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 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).

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 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.

These data are derived from a dust leaching experiment, an in-lake mesocosm experiment and from sediment cores obtained from lakes in the Kangerlussuaq area of West Greenland. The dust leaching experiment was set up in 2017 and the data show which elements and ions were leached from dust into different types of waters. The in-lake mesocosm experiment applied dust over a two week period in July 2018 resulting in chemical and algal pigment data. Data on chlorophyll and carotenoid pigments are presented from sediment cores sampled from six lakes 2017 and sectioned into 0.5-1cm intervals. Full details about this dataset can be found at https://doi.org/10.5285/9115bc7a-adb6-4a3c-8506-32d0b39bcf6f

Airborne and model data collected during the ACCACIA - Aerosol-Cloud Coupling And Climate Interactions in the Arctic project. The dataset comprises airborne in situ measurements of cloud microphysical properties, the vertical structure of the boundary layer and aerosol properties, and the fluxes of solar and infra red radiation above, below, and within cloud. Data was collected on board the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 aircraft and the British Antarctic Survey (BAS) Masin aircraft. It also contains data from specially configured Met Office Unified Model runs. AMS and SP2 data measured on board the Research Ship James Clark Ross during ACCACIA is also available. This project is part of the NERC Arctic research programme. (NERC Reference: NE/I028858/1).

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 (https://www.npolar.no/en/projects/n-ice2015/), 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 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).

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.

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.