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 a 308 year record of methansulphonic acid (MSA) from coastal West Antarctica, representing sea ice conditions in the Amundsen-Ross Sea. Annual average MSA has been calculated from the 136 m Ferrigno ice core (F10), drilled on the Bryan Coast in Ellsworth Land, West Antarctica during the austral summer 2010/11. The sea ice extent is based on geometric mean regression of MSA flux with satellite sea ice extent from 146 degrees west. The record was measured using a Dionex ICS2500 anion system at 5 cm resolution, corresponding to approximately 14 samples a year. Funding was provided by the NERC grant NE/J020710/1.

The dataset (FSD-GFL-res2m-Preponding) contains sea-ice floe ice distribution (FSD) data derived from the Global Fiducials Library (GFL) imagery during the pre-ponding period at the three fiducial sites, using the algorithms described in Hwang et al. (2017). The GFL imagery is 1-m resolution declassified National Technical Means satellite imagery, also known as the Literal Image Derived Products (LIDPs) (Kwok, 2014). The FSD data derived from the GFL imagery cover the period of 2000 to 2014 at the three fiducial sites at Chukchi Sea (70 deg N and 170 deg W), East Siberian Sea (82 deg N and 150 deg E), and Fram Strait (84.9 deg N and 0.5 deg E). For the production of this dataset, the spatial resolution of the GFL imagery degraded to 2 meters ("res2m") for fast processing. The FSD data are produced for robust model calibration and validation for FSD parameterisations within sea-ice models, and also to improve our understanding of spatial and temporal variations of FSD across the Arctic Ocean. The FSD data have been generated by B. Hwang. This FSD dataset is produced as part of NERC MIZ NE/R000654/1 (Towards a marginal Arctic sea ice cover).

This dataset contains the floe size distribution (FSD) data derived from multi-satellite imagery data acquired across the Arctic Ocean. Satellite imagery data includes high-resolution visible images from the USGS Global Fiducials Library (MEDEA), TerraSAR-X/TanDEM-X and Worldview-3 (WV3). The derived data contain floe size (calliper diameter), shape factor, minor/major axis, perimeter and area of the floes. This data set has been used to investigate the characteristics of the FSD during major seasonal evaluation stages of Arctic sea ice floes. The retrieval of the FSD data was done by the University of Huddersfield team. This work was funded by NERC MOSAiC program NE/S002545/1.

This dataset contains the floe size distribution (FSD) data derived from high-resolution satellite imagery data acquired at two fixed locations in the Arctic Ocean. Satellite imagery data include MEDEA images and WorldView images. These satellite images have a spatial resolution of 1 m or higher, thus providing the FSD information, especially for small floes. The derived data contain floe size (calliper diameter), shape factor, minor/major axis, perimeter and area of the floes. This dataset has been used to evaluate the sea ice models with the FSD parameterisations. The retrieval of the FSD data was done by the University of Huddersfield team. This work was funded by NERC standard grant NE/R000654/1 and NERC MOSAiC program NE/S002545/1.

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.

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.