TY - JOUR

T1 - Optimizing the Isoprene Emission Model MEGAN With Satellite and Ground-Based Observational Constraints

AU - DiMaria, Christian A.

AU - Jones, Dylan B.A.

AU - Worden, Helen

AU - Bloom, A. Anthony

AU - Bowman, Kevin

AU - Stavrakou, Trissevgeni

AU - Miyazaki, Kazuyuki

AU - Worden, John

AU - Guenther, Alex

AU - Sarkar, Chinmoy

AU - Seco, Roger

AU - Park, Jeong Hoo

AU - Tota, Julio

AU - Alves, Eliane Gomes

AU - Ferracci, Valerio

N1 - Funding Information: C.A. DiMaria acknowledges a Canada Graduate Scholarship—Doctoral (CGS D) Grant funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) (application no. PGSD3-546,721-2020). This work was also supported by Grant 16SUASEMIS from the Canadian Space Agency. R. Seco acknowledges a Ramón y Cajal Grant (RYC2020-029216-I) funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future.” IDAEA-CSIC is a Severo Ochoa Centre of Research Excellence (MCIN/AEI, Project CEX2018-000794-S). The BR-Sa1 field measurements were supported by Núcleo de Apoio à Pesquisa no Pará (NAPPA) em Santarém-Pa/Instituto Nacional de Pesquisas da Amazônia (INPA), Programa de Grande Escala Biosfera Atmosfera na Amazônia (LBA) and Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) em Santarém-Pa. V. Ferracci acknowledges funding from the Natural Environmental Research Council (NERC) project “Biodiversity and land-use impacts (BALI) on tropical ecosystems” (NE/K016377/1) in support of the Wytham Woods measurements. Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). Funding Information: C.A. DiMaria acknowledges a Canada Graduate Scholarship—Doctoral (CGS D) Grant funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) (application no. PGSD3‐546,721‐2020). This work was also supported by Grant 16SUASEMIS from the Canadian Space Agency. R. Seco acknowledges a Ramón y Cajal Grant (RYC2020‐029216‐I) funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future.” IDAEA‐CSIC is a Severo Ochoa Centre of Research Excellence (MCIN/AEI, Project CEX2018‐000794‐S). The BR‐Sa1 field measurements were supported by Núcleo de Apoio à Pesquisa no Pará (NAPPA) em Santarém‐Pa/Instituto Nacional de Pesquisas da Amazônia (INPA), Programa de Grande Escala Biosfera Atmosfera na Amazônia (LBA) and Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) em Santarém‐Pa. V. Ferracci acknowledges funding from the Natural Environmental Research Council (NERC) project “Biodiversity and land‐use impacts (BALI) on tropical ecosystems” (NE/K016377/1) in support of the Wytham Woods measurements. Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). Publisher Copyright: © 2023. The Authors.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - eddy covariance

KW - isoprene emissions

KW - model optimization

KW - model-data fusion

KW - Monte Carlo algorithm

KW - remote sensing

U2 - 10.1029/2022JD037822

DO - 10.1029/2022JD037822

M3 - Journal article

AN - SCOPUS:85148611757

VL - 128

JO - Journal of Geophysical Research: Solid Earth

JF - Journal of Geophysical Research: Solid Earth

SN - 0148-0227

IS - 4

M1 - e2022JD037822

ER -