Establishing the Suitability of the Model for Prediction Across Scales for Global Retrospective Air Quality Modeling. Issue 10 (12th May 2021)

• Record Type: Journal Article
• Authors: Gilliam, Robert C.; Herwehe, Jerold A.; Bullock, O. Russell; Pleim, Jonathan E.; Ran, Limei; Campbell, Patrick C.; Foroutan, Hosein
• Is Part Of: Journal of geophysical research. Volume 126:Issue 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-12
• Subjects: CMAQ -- FDDA -- global retrospective modeling -- MPAS -- Pleim‐Xiu LSM -- WRF; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2020JD033588
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: The U.S. EPA (United States Environmental Protection Agency) is leveraging recent advances in meteorological modeling to construct an air quality modeling system to allow consistency from global to local scales. The Model for Prediction Across Scales‐Atmosphere (MPAS‐A or MPAS) has been developed by the National Center for Atmospheric Research (NCAR) as a global complement to the Weather Research and Forecasting model (WRF). Patterned after a regional coupled system with WRF, the Community Multiscale Air Quality (CMAQ) modeling system has been coupled within MPAS to explore global‐to‐local chemical transport modeling. Several options were implemented into MPAS for retrospective applications. Nudging‐based data assimilation was added to support continuous simulations of past weather to minimize error growth that exists with a weather forecast configuration. The Pleim‐Xiu land‐surface model, the Asymmetric Convective Model 2 boundary layer scheme, and the Pleim surface layer scheme were added as the preferred options for retrospective air quality applications with WRF. Annual simulations were conducted using this EPA‐enhanced MPAS configuration on two different mesh structures and compared against WRF. MPAS generally compares well with WRF over the conterminous United States. Errors in MPAS surface meteorology are comparable to WRF throughout the year. Precipitation statistics indicate MPAS performs slightly better than WRF. Solar radiation in MPAS is higher than WRF and measurements, suggesting fewer clouds in MPAS than WRF. Upper‐air meteorology is well‐simulated by MPAS, but errors are slightly higher than WRF. These comparisons lend confidence to use MPAS for retrospective air quality modeling and suggest ways it can be further improved in the future. Plain Language Summary: The US EPA analyses and performs research on the past, present and future air quality of the United States using the Community Multiscale Air Quality model (CMAQ). Historically, the modeling was focused on the U.S. as regulations and impact are first order local issues. Global modeling is becoming more attainable and common as computer potential has increased, modeling systems advanced and air quality viewed as a global issue. This research demonstrates that we now have a meteorological modeling system that is capable of modeling air quality from global to local scales. The more comprehensive air quality modeling will directly address research issues on the link between air quality and human/ecological health. Specific results presented with this work indicate that the meteorology from the new global model is on par with the meteorology we have been using for the last decade. This suggests that the global air quality simulations will not be significantly restricted by poor driving meteorology and we can move forward with some confidence. Key Points: Pleim‐Xiu land‐surface model, Asymmetric Convective Model version 2 (ACM2) planetary boundary layer model, and Pleim surface layer scheme from the Weather Research and Forecasting model (WRF) were added as options to the Model for Prediction Across Scales (MPAS) model Errors over the United States were substantially reduced in MPAS by using the Pleim‐Xiu Land Surface Model (P‐X LSM), ACM2 and Pleim surface layer configuration The magnitude of errors in MPAS are comparable to WRF, so MPAS is suitable for driving retrospective air quality models