Using Inverse Modeling of Biogeochemical Processes to Constrain Uncertainty in Monitoring, Recording, and Verification for Marine Carbon Dioxide Removal

Research and investment into marine carbon dioxide removal (mCDR) have grown at a stunning pace in response to the Intergovernmental Panel on Climate Change’s 2022 assertion that removal of atmospheric carbon dioxide will be required to meet Paris Agreement goals for climate mitigation. Many scientific questions remain, including the possible influence of Earth system feedbacks on the efficiency of this technology. To fully explore the uncertainty space surrounding questions of mCDR efficiency, many plausible parameterizations of these feedbacks must be tested, which is an approach currently out of reach for traditional simulations due to computational constraints. As shifts induced in ocean carbon content due to the use of mCDR will generally be smaller than natural variability inherent to these systems and can therefore not be measured directly, developing methods for monitoring, recording, and verification (MRV) of carbon removal is an additional fundamental challenge in implementing gigaton-scale mCDR projects. Key to any MRV protocol will be models that represent physical, chemical, and biological processes with sufficiently small uncertainties and the lowest possible computational cost. In this work, I present an adapted version of the Ocean Circulation Inverse Model (OCIM) that includes biogeochemical processes relevant to mCDR. OCIM is advantageous in that it uses a transport matrix to simplify physical processes in the ocean, reducing computation time by orders of magnitude and requiring only the processing power available on a laptop. With this model, a suite of parameterizations of the CO2-biotic calcification feedback under different forcing scenarios is run to test our mechanistic understanding of this process and more fully constrain the possible inefficiency that this feedback could create in mCDR deployments.