Project description
Towards reliable extreme weather attribution
Extreme weather events are unusual, unpredictable, severe or unseasonal, and can be attributed either to human influence on climate or quantifying specific thermodynamic meteorological processes. The EU-funded ANDANTE project aims to distinguish between the two and to identify their contributions to the risk of extreme weather events in Africa and Europe. The project will be a big boost for the development of new prediction systems in this relatively new scientific area of event attribution.
Objective
Extreme weather and climate events, such as heat waves, droughts and their combinations, are intrinsic aspects of the evolution of the climate system, and they can have substantial environmental and socio-economic impacts. Every extreme event is the result of a superposition of external drivers, natural and anthropogenic, and internal variability. Risk-based or probabilistic extreme event attribution assesses to what extent anthropogenic drivers modify the probability and magnitude, and hence the risk of an extreme event or a class of events to understand regional impacts of climate change. Surface conditions depend on the patterns of atmospheric circulation. Thus, in a specific region human-induced thermodynamic influence can be amplified or counteracted by human-induced change in the atmospheric circulation. The main goal of ANDANTE is to separate human-induced dynamic (i.e. circulation/flow) and thermodynamic contributions to the risk of selected extreme events in Europe and Africa. Since we are dealing with rare events we need large or even better very large ensembles of model simulations (~1,000 members) to do the flow-conditional probabilistic event attribution in statistically sound way (i.e. to get well-resolved probability distributions) with the methods of flow clusters (weather regimes/climate modes) and flow analogues. The project will make a key contribution to the development of the next-generation prediction and event attribution system. The produced new very large ensembles will be combined with the current-generation ensembles as well as multi-model climate simulations and multi-member reanalysis products to perform robust multi-method estimates of the univariate and multivariate (i.e. multi-variable) risk indicators. The risk assessment of selected extreme events manifested in surface temperature, precipitation, potential evapotranspiration and fire weather index can be useful to a wide spectrum of stakeholders interested in climate change impacts.
Fields of science
Keywords
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
OX1 2JD Oxford
United Kingdom