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 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.
With this carbon/temperature interactive model, students investigate the role of atmospheric carbon in the greenhouse effect using a relationship between atmospheric carbon dioxide and global temperature.
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 learning activity, students use a web-based carbon calculator to determine their carbon footprint on the basis of their personal and household habits and choices. Students identify which personal activities and household choices produce the most CO2 emissions, compare their carbon footprint to the U.S. and global averages, and identify lifestyle changes they can make to reduce their footprint.
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.
In this learning activity, students use a web-based geologic timeline to examine temperature, CO2 concentration, and ice cover data to investigate how climate has changed during the last 715 million years.
This lesson explores El Nino 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 Nino 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 lesson, students explore several facets of the impact of volcanic eruptions on the atmosphere. Students analyze three types of visual information: a graph of aerosol optical depth v. global temperature, a global map with temperature anomalies, and an ash plume photograph. In the hands-on activity, students use math to determine the rate and estimated time of arrival of an ash plume at an airfield.
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.