Implementation and evaluation of the automated model reduction (AMORE) version 1.1 isoprene oxidation mechanism in GEOS-Chem

Benjamin Yang, Forwood C. Wiser,V. Faye McNeill,Arlene M. Fiore,Madankui Tao, Daven K. Henze,Siddhartha Sen,Daniel M. Westervelt

ENVIRONMENTAL SCIENCE-ATMOSPHERES(2023)

引用 0|浏览5
暂无评分
摘要
Detailed chemical mechanisms are computationally challenging to include in large-scale chemical transport models such as GEOS-Chem. Employing a graph theory-based automated model reduction (AMORE) algorithm, we developed a new reduced (12 species and 23 reactions) gas-phase isoprene oxidation mechanism. We performed GEOS-Chem simulations for a full year (June 2018-May 2019) with the default (BASE) and AMORE version 1.1 isoprene mechanisms at 2 degrees x 2.5 degrees horizontal resolution globally and 0.25 degrees x 0.3125 degrees resolution over the eastern United States (EUS). Additionally, we conducted BASE and AMORE sensitivity simulations in which biogenic isoprene and anthropogenic emissions were sequentially set to zero in the model. For the entire year simulated, GEOS-Chem was faster by 10% in total and 25% in the chemical reaction solver (KPP) with the AMORE mechanism. Evaluating GEOS-Chem against surface observations from the Air Quality System (AQS) and Interagency Monitoring of Protected Visual Environments (IMPROVE) networks as well as satellite columns from the Tropospheric Monitoring Instrument (TROPOMI) and Cross-track Infrared Sounder (CrIS), our results show comparable accuracy in BASE and AMORE nested-grid simulations of air pollutants, with annual mean model bias changes of 1% for both surface PM2.5 and O-3 over the EUS. From the sensitivity simulations, we find that US biogenic isoprene contributes to 8-9% of surface PM2.5 and 3-4% of surface O-3 on average in summer over the EUS. This study indicates that AMORE is an attractive option for future GEOS-Chem modeling studies, especially where detailed isoprene chemistry is not the focus.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要