Optimizing the Isoprene Emission Model MEGAN With Satellite and Ground-Based Observational Constraints

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 modeled 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.

Detalhes bibliográficos

Principais autores:	DiMaria, Christian A., Jones, Dylan B.A., Worden, Helen, Bloom, A. Anthony, Bowman, Kevin, Stavrakou, Trissevgeni, Miyazaki, Kazuyuki, Worden, John, Guenther, Alex, Sarkar, Chinmoy, Seco, Roger, Park, Jeong Hoo, Tota, Julio, Alves, Eliane Gomes, Ferracci, Valerio
Outros Autores:	Ministerio de Ciencia e Innovación (España)
Formato:	artículo biblioteca
Idioma:	English
Publicado em:	Wiley-Blackwell 2023-02-27
Assuntos:	Remote sensing, Eddy covariance, Isoprene emissions, Model optimization, Model-data fusion, Monte Carlo algorithm,
Acesso em linha:	http://hdl.handle.net/10261/303481 https://api.elsevier.com/content/abstract/scopus_id/85148611757
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