Global and regional carbon budget for 2015–2020 inferred from OCO-2 based on an ensemble Kalman filter coupled with GEOS-Chem

Abstract

Abstract. Understanding carbon sources and sinks across the Earth's surface is fundamental in climate science and policy; thus, these topics have been extensively studied but have yet to be fully resolved and are associated with massive debate regarding the sign and magnitude of the carbon budget from global to regional scales. Developing new models and estimates based on state-of-the-art algorithms and data constraints can provide valuable knowledge and contribute to a final ensemble model in which various optimal carbon budget estimates are integrated, such as the annual global carbon budget paper. Here, we develop a new atmospheric inversion system based on the 4D local ensemble transform Kalman filter (4D-LETKF) coupled with the GEOS-Chem global transport model to infer surface-to-atmosphere net carbon fluxes from Orbiting Carbon Observatory-2 (OCO-2) V10r XCO2 retrievals. The 4D-LETKF algorithm is adapted to an OCO-2-based global carbon inversion system for the first time in this work. On average, the mean annual terrestrial and oceanic fluxes between 2015 and 2020 are estimated as − 2.02 and − 2.34 GtC yr−1, respectively, compensating for 21 % and 24 %, respectively, of global fossil carbon dioxide (CO2) emissions (9.80 GtC yr−1). Our inversion results agree with the CO2 atmospheric growth rates reported by the National Oceanic and Atmospheric Administration (NOAA) and reduce the modeled CO2 concentration biases relative to the prior fluxes against surface and aircraft measurements. Our inversion-based carbon fluxes are broadly consistent with those provided by other global atmospheric inversion models, although discrepancies still occur in the land–ocean flux partitioning schemes and seasonal flux amplitudes over boreal and tropical regions, possibly due to the sparse observational constraints of the OCO-2 satellite and the divergent prior fluxes used in different inversion models. Four sensitivity experiments are performed herein to vary the prior fluxes and uncertainties in our inversion system, suggesting that regions that lack OCO-2 coverage are sensitive to the priors, especially over the tropics and high latitudes. In the further development of our inversion system, we will optimize the data-assimilation configuration to fully utilize current observations and increase the spatial and seasonal representativeness of the prior fluxes over regions that lack observations.

Evaluating the consistency between OCO-2 and OCO-3 XCO2 estimates derived from the NASA ACOS version 10 retrieval algorithm

“…As the science community continues work to better constrain the global carbon cycle Friedlingstein et al, 2022), top-down flux and inventory estimates utilizing XCO 2 observations from space have demonstrated promise for answering key questions about the present and future response of the system to continued human activities and climate change (e.g., Byrne et al, 2021Byrne et al, , 2022Kong et al, 2022;. The need for an international fleet of robust, dedicated carbonmonitoring satellites is paramount to this effort (Ciais et al, 2014;Crisp et al, 2018;Janssens-Maenhout et al, 2020;Palmer et al, 2022).…”

Evaluating the consistency between OCO-2 and OCO-3 XCO2 estimates derived from the NASA ACOS version 10 retrieval algorithm

“…ing key questions about the present and future response of the system to continued human activities and climate change (e.g., Byrne et al, 2021Byrne et al, , 2022bPhilip et al, 2022;Kong et al, 2022;Chevallier et al, 2022). The need for an international fleet of robust, dedicated carbon monitoring satellites is paramount to this effort (Ciais et al, 2014;Crisp et al, 2018;Janssens-Maenhout et al, 2020;Palmer et al, 2022).…”

“…the published literature. Examples include an evaluation of the CO 2 concentrations against the NOAA in situ network (Rastogi et al, 2021), quantification of power plant emissions (Nassar et al, 2021(Nassar et al, , 2022, detection of urban XCO 2 gradients (Rißmann et al, 2022), a global and regional carbon budget analysis (Kong et al, 2022;Byrne et al, 2022b), and an evaluation of global net carbon exchange based on a multi model inter-comparison project (Byrne et al, 2022a).…”

Improved Consistency of Satellite XCO2 Retrievals Based on Machine Learning

“…Besides, given the higher spatial resolution of OCO‐2 compared to GOSAT, the number of matched OCO‐2 soundings for each GOSAT footprint can range from zero to hundreds. Consequently, we first excluded XCO 2 values from matched OCO‐2 data deviating beyond three standard deviations from the mean and then averaged the remaining values to formulate labels for each corresponding pair, weighted by the inverse of the retrieval uncertainty (Crowell et al., 2019; Kong et al., 2022). This aggregation is uniformly applied to the retrieved XCO 2 , averaging kernels, and prior CO 2 profiles.…”