In this activity, students compare carbon dioxide (CO2) data from Mauna Loa Observatory, Barrow (Alaska), and the South Pole over the past 40 years to help them better understand what controls atmospheric carbon dioxide. This activity makes extensive use of Excel.

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 Nino and La Nina 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.

This activity focuses on reconstructing the Paleocene-Eocene Thermal Maximum (PETM) as an example of a relatively abrupt global warming period. Students access Integrated Ocean Drilling Program (IODP) sediment core data with Virtual Ocean software in order to display relevant marine sediments and their biostratigraphy.

A detailed Google Earth tour of glacier change over the last 50 years is given in class as an introduction. Students are then asked to select from a group of glaciers and create their own Google Earth tour exploring key characteristics and evident changes in that glacier.

This activity engages learners in examining data pertaining to the disappearing glaciers in Glacier National Park. After calculating percentage change of the number of glaciers from 1850 (150) to 1968 (50) and 2009 (26), students move on to the main glacier-monitoring content of the module--area vs. time data for the Grinnell Glacier, one of 26 glaciers that remain in the park. Using a second-order polynomial (quadratic function) fitted to the data, they extrapolate to estimate when there will be no Grinnell Glacier remaining (illustrating the relevance of the question mark in the title of the module).

This teaching activity addresses regional variability as predicted in climate change models for the next century. Using real climatological data from climate models, students will obtain annual predictions for minimum temperature, maximum temperature, precipitation, and solar radiation for Minnesota and California to explore this regional variability. Students import the data into a spreadsheet application and analyze it to interpret regional differences. Finally, students download data for their state and compare them with other states to answer a series of questions about regional differences in climate change.

This teaching activity is an introduction to how ice cores from the cryosphere are used as indicators and record-keepers of climate change as well as how climate change will affect the cryosphere. Students learn through a guided web exercise how scientists analyze ice cores to learn about past climate conditions, how melting sea and land ice will contribute to sea level rise, and what areas of the world would be at risk if Antarctic and/or Greenland ice sheets were to melt away.

This activity develops students' understanding of climate by having them make in-depth examinations of historical climate patterns using both graphical and map image formats rather than presenting a general definition of climate. Students explore local climate in order to inform a pen pal what type of weather to expect during an upcoming visit. Students generate and explore a variety of graphs, charts, and map images and interpret them to develop an understanding of climate.

In this activity, students download historic temperature datasets and then graph and compare with different locations. As an extension, students can download and examine data sets for other sites to compare the variability of changes at different distinct locations, and it is at this stage where learning can be individualized and very meaningful.

In this intermediate Excel activity, students import US Historical Climate Network mean temperature data into Excel from a station of their choice. They are then guided through the activity on how to use Excel for statistical calculations, graphing, and linear trend estimates. The activity assumes some familiarity with Excel and graphing in Excel.