A Comparative Study of Two-way and Offline Coupled WRF v3.4 and CMAQ v5.0.2 over the Contiguous U.S.: Performance Evaluation and Impacts of Chemistry-Meteorology Feedbacks on Air Quality

Abstract

Abstract. The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry-meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous U.S. Long-term (five-year of 2008–2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, and precipitation (except for against the National Climatic Data Center data) as well as shortwave/longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O3) and fine particulate matter (PM2.5). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline-coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O3, sulfate, nitrate, ammonium, and elemental carbon as well as tropospheric O3 residual and column nitrogen dioxide (NO2). For example, the mean biases have been reduced by more than 10 W m−2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8-h O3. However, relatively large biases still exist for cloud predictions, some PM2.5 species, and PM10, which warrant follow-up studies to better understand those issues. The impacts of chemistry-meteorological feedbacks are found to play important roles in affecting regional air quality in the U.S. by reducing domain-average concentrations of carbon monoxide (CO), O3, nitrogen oxide (NOx), volatile organic compounds (VOCs), and PM2.5 by 3.1 % (up to 27.8 %), 4.2 % (up to 16.2 %), 6.6 % (up to 50.9 %), 5.8 % (up to 46.6 %), and 8.6 % (up to 49.1 %), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.

Performance Evaluation of the Meteorology and Air Quality Conditions From Multiscale WRF‐CMAQ Simulations for the Long Island Sound Tropospheric Ozone Study (LISTOS)

“…(2015) and Wang et al. (2020) described similar warm biases in their long‐term WRF‐CMAQ simulations, which Wang et al. attribute to deeper PBL heights related to the ACM2 PBL scheme in WRF and to the use of the soil moisture nudging technique used in the PX land surface model scheme in WRF.…”

“…Model biases ( Figure S2 in Supporting Information S1 ) show comparable behavior. Hogrefe et al (2015) and Wang et al (2020) described similar warm biases in their long-term WRF-CMAQ simulations, which Wang et al attribute to deeper PBL heights related to the ACM2 PBL scheme in WRF and to the use of the soil moisture nudging technique used in the PX land surface model scheme in WRF. A comparison between the 1.33-km modeled PBL heights and PBL measurements made at Queens College and at CCNY between June 27 and July 12 ( Figure S1 in Supporting Information S1 ) revealed that all simulations produced deeper daytime PBL heights compared to observations.…”