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  • Oceanic mixed layer  (1)
  • Spatial filtering  (1)
  • 1
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    Seasonality of the mesoscale inverse cascade as inferred from global scale-dependent eddy energy observations (2022)
    American Meteorological Society
    Details
    Publication Date: 2022-09-01
    Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(8), (2022): 1677-1691, https://doi.org/10.1175/jpo-d-21-0269.1.
    Description: Oceanic mesoscale motions including eddies, meanders, fronts, and filaments comprise a dominant fraction of oceanic kinetic energy and contribute to the redistribution of tracers in the ocean such as heat, salt, and nutrients. This reservoir of mesoscale energy is regulated by the conversion of potential energy and transfers of kinetic energy across spatial scales. Whether and under what circumstances mesoscale turbulence precipitates forward or inverse cascades, and the rates of these cascades, remain difficult to directly observe and quantify despite their impacts on physical and biological processes. Here we use global observations to investigate the seasonality of surface kinetic energy and upper-ocean potential energy. We apply spatial filters to along-track satellite measurements of sea surface height to diagnose surface eddy kinetic energy across 60–300-km scales. A geographic and scale-dependent seasonal cycle appears throughout much of the midlatitudes, with eddy kinetic energy at scales less than 60 km peaking 1–4 months before that at 60–300-km scales. Spatial patterns in this lag align with geographic regions where an Argo-derived estimate of the conversion of potential to kinetic energy is seasonally varying. In midlatitudes, the conversion rate peaks 0–2 months prior to kinetic energy at scales less than 60 km. The consistent geographic patterns between the seasonality of potential energy conversion and kinetic energy across spatial scale provide observational evidence for the inverse cascade and demonstrate that some component of it is seasonally modulated. Implications for mesoscale parameterizations and numerical modeling are discussed.
    Description: This work was generously funded by NSF Grants OCE-1912302, OCE-1912125 (Drushka), and OCE-1912325 (Abernathey) as part of the Ocean Energy and Eddy Transport Climate Process Team.
    Keywords: Eddies ; Energy transport ; Mesoscale processes ; Turbulence ; Oceanic mixed layer ; Altimetry ; Seasonal cycle
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
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    Diffusion-Based smoothers for spatial filtering of gridded geophysical data (2021)
    American Geophysical Union
    Details
    Publication Date: 2022-10-26
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Grooms, I., Loose, N., Abernathey, R., Steinberg, J. M., Bachman, S. D., Marques, G., Guillaumin, A. P., & Yankovsky, E. Diffusion-Based smoothers for spatial filtering of gridded geophysical data. Journal of Advances in Modeling Earth Systems, 13(9), (2021): e2021MS002552, https://doi.org/10.1029/2021MS002552.
    Description: We describe a new way to apply a spatial filter to gridded data from models or observations, focusing on low-pass filters. The new method is analogous to smoothing via diffusion, and its implementation requires only a discrete Laplacian operator appropriate to the data. The new method can approximate arbitrary filter shapes, including Gaussian filters, and can be extended to spatially varying and anisotropic filters. The new diffusion-based smoother's properties are illustrated with examples from ocean model data and ocean observational products. An open-source Python package implementing this algorithm, called gcm-filters, is currently under development.
    Description: I.G. and N.L. are supported by NSF OCE 1912332. R.A. is supported by NSF OCE 1912325. J.S. is supported by NSF OCE 1912302. S.B. and G.M. are supported by NSF OCE 1912420. A.G. and E.Y. are supported by NSF GEO 1912357 and NOAA CVP NA19OAR4310364.
    Keywords: Spatial filtering ; Coarse graining ; Data analysis
    Repository Name: Woods Hole Open Access Server
    Type: Article
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