EGU21-9538, updated on 11 Jan 2022
EGU General Assembly 2021
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Exploring winter predictability in Europe using the ECMWF hindcasts

Daniele Mastrangelo1, Ignazio Giuntoli1,2, and Piero Malguzzi1
Daniele Mastrangelo et al.
  • 1CNR-ISAC, Bologna, Italy (
  • 2School of Geography Earth and Environmental Sciences, University of Birmingham, UK

The accuracy and reliability in predicting winter anomalies, particularly high-impact events, is crucial for economic sectors like energy production and trade. Sub-seasonal predictions can provide a useful tool for early detection of these events. In this context, this study aims to target atmospheric patterns leading to skillful winter predictions at S2S lead times.

With a focus on Europe, we explore extended range predictability (up to 35 days) in the ECMWF hindcast dataset (1999-2018). This dataset provides a sizable sample for assessing the winter months predictive skill of the model and can be considered as a preparatory step to the use of the more comprehensive real-time ensemble forecasts.

The verification is performed on geopotential and temperature fields against the ERA5 reanalysis. We first identify the most skillful predictions both in terms of lead-time and period of initialization. Later we assess whether these skillful predictions correspond to high-impact events, especially cold spells. Finally, in the attempt to identify the potential drivers of improved predictability, we track back to the dominant Euro-Atlantic modes of variability present in the initial atmospheric states of the well predicted events.

How to cite: Mastrangelo, D., Giuntoli, I., and Malguzzi, P.: Exploring winter predictability in Europe using the ECMWF hindcasts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9538,, 2021.


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