Assessing peak warming of SST over equatorial eastern Pacific Ocean (Nino 3 region) with the help of circulation pattern over India during El Nino events

Citation
Ak. Srivastava et Kcs. Ray, Assessing peak warming of SST over equatorial eastern Pacific Ocean (Nino 3 region) with the help of circulation pattern over India during El Nino events, J METEO JPN, 78(3), 2000, pp. 279-288
Citations number
22
Categorie Soggetti
Earth Sciences
Journal title
JOURNAL OF THE METEOROLOGICAL SOCIETY OF JAPAN
ISSN journal
00261165 → ACNP
Volume
78
Issue
3
Year of publication
2000
Pages
279 - 288
Database
ISI
SICI code
0026-1165(200006)78:3<279:APWOSO>2.0.ZU;2-F
Abstract
This study investigates the relationship between circulation anomalies over India during the month of April, and sea surface temperature (SST) anomali es of the eastern Pacific Ocean (Nino 3 region). It is found that rain over northern parts of India and position of 500 hPa ridge at 75 degrees E, res pectively, have significant correlation coefficients with subsequent SST an omalies of eastern Pacific Ocean (Nino 3 region). Moreover, these relations hips are stronger during El Nino years. Since, during El Nino years, peak w arming in SST occurs during October-December months, it could well be asses sed with the help of these two parameters with the lead time of six months, once occurrence of an El Nino event is indicated by already existing dynam ical models. To predict the peak warming during the El Nino years, data of April rain and position of 500 hPa April ridge for eleven El Nino years, (1 951, 1953, 1957, 1965, 1969, 1972, 1976, 1982, 1987, 1992, 1997) were subje cted to the principal component analysis. First principal component was use d to predict the average SST anomalies of October-December months through a simple regression equation using cross validation method. The standard dev iation of average SST anomalies of October-December months for these eleven years is 0.96, while the root mean square error of the predicted value is 0.52, which is indicative of the good skill of prediction.