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Substantial carbon drawdown potential from enhanced rock weathering in the United Kingdom

Nature Geoscience volume 15, pages 382–389 (2022)Cite this article

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Abstract

Achieving national targets for net-zero carbon emissions will require atmospheric carbon dioxide removal strategies compatible with rising agricultural production. One possible method for delivering on these goals is enhanced rock weathering, which involves modifying soils with crushed silicate rocks, such as basalt. Here we use dynamic carbon budget modelling to assess the carbon dioxide removal potential and agricultural benefits of implementing enhanced rock weathering strategies across UK arable croplands. We find that enhanced rock weathering could deliver net carbon dioxide removal of 6–30 MtCO2 yr−1 for the United Kingdom by 2050, representing up to 45% of the atmospheric carbon removal required nationally to meet net-zero emissions. This suggests that enhanced rock weathering could play a crucial role in national climate mitigation strategies if it were to gain acceptance across national political, local community and farm scales. We show that it is feasible to eliminate the energy-demanding requirement for milling rocks to fine particle sizes. Co-benefits of enhanced rock weathering include substantial mitigation of nitrous oxide, the third most important greenhouse gas, widespread reversal of soil acidification and considerable cost savings from reduced fertilizer usage. Our analyses provide a guide for other nations to pursue their carbon dioxide removal ambitions and decarbonize agriculture—a key source of greenhouse gases.

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Fig. 1: Net CDR by ERW deployed on UK arable croplands.
Fig. 2: Mapped fine-scale decadal average UK net CDR.
Fig. 3: Costs of CDR by ERW deployed on UK arable croplands.
Fig. 4: Mapped fine-scale decadal average UK net CDR costs.
Fig. 5: Agricultural ecosystem co-benefits of ERW.

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Data availability

Soil pH data were obtained from https://daac.ornl.gov/SOILS/guides/HWSD.html and http://www.ukso.org/. The high-resolution monthly fields of soil temperature and precipitation data were obtained from https://disc.gsfc.nasa.gov/datasets/FLDAS_NOAH01_C_GL_M_001/summary. Additional environmental and climate drivers were acquired through simulations of CLM5 available at https://github.com/ESCOMP/ctsm. The UK crop cover map was obtained from https://www.ceh.ac.uk/ukceh-land-cover-maps, annual time series of crop yields from https://www.fao.org/faostat/en/#data and UK fertilizer usage data from https://www.gov.uk/government/collections/fertiliser-usage. UK national border data were obtained from https://thematicmapping.org/downloads/world_borders.php. The GLiM v1.0 dataset used to identify rock sources is available at https://www.geo.uni-hamburg.de/en/geologie/forschung/aquatische-geochemie/glim.html. Datasets with 5 min resolution on global crop production and yield area to identify cropland are available at http://www.earthstat.org/harvested-area-yield-175-crops/. Datasets on road and rail vector data used for transport network analysis are available at http://www.diva-gis.org/gdata. Datasets on LCA impact factors used for K and P fertilizers are available within Ecoinvent 3.6 at https://ecoinvent.org/. Source data are provided with this paper.

Code availability

The weathering model was developed in MATLAB v.R2019a, and data processing was conducted in both MATLAB v.R2019a and Python v.3.7. MATLAB and Python codes developed for this study belong to the Leverhulme Centre for Climate Change Mitigation. These codes, and the modified codes in CLM5 developed in this study, are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank C. Le Quéré, A. Collins, R. Freckleton, J. Leake and J. Scurlock for comments on an earlier draft. We gratefully acknowledge funding of this research under a Leverhulme Research Centre Award (RC-2015–029; D.J.B.) from the Leverhulme Trust. D.J.B. and P.R. acknowledge UKRI funding under the UK Greenhouse Gas Removal Programme (BB/V011359/1, D.J.B.; NE/P019943/1, NE/P019730/1, P.R.). M.V.M. acknowledges funding from the UKRI Future Leaders Fellowship Programme (MR/T019867/1). We to acknowledge high-performance computing support from Cheyenne (doi: 10.5065/D6RX99HX) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling responsible for CMIP and thank the climate modelling groups for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

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Authors and Affiliations

  1. Leverhulme Centre for Climate Change Mitigation, School of Biosciences, University of Sheffield, Sheffield, UK

    Euripides P. Kantzas, Maria Val Martin, Mark R. Lomas, Amy L. Lewis, Lyla L. Taylor & David J. Beerling

  2. Advanced Resource Efficiency Centre, Management School, University of Sheffield, Sheffield, UK

    Rafael M. Eufrasio & Lenny Koh

  3. School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK

    Phil Renforth

  4. Global Systems Institute, Department of Geography, University of Exeter, Exeter, UK

    Jean-Francois Mecure & Pim V. Vercoulen

  5. Cambridge Centre for Energy, Environment and Natural Resource Governance, University of Cambridge, Cambridge, UK

    Jean-Francois Mecure, Hector Pollitt & Neil R. Edwards

  6. Cambridge Econometrics Ltd, Cambridge, UK

    Hector Pollitt & Pim V. Vercoulen

  7. Environment, Earth and Ecosystems, The Open University, Milton Keynes, UK

    Negar Vakilifard, Philip B. Holden & Neil R. Edwards

  8. Understanding Risk Research Group, School of Psychology, Cardiff University, Cardiff, UK

    Nick F. Pidgeon

  9. Global Food and Environment Institute, University of Leeds, Leeds, UK

    Steven A. Banwart

  10. School of Earth and Environment, University of Leeds, Leeds, UK

    Steven A. Banwart

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  1. Euripides P. Kantzas
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Contributions

D.J.B., E.P.K., M.V.M., M.R.L., P.R. and S.A.B. designed the study. E.P.K., M.V.M and M.R.L. undertook model development, simulations and coding, with input from L.L.T., S.A.B. and D.J.B. E.P.K. undertook data analysis and synthesis. R.M.E. undertook the UK transport analyses with input from L.K. P.R. developed the silicate supply scenarios, A.L.L. undertook the X-ray diffraction analyses of UK basalts and N.F.P. wrote sections on public perception. N.V. and P.B.H. undertook the GENIE analyses. J.-F.M., H.P., P.V.V., N.R.E. and P.B.H. provided analyses and data on UK national economics, energy production and CO2 emissions. L.L.T. led the drafting of the Supplementary Information. D.J.B. and S.A.B. wrote the manuscript with input from co-authors.

Corresponding author

Correspondence to David J. Beerling.

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The authors declare no competing interests.

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Nature Geoscience thanks Thorben Amann and Ward Smith for their contribution to the peer review of this work. Primary Handling Editors: Kyle Frischkorn and Tom Richardson, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Coupled climate-C-N cycle modelling framework.

Schematic overview of coupled climate-carbon-nitrogen modelling strategy for simulating carbon dioxide removal (CDR) with enhanced rock weathering (ERW). CESM, the Community Earth System Model v.2; CLM5, the Community Land Model v.5. Abbreviations. m = monthly time step, y = yearly time step, z = depth.

Extended Data Fig. 2 Logistical CO2 emissions and costs of ERW modelling framework.

Annual (y) energy, economic and transport outputs from E3ME, together with road and rail network analyses, are used to calculate CO2 emissions from ERW logistical operations and costs. The resulting CO2 emissions are subtracted from gross CDR to estimate net CDR and costs of net CDR.

Extended Data Fig. 3 Representation of nitrogen cycle transformations on soil acidity.

Nitrogen cycle transformations represented by the CLM5 land surface model coupled to our soil profile ERW modelling. Indicated is the proton gain or loss associated with each reaction.

Extended Data Fig. 4 Resource extraction scenarios compared with the historical record.

Resource extraction scenarios compared with the historical record. Estimated basalt rock extraction (grey line) and three basalt extraction scenarios (S1-low): ‘maintain 2018 number of mines (+32 Mt yr−1)’, (S2-medium): ‘total rock extraction equivalent to previous 1990 maximum rate (+97 Mt yr−1)’, and S3-high ‘total capacity increase equivalent to 1945 to 1990 (166 Mt yr−1)’) to provide material for ERW simulations reported here. S2 essentially diverts the 100 Mt yr−1 drop in production since 1990 into basalt extraction for CDR, and S3 adopts a rock extraction rate (+166 Mt yr−1) comparable to that reported for 1945–1990 (~180 Mt yr-1). These historical constraints underpin the plausibility of S2 and S3.

Source data

Extended Data Fig. 5 UK energy production and demand for rock grinding.

UK energy production and demand for rock grinding. (a) Energy mix and total annual UK supply modelled by E3ME, (b) absolute energy requirement for rock grinding for three scenarios and (c) percentage of annual UK energy required for rock grinding for the three scenarios.

Source data

Extended Data Fig. 6 Uncertainty in ocean CDR with ERW.

Uncertainty in ocean CDR with ERW. Results show ocean CDR simulated by the intermediate complexity Earth System Model, Genie (blue line), and an empirical function accounting CO2, ocean temperature and salinity6 (red dashed line). Panels display results for simulations of scenario 1 for rock grain (a) p80 = 10 µm and (b) p80 =100 µm, scenario 2 for rock grain (c) p80 = 10 µm and (d) p80 =100 µm, and scenario 3 for rock gain (e) p80 = 10 µm and (f) p80 =100 µm. Genie uncertainties represent 90% confidence limits based on ensemble simulations with 84 different parameter sets. The difference the result of Earth system feedbacks in Genie whereby atmospheric CO2 lowered by ERW causes oceanic outgassing, and sediment CaCO3 uptake reduces alkalinity. Simulations use annual alkalinity fluxes generated by the 1-D soil profile weathering with three UK basalts.

Source data

Extended Data Fig. 7 Seasonal variations in soil CDR pathway time-series.

Seasonal variations in soil CDR pathway time-series. Illustrative time-series of model dynamics of the two CDR pathways (soil export of alkalinity and formation/dissolution of carbonates) and pH. Simulations were undertaken for Cragmill basalt mineralogy with p80 particle diameter of 100 µm. Left-hand site: winter barley (midlands), right hand site: winter wheat (south east).

Source data

Extended Data Fig. 8 Simulated UK energy and economic drivers (2020–2070).

Simulated UK energy and economic drivers (2020–2070). (a) modelled evolution of UK energy supply and mix consistent with specific policies in the 1.5 °C scenario, (b) life cycle CO2 emissions of energy production, (c) median wage rates, (d) industrial electricity tariff with the rapid transition to renewable generation, (e) diesel fuel prices and (f) modelled transitional uptake of electric heavy goods transportation.

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Extended Data Fig. 9 Potential mineral nutrient release rates from ERW.

Modelled mineral nutrient release rates from ERW. P- and K-mass transfer from rocks to soil are the mean of simulations for three UK-specific basalts and two p80 values (10 µm and 100 µm diameter). Shaded envelopes show the 95% confidence limits. Dashed lines indicate upper and lower range of major UK tillage crops. Scenario 1 has fewest grid cells but ERW deployment starts early and average release rate increases with repeated rock dust applications. Scenarios 2 and 3 add more grid cells with initial lower release rates causing the average release rate to remain constant. Elements are likely to be retained in the soil column on ion exchange clays (K), and sorbed on secondary minerals (P), for example iron oxides.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Tables 1–7, Methods and references.

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Source Data Fig. 1

x–y data with uncertainties for all panels.

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Zipped file of mapped datasets.

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Zipped file of mapped datasets.

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x–y data with uncertainties for all panels.

Source Data Extended Data Fig. 5

x–y data with uncertainties for all panels.

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x–y data with uncertainties for all panels.

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x–y data with uncertainties for all panels.

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Source Data Extended Data Fig. 9

x–y data with uncertainties for all panels.

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Kantzas, E.P., Val Martin, M., Lomas, M.R. et al. Substantial carbon drawdown potential from enhanced rock weathering in the United Kingdom. Nat. Geosci. 15, 382–389 (2022). https://doi.org/10.1038/s41561-022-00925-2

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