Using rOpenscmRunner, you can simulate the climate
response to emissions. To do this, it uses the python package openscm-runner. This
makes it possible to run several simple climate models using a unified
interface. At the moment, the models FaIR, MAGICC, and Cicero-SCM are
supported by openscm-runner, and therefore usable from
rOpenscmRunner.
openscm-runner only supports emissions-driven runs. This
means that you provide an emissions scenario specifying the emissions of
forcing agents over time, choose which model(s) you want to run with
which configuration(s) and which outputs you are interested in, and hand
this all over to rOpenscmRunner and you will get back the
simulated climate response(s), e.g. temperatures. The available response
variables depend on the chosen model and configuration.
For installation of rOpenscmRunner an additional
repository has to be added in R:
The additional repository can be made available permanently by adding
the line above to a file called .Rprofile stored in the
home folder of your system (Sys.glob("~") in R returns the
home directory).
After that the most recent version of the package can be installed
using install.packages:
Additionally, you will need a Python installation and the
openscm-runner Python package installed, as well as some
climate models. If you don’t have a Python installation so far, you can
find one at anaconda. Use
the package management of your Python distribution to install
openscm-runner. You can then specify which simple climate
models you want to also install. If you use pip, choose
what you need and run from the command line:
$ pip install --user openscm-runner[fair] # openscm-runner and everything needed to run the model FaIR
$ pip install --user openscm-runner[magicc] # openscm-runner and everything needed to integrate with the model MAGICC
$ pip install --user openscm-runner[models] # openscm-runner and all available modelsIf you are using conda instead of pip, or
want to know further details about the installation, check the openscm-runner
installation documentation.
If you want to run the MAGICC model, you also have to install it.
Download the most recent version from magicc.org. Extract it to
a location suitable to you, and copy the default configuration from the
run/defaults/ folder into the run/ folder.
FaIR and Cicero-SCM are installed via the Python package management
or included in openscm-runner, so it knows where to find
them. However, MAGICC is installed separately, so you have to tell
rOpenscmRunner where to find it using the
rOpenscmRunner::setup function. Additionally, you have to
also provide a path to a folder where MAGICC can store temporary
files:
rOpenscmRunner::setup(magiccExecutable7 = "/path/to/magicc/bin/magicc", # nolint: absolute_path_linter
magiccWorkerRootDir = "/path/to/temp/folder") # nolint: absolute_path_linterIf you are on windows, you have to give the full path to the
magicc.exe file in the MAGICC installation in the
bin/ folder. On Linux, give the full path to the
magicc file in the bin/ folder in the MAGICC
installation. The temporary folder must not be inside the
magicc folder where the magiccExecutable7 is.
You can simply use R’s standard temporary folder like this:
Using the same mechanism, you can also configure other parts of the MAGICC integration, but they are not required:
The default for the number of workers is to use as many workers as there are processors in your system. This should be a sensible default on laptops and workstations, but probably needs manual setup on shared machines like compute clusters.
All settings supplied via rOpenscmRunner::setup() are
remembered for the duration of your current R session. If you don’t want
to call rOpenscmRunner::setup() in each session, you can
also supply these settings via environment variables, check the
documentation of rOpenscmRunner::setup() for details.
To run simple climate model(s), use the
rOpenscmRunner::run() function. It takes the emission
scenario(s) as a data.frame in a format like the IAMC
format for scenarios. In particular, it is a data.frame
where each row is a time series and the columns are model
for the model that produced the emissions scenario,
scenario for a name of the scenario, region
for the area (most likely, World for simple climate
models), and variable and unit to specify what
is in the timeseries. The remaining columns are the time points, most
likely years. An example for a very short and incomplete emissions
scenario would be:
# create very minimal emissions scenario.
df <- data.frame(
model = c("rand", "rand"),
scenario = c("weirdEMI", "weirdEMI"),
region = c("World", "World"),
variable = c("Emissions|CO2", "Emissions|CH4"),
unit = c("Mt CO2 / yr", "Mt CH4 / yr"),
"2015" = c(9., 12.),
"2020" = c(10., 11.),
check.names = FALSE
)
df
#> model scenario region variable unit 2015 2020
#> 1 rand weirdEMI World Emissions|CO2 Mt CO2 / yr 9 10
#> 2 rand weirdEMI World Emissions|CH4 Mt CH4 / yr 12 11Note the usage of check.names = FALSE to be able to use
years as column names.
With the rOpenscmRunner::run() function, you can then
simulate all scenarios in the data frame at once:
# simulate the scenario(s) using MAGICC7 with default settings.
result <- rOpenscmRunner::run(climateModelsConfigs = list("MAGICC7" = NULL), scenarios = df)You will then find the simulated climate scenario in the same format
as the input scenarios in result$df and any metadata
generated by the climate model in result$metadata. Note
that if you run these examples as-is, you will get a lot of warnings and
errors from MAGICC because the example emissions scenario is so
incomplete. For serious work, you will obviously need a complete
emissions scenario.
You can also change configuration values in the models or even run
multiple configurations at once and request other outputs from the
model(s). As an example, we simulate the surface temperature and sea
level rise using MAGICC with two different non-standard climate
sensitivity settings. Note that we have to include
CORE_CLIMATESENSITIVITY in outConfig so that
we can distinguish the runs in the result data frame.
# output sea level rise and surface temperature for two different climate sensitivity settings.
res <- rOpenscmRunner::run(
climateModelsConfigs = list("MAGICC7" = list(list("CORE_CLIMATESENSITIVITY" = 3.),
list("CORE_CLIMATESENSITIVITY" = 4.))),
scenarios = df,
outConfig = list("MAGICC7" = list("CORE_CLIMATESENSITIVITY")),
outputVariables = list("Surface Temperature", "Sea Level Rise")
)The available configuration settings depend on the climate model and
have to be looked up in the respective documentation. The names of
outputVariables are standardized for the most common output
variables by openscm-runner according to the names used in
the Special Report: Global Warming
of 1,5°. You can find them all in the SR15 database
in the tab “Documentation” in the section “Variables” (available after
login, guest login works). Some climate models provide additional
non-standard outputs, if you want to request any of those, you have to
use the non-standard name of the climate model.
It is also possible to run multiple models at once. Expanding on the sea level rise example, we could also add a run of FaIR in the default configuration:
# output sea level rise and surface temperature for two different climate
# sensitivity settings for MAGICC and a default run of FaIR for comparison.
res <- rOpenscmRunner::run(
climateModelsConfigs = list("MAGICC7" = list(list("CORE_CLIMATESENSITIVITY" = 3.),
list("CORE_CLIMATESENSITIVITY" = 4.)),
"FaIR" = NULL),
scenarios = df,
outConfig = list("MAGICC7" = list("CORE_CLIMATESENSITIVITY")),
outputVariables = list("Surface Temperature", "Sea Level Rise")
)If you run into problems and errors, it might be easier to try and
run openscm-runner directly from Python to isolate your
problem. You will get more complete error messages from
openscm-runner when run directly from Python.