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

Related authors

Temperature response measurements from eucalypts give insight into the impact of Australian isoprene emissions on air quality in 2050
Kathryn M. Emmerson, Malcolm Possell, Michael J. Aspinwall, Sebastian Pfautsch, and Mark G. Tjoelker
Atmos. Chem. Phys., 20, 6193–6206, https://doi.org/10.5194/acp-20-6193-2020,https://doi.org/10.5194/acp-20-6193-2020, 2020
Short summary
Comprehensive aerosol and gas data set from the Sydney Particle Study
Melita Keywood, Paul Selleck, Fabienne Reisen, David Cohen, Scott Chambers, Min Cheng, Martin Cope, Suzanne Crumeyrolle, Erin Dunne, Kathryn Emmerson, Rosemary Fedele, Ian Galbally, Rob Gillett, Alan Griffiths, Elise-Andree Guerette, James Harnwell, Ruhi Humphries, Sarah Lawson, Branka Miljevic, Suzie Molloy, Jennifer Powell, Jack Simmons, Zoran Ristovski, and Jason Ward
Earth Syst. Sci. Data, 11, 1883–1903, https://doi.org/10.5194/essd-11-1883-2019,https://doi.org/10.5194/essd-11-1883-2019, 2019
Short summary
Development and evaluation of pollen source methodologies for the Victorian Grass Pollen Emissions Module VGPEM1.0
Kathryn M. Emmerson, Jeremy D. Silver, Edward Newbigin, Edwin R. Lampugnani, Cenk Suphioglu, Alan Wain, and Elizabeth Ebert
Geosci. Model Dev., 12, 2195–2214, https://doi.org/10.5194/gmd-12-2195-2019,https://doi.org/10.5194/gmd-12-2195-2019, 2019
Short summary
The TOMCAT global chemical transport model v1.6: description of chemical mechanism and model evaluation
Sarah A. Monks, Stephen R. Arnold, Michael J. Hollaway, Richard J. Pope, Chris Wilson, Wuhu Feng, Kathryn M. Emmerson, Brian J. Kerridge, Barry L. Latter, Georgina M. Miles, Richard Siddans, and Martyn P. Chipperfield
Geosci. Model Dev., 10, 3025–3057, https://doi.org/10.5194/gmd-10-3025-2017,https://doi.org/10.5194/gmd-10-3025-2017, 2017
Short summary
The MUMBA campaign: measurements of urban, marine and biogenic air
Clare Paton-Walsh, Élise-Andrée Guérette, Dagmar Kubistin, Ruhi Humphries, Stephen R. Wilson, Doreena Dominick, Ian Galbally, Rebecca Buchholz, Mahendra Bhujel, Scott Chambers, Min Cheng, Martin Cope, Perry Davy, Kathryn Emmerson, David W. T. Griffith, Alan Griffiths, Melita Keywood, Sarah Lawson, Suzie Molloy, Géraldine Rea, Paul Selleck, Xue Shi, Jack Simmons, and Voltaire Velazco
Earth Syst. Sci. Data, 9, 349–362, https://doi.org/10.5194/essd-9-349-2017,https://doi.org/10.5194/essd-9-349-2017, 2017
Short summary

Related subject area

Subject: Biosphere Interactions | Research Activity: Atmospheric Modelling | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Why do inverse models disagree? A case study with two European CO2 inversions
Saqr Munassar, Guillaume Monteil, Marko Scholze, Ute Karstens, Christian Rödenbeck, Frank-Thomas Koch, Kai U. Totsche, and Christoph Gerbig
Atmos. Chem. Phys., 23, 2813–2828, https://doi.org/10.5194/acp-23-2813-2023,https://doi.org/10.5194/acp-23-2813-2023, 2023
Short summary
Net ecosystem exchange (NEE) estimates 2006–2019 over Europe from a pre-operational ensemble-inversion system
Saqr Munassar, Christian Rödenbeck, Frank-Thomas Koch, Kai U. Totsche, Michał Gałkowski, Sophia Walther, and Christoph Gerbig
Atmos. Chem. Phys., 22, 7875–7892, https://doi.org/10.5194/acp-22-7875-2022,https://doi.org/10.5194/acp-22-7875-2022, 2022
Short summary
Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models
Sally S.-C. Wang, Yun Qian, L. Ruby Leung, and Yang Zhang
Atmos. Chem. Phys., 22, 3445–3468, https://doi.org/10.5194/acp-22-3445-2022,https://doi.org/10.5194/acp-22-3445-2022, 2022
Short summary
Distinguishing the impacts of natural and anthropogenic aerosols on global gross primary productivity through diffuse fertilization effect
Hao Zhou, Xu Yue, Yadong Lei, Chenguang Tian, Jun Zhu, Yimian Ma, Yang Cao, Xixi Yin, and Zhiding Zhang
Atmos. Chem. Phys., 22, 693–709, https://doi.org/10.5194/acp-22-693-2022,https://doi.org/10.5194/acp-22-693-2022, 2022
Short summary
Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements
Yohanna Villalobos, Peter J. Rayner, Jeremy D. Silver, Steven Thomas, Vanessa Haverd, Jürgen Knauer, Zoë M. Loh, Nicholas M. Deutscher, David W. T. Griffith, and David F. Pollard
Atmos. Chem. Phys., 21, 17453–17494, https://doi.org/10.5194/acp-21-17453-2021,https://doi.org/10.5194/acp-21-17453-2021, 2021
Short summary

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.
Download
Short summary
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.
Altmetrics
Final-revised paper
Preprint