Inter-comparison of multiple Global Climate Model (GCM) data based on spatial pattern of rainfall over Indonesia

5th International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring 2018

生态环境科学

Aflahah, Elania^1 ; Latifah, Arnida Lailatul^2 ; Hidayat, Rahmat^3 ; Hidayati, Rini^3 ; Ihwan, Andi^1

Applied Climatology Study Program, Graduate School, Bogor Agricultural University, Bogor

16680, Indonesia^1

Laboratorium of High-Performance Computing, Research Center for Informatics, Indonesian Institute of Sciences, West Java, Bogor

16916, Indonesia^2

Department of Geophysics and Meteorology, Bogor Agricultural University, Bogor

16680, Indonesia^3

关键词: Annual precipitation;    Composite analysis;    Global climate model;    Mean and standard deviations;    Precipitation characteristics;    Regional climate modeling;    Seasonal rainfall;    Standard deviation;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/284/1/012017/pdf
DOI  :  10.1088/1755-1315/284/1/012017

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

The Coupled Model Inter-comparison Project Phase 5 (CMIP5) is the output of many coupled atmosphere-ocean of global climate models (GCMs) and widely used for climate research, especially for driving regional climate model. There are more than 40 CMIP5 GCMs data available, but no single model can be considered as the best for every region. The use of CMIP5 GCMs data for rainfall projection in Indonesia is important to improve the accuracy of the monthly and seasonal rainfall forecast. Then, this study evaluates the capability of the CMIP5 GCMs data for Indonesia region by quantitatively comparing the spatial pattern of the precipitation mean and standard deviation of the CMIP5 data against GPCP, GPCC, and CRU data in the period 1980-2005. Furthermore, the composite analysis is conducted to observe the model performance in reproducing the precipitation characteristic over some areas in Indonesia. In conclusion, the models NorESM1-M, NorESM1-ME, GFDL-ESM2M, CSIRO-MK3-6-0 perform the rainfall mean better than others, while the standard deviation of the rainfall show that the models NorESM1-M, BNU-ESM, CMCC-CMS are superior in which NorESM1-M gives the best performance. The annual precipitation pattern of the model NorESM1-M over various areas in Indonesia is also highly correlated with the observations. Thus, the most suitable model for Indonesia region is NorESM1-M.