Interannual variability of the global carbon cycle (1992–2005) inferred by inversion of atmospheric CO2 and δ13CO2 measurements

Rayner, P. J.; Law, R. M.; Allison, C. E.; Francey, R. J.; Trudinger, C. M.; Pickett-Heaps, C.

doi:10.1029/2007gb003068

Cited by 126 publications

(157 citation statements)

References 61 publications

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“…The sensitivity of the inferred flux estimates for boreal North America is not surprising since the GOSAT observational coverage is limited at high latitudes over North America. The temperate North America region has been described as a sink in previous inversions using flask observations of atmosphere CO 2 (Deng and Chen, 2011;Gurney et al, 2004;Peters et al, 2007;Rayner et al, 2008;Deng et al, 2007). Here we estimated the region to be a significant source in RUN_A, but a weak sink in RUN_B and a strong sink in RUN_C.…”

Section: Regional Flux Estimatesmentioning

confidence: 67%

Inferring regional sources and sinks of atmospheric CO2 from GOSAT XCO2 data

et al. 2014

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Abstract.We have examined the utility of retrieved columnaveraged, dry-air mole fractions of CO 2 (XCO 2 ) from the Greenhouse Gases Observing Satellite (GOSAT) for quantifying monthly, regional flux estimates of CO 2 , using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system. We focused on assessing the potential impact of biases in the GOSAT CO 2 data on the regional flux estimates. Using different screening and bias correction approaches, we selected three different subsets of the GOSAT XCO 2 data for the 4D-Var inversion analyses, and found that the inferred global fluxes were consistent across the three XCO 2 inversions. However, the GOSAT observational coverage was a challenge for the regional flux estimates. In the northern extratropics, the inversions were more sensitive to North American fluxes than to European and Asian fluxes due to the lack of observations over Eurasia in winter and over eastern and southern Asia in summer. The regional flux estimates were also sensitive to the treatment of the residual bias in the GOSAT XCO 2 data. The largest differences obtained were for temperate North America and temperate South America, for which the largest spread between the inversions was 1.02 and 0.96 Pg C, respectively. In the case of temperate North America, one inversion suggested a strong source, whereas the second and third XCO 2 inversions produced a weak and strong sink, respectively. Despite the discrepancies in the regional flux estimates between the three XCO 2 inversions, the a posteriori CO 2 distributions were in good agreement (with a mean difference between the three inversions of typically less than 0.5 ppm) with independent data from the Total Carbon Column Observing Network (TC-CON), the surface flask network, and from the HIAPER Pole-to-Pole Observations (HIPPO) aircraft campaign. The discrepancy in the regional flux estimates from the different inversions, despite the agreement of the global flux estimatesPublished by Copernicus Publications on behalf of the European Geosciences Union. 3704F. Deng et al.: Inferring regional sources and sinks of atmospheric CO 2 from GOSAT XCO 2 data suggests the need for additional work to determine the minimum spatial scales at which we can reliably quantify the fluxes using GOSAT XCO 2 . The fact that the a posteriori CO 2 from the different inversions were in good agreement with the independent data although the regional flux estimates differed significantly, suggests that innovative ways of exploiting existing data sets, and possibly additional observations, are needed to better evaluate the inferred regional flux estimates.

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Section: Regional Flux Estimatesmentioning

confidence: 67%

Inferring regional sources and sinks of atmospheric CO2 from GOSAT XCO2 data

et al. 2014

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“…This method has been employed for global inversions (Bousquet et al, 1999;Kaminski et al, 1999;Rayner et al, 1999;Gurney et al, 2002;Peylin et al, 2002;Gurney et al, 2003;Law et al, 2003;Baker et al, 2006;Rayner et al, 2008;Ciais 20 et al, 2010), as well as for many of the city scale inversions (Lauvaux et al, 2016;Bréon et al, 2015). This methodology was employed over South Africa in a previous optimal network design study (Nickless et al, 2015b).…”

Section: Bayesian Inverse Modelling Approachmentioning

confidence: 99%

Estimates of CO2 fluxes over the City of Cape Town, South Africa, through Bayesian inverse modelling

Nickless¹,

Engelbrecht²,

Brunke³

et al. 2017

Preprint

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Abstract. The results of a high resolution Bayesian inversion over the City of Cape Town, South Africa, are presented, which used observations of atmospheric CO 2 from sites at Robben Island and Hangklip lighthouses collected over a sixteen month period from March 2012 until June 2013. A Lagrangian particle dispersion model driven by the regional climate model Conformal Cubic Atmospheric Model (CCAM) was used to provide the sensitivities of the observations to the surface sources and boundary concentrations. This regional climate model was dynamically coupled to the CABLE (Community Atmosphere 5Biosphere Land Exchange) model, which provided prior estimates of the biogenic fluxes. Prior estimates of the fossil fuel emissions were obtained from an inventory analysis specifically carried out for this inversion exercise, making use of vehicle count data, population census data, fuel usage at industrial point sources, and aviation and shipping vessel counts. The inversion solved for the actual concentration measurements at each site, which was made possible by the use of the Cape Point background site to provide information on the boundaries, and was necessary due to the effect of topography on the atmospheric 10 transport, affecting particularly the sensitivity of the Robben Island site to the surface fluxes. Night-time observations were included, but allocated much larger errors compared to the daytime observations. The inversion was able to substantially improve the agreement between the modelled and observed concentrations, and able to better represent the diurnal cycle in the concentrations compared with the prior modelled concentrations. The mean bias in the modelled concentrations was reduced from -2.9 ppm, with interquartile range -9.1 to 3.7, for the prior modelled 15 concentrations, to 0.5, with interquartile range -1.5 to 1.5, for the posterior modelled concentrations at Robben Island, and from a bias of 2.4 ppm in the prior modelled concentrations at the Hangklip site, with interquartile range -2.3 to 6.5, to a bias of 0.04, with interquartile range -1.1 to 0.8. The standard deviations of the posterior residuals at both sites were reduced to values below that of the observed concentrations.The inversion solved for working week and weekend fossil fuel emissions, and weekly biogenic fluxes, each split into day model errors or the uncertainty in the fluxes was not specified high enough for some months. A companion paper on sensitivity analyses will address different options for the specification of the correlations between errors in the modelled concentrations, how these prior errors are determined, how correlations are determined between the prior fluxes, and how the state vector is specified. Greater confidence is given to the inversion's ability to correct the total flux within each pixel, rather than the individual flux estimates.

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“…The 13 CO 2 records are also extended to 2012. Calculations for the data uncertainties are as in Rayner et al (2008). We use only the CCAM model from the earlier study.…”

Section: Diagnostics For Inversionsmentioning

confidence: 99%

“…Regional flux estimates can come either from atmospheric inversions or models. In this section we use an update of the Cubic Conformal Atmospheric Model (CCAM) inversion used in Peylin et al (2013) which extended the study of Rayner et al (2008). The update extends the study period from 1992-2012.…”

Section: Diagnostics For Inversionsmentioning

confidence: 99%

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Recent changes in the global and regional carbon cycle: analysis of first-order diagnostics

Stavert

Scholze

Ahlström³

et al. 2015

Biogeosciences

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Abstract.We analyse global and regional changes in CO 2 fluxes using two simple models, an airborne fraction of anthropogenic emissions and a linear relationship with CO 2 concentrations. We show that both models are able to fit the non-anthropogenic (hereafter natural) flux over the length of the atmospheric concentration record. Analysis of the linear model (including its uncertainties) suggests no significant decrease in the response of the natural carbon cycle. Recent data points rather to an increase. We apply the same linear diagnostic to fluxes from atmospheric inversions. Flux responses show clear regional and seasonal patterns driven by terrestrial uptake in the northern summer. Ocean fluxes show little or no linear response. Terrestrial models show clear responses, agreeing globally with the inversion responses, however the spatial structure is quite different, with dominant responses in the tropics rather than the northern extratropics.

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Interannual variability of the global carbon cycle (1992–2005) inferred by inversion of atmospheric CO₂ and δ¹³CO₂ measurements

Cited by 126 publications

References 61 publications

Inferring regional sources and sinks of atmospheric CO<sub>2</sub> from GOSAT XCO<sub>2</sub> data

Inferring regional sources and sinks of atmospheric CO<sub>2</sub> from GOSAT XCO<sub>2</sub> data

Estimates of CO<sub>2</sub> fluxes over the City of Cape Town, South Africa, through Bayesian inverse modelling

Recent changes in the global and regional carbon cycle: analysis of first-order diagnostics

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Interannual variability of the global carbon cycle (1992–2005) inferred by inversion of atmospheric CO2 and δ13CO2 measurements

Cited by 126 publications

References 61 publications

Inferring regional sources and sinks of atmospheric CO&lt;sub&gt;2&lt;/sub&gt; from GOSAT XCO&lt;sub&gt;2&lt;/sub&gt; data

Inferring regional sources and sinks of atmospheric CO&lt;sub&gt;2&lt;/sub&gt; from GOSAT XCO&lt;sub&gt;2&lt;/sub&gt; data

Estimates of CO&lt;sub&gt;2&lt;/sub&gt; fluxes over the City of Cape Town, South Africa, through Bayesian inverse modelling

Recent changes in the global and regional carbon cycle: analysis of first-order diagnostics

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Interannual variability of the global carbon cycle (1992–2005) inferred by inversion of atmospheric CO₂ and δ¹³CO₂ measurements

Inferring regional sources and sinks of atmospheric CO<sub>2</sub> from GOSAT XCO<sub>2</sub> data

Inferring regional sources and sinks of atmospheric CO<sub>2</sub> from GOSAT XCO<sub>2</sub> data

Estimates of CO<sub>2</sub> fluxes over the City of Cape Town, South Africa, through Bayesian inverse modelling