Predictability of Sri Lankan rainfall based on ENSO

Lareef M. Zubair; Manjula Siriwardhana; Janaki Chandimala; Zeenas Yahiya

Predictability of Sri Lankan rainfall based on ENSO
Zubair, Lareef M.
Siriwardhana, Manjula
Chandimala, Janaki
Yahiya, Zeenas
International Research Institute for Climate and Society
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Book/Journal Title:
International Journal of Climatology
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Sri Lanka
Investigating the year-round rainfall of Sri Lanka provides understanding into the South Asian monsoon system as it compliments studies on the Indian summer monsoon. The El Niño-Southern Oscillation (ENSO) is a primary mode of climate variability of this area. Here, the predictability of Sri Lanka rainfall based on ENSO is quantified based on composite analysis, correlations and contingency tables. The rainfall is modestly predictable based on ENSO during January-March, July-August and October-December. El Niño typically leads to wetter conditions during October to December and drier conditions during January to March and July to August on average. The correlations of ENSO indices with rainfall are statistically significant for October to December, January to March and July to August and an analysis based on contingency tables shows modest predictability. The use of ENSO indices derived from the central Pacific sea surfaces improves the predictability from January to June. The predictability in the mountain regions is diminished when garnering orographic rainfall. The predictability in the east is diminished during the cyclone season. The predictability based on ENSO for October to December rainfall is robust on a decadal scale while the predictability of January to March and July to August rainfall has acquired significance in recent decades. An ENSO-based scheme that is adapted to each season and region, and takes account of decadal variations can thus provide skillful rainfall predictions.
Science--Social aspects
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Suggested Citation:
Lareef M. Zubair, Manjula Siriwardhana, Janaki Chandimala, Zeenas Yahiya, , Predictability of Sri Lankan rainfall based on ENSO, Columbia University Academic Commons, .

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