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Understanding Climate Change

Climate change is the defining environmental challenge facing our planet, yet there is considerable uncertainty regarding the social and environmental impact due to the limited capabilities of existing physics-based models of the Earth system. Consequently, important questions relating to food security, water resources, biodiversity, and other socio-economic issues over relevant temporal and spatial scales remain unresolved. A new and transformative approach is required to understand the potential impact of climate change. Data driven approaches that have been highly successful in other scientific disciplines hold significant potential for application in environmental sciences. This Expeditions project addresses key challenges in the science of climate change by developing methods that take advantage of the wealth of climate and ecosystem data available from satellite and ground-based sensors, the observational record for atmospheric, oceanic, and terrestrial processes, and physics-based climate model simulations. These innovative approaches help provide an improved understanding of the complex nature of the Earth system and the mechanisms contributing to the adverse consequences of climate change, such as increased frequency and intensity of hurricanes, precipitation regime shifts, and the propensity for extreme weather events that result in environmental disasters. Methodologies developed as part of this project will be used to gain actionable insights and to inform policymakers.


Expeditions in Computing
NSF Awards: 1029711, 1029166, 1029731, 1028746

This 5-year, $10 Million project is funded by an award from the National Science Foundation's Expeditions in Computing program. The program, established in 2008 by NSF's Directorate for Computer and Information Science and Engineering (CISE), is aimed at pushing the boundaries of computer science research. The awards represent the single largest investments by the directorate in basic computer science research.

The Team

The project team, led by the University of Minnesota, includes faculty and researchers from Minnesota's College of Science and Engineering, College of Food, Agricultural and Natural Resource Sciences, College of Liberal Arts, and the Institute on the Environment, as well as researchers from North Carolina A & T State University, North Carolina State University, Northwestern University, and Northeastern University.


August 23, 2017
An article in Nature News cites our work on automatic discovery of teleconnections in climate, titled: How machine learning could help to improve climate forecasts.


February 17, 2018
Vipin Kumar gave a talk "Using Machine Learning to Monitor Global Surface Water Dynamics With Remote Sensing" at the AAAS Annual Meeting Session Finding Water Management Solutions With Artificial Intelligence in Austin, Texas.

Noteworthy News

November 13-18, 2016
Vipin Kumar is the 2016 recipient of the IEEE Computer Society Sidney Fernbach Award, which was presented at the SC16 Conference in Salt Lake City in November.