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Varghese, Stella Jes
- Do Seasonal Forecasts of Indian Summer Monsoon Rainfall Show Better Skill with February Initial Conditions?
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Authors
Affiliations
1 Multi-Scale Modelling Programme, CSIR Fourth Paradigm Institute, Bengaluru 560 037, IN
2 Multi-Scale Modelling Programme, CSIR Fourth Paradigm Institute, Bengaluru 560 037, IN
3 Academy of Scientific and Innovative Research, Ghaziabad 201 002, IN
4 Centre for Atmospheric and Oceanic Sciences; and DST Centre for Excellence in Climate Change, Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012India, IN
1 Multi-Scale Modelling Programme, CSIR Fourth Paradigm Institute, Bengaluru 560 037, IN
2 Multi-Scale Modelling Programme, CSIR Fourth Paradigm Institute, Bengaluru 560 037, IN
3 Academy of Scientific and Innovative Research, Ghaziabad 201 002, IN
4 Centre for Atmospheric and Oceanic Sciences; and DST Centre for Excellence in Climate Change, Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012India, IN
Source
Current Science, Vol 120, No 12 (2021), Pagination: 1863-1874Abstract
Prediction for Indian summer monsoon rainfall (ISMR) is generated by integrating model from initial conditions (ICs) of weather at some time prior to season. We examine the factors responsible for the widely reported highest ISMR forecast skill for February ICs in climate forecast system (CFSv2) model. Skill for February ICs is highest only based on correlation between observed and predicted year-to-year variation of ISMR, whereas other skill scores indicate highest skill for late-April/early-May ICs having shorter yet useful forecast lead-time. Higher correlation for February ICs arises from correct forecasting of 1983 ISMR excess, which is however due to wrong forecast of La Niña and correlation drops to lower value than that for late-April/early-May ICs if 1983 is excluded. Forecast skill for sea-surface temperature variation over equatorial central Pacific (ENSO) in Boreal summer is lowest for February ICs indicating role of dynamical drift induced by long forecast lead-time. Model deficiencies such as oversensitivity of ISMR to ENSO and unrealistic ENSO influence on variation of convection over equatorial Indian Ocean (EQUINOO) lead to errors in ISMR forecasting. In CFSv2, ISMR is mostly decided by ENSO whereas in observation it is influenced by ENSO and EQUINOO independently.Keywords
Boundary Forcing, Forecast Skill, Seasonal Forecasts, Sea-Surface Temperature, Summer Monsoon Rainfall.Full Text
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