compound Climate Extremes in North America and Europe: from dynamics to predictability

Descripción del proyecto

Nuevas herramientas para predecir fenómenos climáticos extremos

El cambio climático sigue siendo uno de los problemas más acuciantes a los que se enfrenta la sociedad. Mientras que nuestra comprensión de las causas del cambio climático ha avanzado considerablemente, el Programa Mundial de Investigaciones Climáticas ha destacado la necesidad de ampliar nuestros conocimientos sobre los fenómenos climáticos extremos actuales y futuros. El proyecto financiado con fondos europeos CENAE arrojará luz sobre cómo interactúan diferentes fenómenos climáticos extremos (olas de frío, lluvias intensas y vientos fuertes) y dan lugar a fenómenos extremos compuestos con un impacto socioeconómico mayor que la suma de sus componentes individuales. La naturaleza con múltiples variantes y la singularidad inherente de los fenómenos extremos compuestos supone un verdadero desafío para las técnicas analíticas actuales. El proyecto desarrollará herramientas basadas en sistemas dinámicos y aprendizaje automático para analizar fenómenos climáticos extremos. Estas herramientas serán extremadamente flexibles y se aplicarán a fenómenos extremos con múltiples variantes que van más allá de la ciencia climática.

Objetivo

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.