Data analytics can predict global warming trends, heat waves

Data analytics can predict global warming trends, heat waves
Early warning signals as increasing autocorrelation coefficient and standard deviation prior to the early 20th century global warming (left) and mega heat wave during 2010 in Russia (right). Credit: Chenghao Wang, Stanford University

New research from Arizona State and Stanford Universities is augmenting meteorological studies that predict global warming trends and heat waves, adding human originated factors into the equation.

The process quantifies the changing statistics of temperature evolution before global warming in the early 20th century and recent heat wave events to serve as the early warning signals for potential catastrophic changes. In addition, the study illustrates the contrast between urban and rural early warning signals for extreme .

Tracking the pre-event signatures, or tipping points, of the increasing frequency and intensity of heat extremes will support the development of countermeasures to restore system resilience.

"Many studies have identified such changes in climate systems, like the sudden end of glacial period," said Chenghao Wang, a former ASU Research Scientist now at the Department of Earth System Science at Stanford University. "These qualitative changes usually have early-warning signals several thousand years before them."

"We detected similar signals in events much shorter than previous studies," said Chenghao Wang. "We found early-warning signals also exist before and heat waves on the time scale of years and days."

In addition to global historical temperature data, the team tracks current temperature variances from airport weather stations. If it's abnormally hot, compared to 30 years of record, for at least three consecutive days, it's considered a heat wave.

"This method isn't just applicable for predicting extreme weather events in the next few days or weeks, said Zhihua Wang, an ASU environmental and water research engineering associate professor. "It observes human-induced variabilities and will support prediction over the next decades or even century." Zhihua Wang also serves as co-director of climate systems research at ASU's National Center of Excellence on Smart Materials for Urban Climate and Energy.

The emergence of early-warning signals before heat waves provides new insights into the underlying mechanisms (e.g., possible feedback via land-atmosphere interactions). In particular, given the increasing frequency and intensity of extremes, the results will facilitate the design of countermeasures to reserve the tipping and restore the resilience of climate systems.

According to Zhihua Wang, this method creates a "completely new frontier" for evaluating how things like global energy consumption and, conversely, the introduction of urban green infrastructure, are affecting climate change. "We're not replacing existing evaluation tools," he said. "The data is already there. It's enabling us to gauge what actions are having an impact."

Based on the study results, researchers surmise that urban greening, or the use of public landscaping and forestry projects, along with adequate irrigation, may promote reverse tipping.

In addition to Chenghao Wang and Zhihua Wang, the team included rising high school junior Linda Sun from Horace Greely High School in Chappaqua, NY.


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More information: Chenghao Wang et al, Early-warning Signals for Critical Temperature Transitions, Geophysical Research Letters (2020). DOI: 10.1029/2020GL088503
Journal information: Geophysical Research Letters

Citation: Data analytics can predict global warming trends, heat waves (2020, July 15) retrieved 6 August 2020 from https://phys.org/news/2020-07-analytics-global-trends.html
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