This is a partially developed activity description. It is included in the collection because it contains ideas useful for teaching even though it is incomplete.

Initial Publication Date: October 22, 2012 | Reviewed: December 10, 2020
This activity was developed during the workshop, Teaching Climate Change: Insight from Large Lakes, held in June 2012.

Up-scaling Great Lakes sediment cores to the regional climate scale

by Daria Kluver, Central Michigan University
Rhett Mohler, Kansas State University
Katryn Wiese, City College of San Francisco

Topic: scaling lake core data from individual points to the regional and global climate

Course Type: climate change module in an intro-level course

Learning Goals

After completing this activity students should be able to:

  1. demonstrate sediment core collection through a field exercise (whether or not you use that core's data).
  2. recognize potential errors and contamination risks and identify ways to minimize them.
  3. distinguish different layers within the cores and use appropriate terminology to describe them.
  4. identify cyclical variations in the core layers, and correlate those with remotely sensed lake temperature data over a regional spatial scale.
  5. estimate lake evaporation rates from lake temperatures.
  6. reconstruct regional lake evaporation based on core layers.
  7. evaluate the effectiveness of point observations to represent regional paleoclimate


Background: Sediment cores are used by scientists around the world to gather data on past environments, including climate phenomenon such as temperature, rainfall, etc.

In this activity, students will:

  1. Review 3 sediment cores from a single lake and the core data over the last 10 years to correlate with remotely sensed lake surface temperature data, specifically looking for a relationship between temperature and sediment thickness.
  2. Calculate the correlation coefficients at each remotely sensed grid cell with the core data to determine how spatially representative the cores are for lake temperature.
  3. Compute the change in evaporation given a change in temperature.
  4. Given a significant correlation, students will use the lake core data to estimate temperature and evaporation in the region down wind of the lakes pre-in-situ data availability.

Note: it's possible that we won't find a good site or set of sites that correlate sediment thickness with lake-surface temperature. If data were available, we could extend to stable-isotope data or fossil species identification as a way of correlating core data to lake-surface temp data. However... that's expensive data to get.


Students will submit a completed lab report.

Elements of a complete report are:

  • Background
  • Core descriptions including thicknesses and number of layers
  • Appropriate graphics (plot core locations, correlations with remotely sensed grids)
  • Excel -- including calculations of correlation efficiency and evaporation rates related to temperature
  • synthesis and conclusions about the core data's usefulness and applications.

Questions for students to consider along the way:

  • How does one tell how long 10 years is in the core? How does one go back in time through the core?
  • Is there a trend in the data (is temperature rising?)

Questions for students to consider at end and to incorporate into the lab report conclusion:

  • How good was the correlation?
  • How significant is 10 years as a data set for correlation? What are some problems/challenges? What effects might be covered and not covered in that 10-year period?
  • What could be done to verify or improve correlation? (What other data could be collected and how?)
  • What were the minimums and maximums of past temperature and rainfall data? Where are we today?

Data or other materials needed

Sediment cores from lakes distributed across the Great Lakes region:

  • 3 good/clean cores that are well-correlated. We would need to first collect at least a dozen and see which would work best for our students.
  • Either collect cores or get cores from the online repository:

Remotely sensed data:

  • Earth Observation System Data and Information System (EOSDIS) thermal imagery for the month of June (monthly mean calculated from daily data)

Climate data: