4.6 Article

Spatio-temporal characteristics of Indonesian drought related to El Nino events and its predictability using the multi-model ensemble

Journal

INTERNATIONAL JOURNAL OF CLIMATOLOGY
Volume 37, Issue 13, Pages 4700-4719

Publisher

WILEY
DOI: 10.1002/joc.5117

Keywords

meteorological drought; drought characteristics; El Nino; Indonesia; multi-model ensemble; Standardized Precipitation Index

Funding

  1. APEC Climate Center (APCC) Young Scientist Support Program (YSSP)
  2. NOAA
  3. National Science Foundation (NSF)
  4. NASA
  5. Department of Energy (DOE)

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Spatio-temporal drought characteristics related to weak, moderate, and strong El Nino 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 Nino events are better represented by SPI. The effects of El Nino events on meteorological drought were investigated based on a composite analysis. The effects of El Nino 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 Nino events. Spatial distributions of affected areas varied by season, intensity of El Nino events, and local factors. During moderate and strong El Nino 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 Nino 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 Nino effects on drought, implying that serious drought conditions during the most harshly affected season can be predicted using the MME forecast.

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