Regional CO<sub>2</sub> Fluxes during 2010–2015 Inferred from GOSAT XCO<sub>2</sub> retrievals using a new version of Global Carbon Assimilation System

Abstract

Abstract. Satellite XCO2 retrievals could help to improve carbon flux estimation because of their good spatial coverage. In this study, to assimilate the GOSAT XCO2 retrievals, the Global Carbon Assimilation System (GCAS) is upgraded with new assimilation algorithms, procedures and a localization scheme, a higher assimilation parameter resolution and so on, and hence is named as GCASv2. Based on this new system, the global terrestrial ecosystem (BIO) and ocean (OCN) carbon fluxes from May 1, 2009 to Dec 31, 2015 are constrained using the GOSAT ACOS 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. Globally, the mean annual BIO and OCN carbon sinks and their interannual variations inferred in this study are very close to the estimates of 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, there are the strongest carbon sinks in North America Temperate, followed by Europe, Boreal Asia, and Temperate Asia; and in 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 in each continent and some key regions like Boreal Asia and Amazon are comparable or in the range of previous bottom-up estimates. The inversion also changes the interannual variations of carbon fluxes in most TRANSCOM land regions, which have a better relationship with the changes of severe drought area or 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 has a good performance in estimating the surface carbon fluxes, meanwhile, our results also indicate that the GOSAT XCO2 retrievals could help to better understand the interannual variations of regional carbon fluxes.

China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO2 and Land Surface Variables

“…GCAS directly optimizes the prior surface carbon flux derived from the BEPS model (Ju et al, 2006;Liu et al, 1997), while CCDAS optimizes a set of parameters and state variables of the land surface model BETHY (Knorr, 2000) to derive the surface carbon flux. They have been successfully applied for estimating regional carbon fluxes (Jiang et al, 2021;Scholze et al, 2019). Jiang et al (2021) found that although the estimated carbon sinks are comparable with the CarbonTracker estimate (Peters et al, 2007, with updates documented at http://carbontracker.noaa.gov) at continental scales and key regions, the geographical pattern is significantly different, and their estimates better reveal the interannual variations (IAV) in regional carbon fluxes and indicate drought impacts.…”

“…GCAS was originally developed to assimilate in situ CO 2 data (Zhang et al, 2015b) and updated recently with a new scheme to assimilate satellite XCO 2 retrievals (Jiang et al, 2021). It adopts an atmospheric transport Model for OZone And Related chemical Tracers (MOZART-4; Emmons et al, 2010) driven by the ERA-Interim meteorological data and the Ensemble Square Root Filter (EnSRF) optimization technique (Whitaker & Hamill, 2002).…”

“…Scholze et al (2019) reported their carbon sink estimate over Europe by simultaneously assimilation of atmospheric CO 2 , satellite SM and vegetation optical depth (VOD), which agreed well bottom-up studies and atmospheric inversions using in situ CO 2 or OCO-2 XCO 2 observations. Here, we estimated China's carbon sink over 2010-2015 using two CCDASs, the updated version of the GCAS (v2; Jiang et al, 2021) and the upgraded CCDAS for assimilating satellite land surface data (Scholze et al, 2016;Wu et al, 2020). We provide a timely report of China's carbon sink from satellite observation-constrained estimates, which helps to improve our understanding on the magnitude of China's carbon sink and its uncertainty.…”

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

“…Our inversion system configuration is summarized in Table 1. The data-assimilation window was set to 7 d based on the inversion system configurations of Zhang et al (2015), Liu et al (2019), andJiang et al (2021). In each dataassimilation cycle, the ensemble members x b(i) (with the ensemble mean xb and perturbations X b to approximate B) are initialized on the first day of the data-assimilation window, and the following 6 d use the same xb without perturbation.…”

“…To predict and mitigate climate change, it is of critical importance to understand how much CO 2 is released and absorbed by human and natural systems, where these exchanges occur, and how these carbon fluxes respond to anthropogenic and natural forcings (Canadell et al, 2021). Atmospheric CO 2 measurements have indicated that on average, half of the CO 2 emitted by humans from fossil fuels and land-use changes globally (Tian et al, 2021) is taken up by the oceans and land each year (Ciais et al, 2019), and the spatiotemporal distributions of global and regional carbon budget must be further reconstructed and analyzed using increasingly sophisticated bottom-up and top-down approaches.…”