Atmospheric carbon gases retrieved from SCIAMACHY by WFM-DOAS:  version 0.5 CO and CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; and impact of calibration improvements on CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; retrieval

Buchwitz, Michael; Beek, R. de; Noël, Stefan; Burrows, J. P.; Bovensmann, Heinrich; Schneising, Oliver; Khlystova, Iryna; Bruns, M.; Bremer, H.; Bergamaschi, P.; Körner, Stefan; Heimann, Martin

doi:10.5194/acp-6-2727-2006

Cited by 160 publications

(100 citation statements)

References 47 publications

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“…Bösch et al [33] use X CO 2 Park Falls data to reveal biases in the SCIAMACHY data. De Beek et al [34], Buchwitz et al [35] and Reuter et al [36,37] have significantly improved the SCIAMACHY retrieval algorithms, again using TCCON data as their ground reference.…”

Section: (B) Satellite Validationmentioning

confidence: 99%

The Total Carbon Column Observing Network

Wunch

Toon

Blavier

et al. 2011

Phil. Trans. R. Soc. A.

1,095

1,243

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A global network of ground-based Fourier transform spectrometers has been founded to remotely measure column abundances of CO 2 , CO, CH 4 , N 2 O and other molecules that absorb in the near-infrared. These measurements are directly comparable with the near-infrared total column measurements from space-based instruments. With stringent requirements on the instrumentation, acquisition procedures, data processing and calibration, the Total Carbon Column Observing Network (TCCON) achieves an accuracy and precision in total column measurements that is unprecedented for remotesensing observations (better than 0.25% for CO 2 ). This has enabled carbon-cycle science investigations using the TCCON dataset, and allows the TCCON to provide a link between satellite measurements and the extensive ground-based in situ network.

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Section: (B) Satellite Validationmentioning

confidence: 99%

The Total Carbon Column Observing Network

Wunch

Toon

Blavier

et al. 2011

Phil. Trans. R. Soc. A.

1,095

1,243

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show abstract

“…Between 2003 and 2012, the Scanning Imaging Absorption spectrometer for Atmospheric CartograpHY (SCIA-MACHY) was operated on board the ESA ENVIronmental SATellite (ENVISAT), providing nearly 10 years of XCH 4 sensitive to the atmospheric boundary layer (Burrows et al, 1995;Buchwitz et al, 2006;Dils et al, 2006;. These satellite retrievals were the first to be used for global and regional inverse modelling of methane fluxes (Meirink et al, 2008a;Bergamaschi et al, 2007Bergamaschi et al, , 2009).…”

Section: Satellite Data Of Column-averaged Chmentioning

confidence: 99%

The global methane budget 2000–2012

Saunois

Bousquet

Poulter

et al. 2016

Earth Syst. Sci. Data

934

733

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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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“…These measurements form a record of integrated net CO 2 exchange from multiple processes, geographic areas, and times (Peters et al 2007). Retrievals of XCO 2 from the near-infrared nadir spectral radiance and solar irradiance measurements of the SCIAMACHY satellite instrument between 2003 and 2005 are compared with global XCO 2 obtained from CarbonTracker, which showed that the measured CO 2 year-toyear increase agrees within about 1 ppm/year with CarbonTracker (Buchwitz et al 2006). The Carbon Tracker was used to estimate net ecosystem CO 2 exchange over China for the period from 2001 to 2010, in which additional Chinese and Asian CO 2 observations were input to improve its estimation accuracy (Zhang et al 2014a).…”

Section: Classical Methodsmentioning

confidence: 99%

A fundamental theorem of Earth’s surface modelling

et al. 2016

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Ground observation is able to obtain highly accurate data with high temporal resolution at observation points, but these observation points are too sparse to satisfy some application requirements. Satellite remote sensing and system models can frequently supply spatially continuous information about the Earth's surface, which is impossible from ground-based investigations, but remote sensing description and system model simulation are not able to directly obtain process parameters. The most effective method for Earth surface modelling entails the integration of satellite observations or system models with ground observations. However, the full integration was ignored in most of the methods. For finding a solution for this problem, we suggest an alternative method, high accuracy surface modeling (HASM), which takes global approximate information (e.g., remote sensing images or model simulation results) as its driving field and local accurate information (e.g., ground observation data and/or sampling data) as its optimum control constraints. HASM completes its operation when its output satisfies the iteration stopping criterion which is determined by application requirement for accuracy. A Fundamental Theorem of Earth Surface Modelling (FTESM) is abstracted on the basis of applying HASM to simulating surfaces of elevation, soil properties, changes of ecosystem services, and driving forces of the changes on multi-scales for about 20 years. FTESM is described as ''an Earth's surface system or a component surface of the Earth's surface environment can be simulated with HASM when its spatial resolution is fine enough, which is uniquely defined by both extrinsic and intrinsic invariants of the surface''. From FTESM, seven corollaries have been deduced, corresponding to interpolation, upsaling, downscaling, data fusion and data assimilation respectively.

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Atmospheric carbon gases retrieved from SCIAMACHY by WFM-DOAS: version 0.5 CO and CH<sub>4</sub> and impact of calibration improvements on CO<sub>2</sub> retrieval

Cited by 160 publications

References 47 publications

The Total Carbon Column Observing Network

The Total Carbon Column Observing Network

The global methane budget 2000–2012

A fundamental theorem of Earth’s surface modelling

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