In this learning activity, students use a web-based geologic timeline to examine temperature, CO2 concentration, and ice cover data to investigate how climate has changed during the last 715 million years.

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.

In this activity students learn how Earth's energy balance is regulating climate. This activity is lesson 4 in the nine-lesson module Visualizing and Understanding the Science of 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.

In this activity, students are guided through graphs of surface air temperature anomaly data and Vostok ice core data to illustrate how scientists use these data to develop the basis for modeling how climate is likely to change in the future.

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.