Gibson Research Associate in Hydrogeology
Research Interests: Hydrologic Cycle, Climate Modeling, Climate Dynamics
My research interests focus on multiple aspects of the hydrologic cycle including surface and subsurface processes, atmospheric dynamics, and climate change in observations and simulations.
I am interested in global climate patterns such as two new teleconnections that my group recently discovered, one between the West Siberian Plain and the ENSO region and the other between the Tasman Sea and the Southern Ocean. I also perform regional downscaling of global climate scenarios, for which I am using the regional and global models WRF and CESM. Furthermore, I explore interactions of climate and vegetation, such as the impacts of tropical cyclones on regional soil moisture availability and the impacts of the northward shift of boreal forest, which is anticipated due to increased surface temperatures in the Arctic, also known as Arctic amplification. Previously, my simulations with the ECHAM 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 interdisciplinary and outreach activities include a collaboration with the Computer Science and Engineering department at http://climatechange.cs.umn.edu in order to combine research efforts on climate simulations and data analysis with methodologies used in both fields.
Previously, I worked as responsible scientist and system administrator for the SPARC (Stratosphere-troposphere Processes and Their Role in Climate) Data Center at http://www.sparc.sunysb.edu and studied the impacts of the stratosphere - namely the downward propagating QBO (quasi-biennial oscillation) - on tropical convection. My research has been funded by NSF, NASA, and NOAA.
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) (abstract)
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) (abstract)
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) (pdf)
Karpatne, A. and S. Liess, 2015: A guide to Earth science data: Summary and research challenges. Computing in Science & Engineering, 17, 14-18. (pdf)
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. (abstract)
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) (abstract)
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/s00382-00013-01746-00384) (abstract)
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) (abstract) (pdf)
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) (abstract) (pdf)
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) (abstract)
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) (abstract)
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. (abstract)
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) (abstract)
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. (abstract)
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. (abstract)
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. and L. Bengtsson, 2004: The intraseasonal oscillation in ECHAM4. Part II: sensitivity studies. Climate Dyn., 22, 671-688. (abstract)
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. (abstract)
Liess, S. and L. Gates, 2004: Impacts of land use changes (in German). Promet, 30, 134-140. (pdf)
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. (abstract)
Page last modified on Friday, April 21, 2017 by Stefan Liess