From 1 - 10 / 18
  • 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 and 21st-century ice-ocean simulations in the Amundsen Sea. 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 CESM1 climate model for the historical period (1920-2005) and four future scenarios (2006-2100), using 5-10 ensemble members each. The open ocean boundaries are forced by either the corresponding CESM1 simulation or a present-day climatology. The simulations were completed in 2022 by Kaitlin Naughten at the British Antarctic Survey (Polar Oceans team). UKRI Fund for International Collaboration NE/S011994/1

  • 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.

  • 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.

  • 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.

  • 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.

  • 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 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.

  • 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.