Hands-on laboratory activity that allows students to investigate the effects of distance and angle on the input of solar radiation at Earth's surface, the role played by albedo, the heat capacity of land and water, and how these cause the seasons. Students predict radiative heating based on simple geometry and experiment to test their hypotheses.

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.

Students explore the increase in atmospheric carbon dioxide over the past 40 years with an interactive online model. They use the model and observations to estimate present emission rates and emission growth rates. The model is then used to estimate future levels of carbon dioxide using different future emission scenarios. These different scenarios are then linked by students to climate model predictions also used by the Intergovernmental Panel on Climate Change.

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 use climate data to develop a simple graph of how climate has changed over time and then present the result in a blog, emphasizing effective science communication.

In this classroom activity, students analyze visualizations and graphs that show the annual cycle of plant growth and decline. They explore patterns of annual change for the globe and several regions in each hemisphere that have different land cover and will match graphs that show annual green-up and green-down patterns with a specific land cover type.

In this hands-on lesson, students measure the effect of distance and inclination on the amount of heat felt by an object and apply this experiment to building an understanding of seasonality. In Part 1, the students set up two thermometers at different distances from a light bulb and record their temperatures to determine how distance from a heat source affects temperature. In Part 2, students construct a device designed to measure the temperature as a function of viewing angle toward the Sun by placing a thermometer inside a black construction paper sleeve, and placing the device at different angles toward the Sun. They then explain how distance and inclination affect heat and identify situations where these concepts apply, such as the seasons on Earth and the NASA Mercury MESSENGER mission.

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.

In this activity, students use the GLOBE Student Data Archive and visualizations to explore changes in regional and seasonal temperature patterns.

Students consider why the observed atmospheric CO2 increase rate is only ~60% of the CO2 loading rate due to fossil fuel combustion. They develop a box-model to simulate the atmospheric CO2 increase during the industrial era and compare it to the historic observations of atmospheric CO2 concentrations. The model is then used to forecast future concentrations of atmospheric CO2 during the next century.

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