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<p>Tropical cyclones (TCs) are critical components of the atmospheric system, with their attributes (intensity, trajectory, and frequency) primarily influenced by large-scale climate modes/patterns. However, the relationships between global TC attributes and combinations of different phases of multiple climate modes, especially within the latest CMIP6 HighResMIP models, remain underexplored. Additionally, the underestimation of wind speeds in these climate models complicates the accurate representation of TC intensity. Hence, in this study, a composite clustering and discriminant analysis (CDA) method is developed to robustly reclassify global TC intensity using the latest CMIP6 models and project future changes in various TC categories. Following a global assessment to identify key regions with robust signals, the main analysis focuses on the Northern Hemisphere (NH), particularly the North Atlantic (NA), where statistically significant changes are observed. Results indicate a consistent northward shift of high-intensity TCs in the NH during both historical and future periods, with the NA recording more northward-moving, intensified landfalling TCs. The study further explores how individual climate modes, and their interactions influenced observed TC attribute changes in the NA region within the historical period. Beyond the known modulation effects of individual El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and Western Hemisphere Warm Pool (WHWP) on Atlantic TCs, their complex interactions and nonlinear relationships significantly increase the frequency and northward shift of high-intensity TCs in the NA region. However, these nonlinear statistical relationships between climate modes and TCs are reflected in only a few CMIP6 HighResMIP models, though they exhibit similar trends of climate mode-TC interactions as observed; this highlights the need for future model improvements focusing on climate mode interactions. These findings offer new insights into the multifaceted impacts of climate change on TCs and suggest potential directions for enhancing seasonal TC predictions.</p>

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Category-resolved tropical cyclone dynamics and climate modes interactions in CMIP6 HighResMIP: reclassification, projection, and model evaluation

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Xiajing Lin,
  • Guohe Huang,
  • Chen Lu,
  • Tangnyu Song,
  • Yinghui Wu,
  • Chuyin Tian

摘要

Tropical cyclones (TCs) are critical components of the atmospheric system, with their attributes (intensity, trajectory, and frequency) primarily influenced by large-scale climate modes/patterns. However, the relationships between global TC attributes and combinations of different phases of multiple climate modes, especially within the latest CMIP6 HighResMIP models, remain underexplored. Additionally, the underestimation of wind speeds in these climate models complicates the accurate representation of TC intensity. Hence, in this study, a composite clustering and discriminant analysis (CDA) method is developed to robustly reclassify global TC intensity using the latest CMIP6 models and project future changes in various TC categories. Following a global assessment to identify key regions with robust signals, the main analysis focuses on the Northern Hemisphere (NH), particularly the North Atlantic (NA), where statistically significant changes are observed. Results indicate a consistent northward shift of high-intensity TCs in the NH during both historical and future periods, with the NA recording more northward-moving, intensified landfalling TCs. The study further explores how individual climate modes, and their interactions influenced observed TC attribute changes in the NA region within the historical period. Beyond the known modulation effects of individual El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and Western Hemisphere Warm Pool (WHWP) on Atlantic TCs, their complex interactions and nonlinear relationships significantly increase the frequency and northward shift of high-intensity TCs in the NA region. However, these nonlinear statistical relationships between climate modes and TCs are reflected in only a few CMIP6 HighResMIP models, though they exhibit similar trends of climate mode-TC interactions as observed; this highlights the need for future model improvements focusing on climate mode interactions. These findings offer new insights into the multifaceted impacts of climate change on TCs and suggest potential directions for enhancing seasonal TC predictions.