Comparison of atmospheric CO<sub>2</sub> observed by GOSAT and two ground stations in China

Qu, Yan Chen; Zhang, Chunmin; Wang, Dingyi; Peng-bo, Tian; Bai, Wenguang; Zhang, Xingying; Zhang, Peng; Dai, Haishan; Wu, Qingmiao

doi:10.1080/01431161.2013.768362

Cited by 28 publications

(13 citation statements)

References 8 publications

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“…The spatial distributions of CTDAS and GOSAT XCO 2 change seasonally, all with the highest CO 2 concentration in spring and the lowest CO 2 in summer. These global seasonal variations are reasonable and agree with many previously published results [19,48]. For the whole study period, there is general agreement between CTDAS and GOSAT with point-by-point correlation of 0.77 (P \ 0.05).…”

Section: Comparison Between Ctdas and Gosatsupporting

confidence: 80%

“…We found that the seasonal cycle agrees well between CTDAS and GO-SAT for the NH (point-by-point correlation r = 0.79 P \ 0.05) and the SH (point-by-point correlation r = 0.65 P \ 0.05), with bias of -0.04 ± 1.96 and -0.25 ± 1.45 ppm, respectively. This is inconsistent with Cogan et al [20] and Qu et al [48] that show a larger bias in the NH. This discrepancy is partly due to the compensation effect of a positive bias (overestimate) in the lowest peak and a negative bias (underestimate) in the highest peak of CTDAS XCO 2 time series, leading to a small mean bias in the NH, but still with a large amount of scatter (e.g., standard deviation of *1.96 ppm) in simulated XCO 2 .…”

Section: Comparison Between Ctdas and Gosatcontrasting

confidence: 57%

“…Figure 3 shows the time series of XCO 2 from CTDAS and GOSAT, for NH and SH in (a-b) as well as seven different continents in (c-i). As the effect of the natural CO 2 sources and human activities are more variable on the land, the hemispheric variations of both the CTDAS and GOSAT XCO 2 are significantly different, with higher (lower) XCO 2 concentrations and more (less) seasonal fluctuation over the NH (SH) land [20,48]. We found that the seasonal cycle agrees well between CTDAS and GO-SAT for the NH (point-by-point correlation r = 0.79 P \ 0.05) and the SH (point-by-point correlation r = 0.65 P \ 0.05), with bias of -0.04 ± 1.96 and -0.25 ± 1.45 ppm, respectively.…”

Section: Comparison Between Ctdas and Gosatmentioning

confidence: 99%

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Comparing simulated atmospheric carbon dioxide concentration with GOSAT retrievals

Zhang

Chen

et al. 2015

Science Bulletin

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Satellite observations of atmospheric carbon dioxide (CO 2 ) provide a useful way to improve the understanding of global carbon cycling. In this paper, we present a comparison between simulated CO 2 concentrations from an inversion model of the CarbonTracker Data Assimilation System (CTDAS) and satellite-based CO 2 measurements of column-averaged dry air mole fraction (denoted XCO 2 ) derived from version 3.3 Atmospheric CO 2 Observations from Space retrievals of the Greenhouse Gases Observing SATellite (ACOS-GOSAT) L2 data products. We examine the differences of CTDAS and GOSAT to provide important guidance for the further investigation of CTDAS in order to quantify the corresponding flux estimates with satellite-based CO 2 observations. We find that the mean point-by-point difference (CTDAS-GOSAT) between CTDAS and GOSAT XCO 2 is -0.11 ± 1.81 ppm, with a high agreement (correlation r = 0.77, P \ 0.05) over the studied period. The latitudinal zonal variations of CTDAS and GOSAT are in general agreement with clear seasonal fluctuations. The major exception occurs in the zonal band of 0°-15°N where the difference is approximately 4 ppm, indicating that large uncertainty may exist in the assimilated CO 2 for the lowlatitude region of the Northern Hemisphere (NH). Additionally, we find that the hemispherical/continental differences between CTDAS and GOSAT are typically less than 1 ppm, but obvious discrepancies occur in different hemispheres/continents, with high consistency (point-bypoint correlation r = 0.79, P \ 0.05) in the NH and a weak correlation (point-by-point correlation r = 0.65, P \ 0.05) in the Southern Hemisphere. Overall, the difference of CTDAS and GOSAT is small, and the comparison of CTDAS and GOSAT will further instruct the inverse modeling of CO 2 fluxes using GOSAT.

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Section: Comparison Between Ctdas and Gosatsupporting

confidence: 80%

Section: Comparison Between Ctdas and Gosatcontrasting

confidence: 57%

Section: Comparison Between Ctdas and Gosatmentioning

confidence: 99%

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Comparing simulated atmospheric carbon dioxide concentration with GOSAT retrievals

Zhang

Chen

et al. 2015

Science Bulletin

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“…The differences among the forest, grassland and savanna were most likely due to the regional carbon emissions (Zhou et al, 2005;Wu et al, 2012). Most forest measurement sites in this study located in Europe and Northern America, in which intensive human activities and large amount of carbon emissions were reported (Le Quere et al, 2009;Qu et al, 2013). This result could be interpreted by the comparison of atmospheric CO 2 over terrestrial ecosystems and urban area, which were responsible for the largest proportion of anthropogenic emissions in the world (Churkina, 2008).…”

Section: Evolution and Variation Of Co 2 Concentrations At Individualmentioning

confidence: 69%

Evolution and variation of atmospheric carbon dioxide concentration over terrestrial ecosystems as derived from eddy covariance measurements

Liu

Zhu

et al. 2015

Atmospheric Environment

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h i g h l i g h t sAtmospheric CO 2 concentration over terrestrial ecosystems (ACTE) was analyzed. ACTE was higher by 9.0 ppm in winter and lower 2.1 ppm in summer than global means. Annual mean and seasonal amplitude of ACTE increased with 2.04 and 0.60 ppm yr À1 . The annual CO 2 concentration showed large variation among ecosystems. a r t i c l e i n f o (CO 2 ) is the most important anthropogenic greenhouse gas contributing to global climate change. Understanding the temporal and spatial variations of CO 2 concentration over terrestrial ecosystems provides additional insight into global atmospheric variability of CO 2 concentration. Using 355 site-years of CO 2 concentration observations at 104 eddy-covariance flux tower sites in Northern Hemisphere, we presented a comprehensive analysis of evolution and variation of atmospheric CO 2 concentration over terrestrial ecosystem (ACTE) for the period of 1997e2006. Our results showed that ACTE exhibited a strong seasonal variations, with an average seaonsal amplitude (peak-trough difference) of 14.8 ppm, which was approximately threefold that global mean CO 2 observed in Mauna Loa in the United States (MLO). The seasonal variation of CO 2 were mostly dominant by terrestrial carbon fluxes, i.e., net ecosystem procution (NEP) and gross primary produciton (GPP), with correlation coefficient(r) were À0.55 and À0.60 for NEP and GPP, respectively. However, the influence of carbon fluxes on CO 2 were not significant at interannual scale, which implyed that the inter-annual changing trends of atmospheric CO 2 in Northern Hemisphere were likely to depend more on anthropogenic CO 2 emissions sources than on ecosystem change. It was estimated, by fitting a harmonic model to monthly-mean ACTE, that both annual mean and seasonal amplitude of ACTE increased over the 10-year period at rates of 2.04 and 0.60 ppm yr À1 , respectively. The uptrend of annual ACTE could be attributed to the dramatic global increase of CO 2 emissions during the study period, whereas the increasing amplitude could be related to the increases in Northern Hemisphere biospheric activity. This study also found that the annual CO 2 concentration showed large variation among ecosystems, with the high value appeared in deciduous broadleaf forest, evergreen broadleaf forest and cropland. We attribute these discrepancies to both differential local anthropogenic impacts and carbon sequestration abilities across ecosystem types.

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“…The kriging and minimum curvature methods have been the most commonly used interpolation methods in recent geostatistical applications (Cressie and Wikle 2011;Chiles and Delfiner 1999). Kriging was originally developed in the science of mining, but it is currently used in a wide variety of fields in the geophysical and geographical sciences (Chiles and Delfiner 1999;Qu et al 2013). Kriging is a spatial statistical method for interpolating, or sometimes extrapolating, data.…”

Section: Introductionmentioning

confidence: 99%

Global mapping of greenhouse gases retrieved from GOSAT Level 2 products by using a kriging method

Watanabe

Hayashi

Saeki

et al. 2015

International Journal of Remote Sensing

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Because a synoptic overview facilitates understanding of the temporal and spatial changes in the global distribution of greenhouse gases, we developed a statistical spatial estimation method using kriging. Level 3 (L3) data products for the Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for Carbon Observation (TANSO) Fourier Transform Spectrometer (FTS) Short Wave Infrared (SWIR) were generated from column-averaged, dry-air mole fractions of carbon dioxide (XCO 2 ) and methane (XCH 4 ) TANSO-FTS SWIR Level 2 (L2) products using this method. Although there have been some reports on the use of kriging for analysing GOSAT products, the kriging method used in this research was specifically adapted to the statistical characteristics of GOSAT L2 products. In the context of using data for atmospheric research, spatially interpolated data (GOSAT L3 products) cannot be more accurate than modelsimulated global distributions of gas concentrations (GOSAT Level 4B (L4B) products), which are generated using an atmospheric tracer transport model. However, the L3 product takes much less time to generate than the L4B. It would take about a year to produce the L4B after generation of an L2 product. The great advantage of the L3 product is that it gives a comprehensive and reasonable monthly global distribution of gas concentrations with little delay. The L3 product using the kriging method can be generated on a monthly basis by estimating global semi-variogram curves from the L2 products for each month and interpolating spatially within a region with a radius of 1000 km from existing L2 data locations. The main purpose of this paper is to describe the methodology and characteristics of kriging used to generate the GOSAT L3 product, not for strictly scientific use of the estimated values, but for a reasonable global map of gas concentrations derived statistically from the sparsely observed L2 products within a short time frame. The characteristics of this method are compared to XCO 2 products simulated with an atmospheric tracer transport model. The results show that the method proposed in this study is of practical use for generating L3 products from L2 products.

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Comparison of atmospheric CO₂ observed by GOSAT and two ground stations in China

Cited by 28 publications

References 8 publications

Comparing simulated atmospheric carbon dioxide concentration with GOSAT retrievals

Comparing simulated atmospheric carbon dioxide concentration with GOSAT retrievals

Evolution and variation of atmospheric carbon dioxide concentration over terrestrial ecosystems as derived from eddy covariance measurements

Global mapping of greenhouse gases retrieved from GOSAT Level 2 products by using a kriging method

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Comparison of atmospheric CO2 observed by GOSAT and two ground stations in China

Cited by 28 publications

References 8 publications

Comparing simulated atmospheric carbon dioxide concentration with GOSAT retrievals

Comparing simulated atmospheric carbon dioxide concentration with GOSAT retrievals

Evolution and variation of atmospheric carbon dioxide concentration over terrestrial ecosystems as derived from eddy covariance measurements

Global mapping of greenhouse gases retrieved from GOSAT Level 2 products by using a kriging method

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Product

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Comparison of atmospheric CO₂ observed by GOSAT and two ground stations in China