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Creators/Authors contains: "Subhas, Adam V."

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  1. Abstract

    The cycling of marine particulate matter is critical for sequestering carbon in the deep ocean and in marine sediments. Biogenic minerals such as calcium carbonate (CaCO3) and opal add density to more buoyant organic material, facilitating particle sinking and export. Here, we compile and analyze a global data set of particulate organic carbon (POC), particulate inorganic carbon (PIC, or CaCO3), and biogenic silica (bSi, or opal) concentrations collected using large volume pumps (LVPs). We analyze the distribution of all three biogenic phases in the small (1–53 μm) and large (>53 μm) size classes. Over the entire water column 76% of POC exists in the small size fraction. Similarly, the small size class contains 82% of PIC, indicating the importance of small‐sized coccolithophores to the PIC budget of the ocean. In contrast, 50% of bSi exists in the large size fraction, reflecting the larger size of diatoms and radiolarians compared with coccolithophores. We use PIC:POC and bSi:POC ratios in the upper ocean to document a consistent signal of shallow mineral dissolution, likely linked to biologically mediated processes. Sediment trap PIC:POC and bSi:POC are elevated with respect to LVP samples and increase strongly with depth, indicating the concentration of mineral phases and/or a deficit of POC in large sinking particles. We suggest that future sampling campaigns pair LVPs with sediment traps to capture the full particulate field, especially the large aggregates that contribute to mineral‐rich deep ocean fluxes, and may be missed by LVPs.

     
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  2. 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). 
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  3. Ubiquitous fractionation processes in the subsurface obscure mantle-derived volatile signals in hydrothermal systems. 
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  4. Abstract

    Planktonic calcifying organisms play a key role in regulating ocean carbonate chemistry and atmospheric CO2. Surprisingly, references to the absolute and relative contribution of these organisms to calcium carbonate production are lacking. Here we report quantification of pelagic calcium carbonate production in the North Pacific, providing new insights on the contribution of the three main planktonic calcifying groups. Our results show that coccolithophores dominate the living calcium carbonate (CaCO3) standing stock, with coccolithophore calcite comprising ~90% of total CaCO3production, and pteropods and foraminifera playing a secondary role. We show that pelagic CaCO3production is higher than the sinking flux of CaCO3at 150 and 200 m at ocean stations ALOHA and PAPA, implying that a large portion of pelagic calcium carbonate is remineralised within the photic zone; this extensive shallow dissolution explains the apparent discrepancy between previous estimates of CaCO3production derived from satellite observations/biogeochemical modeling versus estimates from shallow sediment traps. We suggest future changes in the CaCO3cycle and its impact on atmospheric CO2will largely depend on how the poorly-understood processes that determine whether CaCO3is remineralised in the photic zone or exported to depth respond to anthropogenic warming and acidification.

     
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  5. Abstract

    Scarce dissolved surface ocean concentrations of the essential algal micronutrient zinc suggest that Zn may influence the growth of phytoplankton such as diatoms, which are major contributors to marine primary productivity. However, the specific mechanisms by which diatoms acclimate to Zn deficiency are poorly understood. Using global proteomic analysis, we identified two proteins (ZCRP-A/B, Zn/Co Responsive Protein A/B) among four diatom species that became abundant under Zn/Co limitation. Characterization using reverse genetic techniques and homology data suggests putative Zn/Co chaperone and membrane-bound transport complex component roles for ZCRP-A (a COG0523 domain protein) and ZCRP-B, respectively. Metaproteomic detection of ZCRPs along a Pacific Ocean transect revealed increased abundances at the surface (<200 m) where dZn and dCo were scarcest, implying Zn nutritional stress in marine algae is more prevalent than previously recognized. These results demonstrate multiple adaptive responses to Zn scarcity in marine diatoms that are deployed in low Zn regions of the Pacific Ocean.

     
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  6. null (Ed.)
    The dissolution of CaCO 3 minerals in the ocean is a fundamental part of the marine alkalinity and carbon cycles. While there have been decades of work aimed at deriving the relationship between dissolution rate and mineral saturation state (a so-called rate law), no real consensus has been reached. There are disagreements between laboratory- and field-based studies and differences in rates for inorganic and biogenic materials. Rates based on measurements on suspended particles do not always agree with rates inferred from measurements made near the sediment–water interface of the actual ocean. By contrast, the freshwater dissolution rate of calcite has been well described by bulk rate measurements from a number of different laboratories, fit by basic kinetic theory, and well studied by atomic force microscopy and vertical scanning interferometry to document the processes at the atomic scale. In this review, we try to better unify our understanding of carbonate dissolution in the ocean via a relatively new, highly sensitive method we have developed combined with a theoretical framework guided by the success of the freshwater studies. We show that empirical curve fits of seawater data as a function of saturation state do not agree, largely because the curvature is itself a function of the thermodynamics. Instead, we show that models that consider both surface energetic theory and the complicated speciation of seawater and calcite surfaces in seawater are able to explain most of the most recent data.This new framework can also explain features of the historical data that have not been previously explained. The existence of a kink in the relationship between rate and saturation state, reflecting a change in dissolution mechanism, may be playing an important role in accelerating CaCO 3 dissolution in key sedimentary environments. 
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  7. null (Ed.)