As a marine Carbon Dioxide Removal (mCDR) approach, Ocean Alkalinity
Enhancement (OAE) is emerging as a viable method for removing
anthropogenic CO2 emissions from the atmosphere to mitigate climate
change. To achieve substantial carbon reduction using this method, OAE
would need to be widespread and scaled-up across the global ocean.
However, the efficiency of OAE varies substantially across a range of
space-time scales and as such field deployments must be carefully
planned to maximize efficiency and minimize logistical costs and risks.
Here we develop a mCDR efficiency framework based on the
data-assimilative ECCO-Darwin ocean biogeochemistry model, which
examines two key factors over seasonal to multi-decadal timescales: 1)
mCDR potential, which quantifies the CO2 solubility of the upper ocean;
and 2) dynamical mCDR efficiency, representing the full-depth impact of
ocean advection, mixing, and air-sea CO2 exchange. To isolate and
quantify the factors that determine dynamical efficiency, we develop a
reduced complexity 1-D model, rapid-mCDR, as a computationally-efficient
tool for evaluation of mCDR efficiency. Combining the rapid-mCDR model
with ECCO-Darwin allows for rapid characterization of OAE efficiency at
any location globally. This research contributes to our understanding
and optimization of OAE deployments (i.e., deploying experiments in the
real-world ocean) as an effective mCDR strategy and elucidates the
regional differences and mechanistic processes that impact mCDR
efficiency. The modeling tools developed in this study can be readily
employed by research teams and industry to plan and complement future
field deployments and provide essential Monitoring, Reporting, and
Verification (MRV).