EGU2020-1618, updated on 11 Dec 2023
EGU General Assembly 2020
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Isoprene emission in central Amazonia - from measurements to model estimates

Eliane Gomes-Alves1, Tyeen Taylor2, Pedro Assis3, Giordane Martins3, Rodrigo Souza4, Sergio Duvoisin-Junior4, Alex Guenther5, Dasa Gu6, Ana Maria Yáñez-Serrano7, Jürgen Kesselmeier8, Anywhere Tsokankunku8, Matthias Sörgel8, Bruce Nelson3, Davieliton Pinho3, Aline Lopes9, Nathan Gonçalves10, Trissevgeni Stavrakou11, Maite Bauwens11, Antonio Manzi12, and Susan Trumbore1
Eliane Gomes-Alves et al.
  • 1Biogeochemical Processes, Max Planck Institute for Biogeochemistry, Jena, Germany (
  • 2University of Florida, Miami, USA
  • 3Department of Environmental Dynamics, National Institute for Amazonian Research, Manaus, Brazil
  • 4Superior School of Technology, State University of Amazonas, Manaus, Brazil
  • 5Department of Earth System Science, University of California, Irvine, USA
  • 6Division of Environment and Sustainability, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
  • 7CREAF, E08193 Cerdanyola del Vallès, Catalonia, Spain
  • 8Atmospheric Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany
  • 9Remote Sensing Division, National Institute for Spatial Research, Sao Jose dos Campos, Brazil
  • 10Department of Forestry, Michigan State University, East Lansing, USA
  • 11Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
  • 12Center of Weather Forecasting and Climate Studies, National Institute for Spatial Research, Cachoeira Paulista, Brazil

Isoprene regulates large-scale biogeochemical cycles by influencing atmospheric chemical and physical processes, and its dominant sources to the global atmosphere are the tropical forests. Although global and regional model estimates of isoprene emission have been optimized in the last decades, modeled emissions from tropical vegetation still carry high uncertainty due to a poor understanding of the biological and environmental controls on emissions. It is already known that isoprene emission quantities may vary significantly with plant traits, such as leaf phenology, and with the environment; however, current models still lack of good representation for tropical plant species due to the very few observations available. In order to create a predictive framework for the isoprene emission capacity of tropical forests, it is necessary an improved mechanistic understanding on how the magnitude of emissions varies with plant traits and the environment in such ecosystems. In this light, we aimed to quantify the isoprene emission capacity of different tree species across leaf ages, and combine these leaf measurements with long-term canopy measurements of isoprene and its biological and environmental drivers; then, use these results to better parameterize isoprene emissions estimated by MEGAN. We measured at the Amazon Tall Tower Observatory (ATTO) site, central Amazonia: (1) isoprene emission capacity at different leaf ages of 21 trees species; (2) isoprene canopy mixing ratios during six campaigns from 2013 to 2015; (3) isoprene tower flux during the dry season of 2015 (El-Niño year); (3) environmental factors – air temperature and photosynthetic active radiation (PAR) - from 2013 to 2018; and (4) biological factors – leaf demography and phenology (tower based measurements) from 2013 to 2018. We then parameterized the leaf age algorithm of MEGAN with the measurements of isoprene emission capacity at different leaf ages and the tower-based measurements of leaf demography and phenology. Modeling estimates were later compared with measurements (canopy level) and five years of satellite-derived isoprene emission (OMI) from the ATTO domain (2013-2017). Leaf level of isoprene emission capacity showed lower values for old leaves (> 6 months) and young leaves (< 2 months), compared to mature leaves (2-6 months); and our model results suggested that this affects seasonal ecosystem isoprene emission capacity, since the demography of the different leaf age classes varied a long of the year. We will present more results on how changes in leaf demography and phenology and in temperature and PAR affect seasonal ecosystem isoprene emission, and how modeling can be improved with the optimization of the leaf age algorithm. In addition, we will present a comparison of ecosystem isoprene emission of normal years (2013, 2014, 2017 years) and anomalous years (2015 - El-Niño; and 2016 - post El-Niño), and discuss how a strong El-Niño year can influence plant functional strategies that can be carried over to the consecutive year and potentially affect isoprene emission.

How to cite: Gomes-Alves, E., Taylor, T., Assis, P., Martins, G., Souza, R., Duvoisin-Junior, S., Guenther, A., Gu, D., Yáñez-Serrano, A. M., Kesselmeier, J., Tsokankunku, A., Sörgel, M., Nelson, B., Pinho, D., Lopes, A., Gonçalves, N., Stavrakou, T., Bauwens, M., Manzi, A., and Trumbore, S.: Isoprene emission in central Amazonia - from measurements to model estimates, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1618,, 2020.


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