Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-885-2023
https://doi.org/10.5194/gmd-16-885-2023
Model description paper
 | 
03 Feb 2023
Model description paper |  | 03 Feb 2023

Isoprene and monoterpene simulations using the chemistry–climate model EMAC (v2.55) with interactive vegetation from LPJ-GUESS (v4.0)

Ryan Vella, Matthew Forrest, Jos Lelieveld, and Holger Tost

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Cited articles

Alessandri, A., Catalano, F., De Felice, M., Van Den Hurk, B., Reyes, F. D., Boussetta, S., Balsamo, G., and Miller, P. A.: Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth, Clim. Dynam., 49, 1215–1237, 2017. a
Allen, J. R., Forrest, M., Hickler, T., Singarayer, J. S., Valdes, P. J., and Huntley, B.: Global vegetation patterns of the past 140,000 years, J. Biogeogr., 47, 2073–2090, 2020. a
Arey, J., Winer, A. M., Atkinson, R., Aschmann, S. M., Long, W. D., Morrison, C. L., and Olszyk, D. M.: Terpenes emitted from agricultural species found in California's Central Valley, J. Geophys. Res.-Atmos., 96, 9329–9336, 1991. a
Arneth, A., Miller, P. A., Scholze, M., Hickler, T., Schurgers, G., Smith, B., and Prentice, I. C.: CO2 inhibition of global terrestrial isoprene emissions: Potential implications for atmospheric chemistry, Geophys. Res. Lett., 34, L18813, https://doi.org/10.1029/2007GL030615, 2007a. a, b
Arneth, A., Niinemets, Ü., Pressley, S., Bäck, J., Hari, P., Karl, T., Noe, S., Prentice, I. C., Serça, D., Hickler, T., Wolf, A., and Smith, B.: Process-based estimates of terrestrial ecosystem isoprene emissions: incorporating the effects of a direct CO2-isoprene interaction, Atmos. Chem. Phys., 7, 31–53, https://doi.org/10.5194/acp-7-31-2007, 2007b. a, b, c, d
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Short summary
Biogenic volatile organic compounds (BVOCs) are released by vegetation and have a major impact on atmospheric chemistry and aerosol formation. Non-interacting vegetation constrains the majority of numerical models used to estimate global BVOC emissions, and thus, the effects of changing vegetation on emissions are not addressed. In this work, we replace the offline vegetation with dynamic vegetation states by linking a chemistry–climate model with a global dynamic vegetation model.
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