Development of a multimodel‐based seasonal prediction system for extreme droughts and floods: a case study for South Korea

Abstract

An experimental, district-level system was developed to forecast droughts and floods over South Korea to properly represent local precipitation extremes. The system is based on the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multimodel ensemble (MME) seasonal prediction products. Three-month lead precipitation forecasts for 60 stations in South Korea for the season of March to May are first obtained from the coarse-scale MME prediction using statistical downscaling. Owing to the relatively small variance of the MME and regression-based downscaling outputs, the downscaled MME (DMME) products need to be subsequently inflated. The final station-scale precipitation predictions are then used to produce drought and flood forecasts on the basis of the Standardized Precipitation Index (SPI).The performance of three different inflation schemes was also assessed. Of these three schemes, the method that simply rescales the variance of predicted rainfall to that based on climate records, irrespective of the prediction skill or the DMME variance itself at a particular station, gives the best overall improvement in the SPI predictions. However, systematic biases in the prediction system cannot be removed by variance inflation. This implies that DMME techniques must be further improved to correct the bias in extreme drought/flood predictions. Overall, it is seen that DMME, in conjunction with variance inflation, can predict hydrological extremes with reasonable skill. Our results could inform the development of a reliable early warning system for droughts and floods, which is invaluable to policy makers and stakeholders in agricultural and water management sectors, and so forth and is important for mitigation and adaptation measures.

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

“…The regression-based coupled pattern projection method with optimal predictor selection was used for statistical downscaling [Kang et al, 2009;Sohn et al, 2012b]. The novelty of this approach is the use of model output statistics [Wilks, 1995] for predicting meteorological variables on the station scale.…”

Drought hazard assessment in the context of climate change for South Korea

“…In addition, the climate of South Korea experienced a gradual warming throughout the 20th century (Im et al, 2011). The change of the mean climate state could lead to a change of climate extremes due to a shift in the temperature and precipitation distribution (Sohn et al, 2013). Anthropogenic climate change is projected to accelerate in the future, and South Korea will be vulnerable to changes in various types of extreme climate events.…”

Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based downscaling

“…go.kr/weather/observation/past_table.jsp). These data were used to calibrate and validate temperature and precipitation predictions at the target station locations (for more information, see Sohn et al 2013b). National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis products (Kalnay et al 1996) covering the same time period were also used in this study.…”