A first projection in OGGM

A first projection in OGGM#

The OGGM workflow is best explained with an example. In the following, we will show you the OGGM fundamentals (Doc page: model structure and fundamentals) and bring you to realize your first OGGM glacier change projection!

First time set-up#

If you are using classroom or hub: nothing to do here, you are all setup! All data is on the online server already.

If you are using your own computer: before you start, make sure that you have set-up the input data configuration file at your wish.

cfg.initialize() and cfg.PARAMS#

An OGGM simulation script will always start with the following commands:

from oggm import cfg, utils
cfg.initialize(logging_level='WARNING')

2023-03-23 08:16:52: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.

2023-03-23 08:16:52: oggm.cfg: Multiprocessing switched OFF according to the parameter file.

2023-03-23 08:16:52: oggm.cfg: Multiprocessing: using all available processors (N=8)

A call to cfg.initialize() will read the default parameter file (or any user-provided file) and make them available to all other OGGM tools via the cfg.PARAMS dictionary. Here are some examples of these parameters:

cfg.PARAMS['melt_f'], cfg.PARAMS['ice_density'], cfg.PARAMS['continue_on_error']

(5.0, 900.0, False)

We set an additional parameter to True for this run:

# Add additional outputs for the maps below
cfg.PARAMS['store_fl_diagnostics'] = True

2023-03-23 08:16:52: oggm.cfg: PARAMS['store_fl_diagnostics'] changed from `False` to `True`.

More details: see here for the default parameter file and a description of their role and default value. Activity: what does store_fl_diagnostics do?

Download and process GCM data from ISIMIP3b (bias-corrected CMIP6)#

A typical use case for OGGM will be to use climate model output (here bias-corrected CMIP6 GCMs from ISIMIP3b). We use some of the files we mirrored in Bremen here, but you can use whichever you want. From ISIMIP3b, we have 5 GCMs and 3 SSPs on the cluster. You can find more information on the ISIMIP website.

Let’s download and process the data for one model and one scenario:

from oggm.shop import gcm_climate

# you can choose one of these 5 different GCMs:
# 'gfdl-esm4_r1i1p1f1', 'mpi-esm1-2-hr_r1i1p1f1', 'mri-esm2-0_r1i1p1f1' ("low sensitivity" models, within typical ranges from AR6)
# 'ipsl-cm6a-lr_r1i1p1f1', 'ukesm1-0-ll_r1i1p1f2' ("hotter" models, especially ukesm1-0-ll)
member = 'mri-esm2-0_r1i1p1f1'

# The sscenario. You can choose from 'ssp126', 'ssp370','ssp585'
ssp = 'ssp126'

workflow.execute_entity_task(gcm_climate.process_monthly_isimip_data, gdirs, 
                             ssp=ssp,  # SSP scenario -> you can choose another one later
                             member=member,  # ensemble member -> you can choose another one later
                             output_filesuffix=f'_ISIMIP3b_{member}_{ssp}',  # make the output file recognizable for later
                             );

2023-03-23 08:16:55: oggm.workflow: Execute entity tasks [process_monthly_isimip_data] on 1 glaciers

The advantage of using ISIMIP3b data is that they have been bias-corrected by the ISIMIP consortium. Since we are using the W5E5 dataset as baseline historical data in OGGM v1.6, there is no need for us to bias correct any further.

More detail: if you want to bias-correct yourself the projections and want to have a larger variety of GCMs, you can also use the original CMIP5 or CMIP6 GCMs: more information in a further tutorial.

OK, let’s have a look at the data, once again with xarray:

# and open the NetCDF file and load it in memory
with xr.open_dataset(gdir.get_filepath('gcm_data', filesuffix=f'_ISIMIP3b_{member}_{ssp}')) as dsgcm:
    dsgcm = dsgcm.load()

Activity: plot the annual GCM temperature overlayed to the historical data. Discuss the length of the timeseries and the bias correction.

# Your answer here

Now run the two other scenarios and plot their volume change#

Activity: similar to the run above, run the ssp370 and ssp585 scenarios. Compare them to the ssp126 scenario.

Tip: if you can’t go any further, you can have a look at the solution.

Recap#

OGGM is a “glacier centric” model, i.e. it operates on a list of glaciers
OGGM relies on saving files to disk during the workflow. Therefore, users must always specify a working directory while running OGGM
The working directory can be used to restart a run at a later stage
Put simply, this “restart workflow” is what OGGM uses to deliver data to its users. Pre-processed directories are online folders filled with glacier data that users can download
Once initialized locally, “glacier directories” allow OGGM to do what it does best: apply processing tasks to a list of glacier entities. These “entity tasks” get the data they need from disk, and write to disk once completed.
Users (or subsequent OGGM tasks) can use these data for new computations.

A first projection in OGGM

Contents

A first projection in OGGM#

First time set-up#

cfg.initialize() and cfg.PARAMS#

Workflow#

Working directory#

Define the glaciers for the run#

Glacier directories#

Accessing data in the preprocessed directories#

Working with Xarray#

OGGM tasks#

Download and process GCM data from ISIMIP3b (bias-corrected CMIP6)#

Run OGGM with ISIMIP3b data#

Now run the two other scenarios and plot their volume change#

Bonus: plot the glacier geometry#

Recap#