This dataset contains estimations of Arctic sea level anomalies produced by the ESA Sea Level Climate Change Initiative project (Sea_level_cci), based on satellite altimetry from the ENVISAT and SARAL/Altika satellites. It has been produced by Collecte Localisation Satellites (CLS) and the Plymouth Marine Laboratory (PML). The retrieval of sea level in the Arctic sea ice covered region requires specific processing steps of the satellite altimetry measurements. For this dataset, a specific radar waveform classification method has been applied based on a neural network approach, and the waveform retracking is based on a new adaptive retracking that is able to process both open ocean and peaky echoes measured in leads without introducing any bias between the two types of surfaces. Editing and mapping processing steps have been optimized for this dataset

The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises images of Absorbing Aerosol Index (AAI) products, using the Multi-Sensor UVAI algorithm, Version 1.5.7. Images are available for monthly and climatology products. For further details about these data products please see the linked documentation.

This dataset consists of monthly averaged total column water vapour (TCWV) over land, at a 0.05 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1). This version of the data is v3.1.

The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies. This dataset contains their Version 2.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.) Please note, this dataset has been superseded. Later versions of the data are now available.

The future 25km regional pan-Africa (P25: Present) data were produced using the Met Office's Unified Model, the IMPALA (Improving Model Processes for African cLimAte) project ran a ten year timeslice simulation that is representative of end the 21st century (2095-2105) using a 30-year averaged sea surface temperature (SST) anomaly (2085-2115 relative to 1975-2005). Parameters include (but not limited to); near-surface air temperature, outgoing longwave radiation, surface latent heat flux and surface sensible heat flux. The NERC funded IMPALA project is within the Future Climate for Africa (FCFA) programme. Here a subset of variables of the data produced is provided in NetCDF format for community reuse, variables are available at a range of temporal frequencies.

Model data from CHASER MIROC-ESM, the atmospheric chemistry coupled version of the MIROC Earth System Model, part of the International Global Atmospheric Chemistry (IGAC)/ Stratosphere-troposphere Processes and their Role in Climate (SPARC) Chemistry-Climate Model Initiative phase 1 (CCMI-1). CCMI-1 is a global chemistry climate model intercomparison project, coordinated by the University of Reading on behalf of the World Climate Research Programme (WCRP). The dataset includes data for the following CCMI-1 experiments: Reference experiments: ref-C1SD and ref-C2. Sensitivity experiments: senC1SDfEmis, senC2fCH4, senC2fEmis, senC2fGHG, senC2fN2O and senC2fODS. ref-C1SD: Similar to ref-C1 but the models are nudged towards reanalysis datasets, and correspondingly the simulations only cover 1980–2010. (“SD” stands for specified dynamics.) ref-C2: Simulations spanning the period 1960–2100. The experiments follow the WMO (2011) A1 baseline scenario for ozone depleting substances and the RCP 6.0 (Meinshausen et al., 2011) for other greenhouse gases, tropospheric ozone (O3) precursors, and aerosol and aerosol precursor emissions. senC1SDfEmis: Surface emissions such as nitrogen oxides (NOx ), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), and aerosol precursors are prescribed at 1960 levels throughout, allowing the influence of meteorological variability on tropospheric composition to be established. senC2fEmis: Similar to ref-C2 but with surface and aircraft emissions fixed to their respective 1960 levels. senC2fGHG: Similar to ref-C2 but with greenhouse gasses (GHGs) fixed at their 1960 levels, and sea surface and sea ice conditions prescribed as the 1955–1964 average (where these conditions are imposed). senC2fCH4: Similar to ref-C2 but the methane surface-mixing ratio is fixed to its 1960 value. senC2fN2O: Similar to ref-C2 but the nitrous oxide surface-mixing ratio is fixed to its 1960 value. senC2fODS: Similar to ref-C2 but with ozone-depleting (halogenated) substances (ODSs) fixed at their 1960 levels.

This dataset comprises gridded limb ozone monthly zonal mean profiles from the ACE FTS instrument on the SCISAT satellite. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean. The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-ACE_FTS_SCISAT-MZM-2008-fv0001.nc” contains monthly zonal mean data for ACE in 2008.

Terrestrial laser scanning (TLS) was conducted at three ForestScan 1ha (100m x 100m) Forest Biomass Reference Measurement Site (FBRMS) plots in French Guiana from September to October 2022 by Cecilia Chavana-Bryant using a Riegl VZ-400i scanner. Data collection assistance was provided by UCL PhD student Wanxin Yang and a local team of field assistants, data processing assistance was provided by Mr Peter Vines. This data collection was part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing terrestial (TLS), unpiloted airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. Scans were acquired using chain sampling at 121 locations along a 10m Cartesian grid to ensure sufficient data overlap to produce high-quality point clouds for all ForestScan 1ha FBRMS plots. Due to the scanner's 100° field of view, capturing a complete sample of the scene at each scan location required two scans -an upright scan and a tilt scan. Upright scans are odd-numbered while tilt scans are even-numbered. The first scan at each plot is collected at the southwest corner, i.e. scan position 0,0 (unless something impedes it, e.g. stream, large tree fall, etc. or if the plot is oriented differently). To facilitate scan registration, five retro-reflective targets were located between scan positions with all tilt scans along the first sampling line were oriented towards the same sampling position along the next sampling line and tilt scans at the ends of sampling lines (i.e. tilt scans along plot edges) were oriented towards the inside of the plot. This aids scan registration as it allows tilt scans to capture the previous scan location within its field of view. A total of 242 scans were collected at each plot. The Riegl operating and processing software RiSCAN PRO version 2.14.1 was used to generate a plot-level point cloud, scans were coarse registered using the shared retro-reflective targets located between consecutive scan positions. Coarse registration was then fine-tuned using Multi Station Adjustment 2 (MSA2). Data for each of the three FBRMS plots is found within plot directories: FG5c1, FG6c2 and FG8c4. Plot directories contain a main project directory (named using the starting date of data collection, e.g. 2022-10-10_FG5c1.PROJ) with nine data subdirectories and a tile_index.dat file as shown in the archived document /neodc/forestscan/data/french_guiana/paracou/TLS_Plot_FG5c1/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets.

The Gridded daily Agricultural Burning Emission Inventory of Eastern China dataset contains a unique high Spatio-temporal resolution agricultural burning inventory for eastern China for the years 2012-2015. The data was generated using twice daily fire radiative power (FRP) observations from the ‘small fire optimised’ VIIRS-IM FRP product, and combined with fire diurnal cycle information taken from the geostationary Himawari-8 satellite. This dataset was designed to fully take into account small fires well below the MODIS burned area or active fire detection limit, focusing on dry matter burned (DMB) and emissions of CO2, CO, PM2.5 and black carbon. The fuel for these fires is waste straw and other agricultural residues. Information from a crop rotation map to classify the type of agricultural residue being burned at each observed location and time, in addition to an agricultural area land map was also incorporated in consideration of this.

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.