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The South Orkney Fast-Ice series (SOFI) is an annual record of the timing of formation and breakout of fast-ice in a bay in the South Orkney Islands on the Scotia Arc in the northern Weddell Sea, Antarctica.
The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data (AMSR-E and AMSR-2). It is processed with an algorithm using coarse resolution (6 GHz and 37 GHz) imaging channels, and has been gridded at 50km grid spacing. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea_Ice_CCI project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities. A SIC CDR at 25km grid spacing is also available (doi: 10.5285/c61bfe88-873b-44d8-9b0e-6a0ee884ad95) and a 12.5km product is in preparation.
The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data (AMSR-E and AMSR-2). It is processed with an algorithm using medium resolution (19 GHz and 37 GHz) imaging channels, and has been gridded at 25km grid spacing. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities. A SIC CDR at 50 km grid spacing is also available (doi:10.5285/70f611b0-ba82-48e6-9190-a62cf9f925f2) and a 12.5km product is in preparation.
This dataset comprises Sentinel2 satellite imagery and derived geographic locations of five emperor penguin colonies located in the central and eastern Bellingshausen Sea, between October to December 2022. Medium-resolution satellite data was monitored for the presence of emperor penguin colonies, and when colonies were found, imagery was digitised and downloaded from Copernicus playground. Satellite data indicate early sea ice break-up at three of the four colonies, and the disappearance of the fourth colony. Digitisation and annotation of satellite imagery was carried out by the British Antarctic Survey, and supported by NERC core funding and WWF grant NEB 2181.
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).
Small particles (known as aerosol) in the atmosphere play several critical roles. They affect the transmission of sunlight to the underlying surface; they affect the formation of clouds, and they host and enhance important chemical reactions. When they are deposited on ice they leave a record of past conditions that can be accessed by drilling ice cores. The most significant aerosol component over marine areas is sea salt aerosol. Over most of the world''s oceans this is created by bubble bursting in sea spray. However there is strong evidence that another source of sea salt aerosol is important in the polar regions, and that this ultimately derives from the surface of sea ice. The existence of this source forms the basis for a proposed method using ice core data for determining changes in sea ice extent over long time periods. Additionally sea salt aerosol, along with salty sea ice surfaces, is the host for the production of halogen compounds which seem to play a key role in the oxidation chemistry of the polar regions. It is therefore important to understand the sources of polar sea salt aerosol and therefore to be able to predict how they may vary with, and feedback to, climate. It was recently proposed that the main source of this polar sea salt aerosol was the sublimation of salty blowing snow. The idea is that snow on sea ice has a significant salinity. When this salty snow is mobilised into blowing snow, sublimation from the (top of) the blowing snow layer will allow the formation of sea salt aerosol above the blowing snow layer, that can remain airborne after the blowing snow has ceased. First calculations suggested that this would provide a strong source of aerosol (greater than that from open ocean processes over an equivalent area). It was proposed that this would have a strong influence on polar halogen chemistry and a noticeable influence on halogens at lower latitudes. However, this was based on estimates of the relevant parameters as there were no data about aerosol production from this source, and almost no data about blowing snow over sea ice in general. Participation in a rare sea ice cruise onboard the German ice breaker Polarstern operated by Alfred-Wegener-Institut (AWI) provided the opportunity to access the sea ice covered Weddell Sea during Austral winter 2013. Snow on sea ice was sampled at various locations, and the snow salinity was subsequently measured in the ship''s laboratory.
This dataset contains data for the plots in Figures 3 and 4 in the article: Effective rheology across the fragmentation transition for sea ice and ice shelves, Åström, and D.I. Benn, GRL, 2019. The data is produced with the numerical simulation code HiDEM, which is an open source code that can be found at: https://github.com/joeatodd/HiDEM. The data plots in the paper contain the data used as benchmarks for testing the reliability of the simulations (Fig.3), and the main results (Fig. 4), the effective rheology of sea ice across the fragmentation transition. Funding was provided by the NERC grant NE/P011365/1 Calving Laws for Ice Sheet Models CALISMO.
This dataset provides model output for 20th and 21st-century ice-ocean simulations in the Amundsen Sea. 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 CESM1 climate model for the historical period (1920-2005) and four future scenarios (2006-2100), using 5-10 ensemble members each. The open ocean boundaries are forced by either the corresponding CESM1 simulation or a present-day climatology. The simulations were completed in 2022 by Kaitlin Naughten at the British Antarctic Survey (Polar Oceans team). UKRI Fund for International Collaboration NE/S011994/1
This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project. It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Data are only available for the NH winter months, October - April.
This dataset provides a Climate Data Record of Sea Ice Thickness for the NH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project. It provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2017.