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Titel Cloud-scale model intercomparison of chemical constituent transport in deep convection
VerfasserIn M. C. Barth, S.-W. Kim, C. Wang, K. E. Pickering, L. E. Ott, G. Stenchikov, M. Leriche, S. Cautenet, J.-P. Pinty, Ch. Barthe, C. Mari, J. H. Helsdon, R. D. Farley, A. M. Fridlind, A. S. Ackerman, V. Spiridonov, B. Telenta
Medientyp Artikel
Sprache Englisch
ISSN 1680-7316
Digitales Dokument URL
Erschienen In: Atmospheric Chemistry and Physics ; 7, no. 18 ; Nr. 7, no. 18 (2007-09-18), S.4709-4731
Datensatznummer 250005193
Publikation (Nr.) Volltext-Dokument vorhandencopernicus.org/acp-7-4709-2007.pdf
 
Zusammenfassung
Transport and scavenging of chemical constituents in deep convection is important to understanding the composition of the troposphere and therefore chemistry-climate and air quality issues. High resolution cloud chemistry models have been shown to represent convective processing of trace gases quite well. To improve the representation of sub-grid convective transport and wet deposition in large-scale models, general characteristics, such as species mass flux, from the high resolution cloud chemistry models can be used. However, it is important to understand how these models behave when simulating the same storm. The intercomparison described here examines transport of six species. CO and O3, which are primarily transported, show good agreement among models and compare well with observations. Models that included lightning production of NOx reasonably predict NOx mixing ratios in the anvil compared with observations, but the NOx variability is much larger than that seen for CO and O3. Predicted anvil mixing ratios of the soluble species, HNO3, H2O2, and CH2O, exhibit significant differences among models, attributed to different schemes in these models of cloud processing including the role of the ice phase, the impact of cloud-modified photolysis rates on the chemistry, and the representation of the species chemical reactivity. The lack of measurements of these species in the convective outflow region does not allow us to evaluate the model results with observations.
 
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