Seasonal prediction is one important element in a seamless prediction chain between weather forecast and climate projections. After several years of common development in collaboration with Universität Hamburg and Max Planck Institute for Meteorology, the Deutscher Wetterdienst performs operational seasonal forecasts since 2016 with the German Climate Forecast System, now in Version 2 (GCFS2.0). Here, the configuration of previous system GCFS1.0 and the current GCFS2.0 are described and the performance of the two systems is compared over the common hindcast period of 1990-2014. In GCFS2.0, the forecast skill is improved compared to GCFS1.0 during boreal winter, especially for the Northern Hemisphere where the Pearson correlation has doubled for the North Atlantic Oscillation index. During boreal summer, overall a similar performance of GCFS2.0 in comparison to GCFS1.0 is assessed. Future developments for climate forecasts need a stronger focus on the performance of seasonal dependent processes in a model system.

The variability of the sea level pressure in the North Atlantic sector is the most important driver of weather and climate in Europe. The main mode of this variability, the North Atlantic Oscillation (NAO), explains up to 50% of the total variance. Other modes, known as the Scandinavian index, East Atlantic and East Atlantic/West Russian pattern, complement the variability of the sea level pressure, thereby influencing the European climate. It has been shown previously that a seasonal prediction system with enhanced winter NAO skill due to ensemble subsampling entails an improved prediction of the surface climate variables as well. Here, we show that a refined subselection procedure that accounts both for the NAO index and for the three additional modes of sea level pressure variability, is able to further increase the prediction skill of wintertime mean sea level pressure, near-surface temperature and precipitation across Europe.

Inter-annual to decadal variability in the strength of the land and ocean carbon sinks impede accurate predictions of year-to-year atmospheric carbon dioxide (CO2) growth rate. Such information is crucial to verify the effectiveness of fossil fuel emissions reduction measures. Using a multi-model framework comprising prediction systems based on Earth system models, we find a predictive skill for the global ocean carbon sink of up to 6 years. Longer regional predictability horizons and robust spatial patterns are found across single models. On land, a predictive skill of up to 2 years is primarily maintained in the tropics and extra-tropics enabled by the initialization of the physical climate variables towards observations. We further show that anomalies of atmospheric CO2 growth rate inferred from natural variations of the land and ocean carbon sinks are predictable at lead time of 2 years and the skill is limited by the land carbon sink predictability horizon.

Variability of the North Atlantic Oscillation (NAO) drives wintertime temperature anomalies in the Northern Hemisphere. Dynamical seasonal prediction systems can skilfully predict the winter NAO. However, prediction of the NAO-dependent air temperature anomalies remains elusive, partially due to the low variability of predicted NAO. Here, we demonstrate a hidden potential of a multi-model ensemble of operational seasonal prediction systems for predicting wintertime temperature by increasing the variability of predicted NAO. We identify and subsample those ensemble members which are close to NAO index estimated from initial autumn conditions. In our novel multi-model approach, the correlation prediction skill for wintertime Central Europe temperature is improved from 0.25 to 0.66, accompanied by an increased winter NAO prediction skill of 0.9. Thereby, temperature anomalies can be skilfully predicted for the upcoming winter over a large part of the Northern Hemisphere through increased variability and skill of predicted NAO.