Preprints
https://doi.org/10.5194/os-2021-87
https://doi.org/10.5194/os-2021-87

  28 Sep 2021

28 Sep 2021

Review status: this preprint is currently under review for the journal OS.

Tracer and observationally-derived constraints on diapycnal diffusivities in an ocean state estimate

David S. Trossman1,2, Caitlin B. Whalen3, Thomas W. N. Haine4, Amy F. Waterhouse5, An T. Nguyen6, Arash Bigdeli7, Matthew Mazloff5, and Patrick Heimbach6,8 David S. Trossman et al.
  • 1Department of Oceanography & Coastal Sciences, Louisiana State University, Baton Rouge, USA
  • 2Center for Computation & Technology, Louisiana State University, Baton Rouge, USA
  • 3Applied Physics Laboratory, University of Washington, Seattle, USA
  • 4Department of Earth & Planetary Sciences, Johns Hopkins University, Baltimore, USA
  • 5Scripps Institution of Oceanography, University of California, San Diego, USA
  • 6Oden Institute for Computational Engineering & Sciences, University of Texas, Austin, USA
  • 7EP Analytics, Inc., Austin, USA
  • 8Jackson School of Geosciences & Institute for Geophysics, University of Texas, Austin, USA

Abstract. Use of an ocean parameter and state estimation framework–such as the Estimating the Circulation & Climate of the Ocean (ECCO) framework–could provide an opportunity to learn about the spatial distribution of the diapycnal diffusivity parameter (κρ) that observations alone cannot due to gaps in coverage. However, we show that the assimilation of existing in situ temperature, salinity, and pressure observations is not sufficient to constrain κρ estimated with ECCO, as κρ from ECCO does not agree closely with observations–specifically, κρ inferred from microstructure measurements. We investigate whether there are observations with more global coverage and well-understood measurement uncertainties that can be assimilated by ECCO to improve its representation of κρ. Argo-derived κρ using a strain-based parameterization of finescale hydrographic structure is one potential source of information. Argo-derived κρ agrees well with microstructure. However, because Argo- derived κρ has both measurement and structural uncertainties, we propose dissolved oxygen concentrations as a candidate for future data assimilation with ECCO. We perform sensitivity analyses with ECCO to test whether oxygen concentrations provide information about κρ. We compare two adjoint sensitivity calculations: one that uses misfits to Argo-derived κρ and the other uses misfits to dissolved oxygen concentrations. We show that adjoint sensitivities of dissolved oxygen concentration misfits to the state estimate's control space typically direct κρ to improve relative to the Argo-derived and microstructure-inferred values. However, assimilation of dissolved oxygen concentrations would likely not serve as a substitute for assimilating accurately measured κρ.

David S. Trossman et al.

Status: open (until 23 Nov 2021)

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David S. Trossman et al.

David S. Trossman et al.

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Short summary
How the ocean mixes is not yet adequately represented by models. There are many challenges with representing this mixing. A model that minimizes disagreements between observations and the model could, in principle, be used to fill in the gaps from observations to better represent ocean mixing. But observations of ocean mixing have large uncertainties. Here, we show that ocean oxygen, which has relatively small uncertainties, is not a perfect substitute for accurately observing ocean mixing.