A University of California (UCLA) study published in Nature Communications shows definitively that abnormally heavy snowfall events around the world are becoming more severe due to human-driven climate change.
While climate change is widely understood to cause higher temperatures and more devastating droughts, it is also linked to heavier precipitation. That’s mainly because the warmer atmosphere can hold more moisture, which can lead to more dramatic wet seasons, said Gavin Madakumbura, the study’s lead author, a UCLA doctoral candidate studying climate modelling.
The study clearly shows that human activity is causing the intensification of heavy rain and snow events, which can lead to problems like floods, soil erosion, crop damage and problems with water resource management.
“These findings further elevate the urgency of reducing greenhouse gas emissions to prevent even larger impacts down the road,” said senior author Alex Hall, director of the UCLA Center for Climate Science, which is a part of the UCLA Institute of the Environment and Sustainability. “We can now say that extreme precipitation is increasing globally due to human-induced climate change.”
For more than a decade, scientists have been able to demonstrate in climate models and smaller, regional studies what they could not yet detect globally from data gathered from real-world observations: that human-induced, or anthropogenic, climate change had already begun causing more intense extreme precipitation events worldwide.
Scientists had long sought to understand and explain the phenomenon on a worldwide scale. But those efforts were hampered in large part because of the challenges of reconciling climate information from around the world.
What’s new in the UCLA study is that researchers applied machine learning, in which a computer can, without being explicitly programmed, produce conclusions as it “learns” by analysing data. For the first time, the researchers were able to present clear evidence that human activities have already caused extreme precipitation globally.
The UCLA researchers compared 11 global datasets of land precipitation records taken from 1982 to 2015. In every dataset they reviewed, they found a link between human activity and precipitation patterns.
In addition, the amount of precipitation considered “extreme” in one region can differ from one area to the next — unusually heavy rain in an otherwise arid desert might appear mild compared to the same rainfall in a monsoon region — making it even more difficult to evaluate the planet as a whole. As a result, most previous studies on the topic have been regional, rather than global, Hall said.
“This is the first time anyone has taken into account these deep uncertainties to detect a human influence on extreme precipitation over the whole globe,” Hall said.
The UCLA scientists used climate models to create yearly global maps to display each area’s largest single-day precipitation. They fed that data into the machine learning program, which was trained to review world maps displaying the precipitation data and then “guess” which year each map represented.
After the machine learning program was trained on the computer-modelled data, the researchers put it to work analysing actual observed precipitation data from 1982 to 2015.
Tellingly, in climate models, the system struggled to accurately infer which year it was reviewing when it was prompted to analyse data from the 1920s through the 1970s, when the signs of human activity were not strong enough to identify separately from natural climate variations.
But for climate model data beginning in the 1970s, thanks to the strength of unnatural precipitation events that were being caused by human activities, the system became increasingly accurate in its predictions.
“We know that the signs of human influence started in the Industrial Revolution in the 18th century, but it takes some time for the signal to be strong enough to recognize compared to natural variations,” Madakumbura said. “We used this new methodology to build on the work that came before, and it means we can look at these disparate datasets from different regions around the world and still detect the human influence.
“Greater precipitation extremes will not be detectable yet in every individual location on the globe, but we do see them in the global pattern.”