δ18Oara profile for shelled pteropod calcification depth estimate based on seawater salinity and temperature measured by Argo floats. The oxygen isotopic composition of pteropod shells (δ18Optero) was compared to the δ18Oara at different depths. The calcification depth is defined as the depth where the values of δ18Optero and δ18Oara were equal within their respective uncertainties. Shelled pteropods are at risk from ocean acidification, with known effects on their shell durability and calcification. Pteropods typically form their aragonitic shells over specific depth ranges known as the ‘calcification depth’, which varies depending on species and habitats. Some Southern Ocean waters are already undersaturated with respect to aragonite and this could negatively affect shelled pteropods. However, the calcification depths of the pteropods have not been determined nor used to infer consequences of changing seawater carbonate chemistry in the Southern Ocean. In this study, we analysed the stable oxygen isotopic composition of Limacina rangii shells, collected by sediment traps in the subantarctic zone, to estimate their calcification depth. Shallow calcification depths (13-126 m) were detected in summer-autumn, while L. rangii calcified their shells deeper in the water column during winter-spring, with an average maximum calcification depth of 525 m. Recent shoaling of the aragonite saturation horizon is likely to negatively affect pteropods that calcify their shells in deep water. Such shoaling is likely to reduce shell formation and threaten population viability.

This dataset is derived from a comparative study evaluating six DNA extraction methods for their efficiency in recovering diatom sedimentary ancient DNA (sedaDNA) from Antarctic marine sediments. Sediment samples were collected from two sites: U1536C (Scotia Sea, West Antarctica) and KC02 (Sabrina Coast, Totten Glacier Region, East Antarctica). Each of the six extraction methods was applied to the same set of samples. Following shotgun metagenomic sequencing, the methods were assessed based on metrics such as diatom DNA recovery, average fragment length, and taxonomic diversity. The purpose of the study was to identify the optimal extraction approach for maximizing the yield and quality of diatom sedaDNA, thereby improving its utility for paleoenvironmental reconstruction.

Internal climate variability encompasses processes ranging from daily weather fluctuations to multidecadal interactions within the climate system. Understanding these processes is crucial for distinguishing natural variability from human-induced climate change. A large component of internal variability on sub-seasonal to multi-decadal time scales are associated with recurring patterns or ‘climate modes’. Using pre-industrial control (piControl) simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we investigate eight critical climate modes: Eastern Pacific El Niño (EP-El Niño), Central Pacific El Niño (CP-El Niño), Interdecadal Pacific Oscillation (IPO), Indian Ocean Dipole (IOD), Subsurface Dipole Mode (SDM), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), and Southern Annular Mode (SAM). These modes were derived from 23 CMIP6 models, each with over 500 years of simulation data, ensuring robust statistical insights into their spatial and temporal structures. The datasets were validated against observational data, revealing broad-scale consistency and highlighting biases in regional features and amplitudes. For example, the models effectively capture spatial patterns such as the tripolar SST anomaly of the IPO and the equatorial Pacific warming of EP-El Niño. However, regional discrepancies, like exaggerated warming or cooling in specific areas, were observed. Despite these biases, the datasets provide critical tools for understanding climate variability, conducting detection and attribution studies, and improving climate projections. Details regarding the generated NetCDF files are provided in the accompanying README file. All datasets are publicly accessible (https://doi.org/10.5281/zenodo.17274477, and additionally linked to this record), supporting future research and policy development to address climate variability and its implications for climate change adaptation and mitigation.

At the inception of our project, no study had examined particle fluxes in the Subantarctic Zone (SAZ) of the Southern Ocean, despite the fact that the SAZ represents a large portion of the total area of the Southern Ocean, serve as a strong sink for atmospheric (~1G t C yr-1 [Metzl et al., 1999]), and is central to hypotheses linking particle fluxes and climate change [Francois et al., 1997; Kumar et al., 1995; Sigman et al., 1999]. The SAZ serves as an interface between the cold nutrient-rich waters to its south and the nutrient-depleted subtropical gyres to its north. SAZ upper layers are marked by a thick layer of relatively homogenous Subantarctic Mode Water (SAMW), which overlies Antarctic Intermediate Water (AAIW). Both water masses are subducted northward beneath the subtropical gyres. Thus particles leaving the surface in these regions contribute to carbon redistribution via both the fraction that reaches the deep sea by settling and the fraction that is remineralized within SAMW or AAIW and subsequently subducted. The SAZ exhibits surface water carbon dioxide partial pressures well below atmospheric equilibrium, but PFZ waters are closer to atmospheric equilibrium in this sector [Metal et al., 1999; Poppet al., 1999]. The relative physical and biological contributions to these carbon dioxide partial pressure variations are unclear, but it is important to determine them because physical and biological carbon dioxide transfers are expected to show different responses to climate change [ Matear et al., 1999; Sarmiento and LeQuere, 1996]. For these reasons we focused on the SAZ and, for comparative purposes, on the PFZ to its south. We measured particle fluxes using moored sinking particle traps at three sites in the SAZ, in the PFZ, and beneath the Subantarctic Front (SAF), which separates them. This record describes particle flux data collected between 2004 and 2005. The NetCDF data contains the following variables. Please note not all variables are supplied in all files, specifically there are not uncertainty estimates and no quality control flags for this data. -----DATA DICTIONARY----- Name, description, units, standard name TIME, time, YYYY-MM-DD, time of sample midpoint TIME_START, time sample open, YYYY-MM-DD, time sample open NOMINAL_DEPTH, depth, m, nominal depth LATITUDE, latitude, degrees_north, latitude of anchor LONGITUDE, longitude, degrees_east, longitude of anchor pressure_actual, actual, dbar, actual pressure sample, sample number, 1, sample number sample_quality_control, quality flag for sample number, unitless, quality flag for sample number mass_flux, <1mm, mg m-2 d-1, particulate total mass flux mass_flux_uncertainty, uncertainty for particulate total mass flux, mg m-2 d-1,), uncertainty for particulate total mass flux mass_flux_quality_control, quality flag for particulate total mass flux, unitless, quality flag for particulate total mass flux SAL_BRINE, supernatant, 1, sample supernatant practical salinity SAL_BRINE_uncertainty, uncertainty for sample supernatant practical salinity, 1, uncertainty for sample supernatant practical salinity SAL_BRINE_quality_control, quality flag for sample supernatant practical salinity, unitless, quality flag for sample supernatant practical salinity pH_BRINE, supernatant, 1, sample supernatant pH NBS scale pH_BRINE_uncertainty, uncertainty for sample supernatant pH NBS scale, 1, uncertainty for sample supernatant pH NBS scale pH_BRINE_quality_control, quality flag for sample supernatant pH NBS scale, unitless, quality flag for sample supernatant pH NBS scale PC_mass_flux, <1mm, mg m-2 d-1, particulate total carbon mass flux PC_mass_flux_uncertainty, uncertainty for particulate total carbon mass flux, mg m-2 d-1, uncertainty for particulate total carbon mass flux PC_mass_flux_quality_control, quality flag for particulate total carbon mass flux, unitless, quality flag for particulate total carbon mass flux PN_mass_flux, <1mm, mg m-2 d-1, particulate total nitrogen mass flux PN_mass_flux_uncertainty, uncertainty for particulate total nitrogen mass flux, mg m-2 d-1, uncertainty for particulate total nitrogen mass flux PN_mass_flux_quality_control, quality flag for particulate total nitrogen mass flux, unitless, quality flag for particulate total nitrogen mass flux POC_mass_flux, <1mm, mg m-2 d-1, particulate organic carbon mass flux POC_mass_flux_uncertainty, uncertainty for particulate organic carbon mass flux, mg m-2 d-1, uncertainty for particulate organic carbon mass flux POC_mass_flux_quality_control, quality flag for particulate organic carbon mass flux, unitless, quality flag for particulate organic carbon mass flux PIC_mass_flux, <1mm, mg m-2 d-1, particulate inorganic carbon mass flux PIC_mass_flux_uncertainty, uncertainty for particulate inorganic carbon mass flux, mg m-2 d-1, uncertainty for particulate inorganic carbon mass flux PIC_mass_flux_quality_control, quality flag for particulate inorganic carbon mass flux, unitless, quality flag for particulate inorganic carbon mass flux BSi_mass_flux, <1mm, mg m-2 d-1, particulate biogenic silicon mass flux BSi_mass_flux_uncertainty, uncertainty for particulate biogenic silicon mass flux, mg m-2 d-1, uncertainty for particulate biogenic silicon mass flux BSi_mass_flux_quality_control, quality flag for particulate biogenic silicon mass flux, unitless, quality flag for particulate biogenic silicon mass flux TIME_END, time sample closed, YYYY-MM-DD, time sample closed Reference, citable reference DOI, DOI

This dataset contains the supplementary data and code accompanying the publication: Ward, D., Hill, N., Melbourne-Thomas, J., Welsford, D., Arangio, R., McNeill, M., Wotherspoon, S., Ziegler, P., Corney, S. (2025) Exploring mechanisms of change in a Southern Ocean fishery with a co-produced network model, ICES Journal of Marine Science. The study aimed to elucidate likely responses of the system to change, and mechanisms by which environmental, ecological or fishery behavioural changes could affect catch rates and toothfish stocks in the Heard and McDonald Islands (HIMI) fishing area. In the study, we co-developed a set of 128 qualitative network models of the Heard and McDonald Islands (HIMI) Patagonian toothfish fishery–stock–ecosystem interactions system with knowledge holders from industry and science. These different model versions capture different possible configurations of interactions for which the sign of the effects (positive or negative) are unknown. For each model versions, we simulated 10,000 quantitative instances of the qualitative model. We analysed these model outputs to understand 1) mean qualitative responses of system components (nodes) in 8 test scenarios (press increases in environmental, fishery, or ecosystem nodes); 2) how ‘unknown’ effect signs affected qualitative responses of key nodes to change; and 3) relationships between responses and the strength of effects originating from nodes being perturbed. Please refer to Ward et al. (2025) for more detailed description of the methodology and findings, and the README file for description of data and code contained in this repository.

At the inception of our project, no study had examined particle fluxes in the Subantarctic Zone (SAZ) of the Southern Ocean, despite the fact that the SAZ represents a large portion of the total area of the Southern Ocean, serve as a strong sink for atmospheric (~1G t C yr-1 [Metzl et al., 1999]), and is central to hypotheses linking particle fluxes and climate change [Francois et al., 1997; Kumar et al., 1995; Sigman et al., 1999]. The SAZ serves as an interface between the cold nutrient-rich waters to its south and the nutrient-depleted subtropical gyres to its north. SAZ upper layers are marked by a thick layer of relatively homogenous Subantarctic Mode Water (SAMW), which overlies Antarctic Intermediate Water (AAIW). Both water masses are subducted northward beneath the subtropical gyres. Thus particles leaving the surface in these regions contribute to carbon redistribution via both the fraction that reaches the deep sea by settling and the fraction that is remineralized within SAMW or AAIW and subsequently subducted. The SAZ exhibits surface water carbon dioxide partial pressures well below atmospheric equilibrium, but PFZ waters are closer to atmospheric equilibrium in this sector [Metal et al., 1999; Poppet al., 1999]. The relative physical and biological contributions to these carbon dioxide partial pressure variations are unclear, but it is important to determine them because physical and biological carbon dioxide transfers are expected to show different responses to climate change [ Matear et al., 1999; Sarmiento and LeQuere, 1996]. For these reasons we focused on the SAZ and, for comparative purposes, on the PFZ to its south. We measured particle fluxes using moored sinking particle traps at three sites in the SAZ, in the PFZ, and beneath the Subantarctic Front (SAF), which separates them. This record describes particle flux data collected between 2000 and 2001. The NetCDF data contains the following variables. Please note not all variables are supplied in all files, specifically there are not uncertainty estimates and no quality control flags for this data. -----DATA DICTIONARY----- Name, description, units, standard name TIME, time, YYYY-MM-DD, time of sample midpoint TIME_START, time sample open, YYYY-MM-DD, time sample open NOMINAL_DEPTH, depth, m, nominal depth LATITUDE, latitude, degrees_north, latitude of anchor LONGITUDE, longitude, degrees_east, longitude of anchor pressure_actual, actual, dbar, actual pressure sample, sample number, 1, sample number sample_quality_control, quality flag for sample number, unitless, quality flag for sample number mass_flux, <1mm, mg m-2 d-1, particulate total mass flux mass_flux_uncertainty, uncertainty for particulate total mass flux, mg m-2 d-1,), uncertainty for particulate total mass flux mass_flux_quality_control, quality flag for particulate total mass flux, unitless, quality flag for particulate total mass flux SAL_BRINE, supernatant, 1, sample supernatant practical salinity SAL_BRINE_uncertainty, uncertainty for sample supernatant practical salinity, 1, uncertainty for sample supernatant practical salinity SAL_BRINE_quality_control, quality flag for sample supernatant practical salinity, unitless, quality flag for sample supernatant practical salinity pH_BRINE, supernatant, 1, sample supernatant pH NBS scale pH_BRINE_uncertainty, uncertainty for sample supernatant pH NBS scale, 1, uncertainty for sample supernatant pH NBS scale pH_BRINE_quality_control, quality flag for sample supernatant pH NBS scale, unitless, quality flag for sample supernatant pH NBS scale PC_mass_flux, <1mm, mg m-2 d-1, particulate total carbon mass flux PC_mass_flux_uncertainty, uncertainty for particulate total carbon mass flux, mg m-2 d-1, uncertainty for particulate total carbon mass flux PC_mass_flux_quality_control, quality flag for particulate total carbon mass flux, unitless, quality flag for particulate total carbon mass flux PN_mass_flux, <1mm, mg m-2 d-1, particulate total nitrogen mass flux PN_mass_flux_uncertainty, uncertainty for particulate total nitrogen mass flux, mg m-2 d-1, uncertainty for particulate total nitrogen mass flux PN_mass_flux_quality_control, quality flag for particulate total nitrogen mass flux, unitless, quality flag for particulate total nitrogen mass flux POC_mass_flux, <1mm, mg m-2 d-1, particulate organic carbon mass flux POC_mass_flux_uncertainty, uncertainty for particulate organic carbon mass flux, mg m-2 d-1, uncertainty for particulate organic carbon mass flux POC_mass_flux_quality_control, quality flag for particulate organic carbon mass flux, unitless, quality flag for particulate organic carbon mass flux PIC_mass_flux, <1mm, mg m-2 d-1, particulate inorganic carbon mass flux PIC_mass_flux_uncertainty, uncertainty for particulate inorganic carbon mass flux, mg m-2 d-1, uncertainty for particulate inorganic carbon mass flux PIC_mass_flux_quality_control, quality flag for particulate inorganic carbon mass flux, unitless, quality flag for particulate inorganic carbon mass flux BSi_mass_flux, <1mm, mg m-2 d-1, particulate biogenic silicon mass flux BSi_mass_flux_uncertainty, uncertainty for particulate biogenic silicon mass flux, mg m-2 d-1, uncertainty for particulate biogenic silicon mass flux BSi_mass_flux_quality_control, quality flag for particulate biogenic silicon mass flux, unitless, quality flag for particulate biogenic silicon mass flux TIME_END, time sample closed, YYYY-MM-DD, time sample closed Reference, citable reference DOI, DOI

Southern Ocean phytoplankton growth is limited by low iron (Fe) supply and irradiance, impacting the strength of the biological carbon pump. Unfavourable upper ocean conditions such as low nutrient concentrations can lead to the formation of deep chlorophyll or biomass maxima (DCM/DBM). While common in the Southern Ocean, these features remain under-studied due to their subsurface location. To increase our understanding of their occurrence, we studied the responses of phytoplankton communities from a Southern Ocean DCM to increasing light, Fe, and manganese (Mn) levels. The DCM communities were light- and Fe-limited, but light limitation did not increase phytoplankton Fe requirements. The greatest physiological responses were observed under combined Fe/light additions, which stimulated macronutrient drawdown, biomass production and the growth of large diatoms. Combined Mn/light additions induced subtle changes in Fe uptake rates and community composition, suggesting species-specific Mn requirements. These results provide valuable information on Southern Ocean DCM phytoplankton physiology.