Descrizione del progetto
Nuovi strumenti per prevedere fenomeni climatici estremi
I cambiamenti climatici restano una delle questioni più difficili che le società si trovano a dover affrontare. Sebbene la nostra comprensione delle cause dei cambiamenti climatici abbia fatto notevoli passi avanti, il Programma mondiale di ricerca sul clima ha ribadito la necessità di approfondire ulteriormente le nostre conoscenze sui fenomeni climatici estremi presenti e futuri. Il progetto CENAE, finanziato dall’UE, farà luce su come diversi fenomeni climatici estremi (ondate di freddo, piogge intense e forti venti) interagiscano tra loro, traducendosi in eventi estremi composti con un impatto socioeconomico più vasto rispetto alla somma dei loro singoli componenti. La natura multivariata e la rarità intrinseca degli eventi estremi composti rappresenta un’ardua sfida per le attuali tecniche di analisi. Il progetto svilupperà degli strumenti basati su sistemi dinamici e sull’apprendimento automatico per analizzare i fenomeni climatici estremi. Tali strumenti saranno altamente flessibili e potranno essere applicati a fenomeni estremi multivariati al di là della climatologia.
Obiettivo
Different climate extremes, such as heavy rains and strong winds, can interact and result in compound extremes with a larger socio-economic impact than the sum of their individual components. Elucidating the nature of these compound extremes is both a key step in furthering our scientific understanding of the climate system and a societally relevant goal. However, it is not easily realised, as the multivariate nature and inherent rarity of the compound extremes poses a formidable challenge to current analysis techniques.
In CENÆ I aim to provide a step-change in our understanding of the drivers and predictability of compound climate extremes, and illuminate how climate change may affect these two aspects. I will specifically focus on two high-impact compound extremes which have occurred with an ostensibly high frequency in recent years: (i) wintertime wet and windy extremes in Europe; and (ii) same as (i) but with the additional occurrence of (near-)simultaneous cold spells in North America.
CENÆ builds upon my ongoing contribution to developing dynamical systems analysis tools for climate extremes. It further leverages the work of my research group on the atmospheric circulation and machine learning for the study of atmospheric predictability. I will use this interdisciplinary knowledge base to elucidate the atmospheric precursors to compound extremes, provide a nuanced understanding of their predictability and point to new predictability pathways. The analysis framework I will develop in CENÆ will be highly flexible and applicable to multivariate extremes beyond climate science.
This effort is timely: the World Climate Research Programme has highlighted understanding current and future climate extremes as a grand challenge of climate science. Moreover, my unconventional research in dynamical systems and machine learning has opened up previously unforeseen opportunities for the study of compound climate extremes which should be rapidly and systematically exploited.
Campo scientifico
- natural sciencesmathematicsapplied mathematicsdynamical systems
- natural sciencesearth and related environmental sciencesatmospheric sciencesmeteorologyatmospheric circulation
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-STG - Starting GrantIstituzione ospitante
751 05 Uppsala
Svezia