Utilizing Public Access Data and Open Source Statistical Programs to Teach Climate Science to Interdisciplinary Undergraduate Students

Abstract:

Students in the Environmental Studies major at the University of Southern California fulfill their curriculum requirements by taking a broad range of courses in the social and natural sciences. Climate change is often taught in 1-2 lectures in these courses with limited examination of this complex topic. Several upper division elective courses focus on the science, policy, and social impacts of climate change. In an upper division course focused on the scientific tools used to determine paleoclimate and predict future climate, I have developed a project where students download, manipulate, and analyze data from the National Climatic Data Center. Students are required to download 100 or more years of daily temperature records and use the statistical program R to analyze that data, calculating daily, monthly, and yearly temperature averages along with changes in the number of extreme hot or cold days (≥90˚F and ≤30˚F, respectively). In parallel, they examine population growth, city expansion, and changes in transportation looking for correlations between the social data and trends observed in the temperature data. Students examine trends over time to determine correlations to urban heat island effect. This project exposes students to “real” data, giving them the tools necessary to critically analyze scientific studies without being experts in the field. Utilizing the existing, public, online databases provides almost unlimited, free data. Open source statistical programs provide a cost-free platform for examining the data although some in-class time is required to help students navigate initial data importation and analysis. Results presented will highlight data compiled over three years of course projects.