2023 was the warmest year ever recorded and since environmental and climate issues are one of the reasons for starting this site, it’s time to start working. Here, we’ll use R to download and plot a few basic climate realted data sets. R scripts included.

CO₂ levels 1974-2024

The most well known greenhouse gas. Data from https://gml.noaa.gov/ccgg/trends/data.html.

The trend seems clear. I´m not saying it´s bad news, but it´s bad news.

# Data from https://gml.noaa.gov/ccgg/trends/data.html

# Load libraries
library(ggplot2)

# Set working directory
setwd("C:\\Eget\\R\\")

# Read CO2 data and create column names
url <- "https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_daily_mlo.txt"
data <- read.table(url, skip = 34, col.names = c("year", "month", "day", "decimal", "CO2_molfrac"))

# Create column "Date"using YYYY-MM-DD
data$Date <- as.Date(paste(data$year, data$month, data$day, sep = "-"))

# Create plot
ggplot(data, aes(x = Date, y = CO2_molfrac)) +
  geom_line(color = "blue", size = 1.0) +
  labs(title = "CO2 concentration at Mauna Loa Observatory",
       x = "Datum",
       y = "CO2 (ppm)") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

CH₄ levels 1984-2022

Methane is more a potent greenhouse gas than CO₂, but also resides for a shorter period of time in the atmosphere.

# Data from https://gml.noaa.gov/ccgg/trends_ch4/

# Load libraries
library(ggplot2)

# Set working directory
setwd("C:\\Eget\\R\\")

# Read CH4 data and create column names
url <- "https://gml.noaa.gov/webdata/ccgg/trends/ch4/ch4_annmean_gl.txt"
data <- read.table(url, skip = 46, col.names = c("year", "mean", "unc"))

# Create plot
ggplot(data, aes(x = year, y = mean)) +
  geom_line(color = "red", size = 1.5) +
  labs(title = "Global CH4 concentration",
       x = "Year",
       y = "CH4 (ppm)") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

Sea level change 1992-2023

Data from https://sealevel.colorado.edu/ shows a sea level rise of about 11 cm for the period 1992-2023.

# Data from https://sealevel.colorado.edu/files/2023_rel2/gmsl_2023rel2_seasons_rmvd.txt

# Load libraries
library(ggplot2)

# Read sea level data and create column names
url <- "https://sealevel.colorado.edu/files/2023_rel2/gmsl_2023rel2_seasons_rmvd.txt"
data <- read.table(url, skip = 46, col.names = c("date", "sea_level"))


# Create plot
ggplot(data, aes(x = date, y = sea_level)) +
  geom_line(color = "blue", size = 1.0) +
  labs(title = "Sea level change 1992-2023",
       x = "Year",
       y = "Sea level change (mm)") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

Global temperature

What about the global temperature? Also trending upwards, it seems.

# Data from https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/global/time-series

# Load libraries
library(ggplot2)

# Set working directory
setwd("C:\\Eget\\R\\")

# Read global temperature data
data <- read.csv("https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/global/time-series/globe/land_ocean/1/2/1850-2024/data.csv", 
                 skip = 4, header = TRUE)

# Create plot
ggplot(data, aes(x = Year, y = Anomaly)) +
  geom_line(color = "orange", size = 1.5) +
  labs(title = "Global Temparature Anomalies 1850-2024",
       x = "Year",
       y = "Anomaly (C)") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5)) + 
  scale_x_continuous(breaks = seq(1850, 2024, by = 25))

The are of course many more data sets to visualize, such as SO₂ levels (reflects incoming radiation) and planet albedo (the ratio of reflected light compared to the total sunlight that falls on it). Still, this is starting to look like a personal climate dashboard. Perhaps future a Shiny climate dashboard? Always check the planet albedo before breakfast. We´ll get back to that Shiny app in a later post.