Evaluating rainfall errors in global climate models through cloud regimes

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.