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  • 标题:Reduced Tropical Cyclone Genesis in the Future as Predicted by a Machine Learning Model
  • 本地全文:下载
  • 作者:QiFeng Qian ; XiaoJing Jia ; Yanluan Lin
  • 期刊名称:Earth's Future
  • 电子版ISSN:2328-4277
  • 出版年度:2022
  • 卷号:10
  • 期号:2
  • 页码:n/a-n/a
  • DOI:10.1029/2021EF002455
  • 语种:English
  • 出版社:John Wiley & Sons, Ltd.
  • 摘要:Abstract Due to a lack of observations and limited understanding of the complex mechanisms of tropical cyclone (TC) genesis, the possible TC activity response to future climate change remains controversial. In this work, a machine learning model, called the maximum entropy (MaxEnt) model, is established using various environmental variables. The model performs slightly better than the genesis potential index for historical TC activities based on the spatial correlation coefficient. Using coupled model intercomparison project phase 6 model projections, the MaxEnt model predicts a statistically significant decreasing trend of TC genesis probability under all shared socioeconomic pathway scenarios. In addition, our analysis reveals that TC genesis might have a complex nonlinear relationship with potential intensity, which is different from the positive relationship reported in previous studies and might be the key factor leading to the model predicting reduced TC genesis in the future.
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