These are MITgcm ocean model outputs under Pine Island Glacier Ice Shelf. The simulations were designed to investigate the relative role of ocean conditions and ice shelf geometric changes between 2011 and 2021. Each set of runs contains one run for each year. BOTH-2011 to BOTH-2021 contain annual runs using the ocean and geometry from the corresponding year, OCEAN-2011 to OCEAN-2021 contain annual runs using the annual ocean conditions and 2012 ice shelf geometry and GEOM-2011 to GEOM-2021 contain annual runs using the 2012 ocean conditions and the annual ice shelf geometry. The ice shelf geometries used are derived from CryoSat-2 Digital Elevation Models (Lowery et al., 2025) and converted to ice shelf draft by assuming the ice is in hydrostatic equilibrium. The ocean boundary conditions are from observations from two moorings in Pine Island Bay. The output data contain fields of potential temperature, salinity, velocity and 2D variables from the boundary layer such as freshwater flux, thermal driving and sub-ice shelf velocity. The work took place as part of the Natural Environment Research Council (NERC) Satellite Data in Environmental Science (SENSE) Centre for Doctoral Training (grant no. NE/T00939X/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.

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.

The South Orkney Fast-Ice series (SOFI) is an annual record of the timing of formation and breakout of fast-ice in Factory Cove, Signy Island, in the South Orkney Islands on the Scotia Arc in the northern Weddell Sea, Antarctica. Fast-ice formation and break-up has been studied at the South Orkeny Islands since the early 1900s, with this dataset covering the period of 1903 to 2019. This dataset is produced by personnel from the British Antarctic Survey, in efforts to study sea-ice variability in the Southern Hemisphere. Data was collected using various methods over the reporting period, namely an offset date from Laurie Island's fast-ice, direct observation, and with camera equipment. This is an updated version (2.0) of the dataset, that includes data from 2008 to 2019.

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.