The Total Carbon Column Observing Network

Wunch, Debra; Toon, G. C.; Blavier, Jean-François L.; Washenfelder, R. A.; Notholt, Justus; Connor, B. J.; Griffith, David; Sherlock, V.; Wennberg, P. O.

doi:10.1098/rsta.2010.0240

Cited by 1,059 publications

(1,293 citation statements)

References 59 publications

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“…The partition of anthropogenic emissions between agriculture and waste, fossil fuel extraction and use, and biomass and biofuel burning also shows good consistency between top-down and bottom-up approaches (Table 2 and Fig. 7 (Wuebbles and Hayhoe, 2002;GAINS model). Although biofuel is a small source globally (∼ 12 Tg CH 4 yr −1 ), more estimates are needed to allow a proper uncertainty assessment.…”

Section: Anthropogenic Emissionsmentioning

confidence: 99%

“…4) and for the continental shelf (saturation anomaly ∼ 0.2). In their synthesis indirectly referring to the original observations from Lambert and Schmidt (1993), Wuebbles and Hayhoe (2002) use a value of 5 Tg CH 4 yr −1 . Proposed explanations for discrepancies regarding sea-to-air methane emissions in the open ocean rely on experimental biases in the former study of Swinnerton and Linnenbom (1967) (Rhee et al, 2009).…”

Section: Oceanic Sourcesmentioning

confidence: 99%

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The global methane budget 2000–2012

Saunois

Bousquet

Poulter

et al. 2016

Earth Syst. Sci. Data

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Abstract. The global methane (CH 4 ) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH 4 over the past decade. Emissions and concentrations of CH 4 are continuing to increase, making CH 4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH 4 sources that overlap geographically, and from the destruction of CH 4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (∼ biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). . Top-down inversions consistently infer lower emissions in China (∼ 58 Tg CH 4 yr −1 , range 51-72, −14 %) and higher emissions in Africa (86 Tg CH 4 yr −1 , range 73-108, +19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models.The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40 % on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions.

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Section: Anthropogenic Emissionsmentioning

confidence: 99%

Section: Oceanic Sourcesmentioning

confidence: 99%

The global methane budget 2000–2012

Saunois

Bousquet

Poulter

et al. 2016

Earth Syst. Sci. Data

Self Cite

906

733

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“…These constituents, which include CO 2 and CH 4 , are retrieved from near-infrared solar absorption spectra using a nonlinear least-squares fitting algorithm referred to as GFIT (Wunch et al, , 2011a. The TC-CON data have been used to compare with satellite data and model simulations (Dils et al, 2006;Morino et al, 2011;Schneising et al, 2012;Saito et al, 2012;Heymann et al, 2012;Oshchepkov et al, 2013;Yoshida et al, 2013;Belikov et al, 2013;Dils et al, 2014;Nguyen et al, 2014;Scheepmaker et al, 2015) and elucidate the temporal behavior of greenhouse gases (Wunch et al, 2009;Messerschmidt et al, 2010;Ishizawa et al, 2016a).…”

Section: Tccon Datamentioning

confidence: 99%

Bias corrections of GOSAT SWIR XCO2 and XCH4 with TCCON data and their evaluation using aircraft measurement data

et al. 2016

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Abstract. We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO 2 (XCO 2 ) and CH 4 (XCH 4 ) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TC-CON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO 2 and XCH 4 data derived from aircraft measurements taken for the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO 2 /XCH 4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.

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“…The retrieved CO 2 column is then divided by this retrieved O 2 column to derive the column average dry-air mole fraction. The method is explained in further detail in Wunch et al (2011a).…”

Section: Xco 2 Retrievalmentioning

confidence: 99%

“…Since its inception in 2004, the Total Carbon Column Observing Network (TCCON) has created a high-quality database of near-infrared (NIR) solar absorption spectra. Both networks have been used to deduce total column amounts of various atmospheric constituents, the main focus being centered around O 3 chemistry (NDACC; see, e.g., Notholt et al, 1995) and the carbon cycle (TCCON; see, e.g., Wunch et al, 2011a).…”

Section: Introductionmentioning

confidence: 99%

Retrieval of xCO2 from ground-based mid-infrared (NDACC) solar absorption spectra and comparison to TCCON

et al. 2016

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Abstract. High-resolution solar absorption spectra, taken within the Network for the Detection of Atmospheric Composition Change Infrared Working Group (NDACC-IRWG) in the mid-infrared spectral region, are used to infer partial or total column abundances of many gases. In this paper we present the retrieval of a column-averaged mole fraction of carbon dioxide from NDACC-IRWG spectra taken with a Fourier transform infrared (FTIR) spectrometer at the site in Ny-Ålesund, Spitsbergen. The retrieved time series is compared to colocated standard TCCON (Total Carbon Column Observing Network) measurements of column-averaged dry-air mole fractions of CO 2 (denoted by xCO 2 ). Comparing the NDACC and TCCON retrievals, we find that the sensitivity of the NDACC retrieval is lower in the troposphere (by a factor of 2) and higher in the stratosphere, compared to TCCON. Thus, the NDACC retrieval is less sensitive to tropospheric changes (e.g., the seasonal cycle) in the column average.

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The Total Carbon Column Observing Network

Cited by 1,059 publications

References 59 publications

The global methane budget 2000–2012

The global methane budget 2000–2012

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

Retrieval of xCO<sub>2</sub> from ground-based mid-infrared (NDACC) solar absorption spectra and comparison to TCCON

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The Total Carbon Column Observing Network

Cited by 1,059 publications

References 59 publications

The global methane budget 2000–2012

The global methane budget 2000–2012

Bias corrections of GOSAT SWIR XCO&lt;sub&gt;2&lt;/sub&gt; and XCH&lt;sub&gt;4&lt;/sub&gt; with TCCON data and their evaluation using aircraft measurement data

Retrieval of xCO&lt;sub&gt;2&lt;/sub&gt; from ground-based mid-infrared (NDACC) solar absorption spectra and comparison to TCCON

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

Retrieval of xCO<sub>2</sub> from ground-based mid-infrared (NDACC) solar absorption spectra and comparison to TCCON