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

• Published in: Journal of geophysical research. Atmospheres 2017-11, Vol.122 (21), p.11,806-11,824
• Main Authors: Jing, Xianwen, Suzuki, Kentaroh, Guo, Huan, Goto, Daisuke, Ogura, Tomoo, Koshiro, Tsuyoshi, Mülmenstädt, Johannes
• Format: Article
• Language: English
• Subjects: Atmospheric precipitations; autoconversion; Climate; Climate models; cloud; Cloud microphysics; Cloud particles; Clouds; Computer simulation; Errors; Evolution; GCMs; Geophysics; Global climate; Global climate models; High resolution; Microphysics; Particle size; Precipitation; Precipitation formation; Properties; Properties (attributes); Rain; Representations; Resolution; Satellite observation; Satellites; State of the art; Statistical analysis; Statistical methods; Water
• ISSN: 2169-897X; 2169-8996
• DOI: 10.1002/2017JD027310

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. Key Points Warm precipitation was found to be triggered too easily and too frequently in state‐of‐the‐art global climate models Biases in cloud microphysics properties compensate partly for the too‐easy formation of warm rain Better representation of autoconversion process and subgrid effects was found to improve warm rain simulation in some aspects