Articles | Volume 16, issue 6
https://doi.org/10.5194/os-16-1491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-16-1491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Dec 2020
Research article |
| 03 Dec 2020
| 03 Dec 2020
Model uncertainties of a storm and their influence on microplastics and sediment transport in the Baltic Sea
Robert Daniel Osinski
Leibniz Institute for Baltic Sea Research Warnemünde, Seestrasse 15, 18119 Rostock, Germany
Kristina Enders
Leibniz Institute for Baltic Sea Research Warnemünde, Seestrasse 15, 18119 Rostock, Germany
Ulf Gräwe
Leibniz Institute for Baltic Sea Research Warnemünde, Seestrasse 15, 18119 Rostock, Germany
Knut Klingbeil
Leibniz Institute for Baltic Sea Research Warnemünde, Seestrasse 15, 18119 Rostock, Germany
Hagen Radtke
CORRESPONDING AUTHOR
Leibniz Institute for Baltic Sea Research Warnemünde, Seestrasse 15, 18119 Rostock, Germany
Related authors
Robert Daniel Osinski and Hagen Radtke
Ocean Sci., 16, 355–371, https://doi.org/10.5194/os-16-355-2020, https://doi.org/10.5194/os-16-355-2020, 2020
Short summary
Short summary
The idea of this study is to quantify the uncertainty in hindcasts of severe storm events by applying a state-of-the-art ensemble generation technique. Other ensemble generation techniques are tested. The atmospheric WRF model is driven by the ERA5 reanalysis. A setup of the Wavewatch III® wave model for the Baltic Sea is used with the wind fields produced with the WRF ensemble. The effect of different spatio-temporal resolutions of the wind fields on the significant wave height is investigated.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dimitry Sidorenko
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-208, https://doi.org/10.5194/gmd-2023-208, 2024
Preprint under review for GMD
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of sea-ice model FESOM2. The alternative we propose allows for the model to capture and simulate more accurately fast changes in quantities like sea-surface-elevation. We also demonstrate that the new alternative is faster and is more adept in taking advantages of highly parallelised computing infrastructure. We show that this new alternative can in future became a great addition to the sea-ice model FESOM2.
Marvin Lorenz and Ulf Gräwe
Ocean Sci., 19, 1753–1771, https://doi.org/10.5194/os-19-1753-2023, https://doi.org/10.5194/os-19-1753-2023, 2023
Short summary
Short summary
We study the variability of extreme sea levels in a 13 member hindcast ensemble for the Baltic Sea. The ensemble mean shows good agreement with observations regarding return levels and trends. However, we find great variability and uncertainty within the ensemble. We argue that the variability of storms in the atmospheric data directly translates into the variability of the return levels. These results highlight the need for large regional ensembles to minimise uncertainties.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Joshua Kiesel, Marvin Lorenz, Marcel König, Ulf Gräwe, and Athanasios T. Vafeidis
Nat. Hazards Earth Syst. Sci., 23, 2961–2985, https://doi.org/10.5194/nhess-23-2961-2023, https://doi.org/10.5194/nhess-23-2961-2023, 2023
Short summary
Short summary
Among the Baltic Sea littoral states, Germany is anticipated to experience considerable damage as a result of increased coastal flooding due to sea-level rise (SLR). Here we apply a new modelling framework to simulate how flooding along the German Baltic Sea coast may change until 2100 if dikes are not upgraded. We find that the study region is highly exposed to flooding, and we emphasise the importance of current plans to update coastal protection in the future.
Bronwyn E. Cahill, Piotr Kowalczuk, Lena Kritten, Ulf Gräwe, John Wilkin, and Jürgen Fischer
Biogeosciences, 20, 2743–2768, https://doi.org/10.5194/bg-20-2743-2023, https://doi.org/10.5194/bg-20-2743-2023, 2023
Short summary
Short summary
We quantify the impact of optically significant water constituents on surface heating rates and thermal energy fluxes in the western Baltic Sea. During productive months in 2018 (April to September) we found that the combined effect of coloured
dissolved organic matter and particulate absorption contributes to sea surface heating of between 0.4 and 0.9 K m−1 d−1 and a mean loss of heat (ca. 5 W m−2) from the sea to the atmosphere. This may be important for regional heat balance budgets.
Pia Kolb, Anna Zorndt, Hans Burchard, Ulf Gräwe, and Frank Kösters
Ocean Sci., 18, 1725–1739, https://doi.org/10.5194/os-18-1725-2022, https://doi.org/10.5194/os-18-1725-2022, 2022
Short summary
Short summary
River engineering measures greatly changed tidal dynamics in the Weser estuary. We studied the effect on saltwater intrusion with numerical models. Our analysis shows that a deepening of the navigation channel causes saltwater to intrude further into the Weser estuary. This effect is mostly masked by the natural variability of river discharge. In our study, it proved essential to recalibrate individual hindcast models due to differences in sediments, bed forms, and underlying bathymetric data.
Matthias Gröger, Manja Placke, H. E. Markus Meier, Florian Börgel, Sandra-Esther Brunnabend, Cyril Dutheil, Ulf Gräwe, Magnus Hieronymus, Thomas Neumann, Hagen Radtke, Semjon Schimanke, Jian Su, and Germo Väli
Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
Short summary
Short summary
Comparisons of oceanographic climate data from different models often suffer from different model setups, forcing fields, and output of variables. This paper provides a protocol to harmonize these elements to set up multidecadal simulations for the Baltic Sea, a marginal sea in Europe. First results are shown from six different model simulations from four different model platforms. Topical studies for upwelling, marine heat waves, and stratification are also assessed.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
Short summary
Short summary
Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Vera Fofonova, Tuomas Kärnä, Knut Klingbeil, Alexey Androsov, Ivan Kuznetsov, Dmitry Sidorenko, Sergey Danilov, Hans Burchard, and Karen Helen Wiltshire
Geosci. Model Dev., 14, 6945–6975, https://doi.org/10.5194/gmd-14-6945-2021, https://doi.org/10.5194/gmd-14-6945-2021, 2021
Short summary
Short summary
We present a test case of river plume spreading to evaluate coastal ocean models. Our test case reveals the level of numerical mixing (due to parameterizations used and numerical treatment of processes in the model) and the ability of models to reproduce complex dynamics. The major result of our comparative study is that accuracy in reproducing the analytical solution depends less on the type of applied model architecture or numerical grid than it does on the type of advection scheme.
Jens Daniel Müller, Bernd Schneider, Ulf Gräwe, Peer Fietzek, Marcus Bo Wallin, Anna Rutgersson, Norbert Wasmund, Siegfried Krüger, and Gregor Rehder
Biogeosciences, 18, 4889–4917, https://doi.org/10.5194/bg-18-4889-2021, https://doi.org/10.5194/bg-18-4889-2021, 2021
Short summary
Short summary
Based on profiling pCO2 measurements from a field campaign, we quantify the biomass production of a cyanobacteria bloom in the Baltic Sea, the export of which would foster deep water deoxygenation. We further demonstrate how this biomass production can be accurately reconstructed from long-term surface measurements made on cargo vessels in combination with modelled temperature profiles. This approach enables a better understanding of a severe concern for the Baltic’s good environmental status.
Tobias Peter Bauer, Peter Holtermann, Bernd Heinold, Hagen Radtke, Oswald Knoth, and Knut Klingbeil
Geosci. Model Dev., 14, 4843–4863, https://doi.org/10.5194/gmd-14-4843-2021, https://doi.org/10.5194/gmd-14-4843-2021, 2021
Short summary
Short summary
We present the coupled atmosphere–ocean model system ICONGETM. The added value and potential of using the latest coupling technologies are discussed in detail. An exchange grid handles the different coastlines from the unstructured atmosphere and the structured ocean grids. Due to a high level of automated processing, ICONGETM requires only minimal user input. The application to a coastal upwelling scenario demonstrates significantly improved model results compared to uncoupled simulations.
Qing Li, Jorn Bruggeman, Hans Burchard, Knut Klingbeil, Lars Umlauf, and Karsten Bolding
Geosci. Model Dev., 14, 4261–4282, https://doi.org/10.5194/gmd-14-4261-2021, https://doi.org/10.5194/gmd-14-4261-2021, 2021
Short summary
Short summary
Different ocean vertical mixing schemes are usually developed in different modeling framework, making the comparison across such schemes difficult. Here, we develop a consistent framework for testing, comparing, and applying different ocean mixing schemes by integrating CVMix into GOTM, which also extends the capability of GOTM towards including the effects of ocean surface waves. A suite of test cases and toolsets for developing and evaluating ocean mixing schemes is also described.
Erik Jacobs, Henry C. Bittig, Ulf Gräwe, Carolyn A. Graves, Michael Glockzin, Jens D. Müller, Bernd Schneider, and Gregor Rehder
Biogeosciences, 18, 2679–2709, https://doi.org/10.5194/bg-18-2679-2021, https://doi.org/10.5194/bg-18-2679-2021, 2021
Short summary
Short summary
We use a unique data set of 8 years of continuous carbon dioxide (CO2) and methane (CH4) surface water measurements from a commercial ferry to study upwelling in the Baltic Sea. Its seasonality and regional and interannual variability are examined. Strong upwelling events drastically increase local surface CO2 and CH4 levels and are mostly detected in late summer after long periods of impaired mixing. We introduce an extrapolation method to estimate regional upwelling-induced trace gas fluxes.
Onur Kerimoglu, Yoana G. Voynova, Fatemeh Chegini, Holger Brix, Ulrich Callies, Richard Hofmeister, Knut Klingbeil, Corinna Schrum, and Justus E. E. van Beusekom
Biogeosciences, 17, 5097–5127, https://doi.org/10.5194/bg-17-5097-2020, https://doi.org/10.5194/bg-17-5097-2020, 2020
Short summary
Short summary
In this study, using extensive field observations and a numerical model, we analyzed the physical and biogeochemical structure of a coastal system following an extreme flood event. Our results suggest that a number of anomalous observations were driven by a co-occurrence of peculiar meteorological conditions and increased riverine discharges. Our results call for attention to the combined effects of hydrological and meteorological extremes that are anticipated to increase in frequency.
Hagen Radtke, Sandra-Esther Brunnabend, Ulf Gräwe, and H. E. Markus Meier
Clim. Past, 16, 1617–1642, https://doi.org/10.5194/cp-16-1617-2020, https://doi.org/10.5194/cp-16-1617-2020, 2020
Short summary
Short summary
During the last century, salinity in the Baltic Sea showed a multidecadal oscillation with a period of 30 years. Using a numerical circulation model and wavelet coherence analysis, we demonstrate that this variation has at least two possible causes. One driver is river runoff which shows a 30-year variation. The second one is a variation in the frequency of strong inflows of saline water across Darss Sill which also contains a pronounced 30-year period.
Robert Daniel Osinski and Hagen Radtke
Ocean Sci., 16, 355–371, https://doi.org/10.5194/os-16-355-2020, https://doi.org/10.5194/os-16-355-2020, 2020
Short summary
Short summary
The idea of this study is to quantify the uncertainty in hindcasts of severe storm events by applying a state-of-the-art ensemble generation technique. Other ensemble generation techniques are tested. The atmospheric WRF model is driven by the ERA5 reanalysis. A setup of the Wavewatch III® wave model for the Baltic Sea is used with the wind fields produced with the WRF ensemble. The effect of different spatio-temporal resolutions of the wind fields on the significant wave height is investigated.
Daniel Neumann, Matthias Karl, Hagen Radtke, Volker Matthias, René Friedland, and Thomas Neumann
Ocean Sci., 16, 115–134, https://doi.org/10.5194/os-16-115-2020, https://doi.org/10.5194/os-16-115-2020, 2020
Short summary
Short summary
The study evaluates how much bioavailable nitrogen is contributed to the nitrogen budget of the western Baltic Sea by deposition of shipping-emitted nitrogen oxides. Bioavailable nitrogen compounds are nutrients for phytoplankton (algae). Excessive input of nutrients into water bodies may lead to eutrophication: more algal blooms with subsequently more oxygen limitation at the seafloor. Hence, reducing shipping emissions might reduce the anthropogenic pressure on the marine ecosystem.
Marvin Lorenz, Knut Klingbeil, Parker MacCready, and Hans Burchard
Ocean Sci., 15, 601–614, https://doi.org/10.5194/os-15-601-2019, https://doi.org/10.5194/os-15-601-2019, 2019
Short summary
Short summary
Estuaries are areas where riverine and oceanic waters meet and mix. The exchange flow of an estuary describes the water properties of the inflowing and outflowing water. These can be described by simple bulk values for volume fluxes and salinities. This work focuses on the numerics of one computational method for these values, the Total Exchange Flow. We show that only the so-called dividing salinity method is able to reliably calculate the correct values, even for complex situations.
Hagen Radtke, Marko Lipka, Dennis Bunke, Claudia Morys, Jana Woelfel, Bronwyn Cahill, Michael E. Böttcher, Stefan Forster, Thomas Leipe, Gregor Rehder, and Thomas Neumann
Geosci. Model Dev., 12, 275–320, https://doi.org/10.5194/gmd-12-275-2019, https://doi.org/10.5194/gmd-12-275-2019, 2019
Short summary
Short summary
This paper describes a coupled benthic–pelagic biogeochemical model, ERGOM-SED. We demonstrate its use in a one-dimensional physical model, which is horizontally integrated and vertically resolved. We describe the application of the model to seven stations in the south-western Baltic Sea. The model was calibrated using pore water profiles from these stations. We compare the model results to these and to measured sediment compositions, benthopelagic fluxes and bioturbation intensities.
Beate Stawiarski, Stefan Otto, Volker Thiel, Ulf Gräwe, Natalie Loick-Wilde, Anna K. Wittenborn, Stefan Schloemer, Janine Wäge, Gregor Rehder, Matthias Labrenz, Norbert Wasmund, and Oliver Schmale
Biogeosciences, 16, 1–16, https://doi.org/10.5194/bg-16-1-2019, https://doi.org/10.5194/bg-16-1-2019, 2019
Short summary
Short summary
The understanding of surface water methane production in the world oceans is still poor. By combining field studies and incubation experiments, our investigations suggest that zooplankton contributes to subthermocline methane enrichments in the central Baltic Sea by methane production within the digestive tract of copepods and/or by methane production through release of methane precursor substances into the surrounding water, followed by microbial degradation to methane.
Daniel Neumann, Hagen Radtke, Matthias Karl, and Thomas Neumann
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-365, https://doi.org/10.5194/bg-2018-365, 2018
Publication in BG not foreseen
Short summary
Short summary
The contribution of atmospheric nitrogen deposition to the marine dissolved inorganic nitrogen (DIN) pool of the North and Baltic Sea was assessed for the year 2012. Atmospheric deposition accounted for approximately 10 % to 15 % of the DIN but its residence time differed between both water bodies. The nitrogen contributions of atmospheric shipping and agricultural imissions also were assessed. Particularly the latter source had a large impact in coastal regions.
Daniel Neumann, Matthias Karl, Hagen Radtke, and Thomas Neumann
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-364, https://doi.org/10.5194/bg-2018-364, 2018
Manuscript not accepted for further review
Short summary
Short summary
Atmospheric nitrogen deposition contributes 20 % to 40 % to bioavailable nitrogen inputs into the North Sea and Baltic Sea. Excessive bioavailable nitrogen may lead to intensified algal blooms in these water bodies resulting in several negative consequences for the marine ecosystem. We traced atmospheric nitrogen in the marine ecosystem via an ecosystem model and estimated the contribution of atmospheric nitrogen to plankton biomass in different regions of the North and Baltic Sea over five years.
Daniel Neumann, René Friedland, Matthias Karl, Hagen Radtke, Volker Matthias, and Thomas Neumann
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-71, https://doi.org/10.5194/os-2018-71, 2018
Revised manuscript not accepted
Short summary
Short summary
We found that refining the spatial resolution of nitrogen deposition data had low impact on marine nitrogen compounds compared to the impact by nitrogen deposition data sets of different origin (other model). The shipping sector had a contribution of up to 10 % to the marine dissolved inorganic nitrogen.
Joeran Maerz, Richard Hofmeister, Eefke M. van der Lee, Ulf Gräwe, Rolf Riethmüller, and Kai W. Wirtz
Biogeosciences, 13, 4863–4876, https://doi.org/10.5194/bg-13-4863-2016, https://doi.org/10.5194/bg-13-4863-2016, 2016
Short summary
Short summary
We investigated sinking velocity (ws) of suspended particulate matter (SPM) in the German Bight. By inferring ws indirectly from an extensive turbidity data set and hydrodynamic model results, we found enhanced ws in a coastal transition zone. Combined with known residual circulation patterns, this led to a new conceptual understanding of the retention of fine minerals and nutrients in shallow coastal areas. The retention is likely modulated by algal excretions enhancing flocculation of SPM.
Rahel Vortmeyer-Kley, Ulf Gräwe, and Ulrike Feudel
Nonlin. Processes Geophys., 23, 159–173, https://doi.org/10.5194/npg-23-159-2016, https://doi.org/10.5194/npg-23-159-2016, 2016
Short summary
Short summary
Since eddies play a major role in the dynamics of oceanic flows, it is of great interest to gain information about their tracks, lifetimes and shapes. We develop an eddy tracking tool based on structures in the flow with collecting (attracting) or separating (repelling) properties. In test cases mimicking oceanic flows it yields eddy lifetimes close to the analytical ones. It even provides a detailed view of the dynamics that can be useful to gain more insight into eddy dynamics in oceanic flows.
M. Duran-Matute, T. Gerkema, G. J. de Boer, J. J. Nauw, and U. Gräwe
Ocean Sci., 10, 611–632, https://doi.org/10.5194/os-10-611-2014, https://doi.org/10.5194/os-10-611-2014, 2014
Cited articles
Andrady, A. L.: Microplastics in the marine environment, Mar. Pollut.
Bull., 62, 1596–1605,
2011. a
Andrady, A. L. and Neal, M. A.: Applications and societal benefits of
plastics., Philos. T. R. Soc. Lon. B, 64, 1977–84, 2009. a
Ardhuin, F., Herbers, T. H. C., O'Reilly, W., and Jessen, P.: Swell
Transformation across the Continental Shelf. Part I: Attenuation and
Directional Broadening, J. Phys. Oceanogr., 33, 1921,
https://doi.org/10.1175/1520-0485(2003)033<1921:STATCS>2.0.CO;2, 2003. a
Ardhuin, F., Rogers, E., Babanin, A. V., Filipot, J.-F., Magne, R., Roland, A.,
van der Westhuysen, A., Queffeulou, P., Lefevre, J.-M., Aouf, L., and
Collard, F.: Semiempirical Dissipation Source Functions for Ocean Waves. Part
I: Definition, Calibration, and Validation, J. Phys. Oceanogr.,
40, 1917–1941, https://doi.org/10.1175/2010JPO4324.1, 2010. a
Baart, F., van Gelder, P. H. A. J. M., and van Koningsveld, M.: Confidence in
real-time forecasting of morphological storm impacts, J. Coastal
Res., 64, 1835–1839, 2011. a
Bagaev, A., Mizyuk, A., Khatmullina, L., Isachenko, I., and Chubarenko, I.:
Anthropogenic fibres in the Baltic Sea water column: Field data, laboratory
and numerical testing of their motion, Sci. Total Environ.,
599/600, 560–571, https://doi.org/10.1016/j.scitotenv.2017.04.185, 2017. a
Ballent, A., Pando, S., Purser, A., Juliano, M. F., and Thomsen, L.: Modelled transport of benthic marine microplastic pollution in the Nazaré Canyon, Biogeosciences, 10, 7957–7970, https://doi.org/10.5194/bg-10-7957-2013, 2013. a
Bartholomä, A., Kubicki, A., Badewien, T. H., and Flemming, B. W.: Suspended
sediment transport in the German Wadden Sea–seasonal variations and extreme
events, Ocean Dynam., 59, 213–225,
https://doi.org/10.1007/s10236-009-0193-6, 2009. a
Besseling, E., Redondo-Hasselerharm, P., Foekema, E. M., and Koelmans, A. A.:
Quantifying ecological risks of aquatic micro- and nanoplastic, Crit.
Rev. Env. Sci. Tec., 49, 32–80,
https://doi.org/10.1080/10643389.2018.1531688, 2019. a
Brankart, J.-M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P.-A., Brasseur, P., and Verron, J.: A generic approach to explicit simulation of uncertainty in the NEMO ocean model, Geosci. Model Dev., 8, 1285–1297, https://doi.org/10.5194/gmd-8-1285-2015, 2015. a
Bruggeman, J. and Bolding, K.: A general framework for aquatic biogeochemical
models, Environ. Modell. Softw., 61, 249–265,
https://doi.org/10.1016/j.envsoft.2014.04.002, 2014. a
Buizza, R.: Introduction to the special issue on “25 years of ensemble
forecasting”, Q. J. Roy. Meteor. Soc., 145 (Suppl. 1), 1–11,
https://doi.org/10.1002/qj.3370, 2018. a
Buizza, R., Milleer, M., and Palmer, T. N.: Stochastic representation of model
uncertainties in the ECMWF ensemble prediction system, Q. J. Roy. Meteor. Soc., 125, 2887–2908,
https://doi.org/10.1002/qj.49712556006, 1999. a
Bunke, D., Leipe, T., Moros, M., Morys, C., Tauber, F., Virtasalo, J. J.,
Forster, S., and Arz, H. W.: Natural and Anthropogenic Sediment Mixing
Processes in the South-Western Baltic Sea, Front. Mar.
Sci., 6, 677, https://doi.org/10.3389/fmars.2019.00677,
2019. a
Burchard, H. and Bolding, K.: GETM – a General Estuarine Transport Model,
Scientific Documentation, Tech. Rep. EUR 20253 EN, JRC23237, European
Commission, 155 pp., 2002. a
Burchard, H., Bolding, K., and Villarreal, M. R.: GOTM – a General Ocean
Turbulence Model. Theory, implementation and test cases, Tech. Rep. EUR
18745 EN, European Commission, 103 pp., 1999. a
Chubarenko, I. and Stepanova, N.: Microplastics in sea coastal zone: Lessons
learned from the Baltic amber, Environ. Pollut., 224, 243–254,
https://doi.org/10.1016/j.envpol.2017.01.085, 2017. a, b
Copernicus Climate Change Service (C3S): ERA5: Fifth generation of ECMWF
atmospheric reanalyses of the global climate, Copernicus Climate Change
Service Climate Data Store (CDS), available at: https://cds.climate.copernicus.eu/cdsapp#!/home (last access: 26 March 2019), 2017. a
Copernicus: Climate Data Store, available at: https://cds.climate.copernicus.eu, last access: 30 November 2020.
EMODnet: Map viewer Geology – seabed substrate, available at:
https://www.emodnet-geology.eu/map-viewer/?p=seabed_substrate (last access: 30 November 2020),
2020. a
Enders, K., Käppler, A., Biniasch, O., Feldens, P., Stollberg, N., Lange, X.,
Fischer, D., Eichhorn, K.-J., Pollehne, F., Oberbeckmann, S., and Labrenz,
M.: Tracing microplastics in aquatic environments based on sediment
analogies, Sci. Rep.-UK, 9, 15207,
https://doi.org/10.1038/s41598-019-50508-2, 2019. a, b, c, d
Gräwe, U., Holtermann, P. L., Klingbeil, K., and Burchard, H.: Advantages of
vertically adaptive coordinates in numerical models of stratified shelf
seas, Ocean Model., 92, 56–68, https://doi.org/10.1016/j.ocemod.2015.05.008, 2015. a
Hofmeister, R., Burchard, H., and Beckers, J.-M.: Non-uniform adaptive
vertical grids for 3D numerical ocean models, Ocean Model., 33, 70–86,
https://doi.org/10.1016/j.ocemod.2009.12.003, 2010. a
Huerta Lwanga, E., Gertsen, H., Gooren, H., Peters, P., Salánki, T., van der
Ploeg, M., Besseling, E., Koelmans, A. A., and Geissen, V.: Microplastics in
the Terrestrial Ecosystem: Implications for Lumbricus terrestris
(Oligochaeta, Lumbricidae), Environ. Sci. Technol., 50, 2685–2691,
https://doi.org/10.1021/acs.est.5b05478, 2016. a
Jalón-Rojas, I., Wang, X.-H., and Fredj, E.: Technical note: On the importance of a three-dimensional approach for modelling the transport of neustic microplastics, Ocean Sci., 15, 717–724, https://doi.org/10.5194/os-15-717-2019, 2019a. a
Jalón-Rojas, I., Wang, X. H., and Fredj, E.: A 3D numerical model to Track
Marine Plastic Debris (TrackMPD): Sensitivity of microplastic trajectories
and fates to particle dynamical properties and physical processes, Mar. Pollut. Bull., 141, 256–272, 2019b. a
Khalaf, H. K.: The theoretical investigation of drag coefficient and settling
velocity correlations, PhD thesis, Nahrain University, Nahrain, Iraq, 138 pp.,
2009. a
Khatmullina, L. and Isachenko, I.: Settling velocity of microplastic particles
of regular shapes, Mar. Pollut. Bull., 114, 871–880,
https://doi.org/10.1016/j.marpolbul.2016.11.024, 2017. a
Klingbeil, K.: GETM & Co., available at: https://getm.io-warnemuende.de (last accessed: 30 November 2020), 2020.
Klingbeil, K. and Burchard, H.: Implementation of a direct nonhydrostatic
pressure gradient discretisation into a layered ocean model, Ocean
Model., 65, 64–77, https://doi.org/10.1016/j.ocemod.2013.02.002, 2013. a
Klingbeil, K., Mohammadi-Aragh, M., Gräwe, U., and Burchard, H.:
Quantification of spurious dissipation and mixing – Discrete Variance Decay
in a Finite-Volume framework, Ocean Model., 81, 49–64,
https://doi.org/10.1016/j.ocemod.2014.06.001, 2014. a
Klingbeil, K., Lemarié, F., Debreu, L., and Burchard, H.: The numerics of
hydrostatic structured-grid coastal ocean models: state of the art and future
perspectives, Ocean Model., 125, 80–105,
https://doi.org/10.1016/j.ocemod.2018.01.007, 2018. a
Kondo, J.: Air-sea bulk transfer coefficients in diabatic conditions,
Bound.-Lay. Meteorol., 9, 91–112, https://doi.org/10.1007/BF00232256, 1975. a
Kriaučiūnienė, J., Gailiušis, B., and Kovalenkovienė, M.: Peculiarities of
sea wave propagation in the Klaipėda Strait, Lithuania, available at:
http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=D6B2487835980E6828B2FD6D4D63DB9D?doi=10.1.1.133.7480&rep=rep1&type=pdf (last access: 30 November 2020),
1961. a
Kundu, P. K. and Cohen, I. M.: Fluid Mechanics, Academic Press, San Diego, USA, 2001. a
Liubartseva, S., Coppini, G., Lecci, R., and Clementi, E.: Tracking plastics in
the Mediterranean: 2D Lagrangian model, Mar. Pollut. Bull., 129, 151–162, https://doi.org/10.1016/j.marpolbul.2018.02.019, 2018. a
NOAA Environmental Modeling Center (EMC): WW3, GitHub repository, available at: https://github.com/NOAA-EMC/WW3, last accessed: 30 November 2020.
Osinski, R. and Bouttier, F.: Short-range probabilistic forecasting of
convective risks for aviation based on a lagged-average-forecast ensemble
approach, Meteorol.l Appl., 25, 105–118, https://doi.org/10.1002/met.1674,
2018. a
Osinski, R. D. and Radtke, H.: Ensemble hindcasting of wind and wave conditions with WRF and WAVEWATCH III® driven by ERA5, Ocean Sci., 16, 355–371, https://doi.org/10.5194/os-16-355-2020, 2020. a, b, c, d
Osinski, R., Lorenz, P., Kruschke, T., Voigt, M., Ulbrich, U., Leckebusch, G. C., Faust, E., Hofherr, T., and Majewski, D.: An approach to build an event set of European windstorms based on ECMWF EPS, Nat. Hazards Earth Syst. Sci., 16, 255–268, https://doi.org/10.5194/nhess-16-255-2016, 2016. a
Pappenberger, F., Bartholmes, J., Thielen, J., Cloke, H. L., Buizza, R., and
de Roo, A.: New dimensions in early flood warning across the globe using
grand-ensemble weather predictions, Geophys. Res. Lett., 35, L10404,
https://doi.org/10.1029/2008GL033837, 2008. a
PlasticsEurope: Plastics – the Facts 2019, Tech. rep., PlasticsEurope
Deutschland e. V., available at: https://www.plasticseurope.org/application/files/6015/7908/8734/Plastics_the_facts_2019.pdf (last access: 30 November 2020),
2019. a
Radtke, H., Brunnabend, S.-E., Gräwe, U., and Meier, H. E. M.: Investigating interdecadal salinity changes in the Baltic Sea in a 1850–2008 hindcast simulation, Clim. Past, 16, 1617–1642, https://doi.org/10.5194/cp-16-1617-2020, 2020. a
Ridal, M., Olsson, E., Unden, P., Zimmermann, K., and Ohlsson, A.:
Uncertainties in Ensembles of Regional Re-Analyses – Deliverable D2.7
HARMONIE reanalysis report of results and dataset, available at:
http://www.uerra.eu/component/dpattachments/?task=attachment.download&id=296 (last access: 30 November 2020),
2017. a
Rijn, L. C. V.: Sediment transport, part II: suspended load transport,
J. Hydraul.Eng., 110, 1613–1641, 1984. a
Sassi, M., Duran-Matute, M., van Kessel, T., and Gerkema, T.: Variability of
residual fluxes of suspended sediment in a multiple tidal-inlet system: the
Dutch Wadden Sea, Ocean Dynam., 65, 1321–1333,
https://doi.org/10.1007/s10236-015-0866-2, 2015. a, b
Seifert, T., Tauber, F., and Kayser, B.: A high resolution spherical grid
topography of the Baltic Sea, 2nd Edn., Baltic Sea Science Congress, Stockholm, Sweden, 25–29 November 2001, 147, 2001. a
Shields, A. R.: Application of similarity principles and turbulence research to bed-load movement, https://authors.library.caltech.edu/25992/1/Sheilds.pdf
(last access: 30 November 2020),
translated by: Ott, W. P. and van Uchelen, J. C.,
1936. a
Shutts, G.: A kinetic energy backscatter algorithm for use in ensemble
prediction systems, Q. J. Roy. Meteor. Soc.,
131, 3079–3102, https://doi.org/10.1256/qj.04.106, 2005. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J.,
Wang, W., Powers, J. G., Duda, M. G., Barker, D., and Yu Huang, X.: A
Description of the Advanced Research WRF Model Version 4, Tech. rep.,
NCAR/TN-556+STR, National Center for Atmospheric Research, Boulder, Colorado, USA, 162 pp., 2019. a, b
Smagorinsky, J.: General Circulation Experiments with the Primitive
Equations, Mon. Weather Rev., 91, 99–164, 1963. a
Stokes, G. G.: On the Effect of the Internal Friction of Fluids on the
Motion of Pendulums, Transactions of the Cambridge Philosophical Society, 9, 8–106, 1851. a
Stuparu, D., der Meulen, M. V., Kleissen, F., Vethaak, D., and el Serafy, G.:
Developing a transport model for plastic distribution in the North Sea, 36th
IAHR World Congress, The Hague, The Netherlands, 28 June–3 July 2015, available at: https://research.vu.nl/en/publications/developing-a-transport-model-for-plastic-distribution-in-the-nort
(last access: 30 November 2020), 2015. a
Taylor, J. W. and Buizza, R.: Using weather ensemble predictions in electricity
demand forecasting, Int. J. Forecasting, 19, 57–70,
https://doi.org/10.1016/S0169-2070(01)00123-6, 2003.
a
The Local: Thousands without power and traffic disrupted as 2019's first
storm hits Sweden, availabe at:
https://www.thelocal.se/20190102/ (last access: 30 November 2020),
2019. a
The WAVEWATCH III Development Group (WW3DG):
User manual and system documentation of WAVEWATCH
III version 6.07. Tech. Note 333, NOAA/NWS/NCEP/MMAB, College Park, MD, USA, 465 pp. + Appendices, available at: https://raw.githubusercontent.com/wiki/NOAA-EMC/WW3/files/manual.pdf (30 November 2020), 2019. a
Tolman, H. L.: A Third-Generation Model for Wind Waves on Slowly Varying,
Unsteady, and Inhomogeneous Depths and Currents, J. Phys. Oceanogr., 21, 782–797,
https://doi.org/10.1175/1520-0485(1991)021<0782:ATGMFW>2.0.CO;2, 1991. a
Umlauf, L. and Burchard, H.: Second-order turbulence closure models for
geophysical boundary layers. A review of recent work, Cont. Shelf
Res., 25, 795–827, https://doi.org/10.1016/j.csr.2004.08.004, 2005. a
University Corporation for Atmospheric Research (UCAR): WRF, GitHub repository, available at: https://github.com/wrf-model/WRF/releases, last access: 30 November 2020.
Waldschläger, K. and Schüttrumpf, H.: Effects of Particle Properties on
the Settling and Rise Velocities of Microplastics in Freshwater
under Laboratory Conditions, Environ. Sci. Tech., 53,
1958–1966, https://doi.org/10.1021/acs.est.8b06794, 2019. a
Watkins, E., ten Brink, P., Sirini Withana, M. K., Daniela Russi, K. M.,
Schweitzer, J.-P., and Gitti, G.: The socio-economic impacts of marine
litter, including the costs of policy inaction and action, in: Handbook on
the Economics and Management of Sustainable Oceans, edited by: Nunes, P. A.,
Svensson, L. E., and Markandya, A., Edward Elgar Publishing, Cheltenham, UK, 296–319, 2017. a
Wessel, P. and Smith, W. H. F.: A global, self-consistent, hierarchical,
high-resolution shoreline database, J. Geophys. Res.-Sol. Ea., 101, 8741–8743, https://doi.org/10.1029/96JB00104, 1996. a
Zambresky, L.: A verification study of the global WAM model December 1987– November 1988, availabe at: https://www.ecmwf.int/node/13201 (last access: 30 November 2020), 1989. a
Ziervogel, K. and Bohling, B.: Sedimentological parameters and erosion
behaviour of submarine coastal sediments in the south-western Baltic Sea,
Geo-Mar. Lett., 23, 43–52, https://doi.org/10.1007/s00367-003-0123-4, 2003. a
Short summary
This study investigates the impact of the uncertainty in atmospheric data of a storm event on the transport of microplastics and sediments. The model chain includes the WRF atmospheric model, the WAVEWATCH III® wave model, and the GETM regional ocean model as well as a sediment transport model based on the FABM framework. An ensemble approach based on stochastic perturbations of the WRF model is used. We found a strong impact of atmospheric uncertainty on the amount of transported material.
This study investigates the impact of the uncertainty in atmospheric data of a storm event on...
