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  • 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 dataset comprises summary statistics regarding historical and projected Southern Hemisphere total sea ice area (SIA) and 21st century global temperature change (dTAS), evaluated from the multi-model ensembles contributing to CMIP5 and CMIP6 (Coupled Model Intercomparison Project phases 5 and 6). The metrics are evaluated for two climatological periods (1979-2014 and 2081-2100) from a number of CMIP experiments; historical, and ScenarioMIP or RCP runs. These metrics were calculated to calculate projections of future Antarctic sea ice loss, and drivers of ensemble spread in this variable, for Holmes et al. (2022) "Antarctic sea ice projections constrained by historical ice cover and future global temperature change". Funding was provided by the British Antarctic Survey Polar Science for Planet Earth Programme and under NERC large grant NE/N01829X/1

  • This dataset contains data for the plots in Figures 3 and 4 in the article: Effective rheology across the fragmentation transition for sea ice and ice shelves, Åström, and D.I. Benn, GRL, 2019. The data is produced with the numerical simulation code HiDEM, which is an open source code that can be found at: https://github.com/joeatodd/HiDEM. The data plots in the paper contain the data used as benchmarks for testing the reliability of the simulations (Fig.3), and the main results (Fig. 4), the effective rheology of sea ice across the fragmentation transition. Funding was provided by the NERC grant NE/P011365/1 Calving Laws for Ice Sheet Models CALISMO.

  • The flow-line model was designed to enable estimation of the age and surface origin for various ice bodies identified within hot-water drilled boreholes on Larsen C Ice Shelf. Surface fluxes are accumulated, converted to thicknesses, and advected down flow from a fixed number of selected points. The model requires input datasets of surface mass balance, surface velocity, vertical strain rates, ice-shelf thickness, and a vertical density profile. This model is part of a larger project. Input datasets such as density profiles and trajectory vectors are available separately. Resolution is dependent on the input datasets. Funding was provided by the NERC grant NE/L005409/1.

  • Two consecutive cruises in the Weddell Sea, Antarctica, in winter/spring 2013 provided the first direct observations of sea salt aerosol (SSA) production from blowing snow above sea ice, thereby validating a model hypothesis to account for winter time SSA maxima in polar regions not explained otherwise. Concentration, size distribution and chemical composition of airborne snow particles, sea salt aerosol and snow on sea ice where measured on board RV Polarstern as well as on the sea ice during ice stations. Funding was provided by NERC projects NE/J023051/1 and NE/J020303/1.

  • The datasets are output from a flow-line model designed to estimate the age and surface origin for various ice bodies identified within hot-water drilled boreholes on Larsen C Ice Shelf (Hubbard et al., 2016, Ashmore et al., 2017). Two trajectories, based on remotely sensed velocities, allow surface fluxes from a regional climate model to be accumulated and advected down flow from selected points on the shelf. Vertical strain rates are taken into account, and surface mass balance is converted to thickness according to density profiles based on borehole data (Ashmore et al., 2017). The model output has a 250m horizontal resolution. These data are part of a larger project. The flow-line model code, the SMB datasets, and the borehole density profiles are also available. Funding was provided by the NERC grant NE/L005409/1.

  • These 21 Last Interglacial (LIG) summer surface air temperature (SSAT) observations were compiled to assess LIG Arctic sea ice (Guarino et al 2020). Twenty of the observations were also previously used in the IPCC-AR5 report. Each observation is thought to be of summer LIG air temperature anomaly relative to present day and is located in the circum-Arctic region. All sites are from north of 51N. There are 7 terrestrial based temperature records; 8 lacustrine records; 2 marine pollen-based records; and 3 ice core records included in the original compilation. This compilation includes 1 additional ice core record. This work was funded by NERC standard research grant nos. NE/P013279/1 and NE/P009271/1.

  • The HadGEM3 (HadGEM3-GC3.1 or HadGEM3-GC3.1-N96ORCA1) PI simulation was initialized using the standard CMIP6 protocol using constant 1850 GHGs, ozone, solar, tropospheric aerosol, stratospheric volcanic aerosol and land-use forcing. The PI spin-up was 700 model-years, which allowed the land and oceanic masses to attain approximate steady state. The HadGEM3 LIG (Last Interglacial) simulation was initialized from the end of the spin-up phase of the equivalent pre-industrial (PI) simulation. After initialization, the LIG was run for 350 model-years. This 350 LIG spin-up permits the model to reach atmospheric equilibrium and to achieve an upper-ocean equilibrium. The model was then run for a further 200 model-years of LIG production run. This has been demonstrated to be an adequate run length to appropriately capture the model internal variability. This dataset contains outputs from the 200 years of production run of the period. The HadCM3 PI simulation was run for a period of over 600 years. The HadCM3 LIG simulation was initialized from the end of a previous CMIP5 LIG simulation, which was of length 400 years and initiated from the end of the corresponding PI, and run for further 250 years. The total spin-up phase for the HadCM3 LIG simulation used in this study was thus 600 model-years, and the length of the production (at atmospheric and upper-oceanic equilibrium) LIG HadCM3 simulation is 50 model-years. This work was funded by NERC standard research grant nos. NE/P013279/1 and NE/P009271/1.

  • This dataset provides Arctic monthly mean 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.

  • Persistent organic pollutant concentrations in artificial sea ice experiments at the Roland von Glasow Air-Sea-Ice Chamber (RvG-ASIC) at the University of East Anglia, UK. Experiments involved investigating chemical contaminant behaviours during sea ice formation and melt in order to assess possible exposure risk to sea ice biota. Funding was provided by: NERC ENVISION Doctoral Training Centre (NE/L002604/1). NERC and the German Federal Ministry of Education and Research (BMBF) funded Changing Arctic Ocean program EISPAC project (NE/R012857/1). British Antarctic Survey Collaboration Voucher. EUROCHAMP-2020 Infrastructure Activity under grant agreement (No 730997).