Stefan Liess

Research Interests: Climate Dynamics, Climate Modeling, Hydrologic Cycle

As a member of  the University of Minnesota Climate Adaptation Partnership, I am interested in global and regional climate patterns as well as future climate projections. My regional climate projections over Minnesota have recently been covered in the media, for example Washington Post, ABC News, KTTC News, and the Star Tribune. An online poster on urban climate simulations at the street scale is available from the American Geophysical Union. Furthermore, I explore interactions of climate and vegetation with regional downscaling efforts of global climate scenarios, for which I use the regional and global models WRF and CESM.  These efforts include the impacts of tropical cyclones on regional soil moisture availability and the impacts of the northward shift of boreal forest, which is mentioned in climatecentral.org. In tandem with reduced ice cover, this northward shift due to increased surface temperatures further amplifies warming in the Arctic, also known as Arctic amplification. Previously, my group discovered two new teleconnections, one between the West Siberian Plain and the ENSO region and the other between the Tasman Sea and the Southern Ocean. My other simulations with the ECHAM climate model over the Amazonian rainforest and the Mediterranean region showed that local climate change due to deforestation in humid regions such as the tropics is dominated by changes in evaporation, whereas the change in arid and semi-arid regions is dominated by reflectivity.

Other foci of my research include phenomena in tropical climate: monsoon circulations, air-sea interaction, and tropical convection. I am assessing the capabilities of general circulation models (GCM) to simulate and forecast tropical intraseasonal variability, especially the Madden-Julian Oscillation (MJO). I perform sensitivity studies to point out the mechanisms, that are important for the evolution of the MJO. A predictability study focuses on time scales of a few weeks, which is longer than numerical weather prediction. One goal is to predict the active and break phases of the Asian summer monsoon. I create ensemble forecast experiments with the ECHAM5 GCM and compare them to control simulations and satellite observations. In order to obtain useful initial conditions for the forecasts, I utilize the breeding method that allows large model uncertainties to grow and small uncertainties to be damped. I also carry out forecast skill experiments for observed events. For this, the prognostic variables of the model are "nudged" toward the observations. The nudging mechanism is a powerful tool to direct the model close enough toward the observations but small enough to let the model develop its own physical mechanisms.

My previous interdisciplinary and outreach activities include collaborations with the Computer Science and Engineering department and the Department of Earth & Environmental Sciences in order to combine research efforts on climate simulations and data analysis with methods used in each field. I am also currently serving as academic editor for the journal Atmosphere.

Previously, I worked as responsible scientist and system administrator for the SPARC (Stratosphere-troposphere Processes and Their Role in Climate) Data Center at https://www.sparc-climate.org/data-centre and studied the impacts of the stratosphere - namely the downward propagating QBO (quasi-biennial oscillation) - on tropical deep convection. My research has been funded by NSF, NASA, and NOAA.

Teaching

ESCI 5980: Global and Regional Climate Variability - SOIL 5480: Atmospheric Processes I and II - ESPM 1425: The Atmosphere / Introduction to Meteorology - Visiting Professor, China University of Geosciences, Wuhan, China: Climate Dynamics

Selected Publications

Liu, C., L. Chen, and S. Liess, 2024: Deciphering mean flow–eddy interaction in Pacific–North American teleconnection linked to storm tracks at subseasonal time scales. J. Climate, 37, 3977-3994. (doi: 10.1175/JCLI-D-23-0429.1)

Cai, X., S. Li, S. Liess, and C. Zhang, 2024: The impact of the early summer Tasman Sea–Southern Ocean hybrid teleconnection pattern on middle summer rainfall in East Asia. Climate Dyn., (doi: 10.1007/s00382-024-07161-x)

Roop, H., et al., 2024: Climate change impacts on Minnesota agriculture. Ames, Iowa: United States Department of Agriculture Climate Hubs, University of Minnesota Climate Adaptation Partnership and Great Lakes Research Integrated Science Assessment. (full report)

Ferin, K. M., T. Balson, E. Audia, A. Ward, S. Liess, T. Twine, and A. VanLoocke, 2023: Field scale analysis of miscanthus production indicates climate change may increase the opportunity for water quality improvement in a key Iowa watershed. GCB Bioenergy, 15, 994-1010, (doi:10.1111/gcbb.13078)

Birkel, J. F. H., T. E. Twine, S. Liess, L. Kalkstein, and S. Sheridan, 2022: Trends in synoptic heat events in four Minnesota urban areas through the 21st century. Urban Climate, 46. ((doi:10.1016/j.uclim.2022.101307))

Liess, S., T. E. Twine, P. K. Snyder, W. D. Hutchison, G. Konar-Steenberg, B. L. Keeler, and K. A. Brauman, 2022: High-resolution climate projections over Minnesota for the 21st century. Earth and Space Science, 9, e2021EA001893. (doi:10.1029/2021EA001893)

Sun, X., S. Li, S. Liess, 2022: The asymmetric connection of SST in the Tasman Sea with respect to the opposite phases of ENSO in austral summer. Adv. Atm. Sci., 39, 1897-1913. (doi: 10.1007/s00376-022-0421-y) 

Wang, X., F. Xie, Z. Zhang, S. Liess, K. Fang, C. Xu, and F. Shi, 2021: Complex network of synchronous climate events in East Asian tree-ring data. Climatic Change, 165, (54). (doi:10.1007/s10584-021-03008-0)

Zhang, Z. et al., 2020: Rapid waxing and waning of Beringian ice sheet reconcile glacial climate records from around North Pacific. Clim. Past Discuss. (doi:10.5194/cp-2020-38)

Laakso, A., P. K. Snyder, S. Liess, A.-I. Partanen, and D. B. Millet, 2020: Differing precipitation response between Solar Radiation Management and Carbon Dioxide Removal due to fast and slow components. Earth Syst. Dynam., 11, 415–434. (doi:10.5194/esd-11-415-2020)

Liess, S., P. K. Snyder, A. Kumar, and V. Kumar, 2018: A cautionary note on decadal sea level pressure projections from GCMs. Adv. Clim. Change Res., 9, 43-56. (doi:10.1016/j.accre.2018.02.002)

Liess, S., S. Agrawal, S. Chatterjee, and V. Kumar, 2017: A teleconnection between the West Siberian Plain and the ENSO region. J. Climate, 30, 301-315.  (doi:10.1175/JCLI-D-15-0884.1)

Lu, M., U. Lall, J. Kawale, S. Liess, and V. Kumar, 2016: Exploring the predictability of 30-day extreme precipitation occurrence using a global SST - SLP correlation network. J. Climate, 29, 1013-1029. (doi:10.1175/JCLI-D-14-00452.1)

Semazzi, F., B. Liu, L. Xie, K. Smith, M. Angus, M. Gudoshava, R. Argent, X. Sun, S. Liess, and A. Bhattacharya, 2015: Decadal variability of the East African monsoon. CLIVAR Exchanges, 66, 15-19. (ISSN No: 1026-0471)

Karpatne, A. and S. Liess, 2015: A guide to Earth science data: Summary and research challenges. Computing in Science & Engineering, 17, 14-18. (doi: 10.1109/MCSE.2015.127)

Gorji Sefidmazgi, M., M. Moradi Kordmahalleh, A. Homaifar, and S. Liess, 2015: Change detection in climate time series based on bounded-variation clustering. Machine Learning and Data Mining Approaches to Climate Science., V. Lakshmanan, E. Gilleland, A. McGovern, and M. Tingley, Eds., Springer, 185-194. (doi:10.1007/978-3-319-17220-0_17)

Liess, S., A. Kumar, P. K. Snyder, J. Kawale, K. Steinhaeuser, F. H. M. Semazzi, A. R. Ganguly, N. F. Samatova, and V. Kumar, 2014: Different modes of variability over the Tasman Sea: Implications for regional climate. J. Climate, 27, 8466-8486. (doi:10.1175/JCLI-D-13-00713.1)

Snyder, P. K. and S. Liess, 2014: The simulated atmospheric response to expansion of the Arctic boreal forest biome. Climate Dyn., 42, 487-503. (doi:10.1007/s00382-013-1746-4)

Ganguly, A. R. et al., 2014: Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques. Nonlin. Processes Geophys., 21, 777-795. (doi:10.5194/npg-21-777-2014)

Gorji Sefidmazgi, M., M. Sayemuzzaman, A. Homaifar, M. K. Jha, and S. Liess, 2014: Trend analysis using non-stationary time series clustering based on the finite element method. Nonlin. Processes Geophys., 21, 605-615. (doi:10.5194/npg-21-605-2014)

Harding, K. J., P. K. Snyder, and S. Liess, 2013: Use of dynamical downscaling to improve the simulation of central U.S. warm-season precipitation in CMIP5 models. J. Geophys. Res., 118, 12,522–12,536. (doi:10.1002/2013JD019994)

Kawale, J., S. Liess, A. Kumar, M. Steinbach, P. Snyder, V. Kumar, A. R. Ganguly, N. F. Samatova, and F. Semazzi, 2013: A graph-based approach to find teleconnections in climate data. Statistical Analysis and Data Mining, 6, 158–179. (doi:10.1002/sam.11181)

Karpatne, A., J. Faghmous, J. Kawale, L. Styles, M. Blank, V. Mithal, X. Chen, A. Khandelwal, S. Boriah, K. Steinhaeuser, M. Steinbach, V. Kumar, and S. Liess, 2013: Earth science applications of sensor data. Managing and Mining Sensor Data, C. C. Aggarwal, Ed., Springer, 505-530. (doi:10.1007/978-1-4614-6309-2_15)

Liess, S., P. K. Snyder, and K. J. Harding, 2012: The effects of boreal forest expansion on the summer Arctic frontal zone. Climate Dyn., 38, 1805-1827. (doi:10.1007/s00382-011-1064-7)

Liess, S. and M. A. Geller, 2012: On the relationship between QBO and distribution of tropical deep convection. J. Geophys. Res., 117, D3, (doi:10.1029/2011JD016317)

Zhou, X., S. Shekhar, P. Mohan, S. Liess, and P. K. Snyder, 2011: Discovering interesting sub-paths in spatiotemporal datasets: a summary of results. Proc. 19th ACM SIGSPATIAL International Conference on Advances in Geographical Information Systems, 44-53, (doi:10.1145/2093973.2093981)

Kawale, J., S. Chatterjee, A. Kumar, S. Liess, M. Steinbach, and V. Kumar, 2011: Anomaly construction in climate data: issues and challenges. Proc. 2011 NASA Conference on Intelligent Data Understanding (CIDU), 189-203. (pdf)

Kawale, J., S. Liess, A. Kumar, A. Ganguly, N. F. Samatova, F. Semazzi, P. K. Snyder, M. Steinbach, and V. Kumar, 2011: Data guided discovery of dynamic climate dipoles. Proc. 2011 NASA Conference on Intelligent Data Understanding (CIDU), 30-44 (best student paper). (pdf)

Liess, S., D. E. Waliser, and S. D. Schubert, 2005: Predictability studies of the intraseasonal oscillation with the ECHAM5 GCM. J. Atmos. Sci., 62, 3320-3336. (doi:10.1175/JAS3542.1)

Liess, S. and L. Bengtsson, 2004: The intraseasonal oscillation in ECHAM4. Part II: sensitivity studies. Climate Dyn., 22, 671-688. (doi:10.1007/s00382-004-0407-z)

Liess, S., L. Bengtsson, and K. Arpe, 2004: The intraseasonal oscillation in ECHAM4. Part I: coupled to a comprehensive ocean model. Climate Dyn., 22, 653-669. (doi:10.1007/s00382-004-0406-0)

Liess, S. and L. Gates, 2004: Impacts of land use changes (in German). Promet, 30, 134-140. (pdf) (pdf to this issue)

Dümenil Gates, L. and S. Liess, 2001: Impacts of deforestation and afforestation in the Mediterranean region as simulated by the MPI atmospheric GCM. Global and Planetary Change, 30, 305-324. (doi:10.1016/S0921-8181(00)00091-6)

Page last modified on Thursday, July 25, 2024 by Stefan Liess