Global surface air temperature anomalies

jun-temps.R

library(needs)
needs(tidyverse, readxl, googlesheets4, stringr, plotly, magrittr, janitor, RcppRoll, lubridate, ggrepel, zoo, scales, rvest, jsonlite, splitstackshape, ncdf4)

options(scipen = 20)

############# 
############# GLOBAL TEMP
#############

#Load in data
global_temp_raw <- read.csv('https://sites.ecmwf.int/data/c3sci/bulletin/202406/press_release/timeseries_era5_monthly_2t_global_allmonths_up_to_june_2024.csv', comment.char = "#")

# Assign decades and highlight years 
global_temp <- global_temp_raw %>% 
  select(1,6) %>% 
  rename(anomaly = ano_pi) %>% 
  mutate(month = ymd(month),
         year = year(month),
         new_date = month %m-% months(6),
         month_gg = month(month),
         decade = case_when(year < 1950 ~ "1940s",
                     year < 1960 & year >=1950 ~ "1950s",
                     year < 1970 & year >=1960 ~ "1960s",
                     year < 1980 & year >=1970 ~ "1970s",
                     year < 1990 & year >=1980 ~ "1980s",
                     year < 2000 & year >=1990 ~ "1990s",
                     year < 2010 & year >=2000 ~ "2000s",
                     year < 2020 & year >=2010 ~ "2010s",
                     year >=2020 ~ "2020s",
                     T ~ "other"),
highlight = case_when(year == 2023 ~ "2023",
                      year == 2024 ~ "2024",
                      year == 2016 ~ "2016",
                      year == 2015 ~ "2015",
                      T ~ "other"))

ggplot(global_temp, aes(month_gg, anomaly, group = year,colour = year)) +
  geom_line() +
  scale_x_continuous(breaks = 1:12, labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun","Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))+
  geom_text(data = last_month_data, aes(label = year), hjust = -0.2, size = 1) +
  scale_color_gradient2(low = "#4393c3", mid = "#f7f7f7", high = "#d6604d", midpoint = 1980)
#facet_wrap(~decade)