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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 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.

  • 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).

  • Perfluoroalkyl substances (PFAS) 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. 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).

  • Sediments cores collected aboard the RRS James Clark Ross (JR104) in the Bellingshausen Sea, 2004. This work was carried out as part of the first systematic investigation of the former ice drainage basin in the southern Bellingshausen Sea. Reconnaissance data collected on previous cruises JR04 (1993) and cruises of R/V Polarstern in 1994 and 1995 suggested that this area contained the outlet of a very large ice drainage basin during late Quaternary glacial periods. The data and samples collected allowed us to address questions about the timing and rate of grounding line retreat from the continental shelf, the dynamic character of the ice that covered the shelf, and its influence on glaciomarine processes on the adjacent continental slope.

  • This dataset presents monthly gridded sea ice and ocean parameters for the Arctic derived from the European Space Agency''s satellite CryoSat-2. Parameters include sea ice freeboard, sea ice thickness, sea ice surface roughness, mean sea surface height, sea level anomaly, and geostrophic circulation. Data are provided as monthly grids with a resolution of 25 km, mapped onto the NSIDC EASE2-Grid, covering the Arctic region north of 50 degrees latitude, for all winter months (Oct-Apr) between 2010 and 2018. CryoSat-2 Level 1b Baseline C observed waveforms have been retracked using a numerical model for the SAR altimeter backscattered echo from snow-covered sea ice presented in Landy et al. (2019), which offers a sophisticated physically-based treatment of the effect of ice surface roughness on retracked ice and ocean elevations. Methods for optimizing echo model fits to observed CryoSat-2 waveforms, retracking waveforms, classifying returns, deriving sea ice freeboard, and converting to thickness are detailed in Landy et al. (In Review). This dataset contains derived sea ice thicknesses from two processing chains, the first using the conventional snow depth and density climatology from Warren et al. (1999) and the second using reanalysis and model-based snow data from SnowModel (Stroeve et al., In Review). Sea surface height and ocean topography grids were derived from only those CryoSat-2 samples classified as leads. Both the random and systematic uncertainties relevant for each parameter have been carefully estimated and are provided in the data files. NetCDF files contain detailed descriptions of each derived parameter. Funding was provided by ESA Living Planet Fellowship Arctic-SummIT grant ESA/4000125582/18/I-NS and NERC Project PRE-MELT grant NE/T000546/1.

  • Samples of snow, sea ice, seawater (0.5 m and 5 m depths) and meltponds were collected from two ice-covered stations located in the Barents Sea (81 N), during the "Nansen Legacy Q3" summer cruise of the Norwegian research vessel Kronprins Haakon on 26-28 August 2019. Perfluoroalkyl substances (PFAS) concentrations, salinity and stable oxygen isotopes were measured in all samples to determine sources and environmental fate of PFAS during late summer. 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). The Nansen Legacy research is funded by the Research Council of Norway (# 276730).

  • This dataset presents biweekly gridded sea ice thickness and uncertainty for the Arctic derived from the European Space Agency''s satellite CryoSat-2. An associated ''developer''s product'' also includes intermediate parameters used or output in the sea ice thickness processing chain. Data are provided as biweekly grids with a resolution of 80 km, mapped onto a Northern Polar Stereographic Grid, covering the Arctic region north of 50 degrees latitude, for all months of the year between October 2010 and July 2020. CryoSat-2 Level 1b Baseline-D observed radar waveforms have been retracked using two different approaches, one for the ''cold season'' months of October-April and the second for ''melting season'' months of May-September. The cold season retracking algorithm uses a numerical model for the SAR altimeter backscattered echo from snow-covered sea ice presented in Landy et al. (2019), which offers a physical treatment of the effect of ice surface roughness on retracked ice and ocean elevations. The method for optimizing echo model fits to observed CryoSat-2 waveforms, retracking waveforms, classifying returns, and deriving sea ice radar freeboard are detailed in Landy et al. (2020). The melting season retracking algorithm uses the SAMOSA+ analytical echo model with optimization to observed CryoSat-2 waveforms through the SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research and Exploitation) service available through ESA Grid Processing on Demand (GPOD). The method for classifying radar returns and deriving sea ice radar freeboard in the melting season are detailed in Dawson et al. (2022). The melting season sea ice radar freeboards require a correction for an electromagnetic range bias, as described in Landy et al. (In Review). After applying the correction, year-round freeboards are converted to sea ice thickness using auxiliary satellite observations of the sea ice concentration and type, as well as snow depth and density estimates from a Lagrangian snow evolution scheme: SnowModel-LG (Stroeve et al., 2020; Liston et al., 2020). The sea ice thickness uncertainties have been estimated based on methods described in Landy et al. (In Review). NetCDF files contain detailed descriptions of each parameter. Funding was provided by the NERC PRE-MELT grant NE/T000546/1 and the ESA Living Planet Fellowship Arctic-SummIT grant ESA/4000125582/18/I-NS.