Data requirements for quantifying natural variability and the background ocean carbon sink in mCDR models

PI: Galen McKinley, Columbia University
Start Year: 2023 | Duration: 3 years
Partners: Columbia University, NOAA PMEL

Project Abstract:

“Marine carbon dioxide removal presents exciting new challenges for scientists who have been working for decades to measure the ocean carbon sink that naturally removes 25% of humanity’s carbon dioxide emissions from the atmosphere each year” says Dr. Galen McKinley of Columbia University. “In this project, we will apply state-of-the-art methods we’ve developed for ocean carbon sink studies to the challenge of marine carbon dioxide removal monitoring, reporting and verification.” To validate the models that will be required to estimate carbon credits for marine carbon dioxide, the team will determine the natural background carbon uptake, its variability, and the degree of certainty with which it is known in areas where marine carbon dioxide removal deployments are likely to occur. The project will also determine the requirements for additional sampling of pCO2, a measure of the carbon dioxide in seawater, needed to quantify the baseline ocean carbon sink in models. This work will develop machine learning approaches for use in marine carbon dioxide removal monitoring, reporting and verification. It will also support future observing system development, both of which are critical for future development of observation-based benchmarks for evaluation of proposed marine carbon dioxide removal models.