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Limited area cloud‐resolving model (CRM) simulations called LASAM are used to reproduce and understand negative subtropical shallow cumulus cloud feedbacks in a near‐global aquaplanet CRM (NGAqua) with 4‐K sea surface temperature (SST) warming. NGAqua spans a large tropical channel domain, with 4‐km horizontal resolution, zonally symmetric equatorially peaked SST, and no cumulus parameterization. Prior work showed that its coarsely resolved shallow cumulus increases with warming. It was suggested that with warmer SST, the moister boundary layer is destabilized by more clear‐sky radiative cooling, driving more cumulus convection. A small doubly periodic version of the same CRM is configured to analyze this low cloud increase in a simpler context. It is driven by steady thermodynamic and advective forcing profiles averaged over the driest subtropical column humidity quartile of NGAqua. Sensitivity studies separate effects of radiative cooling and free tropospheric relative humidity changes from other aspects of NGAqua's warmer climate. Enhanced clear‐sky radiative cooling explains most of the cloud increase due to SST warming, regardless of CRM model resolution and advection scheme. A boundary layer energy budget shows that the downward entrainment heat flux strengthens to balance enhanced radiative cooling, carried by a stronger updraft cloud mass flux from a larger cumulus cloud fraction. In deeper trade cumulus layers, the enhanced radiative cooling in a warming climate may be balanced by increased precipitation warming, leaving the cloud coverage area almost unchanged. With larger domain sizes, shallow cumulus self‐aggregates, especially with higher SST, marginally increasing domain‐mean cloud fraction, but this is a secondary contributor to the cloud feedback. Plain Language Summary Cumulus clouds less than 2 km deep are widespread over the subtropical oceans. Hence, even small shallow cumulus cloudiness changes affect how much the underlying oceans are warmed by sunlight and the sensitivity of climate to greenhouse gas increases. Most climate models suggest slightly decreased shallow cumulus cloudiness in a warmer climate. However, a recent study using a near‐global model with 4‐km horizontal grid spacing, much finer than conventional climate models and capable of simulating individual cumuli, found increased subtropical shallow cumulus cloudiness when the surface is uniformly warmed. We use a simpler version of the same model with a much smaller domain to represent the drier parts of the full model's subtropical oceans, reproduce its cloudiness increase, isolate the possible causes, and show the robustness of this finding. Using this simpler model, we confirm an earlier hypothesis: The cloudiness increase is mainly from more infrared cooling of the cumulus layer that holds more water vapor in a warmer climate. Shallow cumuli are largely driven by this cooling, so more infrared cooling leads to more cumuli. An important caveat is that if the cumuli were deeper so they rained more, warming could lead to more rain rather than more cloud. Key Points A 4‐K SST warming increases subtropical trade cumulus cloud cover in limited area cloud‐resolving and large eddy simulations Enhanced radiative cooling of moister boundary layer under dry free troposphere destabilizes the cloud layer and drives more shallow cumuli The response of shallow cumulus to enhanced radiative cooling is robust across all model configurations

The response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the “cloud-controlling factors” of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming, one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloud-controlling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is 0.25 ± 0.18 W m −2  K −1 (90% confidence interval), suggesting it is very unlikely that tropical low clouds reduce total global cloud feedback. Because the prediction of positive tropical low-cloud feedback with this approach is consistent with independent evidence from low-cloud feedback studies using high-resolution cloud models, progress is being made in reducing this key climate uncertainty.

Inconsistent findings in soil moisture (SM)‐precipitation feedback literature motivate further research into the role of the boundary layer in these feedbacks. The present study explores mechanisms that can explain the spatial patterns found in a previous analysis employing satellite measured SM: positive feedback in the semi‐arid western U.S. (higher morning SM predicting greater likelihood of afternoon rainfall), and negative feedback in the humid east. Using a cloud–topped boundary layer model, we examine how evaporative fraction (EF, a proxy for SM) influences cloud mass flux (CMF). We then use logistic regression to relate CMF to precipitation. The results are consistent with the previous analysis: in semi‐arid areas, increased humidification with increased EF dominates CMF strength, yielding net positive feedbacks; in humid areas, reductions in convective velocity with increasing EF dominate the CMF, yielding net negative feedbacks. Such offsetting feedbacks may contribute to inconsistencies reported in the literature.

Using five climate model simulations of the response to an abrupt quadrupling of CO₂, the authors perform the first simultaneous model intercomparison of cloud feedbacks and rapid radiative adjustments with cloud masking effects removed, partitioned among changes in cloud types and gross cloud properties. Upon CO₂ quadrupling, clouds exhibit a rapid reduction in fractional coverage, cloud-top pressure, and optical depth, with each contributing equally to a 1.1 W m−2net cloud radiative adjustment, primarily from shortwave radiation. Rapid reductions in midlevel clouds and optically thick clouds are important in reducing planetary albedo in every model. As the planet warms, clouds become fewer, higher, and thicker, and global mean net cloud feedback is positive in all but one model and results primarily from increased trapping of longwave radiation. As was true for earlier models, high cloud changes are the largest contributor to intermodel spread in longwave and shortwave cloud feedbacks, but low cloud changes are the largest contributor to the mean and spread in net cloud feedback. The importance of the negative optical depth feedback relative to the amount feedback at high latitudes is even more marked than in earlier models. The authors show that the negative longwave cloud adjustment inferred in previous studies is primarily caused by a 1.3 W m−2cloud masking of CO₂ forcing. Properly accounting for cloud masking increases net cloud feedback by 0.3 W m−2K−1, whereas accounting for rapid adjustments reduces by 0.14 W m−2K−1the ensemble mean net cloud feedback through a combination of smaller positive cloud amount and altitude feedbacks and larger negative optical depth feedbacks.

The major evolution of the National Center for Atmospheric Research Community Atmosphere Model (CAM) is used to diagnose climate feedbacks, understand how climate feedbacks change with different physical parameterizations, and identify the processes and regions that determine climate sensitivity. In the evolution of CAM from version 4 to version 5, the water vapor, temperature, surface albedo, and lapse rate feedbacks are remarkably stable across changes to the physical parameterization suite. However, the climate sensitivity increases from 3.2 K in CAM4 to 4.0 K in CAM5. The difference is mostly due to (i) more positive cloud feedbacks and (ii) higher CO₂ radiative forcing in CAM5. The intermodel differences in cloud feedbacks are largest in the tropical trade cumulus regime and in the midlatitude storm tracks. The subtropical stratocumulus regions do not contribute strongly to climate feedbacks owing to their small area coverage. A “modified Cess” configuration for atmosphere-only model experiments is shown to reproduce slab ocean model results. Several parameterizations contribute to changes in tropical cloud feedbacks between CAM4 and CAM5, but the new shallow convection scheme causes the largest midlatitude feedback differences and the largest change in climate sensitivity. Simulations with greater cloud forcing in the mean state have lower climate sensitivity. This work provides a methodology for further analysis of climate sensitivity across models and a framework for targeted comparisons with observations that can help constrain climate sensitivity to radiative forcing.

Cloud radiative kernels and histograms of cloud fraction, both as functions of cloud-top pressure and optical depth, are used to quantify cloud amount, altitude, and optical depth feedbacks. The analysis is applied to doubled-CO₂ simulations from 11 global climate models in the Cloud Feedback Model Intercomparison Project. Global, annual, and ensemble mean longwave (LW) and shortwave (SW) cloud feedbacks are positive, with the latter nearly twice as large as the former. The robust increase in cloud-top altitude in both the tropics and extratropics is the dominant contributor to the positive LW cloud feedback. The negative impact of reductions in cloud amount offsets more than half of the positive impact of rising clouds on LW cloud feedback, but the magnitude of compensation varies considerably across the models. In contrast, robust reductions in cloud amount make a large and virtually unopposed positive contribution to SW cloud feedback, though the intermodel spread is greater than for any other individual feedback component. Overall reductions in cloud amount have twice as large an impact on SW fluxes as on LW fluxes, such that the net cloud amount feedback is moderately positive, with no models exhibiting a negative value. As a consequence of large but partially offsetting effects of cloud amount reductions on LW and SW feedbacks, both the mean and intermodel spread in net cloud amount feedback are smaller than those of the net cloud altitude feedback. Finally, the study finds that the large negative cloud feedback at high latitudes results from robust increases in cloud optical depth, not from increases in total cloud amount as is commonly assumed.

CGILS—the CFMIP‐GASS Intercomparison of Large Eddy Models (LESs) and single column models (SCMs)—investigates the mechanisms of cloud feedback in SCMs and LESs under idealized climate change perturbation. This paper describes the CGILS results from 15 SCMs and 8 LES models. Three cloud regimes over the subtropical oceans are studied: shallow cumulus, cumulus under stratocumulus, and well‐mixed coastal stratus/stratocumulus. In the stratocumulus and coastal stratus regimes, SCMs without activated shallow convection generally simulated negative cloud feedbacks, while models with active shallow convection generally simulated positive cloud feedbacks. In the shallow cumulus alone regime, this relationship is less clear, likely due to the changes in cloud depth, lateral mixing, and precipitation or a combination of them. The majority of LES models simulated negative cloud feedback in the well‐mixed coastal stratus/stratocumulus regime, and positive feedback in the shallow cumulus and stratocumulus regime. A general framework is provided to interpret SCM results: in a warmer climate, the moistening rate of the cloudy layer associated with the surface‐based turbulence parameterization is enhanced; together with weaker large‐scale subsidence, it causes negative cloud feedback. In contrast, in the warmer climate, the drying rate associated with the shallow convection scheme is enhanced. This causes positive cloud feedback. These mechanisms are summarized as the “NESTS” negative cloud feedback and the “SCOPE” positive cloud feedback (Negative feedback from Surface Turbulence under weaker Subsidence—Shallow Convection PositivE feedback) with the net cloud feedback depending on how the two opposing effects counteract each other. The LES results are consistent with these interpretations. Key Points Reasons of negative and positive cloud feedbacks in SCMs are explained A framework is provided to interpret cloud feedbacks in models SCM results are compared with LES simulations

Moist convection is a key aspect of the extratropical summer climate and strongly affects the delicate balance of processes that determines the surface climate in response to larger-scale forcings. Previous studies using parameterized convection have found that the feedback between soil moisture and precipitation is predominantly positive (more precipitation over wet soils) over Europe. Here this feedback is investigated for one full month (July 2006) over the Alpine region using two different model configurations. The first one employs regional climate simulations performed with the Consortium for Small-Scale Modeling Model in Climate Mode (CCLM) on a grid spacing of 25 km. The second one uses the same model but integrated on a cloud-resolving grid of 2.2 km, allowing an explicit treatment of convection. Each configuration comprises one control and two sensitivity experiments. The latter start from perturbed soil moisture initial conditions. Comparison of the simulated soil moisture–precipitation feedback reveals significant differences between the two systems. The 25-km simulations sustain a strong positive feedback, while those at 2.2-km resolution are associated with a predominantly negative feedback. Thus the two systems yield not only different strengths of this key feedback but also different signs. This has important implications, with the cloud-resolving model exhibiting a shorter soil moisture memory and a smaller soil moisture–temperature feedback. Analysis shows that the different feedback signs relate to the sensitivity of the simulated convective development to the presence of a stable layer sitting on top of the planetary boundary layer. In the 2.2-km integrations, dry initial soil moisture conditions yield more vigorous thermals (owing to stronger daytime heating), which can more easily break through the stable air barrier, thereby leading to deep convection and ultimately to a negative soil moisture–precipitation feedback loop. In the 25-km integrations, deep convection is much less sensitive to the stable layer because of the design of the employed convective parameterization. The authors also show that there are considerable differences in the simulated soil moisture–precipitation feedback between low-resolution modeling frameworks using different cloud convection schemes.

The response of boundary layer properties and cloudiness to changes in surface evaporative fraction (EF) is investigated in a single-column model to quantify the locally coupled impact of subgrid surface variations on the atmosphere during summer. Sensitive coupling days are defined when the model atmosphere exhibits large variations across a range of EFs centered on the analyzed value. Coupling sensitivity exists as both positive feedback (cloudiness increases with EF) and negative feedback (clouds increase with decreasing EF) regimes. The positive regime manifests in shallow convection situations, which are capped by a strengthened inversion and subsidence, restricting the vertical extent of convection to just above the boundary layer. Surfaces with larger EF (greater surface latent heat flux) can inject more moisture into the vertically confined system, lowering the cloud base and an increasing cloud liquid water path (LWP). Negative feedback regimes tend to manifest when large-scale deep convection, such as from mesoscale convective systems and fronts, is advected through the domain, where convection strengthens over surfaces with a lower EF (greater surface sensible heat flux). The invigoration of these systems by the land surface leads to an increase in LWP through strengthened updrafts and stronger coupling between the boundary layer and the free atmosphere. These results apply in the absence of heterogeneity-induced mesoscale circulations, providing a one-dimensional dynamical perspective on the effect of surface heterogeneity. This study provides a framework of intermediate complexity, lying between parcel theory and high-resolution coupled land–atmosphere modeling, and therefore isolates the relevant first-order processes in land–atmosphere interactions.

This paper investigates the relative importance of turbulence and aerosol effects on the broadening of the droplet size distribution (DSD) during the early stage of cloud and raindrop formation. A parcel–DNS (direct numerical simulation) hybrid approach is developed to seamlessly simulate the evolution of cloud droplets in an ascending cloud parcel. The results show that turbulence and cloud condensation nuclei (CCN) hygroscopicity are key to the efficient formation of large droplets. The ultragiant aerosols can quickly form embryonic drizzle drops and thus determine the onset time of autoconversion. However, due to their scarcity in natural clouds, their contribution to the total mass of drizzle drops is insignificant. In the meantime, turbulence sustains the formation of large droplets by effectively accelerating the collisions of small droplets. The DSD broadening through turbulent collisions is significant and therefore yields a higher autoconversion rate compared to that in a nonturbulent case. It is argued that the level of autoconversion is heavily determined by turbulence intensity. This paper also presents an in-cloud seeding scenario designed to scrutinize the effect of aerosols in terms of number concentration and size. It is found that seeding more aerosols leads to higher competition for water vapor, reduces the mean droplet radius, and therefore slows down the autoconversion rate. On the other hand, increasing the seeding particle size can buffer such a negative feedback. Despite the fact that the autoconversion rate is prominently altered by turbulence and seeding, bulk variables such as liquid water content (LWC) stays nearly identical among all cases. Additionally, the lowest autoconversion rate is not co-located with the smallest mean droplet radius. The finding indicates that the traditional Kessler-type or Sundqvist-type autoconversion parameterizations, which depend on the LWC or mean radius, cannot capture the drizzle formation process very well. Properties related to the width or the shape of the DSD are also needed, suggesting that the scheme of Berry and Reinhardt (1974) is conceptually better. It is also suggested that a turbulence-dependent relative-dispersion parameter should be considered.