A full-coverage satellite-based global atmospheric CO2 dataset at 0.05° resolution from 2015 to 2021 for exploring global carbon dynamics

Wang, Zhige; Zhang, Ce ORCID: https://orcid.org/0000-0001-5100-3584; Shi, Kejian; Shangguan, Yulin; Hu, Bifeng; Chen, Xueyao; Wei, Danqing; Chen, Songchao; Atkinson, Peter M.; Zhang, Qiang. 2024 A full-coverage satellite-based global atmospheric CO2 dataset at 0.05° resolution from 2015 to 2021 for exploring global carbon dynamics. Earth System Science Data Discussions, essd-2024-315. 35, pp. 10.5194/essd-2024-315

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Official URL: http://dx.doi.org/10.5194/essd-2024-315

Abstract/Summary

The irreversible trend for global warming underscores the necessity for accurate monitoring and analysis of atmospheric carbon dynamics on a global scale. Carbon satellites hold significant potential for atmospheric CO2 monitoring. However, existing studies on global CO2 are constrained by coarse resolution (ranging from 0.25° to 2°) and limited spatial coverage. In this study, we developed a new global dataset of column-averaged dry-air mole fraction of CO2 (XCO2) at 0.05° resolution with full coverage using carbon satellite observations, multi-source satellite products, and an improved deep learning model. We then investigated changes in global atmospheric CO2 and anomalies from 2015 to 2021. The reconstructed XCO2 products show a better agreement with Total Carbon Column Observing Network (TCCON) measurements, with R2 of 0.92 and RSME of 1.54 ppm. The products also provide more accurate information on the global and regional spatial patterns of XCO2 compared to origin carbon satellite monitoring and previous XCO2 products. The global pattern of XCO2 exhibited a distinct increasing trend with a growth rate of 2.32 ppm/year, reaching 414.00 ppm in 2021. Globally, XCO2 showed obvious spatial variability across different latitudes and continents. Higher XCO2 concentrations were primarily observed in the Northern Hemisphere, particularly in regions with intensive anthropogenic activity, such as East Asia and North America. We also validated the effectiveness of our XCO2 products in detecting intensive CO2 emission sources. The XCO2 dataset is publicly accessible on the Zenodo platform at https://doi.org/10.5281/zenodo.12706142 (Wang et al., 2024). Our findings represent a promising advancement in monitoring carbon emission across various countries and enhancing the understanding of global carbon dynamics.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.5194/essd-2024-315
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	1866-3591
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	atmospheric carbon dioxide, satellite carbon monitoring, deep learning, OCO-2/3
NORA Subject Terms:	Meteorology and Climatology Atmospheric Sciences Data and Information
Related URLs:	Dataset
Date made live:	06 Nov 2024 16:30 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538335

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.5194/essd-2024-315

UKCEH and CEH Sections/Science Areas:

Soils and Land Use (Science Area 2017-)

ISSN:

1866-3591

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

atmospheric carbon dioxide, satellite carbon monitoring, deep learning, OCO-2/3