Assessing Clouds in GFDL's AM4.0 With Different Microphysical Parameterizations Using the Satellite Simulator Package COSP

Abstract We evaluate cloud simulations using satellite simulators against multiple observational data sets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a fully two‐moment Morrison‐Gettelman cloud microphysical parameterization with prognostic precipitation (MG2) is applied, denoted as AM4‐MG2. The modeled cloud spatial distributions, vertical profiles, phase partitioning, cloud‐to‐precipitation transitions, and radiative effects compare reasonably well with satellite observations. Model biases include the under‐prediction of total and low‐level clouds, especially optically thin/intermediate clouds with cloud optical depth of less than 23, but the over‐prediction of thick clouds, indicating “too few, too bright” biases. These biases counteract each other, and give rise to reasonable estimates of cloud radiative effects. The underestimate of low‐level clouds is associated with too early and too frequent drizzle/precipitation formation. The precipitation bias is improved in AM4‐MG2, where the autoconversion scheme initiates the precipitation more realistically. There also exist discrepancies between models and observations for midlevel and high‐level clouds. Additional biases include the underestimate of liquid cloud fraction and the overestimate of ice cloud fraction.

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Publication Details

Journal
Earth and Space Science
Published
2025-05-29
DOI
https://doi.org/10.1029/2024ea004053
Citations
1
Primary Topic
Precipitation Measurement and Analysis
Type
article
Field-Weighted Citation Impact
1.30

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article

Assessing Clouds in GFDL's AM4.0 With Different Microphysical Parameterizations Using the Satellite Simulator Package COSP

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1 citations
Earth and Space Science
Precipitation Measurement and Analysis
1.30
article

Assessing Clouds in GFDL's AM4.0 With Different Microphysical Parameterizations Using the Satellite Simulator Package COSP

Xianwen Jing, Kentaroh Suzuki, Ryan J. Kramer, Ming Zhao, Huan Guo, David Paynter, Songmiao Fan, Levi G. Silvers, Kristopher Rand, Wenhao Dong, Yuying Zhang
article en
1 citations

Abstract

Abstract We evaluate cloud simulations using satellite simulators against multiple observational data sets. These simulators have been run within the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0), as well as an alternative configuration where a fully two‐moment Morrison‐Gettelman cloud microphysical parameterization with prognostic precipitation (MG2) is applied, denoted as AM4‐MG2. The modeled cloud spatial distributions, vertical profiles, phase partitioning, cloud‐to‐precipitation transitions, and radiative effects compare reasonably well with satellite observations. Model biases include the under‐prediction of total and low‐level clouds, especially optically thin/intermediate clouds with cloud optical depth of less than 23, but the over‐prediction of thick clouds, indicating “too few, too bright” biases. These biases counteract each other, and give rise to reasonable estimates of cloud radiative effects. The underestimate of low‐level clouds is associated with too early and too frequent drizzle/precipitation formation. The precipitation bias is improved in AM4‐MG2, where the autoconversion scheme initiates the precipitation more realistically. There also exist discrepancies between models and observations for midlevel and high‐level clouds. Additional biases include the underestimate of liquid cloud fraction and the overestimate of ice cloud fraction.

Earth and Space ScienceVol. 12(6)
Lawrence Livermore National Laboratory (US), NOAA Geophysical Fluid Dynamics Laboratory (US), University Corporation for Atmospheric Research (US), Central China Normal University (CN), Hubei Normal University (CN), The University of Tokyo (JP), Science Applications International Corporation (United States) (US), Colorado State University (US)
National Science Foundation, U.S. Department of Energy, Ministry of Education, Culture, Sports, Science and Technology, Japan Aerospace Exploration Agency, Lawrence Livermore National Laboratory
Openalex Percentile: Top 22%
Precipitation Measurement and Analysis
1.30
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