This Earth Exploration Toolbook chapter is a detailed computer-based exploration in which students learn how various climatic conditions impact the formations of sediment layers on the ocean floor. They analyze sediment core data from the Ross Ice Shelf in Antarctica for evidence of climate changes over time. In addition, they interact with various tools and animations throughout the activity, in particular the Paleontological Stratigraphic Interval Construction and Analysis Tool (PSICAT) that is used to construct a climate change model of a sediment core from core images.

In this JAVA-based interactive modeling activity, students are introduced to the concepts of mass balance, flow rates, and equilibrium using a simple water bucket model. Students can vary flow rate into the bucket, initial water level in the bucket, and residence time of water in the bucket. After running the model, the bucket's water level as a function of time is presented graphically and in tabular form.

In this role-playing activity, learners are presented with a scenario in which they determine whether the Gulf Stream is responsible for keeping northern Europe warm. They must also address the potential future of the Gulf Stream if polar ice were to continue melting. The students work in small groups to identify the issue, discuss the problem, and develop a problem statement. They are then asked what they need to know to solve the problem.

This activity supports educators in the use of the activities that accompany the GLOBE Program's Earth System Poster 'Exploring Connections in Year 2007'. Students identify global patterns and connections in environmental data that include soil moisture, insolation, surface temperature, cloud fraction, precipitation, world topography/bathymetry, aerosol optical thickness, and biosphere (from different times of the year) with the goal of recognizing patterns and trends in global data sets.

This lab exercise is designed to provide a basic understanding of a real-world scientific investigation. Learners are introduced to the concept of tropospheric ozone as an air pollutant due to human activities and burning of fossil fuel energy. The activity uses, analyzes, and visualizes data to investigate this air pollution and climate change problem, determines the season in which it commonly occurs, and communicates the analysis to others in a standard scientific format.

This video focuses on the conifer forest in Alaska to explore the carbon cycle and how the forest responds to rising atmospheric carbon dioxide. Topics addressed in the video include wildfires, reflectivity, and the role of permafrost in the global carbon cycle.

This lesson explores El Nino by looking at sea surface temperature, sea surface height, and wind vectors in order to seek out any correlations there may be among these three variables, using the My NASA Data Live Access Server. The lesson guides the students through data representing the strong El Nino from 1997 to 1998. In this way, students will model the methods of researchers who bring their expertise to study integrated science questions.

In this activity, students gain experience using a spreadsheet and working with others to decide how to conduct their model 'experiments' with the NASA GEEBITT (Global Equilibrium Energy Balance Interactive Tinker Toy). While becoming more familiar with the physical processes that made Earth's early climate so different from that of today, they also acquire first-hand experience with a limitation in modeling, specifically, parameterization of critical processes.

In this video, a spokesperson for the National Climactic Data Center describes the methods of using satellites (originally designed for observing changes in the weather) to study changes in climate from decade to decade. The video clearly illustrates the value of satellite data and begins to address connections between weather and climate.

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.