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\begin{document}

\title{Using Convolutional Neural Networks to Emulate Seasonal Tropical Cyclone
Activity}



\author[1]{Dan Fu}%
\author[1]{Ping Chang}%
\author[1]{Xue Liu}%
\affil[1]{Texas A\&M University}%


\vspace{-1em}



  
  \date{May 19, 2024}


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\begin{abstract}
It has been widely recognized that tropical cyclone (TC) genesis
requires favorable large-scale environmental conditions. Based on these
linkages, numerous efforts have been made to establish an empirical
relationship between seasonal TC activities and large-scale
environmental favorabilities in a quantitative way, which lead to
conceptual functions such as the TC genesis index. However, due to the
limited amount of reliable TC observations and complexity of the climate
system, a simple analytic function may not be an accurate portrait of
the empirical relation between TCs and their ambiences. In this
research, we use convolution neural networks (CNNs) to disentangle this
complex relationship. To circumvent the limited amount of seasonal TC
observation records, we implement transfer-learning technique to train
ensembles of CNNs first on suites of high-resolution climate simulations
with realistic seasonal TC activities and large-scale environmental
conditions, and then subsequently on the state-of-the-art reanalysis
from 1950 to 2019. Our CNNs can remarkably reproduce the historical TC
records, and yields significant seasonal prediction skills when the
large-scale environmental inputs are provided by operational climate
forecasts. Furthermore, by forcing the ensemble CNNs with 20th century
reanalysis products and phase 6 of the Coupled Model Intercomparison
Project (CMIP6) experiments, we attempted to investigate TC
variabilities and their changes in the past and future climates.
Specifically, our ensemble CNNs project a decreasing trend of global
mean TC activity in the future warming scenario, which is consistent
with our dynamic projections using TC-permitting high-resolution coupled
climate model.%
\end{abstract}%



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