# Modeling Atmospheric CO2 Data

**This activity was selected for the On the Cutting Edge Reviewed Teaching Collection**

This activity has received positive reviews in a peer review process involving five review categories. The five categories included in the process are

- Scientific Accuracy
- Alignment of Learning Goals, Activities, and Assessments
- Pedagogic Effectiveness
- Robustness (usability and dependability of all components)
- Completeness of the ActivitySheet web page

For more information about the peer review process itself, please see http://serc.carleton.edu/NAGTWorkshops/review.html.

This page first made public: Oct 9, 2012

#### Summary

The buildup of carbon dioxide in Earth's atmosphere as a result of human industrial and agricultural activities is amplifying the planet's natural greenhouse effect and producing global climate change. Data on global atmospheric CO2 has been collected since 1958 when Charles Keeling began sampling and recording the data from the Mauna Loa Observatory in Hawaii. The data show a variety of cyclical features, but the most profound is the consistent rise in concentrations. From a mathematical point of view, the question arises as to how well the data can be matched to a particular function. From both a mathematical and a scientific point of view, regression analysis of the data could suggest how the future of CO2 concentrations may appear. In this activity, students will use actual CO2 data from the Mauna Loa Observatory in Hawaii to create their own "Keeling Curve"; conduct an analysis of the data; and, attempt to match it to a mathematical function. They will then use the function to predict increases in CO2, both historical and future.

## Learning Goals

## Context for Use

Depending on how the exercise is used, it could be a 20 minute demonstration (with little hands-on experience) or a complete 90 minute laboratory. The length of time can be adjusted by varying the amount of work students actually do in discovering the issues and working with the data. The activity could take place any time during the quarter depending on in which class it is used.

## Description and Teaching Materials

How to introduce this activity depends heavily on the specific course in which it is used. In a mathematics course this activity (or demonstration) can be used as a legitimate real-world example, presuming that the idea of functions has been introduced. In this case, it will be useful if there has been some level of discussion or pre-reading on the subject of climate change so that students understood the context. Examples of such readings can be found below under "Resources". If this activity is to be used in a science course, the teacher may decide to have little background in place, and use this activity in a series that has students discover climate change for themselves. In this case, the only prerequisite is having some working knowledge of spreadsheets; a deep understanding of functions and regression is not necessary to complete the activity. However, such knowledge will permit deeper analysis of both the data and the activity as a whole. Ideally, this activity could be used within a joint integrated course, such as a learning community combining math and science.

Procedure:

- Go to http://scrippsco2.ucsd.edu
- Click Data
- Select Atmospheric CO2 Data
- Click Mauna Loa Observatory, Hawaii
- Select Monthly
- This is the data we are working with
- Discuss the cyclic nature
- Graph the data for 1960-1990 inclusive (CO2 vs. Date) can compare to published graph (see attached pdf file) "Mauna Loa Observatory_Monthly Average CO2 Concentration"
- Fit linear, exponential, quadratic for 1960-1990 inclusive (CO2 vs. Date)
- Look at the R2 value
- Discuss making the cut either a month per year or average the averages for each year
- Re-run regressions for the cut(s) (January Data for each year used in the completed (see attached Excel spreadsheet - "Modeling CO2 Data_raw"), but many other cuts possible, depending upon time you may wish to try and compare various cuts) 1960-1990 inclusive CO2 (CO2 vs. Yr)
- Look at the R2 values
- Make prediction for 2009 for each model
- Discuss the dangers of extrapolation
- Notice that the prediction for the quadratic makes no sense (exponential should really not be below linear value either, discuss), value BELOW where is should be. Need to have the coefficients of the exponential and quadratic models to more decimal places
- Right click the equation for the quadratic model -> Select Format Trendline Label -> Change the Category to Number -> Change Decimal Places to 8
- Modify the quadratic predictions, change the decimal places for the other models to 8 and see how this changes (or does not change) their predictions.
- Discuss which coefficients need to be most accurate, discuss is 8 good enough for our model? Try 12 and compare the differences in the predictions. Discuss why using number of years since first year versus keeping the year in the thousands could have avoided this issues altogether.
- Re-run regression adding the rest of the data. Observe the changes that occurred in each of the three models.
- Calculate the prediction for 2060 for each revised model. Make sure to change the number of decimal places in the model coefficients to 8
- Discuss

Mauna Loa Observatory- Monthly Average CO2 Concentration (Acrobat (PDF) 81kB Nov9 11)

Modeling CO2 Data_Raw (Excel 2007 (.xlsx) 48kB Oct8 12)

## Teaching Notes and Tips

Students are instructed to make predictions through 2009 for each model because 2009 is the most recent annualized data available at the time this activity was created; later in the activity they will look at the actual data from 1991-2009 and compare it to their forecast. Instructors may choose to revise the year 2009 end point as later years' data becomes available.

There are three Excel spreadsheets attached to this teaching-and-learning activity. One, "Modeling CO2 Data_raw", is the rawest data that was downloaded directly from the website. When an instructor does this activity, she may choose to have the students download the data from the Scripps site themselves, or, to save time, she may just hand them this file.

Modeling CO2 Data_Raw (Excel 2007 (.xlsx) 48kB Oct8 12)

The second attached file, "Modeling CO2 Data_2nd", includes that same data as a tab, but two additional tabs, one with some unknown data removed (since it's not useful) and a third that simply has the year and the concentrations. Again, depending on the instructor, one could give any of these to students depending on how much work and time was expected.

Modeling CO2 Data_2nd (Excel 2007 (.xlsx) 48kB Oct8 12)

The third, "Instructor Key Modeling CO2 Data_lab_step by step_completed_year and concentrations", is sort of an answer key or instructor copy. It has the data, the completed graphs, and predictions. We presented them in these different forms so as to allow for flexibility on the part of the instructor.

Instructor Key Modeling CO2 Data_lab_step by step_completed_year and concentrations (Excel 2007 (.xlsx) 101kB Oct8 12)

## Assessment

## References and Resources

- Modeling CO2 Data_raw (Excel File)
- Modeling CO2 Data_2nd (Excel File)
- Instructor Key Modeling CO2 Data_lab_step by step_completed_year and concentrations (Excel File)
- Mauna Loa Observatory_Monthly Average CO2 Concentration (PDF File)

- Intergovernmental Panel on Climate Change: http://www.ipcc.ch/
- Real Climate: http://www.realclimate.org/index.php/archives/2004/12/michael-mann/
- Scripps' CO2 site: http://scrippsco2.ucsd.edu/
- Excel Document: "A History of Atmospheric CO2 and its effects on Plants, Animals and Ecosystems" editors, Ehleringer, J.R.
- T.E. Cerling, M.D.
*Dearing, Springer Verlag*, New York, 2005. - MEDIA: NOVA's "What's Up with the Weather." Dated, but useful for this activity and as an example of changing perspectives. http://www.pbs.org/wgbh/warming/
- MEDIA:
*PBS Frontline's*"HEAT." Streaming video. http://www.pbs.org/wgbh/pages/frontline/heat/