Articles | Volume 17, issue 12
https://doi.org/10.5194/gmd-17-4821-2024
© 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-17-4821-2024
© 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
| 
20 Jun 2024
Model evaluation paper |
| 20 Jun 2024
| 20 Jun 2024
Evaluation of CMIP6 model simulations of PM2.5 and its components over China
Fangxuan Ren
Laboratory for Climate and Ocean–Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Laboratory for Climate and Ocean–Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Institute of Carbon Neutrality, Peking University, Beijing 100871, China
Chenghao Xu
Laboratory for Climate and Ocean–Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Jamiu A. Adeniran
Laboratory for Climate and Ocean–Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Jingxu Wang
Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
Randall V. Martin
Department of Energy, Environmental, and Chemical Engineering, Washington University, St. Louis, MO, USA
Aaron van Donkelaar
Department of Energy, Environmental, and Chemical Engineering, Washington University, St. Louis, MO, USA
Melanie S. Hammer
St. Francis Xavier University, Department of Earth Sciences, Antigonish, NS, Canada
Larry W. Horowitz
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
Steven T. Turnock
Met Office Hadley Center, Exeter, UK
University of Leeds Met Office Strategic (LUMOS) Research Group, University of Leeds, Leeds, UK
Naga Oshima
Meteorological Research Institute, Tsukuba, Japan
Jie Zhang
Beijing Climate Center, China Meteorological Administration, Beijing 100081, China
Susanne Bauer
NASA Goddard Institute for Space Studies, New York, NY, USA
Kostas Tsigaridis
Center for Climate Systems Research, Columbia University, New York, NY, USA
NASA Goddard Institute for Space Studies, New York, NY, USA
Øyvind Seland
Norwegian Meteorological Institute, P.O. Box 43 Blindern, Oslo, Norway
Pierre Nabat
Centre National de Recherches Météorologiques (CNRM), Météo-France, CNRS, Toulouse, France
David Neubauer
Institute of Atmospheric and Climate Science, ETH Zürich, Zurich, Switzerland
Gary Strand
Climate and Global Dynamics Laboratory, the National Center for Atmospheric Research, Boulder, CO, USA
Twan van Noije
Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Philippe Le Sager
Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Toshihiko Takemura
Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan
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Cited
6 citations as recorded by crossref.
- Global fully coupled climate-aerosol CMA-CPSv4 – Part 1: Aerosol simulation performance M. Zheng et al. https://doi.org/10.5194/gmd-19-7279-2026
- Coupling natural-social models can better quantify the interactions in achieving carbon neutrality in China T. Wang et al. https://doi.org/10.1088/1748-9326/ada8c3
- Pesticide fate and transport in the atmosphere and implications for risk assessment C. Bedos et al. https://doi.org/10.1016/j.jhazmat.2026.141769
- Assessing atmospheric particulate matters and their removal potential through roadside trees in Chattogram city, Bangladesh N. Emon et al. https://doi.org/10.1016/j.apr.2025.102535
- High-resolution anthropogenic emission inventory for China (2015–2024): Spatiotemporal changes and environmental application D. Li et al. https://doi.org/10.1016/j.atmosenv.2025.121495
- Spatial–temporal patterns in anthropogenic and biomass burning emission contributions to air pollution and mortality burden changes in India from 1995 to 2014 B. Luo et al. https://doi.org/10.5194/acp-25-4767-2025
6 citations as recorded by crossref.
- Global fully coupled climate-aerosol CMA-CPSv4 – Part 1: Aerosol simulation performance M. Zheng et al. https://doi.org/10.5194/gmd-19-7279-2026
- Coupling natural-social models can better quantify the interactions in achieving carbon neutrality in China T. Wang et al. https://doi.org/10.1088/1748-9326/ada8c3
- Pesticide fate and transport in the atmosphere and implications for risk assessment C. Bedos et al. https://doi.org/10.1016/j.jhazmat.2026.141769
- Assessing atmospheric particulate matters and their removal potential through roadside trees in Chattogram city, Bangladesh N. Emon et al. https://doi.org/10.1016/j.apr.2025.102535
- High-resolution anthropogenic emission inventory for China (2015–2024): Spatiotemporal changes and environmental application D. Li et al. https://doi.org/10.1016/j.atmosenv.2025.121495
- Spatial–temporal patterns in anthropogenic and biomass burning emission contributions to air pollution and mortality burden changes in India from 1995 to 2014 B. Luo et al. https://doi.org/10.5194/acp-25-4767-2025
Saved (final revised paper)
Latest update: 14 Aug 2026
Short summary
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over...
