Procedure Computation List

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        {
            "ob_id": 43084,
            "uuid": "08add2606145470a942ac0ff2d00e743",
            "title": "ESA Snow Climate Change Initiative: Derivation of SCFV AVHRR v3.0 product.",
            "abstract": "Based on the EUMETSAT AVHRR GAC FDR (Global Area Coverage), released in May 2023, a time series (1979–2022) was generated utilising the SCAmod algorithm of Metsämäki et al. (2015). Cloud masking relies on the probability mask of CLARA-A3, an upgrade of the existing cloud albedo and radiation (CLARA) data record developed and generated by EUMETSAT CM SAF, SMHI, which has also been produced from this FDR.\r\n\r\nAs a pre-condition, (FSC (NDSI) > 5%) was implemented based on Normalized Difference Snow Index (NDSI) calculations (Salomonson and Appel, 2006) before utilising the SCAmod algorithm (Metsämäki et al. 2015) for viewable snow (SCFV) and snow on ground (SCFG). A pre-classification was implemented to minimise erroneous results (solar zenith angle > 88°; cloud probability > 80%; water if percentage of pixel > 50; and permanent ice if percentage of pixel > 50). In addition, two thresholds were included to test whether a pixel potentially is snow free or snow covered (snow free if channel 1 > 0.12 or channel 4 > 283 K).  \r\n\r\nIn addition, different post-processing steps were implemented to improve the quality of the snow products: for latitudes ±15° and elevations below 1000 m a.s.l. a test was added (channel 1 < 0.30 or channel 4 > 270 K) to remove erroneous data in the tropical regions. A second test considers channel 3b: (channel 1 – channel 3b ≥ 0.2 and channel 3b < 0.1) for the Southern Hemisphere to remove erroneous snow pixels, with an additional adaptation for the globe (channel 1 – channel 3b < 0.1 and channel 3b < 0.2). The erroneous snow pixels with (channel_3a(/b)/channel_1 > 1 and channel_3a(/b)/channel_1 > 1) or (channel_2/channel_1 < 0.999 and channel_4 > 274 K) was removed. Additionally, the pixels with high solar zenith angle (VZA > 75°) and NDSI < 0.5 was removed. Some other criteria were also added in post-processing steps.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43096,
            "uuid": "b7ef2e222655403c9b95673f8fe8e110",
            "title": "Climatic Research Unit (CRU) procedure to produce the CRU JRA v2.5 data.",
            "abstract": "The CRU JRA (Japanese reanalysis) data is a replacement to the CRU NCEP dataset, CRU JRA data follows the style of Nicolas Viovy's original dataset rather than that which is available from UCAR.\r\n\r\nThe CRU JRA dataset is based on the JRA-55 reanalysis dataset and aligned where appropriate with the CRU TS dataset version 4.08 (1901-2023).\r\n\r\nAll JRA variables are regridded from their native TL319 Gaussian grid to the CRU regular 0.5° x 0.5° grid, using the g2fsh spherical harmonics routine from NCL (NCAR Command Language), based on the 'Spherepack' code. The exception is precipitation, which is regridded using ESMF 'nearest neighbour': all other algorithms tried exhibited unwanted artifacts.\r\n\r\nThe JRA-55 reanalysis dataset starts in 1958. The years 1901-1957 are constructed using randomly-selected years between 1958 and 1967. Where alignment with CRU TS occurs, the relevant CRU TS data is used.\r\n\r\nOf the ten variables listed above, the last four do not have analogs in the CRU TS dataset. These are simply regridded, masked for land only, and output as CRUJRA. The other six are aligned with CRU TS as follows:\r\n\r\nTMP is aligned with CRU TS TMP. A monthly mean for the JRA data is\r\ncalculated and compared with the equivalent CRU TS mean. The difference\r\nbetween the means is added to every JRA value.\r\n\r\n---\r\n\r\nTMAX and TMIN are aligned with CRUJRA TMP and CRU TS DTR. Firstly, at\r\neach time step, the TMAX-TMP-TMIN triplets are checked and adjusted so\r\nthat TMAX is always >= TMP, and TMIN is always <= TMP. This triplet\r\nalignment is prioritised above DTR alignment. Secondly, monthly JRA DTR\r\nis calculated by first establishing the daily maxima and minima (max and\r\nmin of the subdaily values in TMAX and TMIN respectively), then monthly\r\nmaxima and minima, (means of the daily DTR values), giving JRA monthly\r\nDTR. This is compared with CRU TS DTR and the fractional difference\r\n(factor) calculated as (CRU TS DTR) / (JRA monthly DTR). This factor is\r\nthen used to adjust the DTR of each pair of subdaily TMAX and TMIN\r\nvalues, though not if the triplet alignment would be broken.\r\n\r\n---\r\n\r\nPRE is aligned with CRU TS PRE and WET (rain day counts). Firstly, the\r\nmonthly total precipitation is calculated for JRA and compared to CRU TS\r\nPRE; an adjustment factor is acquired (crupre/jrapre) and all values\r\nadjusted. Precipitation amounts are now aligned at a monthly level, and\r\nthis alignment is prioritised above WET alignment. Secondly, the number\r\nof rain days is calculated for JRA: a day is declared wet if the total\r\nprecipitation is equal to, or exceeds, 0.1mm (the same threshold as CRU\r\nTS WET). If JRA has more wet days than CRU TS, then the driest of those\r\nare reduced to a random amount below 0.1 (an adjustment factor is\r\ncalculated and applied to each time step, to preserve the subdaily\r\ndistribution). If JRA has fewer wet days than CRU TS, then sufficient\r\ndry days are set to a random amount equal to or closely above 0.1mm,\r\nagain using an adjustment factor to preserve the subdaily distribution. \r\nWhere wet day alignment threatens precipitation alignment, the process\r\nis abandoned and the cell/month reverts to the previously-aligned\r\nprecip version. Exception handling is very complicated and cannot be\r\nsummarised here.\r\n\r\n---\r\n\r\nSPFH is aligned with CRU TS VAP. VAP is converted to SPFH, and JRA mean\r\nmonthly SPFH is calculated. The fractional difference (factor) is\r\ncalculated as (CRU TS SPFH) / (JRA monthly SPFH), this factor is then\r\napplied to the JRA subdaily humidity values.\r\n\r\n---\r\n\r\nDSWRF is aligned with CRU TS CLD. CLD is converted to shortwave\r\nradiation, and JRA mean monthly DSWRF is calculated. The fractional\r\ndifference (factor) is calculated as (CRU TS SWR) / (JRA monthly DSWRF),\r\nthis factor is then applied to the JRA subdaily radiation values.\r\n\r\n---\r\n\r\nWhere appropriate, CRUJRA values are kept within physically-appropriate\r\nconstraints (such as negative precipitation), which could result from\r\nregridding as well as adjustments.",
            "keywords": "Climatic Research Unit, CRU, TS, CY",
            "inputDescription": null,
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        },
        {
            "ob_id": 43118,
            "uuid": "5f4a954afcc34c099445058b6f8fc657",
            "title": "PROFFAST retrieval code for trace gas concentration",
            "abstract": "PROFFAST is a software package for retrieving trace gas concentrations from interferograms measured with Bruker EM27/SUN solar absorption FTIR spectrometer by using Bruker OPUS software. The PROFFAST software package is developed at the Karlsruhe Institute of Technology (KIT) and funded by the European Space Agency (ESA). Recently, PROFFASTpylot was created to run PROFFAST under Python.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43138,
            "uuid": "4934272199f44117bfaf0851f35c64a1",
            "title": "https://catalogue.ceda.ac.uk/uuid/0e33b1c8c7324783996b85663e03e60b",
            "abstract": "",
            "keywords": "",
            "inputDescription": null,
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        },
        {
            "ob_id": 43144,
            "uuid": "b2ea0c5a99f849728e1b7640ade097a6",
            "title": "Mass flow rate ice discharge (MFID) derived by the ESA Greenland Ice Sheets Climate Change Initiative project, v2.0",
            "abstract": "Ice discharge is calculated from the CCI Ice Velocity (IV) product, the CCI Surface Elevation Change (SEC) product (where it overlaps with the ice discharge gates), and ice thickness from BedMachine. Ice discharge gates are placed 10 km upstream from all marine terminating glacier termini that have baseline velocities of more than 150 m/yr. Results are summed by Zwally et al. (2012) sectors. The methods, including description of \"coverage\", are described in Mankoff et al. 2020. \r\n\r\nFor further details see the documentation.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43169,
            "uuid": "7df7d3cbfe924902a10fe4beade0b747",
            "title": "GEOS-Chem High-Performance (GCHP) model v14.2.2",
            "abstract": "This is an atmospheric chemistry transport model (www.geos-chem.org) The model is run with the standard mechanism and our new rate for OH+NO2. The standard model can be found at https://doi.org/10.5281/zenodo.8411829. Details of our new rate can be found at https://zenodo.org/records/13381188.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43177,
            "uuid": "352307ef566842b2adc4b0102d976322",
            "title": "Integrated Multi-satellitE Retrievals for GPM (IMERG) v7",
            "abstract": "The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields.\r\n\r\nVersion 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07. The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team. The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases. The half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme. The KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR. The IMERG system is run twice in near-real time: \"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and \"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing and once after the monthly gauge analysis is received: \"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses. In V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users. Briefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then \"forward/backward morphed\" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months). The Integrated Multi-Satellite Retrievals for GPM (IMERG) algorithm is designed to leverage the international constellation of precipitation-relevant satellites to create a long record of uniformly time/space gridded precipitation estimates for the globe. The algorithm is focused on creating the best estimate at each time step, meaning that it is not a Climate Data Record, although the ideal is as homogenous a record as possible",
            "keywords": "",
            "inputDescription": null,
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        },
        {
            "ob_id": 43209,
            "uuid": "686e9eebf27340159d6d5dd8def14672",
            "title": "Derivation of the ESA River Discharge Climate Change Initiative (RD_cci): Multispectral indices-based River Discharge Product, v1.2",
            "abstract": "The multispectral indices-based river discharge data has been computed at different locations from several satellite multispectral missions.  At each location, time series are provided for each available single sensor and then merged in a unique time series. \r\n\r\nThe river discharges are derived following several approaches:\r\n\r\nBestFit: by non-linear regression relationship between the multi-mission time series and the ground observed river discharge;\r\n\r\nCopula: by a bivariate cumulative distribution function which is applied between the multi-mission time series and the ground observed river discharge to get their joint probability distribution;\r\n\r\nuncalCDF: by Cumulative Distribution Function curves calculated to generate the percentiles associated to the discharges from the reflectance time series.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43212,
            "uuid": "44b4a239478248978d6b4089d8f2e538",
            "title": "Derivation of the ESA River Discharge Climate Change Initiative (RD_cci): combined river discharge product, v1.0",
            "abstract": "These river discharge time series have been computed at different locations by the combination of data derived from satellite altimeters and multispectral sensors. Two levels of combination are implemented based on the original products: Level-2, in which the data has been derived by merging multi-mission multispectral time series and radar altimeters water level product and Level-3, in which the river discharge products obtained from altimeters and multispectral imagers are used. The river discharges are derived following several approaches:\r\n\r\n1) L2 Merged river discharge:\r\n\r\na) COPULA Altimetry – CM: by a bivariate cumulative distribution function (CDF) which is applied between the multispectral indices and the water level from altimetry to get their joint probability distribution.\r\n\r\nb) RIDESAT Altimetry - CM: by a three-parameter non-linear relationship that merges the multispectral indices and the water level from altimetry\r\n\r\n2) L3 Merged river discharge:\r\n\r\na) Altimetry - CM cal_BestFIT: by the combination of river discharges obtained by the procedure of BestFIT applied to the multispectral and river discharges obtained by the altimetry through a weighted approach\r\n\r\nb) Altimetry – CM cal_Copula: by the combination of river discharges obtained by the procedure of Copula applied to the multispectral and river discharges obtained by the altimetry through a weighted approach\r\n\r\nc) Altimetry – CM uncal_CDF: by the combination of river discharges obtained by the procedure of CDF applied to the multispectral and the altimetry through a weighted approach",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
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            "identifier_set": []
        },
        {
            "ob_id": 43218,
            "uuid": "86be2f87e1554523865eab2a182e556d",
            "title": "Met Office Unified Model version 13.3, configuration GAL9.0",
            "abstract": "The scientific configuration is GAL9.0 and the UM version is 13.3.\r\n\r\nThe ‘N96’ horizontal resolution version of the UM is used which has 192 longitude points by 144 latitude points, with a mid-latitude resolution of 135km. There are 85 vertical levels with 50 levels below 18km and a fixed model lid at 85km above sea level.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
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            "identifier_set": []
        },
        {
            "ob_id": 43224,
            "uuid": "b76749a181e04042b4d0ef79e60b7dd4",
            "title": "Altimetry",
            "abstract": "",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
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        },
        {
            "ob_id": 43231,
            "uuid": "65ad73a5dc2242f8960b76b9f838445a",
            "title": "ESA Fire_cci Burned Area SFDL algorithm",
            "abstract": "Fore further details regarding the Burned Area (BA) algorithm used for producing the Fire_CCI Burned Area product, please see the Fire_CCI Algoirthm Theoretical Basis Document (Bastarrika and Roteta, 2018), available at https://climate.esa.int/en/projects/fire/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43247,
            "uuid": "035e22771d25491294dd89e61ecf3c66",
            "title": "Derivation of the EOCIS: Ice Sheet Surface Elevation, v1.0",
            "abstract": "Time-series calculated from radar altimetry measurements from ERS-1, ERS-2, ENVISAT (FDR4ALT v1), and CryoSat-2 (CryoTEMPO Baseline-C), using the method from Shepherd et al, doi:10.1029/2019GL082182.\r\n\r\nFor more information on the EOCIS Ice Surface Elevation project see the documentation.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43248,
            "uuid": "850720a2b3854f3ba5f073e83c406df1",
            "title": "Derivation of the EOCIS: Ice Sheet Mass Balance, V1.00",
            "abstract": "Mass balance calculated from radar altimetry measurements from ERS-1, ERS-2, ENVISAT, and CryoSat-2, using the method from Shepherd et al. (2019).\r\n\r\nFor more information on the EOCIS Ice Surface Elevation project see the documentation.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43271,
            "uuid": "e533e4eb715e4f26a350a31258a0f820",
            "title": "Calimnos",
            "abstract": "Calimnos is a multi-sensor satellite processing chain developed at Plymouth Marine Laboratory to observe lakes and other isolated waterbodies. It is built around per-pixel dynamic algorithm selection and blending to accommodate the wide optical diversity of lakes.  Processor versions >= 2 include per-pixel product uncertainty for key variables. Calimnos calls several external libraries including modules for pixel identification (SNAP Idepix) and atmospheric correction using Polymer by Hygeos.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43274,
            "uuid": "8d6e1f181fb643f5a64f2a37d31c70fd",
            "title": "Computation for Lava Aerosol Gas and Trace Element data from the Fagradalsfjall 2021-2023 eruption, Iceland",
            "abstract": "Thermochemical equilibrium modelling performed on HSC Chemistry software for Lava Aerosol Gas and Trace Element data from the Fagradalsfjall 2021-2023 eruption, Iceland case study - See the paper https://essopenarchive.org/users/814040/articles/1215204-trace-element-emissions-vary-with-lava-flow-age-and-thermal-evolution-during-the-fagradalsfjall-2021-2023-eruptions-iceland for details",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43296,
            "uuid": "bf23c3ea947a4cd6a4822e1bd656d8a1",
            "title": "Derivation of the Swansea University Global Aerosol Optical Depth products from the Along-Track Scanning Radiometers and Sea and Land Surface Temperature Radiometers",
            "abstract": "For information on the derivation of the Swansea University Global Aerosol Optical Depth products see the associated documentation.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43307,
            "uuid": "bf96d10be6a7466c9d0bef329ffaeedd",
            "title": "Subset of CMIP6 models",
            "abstract": "8 Coupled climate and earth system models participating in CMIP6: CanESM5, CESM2, EC-Earth3, HadGEM3-GC3-1LL, HadGEM3-GC3-1MM, IPSL-CM6A-LR, MPI-ESM1-2-HR, MPI-ESM1-2-LR. All experiments were initialised from the CMIP6 preindustrial control simulations and are identical to these apart from the presence of hosing.  u03-hos and g01-hos were started from the same start year as the piControl experiments, apart from CESM2 which started the hosing experiments after 500 years of the piControl.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43325,
            "uuid": "f1c83717268241b48ca60519d844fc5b",
            "title": "Derivation of the EOCIS: Time Series of Sea Ice Arctic Thickness, Volume and Mass, V1.00 product",
            "abstract": "For information on the derivation of this dataset see the related documents section",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43331,
            "uuid": "bfcb9dc8593b4fb49bd068db6ed5fc77",
            "title": "Derivation of the EOCIS gridded Sea Ice Arctic Thickness V1.00 product",
            "abstract": "For information on the derivation of this dataset see the related documents section",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43339,
            "uuid": "952d3e8971b4414d9448f04b68e59245",
            "title": "Derivation of the EOCIS: Antarctic Ice Sheet Mass Balance, v1.0",
            "abstract": "Mass balance calculated from radar altimetry measurements from ERS-1, ERS-2, ENVISAT, and CryoSat-2, using the method from Shepherd et al .,(2019).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43393,
            "uuid": "3e26006a87a24c419cba05f9eab2e4b9",
            "title": "Derivation of EOCIS: Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A and Sentinel 3B level 3 collated (L3C) global products, v4.00",
            "abstract": "For more information on the retrieval algorithm used see the following paper: Ghent, D. J., Anand, J. S., Veal, K., Remedios, J. J. (2024). The operational and climate land surface temperature products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B. Remote Sensing, 16, 3403. https://doi.org/10.3390/rs16183403",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43407,
            "uuid": "b72285dc95484e039334713dee387ac3",
            "title": "TLS2trees processing pipeline for FBRMS-03: Kabili-Sepilok, Malaysian Borneo 1ha plots",
            "abstract": "Data for each of the three French Guiana FBRMS plots is found within plot directories: SEP-11; Sep-12 and SEP-30. Plot directories contain a main project directory (named using the starting date of data collection, e.g. 2017-03-14.001.riproject) with nine data subdirectories, a tile_index.dat file as shown in the attached ForestScan_example_directory_structure.pdf document.\r\n\r\nThe raw project subdirectory contains all registered scans for each FBRMS 1ha plot. The matrix project subdirectory contains each scan’s Sensor's Orientation and Position (SOP) matrix.\r\nIn order to estimate woody volume and above ground biomass (AGB) for each plot, the TLS2trees processing pipeline was used. TLS2trees is an automated processing pipeline and set of Python command line tools that segments individual trees from plot level point clouds. It consists of existing and new methods and is specifically designed to be horizontally scalable. The TLS2trees pipeline includes three preparatory data steps followed by two segmentation steps: semantic & instance segmentation. Quantitative Structure Modelling (QSM) is then used to estimate morphological and topological tree traits via a four-step process: generate TreeQSM inputs, run TreeQSM, generate optQSM commands and run optQSM. Two final processing steps generated 1) a tree attributes .csv file and 2) tree figures of individually segmented trees arranged by tree DBH size. The complete set of TLS2trees processing files is available for each of the three ForestScan FBRMS plots in French Guiana, the step-by-step processing summary below provides details for these files.\r\n\r\nThe first of three preparatory data steps segmented the 100m x 100m plot point clouds into 10m x 10m data tiles and converted each tile from the RIEGL proprietary file format .rxp to .ply format. The resulting <0-NNN>.ply files (NNN is the assigned tile ID number) + a subdirectory named bounding_box containing bounding geometry files + a tile_index.dat file were saved into the rxp2ply project subdirectory. The second preparatory data step down-sampled the data tiles with results saved as tileID.downsample.ply files in the downsample project subdirectory, e.g. 000.downsample.ply. The third preparatory data step generated a tile_index.dat file saved under the project directory. Next, a semantic segmentation step classified the tiled data into leaf, wood, ground or coarse woody debris. For each data tile, three different files tileID.downsample.dem.csv, tileID.downsample.params.pickle, tileID.downsample.segmented.ply + a temporary subdirectory tileID.downsample.tmp were generated and saved in the fsct project subdirectory. Instance segmentation was then used to automatically segment the semantically classified tiled data into individual tree files. Two automatically segmented versions of each tree (with and without canopy leaves) were generated and saved in subdirectories arranged by increasing DBH size (i.e. subdirectory 0.0 contains the smallest trees in the plot) under the clouds project subdirectory, e.g. clouds/N.N/tileID_TreeID.leafon.ply and clouds/N.N/tileID_TreeID.leafoff.ply.\r\n\r\nQuantitative Structure Modelling (QSM) was then used to enclose the wood-only file version (i.e. tileID_TreeID.leafoff.ply) of each individually segmented tree in a set of geometric primitives i.e. cylinders. This allowed for the estimation of morphological and topological traits such as volume, length and surface area metrics for each successfully modelled tree. The first QSM processing step generated 125 modelling input files representing 125 different parameter combinations for each individually segmented tree. These files were saved as tileID_TreeID_NNN.m (NNN ranges from 0 to 124) in the models/intermediate/inputs project subdirectory, e.g. models/intermediate/inputs/tileID_TreeID/tileID_TreeID_<0-124>.m. Next, up to 625 different model candidates for each segmented tree were generated from the modelling input files and saved as tileID_TreeID-NNN.mat files (NNN ranges from 0 to 624) in the models/intermediate/results project subdirectory, e.g. models/intermediate/results/tileID_TreeID/tileID_TreeID-NNN.mat. QSM command files to find the optimal QSM for each segmented tree were then generated and saved as tileID_TreeID_opt.m files in the models/optqsm/commands project subdirectory, e.g. models/optqsm/commands/tileID_TreeID_opt.m. During the final QSM step, an optimal model was found for each successfully modelled segmented tree and saved as a tileID_TreeID.mat file in the models/optqsm/results project subdirectory, e.g. models/optqsm/results/tileID_TreeID.mat.\r\n\r\nAfter QSM modelling, a report file named projectID.tree-attributes.csv was generated for each plot and saved in the attributes project subdirectory, e.g. attributes/projectID.tree-attributes.csv. This report contains estimates of morphological and topological traits for all modelled trees. Due to the >300m scanning range of the Riegl VZ-400i scanner, reports contain trees located both inside and outside the plots which can be filtered using the in_plot variable. Each row in these reports represents a tree with both successfully and unsuccessfully (empty attribute variables) modelled trees included in the reports. \r\n\r\nThe last processing step generated tree figures arranged by descending tree DBH size and saved as projectID.nn.png files (nn refers to the order in which the figures were generated with figure projectID.0.png containing the largest trees) in the figures project subdirectory, e.g. figures/SEP_12.0.png.",
            "keywords": "Terrestrial LiDAR Scanning (TLS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, TLS2trees processing pipeline",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43417,
            "uuid": "3cd640c8c6504e93a8e8b200e0663182",
            "title": "Computation for Gridded actual groundwater, surface water and tidal water abstraction, discharge and Hands-off Flow datasets for England (1999 to 2014)",
            "abstract": "This computation aligned the abstraction and discharge datasets sourced from the Environment Agency onto a 1 km x 1 km grid. \r\nSpecifically, monthly groundwater, surface water and tidal water abstraction data were obtained from 1999 to 2014, and annual discharges and surface water Hands-off Flow (HoF) conditions were obtained from the EA’s Water Resources Geographic Information System (WRGIS 2017 and 2022 versions respectively). Due to national security data restrictions regarding the location of public water supply abstractions, publication of the dataset is limited to a 1 km x 1 km resolution. Information at a higher resolution has been removed or converted to a 1 km x 1 km resolution as appropriate, and any personal or identifying data has been removed.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43420,
            "uuid": "032147637bad4ee9b3d0488a58daaeae",
            "title": "Computation to derive surface turbulence from FAAM aircraft measurements",
            "abstract": "Computation to derive surface turbulence from FAAM aircraft measurements",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43446,
            "uuid": "29ae1a98a55c44f6bf0716fda2db76fd",
            "title": "MRI-ESM2.0",
            "abstract": "The MRI-ESM2.0 climate model, released in 2017, includes the following components:\r\naerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4.\r\n\r\nThe model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43448,
            "uuid": "3bda2748bd714a239d4974648c015b97",
            "title": "CNRM-ESM2-1",
            "abstract": "The CNRM-ESM2-1 climate model, released in 2017, includes the following components:\r\naerosol: TACTIC_v2, atmos: Arpege 6.3 (T127; Gaussian Reduced with 24572 grid points in total distributed over 128 latitude circles (with 256 grid points per latitude circle between 30degN and 30degS reducing to 20 grid points per latitude circle at 88.9degN and 88.9degS); 91 levels; top level 78.4 km), atmosChem: REPROBUS-C_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA1, tripolar primarily 1deg; 362 x 294 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: Pisces 2.s, seaIce: Gelato 6.1.\r\n\r\n The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43450,
            "uuid": "e663d3500a2a402a88e8a73947aad1f5",
            "title": "CanESM5-1",
            "abstract": "The CanESM5.1 climate model, released in 2022, includes the following components:\r\naerosol: interactive, atmos: CanAM5.1 (T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa), atmosChem: specified oxidants for aerosols, land: CLASS3.6/CTEM1.2, landIce: specified ice sheets, ocean: NEMO3.4.1 (ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m), ocnBgchem: Canadian Model of Ocean Carbon (CMOC); NPZD ecosystem with OMIP prescribed carbonate chemistry, seaIce: LIM2. \r\n\r\nFor CMIP6, the model was run by the Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8P 5C2, Canada (CCCma) in native nominal resolutions: aerosol: 500 km, atmos: 500 km, atmosChem: 500 km, land: 500 km, landIce: 500 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. For RAMIP, the model was run by the University of Toronto on the Niagara computer of SciNet using the same nominal resolutions.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43452,
            "uuid": "7931a6294a684b7083e4ffb807532b9c",
            "title": "GISS-E2-1-G",
            "abstract": "The GISS-E2.1G climate model, released in 2019, includes the following components:\r\naerosol: Varies with physics-version (p==1 none, p==3 OMA, p==4 TOMAS, p==5 MATRIX), atmos: GISS-E2.1 (2.5x2 degree; 144 x 90 longitude/latitude; 40 levels; top level 0.1 hPa), atmosChem: Varies with physics-version (p==1 Non-interactive, p>1 GPUCCINI), land: GISS LSM, ocean: GISS Ocean (GO1, 1 degree; 360 x 180 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: GISS SI.\r\n\r\nThe model was run by the Goddard Institute for Space Studies, New York, NY 10025, USA (NASA-GISS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, seaIce: 250 km.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43454,
            "uuid": "0f7578a9d55f42eead6e66014f8e4cd4",
            "title": "MIROC6",
            "abstract": "The MIROC6 climate model, released in 2017, includes the following components: \r\naerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. \r\n\r\nFor CMIP6, the model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km. For RAMIP, the model was run by JAMSTEC.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43456,
            "uuid": "cf7f9d78c4d7425f9b0b45f44f63ec19",
            "title": "UKESM1-0-LL",
            "abstract": "The UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components:\r\naerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). \r\n\r\nThe model was run by the National Centre for Atmospheric Science at the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43461,
            "uuid": "14416e8347a64c31bb0b2fc744a21331",
            "title": "CESM2",
            "abstract": "The CESM2 climate model, released in 2018, includes the following components:\r\naerosol: MAM4 (same grid as atmos), atmos: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb), atmosChem: MAM4 (same grid as atmos), land: CLM5 (same grid as atmos), landIce: CISM2.1, ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m), ocnBgchem: MARBL (same grid as ocean), seaIce: CICE5.1 (same grid as ocean). \r\n\r\nFor CESM2-LE, the model was run by the National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA (NCAR) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 5 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. For RAMIP, the model was run by the University of California Riverside at NCAR using the cheyenne supercomputer using the same native nominal resolutions.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43557,
            "uuid": "91b3899a155146a8be7bc528cfe9bc1e",
            "title": "TLS2trees processing pipeline for ForestScan 1ha plots FG5c1, FG6c2 and FG8c4 in FBRMS-01: Paracou, French Guiana",
            "abstract": "Data for each of the four Gabon FBRMS plots is found within plot directories: OKO-01; OKO-02; OKO-03 and LPG-01. Plot directories contain a main project directory (named using the starting date of data collection and the plot ID, e.g. 2022-06-24_LPG-01_PROJ) with nine data sub-directories, a tile_index.dat file and a 2022-06-24_LPG-01.kmz file as shown in the archived ForestScan_example_data_directory_structure.pdf document.\r\n\r\nThe raw project sub-directory contains all registered scans for each FBRMS 1ha plot. The matrix project sub-directory contains each scan’s Sensor's Orientation and Position (SOP) matrix with the GNSS coordinates (geographical coordinate system: WGS84 Cartesian) for all scans saved separately and made available via a .kmz file under the project main directory, e.g. 2022-06-24_LPG-01.kmz.\r\n\r\nIn order to estimate woody volume and above ground biomass (AGB) for each plot, the TLS2trees processing pipeline was used. TLS2trees is an automated processing pipeline and set of Python command line tools that segments individual trees from plot level point clouds. It consists of existing and new methods and is specifically designed to be horizontally scalable. The TLS2trees pipeline includes three preparatory data steps followed by two segmentation steps: semantic & instance segmentation. Quantitative Structure Modelling (QSM) is then used to estimate morphological and topological tree traits via a four-step process: generate TreeQSM inputs, run TreeQSM, generate optQSM commands and run optQSM. Two final processing steps generated 1) a tree attributes .csv file and 2) tree figures of individually segmented trees arranged by tree DBH size. The complete set of TLS2trees processing files is available for each of the four ForestScan FBRMS plots in Gabon, the step-by-step processing summary below provides details for these files.\r\n\r\nThe first of three preparatory data steps segmented the 100m x 100m plot point clouds into 10m x 10m data tiles and converted each tile from the RIEGL proprietary file format .rxp to .ply format. The resulting <0-NNN>.ply files (NNN is the assigned tile ID number) + a subdirectory named bounding_box containing bounding geometry files + a tile_index.dat file were saved into the rxp2ply project subdirectory. The second preparatory data step down-sampled the data tiles with results saved as tileID.downsample.ply files in the downsample project subdirectory, e.g. 000.downsample.ply. The third preparatory data step generated a tile_index.dat file saved under the project directory. Next, a semantic segmentation step classified the tiled data into leaf, wood, ground or coarse woody debris. For each data tile, three different files tileID.downsample.dem.csv, tileID.downsample.params.pickle, tileID.downsample.segmented.ply + a temporary subdirectory tileID.downsample.tmp were generated and saved in the fsct project subdirectory. Instance segmentation was then used to automatically segment the semantically classified tiled data into individual tree files. Two automatically segmented versions of each tree (with and without canopy leaves) were generated and saved in subdirectories arranged by increasing DBH size (i.e. subdirectory 0.0 contains the smallest trees in the plot) under the clouds project subdirectory, e.g. clouds/N.N/tileID_TreeID.leafon.ply and clouds/N.N/tileID_TreeID.leafoff.ply.\r\n\r\nQuantitative Structure Modelling (QSM) was then used to enclose the wood-only file version (i.e. tileID_TreeID.leafoff.ply) of each individually segmented tree in a set of geometric primitives i.e. cylinders. This allowed for the estimation of morphological and topological traits such as volume, length and surface area metrics for each successfully modelled tree. The first QSM processing step generated 125 modelling input files representing 125 different parameter combinations for each individually segmented tree. These files were saved as tileID_TreeID_NNN.m (NNN ranges from 0 to 124) in the models/intermediate/inputs project subdirectory, e.g. models/intermediate/inputs/tileID_TreeID/tileID_TreeID_<0-124>.m. Next, up to 625 different model candidates for each segmented tree were generated from the modelling input files and saved as tileID_TreeID-NNN.mat files (NNN ranges from 0 to 624) in the models/intermediate/results project subdirectory, e.g. models/intermediate/results/tileID_TreeID/tileID_TreeID-NNN.mat. QSM command files to find the optimal QSM for each segmented tree were then generated and saved as tileID_TreeID_opt.m files in the models/optqsm/commands project subdirectory, e.g. models/optqsm/commands/tileID_TreeID_opt.m. During the final QSM step, an optimal model was found for each successfully modelled segmented tree and saved as a tileID_TreeID.mat file in the models/optqsm/results project subdirectory, e.g. models/optqsm/results/tileID_TreeID.mat.\r\n\r\nAfter QSM modelling, a report file named projectID.tree-attributes.csv was generated for each plot and saved in the attributes project sub-directory, e.g. attributes/projectID.tree-attributes.csv. This report contains estimates of morphological and topological traits for all modelled trees. Due to the >300m scanning range of the Riegl VZ-400i scanner, reports contain trees located both inside and outside the plots which can be filtered using the in_plot variable. Each row in these reports represents a tree with both successfully and unsuccessfully (empty attribute variables) modelled trees included in the reports.\r\n\r\nThe last processing step generated tree figures arranged by descending tree DBH size and saved as projectID.nn.png files (nn refers to the order in which the figures were generated with figure projectID.0.png containing the largest trees) in the figures project sub-directory, e.g. figures/lpg_01.0.png.",
            "keywords": "Terrestrial LiDAR Scanning (TLS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, TLS2trees processing pipeline",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43564,
            "uuid": "af3b26877032453d9f95ca9bb842f941",
            "title": "TLS2trees processing pipeline for FBRMS-02: Lopé, Gabon 1ha plots",
            "abstract": "Data for each of the four Gabon FBRMS plots is found within plot directories: OKO-01; OKO-02; OKO-03 and LPG-01. Plot directories contain a main project directory (named using the starting date of data collection and the plot ID, e.g. 2022-06-04_OKO-01.PROJ) with nine data subdirectories, a tile_index.dat file and a 2022-06-04_OKO-01.kmz file as shown in the attached ForestScan_example_data_directory_structure.pdf document.\r\n\r\nThe raw project subdirectory contains all registered scans for each FBRMS 1ha plot. The matrix project subdirectory contains each scan’s Sensor's Orientation and Position (SOP) matrix with the GNSS coordinates (geographical coordinate system: WGS84 Cartesian) for all scans saved separately and made available via a .kmz file under the project main directory, e.g. 2022-06-04_OKO-01.kmz.\r\n\r\nIn order to estimate woody volume and above ground biomass (AGB) for each plot, the TLS2trees processing pipeline was used. TLS2trees is an automated processing pipeline and set of Python command line tools that segments individual trees from plot level point clouds. It consists of existing and new methods and is specifically designed to be horizontally scalable. The TLS2trees pipeline includes three preparatory data steps followed by two segmentation steps: semantic & instance segmentation. Quantitative Structure Modelling (QSM) is then used to estimate morphological and topological tree traits via a four-step process: generate TreeQSM inputs, run TreeQSM, generate optQSM commands and run optQSM. Two final processing steps generated 1) a tree attributes .csv file and 2) tree figures of individually segmented trees arranged by tree DBH size. The complete set of TLS2trees processing files is available for each of the four ForestScan FBRMS plots in Gabon, the step-by-step processing summary below provides details for these files.\r\n\r\nThe first of three preparatory data steps segmented the 100m x 100m plot point clouds into 10m x 10m data tiles and converted each tile from the RIEGL proprietary file format .rxp to .ply format. The resulting <0-NNN>.ply files (NNN is the assigned tile ID number) + a subdirectory named bounding_box containing bounding geometry files + a tile_index.dat file were saved into the rxp2ply project subdirectory. The second preparatory data step down-sampled the data tiles with results saved as tileID.downsample.ply files in the downsample project subdirectory, e.g. 000.downsample.ply. The third preparatory data step generated a tile_index.dat file saved under the project directory. Next, a semantic segmentation step classified the tiled data into leaf, wood, ground or coarse woody debris. For each data tile, three different files tileID.downsample.dem.csv, tileID.downsample.params.pickle, tileID.downsample.segmented.ply + a temporary subdirectory tileID.downsample.tmp were generated and saved in the fsct project subdirectory. Instance segmentation was then used to automatically segment the semantically classified tiled data into individual tree files. Two automatically segmented versions of each tree (with and without canopy leaves) were generated and saved in subdirectories arranged by increasing DBH size (i.e. subdirectory 0.0 contains the smallest trees in the plot) under the clouds project subdirectory, e.g.  clouds/N.N/tileID_TreeID.leafon.ply and clouds/N.N/tileID_TreeID.leafoff.ply.\r\n\r\nQuantitative Structure Modelling (QSM) was then used to enclose the wood-only file version (i.e. tileID_TreeID.leafoff.ply) of each individually segmented tree in a set of geometric primitives i.e. cylinders. This allowed for the estimation of morphological and topological traits such as volume, length and surface area metrics for each successfully modelled tree. The first QSM processing step generated 125 modelling input files representing 125 different parameter combinations for each individually segmented tree. These files were saved as tileID_TreeID_NNN.m (NNN ranges from 0 to 124) in the models/intermediate/inputs project subdirectory, e.g. models/intermediate/inputs/tileID_TreeID/tileID_TreeID_<0-124>.m. Next, up to 625 different model candidates for each segmented tree were generated from the modelling input files and saved as tileID_TreeID-NNN.mat files (NNN ranges from 0 to 624) in the models/intermediate/results project subdirectory, e.g. models/intermediate/results/tileID_TreeID/tileID_TreeID-NNN.mat. QSM command files to find the optimal QSM for each segmented tree were then generated and saved as tileID_TreeID_opt.m files in the models/optqsm/commands project subdirectory, e.g. models/optqsm/commands/tileID_TreeID_opt.m. During the final QSM step, an optimal model was found for each successfully modelled segmented tree and saved as a tileID_TreeID.mat file in the models/optqsm/results project subdirectory, e.g. models/optqsm/results/tileID_TreeID.mat.\r\n\r\nAfter QSM modelling, a report file named projectID.tree-attributes.csv was generated for each plot and saved in the attributes project subdirectory, e.g. attributes/projectID.tree-attributes.csv. This report contains estimates of morphological and topological traits for all modelled trees. Due to the >300m scanning range of the Riegl VZ-400i scanner, reports contain trees located both inside and outside the plots which can be filtered using the in_plot variable. Each row in these reports represents a tree with both successfully and unsuccessfully (empty attribute variables) modelled trees included in the reports. \r\n\r\nThe last processing step generated tree figures arranged by descending tree DBH size and saved as projectID.nn.png files (nn refers to the order in which the figures were generated with figure projectID.0.png containing the largest trees) in the figures project subdirectory, e.g. figures/oko_01.0.png.",
            "keywords": "Terrestrial LiDAR Scanning (TLS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, TLS2trees processing pipeline",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43580,
            "uuid": "1cef7aff4035496cbde00291b498c0fd",
            "title": "Derivation of the EOCIS: University of Leicester GOSAT and GOSAT-2 Proxy XCH4 v9.0_eocis data",
            "abstract": "The GOSAT and GOSAT-2 EOCIS datasets were derived using the UoL-FP retrieval algorithm using a proxy retrieval approach.  See the linked documentation for further information",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43585,
            "uuid": "c8effc64e12a4f51b8e303706540e4d0",
            "title": "ESA SAFE processor for generating AATSR multimission data into AT_1_RBT in SAFE format",
            "abstract": "The datasets have been fully reprocessed into a new product which aligns them with the format adopted by the instruments’ successors,  Sentinel-3 SLSTR, and includes the following updates from the former L1B datasets [AT1_TOA_1P / AT2_TOA_1P] generated by the ERS ATSR 3rd Reprocessing in 2013:\r\n\r\nImproved and extended Level 1B datasets, beginning with recovery of Level 0 data, via the reprocessing of Level 0 data to ungridded brightness temperature (UBT) scenes to Level 1A to retain extra instrument information.\r\n\r\nLevel 1B products in a Sentinel-SAFE/NetCDF format: a product folder containing NetCDF files and an XML manifest file, holding extensive metadata. This means new information is now available, such as per-pixel uncertainty estimates, associated meteorological data from ECMWF ERA-Interim, instrument scan and pixel quality information, and channel-specific exception and flag values.\r\n\r\nGeolocation improvement via orthogeolocation to a Digital Elevation Model.\r\n\r\nImproved surface classification via use of the Sentinel-3 Land Water Masks.\r\n\r\nImproved cloud information via a latitude correction within cloud look-up tables.",
            "keywords": "SAFE ATSR1 ATSR2 AATSR",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43593,
            "uuid": "42d1afdbee6c4a04bf96c40b690e18ce",
            "title": "Derivation of the EOCIS: Geospatial Information Files V1.1",
            "abstract": "For more information on the derivation of the Earth Observation Climate Information Service (EOCIS) Geospatial Information files see  https://eocis.org/portal/documents/IEA-EOCISAuxfiles-TR_Additional_v8.pdf.\r\n\r\nThese datasets have been created following the specific format and nature of the EOCIS Climate information at Hi-res for the UK (CHUK) grid.   The CHUK grid consists of a 100m x 100m grid over the whole of the British Isles, an area approximately 1,000km x 1,500km.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43612,
            "uuid": "bcc2f83d1f144316ac299399f0a8adca",
            "title": "UM-CASIM for DCMEX",
            "abstract": "DCMEX-specific setup of UM-CASIM with bespoke microphysical schemes",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43646,
            "uuid": "8129dc20c94b4b53a3802ff83b2667df",
            "title": "TLS2trees processing pipeline for FBRMS-01: Paracou, French Guiana 1ha plots",
            "abstract": "Data for each of the three French Guiana FBRMS plots is found within plot directories: FG5c1; FG6c2 and FG8c4. Plot directories contain a main project directory (named using the starting date of data collection and the plot ID, e.g. 2022-06-04_FG5c1_PROJ) with nine data subdirectories, a tile_index.dat file and a 2022-06-04_FG5c1.kmz file as shown in the attached ForestScan_example_directory_structure.pdf document.  \r\n\r\nThe raw project subdirectory contains all registered scans for each FBRMS 1ha plot. The matrix project subdirectory contains each scan’s Sensor's Orientation and Position (SOP) matrix with the GNSS coordinates (geographical coordinate system: WGS84 Cartesian) for all scans saved separately and made available via a .kmz file under the project main directory, e.g. 2022-06-04_FG5c1.kmz.\r\n\r\nIn order to estimate woody volume and above ground biomass (AGB) for each plot, the TLS2trees processing pipeline was used. TLS2trees is an automated processing pipeline and set of Python command line tools that segments individual trees from plot level point clouds. It consists of existing and new methods and is specifically designed to be horizontally scalable. The TLS2trees pipeline includes three preparatory data steps followed by two segmentation steps: semantic & instance segmentation. Quantitative Structure Modelling (QSM) is then used to estimate morphological and topological tree traits via a four-step process: generate TreeQSM inputs, run TreeQSM, generate optQSM commands and run optQSM. Two final processing steps generated 1) a tree attributes .csv file and 2) tree figures of individually segmented trees arranged by tree DBH size. The complete set of TLS2trees processing files is available for each of the three ForestScan FBRMS plots in French Guiana, the step-by-step processing summary below provides details for these files. \r\n\r\nThe first of three preparatory data steps segmented the 100m x 100m plot point clouds into 10m x 10m data tiles and converted each tile from the RIEGL proprietary file format .rxp to .ply format. The resulting <0-NNN>.ply files (NNN is the assigned tile ID number) + a subdirectory named bounding_box containing bounding geometry files + a tile_index.dat file were saved into the rxp2ply project subdirectory. The second preparatory data step down-sampled the data tiles with results saved as tileID.downsample.ply files in the downsample project subdirectory, e.g. 000.downsample.ply. The third preparatory data step generated a tile_index.dat file saved under the project directory. Next, a semantic segmentation step classified the tiled data into leaf, wood, ground or coarse woody debris. For each data tile, three different files tileID.downsample.dem.csv, tileID.downsample.params.pickle, tileID.downsample.segmented.ply + a temporary subdirectory tileID.downsample.tmp were generated and saved in the fsct project subdirectory. Instance segmentation was then used to automatically segment the semantically classified tiled data into individual tree files. Two automatically segmented versions of each tree (with and without canopy leaves) were generated and saved in subdirectories arranged by increasing DBH size (i.e. subdirectory 0.0 contains the smallest trees in the plot) under the clouds project subdirectory, e.g. clouds/N.N/tileID_TreeID.leafon.ply and clouds/N.N/tileID_TreeID.leafoff.ply. \r\n\r\nQuantitative Structure Modelling (QSM) was then used to enclose the wood-only file version (i.e. tileID_TreeID.leafoff.ply) of each individually segmented tree in a set of geometric primitives i.e. cylinders. This allowed for the estimation of morphological and topological traits such as volume, length and surface area metrics for each successfully modelled tree. The first QSM processing step generated 125 modelling input files representing 125 different parameter combinations for each individually segmented tree. These files were saved as tileID_TreeID_NNN.m (NNN ranges from 0 to 124) in the models/intermediate/inputs project subdirectory, e.g. models/intermediate/inputs/tileID_TreeID/tileID_TreeID_<0-124>.m. Next, up to 625 different model candidates for each segmented tree were generated from the modelling input files and saved as tileID_TreeID-NNN.mat files (NNN ranges from 0 to 624) in the models/intermediate/results project subdirectory, e.g. models/intermediate/results/tileID_TreeID/tileID_TreeID-NNN.mat. QSM command files to find the optimal QSM for each segmented tree were then generated and saved as tileID_TreeID_opt.m files in the models/optqsm/commands project subdirectory, e.g. models/optqsm/commands/tileID_TreeID_opt.m. During the final QSM step, an optimal model was found for each successfully modelled segmented tree and saved as a tileID_TreeID.mat file in the models/optqsm/results project subdirectory, e.g. models/optqsm/results/tileID_TreeID.mat. \r\n\r\nAfter QSM modelling, a report file named projectID.tree-attributes.csv was generated for each plot and saved in the attributes project subdirectory, e.g. attributes/projectID.tree-attributes.csv. This report contains estimates of morphological and topological traits for all modelled trees. Due to the >300m scanning range of the Riegl VZ-400i scanner, reports contain trees located both inside and outside the plots which can be filtered using the in_plot variable. Each row in these reports represents a tree with both successfully and unsuccessfully (empty attribute variables) modelled trees included in the reports. \r\n\r\nThe last processing step generated tree figures arranged by descending tree DBH size and saved as projectID.nn.png files (nn refers to the order in which the figures were generated with figure projectID.0.png containing the largest trees) in the figures project subdirectory, e.g. figures/FG5c1.0.png.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43659,
            "uuid": "5d8107778649409f80143ca6db5432bf",
            "title": "Computation for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from VIIRS (Visible Infrared Imaging Radiometer Suite) on Suomi National Polar-orbiting Partnership (SNPP), level 3 collated (L3C) global product (2012-2024), version 1.00",
            "abstract": "For information on the derivation of this dataset see the associated documentation.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43663,
            "uuid": "cc471b458b194741b400947b038ea9c2",
            "title": "Computation for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from VIIRS (Visible Infrared Imaging Radiometer Suite) on NOAA-20  (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product (2018-2024), version 1.00",
            "abstract": "For information on the derivation of this dataset see the associated documentation.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43700,
            "uuid": "844efe6848c44ae5a6dc5eb1d655e9c0",
            "title": "University of Leicester IASI retrieval scheme",
            "abstract": "This computation involved: ULIRS deployed on JASMIN processing cluster. The ULIRS is an optimal estimation-based retrieval scheme, which utilises equidistant pressure levels and a floating pressure grid based on topography. ULIRS was developed at the University of Leicester through the National Centre for Earth Observation (NCEO).\r\n\r\nFor more information see the associated documentation.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43713,
            "uuid": "d170b6e232f74427bd820c1c2fee87aa",
            "title": "ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021",
            "abstract": "Once co-registered using RiScanPro software, individual scans were exported in las extrabyte format (including deviation) using LidarFomartConverter v.1.2.(AMAP code based on RivLib). Reflectance range was set to -30dB to +5dB and stored in the Intensity field as a long integer. Echoes outside this reflectance range were discarded. Coordinate precisions were set to 0.001 m. The full point cloud (all 249 scans) was then cropped to 1.4 ha plot (+10m buffer around 100x100m plot), and tiled per 20 x 20m (no buffer). Cropping and tiling were done with LAStools software. Scan position number was stored as flight line to allow selection of scans if needed. In particular, distant scans which contribute little more than noise could be deleted. LiDAR data were acquired without the “reflectance optimization filter”. In order to keep only returns with reflectance above -20dB (equivalent to setting reflectance optimization filter) all returns with Intensity below 18724 were dropped.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43749,
            "uuid": "1f2d8d035fde4c24b5aec8b1a093ec66",
            "title": "Met Office Cardington: Land surface model (LSM) meteorological driving data derivation",
            "abstract": "Joint UK Land Environment Simulator (JULES) is a community-developed land surface model (LSM) as used by the Met Office Unified Model for both weather and climate land surface representation.\r\n \r\nWe have compiled meteorological forcing datasets with a 30-min time step for the Cardington site to drive the JULES standalone LSM as a single point for the same period as the core archived files. JULES requires the following seven atmospheric input variables at every time step for it to able to run using prescribed meteorology: downwelling shortwave irradiance, downwelling longwave irradiance, rainfall, air temperature, mean horizontal wind, surface barometric pressure, and specific humidity. The drive dataset comprises a NetCDF file for each of the four drive heights (2, 10, 25 and 50 m), such that temperature, wind and humidity drive variables are taken from the different mast heights, and the pressure, radiation and rainfall remaining unchanged as they were only available from fixed levels i.e. pressure at 1.2 m, downwelling radiation at 4 m, upwelling radiation at 2 m, and rainfall at the surface. Due to the instrumentation deployed, the 2 m level drive data only available for 2012–2024. Only whole years are supplied in the forcing dataset. Although the NetCDF forcing dataset has been configured to run with JULES, it should be straightforward to apply the data within other LSMs that can be run offline and forced by prescribed meteorology for a single point\r\n \r\nThe datasets included here were all tested with version 7.4 of standalone JULES. An example use of running JULES using Cardington field data can be found in Osborne and Weedon (2021), wherein can be found soil and canopy properties for configuring JULES based on field data as an alternative to using operational model ancillary data.\r\n \r\nJULES is available to anyone for non-commercial use, free of charge. However, please note the JULES licence conditions, the JULES Fair Use and Publication Policy and the MOSRS user terms and conditions. The JULES source code is available from the Met Office Science Repository Service to authenticated users only.\r\n \r\nThe JULES forcing datasets included here are gap-filled where data are either missing or deemed unreliable to ensure that every time step is populated. Short gaps (≤3 h) were filled via linear interpolation; longer gaps were filled with the long-term (20-yr) mean values calculated from available measurements for each time step. The latter method of gap-filling ensures the preservation of daily and annual cycles. Each driving data variable has a simple flag to indicate whether gap filling has been applied, or not, at each time step. The driving dataset could potentially be used to apply an optional spin-up to the simulations, for example by repeatedly driving the LSM with the first two years of data so that the soil temperature and soil moisture reach stability.",
            "keywords": "land surface model, LSM, JULES, driving data, downwelling shortwave irradiance, downwelling longwave irradiance, rainfall, air temperature, mean horizontal wind, surface barometric pressure, specific humidity, Cardington, Met Office",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43862,
            "uuid": "301bd8b1a6dc41efb86385754a515236",
            "title": "Non-linear AutoRegressive Moving Average with eXogenous inputs (NARMAX) systems identification (an interpretable form of machine learning) approach",
            "abstract": "Using a novel Non-linear AutoRegressive Moving Average with eXogenous inputs (NARMAX) systems identification (an interpretable form of machine learning) approach, that identifies and models linear and non-linear dynamic relationships between a range of variables, this project seeks to extend skilful seasonal forecasting to seasons beyond winter, identify factors that contribute skill to the forecast, develop regional seasonal forecasts for Northwest Europe and assess the benefits of skilful probabilistic seasonal forecasts to potential users such as the agri-food industry.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43872,
            "uuid": "9fdd3f704cfa4e0d8575decd494fb038",
            "title": "Static adjustment process applied to Hadley Centre Central England Temperature Version 2 Data",
            "abstract": "A series of adjustments and homogenisations are applied to station data to correct for urbanisation and alignment to the station selection described in Manley 1974. See Legg et al. (2025) for a full description of these processes and adjustments applied. References are available as online references on this record.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43873,
            "uuid": "dcae52d88dea47e0947e2eed2e08bd31",
            "title": "Met Office Hadley Centre Daily Central England Temperature series v2 Data Processing Procedure",
            "abstract": "The Met Office Hadley Centre Central England Temperature series is computed through a series of processes applied to temperature observations from a range of sites around Central England.\r\n\r\nFor a description of this process please see Packman (2025, https://doi.org/10.5281/zenodo.15131212) linked to on this record.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43887,
            "uuid": "81e8949519894720abd964b782e13275",
            "title": "Machine-Learning-Based Prediction and Aggregation of Air Pollution Estimates into \"Typical Day\" Profiles",
            "abstract": "The dataset was created using a supervised machine-learning pipeline. The pipeline generates air pollution concentration predictions across a 1 km^2^ grid over England, subsequently aggregated to form representative \"typical\" hourly cycles for each day of the week and month. This approach simplifies downstream use cases such as policy assessment and public communication. The underlying methodology is implemented in the accompanying open-source Python package Environmental Insights, available at https://github.com/berrli/Environmental-Insights",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43894,
            "uuid": "cb2e1150ed9a4d3e9f4a3856b1f046c2",
            "title": "The ESA Biomass Climate Change Initiative above ground biomass retrieval algorithm, v6.0",
            "abstract": "For information on the derivation of the Biomass CCI data, please see the ATBD (Algorithm Theoretical Baseline Document).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43927,
            "uuid": "d5a40c7e64a24b52ad7a8b77bb72f020",
            "title": "Derivation of the ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v3.0",
            "abstract": "Estimates of mass change have been derived based on inversion methods developped at DTU Space.\r\n\r\nThe underlying L2 monthly gravity field solutions used in the derivation were generated by the Center for Space Research (University of Texas at Austin) primarily using K-Band ranging, accelerometer and GPS observations acquired by the GRACE and GRACE-FO twin-satellite missions.\r\n\r\n For more information see the linked documentation.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43960,
            "uuid": "8151b22e8b794c118c73cdd6d85fbb1b",
            "title": "University of Arizona - Department of Geosciences (UA) running: experiment 1pctCO2 using the MCM-UA-1-0 model.",
            "abstract": "University of Arizona - Department of Geosciences (UA) running the \"1 percent per year increase in CO2\" (1pctCO2) experiment using the MCM-UA-1-0 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, UA, MCM-UA-1-0, 1pctCO2, Amon, Ofx, Omon, SImon, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43965,
            "uuid": "29e87bafd01f46208ea8a567f0efe3ae",
            "title": "Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Satellite VFM product computation",
            "abstract": "Deployed on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Satellite. Computation to create the Version 4-51 VFM product. The new version 4.51 (V4.51) of the CALIPSO lidar (CALIOP) Level 2 (L2) data products contain a number of improvements and additions over the previous version (V4.2) that was released in October 2018. A summary of the major changes addressed in this release are detailed in the data quality documentation link, as well as a section high-lighting known issues.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43986,
            "uuid": "34778fab34674713af94c5f2d02ddfdf",
            "title": "University of Arizona - Department of Geosciences (UA) running: experiment abrupt-4xCO2 using the MCM-UA-1-0 model.",
            "abstract": "University of Arizona - Department of Geosciences (UA) running the \"abrupt quadrupling of CO2\" (abrupt-4xCO2) experiment using the MCM-UA-1-0 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, UA, MCM-UA-1-0, abrupt-4xCO2, Amon, Ofx, Omon, SImon, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43989,
            "uuid": "3c74fd0257824514b475f473fe24e2c0",
            "title": "University of Arizona - Department of Geosciences (UA) running: experiment piControl using the MCM-UA-1-0 model.",
            "abstract": "University of Arizona - Department of Geosciences (UA) running the \"pre-industrial control\" (piControl) experiment using the MCM-UA-1-0 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, UA, MCM-UA-1-0, piControl, Amon, Lmon, Ofx, Omon, SImon, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43992,
            "uuid": "467d1f86016f4d90b9ff1e0687be4f77",
            "title": "Chinese Academy of Sciences (CAS) running: experiment amip using the FGOALS-g3 model.",
            "abstract": "Chinese Academy of Sciences (CAS) running the \"AMIP\" (amip) experiment using the FGOALS-g3 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, CAS, FGOALS-g3, amip, 3hr, 6hrPlev, Amon, LImon, Lmon, day, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 43995,
            "uuid": "b8ac9917b17e441b8e51b6cb77f0038b",
            "title": "UK Integrated Assessment Model (UKIAM)",
            "abstract": "UKIAM has been designed to study future air pollution abatement scenarios in the UK. It works at a 1 km resolution and can map how air quality might vary across the country in the future with associated assessment of health impacts and economic costs. UKIAM has previously been used by Defra for the PM2.5 target setting for 2040.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44099,
            "uuid": "62091b3c6ceb4fa5882e93184f804642",
            "title": "ECMWF IFS cycle 48r1, with adjustments to enable aerosol forcing perturbations",
            "abstract": "The experiments were all performed using the ECMWF's IFS cycle 48r1, with adjustments made to enable aerosol forcings to be perturbed.\r\nThem model uses spectral resolution of TCO199 and 137 vertical levels. The ocean model uses the Nucleus for European Modelling of the Ocean (NEMO) with a 1 degree horizontal resolution and 42 vertical levels.\r\nThe aerosol scheme includes sea salt, mineral dust, organic carbon, black carbon, sulfates, nitrates and ammonium. The radiation scheme is ecRad.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44114,
            "uuid": "c88f8e79c70f4d68b5232ab0384a81b7",
            "title": "EOCIS Ice Sheet Velocity, V1",
            "abstract": "These annual mosaics of ice velocity are generated from Sentinel-1 synthetic aperture radar (SAR) interferometric wide (IW) mode data. Measurements which are input these annual mosaics were generated using the intensity feature tracking method implemented with the GAMMA Remote Sensing software package. Georeferencing was completed within this processing chain using the NSIDC 1km Antarctic DEM (https://doi.org/10.5067/H0FQ1KL9NEKM) and the Reference Elevation Model of Antarctica version 1 (https://doi.org/10.5194/tc-13-665-2019) Annual mosaics are the mean of all available tracking results. For full details of the processing chain, please see: https://doi.org/10.1038/s41467-021-21321-1 and https://doi.org/10.1038/s41467-023-36990-3.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44165,
            "uuid": "fa98b009af3949acb8602f3a2fcc30a8",
            "title": "NorESM2-LM",
            "abstract": "The NorESM2-LM (low atmosphere-medium ocean resolution, GHG concentration driven) climate model, released in 2017, includes the following components: \r\naerosol: OsloAero, atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb), atmosChem: OsloChemSimp, land: CLM, landIce: CISM, ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC, seaIce: CICE. \r\n\r\nFor CMIP6, the model was run by the NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway (NCC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. For RAMIP, the model was run by Stockholm University on the Swedish supercomputer.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44176,
            "uuid": "83b559ed100c433184498741e7e9df64",
            "title": "Machine-Learning-Based Prediction of Air Pollution Estimates in England",
            "abstract": "The dataset was created using a supervised machine-learning pipeline. The pipeline generates air pollution concentration predictions across a 1 km^2^ grid over England.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44177,
            "uuid": "4aee97c1556a4145ac1bb33d97dee9fa",
            "title": "The SRON-RemoTeC algorithm used to generate the CO2_GO2_SRFP and CH4_GO2_SRFP (SRON Full Physics) v2.0.3 products.",
            "abstract": "The SRON-RemoTeC retrieval algorithm retrieves column-averaged methane and carbon dioxide using a 'Full Physics' retrieval technique.   \r\n\r\nDetails of the technical aspects of the retrievals can be found in the ATBD (see documentation links)",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44180,
            "uuid": "43226643eaef4460aa5076b4f6575755",
            "title": "The SRON-RemoTeC algorithm used to generate the CH4_GO2_SRPR (SRON Proxy) v2.0.3 product.",
            "abstract": "The SRON-RemoTeC retrieval algorithm retrieves column-averaged methane using a 'Proxy' retrieval technique.   \r\n\r\n\r\nDetails of the technical aspects of the retrievals can be found in the ATBD (see documentation links)",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44234,
            "uuid": "1452985364534304a927fbe84d99f1ab",
            "title": "cesm2.2.0 (WACCM-X)",
            "abstract": "The model cesm2.2.0 (SD-WACCM-X) is used a hybrid simulation coming from a scientific released version CESM2_1_3 simulation (composet FXSD with resolution f19_f19_mg16) to produce the initialisation files on 5 March 2015.  The model is nudged with MERRA2 reanalysis dataset. The simulation is from 0UTC 5 March 2015 to 0UTC 22 March 2015 from surface up to 500-700 km. Here we use CAM6 physics, MA chemistry-MAM4 aerosol scheme. It ran on ARC4 HPCx at University of Leeds. Model output is 1 hour.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44238,
            "uuid": "1b58c327cc3248908cc60c9914ab7f96",
            "title": "Computation for Gridded projections of ground and surface water abstraction and discharge for England 2020-2080",
            "abstract": "This computation applied future scaling factors (derived from various publicly available water demand projections) to baseline datasets available at: https://dx.doi.org/10.5285/18886f95ba84447f997efac96df456ad (Rameshwaran et al., 2025).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44252,
            "uuid": "ed655ef13f64459e8665436a7661dcd6",
            "title": "GloSATMAT computation",
            "abstract": "The temperature values are taken from version 3.0.0 of the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) for the period 1880-2014 and from version 3.0.2 of ICOADS thereafter. The ship data have been adjusted to reduce the effects of varying thermometer heights and the effects of artificial heating biases. The data have been adjusted from their respective recording heights to a reference height of 2m. The air temperature readings have been subjected to a quality-control procedure and values that fail these tests have been excluded. Duplicate values have also been excluded. Additional adjustments have been applied to the data during the Second World War to account for non-standard thermometer exposures on some ships.\r\n\r\nThe adjusted data have been aggregated into monthly mean values in each grid-cell; uncertainty estimates of these gridded values are also provided. The data have not been interpolated across missing grid boxes. In addition to the absolute temperature values, the gridded data also contain anomalies that are expressed with respect to 1961-90 climatological averages.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44318,
            "uuid": "9e1b8173d7f3441dadaa60d513cfe32f",
            "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v2",
            "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NDVI index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44333,
            "uuid": "bf9ac3f9051d4988a45fb7acf9aa944c",
            "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v2",
            "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NBR index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025.\r\nInformation on the software packages can be found in the details/docs tab.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44337,
            "uuid": "6c6e9a5ace66493aae24e254f1177c7d",
            "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v2",
            "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NDMI index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44341,
            "uuid": "dbdfa589d8ba4e27ab3a67d3e5043a8f",
            "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v2",
            "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NDWI index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44351,
            "uuid": "a838df50d8434452a735ae90360a9a0c",
            "title": "Global Mantle Circulation Models run on ARCHER2 - The UK's National Supercomputing System",
            "abstract": "",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44352,
            "uuid": "3f9fbc185dcc4c5ca76c41fb76ceb5a1",
            "title": "Computation for ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Retrieval of drift-aware sea-ice thickness from radar altimetry and passive microwave drift data",
            "abstract": "The method used to extract sea-ice thickness from radar altimetry data is based on the pioneering work of Peacock and Laxon, 2004; Laxon et al., 2003 for the ERS-2 mission. The method involves separating the radar echoes returning from the ice floes from those returning from the sea surface in the leads between the floes. This step of a surface-type classification is crucial and allows for a separate determination of the ice floe and sea-surface heights. The freeboard that is the elevation of the ice upper side (or ice/snow interface) above the sea level can then be computed by deducting the interpolated sea-surface height at the floe location from the height of the floe. Using the freeboard and additional snow load information, sea-ice thickness is then computed along the satellite track. To account for sea-ice motion, the along-track thickness estimates are combined with drift data from passive microwave sensors (Lavergne & Down, 2023). Individual measurement parcels are advected daily over a one-month period, resulting in drift-aware sea-ice thickness maps that reflect the evolving distribution of the ice.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44382,
            "uuid": "2680421bce5c45dd99e0ce6db049e253",
            "title": "the E3SM-Project team running: experiment hist-bgc using the E3SM-1-1-ECA model.",
            "abstract": "The the E3SM-Project team team consisted of the following agencies: Lawrence Livermore National Laboratory (NCAR LLNL), Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL) and Sandia National Laboratories (SNL).the E3SM-Project team running the \"biogeochemically-coupled version of the simulation of the recent past with CO2 concentration prescribed\" (hist-bgc) experiment using the E3SM-1-1-ECA model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, E3SM-Project, E3SM-1-1-ECA, hist-bgc, Amon, Lmon, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44385,
            "uuid": "7bb45dae98e44ddd9478b84a4f051f75",
            "title": "the E3SM-Project team running: experiment ssp585-bgc using the E3SM-1-1-ECA model.",
            "abstract": "The the E3SM-Project team team consisted of the following agencies: Lawrence Livermore National Laboratory (NCAR LLNL), Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL) and Sandia National Laboratories (SNL).the E3SM-Project team running the \"biogeochemically-coupled version of the RCP8.5 based on SSP5\" (ssp585-bgc) experiment using the E3SM-1-1-ECA model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, E3SM-Project, E3SM-1-1-ECA, ssp585-bgc, AERmon, Amon, CFmon, Lmon, Ofx, SImon, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44388,
            "uuid": "2d0ab9f78e504de99adfdfb3a6374bb7",
            "title": "the E3SM-Project team running: experiment hist-bgc using the E3SM-1-1 model.",
            "abstract": "The the E3SM-Project team team consisted of the following agencies: Lawrence Livermore National Laboratory (NCAR LLNL), Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL) and Sandia National Laboratories (SNL).the E3SM-Project team running the \"biogeochemically-coupled version of the simulation of the recent past with CO2 concentration prescribed\" (hist-bgc) experiment using the E3SM-1-1 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, E3SM-Project, E3SM-1-1, hist-bgc, Amon, Lmon, Omon, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44391,
            "uuid": "3b9f6ddd614849ca885d5ba3b0a407da",
            "title": "the E3SM-Project team running: experiment ssp585-bgc using the E3SM-1-1 model.",
            "abstract": "The the E3SM-Project team team consisted of the following agencies: Lawrence Livermore National Laboratory (NCAR LLNL), Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL) and Sandia National Laboratories (SNL).the E3SM-Project team running the \"biogeochemically-coupled version of the RCP8.5 based on SSP5\" (ssp585-bgc) experiment using the E3SM-1-1 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, E3SM-Project, E3SM-1-1, ssp585-bgc, AERmon, Amon, CFmon, Lmon, SImon, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44394,
            "uuid": "3b9043a1c2dc4bff88e6793f5cf3d83c",
            "title": "NASA Goddard Institute for Space Studies (NASA GISS) running: experiment 1pctCO2-bgc using the GISS-E2-1-G model.",
            "abstract": "NASA Goddard Institute for Space Studies (NASA GISS) running the \"biogeochemically-coupled version of 1 percent per year increasing CO2 experiment\" (1pctCO2-bgc) experiment using the GISS-E2-1-G model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NASA-GISS, GISS-E2-1-G, 1pctCO2-bgc, AERmon, Amon, CFmon, Emon, EmonZ, LImon, Lmon, Omon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44397,
            "uuid": "64b1bd4a50914ef5a72c82527d52cf7b",
            "title": "NASA Goddard Institute for Space Studies (NASA GISS) running: experiment esm-ssp585 using the GISS-E2-1-G-CC model.",
            "abstract": "NASA Goddard Institute for Space Studies (NASA GISS) running the \"emission-driven RCP8.5 based on SSP5\" (esm-ssp585) experiment using the GISS-E2-1-G-CC model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NASA-GISS, GISS-E2-1-G-CC, esm-ssp585, AERmon, Amon, CFmon, Emon, EmonZ, LImon, Lmon, Omon, SImon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44402,
            "uuid": "ab4768a38ef94aaea7f8d3d38b923f74",
            "title": "NASA Goddard Institute for Space Studies (NASA GISS) running: experiment ssp585-bgc using the GISS-E2-1-G-CC model.",
            "abstract": "NASA Goddard Institute for Space Studies (NASA GISS) running the \"biogeochemically-coupled version of the RCP8.5 based on SSP5\" (ssp585-bgc) experiment using the GISS-E2-1-G-CC model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NASA-GISS, GISS-E2-1-G-CC, ssp585-bgc, AERmon, Amon, CFmon, Emon, EmonZ, LImon, Lmon, Omon, SImon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44405,
            "uuid": "23abb0252c804fe48b25524a5d6e6f76",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment esm-1pct-brch-1000PgC using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"zero emissions simulation branched from 1% run after 1000 PgC cumulative emission\" (esm-1pct-brch-1000PgC) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, esm-1pct-brch-1000PgC, Emon, Lmon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44409,
            "uuid": "59937a91063448e39fe2002fae2dcf1b",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment esm-ssp585 using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"emission-driven RCP8.5 based on SSP5\" (esm-ssp585) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, esm-ssp585, Amon, Emon, LImon, Lmon, Omon, SImon, day",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44413,
            "uuid": "48ca8ba2f02e4ba094acd5c96816769c",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment 1pctCO2-cdr using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"1 percent per year decrease in CO2 from 4xCO2\" (1pctCO2-cdr) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, 1pctCO2-cdr, Amon, Emon, Eyr, LImon, Lmon, Omon, Oyr, SImon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44416,
            "uuid": "f4f30e72394c443bb4b20dfdd716dca8",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment esm-pi-cdr-pulse using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"pulse removal of 100 Gt carbon from pre-industrial atmosphere\" (esm-pi-cdr-pulse) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, esm-pi-cdr-pulse, Amon, Emon, Eyr, LImon, Lmon, Omon, Oyr, day",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44419,
            "uuid": "72c992ce2f8d4b5cb52f690a370a4faa",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment esm-pi-CO2pulse using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"pulse addition of 100 Gt carbon to pre-industrial atmosphere\" (esm-pi-CO2pulse) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, esm-pi-CO2pulse, Amon, Emon, Eyr, LImon, Lmon, Omon, Oyr",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44423,
            "uuid": "7454d458a8d24386ba38a369f85f2890",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment faf-heat-NA0pct using the GFDL-ESM2M model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"control plus perturbative surface flux of heat into ocean\" (faf-heat-NA0pct) experiment using the GFDL-ESM2M model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM2M, faf-heat-NA0pct, Omon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44426,
            "uuid": "6b8f8b57f01c4db88f580b399ace5639",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment faf-heat-NA50pct using the GFDL-ESM2M model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"control plus perturbative surface flux of heat into ocean\" (faf-heat-NA50pct) experiment using the GFDL-ESM2M model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM2M, faf-heat-NA50pct, Omon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44429,
            "uuid": "f73eee5e2b2e4e9d9fdd243e6502aa1e",
            "title": "National Center for Atmospheric Research (NCAR) running: experiment amip-hist using the CESM2 model.",
            "abstract": "National Center for Atmospheric Research (NCAR) running the \"AMIP-style simulation covering the period 1870-2014\" (amip-hist) experiment using the CESM2 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NCAR, CESM2, amip-hist, Amon, Emon, Lmon, day, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44432,
            "uuid": "4d0d60690a04412fbc77d84880155ffc",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment amip-hist using the GFDL-CM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"AMIP-style simulation covering the period 1870-2014\" (amip-hist) experiment using the GFDL-CM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-CM4, amip-hist, Amon, LImon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44435,
            "uuid": "af701d11f4d54bf9be8843b890cca99f",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment esm-ssp585-ssp126Lu using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"emissions-driven SSP5-8.5 with SSP1-2.6 land use\" (esm-ssp585-ssp126Lu) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, esm-ssp585-ssp126Lu, Amon, Omon, SImon, day",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44438,
            "uuid": "cd2b2e2d48e545bb8151949296eb00e0",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment hist-noLu using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"historical with no land-use change\" (hist-noLu) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, hist-noLu, Amon, Emon, Eyr, LImon, Lmon, Omon, day, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44441,
            "uuid": "2778b8f41e9b406298e42ac0a2e98747",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment land-hist using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"historical land-only\" (land-hist) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, land-hist, LImon, Lmon, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44444,
            "uuid": "9eaca2b3177748f3ae4af8205a417990",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment land-hist-altStartYear using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"historical land-only alternate start year\" (land-hist-altStartYear) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, land-hist-altStartYear, LImon, Lmon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44447,
            "uuid": "7c68620659224b0e986a500949c4f955",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment land-noLu using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"historical land-only with no land-use change\" (land-noLu) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, land-noLu, Emon, LImon, Lmon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44450,
            "uuid": "63e5b825652a402a8abaa1cba29b5ae1",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment ssp126-ssp370Lu using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"SSP1-2.6 with SSP3-7.0 land use\" (ssp126-ssp370Lu) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, ssp126-ssp370Lu, Amon, Emon, Eyr, LImon, Lmon, Omon, day",
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        },
        {
            "ob_id": 44453,
            "uuid": "c16fe27af50f4d19977c74b644c326fd",
            "title": "Geophysical Fluid Dynamics Laboratory (GFDL) running: experiment ssp370-ssp126Lu using the GFDL-ESM4 model.",
            "abstract": "Geophysical Fluid Dynamics Laboratory (GFDL) running the \"SSP3-7.0 with SSP1-2.6 land use\" (ssp370-ssp126Lu) experiment using the GFDL-ESM4 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NOAA-GFDL, GFDL-ESM4, ssp370-ssp126Lu, Amon, Emon, Eyr, Lmon, day",
            "inputDescription": null,
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            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44456,
            "uuid": "002260a5b046423f8697f9d47824bcc1",
            "title": "Commonwealth Scientific and Industrial Research Organisation (CSIRO) running: experiment past1000 using the ACCESS-ESM1-5 model.",
            "abstract": "Commonwealth Scientific and Industrial Research Organisation (CSIRO) running the \"last millennium\" (past1000) experiment using the ACCESS-ESM1-5 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, CSIRO, ACCESS-ESM1-5, past1000, Amon, Lmon, Omon, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44459,
            "uuid": "f644747c8b7d43eebcaa659f9412932b",
            "title": "Institute for Numerical Mathematics (INM) running: experiment past1000 using the INM-CM4-8 model.",
            "abstract": "Institute for Numerical Mathematics (INM) running the \"last millennium\" (past1000) experiment using the INM-CM4-8 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, INM, INM-CM4-8, past1000, AERmon, Amon, Lmon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44462,
            "uuid": "e3c6b769ca644de3914dc8be07f29ab4",
            "title": "Max Planck Institute for Meteorology (MPI-M) running: experiment past2k using the MPI-ESM1-2-LR model.",
            "abstract": "Max Planck Institute for Meteorology (MPI-M) running the \"last two millennia experiment\" (past2k) experiment using the MPI-ESM1-2-LR model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, MPI-M, MPI-ESM1-2-LR, past2k, AERmon, Amon, Lmon, Omon, SImon, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44465,
            "uuid": "0ca9cb14018d40f98cf5e90c64cb0bcf",
            "title": "Meteorological Research Institute of the Japan Meteorological Agency (MRI/JMA) running: experiment past1000 using the MRI-ESM2-0 model.",
            "abstract": "Meteorological Research Institute of the Japan Meteorological Agency (MRI/JMA) running the \"last millennium\" (past1000) experiment using the MRI-ESM2-0 model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, MRI, MRI-ESM2-0, past1000, AERmon, Amon, Omon, SImon, fx",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44470,
            "uuid": "918838d4bb7642158ed42811935fb102",
            "title": "NASA Goddard Institute for Space Studies (NASA GISS) running: experiment volc-pinatubo-full using the GISS-E2-1-G model.",
            "abstract": "NASA Goddard Institute for Space Studies (NASA GISS) running the \"Pinatubo experiment\" (volc-pinatubo-full) experiment using the GISS-E2-1-G model. See linked documentation for available information for each component.",
            "keywords": "CMIP6, WCRP, climate change, NASA-GISS, GISS-E2-1-G, volc-pinatubo-full, AERmon, Amon, CFmon, Emon, EmonZ, LImon, Lmon, Omon, SImon",
            "inputDescription": null,
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            "identifier_set": []
        },
        {
            "ob_id": 44497,
            "uuid": "b2d74129a0a94d1d9ee1bdc8fc5302dd",
            "title": "EOCIS: Soil Moisture Africa, v2.3.1",
            "abstract": "Soil moisture and related variables were derived using the JULES land surface model forced by TAMSAT v3.1 satellite rainfall estimates and NCEP meteorological variables. JULES soil hydraulic parameters were calibrated using SMAP satellite soil moisture observations.",
            "keywords": "",
            "inputDescription": null,
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        }
    ]
}