This visualization graphically displays temperature and CO2 concentration in the atmosphere as derived from ice core data from 400,000 years ago to 1950. The data originates from UNEP GRID Arendal's graphic library of CO2 levels from Vostok ice core.
This NASA video explores the relationship between climate and agriculture, including the variability of climate change impacts that may occur in different regions and the effects of population growth and higher demands for food in areas that already struggle to supply food for the people. The video highlights the need for accurate, continuous, and accessible data and computer models from NASA satellites to track and predict the challenges farmers face as they adjust to a changing climate.
In this lab activity students generate their own biomass gases by heating wood pellets or wood splints in a test tube. They collect the resulting gases and use the gas to roast a marshmallow. Students also evaluate which biomass fuel is the best by their own criteria or by examining the volume of gas produced by each type of fuel.
In this JAVA-based interactive modeling activity, students are introduced to the concept of mass balance, flow rates, and equilibrium using a simple water bucket model. Students can vary flow rate into the bucket, initial water level in the bucket, and residence time of water in the bucket. After running the model, the bucket's water level as a function of time is presented graphically and in tabular form.
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.