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8 results for “GONGGA”
Mechanism for the Uplift of Gongga Shan in the Southeastern Tibetan Plateau Constrained by 3D Magnetotelluric Data
<p>The *.data, *.rho, and *.zip files are associated with a paper titled 'Mechanism for the Uplift of Gongga Shan in the Southeastern Tibetan Plateau Constrained by 3D Magnetotelluric Data' in Geophysical Research Letters published in 2022. On the basis of this data and inversion model, we addressed that the rapid uplift of the Gongga Shan massif likely occurred by the underthrusting of the Yangtze Craton. More details about the electrical resistivity model and its interpretations can be found in our journal paper. </p> <p>All the resulting files from ModEM are included in the 'ModEM_Inversion_Results.zip'. All the figures in the paper and supplementary are included in the 'GRL_All_Figures.zip' and 'Figure_S5_All_Responses.zip'.</p> <p>The resulting model and data output in ModEM format can be found in .rho and .data files. The ModEM is an open-source code package for MT 3D inversion, which is provided by Gary Egbert, Anna Kelbert, and Naser Meqbel and can be found on this website: <a href="https://sites.google.com/site/modularem/download">https://sites.google.com/site/modularem/download</a>. </p> <p>Please note that the 3D resistivity model files in general format includes four columns -- longitude, latitude, depth, and resistivity, the one who wants to plot the model via GMT, MATLAB, Surface, etc., can find these files in 'Gongga_3D_Resistivity_Model_Files.zip'. In this zip, you will find the resistivity model of each horizontal slice of different depths and a file including all the slices. A MATLAB script called 'see_slice.m' is included in the folder which can help to quickly view these resistivity slices.</p>
A global gridded CO2 flux dataset inferred from OCO-2 retrievals using the GONGGA inversion system (v2025)
<p><strong>Data Description</strong></p> <p>Here we provide a global monthly CO2 flux dataset at 1° × 1° spatial resolution for the period 2014.9-2024.12. The dataset is generated using the GONGGA (Global ObservatioN-based system for monitoring Greenhouse GAs) inversion system by assimilating OCO-2 (Observing Carbon Observatory 2) v11.2r column CO2 retrievals that scaled to the WMO X2019 standard. The dataset contains fluxes from biosphere (Net Ecosystem Exchange, NEE) (both prior and posterior), ocean (both prior and posterior), biomass burning emissions and fossil fuel emissions.</p> <p>We also provide the posterior model simulated values corresponding to all measurements contained in the lastest release of NOAA’s ObsPack database (obspack_co2_1_GLOBALVIEWplus_v10.1_2024-11-13 and obspack_co2_1_NRT_v10.1_2025-02-07).</p> <p><strong>Change from v2024</strong></p> <ul> <li>Assimilation of OCO-2 v11.2r retrievals</li> <li>Update of prior fluxes</li> </ul> <p><strong>Data version specification</strong></p> <p>v202x.ori refers to original GONGGA flux data with 3-hourly time resolution and 2° latitude × 2.5° longitude spatial resolution, v202x refers to GONGGA flux data resampled to monthly time resolution and 1° latitude × 1° longitude spatial resolution for facilitating comparisons with other GCP inversion results.</p> <p><strong>Article citation</strong></p> <p>Jin, Z., Wang, T., Zhang, H., Wang, Y., Ding, J., Tian, X., Constraint of satellite CO2 retrieval on the global carbon cycle from a Chinese atmospheric inversion system. Science China Earth Sciences, 2023, 66: 609-618, doi: 10.1007/s11430-022-1036-7.</p> <p>Jin, Z., Tian, X., Wang, Y., Zhang, H., Zhao, M., Wang, T., Ding, J., and Piao, S.: A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system, Earth System Science Data, 2024, 16: 2857-2876, doi: 10.5194/essd-16-2857-2024.</p>
Leaf traits data from Gongga Mountain, China
<p>This dataset reports on leaf traits of plants and climate data used in the paper: Xu et al. (under review in Tree Physiology) at 18 sites from 1143 to 4361 m in Gongga Mountain region (29° 22' to 29° 55' N and 101° 1' to 102° 9' E). The trait data were collected during the active growing season in 2018 and 2019, as part of a Chinese research project. The vegetation type changes from deciduous broad-leaved forest dominated by Betulaceae, Urticaceae, Caprifoliaceae and Rosaceae, to evergreen needle-leaved forest and deciduous shrubland dominated by Pinaceae and/or Rosaceae and Ericaceae with increasing elevation. Relevant dataset information can be found: <a href="https://github.com/Huiying-Xu/PTG">https://github.com/Huiying-Xu/PTG</a></p>
Seasonal elevational patterns and the underlying mechanisms of avian diversity and community structure on the eastern slope of Mt. Gongga
<p>This dataset contains bird survey data (Appedenix 2) and bird trait data (Appedenix 1) in the paper: "He et al. (2022) Seasonal elevational patterns and the underlying mechanisms of avian diversity and community structure on the eastern slope of Mt. Gongga. Diversity and Distributions". Main results of the paper are that TD, PD and FD showed similar hump-shaped elevational patterns in both seasons. In the breeding season, TD, PD and FD for small-ranged species, were highly correlated with climatic factors (mean daily temperature, seasonal temperature range) and vegetation factors (enhanced vegetation index), while large-ranged species were correlated with spatial factors (mid-domain effect). In the non-breeding season, TD, PD and FD for all species groupings were positively correlated with climate factors. For small-ranged species in both seasons, community structure was more overdispersed at low and high elevations, and more clustered at middle elevations. For large-ranged species, community structure differed between seasons, showing a general trend toward clustering as elevations increase in the breeding season and trends toward overdispersion and/or evenness as elevations increase in the non-breeding season.</p>
Seasonal elevational patterns and the underlying mechanisms of avian diversity and community structure on the eastern slope of Mt. Gongga
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Data from: Altitudinal biodiversity patterns of seed plants along Gongga Mountain in the southeastern Qinghai-Tibetan Plateau
The mechanisms underlying elevation patterns in species and phylogenetic diversity remain a central issue in ecology and are vital for effective biodiversity conservation in the mountains. Gongga Mountain, located in the southeastern Qinghai-Tibetan Plateau, represents one of the longest elevational gradients (ca. 6500 m, from ca. 1000 - 7556 m) in the world for studying species diversity patterns. However, the elevational gradient and conservation of plant species diversity and phylogenetic diversity in this mountain remain poorly studied. Here, we compiled the elevational distributions of 2,667 native seed plant species occurring in Gongga Mountain, and estimated the species diversity, phylogenetic diversity, species density, and phylogenetic relatedness across ten elevation belts and five vegetation zones. The results indicated that species diversity and phylogenetic diversity of all seed plants showed a hump-shaped pattern, peaking at 1800 - 2200 m. Species diversity was significantly correlated with phylogenetic diversity and species density. The floras in temperate coniferous broad-leaved mixed forests, sub-alpine coniferous forests and alpine shrublands and meadows were significantly phylogenetically clustered, whereas the floras in evergreen broad-leaved forests had phylogenetically random structure. Both climate and human pressure had strong correlation with species diversity, phylogenetic diversity and phylogenetic structure of seed plants. Our results suggest that the evergreen broad-leaved forests and coniferous broad-leaved mixed forests at low to mid elevations deserve more conservation efforts. This study improves our understanding on the elevational gradients of species and phylogenetic diversity and their determinants, and provides support for improving seed plants conservation in Gongga Mountain.
Data from: Altitudinal biodiversity patterns of seed plants along Gongga Mountain in the southeastern Qinghai-Tibetan Plateau
Open the record for dataset details and reuse information.
A global surface CO2 flux dataset (2015–2021) inferred from OCO-2 retrievals using the GONGGA inversion system
<p>Here we present a global spatially-resolved terrestrial and ocean carbon flux dataset for 2015–2021. The dataset is generated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through assimilating Observing Carbon Observatory 2 (OCO-2) v10r XCO<sub>2</sub> retrievals. The flux files contain NEE, ocean carbon fluxes, fossil fuel emissions, and biomass burning emissions. The NEE and ocean carbon fluxes include the prior and posterior estimates. The corresponding gridded uncertainty of NEE and ocean fluxes are also included in the flux files. The global gridded fluxes are 3-hourly with a resolution of 2° latitude × 2.5° longitude . Users can aggregate the gridded fluxes on their preferred regions. The GEOS-Chem simulated CO<sub>2</sub> concentrations driven by posterior NEE and ocean fluxes, as well as fossil fuel emissions and biomass burning emissions, are generated every 3 hours with a resolution of 2° latitude × 2.5° longitude at 47 vertical levels.</p>
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