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.

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

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 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.

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
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Article about recent California wildfires
Hans Chen has been interviewed by Källkritikbyrån about the recent wildfires in California and the links to climate change and increasing “hydroclimate whiplash”:
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Recent paper on volcanic eruptions and wind energy featured on the cover of The Innovation
A recent paper co-authored by Hans Chen is featured on the cover of the relatively new open-access journal The Innovation. The paper, led by Cheng Shen, investigates the effect of strong tropical volcanic eruptions on near-surface wind speed globally.
- Cheng et al. (2025): A robust reduction in near-surface wind speed after volcanic eruptions: Implications for wind energy generation.
Update 2025-02-06: The Guardian has reported on the study:
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Welcome Hari to the group!
Hari Nair recently joined our group as a postdoc. Hari will primarily work on a project exploring the changing carbon cycle dynamics in the Arctic climate system, financed by the Hasselblad Foundation.
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Wrapping up 2024 with new papers
Happy 2025! We close out 2024 on a high note with some new exciting research:
Machine learning and aerosols:
- Yan et al. (2024): Deep learning with pretrained framework unleashes the power of satellite-based global fine-mode aerosol retrieval.
- Yan et al. (2024): Substantial underestimation of fine-mode aerosol loading from wildfires and its radiative effects in current satellite-based retrievals over the United States.
Carbon cycle and ecosystem dynamics:
- Xu et al. (2024): Global patterns and drivers of post-fire vegetation productivity recovery.
- Li et al. (2024): Increasing sensitivity of tree radial growth to precipitation.
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Presentation at Stora Hållbarhetsdagen Göteborg
Hans Chen will give the opening talk at Stora Hållbarhetsdagen Göteborg (“The Big Sustainability Day Gothenburg”) about the current climate situation. The event is arranged by Fastighetssverige.
Date: 21 November 2024
Location: Clarion Hotel Draken
More information: https://fastighetssverige.se/stora_hallbarhetsdagen_goteborg
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.