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  • 1
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    Copernicus Publications (EGU)
    In:  Geoscientific Model Development Discussions . pp. 1-38.
    Publication Date: 2019-01-17
    Description: We present a new near-global coupled biogeochemical ocean-circulation model configuration. The configuration features a horizontal discretization with a grid spacing of less than 11km in the Southern Ocean and gradually coarsens in meridional direction to more than 200km at 64°N where the model is bounded by a solid wall. The underlying code framework is GFDL's Modular Ocean Model coupled to the Biology Light Iron Nutrients and Gasses (BLING) ecosystem model of Galbraith et al. (2010). The configuration is cutting-edge in that it features both a relatively equilibrated oceanic carbon inventory and a realistic representation of eddy kinetic energy – a combination that has, to-date, been precluded by prohibitive computational cost. Results from a simulation with climatological forcing and a sensitivity experiment with increasing winds suggest that the configuration is suited to explore Southern Ocean Carbon uptake dynamics on decadal timescales. Further, the fidelity of simulated bottom water temperatures off and on the Antarctic Shelf suggest that the configuration may be used to provide boundary conditions to ice-sheet models. The configuration is dubbed MOMSO a Modular Ocean Model Southern Ocean configuration.
    Type: Article , NonPeerReviewed
    Format: text
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  • 2
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    PANGAEA
    In:  Supplement to: Löptien, Ulrike; Dietze, Heiner (2014): Sea ice in the Baltic Sea - revisiting BASIS ice, a historical data set covering the period 1960/1961 - 1978/1979. Earth System Science Data, 6(2), 367-374, https://doi.org/10.5194/essd-6-367-2014
    Publication Date: 2018-09-27
    Description: The Baltic Sea is a seasonally ice-covered, marginal sea in central northern Europe. It is an essential waterway connecting highly industrialised countries. Because ship traffic is intermittently hindered by sea ice, the local weather services have been monitoring sea ice conditions for decades. In the present study we revisit a historical monitoring data set, covering the winters 1960/1961 to 1978/1979. This data set, dubbed Data Bank for Baltic Sea Ice and Sea Surface Temperatures (BASIS) ice, is based on hand-drawn maps that were collected and then digitised in 1981 in a joint project of the Finnish Institute of Marine Research (today the Finnish Meteorological Institute (FMI)) and the Swedish Meteorological and Hydrological Institute (SMHI). BASIS ice was designed for storage on punch cards and all ice information is encoded by five digits. This makes the data hard to access. Here we present a post-processed product based on the original five-digit code. Specifically, we convert to standard ice quantities (including information on ice types), which we distribute in the current and free Network Common Data Format (NetCDF). Our post-processed data set will help to assess numerical ice models and provide easy-to-access unique historical reference material for sea ice in the Baltic Sea. In addition we provide statistics showcasing the data quality. The website http://www.baltic-ocean.org hosts the post-processed data and the conversion code.
    Type: Dataset
    Format: application/zip, 2572.0 kBytes
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  • 3
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    Copernicus Publications (EGU)
    In:  Biogeosciences (BG), 16 (9). pp. 1865-1881.
    Publication Date: 2019-05-10
    Description: Anthropogenic emissions of greenhouse gases such as CO2 and N2O impinge on the Earth system, which in turn modulates atmospheric greenhouse gas concentrations. The underlying feedback mechanisms are complex and, at times, counterintuitive. So-called Earth system models have recently matured to standard tools tailored to assess these feedback mechanisms in a warming world. Applications for these models range from being targeted at basic process understanding to the assessment of geo-engineering options. A problem endemic to all these applications is the need to estimate poorly known model parameters, specifically for the biogeochemical component, based on observational data (e.g., nutrient fields). In the present study, we illustrate with an Earth system model that through such an approach biases and other model deficiencies in the physical ocean circulation model component can reciprocally compensate for biases in the pelagic biogeochemical model component (and vice versa). We present two model configurations that share a remarkably similar steady state (based on ad hoc measures) when driven by historical boundary conditions, even though they feature substantially different configurations (parameter sets) of ocean mixing and biogeochemical cycling. When projected into the future the similarity between the model responses breaks. Metrics such as changes in total oceanic carbon content and suboxic volume diverge between the model configurations as the Earth warms. Our results reiterate that advancing the understanding of oceanic mixing processes will reduce the uncertainty of future projections of oceanic biogeochemical cycles. Related to the latter, we suggest that an advanced understanding of oceanic biogeochemical cycles can be used for advancements in ocean circulation modules.
    Type: Article , PeerReviewed
    Format: text
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  • 4
    Publication Date: 2014-05-05
    Repository Name: EPIC Alfred Wegener Institut
    Type: PANGAEA Documentation , NonPeerReviewed
    Format: application/zip
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  • 5
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    In:  [Talk] In: EGU General Assembly, 15.04, Vienna, Austria .
    Publication Date: 2012-02-23
    Type: Conference or Workshop Item , NonPeerReviewed
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  • 6
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    In:  [Talk] In: DYNAMITE-meeting, 07.02, Bologna, Italy .
    Publication Date: 2012-02-23
    Type: Conference or Workshop Item , NonPeerReviewed
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  • 7
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    In:  (Doctoral thesis/PhD), Christian-Albrechts-Universität, Kiel, Kiel, 98 pp
    Publication Date: 2018-11-02
    Description: Die Nordatlantische Oszillation (NAO) ist das dominierende Muster der atmosphärischen Zirkulation im Bereich des Nordatlantiks. Diese erklärt auf Zeitskalen von über einem Monat, besonders im Winter, einen Großteil der Varianz der atmosphärischen Zirkulation in diesem Gebiet. Eine bisher unbeantwortete Frage in diesem Kontext ist, ob die NAO ein rein zufälliges atmosphärische Phänomen ist oder ob sie auf bestimmten Zeitskalen durch andere Prozesse kontrolliert wird. In der vorliegenden Arbeit wird der Zusammenhang zwischen der NAO und dem Ozean näher untersucht. Der Zustand des Ozeans wird dabei durch die Meeresoberflächentemperatur (SST) repräsentiert. Von Interesse ist vor allem eine mögliche Rückwirkung von SST-Anomalien auf die NAO. Ein wichtiger Aspekt dieser Arbeit ist zudem ein verbessertes Verständnis des NAO-Musters. Es wird nachgewiesen, dass die Verteilung der synoptischen Systeme hinreichend ist, das NAO-Muster zu generieren, und auch dekadische Schwankungen hiermit erklärt werden können. Dies führt zu der Auffassung, dass die NAO hauptsächlich eine Verschiebung der Zyklonenzugbahnen widerspiegelt. Dieses deutlichere Bild der NAO gibt Anhaltspunkte für mögliche Mechanismen der Einwirkung von SST-Anomalien.
    Type: Thesis , NonPeerReviewed
    Format: text
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  • 8
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    American Meteorological Society, AMS
    In:  Monthly Weather Review, 133 (10). pp. 2894-2904.
    Publication Date: 2019-01-23
    Description: The North Atlantic Oscillation (NAO) represents the dominant mode of atmospheric variability in the North Atlantic region. In the present study, the role of the synoptic systems (cyclones and anticyclones) in generating the NAO pattern is investigated. To study the intermonthly variations of the NAO, NCEP–NCAR reanalysis data are used, and for the interdecadal variations the results of a 300-yr control integration under present-day conditions of the coupled model ECHAM4/OPYC3 are analyzed. A filtering method is developed for the sea level pressure anomalies. Application of this method to each grid point yields the low-frequency variability in the sea level pressure field that is due to the synoptic systems. The low-frequency variability of the filtered and the original data are in high agreement. This indicates that the low-frequency pressure variability, and with it the variability of the NAO, is essentially caused by the distribution of the synoptic systems. The idea that the distribution of the synoptic systems is the cause of the variation of the NAO is confirmed by high correlation between the latitudinal position of the polar front over the North Atlantic and the NAO index. Since most of the low-frequency variability in sea level pressure can be explained through the distribution of the synoptic systems, the NAO seems to be a reflection of the distribution of the synoptic systems, rather than the source for variations in the cyclone tracks.
    Type: Article , PeerReviewed
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  • 9
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    Elsevier
    In:  Ecological Modelling, 222 (8). pp. 1376-1386.
    Publication Date: 2017-02-21
    Description: Pelagic, coupled ocean circulation-ecosystem models, are widely used in climate research. These tools aim to quantify fluxes of nutrients and carbon in the ocean and are, increasingly, the base of future projections. For this purpose it is crucial to quantify and identify the sources of uncertainties. In contrast to physical models, the underlying equations for ecosystem models are derived from empirical relationships rather than based on first principles. This resulted in the development of a multitude of different ecosystem models – different in respect to both, underlying principles and complexity. Clearly, the question arises, to what extent the sensitivities of these models are comparable. This study focuses on the intrinsic dynamics of some widely used, simple (containing 2–3 prognostic variables) ecosystem models in a 0-D framework (i.e., comprising only the well-mixed oceanic surface layer). A suite of differing model approaches is tuned such that their behavior is similar. The setup resembles the well-mixed oceanic surface layer in the Baltic proper. It is illustrated that strong differences between the model approaches appear due to exemplary, anticipated changes in the external nutrient and light conditions. Herewith, we demonstrate the well-known, but rarely demonstrated fact that, apparent consistency between modeled prognostic variables with today's data bases is not necessarily a good measure of forecast skill. The causes which lead to the different sensitivities are illustrated by considering the steady state solutions. It is pointed out, that apparently small changes in the model formulations can result in very different dynamical behavior and an enormous spread between the model approaches, despite the feasibility to tune a common behavior in a limited range of light and nutrient supply. In our examples, the sensitivity is mainly a function of the formulation of the loss rate of phytoplankton. It is thus, in particular, the formulation of highly unknown heteorotrophic processes that determines the model sensitivity.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
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  • 10
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    In:  [Talk] In: 5. SAFEWIN assembly meeting, 26.-27.10.2011, St. Petersburg, Russia .
    Publication Date: 2012-08-06
    Type: Conference or Workshop Item , NonPeerReviewed
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