Articles | Volume 13, issue 7
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development of a two-way-coupled ocean–wave model: assessment on a global NEMO(v3.6)–WW3(v6.02) coupled configuration
Univ. Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, Brest, France
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
Univ. Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, Brest, France
Univ. Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, Brest, France
LEGOS, University of Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
Sorbonne Universités (UPMC, Univ Paris 06)-CNRS-IRD-MNHN, LOCEAN Laboratory, Paris, France
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
No articles found.
Matias Alday, Fabrice Ardhuin, Guillaume Dodet, and Mickael Accensi
Ocean Sci., 18, 1665–1689,Short summary
Obtaining accurate results from wave models in coastal regions is typically more difficult. This is due to the complex interactions between waves and the local environment characteristics like complex shorelines, sea bottom topography, the presence of strong currents, and other processes that include wave growth and decay. In the present study we analyze which elements can be adjusted and/or included in order to reduce errors in the modeled output.
Nicholas K.-R. Kevlahan and Florian Lemarié
Geosci. Model Dev., 15, 6521–6539,Short summary
WAVETRISK-2.1 is an innovative climate model for the world's oceans. It uses state-of-the-art techniques to change the model's resolution locally, from O(100 km) to O(5 km), as the ocean changes. This dynamic adaptivity makes optimal use of available supercomputer resources, and allows two-dimensional global scales and three-dimensional submesoscales to be captured in the same simulation. WAVETRISK-2.1 is designed to be coupled its companion global atmosphere model, WAVETRISK-1.x.
M. Al Najar, Y. El Bennioui, G. Thoumyre, R. Almar, E. W. J. Bergsma, R. Benshila, J.-M. Delvit, and D. G. Wilson
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 9–16,
Joris Pianezze, Jonathan Beuvier, Cindy Lebeaupin Brossier, Guillaume Samson, Ghislain Faure, and Gilles Garric
Nat. Hazards Earth Syst. Sci., 22, 1301–1324,Short summary
Most numerical weather and oceanic prediction systems do not consider ocean–atmosphere feedback during forecast, and this can lead to significant forecast errors, notably in cases of severe situations. A new high-resolution coupled ocean–atmosphere system is presented in this paper. This forecast-oriented system, based on current regional operational systems and evaluated using satellite and in situ observations, shows that the coupling improves both atmospheric and oceanic forecasts.
Julien Jouanno, Rachid Benshila, Léo Berline, Antonin Soulié, Marie-Hélène Radenac, Guillaume Morvan, Frédéric Diaz, Julio Sheinbaum, Cristele Chevalier, Thierry Thibaut, Thomas Changeux, Frédéric Menard, Sarah Berthet, Olivier Aumont, Christian Ethé, Pierre Nabat, and Marc Mallet
Geosci. Model Dev., 14, 4069–4086,Short summary
The tropical Atlantic has been facing a massive proliferation of Sargassum since 2011, with severe environmental and socioeconomic impacts. We developed a modeling framework based on the NEMO ocean model, which integrates transport by currents and waves, and physiology of Sargassum with varying internal nutrient quota, and considers stranding at the coast. Results demonstrate the ability of the model to reproduce and forecast the seasonal cycle and large-scale distribution of Sargassum biomass.
Achim Wirth and Florian Lemarié
Earth Syst. Dynam., 12, 689–708,Short summary
We show that modern concepts of non-equilibrium statistical mechanics can be applied to large-scale environmental fluid dynamics, where fluctuations are not thermal but come from turbulence. The work theorems developed by Jarzynski and Crooks are applied to air–sea interaction. Rather than looking at the average values of thermodynamic variables, their probability density functions are considered, which allows us to replace the inequalities of equilibrium statistical mechanics with equalities.
Olivier Marti, Sébastien Nguyen, Pascale Braconnot, Sophie Valcke, Florian Lemarié, and Eric Blayo
Geosci. Model Dev., 14, 2959–2975,Short summary
State-of-the-art Earth system models, like the ones used in CMIP6, suffer from temporal inconsistencies at the ocean–atmosphere interface. In this study, a mathematically consistent iterative Schwarz method is used as a reference. Its tremendous computational cost makes it unusable for production runs, but it allows us to evaluate the error made when using legacy coupling schemes. The impact on the climate at longer timescales of days to decades is not evaluated.
Florian Lemarié, Guillaume Samson, Jean-Luc Redelsperger, Hervé Giordani, Théo Brivoal, and Gurvan Madec
Geosci. Model Dev., 14, 543–572,Short summary
A simplified model of the atmospheric boundary layer (ABL) of intermediate complexity between a bulk parameterization and a full three-dimensional atmospheric model has been developed and integrated to the NEMO ocean model. An objective in the derivation of such a simplified model is to reach an apt representation of ocean-only numerical simulations of some of the key processes associated with air–sea interactions at the characteristic scales of the oceanic mesoscale.
Louis Marié, Fabrice Collard, Frédéric Nouguier, Lucia Pineau-Guillou, Danièle Hauser, François Boy, Stéphane Méric, Peter Sutherland, Charles Peureux, Goulven Monnier, Bertrand Chapron, Adrien Martin, Pierre Dubois, Craig Donlon, Tania Casal, and Fabrice Ardhuin
Ocean Sci., 16, 1399–1429,Short summary
With present-day techniques, ocean surface currents are poorly known near the Equator and globally for spatial scales under 200 km and timescales under 30 d. Wide-swath radar Doppler measurements are an alternative technique. Such direct surface current measurements are, however, affected by platform motions and waves. These contributions are analyzed in data collected during the DRIFT4SKIM airborne and in situ experiment, demonstrating the possibility of measuring currents from space globally.
Théo Brivoal, Guillaume Samson, Hervé Giordani, Romain Bourdallé-Badie, Florian Lemarié, and Gurvan Madec
Ocean Sci. Discuss.,
Preprint withdrawnShort summary
We investigate the interactions between near-surface winds and oceanic surface currents on the north-east atlantic region using a simplified lower atmosphere model coupled with an ocean model. we show that the upper ocean kinetic energy is significantly reduced due to these interactions, but in a smaller amplitude than if the wind feedback is ignored. We also show that wind-current interactions affect the deeper ocean by modifying its vertical structure and consequently the pressure field.
Guillaume Boutin, Camille Lique, Fabrice Ardhuin, Clément Rousset, Claude Talandier, Mickael Accensi, and Fanny Girard-Ardhuin
The Cryosphere, 14, 709–735,Short summary
We investigate the interactions of surface ocean waves with sea ice taking place at the interface between the compact sea ice cover and the open ocean. We use a newly developed coupling framework between a wave and an ocean–sea ice numerical model. Our results show how the push on sea ice exerted by waves changes the amount and the location of sea ice melting, with a strong impact on the ocean surface properties close to the ice edge.
Thomas Holding, Ian G. Ashton, Jamie D. Shutler, Peter E. Land, Philip D. Nightingale, Andrew P. Rees, Ian Brown, Jean-Francois Piolle, Annette Kock, Hermann W. Bange, David K. Woolf, Lonneke Goddijn-Murphy, Ryan Pereira, Frederic Paul, Fanny Girard-Ardhuin, Bertrand Chapron, Gregor Rehder, Fabrice Ardhuin, and Craig J. Donlon
Ocean Sci., 15, 1707–1728,Short summary
FluxEngine is an open-source software toolbox designed to allow for the easy and accurate calculation of air–sea gas fluxes. This article describes new functionality and capabilities, which include the ability to calculate fluxes for nitrous oxide and methane, optimisation for running FluxEngine on a stand-alone desktop computer, and extensive new features to support the in situ measurement community. Four research case studies are used to demonstrate these new features.
Marion Lebrun, Martin Vancoppenolle, Gurvan Madec, and François Massonnet
The Cryosphere, 13, 79–96,Short summary
The present analysis shows that the increase in the Arctic ice-free season duration will be asymmetrical, with later autumn freeze-up contributing about twice as much as earlier spring retreat. This feature is robustly found in a hierarchy of climate models and is consistent with a simple mechanism: solar energy is absorbed more efficiently than it can be released in non-solar form and should emerge out of variability within the next few decades.
Pedro Veras Guimarães, Fabrice Ardhuin, Peter Sutherland, Mickael Accensi, Michel Hamon, Yves Pérignon, Jim Thomson, Alvise Benetazzo, and Pierre Ferrant
Ocean Sci., 14, 1449–1460,Short summary
This paper introduces a new design of drifting buoy. The "surface kinematics buoy'' (SKIB) is particularly optimized for measuring wave–current interactions, including relatively short wave components, from 0.09 to 1 Hz, that are important for air–sea interactions and remote-sensing applications. The capability of this instrument is compared to other sensors, and the ability to measure current-induced wave variations is illustrated with data acquired in a macro-tidal coastal environment.
Christine Lac, Jean-Pierre Chaboureau, Valéry Masson, Jean-Pierre Pinty, Pierre Tulet, Juan Escobar, Maud Leriche, Christelle Barthe, Benjamin Aouizerats, Clotilde Augros, Pierre Aumond, Franck Auguste, Peter Bechtold, Sarah Berthet, Soline Bielli, Frédéric Bosseur, Olivier Caumont, Jean-Martial Cohard, Jeanne Colin, Fleur Couvreux, Joan Cuxart, Gaëlle Delautier, Thibaut Dauhut, Véronique Ducrocq, Jean-Baptiste Filippi, Didier Gazen, Olivier Geoffroy, François Gheusi, Rachel Honnert, Jean-Philippe Lafore, Cindy Lebeaupin Brossier, Quentin Libois, Thibaut Lunet, Céline Mari, Tomislav Maric, Patrick Mascart, Maxime Mogé, Gilles Molinié, Olivier Nuissier, Florian Pantillon, Philippe Peyrillé, Julien Pergaud, Emilie Perraud, Joris Pianezze, Jean-Luc Redelsperger, Didier Ricard, Evelyne Richard, Sébastien Riette, Quentin Rodier, Robert Schoetter, Léo Seyfried, Joël Stein, Karsten Suhre, Marie Taufour, Odile Thouron, Sandra Turner, Antoine Verrelle, Benoît Vié, Florian Visentin, Vincent Vionnet, and Philippe Wautelet
Geosci. Model Dev., 11, 1929–1969,Short summary
This paper presents the Meso-NH model version 5.4, which is an atmospheric non-hydrostatic research model that is applied on synoptic to turbulent scales. The model includes advanced numerical techniques and state-of-the-art physics parameterization schemes. It has been expanded to provide capabilities for a range of Earth system prediction applications such as chemistry and aerosols, electricity and lightning, hydrology, wildland fires, volcanic eruptions, and cyclones with ocean coupling.
Fabrice Ardhuin, Yevgueny Aksenov, Alvise Benetazzo, Laurent Bertino, Peter Brandt, Eric Caubet, Bertrand Chapron, Fabrice Collard, Sophie Cravatte, Jean-Marc Delouis, Frederic Dias, Gérald Dibarboure, Lucile Gaultier, Johnny Johannessen, Anton Korosov, Georgy Manucharyan, Dimitris Menemenlis, Melisa Menendez, Goulven Monnier, Alexis Mouche, Frédéric Nouguier, George Nurser, Pierre Rampal, Ad Reniers, Ernesto Rodriguez, Justin Stopa, Céline Tison, Clément Ubelmann, Erik van Sebille, and Jiping Xie
Ocean Sci., 14, 337–354,Short summary
The Sea surface KInematics Multiscale (SKIM) monitoring mission is a proposal for a future satellite that is designed to measure ocean currents and waves. Using a Doppler radar, the accurate measurement of currents requires the removal of the mean velocity due to ocean wave motions. This paper describes the main processing steps needed to produce currents and wave data from the radar measurements. With this technique, SKIM can provide unprecedented coverage and resolution, over the global ocean.
Charles Peureux, Alvise Benetazzo, and Fabrice Ardhuin
Ocean Sci., 14, 41–52,Short summary
Little is known on the short ocean wave (1 to 20 m wave length typically) directional distribution. It has been measured from a platform in the Adriatic Sea using a three-dimensional reconstruction technique, used for the first time for this purpose. In this record, while longer waves propagate along the wind direction, shorter waves have been found to propagate mainly along two oblique directions, more and more separated towards smaller scales.
Aurore Voldoire, Bertrand Decharme, Joris Pianezze, Cindy Lebeaupin Brossier, Florence Sevault, Léo Seyfried, Valérie Garnier, Soline Bielli, Sophie Valcke, Antoinette Alias, Mickael Accensi, Fabrice Ardhuin, Marie-Noëlle Bouin, Véronique Ducrocq, Stéphanie Faroux, Hervé Giordani, Fabien Léger, Patrick Marsaleix, Romain Rainaud, Jean-Luc Redelsperger, Evelyne Richard, and Sébastien Riette
Geosci. Model Dev., 10, 4207–4227,Short summary
This study presents the principles of the new coupling interface based on the SURFEX multi-surface model and the OASIS3-MCT coupler. As SURFEX can be plugged into several atmospheric models, it can be used in a wide range of applications. The objective of this development is to build and share a common structure for the atmosphere–surface coupling of all these applications, involving on the one hand atmospheric models and on the other hand ocean, ice, hydrology, and wave models.
Anton Beljaars, Emanuel Dutra, Gianpaolo Balsamo, and Florian Lemarié
Geosci. Model Dev., 10, 977–989,Short summary
Coupling an atmospheric model with snow and sea ice modules presents numerical stability challenges in integrations with long time steps as commonly used for weather prediction and climate simulations. Explicit flux coupling is often applied for simplicity. In this paper a simple method is presented to stabilize the coupling without having to introduce fully implicit coupling. A formal stability analysis confirms that the method is unconditionally stable.
Justin E. Stopa, Fabrice Ardhuin, and Fanny Girard-Ardhuin
The Cryosphere, 10, 1605–1629,Short summary
Satellite observations show the Arctic sea ice has decreased the last 30 years. From our wave model hindcast and satellite altimeter datasets we observe profound increasing wave heights, which are caused by the loss of sea ice and not the driving winds. If ice-free conditions persist later into fall, then regions like the Beaufort–Chukchi Sea will be prone to developing larger waves since the driving winds are strong this time of year.
Related subject area
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Bruno K. Zürcher
Geosci. Model Dev., 16, 1697–1711,Short summary
We present a novel algorithm to efficiently compute Barnes interpolation, which is a method for transforming data values recorded at irregularly spaced points into a corresponding regular grid. In contrast to naive implementations with an algorithmic complexity that depends on the product of the number of sample points and the number of grid points, our approach reduces this dependency to their sum.
David H. Marsico and Paul A. Ullrich
Geosci. Model Dev., 16, 1537–1551,Short summary
Climate models involve several different components, such as the atmosphere, ocean, and land models. Information needs to be exchanged, or remapped, between these models, and devising algorithms for performing this exchange is important for ensuring the accuracy of climate simulations. In this paper, we examine the efficacy of several traditional and novel approaches to remapping on the sphere and demonstrate where our approaches offer improvement.
Moritz Liebl, Jörg Robl, Stefan Hergarten, David Lundbek Egholm, and Kurt Stüwe
Geosci. Model Dev., 16, 1315–1343,Short summary
In this study, we benchmark a topography-based model for glacier erosion (OpenLEM) with a well-established process-based model (iSOSIA). Our experiments show that large-scale erosion patterns and particularly the transformation of valley length geometry from fluvial to glacial conditions are very similar in both models. This finding enables the application of OpenLEM to study the influence of climate and tectonics on glaciated mountains with reasonable computational effort on standard PCs.
James Kent, Thomas Melvin, and Golo Albert Wimmer
Geosci. Model Dev., 16, 1265–1276,Short summary
This paper introduces the Met Office's new shallow water model. The shallow water model is a building block towards the Met Office's new atmospheric dynamical core. The shallow water model is tested on a number of standard spherical shallow water test cases, including flow over mountains and unstable jets. Results show that the model produces similar results to other shallow water models in the literature.
Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, and Zhu Wang
Geosci. Model Dev., 16, 1213–1229,Short summary
This work applies a novel technical tool, multifidelity Monte Carlo (MFMC) estimation, to three climate-related benchmark experiments involving oceanic, atmospheric, and glacial modeling. By considering useful quantities such as maximum sea height and total (kinetic) energy, we show that MFMC leads to predictions which are more accurate and less costly than those obtained by standard methods. This suggests MFMC as a potential drop-in replacement for estimation in realistic climate models.
Piyoosh Jaysaval, Glenn E. Hammond, and Timothy C. Johnson
Geosci. Model Dev., 16, 961–976,Short summary
We present a robust and highly scalable implementation of numerical forward modeling and inversion algorithms for geophysical electrical resistivity tomography data. The implementation is publicly available and developed within the framework of PFLOTRAN (http://www.pflotran.org), an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The paper details all the theoretical and implementation aspects of the new capabilities along with test examples.
Lucas Schauer, Michael J. Schmidt, Nicholas B. Engdahl, Stephen D. Pankavich, David A. Benson, and Diogo Bolster
Geosci. Model Dev., 16, 833–849,Short summary
We develop a multi-dimensional, parallelized domain decomposition strategy for mass-transfer particle tracking methods in two and three dimensions, investigate different procedures for decomposing the domain, and prescribe an optimal tiling based on physical problem parameters and the number of available CPU cores. For an optimally subdivided diffusion problem, the parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to thousands of cores.
John Mern and Jef Caers
Geosci. Model Dev., 16, 289–313,Short summary
In this work, we formulate the sequential geoscientific data acquisition problem as a problem that is similar to playing chess against nature, except the pieces are not fully observed. Solutions to these problems are given in AI and rarely used in geoscientific data planning. We illustrate our approach to a simple 2D problem of mineral exploration.
Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Khanh Do, and Armistead G. Russell
Geosci. Model Dev., 15, 9015–9029,Short summary
While the national ambient air quality standard of ozone is based on the 3-year average of the fourth highest 8 h maximum (MDA8) ozone concentrations, these predicted extreme values using numerical methods are always biased low. We built four computational models (GAM, MARS, random forest and SVR) to predict the fourth highest MDA8 ozone in Southern California using precursor emissions, meteorology and climatological patterns. All models presented acceptable performance, with GAM being the best.
Zhihao Wang, Jason Goetz, and Alexander Brenning
Geosci. Model Dev., 15, 8765–8784,Short summary
A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.
Till Sachau, Haibin Yang, Justin Lang, Paul D. Bons, and Louis Moresi
Geosci. Model Dev., 15, 8749–8764,Short summary
Knowledge of the internal structures of the major continental ice sheets is improving, thanks to new investigative techniques. These structures are an essential indication of the flow behavior and dynamics of ice transport, which in turn is important for understanding the actual impact of the vast amounts of water trapped in continental ice sheets on global sea-level rise. The software studied here is specifically designed to simulate such structures and their evolution.
Keith J. Roberts, Alexandre Olender, Lucas Franceschini, Robert C. Kirby, Rafael S. Gioria, and Bruno S. Carmo
Geosci. Model Dev., 15, 8639–8667,Short summary
Finite-element methods (FEMs) permit the use of more flexible unstructured meshes but are rarely used in full waveform inversions (FWIs), an iterative process that reconstructs velocity models of earth’s subsurface, due to computational and memory storage costs. To reduce those costs, novel software is presented allowing the use of high-order mass-lumped FEMs on triangular meshes, together with a material-property mesh-adaptation performance-enhancing strategy, enabling its use in FWIs.
Grant Thomas Euen, Shangxin Liu, Rene Gassmöller, Timo Heister, and Scott David King
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
Due to the increasing availability of high-performance computing over the past decades numerical models have become an important tool for research. Here we test two geodynamic codes that produce such models: ASPECT, a newer code, and CitcomS, an older one. We show that they produce solutions that are extremely close. As methods and codes become more complex over time, showing reproducibility allows us to seamlessly link previously-known information to modern methodologies.
Konstantinos Papadakis, Yann Pfau-Kempf, Urs Ganse, Markus Battarbee, Markku Alho, Maxime Grandin, Maxime Dubart, Lucile Turc, Hongyang Zhou, Konstantinos Horaites, Ivan Zaitsev, Giulia Cozzani, Maarja Bussov, Evgeny Gordeev, Fasil Tesema, Harriet George, Jonas Suni, Vertti Tarvus, and Minna Palmroth
Geosci. Model Dev., 15, 7903–7912,Short summary
Vlasiator is a plasma simulation code that simulates the entire near-Earth space at a global scale. As 6D simulations require enormous amounts of computational resources, Vlasiator uses adaptive mesh refinement (AMR) to lighten the computational burden. However, due to Vlasiator’s grid topology, AMR simulations suffer from grid aliasing artifacts that affect the global results. In this work, we present and evaluate the performance of a mechanism for alleviating those artifacts.
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
This paper describes a new release for LISFLOOD-FP model for fast and efficient flood simulations. The model features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPU) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
Artur Safin, Damien Bouffard, Firat Ozdemir, Cintia L. Ramón, James Runnalls, Fotis Georgatos, Camille Minaudo, and Jonas Šukys
Geosci. Model Dev., 15, 7715–7730,Short summary
Reconciling the differences between numerical model predictions and observational data is always a challenge. In this paper, we investigate the viability of a novel approach to the calibration of a three-dimensional hydrodynamic model of Lake Geneva, where the target parameters are inferred in terms of distributions. We employ a filtering technique that generates physically consistent model trajectories and implement a neural network to enable bulk-to-skin temperature conversion.
Colin Grudzien and Marc Bocquet
Geosci. Model Dev., 15, 7641–7681,Short summary
Iterative optimization techniques, the state of the art in data assimilation, have largely focused on extending forecast accuracy to moderate- to long-range forecast systems. However, current methodology may not be cost-effective in reducing forecast errors in online, short-range forecast systems. We propose a novel optimization of these techniques for online, short-range forecast cycles, simultaneously providing an improvement in forecast accuracy and a reduction in the computational cost.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
Geosci. Model Dev., 15, 6695–6708,Short summary
To understand the scientific consequence of perturbations caused by slave cores in heterogeneous computing environments, we examine the influence of perturbation amplitudes on the determination of the cloud bottom and cloud top and compute the probability density function (PDF) of generated clouds. A series of comparisons of the PDFs between homogeneous and heterogeneous systems show consistently acceptable error tolerances when using slave cores in heterogeneous computing environments.
Vijay S. Mahadevan, Jorge E. Guerra, Xiangmin Jiao, Paul Kuberry, Yipeng Li, Paul Ullrich, David Marsico, Robert Jacob, Pavel Bochev, and Philip Jones
Geosci. Model Dev., 15, 6601–6635,Short summary
Coupled Earth system models require transfer of field data between multiple components with varying spatial resolutions to determine the correct climate behavior. We present the Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol to evaluate the accuracy, conservation properties, monotonicity, and local feature preservation of four different remapper algorithms for various unstructured mesh problems of interest. Future extensions to more practical use cases are also discussed.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398,Short summary
Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Andrew M. Bradley, Peter A. Bosler, and Oksana Guba
Geosci. Model Dev., 15, 6285–6310,Short summary
Tracer transport in atmosphere models can be computationally expensive. We describe a flexible and efficient interpolation semi-Lagrangian method, the Islet method. It permits using up to three grids that share an element grid: a dynamics grid for computing quantities such as the wind velocity; a physics parameterizations grid; and a tracer grid. The Islet method performs well on a number of verification problems and achieves high performance in the E3SM Atmosphere Model version 2.
Léo Pujol, Pierre-André Garambois, and Jérôme Monnier
Geosci. Model Dev., 15, 6085–6113,Short summary
This contribution presents a new numerical model for representing hydraulic–hydrological quantities at the basin scale. It allows modeling large areas at a low computational cost, with fine zooms where needed. It allows the integration of local and satellite measurements, via data assimilation methods, to improve the model's match to observations. Using this capability, good matches to in situ observations are obtained on a model of the complex Adour river network with fine zooms on floodplains.
Ludovic Räss, Ivan Utkin, Thibault Duretz, Samuel Omlin, and Yuri Y. Podladchikov
Geosci. Model Dev., 15, 5757–5786,Short summary
Continuum mechanics-based modelling of physical processes at large scale requires huge computational resources provided by massively parallel hardware such as graphical processing units. We present a suite of numerical algorithms, implemented using the Julia language, that efficiently leverages the parallelism. We demonstrate that our implementation is efficient, scalable and robust and showcase applications to various geophysical problems.
Meriem Krouma, Pascal Yiou, Céline Déandreis, and Soulivanh Thao
Geosci. Model Dev., 15, 4941–4958,Short summary
We evaluated the skill of a stochastic weather generator (SWG) to forecast precipitation at different time scales and in different areas of western Europe from analogs of Z500 hPa. The SWG has the skill to simulate precipitation for 5 and 10 d. We found that forecast weaknesses can be associated with specific weather patterns. The comparison with ECMWF forecasts confirms the skill of our model. This work is important because it provides information about weather forecasts over specific areas.
Piotr Dziekan and Piotr Zmijewski
Geosci. Model Dev., 15, 4489–4501,Short summary
Detailed computer simulations of clouds are important for understanding Earth's atmosphere and climate. The paper describes how the UWLCM has been adapted to work on supercomputers. A distinctive feature of UWLCM is that air flow is calculated by processors at the same time as cloud droplets are modeled by graphics cards. Thanks to this, use of computing resources is maximized and the time to complete simulations of large domains is not affected by communications between supercomputer nodes.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 15, 4147–4161,Short summary
A scale-dependent error growth described by a power law or by a quadratic hypothesis is studied in Lorenz’s system with three spatiotemporal levels. The validity of power law is extended by including a saturation effect. The quadratic hypothesis can only serve as a first guess. In addition, we study the initial error growth for the ECMWF forecast system. Fitting the parameters, we conclude that there is an intrinsic limit of predictability after 22 days.
Michael A. Olesik, Jakub Banaśkiewicz, Piotr Bartman, Manuel Baumgartner, Simon Unterstrasser, and Sylwester Arabas
Geosci. Model Dev., 15, 3879–3899,Short summary
In systems such as atmospheric clouds, droplets undergo growth through condensation of vapor. The broadness of the resultant size spectrum of droplets influences precipitation likelihood and the radiative properties of clouds. One of the inherent limitations of simulations of the problem is the so-called numerical diffusion causing overestimation of the spectrum width, hence the term numerical broadening. In the paper, we take a closer look at one of the algorithms used in this context: MPDATA.
Navjot Kukreja, Jan Hückelheim, Mathias Louboutin, John Washbourne, Paul H. J. Kelly, and Gerard J. Gorman
Geosci. Model Dev., 15, 3815–3829,Short summary
Full waveform inversion (FWI) is a partial-differential equation (PDE)-constrained optimization problem that is notorious for its high computational load and memory footprint. In this paper we present a method that combines recomputation with lossy compression to accelerate the computation with minimal loss of precision in the results. We show this using experiments running FWI with a variety of compression settings on a popular academic dataset.
Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
Geosci. Model Dev., 15, 3641–3662,Short summary
This paper addresses numerical challenges in reasoning about geological models constrained by sensor data, especially models that describe the history of an area in terms of a sequence of events. Our method ensures that small changes in simulated geological features, such as the position of a boundary between two rock layers, do not result in unrealistically large changes to resulting sensor measurements, as occur presently using several popular modeling packages.
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
Geosci. Model Dev., 15, 3433–3445,Short summary
In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
Geosci. Model Dev., 15, 2561–2597,Short summary
This paper proposes a new double Fourier series (DFS) method on a sphere that improves the numerical stability of a model compared with conventional DFS methods. The shallow-water model and the advection model using the new DFS method give stable results without the appearance of high-wavenumber noise near the poles. The model using the new DFS method is faster than the model using spherical harmonics (especially at high resolutions) and gives almost the same results.
Geosci. Model Dev., 15, 2505–2532,Short summary
I preset SciKit-GStat, a well-documented and tested Python package for variogram estimation. The variogram is the core means of geostatistics, which almost all other methods rely on. Geostatistical interpolation and field generation are widely spread in geoscience, i.e., for data assimilation or modeling. While SciKit-GStat focuses on effective and intuitive variogram estimation, it can interface with other prominent packages and make its variograms available for a multitude of methods.
Christopher J. L. D'Amboise, Michael Neuhauser, Michaela Teich, Andreas Huber, Andreas Kofler, Frank Perzl, Reinhard Fromm, Karl Kleemayr, and Jan-Thomas Fischer
Geosci. Model Dev., 15, 2423–2439,Short summary
The term gravitational mass flow (GMF) covers various natural hazard processes such as snow avalanches, rockfall, landslides, and debris flows. Here we present the open-source GMF simulation tool Flow-Py. The model equations are based on simple geometrical relations in three-dimensional terrain. We show that Flow-Py is an educational, innovative GMF simulation tool with three computational experiments: 1. validation of implementation, 2. performance, and 3. expandability.
Evan Baker, Anna B. Harper, Daniel Williamson, and Peter Challenor
Geosci. Model Dev., 15, 1913–1929,Short summary
We have adapted machine learning techniques to build a model of the land surface in Great Britain. The model was trained using data from a very complex land surface model called JULES. Our model is faster at producing simulations and predictions and can investigate many different scenarios, which can be used to improve our understanding of the climate and could also be used to help make local decisions.
Daichun Wang, Wei You, Zengliang Zang, Xiaobin Pan, Yiwen Hu, and Yanfei Liang
Geosci. Model Dev., 15, 1821–1840,Short summary
This paper presents a 3D variational data assimilation system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, which was developed for a size-resolved sectional model in WRF-Chem. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was designed. The results show that Himawari-8 AOT assimilation can significantly improve model aerosol analyses and forecasts.
Kevin Bulthuis and Eric Larour
Geosci. Model Dev., 15, 1195–1217,Short summary
We present and implement a stochastic solver to sample spatially and temporal varying uncertain input parameters in the Ice-sheet and Sea-level System Model, such as ice thickness or surface mass balance. We represent these sources of uncertainty using Gaussian random fields with Matérn covariance function. We generate random samples of this random field using an efficient computational approach based on solving a stochastic partial differential equation.
Urmas Raudsepp and Ilja Maljutenko
Geosci. Model Dev., 15, 535–551,Short summary
A model's ability to reproduce the state of a simulated object is always a subject of discussion. A new method for the multivariate assessment of numerical model skills uses the K-means algorithm for clustering model errors. All available data that fall into the model domain and simulation period are incorporated into the skill assessment. The clustered errors are used for spatial and temporal analysis of the model accuracy. The method can be applied to different types of geoscientific models.
Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov
Geosci. Model Dev., 14, 7749–7774,Short summary
We propose an implementation of the material point method using graphical processing units (GPUs) to solve elastoplastic problems in three-dimensional configurations, such as the granular collapse or the slumping mechanics, i.e., landslide. The computational power of GPUs promotes fast code executions, compared to a traditional implementation using central processing units (CPUs). This allows us to study complex three-dimensional problems tackling high spatial resolution.
Rafael Lago, Thomas Gastine, Tilman Dannert, Markus Rampp, and Johannes Wicht
Geosci. Model Dev., 14, 7477–7495,Short summary
In this work we discuss a two-dimensional distributed parallelization of MagIC, an open-source code for the numerical solution of the magnetohydrodynamics equations. Such a parallelization involves several challenges concerning the distribution of work and data. We detail our algorithm and compare it with the established, optimized, one-dimensional distribution in the context of the dynamo benchmark and discuss the merits of both implementations.
Moritz Lange, Henri Suominen, Mona Kurppa, Leena Järvi, Emilia Oikarinen, Rafael Savvides, and Kai Puolamäki
Geosci. Model Dev., 14, 7411–7424,Short summary
This study aims to replicate computationally expensive high-resolution large-eddy simulations (LESs) with regression models to simulate urban air quality and pollutant dispersion. The model development, including feature selection, model training and cross-validation, and detection of concept drift, has been described in detail. Of the models applied, log-linear regression shows the best performance. A regression model can replace LES unless high accuracy is needed.
Hynek Bednář, Aleš Raidl, and Jiří Mikšovský
Geosci. Model Dev., 14, 7377–7389,Short summary
Forecast errors in numerical weather prediction systems grow in time. To quantify the impacts of this growth, parametric error growth models may be employed. This study recalculates and newly defines parameters for several statistic models approximating error growth in the ECMWF forecasting system. Accurate values of parameters are important because they are used to evaluate improvements of the forecasting systems or to estimate predictability.
Denise Degen, Cameron Spooner, Magdalena Scheck-Wenderoth, and Mauro Cacace
Geosci. Model Dev., 14, 7133–7153,Short summary
In times of worldwide energy transitions, an understanding of the subsurface is increasingly important to provide renewable energy sources such as geothermal energy. To validate our understanding of the subsurface we require data. However, the data are usually not distributed equally and introduce a potential misinterpretation of the subsurface. Therefore, in this study we investigate the influence of measurements on temperature distribution in the European Alps.
Geoffroy Kirstetter, Olivier Delestre, Pierre-Yves Lagrée, Stéphane Popinet, and Christophe Josserand
Geosci. Model Dev., 14, 7117–7132,Short summary
The development of forecasting tools may help to limit the impacts of flash floods. Our purpose here is to demonstrate the possibility of using b-flood, which is a 2D tool based on shallow-water equations and adaptive mesh refinement.
Sojung Park and Seon K. Park
Geosci. Model Dev., 14, 6241–6255,Short summary
One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating subgrid-scale physical processes. Physical processes, such as cumulus, microphysics, and planetary boundary layer processes, are parameterized in NWP models by empirical and theoretical backgrounds. We developed an interface between a micro-genetic algorithm and the WRF model for a combinatorial optimization of physics for heavy rainfall events in Korea. The system improved precipitation forecasts.
Olivier Pannekoucke and Philippe Arbogast
Geosci. Model Dev., 14, 5957–5976,Short summary
This contributes to research on uncertainty prediction, which is important either for determining the weather today or estimating the risk in prediction. The problem is that uncertainty prediction is numerically very expensive. An alternative has been proposed wherein uncertainty is presented in a simplified form with only the dynamics of certain parameters required. This tool allows for the determination of the symbolic equations of these parameter dynamics and their numerical computation.
Annika Günther, Johannes Gütschow, and Mairi Louise Jeffery
Geosci. Model Dev., 14, 5695–5730,Short summary
The mitigation components of the nationally determined contributions (NDCs) under the Paris Agreement are essential in our fight against climate change. Regular updates with increased ambition are requested to limit global warming to 1.5–2 °C. The new and easy-to-update open-source tool NDCmitiQ can be used to quantify the NDCs' mitigation targets and construct resulting emissions pathways. In use cases, we show target uncertainties from missing clarity, data, and methodological challenges.
Futo Tomizawa and Yohei Sawada
Geosci. Model Dev., 14, 5623–5635,Short summary
A new method to predict chaotic systems from observation and process-based models is proposed by combining machine learning with data assimilation. Our method is robust to the sparsity of observation networks and can predict more accurately than a process-based model when it is biased. Our method effectively works when both observations and models are imperfect, which is often the case in geoscience. Therefore, our method is useful to solve a wide variety of prediction problems in this field.
Chloe Leach, Tom Coulthard, Andrew Barkwith, Daniel R. Parsons, and Susan Manson
Geosci. Model Dev., 14, 5507–5523,Short summary
Numerical models can be used to understand how coastal systems evolve over time, including likely responses to climate change. However, many existing models are aimed at simulating 10- to 100-year time periods do not represent a vertical dimension and are thus unable to include the effect of sea-level rise. The Coastline Evolution Model 2D (CEM2D) presented in this paper is an advance in this field, with the inclusion of the vertical coastal profile against which the water level can be altered.
Steven J. Phipps, Jason L. Roberts, and Matt A. King
Geosci. Model Dev., 14, 5107–5124,Short summary
Simplified schemes, known as parameterisations, are sometimes used to describe physical processes within numerical models. However, the values of the parameters are uncertain. This introduces uncertainty into the model outputs. We develop a simple approach to identify plausible ranges for model parameters. Using a model of the Antarctic Ice Sheet, we find that the value of one parameter can depend on the values of others. We conclude that a single optimal set of parameter values does not exist.
Axel Peytavin, Bruno Sainte-Rose, Gael Forget, and Jean-Michel Campin
Geosci. Model Dev., 14, 4769–4780,Short summary
We present a new algorithm developed at The Ocean Cleanup to update ocean plastic models based on measurements from the field to improve future cleaning operations. Prepared in collaboration with MIT researchers, this initial study presents its use in several analytical and real test cases in which two observers in a flow field record regular observations to update a plastic forecast. We demonstrate this improves the prediction, even with inaccurate knowledge of the water flows driving plastic.
Alari, V., Staneva, J., Breivik, Ø., Bidlot, J.-R., Mogensen, K., and Janssen, P.: Surface wave effects on water temperature in the Baltic Sea: simulation with the coupled NEMO-WAM model, Ocean Dynam., 66, 917–930, https://doi.org/10.1007/s10236-016-0963-x, 2016. a, b, c
Ali, A., Christensen, K. H., Øyvind Breivik, Malila, M., Raj, R. P., Bertino, L., Chassignet, E. P., and Bakhoday-Paskyabi, M.: A comparison of Langmuir turbulence parameterizations and key wave effects in a numerical model of the North Atlantic and Arctic Oceans, Ocean Model., 137, 76–97, https://doi.org/10.1016/j.ocemod.2019.02.005, 2019. a
Arakawa, A. and Lamb, V. R.: A Potential Enstrophy and Energy Conserving Scheme for the Shallow Water Equations, Mon. Weather Rev., 109, 18–36, https://doi.org/10.1175/1520-0493(1981)109<0018:APEAEC>2.0.CO;2, 1981. a
Ardhuin, F. and Jenkins, A. D.: On the interaction of surface waves and upper ocean turbulence, J. Phys. Oceanogr., 36, 551–557, https://doi.org/10.1175/JPO2862.1, 2006. a
Ardhuin, F., Herbers, T. H. C., Watts, K. P., van Vledder, G. P., Jensen, R., and Graber, H. C.: Swell and Slanting-Fetch Effects on Wind Wave Growth, J. Phys. Oceanogr., 37, 908–931, https://doi.org/10.1175/JPO3039.1, 2007. a
Ardhuin, F., Marié, L., Rascle, N., Forget, P., and Roland, A.: Observation and estimation of Lagrangian, Stokes and Eulerian currents induced by wind and waves at the sea surface, J. Phys. Oceanogr., 39, 2820–2838, https://doi.org/10.1175/2009JPO4169.1, 2009. a
Ardhuin, F., Rogers, E., Babanin, A., Filipot, J.-F., Magne, R., Roland, A., van der Westhuysen, A., Queffeulou, P., Lefevre, J.-M., Aouf, L., and Collard, F.: Semi-empirical dissipation source functions for wind-wave models: part I, definition, calibration and validation, J. Phys. Oceanogr., 40, 1917–1941, https://doi.org/10.1175/2010JPO4324.1, 2010a. a
Ardhuin, F., Rogers, E., Babanin, A. V., Filipot, J.-F., Magne, R., Roland, A., van der Westhuysen, A., Queffeulou, P., Lefevre, J.-M., Aouf, L., and Collard, F.: Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation, J. Phys. Oceanogr., 40, 1917–1941, https://doi.org/10.1175/2010JPO4324.1, 2010b. a, b
Ardhuin, F., Rascle, N., Chapron, B., Gula, J., Molemaker, J., Gille, S. T., Menemenlis, D., and Rocha, C.: Small scale currents have large effects on wind wave heights, J. Geophys. Res., 122, 4500–4517, https://doi.org/10.1002/2016JC012413, 2017a. a
Ardhuin, F., Suzuki, N., McWilliams, J. C., and Aiki, H.: Comments on “A Combined Derivation of the Integrated and Vertically Resolved, Coupled Wave–Current Equations”, J. Phys. Oceanogr., 47, 2377–2385, https://doi.org/10.1175/JPO-D-17-0065.1, 2017b. a
Banner, M. L. and Morison, R. P.: Refined source terms in wind wave models with explicit wave breaking prediction. Part I: Model framework and validation against field data, Ocean Model., 33, 177–189, https://doi.org/10.1016/j.ocemod.2010.01.002, 2010. a
Banner, M. L. and Young, I. R.: Modeling Spectral Dissipation in the Evolution of Wind Waves. Part I: Assessment of Existing Model Performance, J. Phys. Oceanogr., 24, 1550–1571, https://doi.org/10.1175/1520-0485(1994)024<1550:MSDITE>2.0.CO;2, 1994. a
Barnier, B., Madec, G., Penduff, T., Molines, J.-M., Treguier, A.-M., Le Sommer, J., Beckmann, A., Biastoch, A., Böning, C., Dengg, J., Derval, C., Durand, E., Gulev, S., Remy, E., Talandier, C., Theetten, S., Maltrud, M., McClean, J., and De Cuevas, B.: Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution, Ocean Dynam., 56, 543–567, https://doi.org/10.1007/s10236-006-0082-1, 2006. a
Belcher, S. E., Grant, A. L. M., Hanley, K. E., Fox-Kemper, B., Van Roekel, L., Sullivan, P. P., Large, W. G., Brown, A., Hines, A., Calvert, D., Rutgersson, A., Pettersson, H., Bidlot, J.-R., Janssen, P. A. E. M., and Polton, J. A.: A global perspective on Langmuir turbulence in the ocean surface boundary layer, Geophys. Res. Lett., 39, L18605, https://doi.org/10.1029/2012GL052932, 2012. a
Bennis, A.-C., Ardhuin, F., and Dumas, F.: On the coupling of wave and three-dimensional circulation models: Choice of theoretical framework, practical implementation and adiabatic tests, Ocean Model., 40, 260–272, https://doi.org/10.1016/j.ocemod.2011.09.003, 2011. a, b, c
Boccaletti, G., Ferrari, R., and Fox-Kemper, B.: Mixed Layer Instabilities and Restratification, J. Phys. Oceanogr., 37, 2228–2250, https://doi.org/10.1175/JPO3101.1, 2007. a
Boutin, G., Lique, C., Ardhuin, F., Accensi, M., Rousset, C., Talandier, C., and Girard-Ardhuin, F.: Coupling a spectral wave model with a coupled ocean-ice model, Drakkar meeting, Grenoble, France, 21–23 January, available at: http://pp.ige-grenoble.fr/pageperso/barnierb/WEBDRAKKAR2019/ (last access: 2 July 2020), 2019. a
Breivik, Ø., Bidlot, J.-R., and Janssen, P.: A Stokes drift approximation based on the Phillips spectrum, Ocean Model., 100, 49–56, https://doi.org/10.1016/j.ocemod.2016.01.005, 2016. a, b, c, d
Brodeau, L., Barnier, B., Gulev, S. K., and Woods, C.: Climatologically significant effects of some approximations in the bulk parameterizations of turbulent air-sea fluxes, J. Phys. Oceanogr., 47, 5–28, https://doi.org/10.1175/JPO-D-16-0169.1, 2016. a, b, c
Charnock, H.: Wind stress on a water surface, Q. J. Roy. Meteor. Soc., 81, 639–640, https://doi.org/10.1002/qj.49708135027, 1955. a
Chen, G. and Belcher, S. E.: Effects of Long Waves on Wind-Generated Waves, J. Phys. Oceanogr., 30, 2246–2256, 2000. a
Chen, S. and Curnic, M.: Ocean surface waves in Hurricane Ike (2008) and Superstorm Sandy (2012): Coupled model predictions and observations, Oceanogr. Meteorol., 103, 161–176, https://doi.org/10.5670/oceanog.2010.05, 2015. a
Couvelard, X.: ORCA025-WW3-Couvelard_etal_GMD, https://doi.org/10.5281/zenodo.3331463, 2019. a, b
Couvelard, X., Dumas, F., Garnier, V., Ponte, A., Talandier, C., and Treguier, A.: Mixed layer formation and restratification in presence of mesoscale and submesoscale turbulence, Ocean Model., 96, 243–253, https://doi.org/10.1016/j.ocemod.2015.10.004, 2015. 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, b, c
Craig, P. D. and Banner, M. L.: Modeling Wave-Enhanced Turbulence in the Ocean Surface Layer, J. Phys. Oceanogr., 24, 2546–2559, 1994. a
D'Asaro, E. A., Thomson, J., Shcherbina, A. Y., Harcourt, R. R., Cronin, M. F., Hemer, M. A., and Fox-Kemper, B.: Quantifying upper ocean turbulence driven by surface waves, Geophys. Res. Lett., 41, 102–107, https://doi.org/10.1002/2013GL058193, 2014. a
Deardorff, J. W.: Stratocumulus-capped mixing layers derived from a threedimensional model, Bound.-Lay. Meteorol., 18, 495–527, 1980. a
de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A., and Iudicone, D.: Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology, J. Geophys. Res., 109, C12003, https://doi.org/10.1029/2004JC002378, 2004. a, b
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Ducousso, N., Le Sommer, J., Molines, J.-M., and Bell, M.: Impact of the “ymmetric Instability of the Computational Kind” at mesoscale- and submesoscale-permitting resolutions, Ocean Model., 120, 18–26, https://doi.org/10.1016/j.ocemod.2017.10.006, 2017. a
Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. A., and Edson, J. B.: Bulk Parameterization of Air–Sea Fluxes: Updates and Verification for the COARE Algorithm, J. Climate, 16, 571–591, https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2, 2003. a
Fan, Y. and Griffies, S. M.: Impacts of Parameterized Langmuir Turbulence and Nonbreaking Wave Mixing in Global Climate Simulations, J. Climate, 27, 4752–4775, https://doi.org/10.1175/JCLI-D-13-00583.1, 2014. a
Fox-Kemper, B., Ferrari, R., and Hallberg, R.: Parameterization of Mixed Layer Eddies. Part I: Theory and Diagnosis, J. Phys. Oceanogr., 38, 1145–1165, https://doi.org/10.1175/2007JPO3792.1, 2008. a
Hasselmann, K.: Ocean circulation and climate change, Tellus B, 43, 82–103, https://doi.org/10.3402/tellusb.v43i4.15399, 1991. a
Hasselmann, S., Hasselmann, K., Allender, H., and Barnet, T. P.: Computations and parameterizations of the nonlinear energy transfer in a gravity wave spectrum. Part II. Parameterizations of the nonlinear energy transfer for application in wave models , J. Phys. Oceanogr., 15, 1378–1391, 1985. a
Hilburn, K.: The passive microwave water cycle product, Remote Sensing Systems (REMSS) Technical Report 072409, Santa Rosa (CA), 30 pp., Tech. rep., 2009. a
Hwang, P. A.: Fetch- and Duration-Limited Nature of Surface Wave Growth inside Tropical Cyclones: With Applications to Air–Sea Exchange and Remote Sensing*, J. Phys. Oceanogr., 46, 41–56, 2015. a
Irvine, D. E. and Tilley, D. G.: Ocean wave directional spectra and wave-current interaction in the Agulhas from the Shuttle Imaging Radar-B synthetic aperture radar, J. Geophys. Res.-Oceans, 93, 15389–15401, https://doi.org/10.1029/JC093iC12p15389, 1988. a
Jacob, R., Larson, J., and Ong, E.: M × N Communication and Parallel Interpolation in Community Climate System Model Version 3 Using the Model Coupling Toolkit, Int. J. High Perform. C., 19, 293–307, https://doi.org/10.1177/1094342005056116, 2005. a
Janssen, P. A.: Progress in ocean wave forecasting, J. Comp. Phys., 227, 3572–3594, https://doi.org/10.1016/j.jcp.2007.04.029, 2008. a
Janssen, P. A. E. M.: Quasi-linear Theory of Wind-Wave Generation Applied to Wave Forecasting, J. Phys. Oceanogr., 21, 1631–1642, 1991. a
Janssen, P. A. E. M.: On some consequences of the canonical transformation in the Hamiltonian theory of water waves, J. Fluid Mech., 637, 1–44, https://doi.org/10.1017/S0022112009008131, 2009. a
Komen, G. J., Cavaleri, L., Donelan, M., Hasselmann, K., Hasselmann, S., and Janssen, P. A. E. M.: Dynamics and modelling of ocean waves, Cambridge University Press, Cambridge, 1994. a
Large, W. G. and Yeager, S. G.: The global climatology of an interannually varying air–sea flux data set, Clim. Dynam., 33, 341–364, https://doi.org/10.1007/s00382-008-0441-3, 2009. a
Law Chune, S. and Aouf, L.: Wave effects in global ocean modeling: parametrizations vs. forcing from a wave model, Ocean Dynam., 68, 1739–1758, https://doi.org/10.1007/s10236-018-1220-2, 2018. a, b, c, d
Leclair, M. and Madec, G.: A conservative leap-frog time stepping method, Oceanogr. Meteorol., 30, 88–94, https://doi.org/10.1016/j.ocemod.2009.06.006, 2009. a
Lemarié, F., Samson, G., Redelsperger, J.-L., Giordani, H., Brivoal, T., Masson, S., and Madec, G.: A simplified atmospheric boundary layer model for an improved representation of air-sea interactions in eddying oceanic models: implementation and first evaluation in NEMO(4.0), Geosci. Model Dev. Discuss., submitted, 2020. a
Li, Q., Webb, A., Fox-Kemper, B., Craig, A., and Danabasoglu, G.: Langmuir mixing effects on global climate: WAVEWATCH III in CESM, Oceanogr. Meteorol., 103, 145–160, https://doi.org/10.1016/j.ocemod.2015.07.020, 2016. a
Li, Q., Fox-Kemper, B., Breivik, O., and Webb, A.: Statistical models of global Langmuir mixing, Oceanogr. Meteorol., 113, 95–114, https://doi.org/10.1016/j.ocemod.2017.03.016, 2017. a, b
Lévy, M., Estublier, A., and Madec, G.: Choice of an advection scheme for biogeochemical models, Geophys. Res. Lett., 28, 3725–3728, https://doi.org/10.1029/2001GL012947, 2001. a
Mapp, G. R., Welch, C. S., and Munday, J. C.: Wave refraction by warm core rings, J. Geophys. Res.-Oceans, 90, 7153–7162, https://doi.org/10.1029/JC090iC04p07153, 1985. a
McWilliams, J. C., Restrepo, J. M., and Lane, E. M.: An asymptotic theory for the interaction of waves and currents in coastal waters, J. Fluid Mech., 511, 135–178, https://doi.org/10.1017/S0022112004009358, 2004. a, b, c
Michaud, H., Marsaleix, P., Leredde, Y., Estournel, C., Bourrin, F., Lyard, F., Mayet, C., and Ardhuin, F.: Three-dimensional modelling of wave-induced current from the surf zone to the inner shelf, Ocean Sci., 8, 657–681, https://doi.org/10.5194/os-8-657-2012, 2012. a
Moghimi, S., Klingbeil, K., Gräwe, U., and Burchard, H.: A direct comparison of a depth-dependent Radiation stress formulation and a Vortex force formulation within a three-dimensional coastal ocean model, Ocean Model., 70, 132–144, https://doi.org/10.1016/j.ocemod.2012.10.002, 2013. a
Phillips, O.: On the response of short ocean wave components at fixed wavenumber to ocean current variations, J. Phys. Oceanogr., 14, 1425–1433, 1984. a
Polonichko, V.: Generation of Langmuir circulation for nonaligned wind stress and the Stokes drift, J. Geophys. Res., 102, 15773–15780, https://doi.org/10.1029/97JC00460, 1997. a, b
Rascle, N. and Ardhuin, F.: Drift and mixing under the ocean surface revisited: Stratified conditions and model-data comparisons, J. Geophys. Res., 114, C02016, https://doi.org/10.1029/2007JC004466, 2009. a
Rascle, N. and Ardhuin, F.: A global wave parameter database for geophysical applications. Part 2: model validation with improved source term parameterization, Ocean Modelling, 70, 174–188, https://doi.org/10.1016/j.ocemod.2012.12.001, 2013. a, b
Rascle, N., Ardhuin, F., Queffeulou, P., and Croizé-Fillon, D.: A global wave parameter database for geophysical applications. Part 1: wave-current-turbulence interaction parameters for the open ocean based on traditional parameterizations, Ocean Model., 25, 154–171, https://doi.org/10.1016/j.ocemod.2008.07.006, 2008. a, b, c
Redelsperger, J. L., Mahé, F., and Carlotti, P.: A Simple And General Subgrid Model Suitable Both For Surface Layer And Free-Stream Turbulence, Bound.-Lay. Meteorol., 101, 375–408, 2001. a
Reichl, B. G., Ginis, I., Hara, T., Thomas, B., Kukulka, T., and Wang, D.: Impact of Sea-State-Dependent Langmuir Turbulence on the Ocean Response to a Tropical Cyclone, Mon. Weather Rev., 144, 4569–4590, https://doi.org/10.1175/MWR-D-16-0074.1, 2016. a
Renault, L., Molemaker, M. J., McWilliams, J. C., Shchepetkin, A. F., Lemarié, F., Chelton, D., Illig, S., and Hall, A.: Modulation of Wind Work by Oceanic Current Interaction with the Atmosphere, J. Phys. Oceanogr., 46, 1685–1704, https://doi.org/10.1175/JPO-D-15-0232.1, 2016. a, b
Rodgers, K. B., Aumont, O., Mikaloff Fletcher, S. E., Plancherel, Y., Bopp, L., de Boyer Montégut, C., Iudicone, D., Keeling, R. F., Madec, G., and Wanninkhof, R.: Strong sensitivity of Southern Ocean carbon uptake and nutrient cycling to wind stirring, Biogeosciences, 11, 4077–4098, https://doi.org/10.5194/bg-11-4077-2014, 2014. a, b
Roquet, F., Madec, G., McDougall, T., and Barker, P.: An accurate polynomial expression for the TEOS-10 equation of state for use in ocean general circulation models., Oceanogr. Meteorol., 90, 29–43, https://doi.org/10.1016/j.ocemod.2015.04.002, 2015. a, b
Rousset, C., Vancoppenolle, M., Madec, G., Fichefet, T., Flavoni, S., Barthélemy, A., Benshila, R., Chanut, J., Levy, C., Masson, S., and Vivier, F.: The Louvain-La-Neuve sea ice model LIM3.6: global and regional capabilities, Geosci. Model Dev., 8, 2991–3005, https://doi.org/10.5194/gmd-8-2991-2015, 2015. a
Skyllingstad, E. D. and Denbo, D. W.: An ocean large-eddy simulation of Langmuir circulations and convection in the surface mixed layer, J. Geophys. Res., 100, 8501–8522, https://doi.org/10.1029/94JC03202, 1995. a
Stopa, J. E., Sutherland, P., and Ardhuin, F.: Strong and highly variable push of ocean waves on Southern Ocean sea ice, P. Natl. Acad. Sci. USA, 115, 5861–5865, https://doi.org/10.1073/pnas.1802011115, 2018. a
Suzuki, N. and Fox-Kemper, B.: Understanding Stokes forces in the wave-averaged equations, J. Geophys. Res., 121, 3579–3596, https://doi.org/10.1002/2015JC011566, 2016. a, b
Tennekes, H. and Lumley, J. L.: A first course in turbulence, The MIT Press, 1972. a
Tolman, H. L., Balasubramaniyan, B., Burroughs, L. D., Chalikov, D. V., Chao, Y. Y., Chen, H. S., and Gerald, V. M.: Development and Implementation of Wind-Generated Ocean Surface Wave Modelsat NCEP, Weather Forecast., 17, 311–333, https://doi.org/10.1175/1520-0434(2002)017<0311:DAIOWG>2.0.CO;2, 2002. a
Uchiyama, Y., McWilliams, J. C., and Shchepetkin, A. F.: Wave–current interaction in an oceanic circulation model with a vortex-force formalism: Application to the surf zone, Ocean Model., 34, 16–35, https://doi.org/10.1016/j.ocemod.2010.04.002, 2010. a
UNESCO: Algorithms for computation of fundamental property of sea water, 44, 1983. a
Valcke, S.: The OASIS3 coupler: a European climate modelling community software, Geosci. Model Dev., 6, 373–388, https://doi.org/10.5194/gmd-6-373-2013, 2013. a, b, c
Van Roekel, L. P., Fox-Kemper, B., Sullivan, P. P., Hamlington, P. E., and Haney, S. R.: The form and orientation of Langmuir cells for misaligned winds and waves, J. Geophys. Res., 117, C05001, https://doi.org/10.1029/2011JC007516, 2012. a, b
Wu, L., Staneva, J., Breivik, O., Rutgersson, A., Nurser, A. G., Clementi, E., and Madec, G.: Wave effects on coastal upwelling and water level, Ocean Model., 140, 101405, https://doi.org/10.1016/j.ocemod.2019.101405, 2019. a, b
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Within the framework of the Copernicus Marine Environment Monitoring Service (CMEMS), an objective is to demonstrate the contribution of coupling the high-resolution analysis and forecasting system with a wave model. This study describes the necessary steps and discusses the various choices made for coupling a wave model and an oceanic model for global-scale applications.
Within the framework of the Copernicus Marine Environment Monitoring Service (CMEMS), an...