Accounting for climate change: Modeling and forecasting extreme climate events

What is it about?

Global climate change has amplified the occurrence of extreme climate events. Various modeling methods have, therefore, been developed to predict such events using climate data. One recent method is “event synchronization” (ES). This method divides a study area into grids and uses climate data to assess the synchronization of extreme events between different grids. This creates a climate network which provides a spatiotemporal pattern of these extreme events. The model has successfully predicted the intensity and the distribution of extreme rainfall events. However, the ES method cannot resolve situations with clustered or cascading events. An alternative approach called “event coincidence analysis” (ECA) is used to deal with such situations. Additionally, modified ES and ECA approaches using corrections to decluster events have been developed. A 2020 paper examined the differences in the climate networks derived from the ES and ECA methods by investigating spatial patterns of synchronous heavy rainfalls over the South American continent during the Monsoon season (December to February). The authors found that the corrected and uncorrected ES and ECA methods resulted in climate networks with different biases. Notably, by applying corrections, they observed more prominent features in the cli-mate network underrepresented due to clustering. Further, while both methods could be used to track rainfall patterns, ECA could resolve rain-fall patterns between time scales.

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Photo by Satyan Chawla on Unsplash

Photo by Satyan Chawla on Unsplash

Why is it important?

As climate change has made extreme events more frequent, their timely prediction is of paramount importance. In turn, we need to understand the strengths of different predictive methods to gauge their accuracy. KEY TAKEAWAY The study highlights the differences between the various approaches used to predict extreme climate events.