In this activity, users download and graph modeled climate data to explore variability in climate change. Most people know that climate changes are predicted over the next hundred years, but they may not be aware that these changes are likely to vary from region to region. Using data from the University of New Hampshire's EOS-WEBSTER, a digital library of Earth Science data, users will obtain annual predictions for minimum temperature, maximum temperature, precipitation, and solar radiation for each of these 5 states: New York, Georgia, Colorado, Minnesota, and California. Data will span the years 2000 through 2100. Users will import the data into Excel and analyze it to see what, if any, regional variability exists. Finally, they will download data for their own state, compare these results with the results from the other 5 states and use their results to answer questions related to climate change. This chapter is part of the Earth Exploration Toolbook (EET). Each EET chapter provides teachers and/or students with direct practice for using scientific tools to analyze Earth science data. Students should begin on the Case Study page.


  • Mathematics > General

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Informal Education,Oceanography,NSDL,NSDL_SetSpec_ncs-NSDL-COLLECTION-000-003-112-115,Undergraduate (Lower Division),Biological science,Mathematics,Mathematics:Data collection, analysis and probability,Earth and space science:Earth processes:Climate,Earth and space science:Earth structure:Atmosphere,The nature of science:The scientific process,Atmospheric science,Ecology, Forestry and Agriculture,Life Science,Earth Science,History of science,Climate Variability,Life sciences,Higher Education,Authentic data use,Global Warming,Life sciences:Ecology and ecosystems,History/Policy/Law,Ecosystems and Environmental Change,Engineering and technology:Remote sensing,Earth and space science:Earth structure:Ocean and water,Technology,Earth system science,Engineering,High School,Vocational/Professional Development Education,Engineering and technology:Image processing and visualization,Geoscience,oai:nsdl.org:2200/20140407123602245T



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