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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.

Southern hemisphere subtropical anticyclones are projected to change in a warmer climate during both austral summer and winter. A recent study of CMIP 5 & 6 projections found a combination of local diabatic heating changes and static-stability-induced changes in baroclinic eddy growth as the dominant drivers. Yet the underlying mechanisms forcing these changes still remain uninvestigated. This study aims to enhance our mechanistic understanding of what drives these Southern Hemisphere anticyclones changes during both seasons. Using an AGCM, we decompose the response to CO2-induced warming into two components: (1) the fast atmospheric response to direct CO2 radiative forcing, and (2) the slow atmospheric response due to indirect sea surface temperature warming. Additionally, we isolate the influence of tropical diabatic heating with AGCM added heating experiments. As a complement to our numerical AGCM experiments, we analyze the Atmospheric and Cloud Feedback Model Intercomparison Project experiments. Results from sensitivity experiments show that slow subtropical sea surface temperature warming primarily forces the projected changes in subtropical anticyclones through baroclinicity change. Fast CO2 atmospheric radiative forcing on the other hand plays a secondary role, with the most notable exception being the South Atlantic subtropical anticyclone in austral winter, where it opposes the forcing by sea surface temperature changes resulting in a muted net response. Lastly, we find that tropical diabatic heating changes only significantly influence Southern Hemisphere subtropical anticyclone changes through tropospheric wind shear changes during austral winter.

The newest iteration of the Canadian Earth System Model (CanESM5.0.3) has an effective climate sensitivity (EffCS) of 5.65 K, which is a 54 % increase relative to the model's previous version (CanESM2 – 3.67 K), and the highest sensitivity of all current models participating in the sixth phase of the coupled model inter-comparison project (CMIP6). Here, we explore the underlying causes behind CanESM5's increased EffCS via comparison of forcing and feedbacks between CanESM2 and CanESM5. We find only modest differences in radiative forcing as a response to CO2 between model versions. We find small increases in the surface albedo and longwave cloud feedback, as well as a substantial increase in the SW cloud feedback in CanESM5. Through the use of cloud area fraction output and cloud radiative kernels, we find that more positive low and non-low shortwave cloud feedbacks – particularly with regards to low clouds across the equatorial Pacific, as well as subtropical and extratropical free troposphere cloud optical depth – are the dominant contributors to CanESM5's increased climate sensitivity. Additional simulations with prescribed sea surface temperatures reveal that the spatial pattern of surface temperature change exerts controls on the magnitude and spatial distribution of low-cloud fraction response but does not fully explain the increased EffCS in CanESM5. The results from CanESM5 are consistent with increased EffCS in several other CMIP6 models, which has been primarily attributed to changes in shortwave cloud feedbacks.

Stomatal closure is a major physiological response to the increasing atmospheric carbon dioxide (CO2), which can lead to surface warming by regulating surface energy fluxes—a phenomenon known as CO2 physiological forcing. The magnitude of land surface warming caused by physiological forcing is substantial and varies across models. Here we assess the continental warming response to CO2 physiological forcing and quantify the resultant climate feedback using carbon–climate simulations from phases 5 and 6 of the Coupled Model Intercomparison Project, with a focus on identifying the cause of inter-model spread. It is demonstrated that the continental (40°–70°N) warming response to the physiological forcing in summer (~0.55 K) is amplified primarily due to cloud feedback (~1.05 K), whereas the other climate feedbacks, ranged from –0.57 K to 0.20 K, show relatively minor contributions. In addition, the strength of cloud feedback varies considerably across models, which plays a primary role in leading large diversity of the continental warming response to the physiological forcing.

The COVID-19 pandemic caused disruptions of public life and imposed lockdown measures in 2020 resulted in considerable reductions of anthropogenic aerosol emissions. It still remains unclear how the associated short-term changes in atmospheric chemistry influenced weather and climate on regional scales. To understand the underlying physical mechanisms, we conduct ensemble aerosol perturbation experiments with the Community Earth System Model, version 2. In the simulations reduced anthropogenic aerosol emissions in February generate anomalous surface warming and warm-moist air advection which promotes low-level cloud formation over China. Although the simulated response is weak, it is detectable in some areas, in qualitative agreement with the observations. The negative dynamical cloud feedback offsets the effect from reduced cloud condensation nuclei. Additional perturbation experiments with strongly amplified air pollution over China reveal a nonlinear sensitivity of regional atmospheric conditions to chemical/radiative perturbations. COVID-19-related changes in anthropogenic aerosol emissions provide an excellent testbed to elucidate the interaction between air pollution and climate.

As the atmosphere warms, part of the cloud population shifts from ice and mixed-phase (‘cold’) to liquid (‘warm’) clouds. Because warm clouds are more reflective and longer-lived, this phase change reduces the solar flux absorbed by the Earth and constitutes a negative radiative feedback. This cooling feedback is weaker in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) than in the fifth phase (CMIP5), contributing to greater greenhouse warming. Although this change is often attributed to improvements in the simulated cloud phase, another model bias persists: warm clouds precipitate too readily, potentially leading to underestimated negative lifetime feedbacks. In this study we modified a climate model to better simulate warm-rain probability and found that it exhibits a cloud lifetime feedback nearly three times larger than the default model. This suggests that model errors in cloud-precipitation processes may bias cloud feedbacks by as much as the CMIP5-to-CMIP6 climate sensitivity difference. Reliable climate model projections therefore require improved cloud process realism guided by process-oriented observations and observational constraints. CMIP6 models simulate higher and more accurate cloud liquid water fraction relative to CMIP5, but both ensembles overestimate warm cloud precipitation. Correcting these warm cloud processes in a model exposes compensating biases large enough to offset CMIP5–CMIP6 climate sensitivity differences.

Marine low clouds strongly cool the planet. How this cooling effect will respond to climate change is a leading source of uncertainty in climate sensitivity, the planetary warming resulting from CO2 doubling. Here, we observationally constrain this low cloud feedback at a near-global scale. Satellite observations are used to estimate the sensitivity of low clouds to interannual meteorological perturbations. Combined with model predictions of meteorological changes under greenhouse warming, this permits quantification of spatially resolved cloud feedbacks. We predict positive feedbacks from midlatitude low clouds and eastern ocean stratocumulus, nearly unchanged trade cumulus and a near-global marine low cloud feedback of 0.19 ± 0.12 W m−2 K−1 (90% confidence). These constraints imply a moderate climate sensitivity (~3 K). Despite improved midlatitude cloud feedback simulation by several current-generation climate models, their erroneously positive trade cumulus feedbacks produce unrealistically high climate sensitivities. Conversely, models simulating erroneously weak low cloud feedbacks produce unrealistically low climate sensitivities. Marine low clouds cool the planet, but their response to warming is uncertain and dominates the spread in model-based climate sensitivities. Observational constraints suggest smaller cloud feedbacks than previously reported and imply a more moderate climate sensitivity.

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.

The role of Arctic clouds in the recent rapid Arctic warming has attracted much attention. However, Arctic cloud water paths (CWPs) from reanalysis datasets have not been well evaluated. This study evaluated the CWPs as well as LWPs (cloud liquid water paths) and IWPs (cloud ice water paths) from five reanalysis datasets (MERRA-2, MERRA, ERA-Interim, JRA-55, and ERA5) against the COSP (Cloud Feedback Model Intercomparison Project Observations Simulator Package) output for MODIS from the MERRA-2 CSP (COSP satellite simulator) collection (defined as M2Modis in short). Averaged over 1980–2015 and over the Arctic region (north of 60°N), the mean CWPs of these five datasets range from 49.5 g/m2 (MERRA) to 82.7 g/m2 (ERA-Interim), much smaller than that from M2Modis (140.0 g/m2). However, the spatial distributions of CWPs, show similar patterns among these reanalyses, with relatively small values over Greenland and large values over the North Atlantic. Consistent with M2Modis, these reanalyses show larger LWPs than IWPs, except for ERA-Interim. However, MERRA-2 and MERRA underestimate the ratio of IWPs to CWPs over the entire Arctic, while ERA-Interim and JRA-55 overestimate this ratio. ERA5 shows the best performance in terms of the ratio of IWPs to CWPs. All datasets exhibit larger CWPs and LWPs in summer than in winter. For M2Modis, IWPs hold seasonal variation similar with LWPs over the land but opposite over the ocean. Following the Arctic warming, the trends in LWPs and IWPs during 1980–2015 show that LWPs increase and IWPs decrease across all datasets, although not statistically significant. Correlation analysis suggests that all datasets have similar interannual variability. The study further found that the inclusion of re-evaporation processes increases the humidity in the atmosphere over the land and that a more realistic liquid/ice phase can be obtained by independently treating the liquid and ice water contents.

Global climate models predict warming in response to increasing GHG concentrations, partly due to decreased tropical low-level cloud cover and reflectance. We use satellite observations that discriminate stratocumulus from shallow cumulus clouds to separately evaluate their sensitivity to warming and constrain the tropical contribution to low-cloud feedback. We find an observationally inferred low-level cloud feedback two times smaller than a previous estimate. Shallow cumulus clouds are insensitive to warming, whereas global climate models exhibit a large positive cloud feedback in shallow cumulus regions. In contrast, stratocumulus clouds show sensitivity to warming and the tropical inversion layer strength, controlled by the tropical Pacific sea surface temperature gradient. Models fail to reproduce the historical sea surface temperature gradient trends and therefore changes in inversion strength, generating an overestimate of the positive stratocumulus cloud feedback. Continued weak east Pacific warming would therefore produce a weaker low-cloud feedback and imply a more moderate climate sensitivity (3.47 ± 0.33 K) than many models predict. The response of low clouds to warming is uncertain among climate models and dominates spread in their projections. Satellite estimates of tropical cumulus and stratocumulus cloud feedbacks, derived using surface warming trends, suggest a more moderate climate sensitivity than many models predict.

Equilibrium climate sensitivity (ECS) has been directly estimated using reconstructions of past climates that are different than today's. A challenge to this approach is that temperature proxies integrate over the timescales of the fast feedback processes (e.g., changes in water vapor, snow, and clouds) that are captured in ECS as well as the slower feedback processes (e.g., changes in ice sheets and ocean circulation) that are not. A way around this issue is to treat the slow feedbacks as climate forcings and independently account for their impact on global temperature. Here we conduct a suite of Last Glacial Maximum (LGM) simulations using the Community Earth System Model version 1.2 (CESM1.2) to quantify the forcing and efficacy of land ice sheets (LISs) and greenhouse gases (GHGs) in order to estimate ECS. Our forcing and efficacy quantification adopts the effective radiative forcing (ERF) and adjustment framework and provides a complete accounting for the radiative, topographic, and dynamical impacts of LIS on surface temperatures. ERF and efficacy of LGM LIS are −3.2 W m−2 and 1.1, respectively. The larger-than-unity efficacy is caused by the temperature changes over land and the Northern Hemisphere subtropical oceans which are relatively larger than those in response to a doubling of atmospheric CO2. The subtropical sea-surface temperature (SST) response is linked to LIS-induced wind changes and feedbacks in ocean–atmosphere coupling and clouds. ERF and efficacy of LGM GHG are −2.8 W m−2 and 0.9, respectively. The lower efficacy is primarily attributed to a smaller cloud feedback at colder temperatures. Our simulations further demonstrate that the direct ECS calculation using the forcing, efficacy, and temperature response in CESM1.2 overestimates the true value in the model by approximately 25 % due to the neglect of slow ocean dynamical feedback. This is supported by the greater cooling (6.8 ∘C) in a fully coupled LGM simulation than that (5.3 ∘C) in a slab ocean model simulation with ocean dynamics disabled. The majority (67 %) of the ocean dynamical feedback is attributed to dynamical changes in the Southern Ocean, where interactions between upper-ocean stratification, heat transport, and sea-ice cover are found to amplify the LGM cooling. Our study demonstrates the value of climate models in the quantification of climate forcings and the ocean dynamical feedback, which is necessary for an accurate direct ECS estimation.

To assess the performances of state-of-the-art global climate models on simulating the Arctic clouds and surface radiation balance, the 2001–2014 Arctic Basin surface radiation budget, clouds, and the cloud radiative effects (CREs) in 22 coupled model intercomparison project 6 (CMIP6) models are evaluated against satellite observations. For the results from CMIP6 multi-model mean, cloud fraction (CF) peaks in autumn and is lowest in winter and spring, consistent with that from three satellite observation products (CloudSat-CALIPSO, CERES-MODIS, and APP-x). Simulated CF also shows consistent spatial patterns with those in observations. However, almost all models overestimate the CF amount throughout the year when compared to CERES-MODIS and APP-x. On average, clouds warm the surface of the Arctic Basin mainly via the longwave (LW) radiation cloud warming effect in winter. Simulated surface energy loss of LW is less than that in CERES-EBAF observation, while the net surface shortwave (SW) flux is underestimated. The biases may result from the stronger cloud LW warming effect and SW cooling effect from the overestimated CF by the models. These two biases compensate each other, yielding similar net surface radiation flux between model output (3.0 W/m2) and CERES-EBAF observation (6.1 W/m2). During 2001–2014, significant increasing trend of spring CF is found in the multi-model mean, consistent with previous studies based on surface and satellite observations. Although most of the 22 CMIP6 models show common seasonal cycles of CF and liquid water path/ice water path (LWP/IWP), large inter-model spreads exist in the amounts of CF and LWP/IWP throughout the year, indicating the influences of different cloud parameterization schemes used in different models. Cloud Feedback Model Intercomparison Project (CFMIP) observation simulator package (COSP) is a great tool to accurately assess the performance of climate models on simulating clouds. More intuitive and credible evaluation results can be obtained based on the COSP model output. In the future, with the release of more COSP output of CMIP6 models, it is expected that those inter-model spreads and the model-observation biases can be substantially reduced. Longer term active satellite observations are also necessary to evaluate models’ cloud simulations and to further explore the role of clouds in the rapid Arctic climate changes.

Summer heatwave events have exhibited increasing trends, with sudden increases occurring since the early 2000s over northeastern China and along the northern boundary of Mongolia. However, the mechanism behind heatwaves remains unexplored. To quantitatively examine the feedback attribution of concurrent events related to surface temperature anomalies, the coupled atmosphere–surface climate feedback-response analysis method based on the total energy balance within the atmosphere–surface column was applied. The results demonstrate that the contributions of the latent heat flux and surface dynamic processes served as positive feedback for surface warming by reducing the heat release from the surface to the atmosphere because of deficient soil moisture based on dry conditions. Cloud feedback also led to warm temperature anomalies through increasing solar insolation caused by decreasing cloud amounts associated with anomalous high-pressure systems. In contrast, the sensible heat flux played a role in reducing the warm temperature anomalies by the emission of heat from the surface. Atmospheric dynamic feedback led to cold anomalies. The influence of ozone, surface albedo, and water vapor processes is very weak. This study provides a better understanding of combined extreme climate events in the context of radiative and dynamic feedback processes.

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 CO(2) 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.

The equilibrium climate sensitivity of Earth is defined as the global mean surface air temperature increase that follows a doubling of atmospheric carbon dioxide. For decades, global climate models have predicted it as between approximately 2 and 4.5 °C. However, a large subset of models participating in the 6th Coupled Model Intercomparison Project predict values exceeding 5 °C. The difference has been attributed to the radiative effects of clouds, which are better captured in these models, but the underlying physical mechanism and thus how realistic such high climate sensitivities are remain unclear. Here we analyse Community Earth System Model simulations and find that, as the climate warms, the progressive reduction of ice content in clouds relative to liquid leads to increased reflectivity and a negative feedback that restrains climate warming, in particular over the Southern Ocean. However, once the clouds are predominantly liquid, this negative feedback vanishes. Thereafter, other positive cloud feedback mechanisms dominate, leading to a transition to a high-sensitivity climate state. Although the exact timing and magnitude of the transition may be model dependent, our findings suggest that the state dependence of the cloud-phase feedbacks is a crucial factor in the evolution of Earth’s climate sensitivity with warming. Simulations suggest a shift to a high sensitivity of Earth’s climate to increasing CO2 can be attributed to the decline in the ice content in clouds as the climate warms.

Cloud radiative kernels were built by BCC_RAD (Beijing Climate Center radiative transfer model) radiative transfer code. Then, short-term cloud feedback and its mechanisms in East Asia (0.5°S–60.5°N, 69.5°–150.5°E) were analyzed quantitatively using the kernels combined with MODIS satellite data from July 2002 to June 2018. According to the surface and monsoon types, four subregions in East Asia—the Tibetan Plateau, northwest, temperate monsoon (TM), and subtropical monsoon (SM)—were selected. The average longwave, shortwave, and net cloud feedbacks in East Asia are −0.68 ± 1.20, 1.34 ± 1.08, and 0.66 ± 0.40 W m−2 K−1 (±2σ), respectively, among which the net feedback is dominated by the positive shortwave feedback. Positive feedback in SM is the strongest of all subregions, mainly due to the contributions of nimbostratus and stratus. In East Asia, short-term feedback in spring is primarily caused by marine stratus in SM, in summer is primarily driven by deep convective cloud in TM, in autumn is mainly caused by land nimbostratus in SM, and in winter is mainly driven by land stratus in SM. Cloud feedback in East Asia is chiefly driven by decreases in mid-level and low cloud fraction owing to the changes in relative humidity, and a decrease in low cloud optical thickness due to the changes in cloud water content.

Climate sensitivity represents the response of climate system to doubled CO2 concentration relative to the preindustrial level, which is one of the sources of uncertainty in climate projections. It is unclear how the climate sensitivity and feedbacks will change as a model system is upgraded from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to CMIP6. In this paper, we address this issue by comparing two versions of the Beijing Climate Center Climate System Model (BCC-CSM) participating in CMIP6 and CMIP5, i.e., BCC-CSM2-MR and BCC-CSM1.1m, which have the same horizontal resolution but different physical parameterizations. The results show that the equilibrium climate sensitivity (ECS) of BCC-CSM slightly increases from CMIP5 (2.94 K) to CMIP6 (3.04 K). The small changes in the ECS result from compensation between decreased effective radiative forcing (ERF) and the increased net feedback. In contrast, the transient climate response (TCR) evidently decreases from 2.19 to 1.40 K, nearly the lower bound of the CMIP6 multimodel spread. The low TCR in BCC-CSM2-MR is mainly caused by the small ERF overly even though the ocean heat uptake (OHU) efficiency is substantially improved from that in BCC-CSM1.1m. Cloud shortwave feedback (λSWCL) is found to be the major cause of the increased net feedback in BCC-CSM2-MR, mainly over the Southern Ocean. The strong positive λSWCL in BCC-CSM2-MR is coincidently related to the weakened sea ice-albedo feedback in the same region. This result is caused by reduced sea ice coverage simulated during the preindustrial cold season, which leads to reduced melting per 1-K global warming. As a result, in BCC-CSM2-MR, reduced surface heat flux and strengthened static stability of the planetary boundary layer cause a decrease in low-level clouds and an increase in incident shortwave radiation. This study reveals the important compensation between λSWCL and sea ice-albedo feedback in the Southern Ocean.

Southern Ocean (SO) shortwave (SW) radiation biases are a common problem in contemporary general circulation models (GCMs), with most models exhibiting a tendency to absorb too much incoming SW radiation. These biases have been attributed to deficiencies in the representation of clouds during the austral summer months, either due to cloud cover or cloud optical thickness being too low. The problem has been the focus of many studies, most of which utilised satellite datasets for model evaluation. We use multi-year ship based observations and the CERES spaceborne radiation budget measurements to contrast cloud representation and SW radiation in the atmospheric component Global Atmosphere (GA) version 7.0 and 7.1 of the HadGEM3 GCM and the MERRA-2 reanalysis. We find that MERRA-2 is biased in the opposite direction to GA (reflects too much SW radiation). In addition, MERRA-2 performs better in terms of absolute SW bias than nudged runs of GA7.0 and GA7.1 in the 60–70° S latitude band. GA7.1 reduces the SO SW radiation biases relative to GA7.0, but significant errors remain at up to 20 W m−2 between 60 and 70° S in the austral summer months. Using ship-based ceilometer observations, we find low cloud below 2 km to be predominant in the Ross Sea and the Indian Ocean sector of the SO. Utilising a novel surface lidar simulator developed for this study, derived from an existing COSP-ACTSIM spaceborne lidar simulator, we find that GA7.0 and MERRA-2 both underestimate low cloud occurrence relative to the ship observations by 18–25 % on average, though the cloud cover in MERRA-2 is closer to observations by about 7 %. Based on radiosonde observations, we also find the low cloud to be strongly linked to boundary-layer atmospheric stability and the sea surface temperature. GA7.0 and MERRA-2 agree well with observations in terms of boundary-layer stability, suggesting that subgrid-scale parametrisations do not generate enough cloud in response to the thermodynamic profile of the atmosphere and the surface temperature. Our analysis shows that MERRA-2 has a much greater proportion of cloud liquid water in the SO in January than GA7.0, a likely key contributor to the difference in SW radiation. We show that boundary-layer stability and relative humidity fields are very similar in GA7.0 and MERRA-2, and unlikely to be the cause of the different cloud representation, suggesting that subgrid-scale parametrisations are responsible for the difference between the models.

A key contribution to the latest generation of climate projections for the UK (UKCP18) was a perturbed parameter ensemble (PPE) of global coupled models based on HadGEM3-GC3.05. Together with 13 CMIP5 simulations, this PPE provides users with a dataset that samples modelling uncertainty and is ideal for use in impacts studies. Evaluations of global mean surface temperatures for this PPE have shown twenty-first century warming rates consistently at the top end of the CMIP5 range. Here we investigate one potential contributory factor to this lack of spread: that the methodology to select plausible members from a larger, related PPE of atmosphere-only experiments preferentially ruled out those predicted to have more negative climate feedbacks (i.e. lower climate sensitivities). We confirm that this is indeed the case. We show that performance in extratropical long-wave cloud forcing played a key role in this by constraining ice cloud parameters, which in turn constrained the feedback distribution (though causal links are not established). The relatively weak relationship driving this constraint is shown to arise from stronger relationships for the long-wave and short-wave cloud feedback components, which largely cancel out due to changes in tropical high clouds. Moreover, we show that the strength of these constraints is due to a structural bias in extratropical long-wave cloud forcing across the PPE. We discuss how choices made in the methodology to pick the plausible PPE members may result in an overly strong constraint when there is a structural bias and possible improvements to this methodology for the future.

The main features of climate change patterns, as simulated by the coupled ocean–atmosphere version 2.5 of the Brazilian Earth System Model (BESM), are compared with those of 25 other CMIP5 models, focusing on temperature, precipitation, atmospheric circulation, and radiative feedbacks. The climate sensitivity to quadrupling the atmospheric CO2 concentration was investigated via two methods: linear regression (Gregory et al., 2004) and radiative kernels (Soden and Held, 2006; Soden et al., 2008). Radiative kernels from both the National Center for Atmospheric Research (NCAR) and the Geophysical Fluid Dynamics Laboratory (GFDL) were used to decompose the climate feedback responses of the CMIP5 models and BESM into different processes. By applying the linear regression method for equilibrium climate sensitivity (ECS) estimation, we obtained a BESM value close to the ensemble mean value. This study reveals that the BESM simulations yield zonally average feedbacks, as estimated from radiative kernels, that lie within the ensemble standard deviation. Exceptions were found in the high latitudes of the Northern Hemisphere and over the ocean near Antarctica, where BESM showed values for lapse rate, humidity feedback, and albedo that were marginally outside the standard deviation of the values from the CMIP5 multi-model ensemble. For those areas, BESM also featured a strong positive cloud feedback that appeared as an outlier compared with all analyzed models. However, BESM showed physically consistent changes in the temperature, precipitation, and atmospheric circulation patterns relative to the CMIP5 ensemble mean.

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 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 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, specifically the water vapor triple effect, 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 pursuing the role of episodic variability in AA as a research focus area.

Low-level clouds over the tropical and sub-tropical oceans play a crucial role in the planetary radiative energy budget. However, they are challenging to model in climate simulations because they are affected by local processes that are still partially unknown. The control that mesoscale sea surface temperature structures have on the dynamics of the lower atmosphere on daily scales is emerging to be non-negligible and calls for more effort to be understood. During the EUREC4A field campaign, two of the research vessels (R/Vs) involved in the experiment sampled the edge of a cold mesoscale sea surface temperature (SST) patch in the north-western tropical Atlantic, crossing a gradient of roughly 0.75°C/100 km. The comprehensive set of instruments carried by the R/Vs allows an unprecedented characterization of the atmospheric response to the cold water forcing. The cold ocean patch weakens the vertical atmospheric mixing, reducing the boundary layer depth of roughly 200 m and the horizontal wind intensity of approximately 3 m s−1. At the same time, the humidity content in the sub-cloud layer increases and these conditions decrease the latent heat flux (by roughly 80 W m−2) and reduce vertical velocity fluctuations, making it less likely that moisture exceeds the lifting condensation level. As a consequence, fewer and thinner low-level clouds form over cold water. Independent satellite measurements are found to agree with the in-situ observations. The observed link between sea temperature and low-level clouds highlights its importance in the puzzle of modeling the sea-air-cloud interactions. Key Points Ship-based observations are used to characterize the lower atmospheric response to a cold patch in the north-western subtropical Atlantic Signatures in dynamical and thermodynamical atmospheric properties agree with a reduced vertical mixing over the cold patch Such a weaker vertical mixing is linked to a reduced shallow cloud cover because less moisture reaches the level of saturation Plain Language Summary Puffy clouds typically visible at sea strongly challenge climate models that struggle to represent their interaction with the sea surface and solar radiation. And as a consequence, these climate models cannot precisely estimate how much the Earth’s temperature will increase 100 years from now and its uncertainty. We went into the western Atlantic ocean for a month in Jan-Feb 2020 to measure sea surface temperature and cloud properties and observe how these changes occur. We saw clouds grow deeper over the warm water patches, holding more water and eventually raining. Weaker winds and more humid air occur on cold patches. Satellite observations seem to record the same behavior over a larger area in the same region. We detected a clear difference in cloud properties and amounts over warm and cold patches from all sensors, recording essential evidence of a feature that is hard to predict. We hope these observations will help to properly simulate the intensity and signs of low-cloud feedback over warm and cold oceanic patches of water to improve climate models.

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.

The daily cloud cycle or diurnal cloud cycle (DCC) and its response to global warming are critical to the Earth’s energy budget, but their radiative effects have not been systematically quantified. Toward this goal, here we analyze the radiation at the top of the atmosphere and propose a measure of the DCC radiative effect (DCCRE) as the difference between the total radiative fluxes with the full cloud cycle and its uniformly distributed cloud counterpart. When applied to the frequency of cloud occurrence, DCCRE is linked to the covariance between DCC and cloud radiative effects. Satellite observations show that the daily cloud cycle is strongly linked to pacific decadal oscillation (PDO) and climate hiatus, revealing its potential role in controlling climate variability. Climate model outputs show large inter-model spreads of DCCRE, accounting for approximately 20% inter-model spread of the cloud radiative effects. Climate models also suggest that while DCCRE is not sensitive to rising temperatures at the global scale, it can be important in certain regions. Such a framework can be used to conduct a more systematic evaluation of the DCC in climate models and observations with the goal to understand climate variability and reduce uncertainty in climate projections.

International audience; Climate forcing, sensitivity, and feedback metrics are evaluated in both the United Kingdom's physical climate model HadGEM3‐GC3.1 at low (‐LL) and medium (‐MM) resolution and the United Kingdom's Earth System Model UKESM1. The effective climate sensitivity (EffCS) to a doubling of CO2 is 5.5 K for HadGEM3.1‐GC3.1‐LL and 5.4 K for UKESM1. The transient climate response is 2.5 and 2.8 K, respectively. While the EffCS is larger than that seen in the previous generation of models, none of the model's forcing or feedback processes are found to be atypical of models, though the cloud feedback is at the high end. The relatively large EffCS results from an unusual combination of a typical CO2 forcing with a relatively small feedback parameter. Compared to the previous U.K. climate model, HadGEM3‐GC2.0, the EffCS has increased from 3.2 to 5.5 K due to an increase in CO2 forcing, surface albedo feedback, and midlatitude cloud feedback. All changes are well understood and due to physical improvements in the model. At higher atmospheric and ocean resolution (HadGEM3‐GC3.1‐MM), there is a compensation between increased marine stratocumulus cloud feedback and reduced Antarctic sea‐ice feedback. In UKESM1, a CO2 fertilization effect induces a land surface vegetation change and albedo radiative effect. Historical aerosol forcing in HadGEM3‐GC3.1‐LL is −1.1 W m−2. In HadGEM3‐GC3.1‐LL historical simulations, cloud feedback is found to be less positive than in abrupt‐4xCO2, in agreement with atmosphere‐only experiments forced with observed historical sea surface temperature and sea‐ice variations. However, variability in the coupled model's historical sea‐ice trends hampers accurate diagnosis of the model's total historical feedback.

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 Internation...

A negative shortwave cloud feedback associated with higher extratropical liquid water content in mixed-phase clouds is a common feature of global warming simulations, and multiple mechanisms have b...

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project has provided the climate community more than a 20-year record of top of the atmosphere (TOA) reflected shortwave (SW) and emitted longwave (LW) fluxes. The fluxes are used to monitor the Earth’s energy balance as well as for climate model validation and cloud feedback studies. One of the largest uncertainties in climate models is the response of clouds in feedback studies. Reducing this uncertainty requires a more stringent validation of model-generated fluxes by cloud-type. Rather than relying on radiative transfer model generated fluxes from observed cloud-type retrievals, the CERES FluxByCloudTyp (FBCT) product relies on MODIS empirical narrowband to broadband relationships to convert the cloudy portions of the CERES footprints into fluxes. The overcast footprint and sub-footprint cloud layer fluxes are then stratified by 7 pressure layers and 6 optical depth bins and temporally averaged into daily and monthly regional cloud-type fluxes. The CERES project analyzed a total of 19 MODIS channels for use in the next generation FBCT product narrowband to broadband conversion. This was accomplished by comparing the narrowband to broadband RMS errors from all possible 5-channel combinations. The SW and LW optimized channels were selected by also considering analogous geostationary, MODIS and VIIRS imager channels as well as channel combinations that provided the lowest RMS errors across all surface types. The optimized channel combinations will be tested in the FBCT algorithm to investigate any narrowband to broadband dependencies as a function of cloud fraction, effective pressure, optical depth, PW, solar and view angle, surface type.

Climate models are essential for our comprehensive understanding of Earth's atmosphere and can provide critical insights on future changes decades ahead. Because of these critical roles, today's climate models are continuously being developed and evaluated using constraining observations and measurements obtained by satellites, airborne, and ground-based instruments. Instrument simulators can provide a bridge between the measured or retrieved quantities and their sampling in models and field observations while considering instrument sensitivity limitations. Here we present the Earth Model Column Collaboratory (EMC2), an open-source ground-based lidar and radar instrument simulator and subcolumn generator, specifically designed for large-scale models, in particular climate models, but also applicable to high-resolution model output. EMC2 provides a flexible framework enabling direct comparison of model output with ground-based observations, including generation of subcolumns that may statistically represent finer model spatial resolutions. In addition, EMC2 emulates ground-based (and air- or space-borne) measurements while remaining faithful to large-scale models' physical assumptions implemented in their cloud or radiation schemes. The simulator uses either single particle or bulk particle size distribution lookup tables, depending on the selected scheme approach, to perform the forward calculations. To facilitate model evaluation, EMC2 also includes three hydrometeor classification methods, namely, radar- and sounding-based cloud and precipitation detection and classification, lidar-based phase classification, and a Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) lidar simulator emulator. The software is written in Python, is easy to use, and can be straightforwardly customized for different models, radars and lidars. Following the description of the logic, functionality, features, and software structure of EMC2, we present a case study of highly supercooled mixed-phase cloud based on measurements from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE). We compare observations with the application of EMC2 to outputs from four configurations of the NASA Goddard Institute for Space Studies (GISS) climate model (ModelE3) in single-column model (SCM) mode and from a large-eddy simulation (LES) model. We show that two of the four ModelE3 configurations can form and maintain highly supercooled precipitating cloud for several hours, consistent with observations and LES. While our focus is on one of these ModelE3 configurations, which performed slightly better in this case study, both of these configurations and the LES results post-processed with EMC2 generally provide reasonable agreement with observed lidar and radar variables. As briefly demonstrated here, EMC2 can provide a lightweight and flexible framework for comparing the results of both large-scale and high-resolution models directly with observations, with relatively little overhead and multiple options for achieving consistency with model microphysical or radiation scheme physics.

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.

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.

Global warming drives changes in Earth’s cloud cover, which, in turn, may amplify or dampen climate change. This “cloud feedback” is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)—the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. Considering changes in just these two factors, we are able to constrain global cloud feedback to 0.43 [Formula: see text] 0.35 W [Formula: see text] m(−2) [Formula: see text] K(−1) (90% confidence), implying a robustly amplifying effect of clouds on global warming and only a 0.5% chance of ECS below 2 K. We thus anticipate that our approach will enable tighter constraints on climate change projections, including its manifold socioeconomic and ecological impacts.

The Last Interglacial period (129–116 ka) is characterised by a strong orbital forcing which leads to a different seasonal and latitudinal distribution of insolation compared to the pre-industrial period. In particular, these changes amplify the seasonality of the insolation in the high latitudes of the Northern Hemisphere. Here, we investigate the Arctic climate response to this forcing by comparing the CMIP6 lig127k and piControl simulations performed with the IPSL-CM6A-LR (the global climate model developed at Institut Pierre-Simon Laplace) model. Using an energy budget framework, we analyse the interactions between the atmosphere, ocean, sea ice and continents. In summer, the insolation anomaly reaches its maximum and causes a rise in near-surface air temperature of 3.1 ∘C over the Arctic region. This warming is primarily due to a strong positive anomaly of surface downwelling shortwave radiation over continental surfaces, followed by large heat transfer from the continents to the atmosphere. The surface layers of the Arctic Ocean also receive more energy but in smaller quantity than the continents due to a cloud negative feedback. Furthermore, while heat exchange from the continental surfaces towards the atmosphere is strengthened, the ocean absorbs and stores the heat excess due to a decline in sea ice cover. However, the maximum near-surface air temperature anomaly does not peak in summer like insolation but occurs in autumn with a temperature increase of 4.2 ∘C relative to the pre-industrial period. This strong warming is driven by a positive anomaly of longwave radiation over the Arctic Ocean enhanced by a positive cloud feedback. It is also favoured by the summer and autumn Arctic sea ice retreat (-1.9×106 and -3.4×106 km2, respectively), which exposes the warm oceanic surface and thus allows oceanic heat storage and release of water vapour in summer. This study highlights the crucial role of sea ice cover variations, Arctic Ocean, as well as changes in polar cloud optical properties on the Last Interglacial Arctic warming.

International audience; The equilibrium climate sensitivity (ECS) in the latest version of CNRM climate model, CNRM-CM6-1, and in its high-resolution counterpart, CNRM-CM6-1-HR, is significantly larger than in the previous version (CNRM-CM5.1). The traceability of this climate sensitivity change is investigated using coupled ocean-atmosphere model climate change simulations. These simulations show that the increase in ECS is the result of changes in the atmospheric component. A particular attention is paid to the method used to decompose the equilibrium temperature response difference, by using a linearized decomposition of the individual radiative agents diagnosed by a radiative kernel technique. The climate sensitivity increase is primarily due to the cloud radiative responses, with a predominant contribution of the tropical longwave response (including both feedback and forcing adjustment) and a significant contribution of the extratropical and tropical shortwave feedback changes. A series of stand-alone atmosphere experiments is carried out to quantify the contributions of each atmospheric development to this difference between CNRM-CM5.1 and CNRM-CM6-1. The change of the convection scheme appears to play an important role in driving the cloud changes, with a large effect on the tropical longwave cloud feedback change.

We ask what fundamental insights about the relationship of tropical convection to climate have arisen from recent investigations using simplified models. The vertical distribution of relative humidity should remain approximately constant in a changed climate. The temperature of clouds in the upper troposphere should also remain effectively constant for climate changes likely to occur in response to human-induced warming. The fractional coverage of convective clouds will likely decrease slightly with warming, but it is not known how the albedo and net radiative effect of tropical convective clouds will change. The areal extent and net radiative effect of tropical convective clouds depend on the interactions of radiation, cloud physics, and turbulence within the extended upper-level ice clouds. SST gradients develop naturally as a result of the aggregation of convection and large-scale thermodynamics and circulation act to couple the cloud properties and the SST. Radiative-convective equilibrium continues to provide insight into the structure and energy balance of the atmosphere by incorporating the interactions among radiation, cloud physics, and atmospheric motion.

We analyze the atmospheric processes that explain the large changes in radiative feedbacks between the two latest climate configurations of the Hadley Centre Global Environmental model. We use a large set of atmosphere‐only climate change simulations (amip and amip‐p4K) to separate the contributions to the differences in feedback parameter from all the atmospheric model developments between the two latest model configurations. We show that the differences are mostly driven by changes in the shortwave cloud radiative feedback in the midlatitudes, mainly over the Southern Ocean. Two new schemes explain most of the differences: the introduction of a new aerosol scheme and the development of a new mixed‐phase cloud scheme. Both schemes reduce the strength of the preexisting shortwave negative cloud feedback in the midlatitudes. The new aerosol scheme dampens a strong aerosol‐cloud interaction, and it also suppresses a negative clear‐sky shortwave feedback. The mixed‐phase scheme increases the amount of cloud liquid water path (LWP) in the present day and reduces the increase in LWP with warming. Both changes contribute to reducing the negative radiative feedback of the increase of LWP in the warmer climate. The mixed‐phase scheme also enhances a strong, preexisting, positive cloud fraction feedback. We assess the realism of the changes by comparing present‐day simulations against observations and discuss avenues that could help constrain the relevant processes., Key Points Large changes in shortwave cloud feedbacks exist between two recent configurations of Met Office modelChanges in midlatitude feedbacks explain most of the global‐mean differencesThe new aerosol and mixed‐phase schemes explain most of the feedback differences

Observations from multiple sensors on the NASA Aqua satellite are used to estimate the temporal and spatial variability of short-term cloud responses (CR) and cloud feedbacks λ for different cloud types, with respect to the interannual variability within the A-Train era (July 2002–June 2017). Short-term cloud feedbacks by cloud type are investigated both globally and locally by three different definitions in the literature: 1) the global-mean cloud feedback parameter λGG from regressing the global-mean cloud-induced TOA radiation anomaly ΔRG with the global-mean surface temperature change ΔTGS; 2) the local feedback parameter λLL from regressing the local ΔR with the local surface temperature change ΔTS; and 3) the local feedback parameter λGL from regressing global ΔRG with local ΔTS. Observations show significant temporal variability in the magnitudes and spatial patterns in λGG and λGL, whereas λLL remains essentially time invariant for different cloud types. The global-mean net λGG exhibits a gradual transition from negative to positive in the A-Train era due to a less negative λGG from low clouds and an increased positive λGG from high clouds over the warm pool region associated with the 2015/16 strong El Niño event. Strong temporal variability in λGL is intrinsically linked to its dependence on global ΔRG, and the scaling of λGL with surface temperature change patterns to obtain global feedback λGG does not hold. Despite the shortness of the A-Train record, statistically robust signals can be obtained for different cloud types and regions of interest.

Cloud distribution characteristics over the Tibetan Plateau in the summer monsoon period simulated by the Australian Community Climate and Earth System Simulator (ACCESS) model are evaluated using COSP [the CFMIP (Cloud Feedback Model Intercomparison Project) Observation Simulator Package]. The results show that the ACCESS model simulates less cumulus cloud at atmospheric middle levels when compared with observations from CALIPSO and CloudSat, but more ice cloud at high levels and drizzle drops at low levels. The model also has seasonal biases after the onset of the summer monsoon in May. While observations show that the prevalent high cloud at 9–10 km in spring shifts downward to 7–9 km, the modeled maximum cloud fractions move upward to 12–15 km. The reason for this model deficiency is investigated by comparing model dynamical and thermodynamical fields with those of ERA-Interim. It is found that the lifting effect of the Tibetan Plateau in the ACCESS model is stronger than in ERA-Interim, which means that the vertical velocity in the ACCESS model is stronger and more water vapor is transported to the upper levels of the atmosphere, resulting in more high-level ice clouds and less middle-level cumulus cloud over the Tibetan Plateau. The modeled radiation fields and precipitation are also evaluated against the relevant satellite observations.

A cloud feedback diagnostic package is implemented in the Australian Community Climate and Earth-System Simulator General Circulation Model, based on the methodology of “cloud radiative kernels.” Using separate increased sea surface temperature and CO 2 experiments, both the “rapid response” cloud contribution to forcing and temperature-mediated cloud feedbacks are analyzed. Under increased temperature and CO 2 changes, temperature-mediated cloud radiative feedback dominates over the rapid response in the final radiative response. Cloud feedback is positive in both long and short wave, with short wave dominating global values. Contributing most to this are low to mid-level clouds, of medium-to-high optical thickness. As a means of illustration of the methodology, a number of key parameters related to clouds, precipitation, and convection that are typically used in “tuning” in the model are modified. These changes result in substantial impacts on the model's current climate, but only modest changes to rapid response and feedbacks occur globally, regionally, and as a function of cloud optical thickness and height. This limited set of experiments shows that cloud adjustments and feedbacks in this model are robust under these changes, lending confidence that both model climate change projections and the conclusions of attribution studies are not overly sensitive to such parameterization tuning. Of course, a considerably larger set of experiments would be needed to demonstrate that feedbacks and rapid response are robust under the wider set of tuning adjustments commonly undertaken.

Author(s): Seeley, JT; Jeevanjee, N; Langhans, W; Romps, DM | Abstract: 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.