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. *******PLEASE BE ADVISED TO USE VERSION 2.0 DATA******* Version 2 is available at https://doi.org/10.5285/473eb97c-63a8-4002-8b72-e7f07b2ab228. (Version 1 has the seabed bathymetry and ice shelf topography files incorrectly oriented.)

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. *******PLEASE BE ADVISED TO USE VERSION 2.0 DATA******* (Version 1 had the seabed bathymetry and ice shelf topography files incorrectly oriented.)

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 data set represents the model results plotted in the figures in Bett et al. (2024), produced using the MITgcm/WAVI ice/ocean coupled model. The model domain is the Amundsen Sea sector, where the simulations start in approximately the year 2015 and run for 180 years. Simulations are forced using idealised ocean boundary conditions which represent cold and warm conditions, along with a third extreme case where no ice shelf melting is applied. These simulations were produced in order to examine the ice/ocean processes that occur during future evolution of the region. For full descriptions of the results plotted in each figure see Bett et al. (2024). Funding was provided by NERC Grant NE/S010475/1, ITGC THWAITES MELT (NE/S006656/1), ITGC THWAITES PROPHET (NE/S006796/1) and the European Union''s Horizon 2020 grant PROTECT (869304).

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 study took place from 12 November to 1 December 2015, at the emperor penguin colony at Rothschild Island (-69.5 S, -72.3 W) located on sea ice < 1 km from the eastern coastline of the island in Lazarev Bay. ARGOS telemetry devices were attached to adult emperor penguins en route to, or from, the colony. The last recorded positions were on 26 April 2016 when data collection was terminated; at this date six instruments were still transmitting. PTT devices were deployed as a joint operation between Philip Trathan (British Antarctic Survey), and Barbara Wienecke (Australian Antarctic Division). Catrin Thomas acted as the BAS Field General Assistant. Funding: This work was supported by the UKRI/ BAS ALI-Science project and to the Australian Antarctic Program. Philip Trathan was also supported by WWF (UK) under grant GB095701.

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.

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.

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".

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.