In this activity, students research changes to the environment in the Arctic/Bering Sea over time using oral and photographic histories. Developed for Alaska Native students, this activity can be customized for other regions.
This is a simulation that illustrates how temperature will be affected by global CO2 emission trajectories. It addresses the issue that even if global emissions begin to decrease, the atmospheric concentration of CO2 will continue to increase, resulting in increased global temperatures.
This video features University of Wisconsin-Madison researcher John Magnuson, who studies the ecology of freshwater systems. He explains the difference between weather and climate using data on ice cover from Lake Mendota in Madison, WI. Analysis of the data indicates a long-term trend that can be connected to climate change.
In this activity for undergraduates, students explore the CLIMAP (Climate: Long-Range Investigation, Mapping and Prediction) model results for differences between the modern and the Last Glacial Maximum (LGM) and discover the how climate and vegetation may have changed in different regions of the Earth based on scientific data.
This is a sequence of 5 classroom activities focusing on the El NiÃo climate variability. The activities increase in complexity and student-directedness. The focus of the activities is on accessing and manipulating real data to help students understand El NiÃo as an interaction of Earth systems.
This video shows 15 years of data obtained via Polar-orbiting satellites that are able to detect subtle differences in ocean color, allowing scientists to see where there are higher concentrations of phytoplankton - a proxy for the concentration of chlorophyll in the ocean.
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.