Regional CO<sub>2</sub> fluxes from 2010 to 2015 inferred from GOSAT XCO<sub>2</sub> retrievals using a new version of the Global Carbon Assimilation System

Abstract

Abstract. Satellite retrievals of the column-averaged dry air mole fractions of CO2 (XCO2) could help to improve carbon flux estimation due to their good spatial coverage. In this study, in order to assimilate the GOSAT (Greenhouse Gases Observing Satellite) XCO2 retrievals, the Global Carbon Assimilation System (GCAS) is upgraded with new assimilation algorithms, procedures, a localization scheme, and a higher assimilation parameter resolution. This upgraded system is referred to as GCASv2. Based on this new system, the global terrestrial ecosystem (BIO) and ocean (OCN) carbon fluxes from 1 May 2009 to 31 December 2015 are constrained using the GOSAT ACOS (Atmospheric CO2 Observations from Space) XCO2 retrievals (Version 7.3). The posterior carbon fluxes from 2010 to 2015 are independently evaluated using CO2 observations from 52 surface flask sites. The results show that the posterior carbon fluxes could significantly improve the modeling of atmospheric CO2 concentrations, with global mean bias decreases from a prior value of 1.6 ± 1.8 ppm to −0.5 ± 1.8 ppm. The uncertainty reduction (UR) of the global BIO flux is 17 %, and the highest monthly regional UR could reach 51 %. Globally, the mean annual BIO and OCN carbon sinks and their interannual variations inferred in this study are very close to the estimates of CarbonTracker 2017 (CT2017) during the study period, and the inferred mean atmospheric CO2 growth rate and its interannual changes are also very close to the observations. Regionally, over the northern lands, the strongest carbon sinks are seen in temperate North America, followed by Europe, boreal Asia, and temperate Asia; in the tropics, there are strong sinks in tropical South America and tropical Asia, but a very weak sink in Africa. This pattern is significantly different from the estimates of CT2017, but the estimated carbon sinks for each continent and some key regions like boreal Asia and the Amazon are comparable or within the range of previous bottom-up estimates. The inversion also changes the interannual variations in carbon fluxes in most TransCom land regions, which have a better relationship with the changes in severe drought area (SDA) or leaf area index (LAI), or are more consistent with previous estimates for the impact of drought. These results suggest that the GCASv2 system works well with the GOSAT XCO2 retrievals and shows good performance with respect to estimating the surface carbon fluxes; meanwhile, our results also indicate that the GOSAT XCO2 retrievals could help to better understand the interannual variations in regional carbon fluxes.

A global CO<sub>2</sub> flux dataset (2015–2019) inferred from OCO-2 retrievals using the Tan-Tracker inversion system

“…The estimated URs depended strongly on the number of observations and specification of the observation errors, as well as the a priori error covariance matrix (Deng et al, 2014;Jiang et al, 2021). The UR values reflect the decrease of posterior uncertainties constrained by OCO-2 XCO2 retrievals.…”

“…The fluxes to be optimized are terrestrial ecosystem carbon fluxes (i.e., net ecosystem exchange, NEE) and atmosphere-ocean carbon exchange, whereas other fluxes (e.g., fossil fuel emissions and biomass burning emissions) are assumed to have no errors. This assumption is commonly adopted in global flux inversion systems (Peters et al, 2007;Nassar et al, 2011;Crowell et al, 2019;Jiang et al, 2021 a novel dual-pass inversion strategy with modifications to Tian et al (2014aTian et al ( , 2014b and the NLS-4DVar method (Tian and Feng, 2015;Tian et al, 2018). This dual-pass strategy performs assimilations twice within an inversion window.…”

“…The annual atmospheric CO2 growth rate (AGR) is the net difference between total CO2 emissions and uptake over land and ocean (Jiang et al, 2021). CO2 exchange based on atmospheric measurements using atmospheric transport inversion.…”

A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application

“…The super-observation error decreases as the number of observations used within a super-observation increases. This method has been used in our previous inversions using surface-based (Feng et al, 2020b) and satellite-based (Jiang et al, 2021) observations.…”

“…Although a large improvement has been achieved, this method still has great limitations because the contributions from the emissions and the chemical ICs to the model's bias are difficult to distinguish (Jiang et al, 2017). Besides, the simultaneous optimization means that assimilation window is independent with each other, generally, the uncertainties of the emissions cannot be fully corrected in time in a window, resulting in an accumulation of errors in the estimation (Jiang et al, 2021).…”