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.
This audio slideshow/video describes the Greenland ice sheet and the difficulties in getting scientific measurements at the interface between the ice and the ocean. It features the work of a researcher from Woods Hole Oceanographic Institute researcher. She gives a personal account of her work on the recent increase in melting of glaciers, the challenges of working in Greenland, and the reasons why so many climate scientists are looking there for answers to questions about climate change.
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 exercise learners use statistics (T-test using Excel) to analyze an authentic dataset from Lake Mendota in Madison, WI that spans the last 150 years to explore ice on/ice off dates. In addition, students are asked to investigate the IPCC Likelihoodscale and apply it to their statistical results.
This video provides an excellent summary of the role of the oceans and ocean life and makes the point that despite the important role of life in the oceans, there is still much to be learned about the details of the oceanic biota.
In this activity, students download historic temperature datasets and then graph and compare with different locations. As an extension, students can download and examine data sets for other sites to compare the variability of changes at different distinct locations, and it is at this stage where learning can be individualized and very meaningful.