Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4535-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-4535-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
23 Jul 2021
Development and technical paper |
| 23 Jul 2021
| 23 Jul 2021
A discrete interaction numerical model for coagulation and fragmentation of marine detritic particulate matter (Coagfrag v.1)
Gwenaëlle Gremion
CORRESPONDING AUTHOR
Institut des sciences de la mer de Rimouski, UQAR, Québec-Océan, Rimouski, Canada
Louis-Philippe Nadeau
CORRESPONDING AUTHOR
Institut des sciences de la mer de Rimouski, UQAR, Québec-Océan, Rimouski, Canada
Christiane Dufresne
Institut des sciences de la mer de Rimouski, UQAR, Québec-Océan, Rimouski, Canada
Irene R. Schloss
Instituto Antártico Argentino, Buenos Aires, Argentina
Centro Austral de Investigaciones Científicas (CADIC-CONICET), Ushuaia, Argentina
Instituto de Ciencias Polares y Ambientales, Universidad Nacional de Tierra del Fuego, Ushuaia, Argentina
Philippe Archambault
ArcticNet, Québec-Océan, Université Laval, Quebec, Canada
Dany Dumont
Institut des sciences de la mer de Rimouski, UQAR, Québec-Océan, Rimouski, Canada
Related authors
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.
Elie Dumas-Lefebvre and Dany Dumont
The Cryosphere, 17, 827–842, https://doi.org/10.5194/tc-17-827-2023, https://doi.org/10.5194/tc-17-827-2023, 2023
Short summary
Short summary
By changing the shape of ice floes, wave-induced sea ice breakup dramatically affects the large-scale dynamics of sea ice. As this process is also the trigger of multiple others, it was deemed relevant to study how breakup itself affects the ice floe size distribution. To do so, a ship sailed close to ice floes, and the breakup that it generated was recorded with a drone. The obtained data shed light on the underlying physics of wave-induced sea ice breakup.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Oceanography
PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks
Experimental design for the Marine Ice Sheet–Ocean Model Intercomparison Project – phase 2 (MISOMIP2)
Development of a total variation diminishing (TVD) sea ice transport scheme and its application in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids
Spurious numerical mixing under strong tidal forcing: a case study in the south-east Asian seas using the Symphonie model (v3.1.2)
Modelling the water isotope distribution in the Mediterranean Sea using a high-resolution oceanic model (NEMO-MED12-watiso v1.0): evaluation of model results against in situ observations
LIGHT-bgcArgo-1.0: using synthetic float capabilities in E3SMv2 to assess spatiotemporal variability in ocean physics and biogeochemistry
Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0
A simple approach to represent precipitation-derived freshwater fluxes into nearshore ocean models: an FVCOM4.1 case study of Quatsino Sound, British Columbia
An optimal transformation method applied to diagnose the ocean carbon budget
Implementation and assessment of a model including mixotrophs and the carbonate cycle (Eco3M_MIX-CarbOx v1.0) in a highly dynamic Mediterranean coastal environment (Bay of Marseille, France) – Part 2: Towards a better representation of total alkalinity when modeling the carbonate system and air–sea CO2 fluxes
DALROMS-NWA12 v1.0, a coupled circulation-ice-biogeochemistry modelling system for the northwest Atlantic Ocean: Development and validation
Development of a novel storm surge inundation model framework for efficient prediction
Skin sea surface temperature schemes in coupled ocean–atmosphere modelling: the impact of chlorophyll-interactive e-folding depth
DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin
StraitFlux – precise computations of water strait fluxes on various modeling grids
Comparison of the Coastal and Regional Ocean COmmunity model (CROCO) and NCAR-LES in non-hydrostatic simulations
Intercomparisons of Tracker v1.1 and four other ocean particle-tracking software packages in the Regional Ocean Modeling System
CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean archipelago
Implementation of additional spectral wave field exchanges in a three-dimensional wave–current coupled WAVEWATCH-III (version 6.07) and CROCO (version 1.2) configuration: assessment of their implications for macro-tidal coastal hydrodynamics
Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system
LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2
New insights into the South China Sea throughflow and water budget seasonal cycle: evaluation and analysis of a high-resolution configuration of the ocean model SYMPHONIE version 2.4
MQGeometry-1.0: a multi-layer quasi-geostrophic solver on non-rectangular geometries
Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on El Niño–Southern Oscillation forecasts
A revised ocean mixed layer model for better simulating the diurnal variation of ocean skin temperature
Great Lakes wave forecast system on high-resolution unstructured meshes
Impact of increased resolution on Arctic Ocean simulations in Ocean Model Intercomparison Project phase 2 (OMIP-2)
Evaluating an accelerated forcing approach for improving computational efficiency in coupled ice sheet-ocean modelling
A high-resolution physical–biogeochemical model for marine resource applications in the northwest Atlantic (MOM6-COBALT-NWA12 v1.0)
A flexible z-layers approach for the accurate representation of free surface flows in a coastal ocean model (SHYFEM v. 7_5_71)
Implementation and assessment of a model including mixotrophs and the carbonate cycle (Eco3M_MIX-CarbOx v1.0) in a highly dynamic Mediterranean coastal environment (Bay of Marseille, France) – Part 1: Evolution of ecosystem composition under limited light and nutrient conditions
Ocean wave tracing v.1: a numerical solver of the wave ray equations for ocean waves on variable currents at arbitrary depths
Design and evaluation of an efficient high-precision ocean surface wave model with a multiscale grid system (MSG_Wav1.0)
Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)
Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard
Open-ocean tides simulated by ICON-O, version icon-2.6.6
Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll
An optimal transformation method for inferring ocean tracer sources and sinks
Data assimilation sensitivity experiments in the East Auckland Current system using 4D-Var
Using the COAsT Python package to develop a standardised validation workflow for ocean physics models
Improving Antarctic Bottom Water precursors in NEMO for climate applications
Formulation, optimization, and sensitivity of NitrOMZv1.0, a biogeochemical model of the nitrogen cycle in oceanic oxygen minimum zones
Waves in SKRIPS: WAVEWATCH III coupling implementation and a case study of Tropical Cyclone Mekunu
Adding sea ice effects to a global operational model (NEMO v3.6) for forecasting total water level: approach and impact
Enhanced ocean wave modeling by including effect of breaking under both deep- and shallow-water conditions
An internal solitary wave forecasting model in the northern South China Sea (ISWFM-NSCS)
The 3D biogeochemical marine mercury cycling model MERCY v2.0 – linking atmospheric Hg to methylmercury in fish
Global seamless tidal simulation using a 3D unstructured-grid model (SCHISM v5.10.0)
Arctic Ocean simulations in the CMIP6 Ocean Model Intercomparison Project (OMIP)
ChemicalDrift 1.0: an open-source Lagrangian chemical-fate and transport model for organic aquatic pollutants
Gloria Pietropolli, Luca Manzoni, and Gianpiero Cossarini
Geosci. Model Dev., 17, 7347–7364, https://doi.org/10.5194/gmd-17-7347-2024, https://doi.org/10.5194/gmd-17-7347-2024, 2024
Short summary
Short summary
Monitoring the ocean is essential for studying marine life and human impact. Our new software, PPCon, uses ocean data to predict key factors like nitrate and chlorophyll levels, which are hard to measure directly. By leveraging machine learning, PPCon offers more accurate and efficient predictions.
Jan De Rydt, Nicolas C. Jourdain, Yoshihiro Nakayama, Mathias van Caspel, Ralph Timmermann, Pierre Mathiot, Xylar S. Asay-Davis, Hélène Seroussi, Pierre Dutrieux, Ben Galton-Fenzi, David Holland, and Ronja Reese
Geosci. Model Dev., 17, 7105–7139, https://doi.org/10.5194/gmd-17-7105-2024, https://doi.org/10.5194/gmd-17-7105-2024, 2024
Short summary
Short summary
Global climate models do not reliably simulate sea-level change due to ice-sheet–ocean interactions. We propose a community modelling effort to conduct a series of well-defined experiments to compare models with observations and study how models respond to a range of perturbations in climate and ice-sheet geometry. The second Marine Ice Sheet–Ocean Model Intercomparison Project will continue to lay the groundwork for including ice-sheet–ocean interactions in global-scale IPCC-class models.
Qian Wang, Yang Zhang, Fei Chai, Y. Joseph Zhang, and Lorenzo Zampieri
Geosci. Model Dev., 17, 7067–7081, https://doi.org/10.5194/gmd-17-7067-2024, https://doi.org/10.5194/gmd-17-7067-2024, 2024
Short summary
Short summary
We coupled an unstructured hydro-model with an advanced column sea ice model to meet the growing demand for increased resolution and complexity in unstructured sea ice models. Additionally, we present a novel tracer transport scheme for the sea ice coupled model and demonstrate that this scheme fulfills the requirements for conservation, accuracy, efficiency, and monotonicity in an idealized test. Our new coupled model also has good performance in realistic tests.
Adrien Garinet, Marine Herrmann, Patrick Marsaleix, and Juliette Pénicaud
Geosci. Model Dev., 17, 6967–6986, https://doi.org/10.5194/gmd-17-6967-2024, https://doi.org/10.5194/gmd-17-6967-2024, 2024
Short summary
Short summary
Mixing is a crucial aspect of the ocean, but its accurate representation in computer simulations is made challenging by errors that result in unwanted mixing, compromising simulation realism. Here we illustrate the spurious effect that tides can have on simulations of south-east Asia. Although they play an important role in determining the state of the ocean, they can increase numerical errors and make simulation outputs less realistic. We also provide insights into how to reduce these errors.
Mohamed Ayache, Jean-Claude Dutay, Anne Mouchet, Kazuyo Tachikawa, Camille Risi, and Gilles Ramstein
Geosci. Model Dev., 17, 6627–6655, https://doi.org/10.5194/gmd-17-6627-2024, https://doi.org/10.5194/gmd-17-6627-2024, 2024
Short summary
Short summary
Water isotopes (δ18O, δD) are one of the most widely used proxies in ocean climate research. Previous studies using water isotope observations and modelling have highlighted the importance of understanding spatial and temporal isotopic variability for a quantitative interpretation of these tracers. Here we present the first results of a high-resolution regional dynamical model (at 1/12° horizontal resolution) developed for the Mediterranean Sea, one of the hotspots of ongoing climate change.
Cara Nissen, Nicole S. Lovenduski, Mathew Maltrud, Alison R. Gray, Yohei Takano, Kristen Falcinelli, Jade Sauvé, and Katherine Smith
Geosci. Model Dev., 17, 6415–6435, https://doi.org/10.5194/gmd-17-6415-2024, https://doi.org/10.5194/gmd-17-6415-2024, 2024
Short summary
Short summary
Autonomous profiling floats have provided unprecedented observational coverage of the global ocean, but uncertainties remain about whether their sampling frequency and density capture the true spatiotemporal variability of physical, biogeochemical, and biological properties. Here, we present the novel synthetic biogeochemical float capabilities of the Energy Exascale Earth System Model version 2 and demonstrate their utility as a test bed to address these uncertainties.
Ye Yuan, Fujiang Yu, Zhi Chen, Xueding Li, Fang Hou, Yuanyong Gao, Zhiyi Gao, and Renbo Pang
Geosci. Model Dev., 17, 6123–6136, https://doi.org/10.5194/gmd-17-6123-2024, https://doi.org/10.5194/gmd-17-6123-2024, 2024
Short summary
Short summary
Accurate and timely forecasting of ocean waves is of great importance to the safety of marine transportation and offshore engineering. In this study, GPU-accelerated computing is introduced in WAve Modeling Cycle 6 (WAM6). With this effort, global high-resolution wave simulations can now run on GPUs up to tens of times faster than the currently available models can on a CPU node with results that are just as accurate.
Krysten Rutherford, Laura Bianucci, and William Floyd
Geosci. Model Dev., 17, 6083–6104, https://doi.org/10.5194/gmd-17-6083-2024, https://doi.org/10.5194/gmd-17-6083-2024, 2024
Short summary
Short summary
Nearshore ocean models often lack complete information about freshwater fluxes due to numerous ungauged rivers and streams. We tested a simple rain-based hydrological model as inputs into an ocean model of Quatsino Sound, Canada, with the aim of improving the representation of the land–ocean connection in the nearshore model. Through multiple tests, we found that the performance of the ocean model improved when providing 60 % or more of the freshwater inputs from the simple runoff model.
Neill Mackay, Taimoor Sohail, Jan David Zika, Richard G. Williams, Oliver Andrews, and Andrew James Watson
Geosci. Model Dev., 17, 5987–6005, https://doi.org/10.5194/gmd-17-5987-2024, https://doi.org/10.5194/gmd-17-5987-2024, 2024
Short summary
Short summary
The ocean absorbs carbon dioxide from the atmosphere, mitigating climate change, but estimates of the uptake do not always agree. There is a need to reconcile these differing estimates and to improve our understanding of ocean carbon uptake. We present a new method for estimating ocean carbon uptake and test it with model data. The method effectively diagnoses the ocean carbon uptake from limited data and therefore shows promise for reconciling different observational estimates.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 17, 5851–5882, https://doi.org/10.5194/gmd-17-5851-2024, https://doi.org/10.5194/gmd-17-5851-2024, 2024
Short summary
Short summary
The carbonate system is typically studied using measurements, but modeling can contribute valuable insights. Using a biogeochemical model, we propose a new representation of total alkalinity, dissolved inorganic carbon, pCO2, and pH in a highly dynamic Mediterranean coastal area, the Bay of Marseille, a useful addition to measurements. Through a detailed analysis of pCO2 and air–sea CO2 fluxes, we show that variations are strongly impacted by the hydrodynamic processes that affect the bay.
Kyoko Ohashi, Arnaud Laurent, Christoph Renkl, Jinyu Sheng, Katja Fennel, and Eric Oliver
EGUsphere, https://doi.org/10.5194/egusphere-2024-1372, https://doi.org/10.5194/egusphere-2024-1372, 2024
Short summary
Short summary
We developed a modelling system of the northwest Atlantic Ocean that simulates the currents, temperature, salinity, and parts of the biochemical cycle of the ocean, as well as sea ice. The system combines advanced, open-source models and can be used to study, for example, the oceans’ capture of atmospheric carbon dioxide which is a key process in the global climate. The system produces realistic results, and we use it to investigate the roles of tides and sea ice in the northwest Atlantic Ocean.
Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu
Geosci. Model Dev., 17, 5497–5509, https://doi.org/10.5194/gmd-17-5497-2024, https://doi.org/10.5194/gmd-17-5497-2024, 2024
Short summary
Short summary
Storm surges generate coastal inundation and expose populations and properties to danger. We developed a novel storm surge inundation model for efficient prediction. Estimates compare well with in situ measurements and results from a numerical model. The new model is a significant improvement on existing numerical models, with much higher computational efficiency and stability, which allows timely disaster prevention and mitigation.
Vincenzo de Toma, Daniele Ciani, Yassmin Hesham Essa, Chunxue Yang, Vincenzo Artale, Andrea Pisano, Davide Cavaliere, Rosalia Santoleri, and Andrea Storto
Geosci. Model Dev., 17, 5145–5165, https://doi.org/10.5194/gmd-17-5145-2024, https://doi.org/10.5194/gmd-17-5145-2024, 2024
Short summary
Short summary
This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer
Geosci. Model Dev., 17, 4705–4725, https://doi.org/10.5194/gmd-17-4705-2024, https://doi.org/10.5194/gmd-17-4705-2024, 2024
Short summary
Short summary
We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the Simulating WAves Nearshore model (SWAN) over synoptic to climate timescales. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.
Susanna Winkelbauer, Michael Mayer, and Leopold Haimberger
Geosci. Model Dev., 17, 4603–4620, https://doi.org/10.5194/gmd-17-4603-2024, https://doi.org/10.5194/gmd-17-4603-2024, 2024
Short summary
Short summary
Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data are complicated by the distortion of the used modelling grids and the large number of different grid types. We present two new methods that allow the calculation of oceanic fluxes of volume, heat, salinity, and ice through almost arbitrary sections for various models and reanalyses that are independent of the used modelling grids.
Xiaoyu Fan, Baylor Fox-Kemper, Nobuhiro Suzuki, Qing Li, Patrick Marchesiello, Peter P. Sullivan, and Paul S. Hall
Geosci. Model Dev., 17, 4095–4113, https://doi.org/10.5194/gmd-17-4095-2024, https://doi.org/10.5194/gmd-17-4095-2024, 2024
Short summary
Short summary
Simulations of the oceanic turbulent boundary layer using the nonhydrostatic CROCO ROMS and NCAR-LES models are compared. CROCO and the NCAR-LES are accurate in a similar manner, but CROCO’s additional features (e.g., nesting and realism) and its compressible turbulence formulation carry additional costs.
Jilian Xiong and Parker MacCready
Geosci. Model Dev., 17, 3341–3356, https://doi.org/10.5194/gmd-17-3341-2024, https://doi.org/10.5194/gmd-17-3341-2024, 2024
Short summary
Short summary
The new offline particle tracking package, Tracker v1.1, is introduced to the Regional Ocean Modeling System, featuring an efficient nearest-neighbor algorithm to enhance particle-tracking speed. Its performance was evaluated against four other tracking packages and passive dye. Despite unique features, all packages yield comparable results. Running multiple packages within the same circulation model allows comparison of their performance and ease of use.
Sylvain Cailleau, Laurent Bessières, Léonel Chiendje, Flavie Dubost, Guillaume Reffray, Jean-Michel Lellouche, Simon van Gennip, Charly Régnier, Marie Drevillon, Marc Tressol, Matthieu Clavier, Julien Temple-Boyer, and Léo Berline
Geosci. Model Dev., 17, 3157–3173, https://doi.org/10.5194/gmd-17-3157-2024, https://doi.org/10.5194/gmd-17-3157-2024, 2024
Short summary
Short summary
In order to improve Sargassum drift forecasting in the Caribbean area, drift models can be forced by higher-resolution ocean currents. To this goal a 3 km resolution regional ocean model has been developed. Its assessment is presented with a particular focus on the reproduction of fine structures representing key features of the Caribbean region dynamics and Sargassum transport. The simulated propagation of a North Brazil Current eddy and its dissipation was found to be quite realistic.
Gaetano Porcile, Anne-Claire Bennis, Martial Boutet, Sophie Le Bot, Franck Dumas, and Swen Jullien
Geosci. Model Dev., 17, 2829–2853, https://doi.org/10.5194/gmd-17-2829-2024, https://doi.org/10.5194/gmd-17-2829-2024, 2024
Short summary
Short summary
Here a new method of modelling the interaction between ocean currents and waves is presented. We developed an advanced coupling of two models, one for ocean currents and one for waves. In previous couplings, some wave-related calculations were based on simplified assumptions. Our method uses more complex calculations to better represent wave–current interactions. We tested it in a macro-tidal coastal area and found that it significantly improves the model accuracy, especially during storms.
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, https://doi.org/10.5194/gmd-17-2359-2024, 2024
Short summary
Short summary
Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
Ivan Hernandez, Leidy M. Castro-Rosero, Manuel Espino, and Jose M. Alsina Torrent
Geosci. Model Dev., 17, 2221–2245, https://doi.org/10.5194/gmd-17-2221-2024, https://doi.org/10.5194/gmd-17-2221-2024, 2024
Short summary
Short summary
The LOCATE numerical model was developed to conduct Lagrangian simulations of the transport and dispersion of marine debris at coastal scales. High-resolution hydrodynamic data and a beaching module that used particle distance to the shore for land–water boundary detection were used on a realistic debris discharge scenario comparing hydrodynamic data at various resolutions. Coastal processes and complex geometric structures were resolved when using nested grids and distance-to-shore beaching.
Ngoc B. Trinh, Marine Herrmann, Caroline Ulses, Patrick Marsaleix, Thomas Duhaut, Thai To Duy, Claude Estournel, and R. Kipp Shearman
Geosci. Model Dev., 17, 1831–1867, https://doi.org/10.5194/gmd-17-1831-2024, https://doi.org/10.5194/gmd-17-1831-2024, 2024
Short summary
Short summary
A high-resolution model was built to study the South China Sea (SCS) water, heat, and salt budgets. Model performance is demonstrated by comparison with observations and simulations. Important discards are observed if calculating offline, instead of online, lateral inflows and outflows of water, heat, and salt. The SCS mainly receives water from the Luzon Strait and releases it through the Mindoro, Taiwan, and Karimata straits. SCS surface interocean water exchanges are driven by monsoon winds.
Louis Thiry, Long Li, Guillaume Roullet, and Etienne Mémin
Geosci. Model Dev., 17, 1749–1764, https://doi.org/10.5194/gmd-17-1749-2024, https://doi.org/10.5194/gmd-17-1749-2024, 2024
Short summary
Short summary
We present a new way of solving the quasi-geostrophic (QG) equations, a simple set of equations describing ocean dynamics. Our method is solely based on the numerical methods used to solve the equations and requires no parameter tuning. Moreover, it can handle non-rectangular geometries, opening the way to study QG equations on realistic domains. We release a PyTorch implementation to ease future machine-learning developments on top of the presented method.
Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song
Geosci. Model Dev., 17, 1651–1665, https://doi.org/10.5194/gmd-17-1651-2024, https://doi.org/10.5194/gmd-17-1651-2024, 2024
Short summary
Short summary
Parameter estimation is the process that optimizes model parameters using observations, which could reduce model errors and improve forecasting. In this study, we conducted parameter estimation experiments using the CESM and the ensemble adjustment Kalman filter. The obtained initial conditions and parameters are used to perform ensemble forecast experiments for ENSO forecasting. The results revealed that parameter estimation could reduce analysis errors and improve ENSO forecast skills.
Eui-Jong Kang, Byung-Ju Sohn, Sang-Woo Kim, Wonho Kim, Young-Cheol Kwon, Seung-Bum Kim, Hyoung-Wook Chun, and Chao Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-23, https://doi.org/10.5194/gmd-2024-23, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The recently available ERA5 hourly ocean skin temperature (Tint) data is expected to be valuable for various science studies. However, when analyzing the hourly variations of Tint, questions arise about its reliability, the deficiency of which may be related to errors in the ocean mixed layer (OML) model. To address this, we reexamined and corrected significant errors in the OML model. Validation of the simulated SST using the revised OML model against observations demonstrated good agreement.
Ali Abdolali, Saeideh Banihashemi, Jose Henrique Alves, Aron Roland, Tyler J. Hesser, Mary Anderson Bryant, and Jane McKee Smith
Geosci. Model Dev., 17, 1023–1039, https://doi.org/10.5194/gmd-17-1023-2024, https://doi.org/10.5194/gmd-17-1023-2024, 2024
Short summary
Short summary
This article presents an overview of the development and implementation of Great Lake Wave Unstructured (GLWUv2.0), including the core model and workflow design and development. The validation was conducted against in situ data for the re-forecasted duration for summer and wintertime (ice season). The article describes the limitations and challenges encountered in the operational environment and the path forward for the next generation of wave forecast systems in enclosed basins like the GL.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Qin Zhou, Chen Zhao, Rupert Gladstone, Tore Hattermann, David Gwyther, and Benjamin Galton-Fenzi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-244, https://doi.org/10.5194/gmd-2023-244, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We have introduced an "accelerated forcing" approach to address the discrepancy in timescales between ice sheet and ocean models in coupled modelling, by reducing the ocean model simulation duration. We evaluate the approach's applicability and limitations based on idealized coupled models. Our results suggest that, when used carefully, the approach can be a useful tool in coupled ice sheet-ocean modelling, especially relevant to studies on sea level rise projections.
Andrew C. Ross, Charles A. Stock, Alistair Adcroft, Enrique Curchitser, Robert Hallberg, Matthew J. Harrison, Katherine Hedstrom, Niki Zadeh, Michael Alexander, Wenhao Chen, Elizabeth J. Drenkard, Hubert du Pontavice, Raphael Dussin, Fabian Gomez, Jasmin G. John, Dujuan Kang, Diane Lavoie, Laure Resplandy, Alizée Roobaert, Vincent Saba, Sang-Ik Shin, Samantha Siedlecki, and James Simkins
Geosci. Model Dev., 16, 6943–6985, https://doi.org/10.5194/gmd-16-6943-2023, https://doi.org/10.5194/gmd-16-6943-2023, 2023
Short summary
Short summary
We evaluate a model for northwest Atlantic Ocean dynamics and biogeochemistry that balances high resolution with computational economy by building on the new regional features in the MOM6 ocean model and COBALT biogeochemical model. We test the model's ability to simulate impactful historical variability and find that the model simulates the mean state and variability of most features well, which suggests the model can provide information to inform living-marine-resource applications.
Luca Arpaia, Christian Ferrarin, Marco Bajo, and Georg Umgiesser
Geosci. Model Dev., 16, 6899–6919, https://doi.org/10.5194/gmd-16-6899-2023, https://doi.org/10.5194/gmd-16-6899-2023, 2023
Short summary
Short summary
We propose a discrete multilayer shallow water model based on z-layers which, thanks to the insertion and removal of surface layers, can deal with an arbitrarily large tidal oscillation independently of the vertical resolution. The algorithm is based on a two-step procedure used in numerical simulations with moving boundaries (grid movement followed by a grid topology change, that is, the insertion/removal of surface layers), which avoids the appearance of very thin surface layers.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, France Van Wambeke, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 16, 6701–6739, https://doi.org/10.5194/gmd-16-6701-2023, https://doi.org/10.5194/gmd-16-6701-2023, 2023
Short summary
Short summary
While several studies have shown that mixotrophs play a crucial role in the carbon cycle, the impact of environmental forcings on their dynamics remains poorly investigated. Using a biogeochemical model that considers mixotrophs, we study the impact of light and nutrient concentration on the ecosystem composition in a highly dynamic Mediterranean coastal area: the Bay of Marseille. We show that mixotrophs cope better with oligotrophic conditions compared to strict auto- and heterotrophs.
Trygve Halsne, Kai Håkon Christensen, Gaute Hope, and Øyvind Breivik
Geosci. Model Dev., 16, 6515–6530, https://doi.org/10.5194/gmd-16-6515-2023, https://doi.org/10.5194/gmd-16-6515-2023, 2023
Short summary
Short summary
Surface waves that propagate in oceanic or coastal environments get influenced by their surroundings. Changes in the ambient current or the depth profile affect the wave propagation path, and the change in wave direction is called refraction. Some analytical solutions to the governing equations exist under ideal conditions, but for realistic situations, the equations must be solved numerically. Here we present such a numerical solver under an open-source license.
Jiangyu Li, Shaoqing Zhang, Qingxiang Liu, Xiaolin Yu, and Zhiwei Zhang
Geosci. Model Dev., 16, 6393–6412, https://doi.org/10.5194/gmd-16-6393-2023, https://doi.org/10.5194/gmd-16-6393-2023, 2023
Short summary
Short summary
Ocean surface waves play an important role in the air–sea interface but are rarely activated in high-resolution Earth system simulations due to their expensive computational costs. To alleviate this situation, this paper designs a new wave modeling framework with a multiscale grid system. Evaluations of a series of numerical experiments show that it has good feasibility and applicability in the WAVEWATCH III model, WW3, and can achieve the goals of efficient and high-precision wave simulation.
Doroteaciro Iovino, Pier Giuseppe Fogli, and Simona Masina
Geosci. Model Dev., 16, 6127–6159, https://doi.org/10.5194/gmd-16-6127-2023, https://doi.org/10.5194/gmd-16-6127-2023, 2023
Short summary
Short summary
This paper describes the model performance of three global ocean–sea ice configurations, from non-eddying (1°) to eddy-rich (1/16°) resolutions. Model simulations are obtained following the Ocean Model Intercomparison Project phase 2 (OMIP2) protocol. We compare key global climate variables across the three models and against observations, emphasizing the relative advantages and disadvantages of running forced ocean–sea ice models at higher resolution.
Johannes Röhrs, Yvonne Gusdal, Edel S. U. Rikardsen, Marina Durán Moro, Jostein Brændshøi, Nils Melsom Kristensen, Sindre Fritzner, Keguang Wang, Ann Kristin Sperrevik, Martina Idžanović, Thomas Lavergne, Jens Boldingh Debernard, and Kai H. Christensen
Geosci. Model Dev., 16, 5401–5426, https://doi.org/10.5194/gmd-16-5401-2023, https://doi.org/10.5194/gmd-16-5401-2023, 2023
Short summary
Short summary
A model to predict ocean currents, temperature, and sea ice is presented, covering the Barents Sea and northern Norway. To quantify forecast uncertainties, the model calculates ensemble forecasts with 24 realizations of ocean and ice conditions. Observations from satellites, buoys, and ships are ingested by the model. The model forecasts are compared with observations, and we show that the ocean model has skill in predicting sea surface temperatures.
Jin-Song von Storch, Eileen Hertwig, Veit Lüschow, Nils Brüggemann, Helmuth Haak, Peter Korn, and Vikram Singh
Geosci. Model Dev., 16, 5179–5196, https://doi.org/10.5194/gmd-16-5179-2023, https://doi.org/10.5194/gmd-16-5179-2023, 2023
Short summary
Short summary
The new ocean general circulation model ICON-O is developed for running experiments at kilometer scales and beyond. One targeted application is to simulate internal tides crucial for ocean mixing. To ensure their realism, which is difficult to assess, we evaluate the barotropic tides that generate internal tides. We show that ICON-O is able to realistically simulate the major aspects of the observed barotropic tides and discuss the aspects that impact the quality of the simulated tides.
Bror F. Jönsson, Christopher L. Follett, Jacob Bien, Stephanie Dutkiewicz, Sangwon Hyun, Gemma Kulk, Gael L. Forget, Christian Müller, Marie-Fanny Racault, Christopher N. Hill, Thomas Jackson, and Shubha Sathyendranath
Geosci. Model Dev., 16, 4639–4657, https://doi.org/10.5194/gmd-16-4639-2023, https://doi.org/10.5194/gmd-16-4639-2023, 2023
Short summary
Short summary
While biogeochemical models and satellite-derived ocean color data provide unprecedented information, it is problematic to compare them. Here, we present a new approach based on comparing probability density distributions of model and satellite properties to assess model skills. We also introduce Earth mover's distances as a novel and powerful metric to quantify the misfit between models and observations. We find that how 3D chlorophyll fields are aggregated can be a significant source of error.
Jan David Zika and Sohail Taimoor
EGUsphere, https://doi.org/10.5194/egusphere-2023-1220, https://doi.org/10.5194/egusphere-2023-1220, 2023
Short summary
Short summary
We describe a method to relate the fluxes of heat and fresh water at the sea surface, to the resulting distribution of sea water among categories such as warm and salty, cold and salty, etc. The method exploits the laws that govern how heat and salt change when water mixes. The method will allow the climate community to improve estimates of how much heat the ocean is absorbing and how rainfall and evaporation are changing across the globe.
Rafael Santana, Helen Macdonald, Joanne O'Callaghan, Brian Powell, Sarah Wakes, and Sutara H. Suanda
Geosci. Model Dev., 16, 3675–3698, https://doi.org/10.5194/gmd-16-3675-2023, https://doi.org/10.5194/gmd-16-3675-2023, 2023
Short summary
Short summary
We show the importance of assimilating subsurface temperature and velocity data in a model of the East Auckland Current. Assimilation of velocity increased the representation of large oceanic vortexes. Assimilation of temperature is needed to correctly simulate temperatures around 100 m depth, which is the most difficult region to simulate in ocean models. Our simulations showed improved results in comparison to the US Navy global model and highlight the importance of regional models.
David Byrne, Jeff Polton, Enda O'Dea, and Joanne Williams
Geosci. Model Dev., 16, 3749–3764, https://doi.org/10.5194/gmd-16-3749-2023, https://doi.org/10.5194/gmd-16-3749-2023, 2023
Short summary
Short summary
Validation is a crucial step during the development of models for ocean simulation. The purpose of validation is to assess how accurate a model is. It is most commonly done by comparing output from a model to actual observations. In this paper, we introduce and demonstrate usage of the COAsT Python package to standardise the validation process for physical ocean models. We also discuss our five guiding principles for standardised validation.
Katherine Hutchinson, Julie Deshayes, Christian Éthé, Clément Rousset, Casimir de Lavergne, Martin Vancoppenolle, Nicolas C. Jourdain, and Pierre Mathiot
Geosci. Model Dev., 16, 3629–3650, https://doi.org/10.5194/gmd-16-3629-2023, https://doi.org/10.5194/gmd-16-3629-2023, 2023
Short summary
Short summary
Bottom Water constitutes the lower half of the ocean’s overturning system and is primarily formed in the Weddell and Ross Sea in the Antarctic due to interactions between the atmosphere, ocean, sea ice and ice shelves. Here we use a global ocean 1° resolution model with explicit representation of the three large ice shelves important for the formation of the parent waters of Bottom Water. We find doing so reduces salt biases, improves water mass realism and gives realistic ice shelf melt rates.
Daniele Bianchi, Daniel McCoy, and Simon Yang
Geosci. Model Dev., 16, 3581–3609, https://doi.org/10.5194/gmd-16-3581-2023, https://doi.org/10.5194/gmd-16-3581-2023, 2023
Short summary
Short summary
We present NitrOMZ, a new model of the oceanic nitrogen cycle that simulates chemical transformations within oxygen minimum zones (OMZs). We describe the model formulation and its implementation in a one-dimensional representation of the water column before evaluating its ability to reproduce observations in the eastern tropical South Pacific. We conclude by describing the model sensitivity to parameter choices and environmental factors and its application to nitrogen cycling in the ocean.
Rui Sun, Alison Cobb, Ana B. Villas Bôas, Sabique Langodan, Aneesh C. Subramanian, Matthew R. Mazloff, Bruce D. Cornuelle, Arthur J. Miller, Raju Pathak, and Ibrahim Hoteit
Geosci. Model Dev., 16, 3435–3458, https://doi.org/10.5194/gmd-16-3435-2023, https://doi.org/10.5194/gmd-16-3435-2023, 2023
Short summary
Short summary
In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS. We then performed a case study using the newly implemented model to study Tropical Cyclone Mekunu, which occurred in the Arabian Sea. We found that the coupled model better simulates the cyclone than the uncoupled model, but the impact of waves on the cyclone is not significant. However, the waves change the sea surface temperature and mixed layer, especially in the cold waves produced due to the cyclone.
Pengcheng Wang and Natacha B. Bernier
Geosci. Model Dev., 16, 3335–3354, https://doi.org/10.5194/gmd-16-3335-2023, https://doi.org/10.5194/gmd-16-3335-2023, 2023
Short summary
Short summary
Effects of sea ice are typically neglected in operational flood forecast systems. In this work, we capture these effects via the addition of a parameterized ice–ocean stress. The parameterization takes advantage of forecast fields from an advanced ice–ocean model and features a novel, consistent representation of the tidal relative ice–ocean velocity. The new parameterization leads to improved forecasts of tides and storm surges in polar regions. Associated physical processes are discussed.
Yue Xu and Xiping Yu
Geosci. Model Dev., 16, 2811–2831, https://doi.org/10.5194/gmd-16-2811-2023, https://doi.org/10.5194/gmd-16-2811-2023, 2023
Short summary
Short summary
An accurate description of the wind energy input into ocean waves is crucial to ocean wave modeling, and a physics-based consideration of the effect of wave breaking is absolutely necessary to obtain such an accurate description, particularly under extreme conditions. This study evaluates the performance of a recently improved formula, taking into account not only the effect of breaking but also the effect of airflow separation on the leeside of steep wave crests in a reasonably consistent way.
Yankun Gong, Xueen Chen, Jiexin Xu, Jieshuo Xie, Zhiwu Chen, Yinghui He, and Shuqun Cai
Geosci. Model Dev., 16, 2851–2871, https://doi.org/10.5194/gmd-16-2851-2023, https://doi.org/10.5194/gmd-16-2851-2023, 2023
Short summary
Short summary
Internal solitary waves (ISWs) play crucial roles in mass transport and ocean mixing in the northern South China Sea. Massive numerical investigations have been conducted in this region, but there was no systematic evaluation of a three-dimensional model about precisely simulating ISWs. Here, an ISW forecasting model is employed to evaluate the roles of resolution, tidal forcing and stratification in accurately reproducing wave properties via comparison to field and remote-sensing observations.
Johannes Bieser, David J. Amptmeijer, Ute Daewel, Joachim Kuss, Anne L. Soerensen, and Corinna Schrum
Geosci. Model Dev., 16, 2649–2688, https://doi.org/10.5194/gmd-16-2649-2023, https://doi.org/10.5194/gmd-16-2649-2023, 2023
Short summary
Short summary
MERCY is a 3D model to study mercury (Hg) cycling in the ocean. Hg is a highly harmful pollutant regulated by the UN Minamata Convention on Mercury due to widespread human emissions. These emissions eventually reach the oceans, where Hg transforms into the even more toxic and bioaccumulative pollutant methylmercury. MERCY predicts the fate of Hg in the ocean and its buildup in the food chain. It is the first model to consider Hg accumulation in fish, a major source of Hg exposure for humans.
Y. Joseph Zhang, Tomas Fernandez-Montblanc, William Pringle, Hao-Cheng Yu, Linlin Cui, and Saeed Moghimi
Geosci. Model Dev., 16, 2565–2581, https://doi.org/10.5194/gmd-16-2565-2023, https://doi.org/10.5194/gmd-16-2565-2023, 2023
Short summary
Short summary
Simulating global ocean from deep basins to coastal areas is a daunting task but is important for disaster mitigation efforts. We present a new 3D global ocean model on flexible mesh to study both tidal and nontidal processes and total water prediction. We demonstrate the potential for
seamlesssimulation, on a single mesh, from the global ocean to a few estuaries along the US West Coast. The model can serve as the backbone of a global tide surge and compound flooding forecasting framework.
Qi Shu, Qiang Wang, Chuncheng Guo, Zhenya Song, Shizhu Wang, Yan He, and Fangli Qiao
Geosci. Model Dev., 16, 2539–2563, https://doi.org/10.5194/gmd-16-2539-2023, https://doi.org/10.5194/gmd-16-2539-2023, 2023
Short summary
Short summary
Ocean models are often used for scientific studies on the Arctic Ocean. Here the Arctic Ocean simulations by state-of-the-art global ocean–sea-ice models participating in the Ocean Model Intercomparison Project (OMIP) were evaluated. The simulations on Arctic Ocean hydrography, freshwater content, stratification, sea surface height, and gateway transports were assessed and the common biases were detected. The simulations forced by different atmospheric forcing were also evaluated.
Manuel Aghito, Loris Calgaro, Knut-Frode Dagestad, Christian Ferrarin, Antonio Marcomini, Øyvind Breivik, and Lars Robert Hole
Geosci. Model Dev., 16, 2477–2494, https://doi.org/10.5194/gmd-16-2477-2023, https://doi.org/10.5194/gmd-16-2477-2023, 2023
Short summary
Short summary
The newly developed ChemicalDrift model can simulate the transport and fate of chemicals in the ocean and in coastal regions. The model combines ocean physics, including transport due to currents, turbulence due to surface winds and the sinking of particles to the sea floor, with ocean chemistry, such as the partitioning, the degradation and the evaporation of chemicals. The model will be utilized for risk assessment of ocean and sea-floor contamination from pollutants emitted from shipping.
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...
