Authors: Salvatore Pascale Valerio Lucarini Xue Feng Amilcare Porporato Shabeh ul Hasson
Publish Date: 2014/08/14
Volume: 44, Issue: 11-12, Pages: 3281-3301
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 a distinct wet and dry season it is directly 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 twodimensional maps of RE provide an easy way to compare rainfall seasonality from various datasets and to determine areas of interest Models participating to the Coupled Model Intercomparison Project platform Phase 5 consistently overestimate the RE over tropical Latin America and underestimate it in West Africa western Mexico and East Asia It is demonstrated that positive RE biases in a general circulation model are associated with excessively peaked monthly precipitation fractions too large during the wet months and too small in the months preceding and following the wet season negative biases are instead due in most cases to an excess of rainfall during the premonsoonal monthsThe authors acknowledge the World Climate Research Programmes Working Group on Coupled Modeling which is responsible for CMIP and the NOAA/OAR/ESRL PSD Boulder Colorado USA for providing from their Web site the CMAP GPCP and GPCC precipitation data SP VL and SH wish to acknowledge the financial support provided by the ERCStarting Investigator Grant NAMASTE Grant No 257106 and by the CliSAP/Cluster of excellence in the Integrated Climate System Analysis and Prediction AP gratefully acknowledges NSF Grants CBET 1033467 EAR 1331846 EAR 1316258 as well as the US DOE through the Office of Biological and Environmental Research Terrestrial Carbon Processes program DESC0006967 the Agriculture and Food Research Initiative from the USDA National Institute of Food and Agriculture 20116700330222 XF acknowledges funding from the NSF Graduate Research Fellowship Program F Ragone J M Gregory G Badin and F Laliberté are thanked for useful comments and suggestions The authors also wish to thank B G Liepert and F Lo for providing numerical data about CMIP5 models water biases and two anonymous reviewers for their constructive suggestions which helped us to improve this manuscript
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