The European Space Agency's Synthetic Aperture Radar (SAR) instruments have been flown on board ERS-1, ERS-2 and the Advanced SAR (ASAR) on board Envisat. The ERS-1, ERS-2 and Envisat satellites, launched in 1991, 1995 and 2002 respectively, are ESA multi-payload, Earth observation satellites. This dataset contains Synthetic Aperture Radar(SAR) data from the European Remote Sensing satellites ERS-1. The ERS-1 mission began in 1991 and ended in 2000, and ERS-2 and Envisat are still ongoing. SAR provides high resolution images, ocean wave spectra data and wind direction vector data. They are available through the NEODC to UK based students only.

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 monthly images of Absorbing Aerosol Index (AAI), using the Multi-Sensor UVAI algorithm, Version 1.4.7. For further details about these data products please see the linked documentation.

This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Aqua (Aqua). 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 daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 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 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. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator 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 4th July 2002 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window 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 is a compilation of time series, together with uncertainties, of the following elements of the global mean sea level budget and ocean mass budget: (a) global mean sea level (b) the steric contribution to global mean sea level, that is, the effect of ocean water density change, which is dominated, on a global average, by thermal expansion (c) the mass contribution to global mean sea level (d) the global glaciers contribution (excluding Greenland and Antarctica) (e) the Greenland Ice Sheet and Greenland peripheral glaciers contribution (f) the Antarctic Ice Sheet contribution (g) the contribution from changes in land water storage (including snow cover). The compilation is a result from the Sea-level Budget Closure (SLBC_cci) project conducted in the framework of ESA’s Climate Change Initiative (CCI). It provides assessments of the global mean sea level and ocean mass budgets. Assessment of the global mean sea level budget means to assess how well (a) agrees, within uncertainties, to the sum of (b) and (c) or to the sum of (b), (d), (e), (f) and (g). Assessment of the ocean mass budget means to assess how well (c) agrees to the sum (d), (e), (f) and (g). All time series are expressed in terms of anomalies (in millimetres of equivalent global mean sea level) with respect to the mean value over the 10-year reference period 2006-2015. The temporal resolution is monthly. The temporal range is from January 1993 to December 2016. Some time series do not cover this full temporal range. All time series are complete over the temporal range from January 2003 to August 2016. For some elements, more than one time series are given, as a result of different assessments from different data sources and methods. Data and methods underlying the time series are as follows: (a) satellite altimetry analysis by the Sea Level CCI project. (b) a new analysis of Argo drifter data with incorporation of sea surface temperature data; an alternative time series consists in an ensemble mean over previous global mean steric sea level anomaly time series. (c) analysis of monthly global gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry mission. (d) results from a global glacier model. (e) analysis of satellite radar altimetry over the Greenland Ice Sheet, amended by results from the global glacier model for the Greenland peripheral glaciers; an alternative time series consists of results from GRACE satellite gravimetry. (f) analysis of satellite radar altimetry over the Antarctic Ice Sheet; an alternative time series consists of results from GRACE satellite gravimetry. (g) results from the WaterGAP global hydrological model. Version 2.2 is an update of the previous Version 2.1. The update concerns the estimates of ocean mass change from GRACE.

This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Terra (Terra). 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 Terra equator crossing times which are 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 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. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator 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. The monthly dataset starts from March 2000 and ends December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window 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 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 European Space Agency's Synthetic Aperture Radar (SAR) instruments have been flown on board ERS-1, ERS-2 and the Advanced SAR (ASAR) on board Envisat. The ERS-1, ERS-2 and Envisat satellites, launched in 1991, 1995 and 2002 respectively, are ESA multi-payload, Earth observation satellites. This dataset contains Advanced Synthetic Aperture Radar(ASAR) data from the European Remote Sensing satellites ERS-1 and ERS-2, and Advanced SAR data from Envisat. The ERS-1 mission began in 1991 and ended in 2000, and ERS-2 and Envisat are still ongoing. SAR provides high resolution images, ocean wave spectra data and wind direction vector data. They are available through the NEODC to UK based students only.

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.

This dataset is a compilation of time series, together with uncertainties, of the following elements of the global mean sea level budget and ocean mass budget: (a) global mean sea level (b) the steric contribution to global mean sea level, that is, the effect of ocean water density change, which is dominated, on a global average, by thermal expansion (c) the mass contribution to global mean sea level (d) the global glaciers contribution (excluding Greenland and Antarctica) (e) the Greenland Ice Sheet and Greenland peripheral glaciers contribution (f) the Antarctic Ice Sheet contribution (g) the contribution from changes in land water storage (including snow cover). The compilation is a result from the Sea-level Budget Closure (SLBC_cci) project conducted in the framework of ESA’s Climate Change Initiative (CCI). It provides assessments of the global mean sea level and ocean mass budgets. Assessment of the global mean sea level budget means to assess how well (a) agrees, within uncertainties, to the sum of (b) and (c) or to the sum of (b), (d), (e), (f) and (g). Assessment of the ocean mass budget means to assess how well (c) agrees to the sum (d), (e), (f) and (g). All time series are expressed in terms of anomalies (in millimetres of equivalent global mean sea level) with respect to the mean value over the 10-year reference period 2006-2015. The temporal resolution is monthly. The temporal range is from January 1993 to December 2016. Some time series do not cover this full temporal range. All time series are complete over the temporal range from January 2003 to August 2016. For some elements, more than one time series are given, as a result of different assessments from different data sources and methods. Data and methods underlying the time series are as follows: (a) satellite altimetry analysis by the Sea Level CCI project. (b) a new analysis of Argo drifter data with incorporation of sea surface temperature data; an alternative time series consists in an ensemble mean over previous global mean steric sea level anomaly time series. (c) analysis of monthly global gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry mission. (d) results from a global glacier model. (e) analysis of satellite radar altimetry over the Greenland Ice Sheet, amended by results from the global glacier model for the Greenland peripheral glaciers; an alternative time series consists of results from GRACE satellite gravimetry. (f) analysis of satellite radar altimetry over the Antarctic Ice Sheet; an alternative time series consists of results from GRACE satellite gravimetry. (g) results from the WaterGAP global hydrological model.

This dataset contains 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.