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Optimizing the isoprene emission model MEGAN with satellite and ground-based observational constraints
  • +12
  • Christian A. DiMaria,
  • Dylan B. A. Jones,
  • Helen M Worden,
  • A. Anthony Bloom,
  • Kevin W. Bowman,
  • Trissevgeni Stavrakou,
  • Kazuyuki Miyazaki,
  • John R. Worden,
  • Alex B. Guenther,
  • Chinmoy Sarkar,
  • Roger Seco,
  • Jeonghoo Park,
  • Julio Tota,
  • Eliane Gomes Alvez,
  • Valerio Ferracci
Christian A. DiMaria
University of Toronto

Corresponding Author:christian.dimaria@mail.utoronto.ca

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Dylan B. A. Jones
University of Toronto
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Helen M Worden
National Center for Atmospheric Research (UCAR)
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A. Anthony Bloom
Jet Propulsion Laboratory, California Institute of Technology
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Kevin W. Bowman
Jet Propulsion Lab (NASA)
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Trissevgeni Stavrakou
Royal Belgian Institute for Space Aeronomy
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Kazuyuki Miyazaki
Jet Propulsion Laboratory
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John R. Worden
JPL / Caltech
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Alex B. Guenther
University of California, Irvine
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Chinmoy Sarkar
University of California Irvine
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Roger Seco
Institute of Environmental Assessment and Water Research (IDAEA-CSIC)
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Jeonghoo Park
National Institute of Environmental Research
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Julio Tota
Universidade Federal do Oeste do Para
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Eliane Gomes Alvez
Max Planck Institute for Biogeochemistry
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Valerio Ferracci
Cranfield University
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Isoprene is a hydrocarbon emitted in large quantities by terrestrial vegetation. It is a precursor to several air quality and climate pollutants including ozone. Emission rates vary with plant species and environmental conditions. This variability can be modelled using the Model of Emissions of Gases and Aerosols from Nature (MEGAN). MEGAN parameterizes isoprene emission rates as a vegetation-specific standard rate which is modulated by scaling factors that depend on meteorological and environmental driving variables. Recent experiments have identified large uncertainties in the MEGAN temperature response parameterization, while the emission rates under standard conditions are poorly constrained in some regions due to a lack of representative measurements and uncertainties in landcover. In this study, we use Bayesian model-data fusion to optimize the MEGAN temperature response and standard emission rates using satellite- and ground-based observational constraints. Optimization of the standard emission rate with satellite constraints reduced model biases but was highly sensitive to model input errors and drought stress and was found to be inconsistent with ground-based constraints at an Amazonian field site, reflecting large uncertainties in the satellite-based emissions. Optimization of the temperature response with ground-based constraints increased the temperature sensitivity of the model by a factor of five at an Amazonian field site but had no impact at a UK field site, demonstrating significant ecosystem-dependent variability of the isoprene emission temperature sensitivity. Ground-based measurements of isoprene across a wide range of ecosystems will be key for obtaining an accurate representation of isoprene emission temperature sensitivity in global biogeochemical models.