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  • [This dataset is embargoed until December 31, 2023]. This dataset contains meteorological and atmospheric dust concentration, deposition and particle-size data from Kangerlussuaq, southwest Greenland, 2017-2019. Meteorological and dust concentration data measured at two locations and dust deposition data measured at 5 locations on an east to west transect between 1.8 and 37.4 km from the 2017 Greenland Ice Sheet western margin. The work was supported by the Natural Environment Research Council (grant NE/P011578/1) Full details about this dataset can be found at

  • How feasible is it to predict Arctic climate at seasonal-to-interannual timescales? As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Several different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol. The Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from: - Canadian Centre for Climate Modelling and Analysis (CanCM4) - ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute. - EC-Earth consortium (ec-earth_v2_3) - Geophysical Fluid Dynamics Laboratory (gfdlcm3) - Met Office (hadgem1-2) - Model for Interdisciplinary Research on Climate (MIROC5-2) - Max-Planck-Institut for Meteorologie (mpiesm) Although designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation. A paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal. Note: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary.

  • The Methane and other greenhouse gases in the Artic - Measurements, process studies and Modelling (MAMM) project was a consortium as part of the NERC Artic Research Programme. This project used a range of expertise, from measurements of methane and its isotopes, and other greenhouse gases, through flux measurements to numerical analysis and modelling. Analysis of gas mixing ratios (concentrations), isotopic character, and source fluxes, both from the ground and aircraft. Both past and new measurements were modelled using a suite of techniques. Fluxes were implemented into the JULES land surface model. Atmospheric modelling, including trajectory and inverse modelling will improve understanding on the local/regional scale, placing the role of Arctic emissions in large scale global atmospheric change. The project was led by the University of Cambridge, and in association with the University of Manchester, University of East Anglia, Royal Holloway, University of London, Centre for Ecology and Hydrology and UK and International partners (Met Office, NILU, NOAA, etc).

  • This dataset contains lidar radial velocity and backscatter data for vertical stare mode from the NCAS AMF Halo Doppler lidar mounted on a motion stabilised platform on board the Swedish Icebreaker Oden during the joint Arctic Climate Across Scales (ACAS) and Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA) projects. Both projects are part of the Arctic Ocean 2018 (AO2018) expedition to the High Arctic. AO2018 took place in the Arctic from 1 August until 21 September 2018. These measurements were used to complement a suite of other observations taken during the expedition. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the Bolin Centre for Climate Research MOCCHA/AO2018 holdings. Data were corrected for ship motion. The raw data and detailed instrument information can be obtained from the AMF archive at CEDA. The UK participation of MOCCHA was funded by the Natural Environment Research Council (NERC, grant: NE/R009686/1) and involved instrumentation from the Atmospheric Measurement Facility (AMF) of the UK's National Centre for Atmospheric Science (NCAS AMF).

  • The AAST L3S Combined Surface Temperature (CST) products here primarily provide data on surface temperatures in the Arctic, across all Arctic surfaces, and their associated uncertainties. This dataset consists of CSTs with uncertainty estimates produced from the ATSR-2 and AATSR instruments on ESA's ERS-2 and Enivsat satellite respectively. The CSTs in this dataset are surface temperatures across ocean, snow/ice and land surfaces in the Arctic region, where the Arctic is defined as the area at, or north of, 60°N. Please note, the data here provide daily temperature data, not daily mean data as indicated by the dataset title. For more information see the paper linked under the details tab The data were produced for ATSR Satellite Dataset project, which was funded by The UK Dept. for Business, Energy & Industrial Strategy. Ice (and snow) Surface Temperatures (ISTs) and Land Surface Temperatures (LSTs) used to produce this dataset are sourced from the GlobTemperature Level 2 LST V2.1 product. Sea Surface Temperature (SSTs) are from the ATSR SST L2P V3.0 product. This version of the CST dataset is v2.1, with an earlier version previously provided via the GlobTemperature data portal. ATSR data was sourced from the GlobTemperature data portal and the CEDA archive. It consists of a complete set of CST and accompanying auxiliary (AUX) datafiles for the ATSR-2 and AATSR instruments separately. ATSR-2 data are available for 01/08/1995 - 22/06/2003 while AATSR data are available for 20/05/2002 – 08/04/2012.

  • 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.

  • The ocean surface height is constantly varying under the effects of gravity, density and the Earth''s rotation. Information on the Ocean surface elevation in polar regions is available from the CryoSat2 Radar instrument. We compare ocean surface elevation to a static geoid product (GOCO03s) to give the part of the ocean surface elevation accountable due to surface currents, the Dynamic Ocean Topography (DOT). This measurement is smoothed over 100 km and gives monthly surface currents. NERC NE/R000654/1 Towards a marginal Arctic sea ice cover.

  • Salinity profiles of sea ice and snow on sea ice were measured in the Arctic Ocean during the Norwegian Young Sea Ice cruise in 2015 (, an international sea ice drift expedition led by the Norwegian Polar Institute. Salinity is a key parameter for a range of processes related to biology, photochemistry and physics of sea ice, snow on sea ice as well as atmospheric aerosol. Sea ice cores and snow samples were collected during the sea ice drift expedition from the ice floe and transferred to the ship''s laboratory. The aqueous conductivity of melted sea ice core and snow samples was measured and converted into practical salinity units. Funding was provided by the NERC grant NE/M005852/1, Japan Society for the Promotion of Science (15K16135, 24-4175) and the Centre of Ice, Climate and Ecosystems (ICE) at the Norwegian Polar Institute through the N-ICE project

  • This red-green-blue (RGB) orthomosaic is composite created from 8994 photographs collected withunmanned aerial vehicles (UAVs) over the eastern part of Qikiqtaruk, Herschel Island, in the Canadian Yukon (69.5N, 138.8W). The images were collected on the 10th and 11th of August 2017. Further details on the image processing are provided in the Lineage section. This dataset was created by Andrew Cunliffe, with support from Isla Myers-Smith, William Palmer, Jeffrey Kerby and other members of Team Shrub (, in order to inform ongoing ecological monitoring studies in this area. Part of this orthomosaic was used for a study into permafrost coastline retreat, published in The Cryosphere (Cunliffe, A. M., Tanski, G., Radosavljevic, B., Palmer, W. F., Sachs, T., Lantuit, H., Kerby, J. T., and Myers-Smith, I. H.: Rapid retreat of permafrost coastline observed with aerial drone photogrammetry, The Cryosphere, 13, 1513-1528,, 2019).

  • We present the significant ocean surface wave heights in the Arctic and Southern Oceans from CryoSat-2 data. We use a semi-analytical model for an idealised synthetic aperture satellite radar or pulse-limited radar altimeter echo power. We develop a processing methodology that specifically considers both the Synthetic Aperture and Pulse Limited modes of the radar that change close to the sea ice edge within the Arctic Ocean. All CryoSat-2 echoes to date were matched by our idealised echo revealing wave heights over the period 2011-2019. Our retrieved data were contrasted to existing processing of CryoSat-2 data and wave model data, showing the improved fidelity and accuracy of the semi-analytical echo power model and the newly developed processing methods. We contrasted our data to in situ wave buoy measurements, showing improved data retrievals in seasonal sea ice covered seas. We have shown the importance of directly considering the correct satellite mode of operation in the Arctic Ocean where SAR is the dominant operating mode. Our new data are of specific use for wave model validation close to the sea ice edge and is available at the link in the data availability statement. NERC NE/R000654/1 Towards a marginal Arctic sea ice cover.