Carbonate System Site Selection Characteristics for Ocean Alkalinity Enhancement in the US Northeast Shelf and Slope

[Geoengineering News]

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025JG009063

Authors: Jennie E. Rheuban, Heather H. Kim, Ke Chen, Ivan D. Lima, Daniel C. McCorkle, Anna P. M. Michel, Zhaohui Aleck Wang, Adam V. Subhas

First published: 08 December 2025

https://doi.org/10.1029/2025JG009063

Abstract

Ocean alkalinity enhancement (OAE) is a marine carbon dioxide (CO2) removal strategy that relies on lowering the ocean's pCO2 via the addition of alkaline materials to facilitate enhanced CO2 uptake with the potential for durable, long-term, storage. This strategy has gained recent scientific and private sector attention as a possible component of climate mitigation portfolios, yet many research questions remain. This work describes an analysis of historical reconstructions of regional carbonate chemistry developed via application of machine learning algorithms to an ocean reanalysis product. Model skill assessment demonstrated excellent performance when compared to regional observations, and this work focuses on four carbonate system variables that may influence OAE applications: total scale pH, calcite saturation state, the theoretical molar change in dissolved inorganic carbon associated with a molar change in total alkalinity (ΔDIC/ΔTA), and the timescale of CO2 equilibrium of the surface mixed layer (TCO2). These metrics were combined into a suitability index to quantify locations and times of year more favorable for OAE. Much of the US Northeast Shelf and Slope region has seasonally similar suitability for small-scale OAE applications, with nearshore environments exhibiting high suitability year-round. Lagrangian particle tracking experiments show strong reductions in ΔDIC/ΔTA and increases in TCO2 due to horizontal and vertical transport, suggesting that when water motion is accounted for, reduced efficiency and longer equilibration times may impact successful observations of carbon uptake and storage. This analysis and framework were developed with publicly available tools, data sets, and global data products allowing for global scalability and application.

Plain Language Summary

Ocean alkalinity enhancement (OAE), a proposed marine carbon dioxide removal strategy, relies on altering ocean chemistry via the addition of alkaline materials to reduce pCO2 and allow for additional atmospheric CO2 uptake and storage. An understanding of historical regional ocean chemistry is necessary to inform planning and decision-making. This manuscript applies machine learning models to a high-spatial and temporal resolution ocean reanalysis to reconstruct chemistry on the US Northeast Shelf and Slope (NESS). We evaluated four ocean chemistry metrics, assessed regional thresholds to determine safe levels of alkalinity addition, and evaluated the effects of water motion on two of the four metrics. Ideal geochemical characteristics include: shallow, strong, and spatially-consistent mixed layers, limited water movement, high alkalinity addition thresholds, low pH, low ΩCa, short mixed-layer CO2 equilibration timescales, and high theoretical carbon uptake efficiency. We found, in the summertime, much of the NESS has similar suitability for OAE experiments, but in winter, only nearshore environments showed indices similar to summertime. When water motion was considered, both metrics evaluated suggested worse conditions, showing how water transport might influence OAE field experiments. This analysis was developed entirely with publicly available tools, data sets, and global data products allowing for global scalability and application.

Key Points

Machine learning tools applied to a global ocean reanalysis product reconstruct ocean carbonate chemistry in the US Northeast Shelf and Slope

Assessment of background conditions identifies coastal regions as most suitable for ocean alkalinity enhancement applications

Consideration of water movement reduces the efficacy and equilibration times of alkalinity-enhanced parcels of water

Source: AGU