PENILAIAN TERHADAP SISTEM PREDIKTIF BERBASIS ENSO DI INDONESIA

Abstract

The use of  Seasonal Climate Forecasting (SCF) in  risk  management decisions in  the developing countries such Indonesia, which are most vulnerable to the impacts of climate variability and climate change, has not been widely applied yet. The major limitations are; the limited national capacity for climate monitoring and forecasting; low levels of awareness among decision makers to the local and regional impact of climate variability (e.g. ENSO); and lack of effective policy responses to climate variability and climate change. The specific objectives of this study are, for each main climate regions of the Indonesia, analyse the relationship of seasonal rainfall with key ENSO based predictors  and determine the most “robust” predictive system(s) for each these region. To use FLOWCAST software to undertake three methods of  investigation. Results of  regression and contingency table analyses show that  the synchronous associations between SOI, NINO 3.4, SST    and Indonesia rainfall are significantly stronger in all climate zones for 2 seasonal periods selected (May – October and November – April) in Monsoonal and Local Type. The strength of this relationship also corresponded to high forecasting skill (LEPS) being found especially in Monsoonal and Local Climate Type of Indonesia, with the influences effecting Monsoonal