Impact of Oversimplified Parameters on BVOC Emissions Estimation in China: A Sensitivity Analysis Using the WRF‐CLM4‐MEGAN Model

TL;DR: This study assesses the impact of oversimplified parameters on biogenic volatile organic compound (BVOC) emissions estimation in China using the WRF-CLM4-MEGAN model, revealing significant discrepancies in annual emissions estimates due to temperature, canopy shading, and dynamic weather history.

Abstract: Abstract Biogenic volatile organic compound (BVOC) emissions estimation models are driven by various physical factors. Many studies use weather forecasting models coupled with simple BVOC emission algorithms, where the physical factors driving variations in emissions are largely oversimplified. This study employs the land surface scheme CLM4 (Community Land Model version 4) coupled in the advanced Weather Research and Forecasting model (WRF), and the MEGAN (Model of Emissions of Gases and Aerosols from Nature) algorithms embedded within CLM4, to quantify the effects of three simplified parameters on BVOC emission estimates in China. Our sensitivity analysis results show that the annual BVOC emissions estimated using 2‐m air temperature are about 48% lower than those estimated using leaf temperature in our study. Neglecting the shaded fraction of the canopy leads to a 1.7 times increase in total annual BVOC emissions compared to the separate treatment of sunlit and shaded leaves. Employing fixed values in the default WRF‐CLM4‐MEGAN results in a 51% reduction in total BVOC emissions in July compared to using dynamic weather history for the past few days. Each scenario is evaluated against field measurements, revealing that enhancing a single parameterization does not necessarily lead to improved model performance. Uncertainties from specific simplified parameters can be partially masked by other factors, and vice versa, which therefore pose limitations on overall model performance. Our findings highlight the non‐negligible impact of the three oversimplified parameters and their underlying physical processes on BVOC emission estimates, while also deepening the understanding of uncertainties in BVOC emission modeling.