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

Atmospheric river from satellite

Atmospheric rivers

Atmospheric rivers are long and narrow bands of strong water vapor transport in the atmosphere. We study atmospheric rivers with a focus on their occurrence and impact on climate in the northern high-latitudes and Arctic region.

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.

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.

News

Recent publications

Cai, Z., Q. You, H. W. Chen, R. Zhang, Z. Zuo, D. Chen, J. Cohen, and J. A. Screen, 2024: Assessing Arctic wetting: Performances of CMIP6 models and projections of precipitation changes. Atmospheric Research, 297, 107124, https://doi.org/10.1016/j.atmosres.2023.107124.

Lai, H.-W., D. Chen, and H. W. Chen, 2024: Precipitation variability related to atmospheric circulation patterns over the Tibetan Plateau. International Journal of Climatology, 44, 1–17, https://doi.org/10.1002/joc.8317.

Zhong, Z., B. He, H. W. Chen, D. Chen, T. Zhou, W. Dong, C. Xiao, L. Guo, R. Ding, L. Zhang, X. Song, L. Huang, W. Yuan, X. Hao, and X. Zhao, 2023: Reversed asymmetric warming of sub-diurnal temperature over land during recent decades. Nature Communications, 14, 7189, https://doi.org/10.1038/s41467-023-43007-6.

Wang, S., B. He, H. W. Chen, D. Chen, Y. Chen, W. Yuan, F. Shi, J. Duan, W. Wu, T. Chen, L. Guo, Z. Zhong, W. Duan, Z. Li, W. Jiang, L. Huang, X. Hao, R. Tang, H. Liu, Y. Zhang, and X. Xie, 2023: Fire carbon emissions over Equatorial Asia reduced by shortened dry seasons. npj Climate and Atmospheric Science, 6, 129, https://doi.org/10.1038/s41612-023-00455-7.

Zhong, Z., B. He, Y.-P. Wang, H. W. Chen, D. Chen, Y. H. Fu, Y. Chen, L. Guo, Y. Deng, L. Huang, W. Yuan, X. Hao, R. Tang, H. Liu, L. Sun, X. Xie, and Y. Zhang, 2023: Disentangling the effects of vapor pressure deficit on northern terrestrial vegetation productivity. Science Advances, 9, eadf3166, https://doi.org/10.1126/sciadv.adf3166.