Using data to better understand climate change

Using data to better understand climate change

Using data to better understand climate change
Using data to better understand climate change A global water monitoring system detected meandering of the Ucayali River in South America.

The year 2016 is on pace to be the hottest on record, with each of the first six months setting new temperature records, according to NASA. Climate change, combined with the effects of El Niño, is the main reason behind the record-setting temperatures.

Though the general trends and risks of a warming climate are well-known, researchers remain uncertain about the social and environmental impacts it will cause. Because of this uncertainty, important questions relating to food security, water resources, biodiversity, and other socio-economic issues remain unresolved. How extreme will the weather changes be?

How will these changes impact diverse ecosystems? Answering these and other critical questions requires new approaches that can help governments and individuals better respond to changing climate conditions.

In 2010, the National Science Foundation (NSF) awarded a $10 million Expeditions in Computing grant to a University of Minnesota-led research team that uses data-driven approaches to address key challenges in climate change science.

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Data-driven approaches have already proven useful in a number of scientific disciplines, from materials science to genomics. The project, called Understanding Climate Change: A Data Driven Approach, developed methods that use climate and ecosystem data from a range of sources to refine predictions and identify changes in the climate.

Those sources may include everything from satellite- and ground-based sensors, to climate model simulations and observational records for atmospheric, oceanic and terrestrial processes. “These innovative approaches are helping to provide a new understanding of the complex nature of the Earth system and the mechanisms contributing to the adverse consequences of climate change,” said Vipin Kumar, professor of computer science at the University of Minnesota and lead researcher for the team.

“These consequences may include the increased frequency of forest fires, precipitation regime shifts, and the propensity for extreme events — heat waves, droughts and floods, for example — that result in environmental disasters.”

The research team has applied its data-driven approaches to a variety of specific questions where scientific uncertainty has thus far limited the ability to understand changing conditions and to design proactive policies to address them. In 2012, the team published a paper in Nature Climate Change describing the results of a data-driven study of rainfall in India.

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The paper identified a steady and significant increase in the geographical variability of extreme rainfall events within India over the past half-century, settling a controversy that had long caused gridlock in policy-making. “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,” the researchers wrote in the 2012 paper.

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