Articles | Volume 18, issue 10
https://doi.org/10.5194/acp-18-7539-2018
https://doi.org/10.5194/acp-18-7539-2018
Research article
 | 
31 May 2018
Research article |  | 31 May 2018

Isoprene and monoterpene emissions in south-east Australia: comparison of a multi-layer canopy model with MEGAN and with atmospheric observations

Kathryn M. Emmerson, Martin E. Cope, Ian E. Galbally, Sunhee Lee, and Peter F. Nelson

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Cited articles

Arneth, A., Schurgers, G., Lathiere, J., Duhl, T., Beerling, D. J., Hewitt, C. N., Martin, M., and Guenther, A.: Global terrestrial isoprene emission models: sensitivity to variability in climate and vegetation, Atmos. Chem. Phys., 11, 8037–8052, https://doi.org/10.5194/acp-11-8037-2011, 2011.
Belward, A. S., Estes, J. E., and Kline, K. D.: The IGBP-DIS global 1-km land-cover data set DISCover: A project overview, Photogramm. Eng. Rem. S., 65, 1013–1020, 1999.
Benjamin, M. T., Sudol, M., Bloch, L., and Winer, A. M.: Low-emitting urban forests: A taxonomic methodology for assigning isoprene and monoterpene emission rates, Atmos. Environ., 30, 1437–1452, https://doi.org/10.1016/1352-2310(95)00439-4, 1996.
Carslaw, D. C. and Ropkins, K.: openair – An R package for air quality data analysis, Environ. Modell. Softw., 27–28, 52–61, https://doi.org/10.1016/j.envsoft.2011.09.008, 2012.
Cope, M. E., Hess, G. D., Lee, S., Tory, K., Azzi, M., Carras, J., Lilley, W., Manins, P. C., Nelson, P., Ng, L., Puri, K., Wong, N., Walsh, S., and Young, M.: The Australian Air Quality Forecasting System. Part I: Project description and early outcomes, J. Appl. Meteorol., 43, 649–662, https://doi.org/10.1175/2093.1, 2004.
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We compare the CSIRO in-house biogenic emissions model (ABCGEM) with the Model of Emissions of Gases and Aerosols from Nature (MEGAN), for eucalypt-rich south-east Australia. Differences in emissions are not only due to the emission factors, but also how these emission factors are processed. ABCGEM assumes monoterpenes are not light dependent, whilst MEGAN does. Comparison with observations suggests that Australian monoterpenes may not be as light dependent as other vegetation globally.
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