Quantifying Marine Carbon Dioxide Removal via Alkalinity Enhancement Across Circulation Regimes Using ECCO‐Darwin and 1D Models

Abstract Ocean Alkalinity Enhancement (OAE) is emerging as a viable method for removing anthropogenicC O 2$}_{2}$ emissions from the atmosphere to mitigate climate change. To achieve substantial carbon reductions, OAE would need to be deployed at scale across the global ocean. Hence, there is a need to quantify how the efficiency of OAE varies globally across a range of space‐time scales in preparation for field deployments. Here we develop a marine carbon dioxide removal (mCDR) efficiency evaluation framework based on the data‐assimilative ECCO‐Darwin ocean biogeochemistry model, which separates and quantifies two key factors over seasonal to multi‐annual timescales: (a) mCDR potential, which quantifies the ability of seawater to store additional carbon after an alkalinity perturbation; and (b) dynamical mCDR efficiency, representing the impact of ocean advection, mixing, and air‐seaC O 2$}_{2}$ exchange. We apply this framework to virtual OAE deployments in five archetypal ocean circulation regimes with different mCDR potentials and dynamical efficiencies. The simulations highlight the importance of the dynamical factors, especially vertical transport, in driving differences in efficiency. To rapidly isolate and quantify the factors that determine dynamical efficiency, we develop a reduced complexity 1D model, rapid‐mCDR. We show that combining the rapid‐mCDR model with existing ECCO‐Darwin output allows for rapid characterization of OAE efficiency at any location globally. Thus, these tools can be readily employed by research teams and industry to model future field deployments and contribute to essential monitoring, reporting, and verification efforts.

Distribution of Plastics of Various Sizes and Densities in the Global Ocean From a 3D Eulerian Model

Abstract We study the global transport and distribution of microplastics (MPs) using a three‐dimensional Eulerian model. For the first time, the effects of both particle size and density are accounted for, influencing the vertical transport of MPs, and leading to an advanced understanding of their global distribution. Our simulations reveal two key findings: Only particles with low density and sufficiently large size (e.g., density 900 kgm − 3$-3}$ and size≳ $\gtrsim $ 10  μ ${\upmu }$ m) aggregate in the five subtropical gyres that were identified in previous studies. In contrast, sufficiently small particles ( ≲ $\lesssim $ 1  μ ${\upmu }$ m), regardless of their density, behave like neutrally buoyant particles and can penetrate down to 1 km deep into the ocean. In addition, we observe a seasonal variation in the surface concentration of positively buoyant MPs—higher in summer and lower in winter—which reasonably agrees with the satellite observations made by the Cyclone Global Navigation Satellite System (CYGNSS) in terms of the phase of the variation. A quantitative analysis shows that the seasonal variation in the surface particle concentration correlates well globally with the variation in mixed layer depth. We attribute this correlation to the vertical stretching/squeezing effect of the seasonally varying mixed layer, where the total amount of positively buoyant particles is conserved.

Personalised estimation of exposure to ambient air pollution and application in a longitudinal cohort analysis of cognitive function in London-dwelling older adults

Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals

Bio-inspired electronics: Soft, biohybrid, and “living” neural interfaces

Phototropin connects blue light perception to starch metabolism in green algae

ImmuneLENS characterizes systemic immune dysregulation in aging and cancer

Learning under label noise through few-shot human-in-the-loop refinement

Identifying the metabolic profile of Hashimoto’s thyroiditis from the METHAP clinical study

Shared suffering predicts prosocial commitment among Turkish earthquake survivors