The possible change of global and regional tropical cyclone genesis probability in the future as predicted by machine learning models

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. Previous studies have divergent opinions on how TC activity responds to climate change, as TC activities can be impacted by many environmental variables. One advantage of ML methods is that, compared to traditional analysis methods, they can capture the complex nonlinear relationship between the predictor and predictand; therefore, before the theory of TC genesis is well established, in addition to traditional climate models, ML methods may provide an additional useful tool to predict the possible changes in TC frequency under global warming. Moreover, the ML model may also provide more information about the nonlinear characteristics of TC genesis, which will help to improve our understanding of TC genesis mechanism. 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. Further analysis reveals that PI is the most important environmental variable in the MaxEnt model, which provides the most unique and useful information to the model. 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. We further apply principal component analysis to investigate the TC genesis environment in different ocean basins and show that the TC genesis environments are mainly determined by upper and lower level absolute vorticity. The TC genesis environment in basins are classified into three groups and three machine learning (ML) models are build accordingly. These basin-wide models predict a consistent TC genesis trend in each basin under different future scenarios. Further analysis highlight the importance of absolute vorticity for basin-wide TC genesis. A multivariate environmental similarity surface analysis reveals that climate models predict the weakest change in the TC genesis environment in North Atlantic compared to other basins.