Scientists agree that the climate is changing and that human activities are a primary cause for this change through increased emissions of CO2 and other greenhouse gases to the atmosphere. There have been times in Earth's past that temperature and CO2 concentrations have been much higher than they currently are, so it is not just the actual temperature that is of concern to scientists, but the fact that the rate of change of temperature is unprecedented in the geologic record. We do not know how various factors will respond to such a rapid rate of change, and thus we anticipate that many species will not be able to adapt, leading to widespread extinction. In this module, students will explore how climate is changing from the recent record. They will then compare current patterns to pre-historic rates of change calculated from ice-core data and use their results to support whether or not human activity is likely to have influenced current climate change.
By Jonathan Stetler
Contact information firstname.lastname@example.org
Context for Use
Department: Biological Sciences
Audience Level: Undergraduate
Instructional Setting: Lab
Number of Students: 60 in 1 section, 20 in another section
Activity Length: 3 hours
Resource Learning Goals:
- Students will gain skills graphing and analyzing climate data
- Students will better understand linear regression and associated coefficients (e.g., slope, intercept)
Changes Made (If any)
I started with a youtube video and anonymous Google Doc discussion on climate change.
Instead of part C, I asked students to review their figures and state when we should have started acted to prevent climate change. “When should we have known?”
(Think about what you would like to read about this activity if you came back to it in 2 years. You have complete control of the structure- from a descriptive narrative to bullets)
Some suggestions for this section (not all required, and extras always welcome):
- What did you change and why?
I have many computer science students in my class. I decided to approach this module from the point of view of a Data Scientist. Instead of diving into part C ice core data, I chose to have students reflect on their graphs and the state of our current climate. I felt this was more relevant o my students’ interests.
- How did the activity go?
- What went well and why?
- My students exceled at analyzing and plotting dats in the R program. Students gained a much better understanding of linear regression analysis and what the slope actually means for long-term trends.
- What went wrong and why?
- I could have done a better job explaining what a climate anomaly is, and how the global temperature data set was collected. Many students also left the lab on a somber note after realizing climate change has been occurring for decades and we still haven’t done anything about it. In the future I would like to add an additional concluding activity so students can leave the class on a more positive note.
- What was the prep like?
- How much time went into prep?
- 3+ hours largest prep items included:
- Preparing data for input into R, changing instructions into R not Microsoft Excel, reviewing and modifying the PowerPoint, and finding additional videos/ discussions from a data scientist perspective.
- Did you have to do any prep (i.e. grow cultures, grow seeds, order supplies) ahead of implementation?
- Would you do this activity again?
- What would you change in the future?
- I would like to place more emphasis on how global temperature data is collected. I’d also like to add an activity or discussion on climate solutions at the end of the lab period.
- What do you wish you’d known before you ran the activity?
- Is there anything else you would like to make note of?
- How does this activity fit in your overall course curriculum?
- In what ways, if any, did you modify your teaching practice with this activity?
- Usually I teach data labs in a learn and apply manner where I teach students with one data set, then they explore another similar data set on their own and apply the skills I taught them. This model was student driven from the beginning of the lab which is great. This lab worked out so well because I spent previously labs teaching students the fundamental R skills needed for this exercise such as filtering, plotting, and performing linear regressions in R. I think this lab worked so well because I put it towards the end of the semester when students have already strengthened their computation skills.
Researchers should cite this work as follows: