Articles | Volume 18, issue 2
https://doi.org/10.5194/bg-18-509-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/bg-18-509-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jan 2021
Research article |
| 21 Jan 2021
| 21 Jan 2021
Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design
David Ford
CORRESPONDING AUTHOR
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Related authors
Marion Mittermaier, Rachel North, Jan Maksymczuk, Christine Pequignet, and David Ford
Ocean Sci., 17, 1527–1543, https://doi.org/10.5194/os-17-1527-2021, https://doi.org/10.5194/os-17-1527-2021, 2021
Short summary
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Regions of enhanced chlorophyll-a concentrations can be identified by applying a threshold to the concentration value to a forecast and observed field (or analysis). These regions can then be treated and analysed as features using diagnostic techniques to consider of the evolution of the chlorophyll-a blooms in space and time. This allows us to understand whether the biogeochemistry in the model has any skill in predicting these blooms, their location, intensity, onset, duration and demise.
David Andrew Ford
Ocean Sci., 16, 875–893, https://doi.org/10.5194/os-16-875-2020, https://doi.org/10.5194/os-16-875-2020, 2020
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Satellite observations of the ocean were combined with a numerical model to create simulations of the ocean state between 1998 and 2010. Relationships between physical and biogeochemical quantities were assessed to investigate whether observations of different variables are consistent in their representation of the Earth system. Good consistency was found. The results also highlighted ways in which the model could be improved and the respective impacts of using different observations.
David A. Ford, Johan van der Molen, Kieran Hyder, John Bacon, Rosa Barciela, Veronique Creach, Robert McEwan, Piet Ruardij, and Rodney Forster
Biogeosciences, 14, 1419–1444, https://doi.org/10.5194/bg-14-1419-2017, https://doi.org/10.5194/bg-14-1419-2017, 2017
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This study presents a novel set of in situ observations of phytoplankton community structure for the North Sea. These observations were used to validate two physical–biogeochemical ocean model simulations, each of which used different variants of the widely used European Regional Seas Ecosystem Model (ERSEM). The results suggest the ability of the models to reproduce the observed phytoplankton community structure was dependent on the details of the biogeochemical model parameterizations used.
Marion Mittermaier, Rachel North, Jan Maksymczuk, Christine Pequignet, and David Ford
Ocean Sci., 17, 1527–1543, https://doi.org/10.5194/os-17-1527-2021, https://doi.org/10.5194/os-17-1527-2021, 2021
Short summary
Short summary
Regions of enhanced chlorophyll-a concentrations can be identified by applying a threshold to the concentration value to a forecast and observed field (or analysis). These regions can then be treated and analysed as features using diagnostic techniques to consider of the evolution of the chlorophyll-a blooms in space and time. This allows us to understand whether the biogeochemistry in the model has any skill in predicting these blooms, their location, intensity, onset, duration and demise.
David Andrew Ford
Ocean Sci., 16, 875–893, https://doi.org/10.5194/os-16-875-2020, https://doi.org/10.5194/os-16-875-2020, 2020
Short summary
Short summary
Satellite observations of the ocean were combined with a numerical model to create simulations of the ocean state between 1998 and 2010. Relationships between physical and biogeochemical quantities were assessed to investigate whether observations of different variables are consistent in their representation of the Earth system. Good consistency was found. The results also highlighted ways in which the model could be improved and the respective impacts of using different observations.
David A. Ford, Johan van der Molen, Kieran Hyder, John Bacon, Rosa Barciela, Veronique Creach, Robert McEwan, Piet Ruardij, and Rodney Forster
Biogeosciences, 14, 1419–1444, https://doi.org/10.5194/bg-14-1419-2017, https://doi.org/10.5194/bg-14-1419-2017, 2017
Short summary
Short summary
This study presents a novel set of in situ observations of phytoplankton community structure for the North Sea. These observations were used to validate two physical–biogeochemical ocean model simulations, each of which used different variants of the widely used European Regional Seas Ecosystem Model (ERSEM). The results suggest the ability of the models to reproduce the observed phytoplankton community structure was dependent on the details of the biogeochemical model parameterizations used.
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Anna Belcher, Sian F. Henley, Katharine Hendry, Marianne Wootton, Lisa Friberg, Ursula Dallman, Tong Wang, Christopher Coath, and Clara Manno
Biogeosciences, 20, 3573–3591, https://doi.org/10.5194/bg-20-3573-2023, https://doi.org/10.5194/bg-20-3573-2023, 2023
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The oceans play a crucial role in the uptake of atmospheric carbon dioxide, particularly the Southern Ocean. The biological pumping of carbon from the surface to the deep ocean is key to this. Using sediment trap samples from the Scotia Sea, we examine biogeochemical fluxes of carbon, nitrogen, and biogenic silica and their stable isotope compositions. We find phytoplankton community structure and physically mediated processes are important controls on particulate fluxes to the deep ocean.
Asmita Singh, Susanne Fietz, Sandy J. Thomalla, Nicolas Sanchez, Murat V. Ardelan, Sébastien Moreau, Hanna M. Kauko, Agneta Fransson, Melissa Chierici, Saumik Samanta, Thato N. Mtshali, Alakendra N. Roychoudhury, and Thomas J. Ryan-Keogh
Biogeosciences, 20, 3073–3091, https://doi.org/10.5194/bg-20-3073-2023, https://doi.org/10.5194/bg-20-3073-2023, 2023
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Despite the scarcity of iron in the Southern Ocean, seasonal blooms occur due to changes in nutrient and light availability. Surprisingly, during an autumn bloom in the Antarctic sea-ice zone, the results from incubation experiments showed no significant photophysiological response of phytoplankton to iron addition. This suggests that ambient iron concentrations were sufficient, challenging the notion of iron deficiency in the Southern Ocean through extended iron-replete post-bloom conditions.
Benoît Pasquier, Mark Holzer, Matthew A. Chamberlain, Richard J. Matear, Nathaniel L. Bindoff, and François W. Primeau
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Modeling the ocean's carbon and oxygen cycles accurately is challenging. Parameter optimization improves the fit to observed tracers but can introduce artifacts in the biological pump. Organic-matter production and subsurface remineralization rates adjust to compensate for circulation biases, changing the pathways and timescales with which nutrients return to the surface. Circulation biases can thus strongly alter the system’s response to ecological change, even when parameters are optimized.
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Biogeosciences, 20, 2645–2669, https://doi.org/10.5194/bg-20-2645-2023, https://doi.org/10.5194/bg-20-2645-2023, 2023
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Biogeosciences, 20, 2499–2523, https://doi.org/10.5194/bg-20-2499-2023, https://doi.org/10.5194/bg-20-2499-2023, 2023
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Biogeosciences, 20, 1725–1739, https://doi.org/10.5194/bg-20-1725-2023, https://doi.org/10.5194/bg-20-1725-2023, 2023
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Katherine E. Turner, Doug M. Smith, Anna Katavouta, and Richard G. Williams
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We present a new method for reconstructing ocean carbon using climate models and temperature and salinity observations. To test this method, we reconstruct modelled carbon using synthetic observations consistent with current sampling programmes. Sensitivity tests show skill in reconstructing carbon trends and variability within the upper 2000 m. Our results indicate that this method can be used for a new global estimate for ocean carbon content.
Alexandre Mignot, Hervé Claustre, Gianpiero Cossarini, Fabrizio D'Ortenzio, Elodie Gutknecht, Julien Lamouroux, Paolo Lazzari, Coralie Perruche, Stefano Salon, Raphaëlle Sauzède, Vincent Taillandier, and Anna Teruzzi
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Numerical models of ocean biogeochemistry are becoming a major tool to detect and predict the impact of climate change on marine resources and monitor ocean health. Here, we demonstrate the use of the global array of BGC-Argo floats for the assessment of biogeochemical models. We first detail the handling of the BGC-Argo data set for model assessment purposes. We then present 23 assessment metrics to quantify the consistency of BGC model simulations with respect to BGC-Argo data.
Alban Planchat, Lester Kwiatkowski, Laurent Bopp, Olivier Torres, James R. Christian, Momme Butenschön, Tomas Lovato, Roland Séférian, Matthew A. Chamberlain, Olivier Aumont, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, Tatiana Ilyina, Hiroyuki Tsujino, Kristen M. Krumhardt, Jörg Schwinger, Jerry Tjiputra, John P. Dunne, and Charles Stock
Biogeosciences, 20, 1195–1257, https://doi.org/10.5194/bg-20-1195-2023, https://doi.org/10.5194/bg-20-1195-2023, 2023
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Ocean alkalinity is critical to the uptake of atmospheric carbon and acidification in surface waters. We review the representation of alkalinity and the associated calcium carbonate cycle in Earth system models. While many parameterizations remain present in the latest generation of models, there is a general improvement in the simulated alkalinity distribution. This improvement is related to an increase in the export of biotic calcium carbonate, which closer resembles observations.
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.
Chloé Baumas, Robin Fuchs, Marc Garel, Jean-Christophe Poggiale, Laurent Memery, Frédéric A. C. Le Moigne, and Christian Tamburini
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-34, https://doi.org/10.5194/bg-2023-34, 2023
Revised manuscript accepted for BG
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Through the sink of particles in the ocean, carbon (C) is exported and sequestered when reaching 1000 m. Attempts to quantify the C exported versus consumed by heterotrophs have increased. Yet, most of the conducted estimations have led to C demands several times higher than C export. The choice of parameters greatly impact the results. As theses parameters are overlooked, non-accurate values are often used. In this study we show that C budgets can be well balanced when using appropriate values.
Alastair J. M. Lough, Alessandro Tagliabue, Clément Demasy, Joseph A. Resing, Travis Mellett, Neil J. Wyatt, and Maeve C. Lohan
Biogeosciences, 20, 405–420, https://doi.org/10.5194/bg-20-405-2023, https://doi.org/10.5194/bg-20-405-2023, 2023
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Iron is a key nutrient for ocean primary productivity. Hydrothermal vents are a source of iron to the oceans, but the size of this source is poorly understood. This study examines the variability in iron inputs between hydrothermal vents in different geological settings. The vents studied release different amounts of Fe, resulting in plumes with similar dissolved iron concentrations but different particulate concentrations. This will help to refine modelling of iron-limited ocean productivity.
Nicole M. Travis, Colette L. Kelly, Margaret R. Mulholland, and Karen L. Casciotti
Biogeosciences, 20, 325–347, https://doi.org/10.5194/bg-20-325-2023, https://doi.org/10.5194/bg-20-325-2023, 2023
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The primary nitrite maximum is a ubiquitous upper ocean feature where nitrite accumulates, but we still do not understand its formation and the co-occurring microbial processes involved. Using correlative methods and rates measurements, we found strong spatial patterns between environmental conditions and depths of the nitrite maxima, but not the maximum concentrations. Nitrification was the dominant source of nitrite, with occasional high nitrite production from phytoplankton near the coast.
Daniela König and Alessandro Tagliabue
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-241, https://doi.org/10.5194/bg-2022-241, 2023
Revised manuscript accepted for BG
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Using model simulations, we show that natural and anthropogenic changes in the global climate leave a distinct fingerprint in the isotopic signatures of iron in the surface ocean. We find that these climate effects on iron isotopes are often caused by the redistribution of iron from different external sources to the ocean, due to changes in ocean currents, and by changes in algal growth, which take up iron. Thus, isotopes may help detect climate-induced changes in iron supply and algal uptake.
Natacha Le Grix, Jakob Zscheischler, Keith B. Rodgers, Ryohei Yamaguchi, and Thomas L. Frölicher
Biogeosciences, 19, 5807–5835, https://doi.org/10.5194/bg-19-5807-2022, https://doi.org/10.5194/bg-19-5807-2022, 2022
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Compound events threaten marine ecosystems. Here, we investigate the potentially harmful combination of marine heatwaves with low phytoplankton productivity. Using satellite-based observations, we show that these compound events are frequent in the low latitudes. We then investigate the drivers of these compound events using Earth system models. The models share similar drivers in the low latitudes but disagree in the high latitudes due to divergent factors limiting phytoplankton production.
Abigale M. Wyatt, Laure Resplandy, and Adrian Marchetti
Biogeosciences, 19, 5689–5705, https://doi.org/10.5194/bg-19-5689-2022, https://doi.org/10.5194/bg-19-5689-2022, 2022
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Marine heat waves (MHWs) are a frequent event in the northeast Pacific, with a large impact on the region's ecosystems. Large phytoplankton in the North Pacific Transition Zone are greatly affected by decreased nutrients, with less of an impact in the Alaskan Gyre. For small phytoplankton, MHWs increase the spring small phytoplankton population in both regions thanks to reduced light limitation. In both zones, this results in a significant decrease in the ratio of large to small phytoplankton.
Margot C. F. Debyser, Laetitia Pichevin, Robyn E. Tuerena, Paul A. Dodd, Antonia Doncila, and Raja S. Ganeshram
Biogeosciences, 19, 5499–5520, https://doi.org/10.5194/bg-19-5499-2022, https://doi.org/10.5194/bg-19-5499-2022, 2022
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We focus on the exchange of key nutrients for algae production between the Arctic and Atlantic oceans through the Fram Strait. We show that the export of dissolved silicon here is controlled by the availability of nitrate which is influenced by denitrification on Arctic shelves. We suggest that any future changes in the river inputs of silica and changes in denitrification due to climate change will impact the amount of silicon exported, with impacts on Atlantic algal productivity and ecology.
Emily J. Zakem, Barbara Bayer, Wei Qin, Alyson E. Santoro, Yao Zhang, and Naomi M. Levine
Biogeosciences, 19, 5401–5418, https://doi.org/10.5194/bg-19-5401-2022, https://doi.org/10.5194/bg-19-5401-2022, 2022
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We use a microbial ecosystem model to quantitatively explain the mechanisms controlling observed relative abundances and nitrification rates of ammonia- and nitrite-oxidizing microorganisms in the ocean. We also estimate how much global carbon fixation can be associated with chemoautotrophic nitrification. Our results improve our understanding of the controls on nitrification, laying the groundwork for more accurate predictions in global climate models.
Zuozhu Wen, Thomas J. Browning, Rongbo Dai, Wenwei Wu, Weiying Li, Xiaohua Hu, Wenfang Lin, Lifang Wang, Xin Liu, Zhimian Cao, Haizheng Hong, and Dalin Shi
Biogeosciences, 19, 5237–5250, https://doi.org/10.5194/bg-19-5237-2022, https://doi.org/10.5194/bg-19-5237-2022, 2022
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Fe and P are key factors controlling the biogeography and activity of marine N2-fixing microorganisms. We found lower abundance and activity of N2 fixers in the northern South China Sea than around the western boundary of the North Pacific, and N2 fixation rates switched from Fe–P co-limitation to P limitation. We hypothesize the Fe supply rates and Fe utilization strategies of each N2 fixer are important in regulating spatial variability in community structure across the study area.
Caroline Ulses, Claude Estournel, Patrick Marsaleix, Karline Soetaert, Marine Fourrier, Laurent Coppola, Dominique Lefèvre, Franck Touratier, Catherine Goyet, Véronique Guglielmi, Fayçal Kessouri, Pierre Testor, and Xavier Durrieu de Madron
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-219, https://doi.org/10.5194/bg-2022-219, 2022
Revised manuscript accepted for BG
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Deep convection plays a key role in the circulation, thermodynamics and biogeochemical cycles in the Mediterranean Sea, considered as a hotspot of biodiversity and climate change. In this study, we investigate the seasonal cycle and annual budget of dissolved inorganic carbon in the deep convection area of the northwestern Mediterranean Sea.
Claudia Eisenring, Sophy E. Oliver, Samar Khatiwala, and Gregory F. de Souza
Biogeosciences, 19, 5079–5106, https://doi.org/10.5194/bg-19-5079-2022, https://doi.org/10.5194/bg-19-5079-2022, 2022
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Given the sparsity of observational constraints on micronutrients such as zinc (Zn), we assess the sensitivities of a framework for objective parameter optimisation in an oceanic Zn cycling model. Our ensemble of optimisations towards synthetic data with varying kinds of uncertainty shows that deficiencies related to model complexity and the choice of the misfit function generally have a greater impact on the retrieval of model Zn uptake behaviour than does the limitation of data coverage.
Yoshikazu Sasai, Sherwood Lan Smith, Eko Siswanto, Hideharu Sasaki, and Masami Nonaka
Biogeosciences, 19, 4865–4882, https://doi.org/10.5194/bg-19-4865-2022, https://doi.org/10.5194/bg-19-4865-2022, 2022
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We have investigated the adaptive response of phytoplankton growth to changing light, nutrients, and temperature over the North Pacific using two physical-biological models. We compare modeled chlorophyll and primary production from an inflexible control model (InFlexPFT), which assumes fixed carbon (C):nitrogen (N):chlorophyll (Chl) ratios, to a recently developed flexible phytoplankton functional type model (FlexPFT), which incorporates photoacclimation and variable C:N:Chl ratios.
Jens Terhaar, Thomas L. Frölicher, and Fortunat Joos
Biogeosciences, 19, 4431–4457, https://doi.org/10.5194/bg-19-4431-2022, https://doi.org/10.5194/bg-19-4431-2022, 2022
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Estimates of the ocean sink of anthropogenic carbon vary across various approaches. We show that the global ocean carbon sink can be estimated by three parameters, two of which approximate the ocean ventilation in the Southern Ocean and the North Atlantic, and one of which approximates the chemical capacity of the ocean to take up carbon. With observations of these parameters, we estimate that the global ocean carbon sink is 10 % larger than previously assumed, and we cut uncertainties in half.
Natasha René van Horsten, Hélène Planquette, Géraldine Sarthou, Thomas James Ryan-Keogh, Nolwenn Lemaitre, Thato Nicholas Mtshali, Alakendra Roychoudhury, and Eva Bucciarelli
Biogeosciences, 19, 3209–3224, https://doi.org/10.5194/bg-19-3209-2022, https://doi.org/10.5194/bg-19-3209-2022, 2022
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The remineralisation proxy, barite, was measured along 30°E in the southern Indian Ocean during early austral winter. To our knowledge this is the first reported Southern Ocean winter study. Concentrations throughout the water column were comparable to observations during spring to autumn. By linking satellite primary production to this proxy a possible annual timescale is proposed. These findings also suggest possible carbon remineralisation from satellite data on a basin scale.
Zhibo Shao and Ya-Wei Luo
Biogeosciences, 19, 2939–2952, https://doi.org/10.5194/bg-19-2939-2022, https://doi.org/10.5194/bg-19-2939-2022, 2022
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Non-cyanobacterial diazotrophs (NCDs) may be an important player in fixing N2 in the ocean. By conducting meta-analyses, we found that a representative marine NCD phylotype, Gamma A, tends to inhabit ocean environments with high productivity, low iron concentration and high light intensity. It also appears to be more abundant inside cyclonic eddies. Our study suggests a niche differentiation of NCDs from cyanobacterial diazotrophs as the latter prefers low-productivity and high-iron oceans.
Coraline Leseurre, Claire Lo Monaco, Gilles Reverdin, Nicolas Metzl, Jonathan Fin, Claude Mignon, and Léa Benito
Biogeosciences, 19, 2599–2625, https://doi.org/10.5194/bg-19-2599-2022, https://doi.org/10.5194/bg-19-2599-2022, 2022
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Decadal trends of fugacity of CO2 (fCO2), total alkalinity (AT), total carbon (CT) and pH in surface waters are investigated in different domains of the southern Indian Ocean (45°S–57°S) from ongoing and station observations regularly conducted in summer over the period 1998–2019. The fCO2 increase and pH decrease are mainly driven by anthropogenic CO2 estimated just below the summer mixed layer, as well as by a warming south of the polar front or in the fertilized waters near Kerguelen Island.
Priscilla Le Mézo, Jérôme Guiet, Kim Scherrer, Daniele Bianchi, and Eric Galbraith
Biogeosciences, 19, 2537–2555, https://doi.org/10.5194/bg-19-2537-2022, https://doi.org/10.5194/bg-19-2537-2022, 2022
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This study quantifies the role of commercially targeted fish biomass in the cycling of three important nutrients (N, P, and Fe), relative to nutrients otherwise available in water and to nutrients required by primary producers, and the impact of fishing. We use a model of commercially targeted fish biomass constrained by fish catch and stock assessment data to assess the contributions of fish at the global scale, at the time of the global peak catch and prior to industrial fishing.
Rebecca Chmiel, Nathan Lanning, Allison Laubach, Jong-Mi Lee, Jessica Fitzsimmons, Mariko Hatta, William Jenkins, Phoebe Lam, Matthew McIlvin, Alessandro Tagliabue, and Mak Saito
Biogeosciences, 19, 2365–2395, https://doi.org/10.5194/bg-19-2365-2022, https://doi.org/10.5194/bg-19-2365-2022, 2022
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Dissolved cobalt is present in trace amounts in seawater and is a necessary nutrient for marine microbes. On a transect from the Alaskan coast to Tahiti, we measured seawater concentrations of dissolved cobalt. Here, we describe several interesting features of the Pacific cobalt cycle including cobalt sources along the Alaskan coast and Hawaiian vents, deep-ocean particle formation, cobalt activity in low-oxygen regions, and how our samples compare to a global biogeochemical model’s predictions.
Nicolas Metzl, Claire Lo Monaco, Coraline Leseurre, Céline Ridame, Jonathan Fin, Claude Mignon, Marion Gehlen, and Thi Tuyet Trang Chau
Biogeosciences, 19, 1451–1468, https://doi.org/10.5194/bg-19-1451-2022, https://doi.org/10.5194/bg-19-1451-2022, 2022
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During an oceanographic cruise conducted in January 2020 in the south-western Indian Ocean, we observed very low CO2 concentrations associated with a strong phytoplankton bloom that occurred south-east of Madagascar. This biological event led to a strong regional CO2 ocean sink not previously observed.
Darren R. Clark, Andrew P. Rees, Charissa M. Ferrera, Lisa Al-Moosawi, Paul J. Somerfield, Carolyn Harris, Graham D. Quartly, Stephen Goult, Glen Tarran, and Gennadi Lessin
Biogeosciences, 19, 1355–1376, https://doi.org/10.5194/bg-19-1355-2022, https://doi.org/10.5194/bg-19-1355-2022, 2022
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Measurements of microbial processes were made in the sunlit open ocean during a research cruise (AMT19) between the UK and Chile. These help us to understand how microbial communities maintain the function of remote ecosystems. We find that the nitrogen cycling microbes which produce nitrite respond to changes in the environment. Our insights will aid the development of models that aim to replicate and ultimately project how marine environments may respond to ongoing climate change.
Martí Galí, Marcus Falls, Hervé Claustre, Olivier Aumont, and Raffaele Bernardello
Biogeosciences, 19, 1245–1275, https://doi.org/10.5194/bg-19-1245-2022, https://doi.org/10.5194/bg-19-1245-2022, 2022
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Part of the organic matter produced by plankton in the upper ocean is exported to the deep ocean. This process, known as the biological carbon pump, is key for the regulation of atmospheric carbon dioxide and global climate. However, the dynamics of organic particles below the upper ocean layer are not well understood. Here we compared the measurements acquired by autonomous robots in the top 1000 m of the ocean to a numerical model, which can help improve future climate projections.
Marie Barbieux, Julia Uitz, Alexandre Mignot, Collin Roesler, Hervé Claustre, Bernard Gentili, Vincent Taillandier, Fabrizio D'Ortenzio, Hubert Loisel, Antoine Poteau, Edouard Leymarie, Christophe Penkerc'h, Catherine Schmechtig, and Annick Bricaud
Biogeosciences, 19, 1165–1194, https://doi.org/10.5194/bg-19-1165-2022, https://doi.org/10.5194/bg-19-1165-2022, 2022
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This study assesses marine biological production in two Mediterranean systems representative of vast desert-like (oligotrophic) areas encountered in the global ocean. We use a novel approach based on non-intrusive high-frequency in situ measurements by two profiling robots, the BioGeoChemical-Argo (BGC-Argo) floats. Our results indicate substantial yet variable production rates and contribution to the whole water column of the subsurface layer, typically considered steady and non-productive.
Filippa Fransner, Friederike Fröb, Jerry Tjiputra, Nadine Goris, Siv K. Lauvset, Ingunn Skjelvan, Emil Jeansson, Abdirahman Omar, Melissa Chierici, Elizabeth Jones, Agneta Fransson, Sólveig R. Ólafsdóttir, Truls Johannessen, and Are Olsen
Biogeosciences, 19, 979–1012, https://doi.org/10.5194/bg-19-979-2022, https://doi.org/10.5194/bg-19-979-2022, 2022
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Ocean acidification, a direct consequence of the CO2 release by human activities, is a serious threat to marine ecosystems. In this study, we conduct a detailed investigation of the acidification of the Nordic Seas, from 1850 to 2100, by using a large set of samples taken during research cruises together with numerical model simulations. We estimate the effects of changes in different environmental factors on the rate of acidification and its potential effects on cold-water corals.
Guorong Zhong, Xuegang Li, Jinming Song, Baoxiao Qu, Fan Wang, Yanjun Wang, Bin Zhang, Xiaoxia Sun, Wuchang Zhang, Zhenyan Wang, Jun Ma, Huamao Yuan, and Liqin Duan
Biogeosciences, 19, 845–859, https://doi.org/10.5194/bg-19-845-2022, https://doi.org/10.5194/bg-19-845-2022, 2022
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A predictor selection algorithm was constructed to decrease the predicting error in the surface ocean partial pressure of CO2 (pCO2) mapping by finding better combinations of pCO2 predictors in different regions. Compared with previous research using the same combination of predictors in all regions, using different predictors selected by the algorithm in different regions can effectively decrease pCO2 predicting errors.
Shantelle Smith, Katye E. Altieri, Mhlangabezi Mdutyana, David R. Walker, Ruan G. Parrott, Sedick Gallie, Kurt A. M. Spence, Jessica M. Burger, and Sarah E. Fawcett
Biogeosciences, 19, 715–741, https://doi.org/10.5194/bg-19-715-2022, https://doi.org/10.5194/bg-19-715-2022, 2022
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Ammonium is a crucial yet poorly understood component of the Southern Ocean nitrogen cycle. We attribute our finding of consistently high ammonium concentrations in the winter mixed layer to limited ammonium consumption and sustained ammonium production, conditions under which the Southern Ocean becomes a source of carbon dioxide to the atmosphere. From similar data collected over an annual cycle, we propose a seasonal cycle for ammonium in shallow polar waters – a first for the Southern Ocean.
Jannes Koelling, Dariia Atamanchuk, Johannes Karstensen, Patricia Handmann, and Douglas W. R. Wallace
Biogeosciences, 19, 437–454, https://doi.org/10.5194/bg-19-437-2022, https://doi.org/10.5194/bg-19-437-2022, 2022
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In this study, we investigate oxygen variability in the deep western boundary current in the Labrador Sea from multiyear moored records. We estimate that about half of the oxygen taken up in the interior Labrador Sea by air–sea gas exchange during deep water formation is exported southward the same year. Our results underline the complexity of the oxygen uptake and export in the Labrador Sea and highlight the important role this region plays in supplying oxygen to the deep ocean.
Céline Ridame, Julie Dinasquet, Søren Hallstrøm, Estelle Bigeard, Lasse Riemann, France Van Wambeke, Matthieu Bressac, Elvira Pulido-Villena, Vincent Taillandier, Fréderic Gazeau, Antonio Tovar-Sanchez, Anne-Claire Baudoux, and Cécile Guieu
Biogeosciences, 19, 415–435, https://doi.org/10.5194/bg-19-415-2022, https://doi.org/10.5194/bg-19-415-2022, 2022
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We show that in the Mediterranean Sea spatial variability in N2 fixation is related to the diazotrophic community composition reflecting different nutrient requirements among species. Nutrient supply by Saharan dust is of great importance to diazotrophs, as shown by the strong stimulation of N2 fixation after a simulated dust event under present and future climate conditions; the magnitude of stimulation depends on the degree of limitation related to the diazotrophic community composition.
Matthew P. Humphreys, Erik H. Meesters, Henk de Haas, Szabina Karancz, Louise Delaigue, Karel Bakker, Gerard Duineveld, Siham de Goeyse, Andreas F. Haas, Furu Mienis, Sharyn Ossebaar, and Fleur C. van Duyl
Biogeosciences, 19, 347–358, https://doi.org/10.5194/bg-19-347-2022, https://doi.org/10.5194/bg-19-347-2022, 2022
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A series of submarine sinkholes were recently discovered on Luymes Bank, part of Saba Bank, a carbonate platform in the Caribbean Netherlands. Here, we investigate the waters inside these sinkholes for the first time. One of the sinkholes contained a body of dense, low-oxygen and low-pH water, which we call the
acid lake. We use measurements of seawater chemistry to work out what processes were responsible for forming the acid lake and discuss the consequences for the carbonate platform.
Gerhard Fischer, Oscar E. Romero, Johannes Karstensen, Karl-Heinz Baumann, Nasrollah Moradi, Morten Iversen, Götz Ruhland, Marco Klann, and Arne Körtzinger
Biogeosciences, 18, 6479–6500, https://doi.org/10.5194/bg-18-6479-2021, https://doi.org/10.5194/bg-18-6479-2021, 2021
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Low-oxygen eddies in the eastern subtropical North Atlantic can form an oasis for phytoplankton growth. Here we report on particle flux dynamics at the oligotrophic Cape Verde Ocean Observatory. We observed consistent flux patterns during the passages of low-oxygen eddies. We found distinct flux peaks in late winter, clearly exceeding background fluxes. Our findings suggest that the low-oxygen eddies sequester higher organic carbon than expected for oligotrophic settings.
Matthieu Bressac, Thibaut Wagener, Nathalie Leblond, Antonio Tovar-Sánchez, Céline Ridame, Vincent Taillandier, Samuel Albani, Sophie Guasco, Aurélie Dufour, Stéphanie H. M. Jacquet, François Dulac, Karine Desboeufs, and Cécile Guieu
Biogeosciences, 18, 6435–6453, https://doi.org/10.5194/bg-18-6435-2021, https://doi.org/10.5194/bg-18-6435-2021, 2021
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Phytoplankton growth is limited by the availability of iron in about 50 % of the ocean. Atmospheric deposition of desert dust represents a key source of iron. Here, we present direct observations of dust deposition in the Mediterranean Sea. A key finding is that the input of iron from dust primarily occurred in the deep ocean, while previous studies mainly focused on the ocean surface. This new insight will enable us to better represent controls on global marine productivity in models.
Léo Berline, Andrea Michelangelo Doglioli, Anne Petrenko, Stéphanie Barrillon, Boris Espinasse, Frederic A. C. Le Moigne, François Simon-Bot, Melilotus Thyssen, and François Carlotti
Biogeosciences, 18, 6377–6392, https://doi.org/10.5194/bg-18-6377-2021, https://doi.org/10.5194/bg-18-6377-2021, 2021
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While the Ionian Sea is considered a nutrient-depleted and low-phytoplankton biomass area, it is a crossroad for water mass circulation. In the central Ionian Sea, we observed a strong contrast in particle distribution across a ~100 km long transect. Using remote sensing and Lagrangian simulations, we suggest that this contrast finds its origin in the long-distance transport of particles from the north, west and east of the Ionian Sea, where phytoplankton production was more intense.
Anna Teruzzi, Giorgio Bolzon, Laura Feudale, and Gianpiero Cossarini
Biogeosciences, 18, 6147–6166, https://doi.org/10.5194/bg-18-6147-2021, https://doi.org/10.5194/bg-18-6147-2021, 2021
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During summer, maxima of phytoplankton chlorophyll concentration (DCM) occur in the subsurface of the Mediterranean Sea and can play a relevant role in carbon sequestration into the ocean interior. A numerical model based on in situ and satellite observations provides insights into the range of DCM conditions across the relatively small Mediterranean Sea and shows a western DCM that is 25 % shallower and with a higher phytoplankton chlorophyll concentration than in the eastern Mediterranean.
Elvira Pulido-Villena, Karine Desboeufs, Kahina Djaoudi, France Van Wambeke, Stéphanie Barrillon, Andrea Doglioli, Anne Petrenko, Vincent Taillandier, Franck Fu, Tiphanie Gaillard, Sophie Guasco, Sandra Nunige, Sylvain Triquet, and Cécile Guieu
Biogeosciences, 18, 5871–5889, https://doi.org/10.5194/bg-18-5871-2021, https://doi.org/10.5194/bg-18-5871-2021, 2021
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We report on phosphorus dynamics in the surface layer of the Mediterranean Sea. Highly sensitive phosphate measurements revealed vertical gradients above the phosphacline. The relative contribution of diapycnal fluxes to total external supply of phosphate to the mixed layer decreased towards the east, where atmospheric deposition dominated. Taken together, external sources of phosphate contributed little to total supply, which was mainly sustained by enzymatic hydrolysis of organic phosphorus.
Zouhair Lachkar, Michael Mehari, Muchamad Al Azhar, Marina Lévy, and Shafer Smith
Biogeosciences, 18, 5831–5849, https://doi.org/10.5194/bg-18-5831-2021, https://doi.org/10.5194/bg-18-5831-2021, 2021
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This study documents and quantifies a significant recent oxygen decline in the upper layers of the Arabian Sea and explores its drivers. Using a modeling approach we show that the fast local warming of sea surface is the main factor causing this oxygen drop. Concomitant summer monsoon intensification contributes to this trend, although to a lesser extent. These changes exacerbate oxygen depletion in the subsurface, threatening marine habitats and altering the local biogeochemistry.
France Van Wambeke, Vincent Taillandier, Karine Desboeufs, Elvira Pulido-Villena, Julie Dinasquet, Anja Engel, Emilio Marañón, Céline Ridame, and Cécile Guieu
Biogeosciences, 18, 5699–5717, https://doi.org/10.5194/bg-18-5699-2021, https://doi.org/10.5194/bg-18-5699-2021, 2021
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Simultaneous in situ measurements of (dry and wet) atmospheric deposition and biogeochemical stocks and fluxes in the sunlit waters of the open Mediterranean Sea revealed complex physical and biological processes occurring within the mixed layer. Nitrogen (N) budgets were computed to compare the sources and sinks of N in the mixed layer. The transitory effect observed after a wet dust deposition impacted the microbial food web down to the deep chlorophyll maximum.
Frédéric Gazeau, France Van Wambeke, Emilio Marañón, Maria Pérez-Lorenzo, Samir Alliouane, Christian Stolpe, Thierry Blasco, Nathalie Leblond, Birthe Zäncker, Anja Engel, Barbara Marie, Julie Dinasquet, and Cécile Guieu
Biogeosciences, 18, 5423–5446, https://doi.org/10.5194/bg-18-5423-2021, https://doi.org/10.5194/bg-18-5423-2021, 2021
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Our study shows that the impact of dust deposition on primary production depends on the initial composition and metabolic state of the tested community and is constrained by the amount of nutrients added, to sustain both the fast response of heterotrophic prokaryotes and the delayed one of phytoplankton. Under future environmental conditions, heterotrophic metabolism will be more impacted than primary production, therefore reducing the capacity of surface waters to sequester anthropogenic CO2.
Loes J. A. Gerringa, Martha Gledhill, Indah Ardiningsih, Niels Muntjewerf, and Luis M. Laglera
Biogeosciences, 18, 5265–5289, https://doi.org/10.5194/bg-18-5265-2021, https://doi.org/10.5194/bg-18-5265-2021, 2021
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For 3 decades, competitive ligand exchange–adsorptive cathodic stripping voltammetry was used to estimate the Fe-binding capacity of organic matter in seawater. In this paper the performance of the competing ligands is compared through the analysis of a series of model ligands.
The main finding of this paper is that the determined speciation parameters are not independent of the application, making interpretation of Fe speciation data more complex than it was thought before.
Frédéric Gazeau, Céline Ridame, France Van Wambeke, Samir Alliouane, Christian Stolpe, Jean-Olivier Irisson, Sophie Marro, Jean-Michel Grisoni, Guillaume De Liège, Sandra Nunige, Kahina Djaoudi, Elvira Pulido-Villena, Julie Dinasquet, Ingrid Obernosterer, Philippe Catala, and Cécile Guieu
Biogeosciences, 18, 5011–5034, https://doi.org/10.5194/bg-18-5011-2021, https://doi.org/10.5194/bg-18-5011-2021, 2021
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This paper shows that the impacts of Saharan dust deposition in different Mediterranean basins are as strong as those observed in coastal waters but differed substantially between the three tested stations, differences attributed to variable initial metabolic states. A stronger impact of warming and acidification on mineralization suggests a decreased capacity of Mediterranean surface communities to sequester CO2 following the deposition of atmospheric particles in the coming decades.
Carolin R. Löscher
Biogeosciences, 18, 4953–4963, https://doi.org/10.5194/bg-18-4953-2021, https://doi.org/10.5194/bg-18-4953-2021, 2021
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The Bay of Bengal (BoB) is classically seen as an ocean region with low primary production, which has been predicted to decrease even further. Here, the importance of such a trend is used to explore what could happen to the BoB's low-oxygen core waters if primary production decreases. Lower biological production leads to less oxygen loss in deeper waters by respiration; thus it could be that oxygen will not further decrease and the BoB will not become anoxic, different to other low-oxygen areas.
Cited articles
Altieri, A. H. and Gedan, K. B.: Climate change and dead zones, Glob. Change Biol., 21, 1395–1406, https://doi.org/10.1111/gcb.12754,
2015. a
Anderson, L. A., Robinson, A. R., and Lozano, C. J.: Physical and biological
modeling in the Gulf Stream region: I. Data assimilation methodology, Deep-Sea Res. Pt. I, 47, 1787–1827,
https://doi.org/10.1016/S0967-0637(00)00019-4,
2000. a
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo
GDAC), https://doi.org/10.17882/42182, 2000. a
Arnold, C. P. and Dey, C. H.: Observing-Systems Simulation Experiments: Past,
Present, and Future, B. Am. Meteorol. Soc., 67,
687–695, https://doi.org/10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2,
1986. a, b
Bannister, R. N.: A review of forecast error covariance statistics in
atmospheric variational data assimilation. I: Characteristics and
measurements of forecast error covariances, Q. J. Roy. Meteor. Soc., 134, 1951–1970, https://doi.org/10.1002/qj.339,
2008. a
Bates, N. R., Astor, Y. M., Church, M. J., Currie, K., Dore, J. E.,
González-Dávila, M., Lorenzoni, L., Muller-Karger, F., Olafsson, J.,
and Santana-Casiano, J. M.: A Time-Series View of Changing Surface Ocean
Chemistry Due to Ocean Uptake of Anthropogenic CO2 and Ocean
Acidification, Oceanography, 27, 126–141, 2014. a
Behrenfeld, M. J. and Boss, E. S.: Resurrecting the Ecological Underpinnings of
Ocean Plankton Blooms, Annu. Rev. Mar. Sci., 6, 167–194,
https://doi.org/10.1146/annurev-marine-052913-021325, 2014. a
Biogeochemical-Argo Planning Group: The scientific rationale, design and
Implementation Plan for a Biogeochemical-Argo float array, edited by: Johnson,
K. and Claustre, H., Ifremer, Brest, https://doi.org/10.13155/46601, 2016. a
Blockley, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J., Lea, D. J., Mirouze, I., Peterson, K. A., Sellar, A., and Storkey, D.: Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts, Geosci. Model Dev., 7, 2613–2638, https://doi.org/10.5194/gmd-7-2613-2014, 2014. a
Bloom, S. C., Takacs, L. L., da Silva, A. M., and Ledvina, D.: Data
Assimilation Using Incremental Analysis Updates, Mon. Weather Rev., 124,
1256–1271, https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2,
1996. a
Boss, E., Swift, D., Taylor, L., Brickley, P., Zaneveld, R., Riser, S., Perry, M. J., and Strutton, P. G.: Observations of pigment and particle
distributions in the western North Atlantic from an autonomous float and
ocean color satellite, Limnol. Oceanogr., 53, 2112–2122,
https://doi.org/10.4319/lo.2008.53.5_part_2.2112,
2008. a
Calvert, D. and Siddorn, J.: Revised vertical mixing parameters for the UK
community standard configuration of the global NEMO ocean model, Met Office
Hadley Centre Technical Note, 95,
https://library.metoffice.gov.uk/Portal/Default/en-GB/RecordView/Index/207517 (last access: 29 April 2020),
2013. a
Campbell, J. W.: The lognormal distribution as a model for bio-optical
variability in the sea, J. Geophys. Res.-Oceans, 100,
13237–13254, https://doi.org/10.1029/95JC00458,
1995. a
Ciavatta, S., Torres, R., Saux-Picart, S., and Allen, J. I.: Can ocean color
assimilation improve biogeochemical hindcasts in shelf seas?, J. Geophys. Res.-Oceans, 116, C12043, https://doi.org/10.1029/2011JC007219,
2011. a
Ciavatta, S., Kay, S., Saux-Picart, S., Butenschön, M., and Allen, J. I.:
Decadal reanalysis of biogeochemical indicators and fluxes in the North West
European shelf-sea ecosystem, J. Geophys. Res.-Oceans, 121,
1824–1845, https://doi.org/10.1002/2015JC011496,
2016. a
Cossarini, G., Mariotti, L., Feudale, L., Mignot, A., Salon, S., Taillandier, V., Teruzzi, A., and D'Ortenzio, F.: Towards operational 3D-Var assimilation
of chlorophyll Biogeochemical-Argo float data into a biogeochemical model of
the Mediterranean Sea, Ocean Model., 133, 112–128,
https://doi.org/10.1016/j.ocemod.2018.11.005,
2019. a
Davidson, F., Alvera-Azcárate, A., Barth, A., Brassington, G. B.,
Chassignet, E. P., Clementi, E., De Mey-Frémaux, P., Divakaran, P.,
Harris, C., Hernandez, F., Hogan, P., Hole, L. R., Holt, J., Liu, G., Lu, Y.,
Lorente, P., Maksymczuk, J., Martin, M., Mehra, A., Melsom, A., Mo, H.,
Moore, A., Oddo, P., Pascual, A., Pequignet, A.-C., Kourafalou, V., Ryan, A.,
Siddorn, J., Smith, G., Spindler, D., Spindler, T., Stanev, E. V., Staneva, J., Storto, A., Tanajura, C., Vinayachandran, P. N., Wan, L., Wang, H.,
Zhang, Y., Zhu, X., and Zu, Z.: Synergies in Operational Oceanography: The
Intrinsic Need for Sustained Ocean Observations, Front. Mar. Sci.,
6, 450, https://doi.org/10.3389/fmars.2019.00450,
2019. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kâllberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828,
2011. a
Diaz, R. J. and Rosenberg, R.: Spreading Dead Zones and Consequences for Marine
Ecosystems, Science, 321, 926–929, https://doi.org/10.1126/science.1156401, 2008. a
Doney, S. C., Fabry, V. J., Feely, R. A., and Kleypas, J. A.: Ocean
Acidification: The Other CO2 Problem, Annu. Rev. Mar. Sci., 1, 169–192, https://doi.org/10.1146/annurev.marine.010908.163834, pMID:
21141034, 2009. a
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical
implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
FAO: The State of World Fisheries and Aquaculture 2016. Contributing to food
security and nutrition for all, FAO, Rome, 2016. a
Fennel, K., Gehlen, M., Brasseur, P., Brown, C. W., Ciavatta, S., Cossarini, G., Crise, A., Edwards, C. A., Ford, D., Friedrichs, M. A. M., Gregoire, M.,
Jones, E., Kim, H.-C., Lamouroux, J., Murtugudde, R., Perruche, C., and the
GODAE OceanView Marine Ecosystem Analysis and Prediction Task Team:
Advancing Marine Biogeochemical and Ecosystem Reanalyses and Forecasts as
Tools for Monitoring and Managing Ecosystem Health, Front. Mar. Sci., 6, 89, https://doi.org/10.3389/fmars.2019.00089,
2019. a, b, c, d, e, f
Field, C. B., Behrenfeld, M. J., Randerson, J. T., and Falkowski, P.: Primary
Production of the Biosphere: Integrating Terrestrial and Oceanic Components,
Science, 281, 237–240, https://doi.org/10.1126/science.281.5374.237, 1998. a
Fontana, C., Brasseur, P., and Brankart, J.-M.: Toward a multivariate reanalysis of the North Atlantic Ocean biogeochemistry during 19982006 based on the assimilation of SeaWiFS chlorophyll data, Ocean Sci., 9, 37–56, https://doi.org/10.5194/os-9-37-2013, 2013. a, b
Ford, D. A., Edwards, K. P., Lea, D., Barciela, R. M., Martin, M. J., and Demaria, J.: Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model, Ocean Sci., 8, 751–771, https://doi.org/10.5194/os-8-751-2012, 2012. a, b, c
Fujii, Y., Rémy, E., Zuo, H., Oke, P., Halliwell, G., Gasparin, F.,
Benkiran, M., Loose, N., Cummings, J., Xie, J., Xue, Y., Masuda, S., Smith, G. C., Balmaseda, M., Germineaud, C., Lea, D. J., Larnicol, G., Bertino, L.,
Bonaduce, A., Brasseur, P., Donlon, C., Heimbach, P., Kim, Y., Kourafalou, V., Le Traon, P.-Y., Martin, M., Paturi, S., Tranchant, B., and Usui, N.:
Observing System Evaluation Based on Ocean Data Assimilation and Prediction
Systems: On-Going Challenges and a Future Vision for Designing and Supporting
Ocean Observational Networks, Front. Mar. Sci., 6, 417,
https://doi.org/10.3389/fmars.2019.00417,
2019. a
Garcia, H. E., Weathers, K., Paver, C. R., Smolyar, I., Boyer, T. P.,
Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Baranova, O. K., Seidov, D.,
and Reagan, J. R.: World Ocean Atlas 2018, Volume 3: Dissolved Oxygen,
Apparent Oxygen Utilization, and Oxygen Saturation, edited by:
Mishonov, A., NOAA Atlas NESDIS 83, Maryland, 2018a. a
Garcia, H. E., Weathers, K., Paver, C. R., Smolyar, I., Boyer, T. P.,
Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Baranova, O. K., Seidov, D.,
and Reagan, J. R.: World Ocean Atlas 2018, Volume 4: Dissolved Inorganic
Nutrients (phosphate, nitrate and nitrate+nitrite, silicate), edited by: Mishonov, A.; NOAA Atlas NESDIS 84, Maryland, 2018b. a
Gasparin, F., Guinehut, S., Mao, C., Mirouze, I., Rémy, E., King, R. R.,
Hamon, M., Reid, R., Storto, A., Le Traon, P.-Y., Martin, M. J., and Masina, S.: Requirements for an Integrated in situ Atlantic Ocean Observing System
From Coordinated Observing System Simulation Experiments, Front. Mar. Sci., 6, 83, https://doi.org/10.3389/fmars.2019.00083,
2019. a, b, c, d, e, f, g
Gehlen, M., Barciela, R., Bertino, L., Brasseur, P., Butenschön, M., Chai, F., Crise, A., Drillet, Y., Ford, D., Lavoie, D., Lehodey, P., Perruche, C.,
Samuelsen, A., and Simon, E.: Building the capacity for forecasting marine
biogeochemistry and ecosystems: recent advances and future developments,
J. Oper. Oceanogr., 8, s168–s187,
https://doi.org/10.1080/1755876X.2015.1022350, 2015. a, b
Germineaud, C., Brankart, J.-M., and Brasseur, P.: An Ensemble-Based
Probabilistic Score Approach to Compare Observation Scenarios: An Application
to Biogeochemical-Argo Deployments, J. Atmos. Ocean. Tech., 36, 2307–2326, https://doi.org/10.1175/JTECH-D-19-0002.1, 2019. a, b
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: Quality controlled ocean
temperature and salinity profiles and monthly objective analyses with
uncertainty estimates, J. Geophys. Res.-Oceans, 118,
6704–6716, https://doi.org/10.1002/2013JC009067,
2013. a
Gouretski, V. and Reseghetti, F.: On depth and temperature biases in
bathythermograph data: Development of a new correction scheme based on
analysis of a global ocean database, Deep-Sea Res. Pt. I, 57, 812–833, https://doi.org/10.1016/j.dsr.2010.03.011,
2010. a
Gregg, W. W.: Assimilation of SeaWiFS ocean chlorophyll data into a
three-dimensional global ocean model, J. Marine Syst., 69, 205–225, https://doi.org/10.1016/j.jmarsys.2006.02.015, 2008. a
Groom, S., Sathyendranath, S., Ban, Y., Bernard, S., Brewin, R., Brotas, V.,
Brockmann, C., Chauhan, P., Choi, J.-k., Chuprin, A., Ciavatta, S.,
Cipollini, P., Donlon, C., Franz, B., He, X., Hirata, T., Jackson, T.,
Kampel, M., Krasemann, H., Lavender, S., Pardo-Martinez, S., Mélin, F.,
Platt, T., Santoleri, R., Skákala, J., Schaeffer, B., Smith, M.,
Steinmetz, F., Valente, A., and Wang, M.: Satellite Ocean Colour: Current
Status and Future Perspective, Front. Mar. Sci., 6, 485,
https://doi.org/10.3389/fmars.2019.00485,
2019. a
Guiavarc'h, C., Roberts-Jones, J., Harris, C., Lea, D. J., Ryan, A., and Ascione, I.: Assessment of ocean analysis and forecast from an atmosphereocean coupled data assimilation operational system, Ocean Sci., 15, 1307–1326, https://doi.org/10.5194/os-15-1307-2019, 2019. a
Halliwell, G. R., Mehari, M. F., Hénaff, M. L., Kourafalou, V. H.,
Androulidakis, I. S., Kang, H. S., and Atlas, R.: North Atlantic Ocean OSSE
system: Evaluation of operational ocean observing system components and
supplemental seasonal observations for potentially improving tropical cyclone
prediction in coupled systems, J. Oper. Oceanogr., 10,
154–175, https://doi.org/10.1080/1755876X.2017.1322770, 2017. a
Hemmings, J. C. P., Barciela, R. M., and Bell, M. J.: Ocean color data
assimilation with material conservation for improving model estimates of
air-sea CO2 flux, J. Mar. Res., 66, 87–126,
https://doi.org/10.1357/002224008784815739,
2008. a
Hemmings, J. C. P., Challenor, P. G., and Yool, A.: Mechanistic site-based emulation of a global ocean biogeochemical model (MEDUSA 1.0) for parametric analysis and calibration: an application of the Marine Model Optimization Testbed (MarMOT 1.1), Geosci. Model Dev., 8, 697–731, https://doi.org/10.5194/gmd-8-697-2015, 2015. a
Hoffman, R. N. and Atlas, R.: Future Observing System Simulation Experiments,
B. Am. Meteorol. Soc., 97, 1601–1616,
https://doi.org/10.1175/BAMS-D-15-00200.1, 2016. a
Hovmöller, E.: The Trough-and-Ridge diagram, Tellus, 1, 62–66,
https://doi.org/10.3402/tellusa.v1i2.8498, 1949. a
Hunke, C., E., Lipscomb, H., W., Turner, K., A., Jeffery, N., and Elliott, S.:
CICE: the Los Alamos sea ice model documentation and software users' manual,
Version 5.1, LA-CC-06-012, Los Alamos National Laboratory, N.M., 2015. a
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I,
II and III to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Core Writing Team, Pachauri, R. K. and Meyer, L. A.,
IPCC, Geneva, Switzerland, 2014. a
Jackson, D. R., Keil, M., and Devenish, B. J.: Use of Canadian Quick
covariances in the Met Office data assimilation system, Q. J. Roy. Meteor. Soc., 134, 1567–1582, https://doi.org/10.1002/qj.294,
2008. a
Janjić, T., Bormann, N., Bocquet, M., Carton, J. A., Cohn, S. E., Dance, S. L., Losa, S. N., Nichols, N. K., Potthast, R., Waller, J. A., and Weston, P.: On the representation error in data assimilation, Q. J. Roy. Meteor. Soc., 144, 1257–1278, https://doi.org/10.1002/qj.3130,
2018. a
Johnson, K. S., Plant, J. N., Coletti, L. J., Jannasch, H. W., Sakamoto, C. M.,
Riser, S. C., Swift, D. D., Williams, N. L., Boss, E., Haëntjens, N.,
Talley, L. D., and Sarmiento, J. L.: Biogeochemical sensor performance in the
SOCCOM profiling float array, J. Geophys. Res.-Oceans, 122,
6416–6436, https://doi.org/10.1002/2017JC012838,
2017. a
Kalnay, E.: Atmospheric modeling, data assimilation and predictability,
Cambridge University Press, Cambridge, 2003. a
Key, R. M., Olsen, A., van Heuven, S., Lauvset, S. K., Velo, A., Lin, X.,
Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S.,
Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., and Suzuki, T.: Global
ocean data analysis project, version 2 (GLODAPv2),
available at: https://www.glodap.info/wp-content/uploads/2017/08/NDP_093.pdf (last access: 18 January 2021), 2015. a
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 Reanalysis: General Specifications and Basic Characteristics,
J. Meteorol. Soc. Jpn., 93, 5–48,
https://doi.org/10.2151/jmsj.2015-001, 2015. a
Kwiatkowski, L., Yool, A., Allen, J. I., Anderson, T. R., Barciela, R., Buitenhuis, E. T., Butenschön, M., Enright, C., Halloran, P. R., Le Quéré, C., de Mora, L., Racault, M.-F., Sinha, B., Totterdell, I. J., and Cox, P. M.: iMarNet: an ocean biogeochemistry model intercomparison project within a common physical ocean modelling framework, Biogeosciences, 11, 7291–7304, https://doi.org/10.5194/bg-11-7291-2014, 2014. a
Landschützer, P., Gruber, N., and Bakker, D. C. E.: A 30 years
observation-based global monthly gridded sea surface pCO2
product from 1982 through 2011 (NCEI Accession 0160558),
https://doi.org/10.3334/cdiac/otg.spco2_1982_2011_eth_somffn, 2015a. a, b
Landschützer, P., Gruber, N., Haumann, F. A., Rödenbeck, C., Bakker, D. C. E., van Heuven, S., Hoppema, M., Metzl, N., Sweeney, C., Takahashi, T.,
Tilbrook, B., and Wanninkhof, R.: The reinvigoration of the Southern Ocean
carbon sink, Science, 349, 1221–1224, https://doi.org/10.1126/science.aab2620,
2015b. a, b
Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., Suzuki, T., and Watelet, S.: A new global interior ocean mapped climatology: the 1∘ × 1∘ GLODAP version 2, Earth Syst. Sci. Data, 8, 325–340, https://doi.org/10.5194/essd-8-325-2016, 2016. a
Lévy, M., Estublier, A., and Madec, G.: Choice of an advection scheme for
biogeochemical models, Geophys. Res. Lett., 28, 3725–3728,
https://doi.org/10.1029/2001GL012947,
2001. a
MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J.,
Xavier, P., and Madec, G.: Global Seasonal forecast system version 5
(GloSea5): a high-resolution seasonal forecast system, Q. J. Roy. Meteor. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396,
2015. a
Madec, G.: NEMO ocean engine, Note du Pole de modélisation, Institut
Pierre-Simon Laplace (IPSL), France, No. 27, 1288–1619, 2008. a
Mao, C., King, R. R., Reid, R., Martin, M. J., and Good, S. A.: Assessing the
Potential Impact of Changes to the Argo and Moored Buoy Arrays in an
Operational Ocean Analysis System, Front. Mar. Sci., 7, 905,
https://doi.org/10.3389/fmars.2020.588267,
2020. a
Martin, G. M., Milton, S. F., Senior, C. A., Brooks, M. E., Ineson, S.,
Reichler, T., and Kim, J.: Analysis and Reduction of Systematic Errors
through a Seamless Approach to Modeling Weather and Climate, J.
Climate, 23, 5933–5957, https://doi.org/10.1175/2010JCLI3541.1, 2010. a
Masutani, M., Schlatter, T. W., Errico, R. M., Stoffelen, A., Andersson, E.,
Lahoz, W., Woollen, J. S., Emmitt, G. D., Riishøjgaard, L.-P., and Lord, S. J.: Observing System Simulation Experiments, in: Data Assimilation: Making
Sense of Observations, edited by: Lahoz, W., Khattatov, B., and Menard, R.,
647–679, Springer Berlin Heidelberg, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-540-74703-1_24,
2010. a, b, c, d
McKinley, G. A., Fay, A. R., Lovenduski, N. S., and Pilcher, D. J.: Natural
Variability and Anthropogenic Trends in the Ocean Carbon Sink, Annu. Rev. Mar. Sci., 9, 125–150, https://doi.org/10.1146/annurev-marine-010816-060529, pMID:
27620831, 2017. a
Mirouze, I. and Weaver, A. T.: Representation of correlation functions in
variational assimilation using an implicit diffusion operator, Q. J. Roy. Meteor. Soc., 136, 1421–1443,
https://doi.org/10.1002/qj.643,
2010. a
Mirouze, I., Blockley, E. W., Lea, D. J., Martin, M. J., and Bell, M. J.: A
multiple length scale correlation operator for ocean data assimilation,
Tellus A, 68, 29744,
https://doi.org/10.3402/tellusa.v68.29744, 2016. a, b
Mogensen, K. S., Balmaseda, M. A., Weaver, A., Martin, M., and Vidard, A.: NEMOVAR: a variational data assimilation system for the NEMO ocean model,
ECMWF Newsletter, 120, 17–21, https://doi.org/10.21957/3yj3mh16iq, 2009. a
Mogensen, K. S., Balmaseda, M. A., and Weaver, A.: The NEMOVAR ocean data
assimilation system as implemented in the ECMWF ocean analysis for System 4,
Tech. Rep. 668, ECMWF, https://doi.org/10.21957/x5y9yrtm, 2012. a, b
Munhoven, G.: Mathematics of the total alkalinitypH equation pathway to robust and universal solution algorithms: the SolveSAPHE package v1.0.1, Geosci. Model Dev., 6, 1367–1388, https://doi.org/10.5194/gmd-6-1367-2013, 2013. a, b
Orr, J. C. and Epitalon, J.-M.: Improved routines to model the ocean carbonate system: mocsy 2.0, Geosci. Model Dev., 8, 485–499, https://doi.org/10.5194/gmd-8-485-2015, 2015. a
Palmer, J. and Totterdell, I.: Production and export in a global ocean
ecosystem model, Deep-Sea Res. Pt. I, 48, 1169–1198, https://doi.org/10.1016/S0967-0637(00)00080-7,
2001. a, b
Park, J.-Y., Stock, C. A., Yang, X., Dunne, J. P., Rosati, A., John, J., and
Zhang, S.: Modeling Global Ocean Biogeochemistry With Physical Data
Assimilation: A Pragmatic Solution to the Equatorial Instability, J. Adv. Model. Earth Sy., 10, 891–906, https://doi.org/10.1002/2017MS001223,
2018. a
Polavarapu, S., Ren, S., Rochon, Y., Sankey, D., Ek, N., Koshyk, J., and
Tarasick, D.: Data assimilation with the Canadian middle atmosphere model,
Atmos. Ocean, 43, 77–100, https://doi.org/10.3137/ao.430105, 2005. a
Pradhan, H. K., Völker, C., Losa, S. N., Bracher, A., and Nerger, L.: Global
Assimilation of Ocean-Color Data of Phytoplankton Functional Types: Impact of
Different Data Sets, J. Geophys. Res.-Oceans, 125,
e2019JC015586, https://doi.org/10.1029/2019JC015586, 2020. a
Racault, M.-F., Sathyendranath, S., Brewin, R. J. W., Raitsos, D. E., Jackson, T., and Platt, T.: Impact of El Niño Variability on Oceanic
Phytoplankton, Front. Mar. Sci., 4, 133,
https://doi.org/10.3389/fmars.2017.00133,
2017. a
Raghukumar, K., Edwards, C. A., Goebel, N. L., Broquet, G., Veneziani, M.,
Moore, A. M., and Zehr, J. P.: Impact of assimilating physical oceanographic
data on modeled ecosystem dynamics in the California Current System, Prog. Oceanogr., 138, 546–558, https://doi.org/10.1016/j.pocean.2015.01.004, 2015. a
Ridley, J. K., Blockley, E. W., Keen, A. B., Rae, J. G. L., West, A. E., and Schroeder, D.: The sea ice model component of HadGEM3-GC3.1, Geosci. Model Dev., 11, 713–723, https://doi.org/10.5194/gmd-11-713-2018, 2018. a
Roemmich, D., Alford, M. H., Claustre, H., Johnson, K., King, B., Moum, J.,
Oke, P., Owens, W. B., Pouliquen, S., Purkey, S., Scanderbeg, M., Suga, T.,
Wijffels, S., Zilberman, N., Bakker, D., Baringer, M., Belbeoch, M., Bittig, H. C., Boss, E., Calil, P., Carse, F., Carval, T., Chai, F., Conchubhair, D. O., d'Ortenzio, F., Dall'Olmo, G., Desbruyeres, D., Fennel, K., Fer, I.,
Ferrari, R., Forget, G., Freeland, H., Fujiki, T., Gehlen, M., Greenan, B.,
Hallberg, R., Hibiya, T., Hosoda, S., Jayne, S., Jochum, M., Johnson, G. C.,
Kang, K., Kolodziejczyk, N., Körtzinger, A., Le Traon, P.-Y., Lenn, Y.-D., Maze, G., Mork, K. A., Morris, T., Nagai, T., Nash, J., Garabato, A. N., Olsen, A., Pattabhi, R. R., Prakash, S., Riser, S., Schmechtig, C.,
Schmid, C., Shroyer, E., Sterl, A., Sutton, P., Talley, L., Tanhua, T.,
Thierry, V., Thomalla, S., Toole, J., Troisi, A., Trull, T. W., Turton, J.,
Velez-Belchi, P. J., Walczowski, W., Wang, H., Wanninkhof, R., Waterhouse, A. F., Waterman, S., Watson, A., Wilson, C., Wong, A. P. S., Xu, J., and
Yasuda, I.: On the Future of Argo: A Global, Full-Depth, Multi-Disciplinary
Array, Front. Mar. Sci., 6, 439, https://doi.org/10.3389/fmars.2019.00439,
2019. a, b, c, d
Rousseaux, C. S. and Gregg, W. W.: Climate variability and phytoplankton
composition in the Pacific Ocean, J. Geophys. Res.-Oceans,
117, C10006, https://doi.org/10.1029/2012JC008083,
2012. a
Rousseaux, C. S. and Gregg, W. W.: Recent decadal trends in global
phytoplankton composition, Global Biogeochem. Cy., 29, 1674–1688,
https://doi.org/10.1002/2015GB005139,
2015. a
Sathyendranath, S., Grant, M., Brewin, R., Brockmann, C., Brotas, V., Chuprin, A., Doerffer, R., Dowell, M., Farman, A., Groom, S., Jackson, T., Krasemann, H., Lavender, S., Martinez Vicente, V., Mazeran, C., Mélin, F., Moore, T.,
Müller, D., Platt, T., Regner, P., Roy, S., Steinmetz, F., Swinton, J.,
Valente, A., Zühlke, M., Antoine, D., Arnone, R., Balch, W., Barker, K.,
Barlow, R., Bélanger, S., Berthon, J.-F., Beşiktepe, Ş., Brando, V., Canuti, E., Chavez, F., Claustre, H., Crout, R., Feldman, G., Franz, B.,
Frouin, R., García-Soto, C., Gibb, S., Gould, R., Hooker, S., Kahru, M.,
Klein, H., Kratzer, S., Loisel, H., McKee, D., Mitchell, B., Moisan, T.,
Muller-Karger, F., O'Dowd, L., Ondrusek, M., Poulton, A., Repecaud, M.,
Smyth, T., Sosik, H., Taberner, M., Twardowski, M., Voss, K., Werdell, J.,
Wernand, M., and Zibordi, G.: ESA Ocean Colour Climate Change Initiative
(Ocean_Colour_cci): Version 3.1 Data), Centre for Environmental Data Analysis, Didcot,
https://doi.org/10.5285/9c334fbe6d424a708cf3c4cf0c6a53f5, 2018. a, b
Sathyendranath, S., Brewin, R. J., Brockmann, C., Brotas, V., Calton, B.,
Chuprin, A., Cipollini, P., Couto, A. B., Dingle, J., Doerffer, R., Donlon, C., Dowell, M., Farman, A., Grant, M., Groom, S., Horseman, A., Jackson, T.,
Krasemann, H., Lavender, S., Martinez-Vicente, V., Mazeran, C., Mélin, F., Moore, T. S., Müller, D., Regner, P., Roy, S., Steele, C. J.,
Steinmetz, F., Swinton, J., Taberner, M., Thompson, A., Valente, A.,
Zühlke, M., Brando, V. E., Feng, H., Feldman, G., Franz, B. A., Frouin, R., Gould, R. W., Hooker, S. B., Kahru, M., Kratzer, S., Mitchell, B. G.,
Muller-Karger, F. E., Sosik, H. M., Voss, K. J., Werdell, J., and Platt, T.:
An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the
Ocean-Colour Climate Change Initiative (OC-CCI), Sensors, 19, 4285, https://doi.org/10.3390/s19194285, 2019. a, b
Scaife, A. A., Arribas, A., Blockley, E., Brookshaw, A., Clark, R. T.,
Dunstone, N., Eade, R., Fereday, D., Folland, C. K., Gordon, M., Hermanson, L., Knight, J. R., Lea, D. J., MacLachlan, C., Maidens, A., Martin, M.,
Peterson, A. K., Smith, D., Vellinga, M., Wallace, E., Waters, J., and
Williams, A.: Skillful long-range prediction of European and North American
winters, Geophys. Res. Lett., 41, 2514–2519,
https://doi.org/10.1002/2014GL059637,
2014. a
Schartau, M., Wallhead, P., Hemmings, J., Löptien, U., Kriest, I., Krishna, S., Ward, B. A., Slawig, T., and Oschlies, A.: Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling, Biogeosciences, 14, 1647–1701, https://doi.org/10.5194/bg-14-1647-2017, 2017. a
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A.,
O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E.,
Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T.,
Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J.,
Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A.,
Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A.,
Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.:
UKESM1: Description and Evaluation of the U.K. Earth System Model, J. Adv. Model. Earth Sy., 11, 4513–4558,
https://doi.org/10.1029/2019MS001739,
2019. a
Skákala, J., Ford, D., Brewin, R. J., McEwan, R., Kay, S., Taylor, B.,
de Mora, L., and Ciavatta, S.: The Assimilation of Phytoplankton Functional
Types for Operational Forecasting in the Northwest European Shelf, J. Geophys. Res.-Oceans, 123, 5230–5247, https://doi.org/10.1029/2018JC014153,
2018. a, b, c, d, e
Skákala, J., Bruggeman, J., Brewin, R. J. W., Ford, D. A., and Ciavatta, S.: Improved Representation of Underwater Light Field and Its Impact on
Ecosystem Dynamics: A Study in the North Sea, J. Geophys. Res.-Oceans, 125, e2020JC016122, https://doi.org/10.1029/2020JC016122, 2020. a, b, c
Storkey, D., Blockley, E. W., Furner, R., Guiavarc'h, C., Lea, D., Martin, M. J., Barciela, R. M., Hines, A., Hyder, P., and Siddorn, J. R.: Forecasting
the ocean state using NEMO: The new FOAM system, J. Oper. Oceanogr., 3, 3–15, https://doi.org/10.1080/1755876X.2010.11020109, 2010. a
Storkey, D., Blaker, A. T., Mathiot, P., Megann, A., Aksenov, Y., Blockley, E. W., Calvert, D., Graham, T., Hewitt, H. T., Hyder, P., Kuhlbrodt, T., Rae, J. G. L., and Sinha, B.: UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geosci. Model Dev., 11, 3187–3213, https://doi.org/10.5194/gmd-11-3187-2018, 2018. a, b, c
Teruzzi, A., Dobricic, S., Solidoro, C., and Cossarini, G.: A 3-D variational
assimilation scheme in coupled transport-biogeochemical models: Forecast of
Mediterranean biogeochemical properties, J. Geophys. Res.-Oceans, 119, 200–217, https://doi.org/10.1002/2013JC009277,
2014. a, b, c
Torres, R., Allen, J., and Figueiras, F.: Sequential data assimilation in an
upwelling influenced estuary, J. Marine Syst., 60, 317–329,
https://doi.org/10.1016/j.jmarsys.2006.02.001,
2006. a
Valsala, V. and Maksyutov, S.: Simulation and assimilation of global ocean pCO2
and air-sea CO2 fluxes using ship observations of surface ocean pCO2 in
a simplified biogeochemical offline model, Tellus B, 62, 821–840, https://doi.org/10.1111/j.1600-0889.2010.00495.x, 2010. a
Van Leer, B.: Towards the ultimate conservative difference scheme. I V. A new
approach to numerical convection, J. Comput. Phys., 23,
276–299, 1977. a
Verdy, A. and Mazloff, M. R.: A data assimilating model for estimating Southern
Ocean biogeochemistry, J. Geophys. Res.-Oceans, 122,
6968–6988, https://doi.org/10.1002/2016JC012650,
2017. a
Weaver, A. T., Vialard, J., and Anderson, D. L. T.: Three- and Four-Dimensional
Variational Assimilation with a General Circulation Model of the Tropical
Pacific Ocean. Part I: Formulation, Internal Diagnostics, and Consistency
Checks, Mon. Weather Rev., 131, 1360–1378,
https://doi.org/10.1175/1520-0493(2003)131<1360:TAFVAW>2.0.CO;2,
2003. a
Weaver, A. T., Deltel, C., Machu, E., Ricci, S., and Daget, N.: A multivariate
balance operator for variational ocean data assimilation, Q. J. Roy. Meteor. Soc., 131, 3605–3625,
https://doi.org/10.1256/qj.05.119,
2005. a, b
Wijffels, S., Roemmich, D., Monselesan, D., Church, J., and Gilson, J.: Ocean
temperatures chronicle the ongoing warming of Earth, Nat. Clim. Change,
6, 116–118, https://doi.org/10.1038/nclimate2924, 2016. a
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D.,
Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L.,
Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and
Xavier, P. K.: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and
GC3.1) Configurations, J. Adv. Model. Earth Sy., 10,
357–380, https://doi.org/10.1002/2017MS001115,
2017.
a
Yool, A., Popova, E. E., and Anderson, T. R.: MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies, Geosci. Model Dev., 6, 1767–1811, https://doi.org/10.5194/gmd-6-1767-2013, 2013. a
Yool, A., Palmiéri, J., Jones, C. G., Sellar, A. A., de Mora, L.,
Kuhlbrodt, T., Popova, E. E., Mulcahy, J. P., Wiltshire, A., Rumbold, S. T.,
Stringer, M., Hill, R. S. R., Tang, Y., Walton, J., Blaker, A., Nurser, A. J. G., Coward, A. C., Hirschi, J., Woodward, S., Kelley, D. I., Ellis, R.,
and Rumbold-Jones, S.: Spin-up of UK Earth System Model 1 (UKESM1) for CMIP6,
J. Adv. Model. Earth Sy., e2019MS001933,
https://doi.org/10.1029/2019MS001933, 2020. a
Yu, L., Fennel, K., Bertino, L., Gharamti, M. E., and Thompson, K. R.: Insights
on multivariate updates of physical and biogeochemical ocean variables using
an Ensemble Kalman Filter and an idealized model of upwelling, Ocean
Model., 126, 13–28, https://doi.org/10.1016/j.ocemod.2018.04.005,
2018. a
Yu, L., Fennel, K., Wang, B., Laurent, A., Thompson, K. R., and Shay, L. K.: Evaluation of nonidentical versus identical twin approaches for observation impact assessments: an ensemble-Kalman-filter-based ocean assimilation application for the Gulf of Mexico, Ocean Sci., 15, 1801–1814, https://doi.org/10.5194/os-15-1801-2019, 2019. a
Zalesak, S. T.: Fully multidimensional flux-corrected transport algorithms for
fluids, J. Comput. Phys., 31, 335–362, 1979. a
Short summary
Biogeochemical-Argo floats are starting to routinely measure ocean chlorophyll, nutrients, oxygen, and pH. This study generated synthetic observations representing two potential Biogeochemical-Argo observing system designs and created a data assimilation scheme to combine them with an ocean model. The proposed system of 1000 floats brought clear benefits to model results, with additional floats giving further benefit. Existing satellite ocean colour observations gave complementary information.
Biogeochemical-Argo floats are starting to routinely measure ocean chlorophyll, nutrients,...
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