While climate change is controversial in the media, the scientific basis is relatively simple (much simpler than many of the concepts we apply in our everyday lives) and has been long understood. The recent litigation in New York that attempted to hold big oil accountable for health care costs raises several important issues. We use the long-term Moana Loa CO2 data to examine the evidence, and we consider recent modeling studies on its impacts.
Timeline for vignette
day 1: Course overview, form working groups, install Rstudio, introduce R vignette
day 2: Discussion of science and media issues on greenhouse warming; Gain experience with R using Mauna Loa C02 data
days 3-4: in groups Introduce basics for computing in R; explore data on CO2 and climate
day 5: Discussion of CO2 data
day 6: in groups on vignette containing examples with NC data; can consider how local climate differs from global; how microclimate differs from ‘climate’; extreme events: drought, flood, fire, intensifying hurricanes
day 7: in groups draft answers to questions on R vignettes
day 8: debate litigation
Resources for discussion next time
CO2 data from Mauna Loa trace buildup of greenhouse gasses
Activities today
- Discussion of science and media issues on greenhouse warming
- Gain experience with R using Mauna Loa C02 data
Discussion
The meeting will begin with discussion within working groups, followed by group reports from the coordinator, and a discussion in plenary. Each student will post their responses to Sakai/Resources before class. The initial meeting in groups will be used to sharpen the questions and identify points of disagreement or confusion. The coordinator will provide a brief overview for the full class, including additional information on the subject. Plenary discussion will follow. The coordinator will submit to Sakai their summary at the end of the class meeting as key bullets. Recall the discussion papers from last time:
In the media:
New York’s Global Warming Suit Against Oil Companies Tossed (Bloomberg News). An argument similar to that made by tobacco companies does not work this time.
Why Big Data Could Be a Big Fail (Spectrum IEEE), Jordan on potential and limitations of Big Data (misleading title).
Science resources:
- Estimating economic damage from climate change in the United States (Science). Hsiang et al. estimate the potential economic damage from severe weather related to climate change.
Recall questions for today:
- Is Jordan an optimist or a pessimist on the advances and prospects for ‘big data’? Why?
- What sources of uncertainty are included in the projections from Hsiang et al?
- Locate a state of interest to you on Hsiang et al’s figure S2, and summarize 2 big changes that are projected and why.
- Identify two similarities and two differences between the roles of big tobacco in cancer risk and big oil in climate change.
Help session on the R intro
For next class
Identify a coordinator for next time, who will post to sakai a bullet list of questions encountered by group members during implementation of R code. These will be reviewed next class period.
The next class meeting will be devoted to analysis of the Mauna Loa data at this link. Start this vignette on your own and pose questions to your group as you encounter difficulties. The Halvorson 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.
Here is some simplified code for first steps here. This block of R code will extract long-term CO2 concentrations from 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:
plot(mw$date, mw$co2ppm, type = 'l',
xlab = 'Date', ylab = 'CO2 Concentration PPM',
main = 'Mauna Loa Weekly Carbon Dioxide Concentration'
)