Study of Tropical Cyclones using Climate Networks

Abstract:

Climate network has become a promising approach to study the collective behaviour due to the dynamical interactions among the climate variables of different points on the Earth's surface. Recently, climate networks have been successfully applied to study climate phenomena such as El Niño, Indian monsoon, etc. These phenomena however occur over a rather long period of time. Weather phenomena such as tropical cyclones (TCs) that are relatively short-lived, destructive events are a major concern to life and property especially for densely populated coastlines such as in the North Indian Ocean (NIO) basin. In this work, we aim to study such short-lived extremes through a network-based approach by using evolving networks of overlapping short-length time windows of 10-14 days. We construct evolving climate networks using the ERA5 reanalysis Sea Surface Temperature (SST) and Mean Sea Level Pressure (MSLP) for the post-monsoon (October-November-December) season of the NIO basin which experiences a high frequency of TCs every year. We find that network measures such as degree centrality, giant component size, clustering coefficient, exhibit significant signatures of TCs and have striking similarities with their tracks. This shows the potential of climate networks towards forecasting of tropical cyclones.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.