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<p>Accurate simulation and projection of near-surface wind speed (NSWS) are vital for advancing wind energy development and addressing climate change. Despite extensive use, global climate models continue to face challenges in capturing long-term NSWS trends over land. This study uses data from the High-Resolution Model Intercomparison Project (HighResMIP) under CMIP6, to assess whether the increased horizontal resolution and ocean–atmosphere coupling could improve NSWS simulations. Contrary to expectations, our results reveal that neither higher resolution nor ocean–atmosphere coupling substantially enhances the model’s performance in replicating NSWS. This suggests that other factors, such as dynamical processes and physical parameterizations, play more critical roles in determining model performance. The findings suggest that future research should focus on employing more comprehensive datasets and diverse model configurations to effectively identify and address sources of bias in NSWS modeling.</p>

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Simulated and projected near-surface wind speed in High-Resolution Model Intercomparison Project

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月29日

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  • Hui-Shuang Yuan,
  • Jinling Piao,
  • Youli Chang,
  • Cheng Shen

摘要

Accurate simulation and projection of near-surface wind speed (NSWS) are vital for advancing wind energy development and addressing climate change. Despite extensive use, global climate models continue to face challenges in capturing long-term NSWS trends over land. This study uses data from the High-Resolution Model Intercomparison Project (HighResMIP) under CMIP6, to assess whether the increased horizontal resolution and ocean–atmosphere coupling could improve NSWS simulations. Contrary to expectations, our results reveal that neither higher resolution nor ocean–atmosphere coupling substantially enhances the model’s performance in replicating NSWS. This suggests that other factors, such as dynamical processes and physical parameterizations, play more critical roles in determining model performance. The findings suggest that future research should focus on employing more comprehensive datasets and diverse model configurations to effectively identify and address sources of bias in NSWS modeling.