Descrizione del progetto
Utilizzare l’IA per prevedere gli effetti dei cambiamenti climatici sulle condizioni atmosferiche estreme
I cambiamenti climatici stanno modificando e potenziando gli eventi meteorologici estremi, come le ondate di calore, gli incendi devastanti, i cicloni, le alluvioni e la siccità. Il progetto XAIDA, finanziato dall’UE, caratterizzerà, rileverà e ricercherà le cause degli eventi estremi avvalendosi di un nuovo approccio basato sui dati e sull’impatto. Esso impiegherà nuove tecniche di IA e riunirà specialisti in svariati campi, quali la ricerca delle cause degli eventi estremi, le dinamiche atmosferiche, la modellizzazione del clima, l’apprendimento automatico e l’inferenza causale. I risultati getteranno luce sull’effetto esercitato dai cambiamenti climatici su fenomeni atmosferici come i cicloni e i temporali di convezione, eventi non ben compresi o adeguatamente quantificati. Il progetto fornirà inoltre strumenti per valutare i percorsi causali che portano al verificarsi degli eventi estremi.
Obiettivo
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
Campo scientifico
Programma(i)
Argomento(i)
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Vedi altri progetti per questo bandoBando secondario
H2020-LC-CLA-2020-2
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
75794 Paris
Francia