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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2020-144
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-144
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Jul 2020

07 Jul 2020

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This preprint is currently under review for the journal GMD.

GTS v1.0: A Macrophysics Scheme for Climate Models Based on a Probability Density Function

Chein-Jung Shiu1, Yi-Chi Wang1, Huang-Hsiung Hsu1, Wei-Ting Chen2, Hua-Lu Pan3, Ruiyu Sun4, Yi-Hsuan Chen5, and Cheng-An Chen1 Chein-Jung Shiu et al.
  • 1Research Center for Environmental Changes, Academia Sinica, Taiwan
  • 2Department of Atmospheric Sciences, National Taiwan University, Taiwan
  • 3Retired Senior Scientist, National Centers for Environmental Prediction, NOAA, USA
  • 4National Centers for Environmental Prediction, NOAA, USA
  • 5Department of Climate and Space Sciences and Engineering, University of Michigan, USA

Abstract. Cloud macrophysics schemes are unique parameterizations for general circulation models. We propose an approach based on a probability density function (PDF) that utilizes cloud condensates and saturation ratios to replace the assumption of critical relative humidity (RH). We test this approach, called the GFS-TaiESM-Sundqvist (GTS) scheme, using the macrophysics scheme within the Community Atmospheric Model version 5.3 (CAM5.3) framework. Via single-column model results, the new approach reveals a stronger linear relationship between the cloud fraction (CF) and RH when compared to that of the default CAM5.3 scheme. We also validate the impact of the GTS scheme on global climate simulations with satellite observations. The simulated CF is comparable to CloudSat/CALIPSO data. Comparisons of the vertical distributions of CF and cloud water content (CWC), as functions of large-scale dynamic and thermodynamic parameters, with the CloudSat/CALIPSO data suggest that the GTS scheme can closely simulate observations. This is particularly noticeable for thermodynamic parameters, such as RH, upper-tropospheric temperature, and total precipitable water, implying that our scheme can simulate variation in CF associated with RH more reliably than the default scheme. Changes in CF and CWC would affect climatic fields and large-scale circulation via cloud–radiation interactions. Both climatological means and annual cycles of many of the GTS-simulated variables are improved compared with the default scheme, particularly with respect to water vapor and RH fields. Different PDF shapes in the GTS scheme also significantly affect global simulations.

Chein-Jung Shiu et al.

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Latest update: 14 Aug 2020
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Short summary
Cloud macrophysics schemes are unique parameterizations for climate models to calculate cloud fraction. In this work, we propose an approach based on a probability density function that utilizes cloud condensates and saturation ratios to replace the assumption of critical relative humidity (RH). We validate the impact of this new developed macrophysics scheme on global climate simulations with satellite observations. This scheme can automatically respond to changes in RH in model grids.
Cloud macrophysics schemes are unique parameterizations for climate models to calculate cloud...
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