Chaoqun Ma
Ma Group
Main Focus
I mainly study the biomass burning,
especially wildfires. By combining earth system models, in situ and remote
sensing observations, data assimilation, and machine learning, I explore how wildfires interact
with the earth system, change the climate and affect human societies.
Curriculum Vitae
Orcid:
https://orcid.org/0000-0002-6841-3157
Education
09, 2011 – 07,2015 Nanjing University
Bachelor of Science, majoring
in Atmospheric Science
09, 2015 – 07,2020 Nanjing
University
Ph.D.,
majoring in Atmospheric Science
Work Experience
08, 2020 – 12, 2024 Postdoctoral Researcher, Max Planck Institute for Chemistry, Mainz, Germany
Since 01, 2025 Group Leader, Max Planck Institute for Chemistry, Mainz, Germany
Since 06, 2026 Associate Editor, Journal of Geophyscial Research - Atmospheres
Selected Publications
1. C.
Ma, H. Su, Y. Cheng, Deep learning reveals a stronger fossil fuel influence
than biomass burning in shaping remote tropospheric ozone. arXiv preprint arXiv:2606.09793,
(2026).
2. C.
Ma, H. Su, J. Lelieveld, W. Randel, P. Yu, M. O. Andreae, Y. Cheng,
Smoke-charged vortex doubles hemispheric aerosol in the middle stratosphere and
buffers ozone depletion. Sci. Adv. 10, eadn3657 (2024).
3. C.
Ma, R. Ni, H. Su, Y. Cheng, Enhancing Global Simulation of Smoke Injection
Height for Intense Pyro-Convection Through Coupling an Improved One-Dimensional
Plume Rise Model in CAM-chem. J. Adv.
Model. Earth Syst. 16,
e2023MS004127 (2024).
4. C.
Huang, C. Ma*, T. Wang, Y. Qu, M. Li, S. Li, B. Zhuang, M. Xie, Study on the
assimilation of the sulphate reaction rates based on WRF-Chem/DART. Sci. China Earth Sci. 66, 2239-2253 (2023).
5. C.
Ma, T. Wang, Z. Jiang, H. Wu, M. Zhao, B. Zhuang, S. Li, M. Xie, M. Li, J. Liu,
Importance of bias correction in data assimilation of multiple observations
over eastern China using WRF‐Chem/DART. J.
Geophys. Res. Atmos. 125,
e2019JD031465 (2020).
6. C.
Ma, T. Wang, A. P. Mizzi, J. L. Anderson, B. Zhuang, M. Xie, R. Wu,
Multiconstituent Data Assimilation With WRF-Chem/DART: Potential for Adjusting
Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China.
J. Geophys. Res. Atmos. 124, 7393-7412 (2019).
7. C. Ma, T. Wang, Z. Zang, Z. Li, Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts. Adv. Atmos. Sci. 35, 813-825 (2018).