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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. 
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Research article
02 Mar 2018
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
Received: 18 Aug 2017 – Discussion started: 06 Sep 2017
Revised: 21 Dec 2017 – Accepted: 08 Jan 2018 – Published: 02 Mar 2018
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 150–200 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.
Citation: Sumata, H., Kauker, F., Karcher, M., Rabe, B., Timmermans, M.-L., Behrendt, A., Gerdes, R., Schauer, U., Shimada, K., Cho, K.-H., and Kikuchi, T.: Decorrelation scales for Arctic Ocean hydrography – Part I: Amerasian Basin, Ocean Sci., 14, 161-185, https://doi.org/10.5194/os-14-161-2018, 2018.
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
Citation: Sumata, H., Kauker, F., Karcher, M., Rabe, B., Timmermans, M.-L., Behrendt, A., Gerdes, R., Schauer, U., Shimada, K., Cho, K.-H., and Kikuchi, T.: Decorrelation scales for Arctic Ocean hydrography – Part I: Amerasian Basin, Ocean Sci., 14, 161-185, https://doi.org/10.5194/os-14-161-2018, 2018.
