Summary
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
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
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101003469 |
| Start date: | 01-09-2021 |
| End date: | 31-08-2025 |
| Total budget - Public funding: | 5 999 241,00 Euro - 5 999 241,00 Euro |
Cordis data
Original description
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
Status
SIGNEDCall topic
LC-CLA-12-2020Update Date
27-10-2022
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Organisations
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| CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE (CNRS) (Coördinator)
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| COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES (CEA)
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| DEUTSCHES ZENTRUM FUR LUFT - UND RAUMFAHRT EV
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| EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
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| FOUNDATION POUR L'EDUCATION A LA SCIENCE DANS LE SILLAGE DE LA MAIN A LA PATE
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| HELMHOLTZ-ZENTRUM FUR UMWELTFORSCHUNG GMBH - UFZ
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| IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
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| KONINKLIJK NEDERLANDS METEOROLOGISCH INSTITUUT-KNMI
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| MAX PLANCK GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN E.V.
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| MET OFFICE
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| STICHTING INTERNATIONAL RED CROSS RED CRESCENT CENTRE ON CLIMATE CHANGE AND DISASTER PREPAREDNESS
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| STICHTING VU
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| THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
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| THE UNIVERSITY OF READING
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| UNITED NATIONS EDUCATIONAL SCIENTIFIC AND CULTURAL ORGANIZATION