Climate change and temperature anomalies

If we wanted to study climate change, we can find data on the Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies in the Northern Hemisphere at NASA’s Goddard Institute for Space Studies. The tabular data of temperature anomalies can be found here

To define temperature anomalies I need to have a reference, or base, period which NASA clearly states that it is the period between 1951-1980.

I ran the code below to load the file:

weather <- 
  read_csv("https://data.giss.nasa.gov/gistemp/tabledata_v4/NH.Ts+dSST.csv", 
           skip = 1, 
           na = "***")

NOTE: When using this function, I added two options: skip and na:

1. The skip=1 option is there as the real data table only starts in Row 2, so I need to skip one row.

2. na = "***" option informs R how missing observations in the spreadsheet are coded. When looking at the spreadsheet, you can see that missing data is coded as “***”. It is good practice to specify this here, as otherwise some of the data is not recognized as numeric data.

3. Once the data is loaded, there is an object titled weather in the Environment panel.

Side note: If you cannot see the panel (usually on the top-right), go to Tools > Global Options > Pane Layout and tick the checkbox next to Environment. Click on the weather object, and the dataframe will pop up on a seperate tab. Inspect the dataframe.

For each month and year, the dataframe shows the deviation of temperature from the normal (expected). Further, the dataframe is in wide format.

Cleaning the data

First, I cleaned the data to leave only the relevant ones for the analysis:

• Selected the year and the twelve month variables from the weather dataset.

• Converted the dataframe from wide to ‘long’ format, and named the new dataframe as tidyweather, the variable containing the name of the month as month, and the temperature deviation values as delta.

# Create tidyweather data
tidyweather <- weather %>% 
  select(-c("J-D", "D-N", "DJF", "JJA", "MAM", "SON")) %>% 
  pivot_longer(cols = -Year, names_to = "month", values_to = "delta")

• Then, I inspected this dataframe to have three variables now, one each for:
  • year,
  • month, and
  • delta, or temperature deviation.

# Set date format tidyweather data
tidyweather <- tidyweather %>%
  mutate(date = ymd(paste(as.character(Year), month, "1")),
         month = month(date, label=TRUE),
         year = year(date))

# Plot weather anomalies
ggplot(tidyweather, aes(x=date, y = delta))+
  geom_point()+
  geom_smooth(color="red") +
  theme_bw() +
  labs (
    title = "Weather Anomalies"
  )

Conclusion 1:

Based on the plots, it can be implied that the effect of increasing temperature is more pronounced in the winter months - starting from the end of fall until the start of spring. I can see this from the higher values within the winter months over the other months across time. This reflects the effects of global warming/climate change throughout the last decade.

NOTE: - It is sometimes useful to group data into different time periods to study historical data. For example, we often refer to decades such as 1970s, 1980s, 1990s etc. to refer to a period of time. > Here, NASA calculates a temperature anomaly, as a difference from the base period of 1951-1980.

With the code below I: - Created a new data frame called comparison that groups data in five time periods: 1881-1920, 1921-1950, 1951-1980, 1981-2010 and 2011-present. - Removed data before 1800 and before using filter. - Used the mutate function to create a new variable interval which contains information on which period each observation belongs to. - Assigned the different periods using case_when().

# Assign periods
comparison <- tidyweather %>% 
  filter(Year>= 1881) %>%     #remove years prior to 1881
  #create new variable 'interval', and assign values based on criteria below:
  mutate(interval = case_when(
    Year %in% c(1881:1920) ~ "1881-1920",
    Year %in% c(1921:1950) ~ "1921-1950",
    Year %in% c(1951:1980) ~ "1951-1980",
    Year %in% c(1981:2010) ~ "1981-2010",
    TRUE ~ "2011-present"
  ))

Next: - Inspected the comparison data frame by clicking on it in the Environment pane.

• Now, that I have the interval variable, I could create a density plot to study the distribution of monthly deviations (delta), grouped by the different time periods we are interested in.

• Set fill to interval to group and colour the data by different time periods.

# Create density plot of monthly deviation based off each interval period
ggplot(comparison, aes(delta, fill = interval, alpha = 0.1)) +
  geom_density() +
  labs(title = "Denisty plots of monthly deviations", subtitle = "Separated by Intervals", x = "Delta", y = "Density") +
  theme(plot.title = element_text(size = 14, hjust = 0.5),
        plot.subtitle = element_text(size = 12, hjust = 0.5),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 10)
)