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Evaluating rainfall errors in global climate models through cloud regimes

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Abstract

Global climate models suffer from a persistent shortcoming in their simulation of rainfall by producing too much drizzle and too little intense rain. This erroneous distribution of rainfall is a result of deficiencies in the representation of underlying processes of rainfall formation. In the real world, clouds are precursors to rainfall and the distribution of clouds is intimately linked to the rainfall over the area. This study examines the model representation of tropical rainfall using the cloud regime concept. In observations, these cloud regimes are derived from cluster analysis of joint-histograms of cloud properties retrieved from passive satellite measurements. With the implementation of satellite simulators, comparable cloud regimes can be defined in models. This enables us to contrast the rainfall distributions of cloud regimes in 11 CMIP5 models to observations and decompose the rainfall errors by cloud regimes. Many models underestimate the rainfall from the organized convective cloud regime, which in observation provides half of the total rain in the tropics. Furthermore, these rainfall errors are relatively independent of the model’s accuracy in representing this cloud regime. Error decomposition reveals that the biases are compensated in some models by a more frequent occurrence of the cloud regime and most models exhibit substantial cancellation of rainfall errors from different regimes and regions. Therefore, underlying relatively accurate total rainfall in models are significant cancellation of rainfall errors from different cloud types and regions. The fact that a good representation of clouds does not lead to appreciable improvement in rainfall suggests a certain disconnect in the cloud-precipitation processes of global climate models.

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Notes

  1. Strictly speaking, these are clouds whose tops are located in the mid-level altitudes.

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Acknowledgements

We are grateful for valuable feedback from Dongmin Lee. JT is supported by an appointment to the NASA Postdoctoral Program at Goddard Space Flight Center, administered by USRA through a contract with NASA (NNH15CO48B). LO and DJ gratefully acknowledge support by NASA’s Modeling Analysis and Prediction program. CJ is supported by the ARC Centre of Excellence for Climate System Science (CE110001028). ISCCP data is available at https://isccp.giss.nasa.gov/. TMPA was provided by the NASA GSFC PPS team and NASA GES DISC, and can be downloaded at http://pmm.nasa.gov/data-access. The CMIP5 data was provided by the WCRP and archived by the PCMDI at http://pcmdi.llnl.gov/.

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Authors and Affiliations

  1. NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Jackson Tan, Lazaros Oreopoulos & Daeho Jin

  2. Universities Space Research Association, Columbia, MD, USA

    Jackson Tan & Daeho Jin

  3. ARC Centre of Excellence for Climate System Science and School of Earth, Atmosphere and Environment, Monash University, Clayton, Victoria, Australia

    Christian Jakob

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  1. Jackson Tan
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  2. Lazaros Oreopoulos
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Correspondence to Jackson Tan.

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Tan, J., Oreopoulos, L., Jakob, C. et al. Evaluating rainfall errors in global climate models through cloud regimes. Clim Dyn 50, 3301–3314 (2018). https://doi.org/10.1007/s00382-017-3806-7

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