Frontier Research in Earth System Prediction

Andrea Alessandri

TitleFrontier Research in Earth System Prediction

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Abstract: So far, the lack of observations to constrain the model complexity has determined the development of different modeling systems for different time scales. The models that are developed for short time-scales (i.e. ranging from weather forecast to seasonal climate predictions) include only that part of the variability for which observations are available and that can be suitably modelled/initialized in order to positively contribute to the forecasts. For instance, the land surface model developed at ECMWF (HTESSEL) and included in the ECMWF Integrated Forecasting System (IFS), assumes land cover and vegetation characteristics to be constant in time, therefore evidencing considerable biases and weak prediction signal over the interested land areas. On the other hand, for long time-scales (i.e ranging from interannual to decadal and beyond), the Earth System Models (ESMs) used for climate variability and climate-change research contain comprehensive soil-vegetation-atmosphere-transfer schemes that are intended to represent as many processes as possible, including those that are still poorly constrained or understood. Since most of the applications of climate predictions would serve social and economic interests that are land-based, it is of foremost importance to  improve Earth system predictions over land  by filling the gap between the models used for short-term prediction (verification-based) and the latest developments in the ESMs (process-based). Following this approach, we show that the new and improved observational records can be effectively used to seamlessly enhance land surface, vegetation and hydrology processes in IFS/EC-Earth, leading to significant improvements of the predictions across multiple time-scales. 

Long-term enhancements in climate/Earth system prediction must come from improving the description of the physical & Earth-system processes on the basis of dedicated process studies and observational databases. This is a slow, but necessary process. In the meanwhile, given a set of imperfect models, we can improve predictions by combining individual models through the multi-model approach. Multi-Model Ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model ensembles. Previous works suggested that the potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems (SPSs). Here we combine the two MME SPSs independently developed by the European and by the Asian-Pacific communities. We demonstrate the potential of the combined Grand MME to significantly contribute to obtain useful predictions at the seasonal time-scale applied to the energy sector. These results motivated the application of latest available multi-model seasonal predictions from independent sources [European (Copernicus), North American (NMME) and Asian Pacific (APCC)] that is being performed within the H2020 SECLI-FIRM project (

Most relevant recent publications:

  1. Alessandri, F. Catalano, M. De Felice, B. van den Hurk, and G. Balsamo, 2021: Varying Signatures of Surface Albedo Feedback on the Northern Hemisphere Land Warming., Environ. Res. Lett.,16, 034023.
  2. van Oorschot, F., van der Ent, R. J., Hrachowitz, M., and Alessandri, A, 2021.: Climate controlled root zone parameters show potential to improve water flux simulations by land surface models, Earth Syst. Dynam. Discuss. [preprint],
  3. Alessandri, A., M. De Felice, F. Catalano, J-Y. Lee, B. Wang, D-Y. Lee, J-H. Yoo, A. Weisenheimer, 2018: Grand European and Asian-Pacific multi-model seasonal forecasts: maximization of skill and of potential economical value to end-users, Clim. Dyn., 50 (7-8), 2719-2738
  4. Alessandri, A., F. Catalano, M. De Felice, B. Van Den Hurk, F. Doblas Reyes, S. Boussetta, G. Balsamo, P. Miller, 2017: Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth, Clim. Dyn. doi:10.1007/s00382-016-3372-4
  5. Alessandri  A., M. De Felice , N. Zeng , A. Mariotti , Y. Pan , A. Cherchi , J-Y. Lee , B. Wang , K-J. Ha , P. Ruti, and V. Artale, 2014: Robust assessment of the expansion and retreat of Mediterranean climate in the 21stcentury. Nature Sci. Rep., 4, 7211, doi:10.1038/srep07211



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