Preprints
https://doi.org/10.5194/os-2021-65
https://doi.org/10.5194/os-2021-65

  07 Jul 2021

07 Jul 2021

Review status: this preprint was under review for the journal OS. A revision for further review has not been submitted.

Assimilation of ice compactness data in a strong coupling regime in the ocean – sea ice coupled model

Maxim N. Kaurkin1, Leonid Y. Kalnitski2,1, Konstantin V. Ushakov1,3, and Rashit A. Ibrayev2,1,3 Maxim N. Kaurkin et al.
  • 1Shirshov Institute of Oceanology, Russian Academy of Sciences, Nahimovskiy prospekt, 36, Moscow, 117997, Russia
  • 2Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, ul. Gubkina, 8, Moscow, 119333, Russia
  • 3Moscow Institute of Physics and Technology (State University), Institutskiy per. 9, Dolgoprudny, Moscow reg., 141700, Russia

Abstract. The Arctic Ocean plays an important role in the global climate system, where sea ice regulates the exchange of heat, moisture and momentum between the atmosphere and the ocean. A comprehensive analysis and forecast of the Arctic ocean system requires a detailed numerical ocean and sea ice coupled model supplemented by assimilation of observational data at appropriate time scales.

A new operative ocean – ice state forecast system was developed and implemented. It consists of the INMIO4.1 ocean general circulation model and the CICE5.1 sea ice dynamics and thermodynamics model with common spatial resolution of 0.25°. For the exchange of boundary conditions and service actions (data storage, time synchronization, etc.), the coupled model uses the Compact Modeling Framework (CMF3.0). Data assimilation is implemented in the form of the Data Assimilation Service (DAS) based on the Ensemble Optimal Interpolation (EnOI) method. This technique allows to simultaneously correct the ocean (temperature, salinity, surface level) and ice (concentration) model fields in the DAS service, so they are coordinated not only through the exchange of boundary conditions, but already at the stage of data assimilation (i.e. strong coupling data assimilation). Experiments with the INMIO – CICE model show that the developed algorithm provides a significant improvement in the accuracy of forecasting the state of the ice field in the Arctic Ocean.

Maxim N. Kaurkin et al.

Status: closed (peer review stopped)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on os-2021-65', Anonymous Referee #1, 05 Aug 2021
  • RC2: 'Comment on os-2021-65', Anonymous Referee #2, 31 Aug 2021

Status: closed (peer review stopped)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on os-2021-65', Anonymous Referee #1, 05 Aug 2021
  • RC2: 'Comment on os-2021-65', Anonymous Referee #2, 31 Aug 2021

Maxim N. Kaurkin et al.

Maxim N. Kaurkin et al.

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Short summary
The Arctic plays an important role in the global climate system, where sea ice regulates the exchange of heat and momentum between the atmosphere and the ocean. Interpretation of such changes is difficult due to small amount of observations. Numerical modeling can contribute to understanding these processes, but the lack of knowledge about the physics of ice-ocean interactions limits our ability to realistically reproduce them. The remedy is to correct the model solution by data assimilation.