Campbell Patrick C, Bash Jesse O, Spero Tanya L
National Academies/National Research Council (NRC) Fellowship Participant at National Exposure Research Laboratory U.S. Environmental Protection Agency Durham NC USA.
Now at Department of Atmospheric and Oceanic Science/Cooperative Institute for Climate and Satellites-Maryland University of Maryland College Park MD USA.
J Adv Model Earth Syst. 2019 Jan;11(1):231-256. doi: 10.1029/2018MS001422. Epub 2019 Jan 18.
Regional, state, and local environmental regulatory agencies often use Eulerian models to investigate the potential impacts on pollutant deposition and air quality from changes in land use, anthropogenic and natural emissions, and climate. The Noah land surface model (LSM) in the Weather Research and Forecasting (WRF) model is widely used with the Community Multiscale Air Quality (CMAQ) model for such investigations, but there are many inconsistencies that need to be changed so that they are consistent with dry deposition and emission processes. In this work, the Noah LSM in WRFv3.8.1 is improved in its linkage to CMAQv5.2 by adding important parameters to the WRF/Noah output, updating the WRF soil and vegetation reference tables that influence CMAQ wet and dry photochemical deposition processes, and decreasing WRF/Noah's top soil layer depth to be consistent with CMAQ processes (e.g., windblown dust and bidirectional ammonia exchange). The modified WRF/Noah-CMAQ system (both off-line and coupled) impacts meteorological predictions of 2-m temperature (T2; increases and decreases), 2-m mixing ratio (Q2; decreases), and 10-m wind speed (WSPD10; decreases) in the United States. These changes are mostly driven by leaf area index values and aerodynamic roughness lengths updated in the vegetation tables based on satellite data, with additional impacts from soil tables updated based on recent soil data. Improvements in the consistency in the treatment of land surface processes between CMAQ and WRF resulted in improvements in both estimated meteorological (e.g., T2, WSPD10, and latent heat fluxes) and chemical (e.g., ozone, sulfur dioxide, and windblown dust) model estimates.
区域、州和地方环境监管机构经常使用欧拉模型来研究土地利用变化、人为和自然排放以及气候对污染物沉降和空气质量的潜在影响。天气研究与预报(WRF)模型中的诺亚陆面模型(LSM)与社区多尺度空气质量(CMAQ)模型一起被广泛用于此类研究,但存在许多不一致之处需要改进,以便与干沉降和排放过程保持一致。在这项工作中,通过在WRF/诺亚输出中添加重要参数、更新影响CMAQ湿和干光化学沉降过程的WRF土壤和植被参考表,以及将WRF/诺亚的顶层土壤深度减小到与CMAQ过程一致(例如,风沙扬尘和双向氨交换),对WRFv3.8.1中的诺亚LSM与CMAQv5.2的链接进行了改进。改进后的WRF/诺亚 - CMAQ系统(离线和耦合)对美国2米温度(T2;有升有降)、2米混合比(Q2;下降)和10米风速(WSPD10;下降)的气象预测产生了影响。这些变化主要由基于卫星数据更新的植被表中的叶面积指数值和空气动力学粗糙度长度驱动,同时基于最新土壤数据更新的土壤表也有额外影响。CMAQ和WRF在陆面过程处理上的一致性改进,使得气象(例如T2、WSPD10和潜热通量)和化学(例如臭氧、二氧化硫和风沙扬尘)模型估计都得到了改善。