To help environmental science students analyze the past and forecast with data, Stephen MacAvoy, PhD, turns to carbon storage, volcanoes, and ice cores.
Associate Professor and Department Chair of Environmental Science, American University in Washington, DC
PhD in Environmental Sciences, concentration in Ecology/Geochemistry; MS in Environmental Sciences, concentration in ecology; BS in Biology
When it comes to studying the multitude of contributors to climate change, Stephen MacAvoy, PhD, says there is something to be said for being “down to Earth.” Reading textbooks and listening to lectures can only provide so much insight, he says—and he has not seen these traditional methods generate much passion for the planet.
“What you really don’t want is for students just to be going through the motions, solving problems but having no inkling of why they’re doing things,” says MacAvoy, who is an associate professor and department chair of environmental science at American University in Washington, DC. “In applied science, we have to know why we’re doing things and why they’re important. It’s not just an exercise.”
Unfortunately, MacAvoy notes, predicting our planet’s future is something that is inherently difficult to do, because we cannot know real outcomes until they happen. “Like a lot of environmental work, you can’t do this in a test tube,” he says. So a traditional lab session will not do the trick either.
Then how does MacAvoy give form and substance to students’ studies? He has come up with a series of modeling exercises and real-world scenarios in which students act as environmental consultants who have been called in to analyze various crises. These increase in difficulty as the semester wears on, culminating in a project in which they make predictions—and recommendations.
“I really want students to get an appreciation for using real data and synthesizing it to address an important question,” says MacAvoy. “How do we know what happened in Earth’s past? That’s a huge question, and massively important. Knowledge about the past will change the future.”
Below, MacAvoy shares his approach and how he makes it work.
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“I really want students to get an appreciation for using real data and synthesizing it to address an important question. How do we know what happened in Earth’s past? That’s a huge question, and massively important. Knowledge about the past will change the future.”— Stephen MacAvoy, PhD
Course description: Part of a two-course introduction to environmental science focusing on biological and geological aspects of environmental science such as biogeochemical cycles, ecology and biodiversity, populations, food and agriculture, environmental health, and the impacts of climate change.
MacAvoy’s tips for introducing students to prediction
First, an overview of MacAvoy’s three-step process: He begins by assigning student projects in a place called “Hazard City,” an imaginary town that experiences a series of hypothetical disasters. Next he has students drill down into real ice core data sets, where they use isotopes to reconstruct past temperatures and examine how different variables may affect temperature over time. Finally, he has his class use dynamic mathematical models to project how an increase in carbon dioxide plus associated temperature changes will affect carbon in terrestrial systems, globally.
Here, MacAvoy shares details on the steps involved in prepping students for the analysis work—and beyond.
Study a “virtual volcano” to explain how risk is predicted
Initially, MacAvoy uses an online platform provided by Pearson Education called Hazard City, which is a collection of 11 scenarios of geological and environmental hazards (flooding, earthquake, landslide, etc.) affecting a fictional city in the U.S. “For each assignment, they’re pretending they’re a different kind of environmental consultant,” says MacAvoy.
In one assignment, for instance, students are given data about a volcano and are asked to consider what impact an eruption could have on the community. For this, they gather information on Hazard City demographics and where neighborhoods are located with respect to the volcano. They also analyze geological data on the neighborhoods and the volcano itself. The online modules allow students to access outside data from the U.S. Geological Survey and other online resources.
“They are supposed to interpret what the volcano has done in the past, and then they go to the town and look at what the sediments and geology tell them about what the volcano did to the area before there were any buildings there,” says MacAvoy. “Then the students do an assessment of the risk to the neighborhoods today. Are all neighborhoods at the same degree of risk? What would be the displacement of people if the volcano did erupt?”
In this particular scenario, students might also assess and measure other important aspects of a volcanic eruption, such as lava flow, the likelihood of muddy floods, and ash dispersal around the Hazard City region.
Introduce real data from an ice core that is hundreds of thousands of years old
Next, MacAvoy has students analyze data from an ice core sample. This data was derived from a 1998 drilling at the Russian Vostok base in which Russia, the United States, and France collaborated to recover the deepest ice core ever—3,623 meters (nearly 4,000 yards) below the surface.
Through the ice core assessment, students learn about climate history stretching back nearly one million years. Such sample chronologies help modern-day scientists assess temperature change and climate transition over time.
MacAvoy adds that one of the most important lessons students learn about science is that modeling is imperfect. “Because it’s the future we’re trying to predict, you don’t know if you’re right,” he says. “And if all our models agree about the future, something is really wrong. The fact that models have some uncertainty in them is a good thing. Uncertainty in modeling is totally appropriate.”
Also, they realize that the future is not set in stone—or ice. Having seen their own evidence of the changes that Earth has undergone, students also understand that clues to our climate’s future are revealed by our climate’s past.
Level up modeling experience with a carbon storage study
For an advanced modeling exercise (given that this is a 100-level class), MacAvoy shows how mathematical modeling exercises are really just ways of describing a system’s behavior, using equations rather than words. This allows students see how predictions are made based on data (and why). For this, he introduces simple forms of “dynamic vegetation models” that project how carbon storage in the biosphere could change as atmospheric carbon dioxide concentration increases and the planet warms.
“The exercises give an appreciation for what modeling is,” says MacAvoy. “You make assumptions about the future, and then you calculate what the future might look like for different variables.”
The students are given a set of conditions to model (temperature change, carbon dioxide concentrations, respiration sensitivity to temperature, etc.) and then the overall system of equations. All the equations are simple enough to be solved using a spreadsheet calculator. Then students graph the results and interpret the various scenarios. MacAvoy usually has them model unrealistically “good” and “bad” scenarios so they get an appreciation for the model’s predictive power.