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You are here: Home / Library / RBINS Staff Publications / Uncertainty estimation for operational ocean forecast products—a multi-model ensemble for the North Sea and the Baltic Sea

Inga Golbeck, Xin Li, Frank Janssen, Thorger Brüning, Jacob Nielsen, Vibeke Huess, Johan Söderkvist, Bjarne Büchmann, Simo-Matti Siiriä, Olga Vähä-Piikkiö, Bruce Hackett, Nils M. Kristensen, Harald Engedahl, Ed Blockley, Alistair Sellar, Priidik Lagemaa, Jose Ozer, Sebastien Legrand, Patrik Ljungemyr and Lars Axell (2015)

Uncertainty estimation for operational ocean forecast products—a multi-model ensemble for the North Sea and the Baltic Sea

Ocean Dynamics, 65(12):1603-1631.

Multi-model ensembles for sea surface temperature (SST), sea surface salinity (SSS), sea surface currents (SSC), and water transports have been developed for the North Sea and the Baltic Sea using outputs from several operational ocean forecasting models provided by different institutes. The individual models differ in model code, resolution, boundary conditions, atmospheric forcing, and data assimilation. The ensembles are produced on a daily basis. Daily statistics are calculated for each parameter giving information about the spread of the forecasts with standard deviation, ensemble mean and median, and coefficient of variation. High forecast uncertainty, i.e., for SSS and SSC, was found in the Skagerrak, Kattegat (Transition Area between North Sea and Baltic Sea), and the Norwegian Channel. Based on the data collected, longer-term statistical analyses have been done, such as a comparison with satellite data for SST and evaluation of the deviation between forecasts in temporal and spatial scale. Regions of high forecast uncertainty for SSS and SSC have been detected in the Transition Area and the Norwegian Channel where a large spread between the models might evolve due to differences in simulating the frontal structures and their movements. A distinct seasonal pattern could be distinguished for SST with high uncertainty between the forecasts during summer. Forecasts with relatively high deviation from the multi-model ensemble (MME) products or the other individual forecasts were detected for each region and each parameter. The comparison with satellite data showed that the error of the MME products is lowest compared to those of the ensemble members.
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