Researchers have used a 100-year observational dataset and the most advanced methods to model climate extremes, demonstrating the evolving dynamics of heatwaves throughout Europe under the impact of climate change.
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Heatwaves can have devastating effects on humans, settlements and the environment. They can lead to illness or death, specifically for the frail or elderly, while also triggering wildfires that damage property and huge tracts of wilderness.
It is crucial to understand the behavior of such extreme temperature events over space and time to plan and manage the current and future risks. Yet, most modeling for estimating future heatwaves depends on simulation outputs obtained from climate models instead of direct observations. It largely involves inflexible models that may not precisely capture the dependence relationship among spatially linked locations experiencing extreme conditions.
Raphaël Huser and Peng Zhong from KAUST’s Extreme Statistics Research Group joined hands with Thomas Opitz from France’s National Research Institute for Agriculture, Food and Environment to create a new modeling technique that involves using observational records to tease out the dynamics of extreme heat events more accurately.
Our group is interested in building mathematically sound models to assess the risk associated with climate change. In this study, we looked specifically at the impact of climate change on heatwaves in Europe and developed a model to assess the spatial extent of heatwaves by flexibly modeling and estimating their time-varying dependence strength.
Peng Zhong, Extreme Statistics Research Group, KAUST
Although current statistical models are good at capturing prevalent or common conditions, they lack the statistical flexibility to precisely capture rare extreme events such as extreme rainfall or heatwaves. KAUST researchers created a series of statistical methods for directly solving this extreme value problem.
Our ‘max-infinitely divisible’ model is flexible enough to describe the dependence structure among high temperature values. We use it to derive the effective extremal dependence range, which is when the distance at which the probability that extreme events happen together at two locations becomes negligible.
Peng Zhong, Extreme Statistics Research Group, KAUST
To run their model for large spatial datasets, the researchers had to execute their code on the IBEX supercomputing platform at KAUST. The model used an enormous 100-year temperature dataset covering a large portion of central Europe and offered a new understanding of how heatwaves have transformed over the past century.
Our results provide statistical evidence that the spatial extent of heatwaves in Europe is expanding and the frequency of heatwaves has also increased over the past hundred years. We are now looking at applying our methodology to assess flooding risk in major river basins across the globe.
Peng Zhong, Extreme Statistics Research Group, KAUST