Browsing by Author "Guenther, Alex B."

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Optimizing the isoprene emission model MEGAN with satellite and ground-based observational constraints
    (American Geophysical Union (AGU) - Wiley, 2023-02-02) DiMaria, Christian A.; Jones, Dylan B. A.; Worden, Helen; Bloom, A. Anthony; Bowman, Kevin; Stavrakou, Trissevgeni; Miyazaki, Kazuyuki; Worden, John; Guenther, Alex B.; Sarkar, Chinmoy; Seco, Roger; Park, Jeong-Hoo; Tota, Julio; Gomes Alves, Eliane; Ferracci, Valerio
    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.
  • Thumbnail Image
    ItemOpen Access
    Optimizing the temperature sensitivity of the isoprene emission model MEGAN in different ecosystems using a Metropolis‐Hastings Markov Chain Monte Carlo method
    (American Geophysical Union (AGU), 2025-05-01) DiMaria, Christian A.; Jones, Dylan B. A.; Ferracci, Valerio; Bloom, A. Anthony; Worden, Helen M.; Seco, Roger; Vettikkat, Lejish; Yáñez Serrano, Ana Maria; Guenther, Alex B.; Araujo, A.; Goldstein, Allen H.; Langford, Ben; Cash, James; Harris, Neil R. P.; Brown, Luke; Rinnan, Riikka; Schobesberger, Siegfried; Holst, Thomas; Mak, John E.
    Isoprene is a reactive hydrocarbon emitted to the atmosphere in large quantities by terrestrial vegetation. Annual total isoprene emissions exceed 300 Tg a−1, but emission rates vary widely among plant species and are sensitive to meteorological and environmental conditions including temperature, sunlight, and soil moisture. Due to its high reactivity, isoprene has a large impact on air quality and climate pollutants such as ozone and aerosols. It is also an important sink for the hydroxyl radical which impacts the lifetime of the important greenhouse gas methane along with many other trace gas species. Modeling the impacts of isoprene emissions on atmospheric chemistry and climate requires accurate isoprene emission estimates. These can be obtained using the empirical Model of Emissions of Gases and Aerosols from Nature (MEGAN), but the parameterization of this model is uncertain due in part to limited field observations. In this study, we use ground‐based measurements of isoprene concentrations and fluxes from 11 field sites to assess the variability of the isoprene emission temperature response across ecosystems. We then use these observations in a Metropolis‐Hastings Markov Chain Monte Carlo (MHMCMC) data assimilation framework to optimize the MEGAN temperature response function. We find that the performance of MEGAN can be significantly improved at several high‐latitude field sites by increasing the modeled sensitivity of isoprene emissions to past temperatures. At some sites, the optimized model was nearly four times more sensitive to temperature than the unoptimized model. This has implications for air quality modeling in a warming climate.