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Several independent measurements of warm-season soil moisture and surface atmospheric variables recorded at the ARM Southern Great Plains (SGP) research facility are used to estimate the terrestrial component of land-atmosphere coupling (LAC) strength and its regional uncertainty. The observations reveal substantial variation in coupling strength, as estimated from three soil moisture measurements at a single site, as well as across six other sites having varied soil and land cover types. The observational estimates then serve as references for evaluating SGP terrestrial coupling strength in the Community Atmospheric Model coupled to the Community Land Model. These coupled model components are operated in both a free-running mode and in a controlled configuration, where the atmospheric and land states are reinitialized daily, so that they do not drift very far from observations. Although the controlled simulation deviates less from the observed surface climate than its free-running counterpart, the terrestrial LAC in both configurations is much stronger and displays less spatial variability than the SGP observational estimates. Preliminary investigation of vegetation leaf area index (LAI) substituted for soil moisture suggests that the overly strong coupling between model soil moisture and surface atmospheric variables is associated with too much evaporation from bare ground and too little from the vegetation cover. These results imply that model surface characteristics such as LAI, as well as the physical parameterizations involved in the coupling of the land and atmospheric components, are likely to be important sources of the problematical LAC behaviors.

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 rainforestSCREAMv0 captures the morning development of shallow convection and the early afternoon precipitation peak but lacks mid‐level congestusSCREAMv0 struggles to form large precipitation clusters greater than 128 km and produces smaller ones more often than observed [ABSTRACT FROM AUTHOR]

Heavy precipitation, often associated with weather phenomena such as tropical cyclones, extratropical cyclones (ETCs), atmospheric rivers (ARs), and mesoscale convective systems (MCSs), can cause significant socio‐economic loss. In this study, we apply atmospheric feature trackers to quantify the contributions of these storm types in observational data sets and climate model short‐range hindcasts. We generate a global hourly storm data set at 0.25° spatial resolution covering 2006–2020, based on the tracking results from TempestExtremes and Python FLEXible object TRacKeR. Our analyses show that these four storm types account for 67% of global annual mean precipitation and 82% of top 1% precipitation extremes, with MCSs mainly over the tropics, and ARs and ETCs over the midlatitudes. The percentage of precipitation contributions from these storms also show strong seasonality over many geographical locations. We further apply the tracking results to the Energy Exascale Earth System Model (E3SM) short‐range hindcasts and evaluate how well these storms are simulated. The evaluation show that E3SM, with ∼1° resolution, significantly underestimates storm‐associated precipitation totals and extremes, especially for MCSs in the tropics. Our analysis also suggests that model fails to capture the correct mean diurnal phases and amplitude of MCS precipitation. This phenomenon‐based approach provides a better understanding of precipitation characteristics and can lead to enhanced model evaluation by revealing underlying problems in model physics related to precipitation processes associated with the heavy‐precipitating storms. Plain Language Summary: Earth system models are immensely useful for understanding how the climate system works. However, it is important to recognize that they have limitations including wet or dry precipitation biases caused by complicated factors. On the other hand, different storm types contribute to regional precipitation differently under varying conditions. Attributing precipitation to sourced storm types is a new approach to understanding model precipitation biases. Here we build a new data set to study precipitation from several storm types including tropical cyclones, extratropical cyclones, atmospheric rivers, and mesoscales convection systems. We find that these four storm types explain 67% of global mean precipitation and 82% of extreme precipitation. We also demonstrate the application of this tool for understanding biases in modeled precipitation. The future application of this new tool will shed light on the causes of modeled precipitation biases and underlying model problems. Key Points: A global observational database for tracking four major heavy‐rain storm systems is established for the 2006–2020These four storm systems contribute to over 67% of global annual mean precipitation and over 80% of top 1% precipitation extremesClimate model short‐range hindcasts underestimate the storm‐associated precipitation, especially for heavy precipitation extremes [ABSTRACT FROM AUTHOR]

An evaluation is presented of the impact on tropical climate of continental-scale perturbations given by different representations of land surface processes (LSPs) in a general circulation model that includes atmosphere-ocean interactions. One representation is a simple land scheme, which specifies climatological albedos and soil moisture availability. The other representation is the more comprehensive Simplified Simple Biosphere Model, which allows for interactive soil moisture and vegetation biophysical processes. The results demonstrate that such perturbations have strong impacts on the seasonal mean states and seasonal cycles of global precipitation, clouds, and surface air temperature. The impact is especially significant over the tropical Pacific Ocean. To explore the mechanisms for such impact, model experiments are performed with different LSP representations confined to selected continental-scale regions where strong interactions of climate-vegetation biophysical processes are present. The largest impact found over the tropical Pacific is mainly from perturbations in the tropical African continent where convective heating anomalies associated with perturbed surface heat fluxes trigger global teleconnections through equatorial wave dynamics. In the equatorial Pacific, the remote impacts of the convection anomalies are further enhanced by strong air-sea coupling between surface wind stress and upwelling, as well as by the effects of ocean memory. LSP perturbations over South America and Asia-Australia have much weaker global impacts. The results further suggest that correct representations of LSP, land use change, and associated changes in the deep convection over tropical Africa are crucial to reducing the uncertainty of future climate projections with global climate models under various climate change scenarios. © 2013 American Meteorological Society.

Stratocumulus clouds (SC) often exist over the eastern subtropical oceans during the summer and have significant impacts on the surface radiation budget. Both atmospheric subsidence and lower troposphere stability (LTS) have been found to play important roles in maintaining SC. Using global climate model simulations, we find that irrigation in California's Central Valley results in a decrease of land surface temperature, leading to a smaller land-sea heat contrast, and a corresponding reduction in sea breeze, subsidence, and LTS over the near-coastal region. The decrease in LTS directly drives a reduction in modeled SC coverage, and it would arguably do so in reality because of the well-known link between LTS and SC coverage. Consequently, simulated absorbed surface solar radiation over this region increases by 8 W/m2 (3.7%) due to the reduction in SC cover, resulting in the warming at the Earth's surface. This study has important implications for how SC can change with regard to future climate. In contrast to the general effects of climate change on the formation of SC, our results suggest that irrigation practices in the Central Valley may drive a decrease in nearby SC coverage.

Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and central United States. Common model biases such as excessive precipitation over the tropics, too frequent light‐to‐moderate rain, and the failure to capture propagating convection in the central United States still exist. Over the central United States, the issues of too weak rainfall intensity in climate runs is well improved in their hindcast runs with initial conditions from numerical weather prediction analyses. But the improvement is minimal over the central Amazon. Incorporating the role of the large‐scale environment in convective triggering processes helps resolve the phase‐locking issue in many models where precipitation often incorrectly peaks near noon due to maximum insolation over land. Allowing air parcels to be lifted above the boundary layer improves the simulation of nocturnal precipitation which is often associated with the propagation of mesoscale systems. Including convective memory in cumulus parameterizations acts to suppress light‐to‐moderate rain and promote intense rainfall; however, it also weakens the diurnal variability. Simply increasing model resolution (with cumulus parameterizations still used) cannot fully resolve the biases of low‐resolution climate models in DCP. The hierarchy modeling framework from this study is useful for identifying the missing physics in models and testing new development of model convective processes over different convective regimes. [ABSTRACT FROM AUTHOR]

A stochastic deep convection parameterization is implemented into the U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). This study evaluates its performance on the precipitation simulation. Compared to the default model, the probability distribution function (PDF) of rainfall intensity in the new simulation is greatly improved. Especially, the well-known problem of too much light rain and too little heavy rain is alleviated over the tropics. As a result, the contribution from different rain rates to the total precipitation amount is shifted toward heavier rain. The less frequent occurrence of convection contributes to the suppressed light rain, while both more intense large-scale and convective precipitation contribute to the enhanced heavy total rain. The synoptic and intraseasonal variabilities of precipitation are enhanced as well to be closer to observations. A sensitivity of the rainfall intensity PDF to the model vertical resolution is identified and explained in terms of the relationships between convective precipitation and convective available potential energy (CAPE) and between large-scale precipitation and resolved-scale upward moisture flux. The annual mean precipitation is largely unchanged with the use of the stochastic scheme except over the tropical western Pacific, where a moderate increase in precipitation represents a slight improvement. The responses of precipitation and its extremes to climate warming are similar with or without the stochastic deep convection scheme. [ABSTRACT FROM AUTHOR]