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
Jozef Skákala, David Ford, Keith Haines, Amos Lawless, Matthew J. Martin, Philip Browne, Marcin Chrust, Stefano Ciavatta, Alison Fowler, Daniel Lea, Matthew Palmer, Andrea Rochner, Jennifer Waters, Hao Zuo, Deep S. Banerjee, Mike Bell, Davi M. Carneiro, Yumeng Chen, Susan Kay, Dale Partridge, Martin Price, Richard Renshaw, Georgy Shapiro, and James While
Ocean Sci., 21, 1709–1734, https://doi.org/10.5194/os-21-1709-2025, https://doi.org/10.5194/os-21-1709-2025, 2025
Short summary
Short summary
UK marine data assimilation (MDA) involves a closely collaborating research community. In this paper, we offer both an overview of the state of the art and a vision for the future across all of the main areas of UK MDA, ranging from physics to biogeochemistry to coupled DA. We discuss the current UK MDA stakeholder applications, highlight theoretical developments needed to advance our systems, and reflect upon upcoming opportunities with respect to hardware and observational missions.
Dale Partridge, Deep Banerjee, David Ford, Ke Wang, Jozef Skakala, Juliane Wihsgott, Prathyush Menon, Susan Kay, Daniel Clewley, Andrea Rochner, Emma Sullivan, and Matthew Palmer
EGUsphere, https://doi.org/10.5194/egusphere-2025-3346, https://doi.org/10.5194/egusphere-2025-3346, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed at improving coastal ocean forecasts through the use of autonomous underwater gliders. A fleet of gliders were deployed in the western English Channel during August–September 2024 to collect measurements of temperature, salinity, chlorophyll and oxygen, aiming to track the movement of the harmful algal bloom Karenia mikimotoi.
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc'h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
Geosci. Model Dev., 18, 3405–3425, https://doi.org/10.5194/gmd-18-3405-2025, https://doi.org/10.5194/gmd-18-3405-2025, 2025
Short summary
Short summary
We describe major improvements of the Met Office's global ocean–sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1 d forecasts. The new system performance in past conditions, where subsurface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
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.
Jozef Skákala, David Ford, Keith Haines, Amos Lawless, Matthew J. Martin, Philip Browne, Marcin Chrust, Stefano Ciavatta, Alison Fowler, Daniel Lea, Matthew Palmer, Andrea Rochner, Jennifer Waters, Hao Zuo, Deep S. Banerjee, Mike Bell, Davi M. Carneiro, Yumeng Chen, Susan Kay, Dale Partridge, Martin Price, Richard Renshaw, Georgy Shapiro, and James While
Ocean Sci., 21, 1709–1734, https://doi.org/10.5194/os-21-1709-2025, https://doi.org/10.5194/os-21-1709-2025, 2025
Short summary
Short summary
UK marine data assimilation (MDA) involves a closely collaborating research community. In this paper, we offer both an overview of the state of the art and a vision for the future across all of the main areas of UK MDA, ranging from physics to biogeochemistry to coupled DA. We discuss the current UK MDA stakeholder applications, highlight theoretical developments needed to advance our systems, and reflect upon upcoming opportunities with respect to hardware and observational missions.
Dale Partridge, Deep Banerjee, David Ford, Ke Wang, Jozef Skakala, Juliane Wihsgott, Prathyush Menon, Susan Kay, Daniel Clewley, Andrea Rochner, Emma Sullivan, and Matthew Palmer
EGUsphere, https://doi.org/10.5194/egusphere-2025-3346, https://doi.org/10.5194/egusphere-2025-3346, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed at improving coastal ocean forecasts through the use of autonomous underwater gliders. A fleet of gliders were deployed in the western English Channel during August–September 2024 to collect measurements of temperature, salinity, chlorophyll and oxygen, aiming to track the movement of the harmful algal bloom Karenia mikimotoi.
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc'h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
Geosci. Model Dev., 18, 3405–3425, https://doi.org/10.5194/gmd-18-3405-2025, https://doi.org/10.5194/gmd-18-3405-2025, 2025
Short summary
Short summary
We describe major improvements of the Met Office's global ocean–sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1 d forecasts. The new system performance in past conditions, where subsurface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
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.
Related subject area
Biogeochemistry: Open Ocean
Coccolithophore abundance and production and their impacts on particulate inorganic carbon cycling in the western North Pacific
A time series analysis of transparent exopolymer particle distributions and C : N stoichiometry in the subtropical North Pacific: a key process in net community production and preformed nitrate anomalies?
Marine snow morphology drives sinking and attenuation in the ocean interior
An upper-mesopelagic-zone carbon budget for the subarctic North Pacific
Ocean alkalinity enhancement in an open-ocean ecosystem: biogeochemical responses and carbon storage durability
Relationships between the concentration of particulate organic nitrogen and the inherent optical properties of seawater in oceanic surface waters
Inadequacies in the representation of sub-seasonal phytoplankton dynamics in Earth system models
Nitrogen dynamics and nitrate stable isotopes indicate nitrogen loss in the Bay of Bengal
Interactions between ocean alkalinity enhancement and phytoplankton in an Earth System Model
A tracer study for the development of in-water monitoring, reporting, and verification (MRV) of ship-based ocean alkalinity enhancement
Composite model-based estimate of the ocean carbon sink from 1959 to 2022
Phytoplankton community structure in relation to iron and macronutrient fluxes from subsurface waters in the western North Pacific during summer
Intense and localized export of selected marine snow types at eddy edges in the South Atlantic Ocean
Spatial distributions of iron and manganese in surface waters of the Arctic's Laptev and East Siberian seas
Climate-driven shifts in Southern Ocean primary producers and biogeochemistry in CMIP6 models
Oceanic enrichment of ammonium and its impacts on phytoplankton community composition under a high-emissions scenario
Ocean acidification trends and carbonate system dynamics across the North Atlantic subpolar gyre water masses during 2009–2019
From small scale variability to mesoscale stability in surface ocean pH: implications for air-sea CO2 equilibration
Sedimentary organic matter signature hints at the phytoplankton-driven biological carbon pump in the central Arabian Sea
Hydrological cycle amplification imposes spatial patterns on the climate change response of ocean pH and carbonate chemistry
Assessing the tropical Atlantic biogeochemical processes in the Norwegian Earth System Model
Evolution of oxygen and stratification and their relationship in the North Pacific Ocean in CMIP6 Earth system models
Evaluation of CMIP6 model performance in simulating historical biogeochemistry across the southern South China Sea
Drivers of decadal trends in the ocean carbon sink in the past, present, and future in Earth system models
Anthropogenic carbon storage and its decadal changes in the Atlantic between 1990–2020
Ocean alkalinity enhancement impacts: regrowth of marine microalgae in alkaline mineral concentrations simulating the initial concentrations after ship-based dispersions
Climatic controls on metabolic constraints in the ocean
Effects of grain size and seawater salinity on magnesium hydroxide dissolution and secondary calcium carbonate precipitation kinetics: implications for ocean alkalinity enhancement
Short-term response of Emiliania huxleyi growth and morphology to abrupt salinity stress
Assessing the impact of CO2-equilibrated ocean alkalinity enhancement on microbial metabolic rates in an oligotrophic system
Phosphomonoesterase and phosphodiesterase activities in the eastern Mediterranean in two contrasting seasonal situations
Net primary production annual maxima in the North Atlantic projected to shift in the 21st century
Testing the influence of light on nitrite cycling in the eastern tropical North Pacific
Loss of nitrogen via anaerobic ammonium oxidation (anammox) in the California Current system during the late Quaternary
Technical note: Assessment of float pH data quality control methods – a case study in the subpolar northwest Atlantic Ocean
Linking northeastern North Pacific oxygen changes to upstream surface outcrop variations
Underestimation of multi-decadal global O2 loss due to an optimal interpolation method
Reviews and syntheses: expanding the global coverage of gross primary production and net community production measurements using Biogeochemical-Argo floats
Characteristics of surface physical and biogeochemical parameters within mesoscale eddies in the Southern Ocean
Seasonal dynamics and annual budget of dissolved inorganic carbon in the northwestern Mediterranean deep-convection region
The fingerprint of climate variability on the surface ocean cycling of iron and its isotopes
Reconstructing the ocean's mesopelagic zone carbon budget: sensitivity and estimation of parameters associated with prokaryotic remineralization
Seasonal cycles of biogeochemical fluxes in the Scotia Sea, Southern Ocean: a stable isotope approach
Absence of photophysiological response to iron addition in autumn phytoplankton in the Antarctic sea-ice zone
Optimal parameters for the ocean's nutrient, carbon, and oxygen cycles compensate for circulation biases but replumb the biological pump
Importance of multiple sources of iron for the upper-ocean biogeochemistry over the northern Indian Ocean
Exploring the role of different data types and timescales in the quality of marine biogeochemical model calibration
All about nitrite: exploring nitrite sources and sinks in the eastern tropical North Pacific oxygen minimum zone
Fossil coccolith morphological attributes as a new proxy for deep ocean carbonate chemistry
Reconstructing ocean carbon storage with CMIP6 Earth system models and synthetic Argo observations
Yuye Han, Zvi Steiner, Zhimian Cao, Di Fan, Junhui Chen, Jimin Yu, and Minhan Dai
Biogeosciences, 22, 3681–3697, https://doi.org/10.5194/bg-22-3681-2025, https://doi.org/10.5194/bg-22-3681-2025, 2025
Short summary
Short summary
Our results suggest coccolithophore calcite accounts for a major fraction of PIC (particulate inorganic carbon) standing stocks in the western North Pacific, with a markedly higher contribution in the oligotrophic subtropical gyre than in the Kuroshio–Oyashio transition region, which highlights the importance of coccolithophores for PIC production in the pelagic ocean, particularly in oligotrophic ocean waters.
Kieran Curran, Tracy A. Villareal, and Robert T. Letscher
Biogeosciences, 22, 3515–3531, https://doi.org/10.5194/bg-22-3515-2025, https://doi.org/10.5194/bg-22-3515-2025, 2025
Short summary
Short summary
This work provides a 2-year record of marine organic gel concentrations from an open-ocean site in the subtropical North Pacific Ocean north of Hawaii. These microscopic gels are investigated to understand their importance as an understudied component of organic matter cycling by marine microbes. We find an important role of gel cycling during the summer months, helping explain previously contradictory estimates of nutrient supply and demand for the subtropical ocean.
Yawouvi Dodji Soviadan, Miriam Beck, Joelle Habib, Alberto Baudena, Laetitia Drago, Alexandre Accardo, Remi Laxenaire, Sabrina Speich, Peter Brandt, Rainer Kiko, and Stemmann Lars
Biogeosciences, 22, 3485–3501, https://doi.org/10.5194/bg-22-3485-2025, https://doi.org/10.5194/bg-22-3485-2025, 2025
Short summary
Short summary
Key parameters representing the gravity flux in global models are sinking speed and vertical attenuation of exported material. We calculate, for the first time, these parameters in situ in the ocean for six intermittent blooms followed by export events using high-resolution (3 d) time series of 0–1000 m depth profiles from imaging sensors mounted on an Argo float. We show that sinking speed depends not only on size but also on the morphology of the particles, with density being an important property.
Brandon M. Stephens, Montserrat Roca-Martí, Amy E. Maas, Vinícius J. Amaral, Samantha Clevenger, Shawnee Traylor, Claudia R. Benitez-Nelson, Philip W. Boyd, Ken O. Buesseler, Craig A. Carlson, Nicolas Cassar, Margaret Estapa, Andrea J. Fassbender, Yibin Huang, Phoebe J. Lam, Olivier Marchal, Susanne Menden-Deuer, Nicola L. Paul, Alyson E. Santoro, David A. Siegel, and David P. Nicholson
Biogeosciences, 22, 3301–3328, https://doi.org/10.5194/bg-22-3301-2025, https://doi.org/10.5194/bg-22-3301-2025, 2025
Short summary
Short summary
The ocean’s mesopelagic zone (MZ) plays a crucial role in the global carbon cycle. This study combines new and previously published measurements of organic carbon supply and demand collected in August 2018 in the MZ of the subarctic North Pacific Ocean. Supply was insufficient to meet demand in August, but supply entering into the MZ in the spring of 2018 could have met the August demand. Results suggest observations over seasonal timescales may help to close MZ carbon budgets.
Allanah Joy Paul, Mathias Haunost, Silvan Urs Goldenberg, Jens Hartmann, Nicolás Sánchez, Julieta Schneider, Niels Suitner, and Ulf Riebesell
Biogeosciences, 22, 2749–2766, https://doi.org/10.5194/bg-22-2749-2025, https://doi.org/10.5194/bg-22-2749-2025, 2025
Short summary
Short summary
Ocean alkalinity enhancement (OAE) is being assessed for its potential to absorb atmospheric CO2 and store it for a long time. OAE still needs comprehensive assessment of its safety and effectiveness. We studied an idealised OAE application in a natural low-nutrient ecosystem over 1 month. Our results showed that biogeochemical functioning remained mostly stable but that the long-term capability for storing carbon may be limited at high alkalinity concentration.
Alain Fumenia, Hubert Loisel, Rick A. Reynolds, and Dariusz Stramski
Biogeosciences, 22, 2461–2484, https://doi.org/10.5194/bg-22-2461-2025, https://doi.org/10.5194/bg-22-2461-2025, 2025
Short summary
Short summary
Particulate organic nitrogen (PON) plays a central role in ocean biogeochemistry, yet limited in situ data hinder a full understanding of PON variability and associated processes. Measurements of optical properties offer an alternative for assessing PON across diverse marine environments. Our analysis reveals strong relationships between PON and optical properties, supporting a promising means to assess PON from optical measurements performed in situ or conducted from remote-sensing platforms.
Madhavan Girijakumari Keerthi, Olivier Aumont, Lester Kwiatkowski, and Marina Levy
Biogeosciences, 22, 2163–2180, https://doi.org/10.5194/bg-22-2163-2025, https://doi.org/10.5194/bg-22-2163-2025, 2025
Short summary
Short summary
We assessed how well climate models replicate sub-seasonal changes in ocean chlorophyll observed by satellites. Models struggle to capture these variations accurately. Some overestimate fluctuations and their impact on annual chlorophyll variability, while others underestimate them. The underestimation is likely due to limited model resolution, while the overestimation may come from internal model oscillations.
Gesa Schulz, Kirstin Dähnke, Tina Sanders, Jan Penopp, Hermann W. Bange, Rena Czeschel, and Birgit Gaye
EGUsphere, https://doi.org/10.5194/egusphere-2025-1660, https://doi.org/10.5194/egusphere-2025-1660, 2025
Short summary
Short summary
Oxygen minimum zones (OMZs) are low-oxygen ocean areas that deplete nitrogen, a key marine nutrient. Understanding nitrogen cycling in OMZs is crucial for the global nitrogen cycle. This study examined nitrogen cycling in the OMZ of the Bay of Bengal and East Equatorial Indian Ocean, revealing limited mixing between both regions. Surface phytoplankton consumes nitrate, while deeper nitrification recycles nitrogen. In the BoB’s OMZ (100–300 m), nitrogen loss likely occurs via anammox.
Miriam Seifert, Christopher Danek, Christoph Völker, and Judith Hauck
EGUsphere, https://doi.org/10.5194/egusphere-2025-1495, https://doi.org/10.5194/egusphere-2025-1495, 2025
Short summary
Short summary
Ocean alkalinity enhancement (OAE) can help to remove CO2 from the atmosphere. Yet, our study is the first that investigates the link between OAE and phytoplankton in an Earth System Model. We show that OAE can indirectly decrease primary production, and that biological feedbacks can modify the efficiency of OAE. Our study provides hints for ecological risks of OAE and the quantification of carbon drawdown, and can serve as a kick-start for other models to consider OAE-ecosystem interactions.
Adam V. Subhas, Jennie E. Rheuban, Zhaohui Aleck Wang, Daniel C. McCorkle, Anna P. M. Michel, Lukas Marx, Chloe L. Dean, Kate Morkeski, Matthew G. Hayden, Mary Burkitt-Gray, Francis Elder, Yiming Guo, Heather H. Kim, and Ke Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1348, https://doi.org/10.5194/egusphere-2025-1348, 2025
Short summary
Short summary
Ocean alkalinity enhancement (OAE) is a carbon removal approach in which alkaline materials are added to the marine environment, increasing the ocean's ability to store carbon dioxide. We conducted an open-water experiment releasing and tracking a fluorescent water tracer. Under the right conditions, in-water monitoring of OAE does appear to be possible. We conclude with a series of practical recommendations for open-water OAE monitoring.
Jens Terhaar
Biogeosciences, 22, 1631–1649, https://doi.org/10.5194/bg-22-1631-2025, https://doi.org/10.5194/bg-22-1631-2025, 2025
Short summary
Short summary
The ocean is a major natural carbon sink. Despite its importance, estimates of the ocean carbon sink remain uncertain. Here, I present a hybrid model estimate of the ocean carbon sink from 1959 to 2022. By combining ocean models in hindcast mode and Earth system models, I keep the strength of each approach and remove the respective weaknesses. This composite model estimate is similar in magnitude to the best estimate of the Global Carbon Budget but 70 % less uncertain.
Huailin Deng, Koji Suzuki, Ichiro Yasuda, Hiroshi Ogawa, and Jun Nishioka
Biogeosciences, 22, 1495–1508, https://doi.org/10.5194/bg-22-1495-2025, https://doi.org/10.5194/bg-22-1495-2025, 2025
Short summary
Short summary
Iron (Fe) and nitrate are vital for primary production in the North Pacific. Sedimentary Fe is carried by North Pacific Intermediate Water to the North Pacific, but the nutrient return path and its effect on phytoplankton are unclear. By combining Fe and macronutrient fluxes with phytoplankton composition, this study firstly revealed that Fe supply from the subsurface greatly controls diatom abundance and identified the nutrient return path in the subarctic gyre and Kuroshio–Oyashio transition area.
Alexandre Accardo, Rémi Laxenaire, Alberto Baudena, Sabrina Speich, Rainer Kiko, and Lars Stemmann
Biogeosciences, 22, 1183–1201, https://doi.org/10.5194/bg-22-1183-2025, https://doi.org/10.5194/bg-22-1183-2025, 2025
Short summary
Short summary
The open ocean helps mitigate climate change by storing CO2 via the biological carbon pump (BCP), which involves processes like organic carbon production at the surface and transferring it to the deep ocean via various pathways. By deploying an autonomous platform, we found significant marine snow accumulation from the surface to the mesopelagic zone in frontal regions between eddies. We suggest that the coupling of hydrodynamics at eddy edges and biological activity may enhance this process.
Naoya Kanna, Kazutaka Tateyama, Takuji Waseda, Anna Timofeeva, Maria Papadimitraki, Laura Whitmore, Hajime Obata, Daiki Nomura, Hiroshi Ogawa, Youhei Yamashita, and Igor Polyakov
Biogeosciences, 22, 1057–1076, https://doi.org/10.5194/bg-22-1057-2025, https://doi.org/10.5194/bg-22-1057-2025, 2025
Short summary
Short summary
This article presents data on iron and manganese, essential micronutrients for primary producers in the Arctic Laptev and East Siberian seas (LESS). There, observations were made through international cooperation with the Nansen and Amundsen Basin Observational System expedition during the late summer of 2021. The results from this study indicate that the major sources controlling the iron and manganese distributions on the LESS continental margins are river discharge and shelf sediment input.
Ben J. Fisher, Alex J. Poulton, Michael P. Meredith, Kimberlee Baldry, Oscar Schofield, and Sian F. Henley
Biogeosciences, 22, 975–994, https://doi.org/10.5194/bg-22-975-2025, https://doi.org/10.5194/bg-22-975-2025, 2025
Short summary
Short summary
The Southern Ocean is a rapidly warming environment, with subsequent impacts on ecosystems and biogeochemical cycling. This study examines changes in phytoplankton and biogeochemistry using a range of climate models. Under climate change, the Southern Ocean will be warmer, more acidic and more productive and will have reduced nutrient availability by 2100. However, there is substantial variability between models across key productivity parameters. We propose ways of reducing this uncertainty.
Pearse J. Buchanan, Juan J. Pierella Karlusich, Robyn E. Tuerena, Roxana Shafiee, E. Malcolm S. Woodward, Chris Bowler, and Alessandro Tagliabue
EGUsphere, https://doi.org/10.5194/egusphere-2024-3639, https://doi.org/10.5194/egusphere-2024-3639, 2025
Short summary
Short summary
Ammonium is a form of nitrogen that may become more important for growth of marine primary producers (i.e., phytoplankton) in the future. Because some phytoplankton taxa have a greater affinity for ammonium than others, the relative increase in ammonium could cause shifts in community composition. We quantify ammonium enrichment, identify its drivers, and isolate the possible effect on phytoplankton community composition under a high emissions scenario.
David Curbelo-Hernández, Fiz F. Pérez, Melchor González-Dávila, Sergey V. Gladyshev, Aridane G. González, David González-Santana, Antón Velo, Alexey Sokov, and J. Magdalena Santana-Casiano
Biogeosciences, 21, 5561–5589, https://doi.org/10.5194/bg-21-5561-2024, https://doi.org/10.5194/bg-21-5561-2024, 2024
Short summary
Short summary
The study evaluated CO2–carbonate system dynamics in the North Atlantic subpolar gyre during 2009–2019. Significant ocean acidification, largely due to rising anthropogenic CO2 levels, was found. Cooling, freshening, and enhanced convective processes intensified this trend, affecting calcite and aragonite saturation. The findings contribute to a deeper understanding of ocean acidification and improve our knowledge about its impact on marine ecosystems.
Louise Delaigue, Gert-Jan Reichart, Chris Galley, Yasmina Ourradi, and Matthew Paul Humphreys
EGUsphere, https://doi.org/10.5194/egusphere-2024-2853, https://doi.org/10.5194/egusphere-2024-2853, 2024
Short summary
Short summary
Our study analyzed pH in ocean surface waters to understand how they fluctuate with changes in temperature, salinity, and biological activities. We found that temperature mainly controls daily pH variations, but biological processes also play a role, especially in affecting CO2 levels between the ocean and atmosphere. Our research shows how these factors together maintain the balance of ocean chemistry, which is crucial for predicting changes in marine environments.
Medhavi Pandey, Haimanti Biswas, Daniel Birgel, Nicole Burdanowitz, and Birgit Gaye
Biogeosciences, 21, 4681–4698, https://doi.org/10.5194/bg-21-4681-2024, https://doi.org/10.5194/bg-21-4681-2024, 2024
Short summary
Short summary
We analysed sea surface temperature (SST) proxy and plankton biomarkers in sediments that accumulate sinking material signatures from surface waters in the central Arabian Sea (21°–11° N, 64° E), a tropical basin impacted by monsoons. We saw a north–south SST gradient, and the biological proxies showed more organic matter from larger algae in the north. Smaller algae and zooplankton were more numerous in the south. These trends were related to ocean–atmospheric processes and oxygen availability.
Allison Hogikyan and Laure Resplandy
Biogeosciences, 21, 4621–4636, https://doi.org/10.5194/bg-21-4621-2024, https://doi.org/10.5194/bg-21-4621-2024, 2024
Short summary
Short summary
Rising atmospheric CO2 influences ocean carbon chemistry, leading to ocean acidification. Global warming introduces spatial patterns in the intensity of ocean acidification. We show that the most prominent spatial patterns are controlled by warming-driven changes in rainfall and evaporation, not by the direct effect of warming on carbon chemistry and pH. These evaporation and rainfall patterns oppose acidification in saltier parts of the ocean and enhance acidification in fresher regions.
Shunya Koseki, Lander R. Crespo, Jerry Tjiputra, Filippa Fransner, Noel S. Keenlyside, and David Rivas
Biogeosciences, 21, 4149–4168, https://doi.org/10.5194/bg-21-4149-2024, https://doi.org/10.5194/bg-21-4149-2024, 2024
Short summary
Short summary
We investigated how the physical biases of an Earth system model influence the marine biogeochemical processes in the tropical Atlantic. With four different configurations of the model, we have shown that the versions with better SST reproduction tend to better represent the primary production and air–sea CO2 flux in terms of climatology, seasonal cycle, and response to climate variability.
Lyuba Novi, Annalisa Bracco, Takamitsu Ito, and Yohei Takano
Biogeosciences, 21, 3985–4005, https://doi.org/10.5194/bg-21-3985-2024, https://doi.org/10.5194/bg-21-3985-2024, 2024
Short summary
Short summary
We explored the relationship between oxygen and stratification in the North Pacific Ocean using a combination of data mining and machine learning. We used isopycnic potential vorticity (IPV) as an indicator to quantify ocean ventilation and analyzed its predictability, a strong O2–IPV connection, and predictability for IPV in the tropical Pacific. This opens new routes for monitoring ocean O2 through few observational sites co-located with more abundant IPV measurements in the tropical Pacific.
Winfred Marshal, Jing Xiang Chung, Nur Hidayah Roseli, Roswati Md Amin, and Mohd Fadzil Bin Mohd Akhir
Biogeosciences, 21, 4007–4035, https://doi.org/10.5194/bg-21-4007-2024, https://doi.org/10.5194/bg-21-4007-2024, 2024
Short summary
Short summary
This study stands out for thoroughly examining CMIP6 ESMs' ability to simulate biogeochemical variables in the southern South China Sea, an economically important region. It assesses variables like chlorophyll, phytoplankton, nitrate, and oxygen on annual and seasonal scales. While global assessments exist, this study addresses a gap by objectively ranking 13 CMIP6 ocean biogeochemistry models' performance at a regional level, focusing on replicating specific observed biogeochemical variables.
Jens Terhaar
Biogeosciences, 21, 3903–3926, https://doi.org/10.5194/bg-21-3903-2024, https://doi.org/10.5194/bg-21-3903-2024, 2024
Short summary
Short summary
Despite the ocean’s importance in the carbon cycle and hence the climate, observing the ocean carbon sink remains challenging. Here, I use an ensemble of 12 models to understand drivers of decadal trends of the past, present, and future ocean carbon sink. I show that 80 % of the decadal trends in the multi-model mean ocean carbon sink can be explained by changes in decadal trends in atmospheric CO2. The remaining 20 % are due to internal climate variability and ocean heat uptake.
Reiner Steinfeldt, Monika Rhein, and Dagmar Kieke
Biogeosciences, 21, 3839–3867, https://doi.org/10.5194/bg-21-3839-2024, https://doi.org/10.5194/bg-21-3839-2024, 2024
Short summary
Short summary
We calculate the amount of anthropogenic carbon (Cant) in the Atlantic for the years 1990, 2000, 2010 and 2020. Cant is the carbon that is taken up by the ocean as a result of humanmade CO2 emissions. To determine the amount of Cant, we apply a technique that is based on the observations of other humanmade gases (e.g., chlorofluorocarbons). Regionally, changes in ocean ventilation have an impact on the storage of Cant. Overall, the increase in Cant is driven by the rising CO2 in the atmosphere.
Stephanie Delacroix, Tor Jensen Nystuen, August E. Dessen Tobiesen, Andrew L. King, and Erik Höglund
Biogeosciences, 21, 3677–3690, https://doi.org/10.5194/bg-21-3677-2024, https://doi.org/10.5194/bg-21-3677-2024, 2024
Short summary
Short summary
The addition of alkaline minerals into the ocean might reduce excessive anthropogenic CO2 emissions. Magnesium hydroxide can be added in large amounts because of its low seawater solubility without reaching harmful pH levels. The toxicity effect results of magnesium hydroxide, by simulating the expected concentrations from a ship's dispersion scenario, demonstrated low impacts on both sensitive and local assemblages of marine microalgae when compared to calcium hydroxide.
Precious Mongwe, Matthew Long, Takamitsu Ito, Curtis Deutsch, and Yeray Santana-Falcón
Biogeosciences, 21, 3477–3490, https://doi.org/10.5194/bg-21-3477-2024, https://doi.org/10.5194/bg-21-3477-2024, 2024
Short summary
Short summary
We use a collection of measurements that capture the physiological sensitivity of organisms to temperature and oxygen and a CESM1 large ensemble to investigate how natural climate variations and climate warming will impact the ability of marine heterotrophic marine organisms to support habitats in the future. We find that warming and dissolved oxygen loss over the next several decades will reduce the volume of ocean habitats and will increase organisms' vulnerability to extremes.
Charly A. Moras, Tyler Cyronak, Lennart T. Bach, Renaud Joannes-Boyau, and Kai G. Schulz
Biogeosciences, 21, 3463–3475, https://doi.org/10.5194/bg-21-3463-2024, https://doi.org/10.5194/bg-21-3463-2024, 2024
Short summary
Short summary
We investigate the effects of mineral grain size and seawater salinity on magnesium hydroxide dissolution and calcium carbonate precipitation kinetics for ocean alkalinity enhancement. Salinity did not affect the dissolution, but calcium carbonate formed earlier at lower salinities due to the lower magnesium and dissolved organic carbon concentrations. Smaller grain sizes dissolved faster but calcium carbonate precipitated earlier, suggesting that medium grain sizes are optimal for kinetics.
Rosie M. Sheward, Christina Gebühr, Jörg Bollmann, and Jens O. Herrle
Biogeosciences, 21, 3121–3141, https://doi.org/10.5194/bg-21-3121-2024, https://doi.org/10.5194/bg-21-3121-2024, 2024
Short summary
Short summary
How quickly do marine microorganisms respond to salinity stress? Our experiments with the calcifying marine plankton Emiliania huxleyi show that growth and cell morphology responded to salinity stress within as little as 24–48 hours, demonstrating that morphology and calcification are sensitive to salinity over a range of timescales. Our results have implications for understanding the short-term role of E. huxleyi in biogeochemical cycles and in size-based paleoproxies for salinity.
Laura Marín-Samper, Javier Arístegui, Nauzet Hernández-Hernández, Joaquín Ortiz, Stephen D. Archer, Andrea Ludwig, and Ulf Riebesell
Biogeosciences, 21, 2859–2876, https://doi.org/10.5194/bg-21-2859-2024, https://doi.org/10.5194/bg-21-2859-2024, 2024
Short summary
Short summary
Our planet is facing a climate crisis. Scientists are working on innovative solutions that will aid in capturing the hard to abate emissions before it is too late. Exciting research reveals that ocean alkalinity enhancement, a key climate change mitigation strategy, does not harm phytoplankton, the cornerstone of marine ecosystems. Through meticulous study, we may have uncovered a positive relationship: up to a specific limit, enhancing ocean alkalinity boosts photosynthesis by certain species.
France Van Wambeke, Pascal Conan, Mireille Pujo-Pay, Vincent Taillandier, Olivier Crispi, Alexandra Pavlidou, Sandra Nunige, Morgane Didry, Christophe Salmeron, and Elvira Pulido-Villena
Biogeosciences, 21, 2621–2640, https://doi.org/10.5194/bg-21-2621-2024, https://doi.org/10.5194/bg-21-2621-2024, 2024
Short summary
Short summary
Phosphomonoesterase (PME) and phosphodiesterase (PDE) activities over the epipelagic zone are described in the eastern Mediterranean Sea in winter and autumn. The types of concentration kinetics obtained for PDE (saturation at 50 µM, high Km, high turnover times) compared to those of PME (saturation at 1 µM, low Km, low turnover times) are discussed in regard to the possible inequal distribution of PDE and PME in the size continuum of organic material and accessibility to phosphodiesters.
Jenny Hieronymus, Magnus Hieronymus, Matthias Gröger, Jörg Schwinger, Raffaele Bernadello, Etienne Tourigny, Valentina Sicardi, Itzel Ruvalcaba Baroni, and Klaus Wyser
Biogeosciences, 21, 2189–2206, https://doi.org/10.5194/bg-21-2189-2024, https://doi.org/10.5194/bg-21-2189-2024, 2024
Short summary
Short summary
The timing of the net primary production annual maxima in the North Atlantic in the period 1750–2100 is investigated using two Earth system models and the high-emissions scenario SSP5-8.5. It is found that, for most of the region, the annual maxima occur progressively earlier, with the most change occurring after the year 2000. Shifts in the seasonality of the primary production may impact the entire ecosystem, which highlights the need for long-term monitoring campaigns in this area.
Nicole M. Travis, Colette L. Kelly, and Karen L. Casciotti
Biogeosciences, 21, 1985–2004, https://doi.org/10.5194/bg-21-1985-2024, https://doi.org/10.5194/bg-21-1985-2024, 2024
Short summary
Short summary
We conducted experimental manipulations of light level on microbial communities from the primary nitrite maximum. Overall, while individual microbial processes have different directions and magnitudes in their response to increasing light, the net community response is a decline in nitrite production with increasing light. We conclude that while increased light may decrease net nitrite production, high-light conditions alone do not exclude nitrification from occurring in the surface ocean.
Zoë Rebecca van Kemenade, Zeynep Erdem, Ellen Christine Hopmans, Jaap Smede Sinninghe Damsté, and Darci Rush
Biogeosciences, 21, 1517–1532, https://doi.org/10.5194/bg-21-1517-2024, https://doi.org/10.5194/bg-21-1517-2024, 2024
Short summary
Short summary
The California Current system (CCS) hosts the eastern subtropical North Pacific oxygen minimum zone (ESTNP OMZ). This study shows anaerobic ammonium oxidizing (anammox) bacteria cause a loss of bioavailable nitrogen (N) in the ESTNP OMZ throughout the late Quaternary. Anammox occurred during both glacial and interglacial periods and was driven by the supply of organic matter and changes in ocean currents. These findings may have important consequences for biogeochemical models of the CCS.
Cathy Wimart-Rousseau, Tobias Steinhoff, Birgit Klein, Henry Bittig, and Arne Körtzinger
Biogeosciences, 21, 1191–1211, https://doi.org/10.5194/bg-21-1191-2024, https://doi.org/10.5194/bg-21-1191-2024, 2024
Short summary
Short summary
The marine CO2 system can be measured independently and continuously by BGC-Argo floats since numerous pH sensors have been developed to suit these autonomous measurements platforms. By applying the Argo correction routines to float pH data acquired in the subpolar North Atlantic Ocean, we report the uncertainty and lack of objective criteria associated with the choice of the reference method as well the reference depth for the pH correction.
Sabine Mecking and Kyla Drushka
Biogeosciences, 21, 1117–1133, https://doi.org/10.5194/bg-21-1117-2024, https://doi.org/10.5194/bg-21-1117-2024, 2024
Short summary
Short summary
This study investigates whether northeastern North Pacific oxygen changes may be caused by surface density changes in the northwest as water moves along density horizons from the surface into the subsurface ocean. A correlation is found with a lag that about matches the travel time of water from the northwest to the northeast. Salinity is the main driver causing decadal changes in surface density, whereas salinity and temperature contribute about equally to long-term declining density trends.
Takamitsu Ito, Hernan E. Garcia, Zhankun Wang, Shoshiro Minobe, Matthew C. Long, Just Cebrian, James Reagan, Tim Boyer, Christopher Paver, Courtney Bouchard, Yohei Takano, Seth Bushinsky, Ahron Cervania, and Curtis A. Deutsch
Biogeosciences, 21, 747–759, https://doi.org/10.5194/bg-21-747-2024, https://doi.org/10.5194/bg-21-747-2024, 2024
Short summary
Short summary
This study aims to estimate how much oceanic oxygen has been lost and its uncertainties. One major source of uncertainty comes from the statistical gap-filling methods. Outputs from Earth system models are used to generate synthetic observations where oxygen data are extracted from the model output at the location and time of historical oceanographic cruises. Reconstructed oxygen trend is approximately two-thirds of the true trend.
Robert W. Izett, Katja Fennel, Adam C. Stoer, and David P. Nicholson
Biogeosciences, 21, 13–47, https://doi.org/10.5194/bg-21-13-2024, https://doi.org/10.5194/bg-21-13-2024, 2024
Short summary
Short summary
This paper provides an overview of the capacity to expand the global coverage of marine primary production estimates using autonomous ocean-going instruments, called Biogeochemical-Argo floats. We review existing approaches to quantifying primary production using floats, provide examples of the current implementation of the methods, and offer insights into how they can be better exploited. This paper is timely, given the ongoing expansion of the Biogeochemical-Argo array.
Qian Liu, Yingjie Liu, and Xiaofeng Li
Biogeosciences, 20, 4857–4874, https://doi.org/10.5194/bg-20-4857-2023, https://doi.org/10.5194/bg-20-4857-2023, 2023
Short summary
Short summary
In the Southern Ocean, abundant eddies behave opposite to our expectations. That is, anticyclonic (cyclonic) eddies are cold (warm). By investigating the variations of physical and biochemical parameters in eddies, we find that abnormal eddies have unique and significant effects on modulating the parameters. This study fills a gap in understanding the effects of abnormal eddies on physical and biochemical parameters in the Southern Ocean.
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, 20, 4683–4710, https://doi.org/10.5194/bg-20-4683-2023, https://doi.org/10.5194/bg-20-4683-2023, 2023
Short summary
Short summary
Deep convection plays a key role in the circulation, thermodynamics, and biogeochemical cycles in the Mediterranean Sea, considered to be a hotspot of biodiversity and climate change. In this study, we investigate the seasonal and annual budget of dissolved inorganic carbon in the deep-convection area of the northwestern Mediterranean Sea.
Daniela König and Alessandro Tagliabue
Biogeosciences, 20, 4197–4212, https://doi.org/10.5194/bg-20-4197-2023, https://doi.org/10.5194/bg-20-4197-2023, 2023
Short summary
Short summary
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.
Chloé Baumas, Robin Fuchs, Marc Garel, Jean-Christophe Poggiale, Laurent Memery, Frédéric A. C. Le Moigne, and Christian Tamburini
Biogeosciences, 20, 4165–4182, https://doi.org/10.5194/bg-20-4165-2023, https://doi.org/10.5194/bg-20-4165-2023, 2023
Short summary
Short summary
Through the sink of particles in the ocean, carbon (C) is exported and sequestered when reaching 1000 m. Attempts to quantify C exported vs. C 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 impacts 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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Biogeosciences, 20, 2985–3009, https://doi.org/10.5194/bg-20-2985-2023, https://doi.org/10.5194/bg-20-2985-2023, 2023
Short summary
Short summary
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.
Priyanka Banerjee
Biogeosciences, 20, 2613–2643, https://doi.org/10.5194/bg-20-2613-2023, https://doi.org/10.5194/bg-20-2613-2023, 2023
Short summary
Short summary
This study shows that atmospheric deposition is the most important source of iron to the upper northern Indian Ocean for phytoplankton growth. This is followed by iron from continental-shelf sediment. Phytoplankton increase following iron addition is possible only with high background levels of nitrate. Vertical mixing is the most important physical process supplying iron to the upper ocean in this region throughout the year. The importance of ocean currents in supplying iron varies seasonally.
Iris Kriest, Julia Getzlaff, Angela Landolfi, Volkmar Sauerland, Markus Schartau, and Andreas Oschlies
Biogeosciences, 20, 2645–2669, https://doi.org/10.5194/bg-20-2645-2023, https://doi.org/10.5194/bg-20-2645-2023, 2023
Short summary
Short summary
Global biogeochemical ocean models are often subjectively assessed and tuned against observations. We applied different strategies to calibrate a global model against observations. Although the calibrated models show similar tracer distributions at the surface, they differ in global biogeochemical fluxes, especially in global particle flux. Simulated global volume of oxygen minimum zones varies strongly with calibration strategy and over time, rendering its temporal extrapolation difficult.
John C. Tracey, Andrew R. Babbin, Elizabeth Wallace, Xin Sun, Katherine L. DuRussel, Claudia Frey, Donald E. Martocello III, Tyler Tamasi, Sergey Oleynik, and Bess B. Ward
Biogeosciences, 20, 2499–2523, https://doi.org/10.5194/bg-20-2499-2023, https://doi.org/10.5194/bg-20-2499-2023, 2023
Short summary
Short summary
Nitrogen (N) is essential for life; thus, its availability plays a key role in determining marine productivity. Using incubations of seawater spiked with a rare form of N measurable on a mass spectrometer, we quantified microbial pathways that determine marine N availability. The results show that pathways that recycle N have higher rates than those that result in its loss from biomass and present new evidence for anaerobic nitrite oxidation, a process long thought to be strictly aerobic.
Amanda Gerotto, Hongrui Zhang, Renata Hanae Nagai, Heather M. Stoll, Rubens César Lopes Figueira, Chuanlian Liu, and Iván Hernández-Almeida
Biogeosciences, 20, 1725–1739, https://doi.org/10.5194/bg-20-1725-2023, https://doi.org/10.5194/bg-20-1725-2023, 2023
Short summary
Short summary
Based on the analysis of the response of coccolithophores’ morphological attributes in a laboratory dissolution experiment and surface sediment samples from the South China Sea, we proposed that the thickness shape (ks) factor of fossil coccoliths together with the normalized ks variation, which is the ratio of the standard deviation of ks (σ) over the mean ks (σ/ks), is a robust and novel proxy to reconstruct past changes in deep ocean carbon chemistry.
Katherine E. Turner, Doug M. Smith, Anna Katavouta, and Richard G. Williams
Biogeosciences, 20, 1671–1690, https://doi.org/10.5194/bg-20-1671-2023, https://doi.org/10.5194/bg-20-1671-2023, 2023
Short summary
Short summary
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.
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,...
Altmetrics
Final-revised paper
Preprint