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Global carbonate chemistry gradients reveal a negative feedback on ocean alkalinity enhancement

Nature Geoscience volume 18, pages 232–238 (2025)Cite this article

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Abstract

Ocean alkalinity enhancement is a widely considered approach for marine CO2 removal. Alkalinity enhancement sequesters atmospheric CO2 by shifting the seawater carbonate equilibrium from CO2 towards bicarbonate and carbonate ions. Such re-equilibration has been hypothesized to benefit calcifying organisms, whose increased calcification could strongly reduce the efficiency of alkalinity enhancement. Here we use global ocean satellite data to constrain the sensitivity of coccolithophores—an important group of calcifying phytoplankton—to natural gradients of seawater carbonate chemistry. We show that the ratio of particulate inorganic to particulate organic carbon, reflecting the balance of calcifying versus non-calcifying phytoplankton, is influenced by environmental drivers, including nutrient stoichiometry and carbon substrate within biogeochemical provinces. Across biogeochemical provinces, however, this ratio persistently correlates with carbonate chemistry through combined influences of carbon substrate availability and proton inhibition of calcification. We estimate that extreme alkalinity enhancement may promote the proliferation of coccolithophores, thereby reducing the CO2 removal potential of ocean alkalinity enhancement by 2–29% by 2100. However, less extreme alkalinity enhancement may only mitigate for adverse acidification effects on coccolithophores. Our findings demonstrate the importance of considering large-scale biogeochemical feedbacks when evaluating the efficiency of ocean alkalinity enhancement.

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Fig. 1: Annual surface distributions of PIC/POC and key carbonate system parameters.
Fig. 2: Distribution of PIC/POC and key drivers of the carbonate system across major ocean basins.
Fig. 3: Correlation between PIC/POC and key investigated environmental drivers within oceanographic provinces.
Fig. 4: Linear relationship between observed PIC/POC and key investigated environmental drivers across different spatial scales.
Fig. 5: Global model projections under a high CO2 emissions scenario (RCP 8.5) and global-scale OAE.

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Data availability

All data used in this study are publicly available online. Remotely sensed monthly climatologies of PIC, POC and PAR are made freely available by the NASA Ocean Biology Processing Group at the Goddard Space Flight Center (https://oceancolor.gsfc.nasa.gov). The Argo climatology of monthly mixed-layer depths44 can be accessed via https://mixedlayer.ucsd.edu/. The OceanSODA-ETHZ (v.2023) gridded dataset of surface ocean carbonate chemistry45 is available online (https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.nodc:0220059). Both the globally gridded nutrient datasets (WOA23) and the discrete nutrient data used for marginal sea analyses (WOD18) are available via the National Centers for Environmental Information (NCEI) data portal (https://www.ncei.noaa.gov/products/world-ocean-atlas, https://www.ncei.noaa.gov/products/world-ocean-database). Model outputs used for future projections are available from https://data.geomar.de/downloads/20.500.12085/04eefb69-5509-430b-90c9-467169aba218/. A compilation of the data used for the analyses and depicted in the figures is available for download via Zenodo (https://doi.org/10.5281/zenodo.14489819)84.

Code availability

The R scripts to reproduce the analyses depicted in the figures are available for download via Zenodo (https://doi.org/10.5281/zenodo.14489819)84.

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Acknowledgements

Funding for this project was provided by the Carbon to Sea Initiative. L.T.B. acknowledges funding from the Australian Research Council through a Future Fellowship award (FT200100846). We thank P. Strutton and Z. Chase for constructive early discussions on data products and insightful comments on the paper.

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  1. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia

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Contributions

N.L. and L.T.B. designed the study. N.L. conducted the data analysis. L.T.B. performed calculations on OAE efficiency. N.L. wrote the initial draft of the paper. N.L. and L.T.B. both contributed to the interpretation of the data and preparation of the final paper.

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Correspondence to N. Lehmann.

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Extended data

Extended Data Fig. 1 Longhurst provinces.

Partitioning of the global surface ocean into 4 biomes (color-coded) and 54 biogeochemical provinces (black polygons) as defined by ref. 16.

Extended Data Fig. 2 Density distribution of PIC:POC and key carbonate chemistry data.

PIC:POC versus a) alkalinity and b) SIR across major ocean basins and bloom area. Colour indicates number of counts (n). Box plots display median, 25th and 75th percentile, and minimum and maximum values (whiskers) of the distribution. Outliers are not displayed.

Extended Data Fig. 3 Annual surface distributions of key environmental drivers.

Global annual surface distributions of a) seas surface temperature (SST), b) mixed layer depth (MLD), c) pCO2, d) Si(OH)4, e) Si*, and f) N*. Black polygons indicate Longhurst provinces.

Extended Data Fig. 4 Correlation between PIC:POC and key investigated environmental drivers within oceanographic provinces.

Pearson correlation coefficient (r) between PIC:POC and a) alkalinity, b) pCO2, c) Si(OH)4, d) Si*, e) N*, f) sea surface temperature (SST) and g) mixed layer depth (MLD) for individual Longhurst provinces. Light grey provinces (that is, Boreal Polar Province, Austral Polar Province; Extended Data Fig. 1) are excluded from the analysis (Methods). Diagonal pattern highlights non-significant (P ≥ 0.05) correlations.

Extended Data Fig. 5 Linear relationship between observed PIC:POC and highly co-correlated environmental drivers across different spatial scales.

a) Mean values of gridded annual data ( ± 1σ; error bars) across biogeochemical provinces, color-coded based on full global extent (dark blue) versus bloom areas only (light blue). b) Mean values (±1σ; error bars) across marginal seas (color-coded). Solid line shows regression line and shaded area denotes 95% confidence intervals of the linear regression. Pearson correlation (two-sided) between PIC:POC and environmental drivers is indicated by coefficient (r) and associated P value.

Extended Data Fig. 6 Data distribution across marginal seas.

Box plots showing the range in environmental drivers across marginal seas (color-coded). Box plots display median, 25th and 75th percentile, minimum and maximum values (whiskers) and number of data points (n) of the distribution. Outliers are not displayed.

Extended Data Fig. 7 Spearman’s rank correlation matrix.

Correlation coefficients (ρ) between environmental drivers for a) globally gridded field and b) marginal sea data. Blank squares indicate non-significant (P ≥ 0.05) correlations.

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Lehmann, N., Bach, L.T. Global carbonate chemistry gradients reveal a negative feedback on ocean alkalinity enhancement. Nat. Geosci. 18, 232–238 (2025). https://doi.org/10.1038/s41561-025-01644-0

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