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Warm boundary layer clouds in the eastern North Atlantic region exhibit significant diurnal variations in cloud properties. However, the diurnal cycle of the aerosol indirect effect (AIE) for these clouds remains poorly understood. This study takes advantage of recent advancements in the spatial resolution of geostationary satellites to explore the daytime variation in the AIE by estimating the cloud susceptibilities to changes in cloud droplet number concentration (Nd). Cloud retrievals for the month of July over 4 years (2018–2021) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat-11 over this region are analyzed. Our results reveal a significant “U-shaped” daytime cycle in susceptibilities of the cloud liquid water path (LWP), cloud albedo, and cloud fraction. Clouds are found to be more susceptible to Nd perturbations at noon and less susceptible in the morning and evening. The magnitude and sign of cloud susceptibilities depend heavily on the cloud state defined by cloud LWP and precipitation conditions. Non-precipitating thin clouds account for 44 % of all warm boundary layer clouds in July, and they contribute the most to the observed daytime variation. Non-precipitating thick clouds are the least frequent cloud state (10 %), and they exhibit more negative LWP and albedo susceptibilities compared to thin clouds. Precipitating clouds are the dominant cloud state (46 %), but their cloud susceptibilities show minimal variation throughout the day. We find evidence that the daytime variation in LWP and albedo susceptibilities for non-precipitating clouds is influenced by a combination of the diurnal transition between non-precipitating thick and thin clouds and the “lagged” cloud responses to Nd perturbations. The daytime variation in cloud fraction susceptibility for non-precipitating thick clouds can be attributed to the daytime variation in cloud morphology (e.g., overcast or broken). The dissipation and development of clouds do not adequately explain the observed variation in cloud susceptibilities. Additionally, daytime variation in cloud susceptibility is primarily driven by variation in the intensity of cloud response rather than the frequency of occurrence of cloud states. Our results imply that polar-orbiting satellites with an overpass time at 13:30 LT underestimate daytime mean values of cloud susceptibility, as they observe susceptibility daily minima in the study region.

Abstract The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional “high–resolution” simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud‐Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real‐world climate via a set of four 40 day simulations covering all 4 seasons of the year.

This study documents clouds simulated by the Energy Exascale Earth System Model (E3SM) version 2 (E3SMv2) and attempts to understand what causes the model behavior change in clouds relative to E3SMv1. This is done by analyzing the last 30-year (1985–2014) data from the 165-year historical simulations using E3SMv1 and v2 and four sensitivity tests to isolate the impact of changes in model parameter choices in its turbulence, shallow convection, and cloud macrophysics parameterization (Cloud Layers Unified By Binormals, CLUBB); microphysical parameterization (MG2); and deep-convection scheme (ZM), as well as model physics changes in convective triggering. It is shown that E3SMv2 significantly improves the simulation of subtropical coastal stratocumulus clouds and clouds with optical depth larger than 3.6 over the stratocumulus-to-cumulus transition regimes, where the shortwave cloud radiative effect (SWCRE) is also improved, and the Southern Ocean (SO) while seeing an overall slight degradation in low clouds over other tropical and subtropical oceans. The better performance in E3SMv1 over those regions is partially due to error compensation between its simulated optically thin and intermediate low clouds for which E3SMv2 actually improves simulation of optically intermediate low clouds. Sensitivity tests indicate that the changes in low clouds are primarily due to the tuning done in CLUBB. The impact of the ZM tuning is mainly on optically intermediate and thick high clouds, contributing to an improved SWCRE and longwave cloud radiative effect (LWCRE). The impact of the MG2 tuning and the new convective trigger is primarily on the high latitudes and the SO. They have a relatively smaller impact on clouds than CLUBB tuning and ZM tuning do. This study offers additional insights into clouds simulated in E3SMv2 by utilizing multiple data sets and the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) diagnostic tool as well as sensitivity tests. The improved understanding will benefit future E3SM developments.

Abstract This study examines marine boundary layer cloud regime transition during a cold air outbreak (CAO) over the Norwegian Sea, simulated by a global storm‐resolving model (GSRM) known as the Simple Cloud‐Resolving Energy Exascale Earth System Model Atmosphere Model (SCREAM). By selecting observational references based on a combination of large‐scale conditions rather than strict time‐matched comparisons, this study finds that SCREAM qualitatively captures the CAO cloud transition, including boundary layer growth, cloud mesoscale structure, and phase partitioning. SCREAM also accurately locates the greatest ice and liquid in the mesoscale updrafts, however, underestimates supercooled liquid water in cumulus clouds. The model evaluation approach adopted by this study takes advantages of the existing computational‐expensive global simulations of GSRM and the available observations to understand model performance and can be applied to assessments of other cloud regimes in different regions. Such practice provides valuable guidance on the future effort to correct and improve biased model behaviors.

The E3SM Diagnostics Package (E3SM Diags) is a modern, Python-based Earth system model (ESM) evaluation tool (with Python module name e3sm_diags), developed to support the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM). E3SM Diags provides a wide suite of tools for evaluating native E3SM output, as well as ESM data on regular latitude–longitude grids, including output from Coupled Model Intercomparison Project (CMIP) class models. E3SM Diags is modeled after the National Center for Atmospheric Research (NCAR) Atmosphere Model Working Group (AMWG, 2022) diagnostics package. In its version 1 release, E3SM Diags included a set of core essential diagnostics to evaluate the mean physical climate from model simulations. As of version 2.7, more process-oriented and phenomenon-based evaluation diagnostics have been implemented, such as analysis of the quasi-biennial oscillation (QBO), the El Niño–Southern Oscillation (ENSO), streamflow, the diurnal cycle of precipitation, tropical cyclones, ozone and aerosol properties. An in situ dataset from DOE's Atmospheric Radiation Measurement (ARM) program has been integrated into the package for evaluating the representation of simulated cloud and precipitation processes. This tool is designed with enough flexibility to allow for the addition of new observational datasets and new diagnostic algorithms. Additional features include customizable figures; streamlined installation, configuration and execution; and multiprocessing for fast computation. The package uses an up-to-date observational data repository maintained by its developers, where recent datasets are added to the repository as they become available. Finally, several applications for the E3SM Diags module were introduced to fit a diverse set of use cases from the scientific community.

Abstract This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40‐day prescribed‐sea‐surface‐temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be ∼6× the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long‐standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress.

Abstract Subkilometer processes are critical to the physics of aerosol‐cloud interaction (ACI) but have been dependent on parameterizations in global model simulations. We thus report the strength of ACI in the Ultra‐Parameterized Community Atmosphere Model (UPCAM), a multiscale climate model that uses coarse exterior resolution to embed explicit cloud‐resolving models with enough resolution (250 m horizontal, 20 m vertical) to quasi‐resolve subkilometer eddies. To investigate the impact on ACIs, UPCAM's simulations are compared to a coarser multiscale model with 4 km horizontal resolution. UPCAM produces cloud droplet number concentrations (Nd) and cloud liquid water path (LWP) values that are higher than the coarser model but equally plausible compared to observations. Our analysis focuses on the Northern Hemisphere (20–50°N) oceans, where historical aerosol increases have been largest. We find similarities in the overall radiative forcing from ACIs in the two models, but this belies fundamental underlying differences. The radiative forcing from increases in LWP is weaker in UPCAM, whereas the forcing from increases in Nd is larger. Surprisingly, the weaker LWP increase is not due to a weaker increase in LWP in raining clouds, but a combination of weaker increase in LWP in nonraining clouds and a smaller fraction of raining clouds in UPCAM. The implication is that as global modeling moves toward finer than storm‐resolving grids, nuanced model validation of ACI statistics conditioned on the existence of precipitation and good observational constraints on the baseline probability of precipitation will become key for tighter constraints and better conceptual understanding.

A mistake swapped process rates between autoconversion and accretion in global model solutions. Revised figures are presented. The accretion to autoconversion ratio in the model does increase with Liquid Water Path (LWP) as in the steady state model but biases remain. Simulated autoconversion rates are too high. Adjusting process rates following the steady state model ideas leads to an improvement in process rates. The main conclusion is unaffected.

Five pockets of open cells (POCs) are studied using aircraft flights from the VOCALS Regional Experiment (VOCALS-REx), conducted in October and November 2008 over the southeast Pacific Ocean. Satellite imagery from the geostationary satellite GOES-10 is used to distinguish POC areas, and measurements from the aircraft flights are used to compare aerosol, cloud, precipitation, and boundary layer conditions inside and outside of POCs. Conditions observed across individual POC cases are also compared. POCs are observed in boundary layers with a wide range of inversion heights (1250 to 1600 m) and surface wind speeds (5 to 11 m s−1) and show no remarkable difference from the observed surface and free-tropospheric conditions during the two months of the field campaign. In all cases, compared to the surrounding overcast region the POC boundary layer is more decoupled, supporting both thin stratiform and deeper cumulus clouds. Although cloud-base precipitation rates are higher in the POC than the overcast region in each case, a threshold precipitation rate that differentiates POC precipitation from overcast precipitation does not exist. Mean cloud-base precipitation rates in POCs can range from 1.7 to 5.8 mm d−1 across different POC cases. The occurrence of heavy drizzle (> 0 dBZ) lower in the boundary layer better differentiates POC precipitation from overcast precipitation, likely leading to the more active cold pool formation in POCs. Cloud droplet number concentration is at least a factor of 8 smaller in the POC clouds, and the ratio of drizzle water to cloud water in POC clouds is over an order of magnitude larger than that in overcast clouds, indicating an enhancement of collision–coalescence processes in POC clouds. Despite large variations in the accumulation-mode aerosol concentrations observed in the surrounding overcast region (65 to 324 cm−3), the accumulation-mode aerosol concentrations observed in the subcloud layer of all five POCs exhibit a much narrower range (24 to 40 cm−3), and cloud droplet concentrations within the cumulus updrafts originating in this layer reflect this limited variability. Above the POC subcloud layer exists an ultraclean layer with accumulation-mode aerosol concentrations < 5 cm−3, demonstrating that in-cloud collision–coalescence processes efficiently remove aerosols. The existence of the ultraclean layer also suggests that the major source of accumulation-mode aerosols, and hence of cloud condensation nuclei in POCs, is the ocean surface, while entrainment of free-tropospheric aerosols is weak. The measurements also suggest that at approximately 30 cm−3 a balance of surface source and coalescence scavenging sinks of accumulation-mode aerosols maintain the narrow range of observed subcloud aerosol concentrations.

Cloud microphysical process rates control the amount of condensed water in clouds and impact the susceptibility of precipitation to cloud-drop number and aerosols. The relative importance of different microphysical processes in a climate model is analyzed, and the autoconversion and accretion processes are found to be critical to the condensate budget in most regions. A simple steady-state model of warm rain formation is used to illustrate that the diagnostic rain formulations typical of climate models may result in excessive contributions from autoconversion, compared to observations and large eddy simulation models with explicit bin-resolved microphysics and rain formation processes. The behavior does not appear to be caused by the bulk process rate formulations themselves, because the steady-state model with the same bulk accretion and autoconversion has reduced contributions from autoconversion. Sensitivity tests are conducted to analyze how perturbations to the precipitation microphysics for stratiform clouds impact process rates, precipitation susceptibility and aerosol–cloud interactions (ACI). With similar liquid water path, corrections for the diagnostic rain assumptions in the GCM based on the steady-state model to boost accretion indicate that the radiative effects of ACI may decrease by 20% in the GCM. Links between process rates, susceptibility and ACI are not always clear in the GCM. Better representation of the precipitation process, for example by prognosticating precipitation mass and number, may help better constrain these effects in global models with bulk microphysics schemes.

Although typically associated with precipitating cumuli, cold pools also form under shallower stratocumulus. This study presents cold-pool observations as sampled by the NSF/NCAR C-130, which made cloud and boundary-layer measurements over the southeast Pacific stratocumulus region at an altitude of approximately 150 m during the VOCALS Regional Experiment. Ninety edges of cold pools are found in the C-130 measurements by identifying step-like changes in the potential temperature. Examination of their mesoscale environment shows that the observed cold pools tend to form under heavier precipitation, thicker clouds, and in cleaner environments. Cold pools are also found to form under clouds with high LWP values over the night of or before sampling. When they form, cold pools often form in clusters or on top of each other, rather than as separate, individual entities. Their sizes range from 2 km to 16 km (middle 50th percentile), where the largest of cold pools are associated with the greatest drops in temperature. Composites of various observed thermodynamic and chemical variables along the cold-pool edges indicate increased humidity, equivalent potential temperature, coarse-mode aerosol, and dimethyl sulfide concentration inside cold pools. The enhancements inside cold pools are consistent with increased static stability that traps fluxes from the ocean surface in the lowest levels of the boundary layer. By using pressure perturbations, the average cold pool is estimated to be approximately 300 m deep. The temperature depression in cold pools also leads to density-driven flows that drive convergence of horizontal winds and measurable, mechanically driven vertical wind velocity at the edges of cold pools.

The relationship between precipitation rate and accumulation mode aerosol concentration in marine stratocumulus-topped boundary layers is investigated by applying the precipitation susceptibility metric to aircraft data obtained during the VOCALS Regional Experiment. A new method to calculate the precipitation susceptibility that incorporates non-precipitating clouds is introduced. The mean precipitation rate R over a segment of the data is expressed as the product of a drizzle fraction f and a drizzle intensity I (mean rate for drizzling columns). The susceptibility Sx is then defined as the fractional decrease in precipitation variable x = {R, f, I} per fractional increase in the concentration of aerosols with dry diameter >0.1 μm, with cloud thickness h held fixed. The precipitation susceptibility SR is calculated using data from both precipitating and non-precipitating cloudy columns to quantify how aerosol concentrations affect the mean precipitation rate of all clouds of a given h range and not just the mean precipitation of clouds that are precipitating. SR systematically decreases with increasing h, and this is largely because Sf decreases with h while SI is approximately independent of h. In a general sense, Sf can be thought of as the effect of aerosols on the probability of precipitation, while SI can be thought of as the effect of aerosols on the intensity of precipitation. Since thicker clouds are likely to precipitate regardless of ambient aerosol concentration, we expect Sf to decrease with increasing h. The results are broadly insensitive to the choice of horizontal averaging scale. Similar susceptibilities are found for both cloud base and near-surface drizzle rates. The analysis is repeated with cloud liquid water path held fixed instead of cloud thickness. Simple power law relationships relating precipitation rate to aerosol concentration or cloud droplet concentration do not capture this observed behavior.