Reconsidering the Quality and Utility of Downscaling

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Future change of daily precipitation indices in Japan: a stochastic weather generator-based bootstrap approach to provide probabilistic climate information

This study proposes the stochastic weather generator (WG)-based bootstrap approach to provide the probabilistic climate change information on mean precipitation as well as extremes, which applies a WG (i.e., LARS-WG) to daily precipitation under the present-day and future climate conditions derived from dynamical and statistical downscaling models. Additionally, the study intercompares the precipitation change scenarios derived from the multimodel ensemble for Japan focusing on five precipitation indices (mean precipitation, MEA; number of wet days, FRE; mean precipitation amount per wet day, INT; maximum number of consecutive dry days, CDD; and 90th percentile value of daily precipitation amount in wet days, Q90). Three regional climate models (RCMs: NHRCM, NRAMS and TWRF) are nested into the high-resolution atmosphere-ocean coupled general circulation model (MIROC3.2HI AOGCM) for A1B emission scenario. LARS-WG is validated and used to generate 2000 years of daily precipitation from sets of grid-specific parameters derived from the 20-year simulations from the RCMs and statistical downscaling model (SDM: CDFDM). Then 100 samples of the 20-year of continuous precipitation series are resampled, and mean values of precipitation indices are computed, which represents the randomness inherent in daily precipitation data. Based on these samples, the probabilities of change in the indices and the joint occurrence probability of extremes (CDD and Q90) are computed. High probabilities are found for the increases in heavy precipitation amount in spring and summer and elongated consecutive dry days in winter over Japan in the period 2081-2100, relative to 1981-2000. The joint probability increases in most areas throughout the year, suggesting higher potential risk of droughts and excess water-related disasters (e. g., floods) in a 20 year period in the future. The proposed approach offers more flexible way in estimating probabilities of multiple types of precipitation extremes including their joint probability compared to conventional approaches

Contributions of GCM/RCM Uncertainty in Ensemble Dynamical Downscaling for Precipitation in East Asian Summer Monsoon Season

Targeting to East Asian summer monsoon for the first time, this study presents an assessment of projection uncertainty in ensemble dynamical downscaling (DDS) simulations. Based on 12-member DDS simulations comprised of three global climate models (GCMs) and four regional climate models (RCMs), we evaluate contributions of GCM and RCM uncertainty to the total uncertainty of summer-time precipitation projections around Japan.Our results show that contribution of RCM uncertainty can be comparable to that of GCM uncertainty in magnitudes. This finding draws a distinction from the past studies showing the dominance of GCM uncertainty. Most notably, our results show that RCM uncertainty for number of precipitating days appears around and over the land. RCM uncertainty for precipitation amounts also shows a dependence on topography but to a lessor degree. These RCM uncertainty characteristics are potentially linked to the difference in various RCM configurations such as physics schemes and model topography. In contrast, GCM uncertainty mostly appears over the ocean, which can be attributed to the difference in the GCM\u27s future projections of East Asian summer monsoon. Our finding may be of an importance for water disaster and water resource management with DDS