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In this paper we develop a novel framework aimed to significantly reduce biases related to marine stratocumulus clouds in general circulation models (GCMs) while circumventing excessive computational cost requirements. Our strategy is to increase the horizontal resolution using a regionally refined mesh (RRM) over our region of interest in addition to using the Framework for Improvement by Vertical Enhancement (FIVE) to increase the vertical resolution only for specific physical processes that are important for stratocumulus. We apply the RRM off the coast of Peru in the southeastern Pacific, a region that climatologically contains the most marine stratocumulus in the subtropics. We find that our new modeling framework is able to replicate the results of our high-resolution benchmark simulation with much fidelity, while reducing the computational cost by several orders of magnitude. In addition, this framework is able to greatly reduce the long-standing biases associated with marine stratocumulus in GCMs when compared to the standard-resolution control simulation.

We present a first study showing that organization of trade cumulus (Tc) clouds can significantly enhance Tc response to climate change. Among four recently identified states of Tc organization, the “Flower” state has the highest and the “Sugar” state the lowest cloud fraction and cloud radiative effect. Using large-eddy simulations, we show that the organized “Flower” Tc state is strongly suppressed at the end of the 21st century, unlike the less organized “Sugar” Tc state and Tc studied previously. The primary cause of the suppression is down-welling long-wave radiation from increased greenhouse gas concentrations, which weakens the mesoscale circulation that organizes clouds into the “Flower” Tc state. The cumulus-valve mechanism, which is thought to limit Tc response to climate change, does not prevent this response. Our work unravels an unrecognized role of cloud organization in the cloud response to climate change.

We introduce a new machine learning approach to retrieve cloud optical thickness (COT) fields from visible passive imagery. In contrast to the heritage independent pixel approximation (IPA), our convolutional neural network (CNN) retrieval takes the spatial context of a pixel into account and thereby reduces artifacts arising from net horizontal photon transfer, which is commonly known as independent pixel (IP) bias. The CNN maps radiance fields acquired by imaging radiometers at a single wavelength channel to COT fields. It is trained with a low-complexity and therefore fast U-Net architecture with which the mapping is implemented as a segmentation problem with 36 COT classes. As a training data set, we use a single radiance channel (600 nm) generated from a 3D radiative transfer model using large eddy simulations (LESs) from the Sulu Sea. We study the CNN model under various conditions based on different permutations of cloud aspect ratio and morphology, and we use appropriate cloud morphology metrics to measure the performance of the retrievals. Additionally, we test the general applicability of the CNN on a new geographic location with LES data from the equatorial Atlantic. Results indicate that the CNN is broadly successful in overcoming the IP bias and outperforms IPA retrievals across all morphologies. Over the Atlantic, the CNN tends to overestimate the COT but shows promise in regions with high cloud fractions and high optical thicknesses, despite being outside the general training envelope. This work is intended to be used as a baseline for future implementations of the CNN that can enable generalization to different regions, scales, wavelengths, and sun-sensor geometries with limited training.

In this paper we develop a novel framework aimed to significantly reduce biases related to marine stratocumulus clouds in general circulation models (GCMs) while circumventing excessive computational cost requirements. Our strategy is to increase the horizontal resolution using a regionally refined mesh (RRM) over our region of interest in addition to using the Framework for Improvement of Vertical Enhancement (FIVE) to increase the vertical resolution only for specific physical processes that are important for stratocumulus. We apply the RRM off the coast of Peru in the Southeast Pacific, a region that climatologically contains the most marine stratocumulus in the subtropics. We find that our new modeling framework is able to replicate the results of our high resolution benchmark simulation with much fidelity, while reducing the computational cost by several orders of magnitude. In addition, this framework is able to greatly reduce the longstanding biases associated with marine stratocmulus in GCMs when compared to the standard resolution control simulation.

Data-driven quantification and parameterization of cloud physics in general, and of aerosol-cloud interactions in particular, rely on input data from observations or detailed simulations. These data sources have complementary limitations in terms of their spatial and temporal coverage and resolution; simulation data has the advantage of readily providing causality but cannot represent the full process complexity. In order to base data-driven approaches on comprehensive information, we therefore need ways to integrate different data sources. We discuss how the classical statistical technique of Gaussian-process emulation can be combined with specifically initialized ensembles of detailed cloud simulations (large-eddy simulations, LES) to provide a framework for evaluating data-driven descriptions of cloud characteristics and processes across different data sources. We specifically illustrate this approach for integrating LES and satellite data of aerosol-cloud interactions in subtropical stratocumulus cloud decks. We furthermore explore the extension of our framework to ground-based observations of Arctic mixed-phase clouds.- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -References:Glassmeier, F., F. Hoffmann, J. S. Johnson, T. Yamaguchi, K. S. Carslaw and G. Feingold (2019): “An emulator approach to stratocumulus susceptibility”, Atmos. Chem. Phys., 19, 10191- 10203, doi: 10.5194/acp-19-10191-2019 Hoffmann, F., F. Glassmeier, T. Yamaguchi and G. Feingold (2020): “Liquid water path steady states in stratocumulus: insights from process-level emulation and mixed-layer theory”, J. Atmos. Sci., 77, 2203-2215, doi: 10.1175/JAS-D-19-0241.1 Glassmeier, F., F. Hoffmann, J.S. Johnson, T. Yamaguchi, K. S. Carslaw, and G. Feingold (2021): “Aerosol-cloud climate cooling overestimated by ship-track data”, Science 371, 485–489, doi: 10.1126/science.abd3980

The evaluation of radiative forcing associated with aerosol-cloud interactions remains a significant source of uncertainty in future climate projections. The problem is confounded by the fact that aerosol particles influence clouds locally, and that averaging to larger spatial and/or temporal scales carries biases that depend on the heterogeneity and spatial correlation of the interacting fields and the non-linearity of the responses. Mimicking commonly applied satellite data analyses for calculation of albedo susceptibility So, we quantify So aggregation biases using an ensemble of 127 large eddy simulations of marine stratocumulus. We explore the cloud field properties that control this spatial aggregation bias, and quantify the bias for a large range of shallow stratocumulus cloud conditions manifesting a variety of morphologies and range of cloud fractions. We show that So spatial aggregation biases can be on the order of 100s of percent, depending on methodology. Key uncertainties emanate from the typically applied adiabatic drop concentration Nd retrieval, the correlation between aerosol and cloud fields, and the extent to which averaging reduces the variance in cloud albedo Ac and Nd. Biases are more often positive than negative. So biases are highly correlated to biases in the adjustment. Temporal aggregation biases are shown to offset spatial averaging biases. Both spatial and temporal biases have significant implications for observationally based assessments of aerosol indirect effects and our inferences of underlying aerosol-cloud-radiation effects.

The indirect radiative effect of aerosol variability on shallow cumulus clouds is realized in nature with considerable concurrent meteorological variability. Large-eddy simulations constrained by observations at a continental site in Oklahoma are performed to represent the variability of different meteorological states on days with different aerosol conditions. The total radiative effect of this natural covariation between aerosol and other meteorological drivers of total cloud amount and albedo is quantified. The changes to these bulk quantities are used to understand the response of the cloud radiative effect to aerosol–cloud interactions (ACI) in the context of concurrent processes, as opposed to attempting to untangle the effect of individual processes on a case-by-case basis. Mutual information (MI) analysis suggests that meteorological variability masks the strength of the relationship between cloud drop number concentration and the cloud radiative effect. This is shown to be mostly due to variation in solar zenith angle and cloud field horizontal heterogeneity masking the relationship between cloud drop number and cloud albedo. By combining MI and more traditional differential analyses, a framework to identify important modes of covariation between aerosol, clouds, and meteorological conditions is developed. This shows that accounting for solar zenith angle variation and implementing an albedo bias correction increases the detectability of the radiative effects of ACI in simulations of shallow cumulus.

Stratocumulus clouds constitute one of the largest negative climate forcings in the global radiation budget. This forcing is determined, inter alia, by the cloud liquid water path (LWP), which we analyze using a combination of Gaussian process emulation and mixed-layer theory. For nocturnal, nonprecipitating stratocumuli, we show that LWP steady states constitute an equilibrium primarily between radiative cooling and entrainment warming and drying. These steady states are approached from lower LWPs due to reduced entrainment, while higher LWPs are depleted by stronger entrainment. An analytical solution for the LWP steady state reveals not only the environmental conditions in which a stratocumulus cloud can be maintained, but also distinct analytical properties of the entrainment velocity that are required for a stable LWP steady state that opposes perturbations. In particular, the results highlight the importance of an entrainment velocity that increases strictly monotonically with the LWP if stratocumuli are to attain a stable LWP steady state. This is demonstrated through analysis of two commonly used mixed-layer entrainment parameterizations.

A new framework for the development of global high resolution models using a vertical‐meridional, two‐dimensional pole‐to‐pole domain is proposed. Compared with the three‐dimensional global model, the two‐dimensional framework can simulate cloud‐scale convection at significantly reduced computational cost, data volume, and turn‐around time, and accelerate the development phase. Although idealized, it allows for simulation of various clouds and scale interactions under a wide range of environmental conditions, even though zonal waves such as mid latitude storms cannot be simulated. Physics schemes can be tested and assessed over a full array of simultaneously occurring cloud regimes. The framework can serve as an intermediate step between a one‐dimensional simulation and a full three‐dimensional global model. Using this framework, we analyze the resolution dependencies of simulated clouds and the Hadley circulation in a range of global high resolution models by performing multiple 1000‐day simulations; for some resolutions tested, for example, 2 km horizontal resolution, three‐dimensional simulation is not feasible at the present time. Analysis shows that both horizontal and vertical resolution determine the properties of the Hadley circulation and clouds, and that the differences among the simulated Hadley circulations are understood as differences in scale interactions influenced by resolution.

Many methods for modelling functions over high-dimensional spaces assume global smoothness properties; such assumptions are often violated in practice. We introduce a method for modelling functions that display heterogeneity or contain discontinuities. The heterogeneity is dealt with by using a combination of Voronoi tessellation, to partition the input space, and separate Gaussian processes to model the function over different regions of the partitioned space. The proposed method is highly flexible since it allows the Voronoi cells to combine to form regions, which enables non-convex and disconnected regions to be considered. In such problems, identifying the borders between regions is often of great importance and we propose an adaptive sampling method to gain extra information along such borders. The method is illustrated by simulated examples and an application to real data, in which we see improvements in prediction error over the commonly used stationary Gaussian process and other non-stationary variations. In our application, a computationally expensive computer model that simulates the formation of clouds is investigated, the proposed method more accurately predicts the underlying process at unobserved locations than existing emulation methods. Supplementary materials accompanying this paper are available online.

Low cloud fractions (LCFs) and meteorological factors (MFs) over an oceanic region containing multiple cloud regimes are examined for three data sets: one Energy Exascale Earth System Model (E3SM) simulation with the default 72‐layer vertical grid (E3SM72), another one with 8‐times vertical resolution via the Framework for Improvement by Vertical Enhancement (E3SM×8), and one with MFs from ERA5 reanalysis and LCFs from the CERES SSF product (ERA5‐SSF). Neural networks (NNs) are trained to capture the relationship between MFs and LCF and to select the best‐performing MF subsets for predicting LCF. NN ensembles are used to (a) confirm the performance of selected MF subsets, (b) to serve as proxy models for each data set to predict LCFs for MFs from all data sets, and (c) to classify MFs into those in shared and uniquely occupied MF subspaces. Overall, E3SM72 and E3SM×8 have large fractions of MFs in shared MF subspace, but less so near the Californian and Peruvian stratocumulus decks. E3SM×8 and ERA5 have small fractions of MFs in shared MF subspace but greater than E3SM72 and ERA5, especially in the Southeast Pacific. The differences in LCFs between three pairs of data sets are decomposed into those associated with the differences in the LCF‐MF relationship and those involving different MFs. Given the same MFs, LCFs produced by E3SM×8 are greater than those produced by E3SM72 but are still different from those in ERA5‐SSF. In general, the shift in MFs dominates the difference in the LCFs.

The Atlantic Tradewind Ocean‐Atmosphere Mesoscale Interaction Campaign (ATOMIC) took place in January–February, 2020. It was designed to understand the relationship between shallow convection and the large‐scale environment in the trade‐wind regime. A Lagrangian large eddy simulation, following the trajectory of a boundary‐layer airmass, can reproduce a transition of trade cumulus organization from “sugar” to “flower” clouds with cold pools, observed on February 2–3. The simulation is driven with reanalysis large‐scale meteorology, and in‐situ aerosol data from ATOMIC and its joint field study EUREC4A. During the transition, large‐scale upward motion deepens the cloud layer. The total water path and optical depth increase, especially in the moist regions where flowers aggregate. This is due to mesoscale circulation that renders a net convergence of total water in the already moist and cloudy regions, strengthening the organization. An additional simulation shows that stronger large scale upward motion reinforces the mesoscale circulation and accelerates the organization process by strengthening the cloud‐layer mesoscale buoyant turbulence kinetic energy production.

The low cloud bias in global climate models (GCMs) remains an unsolved problem. Coarse vertical resolution in GCMs has been suggested to be a significant cause of low cloud bias because planetary boundary layer parameterizations cannot resolve sharp temperature and moisture gradients often found at the top of subtropical stratocumulus layers. This work aims to ameliorate the low cloud problem by implementing a new computational method, the Framework for Improvement by Vertical Enhancement (FIVE), into the Energy Exascale Earth System Model (E3SM). Three physics schemes representing microphysics, radiation, and turbulence as well as vertical advection are interfaced to vertically enhanced physics (VEP), which allows for these processes to be computed on a higher vertical resolution grid compared to the rest of the E3SM model. We demonstrate the better representation of subtropical boundary layer clouds with FIVE while limiting additional computational cost from the increased number of levels. When the vertical resolution approaches the large eddy simulation‐like vertical resolution in VEP, the climatological low cloud amount shows a significant increase of more than 30% in the southeastern Pacific Ocean. Using FIVE to improve the representation of low‐level clouds does not come with any negative side effects associated with the simulation of mid‐ and high‐level cloud and precipitation, that can occur when running the full model at higher vertical resolution.

General circulation models (GCMs) are typically run with coarse vertical resolution. For example, the Energy Exascale Earth System Model (E3SM) has a vertical resolution of about 200 m in the boundary layer, which is far too coarse to resolve sharp gradients often found in the thermodynamic fields capping subtropical marine stratocumulus. In this article, we present a series of multiyear atmosphere only simulations of E3SM version 1 where we progressively increase the vertical resolution in the lower troposphere to scales approaching those often used in large eddy simulation (LES). We report marginal impacts in regards to the simulation of boundary layer clouds when vertical resolution is moderately increased, yet find significant positive impacts when the vertical resolution approaches that typically used in LES (∼10 m). In these experiments, there is a marked change in the simulated turbulence and thermodynamics which leads to more abundant marine stratocumulus. However, these simulations are burdened with excessive computational cost. They are also subject to degradations in overall climate metrics due to time step sensitivities and because some processes and parameterizations are sensitive to changes in the vertical resolution.

The effect of 1-D and 3-D thermal radiation on cloud droplet growth in shallow cumulus clouds is investigated using large eddy simulations with size-resolved cloud microphysics. A two-step approach is used for separating microphysical effects from dynamical feedbacks. In step one, an offline parcel model is used to describe the onset of rain. The growth of cloud droplets to raindrops is simulated with bin-resolved microphysics along previously recorded Lagrangian trajectories. It is shown that thermal heating and cooling rates can enhance droplet growth and raindrop production. Droplets grow to larger size bins in the 10–30 µm radius range. The main effect in terms of raindrop production arises from recirculating parcels, where a small number of droplets are exposed to strong thermal cooling at cloud edge. These recirculating parcels, comprising about 6 %–7 % of all parcels investigated, make up 45 % of the rain for the no-radiation simulation and up to 60 % when 3-D radiative effects are considered. The effect of 3-D thermal radiation on rain production is stronger than that of 1-D thermal radiation. Three-dimensional thermal radiation can enhance the rain amount up to 40 % compared to standard droplet growth without radiative effects in this idealized framework. In the second stage, fully coupled large eddy simulations show that dynamical effects are stronger than microphysical effects, as far as the production of rain is concerned. Three-dimensional thermal radiative effects again exceed one-dimensional thermal radiative effects. Small amounts of rain are produced in more clouds (over a larger area of the domain) when thermal radiation is applied to microphysics. The dynamical feedback is shown to be an enhanced cloud circulation with stronger subsiding shells at the cloud edges due to thermal cooling and stronger updraft velocities in the cloud center. It is shown that an evaporation–circulation feedback reduces the amount of rain produced in simulations where 3-D thermal radiation is applied to microphysics and dynamics, in comparison to where 3-D thermal radiation is only applied to dynamics.

The effect of anthropogenic aerosol on the reflectivity of stratocumulus cloud decks through changes in cloud amount is a major uncertainty in climate projections. In frequently occurring nonprecipitating stratocumulus, cloud amount can decrease through aerosol-enhanced cloud-top mixing. The climatological relevance of this effect is debated because ship exhaust only marginally reduces stratocumulus amount. By comparing detailed numerical simulations with satellite analyses, we show that ship-track studies cannot be generalized to estimate the climatological forcing of anthropogenic aerosol. The ship track-derived sensitivity of the radiative effect of nonprecipitating stratocumulus to aerosol overestimates their cooling effect by up to 200%. The offsetting warming effect of decreasing stratocumulus amount needs to be taken into account if we are to constrain the cloud-mediated radiative forcing of anthropogenic aerosol.

The low cloud bias in atmospheric models for climate and weather remains an unsolved problem. Coarse vertical resolution in the current global climate models (GCM) may be a significant cause of low cloud bias because planetary boundary layer (PBL) parameterizations, including higher-order turbulence closure (HOC), cannot resolve sharp temperature and moisture gradients often found at the top of subtropical stratocumulus layers. The aim of this work is to implement a new computational method, the Framework for Improvement by Vertical Enhancement (FIVE) into the Energy Exascale Earth System Model (E3SM) and its single column model. Three physics schemes are interfaced to vertically enhanced physics (VEP), which allows for these schemes to be computed on a higher vertical resolution grid compared to rest of the E3SM model. In this presentation we use VEP for turbulence, microphysics, and radiation parameterizations and demonstrate better representation of subtropical boundary layer clouds while limiting additional computational cost from the increased number of levels. We will also briefly discuss future plans for an adaptive vertical grid for VEP, which will allow for additional layers to be added only when/where they are needed.

The stratocumulus to cumulus transition (SCT) is typically considered to be a slow, multi-day process, caused primarily by dry air entrainment associated with overshooting cumulus, with minor influence of drizzle. This study revisits the role of drizzle in the SCT with large eddy simulations coupled with a two-moment bulk microphysics scheme that includes a budget on aerosol (Na) and cloud droplet number concentrations (Nc). We show a strong precipitation-induced modulation of the SCT by drizzle initiated in penetrative cumulus under stratocumulus. Lagrangian SCT simulations are initiated with various, moderate Na (100-250 cm-3), which produce little to no drizzle from the stratocumulus. As expected, drizzle formation in cumuli is regulated by cloud depth and Nc, with stronger dependence on cloud depth, so that, for the current case, drizzle is generated in all simulations once cumulus clouds become sufficiently deep. The drizzle generated in the cumuli washes out stratocumulus cloud water and much of the aerosol, and a cumulus state appears for approximately 10 hours. With additional simulations with a fixed Nc (100 cm-3), we show that prediction of Nc is necessary for this fast SCT since it is a result of a positive feedback of collision-coalescence induced aerosol depletion that enhances drizzle formation. A fixed Nc does not permit this feedback, and thus results in weak influence of drizzle on the SCT. Simulations with fixed droplet concentrations that bracket the time varying aerosol/drop concentrations are therefore not representative of the role of drizzle in the SCT.

Closed-cell mesoscale organization and its relationship to entrainment and the properties of a low, non-precipitating stratocumulus cloud is investigated. Large eddy simulations were run over 10 periodic diurnal cycles during which mesoscale organization could fully develop and approach a quasi-steady state, on five domains sized from 2.4 km × 2.4 km to 38.4 km × 38.4 km. The four smaller domains hosted a single cell with an aspect ratio that increased with domain size. On the largest domain, mesoscale organization consisted of a cell population that evolved over the course of the diurnal cycle. It is found that with increasing cell aspect ratio, entrainment weakens and the boundary layer becomes shallower, cooler, moister, and more decoupled. This causes an increase in cloud water path and cloud radiative effect up to a cell aspect ratio of 16. With further increase in cell aspect ratio, circulation on the cell scale becomes less effective in supplying moisture to the cloud and in producing turbulent kinetic energy (TKE). This mechanism can explain scale saturation in closed-cell mesoscale organization. The simulations support a maximum stable aspect ratio of closed-cell mesoscale organization between 32 and 64, consistent with the observational limit of ≈40. The simulations show furthermore that entrainment does not, in general, scale with buoyant production of TKE. Instead, entrainment correlates with the vertical component of TKE. This implies vertical motion as a driver of entrainment, and a convective velocity scale based on the vertical component of TKE rather than on buoyant production of TKE.

The climatic relevance of aerosol–cloud interactions depends on the sensitivity of the radiative effect of clouds to cloud droplet number N, and liquid water path LWP. We derive the dependence of cloud fraction CF, cloud albedo AC, and the relative cloud radiative effect rCRE=CF⋅AC on N and LWP from 159 large-eddy simulations of nocturnal stratocumulus. These simulations vary in their initial conditions for temperature, moisture, boundary-layer height, and aerosol concentration but share boundary conditions for surface fluxes and subsidence. Our approach is based on Gaussian-process emulation, a statistical technique related to machine learning. We succeed in building emulators that accurately predict simulated values of CF, AC, and rCRE for given values of N and LWP. Emulator-derived susceptibilities ∂ln⁡rCRE/∂ln⁡N and ∂ln⁡rCRE/∂ln⁡LWP cover the nondrizzling, fully overcast regime as well as the drizzling regime with broken cloud cover. Theoretical results, which are limited to the nondrizzling regime, are reproduced. The susceptibility ∂ln⁡rCRE/∂ln⁡N captures the strong sensitivity of the cloud radiative effect to cloud fraction, while the susceptibility ∂ln⁡rCRE/∂ln⁡LWP describes the influence of cloud amount on cloud albedo irrespective of cloud fraction. Our emulation-based approach provides a powerful tool for summarizing complex data in a simple framework that captures the sensitivities of cloud-field properties over a wide range of states.

Observed and projected trends in large-scale wind speed over the oceans prompt the question: how do marine stratocumulus clouds and their radiative properties respond to changes in large-scale wind speed? Wind speed drives the surface fluxes of sensible heat, moisture, and momentum and thereby acts on cloud liquid water path (LWP) and cloud radiative properties. We present an investigation of the dynamical response of non-precipitating, overcast marine stratocumulus clouds to different wind speeds over the course of a diurnal cycle, all else equal. In cloud-system resolving simulations, we find that higher wind speed leads to faster boundary layer growth and stronger entrainment. The dynamical driver is enhanced buoyant production of turbulence kinetic energy (TKE) from latent heat release in cloud updrafts. LWP is enhanced during the night and in the morning at higher wind speed, and more strongly suppressed later in the day. Wind speed hence accentuates the diurnal LWP cycle by expanding the morning–afternoon contrast. The higher LWP at higher wind speed does not, however, enhance cloud top cooling because in clouds with LWP ⪆ 50 g m−2, longwave emissions are insensitive to LWP. This leads to the general conclusion that in sufficiently thick stratocumulus clouds, additional boundary layer growth and entrainment due to a boundary layer moistening arises by stronger production of TKE from latent heat release in cloud updrafts, rather than from enhanced longwave cooling. We find that large-scale wind modulates boundary layer decoupling. At nighttime and at low wind speed during daytime, it enhances decoupling in part by faster boundary layer growth and stronger entrainment and in part because shear from large-scale wind in the sub-cloud layer hinders vertical moisture transport between the surface and cloud base. With increasing wind speed, however, in decoupled daytime conditions, shear-driven circulation due to large-scale wind takes over from buoyancy-driven circulation in transporting moisture from the surface to cloud base and thereby reduces decoupling and helps maintain LWP. The total (shortwave + longwave) cloud radiative effect (CRE) responds to changes in LWP and cloud fraction, and higher wind speed translates to a stronger diurnally averaged total CRE. However, the sensitivity of the diurnally averaged total CRE to wind speed decreases with increasing wind speed.

The topic of cloud radiative forcing associated with the atmospheric aerosol has been the focus of intense scrutiny for decades. The enormity of the problem is reflected in the need to understand aspects such as aerosol composition, optical properties, cloud condensation, and ice nucleation potential, along with the global distribution of these properties, controlled by emissions, transport, transformation, and sinks. Equally daunting is that clouds themselves are complex, turbulent, microphysical entities and, by their very nature, ephemeral and hard to predict. Atmospheric general circulation models represent aerosol−cloud interactions at ever-increasing levels of detail, but these models lack the resolution to represent clouds and aerosol−cloud interactions adequately. There is a dearth of observational constraints on aerosol−cloud interactions. We develop a conceptual approach to systematically constrain the aerosol−cloud radiative effect in shallow clouds through a combination of routine process modeling and satellite and surface-based shortwave radiation measurements. We heed the call to merge Darwinian and Newtonian strategies by balancing microphysical detail with scaling and emergent properties of the aerosol−cloud radiation system.

The two-way transition between closed and open cellular convection is addressed in an idealized cloud-resolving modeling framework. A series of cloud-resolving simulations shows that the transition between closed and open cellular states is asymmetrical and characterized by a rapid ("runaway") transition from the closed- to the open-cell state but slower recovery to the closed-cell state. Given that precipitation initiates the closed–open cell transition and that the recovery requires a suppression of the precipitation, we apply an ad hoc time-varying drop concentration to initiate and suppress precipitation. We show that the asymmetry in the two-way transition occurs even for very rapid drop concentration replenishment. The primary barrier to recovery is the loss in turbulence kinetic energy (TKE) associated with the loss in cloud water (and associated radiative cooling) and the vertical stratification of the boundary layer during the open-cell period. In transitioning from the open to the closed state, the system faces the task of replenishing cloud water fast enough to counter precipitation losses, such that it can generate radiative cooling and TKE. It is hampered by a stable layer below cloud base that has to be overcome before water vapor can be transported more efficiently into the cloud layer. Recovery to the closed-cell state is slower when radiative cooling is inefficient such as in the presence of free tropospheric clouds or after sunrise, when it is hampered by the absorption of shortwave radiation. Tests suggest that recovery to the closed-cell state is faster when the drizzle is smaller in amount and of shorter duration, i.e., when the precipitation causes less boundary layer stratification. Cloud-resolving model results on recovery rates are supported by simulations with a simple predator–prey dynamical system analogue. It is suggested that the observed closing of open cells by ship effluent likely occurs when aerosol intrusions are large, when contact comes prior to the heaviest drizzle in the early morning hours, and when the free troposphere is cloud free.

Precipitation is thought to be a necessary but insufficient condition for the transformation of stratocumulus-topped closed cellular convection to open cellular cumuliform convection. Here we test the hypothesis that the spatial distribution of precipitation is a key element of the closed-to-open cell transition. A series of idealized 3-D simulations are conducted to evaluate the dependency of the transformation on the areal coverage of rain, and to explore the role of interactions between multiple rainy areas in the formation of the open cells. When rain is restricted to a small area, even substantial rain (order few mm day−1) does not result in a transition. With increasing areal coverage of the rain, the transition becomes possible provided that the rain rate is sufficiently large. When multiple small rain regions interact with each other, the transition occurs and spreads over a wider area, provided that the distance between the rain regions is short. When the distance between the rain areas is large, the transition eventually occurs, albeit slowly. For much longer distances between rain regions the system is anticipated to remain in a closed-cell state. These results suggest a connection to the recently hypothesized remote control of open-cell formation. Finally it is shown that this transition occurs along a consistent path in the phase space of the mean vs. coefficient of variation of the liquid water path, droplet number and optical depth. This could be used as a diagnostic tool for global analyses of the statistics of closed- and open-cell occurrence and transitions between them.

This study examines the effect of spatial gradients in biomass burning (BB) aerosol on mesoscale circulations and clouds in the Amazon through high-resolution numerical modeling over areas of 30 km to 60 km. Inhomogeneous horizontal distribution of BB aerosol results in differential surface heat fluxes and radiative heating of the air, which generates circulation patterns that strongly influence cloud formation. The influence on air circulation and cumulus cloud formation depends on the BB aerosol loading, its vertical location, and the width of the plume. Plumes that reside at higher altitudes (~1.5 km altitude) produce monotonic responses to aerosol loading whereas the response to plumes close to the surface changes nonmonotonically with plume width and aerosol loading. Sensitivity tests highlight the importance of interactive calculations of surface latent and heat fluxes with a coupled land surface model. In the case of the plume residing at higher altitude, failure to use interactive fluxes results in a reversal of the circulation whereas for the plume residing nearer the surface, the interactive surface model weakens the circulation. The influence of the BB aerosol on heating patterns, circulations, surface fluxes, and resultant cloud amount prevails over the BB aerosol-cloud microphysical influences.

In this study, a series of idealized large-eddy simulations is used to understand the relative impact of cloud-top and subcloud-layer sources of moisture on the microphysical–radiative–dynamical feedbacks in an Arctic mixed-phase stratocumulus (AMPS) cloud system. This study focuses on a case derived from observations of a persistent single-layer AMPS cloud deck on 8 April 2008 during the Indirect and Semi-Direct Aerosol Campaign near Barrow, Alaska. Moisture and moist static energy budgets are used to examine the potential impact of ice in mixed-phase clouds, specific humidity inversions coincident with temperature inversions as a source of moisture for the cloud system, and the presence of cloud liquid water above the mixed-layer top. This study demonstrates that AMPS have remarkable insensitivity to changes in moisture source. When the overlying air is dried initially, radiative cooling and turbulent entrainment increase moisture import from the surface layer. When the surface layer is dried initially, the system evolves to a state with reduced mixed-layer water vapor and increased surface-layer moisture, reducing the loss of water through precipitation and entrainment of near-surface air. Only when moisture is reduced both above and below the mixed layer does the AMPS decay without reaching a quasi-equilibrium state. A fundamental finding of this study is that, with or without cloud ice and with or without a specific humidity inversion, the cloud layer eventually extends into the temperature inversion producing a precipitation flux as a source of water into the mixed layer.