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

Photo of Arctic sea ice

Arctic climate system

We are interested in climate change in the Arctic region, including Arctic amplification, changes in sea ice cover, and Arctic greening, as well as linkages with mid-latitude weather and climate variability.

Data assimilation schematic

Data assimilation

Data assimilation is a class of methods that seek to optimally combine information from observations and models. We use and develop data assimilation methods, for example to estimate surface carbon fluxes.

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.

News

Recent publications

Peng, W., Y. Yi, U. Mishra, K. Bakian-Dogaheh, J. S. Kimball, M. Moghaddam, and H. W. Chen, 2025: Characterizing spatial variability of soil organic carbon through improved machine-learning modeling with in situ data resampling: A case study in Alaska. IEEE Transactions on Geoscience and Remote Sensing, 63, 1–14, https://doi.org/10.1109/TGRS.2025.3572344.

Ye, K., J. Cohen, H. W. Chen, S. Zhang, D. Luo, and M. E. Hamouda, 2025: Attributing climate and weather extremes to Northern Hemisphere sea ice and terrestrial snow: progress, challenges and ways forward. npj Climate and Atmospheric Science, 8, 1–22, https://doi.org/10.1038/s41612-025-01012-0.

Cai, Z., Q. You, J. A. Screen, H. W. Chen, R. Zhang, Z. Zuo, D. Chen, J. Cohen, S. Kang, and R. Zhang, 2025: Lessened projections of Arctic warming and wetting after correcting for model errors in global warming and sea ice cover. Science Advances, 11, eadr6413, https://doi.org/10.1126/sciadv.adr6413.

Shen, C., Z.-B. 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, 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/10.1029/2024GL110003.