This is the output from high-resolution model simulations of ocean conditions and melting beneath the floating part of Thwaites Glacier. The model is designed to study how these conditions change as the geometry of Thwaites Glacier evolved from 2011-2022. There is one simulation using the geometry from each year during this period, derived from satellite observations. The simulations are repeated for different ocean model forcing conditions, as described in the associated paper. PH was supported by the NERC/NSF Thwaites-MELT project (NE/S006656/1). ITGC contribution number 099.

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 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 model output for 20th-century ice-ocean simulations in the Amundsen Sea, Antarctica. 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 CESM Pacific Pacemaker Ensemble, using 20 members from 1920-2013. An additional simulation is forced with the ERA5 atmospheric reanalysis from 1920-2013. The simulations were completed in 2021 by Kaitlin Naughten at the British Antarctic Survey (Polar Oceans team). Supported by UKRI Fund for International Collaboration NE/S011994/1.

Sea ice index comprising data extracted from historical records of ship observed ice positions during Weddell Sea voyages between 1820-1843. Extracted data comprise information on the expedition ship and lead, type of document, the date on which the observation was made, the ship''s latitude and longitude at the time of the observation, comments on sea ice and sea ice present (1 if deemed present, 0 if not). Publication assisted by Leverhulme Emeritus Fellowship EM-2022-042 to Professor Grant R. Bigg: "Extending the Southern Ocean marine ice record to the eighteenth century".

Persistent organic pollutant concentrations in artificial sea ice experiments at the Roland von Glasow Air-Sea-Ice Chamber (RvG-ASIC) at the University of East Anglia, UK. Experiments involved investigating chemical contaminant behaviours during sea ice formation and melt in order to assess possible exposure risk to sea ice biota. Funding was provided by: NERC ENVISION Doctoral Training Centre (NE/L002604/1). NERC and the German Federal Ministry of Education and Research (BMBF) funded Changing Arctic Ocean program EISPAC project (NE/R012857/1). British Antarctic Survey Collaboration Voucher. EUROCHAMP-2020 Infrastructure Activity under grant agreement (No 730997).

Year-round measurements of the water column in Ryder Bay, Western Antarctic Peninsula have been collected by the Rothera Marine Assistant and associated researchers, starting in 1997 as part of the Rothera Oceanographic and Biological Time Series (RATS) to assess temporal variability in physical and biogeochemical oceanographic properties. The data were collected using instrumentation deployed from rigid inflatable boats, or through instrumentation deployed through holes cut in the sea ice when the bay is frozen over in winter. Data collected include profiles to about 500m depth with a conductivity-temperature-depth (CTD) system that produces measurements of temperature, salinity, fluorescence and photosynthetically-active radiation (PAR). Individual water samples are collected with a Niskin bottle from a standard 15m depth, with some samples also collected from the surface layer. These individual samples are analysed for size-fractionated chlorophyll, macronutrients (nitrate, nitrite, ammonium, orthophosphate and silicic acid), stable isotopes of oxygen in seawater, and some ancillary parameters. The bottle data have been quality controlled using international reference standards. Profiling and water sample collection occur with quasi-weekly frequency in summer and weekly in winter, but are weather and sea ice dependent. In addition, daily assessments of sea ice concentration and sea ice type are made from nearby Rothera Research Station by visual inspection, to aid interpretation of the ocean data collected. These data constitute one of the longest time series of ocean measurements in Antarctica, with near-unique systematic data collection in winter, within either polar circle. Data collection has been supported since 1997 by the Natural Environment Research Council (NERC) through core funding supplied to the British Antarctic Survey. Since 2017, it has been supported by NERC award "National Capability - Polar Expertise Supporting UK Research" (NE/R016038/1).

Perfluoroalkyl substances (PFAS) concentrations in artificial sea ice experiments at the Roland von Glasow Air-Sea-Ice Chamber (RvG-ASIC) at the University of East Anglia, UK. Experiments involved investigating chemical contaminant behaviours during sea ice formation and melt in order to assess possible exposure risk to sea ice biota. NERC ENVISION Doctoral Training Centre (NE/L002604/1). NERC and the German Federal Ministry of Education and Research (BMBF) funded Changing Arctic Ocean program EISPAC project (NE/R012857/1). British Antarctic Survey Collaboration Voucher. EUROCHAMP-2020 Infrastructure Activity under grant agreement (No 730997).

Sediments cores collected aboard the RRS James Clark Ross (JR104) in the Bellingshausen Sea, 2004. This work was carried out as part of the first systematic investigation of the former ice drainage basin in the southern Bellingshausen Sea. Reconnaissance data collected on previous cruises JR04 (1993) and cruises of R/V Polarstern in 1994 and 1995 suggested that this area contained the outlet of a very large ice drainage basin during late Quaternary glacial periods. The data and samples collected allowed us to address questions about the timing and rate of grounding line retreat from the continental shelf, the dynamic character of the ice that covered the shelf, and its influence on glaciomarine processes on the adjacent continental slope.

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.