Computing Community Consortium Blog

The goal of the Computing Community Consortium (CCC) is to catalyze the computing research community to debate longer range, more audacious research challenges; to build consensus around research visions; to evolve the most promising visions toward clearly defined initiatives; and to work with the funding organizations to move challenges and visions toward funding initiatives. The purpose of this blog is to provide a more immediate, online mechanism for dissemination of visioning concepts and community discussion/debate about them.

“Data-driven Methods for Understanding Climate Change”

December 21st, 2011 / in big science, research horizons, Research News / by Erwin Gianchandani

An excellent example of how novel data-driven methods can advance science and society:

Rainfall patterns on the globe represent a research thrust on global climate change while the artistic depiction of the variability of Indian rainfall extremes represents a focus on regional changes and their extremes as exemplified by a new paper in Nature Climate Change [image courtesy Geneva Hill via University of Minnesota].In February 2012, the journal Nature Climate Change will publish a paper on rainfall extremes in India by principal investigator Vipin Kumar of the University of Minnesota’s computer science and engineering department and co-principal investigator Auroop Ganguly of the civil and environmental engineering department at Northeastern University in Boston, members of the National Science Foundation’s (NSF) [Expeditions in Computing] project team


“This Expeditions in Computing project brings together interdisciplinary researchers from multiple institutions to pursue a bold, ambitious, research agenda by building reliable predictive models from climate data that could potentially transform how we understand and respond to climate change,” explains Vasant Honavar … program manager in NSF’s Division of Information and Intelligent Systems. “The Nature Climate Change piece provides a hint of how sophisticated data mining methods could help fill gaps in our understanding of climate change, and ultimately, produce actionable insights that can help minimize the negative effects of climate change on humans and the environment.”

From the Nature Climate Change paper (after the jump):

Rainfall extremes are rather difficult to characterize over space and time, particularly at regional or local scales. However, our current understanding of the geographical patterns of heavy rainfall and their changes over time guides water resources and flood hazards management as well as policy negotiations related to urbanization or emissions control. Thus, in vulnerable regions of the world where floods may claim many lives and water drives the economy or in emerging nations which may contribute significantly to the atmospheric inventory of greenhouse gases, major science advances are needed…


If we were to use India as a case study, we find that top scientists and peer-reviewed publications do not agree on the nature of observed trends in heavy rainfall over the country. This has led to scientific controversies and uncertainties about adaptation and mitigation strategies in a vulnerable yet rapidly growing region of the world…

The new data-driven methods developed by the Expeditions team identify a steady and significant increase in geographical variability within India over the past half-century. Importantly, the methods “can be generalized not just to other regions beyond India, but to both observed and model-simulated climate data as well.”

For more about this work, read the NSF and University of Minnesota press releases, and check out the full article pre-published by Nature this week (subscription required).

(Contributed by Erwin Gianchandani, CCC Director)

“Data-driven Methods for Understanding Climate Change”

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