Asian Journal of atmospheric environment
[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 5, No. 4, pp.263-277
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 31 Dec 2011
Received 18 Jul 2011 Revised 08 Nov 2011 Accepted 21 Nov 2011

Comparison of Model-simulated Atmospheric Carbon Dioxide with GOSAT Retrievals

Changsub Shim* ; Ray Nassar1) ; Jhoon Kim2)
Korea Environment Institute, 290 Jinheung-ro, Eunpyeong-gu, Seoul, Korea
1)Environment Canada, 4905 Dufferin St, Toronto, Ontario, Canada
2)Department of Atmospheric Sciences, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Korea

Correspondence to: * Tel: +81-2-380-7701, E-mail:


Global atmospheric CO2 distributions were simulated with a chemical transport model (GEOS-Chem) and compared with space-borne observations of CO2 column density by GOSAT from April 2009 to January 2010. The GEOS-Chem model simulated 3-D global atmospheric CO2 at 2°×2.5° horizontal resolution using global CO2 surface sources/sinks as well as 3-D emissions from aviation and the atmospheric oxidation of other carbon species. The seasonal cycle and spatial distribution of GEOS-Chem CO2 columns were generally comparable with GOSAT columns over each continent with a systematic positive bias of ~1.0%. Data from the World Data Center for Greenhouse Gases (WDCGG) from twelve ground stations spanning 90°S-82°N were also compared with the modeled data for the period of 2004-2009 inclusive. The ground-based data show high correlations with the GEOS-Chem simulation (0.66≤R2≤0.99) but the model data have a negative bias of ~1.0%, which is primarily due to the model initial conditions. Together these two comparisons can be used to infer that GOSAT CO2 retrievals underestimate CO2 column concentration by ~2.0%, as demonstrated in recent validation work using other methods. We further estimated individual source/sink contributions to the global atmospheric CO2 budget and trends through 7 tagged CO2 tracers (fossil fuels, ocean exchanges, biomass burning, biofuel burning, net terrestrial exchange, shipping, aviation, and CO oxidation) over 2004-2009. The global CO2 trend over this period (2.1 ppmv/year) has been mainly driven by fossil fuel combustion and cement production (3.2 ppmv/year), reinforcing the fact that rigorous CO2 reductions from human activities are necessary in order to stabilize atmospheric CO2 levels.


Atmospheric CO2, GEOS-Chem, GOSAT CO2, WDCGG


Understanding the global atmospheric CO2 distribution and budget are important for achieving CO2 emission reduction targets in the 21st century. However, the large uncertainties in CO2 biospheric fluxes make it difficult to gauge the pathways of human-made CO2 emissions within the Earth’s system. Recently, there has been growing interest in the global atmospheric CO2 budget and space-borne measurements of the global CO2 distribution (Kulawik et al., 2010; Crevoisier et al., 2009; Yokota et al., 2009; Chahine et al., 2008; Buchwitz et al., 2007) are contributing to our understanding of the topic. The current effort of global CO2 monitoring from space has increased the possibility of constraining CO2 fluxes from the natural sectors such as terrestrial vegetation and the ocean, since the satellite monitoring can give better spatial coverage than ground-based observations. Past studies have used satellite observations of CO2 for constraining fluxes with varying degrees of success (Nassar et al., 2011; Chevallier et al., 2009, 2005), owing to the limited information on CO2 near the Earth’s surface provided by the thermal-infrared satellite measurements used.

In January 2009, the Greenhouse gases Observing Satellite (GOSAT) was launched and has observed carbon dioxide (CO2) and methane (CH4) distributions since April 2009 with good observational sensitivity to these gases near the surface. The main objective of the GOSAT project is to reduce the uncertainties in the greenhouse gas (GHG) fluxes on a subcontinental basis, which can provide additional information to help improve predictions of future global warming (Maksyutov et al., 2008).

In this study, we focused on the comparison of simulated global CO2 by a 3-D global chemical transport model (GEOS-Chem) with the GOSAT retrievals from April 2009 and January 2010. To evaluate the model performance we used ground-based CO2 measurements from 12 stations, which enabled us to remove the model bias and better estimate the bias in GOSAT CO2 observations. In addition, we estimated the contributions of each CO2 source and sink to the global atmospheric CO2 budget for 6 years (from January 2004 and January 2010). The details of the GOSAT data and model simulation are explained in sections 2 and 3 and comparisons are described in sections 4. The estimated source/sink contributions to global atmospheric CO2 concentration are also discussed in sections 4.


2. 1 GOSAT CO2 Products

GOSAT (also known as Ibuki), is the first successful satellite designed specifically to measure the concentrations of atmospheric carbon dioxide and methane (the two greenhouse gases making the largest contribution to climate change) with good sensitivity near the surface. GOSAT has a 666 km sun-synchronous orbit and completes one orbit in ~100 minutes providing global coverage in approximately 3 days (Kadygrov et al., 2009). The GOSAT Thermal And Near-Infrared Sensor for carbon Observation (TANSO) consists of two units: the Fourier Transform Spectrometer (FTS) and the Cloud Aerosol Imager (CAI) (Kuze et al., 2006). The TANSO-FTS has three bands in the Short Wave InfraRed (SWIR) region (0.76, 1.6, and 2.0 μm) and a wide Thermal Infrared (TIR) band (5.5-14.3 μm) with a circular ~10.5 km instantaneous field of view at nadir (Yokota et al., 2009). The retrieval of greenhouse gases from FTS spectra excludes the cloudy pixels by screening using the images from CAI, which results in a significant reduction of data (Kadygrov et al., 2009). The retrieved concentration is obtained from maximum a posteriori (MAP) method with a priori information from a radiative transfer model (RTM) and the pre-processed measured spectra. The overall retrieval algorithm for GOSAT CO2 and CH4 products is described at Yoshida et al. (2011, 2010).

We used the GOSAT CO2 level 2 (L2) products from column abundance retrieved from Short Wave Infra-Red (SWIR) radiance spectra (version 01.1×) based on retrievals led by the National Institute for Environmental Studies (NIES). The details of the products including the retrieval processes and observation results are at


GEOS-Chem is a global chemical transport model that uses GEOS (Goddard Earth Observing System) assimilated meteorological fields from the NASA Global Modeling and Assimilation Office (GMAO). The most common application of this model includes Ox-NOx-VOC chemistry and transport (Bey et al., 2001). The first version of the GEOS-Chem CO2 mode was developed by Suntharalingam et al. (2004), which included atmospheric CO2 fluxes from biomass burning, biofuel burning, fossil fuel combustion and cement production, ocean exchange and terrestrial biospheric exchange. In this work, we applied the CO2 update by Nassar et al. (2010), which improved the CO2 flux inventories and added CO2 emissions from international shipping and aviation (3D). This version also accounts for the chemical production of CO2 from CO oxidation throughout the troposphere (Nassar et al., 2010), a significant source of CO2 (~1.05 Pg C/yr) that most other models count as surface emissions. In addition, use of the tagged tracer simulation, which considers each source/sink as an independent tracer, allows one to estimate the source/sink contributions to global atmospheric CO2 concentration. The GEOS-Chem CO2 module does not consider on-line full chemistry mechanisms (HOx-NOx-VOCs) to estimate CO2 concentration. Instead, the CO2 chemical production uses the monthly CO loss rate from an archived full chemistry run to represent CO2 production (Nassar et al., 2010), since CO oxidation is the primary pathway for atmospheric CO2 production.

The CO2 emissions from fossil fuel burning and cement manufacture were based on the inventory developed at the Carbon Dioxide Information and Analysis Centre (CDIAC) of the Oak Ridge National Laboratory (ORNL) with regional and seasonal variability that spans 1950-2007 (Andres et al., 2011). However, this study only used the annual mean data since the monthly-varying inventory was not publicly available at the outset of this study. CO2 emissions from fossil fuel combustion and cement production in the CDIAC inventory contributed 7.4-8.1 PgC/yr during the period of 2004-2007. The CO2 emissions from biomass burning used year-specific Global Fire Emission Database version 2 (GFEDv2) (van der Werf et al., 2006). The mean global annual CO2 in GFEDv2 (1997-2008) is 2.35 PgC/yr. The biofuel burning CO2 contribution used the annual mean inventory by Yevich and Logan (2003), with emissions of 0.80 Pg C/year. Terrestrial biospheric exchange is the most important CO2 flux and GEOS-Chem represents this with two components: (1) the “balanced biosphere” estimated from the Carnegie-Ames-Stanford-Approach (CASA) model (Potter et al., 1993) with a net annual uptake of 0 PgC/yr to represent the seasonal cycle (2) and the residual annual terrestrial exchange, which was obtained by inverse modeling in the Trans-Com 3 project (Baker et al., 2006) and then adjusted to remove the contribution for biomass burning. Fig. 1 shows the seasonal contrast of net ecosystem exchange (NEE) in 2000 calculated from CASA model. The CO2 ocean fluxes are from Takahashi et al. (2009) with monthly variations, which are based on the 3,000,000 non-El Ni~no measurements of the pressure of CO2 dissolved in ocean water. The ocean exchange at sea surface gives a global annual net CO2 flux of -1.4 PgC/yr, indicating that the ocean is a large sink.

Fig. 1.

The monthly “balanced” net ecosystem exchange (NEE) from the CASA model run used in GEOS-Chem, which gives no global net annual CO2 flux. The left shows the global NEE distribution in July 2000 and the right shows the NEE in January 2000 (unit: 1013 molecules/cm2/s).

Additional fossil fuel emissions from international shipping (~0.20 PgC/yr) and aviation (~0.16 PgC/yr) are also included. Although these are minor in comparison to the main fossil fuel inventory (~8 PgC/yr), they impact the latitudinal and vertical CO2 gradients in the atmosphere. CO2 from the oxidation of other carbon species (~1.05 PgC/yr) makes a larger contribution and has larger impacts on the global 3-D distribution of CO2 (Nassar et al., 2010). The details of the CO2 module and the source/sink inventories (including a surface correction for the CO2 source from oxidation) are explained at Nassar et al. (2010). We initially set a uniform global CO2 concentration on January 1st as 370 ppmv, which is 5 ppmv smaller than Nassar et al. (2010) based on the measurements of marine surface mean CO2 concentration(NOAA-ESRL-GMD sites). But we scaled the global CO2 distribution pattern in January 2010 after 6 year initialization which gives more realistic initial CO2 distribution in the atmosphere.


4. 1 The GOSAT CO2 Data

We used GOSAT (FTS SWIR, L2) CO2 products from versions 01.11-0.1.13. Fig. 2 shows the CO2 column-averaged dry air mole fraction or concentration in each season. This quantity is commonly referred to as XCO2 and is determined by taking the ratio of the retrieved CO2 column density to the dry air column density which is retrieved using the O2 A-band (0.76 μm).

Fig. 2.

The seasonal variability in column-averaged CO2 concentration (xCO2) from GOSAT L2 products (April 2009 to January 2010, unit: ppmv).

Continental observations of XCO2 are made in nadir mode and show a pattern of seasonal variation similar to that from the NOAA-ESRL-GMD (, where the global marine surfacemean CO2 level in January 2004 is 377 ppmv. The oceanic XCO2 values are derived from GOSAT measurements using sun glint mode (Yoshida et al., 2011) and are mostly between 375-380 ppmv (Fig. 2). The hemispheric gradient of CO2 concentration is observed, but is found to be less than the gradient of in-situ or ground measurements, which is expected for column-averaged observations. The relatively low CO2 level below 375 ppmv over the terrestrial biosphere in the northern hemisphere summer (i.e., North America and Siberia) is evident in Fig. 2. Despite these reasonable distributions in GOSAT data, the limited spatial coverage due to observations excluded by cloud and aerosol filtering is one of the major limitations of the product.

4. 2 Comparisons between GOSAT and GEOS-Chem CO2 Data

We compared the total CO2 column densities (unit: 1021 molecules/cm2) between GOSAT and GEOS-Chem data from April 2009 and January 2010, which is shown in Table 1. The comparison between total columns does not consider the averaging kernels of GOSAT data which limits our results from being a truly quantitative validation. We do not include May, November, and December in 2009 since there were serious data exclusions (<5% available) due to the cloud and aerosol interference. Table 1 shows the monthly variation of total CO2 columns over 6 continental regions (East Asia, Europe, North America, Africa, South America, and Australia). In general, the total CO2 column varies between 7.0-9.0×1021 molecules/cm2 and both of the data clearly reproduce the seasonal variation of CO2 (Table 1). GEOS-Chem columns typically have a positive bias of 0.6-1.5% (Table 1). The only exception of GEOS-Chem negative bias occurred over East Asia in April 2009 and January 2010 where the GOSAT CO2 level is slightly higher than the model (Table 1), with the GOSAT columns showing the largest seasonal variability over this region (4.4×1020 molecules/cm2) while GEOS-Chem has a much smaller seasonality (2.4×1020 molecules/cm2). Considering the generally underestimated GOSAT data, these higher GOSAT CO2 concentrations over East Asia in the cold seasons are exceptional. The possible reasons are (1) the anthropogenic CO2 emission over China in the model is still underestimated during these months due to use of an annual mean fossil fuel and cement inventory (the strong NOx seasonality by fossil fuel use over China is also well shown in satellite-based NO2 data (Lamsal et al., 2010)); (2) Biomass burning emissions for 2009-2010 were not available in GFED2 so, regional interannual differences in those years could lead to lower CO2 in the simulation; (3) anomalously low photosynthetic uptake in the region due potentially to climate perturbations like the onset of El Ni~no in spring 2009; (4) Model transport errors over Asia or neighboring regions; (5) Measurement interference by Asian dust over East Asia (Yokota et al., 2009).

The monthly mean, bias, and correlation of GOSAT and GEOS-Chem CO2 total columns (unit: 1021 molecules/cm2).

The column concentrations of the model and GOSAT are largest over Europe (8.02 and 7.96×1021 molecules/cm2, respectively) revealing the latitudinal gradient of CO2 that shows the higher CO2 concentration in the high northern latitudes. GEOS-Chem has a positive bias by ~0.8% but the correlation is relatively poor (R2=0.33) which may be due to the very limited GOSAT data sampled over the higher latitudes in the cold season or imperfections in the CO2 flux inventories, thus the estimation of the bias over Europe is more uncertain.

The correlation between GOSAT and GEOS-Chem columns are highest over North America (R2=0.72) which may indicate relatively more accurate CO2 flux information over this region than that of the rest of the world. GEOS-Chem has ~1.0% bias and almost the same magnitude of seasonal variation between the two data sets (3.9×1020 molecules/cm2), which is driven primarily by vegetation activities since GEOS-Chem here used the annual mean fossil fuel emission inventory.

We excluded the region of Saharan desert during the investigation of Africa because the desert storms seriously interfere with the CO2 retrievals. The seasonal trends show a typical southern hemispheric pattern and the bias is as large as 1.4% (Table 1). The CO2 concentration over Africa is lowest (7.5-7.6×1021 molecules/cm2).

In South America, the GEOS-Chem trends do not reproduce the southern hemispheric CO2 trends which is shown by GOSAT data (Table 1) and the correlation is poor (R2=0.21) with the large bias (~1.3%). This could be attributed to seasonal cycle of CO2 fluxes coming from the CASA run or the residual annual climatology, which recent inverse modeling work suggested overestimated South American biospheric emissions for 2006 (Nassar et al., 2011). The tropical biosphere shows lower seasonality (0.9×1020 molecules/cm2), but we also have to consider the significant number of missing data due to the clouds and aerosols over the Amazon region during the wet season. The data coverage of GOSAT is relatively better over Australia and modeled and GOSAT data show a typical southern hemispheric CO2 trend (Table 1) with the smaller seasonality (1.0×1020 molecules/cm2). The GEOS-Chem bias is closer to the global mean (~1.0%) over Australia. The spatial variance of CO2 data over Australia is smallest (1.5×1020 molecules/cm2), which implies that there are no strong sinks or sources in this region.

The spatial distribution of the differences between GOSAT and GEOS-Chem CO2 total columns in summer and winter seasons are shown in Figs. 3 and 4. In July 2009, the GEOS-Chem total columns of CO2 are slightly higher, particularly over the ocean, which gives systemic overestimation by ~8.8×1019 molecules/ cm2. The global mean difference here is ~1.0% and the spatial pattern is reasonably comparable between two data sets with the correlation (R2=0.6). This difference is entirely consistent with the ~1.0% bias determined by from the 7 months of observations assuming equal-weighting of the continents (Table 1). The value is slightly larger than the mean GOSAT uncertainties (<1%) reported by Yoshida et al. (2011).

Fig. 3.

The monthly average of GOSAT CO2 column density in July 2009 (top), the corresponding GEOS-Chem data (middle) and the difference between GEOS-Chem and GOSAT CO2 column density (GEOS-Chem-GOSAT) (bottom: unit: 1021 molecules/cm2).

Fig. 4.

Same as Fig. 3, but in January 2010.

4. 3 Comparison with WDCGG CO2 Data

Previously, a global GEOS-Chem CO2 simulation was compared with the data from 74 GLOVALVIEW-CO2 sites by Nassar et al. (2010). Although direct comparisons of this type have limitations due to the representation error caused by the size mismatch between the model domain and sites, the GEOS-Chem CO2 generally agreed well with GLOBALVIEW-CO2 data (Nassar et al., 2010). With the same limitation but different initial conditions, we focused on the comparison of the monthly mean GEOS-Chem CO2 with ground-based measurements from 12 sites, available from the World Data Center for Greenhouse Gases (WDCGG) (WMO, 2009) from January 2004 to December 2009. The sites selected represent a range of latitudes and different regions, with most being remote background sites. However, the two Korean sites (Taeahn (36.72°N, 126.12°E) and Gosan (33.17°N, 126.1° E)), Jungfraujoch (45.5°N, 8°E), and Sable Island (43.9°N, 60°W) are somewhat closer to the industrial areas of China, Europe, and North America. The model comparison with the ground-based data is important to help infer the quality of the GOSAT CO2 products since the vertically-integrated GOSAT column data cannot be compared directly with the ground-based “point” measurements. The fact that the model simulates a complete 3-D field allows it to be compared with both measurement approaches.

During the comparison, we first calculated the GEOS-Chem biases which have a range from -1.5% to -0.5% and corrected the model bias by adding the mean bias (3.6 ppmv) for the 12 sites. Correction with a fixed value does not change the correlation between the model and observation data. The red lines in Fig. 5 indicate the model data with the bias correction and black dots shows the observations. As shown in Fig. 5, the GEOS-Chem simulations represent the seasonal cycle of the observation timeseries very well, resulting in a fairly high correlation range (0.66≤R2≤0.99).

Fig. 5.

The comparison of CO2 data between 12 ground-based measurement sites from WDCGG (black) and GEOS-Chem (red). The model data have been adjusted here by correcting them with the average GEOS-Chem bias from 12 sites (3.6 ppmv).

The large gradient of the seasonal variability of CO2 with latitude is shown in Fig. 5. The comparison for the remote stations shows high correlations with the GEOS-Chem data (R2>0.90). Jungfraujoch, Taeahn, and Gosan had relatively lower correlations (0.66-0.75), which likely relates to their inland locations and proximity to industrial sources or natural terrestrial flux regions, which are challenging to model, or from the impact of representativeness errors. The sites of East Asia (Taeahn and Gosan) have a large seasonal variability (>15 ppmv) due to the strong continental influence, which is not captured very well by the model (Fig. 5). The high correlation at Sable Island may imply that the CO2 fluxes or model transport over North America are better represented in the model than for other continents.

The GEOS-Chem data without the correction on Taeahn and Gosan show 0.6%-1.6% negative bias that is particularly larger during the winter and spring season. That large discrepancy may be due in part to the fact that the GEOS-Chem simulation used the annual mean anthropogenic CO2 emission inventory that averaged out the seasonal anthropogenic CO2 trends of China, resulting in the seasonal difference in the bias (~11 ppmv in the winter and ~2.5 ppmv in the summer for the Korean sites) and due in part to the lower initial condition for the CO2 (January 2004) than applied in Nassar et al. (2010).

Based on the comparison with those global ground measurements, GEOS-Chem data reproduce the observed CO2 data well with a systemic bias (3.6 ppmv or ~1.0%) and we can infer that the GOSAT data have a negative bias of ~2.0%, which is consistent with the recent validation study of Morino et al. (2011) that compared the NIES XCO2 with ground-based FTS XCO2 measurements from the Total Carbon Column Observing Network (TCCON, Wunch et al., 2010) and found a low bias of 8.85±4.75 ppm (2.3±1.2%). Another major L2 GOSAT dataset has been developed by the NASA-led Atmospheric Carbon Observations from Space (ACOS) team using a different retrieval algorithm (O’Dell et al., 2011) on the same observations. Wunch et al. (2011) compared the ACOS-GOSAT retrievals to TCCON and found a low bias of 1.8%, which they attribute to multiple sources. Retrieval of GOSAT data by another team (Butz et al., 2011) corrected for a 3% bias in the O2 A-band, which they identify as the primary source of the XCO2 bias in cloud-free observations, such that after the correction, the XCO2 bias for cloud-free observations was only -0.05% relative to TCCON. It should be noted that a different empirical correction approach is used to correct the TCCON ground-based FTS XCO2 data for presumed biases (Wunch et al., 2010). If the source of the GOSAT XCO2 bias overall is predominantly due to the O2 A-band, as suggested by Butz et al. (2011) based on an analysis of cloud-free observations, then overall GOSAT CO2 column densities should only have minor biases perhaps due to retrievals with low levels of cloud and aerosol, that were not excluded based on the CAI data and other screening methods. Until the entire cause of the GOSAT bias can be definitely confirmed, evaluation and comparison of GOSAT L2 CO2 products using a number of different methods (TCCON, model-based approaches, etc.) will be important.

4. 4 Contributions of Sources/Sinks to Atmospheric CO2

We estimated each source/sink contribution to the global atmospheric CO2 budget using a tagged CO2 simulation in which the each source/sink is treated as an individual tracer. The tagged simulation is useful since the geographically defined state vector can be applied for inverse modeling (Nassar et al., 2011) to constrain the surface CO2 fluxes and the spatial distribution and trends of each source/sink’s contribution can be understood. This calculation of the source/sink contribution to atmospheric CO2 can provide useful information for greenhouse gas reduction targets and strategies for policy-makers. Here the tagged simulation estimated the individual source/sink contribution to the global atmospheric CO2 concentration (ppmv) for 6 recent years (January 2004 to December 2009, Figs. 6-10). The tagged simulation results show the spatial and vertical distributions of each contribution. Table 2 represents the accumulated source/sink contributions over the 6-year period in terms of the global CO2 budget and annual trends.

Fig. 6.

The column-averaged CO2 fraction (unit: ppmv) contributed by fossil fuel combustion and cement production from a tagged GEOS-Chem simulation for January 2004 and December 2009.

Fig. 7.

Same as Fig. 6, but for the contribution from biomass burning.

Fig. 8.

Same as Fig. 6, but for the contribution from aviation.

Fig. 9.

Same as Fig. 6, but for the contribution from the terrestrial biosphere (net terrestrial exchange).

Fig. 10.

Same as Fig. 6, but for the contribution from oceanic exchange.

The source/sink contributions to global CO2 concentration and annual trends from 2004-2009.

Fig. 6 represents the global atmospheric CO2 contribution by fossil fuel and cement production. The globally-averaged accumulated contribution is about 19.5 ppmv for the 6 year period (2004-2009). There is a significant latitudinal gradient in the contribution that is particularly strong due to the large emissions from E. Asia and E. US despite inter-hemispheric transport (Fig. 6). The emission of CO2 from biomass burning is largest in the tropics (Africa, Amazonia, Indonesia) and the contribution to atmospheric CO2 is thus stronger within the tropical regions (>6.0 ppmv for 6 years, Fig. 7). GEOS-Chem used a 3-D emission inventory for aviation that is dominant in the intercontinental airline contrails such as over the N. Atlantic and N. Pacific regions. The influence is shown in Fig. 8. The contribution spreads throughout troposphere and low stratosphere due to the flight paths of commercial aircraft.

The tagged CO2 simulation calculated the contribution of the residual annual terrestrial exchange (-13.3 ppmv/6 years), which is higher in the northern hemisphere due to the larger fraction of the continents and hence terrestrial vegetation (Fig. 9). Since these fluxes were based on inversions using the standard 11 Transcom land regions (Baker et al., 2006) they contain very limited information on sources/sinks at sub-continental scales. Regional scale information of net ecosystem exchange based on measurements needs to be applied to more precisely to understand the atmospheric CO2 budget. Fig. 10 represents the spatial distribution of the oceanic contribution that is mostly influenced by the sea surface sinks at the high latitudes (>3.0 ppmv).

The mean global trend of CO2 based on the measurements has been growing (Keeling et al., 1995) from ~1.5 ppmv/year during the 1980s to the early 1990s (Conway et al., 1994) to ~1.8 ppmv/year from 1993 to 2005 (Matsueda et al., 2008). The recent satellite CO2 retrievals from the NASA’s Atmospheric InfraRed Sounder (AIRS) have been recently validated with aircraft data and the annual mean growth rate in the middle troposphere is 1.98 ppmv/year from 2002 to 2008 (Olsen et al., 2008).

The GEOS-Chem generally reproduces the observed trend of global atmospheric CO2 (2.08 ppmv/year) and the simulated global CO2 increase from 2004 to 2009 is shown in Fig. 11. This interannual trend is mainly driven by the national fossil fuel combustion inventory (3.2 ppmv/year, Table 2), which includes a contribution from cement manufacture of ~2-5%. Net terrestrial and oceanic exchange of CO2 are the main sinks (-2.2 ppmv/year and -0.6 ppmv/year, respectively, Table 2). Thus it is clear that a large reduction to the human contribution to the carbon cycle is required to limit the current global atmospheric CO2 increase. However, there are still many unknowns that could be better understood regarding the atmospheric CO2 budget, which could help to achieve emission reduction targets. Carbon exchange in the terrestrial biosphere (and to some extent the ocean) tends to have relatively large spatiotemporal variability, which is one of the key questions to better estimate the global CO2 budget. The influence of the atmospheric transport including El Ni~no/Southern Oscillation (ENSO) events also needs to be quantitatively understood.

Fig. 11.

The global column-averaged CO2 concentration simulated by GEOS-Chem model from 2004 to 2009, demonstrating the seasonal cycle and annual increase.


Here we simulated CO2 concentrations with a global 3-D chemical transport model (GEOS-Chem) and compared the model results with recently available GOSAT satellite observations from April 2009 to January 2010. We found that GEOS-Chem total CO2 columns overestimated the GOSAT data by ~1.0% with a reasonable agreement in the spatial distribution, but there is a significant continental dependence in those agreements. The highest agreement over North America perhaps indicate the best source/sink information based on the intense measurements and study over many years. Larger biases and poor correlations over Africa, South America and Europe might indicate limitations in model inventories and atmospheric transport. After correcting for the systematic underestimation of CO2 in GEOS-Chem data with the global ground-based measurements from WDCGG, a ~2.0% negative bias in GOSAT CO2 is inferred.

The unusual negative model bias with GOSAT over East Asia during the cold season and the larger negative model bias with the Korean stations may suggest that Chinese emission of CO2 from fossil fuel combustion exceeds the inventory values and has a relatively large seasonality. The monthly emission inventory of fossil fuel combustion and cement production by Andreas et al. (2011), recently became publicly available and will be implemented in a future study that will be more focused on the East Asian regions for a quantitative comparison with satellite and insitu observations. Subsequent versions of the GOSAT retrievals are addressing the current negative bias as well as the problem of massive data exclusion due to cloud and aerosol detection, which will enhance the overall quality of the data.

Although the atmospheric CO2 lifetime is approximately a century, our tagged CO2 simulation for a 6-year run (2004-2009) shows that most of sources and sinks have spatial gradients to their CO2 contributions. Human-induced emission from fossil fuel combustion and cement production is likely to be a main driving force to accelerate the current CO2 trends, which supports the international efforts to reduce the anthropogenic CO2 emissions. The information of quantitative CO2 fraction by each source/sink (e.g., biospheric CO2 exchanges) is useful to apply the inverse modeling with CO2 satellite measurements such as GOSAT data will help to better constrain the spatial distribution and efficiency of the individual source/sink, providing important information for CO2 reduction targets and strategies in the 21st century.


This subject is jointly supported by the institutional funding at Korea Environment Institute (2010-RE-17), Korean Ministry of Environment as “The Eco-technopia 21 project (No. 2010-05002-0083-0)”, and National Research Foundation of Korea (No. 201100099 40). We thank the GOSAT project office for providing access to the GOSAT SWIR L2 data products for this work based on their 2nd Research Announcement. We also thank the WDCGG and its contributors for making the ground-based measurements readily available to the scientific community.


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Fig. 1.

Fig. 1.
The monthly “balanced” net ecosystem exchange (NEE) from the CASA model run used in GEOS-Chem, which gives no global net annual CO2 flux. The left shows the global NEE distribution in July 2000 and the right shows the NEE in January 2000 (unit: 1013 molecules/cm2/s).

Fig. 2.

Fig. 2.
The seasonal variability in column-averaged CO2 concentration (xCO2) from GOSAT L2 products (April 2009 to January 2010, unit: ppmv).

Fig. 3.

Fig. 3.
The monthly average of GOSAT CO2 column density in July 2009 (top), the corresponding GEOS-Chem data (middle) and the difference between GEOS-Chem and GOSAT CO2 column density (GEOS-Chem-GOSAT) (bottom: unit: 1021 molecules/cm2).

Fig. 4.

Fig. 4.
Same as Fig. 3, but in January 2010.

Fig. 5.

Fig. 5.
The comparison of CO2 data between 12 ground-based measurement sites from WDCGG (black) and GEOS-Chem (red). The model data have been adjusted here by correcting them with the average GEOS-Chem bias from 12 sites (3.6 ppmv).

Fig. 6.

Fig. 6.
The column-averaged CO2 fraction (unit: ppmv) contributed by fossil fuel combustion and cement production from a tagged GEOS-Chem simulation for January 2004 and December 2009.

Fig. 7.

Fig. 7.
Same as Fig. 6, but for the contribution from biomass burning.

Fig. 8.

Fig. 8.
Same as Fig. 6, but for the contribution from aviation.

Fig. 9.

Fig. 9.
Same as Fig. 6, but for the contribution from the terrestrial biosphere (net terrestrial exchange).

Fig. 10.

Fig. 10.
Same as Fig. 6, but for the contribution from oceanic exchange.

Fig. 11.

Fig. 11.
The global column-averaged CO2 concentration simulated by GEOS-Chem model from 2004 to 2009, demonstrating the seasonal cycle and annual increase.

Table 1.

The monthly mean, bias, and correlation of GOSAT and GEOS-Chem CO2 total columns (unit: 1021 molecules/cm2).

GOSAT GEOS-Chem Model bias (%) R2
E. Asia
2009/04 8.127±0.39 8.024±0.36 -1.28 0.40
2009/06 7.836±0.4 7.968±0.38 1.65 0.58
2009/07 7.784±0.4 7.871±0.44 1.12 0.76
2009/08 7.690±0.45 7.786±0.44 1.24 0.74
2009/09 7.722±0.47 7.780±0.44 0.75 0.81
2009/10 7.721±0.52 7.803±0.43 1.05 0.61
2010/01 7.980±0.49 7.937±0.48 -0.53 0.59
Mean 0.57
2009/04 8.091±0.21 8.098±0.26 0.08 0.18
2009/06 7.988±0.35 8.034±0.28 0.57 0.10
2009/07 7.847±0.37 7.918±0.33 0.89 0.36
2009/08 7.831±0.31 7.929±0.3 1.24 0.34
2009/09 7.917±0.23 8.009±0.29 1.15 0.27
2009/10 7.844±0.41 7.929±0.4 1.08 0.38
2010/01 8.171±0.13 8.201±0.3 0.37 0.81
Mean 0.76
N. America
2009/04 7.938±0.43 8.008±0.47 0.88 0.64
2009/06 7.836±0.43 7.923±0.5 1.11 0.72
2009/07 7.753±0.46 7.834±0.54 1.03 0.79
2009/08 7.773±0.44 7.856±0.5 1.07 0.74
2009/09 7.650±0.46 7.731±0.54 1.05 0.77
2009/10 7.547±0.51 7.610±0.58 0.83 0.74
2010/01 7.675±0.64 7.752±0.66 0.99 0.69
Mean 0.99
2009/04 7.390±0.42 7.502±0.47 1.50 0.46
2009/06 7.435±0.36 7.484±0.38 0.66 0.48
2009/07 7.479±0.36 7.557±0.4 1.03 0.58
2009/08 7.494±0.42 7.610±0.43 1.53 0.53
2009/09 7.588±0.43 7.707±0.44 1.54 0.58
2009/10 7.675±0.51 7.814±0.48 1.78 0.58
2010/01 7.740±0.46 7.858±0.41 1.5 0.69
Mean 1.36
S. America
2009/04 7.765±0.59 7.947±0.46 2.29 0.31
2009/06 7.784±0.65 7.918±0.54 1.7 0.15
2009/07 7.800±0.49 7.879±0.53 1.03 0.16
2009/08 7.806±0.48 7.873±0.54 0.85 0.44
2009/09 7.855±0.36 7.910±0.51 0.69 0.17
2009/10 7.841±0.46 7.959±0.47 1.48 0.15
2010/01 7.855±0.37 7.944±0.47 1.12 0.17
Mean 1.31
2009/04 7.882±0.18 7.981±0.17 1.23 0.44
2009/06 7.922±0.17 7.992±0.16 0.87 0.41
2009/07 7.942±0.16 8.013±0.16 0.90 0.52
2009/08 7.928±0.16 8.013±0.16 1.06 0.58
2009/09 7.932±0.18 8.026±0.17 1.16 0.52
2009/10 7.975±0.18 8.080±0.18 1.30 0.61
2010/01 7.968±0.16 8.056±0.18 1.09 0.58
Mean 1.09
Continental Mean 1.01

Table 2.

The source/sink contributions to global CO2 concentration and annual trends from 2004-2009.

FF (fossil fuel and cement production), OC (ocean), BB (biomass burning), BF (biofuel burning), NTE (net terrestrial exchange), SHIP (shipping), AVI (aviation), Chem (chemical production), Corr (correction factor)
Contribution (ppmv) 19.5 -3.6 5.4 2.1 -13.3 0.5 0.42 2.92 -2.04 11.9
Trends (ppmv/year) 3.2 -0.58 0.88 0.34 -2.17 0.082 0.07 0.48 -0.34 2.08