Climate Change Impacts

Climate Change Impacts

A primary objective is to translate simulations from computational climate models to decision support and policy tools, thus bringing climate prediction closer to integrated assessments. The primary research thrust centers around climate extremes, uncertainty and impacts. Climate extremes are defined in this context to include climate-induced severe weather and hydrological events as well as large changes in regional and decadal climate patterns which may result in extreme stresses on complex interacting natural, engineered and human systems. Knowledge discovery approaches, ranging from hypothesis-guided analysis to relatively hypothesis-free discovery processes, are developed which attempt to combine physics-based insights with interdisciplinary computational data sciences. The priorities are uncertainty reduction as well as a comprehensive characterization of uncertainty in climate change and impacts systems, with a focus on cascading uncertainty from emissions to models and impacts. The thrust on impacts includes the use of climate change and extremes in conjunction with population and global change projections for understanding consequences on critical infrastructures like the electric grid and key resources like water and energy.


  • Uncertainty assessment and reduction for climate extremes and climate change impacts
  • Climate science support for the US DOD’s Quadrennial Defense Review
  • Climate change and impacts assessments
  • Multivariate dependence in climate extremes
  • Science support for a climate change war game

[Note: Synergistic thrust areas include Geographic Data Sciences, Population and Social Dynamical Modeling, and Emergency Preparedness]


Publications Related to Climate Change Impacts

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Machine Vision and Applications proposes a new method to infer road networks from GPS trace data and accurately segment road regions in high resolution aerial images
Congratulations to the authors Jiangye Yuan, Anil M. Cheriyadat, Robert Stewart, Marie Urban, Sam Duchscherer, Jason Kaufman, April Morton, Gautam Thakur, Jesse Piburn, and Jessica Moehl

A Bayesian model for integrating population domain knowledge with open source data under uncertainty
Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling.

Lab's Smart Grid Visualization Research Featured in IEEE Newsletter [June, 2012]
An article titled "Virtualization of the Evolving Power Grid" and authored by Olufemi Omitaomu, Alex Sorokine and Varun Chandola in the Computational Sciences and Engineering Division has been published in the IEEE Smart Grid Newsletter.

New ORNL Tool Developed to Assess Global Freshwater Stress [March 23, 2012]
New method to make better use of vast amounts of data related to global geography, population and climate may help determine the relative importance of population increases vs. climate change.

Off the Map [March 2012]
After the March 11, 2011, earthquake and tsunami devastated the coast along Sendai, Japan, a special team at the software company Esri quickly jumped into action.

New ORNL Technology to help KUB Customers Understand Their Bill
January 27, 2012, WATE.COM – If you think there's a problem with your utility charges or how your meter's being read, all you may be able to do is call your utility company.

Grid Visualization Efforts Helped Heal After Hurricane
September 26, 2011 – By many counts, Irene was the worst hurricane the U.S. East Coast has had to contend with since 2003. For those inland areas hit hardest by flooding, restoration will likely continue for years.

Joplin, Missouri Tornado: Do People Fail to Respond to Tornado Warnings?
Jenniffer Santos-Hernandez at Oak Ridge National Laboratory helped conduct a phone survey of more than 600 people in Oklahoma, Kansas, Minnesota, Illinois, Mississippi, Tennessee, and Alabama.