Projektbeschreibung
Neue Instrumente zur Vorhersage von Klimaextremen
Nach wie vor ist der Klimawandel eines der schwierigsten Probleme, denen unsere Gesellschaft gegenübersteht. Zwar wurden in Bezug auf das Verständnis der Gründe für den Klimawandel wesentliche Fortschritte gemacht, das Weltklimaforschungsprogramm hob jedoch hervor, dass unser Kenntnisstand zu aktuellen und künftigen Klimaextremen dringend erweitert werden muss. Das EU-finanzierte Projekt CENAE möchte beleuchten, wie verschiedene Klimaextreme (Kälteperioden, heftige Regenfälle und starke Winde) zusammenwirken und gemeinsam zu extremen Wetterereignissen mit schwerwiegenderen sozioökonomischen Auswirkungen führen als die Summe ihrer einzelnen Bestandteile. Der multivariate Charakter und die inhärente Seltenheit dieser extremen Wetterereignisse stellt die derzeitigen Analysetechniken vor eine außerordentliche Herausforderung. Das Projekt wird Instrumente zur Analyse von Klimaextremen entwickeln, die auf dynamischen Systemen und maschinellem Lernen aufbauen. Diese Instrumente werden äußerst flexibel und für multivariate Extreme auch außerhalb der Klimawissenschaft anwendbar sein.
Ziel
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.
Wissenschaftliches Gebiet
- 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
Programm/Programme
Thema/Themen
Finanzierungsplan
ERC-STG - Starting GrantGastgebende Einrichtung
751 05 Uppsala
Schweden