In this video, students see how data from the ice core record is used to help scientists predict the future of our climate. Video features ice cores extracted from the WAIS Divide, a research station on the West Antarctic Ice Sheet.
This video highlights research conducted at Woods Hole on how heat absorbed by the ocean and changes of ocean chemistry from human activities could lead to a tipping point for marine life and ecosystems. Includes ice bath experiment that models the tipping point of Arctic sea ice.
This is a sequence of 5 classroom activities focusing on the El NiÃo climate variability. The activities increase in complexity and student-directedness. The focus of the activities is on accessing and manipulating real data to help students understand El NiÃo as an interaction of Earth systems.
This video introduces phytoplankton - the base of the marine food web, the source of half of the oxygen on Earth, and an important remover of CO2 from the atmosphere. The video also explains how satellites are used to monitor phytoplankton and how warming waters and acidification negatively affect phytoplankton.
Hands-on laboratory activity that allows students to investigate the effects of distance and angle on the input of solar radiation at Earth's surface, the role played by albedo, the heat capacity of land and water, and how these cause the seasons. Students predict radiative heating based on simple geometry and experiment to test their hypotheses.
This video shows 15 years of data obtained via Polar-orbiting satellites that are able to detect subtle differences in ocean color, allowing scientists to see where there are higher concentrations of phytoplankton - a proxy for the concentration of chlorophyll in the ocean.
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.