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48 results for “XCO2”
RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.0 operated at Heidelberg University
<p>The data set contains bias-corrected column averaged dry air mole fractions (XCO2) retrieved with the RemoTeCv2.4.0 full-physics algorithm (Butz et al. 2011, Guerlet et al. 2013) applied on GOSAT TANSO-FTS Level 1B (L1B) data from 2009-04-18 to 2019-06-30. The GOSAT TANSO-FTS L1B data product is produced by JAXA/NOIES/MOE and provided by ESA. The XCO2 data together with related variables are aggregated as daily files, only good quality retrievals are included.</p> <p> </p> <p>If the data is used for publications, please contact andre.butz@uni-heidelberg.de to discuss potential co-authorship and technical details.</p> <p>To cite the data in publications:</p> <p>André Butz (2019), RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.0, Institute of Environmental Physics, Heidelberg University, Heidelberg, Germany, Accessed: [Date], 10.5281/zenodo.5886662</p> <p> </p> <p>Summary:</p> <p>Shortname: REMOTEC_L2_CO2_GOSAT</p> <p>Longname: RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.0</p> <p>DOI: 10.5281/zenodo.5886662</p> <p>Version: 2.4.0</p> <p>Format: netCDF</p> <p>Spatial Coverage: -180.0,-90.0,180.0,90.0</p> <p>Temporal Coverage: 2009-04-18 to 2019-06-30</p>
RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 2009-2023, version 2.4.1 operated at Heidelberg University
<p>The data set contains bias-corrected column averaged dry air mole fractions (XCO2) retrieved with the RemoTeCv2.4.1 full-physics algorithm (Butz et al. 2011, Guerlet et al. 2013) applied on GOSAT TANSO-FTS Level 1B (L1B) data from 2009-04-18 to 2023-08-29. The GOSAT TANSO-FTS L1B data product is produced by JAXA/NOIES/MOE and provided by ESA. The XCO2 data together with related variables are aggregated as daily files, only good quality retrievals are included.</p> <p>If the data is used for publications, please contact andre.butz@uni-heidelberg.de to discuss potential co-authorship and technical details.</p> <p> </p> <p>Summary:</p> <p>Shortname: REMOTEC_L2_CO2_GOSAT</p> <p>Longname: RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.1</p> <p>DOI: 10.5281/zenodo.12773070</p> <p>Version: 2.4.1</p> <p>Format: netCDF</p> <p>Spatial Coverage: -180.0,-90.0,180.0,90.0</p> <p>Temporal Coverage: 2009-04-18 to 2023-08-29</p>
Global net ecosystem exchange of CO2 inferred from the OCO-2 XCO2 retrievals (GCAS OCO-2 inversion)
<p>Here is a dataset of global carbon flux estimates over 2015-2019 using the OCO-2 column-averaged dry-air mole fraction (XCO<sub>2</sub>) retrievals (ACOS XCO<sub>2</sub> v10) by the global carbon assimilation system (GCAS v2) (Jiang et al., 2021). </p> <p> </p> <p><strong>Citations:</strong></p> <p>Jiang, F. et al., 2021. Regional CO2 fluxes from 2010 to 2015 inferred from GOSAT XCO2 retrievals using a new version of the Global Carbon Assimilation System. Atmos. Chem. Phys., 21(3): 1963-1985.</p> <p>Jiang, F. et al., 2022. A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021), Earth Syst. Sci. Data., 14, 3013–3037.</p> <p>He, W., Jiang, F., Ju, W., et al. Improved constraints on the recent terrestrial carbon sink over China by assimilating OCO-2 XCO<sub>2 </sub>retrievals, JGR-Atmopsheres, 2022, under review.</p> <p><strong>Contacts: </strong></p> <p>Wei He (weihe@nju.edu.cn); Fei Jiang (jiangf@nju.edu.cn)</p> <p>Note: <strong>If you want to use this dataset for your researches, please contact us in advances. </strong>Thank you!</p>
Global monthly and 0.1° gap-free XCO2 dataset
<p>This dataset consists of the global continental continuous column-averaged dry-air mole fraction of carbon dioxide (XCO2, unit: parts per million i.e., ppm) covering the period from September 2014 to December 2020. </p> <p>The dataset is derived from the official OCO-2 XCO2 product and incorporates data from multiple sources, including CO2 concentration data, vegetation index data, and meteorological data. We utilized a machine learning approach to generate a monthly-scale gapless CO2 product with a spatial resolution of 0.1°, stored in GeoTiff format.</p> <p> </p> <p><strong>Please cite the following article when using the dataset:</strong></p> <p>L. Zhang, T. Li, J. Wu, H. Yang, Global estimates of gap-free and fine-scale CO2 concentrations during 2014–2020 from satellite and reanalysis data, Environment International (2023), doi: https://doi.org/10.1016/j.envint.2023.108057</p>
Global daily high spatial resolution XCO2 product during 2019-2021
<p>Validation results are in preparation. The full dataset will be uploaded after the acceptance of our paper.</p>
SRON Scientific GOSAT-2 Level 2 Data Products for XCO2 and XCH4
<div> <div>Anthropogenic emissions of greenhouse gases such as carbon dioxide and methane over the last century have led to the rapid rise of concentrations of these gases in the Earth's atmosphere, leading to continually increasing surface temperature. The ramifications of a warming climate are serious, with significant negative implications projected well into the future. Consequently, concentrations (or column-averaged dry air mole fractions) of CO2 and CH4, denoted XCO2 and XCH4 respectively, have been classed as Essential Climate Variables.</div> <div> </div> <div>The EU's Copernicus Climate Change Service (C3S) supports climate adaptation and mitigation policies regarding the impact of climate change by offering free and open access to climate data and tools. Within this context conentrations of greenhouse gases are made available by the European Union in an operational manner from a suite of different satellites. The Japanese Aerospace Exploration Agency (JAXA) satellite GOSAT-2 was launched in 2018 with the aim of measuring XCO2 and XCH4 (along with carbon monoxide, nitrous oxide and water vapour). It is the successor to GOSAT (The Greenhouse Gases Observing Satellite), the first satellite dedicated to the observation of greenhouse gases, launched in 2009.</div> <div> </div> <div>Here we provide conentrations of CO2 and CH4 in the form of level 2 data products retrieved from L1b radiance spectra from GOSAT-2, as part of project C3S2_313a. XCO2 and XCH4 are retrieved with the RemoTeC algorithm to compose the SRON GOSAT-2 scientific products. Three products are availabe, two for XCH4 and one for XCO2, and all are contained in this repository. Please see the README for more information on the prodcuts themselves. The user may choose which data to download. One zip file is available per year of data per product. </div> <div> </div> <div>For citation, users are requested to make reference to the DOI of the specific product version available at this repository, in case of any publication resulting from the use of these data.</div> <div> </div> <div>C3S is a programme of the European Union being funded by the European Union (EU) and implemented by the European Centre for MediumRange Weather Forecasts (ECMWF). This document was produced with funding by the European Union. Views and opinions expressed are those of the author(s) only and the European Commission and ECMWF cannot be held responsible for any use which may be made of the information contained therein.</div> </div>
Full-coverage daily 0.1° XCO2 in China
<p>This dataset is generated by a hybrid deep learning model.</p> <p> </p>
Spatial distributions of XCO2 seasonal cycle amplitude and phase over northern high-latitude regions
<p>This dataset is the GEOS-Chem model output used in the following publication, <br> Jacobs, N., Simpson, W. R., Graham, K. A., Holmes, C., Hase, F., Blumenstock, T., Tu, Q., Frey, M., Dubey, M. K., Parker, H. A., Wunch, D., Kivi, R., Heikkinen, P., Notholt, J., Petri, C., and Warneke, T.: Spatial distributions of <em>X<sub>CO</sub></em><sub>2</sub> seasonal cycle amplitude and phase over northern high latitude regions, <em>Atmos. Chem. Phys. Discuss.</em> [preprint], https://doi.org/10.5194/acp-2021-185, in review, 2021.</p> <p>The version 2 zip file contains three directories.<br> CO2_tracers_daily/GEOSChem.taggedCO2.YYYYMMDD.nc files: this directory contains the CO<sub>2</sub> simulation as defined by Jacobs et al. (2021). YYYY indicates year, MM indicates month, and DD indicates day.<br> CO2_tracers_monthly/GEOSChem.taggedCO2.YYYYMM.nc files: this directory contains the CO<sub>2</sub> simulation as defined by Jacobs et al. (2021). YYYY indicates year and MM indicates month.<br> TT_tracers/GEOSChem.TTtagged.YYYYMM.nc files: this directory contains the tagged tracers simulation as defined by Jacobs et al. (2021). YYYY indicates year and MM indicates month.</p> <p>The version 1 zip file contains only the monthly mean directories.</p>
Global Terrestrial Ecosystem Carbon Flux Inferred from TanSat XCO2 Retrievals
<p>TanSat is China’s first greenhouse gases observing satellite. In recent years, substantial progresses have been achieved on retrieving column-averaged CO<sub>2</sub> dry air mole fraction (XCO<sub>2</sub>). However, relatively few attempts have been made to estimate terrestrial net ecosystem exchange (NEE) using TanSat XCO<sub>2</sub> retrievals. In this study, based on the GEOS-Chem 4D-Var data assimilation system, we infer the global NEE from April 2017 to March 2018 using TanSat XCO<sub>2</sub>. Evaluations against independent CO<sub>2</sub> observations and comparison with previous estimates indicate that the inverted land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent, however, they are obviously overestimated in northern high latitudes and underestimated in tropical lands (mainly northern Africa), respectively. </p> <p>There are 4 monthly mean variables in this dataset, including prior NEE, posterior NEE, prior ocean flux, and posterior ocean flux, which are all in a spatial resolution of 5 deg by 4 deg. </p>
A ten-year (2010-2019) global terrestrial NEE inferred from the GOSAT v9 XCO2 retrievals (GCAS2021)
<p>Top-down atmospheric inversion is one of the major methods to estimate the global NEE. Here, by assimilating the GOSAT ACOS v9 XCO<sub>2</sub> product, we generate a ten-year (2010–2019) global monthly terrestrial NEE dataset using the Global Carbon Assimilation System, version 2 (GCASv2), which is named as GCAS2021. It includes(1) monthly and annual prior and posterior NEE and OCN fluxes, and prescribed FIRE and FFC emissions in a global spatial resolution of 1°×1°; (2) globally, latitudinally, and regionally aggregated monthly and annual posterior NEE and NBE, and their uncertainties; and (3) weekly gridded ensemble members of posterior NEE and OCN fluxes. The regional fluxes are aggregated both in the TRANSCOM and RECCAP2 regions. The latitudinal fluxes are aggregated in northern mid-high latitudes (> 30° N, NL), low latitudes (30° S ~ 30° N, TL), and southern middle latitudes (<30° N, SL). The weekly grided ensemble members could be used for calculating the posterior uncertainties of the user's area and time scale. </p> <p>Combining the OCN flux, and FIRE and FFC emissions, the net biosphere flux (NBE) and atmospheric growth rate (AGR) as well as their inter-annual variabilities (IAVs) agree well with the estimates of Global Carbon Budget 2020. Regionally, GCAS2021 shows that eastern North America, Amazon, Congo Basin, Europe, boreal forests, southern China and Southeast Asia are carbon sinks, while western US, African grasslands, Brazilian plateaus and parts of South Asia are carbon sources. In the TRANSCOM land regions, the NBEs of temperate N. America, northern Africa and boreal Asia are between the estimates of CMS-Flux NBE 2020 and CT2019B, and those in temperate Asia, Europe, and Southeast Asia are consistent with CMS-Flux NBE 2020 but significantly different from CT2019B. In the RECCAP2 regions, except for Africa and South Asia, the NBEs are comparable with the latest bottom-up estimate of Ciais et al. (2021). Compared with previous studies, the IAVs and seasonal cycles of NEE of this dateset could clearly reflect the impacts of extreme climates and large-scale climate anomalies on the carbon flux. The evaluations also show that the posterior CO<sub>2</sub> concentrations at remote sites and in regional scale, as well as on vertical CO<sub>2</sub> profiles in the Asia-Pacific region and the Amazon basin, are all consistent with independent CO<sub>2</sub> measurements from surface flask and aircraft CO<sub>2</sub> observations, indicating that this dataset captures surface carbon fluxes well. We believe that this data set will contribute to regional or national-scale carbon cycle and carbon neutrality assessment, and carbon dynamics research.</p>
The role of OCO-3 XCO2 retrievals in estimating global terrestrial net ecosystem exchanges
<p>1.<strong>Exp_OCO3.zip </strong> includes regional posterior carbon fluxes from the assimilation using OCO-3 observations.</p> <p>2.<strong>Exp_OCO2.zip </strong> includes regional posterior carbon fluxes from the assimilation using OCO-2 observations.</p> <p>3.<strong>Exp_OCO3&2.zip </strong> includes regional posterior carbon fluxes from the joint assimilation using OCO-3 and OCO-2 observations together.</p> <p>4.<strong>posterior.fluxes.Exp_OCO3.nc </strong>includes information on the spatial distribution of annual as well as monthly posterior carbon fluxes from the assimilation using OCO-3 observations during August 2019 to December 2022.</p> <p>5.<strong>posterior.fluxes.Exp_OCO2.nc </strong>includes information on the spatial distribution of annual as well as monthly posterior carbon fluxes from the assimilation using OCO-2 observations during August 2019 to December 2022.</p> <p>6.<strong>posterior.fluxes.Exp_OCO3&2.nc </strong>includes information on the spatial distribution of annual as well as monthly posterior carbon fluxes from the joint assimilation using OCO-3 and OCO-2 observations together during August 2019 to December 2022.</p> <p>7.<strong>evaluation_result.txt</strong> includes the results of the evaluation of posterior carbon fluxes using independent CO2 observations from 66 surface flask sites.</p> <p> </p> <p> </p> <p> </p>
Global long-term (2010-2020) daily seamless fused XCO2 and XCH4 from GOSAT, OCO-2, and CAMS-EGG4
<p>A novel spatiotemporally self-supervised fusion method is proposed to establish long-term daily seamless XCO<sub>2</sub> and XCH<sub>4</sub> products from 2010 to 2020 over the globe at grids of 0.25°. More details are provided in https://doi.org/10.5194/essd-2023-28.</p>
Daily Gapless 0.1° XCO2 Dataset for China
<p>This is the daily gapless column-averaged dry-air mole fraction of CO<sub>2</sub> (XCO<sub>2</sub>) dataset with a high spatial resolution of 0.1° in China from 2015 to 2020. This dataset was generated by the deep learning-based multisource data fusion, including satellite XCO<sub>2</sub>, reanalyzed XCO<sub>2</sub>, satellite vegetation data, and meteorological fields. This dataset yields a high accuracy in terms of cross-validation and ground-based validation.</p> <p>This data set is stored in Geotiff format and can be opened with ArcGIS, ENVI, etc.</p>
A top-down method for estimating regional fossil fuel carbon emissions based on satellite XCO2 retrievals
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China XCO2
<p>Annual and seasonal averages of XCO2 across China.</p>
A robust estimate of continental-scale terrestrial carbon sinks using GOSAT XCO2 retrievals
<p>The dataset contains monthly terrestrial ecosystem carbon fluxes (NEE) for 51 terrestrial regions from 2011-2014. We used simulations from 12 terrestrial biosphere models (TBMs) as the prior carbon fluxes, therefore the posterior carbon fluxes correspond to the 12 TBMs.</p>
Data from: A segmentation algorithm for characterizing Rise and Fall segments in seasonal cycles: an application to XCO2 to estimate benchmarks and assess model bias
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Monthly XCO2 of China at 0.25° grid scale
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OCO-2 Gridded bias-corrected XCO2, SIF, and other select fields aggregated as Level 3 daily files V4 (OCO2GriddedXCO2_SIF)
Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) bias corrected data.This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page.
OCO-2 Gridded bias-corrected XCO2 and other select fields aggregated as Level 3 daily files V4 (OCO2GriddedXCO2)
Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) bias corrected data.This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page.
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