Optimizing the isoprene emission model MEGAN with satellite and
ground-based observational constraints
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 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.