climate sensitivity of the latest ensemble is 18% and 13% greater than that of the previous ensemble for equilibrium and transient states. Although these increases are subject to data availability, the latter smaller rate is significant and is consistent with the relationship between feedback strength and response timescales. Plain Language Summary: Pathways of climate change mitigation are based on probabilistic warming projections by a simple emulator that mimics an ensemble of state‐of‐the‐art climate models. To gain insights into the latest multimodel ensemble, this study establishes a new method to analyze a set of key parameters to emulate individual models in terms of the global temperature and energy balance in response to changes in the atmospheric CO2 concentration. This method properly estimates the level of CO2 forcing, proportional to global warming, whereas a commonly used conventional method overestimates forcing levels for most mitigation pathways. On average, a common warming measure to a doubled CO2 concentration in the latest multimodel ensemble is 18% and 13% greater than that in the previous ensemble for a final and transient responses, respectively. The latter smaller change can be explained from a fundamental characteristic in a simplified forcing‐response framework. Although the differences between the two ensembles are subject to data availability, the difference between the final and transient responses is significant and has implications for mitigation pathways during this century. Key Points: A new emulator method is established for diagnosing forcing, feedback, and response functions of state‐of‐the‐art climate modelsConsidering forcing amplification from the first to the second doubling of CO2 is crucial for estimating equilibrium climate sensitivityRealized warming fractions in multimodel ensembles decrease with an increase in feedback strength, as is theoretically suggested [ABSTRACT FROM AUTHOR]

The last-generation CMIP6 global circulation models (GCMs) are currently used to interpret past and future climatic changes and to guide policymakers, but they are very different from each other; for example, their equilibrium climate sensitivity (ECS) varies from 1.83 to 5.67 °C (IPCC AR6, 2021). Even assuming that some of them are sufficiently reliable for scenario forecasts, such a large ECS uncertainty requires a pre-selection of the most reliable models. Herein the performance of 38 CMIP6 models are tested in reproducing the surface temperature changes observed from 1980–1990 to 2011–2021 in three temperature records: ERA5-T2m, ERA5-850mb, and UAH MSU v6.0 Tlt. Alternative temperature records are briefly discussed but found to be not appropriate for the present analysis because they miss data over large regions. Significant issues emerge: (1) most GCMs overestimate the warming observed during the last 40 years; (2) there is great variability among the models in reconstructing the climatic changes observed in the Arctic; (3) the ocean temperature is usually overestimated more than the land one; (4) in the latitude bands 40° N–70° N and 50° S–70° S (which lay at the intersection between the Ferrel and the polar atmospheric cells) the CMIP6 GCMs overestimate the warming; (5) similar discrepancies are present in the east-equatorial pacific region (which regulates the ENSO) and in other regions where cooling trends are observed. Finally, the percentage of the world surface where the (positive or negative) model-data discrepancy exceeds 0.2, 0.5 and 1.0 °C is evaluated. The results indicate that the models with low ECS values (for example, 3 °C or less) perform significantly better than those with larger ECS. Therefore, the low ECS models should be preferred for climate change scenario forecasts while the other models should be dismissed and not used by policymakers. In any case, significant model-data discrepancies are still observed over extended world regions for all models: on average, the GCM predictions disagree from the data by more than 0.2 °C (on a total mean warming of about 0.5 °C from 1980–1990 to 2011–2021) over more than 50% of the global surface. This result suggests that climate change and its natural variability remain poorly modeled by the CMIP6 GCMs. Finally, the ECS uncertainty problem is discussed, and it is argued (also using semi-empirical climate models that implement natural oscillations not predicted by the GCMs) that the real ECS could be between 1 and 2 °C, which implies moderate warming for the next decades.

There is much current debate about the way in which the earth’s climate and temperature are responding to anthropogenic and natural forcing. In this paper we re-assess the current evidence at the globally averaged level by adopting a generic ‘data-based mechanistic’ modelling strategy that incorporates statistically efficient parameter estimation. This identifies a low order, differential equation model that explains how the global average surface temperature variation responds to the influences of total radiative forcing (TRF). The model response includes a novel, stochastic oscillatory component with a period of about 55 years (range 51.6–60 years) that appears to be associated with heat energy interchange between the atmosphere and the ocean. These ‘quasi-cycle’ oscillations, which account for the observed pauses in global temperature increase around 1880, 1940 and 2001, appear to be related to ocean dynamic responses, particularly the Atlantic multidecadal oscillation. The model explains 90% of the variance in the global average surface temperature anomaly and yields estimates of the equilibrium climate sensitivity (ECS) (2.29 ^∘ C with 5%–95% range 2.11 ^∘ C to 2.49 ^∘ C) and the transient climate response (TCR) (1.56 ^∘ C with 5%–95% range 1.43 ^∘ C to 1.68 ^∘ C), both of which are smaller than most previous estimates. When a high level of uncertainty in the TRF is taken into account, the ECS and TCR estimates are unchanged but the ranges are increased to 1.43 ^∘ C to 3.14 ^∘ C and 0.99 ^∘ C to 2.16 ^∘ C, respectively.

Equilibrium climate sensitivity (ECS) and transient climate response (TCR) are both measures of the sensitivity of the climate system to external forcing, in terms of temperature response to CO _2 doubling. Here it is shown that, of the two, TCR in current-generation coupled climate models is better correlated with the model projected temperature change from the pre-industrial state, not only on decadal time scales but throughout much of the 21st century. For strong mitigation scenarios the difference persists until the end of the century. Historical forcing on the other hand has a significant degree of predictive power of past temperature evolution in the models, but is not relevant to the magnitude of temperature change in their future projections. Regional analysis shows a superior predictive power of ECS over TCR during the latter half of the 21st century in areas with slow warming, illustrating that although TCR is a better predictor of warming on a global scale, it does not capture delayed regional feedbacks, or pattern effects. The transient warming at CO _2 quadrupling (T140) is found to be correlated with global mean temperature anomaly for a longer time than TCR, and it also better describes the pattern of regional temperature anomaly at the end of the century. Over the 20th century, there is a weak correlation between total forcing and ECS, contributing to, but not determining, the model agreement with observed warming. ECS and aerosol forcing in the models are not correlated.

[Display omitted] • The role of the Sun in climate change is hotly debated with diverse models. • The Earth's climate likely influenced by the Sun through a variety of physical mechanisms. • Balanced multi-proxy solar records created and their climate effect assessed. • Factors other than direct TSI forcing account for around 80% of the solar influence on the climate. • Important solar-climate mechanisms to be investigated before developing reliable GCMs. The role of the Sun in climate change is hotly debated. Some studies suggest its impact is significant, while others suggest it is minimal. The Intergovernmental Panel on Climate Change (IPCC) supports the latter view and suggests that nearly 100% of the observed surface warming from 1850–1900 to 2020 is due to anthropogenic emissions. However, the IPCC's conclusions are based solely on computer simulations made with global climate models (GCMs) forced with a total solar irradiance (TSI) record showing a low multi-decadal and secular variability. The same models also assume that the Sun affects the climate system only through radiative forcing – such as TSI – even though the climate could also be affected by other solar processes. In this paper I propose three "balanced" multi-proxy models of total solar activity (TSA) that consider all main solar proxies proposed in scientific literature. Their optimal signature on global and sea surface temperature records is assessed together with those produced by the anthropogenic and volcanic radiative forcing functions adopted by the CMIP6 GCMs. This is done by using a basic energy balance model calibrated with a differential multi-linear regression methodology, which allows the climate system to respond to the solar input differently than to radiative forcings alone, and to evaluate the climate's characteristic time-response as well. The proposed methodology reproduces the results of the CMIP6 GCMs when their original forcing functions are applied under similar physical conditions, indicating that, in such a scenario, the likely range of the equilibrium climate sensitivity (ECS) could be 1.4 °C to 2.8 °C, with a mean of 2.1 °C (using the HadCRUT5 temperature record), which is compatible with the low-ECS CMIP6 GCM group. However, if the proposed solar records are used as TSA proxies and the climatic sensitivity to them is allowed to differ from the climatic sensitivity to radiative forcings, a much greater solar impact on climate change is found, along with a significantly reduced radiative effect. In this case, the ECS is found to be 0.9–1.8 °C, with a mean of around 1.3 °C. Lower ECS ranges (up to 20%) are found using HadSST4, HadCRUT4, and HadSST3. The result also suggests that at least about 80% of the solar influence on the climate may not be induced by TSI forcing alone, but rather by other Sun-climate processes (e.g., by a solar magnetic modulation of cosmic ray and other particle fluxes, and/or others), which must be thoroughly investigated and physically understood before trustworthy GCMs can be created. This result explains why empirical studies often found that the solar contribution to climate changes throughout the Holocene has been significant, whereas GCM-based studies, which only adopt radiative forcings, suggest that the Sun plays a relatively modest role. [ABSTRACT FROM AUTHOR]

The Earth's climate sensitivity to radiative forcing remains a key source of uncertainty in future warming projections. There is a growing realization in recent literature that research must go beyond an equilibrium and CO2‐only viewpoint, toward considering how climate sensitivity will evolve over time in response to anthropogenic and natural radiative forcing from multiple sources. Here the transient behavior of climate sensitivity is explored using a modified energy balance model, in which multiple climate feedbacks evolve independently over time to multiple sources of radiative forcing, combined with constraints from observations and from the Climate Model Intercomparison Project phase 5 (CMIP5). First, a large initial ensemble of 107 simulations is generated, with a distribution of climate feedback strengths from subannual to 102‐year timescales constrained by the CMIP5 ensemble, including the Planck feedback, the combined water vapor lapse rate feedback, snow and sea ice albedo feedback, fast cloud feedbacks, and the cloud response to sea surface temperature adjustment feedback. These 107 simulations are then tested against observational metrics representing decadal trends in warming, heat and carbon uptake, leaving only 4.6 × 103 history‐matched simulations consistent with both the CMIP5 ensemble and historical observations. The results reveal an annual timescale climate sensitivity of 2.1 °C (ranging from 1.6 to 2.8 °C at 95% uncertainty), rising to 2.9 °C (from 1.9 to 4.6 °C) on century timescales. These findings provide a link between lower estimates of climate sensitivity, based on the current transient state of the climate system, and higher estimates based on long‐term behavior of complex models and palaeoclimate evidence. Plain Language Summary: The Earth's climate sensitivity is a measure of how much the average surface temperature will increase if atmospheric carbon dioxide levels are doubled. There is currently a wide variation in estimates of the Earth's climate sensitivity at equilibrium, from low estimates around 1.5 °C to high estimates around 4.5 °C. Many different climate processes affect the value of the climate sensitivity, for example the responses to surface warming of clouds, atmospheric water vapor, and changes in the reflectivity of Earth's surface as snow and ice melt. These processes occur on different timescales, for example it takes days for water vapor to change in the atmosphere, but much longer to melt a large ice sheet. This study applies a range of observational constraints to climate model simulations in order to constrain the Earth's climate sensitivity, considering how the climate sensitivity varies on different timescales. A best estimate for climate sensitivity is found to be 2.1 °C (with uncertainty ranging from 1.6 to 2.8 °C) over yearly timescales. However, climate sensitivity increases to 2.9 °C (ranging from 1.9 to 4.6 °C) on century timescales, affecting future anthropogenic warming. Key Points: The climate sensitivity response varies with response timescaleThe climate sensitivity response on annual timescales is 2.1 °C, and between 1.6 and 2.8 ° at 95% uncertaintyThe climate sensitivity response on century timescales is 2.9 °C and between 1.9 and 4.6 ° at 95% uncertainty [ABSTRACT FROM AUTHOR]

Abstract: Estimating the equilibrium climate sensitivity (ECS; the equilibrium warming in response to a doubling of CO2) from observations is one of the big problems in climate science. Using observations of interannual climate variations covering the period 2000 to 2017 and a model‐derived relationship between interannual variations and forced climate change, we estimate that ECS is likely 2.4–4.6 K (17–83% confidence interval), with a mode and median value of 2.9 and 3.3 K, respectively. This analysis provides no support for low values of ECS (below 2 K) suggested by other analyses. The main uncertainty in our estimate is not observational uncertainty but rather uncertainty in converting observations of short term, mainly unforced climate variability to an estimate of the response of the climate system to long‐term forced warming. [ABSTRACT FROM AUTHOR]

Despite the increasing sophistication of climate models, the amount of surface warming expected from a doubling of atmospheric CO2 (equilibrium climate sensitivity) remains stubbornly uncertain, in part because of differences in how models simulate the change in global albedo due to clouds (the shortwave cloud feedback). Here, model differences in the shortwave cloud feedback are found to be closely related to the spatial pattern of the cloud contribution to albedo (α) in simulations of the current climate: high-feedback models exhibit lower (higher) α in regions of warm (cool) sea-surface temperatures, and therefore predict a larger reduction in global-mean α as temperatures rise and warm regions expand. The spatial pattern of α is found to be strongly predictive (r=0.84) of a model’s global cloud feedback, with satellite observations indicating a most-likely value of 0.58±0.31 Wm-2 K-1 (90% confidence). This estimate is higher than the model-average cloud feedback of 0.43 Wm-2 K-1, with half the range of uncertainty. The observational constraint on climate sensitivity is weaker but still significant, suggesting a likely value of 3.68 ± 1.30 K (90% confidence), which also favors the upper range of model estimates. These results suggest that uncertainty in model estimates of the global cloud feedback may be substantially reduced by ensuring a realistic distribution of clouds between regions of warm and cool SSTs in simulations of the current climate. [ABSTRACT FROM AUTHOR]

Abstract: Cloud phase improvements in a state‐of‐the‐art climate model produce a large 1.5 K increase in equilibrium climate sensitivity (ECS, the surface warming in response to instantaneously doubled CO2) via extratropical shortwave cloud feedbacks. Here we show that the same model improvements produce only a small surface warming increase in a realistic 21st century emissions scenario. The small 21st century warming increase is attributed to extratropical ocean heat uptake. Southern Ocean mean‐state circulation takes up heat while a slowdown in North Atlantic circulation acts as a feedback to slow surface warming. Persistent heat uptake by extratropical oceans implies that extratropical cloud biases may not be as important to 21st century warming as biases in other regions. Observational constraints on cloud phase and shortwave radiation that produce a large ECS increase do not imply large changes in 21st century warming. [ABSTRACT FROM AUTHOR]

The degree of physical-biogeochemical equilibration of the climate system determines for how long global warming will continue after anthropogenic CO _2 emissions have ceased. The physical part of this equilibration process is quantified by the realized warming fraction (RWF), but RWF estimates differ strongly between different climate models. Here we analyze the RWF spread and its physical causes in three model ensembles: 1. an ensemble of comprehensive climate models, 2. an ensemble of reduced-complexity models, and 3. an observationally constrained parameter ensemble of the Bern3D-LPX reduced-complexity model. We show that RWF is generally lower in models with higher equilibrium climate sensitivity. The RWF uncertainty from applying different extrapolation methods for climate sensitivity is substantial, but smaller than the inter-model spread in the three ensembles. We decompose the inter-model spread of RWF using a diagnostic global energy balance model, to compare the spread contribution by the climate sensitivity to contributions by other physical quantities: the efficiency and efficacy of ocean heat uptake, and the effective radiative forcing. In the ensembles of the comprehensive climate models and the Bern3D-LPX model, the spread of the RWF is mostly determined by the spread of the climate sensitivity; for the reduced-complexity models, the spread contribution by the ocean heat uptake efficiency is dominant. Compared to the comprehensive models, the reduced-complexity models have a lower range of climate sensitivities and lower, more unitary ocean heat uptake efficacies, resulting in higher RWF. However, by tuning such models to higher climate sensitivities, they can also achieve RWF values in the lower range of comprehensive models, as demonstrated for Bern3D-LPX. This suggests that reduced-complexity models remain useful tools for future climate change projections, but should employ a range of climate sensitivity tunings to account for the uncertainty in both the long-term warming and the RWF.

The correct understanding of the transient response to external radiative perturbation is important for the interpretation of observed climate change, the prediction of near-future climate change, and committed warming under climate stabilization scenarios, as well as the estimation of equilibrium climate sensitivity based on observation data. It has been known for some time that the radiative damping rate per unit of global mean surface temperature increase varies with time, and this inconstancy affects the transient response. Knowledge of the equilibrium response alone is insufficient, but understanding the transient response of the global mean surface temperature has made rapid progress. The recent progress accompanies the relatively new concept of the efficacies of ocean heat uptake and forcing. The ocean heat uptake efficacy associates the temperature response induced by ocean heat uptake with equilibrium temperature response, and the efficacy of forcing compares the temperature response caused by non-CO forcing with that by CO forcing. In this review article, recent studies on these efficacies are discussed, starting from the classical global feedback framework and basis of the transient response. An attempt is made to structure different studies that emphasize different aspects of the transient response and to stress the relevance of those individual studies. The implications on the definition and computation of forcing and on the estimation of the equilibrium response in climate models are also discussed. Along with these discussions, examples are provided with MIROC climate model multi-millennial simulations. [ABSTRACT FROM AUTHOR]

Uncertainty of climate sensitivity is one of the critical issues that may affect climate response strategies. Whereas the equilibrium climate sensitivity (ECS) was specified as 2-4.5 °C with the best estimate of 3 °C in the 4th Assessment Report of IPCC, it was revised to 1.5-4.5 °C in the 5th Assessment Report. The authors examined the impact of a difference in ECS assuming a best estimate of 2.5 °C, instead of 3 °C. The current pledges of several countries including the U.S., EU and China on emission reductions beyond 2020 are not on track for the 2 °C target with an ECS of 3 °C but are compatible with the target with an ECS of 2.5 °C. It is critically important for policymakers in Paris to know that they are in a position to make decisions under large uncertainty of ECS. [ABSTRACT FROM AUTHOR]

We analyzed a dataset from an experiment of an earth system model of intermediate complexity, focusing on the change in transient climate response to cumulative carbon emissions (TCRE) after atmospheric CO _2 concentration was stabilized in the Representative Concentration Pathway (RCP) 4.5. We estimated the TCRE in 2005 at 0.3–2.4 K/TtC for an unconstrained case and 1.1–1.7 K/TtC when constrained with historical and present-day observational data, the latter result being consistent with other studies. The range of TCRE increased when the increase of CO _2 concentration was moderated and then stabilized. This is because the larger (smaller) TCRE members yield even greater (less) TCRE. An additional experiment to assess the equilibrium state revealed significant changes in temperature and cumulative carbon emissions after 2300. We also found that variation of land carbon uptake is significant to the total allowable carbon emissions and subsequent change of the TCRE. Additionally, in our experiment, we revealed that equilibrium climate sensitivity (ECS), one of the 12 parameters perturbed in the ensemble experiment, has a strong positive relationship with the TCRE at the beginning of the stabilization and its subsequent change. We confirmed that for participant models in the Coupled Model Intercomparison Project Phase 5, ECS has a strong positive relationship with TCRE. For models using similar experimental settings, there is a positive relationship with TCRE for the start of the period of stabilization in CO _2 concentration, and rate of change after stabilization. The results of this study are influential regarding the total allowable carbon emissions calculated from the TCRE and the temperature increase set as the mitigation target.

The article discusses the use of a multidimensional kriging method, called an atmosphere ocean general circulation model (AOGCM), to analyze climate model experiments. The method was used to analyze a computer experiment with the Massachusetts Institute of Technology (MIT) two-dimensional (2D) climate model. Results have shown that the method could be uses as an exploratory tool in climate science.

The last-generation CMIP6 global circulation models (GCMs) are currently used to interpret past and future climatic changes and to guide policymakers, but they are very different from each other; for example, their equilibrium climate sensitivity (ECS) varies from 1.83 to 5.67 °C (IPCC AR6, 2021). Even assuming that some of them are sufficiently reliable for scenario forecasts, such a large ECS uncertainty requires a pre-selection of the most reliable models. Herein the performance of 38 CMIP6 models are tested in reproducing the surface temperature changes observed from 1980–1990 to 2011–2021 in three temperature records: ERA5-T2m, ERA5-850mb, and UAH MSU v6.0 Tlt. Alternative temperature records are briefly discussed but found to be not appropriate for the present analysis because they miss data over large regions. Significant issues emerge: (1) most GCMs overestimate the warming observed during the last 40 years; (2) there is great variability among the models in reconstructing the climatic changes observed in the Arctic; (3) the ocean temperature is usually overestimated more than the land one; (4) in the latitude bands 40° N–70° N and 50° S–70° S (which lay at the intersection between the Ferrel and the polar atmospheric cells) the CMIP6 GCMs overestimate the warming; (5) similar discrepancies are present in the east-equatorial pacific region (which regulates the ENSO) and in other regions where cooling trends are observed. Finally, the percentage of the world surface where the (positive or negative) model-data discrepancy exceeds 0.2, 0.5 and 1.0 °C is evaluated. The results indicate that the models with low ECS values (for example, 3 °C or less) perform significantly better than those with larger ECS. Therefore, the low ECS models should be preferred for climate change scenario forecasts while the other models should be dismissed and not used by policymakers. In any case, significant model-data discrepancies are still observed over extended world regions for all models: on average, the GCM predictions disagree from the data by more than 0.2 °C (on a total mean warming of about 0.5 °C from 1980–1990 to 2011–2021) over more than 50% of the global surface. This result suggests that climate change and its natural variability remain poorly modeled by the CMIP6 GCMs. Finally, the ECS uncertainty problem is discussed, and it is argued (also using semi-empirical climate models that implement natural oscillations not predicted by the GCMs) that the real ECS could be between 1 and 2 °C, which implies moderate warming for the next decades. [ABSTRACT FROM AUTHOR]