Pour voir les références d’un(e) auteur(e), cliquez sur son nom. Pour revenir à la page, utilisez le bouton refresh ci-dessous.
Résultats pour:
Kai Kornhuber, Columbia University, Lamont-Doherty Earth Observatory, Columbia University, New York, USA, Climate Analytics, Climate Analytics, Berlin, Germany, German Council on Foreign Relations, German Council on Foreign Relations, Berlin, Germany, Corey Lesk, Columbia University, Lamont-Doherty Earth Observatory, Columbia University, New York, USA, Dartmouth College, Department of Geography and Neukom Institute, Dartmouth College, Hanover, USA, Carl F. Schleussner, Climate Analytics, Climate Analytics, Berlin, Germany, Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys) and the Geography Department, Humboldt-Universität zu Berlin, Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys) and the Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany, Jonas Jägermeyr, Center for Climate Systems Research, Center for Climate Systems Research, Columbia University, Climate School, USA, Columbia Univ
Simultaneous harvest failures across major crop-producing regions are a threat to global food security. Concurrent weather extremes driven by a strongly meandering jet stream could trigger such events, but so far this has not been quantified. Specifically, the ability of state-of-the art crop and climate models to adequately reproduce such high impact events is a crucial component for estimating risks to global food security. Here we find an increased likelihood of concurrent low yields during summers featuring meandering jets in observations and models. While climate models accurately simulate atmospheric patterns, associated surface weather anomalies and negative effects on crop responses are mostly underestimated in bias-adjusted simulations. Given the identified model biases, future assessments of regional and concurrent crop losses from meandering jet states remain highly uncertain. Our results suggest that model-blind spots for such high-impact but deeply-uncertain hazards have to be anticipated and acc