Exposing Global Cloud Biases in the Community Atmosphere Model (CAM) Using Satellite Observations and Their Corresponding Instrument Simulators

J. E. Kay * Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado

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B. R. Hillman Department of Atmospheric Sciences, University of Washington, Seattle, Washington
Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, Washington

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S. A. Klein Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California

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Y. Zhang Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California

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B. Medeiros * Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado

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R. Pincus NOAA/Earth System Research Lab, Physical Sciences Division, and University of Colorado, Boulder, Colorado

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A. Gettelman * Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado

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B. Eaton * Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado

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J. Boyle Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California

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R. Marchand Department of Atmospheric Sciences, University of Washington, Seattle, Washington

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T. P. Ackerman Department of Atmospheric Sciences, University of Washington, Seattle, Washington
Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, Washington

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Abstract

Satellite observations and their corresponding instrument simulators are used to document global cloud biases in the Community Atmosphere Model (CAM) versions 4 and 5. The model–observation comparisons show that, despite having nearly identical cloud radiative forcing, CAM5 has a much more realistic representation of cloud properties than CAM4. In particular, CAM5 exhibits substantial improvement in three long-standing climate model cloud biases: 1) the underestimation of total cloud, 2) the overestimation of optically thick cloud, and 3) the underestimation of midlevel cloud. While the increased total cloud and decreased optically thick cloud in CAM5 result from improved physical process representation, the increased midlevel cloud in CAM5 results from the addition of radiatively active snow. Despite these improvements, both CAM versions have cloud deficiencies. Of particular concern, both models exhibit large but differing biases in the subtropical marine boundary layer cloud regimes that are known to explain intermodel differences in cloud feedbacks and climate sensitivity. More generally, this study demonstrates that simulator-facilitated evaluation of cloud properties, such as amount by vertical level and optical depth, can robustly expose large and at times radiatively compensating climate model cloud biases.

Corresponding author address: J. E. Kay, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307. E-mail: jenkay@ucar.edu

This article is included in the CCSM4 Special Collection.

Abstract

Satellite observations and their corresponding instrument simulators are used to document global cloud biases in the Community Atmosphere Model (CAM) versions 4 and 5. The model–observation comparisons show that, despite having nearly identical cloud radiative forcing, CAM5 has a much more realistic representation of cloud properties than CAM4. In particular, CAM5 exhibits substantial improvement in three long-standing climate model cloud biases: 1) the underestimation of total cloud, 2) the overestimation of optically thick cloud, and 3) the underestimation of midlevel cloud. While the increased total cloud and decreased optically thick cloud in CAM5 result from improved physical process representation, the increased midlevel cloud in CAM5 results from the addition of radiatively active snow. Despite these improvements, both CAM versions have cloud deficiencies. Of particular concern, both models exhibit large but differing biases in the subtropical marine boundary layer cloud regimes that are known to explain intermodel differences in cloud feedbacks and climate sensitivity. More generally, this study demonstrates that simulator-facilitated evaluation of cloud properties, such as amount by vertical level and optical depth, can robustly expose large and at times radiatively compensating climate model cloud biases.

Corresponding author address: J. E. Kay, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307. E-mail: jenkay@ucar.edu

This article is included in the CCSM4 Special Collection.

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