Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO₂ Retrievals

Abstract

The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient observational constraints; satellite column-average dry-air mole fraction carbon dioxide (XCO 2 ) retrievals may fill some of this gap. Here, we estimate China's carbon sink using atmospheric inversions of the Orbiting Carbon Observatory 2 (OCO-2) XCO 2 retrievals within different platforms, including the Global Carbon Assimilation System (GCAS) v2, the Copernicus Atmosphere Monitoring Service, and the OCO-2 Model Inter-comparison Project (MIP). We find that they consistently place the largest net biome production (NBP) in the south on an annual basis compared to the northeast and other main agricultural areas during peak growing season, coinciding well with the distribution of forests and crops, respectively. Moreover, the mean seasonal cycle amplitude of NBP in OCO-2 inversions is obviously larger than that of biosphere model simulations and slightly greater than surface CO 2 inversions. More importantly, the mean seasonal cycle of the OCO-2 inversions is well constrained in the temperate, tropical, and subtropical monsoon climate zones, with better inter-model consistency at a sub-regional scale compared to in situ inversions and biosphere model simulations. In addition, the OCO-2 inversions estimate the mean annual NBP in China for 2015-2019 to be between 0.34 (GCASv2) and 0.47 ± 0.16 PgC/yr (median ± std; OCO-2 v10 MIP), and indicate the impacts of climate extremes (e.g., the 2019 drought) on the interannual variations of NBP. Our results suggest that assimilating OCO-2 XCO 2 retrievals is crucial for improving our understanding of China's terrestrial carbon sink regime.

Satellite-detected large CO2 release in southwestern North America during the 2020–2021 drought and associated wildfires

“…It could be because that, the satellite inversion used vertical column CO 2 data instead of surface CO 2 data to infer carbon fluxes, in theory the column CO 2 data contain more signals than surface CO 2 did, especially the column CO 2 contain more CO 2 sources in the atmosphere, thus larger sink to balance it in an inversion framework (mass balance between sink and source). Similarly, higher estimates from satellite inversions than in-situ inversions were reported in previous studies about the regional carbon sinks in Europe (Feng et al 2016), US (Byrne et al 2023) and China (He et al 2023b). Secondly, the differences in the regional carbon budget estimates among the three OCO-2 MIP experiments and four in-situ based global inversions partly originate from this gap between satellite inversions and in-situ inversions, as well as uncertainties among different inversion frameworks (due to both inversion system design and assimilated data).…”

“…In addition, the SWNA is an arid region, where vegetation signal is relative weak in a large proportion of the domain, making reliable regional flux estimate challenging. In comparison, model ensemble could provide more reliable estimates on regional flux and its anomaly, especially these constrained by satellite XCO 2 observations (He et al 2023a(He et al , 2023b.…”

“…Atmospheric CO 2 inversions offer large-scale constrained estimates on the dynamic of terrestrial net carbon uptake, which could offer more objective impact assessment than using unconstrained terrestrial biosphere model simulations (He et al 2023a(He et al , 2023b. In-situ CO 2 measurements or spaceborne column-averaged CO 2 dry air molar fraction (XCO 2 ) retrievals provide top-down constraints on the net carbon exchange between the atmosphere and terrestrial ecosystems from regional to global scales, thus providing an opportunity to study how largescale carbon fluxes respond to warm and dry climate features under arid conditions (Liu et al 2017, Sun et al 2017.…”

Exploring the Potential of Long Short‐Term Memory Networks for Predicting Net CO2 Exchange Across Various Ecosystems With Multi‐Source Data

“…The top-down approach infers biosphere CO 2 fluxes from atmospheric CO 2 observations onboard different observation platforms, such as tall towers, aircraft, ships, and satellites (Ciais et al, 2014), which utilizes atmospheric CO 2 data and a transport model to deduce the spatiotemporal distribution of carbon fluxes. Atmospheric inversions are particularly beneficial for constraining large-scale carbon fluxes (He et al, 2023a(He et al, , 2023b, but providing limited spatial information on smaller scales, as uncertainties increase with spatial scale decreasing. The process-based TBMs consider the physical processes of energy, carbon, and water cycle regulation.…”

Exploring the Potential of Long Short-Term Memory Networks for Predicting Net CO2 Exchange Across Various Ecosystems With Multi-Source Data

“…The top-down approach infers biosphere CO 2 fluxes from atmospheric CO 2 observations onboard different observation platforms, such as tall towers, aircraft, ships, and satellites (Ciais et al, 2014), which utilizes atmospheric CO 2 data and a transport model to deduce the spatiotemporal distribution of carbon fluxes. Atmospheric inversions are particularly beneficial for constraining large-scale carbon fluxes (He et al, 2023a;He et al, 2023b), but providing limited spatial information on smaller scales, as uncertainties increase with spatial scale decreases. The process-based TBMs consider the physical processes of energy, carbon, and water cycle regulation.…”