Thursday, 26 January 2017: 8:30 AM
609 (Washington State Convention Center )
This study evaluates the seasonal predictability of the Indian summer monsoon (ISM) rainfall using the Climate Forecast System, version 2 (CFSv2), the current operational forecast model for subseasonal-to-seasonal predictions at the National Centers for Environmental Prediction (NCEP). From a 50-year CFSv2 simulation, 21 wet, dry and normal ISM cases are chosen for a set of seasonal “predictions” with initial states from January to May. For each prediction, a five-member ensemble is generated with perturbed atmospheric initial states and all predictions are integrated to the end of September. Based on the measures of correlation and root mean square error, the prediction skill decreases with lead month, with the initial states with the shortest lead (May) generally having the highest skill for predicting the summer mean (June to September; JJAS) rainfall, zonal wind at 850hPa and sea surface temperature over the ISM region.
These predictability experiments are used to understand the finding reported by some recent studies that the CFSv2 seasonal hindcasts generally show higher skill in predicting the ISM rainfall anomalies from February initial states than from May ones. Comparing the May climatologies generated by the February and May initialized CFSv2 hindcasts, it is found that the latter has a larger bias over the Arabian Sea, with stronger monsoon winds, precipitation and surface latent heat loss. Although the atmospheric bias lessens quickly after May, an accompanying cold bias persists in the Arabian Sea for several months. In contrast, the initial shock is negligible in the predictability experiments, by design. Therefore, the model bias and initial shock in the May hindcasts may be a major factor in affecting ISM prediction skill.
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