Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-2857-2024
https://doi.org/10.5194/essd-16-2857-2024
Data description paper
 | 
19 Jun 2024
Data description paper |  | 19 Jun 2024

A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system

Zhe Jin, Xiangjun Tian, Yilong Wang, Hongqin Zhang, Min Zhao, Tao Wang, Jinzhi Ding, and Shilong Piao

Viewed

Total article views: 1,113 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
848 207 58 1,113 74 43 46
  • HTML: 848
  • PDF: 207
  • XML: 58
  • Total: 1,113
  • Supplement: 74
  • BibTeX: 43
  • EndNote: 46
Views and downloads (calculated since 09 Nov 2023)
Cumulative views and downloads (calculated since 09 Nov 2023)

Viewed (geographical distribution)

Total article views: 1,113 (including HTML, PDF, and XML) Thereof 1,099 with geography defined and 14 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 16 Jul 2024
Download
Short summary
An accurate estimate of spatial distribution and temporal evolution of CO2 fluxes is a critical foundation for providing information regarding global carbon cycle and climate mitigation. Here, we present a global carbon flux dataset for 2015–2022, derived by assimilating satellite CO2 observations into the GONGGA inversion system. This dataset will help improve the broader understanding of global carbon cycle dynamics and their response to climate change.
Altmetrics
Final-revised paper
Preprint