In this activity from the Deep Earth Academy, students divide into groups to read and discuss one of nine short articles (1-2 pages) about research done by the Ocean Drilling Program. These articles discuss our understanding about past climate based on collected data. These articles briefly describe the research conducted and the findings. Students use the information from the article to complete a write-up that they share with other students. An extension activity involves examining ocean drilling data using Google Earth.

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 activity uses geophysical and geochemical data to determine climate in Central America during the recent past and to explore the link between climate (wet periods and drought) and population growth/demise among the Maya. Students use ocean drilling data to interpret climate and to consider the influence of climate on the Mayan civilization.

In this activity, students study the relationship between changing climate conditions and the distribution of plants across North America, using a unique tool called the Pollen Viewer. This tool allows the user to animate the retreat of the North American glacier and the migration of plant species during the waning period of the most recent Ice Age.

This is a classroom activity about the forcing mechanisms for the most recent cold period: the Little Ice Age (1350-1850). Students receive data about tree ring records, solar activity, and volcanic eruptions during this time period. By comparing and contrasting time intervals when tree growth was at a minimum, solar activity was low, and major volcanic eruptions occurred, they draw conclusions about possible natural causes of climate change and identify factors that may indicate climate change.

In this activity, students learn about the tools and methods paleoclimatologists use to reconstruct past climates. In constructing sediment cores themselves, students will achieve a very good understanding of the sedimentological interpretation of past climates that scientists can draw from cores.

This activity engages learners to make a model of sediment cores using different kinds of glass beads and sand. They learn how to examine the types, numbers, and conditions of diatom skeletons in the model sediment cores and tell something about the hypothetical paleoclimate that existed when they were deposited. The students get to be climate detectives.

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.

This teaching activity addresses regional variability as predicted in climate change models for the next century. Using real climatological data from climate models, students will obtain annual predictions for minimum temperature, maximum temperature, precipitation, and solar radiation for Minnesota and California to explore this regional variability. Students import the data into a spreadsheet application and analyze it to interpret regional differences. Finally, students download data for their state and compare them with other states to answer a series of questions about regional differences in climate change.

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 or forecast the future, there is value in long-term data collection.

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