Optimizing the Isoprene Emission Model MEGAN With Satellite and Ground‐Based Observational Constraints. Issue 4 (13th February 2023)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Journal of geophysical research. Volume 128:Issue 4(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-13
• Subjects: isoprene emissions -- model‐data fusion -- model optimization -- remote sensing -- eddy covariance -- Monte Carlo algorithm; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2022JD037822
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: 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. Plain Language Summary: Plants emit a reactive gas called isoprene which has a large impact on air quality and climate throughout the world. This impact can be studied and quantified using computer models, but there are large uncertainties in modeled isoprene emission rates. There are few measurements to constrain the emission rates of many vegetation species. There are also uncertainties in the relationship between isoprene emissions and temperature, which makes it difficult to predict how air quality may change in a warming climate. Our goal in this study was to reduce uncertainties in a widely‐used isoprene emission model by constraining the model with observations. We used satellite observations to constrain the emission rates in a diverse range of ecosystems, but these results were sensitive to many different sources of error including drought stress. Using ground‐based observations, we found that isoprene emissions were five times more sensitive to temperature at a measurement site in the Amazon rainforest than they were at a UK measurement site. Updating the temperature sensitivity of isoprene emission models has the potential to improve models of air quality during extreme heat events and in a warming climate. Key Points: Satellite and ground based observations were used to optimize an isoprene emission model in a Bayesian model‐data fusion framework Optimization with satellite‐based emissions was highly uncertain due to observation biases and a high sensitivity to model input errors Ground‐based observations showed that Amazonian isoprene emissions were 5× more sensitive to temperature than UK isoprene emissions