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The Southern Ocean plays a vital role in global CO₂uptake, but the magnitude and even the sign of the flux remain uncertain, and the influence of phytoplankton phenology is underexplored. This study focuses on the West Antarctic Peninsula, a region experiencing rapid climate change, to examine shifts in seasonal carbon uptake. Using 20 years of in situ air‐sea CO₂flux and satellite‐derived Chlorophyll‐a, we observe that the seasonal cycles of both air‐sea CO₂flux and Chlorophyll‐a intensify poleward. The amplitude of the seasonal cycle of the non‐thermal component of surface ocean pCO₂increases with increasing latitude, while the amplitude of the thermal component remains relatively stable. Pronounced biological uptake occurs over the shelf in austral summer despite reduced CO₂solubility in warmer waters, which typically limits carbon uptake through physical processes. These findings underscore the prominence of biological mechanisms in regulating carbon fluxes in this rapidly changing region. 

Organic carbon (OC) sedimentation in marine sediments is the largest long‐term sink of atmospheric CO2 after silicate weathering. Understanding the mechanistic and quantitative aspects of OC delivery and preservation in marine sediments is critical for predicting the role of the oceans in modulating global climate. Yet, estimates of the global OC sedimentation in marginal settings span an order of magnitude, and the primary controls of OC preservation remain highly debated. Here, we provide the first global bottom‐up estimate of OC sedimentation along the margins using a synthesis of literature data. We quantify both terrestrial‐ and marine‐sourced OC fluxes and perform a statistical analysis to discern the key factors influencing their magnitude. We find that the margins host 23.2 ± 3.5 Tmol of OC sedimentation annually, with approximately 84% of marine origin. Accordingly, we calculate that only 2%–3% of OC exported from the euphotic zone escapes remineralization before sedimentation. Surprisingly, over half of all global OC sedimentation occurs below bottom waters with oxygen concentrations greater than 180 μM, while less than 4% occurs in settings with <50 μM oxygen. This challenges the prevailing paradigm that bottom‐water oxygen (BWO) is the primary control on OC preservation. Instead, our statistical analysis reveals that water depth is the most significant predictor of OC sedimentation, surpassing all other factors investigated, including BWO levels and sea‐surface chlorophyll concentrations. This finding suggests that the primary control on OC sedimentation is not production, but the ability of OC to resist remineralization during transit through the water column and while settling on the seafloor. 

Marine heterotrophicBacteria(or referred to as bacteria) play an important role in the ocean carbon cycle by utilizing, respiring, and remineralizing organic matter exported from the surface to deep ocean. Here, we investigate the responses of bacteria to climate change using a three-dimensional coupled ocean biogeochemical model with explicit bacterial dynamics as part of the Coupled Model Intercomparison Project Phase 6. First, we assess the credibility of the century-scale projections (2015–2099) of bacterial carbon stock and rates in the upper 100 m layer using skill scores and compilations of the measurements for the contemporary period (1988–2011). Second, we demonstrate that across different climate scenarios, the simulated bacterial biomass trends (2076–2099) are sensitive to the regional trends in temperature and organic carbon stocks. Bacterial carbon biomass declines by 5–10% globally, while it increases by 3–5% in the Southern Ocean where semi-labile dissolved organic carbon (DOC) stocks are relatively low and particle-attached bacteria dominate. While a full analysis of drivers underpinning the simulated changes in all bacterial stock and rates is not possible due to data constraints, we investigate the mechanisms of the changes in DOC uptake rates of free-living bacteria using the first-order Taylor decomposition. The results demonstrate that the increase in semi-labile DOC stocks drives the increase in DOC uptake rates in the Southern Ocean, while the increase in temperature drives the increase in DOC uptake rates in the northern high and low latitudes. Our study provides a systematic analysis of bacteria at global scale and a critical step toward a better understanding of how bacteria affect the functioning of the biological carbon pump and partitioning of organic carbon pools between surface and deep layers. 

Abstract. Barium is widely used as a proxy for dissolved silicon and particulateorganic carbon fluxes in seawater. However, these proxy applications arelimited by insufficient knowledge of the dissolved distribution of Ba([Ba]). For example, there is significant spatial variability in thebarium–silicon relationship, and ocean chemistry may influence sedimentaryBa preservation. To help address these issues, we developed 4095 models forpredicting [Ba] using Gaussian process regression machine learning. Thesemodels were trained to predict [Ba] from standard oceanographic observationsusing GEOTRACES data from the Arctic, Atlantic, Pacific, and Southernoceans. Trained models were then validated by comparing predictions againstwithheld [Ba] data from the Indian Ocean. We find that a model trained usingdepth, temperature, and salinity, as well as dissolved dioxygen, phosphate,nitrate, and silicate, can accurately predict [Ba] in the Indian Ocean with amean absolute percentage deviation of 6.0 %. We use this model tosimulate [Ba] on a global basis using these same seven predictors in theWorld Ocean Atlas. The resulting [Ba] distribution constrains the Ba budgetof the ocean to 122(±7) × 1012 mol and revealsoceanographically consistent variability in the barium–silicon relationship. We then calculate the saturation state of seawater with respect to barite. This calculation reveals systematic spatial and vertical variations in marine barite saturation and shows that the ocean below 1000 m is at equilibrium with respect tobarite. We describe a number of possible applications for our model outputs, ranging from use in mechanistic biogeochemical models to paleoproxy calibration. Ourapproach demonstrates the utility of machine learning in accurately simulatingthe distributions of tracers in the sea and provides a framework that couldbe extended to other trace elements. Our model, the data used in training and validation, and global outputs are available in Horner and Mete (2023, https://doi.org/10.26008/1912/bco-dmo.885506.2).