T. Izumi, M.A. Semenov, M. Nishimori, Y. Ishigooka, T. Kuwagata ELPIS-JP: A Dataset of Local-scale Daily Climate Change Scenarios for Japan.
Full article (open access) at Royal Society, doi:10.1098/rsta.2011.0305
Keywords
ELPIS-JP; Stochastic weather generator; LARS-WG; Climate change; Impact assessment; Japan
Abstract
We developed a dataset of local-scale daily climate change scenarios for Japan (called ELPIS-JP) using the stochastic weather generators (WGs), mainly the LARS-WG. The ELPIS-JP dataset is based on the observed (or estimated) daily weather data for seven climatic variables (daily mean, maximum, and minimum temperatures, precipitation, solar radiation, relative humidity, and wind speed) at 938 sites in Japan and climate projections from the multi-model ensemble of global climate models used in the Coupled Model Intercomparison Project (CMIP3) and from multi-model ensemble of regional climate models form the Japanese downscaling project (called S-5-3). The capability of the WGs to reproduce the statistical features of the observed data for the period 1981-2000 is assessed using several statistical tests and quantile-quantile plots. Overall performance of the WGs was good. The ELPIS-JP dataset consists of two types of daily data: (1) the transient scenarios throughout the 21st century using projections from ten CMIP3 GCMs under three emission scenarios (A1B, A2, and B1); and (2) the time-slice scenarios for the period 2081-2100 using projections from three S-5-3 RCMs. The ELPIS-JP dataset is designed for use in conjunction with process-based impact models (e.g., crop models) for assessment, not only the impacts of mean climate change, but also the impacts of changes in climate variability, wet/dry spells, and extreme events, as well as the uncertainty of future impacts associated with climate models and emission scenarios. ELPIS-JP offers an excellent platform for probabilistic assessment of climate change impacts and potential adaptation at a local scale in Japan.