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  • Geophysical Research Abstracts Vol. 18,  (1)
  • John Wiley & Sons, Ltd  (1)
  • Taylor & Francis  (1)
  • 1
    Publication Date: 2021-07-20
    Description: We present an Arctic ocean–sea ice reanalysis covering the period 2007–2016 based on the adjoint approach of the Estimating the Circulation and Climate of the Ocean (ECCO) consortium. The spatiotemporal variation of Arctic sea surface temperature (SST), sea ice concentration (SIC), and sea ice thickness (SIT) is substantially improved after the assimilation of ocean and sea ice observations. By assimilating additional World Ocean Atlas 2018 (WOA18) hydrographic data, the freshwater content of the Canadian Basin becomes closer to the observations and translates into changes of the ocean circulation and of transports through the Fram and Davis straits. This new reanalysis compares well with previous filter‐based (TOPAZ4) and nudging‐based (PIOMAS) reanalyses regarding SIC and SST. Benefiting from using the adjoint of the sea ice model, our reanalysis is superior to the ECCOv4r4 product considering sea ice parameters. However, the mean state and variability of the freshwater content and the transport properties of our reanalysis remain different from TOPAZ4 and ECCOv4r4, likely because of a lack of hydrographic observations.
    Description: Arctic sea ice has declined rapidly and reached a record minimum in September, 2012. Arctic ocean–sea ice reanalyses are invaluable sources for understanding the Arctic sea ice changes. We produce an Arctic ocean–sea ice reanalysis of the years 2007–2016 using the adjoint method. The reanalysis is dynamically consistent without introducing unphysical mass and energy discontinuities as in filter‐based data assimilation methods.
    Keywords: 551 ; adjoint method ; data assimilation ; ocean–sea ice reanalysis
    Type: article
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  • 2
    Publication Date: 2017-07-10
    Description: We investigate sea level trends and variability as reconstructed from tide gauge data and ocean data assimilations (ODA) over the last 60 years. Tide gauge reconstructions (TGR) are mostly based on statistical approaches using selected EOFs, or trained from variability patterns, from altimetric sea level and tide gauge data to extrapolate regional sea level evolution backward in time. Reconstructions also exist from dynamical ocean modeling approaches with and without data assimilation. We intercompare all results and provide ensemble mean and ensemble spreads to describe estimates of past regional sea level changes and their uncertainties.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
    Format: application/pdf
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  • 3
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    Taylor & Francis
    In:  Tellus A: Dynamic meteorology and oceanography, 54 . pp. 406-425.
    Publication Date: 2016-06-14
    Description: The study investigates perspectives of the parameter estimation problem with the adjoint method in eddy-resolving models. Sensitivity to initial conditions resulting from the chaotic nature of this type of model limits the direct application of the adjoint method by predictability. Prolonging the period of assimilation is accompanied by the appearance of an increasing number of secondary minima of the cost function that prevents the convergence of this method. In the framework of the Lorenz model it is shown that averaged quantities are suitable for describing invariant properties, and that secondary minima are for this type of data transformed into stochastic deviations. An adjoint method suitable for the assimilation of statistical characteristics of data and applicable on time scales beyond the predictability limit is presented. The approach assumes a greater predictability for averaged quantities. The adjoint to a prognostic model for statistical moments is employed for calculating cost function gradients that ignore the fine structure resulting from secondary minima. Coarse resolution versions of eddy-resolving models are used for this purpose. Identical twin experiments are performed with a quasigeostrophic model to evaluate the performance and limitations of this approach in improving models by estimating parameters. The wind stress curl is estimated from a simulated mean stream function. A very simple parameterization scheme for the assimilation of second-order moments is shown to permit the estimation of gradients that perform efficiently in minimizing cost functions.
    Type: Article , PeerReviewed
    Format: text
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