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Human Energy Systems Unit Activity 6.1: Making Predictions Using Long-Term Trends

Human Energy Systems Unit Activity 6.1: Making Predictions Using Long-Term Trends. Carbon: Transformations in Matter and Energy Environmental Literacy Project Michigan State University. You are here. How do patterns in data allow scientists to make predictions about the future?.

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Human Energy Systems Unit Activity 6.1: Making Predictions Using Long-Term Trends

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  1. Human Energy Systems UnitActivity 6.1: Making Predictions Using Long-Term Trends Carbon: Transformations in Matter and Energy Environmental Literacy ProjectMichigan State University

  2. You are here

  3. 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. http://www.wpc.ncep.noaa.gov • Phases of the moon: the regular pattern allows us to predict the phase of the moon on any given date in the future. http://aa.usno.navy.mil/data/docs/MoonPhase.php • Solar eclipses: NASA scientists are able to predict the exact time and date of all solar eclipses through the year 3000! http://eclipse.gsfc.nasa.gov/SEcat5/SE2901-3000.html

  4. 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

  5. How are the short-term variability in the Arctic sea ice graph and the Keeling curve similar and different?

  6. Arctic Sea Ice Look at the data for these three consecutive years. 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? The average Arctic Sea ice extent in October is NOT predictable from year to year because there is a lot of random variation.

  7. 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?

  8. How are the long- term trends in the Arctic sea ice graph and the Keeling curve similar and different? What approximate values would you predict for Arctic sea ice and atmospheric CO2 in the year 2020?

  9. Why are the short-term variability (gray line) and long-term trend (red line) in the Keeling curve so predictable?

  10. How does Seasonal Change explain the graph? 400 700 800 600 Biomass Atmosphere 1000 900 Fossil Fuels Soil Carbon Jan Jan Jan Jan

  11. How does Fossil Fuel Usage explain the graph? 550 760 600 Biomass Atmosphere 950 Fossil Fuels Soil Carbon Jan Jan Jan Jan

  12. How do Seasonal Change and Fossil Fuel Usage explain the graph? 550 600 Biomass Atmosphere 950 Fossil Fuels Soil Carbon Jan Jan Jan Jan

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

  14. Are the next year’s values for global temperature and sea level predictable or unpredictable? Change in Sea Level Height

  15. 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

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