A Statistical Bias Correction Tool for Generating Climate Change Scenarios in Indonesia based on CMIP5 Datasets

3rd International Seminar On Sciences "Sciences On Precision And Sustainable Agriculture"

Faqih, A.^1

Department of Geophysics and Meteorology, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University (IPB), IPB Dramaga Campus, FMIPA Building, Wing 19, Level 4, Jl. Raya Dramaga, Jawa Barat

16680, Indonesia^1

关键词: Climate change scenarios;    Climate scenarios;    Downscaling process;    Future climate scenarios;    Global climate model;    Project activities;    Spatial resolution;    Supporting capacities;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/58/1/012051/pdf
DOI  :  10.1088/1755-1315/58/1/012051

来源: IOP

【 摘 要 】

Providing information regarding future climate scenarios is very important in climate change study. The climate scenario can be used as basic information to support adaptation and mitigation studies. In order to deliver future climate scenarios over specific region, baseline and projection data from the outputs of global climate models (GCM) is needed. However, due to its coarse resolution, the data have to be downscaled and bias corrected in order to get scenario data with better spatial resolution that match the characteristics of the observed data. Generating this downscaled data is mostly difficult for scientist who do not have specific background, experience and skill in dealing with the complex data from the GCM outputs. In this regards, it is necessary to develop a tool that can be used to simplify the downscaling processes in order to help scientist, especially in Indonesia, for generating future climate scenario data that can be used for their climate change-related studies. In this paper, we introduce a tool called as "Statistical Bias Correction for Climate Scenarios (SiBiaS)". The tool is specially designed to facilitate the use of CMIP5 GCM data outputs and process their statistical bias corrections relative to the reference data from observations. It is prepared for supporting capacity building in climate modeling in Indonesia as part of the Indonesia 3rd National Communication (TNC) project activities.