SOURCE-LINKED INTELLIGENCE

DYnamical drivers, predictability and trends of compound CLimate EXtremes

CORDIS · observation · Publication date unknown

d that of my research group, partly issued from my previous ERC project. Specifically, DYCLEX exploits my cutting-edge work on atmospheric dynamics, dynamical systems theory, statistical analyses and machine learning for the study of extreme events. I will use this interdisciplinary knowledge base to detect and quantify compound climate extremes and elucidate their link to different large-scale atmospheric circulation features. I will then leverage this to identify new predictability pathways for the compound extremes and to explain their long-term occurrence trends. The analysis framework I will develop is a flexible tool, applicable to a wide range of multivariate extremes in the Earth system and beyond. DYCLEX is timely: Climate risks and extremes feature prominently in two Lighthouse Activities of the World Climate Research Programme, as well as in the EU’s strategy on adaptation to climate change. Moreover, the unconventional interdisciplinary toolkit that I plan to adopt offers novel opportunities, which should be rapidly and systematically exploited. Compound climate extremes

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

1986037

unit

EUR