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CENAE

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compound Climate Extremes in North America and Europe from dynamics to predicta...

compound Climate Extremes in North America and Europe from dynamics to predictability 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 compou... ver más

28/02/2026

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

UPPSALA UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-08-25

ERC-2020-STG: ERC STARTING GRANTS

I+D

Cerrada hace 5 años

0% 100% 100%

2 Participantes

1.49M€ | Lider

RKW ITER

1.18M€ | Participante

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Duración del proyecto: 66 meses Fecha Inicio: 2020-08-25
Fecha Fin: 2026-02-28

Líder del proyecto

UPPSALA UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto 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.

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