EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Decadal predictability of European temperature extremes.

Eirini Tsartsali1, Panos Athanasiadis2, Stefano Tibaldi2, and Silvio Gualdi2
Eirini Tsartsali et al.
  • 1Department of Physics and Astronomy, University of Bologna, Italy
  • 2Centro EuroMediterraneo sui Cambiamenti Climatici Foundation, Bologna, Italy

Accurate predictions of climate variations at the decadal timescale are of great interest for decision-making, planning and adaptation strategies for different socio-economic sectors. Notably, decadal predictions have rapidly evolved during the last 15 years and are now produced operationally worldwide. The majority of the studies assessing the skill of decadal prediction systems focus on time-mean anomalies of standard meteorological variables, such as annual mean near-surface air temperature and precipitation. However, the predictability of extreme events frequency may differ substantially from the predictability of multi-year annual or seasonal means. Predicting the frequency of extreme events at different timescales is of major importance, since they are associated with severe impacts on various natural and human systems. In the current study we evaluate the capability of state-of-the-art decadal prediction systems to predict the frequency of temperature extremes in Europe. More specifically, we assess the skill of a multi-model ensemble from the Decadal Climate Prediction Project (DCPP, 163 ensemble members from 12 models in total) to forecast the number of days belonging to heatwaves episodes during summer (June–August). We find statistically significant predictive skill over Europe, except for the United Kingdom and a large part of the Scandinavian Peninsula, most of which is associated with the long-term warming trend. We are progressing with the evaluation of other statistical aspects of extreme events, including warm and cold episodes during winter, and we are also investigating whether there is predictive skill beyond that stemming from the external forcing.  

How to cite: Tsartsali, E., Athanasiadis, P., Tibaldi, S., and Gualdi, S.: Decadal predictability of European temperature extremes., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13736,, 2023.