Climate Dynamics

, Volume 44, Issue 11–12, pp 3281–3301 | Cite as

Analysis of rainfall seasonality from observations and climate models

  • Salvatore Pascale
  • Valerio Lucarini
  • Xue Feng
  • Amilcare Porporato
  • Shabeh ul Hasson


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 two-dimensional 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 months.


Rainfall seasonality Information entropy Hydrological cycle CMIP5 models 



The 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. S.P., V.L. and S.H. wish to acknowledge the financial support provided by the ERC-Starting 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 (DE-SC0006967), the Agriculture and Food Research Initiative from the USDA National Institute of Food and Agriculture (2011-67003-30222). 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Salvatore Pascale
    • 1
  • Valerio Lucarini
    • 1
    • 2
  • Xue Feng
    • 3
  • Amilcare Porporato
    • 3
  • Shabeh ul Hasson
    • 1
  1. 1.Meteorologisches Institute, Center for Earth System Research and Sustainability (CEN)Universität HamburgHamburgGermany
  2. 2.Department of Mathematics and StatisticsUniversity of ReadingReadingUK
  3. 3.Department of Civil and Environmental EngineeringDuke UniversityDurhamUSA

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