Editors’ Highlights are summaries of recent papers by AGU’s journal editors.
Source: Journal of Geophysical Research: Atmospheres
Clouds are a major source of uncertainty in atmospheric predictability and simulating them accurately remains a challenge for large-scale models. Bogenschutz et al. [2025] evaluate a new high-resolution model called the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) developed by the United States Department of Energy (DOE), which is designed to better capture cloud and storm processes. The authors use a fast, small-scale version of the model and compare its output to modern real-world observations from the DOE’s Atmospheric Radiation Measurement (ARM) program.
The model performed better at higher resolutions but still struggled with certain cloud types, especially mid-level “congestus” clouds that form between shallow and deep convection. SCREAM also tended to shift too abruptly from shallow clouds to intense storms, and its performance depended on how finely the vertical layers of the atmosphere were represented.
These results help pinpoint key weaknesses in the model’s treatment of clouds and turbulence. The new library of ARM cases added in this work will help guide future improvements to SCREAM and support more accurate simulations of cloud processes.
Citation: Bogenschutz, P. A., Zhang, Y., Zheng, X., Tian, Y., Zhang, M., Lin, L., et al. (2025). Exposing process-level biases in a global cloud permitting model with ARM observations. Journal of Geophysical Research: Atmospheres, 130, e2024JD043059. https://doi.org/10.1029/2024JD043059
—Yun Qian, Editor, JGR: Atmospheres
Text © 2025. The authors. CC BY-NC-ND 3.0
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