Barents-2.5km v2.0: An operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard

Abstract

Abstract. An operational ocean and sea ice forecast model, Barents-2.5, is implemented at MET Norway for short-term forecasting at the coast off Northern Norway, the Barents Sea, and waters around Svalbard. Primary forecast parameters are the sea ice concentration (SIC), sea surface temperature (SST), and ocean currents. The model is also a substantial input for drift modeling of pollutants, ice berg, and in search-and-rescue pertinent applications in the Arctic domain. Barents-2.5 has recently been upgraded to include an Ensemble Prediction System with 24 daily realizations of the model state. SIC, SST and in-situ hydrography are constrained through the Ensemble Kalman Filter (EnKF) data assimilation scheme executed in daily forecast cycles with lead time up to 66 hours. While the ocean circulation is not directly constrained by assimilation of ocean currents, the model ensemble represents the given uncertainty in the short-term current field by retaining the current state for each member throughout forecast cycles. Here we present the model setup and a validation in terms of SIC, SST and in-situ hydrography. The performance of the ensemble to represent the models uncertainty, and the performance of the EnKF to constrain the model state are discussed, in addition to the model’s forecast capabilities for SIC and SST.

Comment on egusphere-2024-1171

“…As an example for ocean circulation in a coastal area with temporal varying flow, we use data from Barents-2.5 EPS (Röhrs et al, 2023), a coupled numerical ocean and sea-ice model based on the regional ocean modeling system (ROMS) (Shchepetkin and McWilliams, 2005). The model has a 2.5 km horizontal and hourly temporal resolution, covering the Barents Sea, the coast off northern Norway and Svalbard (see Fig.…”

“…The ensemble spread is further controlled by the Ensemble Kalman Filter data assimilation scheme, which reduces the spread of observed variables (Evensen, 1994;Röhrs et al, 2023). The first member in each set (four members) is forced by most recent atmospheric conditions from the AROME-Arctic model (Müller et al, 2017).…”

“…The first member in each set (four members) is forced by most recent atmospheric conditions from the AROME-Arctic model (Müller et al, 2017). The remaining members are forced by 20 members from the integrated forecast system developed by the European Centre for Medium Range Weather Forecasts (ECWF-ENS) (Röhrs et al, 2023).…”

Modes of Operation and Forcing in Oil Spill Modeling: State-of-Art, Deficiencies and Challenges

“…To accommodate the scale of the available observations and to compromise the need to resolve high resolution eddy dynamics, while confining nonlinearities that limit the 4D-Var DA capabilities, a horizontal model resolution of 2.4 km was chosen. The model is intended to be used for the forecasting of ocean circulation and hydrography beyond the coastal area, including the entire shelf sea and the dynamics of the North Atlantic current at the shelf slope [142]. Röhrs et al [23] used the NorShelf model outputs in OpenOil simulations.…”

Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean-sea ice model

“…, could therefore lead to an improvement of short-to medium-range forecasts of the sea ice distribution. As previous studies have only assimilated daily means of SIC (Sakov et al, 2012;Fritzner et al, 2020;Röhrs et al, 2023), we aim in this study to explore the benefits of the assimilation of individual swaths of SIC in order to better constrain the sea ice forecasts both spatially and temporally.…”