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Temperature assimilation into a coastal ocean-biogeochemical model: assessment of weakly and strongly coupled data assimilation

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Abstract

Satellite data of both physical properties as well as ocean colour can be assimilated into coupled ocean-biogeochemical models with the aim to improve the model state. The physical observations like sea surface temperature usually have smaller errors than ocean colour, but it is unclear how far they can also constrain the biogeochemical model variables. Here, the effect of assimilating satellite sea surface temperature into the coastal ocean-biogeochemical model HBM-ERGOM with nested model grids in the North and Baltic Seas is investigated. A weakly and strongly coupled assimilation is performed with an ensemble Kalman filter. For the weakly coupled assimilation, the assimilation only directly influences the physical variables, while the biogeochemical variables react only dynamically during the 12-hour forecast phases in between the assimilation times. For the strongly coupled assimilation, both the physical and biogeochemical variables are directly updated by the assimilation. The strongly coupled assimilation is assessed in two variants using the actual concentrations and the common approach to use the logarithm of the concentrations of the biogeochemical fields. In this coastal domain, both the weakly and strongly coupled assimilation are stable, but only if the actual concentrations are used for the strongly coupled case. Compared to the weakly coupled assimilation, the strongly coupled assimilation leads to stronger changes of the biogeochemical model fields. Validating the resulting field estimates with independent in situ data shows only a clear improvement for the temperature and for oxygen concentrations, while no clear improvement of other biogeochemical fields was found. The oxygen concentrations were more strongly improved with strongly coupled than weakly coupled assimilation. The experiments further indicate that for the strongly coupled assimilation of physical observations the biogeochemical fields should be used with their actual concentrations rather than the logarithmic concentrations.

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Acknowledgements

This work was carried out within the project MeRamo by the German Federal Ministry of Transportation and Digital Infrastructure (BMVI) through the German Aerospace Center (DLR). We thank the German Oceanographic Data Center and International Council for the Exploration of the Sea (ICES Dataset on Ocean Hydrography. The International Council for the Exploration of the Sea, Copenhagen. 2016) for providing the in situ data.

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Authors and Affiliations

  1. Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany

    Michael Goodliff & Lars Nerger

  2. Bundesamt für Seeschifffahrt und Hydrographie, Hamburg, Germany

    Thorger Bruening, Fabian Schwichtenberg, Xin Li, Anja Lindenthal & Ina Lorkowski

  3. Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, USA

    Michael Goodliff

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  1. Michael Goodliff
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  2. Thorger Bruening
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  3. Fabian Schwichtenberg
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  4. Xin Li
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  5. Anja Lindenthal
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  6. Ina Lorkowski
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  7. Lars Nerger
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Correspondence to Lars Nerger.

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Responsible Editor: Vassiliki Kourafalou

This article is part of the Topical Collection on Coastal Ocean Forecasting Science supported by the GODAE OceanView Coastal Oceans and Shelf Seas Task Team (COSS-TT) - Part II

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Goodliff, M., Bruening, T., Schwichtenberg, F. et al. Temperature assimilation into a coastal ocean-biogeochemical model: assessment of weakly and strongly coupled data assimilation. Ocean Dynamics 69, 1217–1237 (2019). https://doi.org/10.1007/s10236-019-01299-7

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