Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-347-2024
© Author(s) 2024. 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-17-347-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
15 Jan 2024
Model evaluation paper |
| 15 Jan 2024
| 15 Jan 2024
Impact of increased resolution on Arctic Ocean simulations in Ocean Model Intercomparison Project phase 2 (OMIP-2)
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven, Germany
First Institute of Oceanography, Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, China
Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao, China
Alexandra Bozec
Center for Ocean–Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA
Eric P. Chassignet
Center for Ocean–Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA
Pier Giuseppe Fogli
Ocean Modeling and Data Assimilation Division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Bologna, Italy
Baylor Fox-Kemper
Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence, RI, USA
Andy McC. Hogg
Research School of Earth Sciences and ARC Centre of Excellence for Climate Extremes, Australian National University, Canberra, Australia
Doroteaciro Iovino
Ocean Modeling and Data Assimilation Division, Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Bologna, Italy
Andrew E. Kiss
Research School of Earth Sciences and ARC Centre of Excellence for Climate Extremes, Australian National University, Canberra, Australia
Nikolay Koldunov
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven, Germany
Julien Le Sommer
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, INRAE, IGE, Grenoble, France
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Pengfei Lin
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Hailong Liu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Igor Polyakov
International Arctic Research Center and College of Natural Science and Mathematics, University of Alaska Fairbanks, Alaska, USA
Finnish Meteorological Institute, Helsinki, Finland
Patrick Scholz
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven, Germany
Dmitry Sidorenko
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven, Germany
Shizhu Wang
First Institute of Oceanography, Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, China
Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao, China
Xiaobiao Xu
Center for Ocean–Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Olmo Zavala-Romero, Alexandra Bozec, Eric P. Chassignet, and Jose R. Miranda
Ocean Sci., 21, 113–132, https://doi.org/10.5194/os-21-113-2025, https://doi.org/10.5194/os-21-113-2025, 2025
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This study shows AI can speed up data assimilation in ocean models. Researchers used convolutional neural networks (CNNs) to assimilate sea surface temperature and height observations in the Gulf of Mexico, learning to replicate corrections made by traditional, computationally expensive methods. CNN design and training window size significantly impacted accuracy, but the percentage of ocean pixels did not. These findings suggest CNNs may accelerate data assimilation in realistic settings.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Claire K. Yung, Madelaine G. Rosevear, Adele K. Morrison, Andrew McC Hogg, and Yoshihiro Nakayama
EGUsphere, https://doi.org/10.5194/egusphere-2024-3513, https://doi.org/10.5194/egusphere-2024-3513, 2024
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Ocean models are used to understand how the ocean interacts with the Antarctic Ice Sheet, but they are too coarse in resolution to capture the small-scale ocean processes driving melting and require a parameterisation to predict melt. Previous parameterisations ignore key processes occurring in some regions of Antarctica. We develop a parameterisation with the feedback of stratification on melting and test it in idealised and regional ocean models, finding changes to melt rate and circulation.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Yann Drillet, Matthew Martin, Yasumasa Miyazawa, Mike Bell, Eric Chassignet, and Stefania Ciliberti
State Planet Discuss., https://doi.org/10.5194/sp-2024-38, https://doi.org/10.5194/sp-2024-38, 2024
Revised manuscript accepted for SP
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This article describes the various stages of research and development that have been carried out over the last few decades to produce an operational reference service for global ocean monitoring and forecasting.
Francesco Cocetta, Lorenzo Zampieri, Julia Selivanova, and Doroteaciro Iovino
The Cryosphere, 18, 4687–4702, https://doi.org/10.5194/tc-18-4687-2024, https://doi.org/10.5194/tc-18-4687-2024, 2024
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Arctic sea ice is thinning and retreating because of global warming. Thus, the region is transitioning to a new state featuring an expansion of the marginal ice zone, a region where mobile ice interacts with waves from the open ocean. By analyzing 30 years of sea ice reconstructions that combine numerical models and observations, this paper proves that an ensemble of global ocean and sea ice reanalyses is an adequate tool for investigating the changing Arctic sea ice cover.
Ibrahim Hoteit, Eric Chassignet, and Mike Bell
State Planet Discuss., https://doi.org/10.5194/sp-2024-10, https://doi.org/10.5194/sp-2024-10, 2024
Revised manuscript accepted for SP
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This paper explores how using multiple predictions instead of just one can improve ocean forecasts and help prepare for changes in ocean conditions. By combining different forecasts, scientists can better understand the uncertainty in predictions, leading to more reliable forecasts and better decision-making. This method is useful for responding to hazards like oil spills, improving climate forecasts, and supporting decision-making in fields like marine safety and resource management.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
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In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Marina Tonani, Eric Chassignet, Mauro Cirano, Yasumasa Miyazawa, and Begoña Pérez Gómez
State Planet Discuss., https://doi.org/10.5194/sp-2024-30, https://doi.org/10.5194/sp-2024-30, 2024
Revised manuscript accepted for SP
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This article provides an overview of the main characteristics of ocean forecast systems covering a limited region of the ocean. Their main components are described, as well as the spatial and temporal scales they resolve. The oceanic variables that these systems are able to predict are also explained. An overview of the main forecasting systems currently in operation is also provided.
Ja-Yeon Moon, Jan Streffing, Sun-Seon Lee, Tido Semmler, Miguel Andrés-Martínez, Jiao Chen, Eun-Byeoul Cho, Jung-Eun Chu, Christian Franzke, Jan P. Gärtner, Rohit Ghosh, Jan Hegewald, Songyee Hong, Nikolay Koldunov, June-Yi Lee, Zihao Lin, Chao Liu, Svetlana Loza, Wonsun Park, Woncheol Roh, Dmitry V. Sein, Sahil Sharma, Dmitry Sidorenko, Jun-Hyeok Son, Malte F. Stuecker, Qiang Wang, Gyuseok Yi, Martina Zapponini, Thomas Jung, and Axel Timmermann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2491, https://doi.org/10.5194/egusphere-2024-2491, 2024
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Based on a series of storm-resolving greenhouse warming simulations conducted with the AWI-CM3 model at 9 km global atmosphere, 4–25 km ocean resolution, we present new projections of regional climate change, modes of climate variability and extreme events. The 10-year-long high resolution simulations for the 2000s, 2030s, 2060s, 2090s were initialized from a coarser resolution transient run (31 km atmosphere) which follows the SSP5-8.5 greenhouse gas emission scenario from 1950–2100 CE.
Julia Selivanova, Doroteaciro Iovino, and Francesco Cocetta
The Cryosphere, 18, 2739–2763, https://doi.org/10.5194/tc-18-2739-2024, https://doi.org/10.5194/tc-18-2739-2024, 2024
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Climate models show differences in sea ice representation in comparison to observations. Increasing the model resolution is a recognized way to improve model realism and obtain more reliable future projections. We find no strong impact of resolution on sea ice representation; it rather depends on the analysed variable and the model used. By 2050, the marginal ice zone (MIZ) becomes a dominant feature of the Arctic ice cover, suggesting a shift to a new regime similar to that in Antarctica.
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
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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.
Elena Bianco, Doroteaciro Iovino, Simona Masina, Stefano Materia, and Paolo Ruggieri
The Cryosphere, 18, 2357–2379, https://doi.org/10.5194/tc-18-2357-2024, https://doi.org/10.5194/tc-18-2357-2024, 2024
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Changes in ocean heat transport and surface heat fluxes in recent decades have altered the Arctic Ocean heat budget and caused warming of the upper ocean. Using two eddy-permitting ocean reanalyses, we show that this has important implications for sea ice variability. In the Arctic regional seas, upper-ocean heat content acts as an important precursor for sea ice anomalies on sub-seasonal timescales, and this link has strengthened since the 2000s.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Laurie C. Menviel, Paul Spence, Andrew E. Kiss, Matthew A. Chamberlain, Hakase Hayashida, Matthew H. England, and Darryn Waugh
Biogeosciences, 20, 4413–4431, https://doi.org/10.5194/bg-20-4413-2023, https://doi.org/10.5194/bg-20-4413-2023, 2023
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As the ocean absorbs 25% of the anthropogenic emissions of carbon, it is important to understand the impact of climate change on the flux of carbon between the ocean and the atmosphere. Here, we use a very high-resolution ocean, sea-ice, carbon cycle model to show that the capability of the Southern Ocean to uptake CO2 has decreased over the last 40 years due to a strengthening and poleward shift of the southern hemispheric westerlies. This trend is expected to continue over the coming century.
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
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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.
Michael Mayer, Takamasa Tsubouchi, Susanna Winkelbauer, Karin Margretha H. Larsen, Barbara Berx, Andreas Macrander, Doroteaciro Iovino, Steingrímur Jónsson, and Richard Renshaw
State Planet, 1-osr7, 14, https://doi.org/10.5194/sp-1-osr7-14-2023, https://doi.org/10.5194/sp-1-osr7-14-2023, 2023
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This paper compares oceanic fluxes across the Greenland–Scotland Ridge (GSR) from ocean reanalyses to largely independent observational data. Reanalyses tend to underestimate the inflow of warm waters of subtropical Atlantic origin and hence oceanic heat transport across the GSR. Investigation of a strong negative heat transport anomaly around 2018 highlights the interplay of variability on different timescales and the need for long-term monitoring of the GSR to detect forced climate signals.
Jonathan Andrew Baker, Richard Renshaw, Laura Claire Jackson, Clotilde Dubois, Doroteaciro Iovino, Hao Zuo, Renellys C. Perez, Shenfu Dong, Marion Kersalé, Michael Mayer, Johannes Mayer, Sabrina Speich, and Tarron Lamont
State Planet, 1-osr7, 4, https://doi.org/10.5194/sp-1-osr7-4-2023, https://doi.org/10.5194/sp-1-osr7-4-2023, 2023
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We use ocean reanalyses, in which ocean models are combined with observations, to infer past changes in ocean circulation and heat transport in the South Atlantic. Comparing these estimates with other observation-based estimates, we find differences in their trends, variability, and mean heat transport but closer agreement in their mean overturning strength. Ocean reanalyses can help us understand the cause of these differences, which could improve estimates of ocean transports in this region.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Andrea Cipollone, Deep Sankar Banerjee, Doroteaciro Iovino, Ali Aydogdu, and Simona Masina
Ocean Sci., 19, 1375–1392, https://doi.org/10.5194/os-19-1375-2023, https://doi.org/10.5194/os-19-1375-2023, 2023
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Sea-ice volume is characterized by low predictability compared to the sea ice area or the extent. A joint initialization of the thickness and concentration using satellite data could improve the predictive power, although it is still absent in the present global analysis–reanalysis systems. This study shows a scheme to correct the two features together that can be easily extended to include ocean variables. The impact of such a joint initialization is shown and compared among different set-ups.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
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We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
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
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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.
Bin Xiao, Fangli Qiao, Qi Shu, Xunqiang Yin, Guansuo Wang, and Shihong Wang
Geosci. Model Dev., 16, 1755–1777, https://doi.org/10.5194/gmd-16-1755-2023, https://doi.org/10.5194/gmd-16-1755-2023, 2023
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A new global surface-wave–tide–circulation coupled ocean model (FIO-COM32) with a resolution of 1/32° × 1/32° is developed and validated. Both the promotion of the horizontal resolution and included physical processes are shown to be important contributors to the significant improvements in FIO-COM32 simulations. It is time to merge these separated model components (surface waves, tidal currents and ocean circulation) and start a new generation of ocean model development.
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.
Stephanie Leroux, Jean-Michel Brankart, Aurélie Albert, Laurent Brodeau, Jean-Marc Molines, Quentin Jamet, Julien Le Sommer, Thierry Penduff, and Pierre Brasseur
Ocean Sci., 18, 1619–1644, https://doi.org/10.5194/os-18-1619-2022, https://doi.org/10.5194/os-18-1619-2022, 2022
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The goal of the study is to evaluate the predictability of the ocean circulation
at a kilometric scale, in order to anticipate the requirements of the future operational forecasting systems. For that purpose, ensemble experiments have been performed with a regional model for the Western Mediterranean (at 1/60° horizontal resolution). From these ensemble experiments, we show that it is possible to compute targeted predictability scores, which depend on initial and model uncertainties.
Sergei Kirillov, Igor Dmitrenko, David G. Babb, Jens K. Ehn, Nikolay Koldunov, Søren Rysgaard, David Jensen, and David G. Barber
Ocean Sci., 18, 1535–1557, https://doi.org/10.5194/os-18-1535-2022, https://doi.org/10.5194/os-18-1535-2022, 2022
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The sea ice bridge usually forms during winter in Nares Strait and prevents ice drifting south. However, this bridge has recently become unstable, and in this study we investigate the role of oceanic heat flux in this decline. Using satellite data, we identify areas where sea ice is relatively thin and further attribute those areas to the heat fluxes from the warm subsurface water masses. We also discuss the potential role of such an impact on ice bridge instability and earlier ice break up.
Sergey Kravtsov, Ilijana Mastilovic, Andrew McC. Hogg, William K. Dewar, and Jeffrey R. Blundell
Geosci. Model Dev., 15, 7449–7469, https://doi.org/10.5194/gmd-15-7449-2022, https://doi.org/10.5194/gmd-15-7449-2022, 2022
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Climate is a complex system whose behavior is shaped by multitudes of processes operating on widely different spatial scales and timescales. In hierarchical modeling, one goes back and forth between highly idealized process models and state-of-the-art models coupling the entire range of climate subsystems to identify specific phenomena and understand their dynamics. The present contribution highlights an intermediate climate model focussing on midlatitude ocean–atmosphere interactions.
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.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, https://doi.org/10.5194/gmd-15-6399-2022, 2022
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We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Takaya Uchida, Julien Le Sommer, Charles Stern, Ryan P. Abernathey, Chris Holdgraf, Aurélie Albert, Laurent Brodeau, Eric P. Chassignet, Xiaobiao Xu, Jonathan Gula, Guillaume Roullet, Nikolay Koldunov, Sergey Danilov, Qiang Wang, Dimitris Menemenlis, Clément Bricaud, Brian K. Arbic, Jay F. Shriver, Fangli Qiao, Bin Xiao, Arne Biastoch, René Schubert, Baylor Fox-Kemper, William K. Dewar, and Alan Wallcraft
Geosci. Model Dev., 15, 5829–5856, https://doi.org/10.5194/gmd-15-5829-2022, https://doi.org/10.5194/gmd-15-5829-2022, 2022
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Ocean and climate scientists have used numerical simulations as a tool to examine the ocean and climate system since the 1970s. Since then, owing to the continuous increase in computational power and advances in numerical methods, we have been able to simulate increasing complex phenomena. However, the fidelity of the simulations in representing the phenomena remains a core issue in the ocean science community. Here we propose a cloud-based framework to inter-compare and assess such simulations.
Yuejin Ye, Zhenya Song, Shengchang Zhou, Yao Liu, Qi Shu, Bingzhuo Wang, Weiguo Liu, Fangli Qiao, and Lanning Wang
Geosci. Model Dev., 15, 5739–5756, https://doi.org/10.5194/gmd-15-5739-2022, https://doi.org/10.5194/gmd-15-5739-2022, 2022
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The swNEMO_v4.0 is developed with ultrahigh scalability through the concepts of hardware–software co-design based on the characteristics of the new Sunway supercomputer and NEMO4. Three breakthroughs, including an adaptive four-level parallelization design, many-core optimization and mixed-precision optimization, are designed. The simulations achieve 71.48 %, 83.40 % and 99.29 % parallel efficiency with resolutions of 2 km, 1 km and 500 m using 27 988 480 cores, respectively.
Xiaoxu Shi, Martin Werner, Carolin Krug, Chris M. Brierley, Anni Zhao, Endurance Igbinosa, Pascale Braconnot, Esther Brady, Jian Cao, Roberta D'Agostino, Johann Jungclaus, Xingxing Liu, Bette Otto-Bliesner, Dmitry Sidorenko, Robert Tomas, Evgeny M. Volodin, Hu Yang, Qiong Zhang, Weipeng Zheng, and Gerrit Lohmann
Clim. Past, 18, 1047–1070, https://doi.org/10.5194/cp-18-1047-2022, https://doi.org/10.5194/cp-18-1047-2022, 2022
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Since the orbital parameters of the past are different from today, applying the modern calendar to the past climate can lead to an artificial bias in seasonal cycles. With the use of multiple model outputs, we found that such a bias is non-ignorable and should be corrected to ensure an accurate comparison between modeled results and observational records, as well as between simulated past and modern climates, especially for the Last Interglacial.
Viorica Nagavciuc, Patrick Scholz, and Monica Ionita
Nat. Hazards Earth Syst. Sci., 22, 1347–1369, https://doi.org/10.5194/nhess-22-1347-2022, https://doi.org/10.5194/nhess-22-1347-2022, 2022
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Here we have assessed the variability and trends of hot and dry summers in Romania. The length, spatial extent, and frequency of heat waves in Romania have increased significantly over the last 70 years, while no significant changes have been observed in the drought conditions. The increased frequency of heat waves, especially after the 1990s, could be partially explained by an increase in the geopotential height over the eastern part of Europe.
Bin Xiao, Fangli Qiao, Qi Shu, Xunqiang Yin, Guansuo Wang, and Shihong Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-52, https://doi.org/10.5194/gmd-2022-52, 2022
Revised manuscript not accepted
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A new global surface wave-tide-circulation coupled ocean model FIO-COM32 with resolution of 1/32° × 1/32° is developed and validated. Both the promotion of the horizontal resolution and included physical processes are proved to be important contributors to the significant improvements of FIO-COM32 simulations. It should be the time to merge these separated model components (surface wave, tidal current and ocean circulation) for new generation ocean model development.
Enrico Scoccimarro, Daniele Peano, Silvio Gualdi, Alessio Bellucci, Tomas Lovato, Pier Giuseppe Fogli, and Antonio Navarra
Geosci. Model Dev., 15, 1841–1854, https://doi.org/10.5194/gmd-15-1841-2022, https://doi.org/10.5194/gmd-15-1841-2022, 2022
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This study evaluated the ability of the CMCC-CM2 climate model participating to the last CMIP6 effort, in representing extreme events of precipitation and temperature at the daily and 6-hourly frequencies. The 1/4° resolution version of the atmospheric model provides better results than the version at 1° resolution for temperature extremes, at both time frequencies. For precipitation extremes, especially at the daily time frequency, the higher resolution does not improve model results.
Patrick Scholz, Dmitry Sidorenko, Sergey Danilov, Qiang Wang, Nikolay Koldunov, Dmitry Sein, and Thomas Jung
Geosci. Model Dev., 15, 335–363, https://doi.org/10.5194/gmd-15-335-2022, https://doi.org/10.5194/gmd-15-335-2022, 2022
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Structured-mesh ocean models are still the most mature in terms of functionality due to their long development history. However, unstructured-mesh ocean models have acquired new features and caught up in their functionality. This paper continues the work by Scholz et al. (2019) of documenting the features available in FESOM2.0. It focuses on the following two aspects: (i) partial bottom cells and embedded sea ice and (ii) dealing with mixing parameterisations enabled by using the CVMix package.
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.
Hakase Hayashida, Meibing Jin, Nadja S. Steiner, Neil C. Swart, Eiji Watanabe, Russell Fiedler, Andrew McC. Hogg, Andrew E. Kiss, Richard J. Matear, and Peter G. Strutton
Geosci. Model Dev., 14, 6847–6861, https://doi.org/10.5194/gmd-14-6847-2021, https://doi.org/10.5194/gmd-14-6847-2021, 2021
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Ice algae are tiny plants like phytoplankton but they grow within sea ice. In polar regions, both phytoplankton and ice algae are the foundation of marine ecosystems and play an important role in taking up carbon dioxide in the atmosphere. However, state-of-the-art climate models typically do not include ice algae, and therefore their role in the climate system remains unclear. This project aims to address this knowledge gap by coordinating a set of experiments using sea-ice–ocean models.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725, https://doi.org/10.5194/tc-15-4703-2021, https://doi.org/10.5194/tc-15-4703-2021, 2021
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Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
Amy Solomon, Céline Heuzé, Benjamin Rabe, Sheldon Bacon, Laurent Bertino, Patrick Heimbach, Jun Inoue, Doroteaciro Iovino, Ruth Mottram, Xiangdong Zhang, Yevgeny Aksenov, Ronan McAdam, An Nguyen, Roshin P. Raj, and Han Tang
Ocean Sci., 17, 1081–1102, https://doi.org/10.5194/os-17-1081-2021, https://doi.org/10.5194/os-17-1081-2021, 2021
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Freshwater in the Arctic Ocean plays a critical role in the global climate system by impacting ocean circulations, stratification, mixing, and emergent regimes. In this review paper we assess how Arctic Ocean freshwater changed in the 2010s relative to the 2000s. Estimates from observations and reanalyses show a qualitative stabilization in the 2010s due to a compensation between a freshening of the Beaufort Gyre and a reduction in freshwater in the Amerasian and Eurasian basins.
H. Jakob Belter, Thomas Krumpen, Luisa von Albedyll, Tatiana A. Alekseeva, Gerit Birnbaum, Sergei V. Frolov, Stefan Hendricks, Andreas Herber, Igor Polyakov, Ian Raphael, Robert Ricker, Sergei S. Serovetnikov, Melinda Webster, and Christian Haas
The Cryosphere, 15, 2575–2591, https://doi.org/10.5194/tc-15-2575-2021, https://doi.org/10.5194/tc-15-2575-2021, 2021
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Summer sea ice thickness observations based on electromagnetic induction measurements north of Fram Strait show a 20 % reduction in mean and modal ice thickness from 2001–2020. The observed variability is caused by changes in drift speeds and consequential variations in sea ice age and number of freezing-degree days. Increased ocean heat fluxes measured upstream in the source regions of Arctic ice seem to precondition ice thickness, which is potentially still measurable more than a year later.
Tongwen Wu, Rucong Yu, Yixiong Lu, Weihua Jie, Yongjie Fang, Jie Zhang, Li Zhang, Xiaoge Xin, Laurent Li, Zaizhi Wang, Yiming Liu, Fang Zhang, Fanghua Wu, Min Chu, Jianglong Li, Weiping Li, Yanwu Zhang, Xueli Shi, Wenyan Zhou, Junchen Yao, Xiangwen Liu, He Zhao, Jinghui Yan, Min Wei, Wei Xue, Anning Huang, Yaocun Zhang, Yu Zhang, Qi Shu, and Aixue Hu
Geosci. Model Dev., 14, 2977–3006, https://doi.org/10.5194/gmd-14-2977-2021, https://doi.org/10.5194/gmd-14-2977-2021, 2021
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This paper presents the high-resolution version of the Beijing Climate Center (BCC) Climate System Model, BCC-CSM2-HR, and describes its climate simulation performance including the atmospheric temperature and wind; precipitation; and the tropical climate phenomena such as TC, MJO, QBO, and ENSO. BCC-CSM2-HR is our model version contributing to the HighResMIP. We focused on its updates and differential characteristics from its predecessor, the medium-resolution version BCC-CSM2-MR.
Pengfei Wang, Jinrong Jiang, Pengfei Lin, Mengrong Ding, Junlin Wei, Feng Zhang, Lian Zhao, Yiwen Li, Zipeng Yu, Weipeng Zheng, Yongqiang Yu, Xuebin Chi, and Hailong Liu
Geosci. Model Dev., 14, 2781–2799, https://doi.org/10.5194/gmd-14-2781-2021, https://doi.org/10.5194/gmd-14-2781-2021, 2021
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Global ocean general circulation models are a fundamental tool for oceanography research, ocean forecast, and climate change research. The increasing resolution will greatly improve simulations of the models, but it also demands much more computing resources. In this study, we have ported an ocean general circulation model to a heterogeneous computing system and have developed a 3–5 km model version. A 14-year integration has been conducted and the preliminary results have been evaluated.
Giulia Bonino, Elisa Lovecchio, Nicolas Gruber, Matthias Münnich, Simona Masina, and Doroteaciro Iovino
Biogeosciences, 18, 2429–2448, https://doi.org/10.5194/bg-18-2429-2021, https://doi.org/10.5194/bg-18-2429-2021, 2021
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Seasonal variations of processes such as upwelling and biological production that happen along the northwestern African coast can modulate the temporal variability of the biological activity of the adjacent open North Atlantic hundreds of kilometers away from the coast thanks to the lateral transport of coastal organic carbon. This happens with a temporal delay, which is smaller than a season up to roughly 500 km from the coast due to the intense transport by small-scale filaments.
Tingfeng Dou, Cunde Xiao, Jiping Liu, Qiang Wang, Shifeng Pan, Jie Su, Xiaojun Yuan, Minghu Ding, Feng Zhang, Kai Xue, Peter A. Bieniek, and Hajo Eicken
The Cryosphere, 15, 883–895, https://doi.org/10.5194/tc-15-883-2021, https://doi.org/10.5194/tc-15-883-2021, 2021
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Rain-on-snow (ROS) events can accelerate the surface ablation of sea ice, greatly influencing the ice–albedo feedback. We found that spring ROS events have shifted to earlier dates over the Arctic Ocean in recent decades, which is correlated with sea ice melt onset in the Pacific sector and most Eurasian marginal seas. There has been a clear transition from solid to liquid precipitation, leading to a reduction in spring snow depth on sea ice by more than −0.5 cm per decade since the 1980s.
Cameron M. O'Neill, Andrew McC. Hogg, Michael J. Ellwood, Bradley N. Opdyke, and Stephen M. Eggins
Clim. Past, 17, 171–201, https://doi.org/10.5194/cp-17-171-2021, https://doi.org/10.5194/cp-17-171-2021, 2021
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We undertake a model–data study of the last glacial–interglacial cycle of atmospheric CO2, spanning 0–130 ka. We apply a carbon cycle box model, constrained with glacial–interglacial observations, and solve for optimal model parameter values against atmospheric and ocean proxy data. The results indicate that the last glacial drawdown in atmospheric CO2 was delivered mainly by slowing ocean circulation, lower sea surface temperatures and also increased Southern Ocean biological productivity.
Clément Bricaud, Julien Le Sommer, Gurvan Madec, Christophe Calone, Julie Deshayes, Christian Ethe, Jérôme Chanut, and Marina Levy
Geosci. Model Dev., 13, 5465–5483, https://doi.org/10.5194/gmd-13-5465-2020, https://doi.org/10.5194/gmd-13-5465-2020, 2020
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In order to reduce the cost of ocean biogeochemical models, a multi-grid approach where ocean dynamics and tracer transport are computed with different spatial resolution has been developed in the NEMO v3.6 OGCM. Different experiments confirm that the spatial resolution of hydrodynamical fields can be coarsened without significantly affecting the resolved passive tracer fields. This approach leads to a factor of 7 reduction of the overhead associated with running a full biogeochemical model.
Claudia Wekerle, Tore Hattermann, Qiang Wang, Laura Crews, Wilken-Jon von Appen, and Sergey Danilov
Ocean Sci., 16, 1225–1246, https://doi.org/10.5194/os-16-1225-2020, https://doi.org/10.5194/os-16-1225-2020, 2020
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The high-resolution ocean models ROMS and FESOM configured for the Fram Strait reveal very energetic ocean conditions there. The two main currents meander strongly and shed circular currents of water, called eddies. Our analysis shows that this region is characterised by small and short-lived eddies (on average around a 5 km radius and 10 d lifetime). Both models agree on eddy properties and show similar patterns of baroclinic and barotropic instability of the West Spitsbergen Current.
Eric P. Chassignet, Stephen G. Yeager, Baylor Fox-Kemper, Alexandra Bozec, Frederic Castruccio, Gokhan Danabasoglu, Christopher Horvat, Who M. Kim, Nikolay Koldunov, Yiwen Li, Pengfei Lin, Hailong Liu, Dmitry V. Sein, Dmitry Sidorenko, Qiang Wang, and Xiaobiao Xu
Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, https://doi.org/10.5194/gmd-13-4595-2020, 2020
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This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations to assess the robustness of climate-relevant improvements in ocean simulations associated with moving from coarse (∼1°) to eddy-resolving (∼0.1°) horizontal resolutions. Despite significant improvements, greatly enhanced horizontal resolution does not deliver unambiguous bias reduction in all regions for all models.
Bingbing Wei, Guodong Jia, Jens Hefter, Manyu Kang, Eunmi Park, Shizhu Wang, and Gesine Mollenhauer
Biogeosciences, 17, 4489–4508, https://doi.org/10.5194/bg-17-4489-2020, https://doi.org/10.5194/bg-17-4489-2020, 2020
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This research reports the applicability of four organic temperature proxies (U37K', LDI, TEX86H, and RI-OH) to the northern South China Sea shelf. The comparison with local sea surface temperature (SST) indicates the impact of terrestrial input on LDI, TEX86H, and RI-OH proxies near the coast. After excluding samples influenced by terrestrial materials, proxy temperatures exhibit different seasonality, providing valuable tools to reconstruct regional SSTs under different monsoonal conditions.
Hiroyuki Tsujino, L. Shogo Urakawa, Stephen M. Griffies, Gokhan Danabasoglu, Alistair J. Adcroft, Arthur E. Amaral, Thomas Arsouze, Mats Bentsen, Raffaele Bernardello, Claus W. Böning, Alexandra Bozec, Eric P. Chassignet, Sergey Danilov, Raphael Dussin, Eleftheria Exarchou, Pier Giuseppe Fogli, Baylor Fox-Kemper, Chuncheng Guo, Mehmet Ilicak, Doroteaciro Iovino, Who M. Kim, Nikolay Koldunov, Vladimir Lapin, Yiwen Li, Pengfei Lin, Keith Lindsay, Hailong Liu, Matthew C. Long, Yoshiki Komuro, Simon J. Marsland, Simona Masina, Aleksi Nummelin, Jan Klaus Rieck, Yohan Ruprich-Robert, Markus Scheinert, Valentina Sicardi, Dmitry Sidorenko, Tatsuo Suzuki, Hiroaki Tatebe, Qiang Wang, Stephen G. Yeager, and Zipeng Yu
Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, https://doi.org/10.5194/gmd-13-3643-2020, 2020
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The OMIP-2 framework for global ocean–sea-ice model simulations is assessed by comparing multi-model means from 11 CMIP6-class global ocean–sea-ice models calculated separately for the OMIP-1 and OMIP-2 simulations. Many features are very similar between OMIP-1 and OMIP-2 simulations, and yet key improvements in transitioning from OMIP-1 to OMIP-2 are also identified. Thus, the present assessment justifies that future ocean–sea-ice model development and analysis studies use the OMIP-2 framework.
Pedro Colombo, Bernard Barnier, Thierry Penduff, Jérôme Chanut, Julie Deshayes, Jean-Marc Molines, Julien Le Sommer, Polina Verezemskaya, Sergey Gulev, and Anne-Marie Treguier
Geosci. Model Dev., 13, 3347–3371, https://doi.org/10.5194/gmd-13-3347-2020, https://doi.org/10.5194/gmd-13-3347-2020, 2020
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In the ocean circulation model NEMO, the representation of the overflow of dense Arctic waters through the Denmark Strait is investigated. In this
z-coordinate context, sensitivity tests show that the mixing parameterizations preferably act along the model grid slope. Thus, the representation of the overflow is more sensitive to resolution than to parameterization and is best when the numerical grid matches the local topographic slope.
Veronika Eyring, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, Omar Bellprat, Björn Brötz, Louis-Philippe Caron, Nuno Carvalhais, Irene Cionni, Nicola Cortesi, Bas Crezee, Edouard L. Davin, Paolo Davini, Kevin Debeire, Lee de Mora, Clara Deser, David Docquier, Paul Earnshaw, Carsten Ehbrecht, Bettina K. Gier, Nube Gonzalez-Reviriego, Paul Goodman, Stefan Hagemann, Steven Hardiman, Birgit Hassler, Alasdair Hunter, Christopher Kadow, Stephan Kindermann, Sujan Koirala, Nikolay Koldunov, Quentin Lejeune, Valerio Lembo, Tomas Lovato, Valerio Lucarini, François Massonnet, Benjamin Müller, Amarjiit Pandde, Núria Pérez-Zanón, Adam Phillips, Valeriu Predoi, Joellen Russell, Alistair Sellar, Federico Serva, Tobias Stacke, Ranjini Swaminathan, Verónica Torralba, Javier Vegas-Regidor, Jost von Hardenberg, Katja Weigel, and Klaus Zimmermann
Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, https://doi.org/10.5194/gmd-13-3383-2020, 2020
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility.
Hailun He, Yuan Wang, Xiqiu Han, Yanzhou Wei, Pengfei Lin, Zhongyan Qiu, and Yejian Wang
Ocean Sci., 16, 895–906, https://doi.org/10.5194/os-16-895-2020, https://doi.org/10.5194/os-16-895-2020, 2020
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Ocean profiling observation in the Indian Ocean is not sufficient. We conducted a hydrographic survey on the Carlsberg Ridge, which is a mid-ocean ridge in the northwest Indian Ocean, to obtain snapshots of sectional temperature, salinity, and density fields by combining the ARGO data. The results show mesoscale eddies located along the specific ridge and the existence of a west-propagating planetary wave. The results provide references in the regional ocean circulation.
Dmitry Sidorenko, Sergey Danilov, Nikolay Koldunov, Patrick Scholz, and Qiang Wang
Geosci. Model Dev., 13, 3337–3345, https://doi.org/10.5194/gmd-13-3337-2020, https://doi.org/10.5194/gmd-13-3337-2020, 2020
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Computation of barotropic and meridional overturning streamfunctions for models formulated on unstructured meshes is commonly preceded by interpolation to a regular mesh. This operation destroys the original conservation, which can be then be artificially imposed to make the computation possible. An elementary method is proposed that avoids interpolation and preserves conservation in a strict model sense.
Taylor A. Shropshire, Steven L. Morey, Eric P. Chassignet, Alexandra Bozec, Victoria J. Coles, Michael R. Landry, Rasmus Swalethorp, Glenn Zapfe, and Michael R. Stukel
Biogeosciences, 17, 3385–3407, https://doi.org/10.5194/bg-17-3385-2020, https://doi.org/10.5194/bg-17-3385-2020, 2020
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Zooplankton are the smallest animals in the ocean and important food for fish. Despite their importance, zooplankton have been relatively undersampled. To better understand the zooplankton community in the Gulf of Mexico (GoM), we developed a model to simulate their dynamics. We found that heterotrophic protists are important for supporting mesozooplankton, which are the primary prey of larval fish. The model developed in this study has the potential to improve fisheries management in the GoM.
Torben Koenigk, Ramon Fuentes-Franco, Virna Meccia, Oliver Gutjahr, Laura C. Jackson, Adrian L. New, Pablo Ortega, Christopher Roberts, Malcolm Roberts, Thomas Arsouze, Doroteaciro Iovino, Marie-Pierre Moine, and Dmitry V. Sein
Ocean Sci. Discuss., https://doi.org/10.5194/os-2020-41, https://doi.org/10.5194/os-2020-41, 2020
Revised manuscript not accepted
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The mixing of water masses into the deep ocean in the North Atlantic is important for the entire global ocean circulation. We use seven global climate models to investigate the effect of increasing the model resolution on this deep ocean mixing. The main result is that increased model resolution leads to a deeper mixing of water masses in the Labrador Sea but has less effect in the Greenland Sea. However, most of the models overestimate the deep ocean mixing compared to observations.
Valentin Resseguier, Wei Pan, and Baylor Fox-Kemper
Nonlin. Processes Geophys., 27, 209–234, https://doi.org/10.5194/npg-27-209-2020, https://doi.org/10.5194/npg-27-209-2020, 2020
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Geophysical flows span a broader range of temporal and spatial scales than can be resolved numerically. One way to alleviate the ensuing numerical errors is to combine simulations with measurements, taking account of the accuracies of these two sources of information. Here we quantify the distribution of numerical simulation errors without relying on high-resolution numerical simulations. Specifically, small-scale random vortices are added to simulations while conserving energy or circulation.
Mattia Righi, Bouwe Andela, Veronika Eyring, Axel Lauer, Valeriu Predoi, Manuel Schlund, Javier Vegas-Regidor, Lisa Bock, Björn Brötz, Lee de Mora, Faruk Diblen, Laura Dreyer, Niels Drost, Paul Earnshaw, Birgit Hassler, Nikolay Koldunov, Bill Little, Saskia Loosveldt Tomas, and Klaus Zimmermann
Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, https://doi.org/10.5194/gmd-13-1179-2020, 2020
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This paper describes the second major release of ESMValTool, a community diagnostic and performance metrics tool for the evaluation of Earth system models. This new version features a brand new design, with an improved interface and a revised preprocessor. It takes advantage of state-of-the-art computational libraries and methods to deploy efficient and user-friendly data processing, improving the performance over its predecessor by more than a factor of 30.
Andrew E. Kiss, Andrew McC. Hogg, Nicholas Hannah, Fabio Boeira Dias, Gary B. Brassington, Matthew A. Chamberlain, Christopher Chapman, Peter Dobrohotoff, Catia M. Domingues, Earl R. Duran, Matthew H. England, Russell Fiedler, Stephen M. Griffies, Aidan Heerdegen, Petra Heil, Ryan M. Holmes, Andreas Klocker, Simon J. Marsland, Adele K. Morrison, James Munroe, Maxim Nikurashin, Peter R. Oke, Gabriela S. Pilo, Océane Richet, Abhishek Savita, Paul Spence, Kial D. Stewart, Marshall L. Ward, Fanghua Wu, and Xihan Zhang
Geosci. Model Dev., 13, 401–442, https://doi.org/10.5194/gmd-13-401-2020, https://doi.org/10.5194/gmd-13-401-2020, 2020
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We describe new computer model configurations which simulate the global ocean and sea ice at three resolutions. The coarsest resolution is suitable for multi-century climate projection experiments, whereas the finest resolution is designed for more detailed studies over time spans of decades. The paper provides technical details of the model configurations and an assessment of their performance relative to observations.
Anson Cheung, Baylor Fox-Kemper, and Timothy Herbert
Clim. Past, 15, 1985–1998, https://doi.org/10.5194/cp-15-1985-2019, https://doi.org/10.5194/cp-15-1985-2019, 2019
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We test two assumptions that are often made in paleoclimate studies by using observations and ask whether temperature and productivity proxy records in the Southern California Current can be used to reconstruct Ekman upwelling. By examining the covariation between alongshore wind stress, temperature, and productivity, we found that the dominant covarying pattern does not reflect Ekman upwelling. Other upwelling patterns found are timescale dependent. Multiple proxies can improve reconstruction.
Patrick Scholz, Dmitry Sidorenko, Ozgur Gurses, Sergey Danilov, Nikolay Koldunov, Qiang Wang, Dmitry Sein, Margarita Smolentseva, Natalja Rakowsky, and Thomas Jung
Geosci. Model Dev., 12, 4875–4899, https://doi.org/10.5194/gmd-12-4875-2019, https://doi.org/10.5194/gmd-12-4875-2019, 2019
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This paper is the first in a series documenting and assessing important key components of the Finite-volumE Sea ice-Ocean Model version 2.0 (FESOM2.0). We assess the hydrographic biases, large-scale circulation, numerical performance and scalability of FESOM2.0 compared with its predecessor, FESOM1.4. The main conclusion is that the results of FESOM2.0 compare well to FESOM1.4 in terms of model biases but with a remarkable performance speedup with a 3 times higher throughput.
Christopher Horvat, Lettie A. Roach, Rachel Tilling, Cecilia M. Bitz, Baylor Fox-Kemper, Colin Guider, Kaitlin Hill, Andy Ridout, and Andrew Shepherd
The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, https://doi.org/10.5194/tc-13-2869-2019, 2019
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Changes in the floe size distribution (FSD) are important for sea ice evolution but to date largely unobserved and unknown. Climate models, forecast centres, ship captains, and logistic specialists cannot currently obtain statistical information about sea ice floe size on demand. We develop a new method to observe the FSD at global scales and high temporal and spatial resolution. With refinement, this method can provide crucial information for polar ship routing and real-time forecasting.
Nikolay V. Koldunov, Vadym Aizinger, Natalja Rakowsky, Patrick Scholz, Dmitry Sidorenko, Sergey Danilov, and Thomas Jung
Geosci. Model Dev., 12, 3991–4012, https://doi.org/10.5194/gmd-12-3991-2019, https://doi.org/10.5194/gmd-12-3991-2019, 2019
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We measure how computational performance of the global FESOM2 ocean model (formulated on an unstructured mesh) changes with the increase in the number of computational cores. We find that for many components of the model the performance increases linearly but we also identify two bottlenecks: sea ice and ssh submodules. We show that FESOM2 is on par with the state-of-the-art ocean models in terms of throughput that reach 16 simulated years per day for eddy resolving configuration (1/10°).
Özgür Gürses, Vanessa Kolatschek, Qiang Wang, and Christian Bernd Rodehacke
The Cryosphere, 13, 2317–2324, https://doi.org/10.5194/tc-13-2317-2019, https://doi.org/10.5194/tc-13-2317-2019, 2019
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The warming of the Earth's climate system causes sea level rise. In Antarctica, ice streams flow into the sea and develop ice shelves. These are floating extensions of the ice streams. Ocean water melts these ice shelves. It has been proposed that a submarine wall could shield these ice shelves from the warm water. Our model simulation shows that the wall protects ice shelves. However, the warm water flows to neighboring ice shelves. There, enhanced melting reduces the effectiveness of the wall.
Thomas Rackow, Dmitry V. Sein, Tido Semmler, Sergey Danilov, Nikolay V. Koldunov, Dmitry Sidorenko, Qiang Wang, and Thomas Jung
Geosci. Model Dev., 12, 2635–2656, https://doi.org/10.5194/gmd-12-2635-2019, https://doi.org/10.5194/gmd-12-2635-2019, 2019
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Current climate models show errors in the deep ocean that are larger than the level of natural variability and the response to enhanced greenhouse gas concentrations. These errors are larger than the signals we aim to predict. With the AWI Climate Model, we show that increasing resolution to resolve eddies can lead to major reductions in deep ocean errors. AWI's next-generation (CMIP6) model configuration will thus use locally eddy-resolving computational grids for projecting climate change.
Xiaolong Zhao, Chun Zhou, Xiaobiao Xu, Ruijie Ye, Jiwei Tian, and Wei Zhao
Ocean Sci. Discuss., https://doi.org/10.5194/os-2019-29, https://doi.org/10.5194/os-2019-29, 2019
Preprint withdrawn
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This study presents a detailed spatial structure and temporal variability of the deep circulation under enhanced mixing in the South China Sea (SCS) based on eddy-resolving model simulations verified by continuous current-meter observations and enables us to investigate sensitivity to distribution of mixing. Comparing the northern shelf of the SCS with the Luzon Strait, deep circulation in the SCS is more sensitive to the large vertical mixing parameters in the Zhongsha Island Chain area.
Cameron M. O'Neill, Andrew McC. Hogg, Michael J. Ellwood, Stephen M. Eggins, and Bradley N. Opdyke
Geosci. Model Dev., 12, 1541–1572, https://doi.org/10.5194/gmd-12-1541-2019, https://doi.org/10.5194/gmd-12-1541-2019, 2019
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The [simple carbon project] model v1.0 (SCP-M) was constructed for simulations of the paleo and modern carbon cycle. In this paper we show its application to the carbon cycle transition from the Last Glacial Maximum to the Holocene period. Our model–data experiment uses SCP-M's fast run time to cover a large range of possible inputs. The results highlight the role of varying the strength of ocean circulation to account for large fluctuations in atmospheric CO2 across the two periods.
Monica Ionita, Klaus Grosfeld, Patrick Scholz, Renate Treffeisen, and Gerrit Lohmann
Earth Syst. Dynam., 10, 189–203, https://doi.org/10.5194/esd-10-189-2019, https://doi.org/10.5194/esd-10-189-2019, 2019
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Based on a simple statistical model we show that the September sea ice extent has a high predictive skill, up to 4 months ahead, based on previous months' oceanic and atmospheric conditions. Our statistical model skillfully captures the interannual variability of the September sea ice extent and could provide a valuable tool for identifying relevant regions and oceanic and atmospheric parameters that are important for the sea ice development in the Arctic.
Andrey V. Pnyushkov, Igor V. Polyakov, Robert Rember, Vladimir V. Ivanov, Matthew B. Alkire, Igor M. Ashik, Till M. Baumann, Genrikh V. Alekseev, and Arild Sundfjord
Ocean Sci., 14, 1349–1371, https://doi.org/10.5194/os-14-1349-2018, https://doi.org/10.5194/os-14-1349-2018, 2018
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This study describes along-slope volume, heat, and salt transports derived from observations collected between 2013 and 2015 in the eastern Eurasian Basin of the Arctic Ocean using a cross-slope array of six moorings. Inferred transport estimates may have wide implications and should be considered when assessing high-latitude ocean dynamics.
Andrey Pnyushkov, Igor V. Polyakov, Laurie Padman, and An T. Nguyen
Ocean Sci., 14, 1329–1347, https://doi.org/10.5194/os-14-1329-2018, https://doi.org/10.5194/os-14-1329-2018, 2018
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A total of 4 years of velocity and hydrography records from moored profilers over the Laptev Sea slope reveal multiple events of eddies passing through the mooring site. These events suggest that the advection of mesoscale eddies is an important component of ocean dynamics in the Eurasian Basin of the Arctic Ocean. Increased vertical shear of current velocities found within eddies produces enhanced diapycnal mixing, suggesting their importance for the redistribution of heat in the Arctic Ocean.
Nikolay V. Koldunov and Luisa Cristini
Adv. Geosci., 45, 295–303, https://doi.org/10.5194/adgeo-45-295-2018, https://doi.org/10.5194/adgeo-45-295-2018, 2018
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We believe that project managers can benefit from using programming languages in their work. In this paper we show several simple examples of how python programming language can be used for some of the basic text manipulation tasks, as well as describe more complicated test cases using a HORIZON 2020 type European project as an example.
Monica Ionita, Patrick Scholz, Klaus Grosfeld, and Renate Treffeisen
Earth Syst. Dynam., 9, 939–954, https://doi.org/10.5194/esd-9-939-2018, https://doi.org/10.5194/esd-9-939-2018, 2018
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In austral spring 2016 the Antarctic region experienced anomalous sea ice retreat in all sectors, with sea ice extent in October and November 2016 being the lowest in the Southern Hemisphere over the observational record (1979–present). The extreme sea ice retreat was accompanied by the wettest and warmest spring on record, over large areas covering the Indian ocean, the Ross Sea, and the Weddell Sea.
Qiang Wang, Claudia Wekerle, Sergey Danilov, Xuezhu Wang, and Thomas Jung
Geosci. Model Dev., 11, 1229–1255, https://doi.org/10.5194/gmd-11-1229-2018, https://doi.org/10.5194/gmd-11-1229-2018, 2018
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For developing a system for Arctic research, we evaluate the Arctic Ocean simulated by FESOM. We use two global meshes differing in the horizontal resolution only in the Arctic Ocean (24 vs. 4.5 km). The high resolution significantly improves the model's representation of the Arctic Ocean. The most pronounced improvement is in the Arctic intermediate layer. The high resolution also improves the ocean surface circulation, mainly through a better representation of the Canadian Arctic Archipelago.
Seonmin Ahn, Baylor Fox-Kemper, Timothy Herbert, and Charles Lawrence
Clim. Past Discuss., https://doi.org/10.5194/cp-2018-1, https://doi.org/10.5194/cp-2018-1, 2018
Revised manuscript not accepted
Matthew B. Alkire, Igor Polyakov, Robert Rember, Andrey Pnyushkov, Vladimir Ivanov, and Igor Ashik
Ocean Sci., 13, 983–995, https://doi.org/10.5194/os-13-983-2017, https://doi.org/10.5194/os-13-983-2017, 2017
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High-resolution measurements of temperature, salinity, and the stable oxygen isotope ratio of seawater were collected along the slopes of the Barents, Kara, and Laptev seas during late summer of 2013 and 2015. Two separate mixing regimes were identified that describe the initial and final stages of halocline water formation. The linear regressions defining the mixing regimes appear to be stable despite the dramatic environmental changes observed over the Arctic Ocean over the past two decades.
Nikolay V. Koldunov, Armin Köhl, Nuno Serra, and Detlef Stammer
The Cryosphere, 11, 2265–2281, https://doi.org/10.5194/tc-11-2265-2017, https://doi.org/10.5194/tc-11-2265-2017, 2017
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The paper describes one of the first attempts to use the so-called adjoint data assimilation method to bring Arctic Ocean model simulations closer to observation, especially in terms of the sea ice. It is shown that after assimilation the model bias in simulating the Arctic sea ice is considerably reduced. There is also additional improvement in the sea ice thickens representation that is not assimilated directly.
Verena Haid, Doroteaciro Iovino, and Simona Masina
The Cryosphere, 11, 1387–1402, https://doi.org/10.5194/tc-11-1387-2017, https://doi.org/10.5194/tc-11-1387-2017, 2017
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Since the Antarctic sea ice extent shows a recent increase, we investigate the sea ice response to changed amount and distribution of surface freshwater addition in the Southern Ocean with the ocean–sea ice model NEMO/LIM2. We find that freshwater addition within the range of current estimates increases the ice extent, but higher amounts could have an opposing effect. The freshwater distribution is of great influence on the ice dynamics and the ice thickness is strongly influenced by it.
Monica Ionita, Lena M. Tallaksen, Daniel G. Kingston, James H. Stagge, Gregor Laaha, Henny A. J. Van Lanen, Patrick Scholz, Silvia M. Chelcea, and Klaus Haslinger
Hydrol. Earth Syst. Sci., 21, 1397–1419, https://doi.org/10.5194/hess-21-1397-2017, https://doi.org/10.5194/hess-21-1397-2017, 2017
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This paper analyses the European summer drought of 2015 from a climatological perspective, including its origin and spatial and temporal development, and how it compares with the 2003 event. It discusses the main contributing factors controlling the occurrence and persistence of the event: temperature and precipitation anomalies, blocking episodes and sea surface temperatures. The results represent the outcome of a collaborative initiative of members of UNESCO’s FRIEND-Water program.
Petteri Uotila, Doroteaciro Iovino, Martin Vancoppenolle, Mikko Lensu, and Clement Rousset
Geosci. Model Dev., 10, 1009–1031, https://doi.org/10.5194/gmd-10-1009-2017, https://doi.org/10.5194/gmd-10-1009-2017, 2017
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We performed ocean model simulations with new and old sea-ice components. Sea ice improved in the new model compared to the earlier one due to better model physics. In the ocean, the largest differences are confined close to the surface within and near the sea-ice zone. The global ocean circulation slowly deviates between the simulations due to dissimilar sea ice in the deep water formation regions, such as the North Atlantic and Antarctic.
Sergey Danilov, Dmitry Sidorenko, Qiang Wang, and Thomas Jung
Geosci. Model Dev., 10, 765–789, https://doi.org/10.5194/gmd-10-765-2017, https://doi.org/10.5194/gmd-10-765-2017, 2017
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Numerical models of global ocean circulation are used to learn about future climate. The ocean circulation is characterized by processes on different spatial scales which are still beyond the reach of present computers. We describe a new model setup that allows one to vary a model's spatial resolution and hence focus the computational power on regional dynamics, reaching a better description of local processes in areas of interest.
Arin D. Nelson, Jeffrey B. Weiss, Baylor Fox-Kemper, Royce K. P. Zia, and Fabienne Gaillard
Ocean Sci. Discuss., https://doi.org/10.5194/os-2016-105, https://doi.org/10.5194/os-2016-105, 2017
Revised manuscript has not been submitted
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We quantify the skill in observing the variability of global upper ocean heat content (OHC) by applying the ISAS13 observing strategy to a CCSM simulation. We find that variability is unreliably observed before 2005, while observed annual running means for 2005–2013 correlate well with model "truth" to a median of 95 %. When scaled to the real ocean, we find signal-to-noise ratios of 1.9 for pre-Argo times (1990–2005) and 14.7 after Argo is introduced (2005–2013). The global warming is robust.
Stephen M. Griffies, Gokhan Danabasoglu, Paul J. Durack, Alistair J. Adcroft, V. Balaji, Claus W. Böning, Eric P. Chassignet, Enrique Curchitser, Julie Deshayes, Helge Drange, Baylor Fox-Kemper, Peter J. Gleckler, Jonathan M. Gregory, Helmuth Haak, Robert W. Hallberg, Patrick Heimbach, Helene T. Hewitt, David M. Holland, Tatiana Ilyina, Johann H. Jungclaus, Yoshiki Komuro, John P. Krasting, William G. Large, Simon J. Marsland, Simona Masina, Trevor J. McDougall, A. J. George Nurser, James C. Orr, Anna Pirani, Fangli Qiao, Ronald J. Stouffer, Karl E. Taylor, Anne Marie Treguier, Hiroyuki Tsujino, Petteri Uotila, Maria Valdivieso, Qiang Wang, Michael Winton, and Stephen G. Yeager
Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, https://doi.org/10.5194/gmd-9-3231-2016, 2016
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The Ocean Model Intercomparison Project (OMIP) aims to provide a framework for evaluating, understanding, and improving the ocean and sea-ice components of global climate and earth system models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This document defines OMIP and details a protocol both for simulating global ocean/sea-ice models and for analysing their output.
Doroteaciro Iovino, Simona Masina, Andrea Storto, Andrea Cipollone, and Vladimir N. Stepanov
Geosci. Model Dev., 9, 2665–2684, https://doi.org/10.5194/gmd-9-2665-2016, https://doi.org/10.5194/gmd-9-2665-2016, 2016
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An 11-year simulation of a global eddying ocean (1/16) configuration is presented. Model performance is evaluated against observations and a twin 1/4 configuration. The model realistically represents the variability at upper and intermediate depths, the position and strength of the surface circulation, and exchanges of mass through key passages. Sea ice properties are close to satellite observations. This simulation constitutes the groundwork for future applications to short range ocean forecasting.
S. Danilov, Q. Wang, R. Timmermann, N. Iakovlev, D. Sidorenko, M. Kimmritz, T. Jung, and J. Schröter
Geosci. Model Dev., 8, 1747–1761, https://doi.org/10.5194/gmd-8-1747-2015, https://doi.org/10.5194/gmd-8-1747-2015, 2015
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Unstructured meshes allow multi-resolution modeling of ocean dynamics. Sea ice models formulated on unstructured meshes are a necessary component of ocean models intended for climate studies. This work presents a description of a finite-element sea ice model which is used as a component of a finite-element sea ice ocean circulation model. The principles underlying its design can be of interest to other groups pursuing ocean modelling on unstructured meshes.
Q. Shu, Z. Song, and F. Qiao
The Cryosphere, 9, 399–409, https://doi.org/10.5194/tc-9-399-2015, https://doi.org/10.5194/tc-9-399-2015, 2015
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We evaluated all CMIP5 sea-ice simulations with more metrics in both the Antarctic and the Arctic, in an attempt to provide the community a useful reference. Generally speaking, our study shows that the performance of an Arctic sea-ice simulation is better than that of an Antarctic sea-ice simulation, that sea-ice extent simulation is better than sea-ice volume simulation, and that mean-state simulation is better than long-term trend simulation.
V. Schourup-Kristensen, D. Sidorenko, D. A. Wolf-Gladrow, and C. Völker
Geosci. Model Dev., 7, 2769–2802, https://doi.org/10.5194/gmd-7-2769-2014, https://doi.org/10.5194/gmd-7-2769-2014, 2014
I. A. Dmitrenko, S. A. Kirillov, N. Serra, N. V. Koldunov, V. V. Ivanov, U. Schauer, I. V. Polyakov, D. Barber, M. Janout, V. S. Lien, M. Makhotin, and Y. Aksenov
Ocean Sci., 10, 719–730, https://doi.org/10.5194/os-10-719-2014, https://doi.org/10.5194/os-10-719-2014, 2014
P. Lin, Y. Song, Y. Yu, and H. Liu
Clim. Past Discuss., https://doi.org/10.5194/cpd-10-2519-2014, https://doi.org/10.5194/cpd-10-2519-2014, 2014
Revised manuscript not accepted
Q. Wang, S. Danilov, D. Sidorenko, R. Timmermann, C. Wekerle, X. Wang, T. Jung, and J. Schröter
Geosci. Model Dev., 7, 663–693, https://doi.org/10.5194/gmd-7-663-2014, https://doi.org/10.5194/gmd-7-663-2014, 2014
D. Bauch, S. Torres-Valdes, I. Polyakov, A. Novikhin, I. Dmitrenko, J. McKay, and A. Mix
Ocean Sci., 10, 141–154, https://doi.org/10.5194/os-10-141-2014, https://doi.org/10.5194/os-10-141-2014, 2014
Related subject area
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Sensitivity of the tropical Atlantic to vertical mixing in two ocean models (ICON-O v2.6.6 and FESOM v2.5)
HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures
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HOTSSea v1: a NEMO-based physical Hindcast of the Salish Sea (1980–2018) supporting ecosystem model development
DalROMS-NWA12 v1.0, a coupled circulation–ice–biogeochemistry modelling system for the northwest Atlantic Ocean: development and validation
A revised ocean mixed layer model for better simulating the diurnal variation in ocean skin temperature
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An optimal transformation method for inferring ocean tracer sources and sinks
PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks
Updates to the Met Office’s global ocean-sea ice forecasting system including model and data assimilation changes
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Experimental design for the Marine Ice Sheet–Ocean Model Intercomparison Project – phase 2 (MISOMIP2)
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GREAT v1.0: Global Real-time Early Assessment of Tsunamis
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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
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Axelle Gaffet, Xavier Bertin, Damien Sous, Héloïse Michaud, Aron Roland, and Emmanuel Cordier
Geosci. Model Dev., 18, 1929–1946, https://doi.org/10.5194/gmd-18-1929-2025, https://doi.org/10.5194/gmd-18-1929-2025, 2025
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This study presents a new global wave model that improves predictions of sea states in tropical areas by using a high-resolution grid and corrected wind fields. The model is validated globally with satellite data and nearshore using in situ data. The model allows for the first time direct comparisons with in situ data collected at 10–30 m water depth, which is very close to shore due to the steep slope usually surrounding volcanic islands.
Antoine Mathieu, Yeulwoo Kim, Tian-Jian Hsu, Cyrille Bonamy, and Julien Chauchat
Geosci. Model Dev., 18, 1561–1573, https://doi.org/10.5194/gmd-18-1561-2025, https://doi.org/10.5194/gmd-18-1561-2025, 2025
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Most of the tools available to model sediment transport do not account for complex physical mechanisms such as surface-wave-driven processes. In this study, a new model, sedInterFoam, allows us to reproduce numerically complex configurations in order to investigate coastal sediment transport applications dominated by surface waves and to gain insight into the complex physical processes associated with breaking waves and morphodynamics.
Zhaoru Zhang, Chuan Xie, Chuning Wang, Yuanjie Chen, Heng Hu, and Xiaoqiao Wang
Geosci. Model Dev., 18, 1375–1393, https://doi.org/10.5194/gmd-18-1375-2025, https://doi.org/10.5194/gmd-18-1375-2025, 2025
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A coupled fine-resolution ocean–ice model is developed for the Ross Sea and adjacent regions in Antarctica, a key area for the formation of global ocean bottom water, the Antarctic Bottom Water (AABW), which affects global ocean circulation. The model has a high skill level in simulating sea ice production driving the AABW source water formation and AABW properties when assessed against observations. A model experiment shows a significant impact of ice shelf melting on the AABW characteristics.
Swantje Bastin, Aleksei Koldunov, Florian Schütte, Oliver Gutjahr, Marta Agnieszka Mrozowska, Tim Fischer, Radomyra Shevchenko, Arjun Kumar, Nikolay Koldunov, Helmuth Haak, Nils Brüggemann, Rebecca Hummels, Mia Sophie Specht, Johann Jungclaus, Sergey Danilov, Marcus Dengler, and Markus Jochum
Geosci. Model Dev., 18, 1189–1220, https://doi.org/10.5194/gmd-18-1189-2025, https://doi.org/10.5194/gmd-18-1189-2025, 2025
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Vertical mixing is an important process, for example, for tropical sea surface temperature, but cannot be resolved by ocean models. Comparisons of mixing schemes and settings have usually been done with a single model, sometimes yielding conflicting results. We systematically compare two widely used schemes with different parameter settings in two different ocean models and show that most effects from mixing scheme parameter changes are model-dependent.
Marko Rus, Hrvoje Mihanović, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev., 18, 605–620, https://doi.org/10.5194/gmd-18-605-2025, https://doi.org/10.5194/gmd-18-605-2025, 2025
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HIDRA3 is a deep-learning model for predicting sea levels and storm surges, offering significant improvements over previous models and numerical simulations. It utilizes data from multiple tide gauges, enhancing predictions even with limited historical data and during sensor outages. With its advanced architecture, HIDRA3 outperforms current state-of-the-art models by achieving a mean absolute error of up to 15 % lower, proving effective for coastal flood forecasting under diverse conditions.
Isabel Jalón-Rojas, Damien Sous, and Vincent Marieu
Geosci. Model Dev., 18, 319–336, https://doi.org/10.5194/gmd-18-319-2025, https://doi.org/10.5194/gmd-18-319-2025, 2025
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This study presents a novel modeling approach for understanding microplastic transport in coastal waters. The model accurately replicates experimental data and reveals key transport mechanisms. The findings enhance our knowledge of how microplastics move in nearshore environments, aiding in coastal management and efforts to combat plastic pollution globally.
Greig Oldford, Tereza Jarníková, Villy Christensen, and Michael Dunphy
Geosci. Model Dev., 18, 211–237, https://doi.org/10.5194/gmd-18-211-2025, https://doi.org/10.5194/gmd-18-211-2025, 2025
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We developed a 3D ocean model called the Hindcast of the Salish Sea (HOTSSea v1) that recreates physical conditions throughout the Salish Sea from 1980 to 2018. It was not clear that acceptable accuracy could be achieved because of computational and data limitations, but the model's predictions agreed well with observations. When we used the model to examine ocean temperature trends in areas that lack observations, it indicated that some seasons and areas are warming faster than others.
Kyoko Ohashi, Arnaud Laurent, Christoph Renkl, Jinyu Sheng, Katja Fennel, and Eric Oliver
Geosci. Model Dev., 17, 8697–8733, https://doi.org/10.5194/gmd-17-8697-2024, https://doi.org/10.5194/gmd-17-8697-2024, 2024
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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 ocean 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.
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., 17, 8553–8568, https://doi.org/10.5194/gmd-17-8553-2024, https://doi.org/10.5194/gmd-17-8553-2024, 2024
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Sea surface temperature (SST) is vital in climate, weather, and ocean sciences because it influences air–sea interactions. Errors in the ECMWF model's scheme for predicting ocean skin temperature prompted a revision of the ocean mixed layer model. Validation against infrared measurements and buoys showed a good correlation with minimal deviations. The revised model accurately simulates SST variations and aligns with solar radiation distributions, showing promise for weather and climate models.
Qin Zhou, Chen Zhao, Rupert Gladstone, Tore Hattermann, David Gwyther, and Benjamin Galton-Fenzi
Geosci. Model Dev., 17, 8243–8265, https://doi.org/10.5194/gmd-17-8243-2024, https://doi.org/10.5194/gmd-17-8243-2024, 2024
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We introduce an accelerated forcing approach to address timescale discrepancies between the ice sheets and ocean components in coupled modelling by reducing the ocean simulation duration. The approach is evaluated using idealized coupled models, and its limitations in real-world applications are discussed. Our results suggest it can be a valuable tool for process-oriented coupled ice sheet–ocean modelling and downscaling climate simulations with such models.
Jan D. Zika and Taimoor Sohail
Geosci. Model Dev., 17, 8049–8068, https://doi.org/10.5194/gmd-17-8049-2024, https://doi.org/10.5194/gmd-17-8049-2024, 2024
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We describe a method to relate fluxes of heat and freshwater at the sea surface to the resulting distribution of seawater among categories such as warm and salty or cold and salty. 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.
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
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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.
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc’h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
EGUsphere, https://doi.org/10.5194/egusphere-2024-3143, https://doi.org/10.5194/egusphere-2024-3143, 2024
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We describe major improvements of the Met Office's global ocean-sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1-day forecasts. The new system performance in past conditions, where sub-surface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard, and Marco Toffolon
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-118, https://doi.org/10.5194/gmd-2024-118, 2024
Revised manuscript accepted for GMD
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Models simplify reality using assumptions, which can sometimes introduce flaws and affect their accuracy. Properly calibrating model parameters is essential, and although automated tools can speed up this process, they may occasionally produce incorrect values due to inconsistencies in the model. We demonstrate that by carefully applying automated tools, we were able to identify and correct a flaw in a widely used model for lake environments.
Renbo Pang, Fujiang Yu, Yuanyong Gao, Ye Yuan, Liang Yuan, and Zhiyi Gao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2515, https://doi.org/10.5194/egusphere-2024-2515, 2024
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The application of the distributed graph communication topology in earth models has been rarely studied. We tested and compared this topology with the traditional point-to-point communication method using a global wave model. We found that this topology is more efficient. Additionally, using this topology can greatly improve the performance of the wave model and could help improve the performance of other earth models.
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
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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
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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
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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
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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
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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.
Usama Kadri, Ali Abdolali, and Maxim Filimonov
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-139, https://doi.org/10.5194/gmd-2024-139, 2024
Revised manuscript accepted for GMD
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The GREAT v1.0 software introduces a novel tsunami warning technology for global real-time analysis. It leverages acoustic signals generated by tsunamis, which propagate faster than the tsunami itself, enabling real-time detection and assessment. Integrating various models, the software provides reliable and rapid assessment, mapping risk areas, and estimating tsunami amplitude. This advancement reduces false alarms and enhances global tsunami warning systems' accuracy and efficiency.
Iria Sala and Bingzhang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-130, https://doi.org/10.5194/gmd-2024-130, 2024
Preprint under review for GMD
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Phytoplankton, tiny photosynthetic organisms, produce nearly half of Earth's oxygen. To analyze their physiology, diversity, and evolution in the ocean, we developed a model that treats phytoplankton as individual particles. Moreover, our model considers phytoplankton size, temperature, and light traits, and allows for mutations in phytoplankton cells. Thus, our model provides a valuable tool for advancing the study of phytoplankton physiology, diversity, and evolution.
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
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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
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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
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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
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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.
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
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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
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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.
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
EGUsphere, https://doi.org/10.5194/egusphere-2024-1414, https://doi.org/10.5194/egusphere-2024-1414, 2024
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The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the HadGEM3 coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the sea floor exerts on ocean currents, and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Cited articles
Aksenov, Y., Karcher, M., Proshutinsky, A., Gerdes, R., de Cuevas, B., Golubeva, E., Kauker, F., Nguyen, A. T., Platov, G. A., Wadley, M., Watanabe, E., Coward, A. C., and Nurser, A. J. G.: Arctic pathways of Pacific Water: Arctic Ocean Model Intercomparison experiments, J. Geophys. Res.-Oceans, 121, 27–59, 2016. a
Allende, S., Fichefet, T., Goosse, H., and Treguier, A. M.: On the ability of OMIP models to simulate the ocean mixed layer depth and its seasonal cycle in the Arctic Ocean, Ocean Model., 184, 102226, https://doi.org/10.1016/j.ocemod.2023.102226, 2023. a
Armitage, T. W. K., Bacon, S., Ridout, A. L., Petty, A. A., Wolbach, S., and Tsamados, M.: Arctic Ocean surface geostrophic circulation 2003–2014, The Cryosphere, 11, 1767–1780, https://doi.org/10.5194/tc-11-1767-2017, 2017. a, b
Årthun, M., Eldevik, T., Viste, E., Drange, H., Furevik, T., Johnson, H. L., and Keenlyside, N. S.: Skillful prediction of northern climate provided by the ocean, Nat. Commun., 8, 15875, https://doi.org/10.1038/ncomms15875, 2017. a
Årthun, M., Eldevik, T., and Smedsrud, L. H.: The Role of Atlantic Heat Transport in Future Arctic Winter Sea Ice Loss, J. Climate, 32, 3327–3341, 2019. a
Bao, Q., Lin, P., Zhou, T., Liu, Y., Yu, Y., Wu, G., He, B., He, J., Li, L., Li, J., Li, Y., Liu, H., Qiao, F., Song, Z., Wang, B., Wang, J., Wang, P., Wang, X., Wang, Z., Wu, B., Wu, T., Xu, Y., Yu, H., Zhao, W., Zheng, W., and Zhou, L.: The Flexible Global Ocean-Atmosphere-Land system model, Spectral Version 2: FGOALS-s2, Adv. Atmos. Sci., 30, 561–576, https://doi.org/10.1007/s00376-012-2113-9, 2013. a
Basedow, S. L., Sundfjord, A., von Appen, W.-J., Halvorsen, E., Kwasniewski, S., and Reigstad, M.: Seasonal Variation in Transport of Zooplankton Into the Arctic Basin Through the Atlantic Gateway, Fram Strait, Front. Mar. Sci., 5, 194, https://doi.org/10.3389/fmars.2018.00194, 2018. a
Beszczynska-Moeller, A., Woodgate, R. A., Lee, C., Melling, H., and Karcher, M.: A Synthesis of Exchanges Through the Main Oceanic Gateways to the Arctic Ocean, Oceanography, 24, 82–99, 2011. a
Beszczynska-Moeller, A., Fahrbach, E., Schauer, U., and Hansen, E.: Variability in Atlantic water temperature and transport at the entrance to the Arctic Ocean, 1997-2010, ICES J. Mar. Sci., 69, 852–863, 2012. a
Blanke, B. and Delecluse, P.: Variability of the Tropical Atlantic Ocean Simulated by a General Circulation Model with Two Different Mixed-Layer Physics, J. Phys. Oceanogr. 23, 1363–1388, https://doi.org/10.1175/1520-0485(1993)023<1363:VOTTAO>2.0.CO;2, 1993. a
Canuto, V. M., Howard, A., Cheng, Y., and Dubovikov, M. S.: Ocean Turbulence. Part II: Vertical Diffusivities of Momentum, Heat, Salt, Mass, and Passive Scalars, J. Phys. Oceanogr., 32, 240–264, https://doi.org/10.1175/1520-0485(2002)032<0240:OTPIVD>2.0.CO;2, 2002. a
Carmack, E., Yamamoto-Kawai, M., Haine, T., Bacon, S., Bluhm, B. A., Lique, C., Melling, H., Polyakov, I. V., Straneo, F., Timmermans, M.-L., and Williams, W. J.: Freshwater and its role in the Arctic Marine System: Sources, disposition, storage, export, and physical and biogeochemical consequences in the Arctic and global oceans, J. Geophys. Res.-Biogeo., 121, 675–717, 2016. a
Chassignet, E. P., Smith, L. T., Halliwell, G. R., and Bleck, R.: North Atlantic Simulations with the Hybrid Coordinate Ocean Model (HYCOM): Impact of the Vertical Coordinate Choice, Reference Pressure, and Thermobaricity, J. Phys. Oceanogr., 33, 2504–2526, https://doi.org/10.1175/1520-0485(2003)033<2504:NASWTH>2.0.CO;2, 2003. a
Chassignet, E. P., Yeager, S. G., Fox-Kemper, B., Bozec, A., Castruccio, F., Danabasoglu, G., Horvat, C., Kim, W. M., Koldunov, N., Li, Y., Lin, P., Liu, H., Sein, D. V., Sidorenko, D., Wang, Q., and Xu, X.: Impact of horizontal resolution on global ocean–sea ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2), Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, 2020. a, b, c, d, e, f, g
Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., Masina, S., Scoccimarro, E., Materia, S., Bellucci, A., and Navarra, A.: Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled Model, J. Adv. Model. Earth Sy., 11, 185–209, https://doi.org/10.1029/2018MS001369, 2019. a
Danabasoglu, G. and Marshall, J.: Effects of vertical variations of thickness diffusivity in an ocean general circulation model, Ocean Model., 18, 122–141, 2007. a
Danilov, S., Wang, Q., Timmermann, R., Iakovlev, N., Sidorenko, D., Kimmritz, M., Jung, T., and Schröter, J.: Finite-Element Sea Ice Model (FESIM), version 2, Geosci. Model Dev., 8, 1747–1761, https://doi.org/10.5194/gmd-8-1747-2015, 2015. a
Danilov, S., Sidorenko, D., Wang, Q., and Jung, T.: The Finite-volumE Sea ice–Ocean Model (FESOM2), Geosci. Model Dev., 10, 765–789, https://doi.org/10.5194/gmd-10-765-2017, 2017. a
de Steur, L., Hansen, E., Gerdes, R., Karcher, M., Fahrbach, E., and Holfort, J.: Freshwater fluxes in the East Greenland Current: A decade of observations, Geophys. Res. Lett., 36, L23611, https://doi.org/10.1029/2009GL041278, 2009. a
de Steur, L., Peralta-Ferriz, C., and Pavlova, O.: Freshwater Export in the East Greenland Current Freshens the North Atlantic, Geophys. Res. Lett., 45, 13359–13366, https://doi.org/10.1029/2018GL080207, 2018. a
Dickson, R. R., Osborn, T. J., Hurrell, J. W., Meincke, J., Blindheim, J., Adlandsvik, B., Vinje, T., Alekseev, G., and Maslowski, W.: The Arctic Ocean Response to the North Atlantic Oscillation, J. Climate, 13, 2671–2696, 2000. a
Docquier, D., Grist, J. P., Roberts, M. J., Roberts, C. D., Semmler, T., Ponsoni, L., Massonnet, F., Sidorenko, D., Sein, D. V., Iovino, D., Bellucci, A., and Fichefet, T.: Impact of model resolution on Arctic sea ice and North Atlantic Ocean heat transport, Clim. Dynam., 53, 4989–5017, https://doi.org/10.1007/s00382-019-04840-y, 2019. a
Fogli, P. G., Iovino, D., and Lovato, T.: CMCC CMCC-CM2-SR5 model output prepared for CMIP6 OMIP omip2, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.13236, 2020. a
Fox-Kemper, B., Ferrari, R., and Hallberg, R.: Parameterization of Mixed Layer Eddies. Part I: Theory and Diagnosis, J. Phys. Oceanogr., 38, 1145–1165, 2008. a
Fox-Kemper, B., Danabasoglu, G., Ferrari, R., Griffies, S. M., Hallberg, R. W., Holland, M. M., Maltrud, M. E., Peacock, S., and Samuels, B. L.: Parameterization of mixed layer eddies. III: Implementation and impact in global ocean climate simulations, Ocean Model., 39, 61–78, 2011. a
Gent, P. R. and McWilliams, J. C.: Isopycnal mixing in ocean circulation models, J. Phys. Oceanogr., 20, 150–155, 1990. a
Gerdes, R., Karcher, M. J., Kauker, F., and Schauer, U.: Causes and development of repeated Arctic Ocean warming events, Geophys. Res. Lett., 30, 1980, https://doi.org/10.1029/2003GL018080, 2003. a
Griffies, S.: Elements of the Modular Ocean Model (MOM) 2012 release, Tech. rep., GFDL, NOAA/Geophysical Fluid Dynamics Laboratory, 632 pp., 2012. a
Griffies, S. M., Biastoch, A., Böning, C., Bryan, F., Danabasoglu, G., Chassignet, E. P., England, M. H., Gerdes, R., Haak, H., Hallberg, R. W., Hazeleger, W., Jungclaus, J., Large, W. G., Madec, G., Pirani, A., Samuels, B. L., Scheinert, M., Gupta, A. S., Severijns, C. A., Simmons, H. L., Treguier, A. M., Winton, M., Yeager, S., and Yin, J.: Coordinated Ocean-ice Reference Experiments (COREs), Ocean Model., 26, 1–46, 2009. a, b
Griffies, S. M., Danabasoglu, G., Durack, P. J., Adcroft, A. J., Balaji, V., Böning, C. W., Chassignet, E. P., Curchitser, E., Deshayes, J., Drange, H., Fox-Kemper, B., Gleckler, P. J., Gregory, J. M., Haak, H., Hallberg, R. W., Heimbach, P., Hewitt, H. T., Holland, D. M., Ilyina, T., Jungclaus, J. H., Komuro, Y., Krasting, J. P., Large, W. G., Marsland, S. J., Masina, S., McDougall, T. J., Nurser, A. J. G., Orr, J. C., Pirani, A., Qiao, F., Stouffer, R. J., Taylor, K. E., Treguier, A. M., Tsujino, H., Uotila, P., Valdivieso, M., Wang, Q., Winton, M., and Yeager, S. G.: OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project, Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, 2016. a, b, c, d
Haine, T., Curry, B., Gerdes, R., Hansen, E., Karcher, M., Lee, C., Rudels, B., Spreen, G., de Steur, L., Stewart, K., and Woodgate, R.: Arctic freshwater export: Status, mechanisms, and prospects, Global Planet. Change, 125, 13–35, 2015. a
Hátún, H., Azetsu-Scott, K., Somavilla, R., Rey, F., Johnson, C., Mathis, M., Mikolajewicz, U., Coupel, P., Tremblay, J. E., Hartman, S., Pacariz, S. V., Salter, I., and Ólafsson, J.: The subpolar gyre regulates silicate concentrations in the North Atlantic, Sci. Rep., 7, 14576, https://doi.org/10.1038/s41598-017-14837-4, 2017. a
Heuzé, C., Zanowski, H., Karam, S., and Muilwijk, M.: The Deep Arctic Ocean and Fram Strait in CMIP6 Models, J. Climate, 36, 2551–2584, https://doi.org/10.1175/JCLI-D-22-0194.1, 2023. a
Hinrichs, C., Wang, Q., Koldunov, N., Mu, L., Semmler, T., Sidorenko, D., and Jung, T.: Atmospheric Wind Biases: A Challenge for Simulating the Arctic Ocean in Coupled Models?, J. Geophys. Res.-Oceans, 126, e2021JC017565, https://doi.org/10.1029/2021JC017565, 2021. a
Holland, M. M. and Bitz, C. M.: Polar amplification of climate change in coupled models, Clim. Dynam., 21, 221–232, https://doi.org/10.1007/s00382-003-0332-6, 2003. a
Holloway, G., Dupont, F., Golubeva, E., Haekkinen, S., Hunke, E., Jin, M., Karcher, M., Kauker, F., Maltrud, M., Maqueda, M. A. M., Maslowski, W., Platov, G., Stark, D., Steele, M., Suzuki, T., Wang, J., and Zhang, J.: Water properties and circulation in Arctic Ocean models, J. Geophys. Res.-Oceans, 112, C04S03, https://doi.org/10.1029/2006JC003642, 2007. a, b, c
Holmes, R., Kiss, A., Hogg, A., Hannah, N., Dias, F. B., Brassington, G., Chamberlain, M., Chapman, C., Dobrohotoff, P., Domingues, C. M., Duran, E., England, M., Fiedler, R., Griffies, S. M., Heerdegen, A., Heil, P., Klocker, A., Marsland, S., Morrison, A., Munroe, J., Nikurashin, M., Oke, P. R., Pilo, G. S., Richet, O., Savita, A., Spence, P., Stewart, K. D., Ward, M., Wu, F., Zhang, X., Mackallah, C., and Druken, K.: CSIRO-COSIMA ACCESS-OM2-025 model output prepared for CMIP6 OMIP omip2, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.14690, 2021. a
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.: CICE: the Los Alamos Sea Ice Model Documentation and Software User's Manual Version 5.1, Tech. Rep. LA-CC-06-012, Los Alamos National Laboratory, Los Alamos NM 87545, 2015. a
Ilicak, M., Drange, H., Wang, Q., Gerdes, R., Aksenov, Y., Bailey, D., Bentsen, M., Biastoch, A., Bozec, A., Böning, C., Cassou, C., Chassignet, E., Coward, A. C., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E., Fogli, P. G., Fujii, Y., Griffies, S. M., Iovino, D., Jahn, A., Jung, T., Large, W. G., Lee, C., Lique, C., Lu, J., Masina, S., George Nurser, A. J., Roth, C., Salas y Mélia, D., Samuels, B. L., Spence, P., Tsujino, H., Valcke, S., Voldoire, A., Wang, X., and Yeager, S. G.: An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part III: Hydrography and fluxes, Ocean Model., 100, 141–161, 2016. a, b, c, d, e, f, g
Ingvaldsen, R., Asplin, L., and Loeng, H.: Velocity field of the western entrance to the Barents Sea, J. Geophys. Res., 109, C03021, https://doi.org/10.1029/2003JC001811, 2004. a
Ingvaldsen, R. B., Assmann, K. M., Primicerio, R., Fossheim, M., Polyakov, I. V., and Dolgov, A. V.: Physical manifestations and ecological implications of Arctic Atlantification, Nat. Rev. Earth Environ., 2, 874–889, 2021. a
Iovino, D., Fogli, P. G., and Masina, S.: Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2), Geosci. Model Dev., 16, 6127–6159, https://doi.org/10.5194/gmd-16-6127-2023, 2023. a
Jahn, A., Aksenov, Y., de Cuevas, B. A., de Steur, L., Hakkinen, S., Hansen, E., Herbaut, C., Houssais, M. N., Karcher, M., Kauker, F., Lique, C., Nguyen, A., Pemberton, P., Worthen, D., and Zhang, J.: Arctic Ocean freshwater: How robust are model simulations?, J. Geophys. Res.-Oceans, 117, C00D16, https://doi.org/10.1029/2012JC007907, 2012. a, b
Jones, E. P., Rudels, B., and Anderson, L. G.: Deep waters of the Arctic Ocean: origins and circulation, Deep-Sea Res. Pt. I, 42, 737–760, https://doi.org/10.1016/0967-0637(95)00013-V, 1995. a
Karcher, M., Kauker, F., Gerdes, R., Hunke, E., and Zhang, J.: On the dynamics of Atlantic Water circulation in the Arctic Ocean, J. Geophys. Res.-Oceans, 112, C04S02, https://doi.org/10.1029/2006JC003630, 2007. a
Karcher, M., Smith, J., Kauker, F., Gerdes, R., and Smethie, W.: Recent changes in Arctic Ocean circulation revealed by iodine-129 observations and modeling, J. Geophys. Res.-Oceans, 117, C08007, https://doi.org/10.1029/2011JC007513, 2012. a
Karpouzoglou, T., de Steur, L., Smedsrud, L. H., and Sumata, H.: Observed Changes in the Arctic Freshwater Outflow in Fram Strait, J. Geophys. Res.-Oceans, 127, e2021JC018122, https://doi.org/10.1029/2021JC018122, 2022. a
Kawasaki, T. and Hasumi, H.: The inflow of Atlantic water at the Fram Strait and its interannual variability, J. Geophys. Res.-Oceans, 121, 502–519, https://doi.org/10.1002/2015JC011375, 2016. a
Khosravi, N., Wang, Q., Koldunov, N., Hinrichs, C., Semmler, T., Danilov, S., and Jung, T.: The Arctic Ocean in CMIP6 models: Biases and projected changes in temperature and salinity, Earth's Future, 10, e2021EF002282, https://doi.org/10.1029/2021EF002282, 2022. a
Kiss, A. E., Hogg, A. McC., Hannah, N., Boeira Dias, F., Brassington, G. B., Chamberlain, M. A., Chapman, C., Dobrohotoff, P., Domingues, C. M., Duran, E. R., England, M. H., Fiedler, R., Griffies, S. M., Heerdegen, A., Heil, P., Holmes, R. M., Klocker, A., Marsland, S. J., Morrison, A. K., Munroe, J., Nikurashin, M., Oke, P. R., Pilo, G. S., Richet, O., Savita, A., Spence, P., Stewart, K. D., Ward, M. L., Wu, F., and Zhang, X.: ACCESS-OM2 v1.0: a global ocean–sea ice model at three resolutions, Geosci. Model Dev., 13, 401–442, https://doi.org/10.5194/gmd-13-401-2020, 2020. a, b
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage: losses and coupled variability (1958–2018), Enviro. Res. Lett., 13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018. a
Large, W. G., Mcwilliams, J. C., and Doney, S. C.: Oceanic Vertical Mixing – A Review and A Model With A Nonlocal Boundary-layer Parameterization, Rev. Geophys., 32, 363–403, 1994. a
Lee, H. C., Rosati, A., and Spelman, M. J.: Barotropic tidal mixing effects in a coupled climate model: Oceanic conditions in the Northern Atlantic, Ocean Model., 11, 464–477, 2006. a
Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., Dong, L., Zhou, T., Liu, L., Wang, L., Pu, Y., Chen, X., Chen, L., Xie, Z., Liu, H., Zhang, L., Huang, X., Feng, T., Zheng, W., Xia, K., Liu, H., Liu, J., Wang, Y., Wang, L., Jia, B., Xie, F., Wang, B., Zhao, S., Yu, Z., Zhao, B., and Wei, J.: The Flexible Global Ocean-Atmosphere-Land System Model Grid-Point Version 3 (FGOALS-g3): Description and Evaluation, J. Adv. Model. Earth Sy., 12, e2019MS002012, https://doi.org/10.1029/2019MS002012, 2020a. a
Li, Y., Liu, H., Ding, M., Lin, P., Yu, Z., Yu, Y., Meng, Y., Li, Y., Jian, X., Jiang, J., Chen, K., Yang, Q., Wang, Y., Zhao, B., Wei, J., Ma, J., Zheng, W., and Wang, P.: Eddy-resolving Simulation of CAS-LICOM3 for Phase 2 of the Ocean Model Intercomparison Project, Adv. Atmos. Sci., 37, 1067–1080, https://doi.org/10.1007/s00376-020-0057-z, 2020b. a
Lin, P.: CAS FGOALS-f3-L model output prepared for CMIP6 OMIP omip1, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3413, 2019. a
Lin, P.: CAS FGOALS-f3-H model output prepared for CMIP6 OMIP omip2, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.13283, 2020. a
Lin, P., Yu, Z., Liu, H., Yu, Y., Li, Y., Jiang, J., Xue, W., Chen, K., Yang, Q., Zhao, B., Wei, J., Ding, M., Sun, Z., Wang, Y., Meng, Y., Zheng, W., and Ma, J.: LICOM Model Datasets for the CMIP6 Ocean Model Intercomparison Project, Adv. Atmos. Sci., 37, 239–249, https://doi.org/10.1007/s00376-019-9208-5, 2020. a
Lique, C., Johnson, H. L., and Davis, P. E. D.: On the Interplay between the Circulation in the Surface and the Intermediate Layers of the Arctic Ocean, J. Phys. Oceanogr., 45, 1393–1409, 2015. a
Lique, C., Holland, M. M., Dibike, Y. B., Lawrence, D. M., and Screen, J. A.: Modeling the Arctic freshwater system and its integration in the global system: Lessons learned and future challenges, J. Geophys. Res.-Biogeo., 121, 540–566, https://doi.org/10.1002/2015JG003120, 2016. a
Manucharyan, G. and Spall, M.: Wind-driven freshwater buildup and release in the Beaufort Gyre constrained by mesoscale eddies, Geophys. Res. Lett., 43, 273–282, 2016. a
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B. (Eds.): Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157896.011, 2021. a
McPhee, M. G., Proshutinsky, A., Morison, J. H., Steele, M., and Alkire, M. B.: Rapid change in freshwater content of the Arctic Ocean, Geophys. Res. Lett., 36, L10602, https://doi.org/10.1029/2009GL037525, 2009. a
Meneghello, G., Marshall, J., Cole, S., and Timmermans, M. L.: Observational inferences of lateral eddy diffusivity in the halocline of the Beaufort Gyre, Geophys. Res. Lett., 44, 12331–12338, 2017. a
Mesinger, F. and Janjic, Z. I.: Problems and numerical methods of the incorporation of mountains in atmospheric models, Lect. Appl. Math., 22, 81–120, 1985. a
Muilwijk, M., Nummelin, A., Heuzé, C., Polyakov, I. V., Zanowski, H., and Smedsrud, L. H.: Divergence in Climate Model Projections of Future Arctic Atlantification, J. Climate, 36, 1727–1748, https://doi.org/10.1175/JCLI-D-22-0349.1, 2023. a
Nguyen, A. T., Menemenlis, D., and Kwok, R.: Improved modeling of the Arctic halocline with a subgrid-scale brine rejection parameterization, J. Geophys. Res.-Oceans, 114, C11014, https://doi.org/10.1029/2008JC005121, 2009. a
Nummelin, A., Li, C., and Hezel, P.: Connecting ocean heat transport changes from the midlatitudes to the Arctic Ocean, Geophys. Res. Lett., 44, 1899–1908, 2017. a
Nurser, A. J. G. and Bacon, S.: The Rossby radius in the Arctic Ocean, Ocean Sci., 10, 967–975, https://doi.org/10.5194/os-10-967-2014, 2014. a
Pan, R., Shu, Q., Wang, Q., Wang, S., Song, Z., He, Y., and Qiao, F.: Future Arctic climate change in CMIP6 strikingly intensified by NEMO-family climate models, Geophys. Res. Lett., 50, e2022GL102077, https://doi.org/10.1029/2022GL102077, 2023. a
Polyakov, I., Bhatt, U., Walsh, J., Abrahamsen, E. P., Pnyushkov, A., and Wassmann, P.: Recent oceanic changes in the Arctic in the context of long-term observations, Ecol. Appl., 23, 1745–1764, 2013. a
Polyakov, I., Pnyushkov, A. V., Alkire, M. B., Ashik, I. M., Baumann, T. M., Carmack, E. C., Goszczko, I., Guthrie, J., Ivanov, V. V., Kanzow, T., Krishfield, R., Kwok, R., Sundfjord, A., Morison, J., Rember, R., and Yulin, A.: Greater role for Atlantic inflows on sea-ice loss in the Eurasian Basin of the Arctic Ocean, Science, 356, 285, https://doi.org/10.1126/science.aai8204, 2017. a, b
Polyakov, I. V., Alkire, M. B., Bluhm, B. A., Brown, K. A., Carmack, E. C., Chierici, M., Danielson, S. L., Ellingsen, I., Ershova, E. A., Gårdfeldt, K., Ingvaldsen, R. B., Pnyushkov, A. V., Slagstad, D., and Wassmann, P.: Borealization of the Arctic Ocean in Response to Anomalous Advection From Sub-Arctic Seas, Front. Mar. Sci., 7, 491, https://doi.org/10.3389/fmars.2020.00491, 2020. a, b, c, d, e, f, g, h, i, j
Popova, E. E., Yool, A., Coward, A. C., Aksenov, Y. K., Alderson, S. G., de Cuevas, B. A., and Anderson, T. R.: Control of primary production in the Arctic by nutrients and light: insights from a high resolution ocean general circulation model, Biogeosciences, 7, 3569–3591, https://doi.org/10.5194/bg-7-3569-2010, 2010. a
Proshutinsky, A., Bourke, R. H., and McLaughlin, F. A.: The role of the Beaufort Gyre in Arctic climate variability: Seasonal to decadal climate scales, Geophys. Res. Lett., 29, 2100, https://doi.org/10.1029/2002GL015847, 2002. a
Proshutinsky, A., Krishfield, R., Timmermans, M.-L., Toole, J., Carmack, E., McLaughlin, F., Williams, W. J., Zimmermann, S., Itoh, M., and Shimada, K.: Beaufort Gyre freshwater reservoir: State and variability from observations, J. Geophys. Res.-Oceans, 114, C00A10, https://doi.org/10.1029/2008JC005104, 2009. a, b
Proshutinsky, A., Krishfield, R., Toole, J. M., Timmermans, M. L., Williams, W., Zimmermann, S., Yamamoto-Kawai, M., Armitage, T. W. K., Dukhovskoy, D., Golubeva, E., Manucharyan, G. E., Platov, G., Watanabe, E., Kikuchi, T., Nishino, S., Itoh, M., Kang, S. H., Cho, K. H., Tateyama, K., and Zhao, J.: Analysis of the Beaufort Gyre Freshwater Content in 2003–2018, J. Geophys. Res.-Oceans, 124, 9658–9689, 2019. a, b
Rabe, B., Karcher, M., Kauker, F., Schauer, U., Toole, J. M., Krishfield, R. A., Pisarev, S., Kikuchi, T., and Su, J.: Arctic Ocean basin liquid freshwater storage trend 1992–2012, Geophys. Res. Lett., 41, 961–968, 2014. a
Årthun, M., Eldevik, T., Smedsrud, L. H., Skagseth, O., and Ingvaldsen, R. B.: Quantifying the Influence of Atlantic Heat on Barents Sea Ice Variability and Retreat, J. Climate, 25, 4736–4743, 2012. a
Redi, M. H.: Oceanic isopycnal mixing by coordinate rotation, J. Phys. Oceanogr., 12, 1154–1158, 1982. a
Richards, A. E., Johnson, H. L., and Lique, C.: Spatial and Temporal Variability of Atlantic Water in the Arctic From 40 Years of Observations, J. Geophys. Res.-Oceans, 127, e2021JC018358, https://doi.org/10.1029/2021JC018358, 2022. a
Rudels, B. and Quadfasel, D.: Convection and deep water formation in the Arctic Ocean-Greenland Sea System, J. Marine Syst., 2, 435–450, https://doi.org/10.1016/0924-7963(91)90045-V, 1991. a
Schauer, U., Muench, R. D., Rudels, B., and Timokhov, L.: Impact of eastern Arctic shelf waters on the Nansen Basin intermediate layers, J. Geophys. Res.-Oceans, 102, 3371–3382, 1997. a
Schauer, U., Østerhus, S., and Rohardt, G.: Arctic warming through the Fram Strait: oceanic heat transport from 3 years of measurement, J. Geophys. Res., 109, C06026, https://doi.org/10.1029/2003JC001823, 2004. a, b
Schauer, U., Beszczynska-Moeller, A., Walczowski, W., Fahrbach, E., Piechura, J., and Hansen, E.: Variation of Measured Heat Flow Through the Fram Strait Between 1997 and 2006, in: Arctic-Subarctic Ocean Fluxes: Defining the Role of the Northern Seas in Climate, edited by: Dickson, R. E. A., Springer, 65–85, https://doi.org/10.1007/978-1-4020-6774-7_4, 2008. a
Schmidtko, S., Johnson, G. C., and Lyman, J. M.: MIMOC: A global monthly isopycnal upper-ocean climatology with mixed layers, J. Geophys. Res.-Oceans, 118, 1658–1672, https://doi.org/10.1002/jgrc.20122, 2013. a
Screen, J. A. and Simmonds, I.: The central role of diminishing sea ice in recent Arctic temperature amplification, Nature, 464, 1334–1337, 2010. a
Serreze, M. C. and Barry, R. G.: Processes and impacts of Arctic amplification: A research synthesis, Global Planet. Change, 77, 85–96, 2011. a
Serreze, M. C., Barrett, A. P., Slater, A. G., Woodgate, R. A., Aagaard, K., Lammers, R. B., Steele, M., Moritz, R., Meredith, M., and Lee, C. M.: The large-scale freshwater cycle of the Arctic, J. Geophys. Res.-Oceans, 111, C11010, https://doi.org/10.1029/2005JC003424, 2006. a, b, c, d
Shu, Q.: Matlab code and data for the paper Impact of high resolution on Arctic Ocean simulations in Ocean Model Intercomparison Project phase 2 (OMIP-2) (v1.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.8046638, 2023. a
Shu, Q., Qiao, F., Song, Z., Zhao, J., and Li, X.: Projected Freshening of the Arctic Ocean in the 21st Century, J. Geophys. Res.-Oceans, 123, 9232–9244, 2018. a
Shu, Q., Wang, Q., Song, Z., and Qiao, F.: The poleward enhanced Arctic Ocean cooling machine in a warming climate, Nat. Commun., 12, 2966, https://doi.org/10.1038/s41467-021-23321-7, 2021. a, b
Shu, Q., Wang, Q., Årthun, M., Wang, S., Song, Z., Zhang, M., and Qiao, F.: Arctic Ocean Amplification in a warming climate in CMIP6 models, Sci. Adv., 8, eabn9755, https://doi.org/10.1126/sciadv.abn9755, 2022. a, b
Sidorenko, D., Goessling, H. F., Koldunov, N. V., Scholz, P., Danilov, S., Barbi, D., Cabos, W., Gurses, O., Harig, S., Hinrichs, C., Juricke, S., Lohmann, G., Losch, M., Mu, L., Rackow, T., Rakowsky, N., Sein, D., Semmler, T., Shi, X., Stepanek, C., Streffing, J., Wang, Q., Wekerle, C., Yang, H., and Jung, T.: Evaluation of FESOM2.0 Coupled to ECHAM6.3: Preindustrial and HighResMIP Simulations, J. Adv. Model. Earth Sy., 11, 3794–3815, https://doi.org/10.1029/2019MS001696, 2019. a
Simmons, H. L., Jayne, S. R., St Laurent, L. C., and Weaver, A. J.: Tidally driven mixing in a numerical model of the ocean general circulation, Ocean Model., 6, 245–263, 2004. a
Skagseth, Ø., Eldevik, T., Årthun, M., Asbjørnsen, H., Lien, V. S., and Smedsrud, L. H.: Reduced efficiency of the Barents Sea cooling machine, Nat. Clim. Change, 10, 661–666, https://doi.org/10.1038/s41558-020-0772-6, 2020. a
Smedsrud, L. H., Ingvaldsen, R., Nilsen, J. E. Ø., and Skagseth, Ø.: Heat in the Barents Sea: transport, storage, and surface fluxes, Ocean Sci., 6, 219–234, https://doi.org/10.5194/os-6-219-2010, 2010. a
Smedsrud, L. H., Esau, I., Ingvaldsen, R. B., Eldevik, T., Haugan, P. M., Li, C., Lien, V. S., Olsen, A., Omar, A. M., Ottera, O. H., Risebrobakken, B., Sando, A. B., Semenov, V. A., and Sorokina, S. A.: The Role of the Barents Sea In the Arctic Climate System, Rev. Geophys., 51, 415–449, 2013. a, b, c, d, e
Solodoch, A., Stewart, A. L., Hogg, A. M., Morrison, A. K., Kiss, A. E., Thompson, A. F., Purkey, S. G., and Cimoli, L.: How does Antarctic Bottom Water Cross the Southern Ocean?, Geophys. Res. Lett., 49, e2021GL097211, https://doi.org/10.1029/2021GL097211, 2022. a
St Laurent, L. C., Simmons, H. L., and Jayne, S. R.: Estimating tidally driven mixing in the deep ocean, Geophys. Res. Lett., 29, 2106, https://doi.org/10.1029/2002GL015633, 2002. a
Streffing, J., Sidorenko, D., Semmler, T., Zampieri, L., Scholz, P., Andrés-Martínez, M., Koldunov, N., Rackow, T., Kjellsson, J., Goessling, H., Athanase, M., Wang, Q., Hegewald, J., Sein, D. V., Mu, L., Fladrich, U., Barbi, D., Gierz, P., Danilov, S., Juricke, S., Lohmann, G., and Jung, T.: AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model, Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, 2022. a
Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all seasons, Environ. Res. Lett., 13, 103001, https://doi.org/10.1088/1748-9326/aade56, 2018. a
Timmermann, R., Goosse, H., Madec, G., Fichefet, T., Ethe, C., and Dulière, V.: On the representation of high latitude processes in the ORCA-LIM global coupled sea ice–ocean model, Ocean Model., 8, 175–201, https://doi.org/10.1016/j.ocemod.2003.12.009, 2005. a
Timmermans, M.-L. and Marshall, J.: Understanding Arctic Ocean Circulation: A Review of Ocean Dynamics in a Changing Climate, J. Geophys. Res.-Oceans, 125, e2018JC014378, https://doi.org/10.1029/2018JC014378, 2020. a
Timmermans, M.-L. and Toole, J. M.: The Arctic Ocean's Beaufort Gyre, Annu. Rev. Mar. Sci., 15, 223–248, https://doi.org/10.1146/annurev-marine-032122-012034, 2023. a
Treguier, A. M., de Boyer Montégut, C., Bozec, A., Chassignet, E. P., Fox-Kemper, B., McC. Hogg, A., Iovino, D., Kiss, A. E., Le Sommer, J., Li, Y., Lin, P., Lique, C., Liu, H., Serazin, G., Sidorenko, D., Wang, Q., Xu, X., and Yeager, S.: The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies, Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, 2023. a, b, c, d, e
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager, S. G., Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C. W., Bozec, A., Chassignet, E. P., Curchitser, E., Boeira Dias, F., Durack, P. J., Griffies, S. M., Harada, Y., Ilicak, M., Josey, S. A., Kobayashi, C., Kobayashi, S., Komuro, Y., Large, W. G., Le Sommer, J., Marsland, S. J., Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.: JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do), Ocean Model., 130, 79–139, https://doi.org/10.1016/j.ocemod.2018.07.002, 2018. a, b
Tsujino, H., Urakawa, L. S., Griffies, S. M., Danabasoglu, G., Adcroft, A. J., Amaral, A. E., Arsouze, T., Bentsen, M., Bernardello, R., Böning, C. W., Bozec, A., Chassignet, E. P., Danilov, S., Dussin, R., Exarchou, E., Fogli, P. G., Fox-Kemper, B., Guo, C., Ilicak, M., Iovino, D., Kim, W. M., Koldunov, N., Lapin, V., Li, Y., Lin, P., Lindsay, K., Liu, H., Long, M. C., Komuro, Y., Marsland, S. J., Masina, S., Nummelin, A., Rieck, J. K., Ruprich-Robert, Y., Scheinert, M., Sicardi, V., Sidorenko, D., Suzuki, T., Tatebe, H., Wang, Q., Yeager, S. G., and Yu, Z.: Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2), Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, 2020. a
Walsh, J. J., McRoy, C. P., Coachman, L. K., Goering, J. J., Nihoul, J. J., Whitledge, T. E., Blackburn, T. H., Parker, P. L., Wirick, C. D., Shuert, P. G., Grebmeier, J. M., Springer, A. M., Tripp, R. D., Hansell, D. A., Djenidi, S., Deleersnijder, E., Henriksen, K., Lund, B. A., Andersen, P., Müller-Karger, F. E., and Dean, K.: Carbon and nitrogen cycling within the Bering/Chukchi Seas: Source regions for organic matter effecting AOU demands of the Arctic Ocean, Prog. Oceanogr., 22, 277–359, https://doi.org/10.1016/0079-6611(89)90006-2, 1989. a
Wang, Q.: Stronger variability in the Arctic Ocean induced by sea ice decline in a warming climate: Freshwater storage, dynamic sea level and surface circulation, J. Geophys. Res.-Oceans, 126, e2020JC016886, https://doi.org/10.1029/2020JC016886, 2021. a, b
Wang, Q. and Danilov, S.: A Synthesis of the Upper Arctic Ocean Circulation During 2000–2019: Understanding the Roles of Wind Forcing and Sea Ice Decline, Front. Mar. Sci., 9, 863204, https://doi.org/10.3389/fmars.2022.863204, 2022. a, b, c
Wang, Q., Ilicak, M., Gerdes, R., Drange, H., Aksenov, Y., Bailey, D. A., Bentsen, M., Biastoch, A., Bozec, A., Böning, C., Cassou, C., Chassignet, E., Coward, A. C., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E., Fogli, P. G., Fujii, Y., Griffies, S. M., Iovino, D., Jahn, A., Jung, T., Large, W. G., Lee, C., Lique, C., Lu, J., Masina, S., Nurser, A. J. G., Rabe, B., Roth, C., Salas y Mélia, D., Samuels, B. L., Spence, P., Tsujino, H., Valcke, S., Voldoire, A., Wang, X., and Yeager, S. G.: An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part II: Liquid freshwater, Ocean Model., 99, 86–109, 2016a. a, b, c, d, e, f, g, h, i
Wang, Q., Ilicak, M., Gerdes, R., Drange, H., Aksenov, Y., Bailey, D. A., Bentsen, M., Biastoch, A., Bozec, A., Böning, C., Cassou, C., Chassignet, E., Coward, A. C., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E., Fogli, P. G., Fujii, Y., Griffies, S. M., Iovino, D., Jahn, A., Jung, T., Large, W. G., Lee, C., Lique, C., Lu, J., Masina, S., Nurser, A. J. G., Rabe, B., Roth, C., Salas y Mélia, D., Samuels, B. L., Spence, P., Tsujino, H., Valcke, S., Voldoire, A., Wang, X., and Yeager, S. G.: An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part I: Sea ice and solid freshwater, Ocean Model., 99, 110–132, 2016b. a
Wang, Q., Wekerle, C., Danilov, S., Wang, X., and Jung, T.: A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4, Geosci. Model Dev., 11, 1229–1255, https://doi.org/10.5194/gmd-11-1229-2018, 2018. a, b
Wang, Q., Koldunov, N. V., Danilov, S., Sidorenko, D., Wekerle, C., Scholz, P., Bashmachnikov, I. L., and Jung, T.: Eddy Kinetic Energy in the Arctic Ocean From a Global Simulation With a 1-km Arctic, Geophys. Res. Lett., 47, e2020GL088550, https://doi.org/10.1029/2020GL088550, 2020a. a
Wang, Q., Wekerle, C., Wang, X., Danilov, S., Koldunov, N., Sein, D., Sidorenko, D., von Appen, W.-J., and Jung, T.: Intensification of the Atlantic Water Supply to the Arctic Ocean Through Fram Strait Induced by Arctic Sea Ice Decline, Geophys. Res. Lett., 47, e2019GL086682, https://doi.org/10.1029/2019GL086682, 2020b. a, b, c, d
Wang, Q., Danilov, S., Sidorenko, D., and Wang, X.: Circulation Pathways and Exports of Arctic River Runoff Influenced by Atmospheric Circulation Regimes, Front. Mar. Sci., 8, 1153, https://doi.org/10.3389/fmars.2021.707593, 2021. a
Wang, Q., Shu, Q., Danilov, S., and Sidorenko, D.: An extreme event of enhanced Arctic Ocean export west of Greenland caused by the pronounced dynamic sea level drop in the North Atlantic subpolar gyre in the mid-to-late 2010s, Environ. Res. Lett., 17, 044046, https://doi.org/10.1088/1748-9326/ac5562, 2022. a, b
Wang, Q., Shu, Q., Wang, S., Beszczynska-Moeller, A., Danilov, S., de Steur, L., Haine, T., Karcher, M., Lee, C., Myers, P., Polyakov, I., Provost, C., Skagseth, O., Spreen, G., and Woodgate, R.: A Review of Arctic–Subarctic Ocean Linkages: Past Changes, Mechanisms, and Future Projections, Ocean-Land-Atmos Res., 2, 0013, https://doi.org/10.34133/olar.0013, 2023. a, b, c, d, e, f, g, h, i, j, k, l
Wang, S., Wang, Q., Wang, M., Lohmann, G., and Qiao, F.: Arctic Ocean Freshwater in CMIP6 Coupled Models, Earth's Future, 10, e2022EF002878, https://doi.org/10.1029/2022EF002878, 2022. a
Wekerle, C., Wang, Q., Danilov, S., Jung, T., and Schröter, J.: The Canadian Arctic Archipelago throughflow in a multiresolution global model: Model assessment and the driving mechanism of interannual variability, J. Geophys. Res.-Oceans, 118, 4525–4541, 2013. a
Woodgate, R. A., Aagaard, K., Muench, R. D., Gunn, J., Björk, G., Rudels, B., Roach, A. T., and Schauer, U.: The Arctic Ocean Boundary Current along the Eurasian slope and the adjacent Lomonosov Ridge: Water mass properties, transports and transformations from moored instruments, Deep-Sea Res. Pt. I, 48, 1757–1792, 2001. a
Woodgate, R. A., Aagaard, K., and Weingartner, T. J.: Interannual changes in the Bering Strait fluxes of volume, heat and freshwater between 1991 and 2004, Geophys. Res. Lett., 33, L15609, https://doi.org/10.1029/2006GL026931, 2006. a
Woodgate, R. A., Weingartner, T., and Lindsay, R.: The 2007 Bering Strait oceanic heat flux and anomalous Arctic sea-ice retreat, Geophys. Res. Lett., 37, L01602, https://doi.org/10.1029/2009GL041621, 2010. a
Yamagami, Y., Watanabe, M., Mori, M., and Ono, J.: Barents-Kara sea-ice decline attributed to surface warming in the Gulf Stream, Nat. Commun., 13, 3767, https://doi.org/10.1038/s41467-022-31117-6, 2022. a
Zanowski, H., Jahn, A., and Holland, M. M.: Arctic Ocean Freshwater in CMIP6 Ensembles: Declining Sea Ice, Increasing Ocean Storage and Export, J. Geophys. Res-Oceans, 126, e2020JC016930, https://doi.org/10.1029/2020JC016930, 2021. a
Zhang, J. and Steele, M.: Effect of vertical mixing on the Atlantic Water layer circulation in the Arctic Ocean, J. Geophys. Res.-Oceans, 112, C04S04, https://doi.org/10.1029/2006JC003732, 2007. a
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.
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors...
