In this activity, students develop an understanding of the relationship between natural phenomena, weather, and climate change: the study known as phenology. In addition, they learn how cultural events are tied to the timing of seasonal events. Students brainstorm annual natural phenomena that are tied to seasonal weather changes. Next, they receive information regarding the Japanese springtime festival of Hanami, celebrating the appearance of cherry blossoms. Students plot and interpret average bloom date data from over the past 1100 years.

This image depicts a representative subset of the atmospheric processes related to aerosol lifecycles, cloud lifecycles, and aerosol-cloud-precipitation interactions that must be understood to improve future climate predictions.

C-Learn is a simplified version of the C-ROADS simulator. Its primary purpose is to help users understand the long-term climate effects (CO2 concentrations, global temperature, sea level rise) of various customized actions to reduce fossil fuel CO2 emissions, reduce deforestation, and grow more trees. Students can ask multiple, customized what-if questions and understand why the system reacts as it does.

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.

This is a multi-media teaching tool to learn about climate change. The tool is comprised of stills, video clips, graphic representations, and explanatory text about climate science. Acclaimed photographer James Balog and his Extreme Ice team put this teaching tool together.

This video is part of the Climate Science in a Nutshell video series. This short video looks at the effects of climate change happening right now around the globe, including: more extreme weather events, droughts, forest fires, land use changes, altered ranges of disease-carrying insects, and the loss of some agricultural products. It concludes with a discussion of the differences between weather, climate variability, and climate change.

In this short video, atmospheric scientist Scott Denning gives a candid and entertaining explanation of how greenhouse gases in Earth's atmosphere warm our planet.

This classroom activity is aimed at an understanding of different ecosystems by understanding the influence of temperature and precipitation. Students correlate graphs of vegetation vigor with those of temperature and precipitation data for four diverse ecosystems, ranging from near-equatorial to polar, and spanning both hemispheres to determine which climatic factor is limiting growth.

In this audio slideshow, an ecologist from the University of Florida describes the radiocarbon dating technique that scientists use to determine the amount of carbon within the permafrost of the Arctic tundra. Understanding the rate of carbon released as permafrost thaws is necessary to understand how this positive feedback mechanism is contributing to climate change that may further increase global surface temperatures.

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.

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