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This paper describes climate simulations of the Community Atmosphere Model, version 5 (CAM5), coupled with a higher-order turbulence closure known as Cloud Layers Unified by Binormals (CLUBB). CLUBB is a unified parameterization of the planetary boundary layer (PBL) and shallow convection that is centered around a trivariate probability density function (PDF) and replaces the conventional PBL, shallow convection, and cloud macrophysics schemes in CAM5. CAM–CLUBB improves many aspects of the base state climate compared to CAM5. Chief among them is the transition of stratocumulus to trade wind cumulus regions in the subtropical oceans. In these regions, CAM–CLUBB provides a much more gradual transition that is in better agreement with observational analysis compared to CAM5, which is too abrupt. The improvement seen in CAM–CLUBB can be largely attributed to the gradual evolution of the simulated turbulence, which is in part a result of the unified nature of the parameterization, and to the general improved representation of shallow cumulus clouds compared to CAM5. In addition, there are large differences in the representation and structure of marine boundary layer clouds between CAM–CLUBB and CAM5. CAM–CLUBB is also shown to be more robust, in terms of boundary layer clouds, to changes in vertical resolution for global simulations in a preliminary test.

A new approach for treating organized convection in global climate models (GCMs) referred to as multiscale coherent structure parameterization (MCSP) introduces physical and dynamical effects of organized convection that are missing from contemporary parameterizations. The effects of vertical shear are approximated by a nonlinear slantwise overturning model based on Lagrangian conservation principles. Simulation of the April 2009 Madden–Julian oscillation event during the Year of Tropical Convection (YOTC) over the Indian Ocean using the Weather Research and Forecasting (WRF) Model at 1.3-km grid spacing identifies self-similar properties for squall lines, MCSs, and superclusters embedded in equatorial waves. The slantwise overturning model approximates this observed self-similarity. The large-scale effects of MCSP are examined in two categories of GCM. First, large-scale convective systems simulated in an aquaplanet model are approximated by slantwise overturning with attention to convective momentum transport. Second, MCSP is utilized in the Community Atmosphere Model, version 5.5 (CAM5.5), as tendency equations for second-baroclinic heating and convective momentum transport. The difference between MCSP and CAM5.5 is a direct measure of the global effects of organized convection. Consistent with TRMM measurements, the MCSP generates large-scale precipitation patterns in the tropical warm pool and the adjoining locale; improves precipitation in the intertropical convergence zone (ITCZ), South Pacific convergence zone (SPCZ), and Maritime Continent regions; and affects tropical wave modes. In conclusion, the treatment of organized convection by MCSP is salient for the next generation of GCMs.

Over the past decade a new type of global climate model (GCM) has emerged, which is known as a multiscale modeling framework (MMF). Colorado State University's MMF represents a coupling between the Community Atmosphere Model and the System for Atmospheric Modeling (SAM) to serve as the cloud‐resolving model (CRM) that replaces traditionally parameterized convection in GCMs. However, due to the high computational expense of the MMF, the grid size of the embedded CRM is typically limited to 4 km for long‐term climate simulations. With grid sizes this coarse, shallow convective processes and turbulence cannot be resolved and must still be parameterized within the context of the embedded CRM. This paper describes a computationally efficient closure that aims to better represent turbulence and shallow convective processes in coarse‐grid CRMs. The closure is based on the assumed probability density function (PDF) technique to serve as the subgrid‐scale (SGS) condensation scheme and turbulence closure that employs a diagnostic method to determine the needed input moments. This paper describes the scheme, as well as the formulation of the eddy length which is empirically determined from large eddy simulation (LES) data. CRM tests utilizing the closure yields good results when compared to LESs for two trade‐wind cumulus cases, a transition from stratocumulus to cumulus, and continental cumulus. This new closure improves the representation of clouds through the use of SGS condensation scheme and turbulence due to better representation of the buoyancy flux and dissipation rates. In addition, the scheme reduces the sensitivity of CRM simulations to horizontal grid spacing. The improvement when compared to the standard low‐order closure configuration of the SAM is especially striking. Key Points Simplified PDF parameter just as good as predictive New turbulence length scale functions well for boundary layer clouds Better representation of boundary layer clouds

This study provides comprehensive insight into the notable differences in clouds and precipitation simulated by the Energy Exascale Earth System Model Atmosphere Model version 0 and version 1 (EAMv1). Several sensitivity experiments are conducted to isolate the impact of changes in model physics, resolution, and parameter choices on these differences. The overall improvement in EAMv1 clouds and precipitation is primarily attributed to the introduction of a simplified third‐order turbulence parameterization Cloud Layers Unified By Binormals (along with the companion changes) for a unified treatment of boundary layer turbulence, shallow convection, and cloud macrophysics, though it also leads to a reduction in subtropical coastal stratocumulus clouds. This lack of stratocumulus clouds is considerably improved by increasing vertical resolution from 30 to 72 layers, but the gain is unfortunately subsequently offset by other retuning to reach the top‐of‐atmosphere energy balance. Increasing vertical resolution also results in a considerable underestimation of high clouds over the tropical warm pool, primarily due to the selection for numerical stability of a higher air parcel launch level in the deep convection scheme. Increasing horizontal resolution from 1° to 0.25° without retuning leads to considerable degradation in cloud and precipitation fields, with much weaker tropical and subtropical short‐ and longwave cloud radiative forcing and much stronger precipitation in the intertropical convergence zone, indicating poor scale awareness of the cloud parameterizations. To avoid this degradation, significantly different parameter settings for the low‐resolution (1°) and high‐resolution (0.25°) were required to achieve optimal performance in EAMv1. Plain Language Summary The Energy Exascale Earth System Model (E3SM) is a new and ongoing U.S. Department of Energy (DOE) climate modeling effort to develop a high‐resolution Earth system model specifically targeting next‐generation DOE supercomputers to meet the science needs of the nation and the mission needs of DOE. The increase of model resolution along with improvements in representing cloud and convective processes in the E3SM atmosphere model version 1 has led to quite significant model behavior changes from its earlier version, particularly in simulated clouds and precipitation. To understand what causes the model behavior changes, this study conducts sensitivity experiments to isolate the impact of changes in model physics, resolution, and parameter choices on these changes. Results from these sensitivity tests and discussions on the underlying physical processes provide substantial insight into the model errors and guidance for future E3SM development. Key Points CLUBB along with the companion changes in EAMv1 primarily account for the overall improvements in clouds and precipitation simulation Underestimate of coastal Sc in EAMv1 is due to CLUBB and model tuning; increased vertical resolution partially offsets this degradation The poor scale awareness of EAMv1 requires retuning as resolution increases, which has a large impact on model cloud behavior

Using the regionally refined mesh (RRM) configuration of the US Department of Energy's Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM), we simulate and evaluate four meteorologically distinct atmospheric river events over California. We test five different RRM configurations, each differing in terms of the areal extent of the refined mesh and the resolution (ranging from 800 m to 3.25 km). We find that SCREAM RRM generally has a good representation of the AR-generated precipitation in CA, even for the control simulation which has a very small 3 km refined patch, and is able to capture the fine-scale regional distributions that are controlled largely by the fine-scale topography of the state. It is found that SCREAM generally has a wet bias over topography, most prominently over the Sierra Nevada mountain range, with a corresponding dry bias on the lee side. We find that refining the resolution beyond 3 km (specifically 1.6 km and 800 m) has virtually no benefit towards reducing systematic precipitation biases but that improvements can be found when increasing the areal extent of the upstream refined mesh. However, these improvements are relatively modest and only realized if the size of the refined mesh is expanded to the scale where employing RRM no longer achieves the substantial cost benefit it was intended for.

This study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses. Plain Language Summary This research examines how well a kilometer grid scale global atmospheric model—the Simple Cloud‐Resolving Energy Exascale Earth System Model (SCREAMv0)—performs in simulating clouds and rainfall over the Amazon rainforest region. The model was assessed by comparing to high‐resolution ground‐based observations from the Green Ocean Amazon field campaign supported by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program. The model struggles to produce enough middle‐level clouds. When comparing the simulated rainfall to radar observations, SCREAMv0 showed good performance on the diurnal pattern of rain rate, but tends to form too many small rain clusters while failing to create large ones. A possible contributor to these errors could be the inaccurate depiction of how the earth's surface and the atmosphere interact within the model. Overall, this study shows that using detailed DOE ARM data can help improve our understanding of clouds and rainfall in global storm resolving kilometer grid scale models. Key Points Convective processes in a global storm resolving model (SCREAMv0) are evaluated using ground‐based observations over a tropical rainforest SCREAMv0 captures the morning development of shallow convection and the early afternoon precipitation peak but lacks mid‐level congestus SCREAMv0 struggles to form large precipitation clusters greater than 128 km and produces smaller ones more often than observed

This paper documents coupled simulations of two developmental versions of the Community Atmosphere Model (CAM) towards CAM6. The configuration called CAM5.4 introduces new microphysics, aerosol, and ice nucleation changes, among others to CAM. The CAM5.5 configuration represents a more radical departure, as it uses an assumed probability density function (PDF)-based unified cloud parameterization to replace the turbulence, shallow convection, and warm cloud macrophysics in CAM. This assumed PDF method has been widely used in the last decade in atmosphere-only climate simulations but has never been documented in coupled mode. Here, we compare the simulated coupled climates of CAM5.4 and CAM5.5 and compare them to the control coupled simulation produced by CAM5.3. We find that CAM5.5 has lower cloud forcing biases when compared to the control simulations. Improvements are also seen in the simulated amplitude of the Niño-3.4 index, an improved representation of the diurnal cycle of precipitation, subtropical surface wind stresses, and double Intertropical Convergence Zone biases. Degradations are seen in Amazon precipitation as well as slightly colder sea surface temperatures and thinner Arctic sea ice. Simulation of the 20th century results in a credible simulation that ends slightly colder than the control coupled simulation. The authors find this is due to aerosol indirect effects that are slightly stronger in the new version of the model and propose a solution to ameliorate this. Overall, in these early coupled simulations, CAM5.5 produces a credible climate that is appropriate for science applications and is ready for integration into the National Center for Atmospheric Research's (NCAR's) next-generation climate model.

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. Plain Language Summary Models that are used to simulate and predict climate often have trouble representing specific cloud types, such as stratocumulus, that are particularly thin in the vertical direction. It has long been speculated that one of the reasons for this deficiency relates to coarse vertical resolution used in these models. In this study, we increase the vertical resolution to scales that previous process oriented studies suggest are needed to represent these cloud types. We find that increasing the vertical resolution is a necessary ingredient toward simulating stratocumulus, though deficiencies remain and the simulations are computationally expensive. Key Points High vertical resolution in the lower troposphere is a crucial ingredient to improve marine stratocumulus (Sc) in GCMs These simulations are expensive and require time step adjustment, which introduces sensitivities Vertical resolution alone cannot improve coastal Sc, likely concurrent increases in horizontal resolution are needed

In this study, we investigate the sensitivity of simulated low clouds to 14 selected tunable parameters of Cloud Layers Unified By Binormals (CLUBB), a higher‐order closure (HOC) scheme, and four parameters of the Zhang‐McFarlane (ZM) deep convection scheme in the Community Atmosphere Model version 5 (CAM5). A Quasi‐Monte Carlo (QMC) sampling approach is adopted to effectively explore the high‐dimensional parameter space and a generalized linear model is applied to study the responses of simulated cloud fields to tunable parameters. Our results show that the variance in simulated low‐cloud properties (cloud fraction and liquid water path) can be explained by the selected tunable parameters in two different ways: macrophysics itself and its interaction with microphysics. First, the parameters related to dynamic and thermodynamic turbulent structure and double Gaussian closure are found to be the most influential parameters for simulating low clouds. The spatial distributions of the parameter contributions show clear cloud‐regime dependence. Second, because of the coupling between cloud macrophysics and cloud microphysics, the coefficient of the dissipation term in the total water variance equation is influential. This parameter affects the variance of in‐cloud cloud water, which further influences microphysical process rates, such as autoconversion, and eventually low‐cloud fraction. This study improves understanding of HOC behavior associated with parameter uncertainties and provides valuable insights for the interaction of macrophysics and microphysics. Key Points: Influential parameters show strong regime dependence Parameters related to turbulent structure are the most influential ones The parameter related to the cloud water variance is influential

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.