Funded through a $900,000 grant from the U.S. Department of Energy, Wang and her collaborators will use climate model simulations of extreme precipitation to assess the performance of the models in reproducing the relationship between temperature and observed extreme precipitation intensity.
To better predict and prepare how extreme rainfall might intensify in the future, a UConn professor is leading a team of researchers investigating the relationship between the intensity of extreme precipitation and temperature.
UConn professor of civil and environmental engineering Guiling Wang and her collaborators received a $900,000 U.S. Department of Energy grant to conduct climate research. Precipitation events of extreme intensity are causing more frequent flash floods worldwide. In the United States, especially the Northeast, flash floods in recent years have claimed lives and destroyed property, roads, bridges, and other critical infrastructure.
Wang and her collaborators, who include L. Ruby Leung, of Pacific Northwest National Laboratory and Xiaoming Sun, of Los Alamos National Laboratory, will use climate model simulations of extreme precipitation to assess the performance of climate models in reproducing the relationship between temperature and observed extreme precipitation intensity. They will also identify the potential causes of disparities or biases in the models and quantify how these biases may influence the projection of extreme precipitation in the future.
"As extreme events become more frequent and more severe, we have to figure out how to adapt," said Wang. "We rely on earth system models to project what might happen in the future. To determine our level of confidence in these projections, we look at how well the models perform in the present-day climate especially in quantifying emergent relationships, the reasons for good performance, and causes for biases."
Wang is a hydrologist specializing in studies of water cycles and how they act in relation to ecosystems. Her work explores two major themes. The first is hydrological extremes, such as intense rainfall and droughts, and trying to understand the mechanisms behind them. The second is the interaction between land and the atmosphere and how factors such as moisture in the soil and vegetation respond to changes in climate or influence the climate itself in a phenomenon known as feedback.
Among other things, this project will explore how local and regional land surface conditions and feedback may influence the intensity of extreme precipitation. Researchers will also look at the relationship between extreme precipitation-temperature scaling and environmental variables, and how it may change in a warmer climate. In addition to climate model outputs, the study will use data collected from sources that include meteorological stations, remote satellite sensors, reanalysis products, and observational tracking of thunderstorms, squall lines, and other so-called mesoscale atmospheric phenomena ranging from 10 to 1,000 kilometers in size.
Model bias refers to systematic errors in a model that can generate consistently incorrect predictions. They can arise from a variety of sources, including coarse spatial resolution and an incomplete understanding of the Earth's systems and their interactions. The models Wang and her team will evaluate include the Energy Exascale Earth System Model (E3SM) and other IPCC CMIP6 Earth System Models (ESMs). The team will also conduct newly designed experiments using E3SM to understand the physical processes that influence the development of extreme events and their sensitivity to temperature.
"The first half of the study will assess the models," says Wang. "The second half is to understand the physical processes, such as soil moisture feedback, in which we look at the land surface and the atmosphere to see how their interaction influences the environment in which extreme precipitation develops."
Wang has published several papers challenging scientific assumptions about extreme precipitation and drought that have garnered media attention. In a 2017 paper published in Nature Climate Change, she confirmed that severe rainstorms of greater intensity and frequency would likely continue as temperatures rise due to global warming, despite some observations that seem to suggest otherwise.
To relate rain intensity to temperature, she concluded, scientists must relate to the temperature at which the rain event occurs, not the mean temperature traditionally used to calculate the long-term average. Last year, her findings from an NSF-funded study that appeared in the journal PNAS, identified vegetation as a way to predict devastating "flash droughts" weeks or even months before they occur. Flash droughts are characterized by a rapid onset that can quickly intensify and have devastating results.
Precipitation extremes interest her, Wang says, because of the potentially catastrophic implications of such events. The increase in the number of storms bringing heavier amounts of rain in the last decade underscores the urgent need for improved prediction, prevention, and adaptation.
This summer saw a spate of torrential rainstorms, such as the one in July that caused widespread flooding in New York, Massachusetts, Connecticut, and Vermont. Vermont was particularly hard-hit, getting nine inches of rain - about two months-worth – in two days, leaving one person dead, homes and businesses destroyed, cars, roads and bridges washed away, and the capital city of Montpelier, inundated.
The same month, flash flooding in Bucks County, Pennsylvania left six people dead and a 9-month-old boy presumed dead. His mother and 2-year-old sister were also killed. The area got 6.5 to 7 inches of rain in under an hour. Two months later, record-breaking rainfall pounded New York City and the surrounding region. That flooding shut down subway lines, swamped major roadways, and sent school children scrambling to the upper floors of flooded school buildings.
"The impact on society is huge," says Wang. "As a hydrologist, I feel I have an obligation to understand how these extreme events happen and how we can better predict them. If we can predict, we can prepare for what's coming up."
