The predictability of Indian summer monsoon rainfall from pre-season circulation indices is explored from observations during 1939–91. The predictand is the all-India average of June–September precipitation NIR, and the precursors examined are the latitude position of the 500 mb ridge along 75°E in April (L), the pressure tendency April minus January at Darwin (DPT), March-April-May temperature at six stations in west central India (T6), the sea surface temperature (SST) anomaly in the northeastern Arabian Sea in May (ASM), SST anomaly in the Arabian Sea in January (ANJ), northern hemisphere temperature anomaly in January–February (NHT), and Eurasian snow cover in January (SNOW). Monsoon rainfall tends to be enhanced with a more northerly ridge position, small Darwin pressure tendency, warmer pre-season conditions, and reduced winter snow cover. However, relationships have varied considerably over the past half-century, with the strongest associations during 1950–80, and a drastic weakening in the 1980s.
Four prediction models were constructed based on stepwise multiple regression, using as predictors combinations of L, DPT, T6, ASM, and NHT, with 1939–68 as “dependent” dataset, or training period, and 1969–91 as “independent” dataset or verification period. For the 1969–80 portion of the verification period calculated and observed NIR values agreed closely, with the models explaining 74–79% of the variance. By contrast, after 1980 predictions deteriorated drastically, with the explained variance for the 1969–89 time span dropping to 25–31%. The monsoon rainfall of 1990 and 1991 turned out to be again highly predictable from models based on stepwise multiple regression and linear discriminant analysis and using as input L + DPT or L + DPT + NHT, and with this encouragement an experimental real-time forecast was issued of the 1992 monsoon rainfall.
These results underline the need for investigations into decadal-scale changes in the general circulation setting and raise concern for the continued success of seasonal forecasting.
Volume 129, 2020
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