R Notebook for Measuring Climate Change

Part 1.1 The behaviour of average surface temperature over time

1. In this dataset, temperature is measured as ‘anomalies’ rather than as absolute temperature. Using NASA’s Frequently Asked Questions section as a reference, explain in your own words what temperature ‘anomalies’ means. Why have researchers chosen this particular measure over other measures (such as absolute temperature)?

library(readr)
dat <- read.csv("data.csv", skip = 1, na.strings = "***")

2. Choose one month and plot a line chart with average temperature anomaly on the vertical axis and time (from 1880 to the latest year available) on the horizontal axis. Label each axis appropriately and give your chart a suitable title (Refer to Figure 1.1 as an example.)

dat$Jan <- ts(dat$Jan,
              start = c(1880), end = c(2020), frequency = 1)
plot(dat$Jan, type = "l", col = "blue", lwd = 2,
     ylab = "Annual temperature anomalies", xlab = "Year")
title("Average annual temperature anomaly in January \n in the northern hemisphere (1880-2020)")

abline(h = 0, col = "darkorange2", lwd = 2)
text(2000, -0.1, "1951-1980 average") 

3. Extra practice: The columns labelled DJF, MAM, JJA, and SON contain seasonal averages (means). For example, the MAM column contains the average of the March, April, and May columns for each year. Plot a separate line chart for each season, using average temperature anomaly for that season on the vertical axis and time (from 1880 to the latest year available) on the horizontal axis.

dat$DJF <- ts(dat$DJF,
              start = c(1880), end = c(2020), frequency = 1)
plot(dat$DJF, type = "l", col = "blue", lwd = 2,
     ylab = "Seasonal temperature anomalies", xlab = "Year")
title("Average temperature anomaly for \n December, January, and February (1881-2020)")

dat$MAM <- ts(dat$MAM,
              start = c(1880), end = c(2020), frequency = 1)
plot(dat$MAM, type = "l", col = "blue", lwd = 2,
     ylab = "Seasonal temperature anomalies", xlab = "Year")
title("Average temperature anomaly for \n March, April, and May (1880-2019)")

dat$SON <- ts(dat$SON,
              start = c(1880), end = c(2020), frequency = 1)
plot(dat$SON, type = "l", col = "blue", lwd = 2,
     ylab = "Seasonal temperature anomalies", xlab = "Year")
title("Average temperature anomaly for \n September, October, and November (1880-2019)")

4. The column labelled J–D contains the average temperature anomaly for each year.
(a) Plot a line chart with annual average temperature anomaly on the vertical axis and time (from 1880 to the latest year available) on the horizontal axis. Extension: Add a horizontal line that intersects the vertical axis at 0, and label it ‘1951–1980 average’.

dat$J.D <- ts(dat$J.D,
              start = c(1880), end = c(2020), frequency = 1)
plot(dat$J.D, type = "l", col = "blue", lwd = 2,
     ylab = "Annual temperature anomalies", xlab = "Year")
title("Average annual temperature anomaly \n in the northern hemisphere (1880-2019)")

abline(h = 0, col = "darkorange2", lwd = 2)
text(2000, -0.1, "1951-1980 average")

(b) What do your charts from Questions 2 to 4(a) suggest about the relationship between temperature and time?