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 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.

异戊二烯（Isoprene）是一种由陆地植被大量排放的烃类化合物。它是臭氧等多种空气质量与气候污染物的前体物。排放速率随植物物种与环境条件的差异而变化，可通过《自然源气体与气溶胶排放模型》（Model of Emissions of Gases and Aerosols from Nature，简称MEGAN）进行模拟。MEGAN将异戊二烯排放速率参数化为植被特异性标准排放速率，再通过依赖于气象与环境驱动变量的缩放因子进行调节。近期研究表明，MEGAN的温度响应参数化方案存在较大不确定性；而部分区域由于缺乏代表性观测数据，且土地覆盖本身存在不确定性，导致标准条件下的排放速率难以被有效约束。本研究采用贝叶斯模型-数据融合方法，结合卫星与地面观测约束，对MEGAN的温度响应参数与标准排放速率开展优化。仅通过卫星约束对标准排放速率进行优化，虽可降低模型偏差，但对模型输入误差与干旱胁迫极为敏感，且在亚马逊流域野外站点中，该优化结果与地面观测约束存在显著矛盾，这反映出基于卫星的排放估算存在较大不确定性。利用地面观测约束对温度响应参数进行优化后，亚马逊流域野外站点的模型温度敏感性提升了5倍，但在英国野外站点未产生显著影响，这表明异戊二烯排放的温度敏感性存在显著的生态系统依赖型差异。未来在广泛的生态系统中开展异戊二烯地面观测，将是全球生物地球化学模型精准表征异戊二烯排放温度敏感性的关键所在。