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XAIDA

Financiado

Cerrado

EXTREME EVENTS ARTIFICIAL INTELLIGENCE FOR DETECTION AND ATTRIBUTION

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... ver más

31/08/2025

CNRS

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 18 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-05-27

LC-CLA-12-2020: Advancing climate services Specific Challenge:Under the Paris Agreement it was agreed to enhance adaptive capacity, strengtheni...

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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18 Participantes

CNRS

1.06M€ | Lider

MIGUEL CARRERAS VERNIS

271.87K€ | Participante

KNMI

260.00K€ | Participante

ETH Zürich

814.77K€ | Participante

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Duración del proyecto: 51 meses Fecha Inicio: 2021-05-27
Fecha Fin: 2025-08-31

Líder del proyecto

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 6M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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

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