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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-218
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-218
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model evaluation paper 03 Sep 2020

Submitted as: model evaluation paper | 03 Sep 2020

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This preprint is currently under review for the journal GMD.

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

Kai Wang1, Yang Zhang1, Shaocai Yu2, David C. Wong3, Jonathan Pleim3, Rohit Mathur3, James T. Kelly4, and Michelle Bell5 Kai Wang et al.
  • 1Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA
  • 2Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education; Research Center for Air Pollution and Health, College of Environment and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
  • 3Center for Environmental Measurement and Modeling, U.S. EPA, RTP, NC 27711, USA
  • 4Office of Air Quality Planning and Standards, U.S. EPA, RTP, NC 27711, USA
  • 5School of Forestry & Environmental Studies, Yale University, New Haven, CT 06511, USA

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.

Kai Wang et al.

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