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      SCOPUS SCIE

      Development of a gridded climate data tool for the COordinated Regional climate Downscaling EXperiment data

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      https://www.riss.kr/link?id=A107504750

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      <P>The assessment of regional climate change impacts on agriculture would benefit from a climate data processing tool that aids preparation of input data to agricultural models. A gridded data tool was developed to process the outputs of regiona...

      <P>The assessment of regional climate change impacts on agriculture would benefit from a climate data processing tool that aids preparation of input data to agricultural models. A gridded data tool was developed to process the outputs of regional climate models including the COordinated Regional climate Downscaling EXperiment (CORDEX) data. The CORDEX Data Support Library (CDSL) was designed to provide functionalities associated with high performance computing and the preparation of input data without additional storage requirement. A set of functions was implemented in the CDSL to facilitate the parallel processing of CORDEX data. The CDSL had functionalities to unify the spatial extent and resolution, projection and calendar system of gridded data for creating ensemble data sets that could be imported into a model of interest using a function call. As a case study, reference evapotranspiration (ETD) in East Asia was calculated using the CDSL to process the outputs of regional climate models (RCMS) available from the website of the CORDEX East Asia. Six sets of ETO (ETcoRDEx) were calculated using CORDEX data as inputs to the FAO 56 formula. Those sets were compared with ET0 calculated using AgMERRA data as inputs (ETAgmERRA). The processing time for climate data decreased with the increasing number of processor cores when the features of parallel processing were used for the CDSL. For example, the running time for data loading reduced by 88% using the CDSL with 16 processor cores. These results demonstrated that the CDSL would facilitate regional climate change impact assessment using a considerably large amount of climate data, e.g., >200 GB, as inputs to agricultural models. (C) 2016 Elsevier B.V. All rights reserved.</P>

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