Regional CO<sub>2</sub> flux estimates for 2009–2010 based on GOSAT and ground-based CO<sub>2</sub> observations

Abstract

Abstract. We present the application of a global carbon cycle modeling system to the estimation of monthly regional CO2 fluxes from the column-averaged mole fractions of CO2 (XCO2) retrieved from spectral observations made by the Greenhouse gases Observing SATellite (GOSAT). The regional flux estimates are to be publicly disseminated as the GOSAT Level 4 data product. The forward modeling components of the system include an atmospheric tracer transport model, an anthropogenic emissions inventory, a terrestrial biosphere exchange model, and an oceanic flux model. The atmospheric tracer transport was simulated using isentropic coordinates in the stratosphere and was tuned to reproduce the age of air. We used a fossil fuel emission inventory based on large point source data and observations of nighttime lights. The terrestrial biospheric model was optimized by fitting model parameters to observed atmospheric CO2 seasonal cycle, net primary production data, and a biomass distribution map. The oceanic surface pCO2 distribution was estimated with a 4-D variational data assimilation system based on reanalyzed ocean currents. Monthly CO2 fluxes of 64 sub-continental regions, between June 2009 and May 2010, were estimated from GOSAT FTS SWIR Level 2 XCO2 retrievals (ver. 02.00) gridded to 5° × 5° cells and averaged on a monthly basis and monthly-mean GLOBALVIEW-CO2 data. Our result indicated that adding the GOSAT XCO2 retrievals to the GLOBALVIEW data in the flux estimation brings changes to fluxes of tropics and other remote regions where the surface-based data are sparse. The uncertainties of these remote fluxes were reduced by as much as 60% through such addition. Optimized fluxes estimated for many of these regions, were brought closer to the prior fluxes by the addition of the GOSAT retrievals. In most of the regions and seasons considered here, the estimated fluxes fell within the range of natural flux variabilities estimated with the component models.

Bias corrections of GOSAT SWIR XCO<sub>2</sub> and XCH<sub>4</sub> with TCCON data and their evaluation using aircraft measurement data

“…These biases can lead to large errors in the estimations of regional fluxes of CO 2 and CH 4 from inversion analyses (Takagi et al, 2011;Maksyutov et al, 2013;Deng et al, 2014;Ishizawa et al, 2016b). Consequently, several studies have described bias corrections of the satellite retrieval data by using linear regression (e.g., Wunch et al, 2011b;Cogan et al, 2012;Guerlet et al, 2013;Schneising et al, 2013;Nguyen et al, 2014).…”

Algorithm update of the GOSAT/TANSO-FTS thermal infrared CO<sub>2</sub> product (version 1) and validation of the UTLS CO<sub>2</sub> data using CONTRAIL measurements

“…Nassar et al (2011) demonstrated that the wide spatial coverage of satellite CO 2 data is beneficial to CO 2 surface flux inversion through the combined use of TES and surface flask CO 2 data, particularly in regions where surface measurements are sparse. In addition to CO 2 surface inversion results using TIR observations, global XCO 2 data observed with the SWIR bands of TANSO-FTS have been actively used for estimating CO 2 source and sink strength (Maksyutov et al, 2013;Saeki et al, 2013a;Chevallier et al, 2014;Basu et al, 2013Basu et al, , 2014Takagi et al, 2014). One of the important things to consider when incorporating satellite data in CO 2 inversion is the accuracy of the data, as suggested by Basu et al (2013).…”

Inferring regional sources and sinks of atmospheric CO<sub>2</sub> from GOSAT XCO<sub>2</sub> data

“…Recent inversion analyses (Takagi et al, 2011;Maksyutov et al, 2013;Basu et al, 2013) have shown that the total column CO 2 abundances inferred from GOSAT measurements can provide constraints on CO 2 flux estimates that are complementary to those obtained from surface observations. We present here an investigation of the impact of biases in the GOSAT CO 2 data on regional flux estimates of CO 2 .…”