Exposing Process-Level Biases in a Global Cloud Permitting Model with
ARM Observations
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.