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    RISS 인기검색어

      Coupled Land‐Atmosphere Regional Model Reduces Dry Bias in Indian Summer Monsoon Rainfall Simulated by CFSv2

      한글로보기

      https://www.riss.kr/link?id=O120845707

      • 저자
      • 발행기관
      • 학술지명
      • 권호사항
      • 발행연도

        2018년

      • 작성언어

        -

      • Print ISSN

        0094-8276

      • Online ISSN

        1944-8007

      • 등재정보

        SCI;SCIE;SCOPUS

      • 자료형태

        학술저널

      • 수록면

        2476-2486   [※수록면이 p5 이하이면, Review, Columns, Editor's Note, Abstract 등일 경우가 있습니다.]

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        • 전북대학교 중앙도서관  
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        • 중앙대학교 서울캠퍼스 중앙도서관  
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        • 충남대학교 중앙도서관  
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        • 이화여자대학교 중앙도서관  
        • 고려대학교 도서관  
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      다국어 초록 (Multilingual Abstract)

      Global climate models including the Climate Forecast System version 2, the operational model used for prediction of Indian summer monsoon rainfall by the India Meteorological Department, has dry precipitation bias, mostly over densely populated Ganga basin. This restricts the use of model output in hydrological simulations/forecasts. We use regional atmospheric Weather Research and Forecasting model coupled with land surface models, driven by the boundary conditions from Climate Forecast System version 2. We find significant reduction in the dry bias of Indian summer monsoon rainfall with regional land‐atmosphere model and this attributes to (a) improved moisture transport from Western and Upper Indian Ocean to Ganga Basin and (b) improved precipitation recycling over the Ganga basin. We find that the smoothened topography in the global model allows advection of cold dry subtropical air into the Indian monsoon region, contributing to the cold temperature and dry precipitation bias. These results have important implications for monsoon simulations in developing operational hydroclimatic prediction system in India.
      The operational monsoon prediction model for India, Climate Forecast System version 2, has significant dry bias in precipitation over the Ganga basin, and this restricts the use of model output for hydrologic prediction. We attribute such bias to the lack of representation of land surface processes and characteristics in the model. We show that an improved representation of land characteristics in a regional coupled atmospheric‐land model improves not only the land‐atmosphere interactions but also the moisture contributions from distant oceanic sources. This finally results into improved simulations of monsoon.


      There is a reduction of the precipitation dry bias over Ganga basin in Indian monsoon simulations using a coupled land‐atmosphere regional model
      Reduction of bias comes from improvement in local recycling and atmospheric moisture transport from the Western and Upper Indian Ocean
      Better land surface process representation in climate models is necessary to capture the monsoon rainfall over the Ganga basin
      번역하기

      Global climate models including the Climate Forecast System version 2, the operational model used for prediction of Indian summer monsoon rainfall by the India Meteorological Department, has dry precipitation bias, mostly over densely populated Ganga ...

      Global climate models including the Climate Forecast System version 2, the operational model used for prediction of Indian summer monsoon rainfall by the India Meteorological Department, has dry precipitation bias, mostly over densely populated Ganga basin. This restricts the use of model output in hydrological simulations/forecasts. We use regional atmospheric Weather Research and Forecasting model coupled with land surface models, driven by the boundary conditions from Climate Forecast System version 2. We find significant reduction in the dry bias of Indian summer monsoon rainfall with regional land‐atmosphere model and this attributes to (a) improved moisture transport from Western and Upper Indian Ocean to Ganga Basin and (b) improved precipitation recycling over the Ganga basin. We find that the smoothened topography in the global model allows advection of cold dry subtropical air into the Indian monsoon region, contributing to the cold temperature and dry precipitation bias. These results have important implications for monsoon simulations in developing operational hydroclimatic prediction system in India.
      The operational monsoon prediction model for India, Climate Forecast System version 2, has significant dry bias in precipitation over the Ganga basin, and this restricts the use of model output for hydrologic prediction. We attribute such bias to the lack of representation of land surface processes and characteristics in the model. We show that an improved representation of land characteristics in a regional coupled atmospheric‐land model improves not only the land‐atmosphere interactions but also the moisture contributions from distant oceanic sources. This finally results into improved simulations of monsoon.


      There is a reduction of the precipitation dry bias over Ganga basin in Indian monsoon simulations using a coupled land‐atmosphere regional model
      Reduction of bias comes from improvement in local recycling and atmospheric moisture transport from the Western and Upper Indian Ocean
      Better land surface process representation in climate models is necessary to capture the monsoon rainfall over the Ganga basin

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