This video focuses on the conifer forest in Alaska to explore the carbon cycle and how the forest responds to rising atmospheric carbon dioxide. Topics addressed in the video include wildfires, reflectivity, and the role of permafrost in the global carbon cycle.

This is a basic animation/simulation with background information about the greenhouse effect by DAMOCLES. The animation has several layers to it that allow users to drill into more detail about the natural greenhouse effect and different aspects of it, including volcanic aerosols and human impacts from burning fossil fuels.

This is a collection of five short videos that show how climate change is affecting fishing, native populations and access for the oil and gas industry in the Arctic. The videos include personal reflections by writers Andrew C. Revkin and Simon Romero, scientists, and residents about their experience of the impacts of the climate change in the Arctic.

This video contains a visualization and explanation of the Arctic sea ice and how it has changed over the 25 years. In September 2012, the National Snow and Ice Data Center recorded the lowest extent of Arctic sea ice. The video discusses the climate importance of ice thickness, reflective properties, and self-reinforcing feedback mechanisms.

This short cartoon video uses a simple baseball analogy (steroid use increases probability of hitting home runs) to explain how small increases in greenhouse gases can cause global temperature changes and increase the probability of extreme weather events.

In this Earth Exploration Toolbook chapter, students select, explore, and analyze satellite imagery. They do so in the context of a case study of the origins of atmospheric carbon monoxide and aerosols, tiny solid airborne particles such as smoke from forest fires and dust from desert wind storms. They use the software tool ImageJ to animate a year of monthly images of aerosol data and then compare the animation to one created for monthly images of carbon monoxide data. Students select, explore, and analyze satellite imagery using NASA Earth Observatory (NEO) satellite data and NEO Image Composite Explorer (ICE) tool to investigate seasonal and geographic patterns and variations in concentration of CO and aerosols in the atmosphere.

This is a figure from the 2007 IPCC Assessment Report 4 on atmospheric concentrations of carbon dioxide, methane and nitrous oxide over the last 10,000 years (large panels) and since 1750 (inset panels).

In this video, students learn that scientific evidence strongly suggests that different regions on Earth do not respond equally to increased temperatures. Ice-covered regions appear to be particularly sensitive to even small changes in global temperature. This video segment adapted from NASA's Goddard Space Flight Center details how global warming may already be responsible for a significant reduction in glacial ice, which may in turn have significant consequences for the planet.

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 work in groups, plotting carbon dioxide concentrations over time on overheads and estimating the rate of change over five years. Stacked together, the overheads for the whole class show an increase on carbon dioxide over five years and annual variation driven by photosynthesis. This exercise enables students to practice basic quantitative skills and understand how important sampling intervals can be when studying changes over time. A goal is to see how small sample size may give incomplete picture of data.

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