Articles | Volume 16, issue 2
https://doi.org/10.5194/os-16-355-2020
© 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-16-355-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Ensemble hindcasting of wind and wave conditions with WRF and WAVEWATCH III® driven by ERA5
Robert Daniel Osinski
CORRESPONDING AUTHOR
Leibniz Institute for Baltic Sea Research Warnemünde, Physical Oceanography and Instrumentation, Seestrasse 15, 18119 Rostock, Germany
Hagen Radtke
Leibniz Institute for Baltic Sea Research Warnemünde, Physical Oceanography and Instrumentation, Seestrasse 15, 18119 Rostock, Germany
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EGUsphere, https://doi.org/10.5194/egusphere-2025-4568, https://doi.org/10.5194/egusphere-2025-4568, 2025
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We used a computer model to study how warming affects biological and chemical processes in the Baltic Sea and controls nutrient cycling in its deep basins. We tested changes across the sea and only along the coast. In oxygen-poor waters, a small increase in the processes caused ammonium buildup and enhanced nitrogen removal. In the Bothnian Sea, the coastal zone had an outsized role, sometimes 2 to 4 times greater than basin-wide changes, altering nitrate, phosphate, and productivity.
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
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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.
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
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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.
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
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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
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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.
Cited articles
Ardhuin, F., H C Herbers, T., 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
Björkqvist, J.-V., Tuomi, L., Tollman, N., Kangas, A., Pettersson, H., Marjamaa, R., Jokinen, H., and Fortelius, C.: Brief communication: Characteristic properties of extreme wave events observed in the northern Baltic Proper, Baltic Sea, Nat. Hazards Earth Syst. Sci., 17, 1653–1658, https://doi.org/10.5194/nhess-17-1653-2017, 2017. a, b, c, d
Bouttier, F., Vié, B., Nuissier, O., and Raynaud, L.: Impact of Stochastic
Physics in a Convection-Permitting Ensemble, Mon. Weather Rev., 140,
3706–3721, https://doi.org/10.1175/MWR-D-12-00031.1,
2012. 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
Buizza, R.: Impact of Horizontal Diffusion on T21, T42, and T63 Singular
Vectors, J. Atmos. Sci., 55, 1069–1083,
https://doi.org/10.1175/1520-0469(1998)055<1069:IOHDOT>2.0.CO;2,
1998. 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, b
Buizza, R., Leutbecher, M., and Isaksen, L.: Potential use of an ensemble of
analyses in the ECMWF Ensemble Prediction System, Q. J. Roy. Meteor. Soc., 134, 2051–2066, https://doi.org/10.1002/qj.346,
2008. a
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#!/dataset/reanalysis-era5-single-levels?tab=overview (last access: 26 March 2019), 2017. a, b, c
Copernicus Climate Data Store: Complete UERRA regional reanalysis for Europe from 1961 to 2019, available at:
https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-complete?tab=overview
(last access: 10 March 2020), 2019. a
ECMWF: Part VII: ECMWF Wave Model, no. 7 in IFS Documentation, ECMWF,
available at: https://www.ecmwf.int/node/16651 (last access: 10 March 2020), 2016. a
Farina, L.: On ensemble prediction of ocean waves, Tellus A, 54, 148–158,
https://doi.org/10.1034/j.1600-0870.2002.01301.x,
2002. a
Gorman, R. M. and Oliver, H. J.: Automated model optimisation using the Cylc workflow engine (Cyclops v1.0), Geosci. Model Dev., 11, 2153–2173, https://doi.org/10.5194/gmd-11-2153-2018, 2018. a
Hagedorn, R., Doblas-Reyes, F. J., and Palmer, T. N.: The rationale behind the
success of multi-model ensembles in seasonal forecasting – I. Basic
concept, Tellus A, 57, 219–233, https://doi.org/10.1111/j.1600-0870.2005.00103.x,
2005. a
Hamill, T. M.: Interpretation of Rank Histograms for Verifying Ensemble
Forecasts, Mon. Weather Rev., 129, 550–560,
https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2,
2001. a
Hoffman, R. N. and Kalnay, E.: Lagged average forecasting, an alternative to
Monte Carlo forecasting, Tellus A, 35A, 100–118,
https://doi.org/10.1111/j.1600-0870.1983.tb00189.x,
1983. a, b
Jolliffe, I. T. and Stephenson, D. B.: Forecast Verification: A Practitioner's
Guide in Atmospheric Science, John Wiley & Sons Ltd.: Chichester, UK, 2003. a
Leutbecher, M., Lock, S.-J., Ollinahob, P., Lang, S. T. K., Balsamo, G.,
Bechtold, P., Bonavita, M., Christensenc, H. M., Diamantakis, M., Dutra, E.,
English, S., Fisher, M., Forbes, R. M., Goddard, J., Haiden, T., Hogan,
R. J., Jurickec, S., Lawrence, H., MacLeodc, D., Magnusson, L., Malardel, S.,
Massart, S., Sandu, I., Smolarkiewicz, P. K., Subramanianc, A., Vitart, F.,
Wedi, N., and Weisheimer, A.: Stochastic representations of
modeluncertainties at ECMWF: State of the art and future vision, Tech. rep.,
European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading,
RG2 9AX, England, 2016. a
Leutbecher, M., Lock, S.-J., Ollinaho, P., Lang, S. T. K., Balsamo, G.,
Bechtold, P., Bonavita, M., Christensen, H. M., Diamantakis, M., Dutra, E.,
English, S., Fisher, M., Forbes, R. M., Goddard, J., Haiden, T., Hogan,
R. J., Juricke, S., Lawrence, H., MacLeod, D., Magnusson, L., Malardel, S.,
Massart, S., Sandu, I., Smolarkiewicz, P. K., Subramanian, A., Vitart, F.,
Wedi, N., and Weisheimer, A.: Stochastic representations of model
uncertainties at ECMWF: state of the art and future vision, Q. J.
Roy. Meteor. Soc., 143, 2315–2339, https://doi.org/10.1002/qj.3094,
2017. a
Lorenz, E. N.: Deterministic Nonperiodic Flow, J. Atmos. Sci., 20, 130–141, https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2,
1963. a
Murphy, A. H. and Winkler, R. L.: Diagnostic verification of probability
forecasts, Int. J. Forecast., 7, 435–455,
1992. a
Nuissier, O., Joly, B., Vié, B., and Ducrocq, V.: Uncertainty of lateral boundary conditions in a convection-permitting ensemble: a strategy of selection for Mediterranean heavy precipitation events, Nat. Hazards Earth Syst. Sci., 12, 2993–3011, https://doi.org/10.5194/nhess-12-2993-2012, 2012. a
Ollinaho, P., Lock, S.-J., Leutbecher, M., Bechtold, P., Beljaars, A., Bozzo,
A., Forbes, R. M., Haiden, T., Hogan, R. J., and Sandu, I.: Towards
process-level representation of model uncertainties: stochastically perturbed
parametrizations in the ECMWF ensemble, Q. J. Roy. Meteor. Soc., 143, 408–422, https://doi.org/10.1002/qj.2931,
2017. a
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
Osinski, R., Dalphinet, A., Aouf, L., and Palany, P.: Estimation of the
hundred year return level of the significant wave height for the French
Guiana coast, Braz. J. Oceanogr., 66, 325–334,
2018. a
Pardowitz, T., Befort, D. J., Leckebusch, G. C., and Ulbrich, U.: Estimating
uncertainties from high resolution simulations of extreme wind storms and
consequences for impacts, Meteorol. Z., 25, 531–541,
https://doi.org/10.1127/metz/2016/0582,
2016. a, b
Peckham, S. E., Smirnova, T. G., Benjamin, S. G., Brown, J. M., and Kenyon,
J. S.: Implementation of a Digital Filter Initialization in the WRF Model and
Its Application in the Rapid Refresh, Mon. Weather Rev., 144, 99–106,
https://doi.org/10.1175/MWR-D-15-0219.1, 2016. a
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using
Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133, 1155–1174, https://doi.org/10.1175/MWR2906.1, 2005. a
Raynaud, L. and Bouttier, F.: The impact of horizontal resolution and ensemble
size for convective-scale probabilistic forecasts, Q. J. Roy. Meteor. Soc., 143, 3037–3047, https://doi.org/10.1002/qj.3159,
2017. a
Ricchi, A., Miglietta, M. M., Bonaldo, D., Cioni, G., Rizza, U., and Carniel,
S.: Multi-Physics Ensemble versus Atmosphere–Ocean Coupled Model
Simulations for a Tropical-Like Cyclone in the Mediterranean Sea, Atmosphere,
10, 2020, https://doi.org/10.3390/atmos10040202,
2019. 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: 10 March 2020),
2017. a
Seifert, T., Tauber, F., and Kayser, B.: A high resolution spherical grid
topography of the Baltic Sea – 2nd edition, in: Baltic Sea Science Congress,
Stockholm, 25–29 November 2001, Poster No. 147,
available at: https://www.io-warnemuende.de/topography-of-the-baltic-sea.html (last access: 10 March 2020), 2001. 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, b
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, https://doi.org/10.5065/1dfh-6p97, 2019. a, b
Soomere, T., Behrens, A., Tuomi, L., and Nielsen, J. W.: Wave conditions in the Baltic Proper and in the Gulf of Finland during windstorm Gudrun, Nat. Hazards Earth Syst. Sci., 8, 37–46, https://doi.org/10.5194/nhess-8-37-2008, 2008. a
Soomere, T., Weisse, R., and Behrens, A.: Wave climate in the Arkona Basin, the Baltic Sea, Ocean Sci., 8, 287–300, https://doi.org/10.5194/os-8-287-2012, 2012. a, b
Swinbank, R., Kyouda, M., Buchanan, P., Froude, L., Hamill, T. M., Hewson,
T. D., Keller, J. H., Matsueda, M., Methven, J., Pappenberger, F., Scheuerer,
M., Titley, H. A., Wilson, L., and Yamaguchi, M.: The TIGGE Project and Its
Achievements, B. Am. Meteor. Soc., 97, 49–67,
https://doi.org/10.1175/BAMS-D-13-00191.1,
2016. a
The WAVEWATCH III® Development Group (WW3DG):
User manual and system documentation of WAVEWATCH
III® version 6.07. Tech. Note 333, 2019. a
Theis, S. E., Hense, A., and Damrath, U.: Probabilistic precipitation forecasts
from a deterministic model: a pragmatic approach, Meteor.
Appl., 12, 257–268, https://doi.org/10.1017/S1350482705001763,
2005. 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
Toth, Z. and Kalnay, E.: Ensemble Forecasting at NCEP and the Breeding Method,
Mon. Weather Rev., 125, 3297–3319,
https://doi.org/10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2,
1997.
a
Tuomi, L., Kahma, K., and Pettersson, H.: Wave hindcast statistics in the
seasonally ice-covered Baltic Sea, Boreal Environ. Res., 16, 451–472,
2011. a
Tuomi, L., Pettersson, H., Fortelius, C., Tikka, K., Björkqvist, J.-V., and
Kahma, K. K.: Wave modelling in archipelagos, Coast. Eng., 83,
205–220, https://doi.org/10.1016/j.coastaleng.2013.10.011,
2014. a, b
Wang, W., Bruyère, C., Duda, M., Dudhia, J., Gill, D., Kavulich, M., Werner,
K., Chen, M., Lin, H.-C., Michalakes, J., Rizvi, S., Zhang, X., Berner, J.,
Munoz-Esparza, D., Reen, B., and Fossel, S. H. K.: User’s Guide for the
Advanced Research WRF (ARW) Modeling System, Version 4,
available at: http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V4/WRFUsersGuide.pdf (last access: 10 March 2020),
2019. a
Yildirim, B. and Karniadakis, G. E.: Stochastic simulations of ocean waves: An
uncertainty quantification study, Ocean Model., 86, 15–35,
https://doi.org/10.1016/j.ocemod.2014.12.001,
2015. a
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.
The idea of this study is to quantify the uncertainty in hindcasts of severe storm events by...