Description: GPS track of albatros MD03i drifters with drag anchors in about 0.9m to 2.0m depth deployed during Havariekommando oil pollution combatting exercise in the offshore wind farm.

Description: In this methodical work, we investigate the applicability of two approaches to determine the biologically induced variations of marine pCO2. The first method was proposed by Takahashi et al. (1993), who used data of temperature and pCO2 in the North Atlantic to linearize the temperature dependency of pCO2. We compare the Takahashi method with a superposition approach by using model results. The superposition approach assumes that biological, chemical and physical influences on the partial pressure of carbon dioxide are adding up. Thus, the biologically induced pCO2 variations are calculated as the difference between a standard run with an ecosystem model and a run in which only physical and chemical processes are taken into account. Both methods agree reasonably for a station in the northern North Sea, characterized by weak dissolved inorganic carbon (DIC) gradients and moderate biological production. The deviations between both approaches are larger for a station in the southern North Sea, characterized by stronger DIC gradients and higher biological activities. In this area, the Takahashi approach has not only the problem that sinks and sources of DIC mask the biologically induced pCO2 variations but also that the constant for the linearization (lp = 0.0423) has to be changed. We show that during strong events which increase the pH (〉 8.3) the pCO2 could not be determined correctly with this method. In all other cases, the use of the Takahashi constant is valid. Locally and temporally calculated constants, which substitute lp, lead to deviations in the calculated pCO2 smaller than 2 µatm

Description: The effect of satellite sea surface temperature assimilation on the forecast quality of the coastal ocean-biogeochemical model HBM-ERGOM in the North- and Baltic Seas is studied. The HBM-ERGOM model is currently used operationally without data assimilation by the German Federal Maritime and Hydrographic Agency (BSH). The model is configured with nested grids with a resolution of 5 km in the North- and Baltic Seas and a resolution of 900 m in the German coastal waters. The biogeochemical model ERGOM contains three phytoplankton groups (Cyanobacteria, Flagellates, Diatoms) and two zooplankton size groups to simulated the biogeochemical cycling in the coastal seas. To improve the predictions of the HBM-ERGOM model, data assimilation was added by coupling the model to the parallel data assimilation framework (PDAF, http://pdaf.awi.de). The ensemble-based error-subspace transform Kalman filter (ESTKF) is applied for the data assimilation. As a first step to improve the biogeochemical forecasts, before the planned assimilation of ocean color data products, the impact of assimilating satellite sea surface temperature data is assessed. Two cases are considered. First, the impact of weakly coupled data assimilation. In this case, the assimilation of temperature only directly influences the physical model variables in the analysis step while the biogeochemical fields react dynamically to the changed physical model state during the ensemble forecasts using the coupled model. The second case is the strongly-coupled data assimilation in which next to the physical model fields also the biogeochemical fields are directly updated in the analysis step through the multivariate covariances estimated by the joined physical-biogeochemical ensemble of model states. Here, it is assessed whether these covariances are sufficiently well estimated to result in an improvement of the biogeochemical fields.

Description: Data assimilation combines observational data with numerical simulation models. The methodology allos to improve the initialization of model predictions, determining model deficiencies, but also to enhance data sets by augmenting the data with dynamical information from numerical models simulating e.g. ocean physics or biogeochemistry. This combination can fill data gaps by an interpolation which accounts for the dynamical information provided by the numerical model. Further the observed information can be used to improve unobserved variables, and even fluxes. This is accomplished through the use of dynamically estimated cross-covariances between the observed and unobserved variables. The assimilation can result in data sets which, at the resolution of the model, exhibit smaller errors than using the observations or the model alone. I will discuss the method of ensemble-based data assimilation on the example of ocean-biogoechemical modeling with the assimilation of satellite ocean color data.

Description: Modellvorhersagen helfen die Datenbasis für die behördlichen Berichtspflichen für die Meeresstrategierahmenrichtlinie zu verbessern. Um die Qualität der Modellvorhersagen zu weiter zu verbessern kann der Modellzustand mit Beobachtungsdaten kombiniert werden. Dieses wird in quantitativer Weise durch Methoden der Datenassimilation vorgenommen. Im Rahmen des Projektes MeRamo wurde das Vorhersagemodell des Bundesamtes für Seeschifffahrt und Hydrographie für den kombinierten ozean-ökosystem Zustand in der Nord- und Ostsee mit Beobachtungsdaten des Satelliten Sentinel-3a sowie Satelliten der amerikanischen Behörde NOAA mit Hilfe der Datenassimilation kombiniert. Hierdurch wird die Simulation sowohl des physikalische Zustands (wie Temperatur und Salzgehalt) als auch ökologischer Größen wie Nährstoffe oder Planktonkonzentrationen beeinflusst. Im Vortrag wird die verwendete Datenassimilationsmethodik diskutiert und der Einfluss der Assimilation auf den Meereszustand, vor allem in Hinblick auf mögliche Indikatoren für die Meeresstrategierahmenrichtlinie betrachtet.