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Exposing Process-Level Biases in a Global Cloud Permitting Model with ARM Observations
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  • Peter A Bogenschutz,
  • Yunyan Zhang,
  • Xue Zheng,
  • Yang Tian,
  • Meng Zhang,
  • Lin Lin,
  • Peng Wu,
  • Shaocheng Xie,
  • Cheng Tao
Peter A Bogenschutz
Lawrence Livermore National Laboratory

Corresponding Author:[email protected]

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Yunyan Zhang
Lawrence Livermore National Laboratory (DOE)
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Xue Zheng
Lawrence Livermore National Laboratory (DOE)
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Yang Tian
National Center for Atmospheric Research
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Meng Zhang
Lawrence Livermore National Laboratory
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Lin Lin
Lawrence Livermore National Laboratory
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Peng Wu
Pacific Northwest National Laboratory
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Shaocheng Xie
Lawrence Livermore National Laboratory
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Cheng Tao
Lawrence Livermore National Laboratory (DOE)
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Abstract

The emergence of global convective-permitting models (GCPMs) represents a significant advancement in climate modeling, offering improved representation of deep convection and complex precipitation patterns. In this study, we evaluate the performance of the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) using its doubly-periodic configuration (DP-SCREAM) against large eddy simulations (LES) and modern observational datasets from the Atmospheric Radiation Measurement (ARM) program. We introduce several new transitional cloud regime cases, such as the transition from shallow to deep convection and from stratocumulus to cumulus, as well as cold-air outbreak scenarios. The results reveal both strengths and limitations of SCREAM, particularly in the accurate simulation of cloud transitions and mid-level convection, with varying degrees of sensitivity to horizontal and vertical resolution. Despite improvements at higher resolutions, key biases remain, including the abrupt transition from shallow to deep convection and the lack of congestus clouds. These findings underscore the need for further refinement in turbulence parameterizations and vertical grid resolution in GCPMs.
27 Nov 2024Submitted to ESS Open Archive
28 Nov 2024Published in ESS Open Archive