The Global Sea Level Observing System (GLOSS) is an international programme co-ordinated by the Intergovernmental Oceanographic Commission (IOC) for the establishment of high quality global and regional sea level networks for application to climate, oceanographic and coastal sea level research. The programme became known as GLOSS as it provides data for deriving the "Global Level Of the Sea Surface"; a smooth level after averaging out waves, tides and short-period meteorological events. The main component of GLOSS is the Global Core Network (GCN) of 308 sea level stations around the world, which are maintained by 87 countries. The GLOSS network has been designed to observe large-scale sea level variations of global implications, and stations were identified at intervals of approximately 1000 km along the continental coasts and on islands, but generally not closer than 500 km. In selecting individual sites, priority is given to gauges which have been functioning for a long period. All gauges are required to aim for an accuracy of 10 mm in level, and 1 minute in time. All must be linked to bench marks against which their datum is checked regularly. This network monitors sea level changes which could be indicative of global warming, ocean circulation patterns, climate variability, etc., and contributes data to global climate research within the World Climate Research Programme (WCRP) including the Tropical Ocean-Global Atmosphere (TOGA) project, the World Ocean Circulation Experiment (WOCE), Climate Variability and Predictability (CLIVAR) and recent vertical crustal movement studies conducted by the International Union of Geodesy and Geophysics (IUGG) of the International Council of Scientific Unions (ICSU) and UNESCO (International Geological Correlation Programme (IGCP)). It also provides high quality data for practical applications of national importance. The measurements by GLOSS gauges complement satellite altimetry measurements. GLOSS is considered as an important potential element of the Global Ocean Observing System (GOOS) initiated by IOC with the World Meteorological Organisation (WMO), the UN Environmental Programme (UNEP) and ICSU. The elements of GLOSS are: A global network of permanent sea level stations to obtain standardised sea level observations; this forms the primary network to which regional and national sea level networks can be related; Data collection for international exchange with unified formats and standard procedures which includes both near-real-time as well as delayed mode data collection; Data analysis and product preparation for scientific and/or practical applications; Assistance and training for establishing and maintaining sea level stations as part of GLOSS and improving national sea level networks; A selected set of GLOSS tide-gauge bench marks accurately connected to a global geodetic reference system (i.e. the conventional terrestrial frame established by the International Earth Rotation Service). The Permanent Service for Mean Sea Level (PSMSL) collects and archives data from GLOSS stations in the form of monthly mean values, but hourly and daily values are also expected to be made available from all stations by the originators. The GLOSS network consists of 308 sea level stations, which are operated and maintained by 87 countries.

HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid covering 1901-2018. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Indices are available on an annual, and for some a monthly, basis. Some indices use a reference period to calculate thresholds, and for these, we provide versions using 1961-90 and 1981-2010. The indices are available in NetCDF files, with one index per file and separate files for annual and monthly values, as well as the different reference periods if appropriate. The codes used to create the dataset are available online, and a wide number of analysis plots are on the dataset homepage. For a detailed description of the methods behind the dataset, please see the paper in Details/Docs.

HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). Spatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010. All indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110'). In September 2020, a user identified some issues in the DTR and TN90p (61-90) indices. These were found to have arisen from erroneous values in a few stations which were not picked up by any quality control checks. These stations were noted on the bad list and these two indices re-run, hence v3.0.1.

HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). Spatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010. All indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110'). Version 3.0.3 was added due to an error in how the Rx1day and Rx5day data were being handled for one of the West African data sources. More details can be found in the HadEX3 blog under 'Details/Docs' tab.

HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). Spatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010. All indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110'). Version 3.0.2 was added due to a correction to the land-sea mask used. More details can be found in the HadEX3 blog under 'Details/Docs' tab.

HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). Spatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010. All indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').

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.

Southern Ocean Atmospheric Photochemistry Experiment 2 (SOAPEX-2) is primarily an experiment to study atmospheric cleansing by free radicals in extremely clean and slightly perturbed tropospheric air and focuses on a field campaign carried out at Cape Grim, Tasmania in January-February 1999. The dataset contains concentrations of atmospheric constituents such as halocarbons, hydrocarbons, methane, nitric oxide, and carbon monoxide. This dataset is public. Oxidation of almost all trace gases released into the atmosphere is initiated by hydroxyl (OH) radicals, produced mainly from the action of near-UV light on ozone in the presence of water vapour. Increasing evidence suggests that the oxidative capacity of the troposphere has been perturbed in recent years due to the emission of gases such as methane, carbon monoxide, non-methane hydrocarbons and nitrogen oxides from man-made sources. These perturbations may be causing changes in the natural atmospheric composition, for instance increasing tropospheric levels of the greenhouse gas ozone, which has important consequences for climate and human health. It is also possible that the rates of oxidation of gases such as methane, and production of sulphate aerosols from the oxidation of sulphur dioxide, have been modified. Taken together a change in the oxidative capacity of the atmosphere has many consequences for the long-term stability of the Earth's climate. SOAPEX-2 builds upon the success of the original SOAPEX-I experiment conducted at Cape Grim in January/February 1995 which resulted in the publication of several papers to the literature on the relationship between concentrations of peroxy radicals and uv light levels in different NOx concentration regimes, and the consequences for ozone production and loss in the marine boundary layer. SOAPEX-2 is a more complete experiment with the addition of atmospheric measurements of key new species including hydroxyl, hydroperoxyl, halogen oxide and nitrate radicals, non methane hydrocarbons, speciated aldehydes, PAN and halocarbons. SOAPEX-2 involves four groups of tropospheric scientists from the UK and Australia, namely the Universities of East Anglia, Leeds and Leicester along with CSIRO (Commonwealth Scientific Research Organisation), Melbourne. The clean air photochemistry experiment is an essential prerequisite for experiments carried out in more polluted atmospheres. The data obtained is allowing rigorous testing of basic mechanisms which describe the behaviour of free radical concentrations at differing light levels, water vapour and nitrogen oxide concentrations, etc. The measurements performed in this project are expected to yield valuable information on chemical changes that are affecting the oxidative capacity of the global troposphere and, therefore, the rate at which the global atmosphere can cleanse itself of pollutants. The measurements are also highly relevant to the situation in more polluted atmospheres, where increased levels of confidence in our understanding of atmospheric chemistry is an essential prerequisite to any legislation designed to reduce regional and global pollution. The specific objectives of SOAPEX-2 are: * To quantitatively test fast photochemical theory in clean air. * To examine perturbations from the baseline situation in polluted continental air containing more complex mixtures of free radical sources and sinks * Investigation of the balance between tropospheric O3 production and destruction in differing NOx regimes * A test of instrumental performance * Testing of models used to simulate chemical processes in the lower atmosphere which are deficient in their description of boundary layer processes

This dataset contains global, monthly marine phytoplankton primary production products (in mg C m-2 d-1) for the period of 1998 to 2018 at 9 km spatial resolution. Data are provided in NetCDF format. Primary production by marine phytoplankton was modelled using ocean-colour remote sensing products and a spectrally-resolved primary production model that incorporates the vertical structure of phytoplankton and simulates changes in photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function (see Kulk et al. 2020, Sathyendranath et al. 2020a, and references therein for details). Chlorophyll-a products were obtained from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI v4.2 dataet). Photosynthetic Active Radiation (PAR) products were obtained from the National Aeronautics and Space Administration (NASA) and were corrected for inter-sensor bias in products. In situ datasets of chlorophyll-a profile parameters and P-I parameters were incorporated as described in Kulk et al. (2020). The primary production products were generated as part of the ESA Living Planet Fellowship programme ‘Primary production, Index of Climate Change in the Ocean: Long-term Observations’ (PICCOLO). Support from the Simons Foundation grant ‘Computational Biogeochemical Modeling of Marine Ecosystems’ (CBIOMES, number 549947), from the ESA Biological Pump and Carbon Exchange Processes (BICEP) project and from the National Centre of Earth Observation (NCEO) is acknowledged. Data are provided as netCDF files containing global, monthly marine phytoplankton primary production products (in mg C m-2 d-1) for the period of 1998 to 2020 at 9 km spatial resolution. References: Kulk, G.; Platt, T.; Dingle, J.; Jackson, T.; Jönsson, B.F.; Bouman, H.A., Babin, M.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; González, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kovac, Z.; Lutz, V.A.; Marañón, E.; Raman, M.; Richardson, K.; Rozema, P.D.; Van de Poll, W.H.; Segura, V.; Tilstone, G.H.; Uitz, J.; van Dongen-Vogels, V.; Yoshikawa, T.; Sathyendranath S. Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades. Remote Sens. 2020, 12, 826. doi:10.3390/rs12050826 Sathyendranath, S.; Platt, T.; Žarko K.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Nón, E.M.; Kulk, G.; Bouman, H. Reconciling models of primary production and photoacclimation. Appl. Opt. 2020a, 59, C100-C114. doi.org/10.1364/AO.386252.

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).