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 audio slideshow, an ecologist from the University of Florida describes the radiocarbon dating technique that scientists use to determine the amount of carbon within the permafrost of the Arctic tundra. Understanding the rate of carbon released as permafrost thaws is necessary to understand how this positive feedback mechanism is contributing to climate change that may further increase global surface temperatures.
This animation shows predicted changes in temperature across the globe, relative to pre-industrial levels, under two different emissions scenarios in the COP 17 climate model. The first is with emissions continuing to increase through the century. The second is with emissions declining through the century.
This video is one of a seven, Climate Change: Lines of Evidence series, produced by the the National Research Council. It outlines and explains what evidence currently exists in support of humans playing a role in contributing to the rise in atmospheric carbon dioxide levels.
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.
This lesson explores El NiÃo by looking at sea surface temperature, sea surface height, and wind vectors in order to seek out any correlations there may be among these three variables using the My NASA Data Live Access Server. The lesson guides the students through data representing the strong El NiÃo from 1997 to 1998. In this way, students will model the methods of researchers who bring their expertise to study integrated science questions.