Reconstruction Of The Rheological Parameters In A Sea Ice Model With Viscoplastic Rheology

Details

• Journal Title:
  Journal of Atmospheric and Oceanic Technology
• Personal Author:
  Panteleev, Gleb ; Yaremchuk, Max ; Francis, Oceana
• NOAA Program & Office:
  Sea Grant ; OAR (Oceanic and Atmospheric Research)
• Sea Grant Program:
  HAWAU (Hawaii Sea Grant)
• Description:
  We analyzed the feasibility of the reconstruction of the spatially varying rheological parameters through the four-dimensional variational data assimilation of the sea ice velocity, thickness, and concentration into the viscoplastic (VP) sea ice model. The feasibility is assessed via idealized variational data assimilation experiments with synthetic observations configured for a 1-day data assimilation window in a 50 × 40 rectangular basin forced by the open boundaries, winds, and ocean currents and should be viewed as a first step in the developing the similar algorithms which can be applied for the more advanced sea ice models. It is found that “true” spatial variability (∼5.8 kN m−2) of the internal maximum ice strength parameter can be retrieved from observations with reasonable accuracy of 2.3–5.3 kN m−2, when an observation of the sea ice state is available daily in each grid point. Similar relative accuracy was achieved for the reconstruction of the compactness strength parameter α. The yield curve eccentricity e is found to be controlled by the data with less efficiency, but the spatial mean value of e could be still reconstructed with a similar degree of confidence. The accuracy of , α, and e retrievals is found to increase in regions of stronger ice velocity convergence, providing prospects for better processing of the observations collected during the recent MOSAiC experiment. The accuracy of the retrievals strongly depends on the number of the control variables characterizing the rheological parameter fields.
• Keywords:
  Data Assimilation Ocean Dynamics Data Assimilation Ocean Dynamics
• Source:
  Journal of Atmospheric and Oceanic Technology, 40(1), 141-157
• DOI:
  https://doi.org/10.1175/JTECH-D-21-0158.1
• ISSN:
  0739-0572 ; 1520-0426
• Publisher:
  American Meteorological Society
• Document Type:
  Journal Article
• Funding:
  Grant no. NA22OAR4170108
• Rights Information:
  Accepted Manuscript
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:ebcdd745cc270379df607dcd646c182daadce584cd5b9960bdddcf1677524fd3
• Download URL:
  https://repository.library.noaa.gov/view/noaa/51680/noaa_51680_DS1.pdf
• File Type:
  [PDF - 3.03 MB ]