Optimizing the Biogeochemical Argo Float Distribution

Paul Chamberlain aUniversity of California, San Diego, La Jolla, California

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Lynne D. Talley aUniversity of California, San Diego, La Jolla, California

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Bruce Cornuelle aUniversity of California, San Diego, La Jolla, California

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Matthew Mazloff aUniversity of California, San Diego, La Jolla, California

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Sarah T. Gille aUniversity of California, San Diego, La Jolla, California

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Abstract

The core Argo array has operated with the design goal of uniform spatial distribution of 3° in latitude and longitude. Recent studies have acknowledged that spatial and temporal scales of variability in some parts of the ocean are not resolved by 3° sampling and have recommended increased core Argo density in the equatorial region, boundary currents, and marginal seas with an integrated vision of other Argo variants. Biogeochemical (BGC) Argo floats currently observe the ocean from a collection of pilot arrays, but recently funded proposals will transition these pilot arrays to a global array. The current BGC Argo implementation plan recommends uniform spatial distribution of BGC Argo floats. For the first time, we estimate the effectiveness of the existing BGC Argo array to resolve the anomaly from the mean using a subset of modeled, full-depth BGC fields. We also study the effectiveness of uniformly distributed BGC Argo arrays with varying float densities at observing the ocean. Then, using previous Argo trajectories, we estimate the Argo array’s future distribution and quantify how well it observes the ocean. Finally, using a novel technique for sequentially identifying the best deployment locations, we suggest the optimal array distribution for BGC Argo floats to minimize objective mapping uncertainty in a subset of BGC fields and to best constrain BGC temporal variability.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Paul Chamberlain, pchamber@ucsd.edu

Abstract

The core Argo array has operated with the design goal of uniform spatial distribution of 3° in latitude and longitude. Recent studies have acknowledged that spatial and temporal scales of variability in some parts of the ocean are not resolved by 3° sampling and have recommended increased core Argo density in the equatorial region, boundary currents, and marginal seas with an integrated vision of other Argo variants. Biogeochemical (BGC) Argo floats currently observe the ocean from a collection of pilot arrays, but recently funded proposals will transition these pilot arrays to a global array. The current BGC Argo implementation plan recommends uniform spatial distribution of BGC Argo floats. For the first time, we estimate the effectiveness of the existing BGC Argo array to resolve the anomaly from the mean using a subset of modeled, full-depth BGC fields. We also study the effectiveness of uniformly distributed BGC Argo arrays with varying float densities at observing the ocean. Then, using previous Argo trajectories, we estimate the Argo array’s future distribution and quantify how well it observes the ocean. Finally, using a novel technique for sequentially identifying the best deployment locations, we suggest the optimal array distribution for BGC Argo floats to minimize objective mapping uncertainty in a subset of BGC fields and to best constrain BGC temporal variability.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Paul Chamberlain, pchamber@ucsd.edu
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  • Adcroft, A., and Coauthors, 2019: The GFDL global ocean and sea ice model OM4.0: Model description and simulation features. J. Adv. Model. Earth Syst., 11, 31673211, https://doi.org/10.1029/2019MS001726.

    • Search Google Scholar
    • Export Citation
  • Argo, 2021: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)—Snapshot of Argo GDAC of May 10st 2021. SEANOE, accessed 10 November 2022, https://doi.org/10.17882/42182.

  • Bretherton, F. P., R. E. Davis, and C. B. Fandry, 1976: A technique for objective analysis and design of oceanographic experiments applied to MODE-73. Deep-Sea Res. Oceanogr. Abstr., 23, 559582, https://doi.org/10.1016/0011-7471(76)90001-2.

    • Search Google Scholar
    • Export Citation
  • Bushinsky, S. M., A. R. Gray, K. S. Johnson, and J. L. Sarmiento, 2017: Oxygen in the Southern Ocean from Argo floats: Determination of processes driving air-sea fluxes. J. Geophys. Res. Oceans, 122, 86618682, https://doi.org/10.1002/2017JC012923.

    • Search Google Scholar
    • Export Citation
  • Bushinsky, S. M., and Coauthors, 2019: Reassessing Southern Ocean air-sea CO2 flux estimates with the addition of biogeochemical float observations. Global Biogeochem. Cycles, 33, 13701388, https://doi.org/10.1029/2019GB006176.

    • Search Google Scholar
    • Export Citation
  • Cai, W., A. Sullivan, and T. Cowan, 2011: Interactions of ENSO, the IOD, and the SAM in CMIP3 models. J. Climate, 24, 16881704, https://doi.org/10.1175/2010JCLI3744.1.

    • Search Google Scholar
    • Export Citation
  • Campbell, E. C., E. A. Wilson, G. W. K. Moore, S. C. Riser, C. E. Brayton, M. R. Mazloff, and L. D. Talley, 2019: Antarctic offshore polynyas linked to Southern Hemisphere climate anomalies. Nature, 570, 319325, https://doi.org/10.1038/s41586-019-1294-0.

    • Search Google Scholar
    • Export Citation
  • Campbell, J. W., 1995: The lognormal distribution as a model for bio-optical variability in the sea. J. Geophys. Res., 100, 13 23713 254, https://doi.org/10.1029/95JC00458.

    • Search Google Scholar
    • Export Citation
  • Chamberlain, P. M., L. D. Talley, M. R. Mazloff, S. C. Riser, K. Speer, A. R. Gray, and A. Schwartzman, 2018: Observing the ice-covered Weddell Gyre with profiling floats: Position uncertainties and correlation statistics. J. Geophys. Res. Oceans, 123, 83838410, https://doi.org/10.1029/2017JC012990.

    • Search Google Scholar
    • Export Citation
  • Chamberlain, P. M., L. D. Talley, M. R. Mazloff, E. van Sebille, S. T. Gille, T. Tucker, M. Scanderbeg, and P. Robbins, 2023: Using existing Argo trajectories to statistically predict future float positions with a transition matrix. J. Atmos. Oceanic Technol., 40, 10831103, https://doi.org/10.1175/JTECH-D-22-0070.1.

    • Search Google Scholar
    • Export Citation
  • Dunne, J. P., and Coauthors, 2020: Simple global ocean Biogeochemistry with Light, Iron, Nutrients and Gas version 2 (BLINGv2): Model description and simulation characteristics in GFDL’s CM4.0. J. Adv. Model. Earth Syst., 12, e2019MS002008, https://doi.org/10.1029/2019MS002008.

    • Search Google Scholar
    • Export Citation
  • Fay, A. R., and G. A. McKinley, 2014: Global open-ocean biomes: Mean and temporal variability. Earth Syst. Sci. Data, 6, 273284, https://doi.org/10.5194/essd-6-273-2014.

    • Search Google Scholar
    • Export Citation
  • Ford, D., 2021: Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design. Biogeosciences, 18, 509534, https://doi.org/10.5194/bg-18-509-2021.

    • Search Google Scholar
    • Export Citation
  • Galbraith, E. D., A. Gnanadesikan, J. P. Dunne, and M. R. Hiscock, 2010: Regional impacts of iron-light colimitation in a global biogeochemical model. Biogeosciences, 7, 10431064, https://doi.org/10.5194/bg-7-1043-2010.

    • Search Google Scholar
    • Export Citation
  • Garcia, H. E., and Coauthors, 2019a: Dissolved Inorganic Nutrients (Phosphate, Nitrate and Nitrate+Nitrite, Silicate). Vol. 4, World Ocean Atlas 2018, NOAA Atlas NESDIS 84, 35 pp., https://www.ncei.noaa.gov/sites/default/files/2020-04/woa18_vol4.pdf.

  • Garcia, H. E., and Coauthors, 2019b: Dissolved Oxygen, Apparent Oxygen Utilization, and Dissolved Oxygen Saturation. Vol. 3, World Ocean Atlas 2018, NOAA Atlas NESDIS 83, 38 pp., https://www.ncei.noaa.gov/sites/default/files/2022-06/woa18_vol3.pdf.

  • Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723757, https://doi.org/10.1002/qj.49712555417.

    • Search Google Scholar
    • Export Citation
  • Hastings, W. K., 1970: Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97109, https://doi.org/10.1093/biomet/57.1.97.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., and Coauthors, 2019: Structure and performance of GFDL’s CM4.0 climate model. J. Adv. Model. Earth Syst., 11, 36913727, https://doi.org/10.1029/2019MS001829.

    • Search Google Scholar
    • Export Citation
  • Ide, K., P. Courtier, M. Ghil, and A. C. Lorenc, 1997: Unified notation for data assimilation: Operational, sequential and variational. J. Meteor. Soc. Japan, 75, 181189, https://doi.org/10.2151/jmsj1965.75.1B_181.

    • Search Google Scholar
    • Export Citation
  • Johnson, K., and H. Claustre, Eds., 2016: The scientific rationale, design, and implementation plan for a Biogeochemical-Argo float array. Biogeochemical-Argo Planning Group, 65 pp., https://doi.org/10.13155/46601.

  • Johnson, K., and Coauthors, 2017: Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) float data archive. UC San Diego Library Digital Collections, accessed 24 October 2022, https://doi.org/10.6075/J0TX3C9X.

  • Kamenkovich, I., W. Cheng, C. Schmid, and D. E. Harrison, 2011: Effects of eddies on an ocean observing system with profiling floats: Idealized simulations of the Argo array. J. Geophys. Res., 116, C06003, https://doi.org/10.1029/2010JC006910.

    • Search Google Scholar
    • Export Citation
  • Kamenkovich, I., A. Haza, A. R. Gray, C. O. Dufour, and Z. Garraffo, 2017: Observing system simulation experiments for an array of autonomous biogeochemical profiling floats in the Southern Ocean. J. Geophys. Res. Oceans, 122, 75957611, https://doi.org/10.1002/2017JC012819.

    • Search Google Scholar
    • Export Citation
  • Landschützer, P., N. Gruber, and D. C. E. Bakker, 2016: Decadal variations and trends of the global ocean carbon sink. Global Biogeochem. Cycles, 30, 13961417, https://doi.org/10.1002/2015GB005359.

    • Search Google Scholar
    • Export Citation
  • Locarnini, M., and Coauthors, 2018: Temperature. Vol. 1, World Ocean Atlas 2018, NOAA Atlas NESDIS 81, 52 pp., https://www.ncei.noaa.gov/sites/default/files/2020-04/woa18_vol1.pdf.

  • Majkut, J. D., B. R. Carter, T. L. Frölicher, C. O. Dufour, K. B. Rodgers, and J. L. Sarmiento, 2014: An observing system simulation for Southern Ocean carbon dioxide uptake. Philos. Trans. Roy. Soc., A372, 20130046, https://doi.org/10.1098/rsta.2013.0046.

    • Search Google Scholar
    • Export Citation
  • Markov, A. A., 1906: Rasprostranenie Zakona Bol’shih chisel na velichiny, zavisyaschie drug ot druga (in Russian). Izv. Fiz.-Mat. Obschestva Kazan. Univ., 15, 135156.

    • Search Google Scholar
    • Export Citation
  • Mazloff, M. R., P. Heimbach, and C. Wunsch, 2010: An eddy-permitting Southern Ocean state estimate. J. Phys. Oceanogr., 40, 880899, https://doi.org/10.1175/2009JPO4236.1.

    • Search Google Scholar
    • Export Citation
  • Monteiro, P. M. S., L. Gregor, M. Lévy, S. Maenner, C. L. Sabine, and S. Swart, 2015: Intraseasonal variability linked to sampling alias in air–sea CO2 fluxes in the Southern Ocean. Geophys. Res. Lett., 42, 85078514, https://doi.org/10.1002/2015GL066009.

    • Search Google Scholar
    • Export Citation
  • NASA, 2022: Ocean Biology Processing Group, AOB. OB.DAAC, accessed 27 June 2022, https://oceancolor.gsfc.nasa.gov/.

  • Prend, C. J., S. T. Gille, L. D. Talley, B. G. Mitchell, I. Rosso, and M. R. Mazloff, 2019: Physical drivers of phytoplankton bloom initiation in the Southern Ocean’s Scotia Sea. J. Geophys. Res. Oceans, 124, 58115826, https://doi.org/10.1029/2019JC015162.

    • Search Google Scholar
    • Export Citation
  • Reygondeau, G., A. Longhurst, E. Martinez, G. Beaugrand, D. Antoine, and O. Maury, 2013: Dynamic biogeochemical provinces in the global ocean. Global Biogeochem. Cycles, 27, 10461058, https://doi.org/10.1002/gbc.20089.

    • Search Google Scholar
    • Export Citation
  • Roemmich, D., and J. Gilson, 2009: The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo program. Prog. Oceanogr., 82, 81100, https://doi.org/10.1016/j.pocean.2009.03.004.

    • Search Google Scholar
    • Export Citation
  • Roemmich, D., and Coauthors, 1999: On the design and implementation of Argo: A global array of profiling floats. Argo Science Team Doc., 35 pp., https://argo.ucsd.edu/wp-content/uploads/sites/361/2020/05/argo-design.pdf.

  • Roemmich, D., and Coauthors, 2019: 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.

    • Search Google Scholar
    • Export Citation
  • Saxon, 2008: Saxon Algebra 2. Houghton Mifflin Harcourt, 1048 pp.

  • Schlunegger, S., and Coauthors, 2020: Time of emergence and large ensemble intercomparison for ocean biogeochemical trends. Global Biogeochem. Cycles, 34, e2019GB006453, https://doi.org/10.1029/2019GB006453.

    • Search Google Scholar
    • Export Citation
  • Sévellec, F., A. Colin de Verdiére, and M. Ollitrault, 2017: Evolution of intermediate water masses based on Argo float displacements. J. Phys. Oceanogr., 47, 15691586, https://doi.org/10.1175/JPO-D-16-0182.1.

    • Search Google Scholar
    • Export Citation
  • Talley, L. D., V. Lobanov, V. Ponomarev, A. Salyuk, P. Tishchenko, I. Zhabin, and S. Riser, 2003: Deep convection and brine rejection in the Japan Sea. Geophys. Res. Lett., 30, 1159, https://doi.org/10.1029/2002GL016451.

    • Search Google Scholar
    • Export Citation
  • Zweng, M. M., and Coauthors, 2019: Salinity. Vol. 2, World Ocean Atlas 2018, NOAA Atlas NESDIS 82, 50 pp., https://www.ncei.noaa.gov/sites/default/files/2022-06/woa18_vol2.pdf.

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