Bias correction of daily precipitation from downscaled CMIP5 climate projections over the Indonesian region

Global Climate Models (GCMs) have been the primary source of information for constructing climate scenarios, and they provide the basis for climate change impacts assessments of climate change for a range of scales, from global down to regional scale. Due to the coarse spatial resolution, the GCM ou...

Full description

Saved in:
Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2019-07, Vol.303 (1), p.12046
Main Authors: Amsal, F, Harsa, H, Sopaheluwakan, A, Linarka, U A, Pradana, R P, Satyaningsih, R
Format: Article
Language:English
Subjects:
Assessments
Bias
Climate change
CMIP5
Distribution functions
Environmental impact
Gamma distribution
Global Climate Models
Precipitation
Probability distribution functions
Quantiles
Regional Climate Model
Simulation
Spatial discrimination
Spatial resolution
Statistical analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Global Climate Models (GCMs) have been the primary source of information for constructing climate scenarios, and they provide the basis for climate change impacts assessments of climate change for a range of scales, from global down to regional scale. Due to the coarse spatial resolution, the GCM outputs have to be downscaled to resolve the scale discrepancy between the resolutions required for impact assessments and the model's resolution. However, it is important to bias-correct (BC) the raw climate projection outputs which ideally correct the discrepancy between a model's climate and the observed historical climate. In this study the results of bias correction of daily precipitation over the Indonesian region from downscaled CMIP5 GCM climate simulations using an optimized configuration of the Regional Climate Model (RegCM) for a baseline period of 16 years (1990-2005) with respect to observation is discussed in detail. The statistical bias correction method validated in this study is based on the initial assumption that both observed and simulated intensity distributions are well approximated by the Gamma distribution and the correction is made by matching the quantiles of the Gamma cumulative distribution functions. Overall, the results suggest that when the bias-correction is applied on dynamically downscaled model, it improved the skill in simulating the precipitation over Indonesia and this is a useful tool for further regional downscaling studies.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/303/1/012046