Exploring Regional Differences in Climate Change

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.

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Denise Blaha
Rita Freuder
Earth Exploration Toolbook from TERC

Notes From Our Reviewers

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Teaching Tips

Teaching Tips

Summary of regional impacts of climate change is weak and not very complete. Ideally the educator would do more research on these topics before teaching the lab so that they can guide students better in understanding the specific local effects of a changing climate.

A few guiding questions are suggested, but educator will have to develop additional assessment questions.

Questions that are given to guide the interpretation of the graphs should stress that the graphs are predictions of the future and, therefore, hypotheses and not facts.

Activity uses information from a model "black box," followed by a detailed, step-by-step instruction - educator has to make sure that the student learning of the content - regional variability of climate change - is achieved.

Summary of regional impacts of climate change is weak and not very complete. Ideally educator would do more research on these topics before teaching the lab.

Shortcut options: 1) Data is available on EET chapter website and does not necessarily need to be downloaded from EOS-Webster, 2) Go directly to Part 3 where the graphs are already presented with the analysis questions. However, to do the extension about the home-state, you need to access the EOS-Webster

Available professional development programs may be helpful in overcoming concerns about manipulation of large data sets.

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