How better climate data supports smarter environmental decisions

UNIVERSITY PARK, Pa. — Accurate measurements are the foundation of effective environmental management and decision-making. Through advanced monitoring networks and computer models, Ken Davis, professor of meteorology and atmospheric science in Penn State’s College of Earth and Mineral Sciences, and his research group are helping scientists, communities and policymakers better understand urban heat, greenhouse gas emissions and air quality.

In this Q&A, Davis discussed how his group's work could shape climate and environmental decisions at local, regional and national scales.

Q: What are some concrete ways your community measurements and models could change future day-to-day experiences of people living there?

Davis: Weather models weren’t designed to simulate urban climate neighborhood by neighborhood. Our research group is developing modeling systems that can simulate urban climate and weather at neighborhood-scale resolution. Ultimately, this work helps communities plan ways to make their environment more comfortable and less hazardous, particularly during extreme heat events that can be genuinely dangerous for residents.

A good example comes from our Baltimore work, where Eliott Foust, a doctoral student in the Department of Meteorology and Atmospheric Sciences, has been analyzing how different heat-mitigation strategies, such as adding vegetation versus using reflective coatings like white roofs and pavements, can change temperatures in the city. He’s compared what happens in specific neighborhoods and produced estimates of how much each approach cools the city, and when during the day those benefits occur.

For instance, reflective surfaces tend to cool daytime air temperatures more, while added vegetation cools nighttime temperatures more — something we did not necessarily expect. That kind of information is extremely useful to a city like Baltimore, which is actively considering these types of interventions but often lacks clear predictions about what they will accomplish. We are trying to provide sound data to help guide those decisions.

Another example comes from Jason Horne, a doctoral student in the Department of Meteorology and Atmospheric Sciences, whose research looks at how detailed weather forecasts need to reflect the conditions people experience in their daily lives. Today’s forecasting models typically divide the atmosphere into grid cells a few kilometers wide, which means they represent average conditions over these large areas. In cities, weather can vary from one block to the next depending on buildings, pavement and green space. Horne studies whether increasing model resolution to much finer scales — down to 100 meters or less — can better capture these neighborhood-level differences in temperature, humidity and wind.

This research is important because it helps guide how forecasting agencies, such as the National Weather Service, invest in next-generation tools, ensuring that future upgrades meaningfully improve predictions that affect public health, energy use and urban planning.

Q: Your group studies greenhouse gases like methane and nitrous oxide from both energy and agriculture. How does quantifying those emissions translate into better decisions by governments, companies or farmers?

Davis: For both methane and nitrous oxide, emissions are often poorly quantified, which makes it difficult to design effective climate policies. Once you know how much is coming from which sources, you can prioritize mitigation where it will have the greatest impact. Our data help inform those priorities.

We have evidence from two cities, using two independent methods — one conducted by Zachary Barkley, an assistant research professor in the College of Earth and Mineral Sciences, and another by Helen Kenion, a recent Penn State alumna and current postdoc at the University of Cincinnati — that a significant fraction of methane leakage from cities may be coming from post-meter sources. In other words, a meaningful portion of urban methane emissions could come from homes and businesses — including natural gas appliances — rather than only from the pipelines that distribute gas throughout the city. This has important implications for reducing methane in the atmosphere. Repairing distribution lines is almost certainly important, but we also need to understand why leakage is occurring in homes and businesses, and how to reduce those emissions.

Emissions of methane from natural gas production are also substantial. Our research has shown that official inventories often underestimate methane leakage by factors of two to four compared with what we observe using atmospheric measurements. That is crucial when planning how to reduce statewide methane emissions because emissions from oil and gas production may play a much larger role than inventories suggest.

In agriculture, nitrous oxide emissions are also poorly known, and the impacts of different management strategies are not well understood. Comparing emissions across different management approaches, for example how best to apply fertilizer, helps identify what practices most effectively reduce emissions.

Q: Why does instrument network and computer model development matter for our health, bills or quality of life?

Davis: We build complex measurement networks and models because good environmental decisions require a process-based understanding. We must understand the physical processes that create environmental conditions like air temperature and air quality. People often assume that observations of the atmosphere are abundant. In truth, we are often measurement limited.

Designing measurement and modeling systems that help us understand how to protect and improve our air quality and climate is challenging.

One way I explain this is with something ordinary, like buying a gallon of orange juice. We all take for granted that when we pay for a gallon, we get a gallon. If you came home and found only half a gallon, you would be upset. In the same way, environmental decisions depend on accurate measurements. I hope our work helps provide the trustworthy information needed so communities and policymakers can have confidence that the actions they take will achieve the results they expect.

A good example is a recent project by Fan Wu, a doctoral student in Penn State’s Department of Meteorology and Atmospheric Science. Her research suggests that models used to predict air pollution can misrepresent how heat and moisture move between irrigated farmlands and the atmosphere, potentially skewing air quality forecasts used for policy decisions.

If you want to reduce air pollution, you should be able to trust that the actions you take will have the impact you expect. If you invest in reducing greenhouse gas emissions, you should be able to trust that those emissions actually go down by the amount you paid for. These measurements are essential for sound, trustworthy environmental management.

Q: Looking ahead five to 10 years, what do you hope to accomplish in your research? What impact might that have for society?

Davis: One concern we have is that a strong focus on methane could distract from the main problem, which is carbon dioxide. Methane is very important for short-term climate change, and it is a quick lever that we can pull to make a difference. But natural gas is still a fossil fuel, so even if you reduce methane leakage, you are still emitting carbon dioxide. If the narrative becomes “we fixed methane leaks, so gas is clean,” that could lock us into a fossil-fuel pathway that would be very harmful to the climate.

We hope our work helps keep the focus on reducing all greenhouse gas emissions, with carbon dioxide squarely at the center of the conversation. We understand human sources of carbon dioxide relatively well, which can make studying the emissions less “exciting” scientifically, and it is often harder to agree on how to reduce those emissions. But cutting carbon dioxide is crucial for our climate future, and we already have many tools available — we need to use them now.

Looking ahead, it would be very satisfying if we could say our measurements and models helped create effective tools for environmental management — tools that guide decisions with solid data instead of guesswork. That kind of science-based guidance matters for people’s climate risk, air quality and overall quality of life.