Six month-lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble

Abstract

The potential of using a dynamical-statistical method for long-lead drought prediction was investigated. In particular, the APEC Climate Center one-tier multimodel ensemble (MME) was downscaled for predicting the standardized precipitation evapotranspiration index (SPEI) over 60 stations in South Korea. SPEI depends on both precipitation and temperature, and can incorporate the effect of global warming on the balance between precipitation and evapotranspiration. It was found that the one-tier MME has difficulty in capturing the local temperature and rainfall variations over extratropical land areas, and has no skill in predicting SPEI during boreal winter and spring. On the other hand, temperature and precipitation predictions were substantially improved in the downscaled MME. In conjunction with variance inflation, downscaled MME can give reasonably skillful 6 month-lead forecasts of SPEI for the winter to spring period. Our results could lead to more reliable hydrological extreme predictions for policymakers and stakeholders in the water management sector, and for better mitigation and climate adaptations. Key Points A dynamical-statistical method for a long-lead drought prediction was developed. The method can increase the skill of up to 6-month lead SPEI predictions. SPEI prediction is suitable for identifying droughts under the global warming. ©2012. American Geophysical Union. All Rights Reserved.