In this activity, students make a model sea floor sediment core using two types of buttons to represent fossil diatoms. They then compare the numbers of diatom fossils in the sediment at different depths to determine whether the seas were free of ice while the diatoms were alive.

In this activity students work with real datasets to investigate a real situation regarding disappearing Arctic sea ice. The case study has students working side-by-side with a scientist from the National Snow and Ice Data Center and an Inuit community in Manitoba.

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.

In this activity, students create graphs of real temperature data to investigate climate trends by analyzing the global temperature record from 1867 to the present. Long-term trends and shorter-term fluctuations are both evaluated. The data is examined for evidence of the impact of natural and anthropogenic climate forcing mechanisms on the global surface temperature variability. Students are prompted to determine the difficulties scientists face in using this data to make climate predictions.

In this intermediate Excel activity, students import US Historical Climate Network mean temperature data into Excel from a station of their choice. They are then guided through the activity on how to use Excel for statistical calculations, graphing, and linear trend estimates. The activity assumes some familiarity with Excel and graphing in Excel.

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.

In this activity, students use the GLOBE Student Data Archive and visualizations to explore changes in regional and seasonal temperature patterns.

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 analyze data maps of sea surface temperature anomalies for a 14-year interval and create an ENSO time line in a case study format. Based on their findings, students determine the recurrence interval of the ENSO system.

This web-based activity tackles the broad reasons for undertaking ocean exploration - studying the interconnected issues of climate change, ocean health, energy and human health. Students examine the types of technology ocean scientists use to collect important data.