Articles | Volume 21, issue 22
https://doi.org/10.5194/bg-21-4951-2024
© 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/bg-21-4951-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Nov 2024
Research article |
| 14 Nov 2024
| 14 Nov 2024
Changes in Arctic Ocean plankton community structure and trophic dynamics on seasonal to interannual timescales
Gabriela Negrete-García
CORRESPONDING AUTHOR
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
Jessica Y. Luo
Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, New Jersey, United States of America
Colleen M. Petrik
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
Manfredi Manizza
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
Andrew D. Barton
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, California, United States of America
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Robert Lampe, Ariel Rabines, Steffaney Wood, Anne Schulberg, Ralf Goericke, Pratap Venepally, Hong Zheng, Michael Stukel, Michael Landry, Andrew Barton, and Andrew Allen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3285, https://doi.org/10.5194/egusphere-2024-3285, 2024
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With the likely emergence of satellite-based phytoplankton pigment data, it is increasingly important to examine relationships between phytoplankton pigments and other metrics of phytoplankton community composition. By using quantitative approaches, we show that phytoplankton pigments correlate with DNA- and RNA-based abundances, and examine how integration of these data addresses ecological questions relating to diversity patterns, harmful algal blooms, and inferring cellular activity.
Mathieu Antoine François Poupon, Laure Resplandy, Jessica Garwood, Charles Stock, Niki Zadeh, and Jessica Luo
EGUsphere, https://doi.org/10.5194/egusphere-2024-3058, https://doi.org/10.5194/egusphere-2024-3058, 2024
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Zooplankton diel vertical migration (DVM) shapes ocean biogeochemical cycles. We present a new DVM model that reproduces migration depths observed in the North Atlantic Ocean. We show that chlorophyll shading contributes to reducing zooplankton migration depth and mainly controls its spatial and temporal variability. Thus, high chlorophyll concentrations may limit carbon sequestration caused by zooplankton migration despite the general abundance of zooplankton migration in these environments.
Mathilde Dugenne, Marco Corrales-Ugalde, Jessica Y. Luo, Rainer Kiko, Todd D. O'Brien, Jean-Olivier Irisson, Fabien Lombard, Lars Stemmann, Charles Stock, Clarissa R. Anderson, Marcel Babin, Nagib Bhairy, Sophie Bonnet, Francois Carlotti, Astrid Cornils, E. Taylor Crockford, Patrick Daniel, Corinne Desnos, Laetitia Drago, Amanda Elineau, Alexis Fischer, Nina Grandrémy, Pierre-Luc Grondin, Lionel Guidi, Cecile Guieu, Helena Hauss, Kendra Hayashi, Jenny A. Huggett, Laetitia Jalabert, Lee Karp-Boss, Kasia M. Kenitz, Raphael M. Kudela, Magali Lescot, Claudie Marec, Andrew McDonnell, Zoe Mériguet, Barbara Niehoff, Margaux Noyon, Thelma Panaïotis, Emily Peacock, Marc Picheral, Emilie Riquier, Collin Roesler, Jean-Baptiste Romagnan, Heidi M. Sosik, Gretchen Spencer, Jan Taucher, Chloé Tilliette, and Marion Vilain
Earth Syst. Sci. Data, 16, 2971–2999, https://doi.org/10.5194/essd-16-2971-2024, https://doi.org/10.5194/essd-16-2971-2024, 2024
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Plankton and particles influence carbon cycling and energy flow in marine ecosystems. We used three types of novel plankton imaging systems to obtain size measurements from a range of plankton and particle sizes and across all major oceans. Data were compiled and cross-calibrated from many thousands of images, showing seasonal and spatial changes in particle size structure in different ocean basins. These datasets form the first release of the Pelagic Size Structure database (PSSdb).
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
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Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jérôme Pinti, Tim DeVries, Tommy Norin, Camila Serra-Pompei, Roland Proud, David A. Siegel, Thomas Kiørboe, Colleen M. Petrik, Ken H. Andersen, Andrew S. Brierley, and André W. Visser
Biogeosciences, 20, 997–1009, https://doi.org/10.5194/bg-20-997-2023, https://doi.org/10.5194/bg-20-997-2023, 2023
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Large numbers of marine organisms such as zooplankton and fish perform daily vertical migration between the surface (at night) and the depths (in the daytime). This fascinating migration is important for the carbon cycle, as these organisms actively bring carbon to depths where it is stored away from the atmosphere for a long time. Here, we quantify the contributions of different animals to this carbon drawdown and storage and show that fish are important to the biological carbon pump.
Angharad C. Stell, Michael Bertolacci, Andrew Zammit-Mangion, Matthew Rigby, Paul J. Fraser, Christina M. Harth, Paul B. Krummel, Xin Lan, Manfredi Manizza, Jens Mühle, Simon O'Doherty, Ronald G. Prinn, Ray F. Weiss, Dickon Young, and Anita L. Ganesan
Atmos. Chem. Phys., 22, 12945–12960, https://doi.org/10.5194/acp-22-12945-2022, https://doi.org/10.5194/acp-22-12945-2022, 2022
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Nitrous oxide is a potent greenhouse gas and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions. We derive increasing global nitrous oxide emissions over 2011–2020, which are mainly driven by emissions between 0° and 30°N, with the highest emissions recorded in 2020.
Vincent Le Fouest, Atsushi Matsuoka, Manfredi Manizza, Mona Shernetsky, Bruno Tremblay, and Marcel Babin
Biogeosciences, 15, 1335–1346, https://doi.org/10.5194/bg-15-1335-2018, https://doi.org/10.5194/bg-15-1335-2018, 2018
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Climate warming could enhance the load of terrigenous dissolved organic carbon (tDOC) of Arctic rivers. We show that tDOC concentrations simulated by an ocean–biogeochemical model in the Canadian Beaufort Sea compare favorably with their satellite counterparts. Over spring–summer, riverine tDOC contributes to 35 % of primary production and an equivalent of ~ 10 % of tDOC is exported westwards with the potential for fueling the biological production of the eastern Alaskan nearshore waters.
V. Le Fouest, M. Manizza, B. Tremblay, and M. Babin
Biogeosciences, 12, 3385–3402, https://doi.org/10.5194/bg-12-3385-2015, https://doi.org/10.5194/bg-12-3385-2015, 2015
C. D. Nevison, M. Manizza, R. F. Keeling, M. Kahru, L. Bopp, J. Dunne, J. Tiputra, T. Ilyina, and B. G. Mitchell
Biogeosciences, 12, 193–208, https://doi.org/10.5194/bg-12-193-2015, https://doi.org/10.5194/bg-12-193-2015, 2015
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The observed seasonal cycles in atmospheric potential oxygen (APO) at five surface monitoring sites are compared to those inferred from the air-sea O2 fluxes of six ocean biogeochemistry models. The simulated air-sea fluxes are translated into APO seasonal cycles using a matrix method that takes into account atmospheric transport model (ATM) uncertainty among 13 different ATMs. Net primary production (NPP), estimated from satellite ocean color data, is also compared to model output.
E. Saikawa, R. G. Prinn, E. Dlugokencky, K. Ishijima, G. S. Dutton, B. D. Hall, R. Langenfelds, Y. Tohjima, T. Machida, M. Manizza, M. Rigby, S. O'Doherty, P. K. Patra, C. M. Harth, R. F. Weiss, P. B. Krummel, M. van der Schoot, P. J. Fraser, L. P. Steele, S. Aoki, T. Nakazawa, and J. W. Elkins
Atmos. Chem. Phys., 14, 4617–4641, https://doi.org/10.5194/acp-14-4617-2014, https://doi.org/10.5194/acp-14-4617-2014, 2014
U. Schuster, G. A. McKinley, N. Bates, F. Chevallier, S. C. Doney, A. R. Fay, M. González-Dávila, N. Gruber, S. Jones, J. Krijnen, P. Landschützer, N. Lefèvre, M. Manizza, J. Mathis, N. Metzl, A. Olsen, A. F. Rios, C. Rödenbeck, J. M. Santana-Casiano, T. Takahashi, R. Wanninkhof, and A. J. Watson
Biogeosciences, 10, 607–627, https://doi.org/10.5194/bg-10-607-2013, https://doi.org/10.5194/bg-10-607-2013, 2013
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Biogeosciences, 21, 4361–4380, https://doi.org/10.5194/bg-21-4361-2024, https://doi.org/10.5194/bg-21-4361-2024, 2024
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Michael Dominik Tyka
EGUsphere, https://doi.org/10.5194/egusphere-2024-2150, https://doi.org/10.5194/egusphere-2024-2150, 2024
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David Talmy, Eric Carr, Harshana Rajakaruna, Selina Våge, and Anne Willem Omta
Biogeosciences, 21, 2493–2507, https://doi.org/10.5194/bg-21-2493-2024, https://doi.org/10.5194/bg-21-2493-2024, 2024
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Yanda Ou and Z. George Xue
Biogeosciences, 21, 2385–2424, https://doi.org/10.5194/bg-21-2385-2024, https://doi.org/10.5194/bg-21-2385-2024, 2024
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Heiner Dietze and Ulrike Löptien
EGUsphere, https://doi.org/10.5194/egusphere-2024-918, https://doi.org/10.5194/egusphere-2024-918, 2024
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Itzel Ruvalcaba Baroni, Elin Almroth-Rosell, Lars Axell, Sam T. Fredriksson, Jenny Hieronymus, Magnus Hieronymus, Sandra-Esther Brunnabend, Matthias Gröger, Ivan Kuznetsov, Filippa Fransner, Robinson Hordoir, Saeed Falahat, and Lars Arneborg
Biogeosciences, 21, 2087–2132, https://doi.org/10.5194/bg-21-2087-2024, https://doi.org/10.5194/bg-21-2087-2024, 2024
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Ieuan Higgs, Jozef Skákala, Ross Bannister, Alberto Carrassi, and Stefano Ciavatta
Biogeosciences, 21, 731–746, https://doi.org/10.5194/bg-21-731-2024, https://doi.org/10.5194/bg-21-731-2024, 2024
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Biogeosciences, 20, 1621–1633, https://doi.org/10.5194/bg-20-1621-2023, https://doi.org/10.5194/bg-20-1621-2023, 2023
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The results of this study indicate that biodegradable dissolved organic matter is responsible for oxygen depletion at low flow during summer seasons when heterotrophic bacterial activity is so intense. Therefore, the dissolved organic matter must be well measured in the water monitoring networks in order to have more accurate water quality models. It also advocates for high-frequency data collection for better quantification of the uncertainties related to organic matter.
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Biogeosciences, 20, 1181–1193, https://doi.org/10.5194/bg-20-1181-2023, https://doi.org/10.5194/bg-20-1181-2023, 2023
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We are among the first to quantify methane emissions from inland water system in the pan-Arctic. The total CH4 emissions are 36.46 Tg CH4 yr−1 during 2000–2015, of which wetlands and lakes were 21.69 Tg yr−1 and 14.76 Tg yr−1, respectively. By using two non-overlap area change datasets with land and lake models, our simulation avoids small lakes being counted twice as both lake and wetland, and it narrows the gap between two different methods used to quantify regional CH4 emissions.
Corentin Clerc, Laurent Bopp, Fabio Benedetti, Meike Vogt, and Olivier Aumont
Biogeosciences, 20, 869–895, https://doi.org/10.5194/bg-20-869-2023, https://doi.org/10.5194/bg-20-869-2023, 2023
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Gelatinous zooplankton play a key role in the ocean carbon cycle. In particular, pelagic tunicates, which feed on a wide size range of prey, produce rapidly sinking detritus. Thus, they efficiently transfer carbon from the surface to the depths. Consequently, we added these organisms to a marine biogeochemical model (PISCES-v2) and evaluated their impact on the global carbon cycle. We found that they contribute significantly to carbon export and that this contribution increases with depth.
Shuang Gao, Jörg Schwinger, Jerry Tjiputra, Ingo Bethke, Jens Hartmann, Emilio Mayorga, and Christoph Heinze
Biogeosciences, 20, 93–119, https://doi.org/10.5194/bg-20-93-2023, https://doi.org/10.5194/bg-20-93-2023, 2023
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We assess the impact of riverine nutrients and carbon (C) on projected marine primary production (PP) and C uptake using a fully coupled Earth system model. Riverine inputs alleviate nutrient limitation and thus lessen the projected PP decline by up to 0.7 Pg C yr−1 globally. The effect of increased riverine C may be larger than the effect of nutrient inputs in the future on the projected ocean C uptake, while in the historical period increased nutrient inputs are considered the largest driver.
Valeria Di Biagio, Stefano Salon, Laura Feudale, and Gianpiero Cossarini
Biogeosciences, 19, 5553–5574, https://doi.org/10.5194/bg-19-5553-2022, https://doi.org/10.5194/bg-19-5553-2022, 2022
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The amount of dissolved oxygen in the ocean is the result of interacting physical and biological processes. Oxygen vertical profiles show a subsurface maximum in a large part of the ocean. We used a numerical model to map this subsurface maximum in the Mediterranean Sea and to link local differences in its properties to the driving processes. This emerging feature can help the marine ecosystem functioning to be better understood, also under the impacts of climate change.
Michael R. Stukel, Moira Décima, and Michael R. Landry
Biogeosciences, 19, 3595–3624, https://doi.org/10.5194/bg-19-3595-2022, https://doi.org/10.5194/bg-19-3595-2022, 2022
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The biological carbon pump (BCP) transports carbon into the deep ocean, leading to long-term marine carbon sequestration. It is driven by many physical, chemical, and ecological processes. We developed a model of the BCP constrained using data from 11 cruises in 4 different ocean regions. Our results show that sinking particles and vertical mixing are more important than transport mediated by vertically migrating zooplankton. They also highlight the uncertainty in current estimates of the BCP.
Ginevra Rosati, Donata Canu, Paolo Lazzari, and Cosimo Solidoro
Biogeosciences, 19, 3663–3682, https://doi.org/10.5194/bg-19-3663-2022, https://doi.org/10.5194/bg-19-3663-2022, 2022
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Methylmercury (MeHg) is produced and bioaccumulated in marine food webs, posing concerns for human exposure through seafood consumption. We modeled and analyzed the fate of MeHg in the lower food web of the Mediterranean Sea. The modeled spatial–temporal distribution of plankton bioaccumulation differs from the distribution of MeHg in surface water. We also show that MeHg exposure concentrations in temperate waters can be lowered by winter convection, which is declining due to climate change.
Yanda Ou, Bin Li, and Z. George Xue
Biogeosciences, 19, 3575–3593, https://doi.org/10.5194/bg-19-3575-2022, https://doi.org/10.5194/bg-19-3575-2022, 2022
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Over the past decades, the Louisiana–Texas shelf has been suffering recurring hypoxia (dissolved oxygen < 2 mg L−1). We developed a novel prediction model using state-of-the-art statistical techniques based on physical and biogeochemical data provided by a numerical model. The model can capture both the magnitude and onset of the annual hypoxia events. This study also demonstrates that it is possible to use a global model forecast to predict regional ocean water quality.
Eva Ehrnsten, Oleg Pavlovitch Savchuk, and Bo Gustav Gustafsson
Biogeosciences, 19, 3337–3367, https://doi.org/10.5194/bg-19-3337-2022, https://doi.org/10.5194/bg-19-3337-2022, 2022
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We studied the effects of benthic fauna, animals living on or in the seafloor, on the biogeochemical cycles of carbon, nitrogen and phosphorus using a model of the Baltic Sea ecosystem. By eating and excreting, the animals transform a large part of organic matter sinking to the seafloor into inorganic forms, which fuel plankton blooms. Simultaneously, when they move around (bioturbate), phosphorus is bound in the sediments. This reduces nitrogen-fixing plankton blooms and oxygen depletion.
Brandon J. McNabb and Philippe D. Tortell
Biogeosciences, 19, 1705–1721, https://doi.org/10.5194/bg-19-1705-2022, https://doi.org/10.5194/bg-19-1705-2022, 2022
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The trace gas dimethyl sulfide (DMS) plays an important role in the ocean sulfur cycle and can also influence Earth’s climate. Our study used two statistical methods to predict surface ocean concentrations and rates of sea–air exchange of DMS in the northeast subarctic Pacific. Our results show improved predictive power over previous approaches and suggest that nutrient availability, light-dependent processes, and physical mixing may be important controls on DMS in this region.
Xi Wei, Josette Garnier, Vincent Thieu, Paul Passy, Romain Le Gendre, Gilles Billen, Maia Akopian, and Goulven Gildas Laruelle
Biogeosciences, 19, 931–955, https://doi.org/10.5194/bg-19-931-2022, https://doi.org/10.5194/bg-19-931-2022, 2022
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Estuaries are key reactive ecosystems along the land–ocean aquatic continuum and are often strongly impacted by anthropogenic activities. We calculated nutrient in and out fluxes by using a 1-D transient model for seven estuaries along the French Atlantic coast. Among these, large estuaries with high residence times showed higher retention rates than medium and small ones. All reveal coastal eutrophication due to the excess of diffused nitrogen from intensive agricultural river basins.
Krysten Rutherford, Katja Fennel, Dariia Atamanchuk, Douglas Wallace, and Helmuth Thomas
Biogeosciences, 18, 6271–6286, https://doi.org/10.5194/bg-18-6271-2021, https://doi.org/10.5194/bg-18-6271-2021, 2021
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Using a regional model of the northwestern North Atlantic shelves in combination with a surface water time series and repeat transect observations, we investigate surface CO2 variability on the Scotian Shelf. The study highlights a strong seasonal cycle in shelf-wide pCO2 and spatial variability throughout the summer months driven by physical events. The simulated net flux of CO2 on the Scotian Shelf is out of the ocean, deviating from the global air–sea CO2 flux trend in continental shelves.
Frerk Pöppelmeier, David J. Janssen, Samuel L. Jaccard, and Thomas F. Stocker
Biogeosciences, 18, 5447–5463, https://doi.org/10.5194/bg-18-5447-2021, https://doi.org/10.5194/bg-18-5447-2021, 2021
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Chromium (Cr) is a redox-sensitive element that holds promise as a tracer of ocean oxygenation and biological activity. We here implemented the oxidation states Cr(III) and Cr(VI) in the Bern3D model to investigate the processes that shape the global Cr distribution. We find a Cr ocean residence time of 5–8 kyr and that the benthic source dominates the tracer budget. Further, regional model–data mismatches suggest strong Cr removal in oxygen minimum zones and a spatially variable benthic source.
Felipe S. Freitas, Philip A. Pika, Sabine Kasten, Bo B. Jørgensen, Jens Rassmann, Christophe Rabouille, Shaun Thomas, Henrik Sass, Richard D. Pancost, and Sandra Arndt
Biogeosciences, 18, 4651–4679, https://doi.org/10.5194/bg-18-4651-2021, https://doi.org/10.5194/bg-18-4651-2021, 2021
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It remains challenging to fully understand what controls carbon burial in marine sediments globally. Thus, we use a model–data approach to identify patterns of organic matter reactivity at the seafloor across distinct environmental conditions. Our findings support the notion that organic matter reactivity is a dynamic ecosystem property and strongly influences biogeochemical cycling and exchange. Our results are essential to improve predictions of future changes in carbon cycling and climate.
Josué Bock, Martine Michou, Pierre Nabat, Manabu Abe, Jane P. Mulcahy, Dirk J. L. Olivié, Jörg Schwinger, Parvadha Suntharalingam, Jerry Tjiputra, Marco van Hulten, Michio Watanabe, Andrew Yool, and Roland Séférian
Biogeosciences, 18, 3823–3860, https://doi.org/10.5194/bg-18-3823-2021, https://doi.org/10.5194/bg-18-3823-2021, 2021
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In this study we analyse surface ocean dimethylsulfide (DMS) concentration and flux to the atmosphere from four CMIP6 Earth system models over the historical and ssp585 simulations.
Our analysis of contemporary (1980–2009) climatologies shows that models better reproduce observations in mid to high latitudes. The models disagree on the sign of the trend of the global DMS flux from 1980 onwards. The models agree on a positive trend of DMS over polar latitudes following sea-ice retreat dynamics.
Mariana Hill Cruz, Iris Kriest, Yonss Saranga José, Rainer Kiko, Helena Hauss, and Andreas Oschlies
Biogeosciences, 18, 2891–2916, https://doi.org/10.5194/bg-18-2891-2021, https://doi.org/10.5194/bg-18-2891-2021, 2021
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In this study we use a regional biogeochemical model of the eastern tropical South Pacific Ocean to implicitly simulate the effect that fluctuations in populations of small pelagic fish, such as anchovy and sardine, may have on the biogeochemistry of the northern Humboldt Current System. To do so, we vary the zooplankton mortality in the model, under the assumption that these fishes eat zooplankton. We also evaluate the model for the first time against mesozooplankton observations.
Roman Bezhenar, Kyeong Ok Kim, Vladimir Maderich, Govert de With, and Kyung Tae Jung
Biogeosciences, 18, 2591–2607, https://doi.org/10.5194/bg-18-2591-2021, https://doi.org/10.5194/bg-18-2591-2021, 2021
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A new approach to predicting the accumulation of radionuclides in fish was developed by taking into account heterogeneity of distribution of contamination in the organism and dependence of metabolic process rates on the fish mass. Predicted concentrations of radionuclides in fish agreed well with the laboratory and field measurements. The model with the defined generic parameters could be used in marine environments without local calibration, which is important for emergency decision support.
Emil De Borger, Justin Tiano, Ulrike Braeckman, Adriaan D. Rijnsdorp, and Karline Soetaert
Biogeosciences, 18, 2539–2557, https://doi.org/10.5194/bg-18-2539-2021, https://doi.org/10.5194/bg-18-2539-2021, 2021
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Bottom trawling alters benthic mineralization: the recycling of organic material (OM) to free nutrients. To better understand how this occurs, trawling events were added to a model of seafloor OM recycling. Results show that bottom trawling reduces OM and free nutrients in sediments through direct removal thereof and of fauna which transport OM to deeper sediment layers protected from fishing. Our results support temporospatial trawl restrictions to allow key sediment functions to recover.
Britta Munkes, Ulrike Löptien, and Heiner Dietze
Biogeosciences, 18, 2347–2378, https://doi.org/10.5194/bg-18-2347-2021, https://doi.org/10.5194/bg-18-2347-2021, 2021
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Cyanobacteria blooms can strongly aggravate eutrophication problems of water bodies. Their controls are, however, not comprehensively understood, which impedes effective management and protection plans. Here we review the current understanding of cyanobacteria blooms. Juxtaposition of respective field and laboratory studies with state-of-the-art mathematical models reveals substantial uncertainty associated with nutrient demands, grazing, and death of cyanobacteria.
Jens Terhaar, Olivier Torres, Timothée Bourgeois, and Lester Kwiatkowski
Biogeosciences, 18, 2221–2240, https://doi.org/10.5194/bg-18-2221-2021, https://doi.org/10.5194/bg-18-2221-2021, 2021
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The uptake of carbon, emitted as a result of human activities, results in ocean acidification. We analyse 21st-century projections of acidification in the Arctic Ocean, a region of particular vulnerability, using the latest generation of Earth system models. In this new generation of models there is a large decrease in the uncertainty associated with projections of Arctic Ocean acidification, with freshening playing a greater role in driving acidification than previously simulated.
Tobias R. Vonnahme, Martial Leroy, Silke Thoms, Dick van Oevelen, H. Rodger Harvey, Svein Kristiansen, Rolf Gradinger, Ulrike Dietrich, and Christoph Völker
Biogeosciences, 18, 1719–1747, https://doi.org/10.5194/bg-18-1719-2021, https://doi.org/10.5194/bg-18-1719-2021, 2021
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Diatoms are crucial for Arctic coastal spring blooms, and their growth is controlled by nutrients and light. At the end of the bloom, inorganic nitrogen or silicon can be limiting, but nitrogen can be regenerated by bacteria, extending the algal growth phase. Modeling these multi-nutrient dynamics and the role of bacteria is challenging yet crucial for accurate modeling. We recreated spring bloom dynamics in a cultivation experiment and developed a representative dynamic model.
Rebecca M. Wright, Corinne Le Quéré, Erik Buitenhuis, Sophie Pitois, and Mark J. Gibbons
Biogeosciences, 18, 1291–1320, https://doi.org/10.5194/bg-18-1291-2021, https://doi.org/10.5194/bg-18-1291-2021, 2021
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Jellyfish have been included in a global ocean biogeochemical model for the first time. The global mean jellyfish biomass in the model is within the observational range. Jellyfish are found to play an important role in the plankton ecosystem, influencing community structure, spatiotemporal dynamics and biomass. The model raises questions about the sensitivity of the zooplankton community to jellyfish mortality and the interactions between macrozooplankton and jellyfish.
Valeria Di Biagio, Gianpiero Cossarini, Stefano Salon, and Cosimo Solidoro
Biogeosciences, 17, 5967–5988, https://doi.org/10.5194/bg-17-5967-2020, https://doi.org/10.5194/bg-17-5967-2020, 2020
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Events that influence the functioning of the Earth’s ecosystems are of interest in relation to a changing climate. We propose a method to identify and characterise
wavesof extreme events affecting marine ecosystems for multi-week periods over wide areas. Our method can be applied to suitable ecosystem variables and has been used to describe different kinds of extreme event waves of phytoplankton chlorophyll in the Mediterranean Sea, by analysing the output from a high-resolution model.
Maria Paula da Silva, Lino A. Sander de Carvalho, Evlyn Novo, Daniel S. F. Jorge, and Claudio C. F. Barbosa
Biogeosciences, 17, 5355–5364, https://doi.org/10.5194/bg-17-5355-2020, https://doi.org/10.5194/bg-17-5355-2020, 2020
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In this study, we analyze the seasonal changes in the dissolved organic matter (DOM) quality (based on its optical properties) in four Amazon floodplain lakes. DOM plays a fundamental role in surface water chemistry, controlling metal bioavailability and mobility, and nutrient cycling. The model proposed in our paper highlights the potential to study DOM quality at a wider spatial scale, which may help to better understand the persistence and fate of DOM in the ecosystem.
Zhengchen Zang, Z. George Xue, Kehui Xu, Samuel J. Bentley, Qin Chen, Eurico J. D'Sa, Le Zhang, and Yanda Ou
Biogeosciences, 17, 5043–5055, https://doi.org/10.5194/bg-17-5043-2020, https://doi.org/10.5194/bg-17-5043-2020, 2020
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.
Iris Kriest, Paul Kähler, Wolfgang Koeve, Karin Kvale, Volkmar Sauerland, and Andreas Oschlies
Biogeosciences, 17, 3057–3082, https://doi.org/10.5194/bg-17-3057-2020, https://doi.org/10.5194/bg-17-3057-2020, 2020
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Constants of global biogeochemical ocean models are often tuned
by handto match observations of nutrients or oxygen. We investigate the effect of this tuning by optimising six constants of a global biogeochemical model, simulated in five different offline circulations. Optimal values for three constants adjust to distinct features of the circulation applied and can afterwards be swapped among the circulations, without losing too much of the model's fit to observed quantities.
Laura Haffert, Matthias Haeckel, Henko de Stigter, and Felix Janssen
Biogeosciences, 17, 2767–2789, https://doi.org/10.5194/bg-17-2767-2020, https://doi.org/10.5194/bg-17-2767-2020, 2020
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Deep-sea mining for polymetallic nodules is expected to have severe environmental impacts. Through prognostic modelling, this study aims to provide a holistic assessment of the biogeochemical recovery after a disturbance event. It was found that the recovery strongly depends on the impact type; e.g. complete removal of the surface sediment reduces seafloor nutrient fluxes over centuries.
Fabian A. Gomez, Rik Wanninkhof, Leticia Barbero, Sang-Ki Lee, and Frank J. Hernandez Jr.
Biogeosciences, 17, 1685–1700, https://doi.org/10.5194/bg-17-1685-2020, https://doi.org/10.5194/bg-17-1685-2020, 2020
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We use a numerical model to infer annual changes of surface carbon chemistry in the Gulf of Mexico (GoM). The main seasonality drivers of partial pressure of carbon dioxide and aragonite saturation state from the model are temperature and river runoff. The GoM basin is a carbon sink in winter–spring and carbon source in summer–fall, but uptake prevails near the Mississippi Delta year-round due to high biological production. Our model results show good correspondence with observational studies.
Simon J. Parker
Biogeosciences, 17, 305–315, https://doi.org/10.5194/bg-17-305-2020, https://doi.org/10.5194/bg-17-305-2020, 2020
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Dissolved oxygen (DO) models typically assume constant ecosystem respiration over the course of a single day. Using a data-driven approach, this research examines this assumption in four streams across two (hydro-)geological types (Chalk and Greensand). Despite hydrogeological equivalence in terms of baseflow index for each hydrogeological pairing, model suitability differed within, rather than across, geology types. This corresponded with associated differences in timings of DO minima.
Fabrice Lacroix, Tatiana Ilyina, and Jens Hartmann
Biogeosciences, 17, 55–88, https://doi.org/10.5194/bg-17-55-2020, https://doi.org/10.5194/bg-17-55-2020, 2020
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Contributions of rivers to the oceanic cycling of carbon have been poorly represented in global models until now. Here, we assess the long–term implications of preindustrial riverine loads in the ocean in a novel framework which estimates the loads through a hierarchy of weathering and land–ocean export models. We investigate their impacts for the oceanic biological production and air–sea carbon flux. Finally, we assess the potential incorporation of the framework in an Earth system model.
Patrick A. Rafter, Aaron Bagnell, Dario Marconi, and Timothy DeVries
Biogeosciences, 16, 2617–2633, https://doi.org/10.5194/bg-16-2617-2019, https://doi.org/10.5194/bg-16-2617-2019, 2019
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The N isotopic composition of nitrate (
nitrate δ15N) is a useful tracer of ocean N cycling and many other ocean processes. Here, we use a global compilation of marine nitrate δ15N as an input, training, and validating dataset for an artificial neural network (a.k.a.,
machine learning) and examine basin-scale trends in marine nitrate δ15N from the surface to the seafloor.
Elena Terzić, Paolo Lazzari, Emanuele Organelli, Cosimo Solidoro, Stefano Salon, Fabrizio D'Ortenzio, and Pascal Conan
Biogeosciences, 16, 2527–2542, https://doi.org/10.5194/bg-16-2527-2019, https://doi.org/10.5194/bg-16-2527-2019, 2019
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Measuring ecosystem properties in the ocean is a hard business. Recent availability of data from Biogeochemical-Argo floats can help make this task easier. Numerical models can integrate these new data in a coherent picture and can be used to investigate the functioning of ecosystem processes. Our new approach merges experimental information and model capabilities to quantitatively demonstrate the importance of light and water vertical mixing for algae dynamics in the Mediterranean Sea.
Jens Terhaar, James C. Orr, Marion Gehlen, Christian Ethé, and Laurent Bopp
Biogeosciences, 16, 2343–2367, https://doi.org/10.5194/bg-16-2343-2019, https://doi.org/10.5194/bg-16-2343-2019, 2019
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A budget of anthropogenic carbon in the Arctic Ocean, the main driver of open-ocean acidification, was constructed for the first time using a high-resolution ocean model. The budget reveals that anthropogenic carbon enters the Arctic Ocean mainly by lateral transport; the air–sea flux plays a minor role. Coarser-resolution versions of the same model, typical of earth system models, store less anthropogenic carbon in the Arctic Ocean and thus underestimate ocean acidification in the Arctic Ocean.
Taylor S. Martin, François Primeau, and Karen L. Casciotti
Biogeosciences, 16, 347–367, https://doi.org/10.5194/bg-16-347-2019, https://doi.org/10.5194/bg-16-347-2019, 2019
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Nitrite is a key intermediate in many nitrogen (N) cycling processes in the ocean, particularly in areas with low oxygen that are hotspots for N loss. We have created a 3-D global N cycle model with nitrite as a tracer. Stable isotopes of N are also included in the model and we are able to model the isotope fractionation associated with each N cycling process. Our model accurately represents N concentrations and isotope distributions in the ocean.
Camille Richon, Jean-Claude Dutay, Laurent Bopp, Briac Le Vu, James C. Orr, Samuel Somot, and François Dulac
Biogeosciences, 16, 135–165, https://doi.org/10.5194/bg-16-135-2019, https://doi.org/10.5194/bg-16-135-2019, 2019
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We evaluate the effects of climate change and biogeochemical forcing evolution on the nutrient and plankton cycles of the Mediterranean Sea for the first time. We use a high-resolution coupled physical and biogeochemical model and perform 120-year transient simulations. The results indicate that changes in external nutrient fluxes and climate change may have synergistic or antagonistic effects on nutrient concentrations, depending on the region and the scenario.
Angela M. Kuhn, Katja Fennel, and Ilana Berman-Frank
Biogeosciences, 15, 7379–7401, https://doi.org/10.5194/bg-15-7379-2018, https://doi.org/10.5194/bg-15-7379-2018, 2018
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Recent studies demonstrate that marine N2 fixation can be carried out without light. However, direct measurements of N2 fixation in dark environments are relatively scarce. This study uses a model that represents biogeochemical cycles at a deep-ocean location in the Gulf of Aqaba (Red Sea). Different model versions are used to test assumptions about N2 fixers. Relaxing light limitation for marine N2 fixers improved the similarity between model results and observations of deep nitrate and oxygen.
Prima Anugerahanti, Shovonlal Roy, and Keith Haines
Biogeosciences, 15, 6685–6711, https://doi.org/10.5194/bg-15-6685-2018, https://doi.org/10.5194/bg-15-6685-2018, 2018
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Minor changes in the biogeochemical model equations lead to major dynamical changes. We assessed this structural sensitivity for the MEDUSA biogeochemical model on chlorophyll and nitrogen concentrations at five oceanographic stations over 10 years, using 1-D ensembles generated by combining different process equations. The ensemble performed better than the default model in most of the stations, suggesting that our approach is useful for generating a probabilistic biogeochemical ensemble model.
Audrey Gimenez, Melika Baklouti, Thibaut Wagener, and Thierry Moutin
Biogeosciences, 15, 6573–6589, https://doi.org/10.5194/bg-15-6573-2018, https://doi.org/10.5194/bg-15-6573-2018, 2018
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During the OUTPACE cruise conducted in the oligotrophic to ultra-oligotrophic region of the western tropical South Pacific, two contrasted regions were sampled in terms of N2 fixation rates, primary production rates and nutrient availability. The aim of this work was to investigate the role of N2 fixation in the differences observed between the two contrasted areas by comparing two simulations only differing by the presence or not of N2 fixers using a 1-D biogeochemical–physical coupled model.
Jenny Hieronymus, Kari Eilola, Magnus Hieronymus, H. E. Markus Meier, Sofia Saraiva, and Bengt Karlson
Biogeosciences, 15, 5113–5129, https://doi.org/10.5194/bg-15-5113-2018, https://doi.org/10.5194/bg-15-5113-2018, 2018
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This paper investigates how phytoplankton concentrations in the Baltic Sea co-vary with nutrient concentrations and other key variables on inter-annual timescales in a model integration over the years 1850–2008. The study area is not only affected by climate change; it has also been subjected to greatly increased nutrient loads due to extensive use of agricultural fertilizers. The results indicate the largest inter-annual coherence of phytoplankton with the limiting nutrient.
Cyril Dutheil, Olivier Aumont, Thomas Gorguès, Anne Lorrain, Sophie Bonnet, Martine Rodier, Cécile Dupouy, Takuhei Shiozaki, and Christophe Menkes
Biogeosciences, 15, 4333–4352, https://doi.org/10.5194/bg-15-4333-2018, https://doi.org/10.5194/bg-15-4333-2018, 2018
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N2 fixation is recognized as one of the major sources of nitrogen in the ocean. Thus, N2 fixation sustains a significant part of the primary production (PP) by supplying the most common limiting nutrient for phytoplankton growth. From numerical simulations, the local maximums of Trichodesmium biomass in the Pacific are found around islands, explained by the iron fluxes from island sediments. We assessed that 15 % of the PP may be due to Trichodesmium in the low-nutrient, low-chlorophyll areas.
Akitomo Yamamoto, Ayako Abe-Ouchi, and Yasuhiro Yamanaka
Biogeosciences, 15, 4163–4180, https://doi.org/10.5194/bg-15-4163-2018, https://doi.org/10.5194/bg-15-4163-2018, 2018
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Millennial-scale changes in oceanic CO2 uptake due to global warming are simulated by a GCM and offline biogeochemical model. Sensitivity studies show that decreases in oceanic CO2 uptake are mainly caused by a weaker biological pump and seawater warming. Enhanced CO2 uptake due to weaker equatorial upwelling cancels out reduced CO2 uptake due to weaker AMOC and AABW formation. Thus, circulation change plays only a small direct role in reduction of CO2 uptake due to global warming.
Cited articles
Andersen, K. H., Berge, T., Gonçalves, R. J., Hartvig, M., Heuschele, J., Hylander, S., Jacobsen, N. S., Lindemann, C., Martens, E. A., Neuheimer, A. B., Olsson, K., Palacz, A., Prowe, A., Sainmont, J., Traving, S., Visser, A., Wadhwa, N., and Kiørboe, T.: Characteristic Sizes of Life in the Oceans, from Bacteria to Whales, Annu. Rev. Mar. Sci., 8, 217–241, https://doi.org/10.1146/annurev-marine-122414-034144, 2016. a
Ardyna, M., Gosselin, M., Michel, C., Poulin, M., and Tremblay, J.-É.: Environmental forcing of phytoplankton community structure and function in the Canadian High Arctic: contrasting oligotrophic and eutrophic regions, Mar. Ecol.-Prog. Ser., 442, 37–57, 2011. a
Ardyna, M., Babin, M., Devred, E., Forest, A., Gosselin, M., Raimbault, P., and Tremblay, J.-É.: Shelf-basin gradients shape ecological phytoplankton niches and community composition in the coastal Arctic Ocean (Beaufort Sea), Limnol. Oceanogr., 62, 2113–2132, https://doi.org/10.1002/lno.10554, 2017. a
Arrhenius, S.: Quantitative laws in biological chemistry, Vol. 1915, G. Bell, https://doi.org/10.5962/bhl.title.4661, 1915. a
Arrigo, K. R. and van Dijken, G. L.: Secular trends in Arctic Ocean net primary production, J. Geophys. Res.-Oceans, 116, https://doi.org/10.1029/2011JC007151, 2011. a, b
Arrigo, K. R., van Dijken, G., and Pabi, S.: Impact of a shrinking Arctic ice cover on marine primary production, Geophys. Res. Lett., 35, L19603, https://doi.org/10.1029/2008GL035028, 2008. a, b
Arrigo, K. R., Perovich, D. K., Pickart, R. S., Brown, Z. W., van Dijken, G. L., Lowry, K. E., Mills, M. M., Palmer, M. A., Balch, W. M., Bates, N. R. B., Benitez-Nelson, C. R., Brownlee, E., Frey, K. E., Laney, S. R., Mathis, J., Matsuoka, A., Greg Mitchell, B., Moore, G., Reynolds, R. A., Sosik, H. M., and Swift, J. H.: Phytoplankton blooms beneath the sea ice in the Chukchi Sea, Deep-Sea Res. Pt. II, 105, 1–16, https://doi.org/10.1016/j.dsr2.2014.03.018, 2014. a
Balazy, K., Trudnowska, E., Wichorowski, M., and Błachowiak-Samołyk, K.: Large versus small zooplankton in relation to temperature in the Arctic shelf region, Polar Res., 37, 1427409, https://doi.org/10.1080/17518369.2018.1427409, 2018. a
Berge, J., Renaud, P. E., Darnis, G., Cottier, F., Last, K., Gabrielsen, T. M., Johnsen, G., Seuthe, L., Weslawski, J. M., Leu, E., Moline, M., Nahrgang, J., Søreide, J. E., Øystein Varpe, Lønne, O. J., Daase, M., and Falk-Petersen, S.: In the dark: a review of ecosystem processes during the Arctic polar night, Prog. Oceanogr., 139, 258–271, https://doi.org/10.1016/j.pocean.2015.08.005, 2015. a
Bethke, I., Furevik, T., and Drange, H.: Towards a more saline North Atlantic and a fresher Arctic under global warming, Geophys. Res. Lett., 33, 258–271, https://doi.org/10.1029/2006GL027264, 2006. a
Beusen, A. H. W., Van Beek, L. P. H., Bouwman, A. F., Mogollón, J. M., and Middelburg, J. J.: Coupling global models for hydrology and nutrient loading to simulate nitrogen and phosphorus retention in surface water – description of IMAGE–GNM and analysis of performance, Geosci. Model Dev., 8, 4045–4067, https://doi.org/10.5194/gmd-8-4045-2015, 2015. a
Beusen, A. H. W., Bouwman, A. F., Van Beek, L. P. H., Mogollón, J. M., and Middelburg, J. J.: Global riverine N and P transport to ocean increased during the 20th century despite increased retention along the aquatic continuum, Biogeosciences, 13, 2441–2451, https://doi.org/10.5194/bg-13-2441-2016, 2016. a
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013. a
Carmack, E. and McLaughlin, F.: Towards recognition of physical and geochemical change in Subarctic and Arctic Seas, Prog. Oceanogr., 90, 90–104, https://doi.org/10.1016/j.pocean.2011.02.007, 2011. a
Carmack, E. and Wassmann, P.: Food webs and physical–biological coupling on pan-Arctic shelves: unifying concepts and comprehensive perspectives, Prog. Oceanogr., 71, 446–477, 2006. a
Carmack, E. C., Macdonald, R. W., and Jasper, S.: Phytoplankton productivity on the Canadian Shelf of the Beaufort Sea, Mar. Ecol.-Prog. Ser., 277, 37–50, 2004. a
Castellani, G., Veyssiere, G., Karcher, M., Stroeve, J., Banas, S. N., Bouman, A. H., Brierley, S. A., Connan, S., Cottier, F., Große, F., Hobbs, L., Katlein, C., Light, B., McKee, D., Orkney, A., Proud, R., and Schourup-Kristensen, V.: Shine a light: Under-ice light and its ecological implications in a changing Arctic Ocean, Ambio, 51, 307–317, https://doi.org/10.1007/s13280-021-01662-3, 2022. a
Cermeño, P., Marañón, E., Harbour, D., and Harris, R. P.: Invariant scaling of phytoplankton abundance and cell size in contrasting marine environments, Ecol. Lett., 9, 1210–1215, 2006. a
Chamberlain, E. J., Rokitta, S., Björn, R., D'Angelo, A., Creamean, J., Loose, B., Ulfsbo, A., Fong, A. A., Hoppe, C. J., Droste, E., Nomura, D., Schulz, K., and Bowman, J.: Microbial predictors of net heterotrophic conditions in the pelagic Arctic Ocean, Limnol. Oceanogr., in preparation, 2024. a
Chen, B., Liu, H., Huang, B., and Wang, J.: Temperature effects on the growth rate of marine picoplankton, Mar. Ecol.-Prog. Ser., 505, 37–47, https://doi.org/10.3354/meps10773, 2014. a
Choi, H., Ha, S.-Y., Lee, S., Kim, J.-H., and Shin, K.-H.: Trophic dynamics of zooplankton before and after polar night in the Kongsfjorden (Svalbard): Evidence of trophic position estimated by δ15N analysis of amino acids, Front. Mar. Sci., 7, 489, https://doi.org/10.3389/fmars.2020.00489, 2020. a
Comiso, J. C.: Large decadal decline of the Arctic multiyear ice cover, J. Climate, 25, 1176–1193, 2012. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The community earth system model version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
Danielson, S. L., Eisner, L., Ladd, C., Mordy, C., Sousa, L., and Weingartner, T. J.: A comparison between late summer 2012 and 2013 water masses, macronutrients, and phytoplankton standing crops in the northern Bering and Chukchi Seas, Deep-Sea Res. Pt. II, 135, 7–26, 2017. a
Duerksen, S. W., Thiemann, G. W., Budge, S. M., Poulin, M., Niemi, A., and Michel, C.: Large, omega-3 rich, pelagic diatoms under Arctic sea ice: sources and implications for food webs, PloS one, 9, e114070, https://doi.org/10.1371/journal.pone.0114070, 2014. a
Edwards, K. F., Thomas, M. K., Klausmeier, C. A., and Litchman, E.: Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton, Limnol. Oceanogr., 57, 554–566, https://doi.org/10.4319/lo.2012.57.2.0554, 2012. a, b, c, d
Edwards, M. and Richardson, A. J.: Impact of climate change on marine pelagic phenology and trophic mismatch, Nature, 430, 881–884, 2004. a
Eppley, R. W.: Temperature and phytoplankton growth in the sea, Fish. Bull., 70, 1063–1085, 1972. a
Freyria, N. J., Joli, N., and Lovejoy, C.: A decadal perspective on north water microbial eukaryotes as Arctic Ocean sentinels, Sci. Rep., 11, 1–14, 2021. a
Fujiwara, A., Nishino, S., Matsuno, K., Onodera, J., Kawaguchi, Y., Hirawake, T., Suzuki, K., Inoue, J., and Kikuchi, T.: Changes in phytoplankton community structure during wind-induced fall bloom on the central Chukchi shelf, Polar Biol., 41, 1279–1295, 2018. a
Gentleman, W. C. and Neuheimer, A. B.: Functional responses and ecosystem dynamics: how clearance rates explain the influence of satiation, food-limitation and acclimation, J. Plankton Res., 30, 1215–1231, 2008. a
Graham, C., Oxtoby, L., Wang, S. W., Budge, S. M., and Wooller, M. J.: Sourcing fatty acids to juvenile polar cod (Boreogadus saida) in the Beaufort Sea using compound-specific stable carbon isotope analyses, Polar Biol., 37, 697–705, 2014. a
Grebmeier, J. M., McRoy, C. P., and Feder, H. M.: Pelagic-benthic coupling on the shelf of the northern Bering and Chukchi seas. 1. Food supply source and benthic biomass, Mar. Ecol.-Prog. Ser., 48, 57–67, 1988. a
Grebmeier, J. M., Feder, H. M., and McRoy, C. P.: Pelagic-benthic coupling on the shelf of the northern Bering and Chukchi Seas. 11. Benthic community structure, Mar. Ecol.-Prog. Ser., 51, 253–68, 1989. a
Gregg, W. W. and Casey, N. W.: Sampling biases in MODIS and SeaWiFS ocean chlorophyll data, Remote Sens. Environ., 111, 25–35, 2007. 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
Hansen, B., Bjornsen, P. K., and Hansen, P. J.: The size ratio between planktonic predators and their prey, Limnol. Oceanogr., 39, 395–403, https://doi.org/10.4319/lo.1994.39.2.0395, 1994. a
Hansen, P. J., Bjørnsen, P. K., and Hansen, B. W.: Zooplankton grazing and growth: Scaling within the 2-2,-µm body size range, Limnol. Oceanogr., 42, 687–704, 1997. a
Hayashida, H., Jin, M., Steiner, N. S., Swart, N. C., Watanabe, E., Fiedler, R., Hogg, A. McC., Kiss, A. E., Matear, R. J., and Strutton, P. G.: Ice Algae Model Intercomparison Project phase 2 (IAMIP2), Geosci. Model Dev., 14, 6847–6861, https://doi.org/10.5194/gmd-14-6847-2021, 2021. a
Huete-Ortega, M., Marañón, E., Varela, M., and Bode, A.: General patterns in the size scaling of phytoplankton abundance in coastal waters during a 10-year time series, J. Plankton Res., 32, 1–14, 2010. a
Ikeda, T.: Metabolic rates of epipelagic marine zooplankton as a function of body mass and temperature, Mar. Biol., 85, 1–11, 1985. a
Ilıcak, 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., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E., Fogli, P. G., Yosuke, F., and Yeager, S.: An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part III: Hydrography and fluxes, Ocean Model., 100, 141–161, https://doi.org/10.1016/j.ocemod.2016.02.004, 2016. a
Jennings, S. and Van Der Molen, J.: Trophic levels of marine consumers from nitrogen stable isotope analysis: estimation and uncertainty, ICES J. Mar. Sci., 72, 2289–2300, 2015. a
Kiørboe, T. and Hirst, A. G.: Shifts in mass scaling of respiration, feeding, and growth rates across life-form transitions in marine pelagic organisms, Am. Nat., 183, E118–E130, https://doi.org/10.1086/675241, 2014. a
Kohlbach, D., Schaafsma, F. L., Graeve, M., Lebreton, B., Lange, B. A., David, C., Vortkamp, M., and Flores, H.: Strong linkage of polar cod (Boreogadus saida) to sea ice algae-produced carbon: evidence from stomach content, fatty acid and stable isotope analyses, Prog. Oceanogr., 152, 62–74, 2017. a
Krumhardt, K. M., Lovenduski, N. S., Long, M. C., Luo, J. Y., Lindsay, K., Yeager, S., and Harrison, C.: Potential predictability of net primary production in the ocean, Global Biogeochem. Cy., 34, e2020GB006531, https://doi.org/10.1029/2020GB006531, 2020. a
Lampe, V., Nöthig, E.-M., and Schartau, M.: Spatio-temporal variations in community size structure of Arctic protist plankton in the Fram Strait, Front. Mar. Sci., 7, 579880, https://doi.org/10.3389/fmars.2020.579880, 2021. a
Lannuzel, D., Tedesco, L., Van Leeuwe, M., Campbell, K., Flores, H., Delille, B., Miller, L., Stefels, J., Assmy, P., Bowman, J., Brown, K., Castellani, G., Chierici, M., Crabeck, O., Damm, E., Else, B., Fransson, A., Fripiat, F., Geilfus, N.-X., and Wongpan, P.: The future of Arctic sea-ice biogeochemistry and ice-associated ecosystems, Nat. Clim. Change, 10, 983–992, https://doi.org/10.1038/s41558-020-00940-4, 2020. a
Large, W. G. and Yeager, S. G.: The global climatology of an interannually varying air – Sea flux data set, Clim. Dynam., 33, 341–364, https://doi.org/10.1007/s00382-008-0441-3, 2009. a, b, c, d
Leu, E., Søreide, J. E., Hessen, D. O., Falk-Petersen, S., and Berge, J.: Consequences of changing sea-ice cover for primary and secondary producers in the European Arctic shelf seas: timing, quantity, and quality, Prog. Oceanogr., 90, 18–32, 2011. a
Li, J., Matsuoka, A., Pang, X., Massicotte, P., and Babin, M.: Performance of Algorithms for Retrieving Chlorophyll a Concentrations in the Arctic Ocean: Impact on Primary Production Estimates, Remote Sens., 16, 892, https://doi.org/10.3390/rs16050892, 2024. a
Li, W. K., McLaughlin, F. A., Lovejoy, C., and Carmack, E. C.: Smallest algae thrive as the Arctic ocean freshens, Science, 326, 539, https://doi.org/10.1126/science.1179798, 2009. a
Limoges, A., Weckström, K., Ribeiro, S., Georgiadis, E., Hansen, K. E., Martinez, P., Seidenkrantz, M.-S., Giraudeau, J., Crosta, X., and Massé, G.: Learning from the past: Impact of the Arctic Oscillation on sea ice and marine productivity off northwest Greenland over the last 9,000 years, Glob. Change Biol., 26, 6767–6786, 2020. a
Locarnini, R. A., Mishonov, A. V., Baranova, O. K., Boyer, T. P., Zweng, M. M., Garcia, H. E., Reagan, J. R., Seidov, D., Weathers, K. W., Paver, C. R., and Smolyar, I. V.: World ocean atlas 2018, volume 1: Temperature, Report, NOAA Atlas NESDIS 81, 52 pp., https://data.nodc.noaa.gov/woa/WOA18/DOC/woa18_vol1.pdf (last access: March 2023), 2018. a
Long, M. C., Lindsay, K., and Holland, M. M.: Modeling photosynthesis in sea ice-covered waters, J. Adv. Model. Earth Sy., 7, 1189–1206, 2015. a
Long, M. C., Moore, J. K., Lindsay, K., Levy, M., Doney, S. C., Luo, J. Y., Krumhardt, K. M., Letscher, R. T., Grover, M., and Sylvester, Z. T.: Simulations with the marine biogeochemistry library (MARBL), J. Adv. Model. Earth Sy., 13, e2021MS002647, https://doi.org/10.1029/2021MS002647, 2021. a, b
Lowry, K. E., Pickart, R. S., Mills, M. M., Brown, Z. W., van Dijken, G. L., Bates, N. R., and Arrigo, K. R.: The influence of winter water on phytoplankton blooms in the Chukchi Sea, Deep-Sea Res. Pt. II, 118, 53–72, 2015. a
Mayorga, E., Seitzinger, S. P., Harrison, J. A., Dumont, E., Beusen, A. H., Bouwman, A., Fekete, B. M., Kroeze, C., and Van Drecht, G.: Global nutrient export from WaterSheds 2 (NEWS 2): model development and implementation, Environ. Modell. Softw., 25, 837–853, 2010. a
Michel, C., Hamilton, J., Hansen, E., Barber, D., Reigstad, M., Iacozza, J., Seuthe, L., and Niemi, A.: Arctic Ocean outflow shelves in the changing Arctic: A review and perspectives, Prog. Oceanogr., 139, 66–88, 2015. a
Mikelsons, K. and Wang, M.: Optimal satellite orbit configuration for global ocean color product coverage, Opt. Express, 27, A445–A457, 2019. a
Moore, J. K., Doney, S. C., and Lindsay, K.: Upper ocean ecosystem dynamics and iron cycling in a global three‐dimensional model, Global Biogeochem. Cy., 18, GB4028, https://doi.org/10.1029/2004GB002220, 2004. a
Moore, J. K., Lindsay, K., Doney, S. C., Long, M. C., and Misumi, K.: Marine ecosystem dynamics and biogeochemical cycling in the Community Earth System Model [CESM1 (BGC)]: Comparison of the 1990s with the 2090s under the RCP4.5 and RCP8.5 scenarios, J. Climate, 26, 9291–9312, 2013. a
Moriarty, R. and O'Brien, T. D.: Distribution of mesozooplankton biomass in the global ocean, Earth Syst. Sci. Data, 5, 45–55, https://doi.org/10.5194/essd-5-45-2013, 2013. a
Mundy, C. J., Gosselin, M., Ehn, J., Gratton, Y., Rossnagel, A., Barber, D. G., Martin, J., Tremblay, J.-É., Palmer, M., Arrigo, K. R., Darnis, G., Fortier, L., Else, B., and Papakyriakou, T.: Contribution of under-ice primary production to an ice-edge upwelling phytoplankton bloom in the Canadian Beaufort Sea, Geophys. Res. Lett., 36, L17601, https://doi.org/10.1029/2009GL038837, 2009. a
Mustapha, S. B., Bélanger, S., and Larouche, P.: Evaluation of ocean color algorithms in the southeastern Beaufort Sea, Canadian Arctic: New parameterization using SeaWiFS, MODIS, and MERIS spectral bands, Can. J. Remote Sens., 38, 535–556, 2012. a
Neeley, A. R., Harris, L. A., and Frey, K. E.: Unraveling phytoplankton community dynamics in the northern Chukchi Sea under sea-ice-covered and sea-ice-free conditions, Geophys. Res. Lett., 45, 7663–7671, 2018. a
Negrete García, G.: gabyneg/arctic_analysis: code for arctic analysis (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.14004652, 2024. a
Negrete-García, G., Luo, J. Y., Long, M. C., Lindsay, K., Levy, M., and Barton, A. D.: Plankton energy flows using a global size-structured and trait-based model, Prog. Oceanogr., 209, 102898, https://doi.org/10.1016/j.pocean.2022.102898, 2022a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x
Negrete-García, G., Luo, J. Y., Long, M. C., Lindsay, K., Levy, M., and Barton, A. D.: Data from: Plankton energy flows using a global size-structured and trait-based model, UC San Diego Library Digital Collections [data set], https://doi.org/10.6075/J0NK3F6D, 2022b. a
Nicolaus, M., Katlein, C., Maslanik, J., and Hendricks, S.: Changes in Arctic sea ice result in increasing light transmittance and absorption, Geophys. Res. Lett., 39, L24501, https://doi.org/10.1029/2012GL053738, 2012. a
Overland, J. E. and Wang, M.: When will the summer Arctic be nearly sea ice free?, Geophys. Res. Lett., 40, 2097–2101, 2013. a
Overland, J. E., Wang, M., Walsh, J. E., and Stroeve, J. C.: Future Arctic climate changes: Adaptation and mitigation time scales, Earth's Future, 2, 68–74, 2014. a
Pauly, D. and Christensen, V.: Primary production required to sustain global fisheries, Nature, 374, 255–257, 1995. a
Renaud, P. E., Carroll, M. L., and Ambrose Jr., W. G.: Effects of global warming on Arctic sea-floor communities and its consequences for higher trophic levels, in: Impacts of global warming on polar ecosystems, edited by: Duarte, C. M., Fundación BBVA, Bilbao, ilus., 3 gráf., Bibliografía: 168–175, 2008. a
Rohr, T., Richardson, A. J., Lenton, A., Chamberlain, M. A., and Shadwick, E. H.: Zooplankton grazing is the largest source of uncertainty for marine carbon cycling in CMIP6 models, Commun. Earth Environ., 4, 212, https://doi.org/10.1038/s43247-023-00871-w, 2023. a
Sakshaug, E.: Primary and secondary production in the Arctic Seas, in: The organic carbon cycle in the Arctic Ocean, edited by: Stein, R. and MacDonald, R. W., 57–81, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-18912-8_3, 2004. a
Sakshaug, E. and Slagstad, D.: Light and productivity of phytoplankton in polar marine ecosystems: a physiological view, Polar Res., 10, 69–86, 1991. a
San Martin, E., Harris, R. P., and Irigoien, X.: Latitudinal variation in plankton size spectra in the Atlantic Ocean, Deep-Sea Res. Pt. II, 53, 1560–1572, 2006. a
Schartau, M., Landry, M. R., and Armstrong, R. A.: Density estimation of plankton size spectra: a reanalysis of IronEx II data, J. Plankton Res., 32, 1167–1184, 2010. a
Schmidt, N. M., Mosbacher, J. B., Eitzinger, B., Vesterinen, E. J., and Roslin, T.: High resistance towards herbivore-induced habitat change in a high Arctic arthropod community, Biol. Lett., 14, 20180054, https://doi.org/10.1098/rsbl.2018.0054, 2018. a
Serra-Pompei, C., Ward, B. A., Pinti, J., Visser, A. W., Kiørboe, T., and Andersen, K. H.: Linking plankton size spectra and community composition to carbon export and its efficiency, Global Biogeochem. Cy., 36, e2021GB007275, https://doi.org/10.1029/2021GB007275, 2022. a
Serreze, M. C., Holland, M. M., and Stroeve, J.: Perspectives on the Arctic's shrinking sea-ice cover, Science, 315, 1533–1536, 2007. a
Sherr, E. B., Sherr, B. F., Wheeler, P. A., and Thompson, K.: Temporal and spatial variation in stocks of autotrophic and heterotrophic microbes in the upper water column of the central Arctic Ocean, Deep-Sea Res. Pt. I, 50, 557–571, 2003. a
Sherr, E. B., Sherr, B. F., and Hartz, A. J.: Microzooplankton grazing impact in the Western Arctic Ocean, Deep-Sea Res. Pt. II, 56, 1264–1273, 2009. a
Sherr, E. B., Sherr, B. F., and Ross, C.: Microzooplankton grazing impact in the Bering Sea during spring sea ice conditions, Deep-Sea Res. Pt. II, 94, 57–67, 2013. a
Siegel, D. A., Maritorena, S., Nelson, N. B., Hansell, D. A., and Lorenzi-Kayser, M.: Global distribution and dynamics of colored dissolved and detrital organic materials, J. Geophys. Res.-Oceans, 107, 3228–3249, https://doi.org/10.1029/2001JC000965, 2002. a
Siegel, D. A., Behrenfeld, M. J., Maritorena, S., McClain, C. R., Antoine, D., Bailey, S. W., Bontempi, P. S., Boss, E. S., Dierssen, H. M., Doney, S. C., Eplee, R. E., Evans, R. H., Feldman, G. C., Fields, E., Franz, B. A., Kuring, N. A., Mengelt, C., Nelson, N. B., Patt, F. S., Robinson, W. D., Sarmiento, J. L., Swan, C. M., Werdell, P. J., Westberry, T. K., Wilding, J. G., and Yoder, J. A.: Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission, Remote Sens. Environ., 135, 77–91, 2013. a
Smith, R., Jones, P., Briegleb, B. P., Bryan, F., Danabasoglu, G., Dennis, J., Dukowicz, J., Eden, C., Fox-Kemper, B., and Gent, P.: The parallel ocean program (POP) reference manual ocean component of the community climate system model (CCSM) and community earth system model (CESM), Los Alamos Technical Report No. LAUR-10-01853, 1–140, https://opensky.ucar.edu/islandora/object/manuscripts:825 (last access: January 2018), 2010. a, b
Song, H., Ji, R., Jin, M., Li, Y., Feng, Z., Varpe, Ø., and Davis, C. S.: Strong and regionally distinct links between ice-retreat timing and phytoplankton production in the Arctic Ocean, Limnol. Oceanogr., 66, 2498–2508, 2021. a
Stawiarski, B., Buitenhuis, E. T., and Quéré, C. L.: The physiological response of picophytoplankton to temperature and its model representation, Front. Mar. Sci., 3, 164, https://doi.org/10.3389/fmars.2016.00164, 2016. a
Steinacher, M., Joos, F., Frölicher, T. L., Bopp, L., Cadule, P., Cocco, V., Doney, S. C., Gehlen, M., Lindsay, K., Moore, J. K., Schneider, B., and Segschneider, J.: Projected 21st century decrease in marine productivity: a multi-model analysis, Biogeosciences, 7, 979–1005, https://doi.org/10.5194/bg-7-979-2010, 2010. a
Stock, C. A., Dunne, J. P., and John, J. G.: Drivers of trophic amplification of ocean productivity trends in a changing climate, Biogeosciences, 11, 7125–7135, https://doi.org/10.5194/bg-11-7125-2014, 2014. a
Tai, T. C., Steiner, N. S., Hoover, C., Cheung, W. W., and Sumaila, U. R.: Evaluating present and future potential of Arctic fisheries in Canada, Mar. Policy, 108, 103637, https://doi.org/10.1016/j.marpol.2019.103637, 2019. a
Tamelander, T., Renaud, P. E., Hop, H., Carroll, M. L., Ambrose Jr., W. G., and Hobson, K. A.: Trophic relationships and pelagic–benthic coupling during summer in the Barents Sea Marginal Ice Zone, revealed by stable carbon and nitrogen isotope measurements, Mar. Ecol.-Prog. Ser., 310, 33–46, 2006. a
Tamelander, T., Reigstad, M., Hop, H., Carroll, M. L., and Wassmann, P.: Pelagic and sympagic contribution of organic matter to zooplankton and vertical export in the Barents Sea marginal ice zone, Deep-Sea Res. Pt. II, 55, 2330–2339, 2008. a
remblay, J.-É. and Gagnon, J.: The effects of irradiance and nutrient supply on the productivity of Arctic waters: a perspective on climate change, in: Influence of climate change on the changing Arctic and sub-Arctic conditions, 73–93, Springer, Dordrecht, https://doi.org/10.1007/978-1-4020-9460-6_7, 2009. a, b, c
Von Appen, W.-J., Waite, A. M., Bergmann, M., Bienhold, C., Boebel, O., Bracher, A., Cisewski, B., Hagemann, J., Hoppema, M., Iversen, M. H., Konrad, C., Krumpen, T., Lochthofen, N., Metfies, K., Niehoff, B., Nöthig, E., Purser, A., Salter, I., Schaber, M., Scholz, D., Soltwedel, T., Torres-Valdes, S., Wekerle, C., Wenzhöfer, F., Wietz, M., and Boetius, A.: Sea-ice derived meltwater stratification slows the biological carbon pump: results from continuous observations, Nat. Commun., 12, 7309, https://doi.org/10.1038/s41467-021-26943-z, 2021. a
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., George Nurser, A., 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, 2016a. a, b
Wang, Q., Ilicak, M., Gerdes, R., Drange, H., Aksenov, Y., Bailey, D. A., Bentsen, M., Biastoch, A., Bozec, A., Böning, C., Caassou, 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. 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, 2016b. a, b
Ward, B. A., Dutkiewicz, S., Jahn, O., and Follows, M. J.: A size-structured food-web model for the global ocean, Limnol. Oceanogr., 57, 1877–1891, https://doi.org/10.4319/lo.2012.57.6.1877, 2012. a
Ward, B. A., Dutkiewicz, S., and Follows, M. J.: Modelling spatial and temporal patterns in size-structured marine plankton communities: top–down and bottom–up controls, J. Plankton Res., 36, 31–47, 2014. a
Wietz, M., Bienhold, C., Metfies, K., Torres-Valdés, S., von Appen, W.-J., Salter, I., and Boetius, A.: The polar night shift: seasonal dynamics and drivers of Arctic Ocean microbiomes revealed by autonomous sampling, ISME Communications, 1, 76, https://doi.org/10.1038/s43705-021-00074-4, 2021. a
Yang, E. J., Ha, H. K., and Kang, S.-H.: Microzooplankton community structure and grazing impact on major phytoplankton in the Chukchi sea and the western Canada basin, Arctic ocean, Deep-Sea Res. Pt. II, 120, 91–102, 2015. a
Zhuang, Y., Jin, H., Cai, W.-J., Li, H., Jin, M., Qi, D., and Chen, J.: Freshening leads to a three-decade trend of declining nutrients in the western Arctic Ocean, Environ. Res. Lett., 16, 054047, https://doi.org/10.1088/1748-9326/abf58b, 2021. a
Zhulay, I., Iken, K., Renaud, P. E., Kosobokova, K., and Bluhm, B. A.: Reduced efficiency of pelagic–benthic coupling in the Arctic deep sea during lower ice cover, Sci. Rep., 13, 6739, https://doi.org/10.1038/s41598-023-33854-0, 2023. a
Zweng, M. M., Seidov, D., Boyer, T. P., Locarnini, R. A., Garcia, H. E., Mishonov, A. V., Baranova, O. K., Weathers, K. W., Paver, C. R., Smolyar, I., Reagan, J., Seidov, D., and Zweng, M. M.: World ocean atlas 2018, volume 2: Salinity, Report, NOAA Atlas NESDIS 82, 50 pp., http://www.nodc.noaa.gov/OC5/indprod.html (last access: March 2023), 2019. a
Short summary
The Arctic Ocean experiences significant seasonal and year-to-year changes, impacting marine plankton populations. Using a plankton community model, we studied these effects on plankton communities and their influence on fish production. Our findings revealed earlier plankton blooms, shifts towards more carnivorous zooplankton, and increased fishery potential during summertime, especially in warmer years with less ice, highlighting the delicate balance of Arctic ecosystems.
The Arctic Ocean experiences significant seasonal and year-to-year changes, impacting marine...
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