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Volume 14, issue 1 | Copyright
Ocean Sci., 14, 161-185, 2018
https://doi.org/10.5194/os-14-161-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Mar 2018

Research article | 02 Mar 2018

Decorrelation scales for Arctic Ocean hydrography – Part I: Amerasian Basin

Hiroshi Sumata1, Frank Kauker1,2, Michael Karcher1,2, Benjamin Rabe1, Mary-Louise Timmermans3, Axel Behrendt1, Rüdiger Gerdes1,4, Ursula Schauer1, Koji Shimada5, Kyoung-Ho Cho6, and Takashi Kikuchi7 Hiroshi Sumata et al.
  • 1Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
  • 2Ocean Atmosphere Systems, Hamburg, Germany
  • 3Yale University, Department of Geology and Geophysics, New Haven, CT, USA
  • 4Jacobs University, Physics and Earth Sciences, Bremen, Germany
  • 5Tokyo University of Marine Science and Technology, Tokyo, Japan
  • 6Korea Polar Research Institute, Incheon, South Korea
  • 7Japan Agency for Marine-Earth Science and Technology, Yokosuka, Japan

Abstract. 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 150–200km 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.

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We estimated spatial and temporal decorrelation scales of temperature and salinity in the Amerasian Basin in the Arctic Ocean. The estimated scales can be applied to representation error assessment in the ocean data assimilation system for the Arctic Ocean.
We estimated spatial and temporal decorrelation scales of temperature and salinity in the...
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