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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. 

Archaea adjust the number of cyclopentane rings in their glycerol dibiphytanyl glycerol tetraether (GDGT) membrane lipids as a homeostatic response to environmental stressors such as temperature, pH, and energy availability shifts. However, archaeal expression patterns that correspond with changes in GDGT composition are less understood. Here we characterize the acid and cold stress responses of the thermoacidophilic crenarchaeonSaccharolobus islandicusREY15A using growth rates, core GDGT lipid profiles, transcriptomics and proteomics. We show that both stressors result in impaired growth, lower average GDGT cyclization, and differences in gene and protein expression. Transcription data revealed differential expression of the GDGT ring synthasegrsBin response to both acid stress and cold stress. Although the GDGT ring synthase encoded bygrsBforms highly cyclized GDGTs with ≥5 ring moieties,S. islandicus grsBupregulation under acidic pH conditions did not correspond with increased abundances of highly cyclized GDGTs. Our observations highlight the inability to predict GDGT changes from transcription data alone. Broader analysis of transcriptomic data revealed thatS. islandicusdifferentially expresses many of the same transcripts in response to both acid and cold stress. These included upregulation of several biosynthetic pathways and downregulation of oxidative phosphorylation and motility. Transcript responses specific to either of the two stressors tested here included upregulation of genes related to proton pumping and molecular turnover in acid stress conditions and upregulation of transposases in cold stress conditions. Overall, our study provides a comprehensive understanding of the GDGT modifications and differential expression characteristic of the acid stress and cold stress responses inS. islandicus. 

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).