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Discussion papers
https://doi.org/10.5194/acp-2018-1113
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-1113
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Feb 2019

Research article | 04 Feb 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Diagnosing spatial error structures in CO2 mole fractions and XCO2 column mole fractions from atmospheric transport

Thomas Lauvaux1,a, Liza I. Díaz-Isaac1,b, Marc Bocquet2, and Nicolas Bousserez3,c Thomas Lauvaux et al.
  • 1Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA, USA
  • 2CEREA, joint laboratory École des Ponts ParisTech and EDF R&D, Université Paris-Est, Champs-sur-Marne, France
  • 3University of Colorado Boulder, Boulder, CO, USA
  • anow at: Laboratoire des Sciences du Climat et de l'Environnement, CEA, CNRS, UVSQ/IPSL, Université Paris-Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette CEDEX, France
  • bnow at: Scripps Institution of Oceanography, University of California, San Diego, 92093, USA
  • cnow at: European Centre for Medium-Range Weather Forecasts, Reading, UK

Abstract. 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 (CO2) mole fractions and total column dry air mole fractions of CO2 (XCO2). 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 CO2 fluxes. Moreover, error variances of in situ near-surface and free-tropospheric CO2 mole fractions differ significantly from total column XCO2 error variances. We conclude that error variances for remote sensing observations need to be quantified independently of in situ CO2 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 CO2 mole fractions, with similarities in seasonal variations and characteristic error length scales.

Thomas Lauvaux et al.
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Thomas Lauvaux et al.
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Latest update: 25 Apr 2019
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Short summary
A small-size ensemble of mesoscale simulations has been filtered to characterize the spatial structures of transport errors in atmospheric CO2 mixing ratios. The extracted error structures in in situ and column CO2 show similar length scales compared to other meteorological variables, including seasonality, which could be used as proxies in regional inversion systems.
A small-size ensemble of mesoscale simulations has been filtered to characterize the spatial...
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