Reduced Tropical Cyclone Genesis in the Future as Predicted by a Machine Learning Model

Qifeng Qian, Xiaojing Jia, Yanluan Lin

Earth s Future · 19 January 2022 · Tsinghua University (NowcastNet), China Meteorological Administration (CMA)

In one sentence. Uses a MaxEnt model on environmental variables to project a significant decline in future tropical cyclone genesis across CMIP6 SSP scenarios, with a nonlinear link to potential intensity.

Abstract

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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