This activity has students examine the misconception that there is no scientific consensus on climate change. Students explore temperature data and report their conclusions to the class. Then students examine techniques of science denial and examine a claim about scientific consensus.

Students consider why the observed atmospheric CO2 increase rate is only ~60% of the CO2 loading rate due to fossil fuel combustion. They develop a box-model to simulate the atmospheric CO2 increase during the industrial era and compare it to the historic observations of atmospheric CO2 concentrations. The model is then used to forecast future concentrations of atmospheric CO2 during the next century.

In this activity, students compare carbon dioxide data from Mauna Loa Observatory, Barrow, Alaska, and the South Pole over the past 40 years. Students use the data to learn about what causes short-term and long-term changes in atmospheric carbon dioxide. This activity makes extensive use of Excel.

Students focus on the three interconnected choices global society faces as Earth's climate continues to changeâsuffer, adapt, and mitigateâto analyze and predict current and future impacts to Earth's systems. Using videos excerpted from NOVA: Decoding the Weather Machine, students explore ways that adaptation and mitigation strategies can work at various levels to minimize suffering and then develop an evidence-based action plan for their local community.

Through learning activities, students learn how weather over a long period of time describes climate, explore how sea level rise can affect coastal communities and environments, and describe how humans are contributing to climate change and how we can take action to solve this problem.

This set of activities is about carbon sources, sinks, and fluxes among them - both with and without anthropogenic components.

This activity involves plotting and comparing monthly data on atmospheric C02 concentrations over two years, as recorded in Mauna Loa and the South Pole, and postulating reasons for differences in their seasonal patterns. Longer-term data is then examined for both sites to see if seasonal variations from one site to the other carry over into longer term trends.