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Abstract Marine carbon dioxide removal (mCDR) is gaining interest as a tool to meet global climate goals. Because the response of the ocean–atmosphere system to mCDR takes years to centuries, modeling is required to assess the impact of mCDR on atmospheric CO₂reduction. Here, we use a coupled ocean–atmosphere model to quantify the atmospheric CO₂reduction in response to a CDR perturbation. We define two metrics to characterize the atmospheric CO₂response to both instantaneous ocean alkalinity enhancement (OAE) and direct air capture (DAC): the cumulative additionality (α) measures the reduction in atmospheric CO₂relative to the magnitude of the CDR perturbation, while the relative efficiency (ϵ) quantifies the cumulative additionality of mCDR relative to that of DAC. For DAC,αis 100% immediately following CDR deployment, but declines to roughly 50% by 100 years post-deployment as the ocean degasses CO₂in response to the removal of carbon from the atmosphere. For instantaneous OAE,αis zero initially and reaches a maximum of 40%–90% several years to decades later, depending on regional CO₂equilibration rates and ocean circulation processes. The global meanϵapproaches 100% after 40 years, showing that instantaneous OAE is nearly as effective as DAC after several decades. However, there are significant geographic variations, withϵapproaching 100% most rapidly in the low latitudes whileϵstays well under 100% for decades to centuries near deep and intermediate water formation sites. These metrics provide a quantitative framework for evaluating sequestration timescales and carbon market valuation that can be applied to any mCDR strategy. 

Abstract The capability of moderate‐spatial‐resolution satellites to accurately resolve submesoscale variations in surface tracers remains an open question, one with relevance to observing physical‐biological interactions in the surface ocean. In this study, we address this question by comparing the variance of two tracers, chlorophyll concentration (Chl) and sea surface temperature (SST), resolved by two satellites—MODIS Aqua, with a resolution of 1.5 km, and Landsat 8/9, with a resolution of 30 m. We quantify tracer variance resolved by both satellites on the submesoscale using spatial variance spectral slopes. We find that MODIS measures significantly higher variance compared to Landsat, in both Chl and SST. This is because, despite higher signal‐to‐noise ratio for MODIS per pixel, Landsat signal‐to‐noise ratio increases considerably when aggregating pixels. Furthermore, by comparing Chl to SST variance for each satellite we find Landsat to be better match to theory for resolving submesoscale physical‐biological interactions. 

Abstract Chromophoric dissolved organic matter (CDOM) is an important part of ocean carbon biogeochemistry with relevance to long‐term observations of ocean biology due to its dominant light absorption properties. Thus, understanding the underlying processes controlling CDOM distribution is important for predicting changes in light availability, primary production, and the cycling of biogeochemically important matter. We present a biogeochemical CDOM model for the open ocean with two classes of biological lability and uncertainty estimates derived from 43 ensemble members that provide a range of model parameter variations. Ensemble members were optimized to match global ocean in situ CDOM measurements and independently assessed against satellite CDOM estimates, which showed good agreement in spatial patterns. Based on the ensemble median, we estimate that about 7% of open‐ocean CDOM is of terrestrial origin, but the ensemble range is large (<0.1–26%). CDOM is rapidly removed in the surface ocean (<200 m) due to biological degradation for short‐lived CDOM and photodegradation for long‐lived CDOM, leading to a net flux of CDOM to the surface ocean from the dark ocean. This deep‐water source (ensemble median 0.001 m⁻¹ yr⁻¹) is similar in magnitude to the riverine flux (0.005 m⁻¹ yr⁻¹) into the surface ocean. Though discrepancies between the model and observational data remain, this work serves as a foundational framework for a mechanistic assessment of global CDOM distribution that is independent of satellite data.