Exploring winter predictability in Europe using the ECMWF hindcasts
- 1CNR-ISAC, Bologna, Italy (d.mastrangelo@isac.cnr.it)
- 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, https://doi.org/10.5194/egusphere-egu21-9538, 2021.