This short video, is the fifth in the National Academies Climate Change, Lines of Evidence series. It focuses on greenhouse gases, climate forcing (natural and human-caused), and global energy balance.
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 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 three-panel figure is an infographic showing how carbon and oxygen isotope ratios, temperature, and carbonate sediments have changed during the Palaeocene-Eocene Thermal Maximum. The figure caption provides sources to scientific articles from which this data was derived. A graphic visualization from the Intergovernmental Panel on Climate Change shows the rapid decrease in carbon isotope ratios that is indicative of a large increase in the atmospheric greenhouse gases CO2 and CH4, which was coincident with approximately 5C of global warming.
This color-coded map displays a progression of changing five-year average global surface temperatures anomalies from 1880 through 2010. The final frame represents global temperature anomalies averaged from 2006 to 2010. The temperature anomalies are computed relative to the base period 1951-1980.
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.