A Multimodel Study on Warm Precipitation Biases in Global Models Compared to Satellite Observations

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  • Xianwen Jing
    Atmosphere and Ocean Research Institute University of Tokyo Kashiwa Japan
  • Kentaroh Suzuki
    Atmosphere and Ocean Research Institute University of Tokyo Kashiwa Japan
  • Huan Guo
    UCAR CPAESS NOAA Geophysical Fluid Dynamics Laboratory Princeton NJ USA
  • Daisuke Goto
    National Institute for Environmental Studies Tsukuba Japan
  • Tomoo Ogura
    National Institute for Environmental Studies Tsukuba Japan
  • Tsuyoshi Koshiro
    Meteorological Research Institute Japan Meteorological Agency Tsukuba Japan
  • Johannes Mülmenstädt
    Institute for Meteorology Universität Leipzig Leipzig Germany

書誌事項

公開日
2017-11-11
資源種別
journal article
権利情報
  • http://onlinelibrary.wiley.com/termsAndConditions#vor
DOI
  • 10.1002/2017jd027310
公開者
American Geophysical Union (AGU)

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説明

<jats:title>Abstract</jats:title><jats:p>The cloud‐to‐precipitation transition process in warm clouds simulated by state‐of‐the‐art global climate models (GCMs), including both traditional climate models and a high‐resolution model, is evaluated against A‐Train satellite observations. The models and satellite observations are compared in the form of the statistics obtained from combined analysis of multiple‐satellite observables that probe signatures of the cloud‐to‐precipitation transition process. One common problem identified among these models is the too‐frequent occurrence of warm precipitation. The precipitation is found to form when the cloud particle size and the liquid water path (LWP) are both much smaller than those in observations. The too‐efficient formation of precipitation is found to be compensated for by errors of cloud microphysical properties, such as underestimated cloud particle size and LWP, to an extent that varies among the models. However, this does not completely cancel the precipitation formation bias. Robust errors are also found in the evolution of cloud microphysical properties from nonprecipitating to drizzling and then to raining clouds in some GCMs, implying unrealistic interaction between precipitation and cloud water. Nevertheless, auspicious information is found for future improvement of warm precipitation representations: the adoption of more realistic autoconversion scheme in the high‐resolution model improves the triggering of precipitation, and the introduction of a sophisticated subgrid variability scheme in a traditional model improves the simulated precipitation frequency over subtropical eastern ocean. However, deterioration in other warm precipitation characteristics is also found accompanying these improvements, implying the multisource nature of warm precipitation biases in GCMs.</jats:p>

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