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).