ECMWF’s Global ECMWF Fire Forecast (GEFF) system is used operationally to produce 10-day fire danger forecasts (using the Canadian Fire Weather index as a proxy) at the European and global scales. The GEFFSP (GEFF for Seasonal Prediction) is an adaptation of GEFF for fire danger, using seasonal predictions from the ECMWF’s long-range forecasting system SEAS5 obtained from the C3S Seasonal Forecasts archive. The results of this methodology applied to California and the Iberian peninsula (regions identified as critical for this work) were mixed, as it was shown that the predictability of the Fire Weather Index is poor beyond 1 month leadtime (due to a poor predictability of precipitation beyond 1 month in these areas) during the fire seasons of both areas. However, literature suggests that predictability should be better in regions affected by ENSO (El Niño/Southern Oscillation) such as the Amazon and Indonesia, and a follow-up of this project will be to use the same technique over these regions. A paper detailing the implementation and assessment of the system is under preparation, and further results are expected shortly. This project has also contributed to the understanding of the predictability of seasonal prediction systems of indices such as the Fire Weather Index for predicting fire danger.
The EC-Earth-Fire seasonal prediction system, using fire models from the LPJ-Guess dynamic vegetation model coupled to the EC-Earth climate model, was developed in offline configuration known as the EC-Earth Land Surface Model. Atmospheric output obtained previously from the decadal (and historical) simulations performed by the Climate Prediction Group of the BSC for DCPP and CMIP6 using the Autosubmit workflow manager were archived in a format suitable to force offline LPJ-Guess simulations. This output was used to generate decadal (and historical) simulations with LPJ-Guess in offline mode. Preliminary results show that the model produces slightly less biomass and more fire emissions than reconstructions. Follow-up projects will evaluate the performance of the vegetation and fire model when forced with seasonal-to-decadal predictions of the Global Climate Model version of EC-Earth, as well as comparing the added value of initialization, by comparing the predictions to free-running historical simulations. This work will be used in further projects in which the Climate Prediction Group is involved, in particular the H2020 project CCiCC (Climate-Carbon Interactions in the Coming Century).
