Performance of general circulation models and thei

Authors: Nachiketa Acharya Ankita Singh U C Mohanty Archana Nair Surajit Chattopadhyay

Publish Date: 2012/12/29

Volume: 66, Issue: 2, Pages: 851-871

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Abstract

The drought during the months of June to September JJAS results in significant deficiency in the annual rainfall and affects the hydrological planning disaster management and the agriculture sector of India Advance information on drought characteristics over the space may help in risk assessment over the country This issue motivated the present study which deals with the prediction of drought during JJAS through standardized precipitation index SPI using nine general circulation models GCM product Among these GCMs three are the atmospheric and six are atmosphere–ocean coupled models The performance of these GCM’s predicted SPI is examined against the observed SPI for the time period of 1982–2010 After a rigorous analysis it can be concluded that the skill of prediction by GCM is not satisfactory whereas the ability of the coupled models is better than the atmospheric models An attempt has been made to improve the accuracy of predicted SPI using two different multimodel ensemble MME schemes viz arithmetic mean and weighted mean using singular value decompositionbased multiple linear regressions SVDMLR of GCMs It is found that among these MME techniques SVDMLRbased MME has more skill as compared to simple MME as well as individual GCMsThis research has been conducted as part of the project entitled “Development and Application of Extended Range Forecast System for Climate Risk Management in Agriculture” at IIT Delhi sponsored by the Department of Agriculture and Cooperation Government of India India Meteorological Department IMD is acknowledged for providing observed gridded rainfall data sets The model data sets used in this study have been provided by the International Research Institute of Climate and Society IRI USA 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 Authors are highly thankful to Dr A Robertson IRI USA for numerous inputs useful suggestions and feedback in course of the development of the paper Authors are gratefully acknowledging the National Centre for Environmental Prediction NCEP for providing the CFS hindcast data sets Japan Agency for MarineEarth Science and Technology JAMSTEC in particular JingJia Luo and Toshio Yamagata is duly acknowledged for providing their model’s products used in this study

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