Get Global climate and weather data with 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 visulaize the layer

tmax %>% 
  raster::plot()

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

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)))

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")