Greenhouse Gases Observing SATellite XCO2 Research Articles

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

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.

Global CO<sub>2</sub> fluxes estimated from GOSAT retrievals of total column CO<sub>2</sub>

Abstract. We present one of the first estimates of the global distribution of CO2 surface fluxes using total column CO2 measurements retrieved by the SRON-KIT RemoTeC algorithm from the Greenhouse gases Observing SATellite (GOSAT). We derive optimized fluxes from June 2009 to December 2010. We estimate fluxes from surface CO2 measurements to use as baselines for comparing GOSAT data-derived fluxes. Assimilating only GOSAT data, we can reproduce the observed CO2 time series at surface and TCCON sites in the tropics and the northern extra-tropics. In contrast, in the southern extra-tropics GOSAT XCO2 leads to enhanced seasonal cycle amplitudes compared to independent measurements, and we identify it as the result of a land–sea bias in our GOSAT XCO2 retrievals. A bias correction in the form of a global offset between GOSAT land and sea pixels in a joint inversion of satellite and surface measurements of CO2 yields plausible global flux estimates which are more tightly constrained than in an inversion using surface CO2 data alone. We show that assimilating the bias-corrected GOSAT data on top of surface CO2 data (a) reduces the estimated global land sink of CO2, and (b) shifts the terrestrial net uptake of carbon from the tropics to the extra-tropics. It is concluded that while GOSAT total column CO2 provide useful constraints for source–sink inversions, small spatiotemporal biases – beyond what can be detected using current validation techniques – have serious consequences for optimized fluxes, even aggregated over continental scales.

Inverse Modeling of CO<sub>2</sub> Fluxes Using GOSAT Data and Multi-Year Ground-Based Observations

We present surface CO2 flux estimates obtained by an inverse modeling analysis from column-averaged dry air mole fractions of CO2 (XCO2) observed by the Greenhouse gases Observing SATellite (GOSAT) and ground-based data. Two inversion cases were examined: 1) a decadal inversion using ground-based CO2 observations by NOAA from 1999 to 2010 to derive CO2 flux interannual variability, and 2) an inversion using NOAA plus NIES GOSAT XCO2 data from June 2009 to October 2010. We used single-shot GOSAT data and individual NOAA flask data for the inversions. Our results show differences in estimated fluxes between the NOAA data inversion and the NOAA plus GOSAT data inversion, especially in Northern Eurasia and in Equatorial Africa and America where the ground-based observational sites were sparse. Uncertainty reduction rates of 40-70 were achieved by inclusion of GOSAT data, compared to the case using just the NOAA data. The inclusion of GOSAT data in the inversion resulted in larger summer sinks in northwest Boreal Eurasia and a smaller summer sink in southeast Boreal Eurasia, with a clear uncertainty reduction in both regions. Adding GOSAT data also led to increase in Tropical African fluxes in boreal winter beyond interannual variability from NOAA data inversions.

A simple empirical model estimating atmospheric CO<sub>2</sub> background concentrations

Abstract. A simple empirical CO2 model (SECM) is presented to estimate column-average dry-air mole fractions of atmospheric CO2 (XCO2) as well as mixing ratio profiles. SECM is based on a simple equation depending on 17 empirical parameters, latitude, and date. The empirical parameters have been determined by least squares fitting to NOAA's (National Oceanic and Atmospheric Administration) assimilation system CarbonTracker version 2010 (CT2010). Comparisons with TCCON (total carbon column observing network) FTS (Fourier transform spectrometer) measurements show that SECM XCO2 agrees quite well with reality. The synthetic XCO2 values have a standard error of 1.39 ppm and systematic station-to-station biases of 0.46 ppm. Typical column averaging kernels of the TCCON FTS, a SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY), and two GOSAT (Greenhouse gases Observing SATellite) XCO2 retrieval algorithms have been used to assess the smoothing error introduced by using SECM profiles instead of CT2010 profiles as a priori. The additional smoothing error amounts to 0.17 ppm for a typical SCIAMACHY averaging kernel and is most times much smaller for the other instruments (e.g. 0.05 ppm for a typical TCCON FTS averaging kernel). Therefore, SECM is well suited to provide a priori information for state-of-the-art ground-based (FTS) and satellite-based (GOSAT, SCIAMACHY) XCO2 retrievals. Other potential applications are: (i) near real-time processing systems (that cannot make use of models like CT2010 operated in delayed mode), (ii) "CO2 proxy" methods for XCH4 retrievals (as correction for the XCO2 background), and (iii) observing system simulation experiments especially for future satellite missions.

Mapping the FTS SWIR L2 product of XCO2 and XCH4 data from the GOSAT by the Kriging method – a case study in East Asia

Developed by Japan, the Greenhouse Gases Observing Satellite (GOSAT), also known as IBUKI, was successfully launched on 23 January 2009 to monitor greenhouse gases on the Earth's surface. Observations started in April 2009, and data on Levels 1, 2 and 3 products became available to general users in November 2009, February 2010 and October 2010, respectively. For this article, the Kriging method was proposed to generate the spatial distribution of the daily GOSAT XCO2 and XCH4 data within the region of East Asia from June 2009 to May 2010. The relationship between the distance and difference of daily data for each month were represented by variogram models. The concentration distributions of XCO2 and XCH4 in East Asia can be intuitively seen on a Kriging interpolation map. Seasonal changes were observed. The concentration of XCO2 was high in winter and spring, which might be due to smoke and dust from coal burning. The concentration of XCH4 changed significantly with latitude in autumn and winter, mainly according to temperature changes. In addition, by comparison, the Level 2 Kriging interpolation values were lower than ground observed data and consistent with the higher tendency of Level 3 data.