Investigating process levels of the German Climate Forecast System

Usually, climate forecast systems are assessed in deterministic and probabilistic quality metrics for error and correlation on reference data sets. Here we apply two diagnostic tools on the seasonal forecast system to learn more about possible mechanisms, which gover the respective forecast skill.

By using the delta maps tool (Falasca et al., 2019) we are looking especially for ENSO-teleconnected regions of the model system during boreal spring and boreal winter in comparison with ERA5. The tool generates clusters of multiple grid points, whose time series correlate and therefore are characterised with similar physical behavior. Delta maps was originally designed for data from climate projections but has been adapted to seasonal forecast output. We aim for a better understanding of the pronounced spring predictability barrier in GCFS2.1 and how this evolves in its successor GCFS2.2.

The Warm Conveyor Belt metric (Quinting and Grams, 2022) is designed up to study the role of physical processes and their influence on the large-scale circulation in NWP and climate models. The metric has been successfully applied to sub-seasonal forecast models (Wandel et al. 2021) where a general underestimation of WCB frequencies and a potential link to circulation biases is found. We here apply the metric to the GCFS2.1 hindcasts (1993-2016) in the boreal winter season. Due to the strong link between air masses from WCBs and large-scale weather regimes, the analysis will help to understand general model behavior  and error development which will ultimately help to increase the prediction skill of the weather regimes in the forecasting system.

Falasca, F., Bracco, A., Nenes, A., & Fountalis, I. . Dimensionality reduction and network inference for climate data using δ‐MAPS: Application to the CESM Large Ensemble sea surface temperature. Journal of Advances in Modeling Earth Systems, 11, 1479– 1515. https://doi.org/10.1029/2019MS001654 (2019).

Quinting, J. F. and Grams, C. M.: EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model, Geosci. Model Dev., 15, 715–730, https://doi.org/10.5194/gmd-15-715-2022, 2022.

Wandel, J., Quinting, J. F., & Grams, C. M. (2021). Toward a Systematic Evaluation of Warm Conveyor Belts in Numerical Weather Prediction and Climate Models. Part II: Verification of Operational Reforecasts. Journal of the Atmospheric Sciences, 78(12), 3965-3982