As the world works to slow climate change, carbon dioxide removal is expected to become an important complement to reducing greenhouse-gas emissions. One promising approach is ocean alkalinity enhancement, which increases the ocean's natural ability to absorb carbon dioxide from the atmosphere. Yet a fundamental challenge remains: how can scientists accurately measure how much carbon has actually been removed and stored?
In a new study, Yale oceanographer Elizabeth Yankovsky and collaborators developed a powerful framework based on impulse response functions, a technique that captures how the ocean responds to a small, short-lived addition of alkalinity. Rather than relying solely on computationally expensive simulations for every possible deployment scenario, the researchers used a series of model experiments to create characteristic response curves that predict carbon uptake over time. The approach successfully reproduced results from full ocean-model simulations, typically within a few percent.
This advance could help establish more transparent and scientifically rigorous methods for monitoring, reporting, and verifying ocean-based carbon removal projects. Reliable accounting is essential for evaluating climate interventions and supporting emerging carbon-removal markets.
Supercomputing enabled the intensive modeling and data-analysis workflows required to test the framework across a wide range of ocean conditions, helping transform a complex climate-modeling challenge into a practical tool for real-world carbon accounting.
Elizabeth Yankovsky, Mengyang Zhou, Michael Tyka, Scott Bachman, David T. Ho, Alicia Karspeck, and Matthew C. Long (2025), Impulse response functions as a framework for quantifying ocean-based carbon dioxide removal, Biogeosciences, doi: 10.5194/bg-22-5723-2025
