In this study, a grid-based bias correction method was developed to calculate the climate application index using CORDEX-EA regional climate change scenarios as a part an improvement in the infrastructure of technology used to apply climate data and the expansion of the APCC information service. For this purpose, CORDEX-EA climate change scenario data was converted to a 0.5 degree rectilinear grid. Daily historical, RCP45, and RCP85 for three regional climate models (HadGEM-RA, RegCM4, SNU-MM5) were used. Precipitation, surface temperature, maximum/minimum temperature, and relative humidity were selected to calculate the climate application index. Daily grid observations such as precipitation, temperature, and relative humidity were needed for grid-based bias correction using QM (Quantile Mapping) and calculation of the application index. Global observations in the form of daily grids were generally provided only for variables such as precipitation and temperature. Therefore, we compared the grid-based observations with three different reanalysis datasets and selected one to replace with observations of precipitation, temperature, and other variables. Using the grid-based observations (CPC unified gauge-based analysis of global daily precipitation and CPC daily maximum/minimum temperature) and the MERRA2 reanalysis data for QM, nine climate application indices were produced using the bias-corrected RCP45 and RCP85 regional climate change scenarios.

1. 서론

2. 연구 자료 및 방법

2.1 자료

2.1.1 CPC 관측 및 재분석 자료

2.1.2 CORDEX-EA 지역기후변화 시나리오 자료

2.2 방법

2.2.1 0.5도 격자 자료 변환

2.2.2 편이보정

2.2.3 기후응용지수

3. 연구내용

3.1 격자 기반 관측 자료 사용을 위한 재분석 자료 선정

3.1.1 CPC 강수 관측과 재분석 자료 비교 분석

3.1.2 CPC 최고/최저기온 관측과 재분석 자료 비교 분석

3.2 CPC 관측과 MERRA2 재분석 자료를 이용한 편이보정 결과

3.2.1 CORDEX-EA 지역기후변화 시나리오 자료의 편이보정 결과

3.2.2 CPC 관측과 MERRA2 재분석 자료를 이용한 편이보정 차이

3.3 편이보정된 CORDEX-EA 지역기후변화 시나리오를 이용한 기후응용지수 산출

3.3.1 동아시아 지역 ETCCDI 기후지수 33 3.3.2 동아시아 지역 분야별 기후응용지수

4. 결론 및 토론

REFERENCES

부록