The CH4_SCI_IMAP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (CH4). It has been produced using data acquired from the SWIR spectra (channel 6) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT using the IMAP-DOAS algorithm. It has been generated as part of ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the dataset is v7.2 and forms part of the Climate Research Data Package 4. The IMAP-DOAS algorithm has been developed at the University of Heidelberg and SRON, and has been applied here to the SCIAMACHY data. This procedure and the algorithms validity are thoroughly described in Frankenberg et al (2011). A second product is also available which has been generated using the Weighting Function Modified DOAS (WFM-DOAS) algorithm. The data product is stored per orbit in a single NetCDF4 file. Retrieval results are provided for the individual SCIAMACHY spatial footprints, no averaging having been applied. The product file contains the key products and information relevant to using the data, such as the vertical layering and averaging kernels. For further details on the product, including the IMAP algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document.

This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water. LST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and the solar geometry angles. The product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time. Data from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on Meteosat Second Generation (MSG) 1, MSG 2, MSG 3, and MSG 4, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1, and MTSAT 2; and polar, Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Sea and Land Surface Temperature Radiometer SLSTR on Sentinel-3A and Sentinel-3B. However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes. Dataset coverage starts on 1st January 2009 and ends on 31st December 2020. LSTs are provided on a global equal angle grid at a resolution of 0.05° longitude and 0.05° latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface. The dataset was produced by the University of Leicester (UoL) and data were processed in the UoL processing chain. The Geostationary data were produced by the Instituto Português do Mar e da Atmosfera (IPMA) before being merged into the final dataset. The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.

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 the Level 3 aerosol daily and monthly gridded products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos. For further details about these data products please see the linked documentation.

The CO2_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. It has been produced using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. The WFM-DOAS algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. Note that this has been designated as an 'alternative' algorithm for the GHG_cci and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.

This dataset contains the Gravimetric Mass Balance (GMB) gridded product for the Antarctic Ice Sheet (AIS), generated by TU Dresden as part of the ESA Antarctic Ice Sheet Climate Change Initiatve (Antarctic_Ice_Sheet_cci). The Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) is based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden. (Groh & Horwath, 2021) The GMB gridded product comprises time series of ice mass changes for cells of polar-stereographic grid with a sampling of 50x50 km² covering the entire AIS. A GMB basin product is also available as a separate dataset. Groh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736

This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water. Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class. The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface. Dataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain. The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.

This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water. Daytime and night-time temperatures are provided in separate files corresponding to 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class. The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width. The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml). LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface. Dataset coverage starts on 1st August 1995 and ends on 31st December 2020. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain. The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.

This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 1982-2018. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology. The Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years.

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 all their Version 4.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided. 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 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 Level 3 Absorbing Aerosol Index (AAI) products, using the Multi-Sensor UVAI algorithm, Version 1.5.7. L3 products are provided as Daily and Monthly gridded products as well as a monthly climatology. For further details about these data products please see the linked documentation.