CONFESS methodology exploits seasonal forecasting as a natural integrator of Copernicus Services. It aims at leveraging efforts in modelling infrastructure and observational datasets across Copernicus Services to further advance the upstream information flow for the production of C3S reanalysis and seasonal forecasts, which should ultimately enhance the quality and authoritative value of the C3S products and cascade down to the downstream applications. Since seasonal forecasts share similar modelling infrastructure with earth system reanalyses, model developments carried out for seasonal forecast systems, if successful, will be applicable to the production of reanalyses. Seasonal forecasts are chosen as a test-bed for evaluation because the proposed developments are expected to have large impact in the low frequency climate signals. Seasonal forecasts have an advantage over non-initialized climate simulation in that they are easier to verify with independent observations, and the model bias often remains small enough to avoid contamination of the model response to a given forcing. Seasonal forecasts are also a more efficient methodology to evaluate low frequency impact than reanalyses: the data assimilation methodology used in reanalyses is quite expensive, and is more effective for evaluation of fast processes.

The proposed methodology follows three basic principles:

1. Modular workflow,
2. Verifiable developments,
3. Robust assessment.

Further details on the different work packages can be found in the links below.