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Understanding and improving Yangtze River Basin summer precipitation prediction using an optimal multi-Physics ensemble

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Abstract

This study employs the regional Climate-Weather Research and Forecasting model (CWRF) to first investigate the primary physical mechanisms causing biases in simulating summer precipitation over the Yangtze River Basin (YRB), and then enhance its predictive ability through an optimal multi-physics ensemble approach. The CWRF 30-km simulations in China are compared among 28 combinations of varying physics parameterizations during 1980–2015. Long-term average summer biases in YRB precipitation are remotely correlated with those of large-scale circulations. These teleconnections of biases are highly consistent with the observed correlation patterns between interannual variations of precipitation and circulations, despite minor shifts in their primary action centers. Increased YRB precipitation aligns with a southward shifted East Asian westerly jet, an intensified low-level southerly flow south of YRB, and a south-eastward shifted South Asian high, alongside higher moisture availability over YRB. Conversely, decreased YRB precipitation corresponds to an opposite circulation pattern. The CWRF control configuration using the ensemble cumulus parameterization (ECP), compared to other cumulus schemes, best captures the observed YRB precipitation characteristics and associated circulation patterns. Coupling ECP with the Morrison or Morrison-aerosol microphysics and the CCCMA or CAML radiation schemes enhances the overall CWRF skills. Compared to the control CWRF, the ensemble average of these skill-enhanced physics configurations more accurately reproduces YRB summer precipitation’s spatial distributions, interannual anomalies, and associated circulation patterns. The Bayesian Joint Probability calibration to these configurations improves the ensemble’s spatial distributions but compromises its interannual anomalies and teleconnection patterns. Our findings highlight substantial potential for refining the representation of climate system physics to improve YRB precipitation prediction. This is notably achieved by realistically coupling cumulus, microphysics, and radiation processes to accurately capture circulation teleconnections. Further enhancements can be achieved by optimizing the multi-physics ensemble among skill-enhanced configurations.

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Data Availability All CWRF experiment simulations used to generate the analysis results in the manuscript are available from the corresponding author upon request. Observational data and analysis results used in this study are accessible online (please email authors for website address).

Abbreviations

BJP:

Bayesian Joint Probability

BMPE:

Best multi-physics ensemble

BMPEbjp:

Ensemble of BJP calibrations applied to the individual BMPE members

CWRF:

Climate-weather research and forecasting model

CTL:

Control simulation

DRI:

Daily rainfall intensity

EAJ:

East Asian westerly jet

ECP:

Ensemble cumulus parameterization

ERI:

European Centre for Medium-Range Weather Forecasts interim reanalysis

ERA5:

European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis

H200:

200-hPa geopotential height

MR:

Comprehensive ranking measure

NRD:

Number of rainy days

PRA:

Average precipitation

SAH:

South Asian High

SCA:

Percentage of areas in the region with significant interannual correlations

TPW:

Total precipitable water

U200:

200-hPa zonal wind

V850:

850-hPa meridional wind

WPSH:

Western Pacific Subtropical High

YRB:

Yangtze River Basin

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Acknowledgments

This research was funded by the US National Science Foundation Innovations at the Nexus of Food, Energy and Water Systems (US-China INFEWS) under Grant EAR1903249, the China Meteorological Administration/National Climate Center research subcontract 2211011816501, and the the Shanghai 2021 “Scientific and technological innovation action plan” Natural Science Foundation (Grant No. 21ZR1420400). The authors thank the Computational and Information Systems Laboratory of the National Center for Atmospheric Research, the ECNU Multifunctional Platform for Innovation 001 facilities and the Wuxi National Supercomputing Center for their support in terms of CWRF simulations and analyses. We would like to express our sincere gratitude to Chief Editor Dr. Wei Gao for the prompt handling of the review process; his efficiency greatly facilitated the publication of our work. We also thank the anonymous reviewers for their constructive comments and suggestions, which significantly improved the presentation of our manuscript.

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Authors and Affiliations

  1. Climate, Environment and Sustainability Center, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yang Zhao

  2. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China

    Fengxue Qiao

  3. School of Geographic Sciences, East China Normal University, Shanghai, 200241, China

    Fengxue Qiao

  4. Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, 20740, USA

    Xin-Zhong Liang

  5. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, 20740, USA

    Xin-Zhong Liang

  6. Key Laboratory of Meteorological Disaster (KLME) (Ministry of Education) / Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jinhua Yu

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Contributions

Author Contributions Conceptualization, Y.Z. and X.L.; methodology, Y.Z. and X.L.; software, Y.Z.; validation, Y.Z.; investigation, Y.Z.; resources, X.L.; data curation, Y.Z.; writing—original draft preparation, Y.Z; writing—review and editing, F.Q., X.L. and J.Y.; supervision, X.L. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Xin-Zhong Liang or Jinhua Yu.

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Zhao, Y., Qiao, F., Liang, XZ. et al. Understanding and improving Yangtze River Basin summer precipitation prediction using an optimal multi-Physics ensemble. Front. Earth Sci. (2024). https://doi.org/10.1007/s11707-024-1118-x

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