Articles | Volume 17, issue 6
https://doi.org/10.5194/gmd-17-2471-2024
© Author(s) 2024. 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-17-2471-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
02 Apr 2024
Model evaluation paper |
| 02 Apr 2024
| 02 Apr 2024
Intercomparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1–CMAQ v5.3.1, WRF–Chem v4.1.1, and WRF v3.7.1–CHIMERE v2020r1) in eastern China
Chao Gao
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Xuelei Zhang
CORRESPONDING AUTHOR
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Aijun Xiu
CORRESPONDING AUTHOR
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Qingqing Tong
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Hongmei Zhao
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Shichun Zhang
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Guangyi Yang
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
Mengduo Zhang
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
Shengjin Xie
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
School of Environment, Harbin Institute of Technology, Harbin, 150000, China
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Geosci. Model Dev., 15, 4503–4528, https://doi.org/10.5194/gmd-15-4503-2022, https://doi.org/10.5194/gmd-15-4503-2022, 2022
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Colm Duffy, Remi Prudhomme, Brian Duffy, James Gibbons, Cathal O'Donoghue, Mary Ryan, and David Styles
Geosci. Model Dev., 15, 2239–2264, https://doi.org/10.5194/gmd-15-2239-2022, https://doi.org/10.5194/gmd-15-2239-2022, 2022
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The GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) model is a new high-resolution integrated
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Samuel Lüthi, Gabriela Aznar-Siguan, Christopher Fairless, and David N. Bresch
Geosci. Model Dev., 14, 7175–7187, https://doi.org/10.5194/gmd-14-7175-2021, https://doi.org/10.5194/gmd-14-7175-2021, 2021
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Lavinia Baumstark, Nico Bauer, Falk Benke, Christoph Bertram, Stephen Bi, Chen Chris Gong, Jan Philipp Dietrich, Alois Dirnaichner, Anastasis Giannousakis, Jérôme Hilaire, David Klein, Johannes Koch, Marian Leimbach, Antoine Levesque, Silvia Madeddu, Aman Malik, Anne Merfort, Leon Merfort, Adrian Odenweller, Michaja Pehl, Robert C. Pietzcker, Franziska Piontek, Sebastian Rauner, Renato Rodrigues, Marianna Rottoli, Felix Schreyer, Anselm Schultes, Bjoern Soergel, Dominika Soergel, Jessica Strefler, Falko Ueckerdt, Elmar Kriegler, and Gunnar Luderer
Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, https://doi.org/10.5194/gmd-14-6571-2021, 2021
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This paper presents the new and open-source version 2.1 of the REgional Model of INvestments and Development (REMIND) with the aim of improving code documentation and transparency. REMIND is an integrated assessment model (IAM) of the energy-economic system. By answering questions like
Can the world keep global warming below 2 °C?and, if so,
Under what socio-economic conditions and applying what technological options?, it is the goal of REMIND to explore consistent transformation pathways.
Phillip D. Alderman
Geosci. Model Dev., 14, 6541–6569, https://doi.org/10.5194/gmd-14-6541-2021, https://doi.org/10.5194/gmd-14-6541-2021, 2021
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This paper documents a framework for accessing crop model input data directly from spatially referenced file formats and running simulations in parallel across a geographic region using the Decision Support System for Agrotechnology Transfer Cropping Systems Model (a widely used crop model system). The framework greatly reduced the execution time when compared to running the standard version of the model.
Abhijeet Mishra, Florian Humpenöder, Jan Philipp Dietrich, Benjamin Leon Bodirsky, Brent Sohngen, Christopher P. O. Reyer, Hermann Lotze-Campen, and Alexander Popp
Geosci. Model Dev., 14, 6467–6494, https://doi.org/10.5194/gmd-14-6467-2021, https://doi.org/10.5194/gmd-14-6467-2021, 2021
Short summary
Short summary
Timber plantations are an increasingly important source of roundwood production, next to harvest from natural forests. However, timber plantations are currently underrepresented in global land-use models. Here, we include timber production and plantations in the MAgPIE modeling framework. This allows one to capture the competition for land between agriculture and forestry. We show that increasing timber plantations in the coming decades partly compete with cropland for limited land resources.
Cited articles
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation 3. Sectional representation, J. Geophys. Res.-Atmos., 107, AAC 1-1–AAC 1-6, https://doi.org/10.1029/2001JD000483, 2002.
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.
Archer-Nicholls, S., Lowe, D., Utembe, S., Allan, J., Zaveri, R. A., Fast, J. D., Hodnebrog, Ø., Denier van der Gon, H., and McFiggans, G.: Gaseous chemistry and aerosol mechanism developments for version 3.5.1 of the online regional model, WRF-Chem, Geosci. Model Dev., 7, 2557–2579, https://doi.org/10.5194/gmd-7-2557-2014, 2014.
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.
Binkowski, F. S. and Roselle, S. J.: Models‐3 Community Multiscale Air Quality (CMAQ) model aerosol component 1. Model description, J. Geophys. Res.-Atmos., 108, https://doi.org/10.1029/2001JD001409, 2003.
Briant, R., Tuccella, P., Deroubaix, A., Khvorostyanov, D., Menut, L., Mailler, S., and Turquety, S.: Aerosol–radiation interaction modelling using online coupling between the WRF 3.7.1 meteorological model and the CHIMERE 2016 chemistry-transport model, through the OASIS3-MCT coupler, Geosci. Model Dev., 10, 927–944, https://doi.org/10.5194/gmd-10-927-2017, 2017.
Brunner, D., Savage, N., Jorba, O., Eder, B., Giordano, L., Badia, A., Balzarini, A., Baro, R., Bianconi, R., and Chemel, C.: 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.
Campbell, P., Zhang, Y., Wang, K., Leung, R., Fan, J., Zheng, B., Zhang, Q., and He, K.: Evaluation of a multi-scale WRF-CAM5 simulation during the 2010 East Asian Summer Monsoon, Atmos. Environ., 169, 204–217, https://doi.org/10.1016/j.atmosenv.2017.09.008, 2017.
Carslaw, K. S., Boucher, O., Spracklen, D. V., Mann, G. W., Rae, J. G. L., Woodward, S., and Kulmala, M.: A review of natural aerosol interactions and feedbacks within the Earth system, Atmos. Chem. Phys., 10, 1701–1737, https://doi.org/10.5194/acp-10-1701-2010, 2010.
Chapman, E. G., Gustafson Jr., W. I., Easter, R. C., Barnard, J. C., Ghan, S. J., Pekour, M. S., and Fast, J. D.: Coupling aerosol-cloud-radiative processes in the WRF-Chem model: Investigating the radiative impact of elevated point sources, Atmos. Chem. Phys., 9, 945–964, https://doi.org/10.5194/acp-9-945-2009, 2009.
Chen, L., Gao, Y., Zhang, M., Fu, J. S., Zhu, J., Liao, H., Li, J., Huang, K., Ge, B., Wang, X., Lam, Y. F., Lin, C.-Y., Itahashi, S., Nagashima, T., Kajino, M., Yamaji, K., Wang, Z., and Kurokawa, J.: MICS-Asia III: multi-model comparison and evaluation of aerosol over East Asia, Atmos. Chem. Phys., 19, 11911–11937, https://doi.org/10.5194/acp-19-11911-2019, 2019.
Ding, Q., Sun, J., Huang, X., Ding, A., Zou, J., Yang, X., and Fu, C.: Impacts of black carbon on the formation of advection–radiation fog during a haze pollution episode in eastern China, Atmos. Chem. Phys., 19, 7759–7774, https://doi.org/10.5194/acp-19-7759-2019, 2019.
Dionne, J., von Salzen, K., Cole, J., Mahmood, R., Leaitch, W. R., Lesins, G., Folkins, I., and Chang, R. Y.-W.: Modelling the relationship between liquid water content and cloud droplet number concentration observed in low clouds in the summer Arctic and its radiative effects, Atmos. Chem. Phys., 20, 29–43, https://doi.org/10.5194/acp-20-29-2020, 2020.
Fan, J., Wang, Y., Rosenfeld, D., and Liu, X.: Review of aerosol-cloud interactions: Mechanisms, significance, and challenges, J. Atmos. Sci., 73, 4221–4252, https://doi.org/10.1175/JAS-D-16-0037.1, 2016.
Feng, X., Lin, H., Fu, T.-M., Sulprizio, M. P., Zhuang, J., Jacob, D. J., Tian, H., Ma, Y., Zhang, L., Wang, X., Chen, Q., and Han, Z.: WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions, Geosci. Model Dev., 14, 3741–3768, https://doi.org/10.5194/gmd-14-3741-2021, 2021.
Forkel, R., Werhahn, J., Hansen, A. B., McKeen, S., Peckham, S., Grell, G., and Suppan, P.: Effect of aerosol-radiation feedback on regional air quality – A case study with WRF/Chem, Atmos. Environ., 53, 202–211, https://doi.org/10.1016/j.atmosenv.2011.10.009,
Gao, C., Zhang, X., Xiu, A., Huang, L., Zhao, H., Wang, K., and Tong, Q.: Spatiotemporal distribution of biogenic volatile organic compounds emissions in China, Acta Sci. Circumstantiae, 39, 4140–4151, https://doi.org/10.13671/j.hjkxxb.2019.0243, 2019.
Gao, C., Xiu, A., Zhang, X., Tong, Q., Zhao, H., Zhang, S., Yang, G., and Zhang, M.: Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality, Atmos. Chem. Phys., 22, 5265–5329, https://doi.org/10.5194/acp-22-5265-2022, 2022a.
Gao, C., Xiu, A., and Zhang, X.: Oservational data for sfdda nudging analysis in WRF model over China during 2017, Zenodo [data set], https://doi.org/10.5281/zenodo.6975602, 2022b.
Gao, C., Xiu, A., Zhang, X., Tong, Q., Zhao, H., Zhang, S., Yang, G., Zhang, M., and Xie, S.: Source codes of WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1, Zenodo [software], https://doi.org/10.5281/zenodo.7901682, 2023a.
Gao, C., Xiu, A., Zhang, X., Tong, Q., Zhao, H., Zhang, S., Yang, G., Zhang, M., and Xie, S.: FNL data used for producing meteorological ICs/BCs of WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1, Zenodo [data set], https://doi.org/10.5281/zenodo.7925012, 2023b.
Gao, C., Xiu, A., Zhang, X., Tong, Q., Zhao, H., Zhang, S., Yang, G., Zhang, M., and Xie, S.: Chemical initial and boundary conditions for WRF-CMAQ, Zenodo [data set], https://doi.org/10.5281/zenodo.7932390, 2023c.
Gao, C., Xiu, A., Zhang, X., Tong, Q., Zhao, H., Zhang, S., Yang, G., Zhang, M., and Xie, S.: Chemical initial and boundary conditions for WRF-Chem, Zenodo [data set], https://doi.org/10.5281/zenodo.7932936, 2023d.
Gao, C., Xiu, A., Zhang, X., Tong, Q., Zhao, H., Zhang, S., Yang, G., Zhang, M., and Xie, S.: Chemical initial and boundary conditions for WRF-CHIMERE, Zenodo [data set], https://doi.org/10.5281/zenodo.7933641, 2023e.
Gao, C., Zhang, X., Xiu, A., Tong, Q., Zhao, H., Zhang, S., Yang, G., Zhang, M., and Xie, S.: Emission input data for WRF-CMAQ, Zenodo [data set], https://doi.org/10.5281/zenodo.7932430, 2023f.
Gao, C., Xiu, A., Zhang, X., Tong, Q., Zhao, H., Zhang, S., Yang, G., Zhang, M., and Xie, S.: Emission input data for WRF-Chem, Zenodo [data set], https://doi.org/10.5281/zenodo.7932734, 2023g.
Gao, C., Xiu, A., Zhang, X., Tong, Q., Zhao, H., Zhang, S., Yang, G., Zhang, M., and Xie, S.: Emission input data for WRF-CHMIERE, Zenodo [data set], https://doi.org/10.5281/zenodo.7931614, 2023h.
Gao, C., Xiu, A., Zhang, X., Tong, Q., Zhao, H., Zhang, S., Yang, G., Zhang, M., and Xie, S.: Data used to create figures and tables in the GMD manuscript “Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China”, Zenodo [data set], https://doi.org/10.5281/zenodo.7750907, 2023i.
Gao, J., Woodward, A., Vardoulakis, S., Kovats, S., Wilkinson, P., Li, L., Xu, L., Li, J., Yang, J., and Cao, L.: Haze, public health and mitigation measures in China: A review of the current evidence for further policy response, Sci. Total Environ., 578, 148–157, https://doi.org/10.1016/j.scitotenv.2016.10.231, 2017.
Gao, M., Han, Z., Liu, Z., Li, M., Xin, J., Tao, Z., Li, J., Kang, J.-E., Huang, K., Dong, X., Zhuang, B., Li, S., Ge, B., Wu, Q., Cheng, Y., Wang, Y., Lee, H.-J., Kim, C.-H., Fu, J. S., Wang, T., Chin, M., Woo, J.-H., Zhang, Q., Wang, Z., and Carmichael, G. R.: Air quality and climate change, Topic 3 of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III) – Part 1: Overview and model evaluation, Atmos. Chem. Phys., 18, 4859–4884, https://doi.org/10.5194/acp-18-4859-2018, 2018.
Gao, M., Han, Z., Tao, Z., Li, J., Kang, J.-E., Huang, K., Dong, X., Zhuang, B., Li, S., Ge, B., Wu, Q., Lee, H.-J., Kim, C.-H., Fu, J. S., Wang, T., Chin, M., Li, M., Woo, J.-H., Zhang, Q., Cheng, Y., Wang, Z., and Carmichael, G. R.: Air quality and climate change, Topic 3 of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III) – Part 2: aerosol radiative effects and aerosol feedbacks, Atmos. Chem. Phys., 20, 1147–1161, https://doi.org/10.5194/acp-20-1147-2020, 2020.
Gao, Y., Zhang, M., Liu, Z., Wang, L., Wang, P., Xia, X., Tao, M., and Zhu, L.: Modeling the feedback between aerosol and meteorological variables in the atmospheric boundary layer during a severe fog–haze event over the North China Plain, Atmos. Chem. Phys., 15, 4279–4295, https://doi.org/10.5194/acp-15-4279-2015, 2015.
Ge, B., Itahashi, S., Sato, K., Xu, D., Wang, J., Fan, F., Tan, Q., Fu, J. S., Wang, X., Yamaji, K., Nagashima, T., Li, J., Kajino, M., Liao, H., Zhang, M., Wang, Z., Li, M., Woo, J.-H., Kurokawa, J., Pan, Y., Wu, Q., Liu, X., and Wang, Z.: Model Inter-Comparison Study for Asia (MICS-Asia) phase III: multimodel comparison of reactive nitrogen deposition over China, Atmos. Chem. Phys., 20, 10587–10610, https://doi.org/10.5194/acp-20-10587-2020, 2020.
Geng, G., Zheng, Y., Zhang, Q., Xue, T., Zhao, H., Tong, D., Zheng, B., Li, M., Liu, F., and Hong, C.: Drivers of PM2.5 air pollution deaths in China 2002–2017, Nat. Geosci., 14, 645–650, https://doi.org/10.1038/s41561-021-00792-3, 2021.
Gillies, S., Ward, B., and Petersen, A. S.: Rasterio: Geospatial raster I/O for Python programmers, GitHub [code], https://github.com/mapbox/rasterio (last access: 20 November 2020), 2013.
Govardhan, G. R., Nanjundiah, R. S., Satheesh, S. K., Moorthy, K. K., and Takemura, T.: Inter-comparison and performance evaluation of chemistry transport models over Indian region, Atmos. Environ., 125, 486–504, https://doi.org/10.1016/j.atmosenv.2015.10.065, 2016.
Grell, G. and Baklanov, A.: Integrated modeling for forecasting weather and air quality: A call for fully coupled approaches, Atmos. Environ., 45, 6845–6851, https://doi.org/10.1016/j.atmosenv.2011.01.017, 2011.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005.
Guo, J., Li, Y., Cohen, J. B., Li, J., Chen, D., Xu, H., Liu, L., Yin, J., Hu, K., and Zhai, P.: Shift in the temporal trend of boundary layer height in China using long-term (1979–2016) radiosonde data, Geophys. Res. Lett., 46, 6080–6089, https://doi.org/10.1029/2019GL082666, 2019.
He, K., Huo, H., and Zhang, Q.: Urban air pollution in China: current status, characteristics, and progress, Annu. Rev. Environ. Resour., 27, 397–431, https://doi.org/10.1146/annurev.energy.27.122001.083421, 2002.
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, https://doi.org/10.1016/j.atmosenv.2014.12.034, 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.
Huang, D. and Gao, S.: Impact of different reanalysis data on WRF dynamical downscaling over China, Atmos. Res., 200, 25–35, https://doi.org/10.1016/j.atmosres.2017.09.017, 2018.
Huang, X., Song, Y., Li, M., Li, J., Huo, Q., Cai, X., Zhu, T., Hu, M., and Zhang, H.: A high-resolution ammonia emission inventory in China, Global Biogeochem. Cy., 26, GB1030, https://doi.org/10.1029/2011GB004161, 2012.
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.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Im, U., Bianconi, R., Solazzo, E., Kioutsioukis, I., Badia, A., Balzarini, A., Baró, R., Bellasio, R., Brunner, D., and Chemel, C.: Evaluation of operational on-line-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part I: Ozone, Atmos. Environ., 115, 404–420, https://doi.org/10.1016/j.atmosenv.2014.09.042, 2015a.
Im, U., Bianconi, R., Solazzo, E., Kioutsioukis, I., Badia, A., Balzarini, A., Baró, R., Bellasio, R., Brunner, D., and Chemel, C.: Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part II: Particulate matter, Atmos. Environ., 115, 421–441, https://doi.org/10.1016/j.atmosenv.2014.08.072, 2015b.
IPCC: Climate change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, https://www.ipcc.ch/site/assets/uploads/2018/02/ar4_syr_full_report.pdf (last access: 20 March 2023), 2007.
IPCC: Climate change 2021: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FullReport_small.pdf (last access: 20 March 2023), 2021.
Itahashi, S., Ge, B., Sato, K., Fu, J. S., Wang, X., Yamaji, K., Nagashima, T., Li, J., Kajino, M., Liao, H., Zhang, M., Wang, Z., Li, M., Kurokawa, J., Carmichael, G. R., and Wang, Z.: MICS-Asia III: overview of model intercomparison and evaluation of acid deposition over Asia, Atmos. Chem. Phys., 20, 2667–2693, https://doi.org/10.5194/acp-20-2667-2020, 2020.
Jacobson, M. Z.: Developing, coupling, and applying a gas, aerosol, transport, and radiation model to study urban and regional air pollution [M], University of California, Los Angeles, 1994.
Jacobson, M. Z.: Development and application of a new air pollution modeling system—Part III. Aerosol-phase simulations, Atmos. Environ., 31, 587–608, https://doi.org/10.1016/S1352-2310(96)00201-4, 1997.
Jacobson, M. Z.: Studying the effects of aerosols on vertical photolysis rate coefficient and temperature profiles over an urban airshed, J. Geophys. Res.-Atmos., 103, 10593–10604, https://doi.org/10.1029/98jd00287, 1998.
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.-Atmos., 106, 5385–5401, https://doi.org/10.1029/2000JD900560, 2001.
Jacobson, M. Z.: Analysis of aerosol interactions with numerical techniques for solving coagulation, nucleation, condensation, dissolution, and reversible chemistry among multiple size distributions, J. Geophys. Res.-Atmos., 107, AAC 2-1–AAC 2-23, https://doi.org/10.1029/2001JD002044, 2002.
Keita, S. A., Girard, E., Raut, J.-C., Leriche, M., Blanchet, J.-P., Pelon, J., Onishi, T., and Cirisan, A.: A new parameterization of ice heterogeneous nucleation coupled to aerosol chemistry in WRF-Chem model version 3.5.1: evaluation through ISDAC measurements, Geosci. Model Dev., 13, 5737–5755, https://doi.org/10.5194/gmd-13-5737-2020, 2020.
Klein, S. A., McCoy, R. B., Morrison, H., Ackerman, A. S., Avramov, A., Boer, G. de, Chen, M., Cole, J. N. S., Del Genio, A. D., and Falk, M.: Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. I: Single-layer cloud, Q. J. Roy. Meteor. Soc., 135, 979–1002, https://doi.org/10.1002/qj.416, 2009.
Knote, C., Tuccella, P., Curci, G., Emmons, L., Orlando, J. J., Madronich, S., Baró, R., Jiménez-Guerrero, P., Luecken, D., and Hogrefe, C.: Influence of the choice of gas-phase mechanism on predictions of key gaseous pollutants during the AQMEII phase-2 intercomparison, Atmos. Environ., 115, 553–568, https://doi.org/10.1016/j.atmosenv.2014.11.066, 2015.
Kong, L., Tang, X., Zhu, J., Wang, Z., Fu, J. S., Wang, X., Itahashi, S., Yamaji, K., Nagashima, T., Lee, H.-J., Kim, C.-H., Lin, C.-Y., Chen, L., Zhang, M., Tao, Z., Li, J., Kajino, M., Liao, H., Wang, Z., Sudo, K., Wang, Y., Pan, Y., Tang, G., Li, M., Wu, Q., Ge, B., and Carmichael, G. R.: Evaluation and uncertainty investigation of the NO2, CO and NH3 modeling over China under the framework of MICS-Asia III, Atmos. Chem. Phys., 20, 181–202, https://doi.org/10.5194/acp-20-181-2020, 2020.
Li, J., Nagashima, T., Kong, L., Ge, B., Yamaji, K., Fu, J. S., Wang, X., Fan, Q., Itahashi, S., Lee, H.-J., Kim, C.-H., Lin, C.-Y., Zhang, M., Tao, Z., Kajino, M., Liao, H., Li, M., Woo, J.-H., Kurokawa, J., Wang, Z., Wu, Q., Akimoto, H., Carmichael, G. R., and Wang, Z.: Model evaluation and intercomparison of surface-level ozone and relevant species in East Asia in the context of MICS-Asia Phase III – Part 1: Overview, Atmos. Chem. Phys., 19, 12993–13015, https://doi.org/10.5194/acp-19-12993-2019, 2019.
Li, M., Liu, H., Geng, G., Hong, C., Liu, F., Song, Y., Tong, D., Zheng, B., Cui, H., and Man, H.: Anthropogenic emission inventories in China: a review, Natl. Sci. Rev., 4, 834–866, https://doi.org/10.1093/nsr/nwx150, 2017.
Liu, Z., Wang, Y., Hu, B., Lu, K., Tang, G., Ji, D., Yang, X., Gao, W., Xie, Y., and Liu, J.: Elucidating the quantitative characterization of atmospheric oxidation capacity in Beijing, China, Sci. Total Environ., 771, 145306, https://doi.org/10.1016/j.scitotenv.2021.145306, 2021.
Ma, Y., Jin, Y., Zhang, M., Gong, W., Hong, J., Jin, S., Shi, Y., Zhang, Y., and Liu, B.: Aerosol optical properties of haze episodes in eastern China based on remote-sensing observations and WRF-Chem simulations, Sci. Total Environ., 757, 143784, https://doi.org/10.1016/j.scitotenv.2020.143784, 2021.
Mailler, S., Menut, L., Khvorostyanov, D., Valari, M., Couvidat, F., Siour, G., Turquety, S., Briant, R., Tuccella, P., Bessagnet, B., Colette, A., Létinois, L., Markakis, K., and Meleux, F.: CHIMERE-2017: from urban to hemispheric chemistry-transport modeling, Geosci. Model Dev., 10, 2397–2423, https://doi.org/10.5194/gmd-10-2397-2017, 2017.
Makar, P. A., Gong, W., Milbrandt, J., Hogrefe, C., Zhang, Y., Curci, G., Žabkar, R., 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. D., 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 1: Effects on weather, Atmos. Environ., 115, 442–469, https://doi.org/10.1016/j.atmosenv.2014.12.003, 2015a.
Makar, P. A., Gong, 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. D., 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, https://doi.org/10.1016/j.atmosenv.2014.10.021, 2015b.
Menut, L., Bessagnet, B., Khvorostyanov, D., Beekmann, M., Blond, N., Colette, A., Coll, I., Curci, G., Foret, G., Hodzic, A., Mailler, S., Meleux, F., Monge, J.-L., Pison, I., Siour, G., Turquety, S., Valari, M., Vautard, R., and Vivanco, M. G.: CHIMERE 2013: a model for regional atmospheric composition modelling, Geosci. Model Dev., 6, 981–1028, https://doi.org/10.5194/gmd-6-981-2013, 2013.
Menut, L., Siour, G., Mailler, S., Couvidat, F., and Bessagnet, B.: Observations and regional modeling of aerosol optical properties, speciation and size distribution over Northern Africa and western Europe, Atmos. Chem. Phys., 16, 12961–12982, https://doi.org/10.5194/acp-16-12961-2016, 2016.
Qu, Y., Voulgarakis, A., Wang, T., Kasoar, M., Wells, C., Yuan, C., Varma, S., and Mansfield, L.: A study of the effect of aerosols on surface ozone through meteorology feedbacks over China, Atmos. Chem. Phys., 21, 5705–5718, https://doi.org/10.5194/acp-21-5705-2021, 2021.
Rosenfeld, D., Andreae, M. O., Asmi, A., Chin, M., de Leeuw, G., Donovan, D. P., Kahn, R., Kinne, S., Kivekäs, N., and Kulmala, M.: Global observations of aerosol-cloud-precipitation-climate interactions, Rev. Geophys., 52, 750–808, https://doi.org/10.1002/2013RG000441, 2014.
Safieddine, S., Boynard, A., Coheur, P.-F., Hurtmans, D., Pfister, G., Quennehen, B., Thomas, J. L., Raut, J.-C., Law, K. S., Klimont, Z., Hadji-Lazaro, J., George, M., and Clerbaux, C.: Summertime tropospheric ozone assessment over the Mediterranean region using the thermal infrared IASI/MetOp sounder and the WRF-Chem model, Atmos. Chem. Phys., 14, 10119–10131, https://doi.org/10.5194/acp-14-10119-2014, 2014.
Stein, O., Schultz, M. G., Bouarar, I., Clark, H., Huijnen, V., Gaudel, A., George, M., and Clerbaux, C.: On the wintertime low bias of Northern Hemisphere carbon monoxide found in global model simulations, Atmos. Chem. Phys., 14, 9295–9316, https://doi.org/10.5194/acp-14-9295-2014, 2014.
Tang, W., Yang, K., Qin, J., Li, X., and Niu, X.: A 16-year dataset (2000–2015) of high-resolution (3 h, 10 km) global surface solar radiation, Earth Syst. Sci. Data, 11, 1905–1915, https://doi.org/10.5194/essd-11-1905-2019, 2019.
Tuccella, P., Menut, L., Briant, R., Deroubaix, A., Khvorostyanov, D., Mailler, S., Siour, G., and Turquety, S.: Implementation of aerosol-cloud interaction within WRF-CHIMERE online coupled model: Evaluation and investigation of the indirect radiative effect from anthropogenic emission reduction on the Benelux Union, Atmosphere (Basel), 10, 20, https://doi.org/10.3390/atmos10010020, 2019.
Wallace, J. M. and Hobbs, P. V: Atmospheric science: an introductory survey, Elsevier, ISBN 9780127329512, 2006.
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, 2015.
Wang, K., Zhang, Y., Zhang, X., Fan, J., Leung, L. R., Zheng, B., Zhang, Q., and He, K.: Fine-scale application of WRF-CAM5 during a dust storm episode over East Asia: Sensitivity to grid resolutions and aerosol activation parameterizations, Atmos. Environ., 176, 1–20, https://doi.org/10.1016/j.atmosenv.2017.12.014, 2018.
Wang, K., Zhang, Y., Yu, S., Wong, D. C., Pleim, J., Mathur, R., Kelly, J. T., and Bell, M.: 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, Geosci. Model Dev., 14, 7189–7221, https://doi.org/10.5194/gmd-14-7189-2021, 2021.
Wang, K., Gao, C., Wu, K., Liu, K., Wang, H., Dan, M., Ji, X., and Tong, Q.: ISAT v2.0: an integrated tool for nested-domain configurations and model-ready emission inventories for WRF-AQM, Geosci. Model Dev., 16, 1961–1973, https://doi.org/10.5194/gmd-16-1961-2023, 2023.
Wang, S. and Hao, J.: Air quality management in China: Issues, challenges, and options, J. Environ. Sci., 24, 2–13, https://doi.org/10.1016/S1001-0742(11)60724-9, 2012.
Wang, Z., Wang, Z., Li, J., Zheng, H., Yan, P., and Li, J.: Development of a meteorology-chemistry two-way coupled numerical model (WRF-NAQPMS) and its application in a severe autumn haze simulation over the Beijing-Tianjin-Hebei area, China. Clim, Environ. Res, 19, 153–163, https://doi.org/10.3878/j.issn.1006-9585.2014.13231, 2014.
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.
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., Wang, J., Gan, C.-M., Sarwar, G., Wong, D. C., and McKeen, S.: Representing the effects of stratosphere–troposphere exchange on 3-D O3 distributions in chemistry transport models using a potential vorticity-based parameterization, Atmos. Chem. Phys., 16, 10865–10877, https://doi.org/10.5194/acp-16-10865-2016, 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.
Xu, K.-M. and Randall, D. A.: A semiempirical cloudiness parameterization for use in climate models, J. Atmos. Sci., 53, 3084–3102, https://doi.org/10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2, 1996.
Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for simulating aerosol interactions and chemistry (MOSAIC), J. Geophys. Res.-Atmos., 113, D13204, https://doi.org/10.1029/2007JD008782, 2008.
Zhang, X., Wu, Y., Liu, X., Reis, S., Jin, J., Dragosits, U., Van Damme, M., Clarisse, L., Whitburn, S., and Coheur, P.-F.: Ammonia emissions may be substantially underestimated in China, Environ. Sci. Technol., 51, 12089–12096, https://doi.org/10.1021/acs.est.7b02171, 2017.
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., Zhang, X., Wang, K., Zhang, Q., Duan, F., and He, K.: Application of WRF/Chem over East Asia: Part II. Model improvement and sensitivity simulations, Atmos. Environ., 124, 301–320, https://doi.org/10.1016/j.atmosenv.2015.07.023, 2016.
Zhao, B., Liou, K., Gu, Y., Li, Q., Jiang, J. H., Su, H., He, C., Tseng, H.-L. R., Wang, S., and Liu, R.: Enhanced PM2.5 pollution in China due to aerosol-cloud interactions, Sci. Rep., 7, 4453, https://doi.org/10.1038/s41598-017-04096-8, 2017.
Zhou, C., Zhang, X., Gong, S., Wang, Y., and Xue, M.: Improving aerosol interaction with clouds and precipitation in a regional chemical weather modeling system, Atmos. Chem. Phys., 16, 145–160, https://doi.org/10.5194/acp-16-145-2016, 2016.
Zhu, J., Wang, S., Wang, H., Jing, S., Lou, S., Saiz-Lopez, A., and Zhou, B.: Observationally constrained modeling of atmospheric oxidation capacity and photochemical reactivity in Shanghai, China, Atmos. Chem. Phys., 20, 1217–1232, https://doi.org/10.5194/acp-20-1217-2020, 2020.
Zhu, J., Chen, L., Liao, H., Yang, H., Yang, Y., and Yue, X.: Enhanced PM2.5 decreases and O3 increases in China during COVID-19 lockdown by aerosol-radiation feedback, Geophys. Res. Lett., 48, e2020GL090260, https://doi.org/10.1029/2020GL090260, 2021.
Short summary
A comprehensive comparison study is conducted targeting the performances of three two-way coupled meteorology and air quality models (WRF-CMAQ, WRF-Chem, and WRF-CHIMERE) for eastern China during 2017. The impacts of aerosol–radiation–cloud interactions on these models’ results are evaluated against satellite and surface observations. Further improvements to the calculation of aerosol–cloud interactions in these models are crucial to ensure more accurate and timely air quality forecasts.
A comprehensive comparison study is conducted targeting the performances of three two-way...
