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  • The Methane and other greenhouse gases in the Artic - Measurements, process studies and Modelling (MAMM) project was a consortium as part of the NERC Artic Research Programme. This project used a range of expertise, from measurements of methane and its isotopes, and other greenhouse gases, through flux measurements to numerical analysis and modelling. Analysis of gas mixing ratios (concentrations), isotopic character, and source fluxes, both from the ground and aircraft. Both past and new measurements were modelled using a suite of techniques. Fluxes were implemented into the JULES land surface model. Atmospheric modelling, including trajectory and inverse modelling will improve understanding on the local/regional scale, placing the role of Arctic emissions in large scale global atmospheric change. The project was led by the University of Cambridge, and in association with the University of Manchester, University of East Anglia, Royal Holloway, University of London, Centre for Ecology and Hydrology and UK and International partners (Met Office, NILU, NOAA, etc).

  • This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. This version of the dataset provides data globally over land. For further information on the dataset, please see the linked documentation. Initially this dataset contains two months of data (June and August 2017), delivered as part of the GHG_cci Climate Research Data Package 6. Additional time periods will be added in the future. This data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).

  • This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA. This version of the data was produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg) and got co-funding from the Univ. Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810). When citing this dataset, please also cite the following peer-review publications: M.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017 M.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017

  • 'Are tropical uplands regional hotspots for methane and nitrous oxide?' was a NERC (Natural Environment Research Council) funded project from 2010-2015 with the following grant references NE/H007849/1, NE/H006753/1 and NE/H006583/2. This dataset collection contains in-situ ground based soil-atmosphere flux and soil condition measurements from 4 different ecosystems located in the Peruvian Andes over ~2.5 years between 2010-2013. The ecosystems included upper montane forest (Wayqecha), lower montane forest (San Pedro), premontane forest (Villa Carmen) and grassland sites. At present, data are only available for 3 ecosystems; Wayqecha, San Pedro and Villa Carman. However, the grassland dataset will follow shortly along with some model output.

  • This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. The data covers the period from March 2017 to May 2018 and is provided for TCCON (Total Carbon Column Observing Network) validation sites only. A full global dataset is in production. For further information on the dataset, please see the linked documentation. This data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).

  • The CH4_GOS_OCFP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the University of Leicester Full-Physics Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version is version 2.1 and forms part of the Climate Research Data Package 4. The University of Leicester Full-Physics Retrieval Algorithm is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and has been modified for use on GOSAT spectra. A second GOSAT CH4 product, generated using the SRFP algorithm, is also available. The XCH4 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG).

  • The CO2_GOS_SRFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) for carbon dioxide (XCO2), from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the RemoTeC Full Physics (SRFP) algorithm, v2.3.8, by the Greenhouse Gases Climate Change Initiative (GHG_cci) project. This forms part of the GHG_cci Climate Research Data Package Number 4 (CRDP#4). The RemoTeC Full Physics (SRFP) algorithm has been jointly developed at SRON and KIT. A second product, generated using the OCFP (University of Leicester Full Physics) algorithm, is also available, and is considered the GHG_cci baseline product, whilst the SRFP product forms an 'alternative' product. It is advised that users who aren't sure whether to use the baseline or alternative product use the OCFP product. For more information on the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document.

  • The CH4_SCI_IMAP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (CH4). It has been produced using data acquired from the SWIR spectra (channel 6) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT using the IMAP-DOAS algorithm. It has been generated as part of ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the dataset is v7.2 and forms part of the Climate Research Data Package 4. The IMAP-DOAS algorithm has been developed at the University of Heidelberg and SRON, and has been applied here to the SCIAMACHY data. This procedure and the algorithms validity are thoroughly described in Frankenberg et al (2011). A second product is also available which has been generated using the Weighting Function Modified DOAS (WFM-DOAS) algorithm. The data product is stored per orbit in a single NetCDF4 file. Retrieval results are provided for the individual SCIAMACHY spatial footprints, no averaging having been applied. The product file contains the key products and information relevant to using the data, such as the vertical layering and averaging kernels. For further details on the product, including the IMAP algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document.

  • The CH4_EMMA dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) for methane (XCH4). It has been produced using the ensemble median algorithm EMMA to several different versions of the Japanes Greenhouse gases Observing Satellite (GOSAT) XCH4 data, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v1.2, and forms part of the Climate Research Data Package 4. The ensemble median algorithm EMMA has been applied to level 2 data of several different retrieval products from the Japanese Greenhouse gases Observing Satellite (GOSAT) This is therefore a merged GOSAT XCH4 Level 2 product, which is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR).

  • The CO2_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. It has been produced using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. The WFM-DOAS algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. Note that this has been designated as an 'alternative' algorithm for the GHG_cci and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.