This dataset contains Weddell Sea limited region ocean ice shelf model (NEMO) outputs. The included experiments were designed to look at the influence of far-field changes in temperature and salinity to changes in melt rates in the Filchner-Ronne Ice Shelf melt rate. Funding was provided by the Filchner Ice Shelf System project NE/L013770/1.

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 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.

This dataset contains output from a hydrodynamic model of the ocean in the Larsen C Ice Shelf (LCIS) cavity and a nearby area of the western Wedell Sea. Simulations were run using the MITgcm numerical ocean model and included an ice shelf with steady thickness. A new LCIS bathymetry was used in the simulations, referred to as the ''Brisbourne'' bathymetry. The data provided here includes these geometry grids and ocean velocity and basal melt rate fields output from the final year of an arbitrary 10-year simulation, or a 6-month extension run. Calculated marine ice fields beneath the ice shelf based on the simulation''s melt rate results are also included. In addition, output from several simulations using different initial and boundary ocean temperature conditions and runs with different cavity geometries are also provided. This work was supported by the Natural Environment Research Council and the EnvEast Doctoral Training Partnership [grant number NE/L002582/1] and PICCOLO [grant number NE/P021395/1].

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.