- 1CMCC Foundation - Euro-Mediterranean Centre on Climate Change, Bologna, Italy (antonello.squintu@cmcc.it)
- 2Department of Signal Processing and Communication, University of Alcalá, Alcalá de Henares, Spain
- 3University Pablo de Olavide, Seville, Spain
Heatwaves heavily affect European public health, society and economy. A full understanding of the drivers behind the occurrence and intensity of heatwaves (HWs) is one of the priorities of H2020 CLimate INTelligence (CLINT) project. Particular attention is given to the detection and attribution of HW and on the understanding of their future evolution thanks to the Storylines method. For the implementation of this technique, it is important to assess the capability of climate models in thoroughly identifying relationships between the drivers and the occurrence and intensity of HW. The relevant drivers of this extreme event are selected among a set of clustered variables on European and Global domains. This step is performed applying a feature selection algorithm (Probabilistic Coral Reef Optimization with Substrate Layers, PCRO-SL, Pérez-Aracil et al., 2023) to ERA5 summer data between 1981 and 2010, using as a target the Po Valley HW occurrence. The PCRO-SL is then applied to CMIP6 models, considering for each of them the period in which their Global Surface Air Temperature (GSAT) corresponds to the one of ERA5 between 1981 and 2010 (“current-climate”, 14.2°C). If a benchmark driver is selected for a CMIP6 model, its relationship with the target event is well resolved. The models that satisfy this requirement can be considered for an inspection of the non-linear and joint impacts of the drivers on Po Valley HWs in a future-climate scenario with higher GSAT. Thanks to this procedure it is possible to identify relevant pairs of drivers, whose combined influence on the target event is inspected by constructing Storylines. The projected evolutions of HWs over Po Valley corresponding to each scenario are displayed, highlighting the role of teleconnections and unveiling undocumented impacts.
Pérez-Aracil, J., Camacho-Gómez, C., Lorente-Ramos, E., Marina, C. M., Cornejo-Bueno, L. M., & Salcedo-Sanz, S. (2023). New probabilistic, dynamic multi-method ensembles for optimization based on the CRO-SL. Mathematics, 11(7), 1666.https://doi.org/10.3390/math11071666
How to cite: Squintu, A. A., McAdam, R., Pérez-Aracíl, J., Peláez Rodríguez, C., Álvarez-Castro, C., and Scoccimarro, E.: Storylines of heatwaves over Po Valley in a warmer World: drivers and impacts , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9371, https://doi.org/10.5194/egusphere-egu25-9371, 2025.
