Articles | Volume 14, issue 11
https://doi.org/10.5194/gmd-14-7189-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-7189-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
26 Nov 2021
Model evaluation paper |
| 26 Nov 2021
| 26 Nov 2021
A comparative study of two-way and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US: performance evaluation and impacts of chemistry–meteorology feedbacks on air quality
Kai Wang
Department of Civil and Environmental Engineering, Northeastern
University, Boston, MA 02115, USA
Yang Zhang
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Northeastern
University, Boston, MA 02115, USA
Key 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, P.R. China
David C. Wong
Center for Environmental Measurement and Modeling, U.S. EPA, Research Triangle Park, NC 27711, USA
Jonathan Pleim
Center for Environmental Measurement and Modeling, U.S. EPA, Research Triangle Park, NC 27711, USA
Rohit Mathur
Center for Environmental Measurement and Modeling, U.S. EPA, Research Triangle Park, NC 27711, USA
James T. Kelly
Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC 27711, USA
Michelle Bell
School of Forestry & Environmental Studies, Yale University, New
Haven, CT 06511, USA
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Stefano Galmarini, Paul Makar, Olivia E. Clifton, Christian Hogrefe, Jesse O. Bash, Roberto Bellasio, Roberto Bianconi, Johannes Bieser, Tim Butler, Jason Ducker, Johannes Flemming, Alma Hodzic, Christopher D. Holmes, Ioannis Kioutsioukis, Richard Kranenburg, Aurelia Lupascu, Juan Luis Perez-Camanyo, Jonathan Pleim, Young-Hee Ryu, Roberto San Jose, Donna Schwede, Sam Silva, and Ralf Wolke
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Syuichi Itahashi, Rohit Mathur, Christian Hogrefe, Sergey L. Napelenok, and Yang Zhang
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The algorithms for applying air pollution emission rates in the Community Multiscale Air Quality (CMAQ) model have been improved to better support users and developers. The new features accommodate emissions perturbation studies that are typical in atmospheric research and output a wealth of metadata for each model run so assumptions can be verified and documented. The new approach dramatically enhances the transparency and functionality of this critical aspect of atmospheric modeling.
Mario Eduardo Gavidia-Calderón, Sergio Ibarra-Espinosa, Youngseob Kim, Yang Zhang, and Maria de Fatima Andrade
Geosci. Model Dev., 14, 3251–3268, https://doi.org/10.5194/gmd-14-3251-2021, https://doi.org/10.5194/gmd-14-3251-2021, 2021
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The MUNICH model was used to calculate pollutant concentrations inside the streets of São Paulo. The VEIN emission model provided the vehicular emissions and the coordinates of the streets. We used information from an air quality station to account for pollutant concentrations over the street rooftops. Results showed that when emissions are calibrated, MUNICH satisfied the performance criteria. MUNICH can be used to evaluate the impact of traffic-related air pollution on public health.
K. Wyat Appel, Jesse O. Bash, Kathleen M. Fahey, Kristen M. Foley, Robert C. Gilliam, Christian Hogrefe, William T. Hutzell, Daiwen Kang, Rohit Mathur, Benjamin N. Murphy, Sergey L. Napelenok, Christopher G. Nolte, Jonathan E. Pleim, George A. Pouliot, Havala O. T. Pye, Limei Ran, Shawn J. Roselle, Golam Sarwar, Donna B. Schwede, Fahim I. Sidi, Tanya L. Spero, and David C. Wong
Geosci. Model Dev., 14, 2867–2897, https://doi.org/10.5194/gmd-14-2867-2021, https://doi.org/10.5194/gmd-14-2867-2021, 2021
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This paper details the scientific updates in the recently released CMAQ version 5.3 (and v5.3.1) and also includes operational and diagnostic evaluations of CMAQv5.3.1 against observations and the previous version of the CMAQ (v5.2.1). This work was done to improve the underlying science in CMAQ. This article is used to inform the CMAQ modeling community of the updates to the modeling system and the expected change in model performance from these updates (versus the previous model version).
Qian Shu, Benjamin Murphy, Jonathan E. Pleim, Donna Schwede, Barron H. Henderson, Havala O.T. Pye, Keith Wyat Appel, Tanvir R. Khan, and Judith A. Perlinger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-129, https://doi.org/10.5194/gmd-2021-129, 2021
Preprint withdrawn
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We have bridged the gap between dry deposition measurement and modeling by rigorous use of box and regional transport models and field measurements, but more efforts are needed. This study highlights that deviation among deposition schemes is most pronounced for small and large particles. This study better links model predictions to available real-world observations and incrementally reduces uncertainties in the magnitude of loss processes important for the lifecycle of air pollutants.
Liqiang Wang, Shaocai Yu, Pengfei Li, Xue Chen, Zhen Li, Yibo Zhang, Mengying Li, Khalid Mehmood, Weiping Liu, Tianfeng Chai, Yannian Zhu, Daniel Rosenfeld, and John H. Seinfeld
Atmos. Chem. Phys., 20, 14787–14800, https://doi.org/10.5194/acp-20-14787-2020, https://doi.org/10.5194/acp-20-14787-2020, 2020
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The Chinese government has made major strides in curbing anthropogenic emissions. In this study, we constrain a state-of-the-art CTM by a reliable data assimilation method with extensive chemical and meteorological observations. This comprehensive technical design provides a crucial advance in isolating the influences of emission changes and meteorological perturbations over the Yangtze River Delta (YRD) from 2016 to 2019, thus establishing the first map of the PM2.5 mitigation across the YRD.
Huiying Luo, Marina Astitha, Christian Hogrefe, Rohit Mathur, and S. Trivikrama Rao
Atmos. Chem. Phys., 20, 13801–13815, https://doi.org/10.5194/acp-20-13801-2020, https://doi.org/10.5194/acp-20-13801-2020, 2020
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A new method is introduced to evaluate nonlinear, nonstationary modeled PM2.5 time series by decomposing decadal PM2.5 concentrations and its species onto various timescales. It does not require preselection of temporal scales and assumptions of linearity and stationarity. It provides a unique opportunity to assess the influence of each species on total PM2.5. The results reveal a phase shift in modeled EC/OC concentrations, indicating the need for improved model treatment of organic aerosols.
Yohei Shinozuka, Pablo E. Saide, Gonzalo A. Ferrada, Sharon P. Burton, Richard Ferrare, Sarah J. Doherty, Hamish Gordon, Karla Longo, Marc Mallet, Yan Feng, Qiaoqiao Wang, Yafang Cheng, Amie Dobracki, Steffen Freitag, Steven G. Howell, Samuel LeBlanc, Connor Flynn, Michal Segal-Rosenhaimer, Kristina Pistone, James R. Podolske, Eric J. Stith, Joseph Ryan Bennett, Gregory R. Carmichael, Arlindo da Silva, Ravi Govindaraju, Ruby Leung, Yang Zhang, Leonhard Pfister, Ju-Mee Ryoo, Jens Redemann, Robert Wood, and Paquita Zuidema
Atmos. Chem. Phys., 20, 11491–11526, https://doi.org/10.5194/acp-20-11491-2020, https://doi.org/10.5194/acp-20-11491-2020, 2020
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In the southeast Atlantic, well-defined smoke plumes from Africa advect over marine boundary layer cloud decks; both are most extensive around September, when most of the smoke resides in the free troposphere. A framework is put forth for evaluating the performance of a range of global and regional atmospheric composition models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016.
Cited articles
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation
2. Multiple aerosol types, J. Geophys. Res., 105, 6837–6844, 2000.
Alapaty, K., Herwehe, J. A., Otte, T. L., Nolte, C. G., Bullock, O. R.,
Mallard, M. S., Kain, J. S., and Dudhia, J.: Introducing subgrid-scale cloud
feedbacks to radiation for regional meteorological and climate modeling,
Geophys. Res. Lett., 39, L24809, https://doi.org/10.1029/2012GL054031, 2012.
Allen, D. J., Pickering, K. E., Pinder, R. W., Henderson, B. H., Appel, K. W., and Prados, A.: Impact of lightning-NO on eastern United States photochemistry during the summer of 2006 as determined using the CMAQ model, Atmos. Chem. Phys., 12, 1737–1758, https://doi.org/10.5194/acp-12-1737-2012, 2012.
Appel, K. W., Gilliland, A. B., Sarwar, G., and Gilliam, R. C.: Evaluation
of the Community Multiscale Air Quality (CMAQ) model version 4.5:
Sensitivities impacting model performance: Part I, Ozone, Atmos. Environ.,
41, 9603–9615, 2007.
Appel, K. W., Pouliot, G. A., Simon, H., Sarwar, G., Pye, H. O. T., Napelenok, S. L., Akhtar, F., and Roselle, S. J.: Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0, Geosci. Model Dev., 6, 883–899, https://doi.org/10.5194/gmd-6-883-2013, 2013.
Appel, K. W., Napelenok, S. L., Foley, K. M., Pye, H. O. T., Hogrefe, C., Luecken, D. J., Bash, J. O., Roselle, S. J., Pleim, J. E., Foroutan, H., Hutzell, W. T., Pouliot, G. A., Sarwar, G., Fahey, K. M., Gantt, B., Gilliam, R. C., Heath, N. K., Kang, D., Mathur, R., Schwede, D. B., Spero, T. L., Wong, D. C., and Young, J. O.: Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1, Geosci. Model Dev., 10, 1703–1732, https://doi.org/10.5194/gmd-10-1703-2017, 2017.
Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D., Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas, E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta, J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos, N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E., Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects, Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, 2014.
Bennartz, R.: Global assessment of marine boundary layer cloud droplet
number concentration from satellite, J. Geophys. Res., 112, D02201,
https://doi.org/10.1029/2006JD007547, 2007.
Boersma, K. F., Eskes, H. J., and Brinksma, E. J.: Error analysis for
tropospheric NO2 retrieval from space, J. Geophys. Res., 109, D04311,
https://doi.org/10.1029/2003JD003962, 2004.
Brunner, D., Savage, N., Jorba, O., Eder, B., Giordano, L., Badia, A.,
Balzarini, A., Baro, R., Bianconi, R., Chemel, C., Curci, G., Forkel, R.,
Jimenez-Guerrero, P., Hirtl, M., Hodzic, A., Hozak, L., Im, U., Knote, C.,
Makar, P., Manders-Groot, A., van Meijgaard, E., Neal, L., Perez, J. L.,
Pirovano, G., San Jose, R., Schroder, W., Sokhi, R. S., Syrakov, D., Torian,
A., Tuccella, P., Werhahn, J., Wolke, R., Yahya, K., Zabkar, R., Zhang, Y.,
Hogrefe, C., and Galmarini, S.: Comparative analysis of meteorological
performance of coupled chemistry-meteorology models in the context of AQMEII
phase 2, Atmos. Environ., 115, 470–498, https://doi.org/10.1016/j.atmosenv.2014.12.032, 2015.
Byun, D. W. and Schere K. L.: Review equations, computational algorithms,
and other components of the Models-3 Community Multi-Scale Air Quality
(CMAQ) modeling system, Appl. Mech. Rev., 59, 51–77,
https://doi.org/10.1115/1.2128636, 2006.
Choi, M. W., Lee, J. H., Woo, J. W., Kim, C. H., and Lee, S. H.: Comparison
of PM2.5 chemical components over East Asia simulated by the WRF-Chem
and WRF/CMAQ models: On the models' prediction inconsistency, Atmosphere, 10,
618, https://doi.org/10.3390/atmos10100618, 2019.
CMAS (Community Modeling and Analysis System):
https://www.cmascenter.org/download/data.cfm#obs, last access: 3 November 2021.
Cohen, A. E., Cavallo, S. M., Coniglio, M. C., and Brooks, H. E.: A review
of planetary boundary layer parameterization schemes and their sensitivity
in simulating southeastern U.S. cold season severe weather environments,
Weather Forecast., 30, 591–612, https://doi.org/10.1175/WAF-D-14-00105.1, 2015.
Dong, X., Fu, J. S., Huang, K., Tong, D., and Zhuang, G.: Model development of dust emission and heterogeneous chemistry within the Community Multiscale Air Quality modeling system and its application over East Asia, Atmos. Chem. Phys., 16, 8157–8180, https://doi.org/10.5194/acp-16-8157-2016, 2016.
Eder, B. and Yu, S.: A performance evaluation of the 2004 release of
Models-3 CMAQ, Atmos. Environ., 40, 4811–4824, 2006.
Emery, C., Liu, Z., Russell, A. G., Odman, M. T., Yarwood, G., and Kumar,
N.: Recommendations on statistics and benchmarks to assess photochemical
model performance, J. Air Waste Manage. Assoc., 67, 582–598,
https://doi.org/10.1080/10962247.2016.1265027, 2017.
Emmons, L. K., Edwards, D. P., Deeter, M. N., Gille, J. C., Campos, T., Nédélec, P., Novelli, P., and Sachse, G.: Measurements of Pollution In The Troposphere (MOPITT) validation through 2006, Atmos. Chem. Phys., 9, 1795–1803, https://doi.org/10.5194/acp-9-1795-2009, 2009.
Gan, C.-M., Pleim, J., Mathur, R., Hogrefe, C., Long, C. N., Xing, J., Wong, D., Gilliam, R., and Wei, C.: Assessment of long-term WRF–CMAQ simulations for understanding direct aerosol effects on radiation “brightening” in the United States, Atmos. Chem. Phys., 15, 12193–12209, https://doi.org/10.5194/acp-15-12193-2015, 2015a.
Gan, C.-M., Binkowski, F., Pleim, J., Xing, J., Wong, D., Mathur, R., and
Gilliam, R.: Assessment of the aerosol optics component of the coupled
WRF–CMAQ model using CARES field campaign data and a single column model,
Atmos. Environ., 115, 670–682, 2015b.
Gantt, B., He, J., Zhang, X., Zhang, Y., and Nenes, A.: Incorporation of advanced aerosol activation treatments into CESM/CAM5: model evaluation and impacts on aerosol indirect effects, Atmos. Chem. Phys., 14, 7485–7497, https://doi.org/10.5194/acp-14-7485-2014, 2014.
Gantt, B., Sarwar, G., Xing, J., Simon, H., Schwede, D., Hutzell, W. T.,
Mathur, R., and Saiz-Lopez, A.: The impact of iodide-mediated ozone
deposition and halogen chemistry on surface ozone concentrations across the
continental United States, Environ. Sci. Technol., 51, 1458–1466, 2017.
Ghan, S. J., Laulainen, N. S., Easter, R. C., Wagener, R., Nemesure, S.,
Chapman, E. G., Zhang, Y., and Leung, L. R.: Evaluation of aerosol direct
radiative forcing in MIRAGE, J. Geophys. Res., 106, 5295–5316, 2001.
Glotfelty, T., He, J., and Zhang, Y.: Impact of future climate policy
scenarios on air quality and aerosol-cloud interactions using an advanced
version of CESM/CAM5: Part I. model evaluation for the current decadal
simulations, Atmos. Environ., 152, 222–239, 2017.
Grell, G. A. and Baklanov, A.: Integrated modelling for forecasting weather
and air quality: A call for fully coupled approaches, Atmos. Environ., 45,
38, 6845–6851, 2011.
Grell, G. A., Peckham, S. E., Schmitz, R., McKenn, S. A., Frost, G.,
Skamarock, W. C., and Eder, B.: Fully Coupled “Online” chemistry within
the WRF Model, Atmos. Environ., 39, 6957–6975, 2005.
He, J. and Zhang, Y.: Improvement and further development in CESM/CAM5: gas-phase chemistry and inorganic aerosol treatments, Atmos. Chem. Phys., 14, 9171–9200, https://doi.org/10.5194/acp-14-9171-2014, 2014.
Heald, C. L., Jacob, D. J., Fiore, A. M., Emmons, L. K., Gille, J. C.,
Deeter, M. N., Warner, J., Edwards, D. P., Crawford, J. H., Hamlin, A. J.,
Sachse, G. W., Browell, E. V., Avery, M. A., Vay, S. A., Westberg, D. J.,
Blake, D. R., Singh, H. B., Sandholm, S. T., Talbot, R. W., and Fuelberg, H.
E.: Asian outflow and trans-Pacific transport of carbon monoxide and ozone
pollution: An integrated satellite, aircraft, and model perspective, J.
Geophys. Res., 108, 4804, https://doi.org/10.1029/2003JD003507, 2003.
Herwehe, J. A., Otte, T. L., Mathur, R., and Rao, S. T.: Diagnostic analysis
of ozone concentrations simulated by two regional-scale air quality models,
Atmos. Environ., 45, 5957–5969, 2011.
Hogrefe, C., Pouliot, G., Wong, D., Torian, A., Roselle, S., Pleim, J., and
Mathur, R.: Annual application and evaluation of the online coupled
WRF–CMAQ system over North America under AQMEII phase 2, Atmos. Environ.,
115, 683–694, 2015.
Hong, C., Zhang, Q., Zhang, Y., Tang, Y., Tong, D., and He, K.: Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects, Geosci. Model Dev., 10, 2447–2470, https://doi.org/10.5194/gmd-10-2447-2017, 2017.
Hong, C.-P., Zhang, Q., Zhang, Y., Davis, S. J., Zhang, X., Tong, D., Guan,
D., Liu, Z., and He, K.-B.: Weakened aerosol radiative effects may mitigate
the climate penalty on Chinese air quality, Nat. Clim. Change, 10, 845–850, https://doi.org/10.1038/s41558-020-0840-y, 2020.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
IPCC: Global warming of 1.5 ∘C, An IPCC Special Report on the
impacts of global warming of 1.5 ∘C above pre-industrial levels
and related global greenhouse gas emission pathways, in the context of
strengthening the global response to the threat of climate change,
sustainable development, and efforts to eradicate poverty, edited by:
Masson-Delmotte, V., Zhai, P., Pörtner, H. O., Roberts, D., Skea, J.,
Shukla, P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R.,
Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy,
E., Maycock, T., Tignor, M., and Waterfield, T., World Meteorological Organization, Geneva, Switzerland, 32 pp., 2018.
Jacobson, M. Z.: GATOR-GCMM: A global- through urban-scale air pollution and
weather forecast model 1. Model design and treatment of subgrid soil,
vegetation, roads, rooftops, water, sea, ice, and snow, J. Geophys. Res.,
106, 5385–5401, 2001.
Jacobson, M. Z., Lu, R., Turco, R. P., and Toon, O. B.: Development and
application of a new air pollution modeling system. Part I: Gas-phase
simulations, Atmos. Environ., 30B, 1939–1963, 1996.
Jung, J., Souri, A. H., Wong, D. C., Lee, S., Jeon, W., Kim, J., and Choi,
Y.: The impact of the direct effect of aerosols on meteorology and air
quality using aerosol optical depth assimilation during the KORUS-AQ campaign, J. Geophys. Res.-Atmos., 124, 8303–8319,
https://doi.org/10.1029/2019JD030641, 2019.
Kain, J. S.: The Kain-Fritsch convective parameterization: An update, J.
Appl. Meteorol., 43, 170–181,
https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Karydis, V. A., Tsimpidi, A. P., and Pandis, S. N.: Evaluation of a
three-dimensional chemical transport model (PMCAMx) in the eastern United
States for all four seasons, J. Geophys. Res., 112, D14211,
https://doi.org/10.1029/2006JD007890, 2007.
Kaufman, Y. J., Smirnov, A., Holben, B., and Dubovik, O.: Baseline maritime
aerosol methodology to derive the optical thickness and scattering
properties, Geophys. Res. Lett., 28, 3251, https://doi.org/10.1029/2001GL013312, 2001.
Kelly, J., Koplitz, S., Baker, K., Holder, A., Pye, H., Murphy, B., Bash,
J., Henderson, B., Possiel, N., Simon, H., Eyth, A., Jang, C., Phillips, S.,
and Timin, B.: Assessing PM2.5 model performance for the conterminous
U.S. with comparison to model performance statistics from 2007–2015, Atmos.
Environ., 214, 116872, https://doi.org/10.1016/j.atmosenv.2019.116872, 2019.
Kukkonen, J., Olsson, T., Schultz, D. M., Baklanov, A., Klein, T., Miranda, A. I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V.-H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lehtinen, K. E. J., Karatzas, K., San José, R., Astitha, M., Kallos, G., Schaap, M., Reimer, E., Jakobs, H., and Eben, K.: A review of operational, regional-scale, chemical weather forecasting models in Europe, Atmos. Chem. Phys., 12, 1–87, https://doi.org/10.5194/acp-12-1-2012, 2012.
Li, P, Wang, L., Guo, P., Yu, S., Mehmood, K., Wang, S., Liu, W., Seinfeld,
J. H., Zhang, Y., Wong, D., Alapaty, K., Pleim, J., and Mathur, R.: High
reduction of ozone and particulate matter during the 2016 G-20 summit in
Hangzhou by forced emission controls of industry and traffic, Environ. Chem.
Lett., 15, 709–715, https://doi.org/10.1007/s10311-017-0642-2, 2017.
Lin, M., Holloway, T., Carmichael, G. R., and Fiore, A. M.: Quantifying pollution inflow and outflow over East Asia in spring with regional and global models, Atmos. Chem. Phys., 10, 4221–4239, https://doi.org/10.5194/acp-10-4221-2010, 2010.
Liu, X.-H., Zhang, Y., Xing, J., Zhang, Q., Wang, K., Streets, D. G., Jang,
C. J., Wang, W.-X., and Hao, J. M.: Understanding of regional air pollution
over China using CMAQ: Part II. Process analysis and ozone sensitivity to
precursor emissions, Atmos. Environ., 44, 3719–3727, 2010.
Lorente, A., Folkert Boersma, K., Yu, H., Dörner, S., Hilboll, A., Richter, A., Liu, M., Lamsal, L. N., Barkley, M., De Smedt, I., Van Roozendael, M., Wang, Y., Wagner, T., Beirle, S., Lin, J.-T., Krotkov, N., Stammes, P., Wang, P., Eskes, H. J., and Krol, M.: Structural uncertainty in air mass factor calculation for NO2 and HCHO satellite retrievals, Atmos. Meas. Tech., 10, 759–782, https://doi.org/10.5194/amt-10-759-2017, 2017.
Ma, P.-L., Rasch, P. J., Fast, J. D., Easter, R. C., Gustafson Jr., W. I., Liu, X., Ghan, S. J., and Singh, B.: Assessing the CAM5 physics suite in the WRF-Chem model: implementation, resolution sensitivity, and a first evaluation for a regional case study, Geosci. Model Dev., 7, 755–778, https://doi.org/10.5194/gmd-7-755-2014, 2014.
Makar, P. A., Gonga, W., Hogrefe, C., Zhang, Y., Curci, G., Žabkar, R.,
Milbrandt, J., Im, U., Balzarini, A., Baró, R., Bianconi, R., Cheung,
P., Forkel, R., Gravel, S., Hirtl, M., Honzak, L., Hou, A.,
Jiménez-Guerrero, P., Langer, M., Moran, M. B., Pabla, B., Pérez, J.
L., Pirovano, G., San José, R., Tuccella, P., Werhahn, J., Zhang, J.,
and Galmarini, S.: Feedbacks between air pollution and weather, Part 2:
Effects on chemistry, Atmos. Environ., 115, 499–526, 2015.
Mathur, R., Xiu, A., Coats, C., Alapaty, K., Shankar, U., and Hanna, A.:
Development of an air quality modeling system with integrated meteorology,
chemistry, and emissions, Proc. Measurement of Toxic and Related Air
Pollutants, AWMA, Cary, NC, 1–3 September 1998.
Mathur, R., Xing, J., Gilliam, R., Sarwar, G., Hogrefe, C., Pleim, J., Pouliot, G., Roselle, S., Spero, T. L., Wong, D. C., and Young, J.: Extending the Community Multiscale Air Quality (CMAQ) modeling system to hemispheric scales: overview of process considerations and initial applications, Atmos. Chem. Phys., 17, 12449–12474, https://doi.org/10.5194/acp-17-12449-2017, 2017.
Matsui, H., Koike, M., Kondo, Y., Takegawa, N., Kita,K., Miyazaki, Y., Hu,
M., Chang, S.-Y., Blake, D. R., Fast, J. D., Zaveri, R. A., Streets, D. G.,
Zhang, Q. and Zhu, T.: Spatial and temporal variations of aerosols around
Beijing in summer 2006: Model evaluation and source apportionment, J.
Geophys. Res., 114, D00G13, https://doi.org/10.1029/2008JD010906, 2009.
Mebust, M. R., Eder, B. K., Binkowski, F. S., and Roselle, S. J.: Models-3
Community Multiscale Air Quality (CMAQ) model aerosol component: 2. Model
evaluation, J. Geophys. Res., 108, 4184, https://doi.org/10.1029/2001JD001410, 2003.
Mehmood, K., Wu, Y., Wang, L., Yu, S., Li, P., Chen, X., Li, Z., Zhang, Y., Li, M., Liu, W., Wang, Y., Liu, Z., Zhu, Y., Rosenfeld, D., and Seinfeld, J. H.: Relative effects of open biomass burning and open crop straw burning on haze formation over central and eastern China: modeling study driven by constrained emissions, Atmos. Chem. Phys., 20, 2419–2443, https://doi.org/10.5194/acp-20-2419-2020, 2020.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of cloud microphysics
on the development of trailing stratiform precipitation in a simulated
squall line: Comparison of one- and two-moment schemes, Mon. Weather Rev.,
137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009.
NASA CERES: CERES_EBAF_Ed4.1 Subsetting and Browsing, NASA [data set], available at: https://ceres-tool.larc.nasa.gov/ord-tool/jsp/EBAF41Selection.jsp, last access: 3 November 2021.
NASA/LARC/SD/ASDC: MOPITT CO gridded monthly means (Near and Thermal Infrared Radiances) V009, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/TERRA/MOPITT/MOP03JM.009, last access: 3 November 2021.
NCAR (National Center for Atmospheric Research): WRFv3.4, NCAR [code], available at: https://www2.mmm.ucar.edu/wrf/src/WRFV3.4.TAR.gz (last access: 3 November 2021), 2012.
NCEI (National Centers for Environmental Information): GPCP data, available at: https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-monthly, last access: 3 November 2021a.
NCEI (National Centers for Environmental Information): NCDC data, available at: https://www.ncei.noaa.gov/data/global-hourly/archive/csv, last access: 3 November 2021b.
Penrod, A., Zhang, Y., Wang, K., Wu, S.-Y., and Leung, R. L.: Impacts of
future climate and emission changes on US air quality, Atmos. Environ., 89,
533–547, https://doi.org/10.1016/j.atmosenv.2014.01.001, 2014.
Platnick, S., Hubanks, P., Meyer, K.. and King, M. D.: MODIS Atmosphere L3 Monthly Product (08_L3), NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], https://doi.org/10.5067/MODIS/MOD08_M3.006 (Terra), 2015.
Pleim, J., Young, J., Wong, D., Gilliam, R., Otte, T., and Mathur, R.:
Two-way coupled meteorology and air quality modeling, in Air Pollution
Modeling and its Application, edited by: Borrego, C. and Miranda, A. I., XIX,
NATO Science for Peace and Security Series, Series C: Environmental
Security, Springer, Dordrecht, 2008.
Pleim, J. E.: A combined local and nonlocal closure model for the
atmospheric boundary layer. Part I: Model description and testing, J. Appl.
Meteorol. Clim., 46, 1383–1395, https://doi.org/10.1175/JAM2539.1, 2007.
Pleim, J. E. and Gilliam, R.: An indirect data assimilation scheme for deep
soil temperature in the Pleim–Xiu land surface model, J. Appl. Meteorol.
Clim., 48, 1362–1376, 2009.
Pye, H. O. T., Murphy, B. N., Xu, L., Ng, N. L., Carlton, A. G., Guo, H., Weber, R., Vasilakos, P., Appel, K. W., Budisulistiorini, S. H., Surratt, J. D., Nenes, A., Hu, W., Jimenez, J. L., Isaacman-VanWertz, G., Misztal, P. K., and Goldstein, A. H.: On the implications of aerosol liquid water and phase separation for organic aerosol mass, Atmos. Chem. Phys., 17, 343–369, https://doi.org/10.5194/acp-17-343-2017, 2017.
Pye, H. O. T., Nenes, A., Alexander, B., Ault, A. P., Barth, M. C., Clegg, S. L., Collett Jr., J. L., Fahey, K. M., Hennigan, C. J., Herrmann, H., Kanakidou, M., Kelly, J. T., Ku, I.-T., McNeill, V. F., Riemer, N., Schaefer, T., Shi, G., Tilgner, A., Walker, J. T., Wang, T., Weber, R., Xing, J., Zaveri, R. A., and Zuend, A.: The acidity of atmospheric particles and clouds, Atmos. Chem. Phys., 20, 4809–4888, https://doi.org/10.5194/acp-20-4809-2020, 2020.
Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A.,
Martins, J. V., Li, R. R., Ichoku, C., Levy, R. C., and Kleidman, R. G.: The
MODIS aerosol algorithm, products, and validation, J. Atmos. Sci., 62,
947–973, 2005.
Roy, B., Pouliot, G. A., Gilliland, A., Pierce, T., Howard, S., Bhave, P.
V., and Benjey, W.: Refining fire emissions for air quality modeling with
remotely sensed fire counts: A wildfire case study, Atmos. Environ., 41,
655–665, https://doi.org/10.1016/j.atmosenv.2006.08.037, 2007.
San Joaquin Valley Air Pollution Control District: 2018 Plan for the 1997,
2006, and 2012 PM2.5 Standards, available at: https://www.valleyair.org/pmplans (last access: 3 November 2021), 15 November 2018.
Sarwar, G., Luecken, D., Yarwood, G., Whitten, G. Z., and Carter, W. P. L.:
Impact of an updated carbon bond mechanism on predictions from the CMAQ
modeling system: Preliminary assessment, J. Appl. Meteorol. Clim., 47, 3–14,
2008.
Sarwar, G., Gantt, B., Schwede, D., Foley, K., Mathur, R., and Saiz-Lopez,
A.: Impact of enhanced ozone deposition and halogen chemistry on
tropospheric ozone over the Northern Hemisphere, Environ. Sci. Technol., 49, 9203-9211, 2015.
Scheffe, R. D., Strum, M., Phillips, S. B., Thurman, J., Eyth, A., Fudge,
S., Morris, M., Palma, T., and Cook, R.: Hybrid modeling approach to
estimate exposures of hazardous air pollutants (HAPs) for the National Air
Toxics Assessment (NATA), Environ. Sci. Technol., 50,
12356–12364, https://doi.org/10.1021/acs.est.6b04752, 2016.
Schwede, D., Pouliot, G. A., and Pierce, T.: Changes to the biogenic
emissions inventory system version 3 (BEIS3), in: Proceedings of the 4th
CMAS Models-3 Users' Conference, Chapel Hill, NC, 26–28 September 2005.
Sekiguchi, A., Shimadera, H., and Kondo, A.: Impact of aerosol direct effect
on wintertime PM2.5 simulated by an online coupled meteorology-air
quality model over East Asia, Aerosol. Air Qual. Res., 18, 1068–1079, 2018.
Solazzo, E., Hogrefe, C., Colette, A., Garcia-Vivanco, M., and Galmarini, S.: Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework, Atmos. Chem. Phys., 17, 10435–10465, https://doi.org/10.5194/acp-17-10435-2017, 2017.
Stavrakou, T., Müller, J.-F., De Smedt, I., Van Roozendael, M., van der Werf, G. R., Giglio, L., and Guenther, A.: Global emissions of non-methane hydrocarbons deduced from SCIAMACHY formaldehyde columns through 2003–2006, Atmos. Chem. Phys., 9, 3663–3679, https://doi.org/10.5194/acp-9-3663-2009, 2009.
TEMIS (Tropospheric Emission Monitoring Internet Service): Tropospheric NO2 from satellites, available at: https://www.temis.nl/airpollution/no2.php, last access: 3 November 2021a.
TEMIS (Tropospheric Emission Monitoring Internet Service): Tropospheric ozone column, available at: https://www.temis.nl/protocols/tropo.php, last access: 3 November 2021b.
U.S. EPA: Our nation's air status and trends through 2010, EPA-454/R-12-001,
available at: https://www.epa.gov/sites/default/files/2017-11/documents/trends_brochure_2010.pdf (last access: 3 November 2021), 2012.
U.S. EPA: Policy assessment for the review of the National Ambient Air
Quality Standards for particulate matter, EPA-452/R-20-002, available at:
https://www.epa.gov/sites/production/files/2020-01/documents/final_policy_assessment_for_the_review_of_the_pm_naaqs_01-2020.pdf (last access: 3 November 2021), 2020.
U.S. EPA Office of Research and Development: CMAQv5.0.2, 5.0.2, Zenodo [code], https://doi.org/10.5281/zenodo.1079898, 2014.
Vasilakos, P., Russell, A., Weber, R., and Nenes, A.: Understanding nitrate formation in a world with less sulfate, Atmos. Chem. Phys., 18, 12765–12775, https://doi.org/10.5194/acp-18-12765-2018, 2018.
Wang, J., Wang, S., Jiang, J., Ding, A., Zheng, M., Zhao, B., Wong, C.-D.,
Zhou, W., Zheng, G., Wang, L., Pleim, J., and Hao, J.: Impact of
aerosol–meteorology interactions on fine particle pollution during China's
severe haze episode in January 2013, Environ. Res. Lett., 9, 094002,
https://doi.org/10.1088/1748-9326/9/9/094002, 2014.
Wang, K. and Zhang, Y.: Application, evaluation, and process analysis of
U.S. EPA's 2002 multiple-pollutant air quality modeling platform,
Atmospheric and Climate Sciences, 2, 254–289, 2012.
Wang, K. and Zhang, Y.: 3-D agricultural air quality modeling: Impacts of
Wang, K., Zhang, Y., Jang, C., Phillips, S., and Wang, B.: Modeling
intercontinental air pollution transport over the trans-Pacific region in
2001 using the Community Multiscale Air Quality modeling system, J. Geophys.
Res., 114, D04307, https://doi.org/10.1029/2008JD010807, 2009.
Wang, K., Zhang, Y., Nenes, A., and Fountoukis, C.: Implementation of dust
emission and chemistry into the Community Multiscale Air Quality modeling
system and initial application to an Asian dust storm episode, Atmos. Chem.
Phys., 12, 10209–10237, https://doi.org/10.5194/acp-12-10209-2012, 2012.
Wang, K., Zhang, Y., Yahya, K., Wu, S.-Y., and Grell, G.: Implementation and
initial application of new chemistry-aerosol options in WRF/Chem for
simulating secondary organic aerosols and aerosol indirect effects for
regional air quality, Atmos. Environ., 115, 716–732,
https://doi.org/10.1016/j.atmosenv.2014.12.007, 2015a.
Wang, K., Yahya, K., Zhang, Y., Hogrefe, C., Pouliot, G., Knote, C., Hodzic,
A., Jose, R. S., Perez, J. L., Jiménez-Guerrero, P., Baro, R., Makar,
P., and Bennartz, R.: A multi-model assessment for the 2006 and 2010
simulations under the Air Quality Model Evaluation International Initiative
(AQMEII) Phase 2 over North America: Part II. Evaluation of column variable
predictions using satellite data, Atmos. Environ., 115, 587–603, https://doi.org/10.1016/j.atmosenv.2014.07.044, 2015b.
Wang, K., Zhang, Y., and Yahya, K.: Decadal application of WRF/Chem over the
continental U.S.: Simulation design, sensitivity simulations, and
climatological model evaluation, Atmos. Environ., 253, 118331,
https://doi.org/10.1016/j.atmosenv.2021.118331, 2021.
West, J. J., Ansari, A. S., and Pandis, S. N.: Marginal PM2.5:
Nonlinear aerosol mass response to sulfate reductions in the Eastern United
States, J. Air Waste Manage. Assoc., 49, 1415–1424, https://doi.org/10.1080/10473289.1999.10463973, 1999.
Wiedinmyer, C., Quayle, B., Geron, C., Belote, A., McKenzie, D., Zhang, X.,
O'Neill, S., and Wynne, K. K.: Estimating emissions from fires in North
America for air quality modeling, Atmos. Environ., 40, 3419–3432,
https://doi.org/10.1016/j.atmosenv.2006.02.010, 2006.
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee III, R. B., Smith,
G. L., and Cooper, J. E.: Clouds and the Earth's Radiant Energy System
(CERES): An earth observing system experiment, B. Am. Meteorol. Soc., 77,
853–868, 1996.
Wilczak, J. M., Djalalova, I., McKeen, S., Bianco, L., Bao, J.-W., Grell,
G., Peckham, S., Mathur, R., McQueen, J., and Lee, P: Analysis of regional
meteorology and surface ozone during the TexAQS II field program and an
evaluation of the NMM-CMAQ and WRF-Chem air quality models, J. Geophys.
Res., 114, D00F14, https://doi.org/10.1029/2008JD011675, 2009.
Wong, D. C., Pleim, J., Mathur, R., Binkowski, F., Otte, T., Gilliam, R., Pouliot, G., Xiu, A., Young, J. O., and Kang, D.: WRF-CMAQ two-way coupled system with aerosol feedback: software development and preliminary results, Geosci. Model Dev., 5, 299–312, https://doi.org/10.5194/gmd-5-299-2012, 2012.
Xing, J., Mathur, R., Pleim, J., Hogrefe, C., Gan, C.-M., Wong, D. C., Wei,
C., and Wang, J.: Air pollution and climate response to aerosol direct
radiative effects: A modeling study of decadal trends across the northern
hemisphere, J. Geophys. Res.-Atmos., 120, 12221–12236,
https://doi.org/10.1002/2015JD023933, 2015a.
Xing, J., Mathur, R., Pleim, J., Hogrefe, C., Gan, C.-M., Wong, D. C., and Wei, C.: Can a coupled meteorology–chemistry model reproduce the historical trend in aerosol direct radiative effects over the Northern Hemisphere?, Atmos. Chem. Phys., 15, 9997–10018, https://doi.org/10.5194/acp-15-9997-2015, 2015b.
Xing, J., Wang, J., Mathur, R., Pleim, J., Wang, S., Hogrefe, C., Gan,
C.-M., Wong, D., and Hao, J.: Unexpected benefits of reducing aerosol
cooling effects, Environ. Sci. Technol., 50, 7527–7534, https://doi.org/10.1021/acs.est.6b00767, 2016.
Xing, J., Wang, J., Mathur, R., Wang, S., Sarwar, G., Pleim, J., Hogrefe, C., Zhang, Y., Jiang, J., Wong, D. C., and Hao, J.: Impacts of aerosol direct effects on tropospheric ozone through changes in atmospheric dynamics and photolysis rates, Atmos. Chem. Phys., 17, 9869–9883, https://doi.org/10.5194/acp-17-9869-2017, 2017.
Xiu, A. and Pleim, J. E.: Development of a land surface model. Part I:
Application in a mesoscale meteorological model, J. Appl. Meteorol., 40,
192–209, https://doi.org/10.1175/1520-0450(2001)040<0192:DOALSM>2.0.CO;2, 2001.
Yahya, K., Wang, K., Gudoshava, M., Glotfelty, T., and Zhang, Y.:
Application of WRF/Chem over North America under the AQMEII Phase 2. Part I.
Comprehensive evaluation of 2006 simulation, Atmos. Environ., 115, 733–755,
https://doi.org/10.1016/j.atmosenv.2014.08.063, 2015a.
Yahya, K., Wang, K., Zhang, Y., and Kleindienst, T. E.: Application of WRF/Chem over North America under the AQMEII Phase 2 – Part 2: Evaluation of 2010 application and responses of air quality and meteorology–chemistry interactions to changes in emissions and meteorology from 2006 to 2010, Geosci. Model Dev., 8, 2095–2117, https://doi.org/10.5194/gmd-8-2095-2015, 2015b.
Yahya, K., Wang, K., Campbell, P., Glotfelty, T., He, J., and Zhang, Y.: Decadal evaluation of regional climate, air quality, and their interactions over the continental US and their interactions using WRF/Chem version 3.6.1, Geosci. Model Dev., 9, 671–695, https://doi.org/10.5194/gmd-9-671-2016, 2016.
Yarwood, G., Rao, S., Yocke, M., and Whitten, G. Z.: Final Report–Updates
to the Carbon Bond Chemical Mechanism: CB05, Rep. RT-04-00675, Yocke and Co.,
Novato, Calif., 246 pp., 2005.
Yoo, J.-W., Jeon, W., Park, S.-Y., Park, C., Jung, J., Lee, S.-H., and Lee,
H. W.: Investigating the regional difference of aerosol feedback effects
over South Korea using the WRF-CMAQ two-way coupled modeling system, Atmos.
Environ., 218, 116968, https://doi.org/10.1016/j.atmosenv.2019.116968, 2019.
Yu, S., Eder, B., Dennis, R., Chu, S., and Schwartz, S.: New unbiased
symmetric metrics for evaluation of air quality models, Atmos. Sci. Lett.,
7, 26–34, 2006.
Yu, S., Mathur, R., Pleim, J., Wong, D., Gilliam, R., Alapaty, K., Zhao, C., and Liu, X.: Aerosol indirect effect on the grid-scale clouds in the two-way coupled WRF–CMAQ: model description, development, evaluation and regional analysis, Atmos. Chem. Phys., 14, 11247–11285, https://doi.org/10.5194/acp-14-11247-2014, 2014 (data available at: https://person.zju.edu.cn/shaocaiyu#674502, last access: 3 November 2021).
Yu, S., Li, P., Wang, L., Wu, Y., Wang, S., Liu, W., Zhu, T., Zhang, Y., Hu,
M., Alapaty, K., Wong, D., Pleim, J., Mathur, R., Rosenfeld, D., and
Seinfeld, J.: Mitigation of severe urban haze pollution by a precision air
pollution control approach, Scientific Reports, 8, 8151,
https://doi.org/10.1038/s41598-018-26344-1, 2018.
Yu, S. C., Mathur, R., Schere, K., Kang, D., Pleim, J., and Otte, T. L.: A
detailed evaluation of the Eta-CMAQ forecast model performance for O3, its related precursors, and meteorological parameters during the 2004 ICARTT Study, J. Geophys. Res, 112, D12S14, https://doi.org/10.1029/2006JD007715, 2007.
Yu, S. C., Mathur, R., Pleim, J., Wong, D., Carlton, A. G., Roselle, S., and
Rao, S. T.: Simulation of the indirect radiative forcing of climate due to
aerosols by the two-way coupled WRF-CMAQ over the eastern United States, in
Air Pollution Modeling and its Applications, edited by: Steyn, D. G. and Castelli, S. T., XXI, Springer Netherlands, Netherlands, C(96), 579–583, 2011.
Yu, X.-Y., Lee, T., Ayres, B., Kreidenweis, S. M., Malm, W., and Collett, J.
L.: Loss of fine particle ammonium from denuded nylon filters, Atmos.
Environ., 40, 4797–4807, 2006.
Zender, C. S., Bian, H., and Newman, D.: Mineral Dust Entrainment and
Deposition (DEAD) model: Description and 1990s dust climatology, J. Geophys.
Res., 108, 4416, https://doi.org/10.1029/2002JD002775, 2003.
Zhang, Y.: Online-coupled meteorology and chemistry models: history, current status, and outlook, Atmos. Chem. Phys., 8, 2895–2932, https://doi.org/10.5194/acp-8-2895-2008, 2008.
Zhang, Y. and Wang, Y.: Climate-driven ground-level ozone extreme in the
fall over the Southeast United States, P. Natl. Acad. Sci. USA, 113,
10025–10030, https://doi.org/10.1073/pnas.1602563113, 2016.
Zhang, Y. and Wang, K.: Project 3 – Air quality and climate modeling:
Multi-model application, evaluation, intercomparison, and ensemble over the
U.S., poster presentation at the Air Climate Energy (ACE) Centers Meeting,
Pittsburgh, PA, 18–19 June 2019.
Zhang, K. M., Knipping, E. M., Wexler, A. S., Bhave, P. V., and Tonnesen, G.
S.: Size distribution of sea-salt emissions as a function of relative humidity, Atmos. Environ., 39, 3373–3379, 2005.
Zhang, Y., Liu, P., Pun, B., and Seigneur, C.: A comprehensive performance
evaluation of MM5-CMAQ for the summer 1999 Southern Oxidants Study episode,
Part-I. Evaluation protocols, databases and meteorological predictions,
Atmos. Environ., 40, 4825–4838, https://doi.org/10.1016/j.atmosenv.2005.12.043, 2006.
Zhang, Y., Vijayaraghavan, K., Wen, X.-Y., Snell, H. E., and Jacobson, M.
Z.: Probing into regional ozone and particulate matter pollution in the
United States: 1. A 1-year CMAQ simulation and evaluation using surface and
satellite data, J. Geophys. Res., 114, D22304, https://doi.org/10.1029/2009JD011898,
2009a.
Zhang, Y., Wen, X.-Y., Wang, K., Vijayaraghavan, K., and Jacobson, M. Z.:
Probing into regional ozone and particulate matter pollution in the United
States: 2. An examination of formation mechanisms through a process analysis
technique and sensitivity study, J. Geophys. Res., 114, D22305,
https://doi.org/10.1029/2009JD011900, 2009b.
Zhang, Y., Wen, X.-Y., and Jang, C. J.: Simulating
chemistry-aerosol-cloud-radiation-climate feedbacks over the continental US
using the online-coupled Weather Research Forecasting Model with chemistry
(WRF/Chem), Atmos. Environ., 44, 3568–3582,
https://doi.org/10.1016/j.atmosenv.2010.05.056, 2010.
Zhang, Y., Sartelet, K., Zhu, S., Wang, W., Wu, S.-Y., Zhang, X., Wang, K., Tran, P., Seigneur, C., and Wang, Z.-F.: Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe – Part 2: Evaluation of chemical concentrations and sensitivity simulations, Atmos. Chem. Phys., 13, 6845–6875, https://doi.org/10.5194/acp-13-6845-2013, 2013.
Zhang, Y., Chen, Y., Fan, J., and Leung, L. R.: Application of an
online-coupled regional climate model, WRF-CAM5, over East Asia for
examination of ice nucleation schemes: Part II. Sensitivity to ice
nucleation parameterizations and dust emissions, Climate, 3, 753–774,
https://doi.org/10.3390/cli3030753, 2015a.
Zhang, Y., Zhang, X., Wang, K., He, J., Leung, L. R., Fan, J.-W., and Nenes,
A.: Incorporating an advanced aerosol activation parameterization into
WRF-CAM5: Model evaluation and parameterization intercomparison, J. Geophys.
Res.-Atmos., 120, 6952–6979, https://doi.org/10.1002/2014JD023051, 2015b.
Zhang, Y., Zhang, X., Wang, L., Zhang, Q., Duan, F., and He, K.: Application
of WRF/Chem over East Asia: Part I. Model evaluation and intercomparison
with MM5/CMAQ, Atmos. Environ., 124, 285–300, 2016a.
Zhang, Y., Hong, C.-P., Yahya, K., Li, Q., Zhang, Q., and He, K.-B.:
Comprehensive evaluation of multi-year real-time air quality forecasting
using an online-coupled meteorology-chemistry model over southeastern United
States, Atmos. Environ., 138, 162–182, https://doi.org/10.1016/j.atmosenv.2016.05.006,
2016b.
Zhang, Y., Wang, K., and He, J.: Multi-year application of WRF-CAM5 over East
Asia-Part II: Interannual variability, trend analysis, and aerosol indirect
effects, Atmos. Environ., 165, 222–239, 2017.
Zhang, Y., Jena, C., Wang, K., Paton-Walsh, C., Guérette, E.-A.,
Utembe, S., Silver, J. D., and Keywood, M.: Multiscale applications of two
online-coupled meteorology-chemistry models during recent field campaigns in
Australia, Part I: Model description and WRF/Chem-ROMS evaluation using
surface and satellite data and sensitivity to spatial grid resolutions,
Atmosphere, 10, 189, https://doi.org/10.3390/atmos10040189, 2019.
Zheng, B., Zhang, Q., Zhang, Y., He, K. B., Wang, K., Zheng, G. J., Duan, F. K., Ma, Y. L., and Kimoto, T.: Heterogeneous chemistry: a mechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China, Atmos. Chem. Phys., 15, 2031–2049, https://doi.org/10.5194/acp-15-2031-2015, 2015.
Short summary
The two-way coupled WRF-CMAQ model accounting for complex chemistry–meteorology feedbacks has been applied to the long-term predictions of regional meteorology and air quality over the US. The model results show superior performance and importance of chemistry–meteorology feedbacks when compared to the offline coupled WRF and CMAQ simulations, which suggests that feedbacks should be considered along with other factors in developing future model applications to inform policy making.
The two-way coupled WRF-CMAQ model accounting for complex chemistry–meteorology feedbacks has...
