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.
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
Related authors
No articles found.
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.
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
Geosci. Model Dev., 17, 1271–1295, https://doi.org/10.5194/gmd-17-1271-2024, https://doi.org/10.5194/gmd-17-1271-2024, 2024
Short summary
Short summary
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.
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.
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
Short summary
Short summary
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
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.
Jamie R. Banks, Bernd Heinold, and Kerstin Schepanski
EGUsphere, https://doi.org/10.5194/egusphere-2023-2772, https://doi.org/10.5194/egusphere-2023-2772, 2023
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
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.
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
Short summary
Short summary
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
Short summary
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.
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.
Christof G. Beer, Johannes Hendricks, Mattia Righi, Bernd Heinold, Ina Tegen, Silke Groß, Daniel Sauer, Adrian Walser, and Bernadett Weinzierl
Geosci. Model Dev., 13, 4287–4303, https://doi.org/10.5194/gmd-13-4287-2020, https://doi.org/10.5194/gmd-13-4287-2020, 2020
Short summary
Short summary
Mineral dust aerosol plays an important role in the climate system. Previously, dust emissions have often been represented in global models by prescribed monthly-mean emission fields representative of a specific year. We now apply an online calculation of wind-driven dust emissions. This results in an improved agreement with observations, due to a better representation of the highly variable dust emissions. Increasing the model resolution led to an additional performance gain.
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.
Christa Genz, Roland Schrödner, Bernd Heinold, Silvia Henning, Holger Baars, Gerald Spindler, and Ina Tegen
Atmos. Chem. Phys., 20, 8787–8806, https://doi.org/10.5194/acp-20-8787-2020, https://doi.org/10.5194/acp-20-8787-2020, 2020
Short summary
Short summary
Atmospheric aerosols are the precondition for the formation of cloud droplets and thus have a large influence on cloud properties. Concentrations of cloud condensation nuclei of the period with highest aerosol concentrations over central Europe are uncertain. In this work, modeled estimates of CCN from today and the mid-1980s are compared to available in situ and remote sensing observations. A scaling factor between today and the 1980s for the CCN concentrations has been derived.
Tobias Donth, Evelyn Jäkel, André Ehrlich, Bernd Heinold, Jacob Schacht, Andreas Herber, Marco Zanatta, and Manfred Wendisch
Atmos. Chem. Phys., 20, 8139–8156, https://doi.org/10.5194/acp-20-8139-2020, https://doi.org/10.5194/acp-20-8139-2020, 2020
Short summary
Short summary
Solar radiative effects of Arctic black carbon (BC) particles (suspended in the atmosphere and in the surface snowpack) were quantified under cloudless and cloudy conditions. An atmospheric and a snow radiative transfer model were coupled to account for radiative interactions between both compartments. It was found that (i) the warming effect of BC in the snowpack overcompensates for the atmospheric BC cooling effect, and (ii) clouds tend to reduce the atmospheric BC cooling and snow BC warming.
Alma Hodzic, Pedro Campuzano-Jost, Huisheng Bian, Mian Chin, Peter R. Colarco, Douglas A. Day, Karl D. Froyd, Bernd Heinold, Duseong S. Jo, Joseph M. Katich, John K. Kodros, Benjamin A. Nault, Jeffrey R. Pierce, Eric Ray, Jacob Schacht, Gregory P. Schill, Jason C. Schroder, Joshua P. Schwarz, Donna T. Sueper, Ina Tegen, Simone Tilmes, Kostas Tsigaridis, Pengfei Yu, and Jose L. Jimenez
Atmos. Chem. Phys., 20, 4607–4635, https://doi.org/10.5194/acp-20-4607-2020, https://doi.org/10.5194/acp-20-4607-2020, 2020
Short summary
Short summary
Organic aerosol (OA) is a key source of uncertainty in aerosol climate effects. We present the first pole-to-pole OA characterization during the NASA Atmospheric Tomography aircraft mission. OA has a strong seasonal and zonal variability, with the highest levels in summer and over fire-influenced regions and the lowest ones in the southern high latitudes. We show that global models predict the OA distribution well but not the relative contribution of OA emissions vs. chemical production.
Mattia Righi, Johannes Hendricks, Ulrike Lohmann, Christof Gerhard Beer, Valerian Hahn, Bernd Heinold, Romy Heller, Martina Krämer, Michael Ponater, Christian Rolf, Ina Tegen, and Christiane Voigt
Geosci. Model Dev., 13, 1635–1661, https://doi.org/10.5194/gmd-13-1635-2020, https://doi.org/10.5194/gmd-13-1635-2020, 2020
Short summary
Short summary
A new cloud microphysical scheme is implemented in the global EMAC-MADE3 aerosol model and evaluated. The new scheme features a detailed parameterization for aerosol-driven ice formation in cirrus clouds, accounting for the competition between homogeneous and heterogeneous ice formation processes. The comparison against satellite data and in situ measurements shows that the model performance is in line with similar global coupled models featuring ice cloud parameterizations.
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.
Diego Villanueva, Bernd Heinold, Patric Seifert, Hartwig Deneke, Martin Radenz, and Ina Tegen
Atmos. Chem. Phys., 20, 2177–2199, https://doi.org/10.5194/acp-20-2177-2020, https://doi.org/10.5194/acp-20-2177-2020, 2020
Short summary
Short summary
Spaceborne retrievals of cloud phase were analysed together with an atmospheric composition model to assess the global frequency of ice and liquid clouds. This analysis showed that at equal temperature the average occurrence of ice clouds increases for higher dust mixing ratios on a day-to-day basis in the middle and high latitudes. This indicates that mineral dust may have a strong impact on the occurrence of ice clouds even in remote areas.
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.
Jacob Schacht, Bernd Heinold, Johannes Quaas, John Backman, Ribu Cherian, Andre Ehrlich, Andreas Herber, Wan Ting Katty Huang, Yutaka Kondo, Andreas Massling, P. R. Sinha, Bernadett Weinzierl, Marco Zanatta, and Ina Tegen
Atmos. Chem. Phys., 19, 11159–11183, https://doi.org/10.5194/acp-19-11159-2019, https://doi.org/10.5194/acp-19-11159-2019, 2019
Short summary
Short summary
The Arctic is warming faster than the rest of Earth. Black carbon (BC) aerosol contributes to this Arctic amplification by direct and indirect aerosol radiative effects while distributed in air or deposited on snow and ice. The aerosol-climate model ECHAM-HAM is used to estimate direct aerosol radiative effect (DRE). Airborne and near-surface BC measurements are used to evaluate the model and give an uncertainty range for the burden and DRE of Arctic BC caused by different emission inventories.
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.
Jamie R. Banks, Anja Hünerbein, Bernd Heinold, Helen E. Brindley, Hartwig Deneke, and Kerstin Schepanski
Atmos. Chem. Phys., 19, 6893–6911, https://doi.org/10.5194/acp-19-6893-2019, https://doi.org/10.5194/acp-19-6893-2019, 2019
Short summary
Short summary
Saharan dust storms may be observed over the desert using false-colour infrared satellite imagery; in one widely used scheme dust displays characteristic pink colours. Simulating satellite imagery using a dust transport model, we confirm that water vapour is a major control on the apparent colour of dust in the false-colour imagery and that dust displays its deepest colours when it is at a high altitude and when the atmosphere is dry. Water vapour can obscure the presence of low-altitude dust.
Ina Tegen, David Neubauer, Sylvaine Ferrachat, Colombe Siegenthaler-Le Drian, Isabelle Bey, Nick Schutgens, Philip Stier, Duncan Watson-Parris, Tanja Stanelle, Hauke Schmidt, Sebastian Rast, Harri Kokkola, Martin Schultz, Sabine Schroeder, Nikos Daskalakis, Stefan Barthel, Bernd Heinold, and Ulrike Lohmann
Geosci. Model Dev., 12, 1643–1677, https://doi.org/10.5194/gmd-12-1643-2019, https://doi.org/10.5194/gmd-12-1643-2019, 2019
Short summary
Short summary
We describe a new version of the aerosol–climate model ECHAM–HAM and show tests of the model performance by comparing different aspects of the aerosol distribution with different datasets. The updated version of HAM contains improved descriptions of aerosol processes, including updated emission fields and cloud processes. While there are regional deviations between the model and observations, the model performs well overall.
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.
Erlend M. Knudsen, Bernd Heinold, Sandro Dahlke, Heiko Bozem, Susanne Crewell, Irina V. Gorodetskaya, Georg Heygster, Daniel Kunkel, Marion Maturilli, Mario Mech, Carolina Viceto, Annette Rinke, Holger Schmithüsen, André Ehrlich, Andreas Macke, Christof Lüpkes, and Manfred Wendisch
Atmos. Chem. Phys., 18, 17995–18022, https://doi.org/10.5194/acp-18-17995-2018, https://doi.org/10.5194/acp-18-17995-2018, 2018
Short summary
Short summary
The paper describes the synoptic development during the ACLOUD/PASCAL airborne and ship-based field campaign near Svalbard in spring 2017. This development is presented using near-surface and upperair meteorological observations, satellite, and model data. We first present time series of these data, from which we identify and characterize three key periods. Finally, we put our observations in historical and regional contexts and compare our findings to other Arctic field campaigns.
Michael Weger, Bernd Heinold, Christa Engler, Ulrich Schumann, Axel Seifert, Romy Fößig, Christiane Voigt, Holger Baars, Ulrich Blahak, Stephan Borrmann, Corinna Hoose, Stefan Kaufmann, Martina Krämer, Patric Seifert, Fabian Senf, Johannes Schneider, and Ina Tegen
Atmos. Chem. Phys., 18, 17545–17572, https://doi.org/10.5194/acp-18-17545-2018, https://doi.org/10.5194/acp-18-17545-2018, 2018
Short summary
Short summary
The impact of desert dust on cloud formation is investigated for a major Saharan dust event over Europe by interactive regional dust modeling. Dust particles are very efficient ice-nucleating particles promoting the formation of ice crystals in clouds. The simulations show that the observed extensive cirrus development was likely related to the above-average dust load. The interactive dust–cloud feedback in the model significantly improves the agreement with aircraft and satellite observations.
Diego Villanueva, Bernd Heinold, Patric Seifert, Hartwig Deneke, Martin Radenz, and Ina Tegen
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-1074, https://doi.org/10.5194/acp-2018-1074, 2018
Revised manuscript not accepted
Short summary
Short summary
Two different satellite products were analysed together with an atmospheric composition model to assess the global frequency of ice and liquid stratiform clouds. This analysis showed that at equal temperature the average occurrence of fully glaciated stratiform clouds was found to increase for higher dust mixing-ratios on a day-to-day basis in the mid- and high latitudes. This indicates that mineral dust may have a strong impact in the occurrence of ice clouds even in remote areas.
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.
Jamie R. Banks, Kerstin Schepanski, Bernd Heinold, Anja Hünerbein, and Helen E. Brindley
Atmos. Chem. Phys., 18, 9681–9703, https://doi.org/10.5194/acp-18-9681-2018, https://doi.org/10.5194/acp-18-9681-2018, 2018
Short summary
Short summary
Satellite observations are used to visualize dust storms over the Sahara, and specific infrared channel combinations can highlight dust with distinctive pink colours. Using output from a dust-atmosphere model to simulate satellite imagery, we explore the consequences of particle size, shape, and refractive index for the colour of dust in the imagery. Particles with a radius of ~ 1.5 microns perturb the colour the most and an assumption of spherical dust appears to be insufficient.
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.
Julian Hofer, Dietrich Althausen, Sabur F. Abdullaev, Abduvosit N. Makhmudov, Bakhron I. Nazarov, Georg Schettler, Ronny Engelmann, Holger Baars, K. Wadinga Fomba, Konrad Müller, Bernd Heinold, Konrad Kandler, and Albert Ansmann
Atmos. Chem. Phys., 17, 14559–14577, https://doi.org/10.5194/acp-17-14559-2017, https://doi.org/10.5194/acp-17-14559-2017, 2017
Short summary
Short summary
The Central Asian Dust Experiment provides unprecedented data on vertically resolved aerosol optical properties over Central Asia from continuous 18-month polarization Raman lidar observations in Dushanbe, Tajikistan. Central Asia is affected by climate change (e.g. glacier retreat) but in a large part missing vertically resolved aerosol measurements, which would help to better understand transport of dust and pollution aerosol across Central Asia and their influence on climate and health.
Kerstin Schepanski, Bernd Heinold, and Ina Tegen
Atmos. Chem. Phys., 17, 10223–10243, https://doi.org/10.5194/acp-17-10223-2017, https://doi.org/10.5194/acp-17-10223-2017, 2017
Short summary
Short summary
This study illustrates the complexity of the interaction among the three major circulation regimes stimulating the North African dust outflow: harmattan, Saharan heat low, and monsoon circulation. We analyse fields from model simulations and satellite observations in concert in order to link atmospheric circulation and dust source activation as well as to characterize their impact on the variability of the dust outflow towards the Atlantic.
Sudhakar Dipu, Johannes Quaas, Ralf Wolke, Jens Stoll, Andreas Mühlbauer, Odran Sourdeval, Marc Salzmann, Bernd Heinold, and Ina Tegen
Geosci. Model Dev., 10, 2231–2246, https://doi.org/10.5194/gmd-10-2231-2017, https://doi.org/10.5194/gmd-10-2231-2017, 2017
Kerstin Schepanski, Marc Mallet, Bernd Heinold, and Max Ulrich
Atmos. Chem. Phys., 16, 14147–14168, https://doi.org/10.5194/acp-16-14147-2016, https://doi.org/10.5194/acp-16-14147-2016, 2016
Short summary
Short summary
EOF analysis is used to link the north African atmospheric dust cycle, particularly active dust source regions, dust emission fluxes, dust transport pathways towards the Mediterranean Sea and Europe as well as dust deposition rates with atmospheric circulation regimes, such as position and strength of the subtropical ridge and the Saharan heat low.
Bernd Heinold, Ina Tegen, Kerstin Schepanski, and Jamie R. Banks
Geosci. Model Dev., 9, 765–777, https://doi.org/10.5194/gmd-9-765-2016, https://doi.org/10.5194/gmd-9-765-2016, 2016
Short summary
Short summary
In the aerosol-climate model ECHAM6-HAM2, dust source activation (DSA) observations from MSG satellite are used to replace the current Saharan source map. The new setup provides more realistically distributed, up to 20 % higher annual Saharan emissions. Modeled dust AOT is partly improved in the Sahara-Sahel region, as is the spatial variability. As a comparison to sub-daily MSG DSAs and a regional model shows, the representation of meteorological drivers of dust uplift remains a critical issue.
S. Fiedler, K. Schepanski, P. Knippertz, B. Heinold, and I. Tegen
Atmos. Chem. Phys., 14, 8983–9000, https://doi.org/10.5194/acp-14-8983-2014, https://doi.org/10.5194/acp-14-8983-2014, 2014
N. Niedermeier, A. Held, T. Müller, B. Heinold, K. Schepanski, I. Tegen, K. Kandler, M. Ebert, S. Weinbruch, K. Read, J. Lee, K. W. Fomba, K. Müller, H. Herrmann, and A. Wiedensohler
Atmos. Chem. Phys., 14, 2245–2266, https://doi.org/10.5194/acp-14-2245-2014, https://doi.org/10.5194/acp-14-2245-2014, 2014
I. Tegen, K. Schepanski, and B. Heinold
Atmos. Chem. Phys., 13, 2381–2390, https://doi.org/10.5194/acp-13-2381-2013, https://doi.org/10.5194/acp-13-2381-2013, 2013
Related subject area
Climate and Earth system modeling
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Towards the definition of a solar forcing dataset for CMIP7
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region
Constraining the carbon cycle in JULES-ES-1.0
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data
The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation
Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1
Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations
Exploring the ocean mesoscale at reduced computational cost with FESOM 2.5: efficient modeling strategies applied to the Southern Ocean
Truly conserving with conservative remapping methods
High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia
Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc
Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts
Understanding changes in cloud simulations from E3SM version 1 to version 2
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a)
Deep learning model based on multi-scale feature fusion for precipitation nowcasting
The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change
Getting the leaves right matters for estimating temperature extremes
The Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative: scientific objectives and experimental design
Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization
The Regional Climate-Chemistry-Ecology Coupling Model RegCM-Chem (v4.6)-YIBs (v1.0): Development and Application
Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections
An evaluation of the LLC4320 global-ocean simulation based on the submesoscale structure of modeled sea surface temperature fields
An emulation-based approach for interrogating reactive transport models
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Objective Evaluation of Earth System Models: PCMDI Metrics Package (PMP) version 3
A Revised Model of Global Silicate Weathering Considering the Influence of Vegetation Cover on Erosion Rate
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
Short summary
Short summary
The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
Short summary
Short summary
This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Short summary
Short summary
Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
Short summary
Short summary
This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
Short summary
Short summary
Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
Short summary
Short summary
Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
Short summary
Short summary
We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
Short summary
Short summary
Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
Short summary
Short summary
As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
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.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
Short summary
Short summary
A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
Short summary
Short summary
Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
Short summary
Short summary
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
Short summary
Short summary
We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
Short summary
Short summary
This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
Short summary
Short summary
By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
Short summary
Short summary
We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
Short summary
Short summary
Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
Short summary
Short summary
We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
Short summary
Short summary
Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
Short summary
Short summary
This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
Short summary
Short summary
This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
Short summary
Short summary
The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
Short summary
Short summary
The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
Short summary
Short summary
Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
Short summary
Short summary
Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
Short summary
Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
Short summary
Short summary
We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
Short summary
Short summary
The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
Short summary
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
Short summary
Short summary
This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
Short summary
Short summary
Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
Short summary
Short summary
Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
Short summary
Short summary
Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Baiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
EGUsphere, https://doi.org/10.5194/egusphere-2023-1733, https://doi.org/10.5194/egusphere-2023-1733, 2023
Short summary
Short summary
For the first time, we coupled a regional climate chemistry model RegCM-Chem with a dynamic vegetation model YIBs to create a regional climate-chemistry-ecology model RegCM-Chem-YIBs. We applied it to simulate climatic, chemical and ecological parameters in East Asia and fully validated it on a variety of observational data. The research results show that RegCM-Chem-YIBs model is a valuable tool for studying terrestrial carbon cycle, atmospheric chemistry, and climate change in regional scale.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
Short summary
Short summary
We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
Short summary
Short summary
This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
Short summary
Short summary
We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-223, https://doi.org/10.5194/gmd-2023-223, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion by either uniform erosion processes where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea level history, material properties, and the relative influence of different erosional processes.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Short summary
In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
EGUsphere, https://doi.org/10.5194/egusphere-2023-2720, https://doi.org/10.5194/egusphere-2023-2720, 2023
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth System Models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-199, https://doi.org/10.5194/gmd-2023-199, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
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...