Student research-Contributions to climate solutions
As part of the University of Wisconsin–Madison, the Space Science and Engineering Center and the Cooperative Institute for Meteorological Satellite Studies are known for providing experiential research and learning exposure to undergraduate and graduate students. These experiences, as the following two stories suggest, offer training opportunities for future scientists while leveraging their creative approaches to help solve real world problems in science, technology, engineering, mathematical and climate fields.
Why study climate?
“Bluntly, studying climate is important because we live in it. In addition to the lofty ideal of furthering humanity’s knowledge, studying climate also has real-world implications for us since it impacts all manners of society — agriculture, resources, cultures. Understanding how our environment behaves and how it’s changing is especially critical given the impacts of global climate change already being observed.”
Anne Sledd is a graduate student with the University of Wisconsin–Madison Department of Atmospheric and Oceanic Sciences who also works with CIMSS scientists to better understand how clouds and cloud cover regulate Arctic temperatures.
Her recent research, published in 2019 in the journal MDPI Atmosphere, investigates Earth’s albedo (or reflectivity) and how changes on the surface impact the albedo higher in the atmosphere. Pairing satellite data and climate models, Sledd has begun to document how the Arctic is changing and estimate how it might change in the future.
“The Arctic is an important area to study because it is warming at a higher rate than the rest of the globe,” says Sledd. “In the past few decades it has warmed 2-5 degrees Celsius, that’s 2-3 times greater than the rest of the world has seen.”
Energy from the Sun enters the atmosphere as short-wave radiation which is absorbed by the Earth’s surface. Generally, darker surfaces like land and ocean water absorb short-wave energy more rapidly than reflective surfaces like sea ice or snow cover (such as in the Arctic). The absorbed energy radiates back toward space in the form of long-wave radiation, but due to the composition of the atmosphere, some of that long-wave radiation never escapes. That stored radiation heats the planet.
This interplay between surface and upper atmosphere albedo is a focal point for her research. She says that while a decrease in sea ice and snow cover result in a lower surface albedo, the atmospheric albedo decreases only half as much as on the surface. This supports the idea that atmospheric albedo is influenced more heavily by other factors such as clouds and is less affected by changes on the surface.
“The Arctic is the ‘canary in a coal mine’ for global climate change, but we still have a lot to learn about it,” says Sledd.
Why study climate?
“It impacts everyone. We only get one planet and it’s our home and we have to look after it. Studying climate change and understanding it — and preventing it to the extent we can — is crucial to society as a whole. We’ve grown accustomed to the current climate we have now. The rate that it’s changing currently has huge social and economic consequences.”
Chuck White is a graduate student in the Department of Atmospheric and Oceanic Sciences and has been working with SSEC and CIMSS scientists to use artificial intelligence to detect the presence of clouds in satellite data.
Weather satellites have played a major part in understanding our climate through continuous data collection since the late 1960s. However, crucial measurements needed to determine the current state of our climate — such as sea surface temperatures or lake temperatures — can be thrown off or obscured by clouds. From a satellite’s point of view, it struggles to identify cloud cover over places like the Arctic due to similarities in ground temperatures and the low-altitude clouds.
“Cloud properties are critical to our understanding of weather and climate variability,” says White. “Clouds are one of the biggest uncertainties in future climate projections and knowing whether or not a cloud is present is the most fundamental of cloud properties.”
Using machine learning techniques, White compares a large database of images captured by the VIIRS imager (onboard the Suomi-NPP satellite) to measurements captured by NASA’s CALIPSO satellite. After running several training modules, the accuracy of the algorithm increases and becomes more consistent, with early results showing improvements of 10 to 25 percent in cloud detection over regions like Antarctica and Greenland.
“The goal is to develop methods that help us better understand one part of the climate system,” says White. “Building confident observational records of global cloud properties helps give context to our current climate and allows us to more accurately diagnose current observed trends and assess future climate projections.”