Articles | Volume 19, issue 1
https://doi.org/10.5194/bg-19-241-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-241-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An empirical MLR for estimating surface layer DIC and a comparative assessment to other gap-filling techniques for ocean carbon time series
Department of Marine Science, University of Otago, Dunedin, 9016, New
Zealand
Kim Currie
National Institute of Water and Atmospheric Research – University of
Otago Research Centre for Oceanography, Dunedin, 9016, New Zealand
John Zeldis
National Institute of Water and Atmospheric Research, Christchurch,
8011, New Zealand
Peter W. Dillingham
Coastal People: Southern Skies Centre of Research Excellence,
Department of Mathematics and Statistics, University of Otago, Dunedin,
9016, New Zealand
Cliff S. Law
Department of Marine Science, University of Otago, Dunedin, 9016, New
Zealand
National Institute of Water and Atmospheric Research, Wellington,
6021, New Zealand
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Nico Lange, Björn Fiedler, Marta Álvarez, Alice Benoit-Cattin, Heather Benway, Pier Luigi Buttigieg, Laurent Coppola, Kim Currie, Susana Flecha, Dana S. Gerlach, Makio Honda, I. Emma Huertas, Siv K. Lauvset, Frank Muller-Karger, Arne Körtzinger, Kevin M. O'Brien, Sólveig R. Ólafsdóttir, Fernando C. Pacheco, Digna Rueda-Roa, Ingunn Skjelvan, Masahide Wakita, Angelicque White, and Toste Tanhua
Earth Syst. Sci. Data, 16, 1901–1931, https://doi.org/10.5194/essd-16-1901-2024, https://doi.org/10.5194/essd-16-1901-2024, 2024
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The Synthesis Product for Ocean Time Series (SPOTS) is a novel achievement expanding and complementing the biogeochemical data landscape by providing consistent and high-quality biogeochemical time-series data from 12 ship-based fixed time-series programs. SPOTS covers multiple unique marine environments and time-series ranges, including data from 1983 to 2021. All in all, it facilitates a variety of applications that benefit from the collective value of biogeochemical time-series observations.
Karine Sellegri, Theresa Barthelmeß, Jonathan Trueblood, Antonia Cristi, Evelyn Freney, Clémence Rose, Neill Barr, Mike Harvey, Karl Safi, Stacy Deppeler, Karen Thompson, Wayne Dillon, Anja Engel, and Cliff Law
Atmos. Chem. Phys., 23, 12949–12964, https://doi.org/10.5194/acp-23-12949-2023, https://doi.org/10.5194/acp-23-12949-2023, 2023
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The amount of sea spray emitted to the atmosphere depends on the ocean temperature, but this dependency is not well understood, especially when ocean biology is involved. In this study, we show that sea spray emissions are increased by up to a factor of 4 at low seawater temperatures compared to moderate temperatures, and we quantify the temperature dependence as a function of the ocean biogeochemistry.
Manon Rocco, Erin Dunne, Alexia Saint-Macary, Maija Peltola, Theresa Barthelmeß, Neill Barr, Karl Safi, Andrew Marriner, Stacy Deppeler, James Harnwell, Anja Engel, Aurélie Colomb, Alfonso Saiz-Lopez, Mike Harvey, Cliff S. Law, and Karine Sellegri
EGUsphere, https://doi.org/10.5194/egusphere-2023-516, https://doi.org/10.5194/egusphere-2023-516, 2023
Preprint archived
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During the Sea2cloud campaign in the Southern Pacific Ocean, we measured air-sea emissions from phytopankton of two key atmospheric compounds: DMS and MeSH. These compounds are well-known to play a great role in atmospheric chemistry and climate. We see in this paper that these compounds are most emited by the nanophytoplankton population. We provide here parameters for climate models to predict future trends of the emissions of these compounds and their roles and impacts on the global warming.
Alexia D. Saint-Macary, Andrew Marriner, Theresa Barthelmeß, Stacy Deppeler, Karl Safi, Rafael Costa Santana, Mike Harvey, and Cliff S. Law
Ocean Sci., 19, 1–15, https://doi.org/10.5194/os-19-1-2023, https://doi.org/10.5194/os-19-1-2023, 2023
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The uppermost oceanic layer was sampled to determine what can explain a potential dimethyl sulfide (DMS) enrichment in this environment. A novel sampling method was used, and the results showed that DMS was not as enriched as expected. Our results showed that the phytoplanktonic composition influenced the DMS concentration, confirming results from another study in this oceanic region. However, additional factors are required to observe a DMS enrichment in the uppermost oceanic layer.
Alexia D. Saint-Macary, Andrew Marriner, Stacy Deppeler, Karl A. Safi, and Cliff S. Law
Ocean Sci., 18, 1559–1571, https://doi.org/10.5194/os-18-1559-2022, https://doi.org/10.5194/os-18-1559-2022, 2022
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To understand how dimethyl sulfide (DMS) enrichment is maintained in the sea surface microlayer (SML) while DMS is lost to the atmosphere, deck-board incubation was carried out to determine DMS sources and sinks. Our results showed that the phytoplankton composition played an essential role in DMS processes in the SML. However, all accumulated DMS processes were lower than the calculated air–sea DMS flux.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
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The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Stefanie Kremser, Mike Harvey, Peter Kuma, Sean Hartery, Alexia Saint-Macary, John McGregor, Alex Schuddeboom, Marc von Hobe, Sinikka T. Lennartz, Alex Geddes, Richard Querel, Adrian McDonald, Maija Peltola, Karine Sellegri, Israel Silber, Cliff S. Law, Connor J. Flynn, Andrew Marriner, Thomas C. J. Hill, Paul J. DeMott, Carson C. Hume, Graeme Plank, Geoffrey Graham, and Simon Parsons
Earth Syst. Sci. Data, 13, 3115–3153, https://doi.org/10.5194/essd-13-3115-2021, https://doi.org/10.5194/essd-13-3115-2021, 2021
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Aerosol–cloud interactions over the Southern Ocean are poorly understood and remain a major source of uncertainty in climate models. This study presents ship-borne measurements, collected during a 6-week voyage into the Southern Ocean in 2018, that are an important supplement to satellite-based measurements. For example, these measurements include data on low-level clouds and aerosol composition in the marine boundary layer, which can be used in climate model evaluation efforts.
Cited articles
Alin, S. R., Feely, R. A., Dickson, A. G., Hernández-Ayón, J. M.,
Juranek, L. W., Ohman, M. D., and Goericke, R.: Robust empirical
relationships for estimating the carbonate system in the southern California
Current System and application to CalCOFI hydrographic cruise data
(2005-2011), J. Geophys. Res.-Oceans, 117, C05033,
https://doi.org/10.1029/2011jc007511, 2012.
Astor, Y. M., Scranton, M. I., Muller-Karger, F., Bohrer, R., and
García, J.: fCO2 variability at the CARIACO tropical coastal upwelling
time series station, Mar. Chem., 97, 245–261,
https://doi.org/10.1016/j.marchem.2005.04.001, 2005.
Astor, Y. M., Lorenzoni, L., Thunell, R., Varela, R., Muller-Karger, F.,
Troccoli, L., Taylor, G. T., Scranton, M. I., Tappa, E., and Rueda, D.:
Interannual variability in sea surface temperature and fCO2 changes in the
Cariaco Basin, Deep-Sea Res. Pt. II,
93, 33–43, https://doi.org/10.1016/j.dsr2.2013.01.002, 2013.
Bates, N., Astor, Y., Church, M., Currie, K., Dore, J.,
Gonaález-Dávila, M., Lorenzoni, L., Muller-Karger, F., Olafsson, J.,
and Santa-Casiano, M.: A Time-Series View of Changing Ocean Chemistry Due to
Ocean Uptake of Anthropogenic CO2 and Ocean Acidification, Oceanography, 27,
126–141, https://doi.org/10.5670/oceanog.2014.16, 2014.
Bates, N. R., Best, M. H. P., Neely, K., Garley, R., Dickson, A. G., and Johnson, R. J.: Detecting anthropogenic carbon dioxide uptake and ocean acidification in the North Atlantic Ocean, Biogeosciences, 9, 2509–2522, https://doi.org/10.5194/bg-9-2509-2012, 2012.
Bernardello, R., Marinov, I., Palter, J. B., Sarmiento, J. L., Galbraith, E.
D., and Slater, R. D.: Response of the Ocean Natural Carbon Storage to
Projected Twenty-First-Century Climate Change, J. Climate, 27,
2033–2053, https://doi.org/10.1175/jcli-d-13-00343.1, 2014.
Bostock, H. C., Mikaloff Fletcher, S. E., and Williams, M. J. M.: Estimating carbonate parameters from hydrographic data for the intermediate and deep waters of the Southern Hemisphere oceans, Biogeosciences, 10, 6199–6213, https://doi.org/10.5194/bg-10-6199-2013, 2013.
Center, G. M. A. F.: GLORYS12V1 – Global Ocean Physical Reanalysis Product
[dataset], 2018.
Coutinho, E. R., Silva, R. M. d., Madeira, J. G. F., Coutinho, P. R. d. O.
d. S., Boloy, R. A. M., and Delgado, A. R. S.: Application of Artificial
Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series,
Revista Brasileira de Meteorologia, 33, 317–328, https://doi.org/10.1590/0102-7786332013,
2018.
Currie, K. I., Reid, M. R., and Hunter, K. A.: Interannual variability of
carbon dioxide drawdown by subantarctic surface water near New Zealand,
Biogeochemistry, 104, 23–34, https://doi.org/10.1007/s10533-009-9355-3, 2011.
de Boyer Montégut, C.: Mixed layer depth over the global ocean: An
examination of profile data and a profile-based climatology, J. Geophys. Res., 109, C12003, https://doi.org/10.1029/2004jc002378, 2004.
de Souza, J. M. A. C., Couto, P., Soutelino, R., and Roughan, M.: Evaluation
of four global ocean reanalysis products for New Zealand waters–A guide for
regional ocean modelling, New Zealand Journal of Marine and Freshwater
Research, 55, 132–155, https://doi.org/10.1080/00288330.2020.1713179, 2020.
Demirhan, H. and Renwick, Z.: Missing value imputation for short to mid-term
horizontal solar irradiance data, Appl. Energ., 225, 998–1012,
https://doi.org/10.1016/j.apenergy.2018.05.054, 2018.
DeVries, T., Le Quere, C., Andrews, O., Berthet, S., Hauck, J., Ilyina, T.,
Landschutzer, P., Lenton, A., Lima, I. D., Nowicki, M., Schwinger, J., and
Seferian, R.: Decadal trends in the ocean carbon sink, P. Natl. Acad. Sci. USA, 116, 11646–11651, https://doi.org/10.1073/pnas.1900371116, 2019.
Dickson, A. G., Wesolowski, D. J., Palmer, D. A., and Mesmer, R. E.:
Dissociation Constant of Bisulfate Ion in Aqueous Sodium Chloride Solutions
to 25∘C, J. Phys. Chem., 94, 7978–7985, 1990.
Dickson, A. G.: The estimation of acid dissociation constants in seawater
media from potentiometric titrations with strong base, Mar. Chem., 7,
101–109, 1979.
Dore, J. E., Lukas, R., Sadler, D. W., Church, M. J., and Karl, D. M.:
Physical and biogeochemical modulation of ocean acidification in the central
North Pacific, P. Natl. Acad. Sci. USA, 106, 12235–12240,
https://doi.org/10.1073/pnas.0906044106, 2009.
Drévillon, M., Regnier, C., Lellouche, J. M., Garric, G., Bricaud, C., and Hernandez, O.:
Quality Information Document for Global Ocean Reanalysis Product
GLOBAL_REANALYSIS_PHY_001_030, E.U. Copernicus Marine Service Information, 2021.
Ducklow, H. W., Doney, S. C., and Steinberg, D. K.: Contributions of
long-term research and time-series observations to marine ecology and
biogeochemistry, Ann. Rev. Mar. Sci., 1, 279–302,
https://doi.org/10.1146/annurev.marine.010908.163801, 2009.
Evans, W., Mathis, J. T., Winsor, P., Statscewich, H., and Whitledge, T. E.:
A regression modeling approach for studying carbonate system variability in
the northern Gulf of Alaska, J. Geophys. Res.-Oceans, 118,
476–489, https://doi.org/10.1029/2012jc008246, 2013.
Fassbender, A. J., Sabine, C. L., and Cronin, M. F.: Net community
production and calcification from 7 years of NOAA Station Papa Mooring
measurements, Global Biogeochem. Cy., 30, 250–267,
https://doi.org/10.1002/2015gb005205, 2016.
Fassbender, A. J., Sabine, C. L., Cronin, M. F., and Sutton, A. J.:
Mixed-layer carbon cycling at the Kuroshio Extension Observatory, Global Biogeochem. Cy., 2, 272–288, https://doi.org/10.1002/2016gb005547, 2017.
Fernandez, E. and Lellouche, J. M.: Product User Manual for the Global Ocean
Physical Reanalysis product GLOBAL_REANALYSIS_PHY_001_030, available at: https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-030.pdf 2021.
Gregor, L., Lebehot, A. D., Kok, S., and Scheel Monteiro, P. M.: A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?, Geosci. Model Dev., 12, 5113–5136, https://doi.org/10.5194/gmd-12-5113-2019, 2019.
Hales, B., Strutton, P. G., Saraceno, M., Letelier, R., Takahashi, T.,
Feely, R., Sabine, C., and Chavez, F.: Satellite-based prediction of pCO2 in
coastal waters of the eastern North Pacific, Prog. Oceanogr., 103,
1–15, https://doi.org/10.1016/j.pocean.2012.03.001, 2012.
Henn, B., Raleigh, M. S., Fisher, A., and Lundquist, J. D.: A Comparison of
Methods for Filling Gaps in Hourly Near-Surface Air Temperature Data,
J. Hydrometeorol., 14, 929–945, https://doi.org/10.1175/jhm-d-12-027.1, 2013.
Henson, S. A., Beaulieu, C., and Lampitt, R.: Observing climate change
trends in ocean biogeochemistry: when and where, Global Change Biol., 22,
1561–1571, https://doi.org/10.1111/gcb.13152, 2016.
James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to
Statistical Learning with Appliations in R, 434 pp., 2013.
Jiang, L.-Q., Cai, W.-J., Wanninkhof, R., Wang, Y., and Lüger, H.:
Air-sea CO2 fluxes on the U.S. South Atlantic Bight: Spatial and seasonal
variability, J. Geophys. Res., 113, C07019, https://doi.org/10.1029/2007jc004366,
2008.
Johengen, T., Schar, D., Atkinson, M., Pinchuk, A., Purcell, H., Robertson,
C., Smith, G. J., and Tamburri, M.: Performance Demonstration Statement PMEL
MAPCO2/Battelle Seaology pCO2 Monitoring System, Chesapeake Biological
Laboratory, Solomons, MD, USA, 24 pp., 2009.
Juranek, L. W., Feely, R. A., Peterson, W. T., Alin, S. R., Hales, B., Lee,
K., Sabine, C. L., and Peterson, J.: A novel method for determination of
aragonite saturation state on the continental shelf of central Oregon using
multi-parameter relationships with hydrographic data, Geophys. Res. Lett., 36, L24601, https://doi.org/10.1029/2009gl040778, 2009.
Juranek, L. W., Feely, R. A., Gilbert, D., Freeland, H., and Miller, L. A.:
Real-time estimation of pH and aragonite saturation state from Argo
profiling floats: Prospects for an autonomous carbon observing strategy,
Geophys. Res. Lett., 38, L17603, https://doi.org/10.1029/2011gl048580, 2011.
Kapsenberg, L. and Hofmann, G. E.: Ocean pH time-series and drivers of
variability along the northern Channel Islands, California, USA, Limnol. Oceanogr., 61, 953–968, https://doi.org/10.1002/lno.10264, 2016.
Kim, T.-W., Lee, K., Feely, R. A., Sabine, C. L., Chen, C.-T. A., Jeong, H.
J., and Kim, K. Y.: Prediction of Sea of Japan (East Sea) acidification over
the past 40 years using a multiparameter regression model, Global
Biogeochem. Cy., 24, GB3005, https://doi.org/10.1029/2009gb003637, 2010.
Krissansen-Totton, J., Arney, G. N., and Catling, D. C.: Constraining the
climate and ocean pH of the early Earth with a geological carbon cycle
model, P. Natl. Acad. Sci. USA, 115, 4105–4110, https://doi.org/10.1073/pnas.1721296115,
2018.
Kroeker, K. J., Kordas, R. L., Crim, R., Hendriks, I. E., Ramajo, L., Singh,
G. S., Duarte, C. M., and Gattuso, J. P.: Impacts of ocean acidification on
marine organisms: quantifying sensitivities and interaction with warming,
Global Change Biol., 19, 1884–1896, https://doi.org/10.1111/gcb.12179, 2013.
Laruelle, G. G., Landschützer, P., Gruber, N., Tison, J.-L., Delille, B., and Regnier, P.: Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation, Biogeosciences, 14, 4545–4561, https://doi.org/10.5194/bg-14-4545-2017, 2017.
Lavoie, M., Phillips, C. L., and Risk, D.: A practical approach for
uncertainty quantification of high-frequency soil respiration using Forced
Diffusion chambers, J. Geophys. Res.-Biogeo., 120,
128–146, https://doi.org/10.1002/2014jg002773, 2015.
Law, C. S., Barr, N., Gall, M., Cummings, V., Currie, K., Murdoch, J.,
Halliday, J., Frost, E., Stevens, C., Plew, D., Vance, J., and Zeldis, J.:
Futureproofing the green-lipped mussel aquaculture industry against ocean
acidification, National Institute for Water and Atmospheric
Research/University of Otago, Wellington, New Zealand, 40 pp., 2020.
Little, R. J. A. and Rubin, D. B.: Statistical Analysis with Missing Data, 2nd,
John Wiley & Sons, Inc., Hoboken, New Jersey, 381 pp., 2002.
Lohrenz, S. E., Cai, W. J., Chakraborty, S., Huang, W. J., Guo, X., He, R.,
Xue, Z., Fennel, K., Howden, S., and Tian, H.: Satellite estimation of
coastal pCO2 and air-sea flux of carbon dioxide in the northern Gulf of
Mexico, Remote Sens. Environ., 207, 71–83,
https://doi.org/10.1016/j.rse.2017.12.039, 2018.
Lueker, T. J., Dickson, A. G., and Keeling, C. D.: Ocean pCO2 calculated from
dissolved inorganic carbon, alkalinity, and equations for K1 and K2:
validation based on laboratory measurements of CO2 in gas and seawater at
equilibrium, Mar. Chem., 70, 105–110, 2000.
Moffat, A. M., Papale, D., Reichstein, M., Hollinger, D. Y., Richardson, A.
D., Barr, A. G., Beckstein, C., Braswell, B. H., Churkina, G., Desai, A. R.,
Falge, E., Gove, J. H., Heimann, M., Hui, D., Jarvis, A. J., Kattge, J.,
Noormets, A., and Stauch, V. J.: Comprehensive comparison of gap-filling
techniques for eddy covariance net carbon fluxes, Agr. Forest
Meteorol., 147, 209–232, https://doi.org/10.1016/j.agrformet.2007.08.011, 2007.
Moritz, S. and Beielstein-Bartz, T.: imputeTS: Time Series Missing Value
Imputation in R, 12 pp., 2017.
Newton, J. A., Feely, R. A., Jewett, E. B., Williamson, P., and Mathis, J.:
Global Ocean Acidification Observing Network: Requirements and Governance
Plan, 61 pp., 2015.
O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K.
L., Garver, S. A., Kahru, M., and McClain, C.: Ocean color chlorophyll
algorithms for SeaWiFS, J. Geophys. Res., 103,
24937–24953, 1998.
Orr, J. C., Epitalon, J.-M., Dickson, A. G., and Gattuso, J.-P.: Routine
uncertainty propagation for the marine carbon dioxide system, Mar. Chem., 207, 84–107, https://doi.org/10.1016/j.marchem.2018.10.006, 2018.
Pappas, C., Papalexiou, S. M., and Koutsoyiannis, D.: A quick gap filling of
missing hydrometeorological data, J. Geophys. Res.-Atmos., 119, 9290–9300, https://doi.org/10.1002/2014jd021633, 2014.
Reimer, J. J., Cai, W.-J., Xue, L., Vargas, R., Noakes, S., Hu, X.,
Signorini, S. R., Mathis, J. T., Feely, R. A., Sutton, A. J., Sabine, C.,
Musielewicz, S., Chen, B., and Wanninkhof, R.: Time series pCO2 at a coastal
mooring: Internal consistency, seasonal cycles, and interannual variability,
Cont. Shelf Res., 145, 95–108, https://doi.org/10.1016/j.csr.2017.06.022, 2017.
Riebesell, U., Fabry, V. J., Hansson, L., Gattuso, J.: Guide to best
practices for ocean acidifcation research and data reporting, Publications
Office of the European Union, Luxembourg, https://doi.org/10.2777/66906, 2011.
Sasse, T. P., McNeil, B. I., and Abramowitz, G.: A new constraint on global
air-sea CO2 fluxes using bottle carbon data, Geophys. Res. Lett., 40,
1594–1599, https://doi.org/10.1002/grl.50342, 2013.
Sea-Bird Electronics, I.: SBE 45 MicroTSG Thermosalinograph Uer Manual
Sea-Bird Electronics, Inc., Bellevue, WA, USA, 58 pp., 2020.
Sea-Bird Electronics, I.: SeaFET V2 and SeapHOx V2 User Manual, Sea-Bird
Electronics, Inc., Bellevue, WA, USA, 56 pp., 2021.
Serrano-Notivoli, R., Tomas-Burguera, M., Beguería, S.,
Peña-Angulo, D., Vicente-Serrano, S. M., and González-Hidalgo,
J.-C.: Gap Filling of Monthly Temperature Data and Its Effect on Climatic
Variability and Trends, J. Climate, 32, 7797–7821,
https://doi.org/10.1175/jcli-d-19-0244.1, 2019.
Simons, R. A.: Chlorophyll-a, Aqua MODIS, NPP, L3SMI, Global, 4 km, Science
Quality, 2003-present (Monthly Composite), NOAA/NMFS/SWFSC/ERD [dataset],
https://doi.org/10.5067/AQUA/MODIS/L3M/CHL/2018, 2020a.
Simons, R. A.: Chlorophyll, NOAA VIIRS, Science Quality, Global, Level 3,
2012-present, Monthly, NOAA/NMFS/SWFSC/ERD [dataset], 2020b.
Stineman, R. W.: A consistently well-behaved method of interpolation,
Creative Computing, 6, 54–57, 1980.
Sutton, A. J., Feely, R. A., Maenner-Jones, S., Musielwicz, S., Osborne, J., Dietrich, C., Monacci, N., Cross, J., Bott, R., Kozyr, A., Andersson, A. J., Bates, N. R., Cai, W.-J., Cronin, M. F., De Carlo, E. H., Hales, B., Howden, S. D., Lee, C. M., Manzello, D. P., McPhaden, M. J., Meléndez, M., Mickett, J. B., Newton, J. A., Noakes, S. E., Noh, J. H., Olafsdottir, S. R., Salisbury, J. E., Send, U., Trull, T. W., Vandemark, D. C., and Weller, R. A.: Autonomous seawater pCO2 and pH time series from 40 surface buoys and the emergence of anthropogenic trends, Earth Syst. Sci. Data, 11, 421–439, https://doi.org/10.5194/essd-11-421-2019, 2019.
Sutton, A. J. S., Christopher, L., Dietrich, C., Maenner, J. S.,
Musielewicz, S., Bott, R., and Osborne, J.: High-resolution ocean and
atmosphere pCO2 time-series measurements from mooring KEO_145E_32N in the North Pacific Ocean (NCEI Accession 0100071)
[dataset], https://doi.org/10.3334/cdiac/otg.tsm_keo_145e_32n, 2012a.
Sutton, A. J. S., Christopher, L., Dietrich, C., Maenner, J. S.,
Musielewicz, S., Bott, R., and Osborne, J.: High-resolution ocean and
atmosphere pCO2 time-series measurements from mooring Papa_145W_50N in the North Pacific Ocean (NCEI Accession 0100074)
[dataset], https://doi.org/10.3334/cdiac/otg.tsm_papa_145w_50n, 2012b.
Takahashi, T., Feely, R. A., Weiss, R. F., Wanninkhof, R. H., Chipman, D.
W., Sutherland, S. C., and Takahashi, T. T.: Global air-sea flux of CO2: an
estimate based on measurements of sea-air pCO2 difference, P. Natl. Acad. Sci. USA, 94, 8292–8299, 1997.
Takahashi, T. and Sutherland, S. C.: Global ocean surface water partial
pressure of CO2 Database: Measurements performed during 1957-2018 (LDEO
Database Version 2018), NOAA National Centers for Environmental Information,
Silver Springs, MD, 2019.
Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A.,
Chipman, D. W., Hales, B., Friederich, G., Chavez, F., Sabine, C., Watson,
A., Bakker, D. C. E., Schuster, U., Metzl, N., Yoshikawa-Inoue, H., Ishii,
M., Midorikawa, T., Nojiri, Y., Körtzinger, A., Steinhoff, T., Hoppema,
M., Olafsson, J., Arnarson, T. S., Tilbrook, B., Johannessen, T., Olsen, A.,
Bellerby, R., Wong, C. S., Delille, B., Bates, N. R., and de Baar, H. J. W.:
Climatological mean and decadal change in surface ocean pCO2, and net
sea–air CO2 flux over the global oceans, Deep-Sea Res. Pt. II, 56, 554–577, https://doi.org/10.1016/j.dsr2.2008.12.009, 2009.
R: A Language and environment for statistical computing, available at: https://www.R-project.org/ (last access: 30 December 2021), 2020.
Terlouw, G. J., Knor, L. A. C. M., De Carlo, E. H., Drupp, P. S., Mackenzie,
F. T., Li, Y. H., Sutton, A. J., Plueddemann, A. J., and Sabine, C. L.:
Hawaii Coastal Seawater CO2 Network: A Statistical Evaluation of a Decade of
Observations on Tropical Coral Reefs, Front. Mar. Sci., 6, 226,
https://doi.org/10.3389/fmars.2019.00226, 2019.
Turk, D., Wang, H., Hu, X., Gledhill, D. K., Wang, Z. A., Jiang, L., and
Cai, W.-J.: Time of Emergence of Surface Ocean Carbon Dioxide Trends in the
North American Coastal Margins in Support of Ocean Acidification Observing
System Design, Front. Mar. Sci., 6, 91, https://doi.org/10.3389/fmars.2019.00091,
2019.
Van Buuren, S. and Groothuis-Oudshoorn, K.: MICE: Multivariate Imputation by
Chained Equations in R, Journal of Statistical Software, 45, 1–67,
https://doi.org/10.18637/jss.v045.i03, 2011.
Vance, J., Currie, K., and Zeldis, J.: An Empirical MLR for Estimating Surface Layer DIC and a Comparative Assessment to Other Gap-filling Techniques for Ocean Carbon Time Series [dataset, code], available at: https://figshare.com/projects/An_Empirical_MLR_for_Estimating_Surface_Layer_DIC_and_a_Comparative_Assessment_to_Other_Gap-filling_Techniques_for_Ocean_Carbon_Time_Series/100349, last access: 23 December 2021.
Velo, A., Pérez, F. F., Tanhua, T., Gilcoto, M., Ríos, A. F., and
Key, R. M.: Total alkalinity estimation using MLR and neural network
techniques, J. Mar. Syst., 11, 11–18,
https://doi.org/10.1016/j.jmarsys.2012.09.002, 2013.
Wang, J., Sun, W., and Zhang, J.: Sea Surface Salinity Products Validation
Based on Triple Match Method, IEEE Journal of Selected Topics in Applied
Earth Observations and Remote Sensing, 12, 4361–4366,
https://doi.org/10.1109/jstars.2019.2945486, 2019.
White, I. R., Royston, P., and Wood, A. M.: Multiple imputation using
chained equations: Issues and guidance for practice, Stat. Med., 30, 377–399,
https://doi.org/10.1002/sim.4067, 2011.
Willcox, S., Meinig, C., Sabine, C., Lawrence-Slavas, N., Richardson, T.,
Hine, R., and Manley, J.: An Autonomous Mobile Platform for Underway Surface
Carbon Measurements in Open-Ocean and Coastal Waters, Seattle, WA, USA, 8 pp.,
2009.
Zeebe, R. E., Ridgwell, A., and Zachos, J. C.: Anthropogenic carbon release
rate unprecedented during the past 66 million years, Nat. Geosci., 9,
325–329, https://doi.org/10.1038/ngeo2681, 2016.
Zeldis, J. R. and Swaney, D. P.: Balance of Catchment and Offshore Nutrient
Loading and Biogeochemical Response in Four New Zealand Coastal Systems:
Implications for Resource Management, Estuar. Coasts, 41, 2240–2259,
https://doi.org/10.1007/s12237-018-0432-5, 2018.
Zhao, J., Lange, H., and Meissner, H.: Gap-filling continuously-measured
soil respiration data: A highlight of time-series-based methods,
Agr. Forest Meteorol., 285–286, 107912,
https://doi.org/10.1016/j.agrformet.2020.107912, 2020.
Short summary
Long-term monitoring is needed to detect changes in our environment. Time series of ocean carbon have aided our understanding of seasonal cycles and provided evidence for ocean acidification. Data gaps are inevitable, yet no standard method for filling gaps exists. We present a regression approach here and compare it to seven other common methods to understand the impact of different approaches when assessing seasonal to climatic variability in ocean carbon.
Long-term monitoring is needed to detect changes in our environment. Time series of ocean carbon...
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