Early detection of extreme drought and flood events either over the whole globe or a broad geographical region, and timely dissemination of this information, is indispensable for mitigation and disaster preparedness. Recently, the APEC Climate Center (APCC) has launched a global precipitation variation monitoring product based on the Climate Anomaly Monitoring System-Outgoing Longwave Radiation Precipitation Index (CAMS-OPI) data. Here we quantify the reliability of CAMS-OPI, as well as other gauge-satellite-merged and reanalysis precipitation datasets, for the purpose of monitoring large-scale precipitation variability in East Asia. The ground truth is the newly available gauge-based data from the project titled 'Asian Precipitation -Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) of the Water Resources'. It is found that the seasonal-to-interannual rainfall deficit and surplus given by various reanalysis systems sometimes do not match the spatial patterns seen in the APHRODITE data. Moreover, maps showing the Standardized Precipitation Index (SPI) become less and less reliable as the time scale based on which values are calculated increases. In contrast, the performance of gauge-satellite-based rainfall datasets is satisfactory and the quality of SPI maps does not decay as the time scale increases. Overall, CAMS-OPI is found to be reliable for monitoring large-scale precipitation variations over the East Asian sector.

[1] 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. Citation: Sohn, S.-J., J.-B.Ahn, and C.-Y. Tam (2013), Six month-lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble, Geophys. Res. Lett., 40,[579][580][581][582][583]

ABSTRACT:The World Meteorological Organization (WMO) Lead Centre for Long-Range Forecast Multi-Model Ensemble (WMO LC-LRFMME) has been established to collect and share long-range forecasts from the WMO designated Global Producing Centres (GPC). In this study, the seasonal skill of the deterministic multi-model prediction of GPCs in WMO LC-LRFMME is investigated. The GPC models included in the analysis cover 30 years of common hindcast period from 1981 to 2010 and real-time forecast for the period from DJF2011/2012 to SON2014. The equal-weighted multi-model ensemble (MME) method is used to produce the MME forecast. We show that the GPC models generally capture the observed climatological patterns and seasonal variations in temperature and precipitation. However, some systematic biases/errors in simulation of the climatological mean patterns and zonal mean profiles are also found, most of which are located in mid-latitudes or high latitudes. The temporal correlation coefficients both of 2 m temperature and precipitation in the tropical region (especially over the ocean) exceed 95%, but drop gradually towards high latitudes and are even negative in the polar region for precipitation. The prediction skills of individual models and the MME over 13 regional climate outlook forum (RCOF) regions for four calendar seasons are also assessed. The prediction skills vary with season and region, with the highest skill being demonstrated by the MME forecasts for the regions of the tropical RCOFs. These predictions are strongly affected by the ENSO over Pacific Islands, Southeast Asia and Central America. Additionally, Southeast of South America and North Eurasian regions show relatively low skills for all seasons when compared to other regions.

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.