MacKay Minutes 2016/05/19

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How did we do on our todo list?

  • Put up small multiples and get the axes to match
  • Find centroids for regions--with cereal boxes!!
  • Create regressions for each species in each year
  • Continue trying to find historical climate data
  • Continue reading assignments

The Readings

2, Week 1: CO2 and other gases (I realized that we're moving at half-speed -- that's okay with me!:)

  1. McKibben: pp. 32-46;
    1. Callendar (1938)
      1. Estimates 3/4 of carbon remains in atmosphere
      2. Estimates CO2 at 274 ppm at the beginning of the century (1900); around 310 by 1936.
      3. .005 degree increase per year for the last half century.... i.e. from 1888-1938 -- right through our data set. But those are global; Nova Scotia is the most moderate of Canadian provinces (due to the ocean). Here are recent climate normals for 13 regions of Nova Scotia. [How can we align them with the regions of MacKay?]
    2. Ravelle and Seuss (1957)
      1. 10 percent increase in CO2 increases temperature by .36 degrees (citing Plass). Climate sensitivity is (roughly) the amount of temperature increase that you would expect, given a doubling of carbon dioxide in the atmosphere. How would you calculate Plass's value of climate sensitivity?
      2. "Thus human beings are now carrying out a large scale geophysical experiment ...."
    3. Keeling: "Keeling's famous curve, 'the single most important environmental data set taken in the 20th century.'"
  2. Tyndall's climate message, 150 years on, from the BBC (2011);
    1. "His discovery, description and analysis of the molecular basis of the greenhouse effect came more than 30 years before the discovery of either radioactivity or the electron."
    2. I've put his 1861 lecture on-line. You might enjoy it! It was read February 7th, 1861 -- almost exactly 100 years before I was born. I feel some sort of weird connection!:)
  3. Kolbert: Chapter Two (A Warmer Sky)
    1. 235 Watts per square meter
    2. Stefan-Boltzmann law: . At some point everyone will need to learn tex (or latex)!
    3. 1959: Keeling data was at 316 ppm. Kolbert asserts that will reach 500 ppm by the middle of the century. How could we predict when?
  4. Archer: Chapter 8: Carbon, pp. 85-98
    1. p. 88: "380 ppm..." (2007); today, 400 ppm
    2. p. 90 bizarre line in Keeling data; attributes smaller variation in CO2 to less land mass; but vegetation is also less deciduous.
    3. ocean/atmosphere exchange of CO2: increasing CO2 storage in the ocean leads to ocean acidification ("global warming's evil twin")
    4. p. 91: We'll talk more about the Milankovich Cycle (Fig. 8.4) down the road, but there are three processes in Earth's orbit involved, each with its own period:
      1. precession (about 26K)
      2. obliquity (about 41K)
      3. eccentricity (100K and 400K)
    5. p. 94: "CO2 in the atmosphere cycles up and down along with the ice sheets"
    6. p. 94: weathering of rock sequesters carbon dioxide (timescales of millions of years).

Moving forward

  • What's our model for phenological first arrival?
    • Some plants require a certain number of cold days before appearing. So we have to factor time into the temperature data....
    • For example, the soil may have to warm to a certain temperature, which is essentially an integral of the warming air temperatures (which is what we typically measure).
  • How do we incorporate the climate data?
  • Use the Keeling data and a reasonable model to predict when CO2 will hit 500 ppm. You might take as an historical level about 270 ppm.

In the news

The biggest coral reef in the continental U.S. is dissolving into the ocean

Todo for next week


  1. will take a crack at getting climate normals for one of the sites from the old climate data
  2. will continue the regressions, using her famous "cereal box centroids".


  1. will continue looking at data quality, and fixing missing data in our summary data sets;
  2. get help from Steve at creating a function for creating the small multiples;
  3. will continue working with Steve on differences from year to year.


  1. will help Laura with regressions in R;
  2. will work on getting the climate data into a better format;
  3. will help Madison with deciding how to fix data problems (e.g. missing data) in individual data sets.


  1. will help Madison turn her process into a function to create small multiples;
  2. will continue working with Madison on looking at differences and second order differences from year to year.

Next meeting