Probabilistic prediction of Indian summer monsoon rainfall using global climate models
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Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.
KeywordsGaussian Mixture Model Monsoon Rainfall Indian Summer Monsoon Rainfall Probabilistic Forecast Seasonal Prediction
The study is conducted as part of a research project entitled “Development and Application of Extended Range Weather Forecasting System for Climate Risk Management in Agriculture,” sponsored by the Department of Agriculture and Cooperation, Government of India. Gridded rain data have been obtained from India Meteorological Department. We gratefully acknowledge the IRI modeling and prediction group led by D. Dewitt for making six of their GCM-based seasonal forecasting systems available to this study, as well as the IRI Data Library group led by B. Blumenthal. We acknowledge the particular contributions of D. Lee, H. Liu, and M. Bell. The computing for the GCM simulations made by IRI was partially provided by a grant from the NCAR Climate System Laboratory (CSL) program to the IRI. The IRI represents a cooperative agreement between the U.S. National Oceanic and Atmospheric Administration (NOAA) Office of Global Programs and Columbia University, and support for MT and AWR through NOAA Grant NA05OAR4311004 is gratefully acknowledged.
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