Rainfall patterns on the globe represent our research thrust on global climate change while the artistic depiction of the variability of Indian rainfall extremes represents our focus on regional changes and their extremes as exemplified by our new paper in Nature Climate Change.
This image shows dipole edges in the year 1948-1967 in the NCEP/NCAR Reanalysis Pressure data. Dipoles in the global sea surface pressure identified via data driven analysis.
This animation shows global forest cover change detected using data mining techniques applied to NASA MODIS EVI data at 1km spatial resolution. Red dots denote large scale changes in the global vegetation due to events such as forest fires, deforestation, droughts, floods, urbanization.
Major droughts over the past 100 years detected using a MRF (Markov Random Field) from CRU data.
Our SRNN approach detects new teleconnections. One is related to longterm droughts over Australia in all seasons.
Data-driven investigations by Auroop Ganguly (co-I) and a team of researchers on the effects of climate change on coastal ocean upwelling have been published in Nature: "Intensification and spatial homogenization of coastal upwelling under climate change.
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 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.
November 13-18, 2016
Vipin Kumar is the 2016 recipient of the IEEE Computer Society Sidney Fernbach Award, which will be presented at the SC16 Conference in Salt Lake City in November.
Several expeditions PhD students and researchers will be presenting their work at the International Conference on Advanced Data Mining and Applications (ADMA2016), December 12-15, 2016 in Gold Coast, Australia.
August 23, 2016
The NSF features the expeditions project in an article titled: "Using data to better understand climate change".