2012
DOI: 10.5194/acp-12-2441-2012
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Error characterization of CO<sub>2</sub> vertical mixing in the atmospheric transport model WRF-VPRM

Abstract: Abstract.One of the dominant uncertainties in inverse estimates of regional CO 2 surface-atmosphere fluxes is related to model errors in vertical transport within the planetary boundary layer (PBL). In this study we present the results from a synthetic experiment using the atmospheric model WRF-VPRM to realistically simulate transport of CO 2 for large parts of the European continent at 10 km spatial resolution. To elucidate the impact of vertical mixing error on modeled CO 2 mixing ratios we simulated a month… Show more

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Cited by 56 publications
(62 citation statements)
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“…As a consequence the MH is one of the most important parameters in air pollution and greenhouse gas transport modelling at regional scales and at the same time considered to be one of the major sources of uncertainty in CO 2 transport modelling (Stephens and Keeling, 2000;Gerbig et al, 2009). For instance, previous model-model and model-data comparisons of mesoscale models found differences in simulated MH ∼25-30 % during daytime over land (Sarrat et al, 2007a, b;Gerbig et al, 2008;Hu et al, 2010, Kretschmer et al, 2012. Gerbig et al (2008) showed that MH discrepancies of this size lead to uncertainties of 3 ppm in CO 2 , which corresponds to about 30 % uncertainty in regional fluxes, simulated in summertime over a domain covering most of Europe.…”
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confidence: 99%
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“…As a consequence the MH is one of the most important parameters in air pollution and greenhouse gas transport modelling at regional scales and at the same time considered to be one of the major sources of uncertainty in CO 2 transport modelling (Stephens and Keeling, 2000;Gerbig et al, 2009). For instance, previous model-model and model-data comparisons of mesoscale models found differences in simulated MH ∼25-30 % during daytime over land (Sarrat et al, 2007a, b;Gerbig et al, 2008;Hu et al, 2010, Kretschmer et al, 2012. Gerbig et al (2008) showed that MH discrepancies of this size lead to uncertainties of 3 ppm in CO 2 , which corresponds to about 30 % uncertainty in regional fluxes, simulated in summertime over a domain covering most of Europe.…”
mentioning
confidence: 99%
“…Nevertheless, wind shear caused by surface friction can very well lead to the development of a mixing layer, and thus a MH can be diagnosed (Stull, 1988;Vogelezang and Holtslag, 1996;Seibert et al, 2000). As a consequence, model errors in MH at night are at least a factor two larger and are substantially biased , which has been shown to cause biases in simulated CO 2 concentrations (Kretschmer et al, 2012), and which in turn leads to potentially serious systematic errors in the retrieved fluxes. For daytime data such biases of the transport model are usually neglected in inversions, while nighttime data obtained within the PBL are not used, to avoid biases in the inferred surface fluxes (e.g.…”
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confidence: 99%
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“…BLH model denotes this wrong boundary layer height. In order to achieve this we use the approach as implemented by Kretschmer et al (2012). This approach assumes that errors in the simulated boundary layer height are caused by incorrect vertical distribution of CO 2 in a given atmospheric column, such that the total column concentration remains unchanged.…”
Section: Experimental Set-upmentioning
confidence: 99%
“…Therefore, we also use the simultaneous assimilation framework; the ensemble Kalman filter (EnKF) was used to constrain CO 2 concentrations and the ensemble Kalman smoother (EnKS) was used to optimize surface CO 2 fluxes. Since the regional chemical transport models, compared to global models, have some advantages in reproducing the effects of meso-microscale transport on atmospheric CO 2 distributions (Ahmadov et al, 2009;Pillai et al, 2011;Kretschmer et al, 2012), we choose a regional model, Regional Atmospheric Modeling System and Community Multi-scale Air Quality (RAMS-CMAQ) (Zhang et al, 2002(Zhang et al, , 2003(Zhang et al, , 2007Kou et al, 2013;Liu et al, 2013;Huang et al, 2014), to develop this inversion system. For simplicity, this system is referred to as CFI-CMAQ (Carbon Flux Inversion system and Community Multi-scale Air Quality).…”
Section: Z Peng Et Al: a Regional Carbon Data Assimilation Systemmentioning
confidence: 99%