getCRUCLdata.R

# Script Description ----------------------
# This script implements a basic workflow to extract the CRU CL v.2.0 climate datasets
# using the getCRUCLdata R package (Sparks 2019). More information about the getCRUCLdata
# R package found here: https://cran.r-project.org/web/packages/getCRUCLdata/index.html.
#
# Script by:      Jose Don T. De Alban
# Date created:   29 Mar 2020
# Date modified:  20 Dec 2021


# Set Working Directory -------------------
setwd("/Users/dondealban/Dropbox/Research/getcrucldata/")

# Load Libraries --------------------------
library(getCRUCLdata)
library(ggplot2)
library(raster)
library(viridis)

# Read Input Data -------------------------
# The get_CRU_df() function automates the download process and creates tidy dataframes of
# the CRU CL v.2.0 climatology elements.
CRU_data <- get_CRU_df(pre = TRUE,    # precipitation (mm/month)
                       pre_cv = TRUE, # cv of precipitation (%)
                       rd0 = TRUE,    # wet-days (number days with >0.1 millimetres rain per month)
                       tmp = TRUE,    # temperature (degrees Celsius)
                       dtr = TRUE,    # mean diurnal temperature range (degrees Celsius)
                       reh = TRUE,    # relative humidity
                       tmn = TRUE,    # minimum temperature values (degrees Celsius)
                       tmx = TRUE,    # maximum temperature values (degrees Celsius)
                       sunp = TRUE,   # percent of maximum possible sunshine (% of day length)
                       frs = TRUE,    # ground-frost records (number of days with ground-frost per month)
                       wnd = TRUE,    # 10m wind speed (m/s)
                       elv = TRUE)    # elevation (and convert to m from km)

# Extract Specific Climate Elements -------
dfTMP <- get_CRU_df(tmp = TRUE)
dfSUN <- get_CRU_df(sun = TRUE)
dfFRS <- get_CRU_df(frs = TRUE)
dfWND <- get_CRU_df(wnd = TRUE)
dfRD0 <- get_CRU_df(rd0 = TRUE)

# Plot Monthly Maps of Climate Elements ---

# Temperature
ggplot(data = dfTMP, aes(x = lon, y = lat)) +
  geom_raster(aes(fill = tmp)) +
  scale_fill_viridis(option = "magma") +
  coord_quickmap() +
  ggtitle("Global Mean Monthly Temperatures 1961-1990") +
  facet_wrap(~ month, nrow = 4)

# Sunshine Duration
ggplot(data = dfSUN, aes(x = lon, y = lat)) +
  geom_raster(aes(fill = sun)) +
  scale_fill_viridis(option = "inferno") +
  coord_quickmap() +
  ggtitle("Global Percentage of Maximum Possible Sunshine 1961-1990") +
  facet_wrap(~ month, nrow = 4)

# Ground Frost Frequency
ggplot(data = dfFRS, aes(x = lon, y = lat)) +
  geom_raster(aes(fill = frs)) +
  scale_fill_viridis(option = "plasma") +
  coord_quickmap() +
  ggtitle("Global Monthly Ground Frost Frequency 1961-1990") +
  facet_wrap(~ month, nrow = 4)

# Wind Speed
ggplot(data = dfWND, aes(x = lon, y = lat)) +
  geom_raster(aes(fill = wnd)) +
  scale_fill_viridis(option = "viridis") +
  coord_quickmap() +
  ggtitle("Global Monthly 10m Wind Speed 1961-1990") +
  facet_wrap(~ month, nrow = 4)

# Relative Humidity
ggplot(data = dfRD0, aes(x = lon, y = lat)) +
  geom_raster(aes(fill = rd0)) +
  scale_fill_viridis(option = "cividis") +
  coord_quickmap() +
  ggtitle("Global Monthly Relative Humidity 1961-1990") +
  facet_wrap(~ month, nrow = 4)

# Create Violin Plots of Climate Elements -

# Temperature
ggplot(data = dfTMP, aes(x = month, y = tmp)) +
  geom_violin() +
  ylab("Temperature (˚C)") +
  labs(title = "Global Monthly Mean Land Surface Temperatures, 1960-1991",
       subtitle = "Excludes Antarctica")

ggplot(data = dfSUN, aes(x = month, y = sun)) +
  geom_violin() +
  ylab("Sunshine Duration (%)") +
  labs(title = "Global Percentage of Maximum Possible Sunshine, 1960-1991",
       subtitle = "Excludes Antarctica")

ggplot(data = dfFRS, aes(x = month, y = frs)) +
  geom_violin() +
  ylab("Number of Days per Month") +
  labs(title = "Global Monthly Ground Frost Frequency, 1960-1991",
       subtitle = "Excludes Antarctica")

ggplot(data = dfWND, aes(x = month, y = wnd)) +
  geom_violin() +
  ylab("10m Wind Speed (m/s)") +
  labs(title = "Global Monthly 10m Wind Speed, 1960-1991",
       subtitle = "Excludes Antarctica")

ggplot(data = dfRD0, aes(x = month, y = rd0)) +
  geom_violin() +
  ylab("Relative Humidity (%)") +
  labs(title = "Global Relative Humidity, 1960-1991",
       subtitle = "Excludes Antarctica")

# Create Raster Stacks --------------------
# The get_CRU_stack() function automates the download process and creates a raster stack
# object of the CRU CL v. 2.0 climatology elements.
CRU_stack <- get_CRU_stack(pre = TRUE,
                           pre_cv = TRUE,
                           rd0 = TRUE,
                           tmp = TRUE,
                           dtr = TRUE,
                           reh = TRUE,
                           tmn = TRUE,
                           tmx = TRUE,
                           sunp = TRUE,
                           frs = TRUE,
                           wnd = TRUE,
                           elv = TRUE)

# Write Raster Stack Objects --------------
writeRaster(CRU_stack$frs,
            filename = "frs.tif",
            bylayer = TRUE,
            format = "GTiff"
            )
writeRaster(CRU_stack$tmp,
            filename = "tmp.tif",
            bylayer = TRUE,
            format = "GTiff"
            )
writeRaster(CRU_stack$wnd,
            filename = "wnd.tif",
            bylayer = TRUE,
            format = "GTiff"
            )
writeRaster(CRU_stack$sun,
            filename = "sun.tif",
            bylayer = TRUE,
            format = "GTiff"
            )
writeRaster(CRU_stack$rd0,
            filename = "rd0.tif",
            bylayer = TRUE,
            format = "GTiff"
            )
writeRaster(CRU_stack$elv,
            filename = "elv.tif",
            bylayer = TRUE,
            format = "GTiff"
            )