This vignette introduces the data that alerted us to the rapid CO2 increases in the atmosphere. Charles Keeling began monitoring atmospheric CO2 from atop Mauna Loa in 1958. The site was selected to be remote from local variations, aiming to provide a global perspective. It came to represent one of the most immediate and persuasive arguments that change is happening fast. The Mauna Loa CO2 data will provide an opportunity to work directly with the data that have been in media on climate change and its consequences.

Activities today

  1. Gain experience with R using Mauna Loa C02 data

Resources for next time

  1. Get started with R: work through the intro to R here

  2. CO2 data from Mauna Loa trace buildup of greenhouse gasses. Work through this vignette and stop when you get to the section The Law Dome Ice Core Record (longer term trends that we’ll pick up on later).

For next class

  1. Reading: Exceeding 1.5°C global warming could trigger multiple climate tipping points
  2. R: The next class meeting will check in on the analysis of the Mauna Loa data. Start with the background on R here. Then try the Halverson vignette on your own. Again, stop when you get to the section The Law Dome Ice Core Record. The Halverson vignette contains explanation and code in R to be implemented in Rstudio. Post your questions on this vignette and the R tutorial to Sakai in advance of the next meeting.

For end of class today

Here is some simplified code for first steps here. This block of R code will extract long-term CO2 concentrations at the Mauna Loa observatory from the NOAA ftp site:

mw <- read.table('ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_weekly_mlo.txt')

# select columns and assign new names
mw <- mw[, c(1, 2, 3, 5)]
names(mw) <- c('year', 'month', 'day', 'co2ppm')
head(mw) 
# date format
mw$date <- as.Date(paste(mw$year, mw$month, mw$day, sep = '-'),
                   format = '%Y-%m-%d')
# only necessary columns
mw <- mw[, c('date', 'co2ppm')]

# missing values
mw[mw$co2ppm == -999.99, ]$co2ppm = NA
head(mw)

Make a plot:

par( bty = 'n' )
plot(mw$date, mw$co2ppm, type = 'l',
  xlab = 'Year', ylab = 'CO2 Concentration (ppmV)',
  main = 'Mauna Loa Weekly Carbon Dioxide Concentration'
)