High carbon dioxide (CO2) concentrations in sea-water (ocean hypercapnia) can induce neurological, physiological and behavioural deficiencies in marine animals1,2,3,4,5,6,7,8,9,10. Prediction of the onset and evolution of hypercapnia in the ocean requires a good understanding of annual variations in oceanic CO2 concentration, but there is a lack of relevant global observational data. Here we identify global ocean patterns of monthly variability in carbon concentration using observations that allow us to examine the evolution of surface-ocean CO2 levels over the entire annual cycle under increasing atmospheric CO2 concentrations. We predict that the present-day amplitude of the natural oscillations in oceanic CO2 concentration will be amplified by up to tenfold in some regions by 2100, if atmospheric CO2 concentrations continue to rise throughout this century (according to the RCP8.5 scenario of the Intergovernmental Panel on Climate Change)11. The findings from our data are broadly consistent with projections from Earth system climate models12,13,14,15. Our predicted amplification of the annual CO2 cycle displays distinct global patterns that may expose major fisheries in the Southern, Pacific and North Atlantic oceans to hypercapnia many decades earlier than is expected from average atmospheric CO2 concentrations. We suggest that these ocean ‘CO2 hotspots’ evolve as a combination of the strong seasonal dynamics of CO2 concentration and the long-term effective storage of anthropogenic CO2 in the oceans that lowers the buffer capacity in these regions, causing a nonlinear amplification of CO2 concentration over the annual cycle. The onset of ocean hypercapnia (when the partial pressure of CO2 in sea-water exceeds 1,000 micro-atmospheres) is forecast for atmospheric CO2 concentrations that exceed 650 parts per million, with hypercapnia expected in up to half the surface ocean by 2100, assuming a high-emissions scenario (RCP8.5)11. Such extensive ocean hypercapnia has detrimental implications for fisheries during the twenty-first century.
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We thank R. Matear and A. Lenton from the CSIRO for conversations, performing initial model runs and analysis of CMIP5 model output for use here. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (Hadley Centre, IPSL, Can and GFDL) for producing their models and making the output openly accessible. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support, and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We acknowledge support from the Australian Research Council discovery grant DP110104955.
The authors declare no competing financial interests.
Extended data figures and tables
Distribution of the mean Revelle or ‘buffer’ factor in the surface ocean in 2000, determined from our study. Source data
Bias in concentrations (expressed as a percentage) in 2100 due to using steady-state assumptions in the SOMLO analysis. Positive (negative) values indicate that SOMLO over-estimates (under-estimates) .
a–d, CO2 amplification factors by the year 2100 as predicted by four ESMs: HadGEM2-ES (a), IPSL-CM5A-MR (b), CanESM2 (c) and GFDL-ESM2M (d); see Extended Data Table 1 for model details.
Extended Data Figure 4 Comparison of present-day natural CO2 variability as predicted by model and data.
Taylor diagram comparing the ability of ESMs to capture the dynamics and magnitude of the annual cycle of CO2 with our data-based approach. Here, the open circle on the x axis represents the standard deviation in our data-based predictions, while the grey contour lines represent the residual standard error between the ESMs and our data-based predictions.
CO2 amplification factors within the HadGEM2 model for each year from 2097 to 2100.
Surface distribution (normalized such that the mean level across the whole surface ocean is shifted to zero) as predicted by SOMLO and two climate models (CanESM2 and HadGEM2-ES), highlighting the change in distribution between 2006 and 2100. The nonlinearity is evidenced by ‘bulging’ in the 2100 distributions for positive , relative to those for negative .
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McNeil, B., Sasse, T. Future ocean hypercapnia driven by anthropogenic amplification of the natural CO2 cycle. Nature 529, 383–386 (2016). https://doi.org/10.1038/nature16156
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