Evaluation of Clouds, Radiation, and Precipitation in CMIP6 Models Using Global Weather States Derived from ISCCP-H Cloud Property Data

George Tselioudis aNASA/GISS, New York, New York
bDepartment of Applied Physics and Applied Math, Columbia University, New York, New York

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William B. Rossow cFranklin, New York

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Christian Jakob dARC Centre of Excellence for Climate Extremes, Monash University, Melbourne, Victoria, Australia

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Jasmine Remillard aNASA/GISS, New York, New York
eSciSpace, New York, New York

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Derek Tropf aNASA/GISS, New York, New York
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Yuanchong Zhang aNASA/GISS, New York, New York
eSciSpace, New York, New York

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Abstract

A clustering methodology is applied to cloud optical depth (τ)–cloud top pressure (TAU-PC) histograms from the new 1° resolution ISCCP-H dataset to derive an updated global weather state (WS) dataset. Then, TAU-PC histograms from current-climate CMIP6 model simulations are assigned to the ISCCP-H WSs along with their concurrent radiation and precipitation properties to evaluate model cloud, radiation, and precipitation properties in the context of the weather states. The new ISCCP-H analysis produces WSs that are very similar to those previously found in the lower-resolution ISCCP-D dataset. The main difference lies in the splitting of the ISCCP-D thin stratocumulus WS between the ISCCP-H shallow cumulus and stratocumulus WSs, which results in the reduction by one of the total WS number. The evaluation of the CMIP6 models against the ISCCP-H weather states shows that, in the ensemble mean, the models are producing an adequate representation of the frequency and geographical distribution of the WSs, with measurable improvements compared to the WSs derived for the CMIP5 ensemble. However, the frequency of shallow cumulus clouds continues to be underestimated, and, in some WSs the good agreement of the ensemble mean with observations comes from averaging models that significantly overpredict and underpredict the ISCCP-H WS frequency. In addition, significant biases exist in the internal cloud properties of the model WSs, such as the model underestimation of cloud fraction in middle-top clouds and secondarily in midlatitude storm and stratocumulus clouds, that result in an underestimation of cloud SW cooling in those regimes.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: George Tselioudis, george.tselioudis@nasa.gov

Abstract

A clustering methodology is applied to cloud optical depth (τ)–cloud top pressure (TAU-PC) histograms from the new 1° resolution ISCCP-H dataset to derive an updated global weather state (WS) dataset. Then, TAU-PC histograms from current-climate CMIP6 model simulations are assigned to the ISCCP-H WSs along with their concurrent radiation and precipitation properties to evaluate model cloud, radiation, and precipitation properties in the context of the weather states. The new ISCCP-H analysis produces WSs that are very similar to those previously found in the lower-resolution ISCCP-D dataset. The main difference lies in the splitting of the ISCCP-D thin stratocumulus WS between the ISCCP-H shallow cumulus and stratocumulus WSs, which results in the reduction by one of the total WS number. The evaluation of the CMIP6 models against the ISCCP-H weather states shows that, in the ensemble mean, the models are producing an adequate representation of the frequency and geographical distribution of the WSs, with measurable improvements compared to the WSs derived for the CMIP5 ensemble. However, the frequency of shallow cumulus clouds continues to be underestimated, and, in some WSs the good agreement of the ensemble mean with observations comes from averaging models that significantly overpredict and underpredict the ISCCP-H WS frequency. In addition, significant biases exist in the internal cloud properties of the model WSs, such as the model underestimation of cloud fraction in middle-top clouds and secondarily in midlatitude storm and stratocumulus clouds, that result in an underestimation of cloud SW cooling in those regimes.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: George Tselioudis, george.tselioudis@nasa.gov
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