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Scientists identify climate signals in global weather

The refrain "weather is not climate" is no long applicable, according to a new study.

Last fall, temperatures in Utah dropped to negative 37.1 degrees Celsius, the lowest temperature ever recorded in the United States outside of Alaska. Photo courtesy of Jacob W. Frank/NPS
Last fall, temperatures in Utah dropped to negative 37.1 degrees Celsius, the lowest temperature ever recorded in the United States outside of Alaska. Photo courtesy of Jacob W. Frank/NPS

Jan. 3 (UPI) -- The refrain "weather is not climate" is no long applicable, according to a new study. An international team of scientists has succeeded in identifying the signature of long-term warming trends in daily weather data.

When extreme weather strikes, whether record lows or prolonged drought, people often wonder about the connections between weather and climate change. Until recently, scientists have been reluctant to explicitly blame climate change for weather events. Instead, meteorologists and climate scientists alike often explain that weather is not climate.

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The latest research doesn't promise to detect a climate signal in the weather affecting one specific place. Instead, scientists suggest they can isolate the signature of global warming in the weather affecting several places at once.

Last fall, temperatures in Utah dropped to negative 37.1 degrees Celsius, the lowest temperature ever recorded in the United States outside of Alaska.

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In isolation, climate change can't account for the frigid temperature, but if elsewhere across the globe, temperatures are warmer than usual, the record low can quickly be canceled out, and the signature of climate change can appear.

"Uncovering the climate change signal in daily weather conditions calls for a global perspective, not a regional one," Sebastian Sippel, a postdoctoral researcher at ETH Zurich, said in a news release.

For the study, researchers used statistical learning techniques to analyze climate model simulations populated with data from weather stations across the globe. By looking at the temperatures across different regions, and comparing the ratio of expected warming to variability, the statistical learning techniques can determine whether or not climate change's fingerprint is present in the weather data.

While weather and temperature in any single place can vary dramatically, global daily mean values occupy a very narrow range. When scientists compared the distribution of global daily mean temperatures measured from 1951 to 1980 to those observed between 2009 and 2018, the curves barely overlapped. The difference between the two curves -- described this week in the journal Nature Climate Change -- represents the warming signature that is present in the daily data.

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"Weather at the global level carries important information about climate," said lead researcher and ETH professor Reto Knutti. "This information could, for example, be used for further studies that quantify changes in the probability of extreme weather events, such as regional cold spells."

"These studies are based on model calculations, and our approach could then provide a global context of the climate change fingerprint in observations made during regional cold spells of this kind," Knutti said. "This gives rise to new opportunities for the communication of regional weather events against the backdrop of global warming."

Researchers hope to continue applying their statistical techniques to identify the impacts of human-caused climate change and other human activities in weather and climate patterns, such as the hydrological cycle.

"In future, we should therefore be able to pick out human-induced patterns and trends in other more complex measurement parameters, such as precipitation, that are hard to detect using traditional statistics," Knutti said.

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