This video shows how scientists use average temperature measurements and measurements of temperature variability to understand how the climate is shifting.
Students will see how global temperature distribution can be represented by a bell curve. When temperatures become more variable, the bell curve widens, and when average temperatures increase, the bell curve moves further down the axis.
This interesting video shows students how statistics can help us to understand changes in climate.
The video uses easy-to-understand graphs to show the temperature data.
Students should be familiar with statistics terms such as bell curves, probability, distribution, and spread.
Math and statistics classes could use this video to show how climate scientists use statistics in real-world applications.
In small groups, students could discuss the following questions:
Who will be affected by greater variability in the weather? Why?
What does a wider bell curve indicate in terms of variability?
Explain how higher average temperatures and greater temperature variability can both lead to extreme heat?
Other resources on this topic include thisNASA videothat shows how land temperature anomaly distributions have changed, thisTED videoon why 1.5 degrees of global warming is a big deal, and thisarticleon how increasing heat will change life on Earth.
This resource explains the relationship and differences between climate averages, variability, and extremes. This resource is recommended for teaching.
Next Generation Science Standards (NGSS)
ESS2: Earth's Systems
HS-ESS2-4 Use a model to describe how variations in the flow of energy into and out of Earth’s systems result in changes in climate.
ESS3: Earth and Human Activity
HS-ESS3-5 Analyze geoscience data and the results from global climate models to make an evidence-based forecast of the current rate of global or regional climate change and associated future impacts to Earth systems.
Common Core Math Standards (CCSS.MATH)
Statistics & Probability (6-8)
CCSS.MATH.CONTENT.7.SP.B.3 Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability. For example, the mean height of players on the basketball team is 10 cm greater than the mean height of players on the soccer team, about twice the variability (mean absolute deviation) on either team; on a dot plot, the separation between the two distributions of heights is noticeable.
Statistics & Probability: Interpreting Categorical & Quantitative Data (9-12)
CCSS.MATH.CONTENT.HSS.ID.A.3 Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).