Ocean Dynamics (2019) 69:1217–1237 https://doi.org/10.1007/s10236-019-01299-7 Temperature assimilation into a coastal ocean-biogeochemical model: assessment of weakly and strongly coupled data assimilation Michael Goodli?1,3 · Thorger Bruening2 · Fabian Schwichtenberg2 · Xin Li2 · Anja Lindenthal2 · Ina Lorkowski2 · Lars Nerger1 Received: 29 January 2019 / Accepted: 7 August 2019 / Published online: 5 September 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019 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. Keywords Data assimilation · Biogeochemistry · North Sea · Baltic Sea 1 Introduction In recent years, ocean forecasting has become more common, e.g. with the European Copernicus Marine Environment Monitoring Service (CMEMS). In Germany, the Federal 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  Lars Nerger lars.nerger@awi.de Extended author information available on the last page of the article. Maritime and Hydrographic Agency (BSH) operates a forecasting system for the North and Baltic Seas based on the HIROMB-BOOS model (HBM, see, e.g. Bruening et al. 2014). The national monitoring duties, e.g. to fulfil the European Marine Strategy Framework Directive (MSFD) require monitoring the seas with regard to water quality, and hence also for the ecosystem. Given that in situ observations are sparse and hence insufficient for the monitoring, the extension of forecast models with an ecosystem component is required. A coupled ocean-biogeochemical model, which simulates phytoplankton and nutrients, can represent, e.g. eutrophication, but can potentially also predict harmful algal blooms. To initialise model forecasts, different observations can be assimilated. Satellite observations, e.g. of temperature or