mCDR 2023: Data Requirements for QuantiFY20ing Natural Variability and the Background Ocean Carbon Sink in Marine Carbon Dioxide Removal (mCDR) Models

Lead PI: Professor Galen A. McKinley , Thea Hatlen Heimdal , Adrienne Sutton, PMEL

Unit Affiliation: Geochemistry, Lamont-Doherty Earth Observatory (LDEO)

September 2023 - August 2026
North America
Project Type: Research

DESCRIPTION: The natural ocean carbon cycle is dynamic over time and space, leading to variability in air-sea CO2 fluxes that is substantially larger than flux changes expected from marine Carbon Dioxide Removal (mCDR). The global ocean is already taking up ~3 PgC/yr with the background ocean carbon sink that occurs naturally as atmospheric pCO2 rises. A major challenge for mCDR will be to quantify additional carbon removal from the atmosphere given this large natural variability and the background carbon sink. Ocean models are expected to be the basis for these estimates. In the interest of validating these models, this project will quantify observational uncertainties for natural variability and the background carbon sink in potential mCDR deployment regions. Current bounds will be established, and requirements for additional sampling to tighten bounds will be determined. Our research objectives are to 1. Quantify uncertainties in air-sea CO2 flux variability and the integrated background carbon sink on regional scales and 2. Set observing requirements for improved quantification of flux variability and background sink. Following our successful prior work at the global scale, these objectives will be achieved by developing and applying a ‘testbed’. Our testbed will be a high-resolution (1/10o ) ocean model that will be sampled with the spatiotemporal pattern of existing surface pCO2 observations in regions on the West and East US Coast, Hawaii and the Bering Sea. Machine learning reconstructions will be performed from these samples to reconstruct full field, time-varying pCO2 . The unique advantage of a testbed is that the fidelity of the reconstructions can be evaluated based on comparison to the original model fields. This approach allows for assessment of how well sparse data and state-of-the-art machine learning techniques can be combined to constrain surface ocean carbon fluxes. In a second phase, observing system simulation experiments (OSSEs) will establish optimal observing designs that can further reduce uncertainties.

BROADER IMPACTS: This work will support future observing system development, and ultimately the future development of observation-based benchmarks against which proposed mCDR models can be evaluated. This project represents a critical important first step in quantiFY20ing mCDR additionality. The project will provide salary support to an early career researcher to become an expert in ocean carbon cycling and machine learning, skills critical to ocean science and the mCDR workforce.