Project description
Using AI to predict climate change effects on extreme weather
Climate change is modifying and enhancing extreme weather events such as heat waves, devastating wildfires, cyclones, floods and droughts. The EU-funded XAIDA project will characterise, detect and attribute extreme events using a novel data-driven, impact-based approach. It will use new AI techniques and bring together specialists in extreme event attribution, atmospheric dynamics, climate modelling, machine learning and causal inference. The findings will shed light on the effect of climate change on atmospheric phenomena like cyclones and convective storms, which are not well understood or quantified. The project will also provide tools to assess the causal pathways leading to extreme events.
Objective
Often, extreme events provide representations of the future climate, but not all extremes are harbingers of the future. Thus, in order to be useful for adaptation in support to future projections, a causal link between events and human influence on climate must be established or refuted. This is why the “Extreme event attribution” field has recently developed. However, extreme event detection, attribution and projections studies currently face major limitations.
XAIDA will fill these gaps. Using new artificial intelligence techniques, and a strong two-way interaction with key stakeholders, it will (i) characterize, detect and attribute extreme events using a novel data-driven, impact-based approach, (ii) assess their underlying causal pathways and physical drivers using causal networks methods, and (iii) simulate high-intensity and as yet unseen events that are physically plausible in present and future climates.
To achieve this, XAIDA brings together teams of specialists in extreme event attribution, atmospheric dynamics, climate modelling, machine learning and causal inference, to:
● Understand the effect of climate change on a variety of impacting atmospheric phenomena currently poorly understood or quantified (cyclones, convective storms, long-lived anomalies, or summer compound events), both for past and future evolutions;
● Develop, in co-design with a community of key stakeholders, a novel, broader and impacts-based attribution and projection framework which extracts causal pathways of extremes;
● Develop storylines of events of unseen intensity, based on machine learning methods;
● Provide new tools for model assessment of causal pathways leading to extreme events and investigate the causes of disagreements between models and observations;
● Develop an interaction and communication platform with stakeholders with the ambition to improve training and education on climate change and impacts and to bring these developments to future operational climate services
Fields of science
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
75794 Paris
France