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  • 1
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    Decorrelation scales for Arctic Ocean hydrogpraphy - Part I: Amerasian Basin (2018)
    COPERNICUS GESELLSCHAFT MBH
    In:  EPIC3Ocean Science, COPERNICUS GESELLSCHAFT MBH, 14, pp. 161-185, ISSN: 1812-0784
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    Publication Date: 2018-03-06
    Description: Any use of observational data for data assimilation requires adequate information of their representativeness in space and time. This is particularly important for sparse, non-synoptic data, which comprise the bulk of oceanic in situ observations in the Arctic. To quantify spatial and temporal scales of temperature and salinity variations, we estimate the autocorrelation function and associated decorrelation scales for the Amerasian Basin of the Arctic Ocean. For this purpose, we compile historical measurements from 1980 to 2015. Assuming spatial and temporal homogeneity of the decorrelation scale in the basin interior (abyssal plain area), we calculate autocorrelations as a function of spatial distance and temporal lag. The examination of the functional form of autocorrelation in each depth range reveals that the autocorrelation is well described by a Gaussian function in space and time. We derive decorrelation scales of 150–200 km in space and 100–300 days in time. These scales are directly applicable to quantify the representation error, which is essential for use of ocean in situ measurements in data assimilation. We also describe how the estimated autocorrelation function and decorrelation scale should be applied for cost function calculation in a data assimilation system.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 2
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    A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model (2013)
    COPERNICUS GESELLSCHAFT MBH
    In:  EPIC3Ocean Science, COPERNICUS GESELLSCHAFT MBH, 9(4), pp. 609-630, ISSN: 1812-0784
    Details
    Publication Date: 2019-07-16
    Description: Two types of optimization methods were applied to a parameter optimization problem in a coupled ocean--sea ice model of the Arctic, and applicability and efficiency of the respective methods were examined. One optimization utilizes a finite difference (FD) method based on a traditional gradient descent approach, while the other adopts a micro-genetic algorithm (\unit{\mu}GA) as an example of a stochastic approach. The opt\imizations were performed by minimizing a cost function composed of model--data misfit of ice concentration, ice drift velocity and ice thickness. A series of optimizations were conducted that differ in the model formulation (``smoothed code'' versus standard code) with respect to the FD method and in the population size and number of possibilities with respect to the \unit{\mu}GA method. The FD method fails to estimate optimal parameters due to the ill-shaped nature of the cost function caused by the strong non-linearity of the system, whereas the genetic algorithms can effectively estimate near optimal parameters. The results of the study indicate that the sophisticated stochastic approach (\unit{\mu}GA) is of practical use for parameter optimization of a coupled ocean--sea ice model with a medium-sized horizontal resolution of 50\,km\,$\times$\,50\,km as used in this study.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 3
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    Arctic Ocean freshwater: How robust are model simulations? (2012)
    AMER GEOPHYSICAL UNION
    In:  EPIC3Journal of Geophysical Research-Oceans, AMER GEOPHYSICAL UNION, 117(C00D16), ISSN: 0148-0227
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    Publication Date: 2016-11-29
    Description: The Arctic freshwater (FW) has been the focus of many modeling studies, due to the potential impact of Arctic FW on the deep water formation in the North Atlantic. A comparison of the hindcasts from ten ocean-sea ice models shows that the simulation of the Arctic FW budget is quite different in the investigated models. While they agree on the general sink and source terms of the Arctic FW budget, the long-term means as well as the variability of the FW export vary among models. The best model-to-model agreement is found for the interannual and seasonal variability of the solid FW export and the solid FW storage, which also agree well with observations. For the interannual and seasonal variability of the liquid FW export, the agreement among models is better for the Canadian Arctic Archipelago (CAA) than for Fram Strait. The reason for this is that models are more consistent in simulating volume flux anomalies than salinity anomalies and volume-flux anomalies dominate the liquid FW export variability in the CAA but not in Fram Strait. The seasonal cycle of the liquid FW export generally shows a better agreement among models than the interannual variability, and compared to observations the models capture the seasonality of the liquid FW export rather well. In order to improve future simulations of the Arctic FW budget, the simulation of the salinity field needs to be improved, so that model results on the variability of the liquid FW export and storage become more robust.
    Repository Name: EPIC Alfred Wegener Institut
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  • 4
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    Impact of Sea-Ice Model Complexity on the Performance of an Unstructured-Mesh Sea-Ice/Ocean Model under Different Atmospheric Forcings (2021)
    AMER GEOPHYSICAL UNION
    In:  EPIC3Journal of Advances in Modeling Earth Systems, AMER GEOPHYSICAL UNION, 13(5), pp. e2020MS002438-e2020MS002438, ISSN: 1942-2466
    Details
    Publication Date: 2024-02-13
    Description: Abstract We have equipped the unstructured-mesh global sea-ice and ocean model FESOM2 with a set of physical parameterizations derived from the single-column sea-ice model Icepack. The update has substantially broadened the range of physical processes that can be represented by the model. The new features are directly implemented on the unstructured FESOM2 mesh, and thereby benefit from the flexibility that comes with it in terms of spatial resolution. A subset of the parameter space of three model configurations, with increasing complexity, has been calibrated with an iterative Green's function optimization method to test the impact of the model update on the sea-ice representation. Furthermore, to explore the sensitivity of the results to different atmospheric forcings, each model configuration was calibrated separately for the NCEP-CFSR/CFSv2 and ERA5 forcings. The results suggest that a complex model formulation leads to a better agreement between modeled and the observed sea-ice concentration and snow thickness, while differences are smaller for sea-ice thickness and drift speed. However, the choice of the atmospheric forcing also impacts the agreement of the FESOM2 simulations and observations, with NCEP-CFSR/CFSv2 being particularly beneficial for the simulated sea-ice concentration and ERA5 for sea-ice drift speed. In this respect, our results indicate that parameter calibration can better compensate for differences among atmospheric forcings in a simpler model (i.e., sea-ice has no heat capacity) than in more realistic formulations with a prognostic sea-ice thickness distribution and sea ice enthalpy.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev , info:eu-repo/semantics/article
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