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Spatio‐temporal characteristics of Indonesian drought related to El Niño events and its predictability using the multi‐model ensemble
Author(s) -
Setiawan Amsari Mudzakir,
Lee WooSeop,
Rhee Jinyoung
Publication year - 2017
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.5117
Subject(s) - empirical orthogonal functions , climatology , predictability , precipitation , environmental science , geography , indonesian , meteorology , geology , mathematics , statistics , linguistics , philosophy
Spatio‐temporal drought characteristics related to weak, moderate, and strong El Niño events in the Indonesian region were investigated. The empirical orthogonal function ( EOF ) patterns of normalized monthly precipitation and Standardized Precipitation Index ( SPI ) for the period 1950–2010 were analysed; the influence of El Niño events are better represented by SPI . The effects of El Niño events on meteorological drought were investigated based on a composite analysis. The effects of El Niño were more obvious during June–July–August and September–October–November, while they were not significant in March–April–May especially during weak and moderate El Niño events. Spatial distributions of affected areas varied by season, intensity of El Niño events, and local factors. During moderate and strong El Niño events, meteorological drought was more common in southern Sumatra, Java, Bali, Nusa Tenggara, southeastern Kalimantan, Sulawesi, Maluku, and Papua. Based on the spatial and temporal variability of droughts, the monthly or seasonal meteorological drought outlook can be improved by using SPI focusing more on the regions and seasons that are affected by El Niño events. The multi‐model ensemble ( MME ) forecast of SPI performed better for the target months of September to November even at medium lead times. The months are consistent with the period of serious El Niño effects on drought, implying that serious drought conditions during the most harshly affected season can be predicted using the MME forecast.