UNDERSTANDING CLIMATE CHANGE:A DATA-DRIVEN APPROACH

PI: Vipin Kumar Co-PIs: A. Banerjee, S. Chaterjee, A. Choudhary, J. Foley, A. Ganguly, A. Homaifar, J. Knight, S. Shekhar, P. Snyder, N. Samatova, F. SemazziParticipating Universities: U. of Minnesota, North Carolina A & T State U., North Carolina State U., Northeastern U., Northwestern U.

NSF Expeditions in Computing PI Meeting May 14-16, 2013

Vision of Research

A new and transformative data-driven approach that:• Makes use of wealth of observational and simulation data• Advances understanding of climate processes• Informs climate change impacts and adaptation

Research Overview

Research Highlights

Research Overview

Climate change is the defining environmental challenge facing our planet, yet there is considerable uncertainty regarding its social and environmental impact 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. 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. Methodologies developed as part of this project will be used to gain actionable insights and to inform policymakers.

Education/Outreach Activities• Undergraduate and graduate courses and programs at the intersection of climate and data sciences• Cross disciplinary training environment• Extensive research opportunities for students from historically underrepresented groups• Fostering interdisciplinary collaborations by organizing workshops and sessions at climate and computer science venues• Engagement with UNEP and IPCC

Teleconnection discovery Precipitation extremes

Ocean eddy monitoring Hurricane track predictions

“The sad truth of climate science is that the most crucial information is the least reliable” (Nature, 2010)

Sparse statistical models

Cyclone intensity estimationHurricane causal networksDrought detection

Annual workshop

Climate Prediction Community Interface

Scalable global cloud resolution

Figure Courtesy: ORNL