Observation List

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                    "short_code": "coll",
                    "title": "Seismic datasets acquired around an active fluid flow structure: Scanner Pockmark, North Sea as part of the CHIMNEY project",
                    "abstract": "A multi-channel seismic dataset was acquired during RRS James Cook cruise JC152 (August - September 2017), around the Scanner Pockmark Complex in the North Sea. Data were recorded using a number of different seismic sources, comprising: 1) a GI airgun array, used in two different configurations for separate parts of the survey, i) a 420 ci (2 x 105/105 ci) array operated in harmonic mode and fired at 8 s intervals, and ii) a 300 ci (2 x 45/105 ci) array operated in true GI mode and fired at 6 s intervals, both towed at 2 m depth below sea surface; 2) an Applied Acoustic Engineering Squid sparker (1750 or 2000 J), towed at the sea surface and triggered at 2 s intervals, and; 3) a Duraspark sparker (2000 J), towed at the sea surface and triggered at 2 s intervals. Signals produced by the GI airguns and surface sparkers were recorded on two towed multi-channel streamers: a) a 60 channel, 1 m group interval streamer recorded on a Geometrics Strataview R60 recording system, and b), a 120 channel, 1.56 m group interval GeoEel streamer, at sampling rates between 0.125 and 0.5 ms depending on the streamer and source pairing. Data are provided in standard SEG-D format. The data were acquired as part of the 'Characterization of major overburden leakage pathways above sub-seafloor CO2 storage reservoirs in the North Sea' (CHIMNEY) project, funded by the Natural Environment Research Council (NERC) under grant reference NE/N016130/1."
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                "abstract": "This computation involved: ECMWF ERA5 Re-analysis Model deployed on ECMWF Computer.  The data assimilation system used to produce ERA5 is based on the Integrated Forecasting System (IFS)Cycle 41r2 release, with several added features specifically developed for reanalysis. The many changes and improvements incorporated into the IFS represent a decade of research and development in modelling and data assimilation. The ERA5 reanalysis benefits from research conducted in the EU-funded ERA-CLIM and ERA-CLIM2 projects carried out by ECMWF and partners. These led to improved input data for the assimilating model that better reflects observed changes in climate forcings, as well as many new or reprocessed observations for data assimilation.\r\n\r\n\r\nThe system includes :\r\n\r\n - Model input:  Appropriate for climate (e.g. CMIP5 greenhouse gases, volcanic eruptions,\r\nSST and sea-ice cover)\r\n - Spatial resolution: 31 km globally, 137 levels to 0.01 hPa\r\n - Uncertainty estimates - From a 10-member Ensemble of Data Assimilations (EDA) at 63 km resolution\r\n - Output frequency: Hourly analysis and forecast fields, 3-hourly for the EDA\r\n - Input observations: As in ERA-40 and from Global Telecommunication System. In addition, various newly reprocessed datasets and recent instruments that could not be ingested in ERA-Interim\r\n - Variational bias scheme: Satellite radiances and also ozone, aircraft and surface pressure data\r\n - Satellite data: RTTOV-11, all-sky for various components\r\n - Additional innovations: Long-term evolution of CO2 in RTTOV, cell-pressure correction SSU, improved bias correction for radiosondes, EDA perturbations for sea-ice cover"
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            "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 2.0.1",
            "abstract": "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. \r\n\r\nThis is version 2.0.1 of the dataset.   The five thematic climate variables included in this dataset are:\r\n• 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.\r\n• 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. .\r\n• 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.\r\n• 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.\r\n• 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).\r\n\r\nData 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.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website.",
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            "title": "CRU TS3.23_clim: 30 year climatologies derived from Climatic Research Unit (CRU) Time-Series (TS) Version 3.23 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2014)",
            "abstract": "These data are 30 year climatologies produced from the gridded CRU TS (time-series) 3.23 data are month-by-month variations in climate over the period 1901-2014, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia. 4 sets of climatologies are produced covering the periods: 1901-1930, 1931-1960, 1961 - 1990 and 1984-2013.\r\n\r\nCRU TS 3.23 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period Jan. 1901 - Dec. 2014.\r\n\r\nCRU TS 3.23 data were produced using the same methodology as for the 3.21 datasets. In addition to updating the dataset with 2014 data, some new stations have been added for TMP and PRE only. Known issues predating this release remain; the 4.00 release, due soon, will address these.\r\n\r\nThe 4.00 release will utilise Angular-Distance Weighting (ADW) gridding, promising more accurate results with far greater adjustability and logging. It will cover the same spatial, temporal and variate spaces as version 3.23 (land areas excluding Antarctica at 0.5°x0.5°, monthly from 1901 to 2014 with no missing values, 10 variables).\r\n\r\nVersions 3.23 and 4.00 will run concurrently until 2016, after which the new (ADW) approach will be used. This is to allow comparisons between the methods and results to be made by users of the dataset.\r\n\r\nThe CRU TS 3.23 data are monthly gridded fields based on monthly observational data, which are calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and netcdf data files both contain monthly mean values for the various parameters.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies.\r\n\r\nCRU TS data are available for download to all CEDA users. The CEDA Web Processing Service (WPS) may be used to extract a subset of the data (please see link to WPS below).",
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                    "title": "National Centre for Earth Observation (NCEO) partnered datasets",
                    "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)."
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            "abstract": "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.\r\n\r\nDaytime 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.\r\n\r\nThe 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.\r\n\r\nThe 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).\r\n\r\nLSTs 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.\r\n\r\nDataset 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. \r\n\r\nThe 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.\r\n\r\nThe 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.",
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            "abstract": "This dataset contains measurements of dispersed Nitric Oxide (NO) made at a relatively low traffic area, Mansfield Road, in the city of Oxford, U.K.  as part of project funded through the TRANSITION AIR QUALITY Network.   NO was released from a bottle with a known concentration of 10,000ppm NO in nitrogen mounted at the back of an electric van and the dispersed NO measured at the roadside using fast response sensors provided by Cambustion Ltd. These measurements were then used to validate Computational Fluid Dynamics Models. The tests were carried out during a single day at different locations on the road. Wind direction, wind speed and the concentration of sensed NO are recorded and presented in the archived dataset.",
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                    "abstract": "A newly emerging air quality and public health challenge comes from exposure to high, momentary peaks of air pollution which arise from vehicles stop-start manoeuvres and accelerations, typical of congested urban areas. Roadside air quality instrumentation does not routinely measure these events, and the health implications – especially for vulnerable groups (e.g. children, the elderly) who use streets and public transport more frequently – remain unknown. While literature is starting to discuss the weaknesses of the “point-fixed/uniform exposure” approach, there is a clear necessity of building up data to support specific air quality and medical research.\r\n\r\nLeveraging years of experience on emissions, and Computational Fluid Dynamics (CFD) modelling, Oxford Brookes University have developed a new ultra-high definition 3-Dimensional CFD urban model, capable of: predicting the complex dynamics of pollutants dispersion from moving traffic; and quantifying actual exposure for the public occupying the space. The model is computationally demanding, but offers a vast accuracy advantage compared to other approaches (e.g. Gaussian Plume Models) for application in dense urban environments.\r\n\r\nThis project aims to: increase the technical capabilities of the Oxford Brookes University model; perform its validation using purposely-collected field data; and carry out a case study on bus stop shelters, to assess the effective protection they may offer from short-term peak concentrations of air pollution. This was funded by UKRI grant NE/V002449/1 https://transition-air.org.uk/research/"
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            "abstract": "This dataset contains outputs generated using the Gorgon Magnetohydrodynamic (MHD) code, for simulations of the magnetosphere-ionosphere system during impact by a series of interplanetary shocks with different solar wind conditions and dipole magnetic field orientations. \r\n\r\nThe MHD equations were solved in the magnetosphere on a regular 3-D cartesian grid of resolution 0.25 RE (Earth radii), covering a domain of dimensions (-30,90) RE in X, (-40,40) RE in Y and (-40,40) RE in Z with an inner boundary at 3 RE. In this coordinate system the Sun lies in the negative X-direction, the Z axis is aligned to the dipole in the 0 degree tilt case (where positive tilt points the north magnetic pole towards the Sun), and Y completes the right-handed set. The ionospheric variables were calculated on a separate 2-D spherical grid of dimensions 128x256 in latitude and longitude (with the north pole at 90 degrees latitude and the Sun at 180 degrees longitude), coupled to the magnetospheric domain at the inner boundary. \r\n\r\n5 different shocks were simulated in total, with the following solar wind jump conditions injected at the sunward edge at 7200s simulation time:\r\n\r\nShocks 1-4: n = 5 /cm^3 -> 10 /cm^3 (number density)         \r\n            v = 400 km/s -> 600 km/s (velocity)        \r\n            T = 5 eV -> 417 eV (temperature)     \r\n            B = 2 nT -> 4 nT (interplanetary magnetic field) \r\n\r\nShock 5:    n = 5 /cm^3 -> 20 /cm^3 (number density)    \r\n            v = 400 km/s -> 1000 km/s (velocity)\r\n            T = 5 eV -> 1250 eV (temperature)  \r\n            B = 2 nT -> 4 nT (interplanetary magnetic field)      \r\n\r\nShocks 1, 3 and 5 had an interplanetary magnetic field (IMF) clock angle of 180 degrees, i.e. B = Bz = -2 nT, whereas Shocks 2 and 3 had IMF clock angles of 135 degrees and 90 degrees, respectively. In addition, Shocks 1, 2, 3 and 5 had zero dipole tilt, whereas Shock 4 had a tilt angle of 30 degrees. These simulations employed zero electrical resistivity. The simulations of Shocks 1, 3 and 5 were then repeated utilising an explicit resistivity eta with value of eta/mu_0 = 5e10 m^2/s. The full set of 8 simulations are labelled 'Shock1', 'Shock2', 'Shock3', 'Shock4', 'Shock5' for the zero resistivity runs and 'Shock1_res', 'Shock3_res' and 'Shock5_res' for those with explicit resistivity. \r\n\r\nOutput grid data are timestamped in seconds and are defined at the centre of the grid cells, stored as .hdf5 files for each timestep. Output time-series data are for a single variable over a simulation time range, stored in .csv files. The simulation data corresponding to each shock are stored in separate directories, according to the simulation labels listed above.\r\n\r\nThe magnetospheric variables are stored in the files 'Gorgon_[YYYYMMDD]_[RUN]_MS_params_[XXXX]s.hdf5' where RUN is the simulation label and XXXX is the simulation time in seconds. The magnetospheric data are in SI units and include the magnetic field vector ('Bvec_c'), electric current density vector ('jvec') and ion thermal pressure ('P') for multiple timesteps following initialisation at 7200s of simulation time. The dataset for each magnetospheric variable is of shape (480,320,320,3) for vectors and (480,320,320) for scalars, where the first 3 dimensions are the grid indices in (X,Y,Z) indexed from negative to positive, and the final dimension is the cartesian vector components in (i,j,k).\r\n\r\nSimilarly, the ionospheric data are stored as 'Gorgon_[YYYYMMDD]_[RUN]_IS_params_[XXXX]s.hdf5', containing the field-aligned current density ('FAC') in SI units for multiple timesteps following initialisation at 7200s of simulation time. The dataset for each ionospheric variable is of shape (130, 256) where the first dimension is the grid index in colatitude, indexed from the north towards the south (i.e. 0 to 180 degrees), and the second dimension is the grid index in longitude, indexed from midnight towards noon via dawn (i.e. 0 to 360 degrees).\r\n\r\nFinally, the time-series data are stored as 'Gorgon_[YYYYMMDD]_[RUN]_[XXX].csv' where 'X' is the simulation label and 'XXX' is the time-series variable. These include the subsolar magnetopause standoff distance 'RMP' in RE, and the North (and South) polar cap flux content 'FPC' in Wb*RE^2; in each case the first column contains the simulation time in seconds, with the variables in the second (and third) column(s).   NE/P017142/1",
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                    "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps."
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            "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from MeteoSwiss's Vaisala CL31 instrument deployed at Grenchen, Switzerland",
            "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from MeteoSwiss's Vaisala CL31 deployed at Grenchen, Switzerland.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-06632.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n    \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.",
            "creationDate": "2022-07-22T09:15:57.183554",
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                    "title": "EUMETNET E-PROFILE",
                    "abstract": "E-PROFILE is part of the EUMETNET Composite Observing System, EUCOS, managing the European networks of radar wind profilers (RWP) and automatic lidars and ceilometers (ALC) for the monitoring of vertical profiles of wind and aerosols including volcanic ash.\r\n \r\n\r\nE-PROFILE coordinates the measurements of vertical profiles of wind from radar wind profilers (vertically pointing Doppler radars) and weather radars from a network of locations across Europe and provides the data to the end users. The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network."
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                    "short_code": "coll",
                    "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide",
                    "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps."
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            "title": "HadUK-Grid Climate Observations by UK river basins, v1.1.0.0 (1836-2021)",
            "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK river basins consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2021, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.",
            "creationDate": "2022-07-22T09:15:57.183554",
            "lastUpdatedDate": "2022-07-22T09:15:57",
            "latestDataUpdateTime": "2025-01-18T03:17:00",
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            "dataLineage": "Data provided by the UK Met Office for archiving in the Centre for Environmental Data Analysis (CEDA) archives.",
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            "dataPublishedTime": "2022-05-26T15:46:31",
            "doiPublishedTime": "2022-05-26T15:46:06",
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                "explanation": "Data quality control details for the HadUK-Grid version 1.0 datasets is available in section 2.2. of Hollis et al. (2019). See linked documentation for further details.",
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                "resultTitle": "HadUK-Grid v1.1 Data Quality Statement",
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                "title": "HadUK-Grid gridded climate observations methodology",
                "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography."
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                    "uuid": "4dc8450d889a491ebb20e724debe2dfb",
                    "short_code": "coll",
                    "title": "HadUK-Grid gridded and regional average climate observations for the UK",
                    "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised."
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            "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 60 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2021, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.",
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            "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.1.0.0 (1836-2021)",
            "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The datasets cover the UK at 1 km x 1 km resolution. These 1 km x 1 km data have been used to provide a range of other resolutions  and across countries, administrative regions and river basins to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2021, but the start time is dependent on climate variable and temporal resolution. \r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.",
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            "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 5 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2021, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.1.0.0 HadUK-Grid datasets are as follows:\r\n\r\n* The addition of data for calendar year 2021\r\n\r\n* The addition of 30 year averages for the new reference period 1991-2020\r\n\r\n* An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.\r\n\r\n* A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.\r\n\r\nNet changes to the input station data used to generate this dataset:\r\n\r\n-Total of 122664065 observations\r\n\r\n-118464870 (96.5%) unchanged\r\n\r\n-4821 (0.004%) modified for this version\r\n\r\n-4194374 (3.4%) added in this version\r\n\r\n-5887 (0.005%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.",
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            "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 2.11 (v20220510)",
            "abstract": "Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 includes mapped and time-series data showing global surface temperature relative to 1850 - 1900 over multiple time scales.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n---------------------------------------------------\r\n Figure has three panels, with data provided for panel (a) (center and right part), and panel (c).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n---------------------------------------------------\r\n Global surface temperature, relative to 1850 - 1900 for:\r\n\r\n Panel a: \r\n \r\n - 1000 to 1900 CE - from PAGES 2k Consortium (modified from the version 2019: 10.1038/s41561-019-0400-0)\r\n - 1850 to 2020 from AR6 assessed mean (same as Figure 2.11c).\r\n\r\n Panel c: \r\n \r\n - Annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n---------------------------------------------------\r\n Panel a:\r\n \r\n - Data file: Figure_2_11a-PAGES_2k_Consortium.csv (yearly data, 1000 to 1900); relates to the center part of the figure showing global surface temperature relative to 1850 -1900. (bold solid green line, column 2, median 10-yr smooth adjusted (+0.37°C), thin solid green lines: 5th (column 3) and 95th (column 4) percentiles of the ensemble members).\r\n - Data file: Figure2_11_panel_a.csv (yearly data, 1850 to 2020); relates to the right part of the figure showing global temperature anomaly AR6 assessed mean. (bold solid violet line, column 2)\r\n\r\nPanel c: \r\n \r\n - Data file: Figure_2_11c-land_and_ocean_time_series.csv (yearly data, 1850 to 2020); relates to the upper part of the figure showing global surface temperature relative to 1850 -1900. (Land, column 2, red line; Ocean, column 3, blue line).\r\n\r\n---------------------------------------------------\r\nNotes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nInput data and code to reproduce panel b and panel c (lower part) plots are provided in the Related Documents section of this catalogue record.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to input data figure 2.11.\r\n - Link to the code for the figure, archived on Zenodo.",
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                "title": "Caption for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
                "abstract": "Earth’s surface temperature history with key findings annotated within each panel. (a) GMST over the Holocene divided into three time scales: (i) 12 kyr–1 kyr in 100-year time steps; (ii) 1000–1900 CE, 10-year smooth; and (iii) 1900–2020 CE (from panel (c)). Median of the multi-method reconstruction (bold lines), with 5th and 95th percentiles of the ensemble members (thin lines). Vertical bars are the assessed medium confidence ranges of GMST for the Last Interglacial and mid-Holocene (Section 2.3.1.1). The last decade value and very likely range arises from Section 2.3.1.1.3. (b) Spatially resolved trends (C per decade) for HadCRUTv5 over (upper map) 1900–1980, and (lower map) 1981–2020.Significance is assessed following AR(1) adjustment after Santer et al. (2008), ‘x’ marks denote non-significant trends. (c) Temperature from instrumental data for 1850–2020, including (upper panel) multi-product mean annual timeseries assessed in Section 2.3.1.1.3 for temperature over the oceans (blue line) and temperature over the land (red line) and indicating the warming to the most recent 10 years; and annually (middle panel) and decadally (bottom panel) resolved averages for the GMST datasets assessed in Section 2.3.1.1.3. The grey shading in each panel shows the uncertainty associated with the HadCRUT5 estimate (Morice et al., 2021). All temperatures relative to the 1850–1900 reference period. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)."
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                    "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible."
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                    "abstract": "This dataset collection contains datasets relating to the figures found in the IPCC Sixth Assessment Report (AR6) Chapter 2: Changing state of the climate system.\r\n\r\nWhen using datasets from this collection please use the citation indicated in each specific dataset rather than the citation for the entire collection.\r\n\r\nFigure datasets related to this collection:\r\n- input data for Figure 2.2\r\n- data for Figure 2.4\r\n- data for Figure 2.5\r\n- data for Figure 2.6\r\n- data for Figure 2.9\r\n- data for Figure 2.11\r\n- input data for Figure 2.11\r\n- data for Figure 2.12\r\n- input data for Figure 2.12\r\n- data for Figure 2.13\r\n- input data for Figure 2.13\r\n- data for Figure 2.14\r\n- data for Figure 2.15\r\n- input data for Figure 2.15\r\n- input data for Figure 2.16\r\n- data for Figure 2.17\r\n- data for Figure 2.22\r\n- input data for Figure 2.23\r\n- data for Figure 2.25\r\n- input data for Figure 2.25\r\n- data for Figure 2.26\r\n- input data for Figure 2.27\r\n- data for Figure 2.28\r\n- input data for Figure 2.29\r\n- data for Figure 2.36\r\n- data for Figure 2.37\r\n- data for Figure 2.38\r\n- data for Cross-Chapter Box 2.1.1\r\n- data for Cross-Chapter Box 2.3.1"
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            "title": "HadISDH.land: gridded global monthly land surface humidity data version 4.4.0.2021f",
            "abstract": "This is the HadISDH.land 4.4.0.2021f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.land is a near-global gridded monthly mean land surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from weather stations. The observations have been quality controlled and homogenised. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2021.  \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2021. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nAs in previous years, the annual scrape of NOAAs Integrated Surface Dataset for HadISD.3.1.2.202101p, which is the basis of HadISDH.land, has pulled through some historical changes to stations. This, and the additional year of data, results in small changes to station selection. The homogeneity adjustments differ slightly due to sensitivity to the addition and loss of stations, historical changes to stations previously included and the additional 12 months of data.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013.",
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            "title": "Chapter 2 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 2.11 (v20220428)",
            "abstract": "Input Data for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 2.11 shows observed global temperature change over a wide range of timescales.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nGulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three subpanels. Input data are provided for panel b and panel c (lower panel).\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n Panel b:\r\n - gridded file of observed trends (as ASCII text) and significance overlay. Separate notes document.\r\n \r\n Panel c (lower panel):\r\n - Global surface temperature, relative to 1850 - 1900 for annual and decadal means from instrumental data for 1850–2020, along with the uncertainty range from HadCRUT5.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel b:\r\n - IndermediateData_Figure-2_11-HadCRUT_significance_overlay_1981-2020.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_significance_overlay_1900-1980.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_trends_1900-1980.txt\r\n - IntermediateData_Figure-2_11-HadCRUT_trends_1981-2020.txt\r\n \r\n Panel c:\r\n - Figure_2_11c-lower_panel.csv; relates to the lower part of the figure. (black line, column 2, HadCRUT 5.0; cyan line, column 3, NOAA Global Temp; pink line, column 4, Berkeley Earth; orange line, column 5, Kadow et al.; grey shadow, columns 6 and 7, HadCRUT confidence limit)\r\n\r\nHadCRUT5 is a gridded dataset of global historical surface temperature anomalies relative to a 1961-1990 reference period produced by the Met Office Hadley Centre. \r\nNOAA Global Temp is a gridded dataset of global historical surface temperature anomalies relative to a 1971-2000 reference period produced by the National Oceanic and Atmospheric Administration. \r\nBerkeley Earth is a global historical land-ocean temperature index produced by Berkeley Earth.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n Figure 2.11b - this is an ASCII grid (described in Figure_2_11-notes_on_HadCRUT_trend_files.txt) with a significance overlay. Should be approximately reproducible with any standard software to produce maps from gridded data.\r\n\r\n\r\nFigure 2.11c (lower panel), link to the code to reproduce this part of the figure is provided in the Related Documents section  of this catalogue record.\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 2)\r\n - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1\r\n - Link to the code for the figure, archived on Zenodo.",
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                    "abstract": "ACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. ACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThis collection includes global ocean simulations generated within the project.\r\nACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1"
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            "title": "ACSIS: Global ocean-ice sea ice simulations CICEv5.1.2 with prognostic melt pond model and EAP rheology with  modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter with NCEP Reanalysis-2 atmospheric forcing data from 1960 - 2015",
            "abstract": "This dataset includes model output from a forced ocean-ice simulation to document the impact of sea ice physics and atmospheric forcing data on the Arctic sea ice evolution produced for the The North Atlantic Climate System Integrated Study (ACSIS). This uses the same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) with an atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2) data (Kanamitsu et al., 2002, updated 2020). Regarding the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default) and an advanced setup (CICE-best) in which a new process is added (snow loss due to drifting snow) and some adjustments have been made to model physics and parameters. \r\n\r\nThe specific parameters for this dataset are:\r\nsea ice model: CICEv5.1.2 with prognostic melt pond model and EAP rheology, but with several modifications including snow drift scheme, bubbly conductivity scheme, increased sea ice emissivity and reduced melt pond max fraction parameter (see Schroeder et al., TC 2019)\r\nocean model: NEMOv3.6\r\nperiod: 1960-2015\r\natmospheric forcing: NCEP2\r\ndomain: global\r\ngrid resolution: 1deg ORCA\r\n\r\nThe simulation was performed by the Centre of Polar Observation and Modelling (CPOM) at University of Reading under the ACSIS project. ACSIS was funded by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) grant NE/N018028/1.",
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                "abstract": "6 forced ocean-ice simulations and 2 stand-alone ice simulations to document the impact of sea ice physics and\\ \\ atmospheric forcing data on the Arctic sea ice evolution. All of them use the\\ \\ same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice\\ \\ ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. Three\\ \\ different atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2)\\ \\ data (Kanamitsu et al., 2002, updated 2020), CORE II surface data (Large & Yeager,\\ \\ 2009) and the atmospheric forcing data set DFS5.2 (Dussin et al., 2016). Regarding\\ \\ the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default)\\ \\ and an advanced setup (CICE-best) in which a new process is added (snow loss due\\ \\ to drifting snow) and some adjustments have been made to model physics and parameters.\\ \\ \\n\\nThe simulations were performed by the Centre of Polar Observation and Modelling\\ \\ (CPOM) at University of Reading under the ACSIS project"
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                    "abstract": "Major changes are occurring across the North Atlantic (NA) climate system: in ocean and atmosphere temperatures and circulation, in sea ice thickness and extent, and in key atmospheric constituents such as ozone, methane and aerosols. Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health.  The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. ACSIS provided advances in understanding and predicting changes in the NA climate system that can be exploited in further research and related activities, for example to assess the impact of these changes on the UK and other countries - e.g. in terms of the consequences for hazardous weather risk, the environment and businesses. ACSIS outputs will also inform policy on climate change adaptation and air quality.\r\nACSIS was fully funded for five years (2016-2021)by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) (grant NE/N018028/1) which aimed to encourage its research centres to work closely together to tackle major scientific and societal challenges. ACSIS is one of the projects funded through this new way of allocating national capability funding, designed to enable more ambitious science than any single research organisation could provide."
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            "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci):  Global Plant Functional Types (PFT) Dataset, v2.0.8",
            "abstract": "This dataset contains Global Plant Functional Types (PFT) data, from the ESA Medium Resolution Land Cover (MRLC) Climate Change Initiative project.   The data provides yearly data, and initially covers the time period from 1992 to 2020.   It is anticipated that the dataset will be updated annually going forward.\r\n\r\nThe PFT v2.0.8 global dataset has 14 layers, each describing the percentage cover (0-100%) of a plant functional type at a spatial resolution of 300 m: broadleaved evergreen trees, broadleaved deciduous trees, needleleaved evergreen trees, needleleaved deciduous trees, broadleaved evergreen shrubs, broadleaved deciduous shrubs, needleleaved evergreen shrubs, needleleaved deciduous shrubs, natural grasses, herbaceous cropland (i.e., managed grasses), built, water, bare areas, and snow and ice.\r\n\r\n\"Plant Functional Types” (PFTs) refer to globally representative and similarly behaving plant types. PFTs can be related to physiognomy and phenology, climate (which defines the geographical ranges in which a plant type can grow and reproduce under natural conditions, and physiological activity (e.g., C3/C4 photosynthetic pathways).\r\n\r\nAll terrestrial zones of the Earth between the parallels 90°N and 90°S are covered. The PFT dataset has a regular latitude-longitude grid with a grid spacing of 0.002777777777778°, corresponding to ~300 m at the equator and ~200 m in the midlatitudes.    The Coordinate Reference System used for the global land cover database is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid.\r\n\r\nThe plant functional type (PFT) distribution was created by combining auxiliary data products with the CCI MRLC map series. The LC classification provides the broad characteristics of the 300 m pixel, including the expected vegetation form(s) (tree, shrub, grass) and/or abiotic land type(s) (water, bare area, snow and ice, built-up) in the pixel. For some classes, the class legend specifies an expected range for the fractional covers of the contributing PFTs and broadly differentiates between natural and cultivated vegetation. We used a quantitative, globally consistent method that fuses the 300-metre MRLC product with a suite of existing high-resolution datasets to develop spatially explicit annual maps of PFT fractional composition at 300 metres. The new PFT product exhibits intraclass spatial variability in PFT fractional cover at the 300-metre pixel level and is complementary to the MRLC maps since the derived PFT fractions maintain consistency with the original LC class legend. \r\n\r\nThis dataset was generated to reduce the cross-walking component of uncertainty by adding spatial variability to the PFT composition within a LC class. This work moved beyond fine-tuning the cross-walking approach for specific LC classes or regions and, instead, separately quantifies the PFT fractional composition for each 300 m pixel globally. The result is a dataset representing the cover fractions of 14 PFTs at 300 m for each year within the time range, consistent with the CCI MRLC LC maps for the corresponding year.\r\n\r\nThis study was carried out with the continued support of the European Space Agency Climate Change Initiative under the contract ESA/No.4000126564 Land_Cover_cci.",
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                "abstract": "The plant functional type (PFT) distribution was created by combining auxiliary data products with the CCI Medium Resolution Land Cover (MRLC) map series. The land cover (LC) classification provides the broad characteristics of the 300 m pixel, including the expected vegetation form(s) (tree, shrub, grass) and/or abiotic land type(s) (water, bare area, snow and ice, built-up) in the pixel. For some classes, the class legend specifies an expected range for the fractional covers of the contributing PFTs and broadly differentiates between natural and cultivated vegetation. We used a quantitative, globally consistent method that fuses the 300-metre MRLC product with a suite of existing high-resolution datasets to develop spatially explicit annual maps of PFT fractional composition at 300 metres. The new PFT product exhibits intraclass spatial variability in PFT fractional cover at the 300-metre pixel level and is complementary to the MRLC maps since the derived PFT fractions maintain consistency with the original LC class legend. \r\n\r\nThis dataset was generated to reduce the cross-walking component of uncertainty by adding spatial variability to the PFT composition within a LC class. This work moved beyond fine-tuning the cross-walking approach for specific LC classes or regions and, instead, separately quantifies the PFT fractional composition for each 300 m pixel globally. The result is a dataset representing the cover fractions of 14 PFTs at 300 m for each year within the time range, consistent with the CCI MRLC LC maps for the corresponding year."
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                    "abstract": "The ESA Land Cover Climate Change Initiative Project is part of the European Space Agency's Climate Change Initiative to produce long term datasets of Essential Climate Variables (ECV's) from historic satellite data.\r\n\r\nLand cover is defined as the (bio) physical cover at the earth surface including grass, trees, bare ground and water. Land cover is fundamental to better understand the climate through the estimation and validation of fluxes of water, carbon, and energy.  It plays a role in adaptation and mitigation assessments at various scales.\r\n\r\nThe  projects objective is to critically revisit all algorithms required for the generation of global land product in the light of GCOS requirements, and to design and demonstrate a mature system delivering in a consistent way over years and from multi-mission Earth Observation instruments, the longest possible global land cover map series at 300m, matching the needs of key users belonging to the climate change and land cover communities. The focus is placed on ESA and Member States missions, providing near daily global surface reflectance observation at moderate spatial resolution from 1999 onwards (MERIS Full Resolution (FR) & Reduced Resolution (RR), SPOT VEGETATION, PROBA-V and ASAR data), while the contribution of the AVHRR sensor tackles specific past years back to 1992."
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                    "abstract": "The Lakes Climate Change Initiative Project (Lakes_cci) is part of the European Space Agency's Climate Change Initiative Programme to produce long term datasets of Essential Climate Variables (ECV's) derived from global satellite data..\r\n\r\nLakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. In this context, the Lakes_cci develops products for the following five thematic climate variables:\r\n•\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\r\n•\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\r\n•\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \r\n•\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \r\n•\tLake Water-Leaving Reflectance (LWLR): 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).\r\n\r\nIn this context, Lakes_cci represents a unique framework to provide consistent and homogenous data to the multiple communities of lake scientists. The project actively engages with this community to assess the utility and future improvement of Lakes_cci products."
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            "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.2 (v20220610)",
            "abstract": "Data for Figure 3.2 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 3.2 shows changes in surface temperature for different paleoclimates.\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has three subpanels, the data provided for all panels in subdirectories named panel_a, panel_b, panel_c\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n For panel (a):\r\n - PMIP3 global temperature anomalies over continents and oceans reconstruction sites\r\n - PMIP4 CMIP6 global temperature anomalies over continents and oceans reconstruction sites\r\n - PMIP4 non-CMIP6 global temperature anomalies over continents and oceans reconstruction sites\r\n - Tierney 2020 reconstructions of marine temperature\r\n - Cleator 2020 reconstructions of continental temperature\r\n \r\n For panel (b):\r\n - CMIP5 temperature data for paleoclimate periods\r\n - CMIP6 temperature data for paleoclimate periods\r\n - non-CMIP temperature data for paleoclimate periods\r\n - Instrumental observational and observations from reconstructions\r\n \r\n For panel (c):\r\n - Volcanic forcing from TS17, CU12, GRA08\r\n - CMIP6 GMST anomaly with respect to 1850-1900 modelled with TS17 volcanic forcing\r\n - CMIP5 GMST anomaly with respect to 1850-1900 modelled with CU12 volcanic forcing\r\n - CMIP5 GMST anomaly with respect to 1850-1900 modelled with GRA08 volcanic forcing\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/temperature_anomalies_scatter_points.csv relates to the scatter points and their standard deviation for panel (a)\r\n - For panel (b) the datasets are stored as following panel_b/temperature_{color}_{marker}_{period}_{model_group}_{additional_info}.csv and relates to the scatter points for panel (b).\r\n - For panel (c) the data is stored in panel_c/gmst_changes_paleo_volcanic_forcings.csv and relates to red, green, blue and black lines on the panel as well as grey shadings.\r\n Additional information about data provided in relation to figure in files headers.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nCMIP5 is the fifth phase of the Coupled Model Intercomparison Project.\r\nPMIP4 is the Paleoclimate Modelling Intercomparison Project phase 4\r\nPMIP3 is the Paleoclimate Modelling Intercomparison Project phase 3\r\n\r\n ---------------------------------------------------\r\n Temporal Range of Paleoclimate Data\r\n ---------------------------------------------------\r\n This dataset covers a paleoclimate timespan from 56 Ma (56 million years ago) to 2010.\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data.\r\n ---------------------------------------------------\r\n For panel (a) the error bar should be plotted as anomalies from columns 2/4 +/- standard deviation. \r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo\r\n - Link to the figure on the IPCC AR6 website.",
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            "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).",
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                "title": "Caption for Figure 3.2 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
                "abstract": ". (a) Comparison of reconstructed and modelled surface temperature anomalies for the Last Glacial Maximum over land and ocean in the Tropics (30°N–30°S). Land-based reconstructions are from Cleator et al. (2020). Ocean-based reconstructions are from Tierney et al. (2020b). Model points are calculated as the difference between Last Glacial Maximum and pre-industrial control simulations of the PMIP3 and PMIP4 ensembles, sampled at the reconstruction data points. (b) Land–sea contrast in global mean surface temperature change for different paleoclimates. Crosses show individual model simulations from the CMIP5 and CMIP6 ensembles. Filled symbols show ensemble means and assessed values. Acronyms are Last Glacial Maximum (LGM), Last Inter Glacial (LIG), mid-Pliocene Warm Period (MPWP), Early Eocene Climatic Optimum (EECO). (c) Upper panel shows time series of volcanic radiative forcing, in W m−2, as used in the CMIP5 (Gao et al., 2008; Crowley and Unterman, 2013; see also Schmidt et al., 2011) and CMIP6 (850 BCE to 1900 CE from Toohey and Sigl (2017), 1850-2015 from Luo (2018)). The forcing was calculated from the stratospheric aerosol optical depth at 550 nm shown in Figure 2.2. Lower panel shows time series of global mean surface temperature anomalies, in °C, with respect to 1850–1900 for the CMIP5 and CMIP6 past 1000 simulations and their historical continuation simulations. Simulations are coloured according to the volcanic radiative forcing dataset they used. The median reconstruction of temperature from PAGES 2k Consortium (2019) is shown in black, the 5–95% confidence interval is shown by grey lines and the grey envelopes show the 1st, 5th, 15th, 25th, 35th, 45th, 55th, 65th, 75th, 85th, 95th, and 99th percentiles. All data in both panels are band-passed, where frequencies longer than 20 years have been retained. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."
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                    "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n\r\nThe initiative to archive the data (and code) from the Climate Change 2021: The Physical Science Basis report was a collective effort with many contributors. We thank the Working Group I Co-Chairs for their long-standing support. We also extend our gratitude to the members of the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) for their constant guidance and encouragement, including its Co-chairs, David Huard and Sebastian Vicuna. \r\n\r\nFor the implementation of the initiative, we recognise project management from Anna Pirani and Robin Matthews of the Working Group I TSU (WGI TSU). For contributing data and metadata for archival, we gratefully acknowledge the numerous WGI Authors and Chapter Scientists. In particular, we highlight the efforts of Katherine Dooley, Lisa Bock, Malinina-Rieger Elizaveta, Chaincy Kuo and Chris Smith for their major contributions.\r\n\r\nFor assistance with preparing data, code and the accompanying metadata for archival and publication, we extend our considerable appreciation to the dedicated contractor, Lina Sitz, along with Diego Cammarano and Özge Yelekçi from the WGI TSU. For the subsequent archival of figure data, we are indebted to Charlotte Pascoe, Kate Winfield, Ellie Fisher, Molly MacRae, and Emily Anderson from the UK Centre for Environmental Data Analysis (CEDA).\r\n\r\nFor the archival of the climate model data used as input to the report, we gratefully acknowledge Martina Stockhause of the German Climate Computing Center (DKRZ). For the development and support of software for data and code archival, we thank Tim Waterfield of the WGI TSU. For administrative contributions to the initiative we thank Clotilde Pean of the WGI TSU and Martin Juckes from CEDA. For the transfer of metadata to the IPCC data catalogue, we thank MetadataWorks. Finally, we gratefully acknowledge funding support from the Governments of France, the United Kingdom and Germany, without which data and code archival would not have been possible."
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            "title": "Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.7 (v20220613)",
            "abstract": "Data for Figure 3.7 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.7 shows regression coefficients and corresponding attributable warming estimates for individual CMIP6 models.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\nWhen citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has four panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c and panel_d.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains information on global temperature attributable warming (2010-2019 relative to 1850-1900) from CMIP6 models: \r\n \r\n - Regression coefficients for two way regression (2010-2019 relative to 1850-1900)\r\n - Regression coefficients for three way regression (2010-2019 relative to 1850-1900)\r\n - Attributable warming for two way regression (2010-2019 relative to 1850-1900)\r\n - Attributable warming for three way regression (2010-2019 relative to 1850-1900)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - panel_a/regression_coeff_two_way_regression.csv has data for brown and green crosses\r\n - panel_b/regression_coeff_three_way_regression.csv has data for grey, green and blue crosses\r\n - panel_c/attributable_warming_two_way_regression.csv has data for brown and green crosses\r\n - panel_d/attributable_warming_three_way_regression.csv has data for grey, green and blue crosses\r\n\r\n Details about the data provided in relation to the figure in the header of every file.\r\n\r\n CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.",
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                "title": "Caption for Figure 3.7 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
                "abstract": "Regression coefficients and corresponding attributable warming estimates for individual CMIP6 models. Upper panels show regression coefficients based on a two-way regression (left) and three-way regression (right), of observed five-year mean, globally averaged, masked and blended surface temperature (HadCRUT4) onto individual model response patterns, and a multi-model mean, labelled ‘Multi’. Anthropogenic, natural, greenhouse gas, and other anthropogenic (aerosols, ozone, land-use change) regression coefficients are shown. Regression coefficients are the scaling factors by which the model responses must be multiplied to best match observations. Regression coefficients consistent with one indicate a consistent magnitude response in observations and models, and regression coefficients inconsistent with zero indicate a detectable response to the forcing concerned. Lower panels show corresponding observationally-constrained estimates of attributable warming in globally-complete GSAT for the period 2010–2019, relative to 1850–1900, and the horizontal black line shows an estimate of observed warming in GSAT for this period. Figure is adapted from Gillett et al. (2021), their Extended Data Figure 3. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."
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            "ob_id": 37394,
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            "abstract": "Data for Figure 3.8 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 3.8 shows assessed contributions to observed warming, and supporting lines of evidence.\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nEyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.\r\n\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains the drivers of the attributable warming (2010-2019 relative to 1850-1900):\r\n \r\n - Observed global warming (2010-2019)\r\n - Global warming and its drivers reported in the literature sources  (2010-2019)\r\n - Global warming and its drivers calculated from CMIP6 models (2010-2019)\r\n\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n - drivers_observed_warming.csv has data for the shadings and markers in the figure.\r\n Additional details of data provided in relation to figure in the file header (BADC-CSV file).\r\n\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the report component containing the figure (Chapter 3)\r\n - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1\r\n - Link to the code for the figure, archived on Zenodo.",
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                "title": "Caption for Figure 3.8 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)",
                "abstract": "Assessed contributions to observed warming, and supporting lines of evidence. Shaded bands show assessed likely ranges of temperature change in GSAT, 2010-2019 relative to 1850-1900, attributable to net human influence, well-mixed greenhouse gases, other human forcings (aerosols, ozone, and land-use change), natural forcings, and internal variability, and the 5-95% range of observed warming. Bars show 5-95% ranges based on (left to right) Haustein et al. (2017), Gillett et al. (2021) and Ribes et al. (2021), and crosses show the associated best estimates. No 5-95% ranges were provided for the Haustein et al. (2017) greenhouse gas or other human forcings contributions. The Ribes et al. (2021) results were updated using a revised natural forcing time series, and the Haustein et al. (2017) results were updated using HadCRUT5. The Chapter 7 best estimates and ranges were derived using assessed forcing time series and a two-layer energy balance model as described in Section 7.3.5.3. Coloured symbols show the simulated responses to the forcings concerned in each of the models indicated. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."
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