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Article Type:
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CWRF Downscaling with Improved Land Surface Initialization Enhances Spring–Summer Seasonal Climate Prediction Skill in China

Han Zhang aSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Science, Sun Yat-sen University, Zhuhai, China

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https://orcid.org/0000-0002-2623-3668
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Xin-Zhong Liang bDepartment of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland
cEarth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Yongjiu Dai aSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Science, Sun Yat-sen University, Zhuhai, China

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Lianchun Song dNational Climate Center, China Meteorological Administration, Beijing, China

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Qingquan Li dNational Climate Center, China Meteorological Administration, Beijing, China

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Fang Wang dNational Climate Center, China Meteorological Administration, Beijing, China

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Shulei Zhang aSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Science, Sun Yat-sen University, Zhuhai, China

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Online Publication:
13 Aug 2024
Print Publication:
01 Sep 2024
DOI:
https://doi.org/10.1175/JCLI-D-23-0565.1
Page(s):
4437–4459
Restricted access

Abstract

This study investigates skill enhancement in operational seasonal forecasts of Beijing Climate Center’s Climate System Model through regional Climate–Weather Research and Forecasting (CWRF) downscaling and improved land initialization in China. The downscaling mitigates regional climate biases, enhancing precipitation pattern correlations by 0.29 in spring and 0.21 in summer. It also strengthens predictive capabilities for interannual anomalies, expanding skillful temperature forecast areas by 6% in spring and 12% in summer. Remarkably, during 7 of 10 years with relatively high predictability, the downscaling increases average seasonal precipitation anomaly correlations by 0.22 and 0.25. Additionally, the substitution of initial land conditions via a Common Land Model integration reduces snow cover and cold biases across the Tibetan Plateau and Mongolia–northeast China, consistently contributing to CWRF’s overall enhanced forecasting capabilities. Improved downscaling predictive skill is attributed to CWRF’s enhanced physics representation, accurately capturing intricate regional interactions and associated teleconnections across China, especially linked to the Tibetan Plateau’s blocking and thermal effects. In summer, CWRF predicts an intensified South Asian high alongside a strengthened East Asian jet compared to CSM, amplifying cold air advection and warm moisture transport over central to northeast regions. Consequently, rainfall distributions and interannual anomalies over these areas experience substantial improvements. Similar enhanced circulation processes elucidate skill improvement from land initialization, where the accurate specification of initial snow cover and soil temperature within sensitive regions persists in influencing local and remote circulations extending beyond two seasons. Our findings emphasize the potential of improving physics representation and surface initialization to markedly enhance regional climate predictions.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Xin-Zhong Liang, xliang@umd.edu; Yongjiu Dai, daiyj6@mail.sysu.edu.cn

Abstract

This study investigates skill enhancement in operational seasonal forecasts of Beijing Climate Center’s Climate System Model through regional Climate–Weather Research and Forecasting (CWRF) downscaling and improved land initialization in China. The downscaling mitigates regional climate biases, enhancing precipitation pattern correlations by 0.29 in spring and 0.21 in summer. It also strengthens predictive capabilities for interannual anomalies, expanding skillful temperature forecast areas by 6% in spring and 12% in summer. Remarkably, during 7 of 10 years with relatively high predictability, the downscaling increases average seasonal precipitation anomaly correlations by 0.22 and 0.25. Additionally, the substitution of initial land conditions via a Common Land Model integration reduces snow cover and cold biases across the Tibetan Plateau and Mongolia–northeast China, consistently contributing to CWRF’s overall enhanced forecasting capabilities. Improved downscaling predictive skill is attributed to CWRF’s enhanced physics representation, accurately capturing intricate regional interactions and associated teleconnections across China, especially linked to the Tibetan Plateau’s blocking and thermal effects. In summer, CWRF predicts an intensified South Asian high alongside a strengthened East Asian jet compared to CSM, amplifying cold air advection and warm moisture transport over central to northeast regions. Consequently, rainfall distributions and interannual anomalies over these areas experience substantial improvements. Similar enhanced circulation processes elucidate skill improvement from land initialization, where the accurate specification of initial snow cover and soil temperature within sensitive regions persists in influencing local and remote circulations extending beyond two seasons. Our findings emphasize the potential of improving physics representation and surface initialization to markedly enhance regional climate predictions.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Xin-Zhong Liang, xliang@umd.edu; Yongjiu Dai, daiyj6@mail.sysu.edu.cn

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