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\begin{document}

\title{Improvements of Biogenic Emission Estimation in China by Using
WRF-CLM4-MEGAN Model}



\author[1]{Lifei Yin}%
\author[1]{Yu Song}%
\author[1]{Hongsheng Zhang}%
\author[1]{Xuhui Cai}%
\author[1]{Zhenying Xu}%
\author[1]{Ling Kang}%
\author[1]{Tiantian Wang}%
\author[2]{Mengmeng Li}%
\author[1]{Mingxu Liu}%
\author[1]{Tingting Xu}%
\author[1]{Wenling Liao}%
\affil[1]{Peking University}%
\affil[2]{Nanjing University}%


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  \date{May 19, 2024}


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\begin{abstract}
Biogenic emission models are developed on the foundation of leaf
physiological processes and driven by a set of physical and biological
factors. To estimate emissions online, many studies used weather
forecasting models coupled with simple biogenic emission algorithms, in
which the canopy physiological parameters were neglected or
oversimplified. In this study, the land surface scheme CLM4 (Community
Land Model version 4) coupled in the advanced Weather Research and
Forecasting model (WRF) was used to determine canopy physiological
parameters. The MEGAN (Model of Emissions of Gases and Aerosols from
Nature) algorithms embedded in CLM4 scheme used these parameters to
estimate biogenic emissions. The emission estimated by using leaf
temperature in our study were about 23\% higher than that based on air
temperature as in the previous methods. Compared with studies neglecting
shaded canopy, the separate treatments of sunlit and shaded leaves in
this study lowered the estimations by a factor of 2 through decreasing
diffuse radiaton absorbed by sunlit canopy. Dynamic weather history was
used in our study to replace the fixed values in the original MEGAN-CLM4
code. An emission inventory of isoprene and monoterpenes in China was
established for the year 2018. The estimates were evaluated against
field measurements. Generally, the coupled model produced a reasonable
simulation in both emission budgets and spatiotemporal distribution of
biogenic emissions.%
\end{abstract}%



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