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Circulation-driven variability of Atlantic anthropogenic carbon transports and uptake

Nature Geoscience volume 14, pages 571–577 (2021)Cite this article

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Abstract

The ocean absorbs approximately a quarter of the carbon dioxide currently released to the atmosphere by human activities (Canth). A disproportionately large fraction accumulates in the North Atlantic due to the combined effects of transport by the Atlantic Meridional Overturning Circulation (AMOC) and air–sea exchange. However, discrepancies exist between modelled and observed estimates of the air–sea exchange due to unresolved ocean transport variability. Here we quantify the strength and variability of Canth transports across 26.5° N in the North Atlantic between 2004 and 2012 using circulation measurements from the RAPID mooring array and hydrographic observations. Over this period, decreasing circulation strength tended to decrease northward Canth transport, while increasing Canth concentrations (preferentially in the upper limb of the overturning circulation) tended to increase northward Canth transport. These two processes compensated each other over the 8.5-year period. While ocean transport and air–sea Canth fluxes are approximately equal in magnitude, the increasing accumulation rate of Canth in the North Atlantic combined with a stable ocean transport supply means we infer a growing contribution from air–sea Canth fluxes over the period. North Atlantic Canth accumulation is thus sensitive to AMOC strength, but growing atmospheric Canth uptake continues to significantly impact Canth transports.

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Fig. 1: AMOC observing system mooring array at 26.5° N with 2010 Canth distribution.
Fig. 2: Canth transports across 26.5° N in the subtropical NA.
Fig. 3: AMOC–Canth transport co-variability and non-AMOC variability.
Fig. 4: Impact of increasing Canth concentrations on ocean Canth transport.

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Data availability

The carbon transport data that support the findings of this study63 are available from the British Oceanographic Data Centre at http://doi.org/10/fn4j. Raw hydrographic datasets are at https://cchdo.ucsd.edu/. Final adjusted hydrographic datasets are available from GLODAP (https://www.glodap.info/). AMOC estimates are available from the RAPID programme website (https://www.rapid.ac.uk/). Atmospheric CO2 is available from the GLOBALVIEW-CO2 web resources (GLOBALVIEW-CO2; Cooperative Atmospheric Data Integration Project—Carbon Dioxide; NOAA ESRL, Boulder, Colorado; Also available on the Internet via anonymous FTP to ftp.cmdl.noaa.gov, path: ccg/co2/GLOBALVIEW). Sea surface pCO2 observations are from the Surface Ocean Carbon Dioxide Atlas SOCAT: https://www.socat.info/. NCEP/NCAR temperature and sea-level pressure fields are available from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.surface.html. Source data are provided with this paper.

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Acknowledgements

The authors are grateful for support from the UK Natural Environment Research Council through projects Radiatively Active Gases from the North Atlantic Region and Climate Change (RAGNARoCC) NE/K00249X/1, Atlantic Biogeochemical Fluxes (ABC-Fluxes) NE/M005046/1 and RAPID-AMOC ((P.J.B., E.L.M., B.A.K., R.S., A.J.W., U.S., M.-J.M., D.A.S.), the NOAA Global Ocean Monitoring and Observation Program (GOMO) (via the Western Boundary Time Series project; FundRef number 100007298) and the NOAA Atlantic Oceanographic and Meteorological Laboratory (M.O.B., C.S.M., R.W.).

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Authors and Affiliations

  1. National Oceanography Centre, Southampton, UK

    Peter J. Brown, Elaine L. McDonagh, Richard Sanders, Brian A. King, David A. Smeed & Andrew Yool

  2. NORCE, Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway

    Elaine L. McDonagh & Richard Sanders

  3. College of Life and Environmental Sciences, University of Exeter, Exeter, UK

    Andrew J. Watson, Ute Schuster & Marie-José Messias

  4. NOAA, Atlantic Oceanographic and Meteorological Laboratory, Miami, FL, USA

    Rik Wanninkhof, Molly O. Baringer & Christopher S. Meinen

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Contributions

E.L.M., B.A.K., R.S. and A.J.W. designed the study. P.J.B. performed the analysis. P.J.B., E.L.M., R.S. and A.J.W. wrote the manuscript. D.A.S., U.S., M.O.B., C.S.M. and R.W. gave technical support and conceptual advice. M.O.B., C.S.M., A.Y., U.S. and M.-J.M. contributed observational data.

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Correspondence to Peter J. Brown.

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Peer review information Nature Geoscience thanks Gabriel Rosón and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson.

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Extended data

Extended Data Table 1 Predictive coefficients a–d and constant y0 from PREs for individual bins
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Extended Data Table 2 Canth transport uncertainty estimates
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Extended Data Fig. 1 PRE regions and performance statistics.

a, Data bin locations for generation of independent predictive multiple linear regressions for Canth estimation, b, individual predictive PRE root mean square error, and c, individual predictive PRE R2 for each data bin for ΔC* Canth. Box colours and numbers relate to Extended Data Fig. 3 & 4.

Source data

Extended Data Fig. 2 PRE residuals (predicted Canth – bottle Canth) plotted against depth.

a, for individual PREs, b, for all outputs binned, for ΔC* Canth. Numbers and colours relate to regions in Extended Data Fig. 1a. Dots relate to Western basin, circles to Eastern basin.

Source data

Extended Data Fig. 3 Bottle Canth estimates versus PRE predicted Canth for ΔC* Canth.

Numbers and colours relate to regions in Extended Data Fig. 1a. Black lines indicate unity. Red lines indicate linear least squares fit of bottle estimates versus predicted. Dots relate to Western basin, circles to Eastern basin.

Source data

Extended Data Fig. 4 Schematic of PreIndustrial DisEquilibrium (SPIDEr) mixed layer Canth calculation.

Blue colour implies preindustrial era, yellow colour implies modern era. Numbers on left refer to explanations in Methods text.

Extended Data Fig. 5 Variability in predicted surface layer Canth.

For 2009 at 62.375° W with Canth plotted against a, pressure, b, neutral density and c, potential temperature. Colour refers to time of year.

Source data

Extended Data Fig. 6 Canth transports across Florida Straits and 26.5°N.

a, transport-weighted Canth transports and volume transports for Florida Straits in 2004, 2010 and 2012. Lines are linear predictive fits. TTD has no data for 2012. b, Canth transports across 26.5°N on 10-day (thin lines) and 3-month filtered (thick lines) timescales for 2004.3-2012.8 for four Canth calculation methods with 2004-2012 averages and standard deviation.

Source data

Extended Data Fig. 7 Application of Canth transport calculation methodology to model outputs.

Anthropogenic carbon transports (a,c) and their residual from the model truth (b,d) for 1980-2100 (a,b) and 2004-2013 (c,d) for five unique applications of the PRE methodology applied to 1° NEMO-MEDUSA model outputs. Legend lists colour schemes of model truth, and different modifications of PRE methodology applied. For conversion of carbon transports, 660 kmol s-1 = 0.25 Pg C yr-1.

Source data

Extended Data Fig. 8 Application of back-calculation Canth methodology to model outputs.

Top: Anthropogenic carbon transports for 1980-2100 for 1. application of ΔC* Canth calculation method to 1° NEMO-MEDUSA model outputs combined with model velocity fields, and 2. model truth. Bottom, as for top but for 2004-2013. Monthly values and 12-month running mean shown for both. For conversion of carbon transports, 660 kmol s-1 = 0.25 Pg C yr-1.

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Brown, P.J., McDonagh, E.L., Sanders, R. et al. Circulation-driven variability of Atlantic anthropogenic carbon transports and uptake. Nat. Geosci. 14, 571–577 (2021). https://doi.org/10.1038/s41561-021-00774-5

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