EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Forecast-based attribution of a winter heatwave within the limit of predictability

Nicholas Leach1, Antje Weisheimer1,2,3, Myles Allen1,4, and Timothy Palmer1
Nicholas Leach et al.
  • 1Oxford University, Department of Physics, Oxford, United Kingdom of Great Britain – England, Scotland, Wales (
  • 2NCAS-Climate, Department of Physics, University of Oxford, Oxford, United Kingdom of Great Britain – England, Scotland, Wales
  • 3European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, United Kingdom of Great Britain – England, Scotland, Wales
  • 4Environmental Change Institute, University of Oxford, Oxford, United Kingdom of Great Britain – England, Scotland, Wales

Between the 21st and 27th February 2019, climatologically exceptional warm temperature anomalies of 10-15 °C were experienced throughout Northern and Western Europe. In particular, the 25th - 27th February saw record-breaking temperatures measured at many weather stations over wide areas of Iberia, France, the British Isles, the Netherlands, Germany and Southern Sweden. 

This heatwave was well-predicted by the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system. Their forecasts indicated "extreme" heat was possible at a lead time of around two weeks, and likely at a lead time of around ten days. The performance of these forecasts in predicting the surface heat is also reflected in their ability to predict the synoptic situation. 

We exploit this successful forecast to perform an attribution analysis of the heatwave that differs from "conventional" analyses in several key regards. Firstly, we are not only confident that the model used is able to simulate the event in question; but that we are unequivocally studying the specific winter heatwave that occurred in Europe during February 2019. 

This analysis is carried out using a state-of-the-art coupled high-resolution forecast model ensemble, as opposed to the prescribed-SST experiments within climate model ensembles traditionally used for attribution.

A crucial distinction between the typical climate model simulations used for attribution and the forecasts used here is that the climate model simulations are usually allowed to spin out for a sufficient length of time such that they have no memory of their initial conditions; an ensemble constructed in this way will therefore be representative of the climatology of the model (possibly conditioned on any prescribed-SST patterns). Unlike these climatological simulations, the successful forecasts used here are clearly heavily dependent on the initial conditions used. Within these simulations, the level of dynamical conditioning can therefore be specified by altering the lead time from initialisation to the event in question. We explore the implications of this aspect of forecast-based attribution, attempting to integrate between the "conventional" climatological and "storyline" frameworks of attribution. 

To simplify the interpretation of our experiments, here we have decided to only change a single feature between our factual and counterfactual experiments. The analysis presented is therefore limited to attributing the impact of diabatic heating due to increased CO2 concentrations above pre-industrial levels just over the days between the model initialisation date and the event. We carry out simulations at four different lead times from the event, allowing us to investigate the balance between the level of conditioning of the ensemble and the relaxation of the ensemble toward a new equilibrium at the lowered CO2 concentrations.

How to cite: Leach, N., Weisheimer, A., Allen, M., and Palmer, T.: Forecast-based attribution of a winter heatwave within the limit of predictability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5731,, 2021.

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