Emergent constraint on Arctic Ocean acidification in the twenty-first century

Abstract

The ongoing uptake of anthropogenic carbon by the ocean leads to ocean acidification, a process that results in a reduction in pH and in the saturation state of biogenic calcium carbonate minerals aragonite (Ωarag) and calcite (Ωcalc)1,2. Because of its naturally low Ωarag and Ωcalc (refs. 2,3), the Arctic Ocean is considered the region most susceptible to future acidification and associated ecosystem impacts4,5,6,7. However, the magnitude of projected twenty-first century acidification differs strongly across Earth system models8. Here we identify an emergent multi-model relationship between the simulated present-day density of Arctic Ocean surface waters, used as a proxy for Arctic deep-water formation, and projections of the anthropogenic carbon inventory and coincident acidification. By applying observations of sea surface density, we constrain the end of twenty-first century Arctic Ocean anthropogenic carbon inventory to 9.0 ± 1.6 petagrams of carbon and the basin-averaged Ωarag and Ωcalc to 0.76 ± 0.06 and 1.19 ± 0.09, respectively, under the high-emissions Representative Concentration Pathway 8.5 climate scenario. Our results indicate greater regional anthropogenic carbon storage and ocean acidification than previously projected3,8 and increase the probability that large parts of the mesopelagic Arctic Ocean will be undersaturated with respect to calcite by the end of the century. This increased rate of Arctic Ocean acidification, combined with rapidly changing physical and biogeochemical Arctic conditions9,10,11, is likely to exacerbate the impact of climate change on vulnerable Arctic marine ecosystems.

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Fig. 1: Projections of Arctic Ocean anthropogenic carbon and aragonite saturation state.
Fig. 2: Arctic Ocean surface water density and the anthropogenic carbon inventory.
Fig. 3: Emergent constraints on the projected anthropogenic carbon inventory and future acidification.
Fig. 4: Constrained end-of century Arctic Ocean vertical profiles of Ωcalc and Ωarag.

Data availability

The Earth system model output used in this study is available via the Earth System Grid Federation (https://esgf-node.ipsl.upmc.fr/projects/esgf-ipsl/). Observations from the World Ocean Atlas 2013 (https://www.nodc.noaa.gov/OC5/woa18/) and GLODAPv2 (https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2019/) are available via the National Oceanic and Atmospheric Administration. The output of ocean-only NEMO-PISCES simulations is freely accessible from the ODATIS-supported centre51.

Code availability

The open-source software Python 2.7 was used for analyses. The module ‘statsmodels’ (https://www.statsmodels.org/stable/index.html) was used for linear regression and the calculation of prediction intervals and the module ‘matplotlib’ (https://matplotlib.org/) was used to create all figures including the maps. The mocsy2.0 routines were used to calculate the ocean carbonate system variables (http://ocmip5.ipsl.jussieu.fr/mocsy/). The Climate Data Operators were used for regridding of CMIP5 model output (https://code.mpimet.mpg.de/projects/cdo/). The code for the NEMO ocean model version 3.2 is available under CeCILL license online (http://www.nemo-ocean.eu).

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Acknowledgements

This study was funded by the H2020 C-CASCADES (grant number 643052), the H2020 CRESCENDO (grant number 641816), the H2020 4C (grant number 821003), the Agence Nationale de la Recherche (grant ANR-18-ERC2-0001-01; CONVINCE), the MTES/FRB Acidoscope project and the ENS-Chanel research chair. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led the development of software infrastructure in partnership with the Global Organisation for Earth System Science Portals. We also thank the IPSL modelling group for the software infrastructure, which facilitated CMIP5 analysis, J.-M. Molines, L. Brodeau and B. Barnier for developing the DRAKKAR ORCA05 and ORCA025 global configurations of NEMO and J. Simeon, C. Ethé, M. Gehlen and J. C. Orr for the implementation of NEMO-PISCES within these configurations.

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Contributions

This study was conceived by all co-authors. J.T. performed the model output analysis and produced the figures, with help from L.K. and L.B. All authors contributed ideas, discussed the results and wrote the manuscript.

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Correspondence to Jens Terhaar.

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Peer review information Nature thanks Leif Anderson, Filippa Fransner, Chad Thackeray and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Projections and emergent constraints on Arctic Ocean calcite saturation state and pH.

a, c, ESM projections of the twenty-first century Arctic Ocean basin-averaged Ωcalc (a) and basin-averaged pH (c). b, d, Vertical profiles of basin-averaged Ωcalc (b) and pH (d) in 2100 for the 11 ESMs. The GLODAPv2 observational profiles of Ωcalc and pH for 2002, respectively, are marked as black lines in b and d. e, g, The projected Arctic Ocean basin-averaged Ωcalc (e) and basin-averaged pH (g) in 2100 against present-day maximum sea surface density (95th percentile waters) for the ESM ensemble (black dots). Linear regression fits (red dashed lines) and the associated 68% prediction intervals are shown, as are data-based estimates of present-day maximum sea surface density (black dashed lines) with the associated standard deviation (grey shaded area). f, h, Probability density functions for the end-of-century Arctic Ocean basin-averaged Ωcalc (f) and basin-averaged pH (h), before (black) and after (red) the emergent constraint is applied.

Extended Data Fig. 2 Arctic Ocean surface water density.

al, Present-day annual-mean sea surface density from the 11 ESMs (a–k) and from World Ocean Atlas 2013 observations (l). Contours delineate regions that contribute to the maximum surface density as defined by the 95th percentile densities.

Extended Data Fig. 3 Arctic Ocean present-day density anomaly and anthropogenic carbon inventory in 2100 against seasonal sea-ice extent, volume of light waters, and number of grid cells in the Arctic Ocean on the native model grid.

ad, The Arctic Ocean anthropogenic carbon inventory in 2100 against winter sea-ice extent (February and March) (a), summer sea-ice extent (August and September) (b), the volume of light waters (c) and the number of grid cells in the Arctic Ocean (d) on the native model grid for each of the 11 ESMs. eh, Arctic Ocean present-day maximum surface density anomaly against winter sea-ice extent (e), summer sea-ice extent (f), the volume of light waters (g) and the number of grid cells in the Arctic Ocean (h). The volume of light waters is defined as the volume of water masses with densities below the respective maximum sea surface density (95th percentile waters).

Extended Data Fig. 4 Correlations between projections of the Arctic Ocean anthropogenic carbon inventory and Ωarag, Ωcalc and pH.

ad, Arctic Ocean basin-averaged Ωarag in 2100 (a), Ωcalc in 2100 (b), pH in 2100 (c) and the anthropogenic carbon inventory in 2002 (d) against the anthropogenic carbon inventory in 2100 for the 11 ESMs.

Extended Data Fig. 5 Emergent constraints on future aragonite saturation state in different depth layers.

The projected end-of-century Arctic Ocean Ωarag, across six depth layers (af) plotted against maximum sea surface density (95th percentile waters) for the ESM ensemble (black dots). Linear regression fits (red dashed lines) and the associated 68% prediction intervals are shown, as are data-based estimates of present-day maximum sea surface density (black dashed lines) with the associated standard deviation (grey shaded area).

Extended Data Fig. 6 Emergent constraints on future calcite saturation state in different depth layers.

The projected end-of-century Arctic Ocean Ωcalc, across six depth layers (af) plotted against maximum sea surface density (95th percentile waters) for the ESM ensemble (black dots). Linear regression fits (red dashed lines) and the associated 68% prediction intervals are shown, as are data-based estimates of present-day maximum sea surface density (black dashed lines) with the associated standard deviation (grey shaded area).

Extended Data Fig. 7 Emergent constraints on future pH in different depth layers.

The projected end-of-century Arctic Ocean pH, across six depth layers (af) plotted against maximum sea surface density (95th percentile waters) for the ESM ensemble (black dots). Linear regression fits (red dashed lines) and the associated 68% prediction intervals are shown, as are data-based estimates of present-day maximum sea surface density (black dashed lines) with the associated standard deviation (grey shaded area). g, Multi-model mean vertical profiles of basin-averaged pH in 2100 (black lines) with the associated standard deviation (n = 11, grey shading). Constrained estimates of pH (red dots) are shown for the six depth layers given in af. The constrained estimates are shown at the mid-point of each layer, with error bars representing ±1 standard deviation.

Extended Data Fig. 8 Emergent constraints on future \({{\boldsymbol{p}}}_{{{\rm{CO}}}_{2}}\) in different depth layers.

The projected end-of-century Arctic Ocean \({p}_{{{\rm{CO}}}_{2}}\), across six depth layers (af) plotted against maximum sea surface density (95th percentile waters) for the ESM ensemble (black dots). Linear regression fits (red dashed lines) and the associated 68% prediction intervals are shown, as are data-based estimates of present-day maximum sea surface density (black dashed lines) with the associated standard deviation (grey shaded area). g, Multi-model mean vertical profiles of basin-averaged \({p}_{{{\rm{CO}}}_{2}}\) in 2100 (black lines) with the associated standard deviation (n = 11, grey shading). Constrained estimates of \({p}_{{{\rm{CO}}}_{2}}\) (red dots) are shown for the six depth layers given in af. The constrained estimates are shown at the mid-point of each layer, with error bars representing ±1 standard deviation.

Extended Data Fig. 9 Internal variability within the context of emergent constraints on the projected anthropogenic carbon inventory.

The projected Arctic Ocean anthropogenic carbon inventory against present-day maximum sea surface density (95th percentile waters) for the ESM ensemble (as shown in Fig. 3). The linear regression fit (red dashed line) and the associated 68% prediction intervals are shown, as are data-based estimates of present-day maximum sea surface density (black dashed lines) with the associated standard deviation (grey shaded area). In addition to the IPSL-CM5A-LR ensemble member used in the main paper and for the regression fit, three additional IPSL-CM5A-LR ensemble members are shown (blue dots).

Extended Data Table 1 The percentage probability of different year-2100 acidification extremes under RCP8.5 in the CMIP5 before and after the application of the maximum surface density emergent constraint

Supplementary information

Supplementary Information

The supplementary information file includes 2 supplementary tables and 2 supplementary figures. Table S1 shows the CMIP5 Earth System Models used in this study and the corresponding model groups. Table S1 and Figures S1 and S2 show analyses from the ocean-biogeochemical model NEMO-PISCES.

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Terhaar, J., Kwiatkowski, L. & Bopp, L. Emergent constraint on Arctic Ocean acidification in the twenty-first century. Nature 582, 379–383 (2020). https://doi.org/10.1038/s41586-020-2360-3

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