Climate Dynamics Group

at Chalmers University of Technology

We study the interactions between different components of the climate system to understand how they give rise to patterns and variations on timescales ranging from days to decades.

On this site you can find more information about our group, what we do, and our output in terms of publications and other related resources. Got a question? Feel free to reach out to us!

About us

We are the Climate Dynamics Group (CDG), led by Hans Chen at the Division of Geoscience and Remote Sensing within the Department of Space, Earth and Environment at Chalmers University of Technology. We are situated in the west coast city of Gothenburg, Sweden.

Photo of members of the Climate Dynamics Group.
Climate Dynamics Group members at the Swedish Climate Symposium 2024.

In our research, we use observations, numerical models, statistical methods, model–data fusion methods such as data assimilation, and machine learning approaches to study the climate system. Our focus is on the atmosphere and its connections with other Earth system components on global to regional scales and diurnal to interdecadal timescales.

Highlighted research topics

ML-illustration of

Machine learning

We use machine learning to combine information from different data sources and to unveil patterns in the climate system. Examples of applications include classification of weather patterns and interpretation of satellite images.

Carbon cycle diagram

Carbon cycle dynamics

Climate change has significantly altered the carbon cycle, but major knowledge gaps remain, especially regarding the land sink. Our research aims to fill these gaps by combining different types of observations and models.

Map of global temperature anomalies

Climate variation

We study climate variability and change on interannual to decadal timescales using observations and models. Our main focus is the atmosphere and its connections with the land, ocean, sea ice, ecosystems, and human systems.

News

Recent publications

Cheng, S., Z. Li, F. Liu, H. W. Chen, and D. Chen, 2025: A robust reduction in near-surface wind speed after volcanic eruptions: Implications for wind energy generation. The Innovation, 6, 1, https://doi.org/10.1016/j.xinn.2024.100734.

Li, T., B. He, D. Chen, H. W. Chen, L. Guo, W. Yuan, K. Fang, F. Shi, L. Liu, H. Zheng, L. Huang, X. Wu, X. Hao, X. Zhao, and W. Jiang, 2024: Increasing sensitivity of tree radial growth to precipitation. Geophysical Research Letters, 51, e2024GL110003, https://doi.org/https://doi.org/10.1029/2024GL110003.

Xu, H., H. W. Chen, D. Chen, Y. Wang, X. Yue, B. He, L. Guo, W. Yuan, Z. Zhong, L. Huang, F. Zheng, T. Li, and X. He, 2024: Global patterns and drivers of post-fire vegetation productivity recovery. Nature Geoscience, 17, 874–881, https://doi.org/10.1038/s41561-024-01520-3.

Yan, X., C. Zuo, Z. Li, H. W. Chen, Y. Jiang, Q. Wang, G. Wang, K. Jia, Y. A, Z. Chen, and J. Chen, 2024: Substantial underestimation of fine-mode aerosol loading from wildfires and its radiative effects in current satellite-based retrievals over the United States. Environmental Science & Technology, 58, 15661–15671, https://doi.org/10.1021/acs.est.4c02498.

Yan, X., Z. Zang, Z. Li, H. W. Chen, J. Chen, Y. Jiang, Y. Chen, B. He, C. Zuo, T. Nakajima, and J. Kim, 2024: Deep learning with pretrained framework unleashes the power of satellite-based global fine-mode aerosol retrieval. Environmental Science & Technology, 58, 14260–14270, https://doi.org/10.1021/acs.est.4c02701.