Abstract. Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1∘ × 1∘ gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website: https://doi.org/10.48588/npf6-sw92 (Byrne et al., 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr−1 (0.90–1.25 Pg C yr−1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

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Abstract. Atmospheric inversion of carbon dioxide (CO2) measurements to better understand carbon sources and sinks has made great progress over the last 2 decades. However, most of the studies, including a four-dimensional variational ensemble Kalman filter and Bayesian synthesis approaches, directly obtain only fluxes, while CO2 concentration is derived with the forward model as part of a post-analysis. Kang et al. (2012) used the local ensemble transform Kalman filter (LETKF), which updates the CO2, surface carbon flux (SCF), and meteorology fields simultaneously. Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this data assimilation system faces the challenge of maintaining carbon mass conservation. To overcome this shortcoming, here we apply a constrained ensemble Kalman filter (CEnKF) approach to ensure the conservation of global CO2 mass. After a standard LETKF procedure, an additional assimilation is used to adjust CO2 at each model grid point and to ensure the consistency between the analysis and the first guess of the global CO2 mass. Compared to an observing system simulation experiment without mass conservation, the CEnKF significantly reduces the annual global SCF bias from ∼ 0.2 to less than 0.06 Gt and slightly improves the seasonal and annual performance over tropical and southern extratropical regions. We show that this system can accurately track the spatial distribution of annual mean SCF. And the system reduces the seasonal flux root mean square error from a priori to analysis by 48 %–90 %, depending on the continental region. Moreover, the 2015–2016 El Niño impact is well captured with anomalies mainly in the tropics.

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The 2019–20 Australian mega-bushfires, which raged particularly over New South Wales and Victoria, released large amounts of toxic haze and CO2. Here, we investigate whether the resulting CO2 enhancement can be directly detected by satellite observations, based on National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2 (OCO-2) column-averaged CO2 (XCO2) product. We find that smoke from wildfires can greatly obscure satellite observations, making the available XCO2 mainly locate over outer fringes of plumes downwind of the major mega-bushfires in eastern Australia in three orbit observations during November–December 2019, with their enhancements of approximately 1.5 ppm. This fire-induced CO2 enhancement is further confirmed using an atmospheric transport model, Goddard Earth Observing System-Chem, forced by satellite observation-derived fire product Global Fire Emissions Database, version 4.1 and wind observations, with comparable simulated XCO2 enhancements. Model simulation also suggests that the sensitivity of the downwind maximum XCO2 enhancement is 0.41 ± 0.04 ppm for 1 TgC d−1 fire emissions. In sum, though detectable to some extent, it remains a challenge to get the accurate maximum XCO2 enhancements due to the gaps in XCO2 detections obscured by smoke. Understanding the capability of OCO-2 XCO2 detection is prerequisite for monitoring and constraining wildfire CO2 emissions by inversions.

Abstract. Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries’ carbon budgets. These estimates are based on "top-down" NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements, or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with "bottom-up" estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1° × 1° gridded dataset and as a country-level dataset. Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 PgCO2 yr-1 (0.90–1.25 PgC yr-1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

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.