Large-scale teleconnection patterns of Indian summer monsoon as revealed by CFSv2 retrospective seasonal forecast runs

The seasonal prediction skill [defined as the linear correlation (cc) between the observed and forecasted rainfall] of the Indian Summer Monsoon Rainfall (ISMR) is evaluated in the Climate Forecast System version 2 (CFSv2) model, at different lead times on the basis of its representation of large scale tropical teleconnection. Surprisingly, the model exhibits reasonable skill at a longer lead time (e.g. forecasts initialized with February initial conditions, Feb IC run, cc > 0.5) that is reasonably better when compared with that with forecast initialized at shorter lead time [April/May IC (Apr/May IC) runs, cc < 0.5]. Although the prediction skill of ISMR improves as lead time increases, the ENSO forecast skill improves as lead time decreases. Probable reasons for these counter-intuitive findings are investigated in this study. The model shows unrealistic teleconnection of ISMR with Indian Ocean Dipole (IOD) and is unable to represent the large scale rainfall pattern over the Indian land region. The Equatorial Indian Ocean Oscillation (EQUINOO) shows unrealistic teleconnection with ISMR as well as equatorial Pacific from all the initial condition runs. Unrealistic EQUINOO and the IOD teleconnection suggest that the air-sea interaction in the Indian Ocean requires to be improved in the model. The relationship between El Nino-Southern Oscillation (ENSO) and monsoon in CFSv2 is realistic in terms of spatial pattern though it is somewhat stronger than that in observations. The equatorial central Pacific sea surface temperature and rainfall show very strong cold and dry biases respectively, and these biases enhance from the Feb IC run to the May IC run. It is found that these biases are due to strong unrealistic coupled feedback in this region. As a result, the associated ENSO-monsoon teleconnection pattern shifts westward with decreasing lead time, resulting in unrealistic patterns as compared with observations, and causing a loss of prediction skill at shorter lead times.