Chaoqun Ma

Team Leader
Aerosol Chemistry
+4961313057700
B.2.15

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 –  08, 2024  Postdoctoral Researcher, Max Planck Institute for Chemistry, Mainz, Germany

Since 09, 2024           Project Leader, Max Planck Institute for Chemistry, Mainz, Germany


Publications

Ma, C., Su, H., Lelieveld, J., Randel, W., Yu, P., Andreae, M. O., and Cheng, Y. (2024), Smoke-charged vortex doubles hemispheric aerosol in the middle stratosphere and buffers ozone depletion. Science Advances, 10(28), eadn3657, doi: doi:10.1126/sciadv.adn3657.

Huang, C., Ma, C., Wang, T., Qu, Y., Li, M., Li, S., et al. (2023), Study on the assimilation of the sulphate reaction rates based on WRF-Chem/DART. Science China Earth Sciences, 66(10), 2239-2253, doi: 10.1007/s11430-023-1153-9.

Ma, C., Wang, T., Jiang, Z., Wu, H., Zhao, M., Zhuang, B., ... & Wu, R. (2020). Importance of bias correction in data assimilation of multiple observations over eastern China using WRF‐Chem/DART. Journal of Geophysical Research: Atmospheres, 125(1), e2019JD031465.

Ma, C., Wang, T., Mizzi, A. P., Anderson, J. L., Zhuang, B., Xie, M., & Wu, R. (2019). Multiconstituent data assimilation with WRF‐Chem/DART: Potential for adjusting anthropogenic emissions and improving air quality forecasts over eastern China. Journal of Geophysical Research: Atmospheres, 124(13), 7393-7412.

Ma, C., Wang, T., Zang, Z., and Li, Z. (2018), Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts. Advances in Atmospheric Sciences, 35(7), 813-825, doi: 10.1007/s00376-017-7179-y.

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