This multi-week project begins with a measurement of baseline consumptive behavior followed by three weeks of working to reduce the use of water, energy, high-impact foods, and other materials. The assignment uses an Excel spreadsheet that calculates direct energy and water use as well as indirect CO2 and water use associated with food consumption. After completing the project, students understand that they do indeed play a role in the big picture. They also learn that making small changes to their lifestyles is not difficult and they can easily reduce their personal impact on the environment.
In this activity, students reconstruct past climates using lake varves as a proxy to interpret long-term climate patterns and to understand annual sediment deposition and how it relates to weather and climate patterns.
In this investigation learners research the effects of melting sea ice in the Bering Sea Ecosystem. They create research proposals to earn a place on the scientific research vessel Healy and present their findings and proposals to a Research Board committee.
This animation depicts global surface warming as simulated by NCAR's Community Climate System Model (CCSM) Version 3. It shows the temperature anomalies relative to the end of the 19th century (1870-1899), both over the entire globe and as a global average. The model shows the temporary cooling effects during the 5 major volcanic eruptions of this time period, and then the model's estimates of warming under the different scenarios taken from the fourth IPCC report.
This short video follows San Francisco inventor and engineer Saul Griffith as he determines his family's carbon footprint and develops a special cargo bike to further reduce his individual footprint. This video highlights innovation, creativity, and design as solutions to problems. The overall message is inspiring and proactive.
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.