Articles | Volume 14, issue 8
https://doi.org/10.5194/gmd-14-4843-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-4843-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
04 Aug 2021
Model description paper |
| 04 Aug 2021
| 04 Aug 2021
ICONGETM v1.0 – flexible NUOPC-driven two-way coupling via ESMF exchange grids between the unstructured-grid atmosphere model ICON and the structured-grid coastal ocean model GETM
Tobias Peter Bauer
CORRESPONDING AUTHOR
Leibniz Institute for Tropospheric Research (TROPOS), Permoserstraße 15, 04318 Leipzig, Germany
Leibniz Institute for Baltic Sea Research Warnemünde (IOW), Seestraße 15, 18119 Rostock, Germany
Peter Holtermann
Leibniz Institute for Baltic Sea Research Warnemünde (IOW), Seestraße 15, 18119 Rostock, Germany
Bernd Heinold
Leibniz Institute for Tropospheric Research (TROPOS), Permoserstraße 15, 04318 Leipzig, Germany
Hagen Radtke
Leibniz Institute for Baltic Sea Research Warnemünde (IOW), Seestraße 15, 18119 Rostock, Germany
Oswald Knoth
Leibniz Institute for Tropospheric Research (TROPOS), Permoserstraße 15, 04318 Leipzig, Germany
Knut Klingbeil
Leibniz Institute for Baltic Sea Research Warnemünde (IOW), Seestraße 15, 18119 Rostock, Germany
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EGUsphere, https://doi.org/10.5194/egusphere-2025-4374, https://doi.org/10.5194/egusphere-2025-4374, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Using model simulations, we studied convective weather events to see how urban aerosol emissions influence cloud microphysics and precipitation. By tracing urban air masses from convective clouds back to their emission sources, we could isolate the effects of emissions. The results show a significant influence of urban emissions. Depending on the weather, urban emissions can either delay, enhance, or suppress precipitation, highlighting cities' complex role in shaping local rainfall.
Moritz Zeising, Laurent Oziel, Silke Thoms, Özgür Gürses, Judith Hauck, Bernd Heinold, Svetlana N. Losa, Manuela van Pinxteren, Christoph Völker, Sebastian Zeppenfeld, and Astrid Bracher
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Sofía Gómez Maqueo Anaya, Dietrich Althausen, Julian Hofer, Moritz Haarig, Ulla Wandinger, Bernd Heinold, Ina Tegen, Matthias Faust, Holger Baars, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Atmos. Chem. Phys., 25, 9737–9764, https://doi.org/10.5194/acp-25-9737-2025, https://doi.org/10.5194/acp-25-9737-2025, 2025
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This study investigates how hematite in Sahara dust affects how dust particles interact with radiation. Using lidar data from Cabo Verde (2021–2022) and hematite content from atmospheric model simulations, the results show that a higher hematite fraction leads to a decrease in the particle backscattering coefficients in a spectrally different way. These findings can improve the representation of mineral dust in climate models, particularly regarding their radiative effect.
Daniel L. Pönisch, Henry C. Bittig, Martin Kolbe, Ingo Schuffenhauer, Stefan Otto, Peter Holtermann, Kusala Premaratne, and Gregor Rehder
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Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025, https://doi.org/10.5194/gmd-18-4183-2025, 2025
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Anisbel Leon-Marcos, Manuela van Pinxteren, Sebastian Zeppenfeld, Moritz Zeising, Astrid Bracher, Laurent Oziel, Ina Tegen, and Bernd Heinold
EGUsphere, https://doi.org/10.5194/egusphere-2025-2829, https://doi.org/10.5194/egusphere-2025-2829, 2025
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Jamie R. Banks, Bernd Heinold, and Kerstin Schepanski
Atmos. Chem. Phys., 24, 11451–11475, https://doi.org/10.5194/acp-24-11451-2024, https://doi.org/10.5194/acp-24-11451-2024, 2024
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The Aralkum is a new desert in Central Asia formed by the desiccation of the Aral Sea. This has created a source of atmospheric dust, with implications for the balance of solar and thermal radiation. Simulating these effects using a dust transport model, we find that Aralkum dust adds radiative cooling effects to the surface and atmosphere on average but also adds heating events. Increases in surface pressure due to Aralkum dust strengthen the Siberian High and weaken the summer Asian heat low.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
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In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Andreas Walbröl, Janosch Michaelis, Sebastian Becker, Henning Dorff, Kerstin Ebell, Irina Gorodetskaya, Bernd Heinold, Benjamin Kirbus, Melanie Lauer, Nina Maherndl, Marion Maturilli, Johanna Mayer, Hanno Müller, Roel A. J. Neggers, Fiona M. Paulus, Johannes Röttenbacher, Janna E. Rückert, Imke Schirmacher, Nils Slättberg, André Ehrlich, Manfred Wendisch, and Susanne Crewell
Atmos. Chem. Phys., 24, 8007–8029, https://doi.org/10.5194/acp-24-8007-2024, https://doi.org/10.5194/acp-24-8007-2024, 2024
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To support the interpretation of the data collected during the HALO-(AC)3 campaign, which took place in the North Atlantic sector of the Arctic from 7 March to 12 April 2022, we analyze how unusual the weather and sea ice conditions were with respect to the long-term climatology. From observations and ERA5 reanalysis, we found record-breaking warm air intrusions and a large variety of marine cold air outbreaks. Sea ice concentration was mostly within the climatological interquartile range.
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.
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Matthias Faust, Holger Baars, Bernd Heinold, Julian Hofer, Ina Tegen, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
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Mineral dust aerosol particles vary greatly in their composition depending on source region, which leads to different physicochemical properties. Most atmosphere–aerosol models consider mineral dust aerosols to be compositionally homogeneous, which ultimately increases model uncertainty. Here, we present an approach to explicitly consider the heterogeneity of the mineralogical composition for simulations of the Saharan atmospheric dust cycle with regard to dust transport towards the Atlantic.
Julia Muchowski, Martin Jakobsson, Lars Umlauf, Lars Arneborg, Bo Gustafsson, Peter Holtermann, Christoph Humborg, and Christian Stranne
Ocean Sci., 19, 1809–1825, https://doi.org/10.5194/os-19-1809-2023, https://doi.org/10.5194/os-19-1809-2023, 2023
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We show observational data of highly increased mixing and vertical salt flux rates in a sparsely sampled region of the northern Baltic Sea. Co-located acoustic observations complement our in situ measurements and visualize turbulent mixing with high spatial resolution. The observed mixing is generally not resolved in numerical models of the area but likely impacts the exchange of water between the adjacent basins as well as nutrient and oxygen conditions in the Bothnian Sea.
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.
Michael Weger and Bernd Heinold
Atmos. Chem. Phys., 23, 13769–13790, https://doi.org/10.5194/acp-23-13769-2023, https://doi.org/10.5194/acp-23-13769-2023, 2023
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This study investigates the effects of complex terrain on air pollution trapping using a numerical model which simulates the dispersion of emissions under real meteorological conditions. The additionally simulated aerosol age allows us to distinguish areas that accumulate aerosol over time from areas that are more influenced by fresh emissions. The Dresden Basin, a widened section of the Elbe Valley in eastern Germany, is selected as the target area in a case study to demonstrate the concept.
Suvarna Fadnavis, Bernd Heinold, T. P. Sabin, Anne Kubin, Katty Huang, Alexandru Rap, and Rolf Müller
Atmos. Chem. Phys., 23, 10439–10449, https://doi.org/10.5194/acp-23-10439-2023, https://doi.org/10.5194/acp-23-10439-2023, 2023
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The influence of the COVID-19 lockdown on the Himalayas caused increases in snow cover and a decrease in runoff, ultimately leading to an enhanced snow water equivalent. Our findings highlight that, out of the two processes causing a retreat of Himalayan glaciers – (1) slow response to global climate change and (2) fast response to local air pollution – a policy action on the latter is more likely to be within the reach of possible policy action to help billions of people in southern Asia.
Fabian Senf, Bernd Heinold, Anne Kubin, Jason Müller, Roland Schrödner, and Ina Tegen
Atmos. Chem. Phys., 23, 8939–8958, https://doi.org/10.5194/acp-23-8939-2023, https://doi.org/10.5194/acp-23-8939-2023, 2023
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Wildfire smoke is a significant source of airborne atmospheric particles that can absorb sunlight. Extreme fires in particular, such as those during the 2019–2020 Australian wildfire season (Black Summer fires), can considerably affect our climate system. In the present study, we investigate the various effects of Australian smoke using a global climate model to clarify how the Earth's atmosphere, including its circulation systems, adjusted to the extraordinary amount of Australian smoke.
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.
Bernd Heinold, Holger Baars, Boris Barja, Matthew Christensen, Anne Kubin, Kevin Ohneiser, Kerstin Schepanski, Nick Schutgens, Fabian Senf, Roland Schrödner, Diego Villanueva, and Ina Tegen
Atmos. Chem. Phys., 22, 9969–9985, https://doi.org/10.5194/acp-22-9969-2022, https://doi.org/10.5194/acp-22-9969-2022, 2022
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The extreme 2019–2020 Australian wildfires produced massive smoke plumes lofted into the lower stratosphere by pyrocumulonimbus convection. Most climate models do not adequately simulate the injection height of such intense fires. By combining aerosol-climate modeling with prescribed pyroconvective smoke injection and lidar observations, this study shows the importance of the representation of the most extreme wildfire events for estimating the atmospheric energy budget.
Michael Weger, Holger Baars, Henriette Gebauer, Maik Merkel, Alfred Wiedensohler, and Bernd Heinold
Geosci. Model Dev., 15, 3315–3345, https://doi.org/10.5194/gmd-15-3315-2022, https://doi.org/10.5194/gmd-15-3315-2022, 2022
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Numerical models are an important tool to assess the air quality in cities,
as they can provide near-continouos data in time and space. In this paper,
air pollution for an entire city is simulated at a high spatial resolution of 40 m.
At this spatial scale, the effects of buildings on the atmosphere,
like channeling or blocking of the air flow, are directly represented by diffuse obstacles in the used model CAIRDIO. For model validation, measurements from air-monitoring sites are used.
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
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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.
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
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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.
Michael Weger, Oswald Knoth, and Bernd Heinold
Geosci. Model Dev., 14, 1469–1492, https://doi.org/10.5194/gmd-14-1469-2021, https://doi.org/10.5194/gmd-14-1469-2021, 2021
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A new numerical air-quality transport model for cities is presented, in which buildings are described diffusively. The used diffusive-obstacles approach helps to reduce the computational costs for high-resolution simulations as the grid spacing can be more coarse than in traditional approaches. The research which led to this model development was primarily motivated by the need for a computationally feasible downscaling tool for urban wind and pollution fields from meteorological model output.
Robert Daniel Osinski, Kristina Enders, Ulf Gräwe, Knut Klingbeil, and Hagen Radtke
Ocean Sci., 16, 1491–1507, https://doi.org/10.5194/os-16-1491-2020, https://doi.org/10.5194/os-16-1491-2020, 2020
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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.
Cited articles
Balaji, V., Anderson, J., Held, I., Winton, M., Durachta, J., Malyshev, S., and
Stouffer, R. J.: The Exchange Grid: A mechanism for data exchange between
Earth System components on independent grids, in: Parallel Computational
Fluid Dynamics 2005, Elsevier, 179–186,
https://doi.org/10.1016/B978-044452206-1/50021-5, 2006. a, b
Barron, C. N., Kara, A. B., Martin, P. J., Rhodes, R. C., and Smedstad, L. F.:
Formulation, implementation and examination of vertical coordinate choices
in the Global Navy Coastal Ocean Model (NCOM), Ocean Model., 11,
347–375, https://doi.org/10.1016/j.ocemod.2005.01.004, 2006. a
Bauer, T. P.: The atmosphere model ICON as used in the two-way coupled atmosphere-ocean model ICONGETM (Version v2.5), Geoscientific Model Development, Zenodo, https://doi.org/10.5281/zenodo.4432739, 2021. a
Bauer, T. P. and Klingbeil, K.: ICONGETM v1.0 – Flexible NUOPC-driven two-way coupling via ESMF exchange grids between the unstructured-grid atmosphere model ICON and the structured-grid coastal ocean model GETM (Version v1.0), Geoscientific Model Development, Zenodo, https://doi.org/10.5281/zenodo.4516568, 2021. a
Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M.,
Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating
atmospheric variability with the ECMWF model: From synoptic to decadal
time-scales, Q. J. Roy. Meteor. Soc., 134,
1337–1351, https://doi.org/10.1002/qj.289, 2008. a
Best, M. J., Beljaars, A., Polcher, J., and Viterbo, P.: A Proposed Structure
for Coupling Tiled Surfaces with the Planetary Boundary Layer, J.
Hydrometeorol., 5, 1271–1278, https://doi.org/10.1175/JHM-382.1, 2004. a
Booij, N., Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model
for coastal regions: 1. Model description and validation, J.
Geophys. Res.-Oceans, 104, 7649–7666, https://doi.org/10.1029/98JC02622, 1999. a
Borchert, S., Zhou, G., Baldauf, M., Schmidt, H., Zängl, G., and Reinert, D.: The upper-atmosphere extension of the ICON general circulation model (version: ua-icon-1.0), Geosci. Model Dev., 12, 3541–3569, https://doi.org/10.5194/gmd-12-3541-2019, 2019. a
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly, A.:
Unified Modeling and Prediction of Weather and Climate: A 25-Year Journey,
B. Am. Meteorol. Soc., 93, 1865–1877,
https://doi.org/10.1175/BAMS-D-12-00018.1, 2012. a
Carniel, S., Benetazzo, A., Bonaldo, D., Falcieri, F. M., Miglietta, M. M.,
Ricchi, A., and Sclavo, M.: Scratching beneath the surface while coupling
atmosphere, ocean and waves: Analysis of a dense water formation event,
Ocean Model., 101, 101–112, https://doi.org/10.1016/j.ocemod.2016.03.007, 2016. a
Chelton, D. B. and Xie, S.-P.: Coupled Ocean-Atmosphere Interaction at Oceanic
Mesoscales, Oceanography, 23, 52–69, https://doi.org/10.5670/oceanog.2010.05, 2010. a
Chen, S., Campbell, T. J., Jin, H., Gaberšek, S., Hodur, R. M., and
Martin, P.: Effect of Two-Way Air–Sea Coupling in High and Low Wind Speed
Regimes, Mon. Weather Rev., 138, 3579–3602,
https://doi.org/10.1175/2009MWR3119.1, 2010. a
Chrysagi, E., Umlauf, L., Holtermann, P. L., Klingbeil, K., and Burchard, H.:
High‐resolution simulations of submesoscale processes in the Baltic Sea:
The role of storm events, J. Geophys. Res.-Oceans, 126, e2020JC016411,
https://doi.org/10.1029/2020JC016411, 2021. a
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017. a
Crueger, T., Giorgetta, M. A., Brokopf, R., Esch, M., Fiedler, S., Hohenegger,
C., Kornblueh, L., Mauritsen, T., Nam, C., Naumann, A. K., Peters, K., Rast,
S., Roeckner, E., Sakradzija, M., Schmidt, H., Vial, J., Vogel, R., and
Stevens, B.: ICON-A, The Atmosphere Component of the ICON Earth System
Model: II. Model Evaluation, J. Adv. Model. Earth Sy.,
10, 1638–1662, https://doi.org/10.1029/2017MS001233, 2018. a, b
Cullen, M. J.: The unified forecast/climate model, Meteorol. Mag.,
122, 81–94,
1993. a
Dipankar, A., Stevens, B., Heinze, R., Moseley, C., Zängl, G., Giorgetta,
M. A., and Brdar, S.: Large eddy simulation using the general circulation
model ICON, J. Adv. Model. Earth Sy., 7, 963–986,
https://doi.org/10.1002/2015MS000431, 2015. a, b
Doms, G., Förstner, J., Heise, E., Herzog, H.-J., Mironov, D.,
Raschendorfer, M., Reinhardt, T., Ritter, B., Schrodin, R., Schulz, J.-P.,
and Vogel, G.: A description of the Nonhydrostatic Regional COSMOS Model,
Part II: Physical parameterization, Tech. rep., Deutscher Wetterdienst,
Offenbach, https://doi.org/10.5676/DWD_pub/nwv/cosmo-doc_5.00_II, 2013. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis
(OSTIA) system, Remote Sens. Environ., 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017, 2012.
DWD: Weather Forecast System, available at:
https://www.dwd.de/EN/research/weatherforecasting/num_modelling/01_num_weather_prediction_modells/icon_description.html, last access: 22 June 2021. a
ECMWF: Atmospheric Model high resolution 10-day forecast (Set I – HRES), available at: https://www.ecmwf.int/en/forecasts/dataset/atmospheric-model-high-resolution-10-day-forecast, last access: 21 December 2018. a
Fallmann, J., Lewis, H. W., Castillo, J. M., Arnold, A., and Ramsdale, S.:
Impact of sea surface temperature on stratiform cloud formation over the
North Sea, Geophys. Res. Lett., 44, 4296–4303,
https://doi.org/10.1002/2017GL073105, 2017. a
Fallmann, J., Lewis, H. W., Sanchez, J. C., and Lock, A.: Impact of
high‐resolution ocean–atmosphere coupling on fog formation over the North
Sea, Q. J. Roy. Meteor. Soc., 145, 1180–1201,
https://doi.org/10.1002/qj.3488, 2019. a, b, c
Gassmann, A. and Herzog, H.-J.: Towards a consistent numerical compressible
non-hydrostatic model using generalized Hamiltonian tools, Q. J.
Roy. Meteor. Soc., 134, 1597–1613, https://doi.org/10.1002/qj.297,
2008. a
Giorgetta, M. A., Brokopf, R., Crueger, T., Esch, M., Fiedler, S., Helmert, J.,
Hohenegger, C., Kornblueh, L., Köhler, M., Manzini, E., Mauritsen, T.,
Nam, C., Raddatz, T., Rast, S., Reinert, D., Sakradzija, M., Schmidt, H.,
Schneck, R., Schnur, R., Silvers, L., Wan, H., Zängl, G., and Stevens,
B.: ICON-A, the Atmosphere Component of the ICON Earth System Model: I.
Model Description, J. Adv. Model. Earth Sy., 10,
1613–1637, https://doi.org/10.1029/2017MS001242, 2018. a, b
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
Gräwe, U., Klingbeil, K., Kelln, J., and Dangendorf, S.: Decomposing
Mean Sea Level Rise in a Semi-Enclosed Basin, the Baltic Sea, J.
Climate, 32, 3089–3108, https://doi.org/10.1175/JCLI-D-18-0174.1, 2019. a, b
Hanke, M., Redler, R., Holfeld, T., and Yastremsky, M.: YAC 1.2.0: new aspects for coupling software in Earth system modelling, Geosci. Model Dev., 9, 2755–2769, https://doi.org/10.5194/gmd-9-2755-2016, 2016. a, b
Heinze, R., Dipankar, A., Henken, C. C., Moseley, C., Sourdeval, O.,
Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C.,
Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S.,
Deneke, H., Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C.,
Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen,
A., Hege, H.-c., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S.,
Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B.,
Meyer, C. I., Muppa, S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F.,
Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf,
F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger,
M., Wulfmeyer, V., Zängl, G., Zhang, D., and Quaas, J.: Large-eddy
simulations over Germany using ICON: a comprehensive evaluation, Q.
J. Roy. Meteor. Soc., 143, 69–100,
https://doi.org/10.1002/qj.2947, 2017. a, b
Hill, C., DeLuca, C., Balaji, V., Suarez, M., and Da Silva, A. M.: The
architecture of the earth system modeling framework, Comput. Sci.
Eng., 6, 18–28, https://doi.org/10.1109/MCISE.2004.1255817, 2004. a
Hodur, R. M.: The Naval Research Laboratory's Coupled Ocean/Atmosphere
Mesoscale Prediction System (COAMPS), Mon. Weather Rev., 125,
1414–1430, https://doi.org/10.1175/1520-0493(1997)125<1414:TNRLSC>2.0.CO;2, 1997. 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
Holtermann, P. L., Burchard, H., Gräwe, U., Klingbeil, K., and Umlauf,
L.: Deep-water dynamics and boundary mixing in a nontidal stratified basin:
A modeling study of the Baltic Sea, J. Geophys. Res.-Oceans,
119, 1465–1487, https://doi.org/10.1002/2013JC009483, 2014. a, b
Holtermann, P. L., Prien, R., Naumann, M., and Umlauf, L.: Interleaving of
oxygenized intrusions into the Baltic Sea redoxcline, Limnol.
Oceanogr., 65, 482–503, https://doi.org/10.1002/lno.11317, 2020. a
Kara, A. B., Wallcraft, A. J., and Hurlburt, H. E.: A Correction for Land
Contamination of Atmospheric Variables near Land–Sea Boundaries, J.
Phys. Oceanogr., 37, 803–818, https://doi.org/10.1175/JPO2984.1, 2007. a
Klingbeil, K.: Source code for the coastal ocean model GETM (iow branch) (Version 2.5.0-0.1), Zenodo [code], https://doi.org/10.5281/zenodo.5017784, 2020. a
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
Lange, X., Klingbeil, K., and Burchard, H.: Inversions of Estuarine
Circulation Are Frequent in a Weakly Tidal Estuary With Variable Wind Forcing
and Seaward Salinity Fluctuations, J. Geophys. Res.-Oceans,
125, e2019JC015789, https://doi.org/10.1029/2019JC015789, 2020. a
Larson, J., Jacob, R., and Ong, E.: The Model Coupling Toolkit: A New
Fortran90 Toolkit for Building Multiphysics Parallel Coupled Models, The
Int. J. High Perform. C., 19,
277–292, https://doi.org/10.1177/1094342005056115, 2005. a
Lemmen, C., Hofmeister, R., Klingbeil, K., Nasermoaddeli, M. H., Kerimoglu, O., Burchard, H., Kösters, F., and Wirtz, K. W.: Modular System for Shelves and Coasts (MOSSCO v1.0) – a flexible and multi-component framework for coupled coastal ocean ecosystem modelling, Geosci. Model Dev., 11, 915–935, https://doi.org/10.5194/gmd-11-915-2018, 2018. a
Lewis, H. W., Castillo Sanchez, J. M., Graham, J., Saulter, A., Bornemann, J., Arnold, A., Fallmann, J., Harris, C., Pearson, D., Ramsdale, S., Martínez-de la Torre, A., Bricheno, L., Blyth, E., Bell, V. A., Davies, H., Marthews, T. R., O'Neill, C., Rumbold, H., O'Dea, E., Brereton, A., Guihou, K., Hines, A., Butenschon, M., Dadson, S. J., Palmer, T., Holt, J., Reynard, N., Best, M., Edwards, J., and Siddorn, J.: The UKC2 regional coupled environmental prediction system, Geosci. Model Dev., 11, 1–42, https://doi.org/10.5194/gmd-11-1-2018, 2018. a
Lewis, H. W., Castillo Sanchez, J. M., Arnold, A., Fallmann, J., Saulter, A., Graham, J., Bush, M., Siddorn, J., Palmer, T., Lock, A., Edwards, J., Bricheno, L., Martínez-de la Torre, A., and Clark, J.: The UKC3 regional coupled environmental prediction system, Geosci. Model Dev., 12, 2357–2400, https://doi.org/10.5194/gmd-12-2357-2019, 2019a. a
Lewis, H. W., Siddorn, J., Castillo Sanchez, J. M., Petch, J., Edwards, J. M., and Smyth, T.: Evaluating the impact of atmospheric forcing and air–sea coupling on near-coastal regional ocean prediction, Ocean Sci., 15, 761–778, https://doi.org/10.5194/os-15-761-2019, 2019b. a
Linardakis, L., Reinert, D., and Gassmann, A.: ICON Grid Documentation, Tech.
rep., DKRZ, Hamburg,
available at: https://www.dkrz.de/SciVis/hd-cp-2/en-icon_grid.pdf?lang=en (last access: 22 June 2021),
2011. a
Luettich Jr., R. A., Westerink, J. J., and Scheffner, N. W.: ADCIRC: An
Advanced Three-Dimensional Circulation Model for Shelves, Coasts, and
Estuaries. Report 1. Theory and Methodology of ADCIRC-2DDI and ADCIRC-3DL.,
Tech. rep., Coastal Engineering Research Center, Vicksburg, MS,
available at: https://apps.dtic.mil/docs/citations/ADA261608 (last access: 22 June 2021), 1992. a
Madec, G., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C.,
Bruciaferri, D., Calvert, D., Chanut, J., Clementi, E., Coward, A., Delrosso,
D., Ethé, C., Flavoni, S., Graham, T., Harle, J., Iovino, D., Lea, D.,
Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S., Paul, J.,
Rousset, C., Storkey, D., Storto, A., and Vancoppenolle, M.: NEMO ocean
engine (Version v3.6-patch), Tech. rep., Pôle De Modélisation De
L'institut Pierre-simon Laplace (IPSL), Zenodo, https://doi.org/10.5281/ZENODO.3248739, 2017. a
Marshall, J., Adcroft, A. J., Hill, C., Perelman, L., and Heisey, C.: A
finite-volume, incompressible Navier Stokes model for studies of the ocean on
parallel computers, J. Geophys. Res.-Oceans, 102,
5753–5766, https://doi.org/10.1029/96JC02775, 1997. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.:
Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997. a
Moghimi, S., Van der Westhuysen, A., Abdolali, A., Myers, E., Vinogradov, S.,
Ma, Z., Liu, F., Mehra, A., and Kurkowski, N.: Development of an ESMF Based
Flexible Coupling Application of ADCIRC and WAVEWATCH III for High Fidelity
Coastal Inundation Studies, J. Mar. Sci. Eng., 8,
308, https://doi.org/10.3390/jmse8050308, 2020. a
Perlin, N., Skyllingstad, E. D., Samelson, R. M., and Barbour, P. L.:
Numerical Simulation of Air–Sea Coupling during Coastal Upwelling,
J. Phys. Oceanogr., 37, 2081–2093, https://doi.org/10.1175/JPO3104.1,
2007. a
Pullen, J., Doyle, J. D., and Signell, R. P.: Two-Way Air–Sea Coupling: A
Study of the Adriatic, Mon. Weather Rev., 134, 1465–1483,
https://doi.org/10.1175/MWR3137.1, 2006. a
Pullen, J., Doyle, J. D., Haack, T., Dorman, C., Signell, R. P., and Lee,
C. M.: Bora event variability and the role of air-sea feedback, J.
Geophys. Res., 112, C03S18, https://doi.org/10.1029/2006JC003726, 2007. a
Pullen, J., Caldeira, R., Doyle, J. D., May, P., and Tomé, R.: Modeling
the air‐sea feedback system of Madeira Island, J. Adv.
Model. Earth Sy., 9, 1641–1664, https://doi.org/10.1002/2016MS000861, 2017. a
Reinert, D., Prill, F., Frank, H., Denhard, M., and Zängl, G.: Database
Reference Manual for ICON and ICON-EPS, Tech. rep., DWD, Offenbach,
https://doi.org/10.5676/DWD_pub/nwv/icon_1.2.12, 2020. a, b
Reissmann, J. H., Burchard, H., Feistel, R., Hagen, E., Lass, H. U., Mohrholz,
V., Nausch, G., Umlauf, L., and Wieczorek, G.: Vertical mixing in the Baltic
Sea and consequences for eutrophication – A review, Prog.
Oceanogr., 82, 47–80, https://doi.org/10.1016/j.pocean.2007.10.004, 2009. a
Renault, L., Chiggiato, J., Warner, J. C., Gomez, M., Vizoso, G., and
Tintoré, J.: Coupled atmosphere-ocean-wave simulations of a storm
event over the Gulf of Lion and Balearic Sea, J. Geophys.
Res.-Oceans, 117, C09019, https://doi.org/10.1029/2012JC007924, 2012. a
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: NCEP Climate Forecast System Version 2 (CFSv2) Monthly Products, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, https://doi.org/10.5065/D69021ZF, 2012. a
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D.,
Hou, Y.-T., Chuang, H.-y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez,
M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The
NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208,
https://doi.org/10.1175/JCLI-D-12-00823.1, 2014. a
Shao, M., Ortiz‐Suslow, D. G., Haus, B. K., Lund, B., Williams, N. J.,
Özgökmen, T. M., Laxague, N. J. M., Horstmann, J., and Klymak,
J. M.: The Variability of Winds and Fluxes Observed Near Submesoscale
Fronts, J. Geophys. Res.-Oceans, 124, 7756–7780,
https://doi.org/10.1029/2019JC015236, 2019. a
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling system
(ROMS): a split-explicit, free-surface, topography-following-coordinate
oceanic model, Ocean Model., 9, 347–404,
https://doi.org/10.1016/j.ocemod.2004.08.002, 2005. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang,
W., and Powers, J. G.: A Description of the Advanced Research WRF Version
2, Tech. rep., UCAR, https://doi.org/10.5065/D6DZ069T, 2005. a
Small, R. J., DeSzoeke, S., Xie, S.-P., O'Neill, L., Seo, H., Song, Q.,
Cornillon, P., Spall, M., and Minobe, S.: Air–sea interaction over ocean
fronts and eddies, Dynam. Atmos. Oceans, 45, 274–319,
https://doi.org/10.1016/j.dynatmoce.2008.01.001, 2008. a
Small, R. J., Campbell, T., Teixeira, J., Carniel, S., Smith, T. A., Dykes, J.,
Chen, S., and Allard, R.: Air–Sea Interaction in the Ligurian Sea:
Assessment of a Coupled Ocean–Atmosphere Model Using In Situ Data from
LASIE07, Mon. Weather Rev., 139, 1785–1808,
https://doi.org/10.1175/2010MWR3431.1, 2011.
a
Sun, R., Subramanian, A. C., Miller, A. J., Mazloff, M. R., Hoteit, I., and Cornuelle, B. D.: SKRIPS v1.0: a regional coupled ocean–atmosphere modeling framework (MITgcm–WRF) using ESMF/NUOPC, description and preliminary results for the Red Sea, Geosci. Model Dev., 12, 4221–4244, https://doi.org/10.5194/gmd-12-4221-2019, 2019. a
Theurich, G., DeLuca, C., Campbell, T., Liu, F., Saint, K., Vertenstein, M.,
Chen, J., Oehmke, R., Doyle, J. D., Whitcomb, T., Wallcraft, A. J., Iredell,
M., Black, T., Da Silva, A. M., Clune, T., Ferraro, R., Li, P., Kelley, M.,
Aleinov, I., Balaji, V., Zadeh, N., Jacob, R., Kirtman, B., Giraldo, F.,
McCarren, D., Sandgathe, S., Peckham, S., and Dunlap, R.: The earth system
prediction suite: Toward a coordinated U.S. modeling capability, B.
Am. Meteorol. Soc., 97, 1229–1247,
https://doi.org/10.1175/BAMS-D-14-00164.1, 2016. a, b
Turuncoglu, U. U.: Toward modular in situ visualization in Earth system models: the regional modeling system RegESM 1.1, Geosci. Model Dev., 12, 233–259, https://doi.org/10.5194/gmd-12-233-2019, 2019. a, b
Ullrich, P. A., Jablonowski, C., Kent, J., Lauritzen, P. H., Nair, R., Reed, K. A., Zarzycki, C. M., Hall, D. M., Dazlich, D., Heikes, R., Konor, C., Randall, D., Dubos, T., Meurdesoif, Y., Chen, X., Harris, L., Kühnlein, C., Lee, V., Qaddouri, A., Girard, C., Giorgetta, M., Reinert, D., Klemp, J., Park, S.-H., Skamarock, W., Miura, H., Ohno, T., Yoshida, R., Walko, R., Reinecke, A., and Viner, K.: DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models, Geosci. Model Dev., 10, 4477–4509, https://doi.org/10.5194/gmd-10-4477-2017, 2017. a
Valcke, S., Balaji, V., Craig, A., DeLuca, C., Dunlap, R., Ford, R. W., Jacob, R., Larson, J., O'Kuinghttons, R., Riley, G. D., and Vertenstein, M.: Coupling technologies for Earth System Modelling, Geosci. Model Dev., 5, 1589–1596, https://doi.org/10.5194/gmd-5-1589-2012, 2012. a
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a
Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) Modeling
System, Ocean Model., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010,
2010. a
WW3DG (The WAVEWATCH III Development Group): User Manual and System
Documentation of WAVEWATCH III version 6.07, Tech. Rep. Tech. Note 33,
NOAA/NWS/NCEP/MMAB,
available at: https://github.com/NOAA-EMC/WW3/wiki/files/manual.pdf (last access: 22 June 2021), 2019. a
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.
We present the coupled atmosphere–ocean model system ICONGETM. The added value and potential of...
