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Cloud feedback remains a significant source of uncertainty in the evaluation of ongoing changes within the Earth’s climate system. The seasonal cycle of cloud cover has crucial effects on natural ecosystems and the human society by altering the distribution of radiation budget and regional hydroclimate. However, the spatiotemporal characteristics and underlying mechanisms of this seasonal cycle require further investigation. Using high-resolution cloud datasets covering 1950 − 2022, we quantified variations of cloud cover seasonality (CCS) in China and explored its spatiotemporal characteristics and driving factors at the national scale. CCS was found to be smaller primarily in southeastern China, indicating a small range of the annual cycle in this region. In recent decades, interannual variations of CCS exhibited a significant declining trend, greatly contributing to similar change trends observed in the seasonality of precipitation and surface radiation (p < 0.001). Wavelet power spectrum analysis revealed a significant periodicity of 2 − 8 years in CCS variations, corresponding to the dynamics of large-scale ocean–atmosphere interactions. Specifically, the El Niño − Southern Oscillation (ENSO) dominates interannual variations of CCS, with lower (higher) CCS observed during the El Niño (La Niña) phase. The CCS − ENSO relationship is non-stationary and can be intensified by the multidecadal strengthening of ENSO variability, which has been taking place since the late 1970s. Under global warming, CCS in China will likely continue to decline with a partial mitigation by the reduction of CO2 and aerosols over the next few decades. This introduces greater uncertainty in assessing hydrological extremes and photovoltaic power potential in China. [ABSTRACT FROM AUTHOR]

Global climate model (GCM) projections of future climate are uncertain largely due to a persistent spread in cloud feedback. This is despite efforts to reduce this model uncertainty through a variety of emergent constraints (ECs); with several studies suggesting an important role for present‐day biases in clouds. Here, we use three generations of GCMs to assess the value of climatological cloud metrics for constraining uncertainty in cloud feedback. We find that shortwave cloud radiative properties across the Southern Hemisphere extratropics are most robustly correlated with tropical cloud feedback (TCF). Using this relationship in conjunction with observations, we produce an EC that yields a TCF value of 0.52 ± 0.34 W/m2/K, which equates to a 34% reduction in uncertainty. Thus, we show that climatological cloud properties can be used to reduce uncertainty in how clouds will respond to future warming. Plain Language Summary: Different global climate models exhibit large variability in how clouds across the tropics will respond to future warming. This is largely due to the complexity and diversity of responses that differing cloud types may experience under warming. A long‐term goal of the community has been to narrow this disagreement between different models. Over the past 15 years, several studies have proposed ways in which the variability in future cloud changes might be related to errors in how these models represent present‐day properties. Here, we use three collections of models to show that variability in tropical cloud changes is closely tied to shortwave cloud radiative properties across the Southern Ocean. We then use this intermodel relationship along with observations to produce a best estimate of cloud feedback across the tropics. Key Points: We find a relationship between tropical cloud feedback and mean‐state biases in Southern Hemisphere extratropical cloud propertiesThis intermodel relationship is found to be present in three different ensembles of global climate models, a sign of robustnessThis relationship suggests a likely tropical cloud feedback value of 0.52 ± 0.34 W/m2/K, which equates to a 34% reduction in uncertainty [ABSTRACT FROM AUTHOR]

Cloud feedback is the largest source of uncertainty in climate sensitivity. This feedback is generally discussed in terms of radiative flux at the top of the atmosphere, but the vertical distribution of the contribution of cloud changes to the top‐of‐atmosphere cloud feedback should be discussed to understand the feedback in greater detail. We have developed a simple analysis method called "vertical profile analysis of cloud feedback," which simply uses radiative flux data from each level of the model. The analysis is applied for typical cloud regimes and the results are discussed together with cloud fraction change profiles. The advantages and disadvantages of the vertical profile analysis, compared with the commonly used International Satellite Cloud Climatology Project (ISCCP) histogram kernel method, are discussed. Our analysis has the advantage of resolving the detailed vertical profiles of the contribution to the top‐of‐atmosphere cloud feedback from the changes in cloud regimes associated with warming, such as reduced cloud cover and upward shifts of the cloud layer in subtropical low‐cloud regions as well as the increased height of the melting layer in tropical deep convection. The main disadvantage is that the vertical profile analysis cannot represent the feedback components sorted by cloud optical thickness as in the ISCCP histogram kernel method. Plain Language Summary: Rising temperature caused by global warming will depend to a large extent on how clouds will change in the future climate. It is therefore important to know in detail how clouds and radiation will change in the future, not only horizontally but also vertically, in order to understand the mechanism. We have developed a simple but useful method for analyzing the vertical profile of changes in radiation and clouds. This detailed information is useful for understanding the mechanism of changes in clouds and radiation. We have compared this analysis method with the conventional method and discussed the advantages and disadvantages of the two methods in detail, based on simulation results. Although our vertical profile analysis provides more detailed vertical profile information, both methods can be used effectively, depending on the purpose of the analysis. Key Points: A simple analysis method is proposed to obtain a detailed vertical profile of cloud feedbackApplications are shown for typical regimes including stratocumulus, shallow convection, mid‐latitude clouds, and tropical deep convectionThe advantages and disadvantages of this analysis method relative to the conventional method are presented [ABSTRACT FROM AUTHOR]

ABSTRACT Under global warming, cloud change and its radiative feedback have often been considered to evolve from thermodynamic processes; however, cloud feedback may also force sea surface temperature to trigger such air–sea interactions. Due to complex cloud physics in air–sea coupling, this contributes to the surface warming pattern formation with significant uncertainty. Here we develop a novel overriding technique for climate projections that substitutes specific variables in control runs to isolate such feedback mechanisms, decoupling thermodynamic, dynamical and radiative responses of the surface ocean to the atmosphere. We apply this to the Community Earth System Model version 2 (CESM2) and perform a series of 150‐year simulations with 1% CO2 increase per year (1pctCO2). In real time, the key variables under 1pctCO2 are replaced with those from the current climate, such as downwelling shortwave radiation, wind speed in latent and sensible heat and wind stress. These experiments provide monthly output of global distributions including surface temperatures, winds and precipitation, with a spatial resolution of 1.9° × 2.5° in latitude and longitude and 32 levels for the atmosphere and of ~1° and 60 layers designated as gx1v7 for the ocean. This open access dataset for partial air–sea coupling under climate change can help understand the tropical and polar warming patterns and quantify the relative contributions of forcing and triggering mechanisms.

At an energy flux imbalance of about 1 W m−2, the ocean stores 90% of the heat accumulating by global warming. However, neither the causes of this nor the responsible geophysical processes are sufficiently well understood. More detailed investigations of the different phenomena contributing to the oceanic energy balance are warranted. Here, the role of low-level marine clouds in the air–sea interaction is analysed. TEOS-10, the International Thermodynamic Equation of State of Seawater—2010, is exploited for a rigorous thermodynamic description of the climatic trends in the lifted condensation level (LCL) of the marine troposphere. Rising sea surface temperature (SST) at a constant relative humidity (RH) is elevating marine clouds, cooling the cloud base, and reducing downward thermal radiation. This LCL feedback effect is negative and counteracts ocean warming. At the current global mean SST of about 292 K, the net radiative heat flux from the ocean surface to the LCL cloud base is estimated to be 24 W m−2. Per degree of SST increase, this net flux is expected to be enhanced by almost 0.5 W m−2. The climatic LCL feedback effect is relevant for the ocean’s energy balance and may be rigorously thermodynamically modelled in terms of TEOS-10 equations. LCL height may serve as a remotely measured, sensitive estimate for the sea surface’s relative fugacity, or conventional relative humidity.

At an energy flux imbalance of about 1 W m−2, the ocean stores 90% of the heat accumulating by global warming. However, neither the causes of this nor the responsible geophysical processes are sufficiently well understood. More detailed investigations of the different phenomena contributing to the oceanic energy balance are warranted. Here, the role of low-level marine clouds in the air–sea interaction is analysed. TEOS-10, the International Thermodynamic Equation of State of Seawater—2010, is exploited for a rigorous thermodynamic description of the climatic trends in the lifted condensation level (LCL) of the marine troposphere. Rising sea surface temperature (SST) at a constant relative humidity (RH) is elevating marine clouds, cooling the cloud base, and reducing downward thermal radiation. This LCL feedback effect is negative and counteracts ocean warming. At the current global mean SST of about 292 K, the net radiative heat flux from the ocean surface to the LCL cloud base is estimated to be 24 W m−2. Per degree of SST increase, this net flux is expected to be enhanced by almost 0.5 W m−2. The climatic LCL feedback effect is relevant for the ocean's energy balance and may be rigorously thermodynamically modelled in terms of TEOS-10 equations. LCL height may serve as a remotely measured, sensitive estimate for the sea surface's relative fugacity, or conventional relative humidity. [ABSTRACT FROM AUTHOR]

Global surface temperature short‐term trends fluctuate between cooling and fast‐warming under the combined action of external forcing and internal variability, significantly influencing the detectability of near‐term climate change. A key driver of these variations is anthropogenic aerosols (AAs), which have undergone a non‐monotonic evolution with rapid reduction in recent decades. However, their reduction is projected to decelerate under a high carbon emission scenario, yet the impact on surface temperature trends remains unknown. Here, using initial‐perturbation large ensembles, we find that future slowdown in AA reduction over Europe and North America expedites the subpolar North Atlantic surface warming by intensifying the Atlantic meridional overturning circulation. Further, it accelerates the South Indian Ocean and Southern Ocean surface warming through positive low‐cloud feedback and oceanic dynamical adjustment, triggered by the poleward migration of westerlies under interhemispheric energy constraint. These AA‐driven warmings exacerbate greenhouse warming, significantly enhancing the detectability of local decadal warming trends. Plain Language Summary: Global surface temperature rises non‐monotonically, exhibiting phases of accelerated warming, warming slowdown, and transitions between warming and cooling. Anthropogenic aerosols (AAs) are a key driver of historical trend variations, making human‐induced trends more detectable. However, AAs follow a nonlinear trajectory with a rapid decline over Europe and North America in recent decades, and such reduction is projected to slow down in the future. The saturation of AA reduction signifies a diminishing proportion of anthropogenic forcings, which were previously considered unrelated to future global warming. Nevertheless, based on large ensembles of climate change simulations, we reveal that this projected slowdown in AA reduction could significantly accelerate surface warming in the subpolar North Atlantic, South Indian and Southern Oceans. This is achieved by the strengthening of Atlantic meridional overturning circulation and the coupled oceanic‐atmospheric adjustments triggered by the southward shift of southern westerlies. Those AA‐driven ocean surface warmings exacerbate greenhouse warming, significantly enhancing the predominance of external signals over strong internal noise and thus the detectability of regional human‐induced decadal warming trends. It suggests that upholding stringent AA mitigation policies is imperative for effectively mitigating the risk of severe ocean warming. Key Points: Recent rapid reduction of anthropogenic aerosols (AA) over the northern extratropics would slow down under a high carbon emission scenarioSlower AA reduction accelerates subpolar North Atlantic surface warming by intensifying Atlantic meridional overturning circulationSlower AA reduction accelerates South Indian and Southern Ocean warming via positive low‐cloud feedback and oceanic dynamical adjustment [ABSTRACT FROM AUTHOR]

The surface temperature‐mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel "double decomposition method" that employs the "cloud radiative kernel" and "cloud regime" concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low‐ and mid‐level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm‐track clouds to thinner oceanic low‐level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid‐ and low‐level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies. Plain Language Summary: Climate models project that rising levels of greenhouse gas emissions will raise Earth's global mean surface air temperature. However, the amount of warming to be expected remains highly uncertain. A main underlying cause for this uncertainty is the representation of atmospheric clouds in these models. In particular, the response of clouds to global warming, known as the "cloud feedback" over the Southern Ocean has been linked to higher levels of projected warming. To address the uncertainty, we apply a novel method to two satellite data sets. This method teases apart contributions from changing cloud properties such as their horizontal spatial coverage and their ability to reflect sunlight, to the cloud feedback, and then further dissects each of these contributions into those arising from changes occurring both within groups of cloud types and changes occurring between these groups. Results suggest that a general shift from thick storm‐track clouds to thinner oceanic low‐level clouds exacerbates warming in the Southern Ocean but are partially counterbalanced by cloud thickening in part due to stronger near‐surface winds in the same region. Our novel methods and results can potentially be used to evaluate climate models and aid in tracing their shortcomings to poorly represented physical processes. Key Points: MODIS satellite observations indicate that in the Southern Ocean the interannual cloud optical depth feedback is slightly positiveMore frequently occurring thin low‐level clouds and fewer storm‐track clouds contribute to a positive feedback in the Southern OceanStrengthened near‐surface winds contribute to a negative low‐level cloud feedback in the Southern Ocean [ABSTRACT FROM AUTHOR]

This study shows that geostationary satellites are critical to estimate the accurate cloud feedback strength over the tropical western Pacific (TWP). Cloud feedback strength was calculated by the simultaneous relation between cloud cover and sea surface temperature (SST) over the TWP [120°E–170°E, 20°S–20°N]. During 2011–2018, the cloud cover was obtained by geostationary earth orbit satellite (GEO) and low‐level earth orbit satellite (LEO) (AGEO, ALEO), and the NOAA's all‐sky SST (To) was weighted with the clear‐sky fraction observed by GEO and LEO (TwGEO; TwLEO). The linear regression coefficients between clouds and SST are very different: −7.93%K−1 (AGEO/TwGEO), −6.94%K−1 (ALEO/TwGEO), −1.35%K−1 (AGEO/TwLEO), −0.69%K−1 (ALEO/TwLEO), −0.02 %K−1 (AGEO/To), and −0.50 %K−1 (ALEO/To). Among these, only the TwGEO values provided a valid cloud feedback signal. This is because GEO's field of view is large enough to simultaneously capture cloud cover over the entire TWP. Plain Language Summary: Geostationary satellites are essential for accurately estimating cloud feedback strength over the tropical western Pacific (TWP). Cloud feedback strength is the change in cloudiness that results from a change in sea surface temperature (SST). When using data from both geostationary and low‐earth orbit satellites, the resulting cloud feedback signals are very different. This is because geostationary satellites have a large enough field of view to capture cloud cover over the entire TWP, while low‐earth orbit satellites do not. Therefore, geostationary satellites are the only reliable source of data for estimating cloud feedback strength over the TWP. This is important because cloud feedback is a major uncertainty in climate models. Key Points: In the tropical western Pacific (TWP), the cloud‐sea surface temperature (SST) relation has been subject to the analysis methods with satellite observationsThe negative relationship is revealed only when the daily SST is weighted with the clear‐sky fraction from a geostationary satelliteThis disparity arises from the capability of geostationary satellites to simultaneously capture a snapshot of the entire TWP area [ABSTRACT FROM AUTHOR]

We conduct a global assessment of the spatial heterogeneity of cloud phase within the temperature range where liquid and ice can coexist. Single‐shot Cloud‐Aerosol Lidar with Orthogonal Polarization lidar retrievals are used to examine cloud phase at scales as fine as 333 m, and horizontal heterogeneity is quantified according to the frequency of switches between liquid and ice along the satellite's path. In the global mean, heterogeneity is greatest between −15 and −4°C with a peak at −5°C, when small patches of ice are prevalent within liquid‐dominated clouds. Heterogeneity "hot spots" are typically found over the extratropical continents, whereas phase is relatively homogeneous over the Southern Ocean and the eastern subtropical ocean basins, where supercooled liquid clouds dominate. Even at a fixed temperature, heterogeneity undergoes a pronounced annual cycle that, in most places, consists of a minimum during autumn or winter and a maximum during spring or summer. Based on this spatial and temporal variability, it is hypothesized that heterogeneity is affected by the availability of ice nucleating particles. These results can be used to improve the representation of subgrid‐scale heterogeneity in general circulation models, which has the potential to reduce longstanding model biases in cloud phase partitioning and radiative fluxes. Plain Language Summary: At temperatures where ice and liquid can coexist within clouds, climate models tend to produce too much ice and too little liquid compared to satellite observations. This bias is likely caused by the assumption that liquid and ice are uniformly mixed, which results in the rapid conversion of liquid to ice for thermodynamic reasons. To reduce this bias, models need to account for the spatial heterogeneity ("patchiness") of liquid and ice that exists in the real atmosphere. The goal of this paper is to quantify this spatial heterogeneity using satellite‐based lidar observations of cloud phase. We find small pockets of ice in liquid‐dominated clouds to be more common than small pockets of liquid in ice‐dominated clouds. The greatest heterogeneity is found over the midlatitude continents, whereas phase is relatively uniform over the Southern Ocean and other maritime regions with extensive low cloud cover. In the mid and high latitudes, cloud phase tends to be more heterogeneous during spring and summer and more homogeneous during autumn and winter. These results can be used in the future to improve model representations of the thermodynamic processes responsible for biases in cloud phase. Key Points: Cloud phase heterogeneity is greatest at −5°C, when small ice patches form in majority‐liquid cloudsCloud phase is relatively homogeneous over the Southern Ocean and heterogeneous over the northern continentsFor a fixed temperature, extratropical phase heterogeneity is generally greatest during local spring and summer [ABSTRACT FROM AUTHOR]

We compared short‐term cloud feedback, defined at the top of the atmosphere (TOA), the atmospheric column (ATM), and the surface (SFC), between observations and models participating in Atmospheric Model Intercomparison Project Phase 6 (AMIP6) for the period 2000–2014. The globally averaged net cloud feedbacks observed at TOA, ATM, and SFC are −0.06 ± 0.63, −0.17 ± 0.70, and 0.11 ± 0.81 W m−2 K−1, respectively. While most models produced TOA cloud feedbacks that agreed with the observations within uncertainty ranges, the intermodel spread at SFC and within ATM was relatively larger. This demonstrates that models are diverse in how their TOA feedback is distributed between ATM and SFC. Because short‐term cloud feedback is mainly driven by El Niño–Southern Oscillation (ENSO), the global‐mean cloud feedback was further decomposed into components from the ENSO and non‐ENSO regions. Results show that cloud feedback in these two regions tends to be inversely related. Compared to observations, almost all models overestimated the longwave cloud feedback in the ENSO region due to the overestimation of cloud amount changes for high‐topped clouds. For these models, it is the offset between deviations in ENSO and non‐ENSO regions that leads to the overall agreement of global mean with observations. Sensitivity tests show that the main conclusions still hold when alternative kernels are used in estimating cloud feedback. Plain Language Summary: The large spread in cloud feedback among climate models contributes to the largest uncertainty in climate sensitivity. There is an urgent need to utilize observational data to validate cloud feedback in climate models. By using a combination of reanalysis data and satellite measurements for the period 2000–2014, results show the observed cloud feedback at TOA, ATM, and SFC is −0.06 ± 0.63, −0.17 ± 0.70, and 0.11 ± 0.81 W m−2 K−1, respectively. Most models produced TOA cloud feedback that was consistent with observations within the uncertainty range, yet there was larger difference at the surface and within the atmosphere, causing some models to produce values outside the uncertainty range. Further study shows that even well‐modeled TOA feedback is due to bias offsetting. The models tend to overestimate longwave cloud feedback in the El Niño–Southern Oscillation (ENSO) region due to an overestimation of high‐topped cloud changes, which is partially offset by an underestimation in the non‐ENSO region, resulting in a global mean that is close to the observed value. Decomposing cloud response and cloud feedback in this way highlights the deficiency in ENSO‐related cloud modeling that would otherwise be hidden by the global average. Key Points: The intermodel spread of the net short‐term cloud feedback is larger at the surface and within the atmosphere than at the top of atmosphereAn inverse relationship between cloud feedbacks in the El Niño–Southern Oscillation (ENSO) and non‐ENSO regions is found in both observations and AMIP6 modelsCompared to observations, models tend to overestimate longwave cloud feedback in ENSO regions due to the overestimation of high‐topped cloud [ABSTRACT FROM AUTHOR]

This study quantifies the contribution of individual cloud feedbacks to the total short‐term cloud feedback in satellite observations over the period 2002–2014 and evaluates how they are represented in climate models. The observed positive total cloud feedback is primarily due to positive high‐cloud altitude, extratropical high‐ and low‐cloud optical depth, and land cloud amount feedbacks partially offset by negative tropical marine low‐cloud feedback. Seventeen models from the Atmosphere Model Intercomparison Project of the sixth Coupled Model Intercomparison Project are analyzed. The models generally reproduce the observed moderate positive short‐term cloud feedback. However, compared to satellite estimates, the models are systematically high‐biased in tropical marine low‐cloud and land cloud amount feedbacks and systematically low‐biased in high‐cloud altitude and extratropical high‐ and low‐cloud optical depth feedbacks. Errors in modeled short‐term cloud feedback components identified in this analysis highlight the need for improvements in model simulations of the response of high clouds and tropical marine low clouds. Our results suggest that skill in simulating interannual cloud feedback components may not indicate skill in simulating long‐term cloud feedback components. Plain Language Summary: Cloud feedback—the radiative response of clouds to changes in temperature—is determined by the contributions from various cloud types and exhibits large uncertainty. Here, we use satellite observations to evaluate how the individual cloud feedback components in response to interannual variability are represented in the latest generation of climate models. The total cloud feedback is positive in the observations, mainly driven by changes in the altitude of high clouds, changes in the cloud cover over land, and changes in the reflectivity of high and low clouds over extratropical regions. The climate models were driven by observed sea‐surface temperature, sea‐ice conditions, and radiative forcing, so they can be directly compared to the observations. We found the models simulate a total cloud feedback that agrees with the observed positive total cloud feedback in general. However, the models consistently overestimate the decrease of cloud amount over land and in oceanic regions of tropical descent and consistently underestimate the increase of high‐cloud altitude and the decrease of extratropical high‐ and low‐cloud optical depth. Models that better simulate cloud feedback in response to short‐term fluctuations do not perform better in simulating long‐term cloud feedback under global warming. Key Points: Individual short‐term cloud feedback components in climate models are assessed by comparing against satellite observationsModel biases are mainly driven by tropical marine low‐cloud, high‐cloud altitude, and extratropical high‐cloud optical depth feedbacksSkill in simulating short‐term cloud feedbacks is not correlated with skill in simulating long‐term cloud feedbacks [ABSTRACT FROM AUTHOR]

The effective climate sensitivity in the Department of Energy's Energy Exascale Earth System Model (E3SM) has decreased from 5.3 K in version 1 to 4.0 K in version 2. This reduction is mainly due to a weaker positive cloud feedback that leads to a stronger negative radiative feedback. Present‐day atmosphere‐only experiments with uniform 4 K sea surface temperature warming are used to separate the contributions of individual model modifications to the reduced cloud feedback. We find that the reduced cloud feedback is mostly driven by changes over the tropical marine low cloud regime, mainly related to a new trigger function for the deep convection scheme and modifications in the cloud microphysics scheme. The new trigger function helps weaken the low cloud reduction by increasing the cloud water detrainment at low levels from deep convection under warming. Changes to the formula of autoconversion rate from liquid to rain and an introduced minimum cloud droplet number concentration threshold in cloud microphysical calculations help sustain clouds against dissipation by suppressing precipitation generation with warming. In the midlatitudes, the increased Wegener‐Bergeron‐Findeisen (WBF) efficiency strongly reduces present‐day liquid water and leads to a stronger negative cloud optical depth feedback. The reduced trade cumulus cloud feedback in v2 is closer to estimates from recent observational and large‐eddy modeling studies but might not be due to the right physical reasons. The reduced mid‐latitude cloud feedback may be more plausible because more realistic present‐day mixed‐phase clouds are produced through the change in the WBF efficiency. Plain Language Summary: Understanding how the Earth responds to greenhouse gas increases is important for climate change research. In the Department of Energy's Energy Exascale Earth System Model, the global temperature response to an abrupt quadrupling of atmospheric carbon dioxide has decreased from 5.3 K in version 1 to 4.0 K in version 2. This reduction is mainly because low‐level clouds over the tropical oceans decrease less as the planet warms in version 2: a weaker amplifying cloud feedback. To understand the reasons behind this reduction, warming simulations were conducted to separate the contributions of individual model changes. We find that the reduced cloud feedback is primarily due to changes in the representation of the vertical movement of air through the depth of the lower atmosphere and of the microscopic properties of clouds. The findings highlight some unexpected impacts on cloud feedback resulting from modifications to the model's physics and emphasize the importance of monitoring and understanding changes in cloud feedback during model development. Key Points: E3SM's effective climate sensitivity is lower in version 2 mainly due to the reduced positive cloud feedback over marine low cloud regionsThe feedback reduction is primarily due to altered cloud microphysical parameters and a new deep convection trigger functionProcess‐level analysis is conducted to understand the impact of these model modifications on cloud feedbacks [ABSTRACT FROM AUTHOR]

Low cloud feedback in global warming projections by climate models is characterized by its positive sign, the mechanism of which is not well understood. Here we propose that the positive sign is primarily caused by the increase in upward longwave radiation from the sea surface. We devise numerical experiments that enable separation of the feedback into components coming from physically distinct causes. Results of these experiments with a climate model indicate that increases in upward longwave radiation from the sea surface cause warming and absolute drying in the boundary layer, leading to the positive low cloud feedback. The absolute drying results from decrease in surface evaporation, and also from decrease in inversion strength which enhances vertical mixing of drier free tropospheric air into the boundary layer. This mechanism is different from previously proposed understanding that positive low cloud feedback is caused by increases in surface evaporation or vertical moisture contrast. Plain Language Summary: We project future climate change induced by atmospheric greenhouse gas increases by conducting numerical simulations using specialized computer codes, namely Global Climate Models. Results of such simulations are characterized by decreases in low cloud with warming at the Earth's surface, which amplifies the warming by reflecting less sunlight back to space and allowing more sunlight to be absorbed at the surface. This amplifying effect, called "positive low cloud feedback," is important because the amount of future warming affects our living and safety. However, the mechanism of the low cloud decreases with warming is not well understood. Here we propose that the low cloud decrease is primarily caused by increase in upward longwave radiation from the sea surface. We devise numerical simulations that enable the separation of the low cloud feedback into components coming from physically distinct causes. Results of the simulations indicate that increases in upward longwave radiation from the sea surface cause warming and drying near the Earth's surface, leading to the low cloud decrease. This mechanism is different from previously proposed understanding that the low cloud decrease is due to increases in sea surface evaporation or vertical moisture contrast. Key Points: The increase in longwave radiation from the sea surface is a leading order cause of the positive low cloud feedback in a climate modelThis increase in longwave radiation leads to warming and drying in the boundary layer, which contributes to the decrease in the low cloudThis mechanism is not associated with increases in surface evaporation or vertical moisture contrast [ABSTRACT FROM AUTHOR]

Tropical upper-level clouds (TUCs) control the radiation budget in a climate system and strongly influence surface temperatures. This study examines global mean surface temperature changes due to the percent change in TUC cover, which is referred to as the tropical upper-level cloud radiative effect (TUCRE, in units of Kelvin per %). We use a radiative-convective equilibrium model that can control both upper- and lower-level cloud layers separately in three idealized regions (extratropics, tropical moist, and tropical dry regions) and two sub-regions (clear-moist and cloudy-moist regions) within the tropical moist regions. In the simulation, tropical reflectivity based on the TUC fraction assumes a primary role in determining the TUCRE. Accurate estimate of the TUCRE requires careful prescriptions according to actual satellite observations. We use the extent of TUC fraction and reflectivity obtained from 18 years (2003–2020) of satellite data on daily MODIS cloud properties. Our results show that the estimated net TUCRE ranges from 0.19 to 0.33 K/%, with a higher TUC fraction leading to higher temperatures (a warming effect) in the climate system. This means that a longwave TUCRE dominates over a shortwave TUCRE. When upper- and lower-level clouds interplay in the model, the range of the TUCRE was greater with a combination of two cloud layers, although all values were positive. The TUCRE is greater by 0.22 to 0.40 K/% when upper- and lower-level clouds are negatively coupled, because the Earth warms due to a decline in the reflectance of solar radiation. When upper- and lower-level clouds are positively coupled, the TUCRE is lower by 0.14 to 0.30 K/%, as less radiation reaches the Earth through combined cloud layers. Finally, we test the sensitivity of the TUCRE with five TUC fractions and 15 combinations of tropical reflectivity. Comparing our results with the TUCREs estimated from climate models will help us understand how TUC cover affects climate, and should greatly reduce uncertainty associated with cloud feedback. [ABSTRACT FROM AUTHOR]

Understanding the cloud feedback (CFB) process remains a critical challenge for climate projection. While previous studies have mainly focused on the globe, the tropics, or the Southern Ocean, this study analyzes the long-term CFB over East Asia (EA) using data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and radiative kernels newly developed. We find that the overall CFBs over EA act to amplify warming in an annual scale, with mean values of 0.66 ± 0.47 and 0.63 ± 0.39 W m−2 K−1 for the SSP2-4.5 and SSP5-8.5 scenarios, respectively, although remarkable seasonal discrepancies. There are two distinct subregions: the positive center over the East Asian monsoon region, primarily due to the significant reduction in low cloud cover; the negative center over the Tibet Plateau, largely owing to the reduced high cloud cover and the ice-to-liquid conversion of cloud phase. Furthermore, the CFB over EA changes from negative to positive in the early 1980s, and then enhance at a rate faster than the global average until the early years of the twenty-first century, which may be linked with the weakening and subsequent recovery of the East Asian summer monsoon. Generally, inter-kernel uncertainty is markedly smaller than inter-model uncertainty; however, in regional scale, inter-kernel uncertainty is larger and inter-model uncertainty is smaller than those in the global scale. This feature implies that ignoring inter-kernel uncertainty may bring additional errors to regional CFB quantification. Notably, these results are comparable between the SSP2-4.5 and SSP5-8.5 scenarios, suggesting that CFB processes may be insensitive to emission scenarios. [ABSTRACT FROM AUTHOR]

We examine the changes in clouds and cloud feedback between Phase 5 (AMIP5) and Phase 6 (AMIP6) of the Atmospheric Model Intercomparison Project. Each model is perturbed by uniformly increasing the sea surface temperature by 4 K. The simulated cloud fraction, the perturbed states and cloud radiative kernels are used to derive cloud feedback in the shortwave (SW), longwave (LW) and their sum (Net). Compared to AMIP5, the cloud fraction in AMIP6 increases by 9.1%, while the perturbation leads to a 0.25% decrease. The Net cloud feedback at the top of the atmosphere (TOA) is almost double (174%). Statistical tests support that this change is mainly due to an increase in the surface SW cloud feedback caused by optically thick, middle and low clouds. The contribution of the atmospheric Net component (12%) stems from the increase in the atmospheric LW cloud feedback, likely to play a role in weakening (strengthening) the northward (southward) meridional atmospheric energy transport, while the opposite is true for the surface LW and Net cloud feedback in the meridional oceanic energy transport. The substantial increase in cloud feedback at the TOA primarily contributes to the higher climate sensitivity. The cloud feedback spread in AMIP6 is comparable to that in AMIP5. [ABSTRACT FROM AUTHOR]

Diagnosing the root causes of cloud feedback in climate models and reasons for inter-model disagreement is a necessary first step in understanding their wide variation in climate sensitivities. Here we bring together two analysis techniques that illuminate complementary aspects of cloud feedback. The first quantifies feedbacks from changes in cloud amount, altitude, and optical depth, while the second separates feedbacks due to cloud property changes within specific cloud regimes from those due to regime occurrence frequency changes. We find that in the global mean, shortwave cloud feedback averaged across ten models comes solely from a positive within-regime cloud amount feedback countered slightly by a negative within-regime optical depth feedback. These within-regime feedbacks are highly uniform: In nearly all regimes, locations, and models, cloud amount decreases and cloud albedo increases with warming. In contrast, global-mean across-regime components vary widely across models but are very small on average. This component, however, is dominant in setting the geographic structure of the shortwave cloud feedback: Thicker, more extensive cloud types increase at the expense of thinner, less extensive cloud types in the extratropics, and vice versa at low latitudes. The prominent negative extratropical optical depth feedback has contributions from both within- and across-regime components, suggesting that thermodynamic processes affecting cloud properties as well as dynamical processes that favor thicker cloud regimes are important. The feedback breakdown presented herein may provide additional targets for observational constraints by isolating cloud property feedbacks within specific regimes without the obfuscating effects of changing dynamics that may differ across timescales. [ABSTRACT FROM AUTHOR]

The third generation of the radiative flux profile data product, called ISCCP‐FH, is described. The revisions over the previous generation (called ISCCP‐FD) include improvements in the radiative model representation of gaseous and aerosol effects, as well as a refined statistical model of cloud vertical layer variations with cloud types, and increased spatial resolution. The new product benefits from the changes in the new H‐version of the ISCCP cloud products (called ISCCP‐H): higher spatial resolution, revised radiance calibration and treatment of ice clouds, treatment of aerosol effects, and revision of all the ancillary atmosphere and surface property products. The ISCCP‐FH product is evaluated against more direct measurements from the Clouds and the Earth's Radiant Energy System and the Baseline Surface Radiation Network products, showing some small, overall reductions in average flux uncertainties; but the main results are similar to ISCCP‐FD: the ISCCP‐FH uncertainties remain ≲10 Wm−2 at the top‐of‐atmosphere (TOA) and ≲15 Wm−2 at surface for monthly, regional averages. The long‐term variations of TOA, surface and in‐atmosphere net fluxes are documented and the possible transient cloud feedback implications of a long‐term change of clouds are investigated. The cloud and flux variations from 1998 to 2012 suggest a positive cloud‐radiative feedback on the oceanic circulation and a negative feedback on the atmospheric circulation. This example demonstrates that the ISCCP‐FH product can provide useful diagnostic information about weather‐to‐interannual scale variations of radiation induced by changes in cloudiness as well as atmospheric and surface properties. Plain Language Summary: The article describes the updated version of the International Satellite Cloud Climatology Project (ISCCP) radiative profile flux product, ISCCP‐FH. This version has several important improvements over its previous two versions in its radiation model and input data sets of clouds, aerosol and other atmospheric and surface physical properties as well as ancillary data sets. Its spatial resolution is increased to 110 km. It now has uncertainties ≲10 Wm−2 at the top‐of‐atmosphere (TOA) and ≲15 Wm−2 at surface for monthly, regional averages based on validations against the direct observations at TOA and surface, slightly improved over the previous versions. We also describe long‐term variations of the radiative energy intensity based on the product and give an example to study cloud‐radiation feedback using this product. We expect the new product to be used in climate studies like its previous versions. Key Points: The radiative flux profile data product (called ISCCP‐FH) is described. It benefits from the new ISCCP cloud products (called ISCCP‐H)The product is evaluated against the Clouds and the Earth's Radiant Energy System and the Baseline Surface Radiation Network measurementsThe long‐term variations of top‐of‐atmosphere, surface and in‐atmosphere net fluxes are documented and a possible cloud feedback is investigated [ABSTRACT FROM AUTHOR]

Tropical anvil clouds play a large role in the Earth's radiation balance, but their effect on global warming is uncertain. The conventional paradigm for these clouds attributes their existence to the rapidly declining convective mass flux below the tropopause, which implies a large source of detraining cloudy air there. Here we test this paradigm by manipulating the sources and sinks of cloudy air in cloud-resolving simulations. We find that anvils form in our simulations because of the long lifetime of upper-tropospheric cloud condensates, not because of an enhanced source of cloudy air below the tropopause. We further show that cloud lifetimes are long in the cold upper troposphere because the saturation specific humidity is much smaller there than the condensed water loading of cloudy updrafts, which causes evaporative cloud decay to act very slowly. Our results highlight the need for novel cloud-fraction schemes that align with this decay-centric framework for anvil clouds.

Cloud radiative kernels (CRK) built with radiative transfer models have been widely used to analyze the cloud radiative effect on top of atmosphere (TOA) fluxes, and it is expected that the CRKs would also be useful in the analyses of surface radiative fluxes, which determines the regional surface temperature change and variability. In this study, CRKs at the surface and TOA were built using the Rapid Radiative Transfer Model (RRTM). Longwave cloud radiative effect (CRE) at the surface is primarily driven by cloud base properties, while TOA CRE is primarily decided by cloud top properties. For this reason, the standard version of surface CRK is a function of latitude, longitude, month, cloud optical thickness (τ) and cloud base pressure (CBP), and the TOA CRK is a function of latitude, longitude, month, τ and cloud top pressure (CTP). Considering that the cloud property histograms provided by climate models are functions of CTP instead of CBP at present, the surface CRKs on CBP-τ histograms were converted to CTP-τ fields using the statistical relationship between CTP, CBP and τ obtained from collocated CloudSat and MODIS observations. For both climate model outputs and satellites observations, the climatology of surface CRE and cloud-induced surface radiative anomalies calculated with the surface CRKs and cloud property histograms are well correlated with those calculated from surface radiative fluxes. The cloud-induced surface radiative anomalies reproduced by surface CRKs and MODIS cloud property histograms are not affected by spurious trends that appear in Clouds and the Earth's Radiant Energy System (CERES) surface irradiances products. [ABSTRACT FROM AUTHOR]

The critical relative humidity (RHc), which approximately measures the subgrid‐scale variability of moisture, is important to cloud parameterization. Based on the diagnostics from CloudSat/CALIPSO satellite data, we propose an improved RHc formula that incorporates geographic dependence and allows for non‐monotonic variations in the vertical. With the parameterized RHc, a cloud macrophysics scheme is constructed in which fractional cloudiness and subgrid‐scale condensation are synergistically solved, with the latter being calculated using two different approaches. Results show the new scheme largely alleviates the underestimation of high‐ and mid‐level clouds in the default model. The performance is also superior to the simulations applying a globally uniform RHc as conventionally used in the literature. Varying RHc and the techniques for computing subgrid‐scale condensation leads to a marked diversity in cloud feedback, which nearly replicates the range of uncertainty found in Coupled Model Intercomparison Project Phase 6 models. Using smaller RHc leads to larger spread than using larger ones. And the spread caused by different techniques for calculating subgrid‐scale condensation is larger than that caused by the choice of RHc. While many previous studies have emphasized the diversity of cloud feedback due to low clouds, varying RHc and its implementation leads to diversity of cloud feedback being mainly due to optically thick clouds. These results highlight the importance of RHc in inducing uncertainty of cloud feedback, as well as notes of caution to modelers when using RHc to tune models. Plain Language Summary: To represent subgrid‐scale cloud condensation in climate models, information on fluctuations of temperature and moisture within a grid is indispensable. By neglecting temperature fluctuations and assuming that the probability density function of total water is uniform, the subgrid‐scale variability of moisture is reduced to the so‐called "critical relative humidity" (RHc). Previous parameterizations assume RHc is globally uniform and varying monotonically in the vertical, which is far from realistic. Based on the diagnostics from CloudSat/CALIPSO satellite data, we propose an improved RHc formula that incorporates geographic dependence and allows for non‐monotonic variations in the vertical. A RHc‐based cloud macrophysics scheme is then constructed to investigate the role of RHc on cloudiness simulation and cloud feedback. Results show varying RHc and the techniques in calculating subgrid‐scale condensation nearly replicates the range of uncertainty found in CMIP6 cloud feedback estimates, thus raising caveats for modelers given that RHc is commonly used as a knob for model tuning. Key Points: We propose an improved critical relative humidity (RHc) formula that incorporates geographic dependence and allows for non‐monotonic variations in the verticalVarying RHc and techniques in calculating subgrid condensation replicates the range of uncertainty found in Coupled Model Intercomparison Project Phase 6 cloud feedback estimatesVarying RHc and its implementation in models leads to the diversity of cloud feedback being mainly due to optically thick clouds [ABSTRACT FROM AUTHOR]

The Sun becomes brighter with time, but Earth's climate is roughly temperate for life during its long‐term history; for early Earth, this is known as the faint young Sun problem (FYSP). Besides the carbonate‐silicate feedback, recent researches suggest that a long‐term cloud feedback may partially solve the FYSP. However, the general circulation models they used cannot resolve convection and clouds explicitly. This study re‐investigates the clouds using a near‐global cloud‐permitting model without cumulus convection parameterization. Our results confirm that a stabilizing shortwave cloud feedback does exist, and its magnitude is ≈6 W m−2 or 14% of the energy required to offset a 20% fainter Sun than today, or ≈10 W m−2 or 16% for a 30% fainter Sun. When insolation increases and meanwhile CO2 concentration decreases, low‐level clouds increase, acting to stabilize the climate by raising planetary albedo, and vice versa. Plain Language Summary: The emergence and evolution of life require a relatively stable climate environment. In the solar system, life has been found only on Earth and appeared since about four billion years ago. The underlying mechanisms for maintaining the long‐term climate on Earth is an important question but the answer is not completely clear. In this study, we re‐investigate a recently proposed mechanism, a stabilizing cloud feedback, using a high‐resolution cloud‐permitting model in a near‐global domain. Our simulations confirm that a stabilizing cloud feedback does exist, but its magnitude is relatively small. Key Points: A near‐global cloud‐permitting model with a resolution of 10 × 14 km is employed to test whether there is a long‐term cloud feedbackThe cloud feedback does have a net cooling effect when the Sun becomes brighter and meanwhile the CO2 concentration decreasesThese results confirm that cloud feedback is a part of the solution to the faint young Sun problem but its magnitude is relatively small [ABSTRACT FROM AUTHOR]

How subtropical marine low cloud cover (LCC) will respond to global warming is a major source of uncertainty in future climate change. Although the estimated inversion strength (EIS) is a good predictive index of LCC, it has a serious limitation when applied to evaluate LCC changes due to warming:The LCC decreases despite increases in EIS in future climate simulations of global climate models (GCMs). In this work, using stateof-the-art GCMs, we show that the recently proposed estimated cloud-top entrainment index (ECTEI) decreases consistently with LCC in warmer sea surface temperature (SST) climates. For the patterned SST warming predicted by coupled GCMs, ECTEI can constrain the subtropical marine LCC feedback to -0.41 ± 0.28% K-1 (90% CI), implying virtually certain positive feedback. ECTEI physically explains the heuristic model for LCC changes based on a linear combination of EIS and SST changes in previous studies in terms of cloud-top entrainment processes. [ABSTRACT FROM AUTHOR]

Various studies reported an elevation dependent precipitation and temperature changes in mountainous regions of the world including the Himalayas. Various mechanisms are proposed to link the possible dependence of the precipitation and temperature on elevation with other variables, including, long- and short-wave radiation, albedo, clouds, humidity, etc. In the present study changes and trends of precipitation and temperature at different elevation ranges in the Indian Himalayan region (IHR) is assessed. Observations and modelling fields during the period 1970–2099 are used. Modelling simulations from the Coordinated Regional Climate Downscaling Experiment-South Asia experiments (CORDEX-SA) suites are considered. In addition, four seasons—winter (Dec, Jan, Feb: DJF), pre-monsoon (Mar, Apr, May: MAM), monsoon (Jun, Jul, Aug, Sep: JJAS) and post-monsoon (Oct, Nov: ON)—are considered to detect the possible seasonal response of elevation dependency. Firstly, precipitation and temperature fields, separately, as well as the diurnal temperature range (DTR) are assessed. Following, their long-term trends are investigated, if varying, at different elevational ranges in the IHR. To explain plausible physical mechanisms due to elevation dependency, trend of other variables viz., surface downward longwave radiation (DLR), total cloud faction, soil moisture, near surface specific humidity, surface snow melt and surface albedo, etc. are investigated. Results point towards an decreased (increased) precipitation in higher (lower) elevation. And amplified warming signals at higher elevations (above 3000 m), both in daytime and nighttime temperatures, during all seasons except the monsoon, are noticed. Increased DLR trends at higher elevation are also simulated well by the model and are likely the main elevation dependent driver in the IHR. [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]

Mixed‐phase clouds play an important role in determining Arctic warming, but are parametrized in models and difficult to constrain with observations. We use two satellite‐derived cloud phase metrics to investigate the vertical structure of Arctic clouds in two global climate models that use the Community Atmosphere Model version 6 (CAM6) atmospheric component. We report a model error limiting ice nucleation, produce a set of Arctic‐constrained model runs by adjusting model microphysical variables to match the cloud phase metrics, and evaluate cloud feedbacks for all simulations. Models in this small ensemble uniformly overestimate total cloud fraction in the summer, but have variable representation of cloud fraction and phase in the winter and spring. By relating modeled cloud phase metrics and changes in low‐level liquid cloud amount under warming to longwave cloud feedback, we show that mixed‐phase processes mediate the Arctic climate by modifying how wintertime and springtime clouds respond to warming. Plain Language Summary: Clouds are important regulators of warming in the Arctic. The thermodynamic phase of a cloud affects its lifetime and transparency to incoming and outgoing radiation. As a result, transitions from ice to liquid in a warming climate change the influence of clouds on surface temperature. At temperatures between −37°C and 0°C, both ice and supercooled liquid water may exist simultaneously in a cloud layer. Global climate models struggle to capture cloud phase in this temperature range because it depends on both cloud temperature and aerosol properties. This study investigates how the fraction of supercooled liquid water changes vertically in Arctic clouds, comparing liquid‐rich cloud tops with their icy interiors. We describe a significant model error that limits the formation of new ice crystals. We also find that global climate models reproduce observations, and that a range of model parameters produce results consistent with observations. Changes in cloud fraction resulting from these adjustments mostly occur in the winter and spring, and cause the models to trap longwave radiation differently. The results of this study highlight the need to capture seasonal changes in cloud phase and amount in order to successfully predict future changes to the Arctic climate. Key Points: CAM6‐Oslo and CAM6 capture the vertical structure of Arctic mixed‐phase clouds, with supercooled liquid cloud tops overlying icy interiorsRemoving an invalid limit on heterogeneous and secondary ice nucleation processes in CAM6 reduces supercooled liquid water in Arctic cloudsParametrizations of mixed‐phase processes control longwave cloud feedback by modifying how winter and spring clouds respond to warming [ABSTRACT FROM AUTHOR]

This study reviews the research of the past 20-years on the role of anvil cirrus in the Earth's climate – research initiated by Lindzen et al. (Bull. Am. Meteor. Soc. 82:417-432, 2001). The original study suggested that the anvil cirrus would shrink with warming, which was estimated to induce longwave cooling for the Earth. This is referred to as the iris effect since the areal change hypothetically resembles the light control by the human eye's iris. If the effect is strong enough, it exerts a significant negative climate feedback which stabilizes tropical temperatures and limits climate sensitivity. Initial responses to Lindzen et al. (Bull. Am. Meteor. Soc. 82:417-432, 2001) denied the existence and effectiveness of the iris effect. Assessment of the debatable issues in these responses will be presented later in this review paper. At this point, the strong areal reduction of cirrus with warming appears very clearly in both climate models and satellite observations. Current studies found that the iris effect may not only come from the decreased cirrus outflow due to increased precipitation efficiency, but also from concentration of cumulus cores over warmer areas (the so-called aggregation effect). Yet, different opinions remain as to the radiative effect of cirrus clouds participating in the iris effect. For the iris effect to be most important, it must involve cirrus clouds that are not as opaque for visible radiation as they are for infrared radiation. However, current climate models often simulate cirrus clouds that are opaque in both visible and infrared radiation. This issue requires thorough examination as it seems to be opposed to conventional wisdom based on explicit observations. This paper was written in the hope of stimulating more effort to carefully evaluate these important issues. [ABSTRACT FROM AUTHOR]

Arctic amplification (AA) is a coupled atmosphere-sea ice-ocean process. This understanding has evolved from the early concept of AA, as a consequence of snow-ice line progressions, through more than a century of research that has clarified the relevant processes and driving mechanisms of AA. The predictions made by early modeling studies, namely the fall/winter maximum, bottom-heavy structure, the prominence of surface albedo feedback, and the importance of stable stratification have withstood the scrutiny of multi-decadal observations and more complex models. Yet, the uncertainty in Arctic climate projections is larger than in any other region of the planet, making the assessment of high-impact, near-term regional changes difficult or impossible. Reducing this large spread in Arctic climate projections requires a quantitative process understanding. This manuscript aims to build such an understanding by synthesizing current knowledge of AA and to produce a set of recommendations to guide future research. It briefly reviews the history of AA science, summarizes observed Arctic changes, discusses modeling approaches and feedback diagnostics, and assesses the current understanding of the most relevant feedbacks to AA. These sections culminate in a conceptual model of the fundamental physical mechanisms causing AA and a collection of recommendations to accelerate progress towards reduced uncertainty in Arctic climate projections. Our conceptual model highlights the need to account for local feedback and remote process interactions within the context of the annual cycle to constrain projected AA. We recommend raising the priority of Arctic climate sensitivity research, improving the accuracy of Arctic surface energy budget observations, rethinking climate feedback definitions, coordinating new model experiments and intercomparisons, and further investigating the role of episodic variability in AA.

The equilibrium climate sensitivity estimated from different sources is inconsistent due to its dependence on the surface warming pattern. Cloud feedbacks have been identified as the major contributor to this so‐called pattern effect. We find a large unforced pattern effect in CERES data, with cloud feedback estimated from two consecutive 125‐month periods (March 2000–July 2010 and August 2010–December 2020) changing from −0.45 ± 0.85 to +1.2 ± 0.78 W/m2/K. When comparing to models, 27% of consecutive 10‐year segments in CMIP6 control runs have differences similar to the observations. We also compare the spatial patterns in the CERES data to those in climate models and find they are similar, with the East Pacific playing a key role. This suggests that the impact of the unforced pattern effect can be significant and that models are capable of reproducing its global‐average magnitude. Plain Language Summary: Climate sensitivity, the amount of surface warming in response to increasing carbon dioxide, is often used as a metric to quantify the severity of climate change. The estimates of climate sensitivity from different sources, however, are not consistent. The main reason is that the response of clouds to surface temperature changes, called cloud feedback, depends on the spatial pattern of surface warming. We investigate the impact of this so‐called pattern effect on cloud feedback in observations and climate models. Here we find that the observed cloud feedbacks calculated from two consecutive decades have significant differences in both global mean value and spatial pattern, and the climate models produce similar results. This highlights the fact that differences of surface warming patterns can have large influences on cloud feedbacks, and climate models have the ability to reproduce what happened in the real world. Key Points: The difference in cloud feedback between two ∼10‐year periods inferred from CERES is 1.6 ± 1.1 W/m2/KThe distinct difference in cloud feedback verifies the existence of a large unforced pattern effectCMIP6 models produce unforced pattern effects of similar magnitude and spatial structure to those seen in the observations [ABSTRACT FROM AUTHOR]

This study aims to create observation‐based cloud radiative kernel (CRK) datasets and evaluate them by direct comparison of CRK and the CRK‐derived cloud feedback datasets. Based on the International Satellite Cloud Climatology Project (ISCCP) H datasets, we calculate CRKs (called ISCCP‐FH or FH CRKs) as 2D joint function/histogram of cloud optical depth and cloud top pressure for shortwave (SW), longwave (LW), and their sum, Net, at the top of atmosphere (TOA), as well as, for the first time, at the surface (SFC) and in the atmosphere (ATM). With cloud fraction change (CFC) datasets from doubled‐CO2 simulation and short‐term observational anomalies, we derive all the TOA, SFC and ATM cloud feedback for SW, LW and Net using our CRKs.The direct comparison with modeled and observed CRKs (or cloud radiative effects), cloud feedback from previous model results and the Clouds and the Earth's Radiant Energy System products show that our CRKs and CRK‐derived cloud feedback are reasonably well validated. We estimate the uncertainty for the CRK‐derived cloud feedback and show that the CFC‐associated uncertainty contributes >98.5% of the total cloud feedback uncertainty while CRK's is very small. Our preliminary evaluation also shows that some near‐zero/small cloud feedback in the TOA‐alone feedback indeed results from the compensation of sizable cloud feedback of the SFC and ATM feedback and reveals some significant surface and atmospheric cloud feedback whose sum appears insignificant in TOA‐alone feedback. In addition, the atmospheric longwave cloud feedback seems to play a role in enhancing meridional atmospheric energy transport. Key Points: The cloud radiative kernel datasets are created for longwave, shortwave and their sum, net, respectively, at the top of atmosphere, the surface and in the atmosphere. They are evaluated by direct comparison with the counterparts of the other three representative datasets and with the cloud radiative effects from the Clouds and the Earth's Radiant Energy System products. In addition, the kernel‐derived cloud feedback is also directly compared with the previous ensemble results from 10 climate models and the observation‐based results from the Clouds and the Earth's Radiant Energy System products. Both comparisons well‐validated our cloud kernels and their derived cloud feedbackThe uncertainty budget for cloud kernel‐derived cloud feedback at the top of atmosphere is estimated based on uncertainties of cloud kernels and cloud fraction change from the direct comparisons. It shows that the cloud fraction change associated uncertainty contributes >98.5% of the total cloud feedback uncertainty while cloud kernels' is very small. We have also tested the effect of dry bias of water vapor on the cloud radiative kernel calculation when the clear‐sky water vapor is used for the cloud‐free layers under cloud layers and estimated error/uncertainty may be caused in the calculation by such a dry bias. The results show such‐caused errors/uncertainties are in high order and need not to be taken into account at presentOur preliminary evaluation also shows that some near‐zero or small cloud feedback in the top‐of‐atmosphere‐alone feedback results from the compensation of sizable cloud feedback of the surface and atmospheric feedback. It demonstrates how the surface and atmospheric cloud kernel‐derived cloud feedback can be used to reveal some significant surface and atmospheric cloud feedback whose sum appears insignificant in the top‐of‐atmosphere‐alone feedback. In addition, the atmospheric longwave cloud feedback may play a role in enhancing meridional atmospheric energy transport [ABSTRACT FROM AUTHOR]

The analysis of radiative feedbacks requires the separation and quantification of the radiative contributions of different feedback variables, such as atmospheric temperature, water vapor, surface albedo, cloud, etc. It has been a challenge to include the nonlinear radiative effects of these variables in the feedback analysis. For instance, the kernel method that is widely used in the literature assumes linearity and completely neglects the nonlinear effects. Nonlinear effects may arise from the nonlinear dependency of radiation on each of the feedback variables, especially when the change in them is of large magnitude such as in the case of the Arctic climate change. Nonlinear effects may also arise from the coupling between different feedback variables, which often occurs as feedback variables including temperature, humidity and cloud tend to vary in a coherent manner. In this paper, we use brute-force radiation model calculations to quantify both univariate and multivariate nonlinear feedback effects and provide a qualitative explanation of their causes based on simple analytical models. We identify these prominent nonlinear effects in the CO2-driven Arctic climate change: 1) the univariate nonlinear effect in the surface albedo feedback, which results from a nonlinear dependency of planetary albedo on the surface albedo, which causes the linear kernel method to overestimate the univariate surface albedo feedback; 2) the coupling effect between surface albedo and cloud, which offsets the univariate surface albedo feedback; 3) the coupling effect between atmospheric temperature and cloud, which offsets the very strong univariate temperature feedback. These results illustrate the hidden biases in the linear feedback analysis methods and highlight the need for nonlinear methods in feedback quantification.

The surprising increase of Earth's climate sensitivity in the most recent coupled model intercomparison project (CMIP) models has been largely attributed to extratropical cloud feedback, which is thought to be driven by greater supercooled water in present‐day cloud phase partitioning (CPP). Here, we report that accounting for precipitation in the Goddard Institute for Space Studies ModelE3 radiation scheme, neglected in more than 60% of CMIP6 and 90% of CMIP5 models, systematically changes its apparent CPP and substantially increases its cloud feedback, consistent with results using CMIP models. Including precipitation in the comparison with cloud–aerosol lidar and infrared pathfinder satellite observations (CALIPSO) measurements and in model radiation schemes is essential to faithfully constrain cloud amount and phase partitioning, and simulate cloud feedbacks. Our findings suggest that making radiation schemes precipitation‐aware (missing in most CMIP6 models) should strengthen their positive cloud feedback and further increase their already high mean climate sensitivity. Plain Language Summary: The surprising increase of Earth's climate sensitivity–a proxy for future global warming–in the most recent climate models (CMIP6) has been largely attributed to the response of extratropical low clouds to warming. This cloud‐climate feedback is thought to be driven by greater supercooled water in present‐day cloud phase partitioning. Here we report that accounting for precipitation in climate model radiation schemes–neglected in more than 60% of CMIP6 and 90% of CMIP5 models–profoundly changes their apparent cloud phase partitioning and substantially increases their cloud‐climate feedbacks, which has not been reported before. Including precipitation in the comparison with observations and in model radiation schemes is essential to faithfully constrain cloud amount and phase partitioning and simulate cloud‐climate feedbacks. Our novel findings suggest that making radiation schemes precipitation‐aware, which is missing in most CMIP6 models, should strengthen their positive cloud feedback and further increase their already high mean climate sensitivity Key Points: Accounting for snow in a lidar simulator to compare with observations systematically reduces the simulated apparent supercooled liquidAllowing radiative schemes to be snow‐aware in climate models greatly increases their net cloud feedbackThe mean climate sensitivity is greater for recently coupled model intercomparison project models with snow‐aware radiative schemes compared to those without [ABSTRACT FROM AUTHOR]

Uncertainty in climate feedbacks is the primary source of the spread in projected surface temperature responses to anthropogenic forcing. Cloud feedback persistently appears as the main source of disagreement in future projections while the combined lapse‐rate plus water vapor (LR + WV) feedback is a smaller (30%), but non‐trivial source of uncertainty in climate sensitivity. Here we attempt to observationally constrain the feedbacks in an effort to reduce their intermodel uncertainties. The observed interannual variation provides a useful constraint on the long‐term cloud feedback, as evidenced by the consistency of global‐mean values and regional contributions to the intermodel spread on both interannual and long‐term timescales. However, interannual variability does not serve to constrain the long‐term LR + WV feedback spread, which we find is dominated by the varying tropical relative humidity (RH) response to interhemispheric warming differences under clear‐sky conditions and the RH‐fixed LR feedback under all‐sky conditions. Plain Language Summary: How much the Earth warms in response to greenhouse gas increases depends on the Earth's efficiency in restoring radiative equilibrium. This efficiency differs significantly among climate models due to differences in feedback processes, particularly the responses of clouds, temperature and water vapor to the initial perturbation. One approach to narrowing the intermodel spread of feedbacks is to only consider models whose observable variability is consistent with available measurements. The magnitude of cloud feedbacks on interannual and long‐term timescales are closely related, which allows us to employ this approach with observational estimates of the interannual cloud feedback to constrain the long‐term cloud feedback. However, this approach does not work for the feedback resulting from changes in the vertical distribution of temperature and water vapor (the combined lapse‐rate plus water vapor feedback). Key Points: Observed interannual variation provides a useful constraint to narrow the uncertainty in long‐term cloud feedbackIt is not possible to constrain the long‐term LR + WV feedback uncertainty with available observations of interannual variabilityDisagreements in the response of tropical relative humidity are responsible for the long‐term clear‐sky LR + WV feedback spread [ABSTRACT FROM AUTHOR]

Because of the large carbon sequestration potential, reforestation and afforestation (R&A) are among the most prominent natural climate solutions. However, while their effectiveness is well established for wet tropics, it is often argued that R&A are less advantageous or even detrimental at higher latitudes, where the reduction of forest albedo (the amount of reflected solar radiation by a surface) tends to nullify or even overcome the carbon benefits. Here, we carefully analyze the situation for R&A at midlatitudes, where the warming effects due to vegetation albedo are regarded to be almost balanced by the cooling effects from an increased carbon storage. Using both satellite data and atmospheric boundary-layer models, we show that by including cloud-albedo effects due to land-atmosphere interactions, the R&A cooling at midlatitudes becomes prevalent. This points to a much greater potential of R&A for wet temperate regions than previously considered. [ABSTRACT FROM AUTHOR]

The most recent generation of climate models (the 6th Phase of the Coupled Model Intercomparison Project) yields estimates of effective climate sensitivity (ECS) that are much higher than past generations due to a stronger amplification from cloud feedback. If plausible, these models require substantially larger greenhouse gas reductions to meet global warming targets. We show that models with a more positive cloud feedback also have a stronger cooling effect from aerosol‐cloud interactions. These two effects offset each other during the historical period when both aerosols and greenhouse gases increase, allowing either more positive or neutral cloud feedback models to reproduce the observed global‐mean temperature change. Since anthropogenic aerosols primarily concentrate in the Northern Hemisphere, strong aerosol‐cloud interaction models produce an interhemispheric asymmetric warming. We show that the observed warming asymmetry during the mid to late 20th century is more consistent with low ECS (weak aerosol indirect effect) models. Plain Language Summary: The response of clouds to surface temperature change can amplify or dampen the greenhouse gas induced warming, also known as cloud feedback. We find that in the latest generation of climate models, those models with a more positive cloud feedback tend to have a stronger cooling effect from aerosol‐cloud interaction. The compensation between cloud feedback and aerosol‐cloud interaction enables models to reproduce the historical global‐mean temperature change. In spite of having significantly different surface temperature sensitivity to increasing CO2, the historical record of global‐mean temperature is not a strong constraint in distinguishing these models. However, the interhemispheric difference in temperature over the 20th century provides a constraint that distinguishes the models that have a large or small sensitivity to increasing CO2. Over the 20th century, changes in anthropogenic aerosols were mostly concentrated in the Northern Hemisphere. Consequently, models with strong or weak aerosol‐cloud interactions produce different warming asymmetry over the historical period, and the observed warming asymmetry is more consistent with the models that have weak aerosol‐cloud interactions (and less positive cloud feedback). This study can help us better understand and reduce the uncertainty in the projected future warming. Key Points: Models with more positive cloud feedback tend to have more negative aerosol‐cloud interactionThis compensation relationship enables the models to match the historical warming even with a large spread in climate sensitivityHistorical interhemispheric warming indicates the high climate sensitivity models overestimate the aerosol‐cloud interaction [ABSTRACT FROM AUTHOR]

General Circulation Model (GCM) simulations with prescribed observed sea surface temperature (SST) over the historical period show systematic global shortwave cloud radiative effect (SWCRE) variations uncorrelated with global surface temperature (known as "pattern effect"). Here, we show that a single parameter that quantifies the difference in SSTs between regions of tropical deep convection and the tropical or global average (Δconv) captures the time‐varying "pattern effect" in the simulations using the PCMDI/AMIPII SST recommended for CMIP6. In particular, a large positive trend in the 1980s–1990s in Δconv explains the change of sign to a strongly negative SWCRE feedback since the late 1970s. In these decades, the regions of deep convection warm about +50% more than the tropical average. Such an amplification is rarely observed in forced coupled atmosphere‐ocean GCM simulations, where the amplified warming is typically about +10%. During the post 2000 global warming hiatus Δconv shows little change, and the more recent period of resumed global warming is too short to robustly detect trends. In the prescribed SST simulations, Δconv is forced by the SST difference between warmer and colder regions. An index thereof (SST#) evaluated for six SST reconstructions shows similar trends for the satellite era, but the difference between the pre‐ and the satellite era is substantially larger in the PCMDI/AMIPII SSTs than in the other reconstructions. Quantification of the cloud feedback depends critically on small changes in the shape of the SST probability density distribution. These sensitivities underscore how essential highly accurate, persistent, and stable global climate records are to determine the cloud feedback. Key Points: Difference between sea surface temperatures (SSTs) in regions of tropical deep convection and average (Δconv) explains time‐varying cloud radiative feedback ("pattern effect")SST reconstructions show an unprecedented trend in Δconv in the 1980s–1990s not seen in coupled atmosphere‐ocean general circulation models (GCMs)Independent confirmation of the resulting negative cloud feedback in the 1980s–1990s in GCM simulations with observed SSTs is lacking [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]

Abstract We examine the response of the Community Earth System Model Versions 1 and 2 (CESM1 and CESM2) to abrupt quadrupling of atmospheric CO2 concentrations (4xCO2) and to 1% annually increasing CO2 concentrations (1%CO2). Different estimates of equilibrium climate sensitivity (ECS) for CESM1 and CESM2 are presented. All estimates show that the sensitivity of CESM2 has increased by 1.5 K or more over that of CESM1. At the same time the transient climate response (TCR) of CESM1 and CESM2 derived from 1%CO2 experiments has not changed significantly—2.1 K in CESM1 and 2.0 K in CESM2. Increased initial forcing as well as stronger shortwave radiation feedbacks are responsible for the increase in ECS seen in CESM2. A decomposition of regional radiation feedbacks and their contribution to global feedbacks shows that the Southern Ocean plays a key role in the overall behavior of 4xCO2 experiments, accounting for about 50% of the total shortwave feedback in both CESM1 and CESM2. The Southern Ocean is also responsible for around half of the increase in shortwave feedback between CESM1 and CESM2, with a comparable contribution arising over tropical ocean. Experiments using a thermodynamic slab‐ocean model (SOM) yield estimates of ECS that are in remarkable agreement with those from fully coupled Earth system model (ESM) experiments for the same level of CO2 increase. Finally, we show that the similarity of TCR in CESM1 and CESM2 masks significant regional differences in warming that occur in the 1%CO2 experiments for each model.

Abstract Tian (2015, https://doi.org/10.1002/2015GL064119) found that Coupled Model Intercomparison Project Phases 3 and 5 (CMIP3 and CMIP5) climate models with too much precipitation in a region of the Southeast Pacific (due to a double‐Intertropical Convergence Zone [ITCZ] bias) tend to have lower climate sensitivities and suggested that this might form the basis of an “emergent constraint,” which could rule out lower values of climate sensitivity. However, no physical mechanism has been proposed to explain this relationship. Here we advance the hypothesis that deep convection encroaching into regions that should be dominated by shallow clouds hampers the formation of shallow clouds in the present climate and reduces the magnitude of positive low‐level cloud feedbacks, resulting in smaller values of climate sensitivity. We test this hypothesis first by performing sensitivity tests with the HadGEM2‐A aquaplanet model subject to a uniform +4 K sea surface temperature (SST) perturbation, in which we vary the degree to which deep convection associated with the single/double ITCZ extends toward subtropical low‐cloud regions. Experiments with more precipitation encroaching into the subtropics have weaker subtropical cloud radiative effects in the present‐day simulations and less positive subtropical cloud feedbacks, consistent with our hypothesis. We test this hypothesis further by looking for the predicted relationships across multimodel ensembles of SST forced Atmospheric Model Intercomparison Project (AMIP) experiments subject to a uniform +4 K SST increase. Relationships of the expected sign are found in the CMIP5 AMIP+4K experiments, but not all are statistically significant at the 5% level. We find no statistically significant support for our hypothesis in the currently available CMIP6 AMIP+4K experiments.

Abstract In estimates of climate sensitivity obtained from global models, the need to represent clouds introduces a great deal of uncertainty. To address this issue, approaches using a high-resolution global non-hydrostatic model are promising: the model captures cloud structure by explicitly simulating meso-scale convective systems, and the results compare reasonably well with satellite observations. We review the outcomes of a 5-year project aimed at reducing the uncertainty in climate models due to cloud processes using a global non-hydrostatic model. In our project, which was conducted as a subgroup of the Program for Risk Information on Climate Change, or SOUSEI, we use the non-hydrostatic icosahedral atmospheric model (NICAM) to study cloud processes related to climate change. NICAM performs numerical simulations with much higher resolution (about 7 km or 14 km mesh) than conventional global climate models (GCMs) using cloud microphysics schemes without a cumulus parameterization scheme, which causes uncertainties in climate projection. The subgroup had three research targets: analyzing cloud changes in global warming simulations with NICAM with the time-slice approach, sensitivity of the results to the cloud microphysics scheme employed, and evaluating circulation changes due to global warming. The research project also implemented a double-moment bulk cloud microphysics scheme and evaluated its results using satellite observation, as well as comparing it with a bin cloud microphysics scheme. The future projection simulations show in general increase in high cloud coverage, contrary to results with other GCMs. Changes in cloud horizontal-size distribution size and structures of tropical/extratropical cyclones can be discussed with high resolution simulations. At the conclusion of our review, we also describe the future prospects of research for global warming using NICAM in the program that followed SOUSEI, known as TOUGOU.

Abstract This study explores how to drive Single‐Column Models (SCMs) with existing data sets of General Circulation Model (GCM) outputs, with the aim of studying the boundary layer cloud response to climate change in the marine subtropical trade wind regime. The EC‐EARTH SCM is driven with the large‐scale tendencies and boundary conditions as derived from two different data sets, consisting of high‐frequency outputs of GCM simulations. SCM simulations are performed near Barbados Cloud Observatory in the dry season (January–April), when fair‐weather cumulus is the dominant low‐cloud regime. This climate regime is characterized by a near equilibrium in the free troposphere between the long‐wave radiative cooling and the large‐scale advection of warm air. In the SCM, this equilibrium is ensured by scaling the monthly mean dynamical tendency of temperature and humidity such that it balances that of the model physics in the free troposphere. In this setup, the high‐frequency variability in the forcing is maintained, and the boundary layer physics acts freely. This technique yields representative cloud amount and structure in the SCM for the current climate. Furthermore, the cloud response to a sea surface warming of 4 K as produced by the SCM is consistent with that of the forcing GCM.

Discussions of countering global warming with solar geoengineering assume that warming owing to rising greenhouse-gas concentrations can be compensated by artificially reducing the amount of sunlight Earth absorbs. However, solar geoengineering may not be fail-safe to prevent global warming because CO2 can directly affect cloud cover: It reduces cloud cover by modulating the longwave radiative cooling within the atmosphere. This effect is not mitigated by solar geoengineering. Here, we use idealized high-resolution simulations of clouds to show that, even under a sustained solar geoengineering scenario with initially only modest warming, subtropical stratocumulus clouds gradually thin and may eventually break up into scattered cumulus clouds, at concentrations exceeding 1,700 parts per million (ppm). Because stratocumulus clouds cover large swaths of subtropical oceans and cool Earth by reflecting incident sunlight, their loss would trigger strong (about 5 K) global warming. Thus, the results highlight that, at least in this extreme and idealized scenario, solar geoengineering may not suffice to counter greenhouse-gas-driven global warming. [ABSTRACT FROM AUTHOR]

We examine the response of the Community Earth System Model Versions 1 and 2 (CESM1 and CESM2) to abrupt quadrupling of atmospheric CO2 concentrations (4xCO2) and to 1% annually increasing CO2 concentrations (1%CO2). Different estimates of equilibrium climate sensitivity (ECS) for CESM1 and CESM2 are presented. All estimates show that the sensitivity of CESM2 has increased by 1.5 K or more over that of CESM1. At the same time the transient climate response (TCR) of CESM1 and CESM2 derived from 1%CO2 experiments has not changed significantly—2.1 K in CESM1 and 2.0 K in CESM2. Increased initial forcing as well as stronger shortwave radiation feedbacks are responsible for the increase in ECS seen in CESM2. A decomposition of regional radiation feedbacks and their contribution to global feedbacks shows that the Southern Ocean plays a key role in the overall behavior of 4xCO2 experiments, accounting for about 50% of the total shortwave feedback in both CESM1 and CESM2. The Southern Ocean is also responsible for around half of the increase in shortwave feedback between CESM1 and CESM2, with a comparable contribution arising over tropical ocean. Experiments using a thermodynamic slab‐ocean model (SOM) yield estimates of ECS that are in remarkable agreement with those from fully coupled Earth system model (ESM) experiments for the same level of CO2 increase. Finally, we show that the similarity of TCR in CESM1 and CESM2 masks significant regional differences in warming that occur in the 1%CO2 experiments for each model. Plain Language Summary: Computer models of the Earth's climate system are complex. Our best guess scenarios for how the climate system will change due to human activity over the next century are also complex. They include estimates of changing greenhouse gas (e.g., CO2) levels in the atmosphere, aerosol (e.g., smog and haze) emissions, and land use changes (e.g., deforestation and urbanization). To help understand this complex system, the climate modeling community has designed two simplified experiments: abrupt CO2 quadrupling (4xCO2) and 1% annual CO2 increase (1%CO2). In these experiments all human‐induced factors in the climate system are held constant (at preindustrial levels) except for CO2 in the atmosphere. Results of these experiments from different climate models can be compared to gain insight into the climate system. We look at two versions of the Community Earth System Model (CESM1 and CESM2). The warming simulated in the 4xCO2 experiment (climate sensitivity) has increased substantially in CESM2. This is related to changes in clouds over the Southern Ocean and tropics. At the same time warming in in the 1%CO2 experiment has not increased. This is related to differences in how CESM1 and CESM2 simulate northern oceans (Arctic, North Atlantic, and North Pacific). Key Points: Climate sensitivity has increased from 4 K to over 5 K in CESM2 compared to CESM1Shortwave radiation feedbacks over the Southern Ocean play a key role in determining the response of CESM to increasing CO2Various measures of climate response, including equilibrium climate sensitivity (ECS) and transient climate response (TCR), are not simply related in CESM [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]