In this activity, students work in groups, plotting carbon dioxide concentrations over time on overheads and estimating the rate of change over five years. Stacked together, the overheads for the whole class show an increase on carbon dioxide over five years and annual variation driven by photosynthesis. This exercise enables students to practice basic quantitative skills and understand how important sampling intervals can be when studying changes over time. A goal is to see how small sample size may give incomplete picture of data.
C-Learn is a simplified version of the C-ROADS simulator. Its primary purpose is to help users understand the long-term climate effects (CO2 concentrations, global temperature, sea level rise) of various customized actions to reduce fossil fuel CO2 emissions, reduce deforestation, and grow more trees. Students can ask multiple, customized what-if questions and understand why the system reacts as it does.
For this lesson, the guiding Concept Question is: What is climate change and how does climate relate to greenhouse gas concentrations over time? This activity is the second lesson in a nine-lesson module 'Visualizing and Understanding the Science of Climate Change' produced by the International Year of Chemistry project (2011).
This is a teaching activity in which students learn about the connection between CO2 emissions, CO2 concentration, and average global temperatures. Through a simple online model, students learn about the relationship between these and learn about climate modeling while predicting temperature change over the 21st century.
Two short, narrated animations about carbon dioxide and Earth's temperature are presented on this webpage. The first animation shows the rise in atmospheric CO2 levels, human carbon emissions, and global temperature rise of the past 1,000 years; the second shows changes in the level of CO2 from 800,000 years ago to the present.
This activity involves plotting and comparing monthly data on atmospheric C02 concentrations over two years, as recorded in Mauna Loa and the South Pole, and postulating reasons for differences in their seasonal patterns. Longer-term data is then examined for both sites to see if seasonal variations from one site to the other carry over into longer term trends.