## 15.3 Overview of the microclimate data

The compressed files referred to above include microclimate data for Australia and North America, and comprises a subset of the the global microclimate data set ‘microclim’ published by Kearney et al. (2014) and discussed in lecture. You can obtain the full data set at this site.

The full data set includes estimates of 24hr cycles of each variable for the entire globe for a typical (average) day for each month of the year, for six shade levels and three substrate types (rock, sand and soil). It is a very large data set (415 gigabytes!) and so, for the purposes of this prac, we have cropped the data set to Australia and North America (using the crop function of the raster package). Moreover, we have only supplied data for the months of January and July (i.e. the hottest/coldest times of year, depending on the hemisphere), for zero or full shade, and for the soil substrate type only.

To decompress the files into your working directory, you can use the unzip function of R where you specify a file to unzip first (without a path as we are assuming it is in our working directory) and a location to unzip to (here putting it within the current folder in a subfolder called ‘microclim_Aust’).

unzip('microclim_Aust.zip', exdir = "./microclim_Aust")

Have a look at the folder ’microclim_Aust. Inside you will see folders for 10 different variables. These are all the variables we need to compute a heat and water budget for an organism.

zenith_angle

wind_speed_ms_10m

wind_speed_ms_1cm

air_temperature_degC_120cm

air_temperature_degC_1cm

relative_humidity_pct_120cm

relative_humidity_pct_1cm

sky_temperature_degC

substrate_temperature_degC

As discussed in Kearney et al. (2014), these microclimate layers have been computed with the NicheMapR microclimate model. We will look at each of these layers in turn.