Complex network approach for detecting tropical cyclones

• Published in: Climate dynamics 2021-12, Vol.57 (11-12), p.3355-3364
• Main Authors: Gupta, Shraddha, Boers, Niklas, Pappenberger, Florian, Kurths, Jürgen
• Format: Article
• Language: English
• Subjects: Climate; Climate change; Climatology; Clustering; Coastal zone; Complex systems; Cyclones; Earth and Environmental Science; Earth Sciences; Earth surface; El Nino; El Nino phenomena; Geophysics/Geodesy; Hurricanes; Mathematical models; Mean sea level; Monsoon climates; Network topologies; Oceanography; Oceans; Pressure distribution; Regions; Sea level; Sea level pressure; Southern Oscillation; Time; Topology; Tropical climate; Tropical cyclones; Volcanic eruption effects; Volcanic eruptions; Weather; Weather conditions; Windows (intervals)
• ISSN: 0930-7575; 1432-0894
• DOI: 10.1007/s00382-021-05871-0

Tropical cyclones (TCs) are one of the most destructive natural hazards that pose a serious threat to society, particularly to those in the coastal regions. In this work, we study the temporal evolution of the regional weather conditions in relation to the occurrence of TCs using climate networks. Climate networks encode the interactions among climate variables at different locations on the Earth’s surface, and in particular, time-evolving climate networks have been successfully applied to study different climate phenomena at comparably long time scales, such as the El Niño Southern Oscillation, different monsoon systems, or the climatic impacts of volcanic eruptions. Here, we develop and apply a complex network approach suitable for the investigation of the relatively short-lived TCs. We show that our proposed methodology has the potential to identify TCs and their tracks from mean sea level pressure (MSLP) data. We use the ERA5 reanalysis MSLP data to construct successive networks of overlapping, short-length time windows for the regions under consideration, where we focus on the north Indian Ocean and the tropical north Atlantic Ocean. We compare the spatial features of various topological properties of the network, and the spatial scales involved, in the absence and presence of a cyclone. We find that network measures such as degree and clustering exhibit significant signatures of TCs and have striking similarities with their tracks. The study of the network topology over time scales relevant to TCs allows us to obtain crucial insights into the effects of TCs on the spatial connectivity structure of sea-level pressure fields.