Public perceptions of the risk posed by natural hazards and climate change are typically associated with extreme events. While there are studies focused on the detailed synergy for processes that lead to the event occurrence. Past literature working on long-term variations of global weather and climate extremes often studies the derived indices and applies further statistical models to data sampled from a fixed grid point or area. Our study, using extreme heatwave events as an example, proposes an alternative framework to track and analyze extremes from an event perspective to facilitate better climate risk communication. With a selected threshold of daily heatwave intensity and an objective spatial and temporal connectivity algorithm, we introduce an event-tracking method to automatically track all global heatwaves based on long-term reanalysis data. Two nearby but separate European heatwave events (Scandinavia and France) in 2003 are used to demonstrate the validity of our event-tracking method. We further highlight the genesis frequency, occurrence density, and tracks of all historical heatwave events over global land areas from 1979 to 2018. The contributions from heatwave intensity, the affected area, and temporal evolution to each extreme event are documented. With the retaining spatial and temporal evolutions of tracked heatwave events during their lifespans, the precursory and concurrent environmental conditions associated with the event can be further studied. The integrated size and scale measures of extreme heatwave events can be compared historically in a region and/or across different climate regimes. They also provide better information for the risk assessment on event-associated loss and damages, and can easily be extended to study compound hazards with intersections of different types of extreme events.
- Depth-first search algorithm
- Extreme event
- Feature tracking
ASJC Scopus subject areas
- Geography, Planning and Development
- Atmospheric Science
- Management, Monitoring, Policy and Law