Emanuele Bevacqua | Searcharxiv

arXiv2026

Omega-blocks with spatially compounding extremes over Europe are highly sensitive to remote atmospheric drivers

Omega-blocks can trigger spatially compounding heat-precipitation extremes with severe societal impacts, as seen in September 2023 when a heatwave over France coincided with devastating floods in the Iberian Peninsula and Greece. Although blocking in general has been linked to moist processes in upstream warm conveyor belts (WCBs), it has remained unexplored whether and how upstream WCB activity influences the evolution of omega-blocks and downstream flood-heat-flood impacts. Here, we show that already five days ahead, small differences in the upstream evolution - particularly in WCB outflow regions - distinguish cases that later produce extreme compound events over Europe from weaker ones, even though their large-scale anomalies initially appear similar. We illustrate the distinct evolution in remote locations by analyzing storylines simulated in a fully coupled climate model. Using ensemble boosting, we generate hundreds of physically plausible simulations of omega-prone situations. Lagrangian air parcel tracking reveals that variations in WCB outflow areas can explain differences in upstream precursors and downstream effects over Europe. Our results highlight ensemble boosting as a powerful approach to systematically track dynamical differences along model-based event storylines, important for understanding and anticipating compound extremes striking multiple regions simultaneously.

physics.ao-ph↗

arXiv2023

Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning

Extreme wildfires are a significant cause of human death and biodiversity destruction within countries that encompass the Mediterranean Basin. Recent worrying trends in wildfire activity (i.e., occurrence and spread) suggest that wildfires are likely to be highly impacted by climate change. In order to facilitate appropriate risk mitigation, we must identify the main drivers of extreme wildfires and assess their spatio-temporal trends, with a view to understanding the impacts of global warming on fire activity. We analyse the monthly burnt area due to wildfires over a region encompassing most of Europe and the Mediterranean Basin from 2001 to 2020, and identify high fire activity during this period in Algeria, Italy and Portugal. We build an extreme quantile regression model with a high-dimensional predictor set describing meteorological conditions, land cover usage, and orography. To model the complex relationships between the predictor variables and wildfires, we use a hybrid statistical deep-learning framework that can disentangle the effects of vapour-pressure deficit (VPD), air temperature, and drought on wildfire activity. Our results highlight that whilst VPD, air temperature, and drought significantly affect wildfire occurrence, only VPD affects wildfire spread. To gain insights into the effect of climate trends on wildfires in the near future, we focus on August 2001 and perturb temperature according to its observed trends (median over Europe: +0.04K per year). We find that, on average over Europe, these trends lead to a relative increase of 17.1\% and 1.6\% in the expected frequency and severity, respectively, of wildfires in August 2001, with spatially non-uniform changes in both aspects.

stat.AP↗