This activity engages learners to investigate the impact of Earth's tilt and the angle of solar insolation as the reason for seasons by doing a series of hands-on activities that include scale models. Students plot the path of the Sun's apparent movement across the sky on two days separated by three months of time.

In this activity, students examine pictures of pollen grains representing several species that show the structural differences that scientists use for identification. Students analyze model soil samples with material mixed in to represent pollen grains. They then determine the type and amount of 'pollen' in the samples and, using information provided to them, determine the type of vegetation and age of their samples. Finally, they make some conclusions about the likely climate at the time the pollen was shed.

In this lab activity, students use a chemical indicator (bromothymol blue) to detect the presence of carbon dioxide in animal and plant respiration and in the burning of fossil fuels and its absence in the products of plant photosynthesis. After completing the five parts of this activity, students compare the colors of the chemical indicator in each part and interpret the results in terms of the qualitative importance of carbon sinks and sources.

In this activity, students explore past examples of climate variability in three locations: the Peruvian and Bolivian Andes, Central America, and coastal Greenland, and consider differences between climate variability and climate change.

This unit allows students to investigate past changes in Earth's climate. Students first explore relationships in climate data such as temperature, solar radiation, carbon dioxide, and biodiversity. They then investigate solar radiation in more depth to learn about changes over time such as seasonal shifts. Students then learn about mechanisms for exploring past changes in Earth's climate such as ice cores, tree rings, fossil records, etc. Finally, students tie all these together by considering the feedbacks throughout the Earth system and reviewing an article on a past mass extinction event.

In this activity, students use Google Earth and team up with fictional students in Chersky, Russia to investigate possible causes of thawing permafrost in Siberia and other Arctic regions. Students explore the nature of permafrost and what the effects of thawing permafrost mean both locally and globally. Next, students use a spreadsheet to explore soil temperature data from permafrost boreholes and surface air temperature datasets from in and around the Chersky region for a 50-year time span.

In this activity, students use Google Earth to explore global temperature changes during a recent 50 - 58 year period. They also explore, analyze, and interpret climate patterns of 13 different cities, and analyze differences between weather and climate patterns.

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 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.

In this activity, students review techniques used by scientists as they analyze a 50-year temperature time series dataset. The exercise helps students understand that data typically has considerable variability from year to year and to predict trends, one needs to consider long-term data.

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