This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.0.2 of the dataset. The five thematic climate variables included in this dataset are: • Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change. • Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. . • Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions). Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop. Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z

A collection of Version 2.0 datasets produced by the Ocean Colour project of the ESA Climate Change Inititative (CCI). The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies. 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 490 nm. Information on uncertainties is also provided. This dataset collection refers to the Version 2.0 data products held in the CEDA archive and available from ftp://anon-ftp.ceda.ac.uk/neodc/esacci/ocean_colour/data/v2-release . Links to the individual datasets that make up this collection are given in the record below. Please note, version 3.0 data products are now currently available directly from the Ocean Colour CCI team (see link in the documentation section).

This collection contains version 4.0 datasets produced by the Ocean Colour project of the ESA Climate Change Inititative (CCI). The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies. 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 490 nm. Information on uncertainties is also provided. This dataset collection refers to the Version 4.0 data products held in the CEDA archive covering the period 1997-2018. Links to the individual datasets that make up this collection are given in the record below.

A collection of Version 1.0 datasets produced by the Ocean Colour project of the ESA Climate Change Inititative (CCI). The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies. 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 490 nm. Information on uncertainties is also provided. This dataset collectionrefers to the Version 1.0 data products products held in the CEDA archive and available from ftp://anon-ftp.ceda.ac.uk/neodc/esacci/ocean_colour/data/v1-release . Links to the individual datasets that make up this collection are given in the record below. Note, version 2.0 data products are now currently available directly from the Ocean Colour CCI team (see link in the documentation section).

Collection of datasets from the ESA Sea Ice Climate Change Initiative (CCI) project. The Sea Ice CCI is developing improved and validated timeseries of ice concentration for the Arctic and Antarctic and ice thickness datasets for the Arctic to support climate research and monitoring. Since sea ice is a sensitive climate indicator with large seasonal and regional variability, the climate research community require long-term and regular observations of the key ice parameters in both Arctic and Antarctic.

This dataset collection contains cloud products produced by the Cloud project within the ESA Climate Change Initiative (CCI). The ultimate objective of the ESA Cloud Climate Change Initiative (Cloud_cci) project is to provide long-term coherent cloud property datasets exploiting the synergic capabilities of different Earth observation missions allowing for improved accuracies and enhanced temporal and spatial sampling better than those provided by the single sources. CC4CL (Community Cloud Retrieval for Climate) and FAME-C (Freie Universität Berlin AATSR MERIS Cloud) are optimal estimation based retrieval systems providing GCOS cloud property Essential Climate Variables (ECVs) including uncertainty estimates. These global datasets contain cloud fraction, cloud top level estimates (pressure, height, and temperature), cloud thermodynamic phase, spectral cloud albedo, cloud effective radius, cloud optical thickness as well as cloud liquid and ice water content. The AATSR-MODIS-AVHRR heritage product family obtained by CC4CL is based on measurements from ATSR-2/ERS-2, AATSR/ENVISAT, MODIS/AQUA, MODIS/TERRA, and AVHRR on-board NOAA-7, 9, 11, 12, 14, 15,16, 17, 18,19, and MetOp-A. The second product family contains cloud properties derived from ENVISAT’s AATSR and MERIS observations using the synergetic retrieval system FAME-C. In the first phase (2010 – 2013) of the Cloud_cci project prototype retrieval versions have been established leading to preliminary results covering 2007, 2008, and 2009, herein referred to as demonstrator datasets. In Phase 2 (2014 – 2016) both retrieval schemes have been substantially improved enhancing the data quality of the cloud products spanning the time period from Jan 1st 1982 to Dec 31st 2014. Considerations for climate applications: Due to the short period (i.e. 3 years) of the current available demonstrator datasets, it is not possible to perform long-term data comparisons or to support long-term climate analysis. Please be aware of the fact that by the end of 2016 at the latest these prototype datasets will be replaced by the complete multi-decadal Cloud_cci climatology (1982 – 2014) together with updated Product User Guide (PUG) and Product Validation and Intercomparison Report (PVIR) documents. We would like to stress that one of the main objectives in the second phase of the Cloud_cci project has been the further development and improvement of both retrieval schemes and their processing systems. As a consequence, the quality and accuracy of the final cloud products have been considerably improved compared to the currently available demonstrator datasets.

This collection contains version 4.2 datasets produced by the Ocean Colour project of the ESA Climate Change Inititative (CCI). The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies. 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 490 nm. Information on uncertainties is also provided. This dataset collection refers to the Version 4.2 data products held in the CEDA archive covering the period 1997-2019. Links to the individual datasets that make up this collection are given in the record below.

The Land Cover CCI has generated a number of data products as part of its Climate Data Research Package. These consist of: - A new time series of consistent global LC maps at 300 m spatial resolution on an annual basis from 1992 to 2015; - 1 user tool for sub-setting, re-projecting and re-sampling the products in a way which is suitable to each climate model. - The full archive of AVHRR HRPT 1 km surface reflectance 7-day composites from 1992 to 1999; - The full archive of MERIS surface reflectance 7-day composites from 2003 to 2011 (300 m and 1 km resolution); - A PROBA-V 1 km time series of surface reflectance 7-day composites from mid March 2014 to end 2015; - 1 static map of open water bodies including ENVISAT ASAR data; - 3 global land surface seasonality products characterizing the vegetation greenness, the snow and the burned areas dynamics. In the context of the CCI Open Data portal, a subset of these data products are held within the CEDA archive. The complete set of data products are available from the CCI Landcover team via their portal at: http://maps.elie.ucl.ac.be/CCI/viewer/

Datasets produced by the Ocean Colour project of the ESA Climate Change Inititative (CCI). The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies. 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 490 nm. Information on uncertainties is also provided. This dataset collection currently holds Version 1.0 data products, and later versions will be added in the future. Note, version 2.0 data products are now currently available directly from the Ocean Colour CCI team (see link in the documentation section).

This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.0.1 of the dataset. The five thematic climate variables included in this dataset are: • Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change. • Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. . • Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions). Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop. Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website.