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<p>Reliable projections of tropical cyclone (TC) frequency, tracks, and intensity are essential for assessing climate risks and facilitating human adaptation to climate change. Systematic biases in general circulation models (GCMs) can be transferred into regional climate models (RCMs) via initial and lateral boundary conditions (ICs and LBSs), thereby lowering the reliability of dynamical downscaling simulations for TCs. To mitigate this issue, this study explores the impacts of GCM bias correction on the dynamical downscaling simulation of western North Pacific (WNP) TCs. We carried out three Weather Research and Forecasting (WRF) simulations over the Asian-Western Pacific region at a 25-km resolution for the period 1981–2014. Three simulations utilize the original GCM outputs (hereafter referred to as WRF_MPI), bias-corrected GCM data (referred to as WRF_MPIbc), and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (referred to as WRF_ERA) as initial and lateral boundary conditions, respectively. The results indicate that the environmental conditions conducive to TC activity, such as wind shear, mid-level humidity, and monsoon troughs, were notably improved in the WRF_MPIbc due to GCM bias correction, particularly in critical regions for TC genesis and development. Consequently, WRF_MPIbc exhibited marked improvements in simulating TC genesis locations, the number of TCs, seasonal variations, tracks, and landfall occurrences. This suggests that bias correction in ICs and LBCs from GCMs is an effective method for improving simulations and projections of TCs and other high-impact weather events using dynamical downscaling.</p>

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Improving dynamical downscaling simulations of western North Pacific tropical cyclones using bias-corrected CMIP6 data

  • 期刊论文

  • 发表日期：

    2025年10月17日

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  • Ying Han,
  • Zhongfeng Xu,
  • Meng-Zhuo Zhang,
  • Chi-Yung Tam,
  • Vincent S. Cheng,
  • Hon-man Wong,
  • Xiaofeng He,
  • Yi Wang,
  • Si-nan Gu

摘要

Reliable projections of tropical cyclone (TC) frequency, tracks, and intensity are essential for assessing climate risks and facilitating human adaptation to climate change. Systematic biases in general circulation models (GCMs) can be transferred into regional climate models (RCMs) via initial and lateral boundary conditions (ICs and LBSs), thereby lowering the reliability of dynamical downscaling simulations for TCs. To mitigate this issue, this study explores the impacts of GCM bias correction on the dynamical downscaling simulation of western North Pacific (WNP) TCs. We carried out three Weather Research and Forecasting (WRF) simulations over the Asian-Western Pacific region at a 25-km resolution for the period 1981–2014. Three simulations utilize the original GCM outputs (hereafter referred to as WRF_MPI), bias-corrected GCM data (referred to as WRF_MPIbc), and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (referred to as WRF_ERA) as initial and lateral boundary conditions, respectively. The results indicate that the environmental conditions conducive to TC activity, such as wind shear, mid-level humidity, and monsoon troughs, were notably improved in the WRF_MPIbc due to GCM bias correction, particularly in critical regions for TC genesis and development. Consequently, WRF_MPIbc exhibited marked improvements in simulating TC genesis locations, the number of TCs, seasonal variations, tracks, and landfall occurrences. This suggests that bias correction in ICs and LBCs from GCMs is an effective method for improving simulations and projections of TCs and other high-impact weather events using dynamical downscaling.