In this activity, students utilize a set of photographs and a 30 minute video on weather to investigate extreme weather events. They are posed with a series of questions that ask them to identify conditions predictive of these events, and record them on a worksheet. Climate and weather concepts defined.
In this video, a team of paleontologists, paleobotanists, soil scientists, and other researchers take to the field in Wyoming's Bighorn Basin to document how the climate, plants, and animals there changed during the Paleocene- Eocene Thermal Maximum (PETM) when a sudden, enormous influx of carbon flooded the ocean and atmosphere for reasons that are still unclear to scientists. The PTEM is used as an analog to the current warming occurring. The scientists' research may help inform our understanding of current increases in carbon in the atmosphere and ocean and the resulting impact on ecosystems. Supporting materials include essay and interactive overview of animals that existed in the Basin after the PETM event.
This activity engages learners to make a model of sediment cores using different kinds of glass beads and sand. They learn how to examine the types, numbers, and conditions of diatom skeletons in the model sediment cores and tell something about the hypothetical paleoclimate that existed when they were deposited. The students get to be climate detectives.
In this activity, students will use oxygen isotope values of two species of modern coral to reconstruct ambient water temperature over a four-year period. They use Microsoft Excel, or similar application, to create a spreadsheet of temperature values calculated from the isotope values of the corals by means of an algebraic equation. Students then use correlation and regression techniques to determine whether isotope records can be considered to be good proxies for records of past 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 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.
This activity focuses on reconstructing the Paleocene-Eocene Thermal Maximum (PETM) as an example of a relatively abrupt global warming period. Students access Integrated Ocean Drilling Program (IODP) sediment core data with Virtual Ocean software in order to display relevant marine sediments and their biostratigraphy.
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 teaching activity is an introduction to how ice cores from the cryosphere are used as indicators and record-keepers of climate change as well as how climate change will affect the cryosphere. Students learn through a guided web exercise how scientists analyze ice cores to learn about past climate conditions, how melting sea and land ice will contribute to sea level rise, and what areas of the world would be at risk if Antarctic and/or Greenland ice sheets were to melt away.
In this activity, students review techniques used by scientists, as they analyze a 50-year temperature time series dataset. The exercise helps students understand that data typically has considerable variability from year to year and to predict trends or forecast the future, there is value in long-term data collection.