Impulse response functions as a framework for quantifying ocean-based carbon dioxide removal

Abstract. Limiting global warming to 2 °C by the end of the century requires dramatically reducing CO2 emissions, and also implementing carbon dioxide removal (CDR) technologies. Ocean-based CDR through ocean alkalinity enhancement (OAE) offers a particularly scalable and promising pathway. However, quantifying carbon removal achieved by OAE deployments is challenging because it requires determining air-to-sea CO2 transfer over large spatiotemporal scales – and there is the possibility that ocean circulation will remove alkalinity from the surface ocean before complete equilibration. This challenge makes it difficult to establish robust accounting frameworks suitable for an effective carbon market. Here, we propose using impulse response functions (IRFs) to address such challenges. We perform model simulations of a short-duration alkalinity release (the “impulse”), compute the resultant air-sea CO2 flux as a function of time, and generate a characteristic carbon uptake curve for the given location (the IRF). Applying the IRF method requires a linear and time-invariant system. We attempt to meet these conditions by using small alkalinity forcing values and creating an IRF ensemble accounting for seasonal variability. The IRF ensemble is used to predict carbon uptake for an arbitrary-duration alkalinity release. We test whether the IRF approach provides a reasonable approximation by performing OAE simulations in a global ocean model at locations that span a variety of dynamical and biogeochemical regimes. We find that the IRF prediction can typically reconstruct the carbon uptake in continuous-release simulations in our model within several percent error. Our simulations elucidate the influences of oceanic variability and deployment duration on carbon uptake efficiency. We discuss the strengths and possible shortcomings of the IRF approach as a basis for quantification and uncertainty assessment of ocean-based CDR, facilitating its potential for adoption as a component of the carbon removal market’s standard approach to Monitoring, Reporting, and Verification (MRV).

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Publication Details

Journal
Biogeosciences
Published
2025-10-21
DOI
https://doi.org/10.5194/bg-22-5723-2025
Citations
1
Primary Topic
Ocean Acidification Effects and Responses
Type
article
Field-Weighted Citation Impact
1.79
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article

Impulse response functions as a framework for quantifying ocean-based carbon dioxide removal

Elizabeth Yankovsky, Alicia Karspeck, Scott Bachman, Matthew C. Long et al.
1 citations
Biogeosciences
Ocean Acidification Effects and Responses
1.79
article

Impulse response functions as a framework for quantifying ocean-based carbon dioxide removal

Elizabeth Yankovsky, Alicia Karspeck, Scott Bachman, Matthew C. Long, Michael D. Tyka, Mengyang Zhou, David T. Ho
article en
1 citations

Abstract

Abstract. Limiting global warming to 2 °C by the end of the century requires dramatically reducing CO2 emissions, and also implementing carbon dioxide removal (CDR) technologies. Ocean-based CDR through ocean alkalinity enhancement (OAE) offers a particularly scalable and promising pathway. However, quantifying carbon removal achieved by OAE deployments is challenging because it requires determining air-to-sea CO2 transfer over large spatiotemporal scales – and there is the possibility that ocean circulation will remove alkalinity from the surface ocean before complete equilibration. This challenge makes it difficult to establish robust accounting frameworks suitable for an effective carbon market. Here, we propose using impulse response functions (IRFs) to address such challenges. We perform model simulations of a short-duration alkalinity release (the “impulse”), compute the resultant air-sea CO2 flux as a function of time, and generate a characteristic carbon uptake curve for the given location (the IRF). Applying the IRF method requires a linear and time-invariant system. We attempt to meet these conditions by using small alkalinity forcing values and creating an IRF ensemble accounting for seasonal variability. The IRF ensemble is used to predict carbon uptake for an arbitrary-duration alkalinity release. We test whether the IRF approach provides a reasonable approximation by performing OAE simulations in a global ocean model at locations that span a variety of dynamical and biogeochemical regimes. We find that the IRF prediction can typically reconstruct the carbon uptake in continuous-release simulations in our model within several percent error. Our simulations elucidate the influences of oceanic variability and deployment duration on carbon uptake efficiency. We discuss the strengths and possible shortcomings of the IRF approach as a basis for quantification and uncertainty assessment of ocean-based CDR, facilitating its potential for adoption as a component of the carbon removal market’s standard approach to Monitoring, Reporting, and Verification (MRV).

BiogeosciencesVol. 22(20)
NSF National Center for Atmospheric Research (US), University of Hawaiʻi at Mānoa (US), Google (United States) (US), Yale University (US), Connecticut Sea Grant (US)
Openalex Percentile: Top 13%
Ocean Acidification Effects and Responses
1.79
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