How low clouds respond to warming constitutes a key uncertainty for climate projections. Here we observationally constrain low-cloud feedback through a controlling factor analysis based on ridge regression. We find a moderately positive global low-cloud feedback (0.45 W m-2 ${\mathrm{m}}<^>{-2}$ K-1 ${\mathrm{K}}<^>{-1}$, 90% range 0.18-0.72 W m-2 ${\mathrm{m}}<^>{-2}$ K-1 ${\mathrm{K}}<^>{-1}$), about twice the mean value (0.22 W m-2 ${\mathrm{m}}<^>{-2}$ K-1 ${\mathrm{K}}<^>{-1}$) of 16 models from the Coupled Model Intercomparison Project. We link this discrepancy to a pervasive model mean-state bias: models underestimate the low-cloud response to warming because (a) they systematically underestimate present-day tropical marine low-cloud amount, and (b) the low-cloud sensitivity to warming is proportional to this present-day low-cloud amount. Our results hence highlight the importance of reducing model biases in both the mean state of clouds and their sensitivity to environmental factors for accurate climate change projections. Low clouds have a large impact on climate by reflecting a portion of incoming sunlight back to space. Hence any future changes in clouds under global warming could amplify or dampen climate change-a phenomenon known as "cloud feedback." Climate models however disagree on future low-cloud changes, resulting in large uncertainty in future global-warming projections. Here we perform a statistical analysis of global satellite observations of low clouds, using observed co-variations between clouds and meteorology to constrain the feedback simulated by climate models. We find evidence of an amplifying feedback by low clouds, stronger than simulated by most climate models. We link this discrepancy to a low-cloud deficit across wide swathes of the tropical oceans characterized by abundant low cloud cover in observations. Thus to reduce climate projection uncertainty, we propose it is important to understand and mitigate the low-cloud deficit in climate models. We implement a statistical learning-based analysis to constrain low-cloud feedback, with strong out-of-sample predictive skill The observationally constrained global-mean feedback is about double the mean value from an ensemble of 16 climate models This discrepancy is consistent with a pervasive underestimate of mean-state tropical marine low-cloud amount in the models

The change in global mean surface temperature is a crucial and broadly used indicator of the evolution of climate change. Any decadal scale changes in warming rate are however obfuscated by internal variability. Here we show that the surface temperature increase through the recent La Nina influenced years (2022) is consistent with the 50-year trend of 0.18 °C/decade. We use an Earth System Model based tool to filter out modulations to the warming rate by sea-surface temperature patterns and find consistent warming rates in four major global temperature data series. However, we also find clear indications, in all observational series, of a step-up in warming rate since around 1990. CMIP6 models generally do not capture this observed combination of long-term warming rate and recent increase.

Abstract The magnitude of global surface temperature change in response to unit radiative forcing depends on the type and magnitude of forcing agent—a concept known as a “forcing efficacy.” However, the mechanisms behind the forcing efficacy are still unclear. In this study, we perform a set of simulations using CESM1 to calculate the efficacy of 10 different forcing agents defined in terms of fixed‐SST effective radiative forcing, and then use a Green's function approach to show that each forcing efficacy can be largely understood in terms of the radiative feedbacks associated with the different surface temperature patterns induced by the forcing agents (a pattern effect). We also quantify how the state dependence of feedbacks on global mean surface temperature anomalies impacts forcing efficacies. The results show that the forcing efficacy can be well reconstructed with a combination of pattern effect and state dependence.

The rate of global surface warming is crucial for tracking progress towards global climate targets, but is strongly influenced by interannual-to-decadal variability, which precludes rapid detection of the temperature response to emission mitigation. Here we use a physics based Green's function approach to filter out modulations to global mean surface temperature from sea-surface temperature (SST) patterns, and show that it results in an earlier emergence of a response to strong emissions mitigation. For observed temperatures, we find a filtered 2011-2020 surface warming rate of 0.24 °C per decade, consistent with long-term trends. Unfiltered observations show 0.35 °C per decade, partly due to the El Nino of 2015-2016. Pattern filtered warming rates can become a strong tool for the climate community to inform policy makers and stakeholder communities about the ongoing and expected climate responses to emission reductions, provided an effort is made to improve and validate standardized Green's functions.

As a step towards understanding the fundamental drivers of polar climate change, we evaluate contributions to polar warming and its seasonal and hemispheric asymmetries in Coupled Model Intercomparison Project phase 6 (CMIP6) as compared with CMIP5. CMIP6 models broadly capture the observed pattern of surface- and winter-dominated Arctic warming that has outpaced both tropical and Antarctic warming in recent decades. For both CMIP5 and CMIP6, CO2 quadrupling experiments reveal that the lapse-rate and surface albedo feedbacks contribute most to stronger warming in the Arctic than the tropics or Antarctic. The relative strength of the polar surface albedo feedback in comparison to the lapse-rate feedback is sensitive to the choice of radiative kernel, and the albedo feedback contributes most to intermodel spread in polar warming at both poles. By separately calculating moist and dry atmospheric heat transport, we show that increased poleward moisture transport is another important driver of Arctic amplification and the largest contributor to projected Antarctic warming. Seasonal ocean heat storage and winter-amplified temperature feedbacks contribute most to the winter peak in warming in the Arctic and a weaker winter peak in the Antarctic. In comparison with CMIP5, stronger polar warming in CMIP6 results from a larger surface albedo feedback at both poles, combined with less-negative cloud feedbacks in the Arctic and increased poleward moisture transport in the Antarctic. However, normalizing by the global-mean surface warming yields a similar degree of Arctic amplification and only slightly increased Antarctic amplification in CMIP6 compared to CMIP5.

We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, 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.

Ground‐based observations from three middle‐ and high‐latitude sites managed by the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program are used to determine the sensitivity of the low‐cloud optical depth to temperature and to test whether observations support mechanisms previously proposed to affect the optical depth feedback. Analysis of cloud optical depth retrievals support previous satellite findings that the optical depth decreases or stays constant with increases in temperature when the cloud is warm but increases when the cloud is cold. The cloud liquid water path sensitivity to warming largely explains the optical depth sensitivity at all sites. Mechanisms examined in this study involve the temperature dependence of (a) the moist‐adiabatic lapse rate, (b) cloud phase partitioning, (c) drying efficiency of cloud top mixing, (d) cloud top inversion strength, and (e) boundary layer decoupling. Mechanism (a) is present across all clouds and explains 30% to 50% of the increase in liquid water path with warming at temperatures below 0 °C. However, the cloud's adiabaticity, the ratio between the liquid water path and its theoretical maximum, is at least as important and determines how the liquid water path sensitivity to temperature varies with temperature. At temperatures below 0 °C, the adiabaticity increases with warming, and the data support mechanism (b). At warmer temperatures, the adiabaticity decreases with warming, overwhelming mechanism (a) and resulting in the liquid water path decreasing with warming. This adiabaticity decrease arises primarily because of mechanism (d), and to a lesser degree because of mechanism (e). No evidence is found supporting mechanism (c).

The increase in cloud optical depth with warming at middle and high latitudes is a robust cloud feedback response found across all climate models. This study builds on results that suggest the optical depth response to temperature is timescale invariant for low‐level clouds. The timescale invariance allows one to use satellite observations to constrain the models' optical depth feedbacks. Three passive‐sensor satellite retrievals are compared against simulations from eight models from the Atmosphere Model Intercomparison Project (AMIP) of the 5th Coupled Model Intercomparison Project (CMIP5). This study confirms that the low‐cloud optical depth response is timescale invariant in the AMIP simulations, generally at latitudes higher than 40°. Compared to satellite estimates, most models overestimate the increase in optical depth with warming at the monthly and interannual timescales. Many models also do not capture the increase in optical depth with estimated inversion strength that is found in all three satellite observations and in previous studies. The discrepancy between models and satellites exists in both hemispheres and in most months of the year. A simple replacement of the models' optical depth sensitivities with the satellites' sensitivities reduces the negative shortwave cloud feedback by at least 50% in the 40°–70°S latitude band and by at least 65% in the 40°–70°N latitude band. Based on this analysis of satellite observations, we conclude that the low‐cloud optical depth feedback at middle and high latitudes is likely too negative in climate models.

Feedbacks modify an initial warming of the climate system, caused, for example, by increasing carbon dioxide. We discuss here the feedbacks that are of primary importance on decadal timescales: the water vapor feedback, the lapse-rate feedback, the surface albedo feedback, and the cloud feedback. Together, they account for approximately two-thirds of the warming we expect over the twenty-first century. The strongest positive feedback is the water vapor feedback, with the surface albedo and cloud feedbacks being smaller, positive feedbacks. The lapse-rate feedback is a negative feedback that offsets some of the water vapor feedback. The cloud feedback is the most uncertain one, and it is responsible for much of the spread among climate models in predictions of future climate change.

The representation of the effect of tropical deep convective (DC) systems on upper-tropospheric moist processes and outgoing longwave radiation is evaluated in the EC-Earth3, ECHAM6, and CAM5 (Community Atmosphere Model) climate models using satellite-retrieved data. A composite technique is applied to thousands of deep convective systems that are identified using local rain rate maxima in order to focus on the temporal evolution of the deep convective processes in the model and satellite-retrieved data.The models tend to over-predict the occurrence of rain rates that are less than approximate to 3 mm h(-1) compared to Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA). While the diurnal distribution of oceanic rain rate maxima in the models is similar to the satellite-retrieved data, the land-based maxima are out of phase.Despite having a larger climatological mean uppertropospheric relative humidity, models closely capture the satellite-derived moistening of the upper troposphere following the peak rain rate in the deep convective systems. Simulated cloud fractions near the tropopause are larger than in the satellite data, but the ice water contents are smaller compared with the satellite-retrieved ice data. The models capture the evolution of ocean-based deep convective systems fairly well, but the land-based systems show significant discrepancies. Over land, the diurnal cycle of rain is too intense, with deep convective systems occurring at the same position on subsequent days, while the satellite-retrieved data vary more in timing and geographical location.Finally, simulated outgoing longwave radiation anomalies associated with deep convection are in reasonable agreement with the satellite data, as well as with each other. Given the fact that there are strong disagreements with, for example, cloud ice water content, and cloud fraction, between the models, this study supports the hypothesis that such agreement with satellite-retrieved data is achieved in the three models due to different representations of deep convection processes and compensating errors.

Cirrus clouds are not only important in determining the current climate but also play an important role in climate change and variability. Analysis of satellite observations shows that the amount and altitude of cirrus clouds (cloud optical depth<3.6, cloud top pressure<440hPa) increase in response to interannual surface warming. Using cirrus cloud radiative kernels, the magnitude of the interannual cirrus feedback is estimated to be 0.200.21W/m(2)/degrees C, which represents an important component of the cloud feedback. Thus, cirrus clouds are likely to act as a positive feedback on interannual climate fluctuations, by reducing the Earth's ability to radiate longwave radiation to space in response to planetary surface warming. Most of the cirrus feedback comes from increasing cloud amount in the tropical tropopause layer (TTL) and subtropical upper troposphere.

An earlier method to determine the mean response of upper-tropospheric water to localised deep convective systems (DC systems) is improved and applied to the EC-Earth climate model. Following Zelinka and Hartmann (2009), several fields related to moist processes and radiation from various satellites are composited with respect to the local maxima in rain rate to determine their spatio-temporal evolution with deep convection in the central Pacific Ocean. Major improvements to the earlier study are the isolation of DC systems in time so as to prevent multiple sampling of the same event, and a revised definition of the mean background state that allows for better characterisation of the DC-system-induced anomalies. The observed DC systems in this study propagate westward at ~4 m s−1. Both the upper-tropospheric relative humidity and the outgoing longwave radiation are substantially perturbed over a broad horizontal extent and for periods >30 h. The cloud fraction anomaly is fairly constant with height but small maximum can be seen around 200 hPa. The cloud ice water content anomaly is mostly confined to pressures greater than 150 hPa and reaches its maximum around 450 hPa, a few hours after the peak convection. Consistent with the large increase in upper-tropospheric cloud ice water content, albedo increases dramatically and persists about 30 h after peak convection. Applying the compositing technique to EC-Earth allows an assessment of the model representation of DC systems. The model captures the large-scale responses, most notably for outgoing longwave radiation, but there are a number of important differences. DC systems appear to propagate eastward in the model, suggesting a strong link to Kelvin waves instead of equatorial Rossby waves. The diurnal cycle in the model is more pronounced and appears to trigger new convection further to the west each time. Finally, the modelled ice water content anomaly peaks at pressures greater than 500 hPa and in the upper troposphere between 250 hPa and 500 hPa, there is less ice than the observations and it does not persist as long after peak convection. The modelled upper-tropospheric cloud fraction anomaly, however, is of a comparable magnitude and exhibits a similar longevity as the observations.