Preprints
https://doi.org/10.5194/gmd-2023-20
https://doi.org/10.5194/gmd-2023-20
Submitted as: development and technical paper
 | 
06 Mar 2023
Submitted as: development and technical paper |  | 06 Mar 2023
Status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

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

Johannes Röhrs, Yvonne Gusdal, Edel Rikardsen, Marina Duran Moro, Jostein Brændshøi, Nils Melsom Kristensen, Sindre Fritzner, Keguang Wang, Ann Kristin Sperrevik, Martina Idžanović, Thomas Lavergne, Jens Debernard, and Kai H. Christensen

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.

Johannes Röhrs et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-20', Benjamin Barton, 19 Apr 2023
    • AC1: 'Reply on RC1', Johannes Röhrs, 25 May 2023
  • RC2: 'Comment on gmd-2023-20', Anonymous Referee #2, 23 Apr 2023
    • AC2: 'Reply on RC2', Johannes Röhrs, 26 May 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-20', Benjamin Barton, 19 Apr 2023
    • AC1: 'Reply on RC1', Johannes Röhrs, 25 May 2023
  • RC2: 'Comment on gmd-2023-20', Anonymous Referee #2, 23 Apr 2023
    • AC2: 'Reply on RC2', Johannes Röhrs, 26 May 2023

Johannes Röhrs et al.

Data sets

Configuration files for Barents-2.5 Ocean and Ice forecast model Johannes Röhrs https://doi.org/10.5281/zenodo.7607191

Barents-2.5km operational forecast model archive Norwegian Meteorological Institute https://thredds.met.no/thredds/fou-hi/barents_eps.html

Model code and software

metno/metroms: Version 0.4.1 Jens Debernard, Nils Melsom Kristensen, Sebastian Maartensson, Keguang Wang, Kate Hedstrom, Jostein Brændshøi, and Nicholas Szapiro https://doi.org/10.5281/zenodo.5067164

Johannes Röhrs et al.

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Latest update: 27 Aug 2023
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Short summary
A model to predict ocean currents, temperature and sea ice is presented, covering the Barents Sea and Northern Norway. To quantify forecast uncertainties, the model calculates ensemble forecasts – 24 realizations of ocean and ice conditions. Observations from satellites, buoys and ships are ingested by the model. The model forecasts are compared with observations, and we show that the ocean model has skill in predicting sea surface temperatures.