At Isometric, we partner with organizations that engage in research to address fundamental knowledge gaps in monitoring, reporting and verification (MRV) of carbon dioxide removal (CDR) projects. We believe that diverse collaborations are an essential part of advancing MRV research, fostering scientific consensus, and building trust in the carbon removal industry.

With this in mind, Isometric is partnering with [C]Worthy—a non-profit “Focused Research Organization” that is building C-Star (Computational Systems for Tracking Ocean Carbon). C-Star is a set of open-source software tools to support oceanographic and biogeochemical modeling and data integration for MRV of marine CDR (mCDR) projects. The goal of our collaboration is to use C-Star to better quantify the uncertainty bounds associated with ocean models that will be used to calculate the net carbon dioxide removal of mCDR projects.

The models used in this collaborative research simulate the time-evolving physics, biology, and chemistry of key regions of the ocean and have been developed and vetted over decades by academic and government scientists. These models are based on the most scientifically up-to-date understanding of the physical and biogeochemical processes relevant to mCDR quantification. Because the models will be heavily relied on for mCDR MRV, their uncertainties must be understood and quantified. To date, there has been little published scientific literature on model uncertainty in the context of mCDR MRV, so Isometric scientists and engineers will be working closely with [C]Worthy to co-design experiments to help fill this knowledge gap. 

The results of this project will be most relevant for mCDR pathways such as Ocean Alkalinity Enhancement (OAE) and Direct Ocean Capture (DOC). OAE and DOC remove carbon dioxide from the atmosphere by inducing a deficit in the partial pressure of carbon dioxide in the surface ocean, priming re-equilibration between the ocean and atmosphere which leads to atmospheric drawdown. 

For many OAE and DOC projects operating in the near future, there is growing consensus that ocean models will play a large role in the quantification of net carbon dioxide removal from the atmosphere. This is because these projects face the following—uniquely ocean-related—challenges if relying solely on direct measurements:

• Signal to noise: The ocean is a highly dynamic and variable environment and any induced changes to ocean chemistry as a result of mCDR projects need to be small for environmental safety reasons. This presents a challenge for detecting small signals above a noisy background. 

• Spatial and temporal scales: Ocean currents will rapidly disperse induced changes to seawater chemistry from mCDR interventions, spreading project signals over much larger areas beyond initial project sites. The eventual ocean uptake of atmospheric carbon dioxide will occur slowly over this larger area for months or years after project deployment. Having the capabilities to make measurements across a large enough area and time period is an operational challenge.

• Additionality: Even if making all the necessary measurements to perfectly quantify ocean carbon dioxide uptake is possible, there is still the challenge of demonstrating that a mCDR project resulted in carbon uptake that was “additional” to what would have otherwise occurred through natural ocean/atmospheric carbon exchange. Unlike some other CDR pathways, the natural dynamism of the ocean makes it impossible to establish a clear “control”.  

Models are a solution for these challenges as they can simulate what happens with and without a mCDR intervention to quantify the additional atmospheric carbon dioxide removal that results from a project. We envision that the results that come out of our research collaboration will feed into ocean modeling requirements in future versions of Isometric mCDR MRV protocols and further contribute to [C]Worthy’s open-source tools (C-Star). 

We plan to publish results of this work with [C]Worthy to help further advance and communicate the state of mCDR science. If you are interested in getting involved or following this work, please consider joining the more than 200 members of the Science Network, where we offer multiple pathways for engagement across our work in all CDR pathways.