COMPound extremes Attribution of climate change: towardS an operational Service

ongoing

Climate change increases the frequency and severity of extreme weather events, such as storms, heatwaves and droughts. Such events can have devastating societal impacts, and it is becoming increasingly clear that the most impactful disasters are often the result of a complex interplay of multiple physical and societal drivers. Climate attribution, which examines the causal links between extreme events, natural variability, and anthropogenic climate change, can help to unravel this complexity and thereby promote societal preparedness and awareness for climate change impacts. The COMPASS project aims to develop a harmonised, yet flexible, methodological framework for climate and impact attribution of various hazard types. COMPASS will go beyond the current frameworks by bridging the gap from the attribution of single-driver extremes to the attribution of more complex extremes (that is compound, sequences and cascading hazard events) and enabling a shift from a hazard-centred analysis to an impact-centred perspective. Main novelties include event-based hazard and impact modelling using a multi-scale approach, the use of weather type analysis for better understanding the physical drivers that give rise compound extremes, and the use of contextualized storylines to communicate attribution results. The framework will be validated and applied to a set of use cases that cover historical extremes for various hazard types and impact context as well as extreme events happening during the project. COMPASS will lay the scientific foundation for the operational deployment as part of the Copernicus Climate Change Services. The project will create a modular and scalable framework for on-the fly analysis, and thus transferable to other extremes and regions. To promote uptake of the project’s results, data, methods and tools will be made openly available, a web-based demonstrator will showcase the results of the use cases, and clear guideline for attribution will be developed.