Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for Volcanic and Atmospheric Near- to far-field Analysis of plumes Helping Interpretation and Modelling (Vanaheim) project.

This data represents the probabilistic climate projections component of the past (observed) and future climate scenario projections data, produced as part of the UK Climate Projections 2018 (UKCP18) project. Data has been produced by the UK Met Office Hadley Centre, and provides information on changes in 21st century climate for the UK, helping to inform adaptation to a changing climate. The data represents anomalies with respect to the baseline period 1981-2000, and cover the period 1 Dec 1960 to 30 Nov 2099. Data for 8 'country' regions in the UK is provided: Channel Islands, England, England and Wales, Isle of Man, Northern Ireland, Scotland, United Kingdom, Wales. The Probabilistic Projections were updated on 4th August 2022, to make improvements to the methodology to improve: consistency between maximum, minimum and mean temperature; consistency in the downscaling; statistical treatment of precipitation particularly at the wet and dry extremes; representation of annual and decadal variability; and adjustment of the data in the 1981-2000 baseline period to ensure the anomalies average to zero. The combination of the improvements means that all variables are modified to some degree. For more information, please refer to the UKCP news article and the documents it links to. On 11th February 2025, the Probabilistic Projections were updated to include information at global warming levels. This information is available for each of the RCP scenarios as cumulative distribution frequency data. Further information about this global warming levels data and approach can be found in the relevant UKCP news article and the guidance document it links to.

Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project.

This dataset contains volatile organic compound (VOC) mixing ratios recorded during two intensive field campaigns in Beijing (winter: 12/11/2016 - 10/12/2016; and summer: 15/05/2017 - 24/06/2017) as part of the Atmospheric Pollution & Human Health in a Chinese Megacity (APHH) programme. The species recorded include methanol, acetonitrile, acetaldehyde, acrolein, acetone, isoprene, methyl vinyl ketone and methacrolein, methyl ethyl ketone, benzene, toluene, C2-benzenes, C3-benzenes and monoterpenes. The data were recorded using a proton transfer reaction-time of flight-mass spectrometer (PTR-ToF-MS) from a sampling height of 100m.

Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for FAAM Test, Calibration, Training and Non-science Flights and other non-specified flight projects (Instrument) project.

These ancillary datasets were used in the production of the "Active", "Passive" and "Combined" soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v04.2 Soil Moisture CCI data. The "Active" "Passive" and "Combined" soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website. Soil moisture CCI data should be cited using all three of the following references: 1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070 3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014

This dataset contains optical ice velocity time series and seasonal product of the 79Fjord Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The data have been produced by S[&]T Norway

The data are projected future still water return levels. The data were produced by the Met Office using projections of future mean sea level change prepared at the Met Office and estimates of present-day still water return levels which were provided by the Environment Agency. The data were produced as a simple indication of the relative sizes and uncertainties in present day extreme water levels and projected future mean sea level change. The data were produced by combining preojections of mean sea level change with best estimates of present day extreme still water levels. The data in marine strand 4.09 cover the period from 2020 to 2100 and are available for 46 UK tide gauge locations.

This dataset contains derived wave statistics, directional wave spectra and raw buoy displacement data from the University of Leeds Datawell DWR-G4-Waverider buoy deployed from the Swedish Icebreaker Oden durning Arctic Cloud Summer Expedition (ACSE). ACSE took place in the Arctic during summer 2014. These measurements were used to complement a suite of other observations taken during the cruise. 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 NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record. The buoy was deployed on numerous occasions during the voyage, including multiple deployments per day (the deployment number during the day is given in the filename following the date of deployment). However, some deployments are omitted from the archive where significant problems with the raw data were found (failure to obtain GPS lock; too-short a deployment, etc.). The deployment number for the day has been maintained to coincide with the buoy's deployment logs. Data are truncated to remove the deployment and recovery. No further quality control has been applied to the data. The raw displacement timeseries data contains raw buoy displacements (up, north, west) from which the spectra were calculated. The spectra were calculated as 10-minute averages. The methodology used is detailed within the files, but include both raw (quite noisy) and smoothed spectra. The overall wave statistics file contains all wave statistics data obtained during the voyage (significant wave height, spectral peak details, etc, along with the spectral moments from which the stats are calculated). These are all given for each spectrum as a whole, and partitioned into windsea and swell components. References to the techniques used to produce these statistics are given in the file. The Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat. ACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud. The UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements.

Daily global cloud droplet number concentrations (Nd) have been calculated at 1x1 degree resolution from pixel-level MODIS (MODerate Imaging Spectroradiometer) Collection 5.1 Joint Level-2 (Aqua satellite) optical depth (tau) and the 3.7 micron effective radius (reff) data (and other supporting data) using the adiabatic cloud assumption (liquid water content increases linearly with height, Nd is constant throughout the cloud depth and the ratio of the volumne mean radius to the effective radius is assumed constant). The Nd data is contained in separate NetCDF files for each year for the period 2003-2015. Nd is contained in the "Nd" variable and has units of cm^{-3}. This is a 360x180xNdays (lon x lat x Ndays) sized array, where Ndays is the number of days in the year. The lon x lat grid is a regular 1x1 degree grid. The time is provided as both a 1D array of size Ndays ("time") with units of days since 1st Jan, 1970 and an array of size Ndays x 3 ("time_vec") that contains numbers for the year month and day for each of the Ndays entries. A number of filters have been applied to the data in order to remove retrievals that are likely to be problematic, or to violate the adiabatic cloud assumptions. Data is only included if: 1) Pixels are determined to be liquid pixels by MODIS. 2) The 1x1 degree mean cloud top height (calculated using the MODIS cloud top temperature and the sea surface temperature) is below 3.2km. 3) The 1x1 degree liquid cloud fraction was larger than 80%. 4) The 1x1 degree mean solar zenith angle was 65 degrees or less to avoid biases at high angles (Grosvenor and Wood, 2014). Note, that the filtering is different to that described in Grosvenor, AMTD, 2018 in the following ways :- 1) 1km resolution tau and reff are used to calculate Nd, which is then aggregated to 1x1 degree resolution (rather than using 1x1 degree tau and reff). 2) Only Nd based on the 3.7 micron reff retrieval is provided here. 3) No filtering for the presence of sea-ice is done here - it is recommended that this is done if using for high latitudes. 4) The data here is not restricted to tau>5. Also note that the vertical penetration bias correction described in Grosvenor, AMTD, 2018 is NOT applied here. In addition, as described in the latter paper, further pixel-level screening is performed in order to select high quality data. Details on the reasons for restricting to low solar zenith angles can be found in Grosvenor and Wood, ACP, 2014. Information on the pixel level filtering applied can be found in Grosvenor et al., AMTD, 2018 (noting the differences explained above). A comparison of this dataset with others can be found in Grosvenor et al., Reviews of Geophysics, 2018. This dataset calculates a product that is not provided as standard by MODIS. It uses improved optical depth and effective radius data compared to the standard MODIS Level-3 data since situations (e.g., high solar zenith angles, broken clouds) that have been shown to cause retrieval issues have been filtered out at the Level-2 stage before being averaged into Level-3 droplet concentration data.