Diagnosing spatial error structures in CO<sub>2</sub> mole fractions and XCO<sub>2</sub> column mole fractions from atmospheric transport

  • Lauvaux T
  • Díaz-Isaac L
  • Bocquet M
  • et al.
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<p><strong>Abstract.</strong> Atmospheric inversions inform about the magnitude and variations of greenhouse gas (GHG) sources and sinks from global to local scales. Deployment of observing systems such as spaceborne sensors and ground-based instruments distributed around the globe has started to offer an unprecedented amount of information to estimate surface exchanges of GHG at finer spatial and temporal scales. However, inversion methods still rely on imperfect atmospheric transport models of which error structures directly affect the inverse estimates of GHG fluxes. The impact of spatial error structures on the inverse fluxes increase concurrently with the density of the available measurements. In this study, we diagnose the spatial structures due to transport model errors affecting modeled in situ carbon dioxide (CO<sub>2</sub>) mole fractions and total column dry air mole fractions of CO<sub>2</sub> (XCO<sub>2</sub>). We implemented a cost-effective filtering technique recently developed in the meteorological data assimilation community to describe spatial error structures using a small-size ensemble. This technique can enable ensemble-based error analysis for multi-year inversions of sources and sinks. The removal of noisy structures in our small-size ensembles is evaluated by comparison to larger-size ensembles. A second filtering approach for error covariances is proposed (Wiener filter), producing similar results over the 1-month simulation period than a Schur filter. We conclude that key information about error variances and spatial error correlation structures are recoverable from small-size ensembles of about ten (10) members down to five (5), improving the representation of transport errors in mesoscale inversions of CO<sub>2</sub> fluxes. Moreover, error variances of in situ near-surface and free-tropospheric CO<sub>2</sub> mole fractions differ significantly from total column XCO<sub>2</sub> error variances. We conclude that error variances for remote sensing observations need to be quantified independently of in situ CO<sub>2</sub> mole fractions due to the complexity of spatial error structures at different altitudes. However, we show the potential use of meteorological error structures such as the mean horizontal wind speed, directly available from Ensemble Prediction Systems, to approximate spatial error correlations of in situ CO<sub>2</sub> mole fractions, with similarities in seasonal variations and characteristic error length scales.</p>




Lauvaux, T., Díaz-Isaac, L. I., Bocquet, M., & Bousserez, N. (2019). Diagnosing spatial error structures in CO&lt;sub&gt;2&lt;/sub&gt; mole fractions and XCO&lt;sub&gt;2&lt;/sub&gt; column mole fractions from atmospheric transport. Atmospheric Chemistry and Physics Discussions, 1–26. https://doi.org/10.5194/acp-2018-1113

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