In this activity, students research changes to the environment in the Arctic/Bering Sea over time using oral and photographic histories. Developed for Alaska Native students, this activity can be customized for other regions.
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, one needs to consider long-term data.
In this activity, students learn about the urban heat island effect by investigating which areas of their schoolyard have higher temperatures - trees, grass, asphalt, and other materials. Based on their results, they hypothesize how concentrations of surfaces that absorb heat might affect the temperature in cities - the urban heat island effect. Then they analyze data about the history of Los Angeles heat waves and look for patterns in the Los Angeles climate data and explore patterns.
This series of activities is designed to introduce students to the role of sediments and sedimentary rocks in the global carbon cycle. Students learn how stable carbon isotopes can be used to reconstruct ancient sedimentary environments. Students will make some simple calculations, formulate hypotheses, and think about the implications of their results. The activity includes an optional demonstration of the density separation of a sediment sample into a light, organic fraction and a heavier, mineral fraction.
In this lesson, students examine and interpret varied observational datasets and are asked to determine whether the data supports or does not support the statement: climate change is occurring in Colorado.
This activity introduces students to plotting and analyzing phenology data. Students use 30 years of data that shows the date of the first lilac bloom and the number of days of ice cover of nearby Gull Lake.
This module contains five activities, in increasing complexity, that focus on understanding how to interpret and manipulate sea level data, using real data from NOAA.
Students first need to understand how to access and interpret sea surface height and tide data. To understand how to interpret these data, students will review and practice computing mean values. Along the way, they will learn how different factors, such as storms, affect tide levels and how to measure them. The goal is for students to become experienced with these kinds of data and the tools for accessing them so that, by the end of the module, they can continue to explore data sets driven by their own inquiry.