An increasing number of studies are considering Fe and ligand concentrations, providing data of trace element availability across the remote Southern Ocean region (Ardiningsih et al., 2021, Gerringa et al., 2020, Hassler et al., 2017, Thuroczy et al., 2012, Thuroczy et al., 2011, Caprara et al., 2016 and references therein). However, studies seldom focus on polar coastal environments which are especially sensitive to climate-induced changes. To anticipate how these changes may impact Fe availability, we must first understand the drivers of ligand supply to the Antarctic coast and offshore. The newly compiled Southern Ocean Ligand (SOLt) Collection includes all publicly available Fe complexation datasets for the Southern Ocean including dissolved Fe concentrations, Fe-binding ligand concentrations, and complexation capacities for 25 studies between 1995 - 2019.

Outline This is the Southern Ocean Monthly Climatology of Yamazaki et al. "Unlocking Southern Ocean Under-ice Seasonality with a New Monthly Climatology". The interpolation method follows Barth et al. (2014) available via DIVAnd Julia package (https://github.com/gher-uliege/DIVAnd.jl). CTD data sourced from Argo, MEOP, and World Ocean Database (including low resolution ocean station data). The dataset covers south of 40S and above 2000 dbar (above 1000 dbar for "_minimal"). The horizontal grid is 1/4 and 1/2 degrees in latitude and longitude, and the vertical grid is the 66 WOA layers. Mixed layer depth, temperature, salinity, crudely derived from max("Δσθ_10m=0.03kg/m³", "Holte&Talley"), are also provided in "_MLD". The following variables are included (* are excluded in "_minimal"): In-situ temperature (°C) in ITS-90 Practical salinity (psu) *Standard deviation of temperature (°C), inferred by the spread of observations *Standard deviation of practical salinity (psu), inferred by the spread of observations *Interpolation error of temperature (°C), inferred by the sparsity of observations *Interpolation error of practical salinity (psu), inferred by the sparsity of observations *Cabbeling correction for temperature (°C) *Cabbeling correction for practical salinity (psu) *Density stabilization factor for temperature (°C) *Density stabilization factor for practical salinity (psu) Project Description The advent of under-ice profiling float and biologging techniques has enabled year-round observation of the Southern Ocean and its Antarctic margin. These under-ice data are often overlooked in widely used oceanographic datasets, despite their importance in understanding seasonality and its role in sea ice changes, water mass formation, and glacial melt. We develop a monthly climatology of the Southern using Data Interpolating Variational Analysis, which excels in multi-dimensional interpolation and consistent handling of topography and horizontal advection. The dataset will be instrumental in investigating the seasonality and improving ocean models, thereby making valuable under-ice observations more accessible.

We assessed the impact of the Zonal Wave-3 atmospheric mode on the Antarctic Margins. The Zonal Wave-3 mode is the first mode of meridional winds over the Southern Ocean and has been linked to important sea ice and heat flux anomalies. It is expected to become stronger in the future, but there is only very limited knowledge on its impact on the Southern Ocean beneath sea ice. We set up a range of atmospheric perturbation on the ACCESS-OM2 ocean–sea ice model to assess the regional impact of the ZW3 mode and its different phases on the subpolar Southern Ocean.

The downward transport of organic particles produced by marine organisms is a key control on the ocean's carbon storage. Measurements of particle attenuation through the water column have historically been used to infer the sequestration potential of the biological carbon pump. While often modelled using a single power-law fit, this simplification may obscure important depth-dependent processes. Using data from biogeochemical-Argo floats in the Southern Ocean, here we show that splitting the water column into two distinct regimes captures depth-dependent variability in particle attenuation more effectively. Compared to the single power-law fit the model reveals greater attenuation just below the productive layer, reducing particulate organic carbon flux into the mesopelagic (200 - 400 m) by 40 - 60 %. Our findings suggest a more mechanistic representation of particle attenuation is needed to improve estimates of carbon transport and the durability of biologically-based marine carbon dioxide removal technologies, and to reduce uncertainties in ocean-climate feedbacks and future climate projections.

This work aimed to understand the influences of tropical to high-latitude Southern Hemisphere teleconnections on Southern Ocean atmospheric circulation, the air-sea-sea ice system, and Antarctic sea ice variability. It also sought to investigate how the Southern Annular Mode (SAM) interacts with tropically-forced climate patterns such as Zonal Wave 3 (ZW3) to affect high-latitude atmospheric circulation and impact sea-ice.

Antarctic krill is a key component of Southern Ocean ecosystems and there is significant interest in identifying regions acting as sources for the krill population. We develop a mechanistic model combining thermal and food requirements for krill egg production, with predation pressure post-spawning, to predict regions that could support high larval production (spawning habitat). We optimise our model on regional data using a maximum likelihood approach and then generate circumpolar predictions of spawning habitat quality. The uploaded datasets represent model predictions of seasonal circumpolar spawning habitat quality of Antarctic krill as well as composite data of the circumpolar mean annual number of weeks in which modelled spawning habitat quality is higher than the summer 80th percentile.

This dataset is a compilation of published records of 230Thorium - normalised lithogenic and biogenic fluxes from the Southern Ocean, south of 30S. All age models and derived fluxes were taken as published. Lithogenic fluxes are based on 232Th concentrations. Opal and carbonate fluxes are also included where available. In some cases fluxes had to be derived from published data. LGM values for each core represent an average of observations between 28 - 18 ka BP and Holocene values represent an average of observations from 10 - 0 ka BP. These data were collated as part of modelling study of the Southern Ocean during the LGM (Saini et al, Southern Ocean ecosystem response to Last Glacial Maximum boundary conditions, Submitted to Paleoceanography and Paleoclimatology, 2021)

Robust prediction of population responses to changing environments requires the integration of factors controlling population dynamics with processes affecting distribution. This is true everywhere but especially in polar pelagic environments. Biological cycles for many polar species are synchronised to extreme seasonality, while their distributions may be influenced by both the prevailing oceanic circulation and sea-ice distribution. Antarctic krill (𝘌𝘶𝘱𝘩𝘢𝘶𝘴𝘪𝘢 𝘴𝘶𝘱𝘦𝘳𝘣𝘢) is one such species exhibiting a complex life history that is finely tuned to the extreme seasonality of the Southern Ocean. Dependencies on the timing of optimal seasonal conditions has led to concerns over the effects of future climate on krill’s population status, particularly given the species’ important role within Southern Ocean ecosystems. Under a changing climate, established correlations between environment and species may breakdown. Developing the capacity for predicting krill responses to climate change therefore requires methods that can explicitly consider the interplay between life history, biological conditions, and transport. The Spatial Ecosystem And Population Dynamics Model (SEAPODYM) is one such framework that integrates population and general circulation modelling to simulate the spatial dynamics of key organisms. Here, we describe a modification to SEAPODYM, creating a novel model – KRILLPODYM – that generates spatially resolved estimates of krill biomass and demographics. This new model consists of three major components: (1) an age-structured population consisting of five key life stages, each with multiple age classes, which undergo age-dependent growth and mortality, (2) six key habitats that mediate the production of larvae and life stage survival, and (3) spatial dynamics driven by both the underlying circulation of ocean currents and advection of sea-ice. Here we present the first results of KRILLPODYM, using published deterministic functions of population processes and habitat suitability rules. Initialising from a non-informative uniform density across the Southern Ocean our model independently develops a circumpolar population distribution of krill that approximates observations. The model framework lends itself to applied experiments aimed at resolving key population parameters, life-stage specific habitat requirements, and dominant transport regimes, ultimately informing sustainable fishery management. ____ This dataset represents KRILLPODYM modelled estimates of Antarctic krill circumpolar biomass distribution for the final year of a 12-year spin up. Biomass distributions are given for each of the five key life stages outlined above. The accompanying background, model framework and initialisation description can be found in the following reference paper: 𝗚𝗿𝗲𝗲𝗻 𝗗𝗕, 𝗧𝗶𝘁𝗮𝘂𝗱 𝗢, 𝗕𝗲𝘀𝘁𝗹𝗲𝘆 𝗦, 𝗖𝗼𝗿𝗻𝗲𝘆 𝗦𝗣, 𝗛𝗶𝗻𝗱𝗲𝗹𝗹 𝗠𝗔, 𝗧𝗿𝗲𝗯𝗶𝗹𝗰𝗼 𝗥, 𝗖𝗼𝗻𝗰𝗵𝗼𝗻 𝗔, & 𝗟𝗲𝗵𝗼𝗱𝗲𝘆 𝗣. (2023) KRILLPODYM: a mechanistic, spatially resolved model of Antarctic krill distribution and abundance. 𝘍𝘳𝘰𝘯𝘵𝘪𝘦𝘳𝘴 𝘪𝘯 𝘔𝘢𝘳𝘪𝘯𝘦 𝘚𝘤𝘪𝘦𝘯𝘤𝘦, 10 Article 1218003. https://doi.org/10.3389/fmars.2023.1218003