Abstract
The complexities in the relationship between winter monsoon rainfall (WMR) over South India and Sea Surface temperature (SST) variability in the southern and tropical Indian Ocean (STIO) are evaluated statistically. The data of the time period of our study (1950-2003) have been divided exactly in two halves to identify predictors. Correlation analysis is done to see the effect of STIO SST variability on winter monsoon rainfall index (WMRI) for South India with a lead-lag of 8 seasons (two years). The significant positive correlation is found between Southern Indian Ocean (SIO) SST and WMRI in July-August-September season having a lag of one season. The SST of the SIO, Bay of Bengal and North Equatorial Indian Ocean are negatively correlated with WMRI at five, six and seven seasons before the onset of winter monsoon. The maximum positive correlation of 0.61 is found from the region south of 500 S having a lag of one season and the negative correlations of 0.60, 0.53 and 0.57 are found with the SST of the regions SIO, Bay of Bengal and North Equatorial Ocean having lags of five, six and seven seasons respectively and these correlation coefficients have confidence level of 99%. Based on the correlation analysis, we defined Antarctic Circumpolar Current Index A and B (ACCIA (A) & ACCIB (B)), Bay of Bengal index (BOBI (C)) and North Equatorial Index (NEI (D)) by averageing SST for the regions having maximum correlation (positive or negative) with WMRI index. These SST indices are used to predict the WMRI using linear and multivariate linear regression models. In addition, we also attempted to detect a dynamic link for the predictability of WMRI using Nino 3.4 index. The predictive skill of these indices is tested by error analysis and Willmott’s index.
Highlights
Indian summer monsoon, which is a part of the Asian monsoon system, is a regular annual phenomenon which brings heavy rainfall to India and adjacent countries during summer monsoon season (June to September; JJAS)
winter monsoon rainfall index (WMRI) is found in Figure 1(a) having a maximum value of 0.61 which is significant above 99% confidence level near 90 ̊E, 55 ̊S
We have extended our analysis by leading Sea Surface temperature (SST) with respect to WMRI for more than a year
Summary
Indian summer monsoon, which is a part of the Asian monsoon system, is a regular annual phenomenon which brings heavy rainfall to India and adjacent countries during summer monsoon season (June to September; JJAS). In all the above studies related to WMR, no attempt was made to understand the effect of sea Surface temperature variability in the Southern as well as tropical Indian Ocean (STIO) on the WMR monsoon on lead-lag of 8 seasons (2 years). The variability of STIO SST and WMRI relationship is examined under the lead-lag time scales of 08 seasons for providing some insight into the possibility of early prediction of winter monsoon rainfall. This relationship will enable us to understand dependency of winter monsoon conditions on Southern Indian Ocean (SIO) SSTA, and in turn will provide important clues to oceanic system memory, which is still poorly understood. The regression and multiple regression technique have been used to study the predictability of the WMRI with above indices individually as well as in various combinations
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