This dataset provides observation-informed latitudinal estimates of apparent grazing parameters used to constrain community-integrated zooplankton grazing dynamics. The products were developed to represent large-scale variation in zooplankton grazing using three independent approaches that draw on observational datasets, empirical grazing relationships and inverse modelling. The first estimate combines latitudinal patterns in zooplankton community composition with empirically derived grazing characteristics. Zooplankton biomass distributions were informed by the MAREDAT database and the statistically interpolated biomass distribution model of Clerc et al. (2024), while grazing parameters were derived from laboratory dilution experiments. Micro- and mesozooplankton contributions were combined according to their relative abundance and empirical grazing characteristics, with temperature-dependent adjustment of grazing parameters. Two additional estimates were derived from inverse-modelling experiments using the WOMBAT biogeochemical model (Rohr et al., 2024). Grazing dynamics were optimised to reproduce satellite-observed phytoplankton phenology while physical transport and bottom-up environmental controls were constrained. Independent optimisations were undertaken using satellite-derived phytoplankton carbon biomass from MODIS backscatter and chlorophyll concentration from VIIRS ocean colour, producing separate carbon-based and chlorophyll-based estimates of community-integrated grazing dynamics. Together, the three products provide alternative observation-informed estimates of the latitudinal structure of apparent zooplankton grazing parameters.

We compare the formulation and emergent dynamics of 11 CMIP6 IPCC marine biogeochemical models. We find that the largest source of uncertainty across model simulations of marine carbon cycling is grazing pressure (i.e. the phytoplankton specific loss rate to grazing). Variability in grazing pressure is driven by large differences in zooplankton specific grazing rates, which are not sufficiently compensated for by offsetting differences in zooplankton specific mortality rates. Models instead must tune the turnover rate of the phytoplankton population to balance large differences in top-down grazing pressure and constrain net primary production. We then run a controlled sensitivity experiment in a global, coupled ocean-biogeochemistry model to test the sensitivity of marine carbon cycling to this uncertainty and find that even when tuned to identical net primary production, export and secondary production remain extremely sensitive to grazing, likely biasing predictions of future climate states and food security.

These files contain the data recorded from a mesocosm experiment conducted in Bergen, Norway 2022 which assessed the effect of simualted mineral-based (silicate or calcium) ocean alkalinity enhancement (OAE) on diatom silicification. Ten mesocosms were used in total, divided into two groups either the silicate- or calcium based group and alkalinity was increased by either 0, 150, 300, 450 or 600 µmol L-1 above natrually occuring levels. The PDMPO-fluorescence (an appropriate proxy for silicification) of diatoms was recorded on eight seperate days during the experiment. Accompanying data includes measured; macronutrients (nitrate, nitrite, phophate, silicate), total alkalinity, biogenic silica in the water column and sediment trap.

Ocean alkalinity enhancement (OAE) is an emerging carbon dioxide removal (CDR) strategy that leverages the natural processes of weathering and acid neutralisation to durably store atmospheric CO2 in seawater. OAE can be achieved with a variety of methods, all of which have different environmental implications. One widely considered method utilizes electrochemistry to remove strong acid from seawater, leaving sodium hydroxide (NaOH) behind. This study evaluates the impacts of OAE via NaOH (NaOH-OAE) on a coastal plankton bloom, with particular focus on how macronutrient regeneration in the aftermath of the bloom responds to the perturbation. To investigate this, we enclosed a natural coastal phytoplankton community, including coccolithophores, in nine microcosms. The microcosms were divided into three groups: control, unequilibrated (512.1 ± 2.5 µmol kg-1 alkalinity increase) and equilibrated (499.3 ±5.65 µmol kg-1 alkalinity increase). Light was provided for 11 days to stimulate a bloom (light phase) and lights were turned off thereafter to investigate alkalinity and nutrient changes for 21 days (dark phase). We found no detectable effect of equilibrated NaOH-OAE on phytoplankton community and bacteria abundances determined with flow cytometry but observed a small yet detectable restructuring of phytoplankton communities under unequilibrated conditions. NaOH-OAE had no significant effect on alkalinity, NOx- and phosphate regeneration, but increased silicate regeneration by 64% over 21 days under darkness in the unequilibrated treatments where seawater pH was highest (8.65 relative to 7.92 in the control). Additional dissolution experiments with two diatom species supported this outcome on silicate regeneration for one of the two species, thereby suggesting that the effect is species specific. Our results point towards the potential of NaOH-OAE to influence regeneration of silicate in the surface ocean and thus the growth of diatoms, at least under the very extreme NaOH-OAE conditions simulated here.

The aim of this project was to estimate the iron recycling and export potential of Antarctic krill faecal pellets. To determine this, we firstly determined the sinking rate of the faecal pellets, characteristics which may influence the sinking rate (e.g., density, length and diameter), and then determined the portion of the total iron in the faecal pellets that is leached over a 12 h period under a continuous flow of seawater. The data sets that can be accessed here are: 1. Faecal pellet characteristics, including the raw and analysed data 2. The dry weight and length of faecal pellets used for total Fe estimation 3. Raw and final iron leachate data

This record presents data used in the paper 'Controls on polar Southern Ocean deep chlorophyll maxima: viewpoints from multiple observational platforms,' Philip W Boyd 𝘦𝘵. 𝘢𝘭., submitted to Global Biogeochemical Cycles, November 2023. All methods for the following datasets are detailed and cross-referenced in the paper. Data were collected from a range of methods, including: • vertical profiles (from 1 m resolved profiling using sensors on a CTD rosette: temperature, salinity, chlorophyll fluorescence, transmissivity - all calibrated) • vertical profiles (from discrete samples collected from CTD rosette or trace metal clean rosette, for nutrients, chlorophyll, POC, dissolved and particulate iron, active fluorescence, net primary productivity, biological iron uptake) • tow-body sections (undulating tow body (Triaxus) for temperature, salinity, chlorophyll fluorescence, transmissivity (and the ratio of chlorophyll fluorescence, transmissivity) • time-series observations from a robotic profiling float (BGC-ARGO) for temperature, salinity, chlorophyll fluorescence, and transmissivity).

This dataset contains the input and output data for an extended optimum multiparameter analysis (eOMP). Input data for parameters are given (temperature, salinity, oxygen, nitrate, phosphate and silicate), as obtained from the cited CSIRO open access CTD bottle data for the 2018 SR3 occupation. Output parameters are the proportional contribution of 8 water masses that were defined in the eOMP analysis. The output remineralization estimate, Delta-O, is also given. All data are referenced to depth and geographical position (latitude, longitude) from corresponding CTD bottle data. The eOMP used here was configured following Pardo et al. (2017). Details on the equations, parameterization and end-members that characterize the regional oceanography can also be found in the Supplementary Materials of Traill et al. (2023), including the robustness of the OMP analysis and the uncertainties of both the SWTs’ contributions and the ΔO parameter (Sections S1.2 and S1.3, Table S1, Table S2, Table S3).