1. Carbon: Transformations in Matter and Energy
Environmental Literacy Project Michigan State University
Human Energy Systems Unit Activity 6.1: Making Predictions Using Long-Term Trends

2. How do patterns in data allow scientists to make predictions about the future?
Can you think of any examples? Share your ideas with a partner.
Share ideas with the class.
Some examples:
Weather forecasting: Meteorologists use past patterns in weather conditions to predict future weather conditions.
Phases of the moon: the regular pattern allows us to predict the phase of the moon on any given date in the future.
Solar eclipses: NASA scientists are able to predict the exact time and date of all solar eclipses through the year 3000!

3. Patterns in large-scale data like those we have been exploring in this unit can be used to predict the future state of Earth’s systems.
Atmospheric CO2
Change in Sea Level Height

4. How are the short-term variability in the Arctic sea ice graph and the Keeling curve similar and different?
Use slide 4 to ask students: How are the short-term variability in the Arctic sea ice graph and the Keeling curve similar and different? Listen for students to suggest that the short-term variability is more consistent (seasonal cycle) in the Keeling curve and more inconsistent (random) in the Arctic sea ice data. If not, point this out.

5. Look at the data for these three consecutive years.
Arctic Sea Ice
Look at the data for these three consecutive years.
The average Arctic Sea ice extent in October is NOT predictable from year to year because there is a lot of random variation.
What does this tell us about the short-term variability for Arctic Sea Ice? Can we use these three years to predict what will happen in one year? How about 20 years?
Use slide 5 to point out the Arctic sea ice extent for three consecutive years and ask students: Can we use this short-term pattern to predict what will happen in one year? How about 20 years? Listen for students to suggest that these three data points are not sufficient for making predictions about the future because there is too much random variability.

6. Look at the data over all 35 years (the blue trend line).
Arctic Sea Ice
Look at the data over all 35 years (the blue trend line).
Although we can’t precisely predict the sea ice extent for the next year, continuing the trend line with the same slope suggests average sea ice extent will continue to decrease over the next few years. Thus this longer-term trend is predictable.
There is a clear long-term trend of decreasing Arctic Sea ice. This pattern suggests that the future is somewhat predictable (i.e. Arctic Sea ice will continue to decline if conditions stay the same).
What does this tell us about the long-term trend for Arctic Sea Ice? Can we use long-term trend to predict what will happen in 1 year? How about 20 years?
Use slide 6 to point out the long-term trend in the Arctic sea ice data. Ask: Can we use long-term trend to predict what will happen in 1 year? How about 20 years? Listen for students to suggest that predicting ice extent the next year would not be very precise, but that we would expect the negative trend to continue over the next 20 years. In other words, the longer term-trend is predictable.
If necessary, remind students that a trend line represents averages for a period of time or a mathematical formula that calculates a best-fit line through the data points (Activity 1.4).

7. How are the long- term trends in the Arctic sea ice graph and the Keeling curve similar and different?
Use slide 7 to ask students: How are the long- term trends in the Arctic sea ice graph and the Keeling curve similar and different? Listen for students suggest that there is a positive long-term trend visible in the Keeling curve and a negative long-term trend for Arctic sea ice.
Ask students: What approximate values would you predict for Arctic sea ice and atmospheric CO2 in the year 2020? Listen for students to suggest values lower than 8.0 million square kilometers for Arctic sea ice and higher than 400 ppm for atmospheric CO2.
What approximate values would you predict for Arctic sea ice and atmospheric CO2 in the year 2020?

8. Why are the short-term variability (gray line) and long-term trend (red line) in the Keeling curve so predictable?
Use slide 8 to ask students: Why are the short-term variability and long-term trend in the Keeling curve are so predictable? Listen to student ideas and see if any suggest that the predictability comes from two sources: the seasonal flux of carbon between the biomass and the atmosphere through photosynthesis and cellular respiration (short-term pattern), and the burning of fossil fuels (long-term trend). If students don’t suggest reasons for the predictability of the short-term trend (seasonal cycle) point out that unlike Arctic sea ice, the shorter-term trend (annual cycle) in the Keeling curve is also very predictable because it is less affected by random variations than is sea ice extent. Then ask: What causes the short-term variation (annual cycle) in the Keeling curve? Probe students to explain that the ratio of photosynthesis to respiration causes the seasonal cycle.

9. How does Seasonal Change explain the graph?
800
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Biomass
Atmosphere
Soil Carbon
Fossil Fuels
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Jan
Jan
Jan

10. How does Fossil Fuel Usage explain the graph?
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760
Biomass
Atmosphere
950
Soil Carbon
Fossil Fuels
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Jan
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Jan

11. How do Seasonal Change and Fossil Fuel Usage explain the graph?
Soil Carbon
Atmosphere
Fossil Fuels
Biomass
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950
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Jan
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Jan

12. Small changes make a large difference over many years
Soil Carbon
Atmosphere
Fossil Fuels
Biomass
550
600
950
Jan
Jan
Jan
Jan

13. Are the next year’s values for global temperature and sea level predictable or unpredictable?
Change in Sea Level Height
Check to see that students recognize that there is quite a bit of random variation from year to year in global temperature anomalies and change in sea level height. Thus, just like Arctic Sea ice extent, we can’t predict the next year’s measurements very precisely.

14. Is the five year mean for the next period (after what is shown on the graphs below) predictable or unpredictable for global temperature and change sea level height?
Change in Sea Level Height
Check to see that students recognize that there are clear long-term positive (increasing) trends in both global temperature anomalies and change in sea level height that make these phenomena relatively predictable (i.e. we would predict that global temperatures and sea level would both continue to rise if atmospheric CO2 continues to increase).