Development and technical paper
23 Jul 2021
Development and technical paper
| 23 Jul 2021
A discrete interaction numerical model for coagulation and fragmentation of marine detritic particulate matter (Coagfrag v.1)
Gwenaëlle Gremion et al.
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Mathieu Casado, Gwenaëlle Gremion, Paul Rosenbaum, Jilda Alicia Caccavo, Kelsey Aho, Nicolas Champollion, Sarah L. Connors, Adrian Dahood, Alfonso Fernandez, Martine Lizotte, Katja Mintenbeck, Elvira Poloczanska, and Gerlis Fugmann
Geosci. Commun., 3, 89–97, https://doi.org/10.5194/gc-3-89-2020, https://doi.org/10.5194/gc-3-89-2020, 2020
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Early-career scientists (ECSs) are rarely invited to act as peer reviewers. Participating in a group peer review of the IPCC Special Report on Ocean and Cryosphere in a Changing Climate, PhD students spent more time reviewing than more established scientists and provided a similar proportion of substantive comments. By soliciting and including ECSs in peer review, the scientific community would reduce the burden on more established scientists and may improve the quality of that process.
Flavienne Bruyant, Rémi Amiraux, Marie-Pier Amyot, Philippe Archambault, Lise Artigue, Lucas Barbedo de Freitas, Guislain Bécu, Simon Bélanger, Pascaline Bourgain, Annick Bricaud, Etienne Brouard, Camille Brunet, Tonya Burgers, Danielle Caleb, Katrine Chalut, Hervé Claustre, Véronique Cornet-Barthaux, Pierre Coupel, Marine Cusa, Fanny Cusset, Laeticia Dadaglio, Marty Davelaar, Gabrièle Deslongchamps, Céline Dimier, Julie Dinasquet, Dany Dumont, Brent Else, Igor Eulaers, Joannie Ferland, Gabrielle Filteau, Marie-Hélène Forget, Jérome Fort, Louis Fortier, Martí Galí, Morgane Gallinari, Svend-Erik Garbus, Nicole Garcia, Catherine Gérikas Ribeiro, Colline Gombault, Priscilla Gourvil, Clémence Goyens, Cindy Grant, Pierre-Luc Grondin, Pascal Guillot, Sandrine Hillion, Rachel Hussherr, Fabien Joux, Hannah Joy-Warren, Gabriel Joyal, David Kieber, Augustin Lafond, José Lagunas, Patrick Lajeunesse, Catherine Lalande, Jade Larivière, Florence Le Gall, Karine Leblanc, Mathieu Leblanc, Justine Legras, Keith Lévesque, Kate-M. Lewis, Edouard Leymarie, Aude Leynaert, Thomas Linkowski, Martine Lizotte, Adriana Lopes dos Santos, Claudie Marec, Dominique Marie, Guillaume Massé, Philippe Massicotte, Atsushi Matsuoka, Lisa A. Miller, Sharif Mirshak, Nathalie Morata, Brivaela Moriceau, Philippe-Israël Morin, Simon Morisset, Anders Mosbech, Alfonso Mucci, Gabrielle Nadaï, Christian Nozais, Ingrid Obernosterer, Thimoté Paire, Christos Panagiotopoulos, Marie Parenteau, Noémie Pelletier, Marc Picheral, Bernard Quéguiner, Patrick Raimbault, Joséphine Ras, Eric Rehm, Llúcia Ribot Lacosta, Jean-François Rontani, Blanche Saint-Béat, Julie Sansoulet, Noé Sardet, Catherine Schmechtig, Antoine Sciandra, Richard Sempéré, Caroline Sévigny, Jordan Toullec, Margot Tragin, Jean-Éric Tremblay, Annie-Pier Trottier, Daniel Vaulot, Anda Vladoiu, Lei Xue, Gustavo Yunda-Guarin, and Marcel Babin
Earth Syst. Sci. Data, 14, 4607–4642, https://doi.org/10.5194/essd-14-4607-2022, https://doi.org/10.5194/essd-14-4607-2022, 2022
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This paper presents a dataset acquired during a research cruise held in Baffin Bay in 2016. We observed that the disappearance of sea ice in the Arctic Ocean increases both the length and spatial extent of the phytoplankton growth season. In the future, this will impact the food webs on which the local populations depend for their food supply and fisheries. This dataset will provide insight into quantifying these impacts and help the decision-making process for policymakers.
Frédéric Dupont, Dany Dumont, Jean-François Lemieux, Elie Dumas-Lefebvre, and Alain Caya
The Cryosphere, 16, 1963–1977, https://doi.org/10.5194/tc-16-1963-2022, https://doi.org/10.5194/tc-16-1963-2022, 2022
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In some shallow seas, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. A scheme has already proposed where the keel thickness varies linearly with the mean thickness. Here, we extend the approach by taking into account the ice thickness and bathymetry distributions. The probabilistic approach shows a reasonably good agreement with observations and previous grounding scheme while potentially offering more physical insights into the formation of landfast ice.
Elie Dumas-Lefebvre and Dany Dumont
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-328, https://doi.org/10.5194/tc-2021-328, 2021
Revised manuscript accepted for TC
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It is a known fact that ocean waves break sea ice but no one could ever capture it with a camera. This was until we brought a drone on a research vessel to break sea ice with ship-generated waves. The resulting footage allows for an in-depth analysis of breakup. We obtain that ice fragments have a thickness-dependent preferential size. More importantly, we demonstrated that this kind of experiment represents a very convenient way for studying wave-ice interaction and improve sea ice models.
Philippe Massicotte, Rainer M. W. Amon, David Antoine, Philippe Archambault, Sergio Balzano, Simon Bélanger, Ronald Benner, Dominique Boeuf, Annick Bricaud, Flavienne Bruyant, Gwenaëlle Chaillou, Malik Chami, Bruno Charrière, Jing Chen, Hervé Claustre, Pierre Coupel, Nicole Delsaut, David Doxaran, Jens Ehn, Cédric Fichot, Marie-Hélène Forget, Pingqing Fu, Jonathan Gagnon, Nicole Garcia, Beat Gasser, Jean-François Ghiglione, Gaby Gorsky, Michel Gosselin, Priscillia Gourvil, Yves Gratton, Pascal Guillot, Hermann J. Heipieper, Serge Heussner, Stanford B. Hooker, Yannick Huot, Christian Jeanthon, Wade Jeffrey, Fabien Joux, Kimitaka Kawamura, Bruno Lansard, Edouard Leymarie, Heike Link, Connie Lovejoy, Claudie Marec, Dominique Marie, Johannie Martin, Jacobo Martín, Guillaume Massé, Atsushi Matsuoka, Vanessa McKague, Alexandre Mignot, William L. Miller, Juan-Carlos Miquel, Alfonso Mucci, Kaori Ono, Eva Ortega-Retuerta, Christos Panagiotopoulos, Tim Papakyriakou, Marc Picheral, Louis Prieur, Patrick Raimbault, Joséphine Ras, Rick A. Reynolds, André Rochon, Jean-François Rontani, Catherine Schmechtig, Sabine Schmidt, Richard Sempéré, Yuan Shen, Guisheng Song, Dariusz Stramski, Eri Tachibana, Alexandre Thirouard, Imma Tolosa, Jean-Éric Tremblay, Mickael Vaïtilingom, Daniel Vaulot, Frédéric Vaultier, John K. Volkman, Huixiang Xie, Guangming Zheng, and Marcel Babin
Earth Syst. Sci. Data, 13, 1561–1592, https://doi.org/10.5194/essd-13-1561-2021, https://doi.org/10.5194/essd-13-1561-2021, 2021
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The MALINA oceanographic expedition was conducted in the Mackenzie River and the Beaufort Sea systems. The sampling was performed across seven shelf–basin transects to capture the meridional gradient between the estuary and the open ocean. The main goal of this research program was to better understand how processes such as primary production are influencing the fate of organic matter originating from the surrounding terrestrial landscape during its transition toward the Arctic Ocean.
Mathieu Casado, Gwenaëlle Gremion, Paul Rosenbaum, Jilda Alicia Caccavo, Kelsey Aho, Nicolas Champollion, Sarah L. Connors, Adrian Dahood, Alfonso Fernandez, Martine Lizotte, Katja Mintenbeck, Elvira Poloczanska, and Gerlis Fugmann
Geosci. Commun., 3, 89–97, https://doi.org/10.5194/gc-3-89-2020, https://doi.org/10.5194/gc-3-89-2020, 2020
Short summary
Short summary
Early-career scientists (ECSs) are rarely invited to act as peer reviewers. Participating in a group peer review of the IPCC Special Report on Ocean and Cryosphere in a Changing Climate, PhD students spent more time reviewing than more established scientists and provided a similar proportion of substantive comments. By soliciting and including ECSs in peer review, the scientific community would reduce the burden on more established scientists and may improve the quality of that process.
Anna J. Crawford, Derek Mueller, Gregory Crocker, Laurent Mingo, Luc Desjardins, Dany Dumont, and Marcel Babin
The Cryosphere, 14, 1067–1081, https://doi.org/10.5194/tc-14-1067-2020, https://doi.org/10.5194/tc-14-1067-2020, 2020
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Large tabular icebergs (
ice islands) are symbols of climate change as well as marine hazards. We measured thickness along radar transects over two visits to a 14 km2 Arctic ice island and left automated equipment to monitor surface ablation and thickness over 1 year. We assess variation in thinning rates and calibrate an ice–ocean melt model with field data. Our work contributes to understanding ice island deterioration via logistically complex fieldwork in a remote environment.
Ariadna Celina Nocera, Dany Dumont, and Irene R. Schloss
Biogeosciences Discuss., https://doi.org/10.5194/bg-2020-10, https://doi.org/10.5194/bg-2020-10, 2020
Manuscript not accepted for further review
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Zooplankton, which means drifting animals, represents a large class of animals that graze the phytoplankton that grows near the surface of oceans, lakes and estuaries and feed many other organisms of aquatic food webs. It is known that zooplankton migrate vertically every day in the water column to avoid visual predation, a process that is not often represented in ecosystem models. This paper presents a model that simulate this behavior and study its impacts on a coastal ocean environment.
Philippe Massicotte, Rémi Amiraux, Marie-Pier Amyot, Philippe Archambault, Mathieu Ardyna, Laurent Arnaud, Lise Artigue, Cyril Aubry, Pierre Ayotte, Guislain Bécu, Simon Bélanger, Ronald Benner, Henry C. Bittig, Annick Bricaud, Éric Brossier, Flavienne Bruyant, Laurent Chauvaud, Debra Christiansen-Stowe, Hervé Claustre, Véronique Cornet-Barthaux, Pierre Coupel, Christine Cox, Aurelie Delaforge, Thibaud Dezutter, Céline Dimier, Florent Domine, Francis Dufour, Christiane Dufresne, Dany Dumont, Jens Ehn, Brent Else, Joannie Ferland, Marie-Hélène Forget, Louis Fortier, Martí Galí, Virginie Galindo, Morgane Gallinari, Nicole Garcia, Catherine Gérikas Ribeiro, Margaux Gourdal, Priscilla Gourvil, Clemence Goyens, Pierre-Luc Grondin, Pascal Guillot, Caroline Guilmette, Marie-Noëlle Houssais, Fabien Joux, Léo Lacour, Thomas Lacour, Augustin Lafond, José Lagunas, Catherine Lalande, Julien Laliberté, Simon Lambert-Girard, Jade Larivière, Johann Lavaud, Anita LeBaron, Karine Leblanc, Florence Le Gall, Justine Legras, Mélanie Lemire, Maurice Levasseur, Edouard Leymarie, Aude Leynaert, Adriana Lopes dos Santos, Antonio Lourenço, David Mah, Claudie Marec, Dominique Marie, Nicolas Martin, Constance Marty, Sabine Marty, Guillaume Massé, Atsushi Matsuoka, Lisa Matthes, Brivaela Moriceau, Pierre-Emmanuel Muller, Christopher-John Mundy, Griet Neukermans, Laurent Oziel, Christos Panagiotopoulos, Jean-Jacques Pangrazi, Ghislain Picard, Marc Picheral, France Pinczon du Sel, Nicole Pogorzelec, Ian Probert, Bernard Quéguiner, Patrick Raimbault, Joséphine Ras, Eric Rehm, Erin Reimer, Jean-François Rontani, Søren Rysgaard, Blanche Saint-Béat, Makoto Sampei, Julie Sansoulet, Catherine Schmechtig, Sabine Schmidt, Richard Sempéré, Caroline Sévigny, Yuan Shen, Margot Tragin, Jean-Éric Tremblay, Daniel Vaulot, Gauthier Verin, Frédéric Vivier, Anda Vladoiu, Jeremy Whitehead, and Marcel Babin
Earth Syst. Sci. Data, 12, 151–176, https://doi.org/10.5194/essd-12-151-2020, https://doi.org/10.5194/essd-12-151-2020, 2020
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The Green Edge initiative was developed to understand the processes controlling the primary productivity and the fate of organic matter produced during the Arctic spring bloom (PSB). In this article, we present an overview of an extensive and comprehensive dataset acquired during two expeditions conducted in 2015 and 2016 on landfast ice southeast of Qikiqtarjuaq Island in Baffin Bay.
Related subject area
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The tidal effects in the Finite-volumE Sea ice–Ocean Model (FESOM2.1): a comparison between parameterised tidal mixing and explicit tidal forcing
HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic
Moana Ocean Hindcast – a > 25-year simulation for New Zealand waters using the Regional Ocean Modeling System (ROMS) v3.9 model
A nonhydrostatic oceanic regional model, ORCTM v1, for internal solitary wave simulation
How does 4DVar data assimilation affect the vertical representation of mesoscale eddies? A case study with observing system simulation experiments (OSSEs) using ROMS v3.9
An ensemble Kalman filter-based ocean data assimilation system improved by adaptive observation error inflation (AOEI)
GULF18, a high-resolution NEMO-based tidal ocean model of the Arabian/Persian Gulf
The Baltic Sea Model Intercomparison Project (BMIP) – a platform for model development, evaluation, and uncertainty assessment
An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0
Wind work at the air-sea interface: a modeling study in anticipation of future space missions
Improved upper-ocean thermodynamical structure modeling with combined effects of surface waves and M2 internal tides on vertical mixing: a case study for the Indian Ocean
The bulk parameterizations of turbulent air–sea fluxes in NEMO4: the origin of sea surface temperature differences in a global model study
NeverWorld2: an idealized model hierarchy to investigate ocean mesoscale eddies across resolutions
Observing system simulation experiments reveal that subsurface temperature observations improve estimates of circulation and heat content in a dynamic western boundary current
Parallel implementation of the SHYFEM (System of HydrodYnamic Finite Element Modules) model
Barotropic Tides in MPAS-Ocean: Impact of Ice Shelf Cavities
Block-structured, equal-workload, multi-grid-nesting interface for the Boussinesq wave model FUNWAVE-TVD (Total Variation Diminishing)
Evaluation of an emergent feature of sub-shelf melt oscillations from an idealized coupled ice sheet–ocean model using FISOC (v1.1) – ROMSIceShelf (v1.0) – Elmer/Ice (v9.0)
GNOM v1.0: an optimized steady-state model of the modern marine neodymium cycle
Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system
ROMSPath v1.0: offline particle tracking for the Regional Ocean Modeling System (ROMS)
DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations
RADIv1: a non-steady-state early diagenetic model for ocean sediments in Julia and MATLAB/GNU Octave
IBI-CCS: a regional high-resolution model to simulate sea level in western Europe
Empirical Lagrangian parametrization for wind-driven mixing of buoyant particles at the ocean surface
Improving ocean modeling software NEMO 4.0 benchmarking and communication efficiency
Improvements in the regional South China Sea Operational Oceanography Forecasting System (SCSOFSv2)
Reconsideration of wind stress, wind waves, and turbulence in simulating wind-driven currents of shallow lakes in the Wave and Current Coupled Model (WCCM) version 1.0
ISWFoam: a numerical model for internal solitary wave simulation in continuously stratified fluids
PyCO2SYS v1.8: marine carbonate system calculations in Python
Plume spreading test case for coastal ocean models
The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content
S2P3-R v2.0: computationally efficient modelling of shelf seas on regional to global scales
The Lagrangian-based Floating Macroalgal Growth and Drift Model (FMGDM v1.0): application to the Yellow Sea green tide
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Sedapp v2021: a nonlinear diffusion-based forward stratigraphic model for shallow marine environments
Parallel computing efficiency of SWAN 40.91
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Pengyang Song, Dmitry Sidorenko, Patrick Scholz, Maik Thomas, and Gerrit Lohmann
Geosci. Model Dev., 16, 383–405, https://doi.org/10.5194/gmd-16-383-2023, https://doi.org/10.5194/gmd-16-383-2023, 2023
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Tides have essential effects on the ocean and climate. Most previous research applies parameterised tidal mixing to discuss their effects in models. By comparing the effect of a tidal mixing parameterisation and tidal forcing on the ocean state, we assess the advantages and disadvantages of the two methods. Our results show that tidal mixing in the North Pacific Ocean strongly affects the global thermohaline circulation. We also list some effects that are not considered in the parameterisation.
Marko Rus, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 16, 271–288, https://doi.org/10.5194/gmd-16-271-2023, https://doi.org/10.5194/gmd-16-271-2023, 2023
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We propose a new fast and reliable deep-learning architecture HIDRA2 for sea level and storm surge modeling. HIDRA2 features new feature encoders and a fusion-regression block. We test HIDRA2 on Adriatic storm surges, which depend on an interaction between tides and seiches. We demonstrate that HIDRA2 learns to effectively mimic the timing and amplitude of Adriatic seiches. This is essential for reliable HIDRA2 predictions of total storm surge sea levels.
Joao Marcos Azevedo Correia de Souza, Sutara H. Suanda, Phellipe P. Couto, Robert O. Smith, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 211–231, https://doi.org/10.5194/gmd-16-211-2023, https://doi.org/10.5194/gmd-16-211-2023, 2023
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The current paper describes the configuration and evaluation of the Moana Ocean Hindcast, a > 25-year simulation of the ocean state around New Zealand using the Regional Ocean Modeling System v3.9. This is the first open-access, long-term, continuous, realistic ocean simulation for this region and provides information for improving the understanding of the ocean processes that affect the New Zealand exclusive economic zone.
Hao Huang, Pengyang Song, Shi Qiu, Jiaqi Guo, and Xueen Chen
Geosci. Model Dev., 16, 109–133, https://doi.org/10.5194/gmd-16-109-2023, https://doi.org/10.5194/gmd-16-109-2023, 2023
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The Oceanic Regional Circulation and Tide Model (ORCTM) is developed to reproduce internal solitary wave dynamics. The three-dimensional nonlinear momentum equations are involved with the nonhydrostatic pressure obtained via solving the Poisson equation. The validation experimental results agree with the internal wave theories and observations, demonstrating that the ORCTM can successfully describe the life cycle of nonlinear internal solitary waves under different oceanic environments.
David E. Gwyther, Shane R. Keating, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 157–178, https://doi.org/10.5194/gmd-16-157-2023, https://doi.org/10.5194/gmd-16-157-2023, 2023
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Ocean eddies are important for weather, climate, biology, navigation, and search and rescue. Since eddies change rapidly, models that incorporate or assimilate observations are required to produce accurate eddy timings and locations, yet the model accuracy is rarely assessed below the surface. We use a unique type of ocean model experiment to assess three-dimensional eddy structure in the East Australian Current and explore two pathways in which this subsurface structure is being degraded.
Shun Ohishi, Takemasa Miyoshi, and Misako Kachi
Geosci. Model Dev., 15, 9057–9073, https://doi.org/10.5194/gmd-15-9057-2022, https://doi.org/10.5194/gmd-15-9057-2022, 2022
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An adaptive observation error inflation (AOEI) method was proposed for atmospheric data assimilation to mitigate erroneous analysis updates caused by large observation-minus-forecast differences for satellite brightness temperature around clear- and cloudy-sky boundaries. This study implemented the AOEI with an ocean data assimilation system, leading to an improvement of analysis accuracy and dynamical balance around the frontal regions with large meridional temperature differences.
Diego Bruciaferri, Marina Tonani, Isabella Ascione, Fahad Al Senafi, Enda O'Dea, Helene T. Hewitt, and Andrew Saulter
Geosci. Model Dev., 15, 8705–8730, https://doi.org/10.5194/gmd-15-8705-2022, https://doi.org/10.5194/gmd-15-8705-2022, 2022
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More accurate predictions of the Gulf's ocean dynamics are needed. We investigate the impact on the predictive skills of a numerical shelf sea model of the Gulf after changing a few key aspects. Increasing the lateral and vertical resolution and optimising the vertical coordinate system to best represent the leading physical processes at stake significantly improve the accuracy of the simulated dynamics. Additional work may be needed to get real benefit from using a more realistic bathymetry.
Matthias Gröger, Manja Placke, H. E. Markus Meier, Florian Börgel, Sandra-Esther Brunnabend, Cyril Dutheil, Ulf Gräwe, Magnus Hieronymus, Thomas Neumann, Hagen Radtke, Semjon Schimanke, Jian Su, and Germo Väli
Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
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Comparisons of oceanographic climate data from different models often suffer from different model setups, forcing fields, and output of variables. This paper provides a protocol to harmonize these elements to set up multidecadal simulations for the Baltic Sea, a marginal sea in Europe. First results are shown from six different model simulations from four different model platforms. Topical studies for upwelling, marine heat waves, and stratification are also assessed.
Shun Ohishi, Tsutomu Hihara, Hidenori Aiki, Joji Ishizaka, Yasumasa Miyazawa, Misako Kachi, and Takemasa Miyoshi
Geosci. Model Dev., 15, 8395–8410, https://doi.org/10.5194/gmd-15-8395-2022, https://doi.org/10.5194/gmd-15-8395-2022, 2022
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We develop an ensemble-Kalman-filter-based regional ocean data assimilation system in which satellite and in situ observations are assimilated at a daily frequency. We find the best setting for dynamical balance and accuracy based on sensitivity experiments focused on how to inflate the ensemble spread and how to apply the analysis update to the model evolution. This study has a broader impact on more general data assimilation systems in which the initial shocks are a significant issue.
Hector S. Torres, Patrice Klein, Jinbo Wang, Alexander Wineteer, Bo Qiu, Andrew F. Thompson, Lionel Renault, Ernesto Rodriguez, Dimitris Menemenlis, Andrea Molod, Christopher N. Hill, Ehud Strobach, Hong Zhang, Mar Flexas, and Dragana Perkovic-Martin
Geosci. Model Dev., 15, 8041–8058, https://doi.org/10.5194/gmd-15-8041-2022, https://doi.org/10.5194/gmd-15-8041-2022, 2022
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Wind work at the air-sea interface is the scalar product of winds and currents and is the transfer of kinetic energy between the ocean and the atmosphere. Using a new global coupled ocean-atmosphere simulation performed at kilometer resolution, we show that all scales of winds and currents impact the ocean dynamics at spatial and temporal scales. The consequential interplay of surface winds and currents in the numerical simulation motivates the need for a winds and currents satellite mission.
Zhanpeng Zhuang, Quanan Zheng, Yongzeng Yang, Zhenya Song, Yeli Yuan, Chaojie Zhou, Xinhua Zhao, Ting Zhang, and Jing Xie
Geosci. Model Dev., 15, 7221–7241, https://doi.org/10.5194/gmd-15-7221-2022, https://doi.org/10.5194/gmd-15-7221-2022, 2022
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We evaluate the impacts of surface waves and internal tides on the upper-ocean mixing in the Indian Ocean. The surface-wave-generated turbulent mixing is dominant if depth is < 30 m, while the internal-tide-induced mixing is larger than surface waves in the ocean interior from 40
to 130 m. The simulated thermal structure, mixed layer depth and surface current are all improved when the mixing schemes are jointly incorporated into the ocean model because of the strengthened vertical mixing.
Giulia Bonino, Doroteaciro Iovino, Laurent Brodeau, and Simona Masina
Geosci. Model Dev., 15, 6873–6889, https://doi.org/10.5194/gmd-15-6873-2022, https://doi.org/10.5194/gmd-15-6873-2022, 2022
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The sea surface temperature (SST) is highly influenced by the transfer of energy driven by turbulent air–sea fluxes (TASFs). In the NEMO ocean general circulation model, TASFs are computed by means of bulk formulas. Bulk formulas require the choice of a given bulk parameterization, which influences the magnitudes of the TASFs. Our results show that parameterization-related SST differences are primarily sensitive to the wind stress differences across parameterizations.
Gustavo M. Marques, Nora Loose, Elizabeth Yankovsky, Jacob M. Steinberg, Chiung-Yin Chang, Neeraja Bhamidipati, Alistair Adcroft, Baylor Fox-Kemper, Stephen M. Griffies, Robert W. Hallberg, Malte F. Jansen, Hemant Khatri, and Laure Zanna
Geosci. Model Dev., 15, 6567–6579, https://doi.org/10.5194/gmd-15-6567-2022, https://doi.org/10.5194/gmd-15-6567-2022, 2022
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We present an idealized ocean model configuration and a set of simulations performed using varying horizontal grid spacing. While the model domain is idealized, it resembles important geometric features of the Atlantic and Southern oceans. The simulations described here serve as a framework to effectively study mesoscale eddy dynamics, to investigate the effect of mesoscale eddies on the large-scale dynamics, and to test and evaluate eddy parameterizations.
David E. Gwyther, Colette Kerry, Moninya Roughan, and Shane R. Keating
Geosci. Model Dev., 15, 6541–6565, https://doi.org/10.5194/gmd-15-6541-2022, https://doi.org/10.5194/gmd-15-6541-2022, 2022
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The ocean current flowing along the southeastern coast of Australia is called the East Australian Current (EAC). Using computer simulations, we tested how surface and subsurface observations might improve models of the EAC. Subsurface observations are particularly important for improving simulations, and if made in the correct location and time, can have impact 600 km upstream. The stability of the current affects model estimates could be capitalized upon in future observing strategies.
Giorgio Micaletto, Ivano Barletta, Silvia Mocavero, Ivan Federico, Italo Epicoco, Giorgia Verri, Giovanni Coppini, Pasquale Schiano, Giovanni Aloisio, and Nadia Pinardi
Geosci. Model Dev., 15, 6025–6046, https://doi.org/10.5194/gmd-15-6025-2022, https://doi.org/10.5194/gmd-15-6025-2022, 2022
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The full exploitation of supercomputing architectures requires a deep revision of the current climate models. This paper presents the parallelization of the three-dimensional hydrodynamic model SHYFEM (System of HydrodYnamic Finite Element Modules). Optimized numerical libraries were used to partition the model domain and solve the sparse linear system of equations in parallel. The performance assessment demonstrates a good level of scalability with a realistic configuration used as a benchmark.
Nairita Pal, Kristin Nicole Barton, Mark Roger Petersen, Steven Richard Brus, Darren Engwirda, Brian K. Arbic, Andrew Frank Roberts, Joannes J. Westerink, and Damrongsak Wiraset
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-188, https://doi.org/10.5194/gmd-2022-188, 2022
Preprint under review for GMD
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Understanding tides is essential to accurately predict ocean currents. Over the next several decades coastal processes such as flooding, erosion will be severely impacted due to climate change. Tides affect currents along the coastal regions the most. In this manuscript we show the results of implementing tides in a global ocean model known as MPAS–Ocean. We also show how Antarctic ice shelf cavities affect global tides. Our work points towards future research with tides-ice interactions.
Young-Kwang Choi, Fengyan Shi, Matt Malej, Jane M. Smith, James T. Kirby, and Stephan T. Grilli
Geosci. Model Dev., 15, 5441–5459, https://doi.org/10.5194/gmd-15-5441-2022, https://doi.org/10.5194/gmd-15-5441-2022, 2022
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The multi-grid-nesting technique is an important methodology used for modeling transoceanic tsunamis and coastal effects. In this study, we developed a two-way nesting interface in a multi-grid-nesting system for the Boussinesq wave model FUNWAVE-TVD. The interface acts as a
backboneof the nesting framework, handling data input, output, time sequencing, and internal interactions between grids at different scales.
Chen Zhao, Rupert Gladstone, Benjamin Keith Galton-Fenzi, David Gwyther, and Tore Hattermann
Geosci. Model Dev., 15, 5421–5439, https://doi.org/10.5194/gmd-15-5421-2022, https://doi.org/10.5194/gmd-15-5421-2022, 2022
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We use a coupled ice–ocean model to explore an oscillation feature found in several contributing models to MISOMIP1. The oscillation is closely related to the discretized grounding line retreat and likely strengthened by the buoyancy–melt feedback and/or melt–geometry feedback near the grounding line, and frequent ice–ocean coupling. Our model choices have a non-trivial impact on mean melt and ocean circulation strength, which might be interesting for the coupled ice–ocean community.
Benoît Pasquier, Sophia K. V. Hines, Hengdi Liang, Yingzhe Wu, Steven L. Goldstein, and Seth G. John
Geosci. Model Dev., 15, 4625–4656, https://doi.org/10.5194/gmd-15-4625-2022, https://doi.org/10.5194/gmd-15-4625-2022, 2022
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Neodymium isotopes in seawater have the potential to provide key information about ocean circulation, both today and in the past. This can shed light on the underlying drivers of global climate, which will improve our ability to predict future climate change, but uncertainties in our understanding of neodymium cycling have limited use of this tracer. We present a new model of neodymium in the modern ocean that runs extremely fast, matches observations, and is freely available for development.
Pedro Duarte, Jostein Brændshøi, Dmitry Shcherbin, Pauline Barras, Jon Albretsen, Yvonne Gusdal, Nicholas Szapiro, Andreas Martinsen, Annette Samuelsen, Keguang Wang, and Jens Boldingh Debernard
Geosci. Model Dev., 15, 4373–4392, https://doi.org/10.5194/gmd-15-4373-2022, https://doi.org/10.5194/gmd-15-4373-2022, 2022
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Sea ice models are often implemented for very large domains beyond the regions of sea ice formation, such as the whole Arctic or all of Antarctica. In this study, we implement changes in the Los Alamos Sea Ice Model, allowing it to be implemented for relatively small regions within the Arctic or Antarctica and yet considering the presence and influence of sea ice outside the represented areas. Such regional implementations are important when spatially detailed results are required.
Elias J. Hunter, Heidi L. Fuchs, John L. Wilkin, Gregory P. Gerbi, Robert J. Chant, and Jessica C. Garwood
Geosci. Model Dev., 15, 4297–4311, https://doi.org/10.5194/gmd-15-4297-2022, https://doi.org/10.5194/gmd-15-4297-2022, 2022
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ROMSPath is an offline particle tracking model tailored for use with output from Regional Ocean Modeling System (ROMS) simulations. It is an update to an established system, the Lagrangian TRANSport (LTRANS) model, including a number of improvements. These include a modification of the model coordinate system which improved accuracy and numerical efficiency, and added functionality for nested grids and Stokes drift.
Alexander Barth, Aida Alvera-Azcárate, Charles Troupin, and Jean-Marie Beckers
Geosci. Model Dev., 15, 2183–2196, https://doi.org/10.5194/gmd-15-2183-2022, https://doi.org/10.5194/gmd-15-2183-2022, 2022
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Earth-observing satellites provide routine measurement of several ocean parameters. However, these datasets have a significant amount of missing data due to the presence of clouds or other limitations of the employed sensors. This paper describes a method to infer the value of the missing satellite data based on a convolutional autoencoder (a specific type of neural network architecture). The technique also provides a reliable error estimate of the interpolated value.
Olivier Sulpis, Matthew P. Humphreys, Monica M. Wilhelmus, Dustin Carroll, William M. Berelson, Dimitris Menemenlis, Jack J. Middelburg, and Jess F. Adkins
Geosci. Model Dev., 15, 2105–2131, https://doi.org/10.5194/gmd-15-2105-2022, https://doi.org/10.5194/gmd-15-2105-2022, 2022
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A quarter of the surface of the Earth is covered by marine sediments rich in calcium carbonates, and their dissolution acts as a giant antacid tablet protecting the ocean against human-made acidification caused by massive CO2 emissions. Here, we present a new model of sediment chemistry that incorporates the latest experimental findings on calcium carbonate dissolution kinetics. This model can be used to predict how marine sediments evolve through time in response to environmental perturbations.
Alisée A. Chaigneau, Guillaume Reffray, Aurore Voldoire, and Angélique Melet
Geosci. Model Dev., 15, 2035–2062, https://doi.org/10.5194/gmd-15-2035-2022, https://doi.org/10.5194/gmd-15-2035-2022, 2022
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Climate-change-induced sea level rise is a major threat for coastal and low-lying regions. Projections of coastal sea level changes are thus of great interest for coastal risk assessment and have significantly developed in recent years. In this paper, the objective is to provide high-resolution (6 km) projections of sea level changes in the northeastern Atlantic region bordering western Europe. For that purpose, a regional model is used to refine existing coarse global projections.
Victor Onink, Erik van Sebille, and Charlotte Laufkötter
Geosci. Model Dev., 15, 1995–2012, https://doi.org/10.5194/gmd-15-1995-2022, https://doi.org/10.5194/gmd-15-1995-2022, 2022
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Turbulent mixing is a vital process in 3D modeling of particle transport in the ocean. However, since turbulence occurs on very short spatial scales and timescales, large-scale ocean models generally have highly simplified turbulence representations. We have developed parametrizations for the vertical turbulent transport of buoyant particles that can be easily applied in a large-scale particle tracking model. The predicted vertical concentration profiles match microplastic observations well.
Gaston Irrmann, Sébastien Masson, Éric Maisonnave, David Guibert, and Erwan Raffin
Geosci. Model Dev., 15, 1567–1582, https://doi.org/10.5194/gmd-15-1567-2022, https://doi.org/10.5194/gmd-15-1567-2022, 2022
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To be efficient on supercomputers, software must be high-performance at computing many concurrent tasks. Communications between tasks is often necessary but time consuming, and ocean modelling software NEMO 4.0 is no exception.
In this work we describe approaches enabling fewer communications, an optimization to share the workload more equally between tasks and a new flexible configuration to assess NEMO's performance easily.
Xueming Zhu, Ziqing Zu, Shihe Ren, Miaoyin Zhang, Yunfei Zhang, Hui Wang, and Ang Li
Geosci. Model Dev., 15, 995–1015, https://doi.org/10.5194/gmd-15-995-2022, https://doi.org/10.5194/gmd-15-995-2022, 2022
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SCSOFS has provided daily updated marine forecasting in the South China Sea for the next 5 d since 2013. Comprehensive updates have been conducted to the configurations of SCSOFS's physical model and data assimilation scheme in order to improve its forecasting skill. The three most sensitive updates are highlighted. Scientific comparison and accuracy assessment results indicate that remarkable improvements have been achieved in SCSOFSv2 with respect to the original version SCSOFSv1.
Tingfeng Wu, Boqiang Qin, Anning Huang, Yongwei Sheng, Shunxin Feng, and Céline Casenave
Geosci. Model Dev., 15, 745–769, https://doi.org/10.5194/gmd-15-745-2022, https://doi.org/10.5194/gmd-15-745-2022, 2022
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Most hydrodynamic models were initially developed based in marine environments. They cannot be directly applied to large lakes. Based on field observations and numerical experiments of a large shallow lake, we developed a hydrodynamic model by adopting new schemes of wind stress, wind waves, and turbulence for large lakes. Our model can greatly improve the simulation of lake currents. This study will be a reminder to limnologists to prudently use ocean models to study lake hydrodynamics.
Jingyuan Li, Qinghe Zhang, and Tongqing Chen
Geosci. Model Dev., 15, 105–127, https://doi.org/10.5194/gmd-15-105-2022, https://doi.org/10.5194/gmd-15-105-2022, 2022
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A numerical model, ISWFoam with a modified k–ω SST model, is developed to simulate internal solitary waves (ISWs) in continuously stratified, incompressible, viscous fluids based on a fully three-dimensional (3D) Navier–Stokes equation with the finite-volume method. ISWFoam can accurately simulate the generation and evolution of ISWs, the ISW breaking phenomenon, waveform inversion of ISWs, and the interaction between ISWs and complex topography.
Matthew P. Humphreys, Ernie R. Lewis, Jonathan D. Sharp, and Denis Pierrot
Geosci. Model Dev., 15, 15–43, https://doi.org/10.5194/gmd-15-15-2022, https://doi.org/10.5194/gmd-15-15-2022, 2022
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The ocean helps to mitigate our impact on Earth's climate by absorbing about a quarter of the carbon dioxide (CO2) released by human activities each year. However, once absorbed, chemical reactions between CO2 and water reduce seawater pH (
ocean acidification), which may have adverse effects on marine ecosystems. Our Python package, PyCO2SYS, models the chemical reactions of CO2 in seawater, allowing us to quantify the corresponding changes in pH and related chemical properties.
Vera Fofonova, Tuomas Kärnä, Knut Klingbeil, Alexey Androsov, Ivan Kuznetsov, Dmitry Sidorenko, Sergey Danilov, Hans Burchard, and Karen Helen Wiltshire
Geosci. Model Dev., 14, 6945–6975, https://doi.org/10.5194/gmd-14-6945-2021, https://doi.org/10.5194/gmd-14-6945-2021, 2021
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We present a test case of river plume spreading to evaluate coastal ocean models. Our test case reveals the level of numerical mixing (due to parameterizations used and numerical treatment of processes in the model) and the ability of models to reproduce complex dynamics. The major result of our comparative study is that accuracy in reproducing the analytical solution depends less on the type of applied model architecture or numerical grid than it does on the type of advection scheme.
Trevor J. McDougall, Paul M. Barker, Ryan M. Holmes, Rich Pawlowicz, Stephen M. Griffies, and Paul J. Durack
Geosci. Model Dev., 14, 6445–6466, https://doi.org/10.5194/gmd-14-6445-2021, https://doi.org/10.5194/gmd-14-6445-2021, 2021
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We show that the way that the air–sea heat flux is treated in ocean models means that the model's temperature variable should be interpreted as being Conservative Temperature, irrespective of whether the equation of state used in an ocean model is EOS-80 or TEOS-10.
Paul R. Halloran, Jennifer K. McWhorter, Beatriz Arellano Nava, Robert Marsh, and William Skirving
Geosci. Model Dev., 14, 6177–6195, https://doi.org/10.5194/gmd-14-6177-2021, https://doi.org/10.5194/gmd-14-6177-2021, 2021
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This paper describes the latest version of a simple model for simulating coastal oceanography in response to changes in weather and climate. The latest revision of this model makes scientific improvements but focuses on improvements that allow the model to be run simply at large scales and for long periods of time to explore the implications of (for example) future climate change along large areas of coastline.
Fucang Zhou, Jianzhong Ge, Dongyan Liu, Pingxing Ding, Changsheng Chen, and Xiaodao Wei
Geosci. Model Dev., 14, 6049–6070, https://doi.org/10.5194/gmd-14-6049-2021, https://doi.org/10.5194/gmd-14-6049-2021, 2021
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In this study, a physical–ecological model, the Floating Macroalgal Growth and Drift Model (FMGDM), was developed to determine the dynamic growth and drifting pattern of floating macroalgae. Based on Lagrangian tracking, the macroalgae bloom is jointly controlled by ocean flows, sea surface wind, temperature, irradiation, and nutrients. The FMGDM was robust in successfully reproducing the spatial and temporal dynamics of the massive green tide around the Yellow Sea.
Tuomas Kärnä, Patrik Ljungemyr, Saeed Falahat, Ida Ringgaard, Lars Axell, Vasily Korabel, Jens Murawski, Ilja Maljutenko, Anja Lindenthal, Simon Jandt-Scheelke, Svetlana Verjovkina, Ina Lorkowski, Priidik Lagemaa, Jun She, Laura Tuomi, Adam Nord, and Vibeke Huess
Geosci. Model Dev., 14, 5731–5749, https://doi.org/10.5194/gmd-14-5731-2021, https://doi.org/10.5194/gmd-14-5731-2021, 2021
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We present Nemo-Nordic 2.0, a novel operational marine model for the Baltic Sea. The model covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. We validate the model's performance against sea level, water temperature, and salinity observations, as well as sea ice charts. The skill analysis demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.
David A. Griffin, Mike Herzfeld, Mark Hemer, and Darren Engwirda
Geosci. Model Dev., 14, 5561–5582, https://doi.org/10.5194/gmd-14-5561-2021, https://doi.org/10.5194/gmd-14-5561-2021, 2021
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In support of the developing ocean renewable energy sector, and indeed all mariners, we have developed a new tidal model for Australian waters and thoroughly evaluated it using a new compilation of tide gauge and current meter data. We show that while there is certainly room for improvement, the model provides useful predictions of tidal currents for about 80 % (by area) of Australian shelf waters. So we intend to commence publishing tidal current predictions for those regions soon.
Jingzhe Li, Piyang Liu, Shuyu Sun, Zhifeng Sun, Yongzhang Zhou, Liang Gong, Jinliang Zhang, and Dongxing Du
Geosci. Model Dev., 14, 4925–4937, https://doi.org/10.5194/gmd-14-4925-2021, https://doi.org/10.5194/gmd-14-4925-2021, 2021
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This paper introduces Sedapp, a basin fill simulation tool. Sedapp is an open-source computer code written in R language. Using this program, one can simulate the formation of sedimentary strata, especially in shallow marine environments injected by rivers. With proper parameter settings, the simulation results are very similar to the real geological bodies. Sedapp can also be used in continental fault basin environments, which may serve as a tool for oil exploration.
Christo Rautenbach, Julia C. Mullarney, and Karin R. Bryan
Geosci. Model Dev., 14, 4241–4247, https://doi.org/10.5194/gmd-14-4241-2021, https://doi.org/10.5194/gmd-14-4241-2021, 2021
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The simulation of ocean waves is important for various reasons, e.g. ship route safety and coastal vulnerability assessments. SWAN is a popular tool with which ocean waves may be predicted. Simulations using this tool can be computationally expensive. The present study thus aimed to understand which typical parallel-computing SWAN model set-up will be most effective. There thus do exist configurations where these simulations are most time-saving and effective.
Qing Li, Jorn Bruggeman, Hans Burchard, Knut Klingbeil, Lars Umlauf, and Karsten Bolding
Geosci. Model Dev., 14, 4261–4282, https://doi.org/10.5194/gmd-14-4261-2021, https://doi.org/10.5194/gmd-14-4261-2021, 2021
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Different ocean vertical mixing schemes are usually developed in different modeling framework, making the comparison across such schemes difficult. Here, we develop a consistent framework for testing, comparing, and applying different ocean mixing schemes by integrating CVMix into GOTM, which also extends the capability of GOTM towards including the effects of ocean surface waves. A suite of test cases and toolsets for developing and evaluating ocean mixing schemes is also described.
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, https://doi.org/10.5194/gmd-14-4069-2021, https://doi.org/10.5194/gmd-14-4069-2021, 2021
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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.
Andrew Yool, Julien Palmiéri, Colin G. Jones, Lee de Mora, Till Kuhlbrodt, Ekatarina E. Popova, A. J. George Nurser, Joel Hirschi, Adam T. Blaker, Andrew C. Coward, Edward W. Blockley, and Alistair A. Sellar
Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, https://doi.org/10.5194/gmd-14-3437-2021, 2021
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The ocean plays a key role in modulating the Earth’s climate. Understanding this role is critical when using models to project future climate change. Consequently, it is necessary to evaluate their realism against the ocean's observed state. Here we validate UKESM1, a new Earth system model, focusing on the realism of its ocean physics and circulation, as well as its biological cycles and productivity. While we identify biases, generally the model performs well over a wide range of properties.
Sebastiaan J. van de Velde, Dominik Hülse, Christopher T. Reinhard, and Andy Ridgwell
Geosci. Model Dev., 14, 2713–2745, https://doi.org/10.5194/gmd-14-2713-2021, https://doi.org/10.5194/gmd-14-2713-2021, 2021
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Biogeochemical interactions between iron and sulfur are central to the long-term biogeochemical evolution of Earth’s oceans. Here, we introduce an iron–sulphur cycle in a model of Earth's oceans. Our analyses show that the results of the model are robust towards parameter choices and that simulated concentrations and reactions are comparable to those observed in ancient ocean analogues (anoxic lakes). Our model represents an important step forward in the study of iron–sulfur cycling.
Katherine M. Smith, Skyler Kern, Peter E. Hamlington, Marco Zavatarelli, Nadia Pinardi, Emily F. Klee, and Kyle E. Niemeyer
Geosci. Model Dev., 14, 2419–2442, https://doi.org/10.5194/gmd-14-2419-2021, https://doi.org/10.5194/gmd-14-2419-2021, 2021
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We present a newly developed reduced-order biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics but reduced enough to incorporate into highly resolved numerical simulations with limited additional computational cost. The model provides improved correlations between model output and field data, indicating that significant improvements in the reproduction of real-world data can be achieved with a small number of variables.
Chia-Wei Hsu, Jianjun Yin, Stephen M. Griffies, and Raphael Dussin
Geosci. Model Dev., 14, 2471–2502, https://doi.org/10.5194/gmd-14-2471-2021, https://doi.org/10.5194/gmd-14-2471-2021, 2021
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The new surface forcing from JRA55-do (OMIP II) significantly improved the underestimated sea level trend across the entire Pacific Ocean along 10° N in the simulation forced by CORE (OMIP I). We summarize and list out the reasons for the existing sea level biases across all studied timescales as a reference for improving the sea level simulation in the future. This study on the evaluation and improvement of ocean climate models should be of broad interest to a large modeling community.
Oliver Gutjahr, Nils Brüggemann, Helmuth Haak, Johann H. Jungclaus, Dian A. Putrasahan, Katja Lohmann, and Jin-Song von Storch
Geosci. Model Dev., 14, 2317–2349, https://doi.org/10.5194/gmd-14-2317-2021, https://doi.org/10.5194/gmd-14-2317-2021, 2021
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We compare four ocean vertical mixing schemes in 100-year coupled simulations with the Max Planck Institute Earth System Model (MPI-ESM1.2) and analyse their model biases. Overall, the mixing schemes modify biases in the ocean interior that vary with region and variable but produce a similar global bias pattern. We therefore cannot classify any scheme as superior but conclude that the chosen mixing scheme may be important for regional biases.
Katsumi Matsumoto, Tatsuro Tanioka, and Jacob Zahn
Geosci. Model Dev., 14, 2265–2288, https://doi.org/10.5194/gmd-14-2265-2021, https://doi.org/10.5194/gmd-14-2265-2021, 2021
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MESMO is a mathematical model that represents the essential components of the Earth, such as the global ocean, atmosphere, and sea ice. It is used to study the global climate and the global carbon cycle. We describe the third version of MESMO. A novel feature of the new version is its mathematical representations of the chemical composition of marine phytoplankton and the marine dissolved organic matter, which are both recognized as important for the global ocean carbon cycle.
Lojze Žust, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 14, 2057–2074, https://doi.org/10.5194/gmd-14-2057-2021, https://doi.org/10.5194/gmd-14-2057-2021, 2021
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Adriatic basin sea level modelling is a challenging problem due to the interplay between terrain, weather, tides and seiches. Current state-of-the-art numerical models (e.g. NEMO) require large computational resources to produce reliable forecasts. In this study we propose HIDRA, a novel deep learning approach for sea level modeling, which drastically reduces the numerical cost while demonstrating predictive capabilities comparable to that of the NEMO model, outperforming it in many instances.
Qing Li and Luke Van Roekel
Geosci. Model Dev., 14, 2011–2028, https://doi.org/10.5194/gmd-14-2011-2021, https://doi.org/10.5194/gmd-14-2011-2021, 2021
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Physical processes in the ocean span multiple spatial and temporal scales. Simultaneously resolving all these in a simulation is computationally challenging. Here we develop a more efficient technique to better study the interactions across scales, particularly focusing on the ocean surface turbulent mixing, by coupling a global ocean circulation model MPAS-Ocean and a large eddy simulation model PALM. The latter is customized and ported on a GPU to further accelerate the computation.
Yang Feng, Dimitris Menemenlis, Huijie Xue, Hong Zhang, Dustin Carroll, Yan Du, and Hui Wu
Geosci. Model Dev., 14, 1801–1819, https://doi.org/10.5194/gmd-14-1801-2021, https://doi.org/10.5194/gmd-14-1801-2021, 2021
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Simulation of coastal plume regions was improved in global ECCOv4 with a series of sensitivity tests. We find modeled SSS is closer to SMAP when using daily point-source runoff as well as increasing the resolution from coarse to intermediate. The plume characteristics, freshwater transport, and critical water properties are modified greatly. But this may not happen with a further increase to high resolution. The study will advance the seamless modeling of land–ocean–atmosphere feedback in ESMs.
Gregory C. Smith, Yimin Liu, Mounir Benkiran, Kamel Chikhar, Dorina Surcel Colan, Audrey-Anne Gauthier, Charles-Emmanuel Testut, Frederic Dupont, Ji Lei, François Roy, Jean-François Lemieux, and Fraser Davidson
Geosci. Model Dev., 14, 1445–1467, https://doi.org/10.5194/gmd-14-1445-2021, https://doi.org/10.5194/gmd-14-1445-2021, 2021
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Canada's coastlines include diverse ocean environments. In response to the strong need to support marine activities and security, we present the first pan-Canadian operational regional ocean analysis system. A novel online tidal harmonic analysis method is introduced that uses a sliding-window approach. Innovations are compared to those from the Canadian global analysis system. Particular improvements are found near the Gulf Stream due to the higher model grid resolution.
Cited articles
Ackleh, A. S.:
Parameter estimation in a structured algal coagulation-fragmentation model,
Nonlinear Anal.-Theor.,
28, 837–854, https://doi.org/10.1016/0362-546X(95)00195-2, 1997. a
Alldredge, A.:
The carbon, nitrogen and mass content of marine snow as a function of aggregate size,
Deep-Sea Res. Pt. I,
45, 529–541, https://doi.org/10.1016/S0967-0637(97)00048-4, 1998. a, b
Alldredge, A. L., Granata, T. C., Gotschalk, C. C., and Dickey, T. D.:
The physical strength of marine snow and its implications for particle disaggregation in the ocean,
Limnol. Oceanogr.,
35, 1415–1428, https://doi.org/10.4319/lo.1990.35.7.1415, 1990. a, b, c
Anderson, T. and Gentleman, W.:
The legacy of Gordon Arthur Riley (1911–1985) and the development of mathematical models in biological oceanography,
J. Mar. Res.,
70, 1–30, https://doi.org/10.1357/002224012800502390, 2012. a
Anderson, T. R.:
Plankton functional type modelling: running before we can walk?,
J. Plankton Res.,
27, 1073–1081, https://doi.org/10.1093/plankt/fbi076, 2005. a
Anderson, T. R., Gentleman, W., and Sinha, B.:
Influence of grazing formulations on the emergent properties of a complex ecosystem model in a global ocean general circulation model,
Prog. Oceanogr.,
87, 201–213, https://doi.org/10.1016/j.pocean.2010.06.003, 2010. a
Aumont, O., Maier-Reimer, E., Blain, S., and Monfray, P.:
An ecosystem model of the global ocean including Fe, Si, P colimitations,
Global Biogeochem. Cy.,
17, 1060, https://doi.org/10.1029/2001GB001745, 2003. a, b
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015. a
Banse, K.:
New views on the degradation and disposition of organic particles as collected by sediment traps in the open sea,
Deep-Sea Res.,
37, 1177–1195, https://doi.org/10.1016/0198-0149(90)90058-4, 1990. a
Blackford, J. and Radford, P.:
A structure and methodology for marine ecosystem modelling,
Neth. J. Sea Res.,
33, 247–260, https://doi.org/10.1016/0077-7579(95)90048-9, 1995. a
Boyd, P., Claustre, H., Levy, M., Siegel, D., and Weber, T.:
Multi-facted particle pumps drive carbon sequestration in the ocean,
Nature,
568, 327–335, https://doi.org/10.1038/s41586-019-1098-2, 2019. a
Burd, A. B.:
Modeling particle aggregation using size class and size spectrum approaches,
J. Geophys. Res.,
118, 3431–3443, https://doi.org/10.1002/jgrc.20255, 2013. a
Burd, A. B., Chanton, J. P., Daly, K. L., Gilbert, S., Passow, U., and Quigg, A.:
The science behind marine-oil snow and MOSSFA: past, present, and future,
Prog. Oceanogr.,
187, https://doi.org/10.1016/j.pocean.2020.102398, 2020. a
Butenschön, M., Clark, J., Aldridge, J. N., Allen, J. I., Artioli, Y., Blackford, J., Bruggeman, J., Cazenave, P., Ciavatta, S., Kay, S., Lessin, G., van Leeuwen, S., van der Molen, J., de Mora, L., Polimene, L., Sailley, S., Stephens, N., and Torres, R.: ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels, Geosci. Model Dev., 9, 1293–1339, https://doi.org/10.5194/gmd-9-1293-2016, 2016. a
De La Rocha, C. L. and Passow, U.:
Factors influencing the sinking of POC and the efficiency of the biological carbon pump,
Deep-Sea Res. Pt. II,
54, 639–658, https://doi.org/10.1016/j.dsr2.2007.01.004, 2007. a, b
Denman, K.:
Modelling planktonic ecosystems: parameterizing complexity,
Prog. Oceanogr.,
57, 429–452, https://doi.org/10.1016/S0079-6611(03)00109-5, 2003. a
Denman, K., Brasseur, G., Chidthaisong, A., Ciais, P., Cox, P., Dickinson, R., Hauglustaine, D., Heinze, C., Holland, E., Jacob, D., Lohmann, U., Ramachandran, S., da Silva Dias, P., Wofsy, S., and Zhang, X.:
2007: Couplings Between Changes in the Climate System and Biogeochemistry,
in: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K., Tignor, M., and Miller, H.,
Cambridge University Press, Cambridge, UK and New York, NY, USA,
availabale at: https://www.ipcc.ch/site/assets/uploads/2018/02/ar4-wg1-chapter7-1.pdf (last access: 28 June 2021), 2007. a
Dilling, L. and Alldredge, A. L.:
Fragmentation of marine snow by swimming macrozooplankton: A new process impacting carbon cycling in the sea,
Deep-Sea Res. Pt. I,
47, 1227–1245, https://doi.org/10.1016/S0967-0637(99)00105-3, 2000. a, b
Dissanayake, A. L., Burd, A. B., Daly, K. L., Francis, S., and Passow, U.:
Numerical modeling of the interactions of oil, marine snow, and riverine sediments in the ocean,
J. Geophys. Res.-Oceans,
123, 5388–5405, https://doi.org/10.1029/2018JC013790, 2018. a
Doney, S., Kleypas, J., Sarmiento, J., and Falkowski, P.:
The US JGOFS Synthesis and Modeling Project – An introduction,
Deep-Sea Res. Pt. II,
49, 1–20, https://doi.org/10.1016/S0967-0645(01)00092-3, 2002. a
Doney, S. C., Glover, D. M., and Najjar, R. G.:
A new coupled, one-dimensional biological-physical model for the upper ocean: Applications to the JGOFS Bermuda Atlantic Time-series Study (BATS) site,
Deep-Sea Res. Pt. II,
43, 591–624, https://doi.org/10.1016/0967-0645(95)00104-2, 1996. a, b
Doney, S. C., Lindsay, K., and Moore, J. K.:
Global Ocean Carbon Cycle Modeling, vol. 4,
in: Ocean Biogeochemistry,
edited by: Fasham, M.,
Springer, Verlag Berlin Heidelberg, 217–238, https://doi.org/10.1007/978-3-642-55844-3_10, 2003. a, b
Dutkiewicz, S., Follows, M. J., and Parekh, P.:
Interactions of the iron and phosphorus cycles: A three-dimensional model study,
Global Biogeochem. Cy.,
19, GB1021, https://doi.org/10.1029/2004GB002342, 2005. a, b
Engel, A.:
The role of transparent exopolymer particles (TEP) in the increase in apparent particle stickiness during the decline of a diatom bloom,
J. Plankton Res.,
22, 485–497, https://doi.org/10.1093/plankt/22.3.485, 2000. a
Fasham, M., Ducklow, H. W., and McKelvie, M.:
A nitrogen-based model of plankton dynamics in the oceanic mixed layer,
J. Mar. Res.,
48, 591–639, https://doi.org/10.1357/002224090784984678, 1990. a
Flynn, K. J.:
Castles built on sand: dysfunctionality in plankton models and the inadequacy of dialogue between biologists and modellers,
J. Plankton Res.,
27, 1205–1210, https://doi.org/10.1093/plankt/fbi099, 2005. a
Gelbard, F., Tambour, Y., and Seinfeld, J. H.:
Sectional representations for simulating aerosol dynamics,
J. Colloid Interf. Sci.,
76, 541–556, https://doi.org/10.1016/0021-9797(80)90394-X, 1980. a, b
Gloege, L., Mc Kinley, G. A., Mouw, C., and Ciochetto, A. B.:
Global evaluation of particulate organic carbon flux parametrizations and implications for atmospheric pCO2,
Global Biogeochem. Cy.,
31, 1192–1215, https://doi.org/10.1002/2016GB005535, 2017. a
Goldthwait, S., Yen, J., Brown, J., and Alldredge, A.:
Quantification of marine snow fragmentation by swimming euphausiids,
Limnol. Oceanogr.,
49, 940–952, https://doi.org/10.4319/lo.2004.49.4.0940, 2004. a, b
Green, E. P. and Dagg, M. J.:
Mesozooplankton associations with medium to large marine snow aggregates in the northern Gulf of Mexico,
J. Plankton Res.,
19, 435–447, https://doi.org/10.1093/plankt/19.4.435, 1997. a
Gregory, J.:
The density of particle aggregates,
Water Sci. Technol.,
36-4, 1–13, https://doi.org/10.1016/S0273-1223(97)00452-6, 1997. a
Gremion, G. and Nadeau, L.-P.:
Source code and user manual of the Coagfrag Model (Version 1),
Zenodo,
2021-01-11, https://doi.org/10.5281/zenodo.4432896, 2021. a, b
Hansen, K.:
Abundance Distributions; Large Scale Features,
Springer International Publishing, Cham, https://doi.org/10.1007/978-3-319-90062-9_8, pp. 205–251, 2018. a
Hill, P. S.:
Reconciling aggregation theory with observed vertical fluxes following phytoplankton blooms,
J. Geophys. Res.-Oceans,
97, 2295–2308, https://doi.org/10.1029/91JC02808, 1992. a
Hill, P. S.:
Sectional and discrete representations of floc breakage in agitated suspensions,
Deep-Sea Res. Pt. I,
43, 679–702, https://doi.org/10.1016/0967-0637(96)00030-1, 1996. a
Hood, R. R., Laws, E. A., Armstrong, R. A., Bates, N. R., Brown, C. W., Carlson, C. A., Chaig, F., Doneyh, S. C., Falkowskii, P. G., Feelyj, R. A., Friedrichsk, M., Landryl, M. R., Moorem, J. K., Nelsonn, D. M., Richardson, T. L., Salihoglup, B., Schartauq, M., Tooleh, D. A., and D., W. J.:
Pelagic functional group modeling: Progress, challenges and prospects,
Deep-Sea Res. Pt. II,
53, 459–512, https://doi.org/10.1016/j.dsr2.2006.01.025, 2006. a
Jackson, G. A.:
A model of the formation of marine algal flocs by physical coagulation processes,
Deep-Sea Res.,
37, 1197–1211, https://doi.org/10.1016/0198-0149(90)90038-W, 1990. a
Jackson, G. A.:
Effect of coagulation on a model planktonic food web,
Deep-Sea Res. Pt. I,
48, 95–123, https://doi.org/10.1016/S0967-0637(00)00040-6, 2001. a, b, c
Jackson, G. A. and Burd, A. B.:
Aggregation in the Marine Environment, Environ. Sci. Technol., 32, 2805–2814, https://doi.org/10.1021/es980251w, 1998. a
Jackson, G. A. and Burd, A. B.:
Simulating aggregate dynamics in ocean biogeochemical models,
Prog. Oceanogr.,
133, 55–65, https://doi.org/10.1016/j.pocean.2014.08.014, 2015. a, b
Jackson, G. A., Logan, B. E., Alldredge, A. L., and Dam, H. G.:
Combining particle size spectra from a mesocosm experiment measured using photographic and aperture impedance (Coulter and Elzone) techniques,
Deep-Sea Res. Pt. II,
42, 139–157, https://doi.org/10.1016/0967-0645(95)00009-F, 1995. a, b
Jackson, G. A., Maffione, R., Costello, D. K., Alldredge, A. L., Logan, B. E., and Dam, H. G.:
Particle size spectra between 1 µm and 1 cm at Monterey Bay determined using multiple instruments,
Deep-Sea Res. Pt. I,
44, 1739–1767, https://doi.org/10.1016/S0967-0637(97)00029-0, 1997. a, b, c
Jokulsdottir, T. and Archer, D.: A stochastic, Lagrangian model of sinking biogenic aggregates in the ocean (SLAMS 1.0): model formulation, validation and sensitivity, Geosci. Model Dev., 9, 1455–1476, https://doi.org/10.5194/gmd-9-1455-2016, 2016. a
Kang, I.-S., Ahn, M.-S., Miura, H., and Subramanian, A.:
Chapter 14 – GCMs With Full Representation of Cloud Microphysics and Their MJO Simulations,
in: Sub-Seasonal to Seasonal Prediction,
edited by: Robertson, A. W. and Vitart, F.,
Elsevier, the Netherlands, UK, USA, https://doi.org/10.1016/B978-0-12-811714-9.00014-0, pp. 305–319, 2019. a
Karakaş, G., Nowald, N., Schäfer-Neth, C., Iversen, M., Barkmann, W., Fischer, G., Marchesiello, P., and Schlitzer, R.:
Impact of particle aggregation on vertical fluxes of organic matter,
Prog. Oceanogr.,
83, 331–341, https://doi.org/10.1016/j.pocean.2009.07.047, 2009. a
Kearney, K., Hermann, A., Cheng, W., Ortiz, I., and Aydin, K.: A coupled pelagic–benthic–sympagic biogeochemical model for the Bering Sea: documentation and validation of the BESTNPZ model (v2019.08.23) within a high-resolution regional ocean model, Geosci. Model Dev., 13, 597–650, https://doi.org/10.5194/gmd-13-597-2020, 2020. a
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O., Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., van den Heever, S. C., and Yano, J.-I.:
Representation of microphysical processes in cloud-resolving models: Spectral (bin) microphysics versus bulk parameterization,
Rev. Geophys.,
53, 247–322, https://doi.org/10.1002/2014RG000468, 2015. a
Kishi, M. J., Kashiwai, M., Ware, D. M., Megrey, B. A., Eslinger, D. L., Werner, F. E., Noguchi-Aita, M., Azumaya, T., Fujii, M., Hashimoto, S., Huang, D., Iizumi, H., Ishida, Y., Kang, S., Kantakov, G. A., Kim, H.-C., Komatsu, K., Navrotsky, V. V., Smith, S. L., Tadokoro, K., Tsuda, A., Yamamura, O., Yamanaka, Y., Yokouchi, K. Yoshie, N., Zhang, J., Zuenko, Y. I., and Zvalinsky, V. I.:
NEMURO–a lower trophic level model for the North Pacific marine ecosystem,
Ecol. Model.,
202, 12–25, https://doi.org/10.1016/j.ecolmodel.2006.08.021, 2007. a
Kiørboe, T.:
Colonization of marine snow aggregates by invertebrate zooplankton: Abundance, scaling, and possible role,
Limnol. Oceanogr.,
45, 479–484, https://doi.org/10.4319/lo.2000.45.2.0479, 2000. a
Kiørboe, T. and Thygesen, U. H.:
Fluid motion and solute distribution around sinking aggregates. II. Implications for remote detection by colonizing zooplankters,
Mar. Ecol.-Prog. Ser.,
211, 15–25, https://doi.org/10.3354/meps211015, 2001. a
Kiørboe, T., Andersen, K. P., and Dam, H. G.:
Coagulation efficiency and aggregate formation in marine phytoplankton,
Mar. Biol.,
107, 235–245, https://doi.org/10.1007/BF01319822, 1990. a
Kobayashi, M., Adachi, Y., and Ooi, S.:
Breakup of Fractal Flocs in a Turbulent Flow,
Langmuir,
15, 4351–4356, https://doi.org/10.1021/la980763o, 1999. a
Kriest, I.:
Different parameterizations of marine snow in a 1D-model and their influence on representation of marine snow, nitrogen budget and sedimentation,
Deep-Sea Res. Pt. I,
49, 2133–2162, https://doi.org/10.1016/S0967-0637(02)00127-9, 2002. a, b
Kriest, I. and Evans, G. T.:
Representing phytoplankton aggregates in biogeochemical models,
Deep-Sea Res. Pt. I,
46, 1841–1859, https://doi.org/10.1016/S0967-0637(99)00032-1, 1999. a
Lee, J. K., Samanta, D., Nam, H. G., and Zare, R. N.:
Spontaneous formation of gold nanostructures in aqueous microdroplets,
Nat. Commun.,
9, 1–9, 2018. a
Leles, S. G., Valentin, J. L., and Figueiredo, G. M.:
Evaluation of the complexity and performance of marine planktonic trophic models,
An. Acad. Bras. Cienc.,
88, 1971–1991, https://doi.org/10.1590/0001-3765201620150588, 2016. a
Le Moigne, F.:
Pathways of Organic Carbon Downward Transport by the Oceanic Biological Carbon Pump,
Front. Mar. Sci.,
6, 634, https://doi.org/10.3389/fmars.2019.00634, 2019. a
Le Quéré, C.:
Reply to Horizons Article 'Plankton functional type modelling: running before we can walk' Anderson (2005): I. Abrupt changes in marine ecosystems?,
J. Plankton Res.,
28, 871–872, https://doi.org/10.1093/plankt/fbl014, 2006. a
Le Quéré, C., Harrison, S. P., Prentice, I. C., Buitenhuis, E. T., Aumont, O., Bopp, L., Claustre, H., Cotrim Da Cunha, L., Geider, R., Giraud, X., Klaas, C., Kohfeld, K. E., Legendre, L., Manizza, M., Platt, T., Rivkin, R. B., Sathyendranath, S., Uitz, J., Watson, A. J., and Wolf-Gladrow, D.:
Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models,
Glob. Change Biol.,
11, 2016–2040, https://doi.org/10.1111/j.1365-2486.2005.1004.x, 2005. a
Li, X., Passow, U., and Logan, B. E.:
Fractal dimensions of small (15–200 µm) particles in Eastern Pacific coastal waters,
Deep-Sea Res. Pt. I,
45, 115–131, https://doi.org/10.1016/S0967-0637(97)00058-7, 1998. a
Li, X. Y., Zhang, J. J., and Lee, J. H.:
Modelling particle size distribution dynamics in marine waters,
Water Res.,
38, 1305–1317, https://doi.org/10.1016/j.watres.2003.11.010, 2004. a, b
Lima, I. D., Olson, D. B., and Doney, S. C.:
Intrinsic dynamics and stability properties of size-structured pelagic ecosystem models,
J. Plankton Res.,
24, 533–556, https://doi.org/10.1093/plankt/24.6.533, 2002. a
McCave, I.:
Size spectra and aggregation of suspended particles in the deep ocean,
Deep-Sea Res.,
31, 329–352, https://doi.org/10.1016/0198-0149(84)90088-8, 1984. a, b, c
Monroy, P., Hernández-García, E., Rossi, V., and López, C.:
Modeling the dynamical sinking of biogenic particles in oceanic flow,
Nonlinear Proc. Geoph.,
24, 293–305, https://doi.org/10.5194/npg-24-293-2017, 2017. a
Moore, J. K., Doney, S. C., Kleypas, J. A., Glover, D. M., and Fung, I. Y.:
An intermediate complexity marine ecosystem model for the global domain,
Deep-Sea Res. Pt. II,
49, 403–462, https://doi.org/10.1016/S0967-0645(01)00108-4, 2002. a
Neumann, T.:
Towards a 3-D ecosystem model of the Baltic Sea,
J. Marine Syst.,
25, 405–419, https://doi.org/10.1016/S0924-7963(00)00030-0, 2000. a
Oriekhova, O. and Stoll, S.:
Heteroaggregation of nanoplastic particles in the presence of inorganic colloids and natural organic matter,
Environ. Sci.-Nano,
5, 792–799, https://doi.org/10.1039/C7EN01119A, 2018. a
Palmer, J. R. and Totterdell, I. J.:
Production and export in a global ocean ecosystem model,
Deep-Sea Res. Pt. I,
48, 1169–1198, https://doi.org/10.1016/S0967-0637(00)00080-7, 2001. a, b
Passow, U. and Carlson, C. A.:
The biological pump in a high CO2 world,
Mar. Ecol.-Prog. Ser.,
470, 249–271, https://doi.org/10.3354/meps09985, 2012. a
Pego, R. L.:
Lectures on dynamics in models of coarsening and coagulation,
in: Dynamics in models of coarsening, coagulation, condensation and quantization,
World Scientific, Pittsburgh, USA,
available at: https://www.math.cmu.edu/CNA/Publications/publications2006/001abs/06-CNA-001.pdf (last access: 28 June 2021), pp. 1–61, 2007. a
Ploug, H. and Grossart, H.-P.:
Bacterial growth and grazing on diatom aggregates: Respiratory carbon turnover as a function of aggregate size and sinking velocity,
Limnol. Oceanogr.,
45, 1467–1475, https://doi.org/10.4319/lo.2000.45.7.1467, 2000. a
Pruppacher, H. R. and Klett, J. D.:
Microphysics of clouds and precipitation, no. v. 18 in Atmos. Oceanogr. Sciences Library,
Springer, Dordrecht, New York, https://doi.org/10.1007/978-0-306-48100-0, 2010. a, b
Raick, C., Soetaert, K., and Grégoire, M.:
Model complexity and performance: How far can we simplify?,
Prog. Oceanogr.,
70, 27–57, https://doi.org/10.1016/j.pocean.2006.03.001, 2006. a
Riley, G. A.:
Factors controlling phytoplankton populations on Georges Bank,
J. Mar. Res.,
6, 54–73,
available at: http://images.peabody.yale.edu/publications/jmr/jmr06-01-04.pdf (last access: 28 June 2021), 1946. a
Sarmiento, J. L.and Slater, R. D., Fasham, M. J. R., Ducklow, H. W., Toggweiler, J. R., and Evans, G. T.:
A seasonal three-dimensional ecosystem model of nitrogen cycling in the North Atlantic euphotic zone,
Global Biogeochem. Cy.,
7, 417–450, https://doi.org/10.1029/93GB00375, 1993. a
Stemmann, L., Picheral, M., and Gorsky, G.:
Diel variation in the vertical distribution of particulate matter (> 0.15 mm) in the NW Mediterranean Sea investigated with the Underwater Video Profiler,
Deep-Sea Res. Pt. I,
47, 505–531, https://doi.org/10.1016/S0967-0637(99)00100-4, 2000. a
Stemmann, L., Jackson, G. A., and Ianson, D.:
A vertical model of particle size distributions and fluxes in the midwater column that includes biological and physical processes–Part I: model formulation,
Deep-Sea Res. Pt. I,
51, 865–884, https://doi.org/10.1016/j.dsr.2004.03.001, 2004. a, b
Vichi, M., Pinardi, N., and Masina, S.:
A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory,
J. Mar. Sys.,
64, 89–109, https://doi.org/10.1016/j.jmarsys.2006.03.006, 2007. a
von Smoluchowski, M.:
Drei Vorträge über Diffusion, Brownsche Molekularbewegung und Koagulation von Kolloidteilchen,
Phys. Z.,
17, 557–571, https://fbc.pionier.net.pl/id/oai:jbc.bj.uj.edu.pl:387533, 1916. a
Ward, B. A., Dutkiewicz, S., Jahn, O., and Follows, M. J.:
A size-structured food-web model for the global ocean,
Limnol. Oceanogr.,
57, 1877–1891, https://doi.org/10.4319/lo.2012.57.6.1877, 2012. a
Wiggert, J., Murtugudde, R., and Christian, J.:
Annual ecosystem variability in the tropical Indian Ocean: Results of a coupled bio-physical ocean general circulation model,
Deep-Sea Res. Pt. II,
53, 644–676, https://doi.org/10.1016/j.dsr2.2006.01.027, 2006. a, b
Yool, A., Popova, E. E., and Anderson, T. R.: Medusa-1.0: a new intermediate complexity plankton ecosystem model for the global domain, Geosci. Model Dev., 4, 381–417, https://doi.org/10.5194/gmd-4-381-2011, 2011. a
Zahnow, J. C., Maerz, J., and Feudel, U.:
Particle-based modeling of aggregation and fragmentation processes: Fractal-like aggregates,
Physica D,
240, 882–893, https://doi.org/10.1016/j.physd.2011.01.003, 2011. a
Short summary
An accurate description of detritic organic particles is key to improving estimations of carbon export into the ocean abyss in ocean general circulation models. Yet, most parametrization are numerically impractical due to the required number of tracers needed to resolve the particle size spectrum. Here, a new parametrization that aims to minimize the tracers number while accurately describing the particles dynamics is developed and tested in a series of idealized numerical experiments.
An accurate description of detritic organic particles is key to improving estimations of carbon...