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G. Nageswara Rao, “Variations of the SO Relationship with Summer and Winter Monsoon Rainfall over India: 1872-1993,” Journal of Climatology, Vol. 5, 1999, pp. 3486-3495.

has been cited by the following article:

  • TITLE: Southern and Tropical Indian Ocean SST: A Possible Predictor of Winter Monsoon Rainfall over South India

    AUTHORS: Ravi P. Shukla, Shailendra Rai, Avinash C. Pandey

    KEYWORDS: Winter Monsoon Rainfall over South India; Southern/Tropical Indian Ocean; Multivariate/Linear Regression Models

    JOURNAL NAME: Atmospheric and Climate Sciences, Vol.3 No.4, August 26, 2013

    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.