Assessing the limitations of commercial sensors and models for supporting marine carbon dioxide removal (mCDR) monitoring: a case study

Tristen Myers Stewart¹Peter Regier^1*Kyle Hinson¹Carolina Torrez Sanchez¹Quinn Mackay¹Nicholas David Ward^1,2Jessica Cross¹

• ¹Marine and Coastal Research Laboratory, Pacific Northwest National Laboratory (DOE), Sequim, United States
• ²School of Oceanography, University of Washington, Seattle, United States

Several unknowns remain surrounding marine Carbon Dioxide Removal (mCDR) monitoring, reporting, and verification (MRV) practices and capabilities. Current in-situ sensor technology is limited (primarily pH and pCO2), requiring calculations and assumptions to estimate changes in carbonate chemistry parameters, including total alkalinity (TA). Considering that cost, energy consumption, and accuracy of commercial sensors can vary by orders of magnitude, understanding how well existing sensors perform in an mCDR context is important for this emerging community. Likewise, documenting sensor limitations and how relatively simple models can optimize sensor deployments will improve MRV efforts and support protocol development. Here we 1) compare performance a variety of commercially available sensors in a blind mesocosm experiment simulating ocean alkalinity enhancement (OAE), and how sensor performance impacted carbonate chemistry estimates; 2) evaluate if sensors distinguish the OAE signal from natural variability during a small scale OAE field test in Sequim Bay, WA, USA, and 3) use an idealized ocean biogeochemistry model to explore optimal sensor network design based on (1) and (2). Our mesocosm results indicate that correctly constraining pH uncertainty will be critical for accurate TA estimates with current sensor technology compared to the less impactful variation caused by uncertainty in pCO2 (pH data that are presented throughout are reported on the total scale (pHT) unless otherwise noted). Our pilot field test demonstrated that sensors were capable of distinguishing mCDR signatures from natural variability under optimal real-world conditions. Idealized modeling simulations of the field test showed that a range of sparse and dense (3 to 100) sensors sampling areas of detectable increases will underestimate the net change in surface pH by at least 35-55%, at realistic and highly elevated alkalinity input levels. We also highlight the limitations of our current sensing technology for MRV, and the importance of ocean biogeochemistry models as critical tools for predicting when and where mCDR signals will be detectable using available sensors. Overall, our findings suggest that commercially available pCO2 sensors and some pH sensors will form an important backbone for mCDR MRV tasks, though complete MRV characterization will require these data to be used in combination with other tools.