This data set represents the model results plotted in the figures in Bett et al. (2024), produced using the MITgcm/WAVI ice/ocean coupled model. The model domain is the Amundsen Sea sector, where the simulations start in approximately the year 2015 and run for 180 years. Simulations are forced using idealised ocean boundary conditions which represent cold and warm conditions, along with a third extreme case where no ice shelf melting is applied. These simulations were produced in order to examine the ice/ocean processes that occur during future evolution of the region. For full descriptions of the results plotted in each figure see Bett et al. (2024). Funding was provided by NERC Grant NE/S010475/1, ITGC THWAITES MELT (NE/S006656/1), ITGC THWAITES PROPHET (NE/S006796/1) and the European Union''s Horizon 2020 grant PROTECT (869304).

This dataset encompasses data produced in the study ''Seasonal Arctic sea ice forecasting with probabilistic deep learning'', published in Nature Communications. The study introduces a new Arctic sea ice forecasting AI system, IceNet, which predicts monthly-averaged sea ice probability (SIP; probability of sea ice concentration > 15%) up to 6 months ahead at 25 km resolution. The study demonstrated IceNet''s superior seasonal forecasting skill over a state-of-the-art physics-based sea ice forecasting system, ECMWF SEAS5, and a statistical benchmark. This dataset includes three types of data from the study. Firstly, IceNet''s SIP forecasts from 2012/1 - 2020/9. Secondly, the 25 neural network files underlying the IceNet model. Thirdly, CSV files of results from the study. The codebase associated with this work includes a script to download this dataset and reproduce all the paper''s figures. This dataset is supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the "AI for Science" theme within that grant and The Alan Turing Institute. The dataset is also supported by the NERC ACSIS project (grant NE/N018028/1).

This is the output from high-resolution model simulations of ocean conditions and melting beneath the floating part of Thwaites Glacier. The model is designed to study how these conditions change as the geometry of Thwaites Glacier evolved from 2011-2022. There is one simulation using the geometry from each year during this period, derived from satellite observations. The simulations are repeated for different ocean model forcing conditions, as described in the associated paper. PH was supported by the NERC/NSF Thwaites-MELT project (NE/S006656/1). ITGC contribution number 099. *******PLEASE BE ADVISED TO USE VERSION 2.0 DATA******* Version 2 is available at https://doi.org/10.5285/473eb97c-63a8-4002-8b72-e7f07b2ab228. (Version 1 has the seabed bathymetry and ice shelf topography files incorrectly oriented.)

This is the output from high-resolution model simulations of ocean conditions and melting beneath the floating part of Thwaites Glacier. The model is designed to study how these conditions change as the geometry of Thwaites Glacier evolved from 2011-2022. There is one simulation using the geometry from each year during this period, derived from satellite observations. The simulations are repeated for different ocean model forcing conditions, as described in the associated paper. PH was supported by the NERC/NSF Thwaites-MELT project (NE/S006656/1). ITGC contribution number 099. *******PLEASE BE ADVISED TO USE VERSION 2.0 DATA******* (Version 1 had the seabed bathymetry and ice shelf topography files incorrectly oriented.)

This study took place from 12 November to 1 December 2015, at the emperor penguin colony at Rothschild Island (-69.5 S, -72.3 W) located on sea ice < 1 km from the eastern coastline of the island in Lazarev Bay. ARGOS telemetry devices were attached to adult emperor penguins en route to, or from, the colony. The last recorded positions were on 26 April 2016 when data collection was terminated; at this date six instruments were still transmitting. PTT devices were deployed as a joint operation between Philip Trathan (British Antarctic Survey), and Barbara Wienecke (Australian Antarctic Division). Catrin Thomas acted as the BAS Field General Assistant. Funding: This work was supported by the UKRI/ BAS ALI-Science project and to the Australian Antarctic Program. Philip Trathan was also supported by WWF (UK) under grant GB095701.

This dataset provides daily, 8-day, and monthly Arctic melt pond fractions and binary classification, from 2021-05-01 to 2022-08-31. Level-2 MODerate resolution Imaging Spectroradiometer (MODIS) top-of-the-atmosphere (TOA) reflectances for bands 1-4 were obtained, to which two machine learning algorithms such as multi-layer neural networks and logistic regression were applied to map melt pond fraction and binary melt pond/ice classification. This work was funded by NERC standard grant NE/R017123/1.

Sea ice index comprising data extracted from historical records of ship observed ice positions during Weddell Sea voyages between 1820-1843. Extracted data comprise information on the expedition ship and lead, type of document, the date on which the observation was made, the ship''s latitude and longitude at the time of the observation, comments on sea ice and sea ice present (1 if deemed present, 0 if not). Publication assisted by Leverhulme Emeritus Fellowship EM-2022-042 to Professor Grant R. Bigg: "Extending the Southern Ocean marine ice record to the eighteenth century".

This dataset provides daily, 8-day, and monthly Arctic melt pond fractions and binary classification, from 2000-06-01 to 2020-08-31. Level-2 MODerate resolution Imaging Spectroradiometer (MODIS) top-of-the-atmosphere (TOA) reflectances for bands 1-4 were obtained, to which two machine learning algorithms such as multi-layer neural networks and logistic regression were applied to map melt pond fraction and binary melt pond/ice classification. This work was funded by NERC standard grant NE/R017123/1.

This dataset provides model output for 20th-century ice-ocean simulations in the Amundsen Sea, Antarctica. The simulations are performed with the MITgcm model at 1/10 degree resolution, including components for the ocean, sea ice, and ice shelf thermodynamics. Atmospheric forcing is provided by the CESM Pacific Pacemaker Ensemble, using 20 members from 1920-2013. An additional simulation is forced with the ERA5 atmospheric reanalysis from 1920-2013. The simulations were completed in 2021 by Kaitlin Naughten at the British Antarctic Survey (Polar Oceans team). Supported by UKRI Fund for International Collaboration NE/S011994/1.

A seasonal cycle of the FA composition of particulate organic matter from surface waters, Chlorophyll-a maximum layer and bottom sea ice, sampled during the MOSAiC expedition in the Central Arctic Ocean (2019-2020), suggests the importance of phylogenetic and environmental drivers. To improve our understanding of these different drivers, we conducted culture experiments with 32 cold-water algal strains where temperature, light intensity, and nutrient supply were manipulated individually or in combination. The culture experiments were carried out at the Culture Collection of Algae and Protozoa (CCAP; Oban, Scotland), the Roscoff Culture Collection (RCC; Roscoff, France) and the Alfred-Wegener-Institute-Helmholtz-Centre for Polar and Marine Research (AWI; Bremerhaven, Germany). The strains were part of the culture collections, had been isolated in the Arctic (25 strains), Southern Ocean (2 strains) or North Atlantic (5 strains), and included diatoms, chlorophytes, haptophytes, cryptophytes, chrysophytes, dinoflagellates and cyanobacteria. Some of the species are Arctic sea ice diatoms (e.g. Nitzschia frigida, Attheya spp.) or pelagic diatoms (e.g. Thalassiosira gravida), while others are non-diatom species that are becoming increasingly prominent in the Arctic, e.g. the coccolithophore Emiliania huxleyi (synonym Gephyrocapsa huxleyi), the prymnesiophyte Phaeocystis pouchetii, the chlorophyte Micromonas spp. and the cyanobacterium Synechococcus spp.. The experiments can be divided into three groups: First, those that tested a low light-low temperature setting, second, those that tested a low light-low temperature and a higher light-higher temperature setting and, third, those that tested the effect of nutrient (nitrate, phosphate and silicate) shortage in combination with low and high light intensity. The first set of experiments was conducted with all 32 strains, the second set with all strains grown at CCAP and AWI, and the third set focuses on the keystone under-ice diatom Melosira arctica. The experiments were run for 4-7 weeks to accumulate sufficient biomass for biomarker extractions (FA and sterols), C:N analysis and light-microscopy of cell size and cell concentration. At the end of the experiments, the algae were filtered onto GF/F filters and deep frozen until analysis. After addition of internal standards for FA and sterols, the filters were saponified with KOH. Thereafter, non-saponifiable lipids (sterols) were extracted with hexane and purified by open column chromatography on silica gel. FA were obtained by adding concentrated HCl to the saponified solution and re-extracted with hexane. Samples were converted into fatty acid methyl esters (FAME) and analysed using an Agilent 6890N gas chromatograph with FID detector. The Clarity chromatography software system (DataApex, Czech Republic) was used for chromatogram data evaluation. FAME were quantified via the internal standard, Tricosanoic acid methyl ester (23:0) (Supelco, Germany) to provide the total amount of FA (TFA) per filter. These FA datasets of cultured algae are presented in a manuscript together with the FA pattern seen in sea ice- and water column POM in the CAO during the MOSAiC expedition and in previously published data from Arctic shelf regions. The manuscript focusses mainly on two important long-chain omega-3 FA (eicosapentaenoic acid and docosahexaenoic acid) that are considered essential for the nutrition of higher trophic levels, including humans, and their production to decline with global temperature rise. Contributions by KS were funded by the UK''s Natural Environment Research Council MOSAiC Thematic project SYM-PEL: ''Quantifying the contribution of sympagic versus pelagic diatoms to Arctic food webs and biogeochemical fluxes: application of source-specific highly branched isoprenoid biomarkers''/ (NE/S002502/1). CRM was funded by the NERC National Capability Services and Facilities Programme (NE/R017050/1).