Access Global Climate and Weather Data in R

Maximum temperature

tmax = raster::getData(name = 'worldclim', var = 'tmax', res = 10)

We check the class of the dataset with class function

tmax %>% class()

[1] "RasterStack"
attr(,"package")
[1] "raster"

We get the information that is rasterStack—a collection of raster layer with the same spatial extent and resolution. Let us use dim function to check how many raster layers are there in this rasterstack.

tmax %>% dim()

[1]  900 2160   12

We see that there are twelve layers—each layer represent a monthly climatological value of maximum temperature from January to December.

tmax

class      : RasterStack 
dimensions : 900, 2160, 1944000, 12  (nrow, ncol, ncell, nlayers)
resolution : 0.1666667, 0.1666667  (x, y)
extent     : -180, 180, -60, 90  (xmin, xmax, ymin, ymax)
crs        : +proj=longlat +datum=WGS84 +no_defs 
names      : tmax1, tmax2, tmax3, tmax4, tmax5, tmax6, tmax7, tmax8, tmax9, tmax10, tmax11, tmax12 
min values :  -478,  -421,  -422,  -335,  -190,   -94,   -59,   -76,  -153,   -265,   -373,   -452 
max values :   418,   414,   420,   429,   441,   467,   489,   474,   441,    401,    401,    413 

We have noticed that the layer names are not descriptive, we know from our experience that the layers represent months, start from January all the way to December. Therefore, we need to change the layers name to a more descriptive variable names.

names(tmax) = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
tmax

We notice that the temperature values are higher or below the range of temperature we know. This is because the values were multiplied by 10 to reduce the file size, we therefore, need to remove the scale factor by dividing by ten

tmax = tmax/10

We then use a plot function from raster to visualize the layer

tmax %>% 
  raster::plot()

Though the base plot is sharp and quick to draw this large dataset, but we are satisfied with default plotting settings. We can customize the plot and make them appeal to the eye, but we switch to ggplot2 package (Wickham 2016), which does have tons of tools for customization of publication quality plots. Unfortunate, ggplot2 require tidy data in data frame format instead of the raster. Therefore, the first thing is to convert raster to a data frame, the chunk bleow highlihgth.

tmax.tb = tmax %>%
  raster::as.data.frame(xy = TRUE) %>%
  pivot_longer(cols = 3:14, values_to = "tmax", names_to = "month")

tmax.tb

# A tibble: 23,328,000 x 4
       x     y month   tmax
   <dbl> <dbl> <chr>  <dbl>
 1 -180.  89.9 tmax1     NA
 2 -180.  89.9 tmax2     NA
 3 -180.  89.9 tmax3     NA
 4 -180.  89.9 tmax4     NA
 5 -180.  89.9 tmax5     NA
 6 -180.  89.9 tmax6     NA
 7 -180.  89.9 tmax7     NA
 8 -180.  89.9 tmax8     NA
 9 -180.  89.9 tmax9     NA
10 -180.  89.9 tmax10    NA
# ... with 23,327,990 more rows

Once we have the data converted to data frame, we can use the power of ggplot2 to visualize land surface maximum temperature at global level by months as highlighted below.

ggplot() +
  geom_raster(data = tmax.tb, aes(x = x, y = y, fill = tmax)) +
  facet_wrap(~month)+
  scale_fill_gradientn(colours = oce::oce.colors9A(120), na.value = NA)+
  theme(panel.background = element_blank(), 
        strip.background = element_blank(), 
        axis.title = element_blank())+
  scale_x_continuous(breaks = seq(-100,100,100), labels = metR::LonLabel(seq(-100,100,100)))+
  scale_y_continuous(breaks = seq(-50,50,50), labels = metR::LatLabel(seq(-50,50,50)))

Figure 1: Global land surface temperature by months

We notice that although facet_wrap function plotted figure 1 well and the variation in temperature is visible, but the order of months follow the alphabetic and rather month’s ascending order. We need to tweek around the order. As a first step, we have to reorder the levels of our grouping variable using the level function:

tmax.tb.order = tmax.tb %>% 
  mutate(group = factor(x = month, levels = month.abb))

Now, we can draw our plot again:

ggplot() +
  geom_raster(data = tmax.tb.order, aes(x = x, y = y, fill = tmax)) +
  facet_wrap(~group)+
  scale_fill_gradientn(colours = oce::oce.colors9A(120), na.value = NA)+
  theme(panel.background = element_blank(), 
        strip.background = element_blank(), 
        axis.title = element_blank())+
  scale_x_continuous(breaks = seq(-100,100,100), labels = metR::LonLabel(seq(-100,100,100)))+
  scale_y_continuous(breaks = seq(-50,50,50), labels = metR::LatLabel(seq(-50,50,50)))

Figure 2: Global land surface temperature by months

Figure 2 shows the output of the reordered facet plot created with the ggplot2.

tmax %>% 
  raster::writeRaster(filename = "wcData/tmax_hist.tif")

Minitmum Temperature

tmin = raster::getData(name = 'worldclim', 
                       var = 'tmin', 
                       res = 10)

Precipitation

prec = raster::getData(name = 'worldclim', 
                       var = 'prec', 
                       res = 10)

Bioclimatic variables

Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). The list of all derived bio–climatic variable is shown in table 1.

bio = raster::getData(name = 'worldclim', var = 'bio', res = 10)

bio[[3]] %>% raster::plot(main = "Isothermality ")

bio %>% 
  raster::writeRaster(filename = "wcData/bio_hist.tif")