Analysis of rainfall seasonality from observations and climate models

Abstract

Two new indicators of rainfall seasonality based on information entropy, the relative entropy (RE) and the dimensionless seasonality index (DSI), together with the mean annual rainfall, are evaluated on a global scale for recently updated precipitation gridded datasets and for historical simulations from coupled atmosphere-ocean general circulation models. The RE provides a measure of the number of wet months and, for precipitation regimes featuring one maximum in the monthly rain distribution, it is related to the duration of the wet season. The DSI combines the rainfall intensity with its degree of seasonality and it is an indicator of the extent of the global monsoon region. We show that the RE and the DSI are fairly independent of the time resolution of the precipitation data, thereby allowing objective metrics for model intercomparison and ranking. Regions with different precipitation regimes are classified and characterized in terms of RE and DSI. Comparison of different land observational datasets reveals substantial difference in their local representation of seasonality. It is shown that two-dimensional maps of RE provide an easy way to compare rainfall seasonality from various datasets and to determine areas of interest. CMIP5 models consistently overestimate the RE over tropical Latin America and underestimate it in Western Africa and East Asia. It is demonstrated that positive RE biases in a GCM are associated with simulated monthly precipitation fractions which are too large during the wet months and too small in the months preceding the wet season; negative biases are instead due to an excess of rainfall during the dry months.