In this lab activity, students use a chemical indicator (bromothymol blue) to detect the presence of carbon dioxide in animal and plant respiration and in the burning of fossil fuels and its absence in the products of plant photosynthesis. After completing the five parts of this activity, students compare the colors of the chemical indicator in each part and interpret the results in terms of the qualitative importance of carbon sinks and sources.

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.

This is a classroom activity about the forcing mechanisms for the most recent cold period: the Little Ice Age (1350-1850). Students receive data about tree ring records, solar activity, and volcanic eruptions during this time period. By comparing and contrasting time intervals when tree growth was at a minimum, solar activity was low, and major volcanic eruptions occurred, they draw conclusions about possible natural causes of climate change and identify factors that may indicate climate change.

This multi-part activity introduces users to normal seasonal sea surface temperature (SST) variation as well as extreme variation, as in the case of El NiÃo and La NiÃa events, in the equatorial Pacific Ocean. Via a THREDDS server, users learn how to download seasonal SST data for the years 1982 to 1998. Using a geographic information system (GIS), they visualize and analyze that data, looking for the tell-tale SST signature of El NiÃo and La NiÃa events that occurred during that time period. At the end, students analyze a season of their own choosing to determine if an El NiÃo or La NiÃa SST pattern emerged in that year's data.