EGU23-9644, updated on 25 Oct 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Analysis of Ensemble Uncertainty Transfer in AI-Based Downscaling of C3S Seasonal Forecast

Qing Lin1, Fatemeh Heidari1, Edgar Fabián Espitia Sarmiento1, Muralidhar Adakudlu1, Andrea Toreti2, and Elena Xoplaki1,3
Qing Lin et al.
  • 1Center for International Development and Environmental Research, Justus Liebig University Giessen, Senckenbergstrasse 3, 35390 Giessen, Germany (
  • 2Joint Research Centre (JRC), European Commission, 21027 Ispra VA, Italy
  • 3Department of Geography, Climatology, Climate Dynamics and Climate Change, Justus Liebig University Giessen, Senckenbergstrasse 1, 35390 Giessen, Germany

Copernicus Climate Change Service (C3S) integrates multiple seasonal forecast models of climate variables with multiple ensemble realizations. Assessing the risks of natural hazards with high impacts on human and natural systems and providing actionable services at the local scale require high-resolution predictions. We implement the AI-based approach proposed by Heidari et al. (2023) to address such needs and reach a kilometer scale. While downscaling seasonal forecasts, it is crucial to transfer the full range of the uncertainties given by the ensembles.

This study assesses how uncertainty is transferred by an AI-based downscaling approach. Quantile-based metrics are here used to measure the ensemble variability between seasonal forecasts and their downscaled products. On the other side, quantile-based metrics can also give an alternative description of the ensemble variabilities, which could replace the raw ensemble members in the downscaling process. In this study, the AI-downscaling system is tested by inputting (a) raw ensemble members and (b) quantile-based metrics. Transferred uncertainty and downscaling accuracy are then evaluated to develop and implement an optimal downscaling approach with hazard-dependent inputs being selected at  regional and local scales.


Heidari F., Lin Q., Espitia Sarmiento E.F., Toreti A., and Xoplaki E. (2023): A deep learning technique to realistically bias correct and downscale seasonal forecast ensembles of climate variables towards the development of an AI-based early warning system, EGU 2023 abstract

How to cite: Lin, Q., Heidari, F., Espitia Sarmiento, E. F., Adakudlu, M., Toreti, A., and Xoplaki, E.: Analysis of Ensemble Uncertainty Transfer in AI-Based Downscaling of C3S Seasonal Forecast, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9644,, 2023.