1605 / 2024-11-07 09:07:45
Ocean Meets Network Science: Exploring Extreme Ocean-Related Events via a Complex Network Approach
extreme ocean - coastal zone events,forecasts, complex networks,tropical cyclones,Indian ocean dipole
Keynote Session
Abstract Accepted
The Ocean system is a very complex and dynamical one basing on various feedbacks. Here, we introduce a recently developed approach via complex networks to study important aspects of the Ocean system. This enables us to study basic Ocean-related phenomena at different spatial and time scales from a different viewpoint. First, the Indian Ocean Dipole (IOD) will be analyzed based on complex networks methods. Due to its complexity, a reliable prediction of the IOD is still a great challenge. In this study, we investigate whether there are early warning signals prior to the start of IOD events. An enhanced seesaw tendency in sea surface temperature (SST) among a large number of grid points between the dipole regions in the tropical Indian Ocean is revealed in boreal winter, which can be used to forewarn the potential occurrence of the IOD in the coming year. We combine this insight with the indicator of the December equatorial zonal wind in the tropical Indian Ocean to propose a network-based predictor that clearly outperforms the current dynamic models. Of the 15 IOD events over the past 37 y (1984 to 2020), 11 events were correctly predicted from December of the previous year, i.e., a hit rate of higher than 70%, and the false alarm rate was around 35%.
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. We study the temporal evolution of the regional weather conditions in relation to the occurrence of TCs using climate networks. We show that our proposed methodology has the potential to identify even such short-lived events as TCs and their tracks from mean sea level pressure data. In certain scenarios, two co-rotating cyclones in close proximity to one another can drift closer and completely merge into a single cyclonic system. We suggest an innovative approach to understand the evolving vortical interactions between the cyclones during such merging. We find that network-based indicators quantify the changes in the interaction between two cyclones and are excellent candidates to classify the interaction stages before a merging.
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. We study the temporal evolution of the regional weather conditions in relation to the occurrence of TCs using climate networks. We show that our proposed methodology has the potential to identify even such short-lived events as TCs and their tracks from mean sea level pressure data. In certain scenarios, two co-rotating cyclones in close proximity to one another can drift closer and completely merge into a single cyclonic system. We suggest an innovative approach to understand the evolving vortical interactions between the cyclones during such merging. We find that network-based indicators quantify the changes in the interaction between two cyclones and are excellent candidates to classify the interaction stages before a merging.