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Convective Biases in the US DOE Global Storm‐Resolving Model: Insights From Regionally Refined Simulations During the CACTI Campaign.

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Title: Convective Biases in the US DOE Global Storm‐Resolving Model: Insights From Regionally Refined Simulations During the CACTI Campaign.
Authors: Su, Tianning1 (AUTHOR) su10@llnl.gov, Zhang, Yunyan1 (AUTHOR) zhang25@llnl.gov, Ma, Hsi‐Yen1 (AUTHOR), Varble, Adam C.2 (AUTHOR), Bogenschutz, Peter A.1 (AUTHOR)
Source: Journal of Geophysical Research. Atmospheres. 3/16/2026, Vol. 131 Issue 5, p1-24. 24p.
Subject Terms: *Meteorological precipitation, Atmospheric models, Free convection, Precipitation (Chemistry), Cloud physics, Convective flow
Geographic Terms: Argentina
Abstract: Accurately simulating convective processes in complex terrain remains a critical challenge for global storm‐resolving models (GSRMs). This study systematically evaluates moist convective biases in the Regionally Refined Mesh configuration of the U.S. Department of Energy Simple Cloud‐Resolving E3SM Atmosphere Model (RRM‐SCREAM) using comprehensive observations and large‐eddy simulations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in the mountainous area of central Argentina. Comparisons of simulations with high‐resolution observations and reanalysis data indicate that RRM‐SCREAM effectively captures large‐scale meteorological patterns, including regional atmospheric gradients and diurnal variability. However, RRM‐SCREAM disproportionately produces smaller precipitation clusters referred to as "popcorn convection," and exaggerated rainfall intensities compared to observations and reference models. Detailed examination of a representative orographic shallow‐to‐deep convective transition case shows that RRM‐SCREAM delays initial shallow convection growth due to lower‐tropospheric dryness and sustained convective inhibition, but once triggered, deep convection becomes overly vigorous with excessively strong vertical velocities and elevated cloud ice content, linked to a thermodynamic structure characterized by suppressed low‐level moistening and excessive upper‐level moisture retention. Our results highlight specific deficiencies in the model representation of convective vertical velocity, cloud microphysical processes, and convective precipitation organization within RRM‐SCREAM. Addressing these biases is essential for improving the predictions of convective clouds and precipitation in the global high‐resolution atmospheric models. Plain Language Summary: Simulating convective storms over complex terrain remains a critical challenge for global storm‐resolving models (GSRMs), which aim to explicitly represent storm‐scale motions. We assess the Regionally Refined Mesh configuration of the U.S. Department of Energy Simple Cloud‐Resolving E3SM Atmosphere Model (RRM‐SCREAM) using observations and large‐eddy simulations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in central Argentina. RRM‐SCREAM reproduces broad weather patterns, including regional variabilities and the daily cycle. However, it tends to produce too many small isolated rain cells (often termed "popcorn convection") and rainfall that is overly intense relative to observations. A representative orographic shallow‐to‐deep transition case shows that initial growth of shallow clouds is delayed by dry air in the lower troposphere and persistent convective inhibition; once storms form, updrafts are too strong and cloud ice is too abundant. These behaviors are linked to a vertical moisture and temperature structure that is too dry near the surface and too moist aloft. The results point to specific areas for improvement in how the model represents updraft strength, cloud microphysics, and the organization of precipitating systems, which are important for better predictions of clouds and rainfall in the global high‐resolution atmospheric models. Key Points: Regionally refined simulations capture large‐scale patterns but overproduce small, intense popcorn cellsA warm, dry lower troposphere delays deep convection initiation; later convection becomes too vigorous with strong updrafts and iceImprovements to turbulent mixing and microphysics are necessary to mitigate convection biases and to effectively represent rainfall intensity [ABSTRACT FROM AUTHOR]
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Abstract:Accurately simulating convective processes in complex terrain remains a critical challenge for global storm‐resolving models (GSRMs). This study systematically evaluates moist convective biases in the Regionally Refined Mesh configuration of the U.S. Department of Energy Simple Cloud‐Resolving E3SM Atmosphere Model (RRM‐SCREAM) using comprehensive observations and large‐eddy simulations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in the mountainous area of central Argentina. Comparisons of simulations with high‐resolution observations and reanalysis data indicate that RRM‐SCREAM effectively captures large‐scale meteorological patterns, including regional atmospheric gradients and diurnal variability. However, RRM‐SCREAM disproportionately produces smaller precipitation clusters referred to as "popcorn convection," and exaggerated rainfall intensities compared to observations and reference models. Detailed examination of a representative orographic shallow‐to‐deep convective transition case shows that RRM‐SCREAM delays initial shallow convection growth due to lower‐tropospheric dryness and sustained convective inhibition, but once triggered, deep convection becomes overly vigorous with excessively strong vertical velocities and elevated cloud ice content, linked to a thermodynamic structure characterized by suppressed low‐level moistening and excessive upper‐level moisture retention. Our results highlight specific deficiencies in the model representation of convective vertical velocity, cloud microphysical processes, and convective precipitation organization within RRM‐SCREAM. Addressing these biases is essential for improving the predictions of convective clouds and precipitation in the global high‐resolution atmospheric models. Plain Language Summary: Simulating convective storms over complex terrain remains a critical challenge for global storm‐resolving models (GSRMs), which aim to explicitly represent storm‐scale motions. We assess the Regionally Refined Mesh configuration of the U.S. Department of Energy Simple Cloud‐Resolving E3SM Atmosphere Model (RRM‐SCREAM) using observations and large‐eddy simulations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in central Argentina. RRM‐SCREAM reproduces broad weather patterns, including regional variabilities and the daily cycle. However, it tends to produce too many small isolated rain cells (often termed "popcorn convection") and rainfall that is overly intense relative to observations. A representative orographic shallow‐to‐deep transition case shows that initial growth of shallow clouds is delayed by dry air in the lower troposphere and persistent convective inhibition; once storms form, updrafts are too strong and cloud ice is too abundant. These behaviors are linked to a vertical moisture and temperature structure that is too dry near the surface and too moist aloft. The results point to specific areas for improvement in how the model represents updraft strength, cloud microphysics, and the organization of precipitating systems, which are important for better predictions of clouds and rainfall in the global high‐resolution atmospheric models. Key Points: Regionally refined simulations capture large‐scale patterns but overproduce small, intense popcorn cellsA warm, dry lower troposphere delays deep convection initiation; later convection becomes too vigorous with strong updrafts and iceImprovements to turbulent mixing and microphysics are necessary to mitigate convection biases and to effectively represent rainfall intensity [ABSTRACT FROM AUTHOR]
ISSN:2169897X
DOI:10.1029/2025JD045449