The file contains Southern Hemisphere winter (September) sea ice concentration (sic) from a simulation performed using the isotope-enabled HadCM3 climate model forced with early last interglacial boundary conditions, centred approximately 128,000 years ago. The resulting sic represents a reduction in winter sea ice area of approximately 54% relative to pre-industrial and is proposed as the best explanation for the Antarctic ice core data from 128,000 years ago. The spatial pattern of sea ice retreat was determined using a large ensemble of model experiments and a pattern search optimization approach to match the last interglacial ice core isotope peak. Further details can be found in the published manuscript (https://doi.org/10.1002/2017GL074594). This work was funded by NERC grants NE/P009271/1, NE/P013279/1, and NE/K004514/1.

This dataset contains four types of data: i) IceNet''s 93-day pan-Arctic sea ice concentration forecasts, initialised each day between 26th July - 12th December for the years 2020-2022 inclusive (140 forecasts per year), ii) neural network weights for the IceNet model used to generate the forecasts, iii) a Shapefile for the coastline of Victoria Island (Nunavut, Canada), which was used to estimate caribou sea ice crossing-start times, and iv) CSV files with results linking sea ice concentration values to caribou sea ice crossing-start times. This data was used to explore if and how sea ice forecasts from the IceNet model could give early-warning of Dolphin and Union caribou migration times from Victoria Island to the mainland, by predicting key sea ice concentration thresholds. This work was supported under the WWF-UK Arctic IceNet grant (project number GB085600), the EPSRC Grant EP/Y028880/1 and the Environment and Sustainability Grand Challenge at the Alan Turing Institute.

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. (2022). 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. (2022). 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.