## This example shows a typical workflow for models2wux, the workhorse of
## the wux package. Going through this example step-by-step, you will
## retrieve NetCDF files of two CMIP5 simulations and aggregate them to
## an R data.frame for further analysis.
## I) Load wux functions and example datasets...
library("wux")
## II) You need to obtain the climate simulations first. You can get
## started with downloading some example CMIP5 NetCDF files from the
## ESGF visiting for example http://pcmdi9.llnl.gov or using the
## CMIP5fromESGF function. Here, we dowload two simulations "NorESM1-M" and
## "CanESM2" into your home directory "~/tmp/CMIP5/" which will be
## created automatically. You will need a valid account at any ESGF
## node for this function to run. See ?CMIP5fromESGF for further help.
CMIP5fromESGF(save.to = "~/tmp/CMIP5/",
models = c("NorESM1-M", "CanESM2"),
variables = c("tas"),
experiments= c("historical", "rcp85"))
## III) Specify those downloaded data for models2wux. models2wux needs
## to know where the data is stored on your HDD and needs to have access
## to certain metadata of the climate simulator, which you have to
## provide as well. This information is stored in a list, which should
## be saved as ONE file somewhere on your computer. We call this
## information "modelinput". You should share this
## file with you collegues using the same IT infrastructure to share
## synergies. You can create such a file based on the data downloade
## by "CMIP5fromESGF":
CMIP5toModelinput(filedir = "~/tmp/CMIP5",
save.to = "~/modelinput.R")
## This file then would look this:
data(modelinput_test)
## It specifies temperature and precipitation files for the two
## simulations "NorESM1-M" and "CanESM2" (RCP8.5), stored in
## "~/tmp/CMIP5/".
str(modelinput_test)
## IV) Next, you need to specify which simulations you want to read in
## with models2wux, what kind of statistics to calculate, what subregion
## to analyze, what time periods and seasons to define, and so on. This
## is done with a user input file, which cntains a list with all the
## necessary information. You typically use different userinput files
## for different analysis, whereas your modelinput should remain in ONE
## file which will be updated each time you obtain a new climate
## simulation. One example user input file, which reads in both
## simulations specified above for the Alpine domain and returns their
## projected climate change signal, could look like follows:
data(userinput_CMIP5_changesignal)
str(userinput_CMIP5_changesignal)
## alternatively following userinput returns a timeseries of both
## models, which only differs by the "time.series" tag and differently
## specified periods:
data(userinput_CMIP5_timeseries)
str(userinput_CMIP5_timeseries)
## V) At last you can run models2wux to obtain a data.frame of the
## specified climatic change features defined above:
climchange.df <- models2wux(userinput = userinput_CMIP5_changesignal,
modelinput = modelinput_test)
## A better practice is to safe both input files containing a named
## list each somewhere on your disk and pass the files directly to the
## models2wux function. If you had stored the two files in your home
## directory as e.g. "~/userinput.R" and "~/modelinput.R" you can call:
climchange.df <- models2wux(userinput = "~/userinput.R",
modelinput = "~/modelinput.R")
## if you downloaded the data correctly, you should obtain a data.frame:
climchange.df
## which should be identical to this example data.frame:
data(CMIP5_example_changesignal)
CMIP5_example_changesignal
## Instead of calculating the climate change signals, you can also
## generate time series of the two models aggregated over the Alpine
## domain, using a different user input file:
climchange.df <- models2wux(userinput = userinput_CMIP5_timeseries,
modelinput = modelinput_test)
## VI) Finally you can make all kind of analysis you are interested in,
## using either functions from wux or from any other R funtionality
summary(CMIP5_example_changesignal, parms = "delta.air_temperature")
## or plot timeseries as
require(lattice)
data(CMIP5_example_timeseries)
xyplot(air_temperature ~ year|season,
groups = acronym,
data = CMIP5_example_timeseries,
type = c("l", "g"),
main = "NorESM1-M and CanESM2 simulations over Alpine Region\nRCP 8.5 forcing")Run the code above in your browser using DataLab