.. currentmodule:: oggm

OGGM needs various data files to run. We rely exclusively on open-access data that can be downloaded automatically for the user. This data needs to be extracted and pre-processed for each individual glacier. To avoid that everyone needs to repeat these steps, we have added a service that we like to call a "shop", allowing users to define a shopping list of data that they wish to add to their :ref:`glacierdir`. The data that can be included your glacier directories range from essentials for standard OGGM workflow to other data, like velocity provided as gridded_data for each respective glacier, that might be of interest to you.

This page describes the various products you will find in the shop. Don't forget to cite the original data sources of the data that you use (all details can be found the glacier directories, but details are also listed throughout the documentation).

The simplest way to run OGGM is to rely on :ref:`glacierdir` which have been prepared for you by the OGGM developers. Depending on your use case, you can start from various stages in the processing chain, map sizes, and model set-ups.

The directories have been generated with the standard parameters of the respective OGGM version (and a few alternative combinations). If you want to change some of these parameters, you may have to start a run from a lower processing level and re-run the processing tasks. Whether or not this is necessary depends on the stage of the workflow you'd like your computations to diverge from the defaults (this will become clearer as we provide examples workflow below).

To start from a pre-processed state, simply use the :py:func:`workflow.init_glacier_directories` function with the prepro_base_url, from_prepro_level and prepro_border keyword arguments set to the values of your choice. This will fetch the desired directories: there are more options to these, which we explain in detail below. If you like to start using the pre-processed directories right away, with out reading about all the different options and details first, you can go to the 10 minutes to… a preprocessed directory tutorial.

There are six available levels of pre-processing:

• Level 0: the lowest level, with directories containing the glacier outlines only.
• Level 1: directories now contain the glacier topography data as well.
• Level 2: at this stage, the glacier flowlines and their downstream lines are computed and ready to be used.
• Level 3: has the baseline climate timeseries (e.g. W5E5, CRU or ERA5) added to the directories. It also contains all necessary pre-processing tasks for a dynamical run, including the mass balance calibration, bed inversion, up to the :py:func:`tasks.init_present_time_glacier` task. These directories still contain all data that were necessary for the processing. Therefore they are large in size but also the most flexible since the processing chain can be re-run from them.
• Level 4: includes a historical simulation from the RGI date to the last possible date of the baseline climate file (currently January 1st 2020 at 00H for most datasets), stored with the file suffix _historical. Moreover, some configurations (called spinup) may include a simulation running a spinup from 1979 to the last possible date of the baseline climate file, stored with the file suffix _spinup_historical. This spinup attempts to conduct a dynamic melt factor calibration and a dynamic spinup matching the RGI area. If this fails, only a dynamic spinup is carried out. If this also fails, a fixed geometry spinup is conducted. To learn more about these different spinup types, check out :ref:`dynamic-spinup`.
• Level 5: is same as level 4 but with all intermediate output files removed. The strong advantage of level 5 directories is that their size is considerably reduced, at the cost that certain operations (like plotting on maps or re-running the bed inversion algorithm) are not possible anymore.

In practice, most users are going to use level 2, level 3 or level 5 files. To save space on our servers, level 4 data might be unavailable for some experiments (but are easily recovered if needed).

Here are some example use cases for glacier directories, and recommendations on which level to pick:

1. Running OGGM from GCM / RCM data with the default settings: start from level 5
2. Using OGGM's flowlines but running your own baseline climate, mass balance or ice thickness inversion models: start at level 2. When using an own module, for instance for the mass balance, one can still decide to use OGGM again further on in the workflow, for instance for the glacier dynamics. This is the workflow used by associated model PyGEM for example.
3. Run sensitivity experiments for the ice thickness inversion: start at level 3 (with climate data available) and re-run the inversion steps.

The size of the local glacier map is given in number of grid points outside the glacier boundaries. The larger the domain, the larger the glacier can become. Here is an example with Hintereisferner in the Alps:

.. ipython:: python
   :suppress:

    import os
    import matplotlib.pyplot as plt
    import numpy as np
    from oggm import cfg, tasks, workflow, graphics, DEFAULT_BASE_URL
    from oggm.utils import gettempdir

    cfg.initialize()
    cfg.PATHS['working_dir'] = os.path.join(gettempdir(), 'Docs_BorderSize')

base_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6'
base_url += '/L1-L2_files/elev_bands'
f, axs = plt.subplots(2, 2, figsize=(8, 6))
for ax, border in zip(np.array(axs).flatten(), [10, 80, 160, 240]):
    gdir = workflow.init_glacier_directories('RGI60-11.00897',
                                             from_prepro_level=1,
                                             prepro_base_url=base_url,
                                             prepro_border=border)
    graphics.plot_domain(gdir, ax=ax, title='Border: {}'.format(border),
                         add_colorbar=False,
                         lonlat_contours_kwargs={'add_tick_labels':False})

Users should choose the border parameter depending on the expected glacier growth in their simulations. For simulations into the 21^st century, a border value of 80 is sufficient. For runs including the Little Ice Age, a border value of 160 or 240 is recommended.

Users should be aware that the amount of data to download isn't small, especially for full directories at processing level 3 and 4. It is recommended to always pick the smallest border value suitable for your research question, and to start your runs from level 5 if possible. Here is an indicative table for the total amount of data with ERA5 centerlines for all 19 RGI regions:

L4 and L5 data are not available for border 10 (the domain is too small for the downstream lines).

Certain regions are much smaller than others of course. As an indication, with prepro level 3 and a map border of 160, the Alps are ~2.1G large, Greenland ~21G, and Iceland ~660M.

OGGM has several configurations and directories to choose from, and the list is getting larger regularly. Don't hesitate to ask us if you are unsure about which to use, or if you'd like to have more configurations to choose from!

To choose from a specific preprocessed configuration, use the prepro_base_url argument in your call to :py:func:`workflow.init_glacier_directories`, and set it to the url of your choice.

The recommended prepro_base_url for a standard OGGM run is:

.. ipython:: python
   :okwarning:

    from oggm import DEFAULT_BASE_URL
    DEFAULT_BASE_URL

This is the set-up that was used to generate the OGGM 1.6.1 standard projections (:doc:`download-projections`). The basic set-up is following:

• all default OGGM parameters
• :ref:`eb-flowlines`
• :ref:`climate-w5e5` reference climate
• "informed three-step" mass balance model calibration (see :ref:`mb-calib`)
• :ref:`dynamic-spinup` with dynamic melt factor calibration

There are however multiple options to choose from. Our tutorials showcase example of applications for some of them. One can explore cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.6 for more options. Here follows a brief guide through the folder structure:

• Step 1:

  L1_L2_files: here the directories with pre-processing level 1 and 2 can be found. L3_L5_files: here the directories with pre-processing level 3 to 5 can be found.

• Step 2: one can select a version of the directories (e.g. 2023.3)
• Step 3: select the flowline type, centerlines or elevation band flowlines (elev_bands), optionally with the extension of you choice in when using L1_L2_files.
• Step 4: This is only needed when taking the L3_L5_files route. The folder name starts with the name of the baseline climate (e.g. w5e5) that has been used, optionally followed by one or more extensions.

Explanation of the naming convention for the folder name extensions:

• _spinup indicates that the dynamic spin-up has been used for the calibration, if left out the calibration was done without the dynamic spin-up.
• w_data indicates that additional data has been added to the directories: ITS-LIVE, Millan et al. ice velocity product and the consensus ice thickness estimate (all described in more detail later).

Deprecated: version 1.4 and 1.5 directories (before v1.6)

All v1.4 directories are still working with OGGM v1.6: however, you may have to change the run parameters back to their previous values. We document them here:

A. Default

If not provided with a specific prepro_base_url argument, :py:func:`workflow.init_glacier_directories` will download the glacier directories from the default urls. Here is a summary of the default configuration:

• model parameters as of the oggm/params.cfg file at the published model version
• flowline glaciers computed from the geometrical centerlines (including tributaries)
• baseline climate from CRU (not available for Antarctica) using a global precipitation factor of 2.5 (path index: pcp2.5)
• baseline climate quality checked and corrected if needed with :py:func:`tasks.historical_climate_qc` with N=3. If the condition of at least 3 months of melt per year at the terminus and 3 months of accumulation at the glacier top is not reached, temperatures are shifted (path index: qc3).
• mass balance parameters calibrated with the standard OGGM procedure (path index: no_match). No calibration against geodetic MB (see options below for regional calibration).
• ice volume inversion calibrated to match the ice volume from [Farinotti_etal_2019]_ at the RGI region level, i.e. glacier estimates might differ. If not specified otherwise, it's also the precalibrated parameters that will be used for the dynamical run.
• frontal ablation by calving (at inversion and for the dynamical runs) is switched off

To see the code that generated these directories (for example if you want to make your own, visit :py:func:`cli.prepro_levels.run_prepro_levels` or this file on github).

The urls used by OGGM per default are in the following ftp servor:

https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/ :

• L1-L2_files/centerlines for level 1 and level 2
• L3-L5_files/CRU/centerlines/qc3/pcp2.5/no_match for level 3 to 5

If you are new to this, we recommend to explore these directories to familiarize yourself to their content. Of course, when provided with an url such as above, OGGM will know where to find the respective files automatically, but is is good to understand how they are structured. The summary folder (example) contains diagnostic files which can be useful as well.

B. Option: Geometrical centerlines or elevation band flowlines

The type of flowline to use (see :doc:`flowlines`) can be decided at level 2 already. Therefore, the two configurations available at level 2 from these urls:

• L1-L2_files/centerlines for centerlines
• L1-L2_files/elev_bands for elevation bands

The default pre-processing set-ups are also available with each of these flowline types. For example with CRU:

• L3-L5_files/CRU/centerlines/qc3/pcp2.5/no_match for centerlines
• L3-L5_files/CRU/elev_bands/qc3/pcp2.5/no_match for elevation bands

C. Option: Baseline climate data

For the two most important default configurations (CRU or ERA5 as baseline climate), we provide all levels for both the geometrical centerlines or the elevation band flowlines:

• L3-L5_files/CRU/centerlines/qc3/pcp2.5/no_match for CRU + centerlines
• L3-L5_files/CRU/elev_bands/qc3/pcp2.5/no_match for CRU + elevation bands
• L3-L5_files/ERA5/centerlines/qc3/pcp1.6/no_match for ERA5 + centerlines
• L3-L5_files/ERA5/elev_bands/qc3/pcp1.6/no_match for ERA5 + elevation bands

Note that the globally calibrated multiplicative precipitation factor (pcp) depends on the used baseline climate (e.g. pcp is 2.5 for CRU and 1.6 for ERA5). If you want to use another baseline climate, you have to calibrate the precipitation factor yourself. Please get in touch with us in that case!

D. Option: Mass balance calibration method

There are different mass balance calibration options available in the preprocessed directories:

• no_match: This is the default calibration option. For calibration, the direct glaciological WGMS data is used and the Marzeion et al., 2012 tstar method is applied to interpolate to glaciers without measurements. With this method, the geodetic estimates are not matched.
• match_geod: The default calibration with direct glaciological WGMS data is still applied on the glacier per glacier level, but on the regional level the epsilon (residual) is corrected to match the geodetic estimates from Hugonnet et al., 2021. For example:
  • L3-L5_files/CRU/elev_bands/qc3/pcp2.5/match_geod for CRU + elevation bands flowlines + matched on regional geodetic mass balances
  • L3-L5_files/ERA5/elev_bands/qc3/pcp1.6/match_geod for ERA5 + elevation bands flowlines + matched on regional geodetic mass balances
• match_geod_pergla: Only the per-glacier geodetic estimates from Hugonnet et al., 2021 (mean mass balance change between 2000 and 2020) are used for calibration. The mass balance model parameter \mu ^{*} of each glacier is calibrated to match individually the geodetic estimates (using :py:func:`oggm.core.climate.mustar_calibration_from_geodetic_mb`). For the preprocessed glacier directories, the allowed \mu ^{*} range is set to 20--600 (more in :doc:`mass-balance-monthly`). This option only works for elevation band flowlines at the moment. match_geod_pergla makes only sense without "external" climate quality check and correction (i.e. qc0) as this is already done internally. For example:
  • L3-L5_files/CRU/elev_bands/qc0/pcp2.5/match_geod_pergla for CRU + elevation bands flowlines + matched geodetic mass balances on individual glacier level
  • L3-L5_files/ERA5/elev_bands/qc0/pcp1.6/match_geod_pergla for ERA5 + elevation bands flowlines + matched geodetic mass balances on individual glacier level

Warning

make sure that you use the oggm_v1.6 directory for match_geod_pergla and match_geod_pergla_massredis! In the gdirs/oggm_v1.4 folder from the OGGM server, the match_geod_pergla preprocessed directories have a minor bug in the calibration (see this GitHUB issue). This bug is removed in the latest OGGM version and the corrected preprocessed glacier directories are inside the gdirs/oggm_v1.6 folder.

E. Further set-ups

Here is a list of other available configurations at the time of writing (explore the server for more!):

• L3-L5_files/CERA+ERA5/elev_bands/qc3/pcp1.6/no_match for CERA+ERA5 + elevation bands flowlines
• L3-L5_files/CERA+ERA5/elev_bands/qc3/pcp1.6/match_geod for CERA+ERA5 + elevation bands flowlines + matched on regional geodetic mass balances
• L3-L5_files/ERA5/elev_bands/qc3/pcp1.8/match_geod for ERA5 + elevation bands flowlines + matched on regional geodetic mass balances + precipitation factor 1.8
• L3-L5_files/CRU/elev_bands/qc0/pcp2.5/match_geod for CRU + elevation bands flowlines + matched on regional geodetic mass balances + no climate quality check
• L3-L5_files/CRU/elev_bands/qc0/pcp2.5/no_match for CRU + elevation bands flowlines + no climate quality check
• L3-L5_files/CRU/elev_bands/qc0/pcp2.5/match_geod_pergla_massredis for CRU + elevation bands flowlines + matched on regional geodetic mass balances + mass redistribution instead of SIA (see: :doc:`mass-redistribution`)
• L3-L5_files/ERA5/elev_bands/qc0/pcp1.6/match_geod_pergla_massredis for ERA5 + elevation bands flowlines + matched on regional geodetic mass balances + mass redistribution instead of SIA

Note: the additional set-ups might not always have all map sizes available. Please get in touch if you have interest in a specific set-up. Remember that per default, the climate quality check and correction (:py:func:`oggm.tasks.historical_climate_qc`) is applied (qc3). However, if the pre-processed directory has the path index "qc0", it was not applied (except for match_geod_pergla where it is applied internally).

F. Error analysis and further volume and mass change comparison for different pre-processed glacier directories

Overall, calibrating with ERA5 using a precipitation factor of 1.6 results in much less errors than CRU with pf=2.5. In addition, less errors occur for elevation bands and when using the match_geod_pergla option.

A more detailed analysis about the type, amount and relative failing glacier area (in total and per RGI region) can be found in this error analysis jupyter notebook.

If you are also interested in how the "common" non-failing glaciers differ in terms of historical volume change, total mass change and specific mass balance between different pre-processed glacier directories, you can check out this jupyter notebook.

Here follows a description of the additional data that can be added to the pre-processed glacier directories, by choosing the 'w_data' option or in code. See also: :ref:`apientitytasks`.

The ITS_LIVE ice velocity products can be downloaded and reprojected to the glacier directory (visit our tutorials if you are interested!).

Example of the reprojected ITS_LIVE products at Malaspina glacier

The data source used is https://its-live.jpl.nasa.gov/#data Currently the only data downloaded is the 120m composite for both (u, v) and their uncertainty. The composite is computed from the 1985 to 2018 average.

If you want more velocity products, feel free to open a new topic on the OGGM issue tracker!

The Farinotti et al., 2019 ice thickness products can be downloaded and reprojected to the glacier directory (visit our tutorials if you are interested!).

Example of the reprojected ice thickness products at Malaspina glacier

You can now add observations from the Glacier Thickness Database (GlaThiDa) to your glacier directory with:

from oggm.shop import glathida
glathida.glathida_to_gdir(gdir)

Checkout :py:func:`shop.glathida.glathida_to_gdir`.

Similarly, we provide data from the recent Millan et al. (2022) global study (visit our tutorials if you are interested!).

Cook et al. (2023) provided a new ice thickness dataset for the Alps. This is now also in the shop, with :py:func:`shop.cook23.cook23_to_gdir`.

from oggm.shop import cook23
cook23.cook23_to_gdir(gdir)

If you want to run your own workflow from A to Z, or if you would like to know which data are used in OGGM before being available in the pre-processed directories, read further for some more details!

Glacier outlines are obtained from the Randolph Glacier Inventory (RGI). We recommend to download them right away by opening a python interpreter and type:

from oggm import cfg, utils
cfg.initialize()
utils.get_rgi_intersects_dir()
utils.get_rgi_dir()

The RGI folders should now contain the glacier outlines in the shapefile format, a format widely used in GIS applications. These files can be read by several software (e.g. qgis), and OGGM can read them too.

The "RGI Intersects" shapefiles contain the locations of the ice divides (intersections between neighboring glaciers). OGGM can make use of them to determine which bed shape should be used (rectangular or parabolic). See the rgi tools documentation for more information about the intersects.

The following table summarizes the RGI attributes used by OGGM. It can be useful to refer to this list if you use your own glacier outlines with OGGM.

For Greenland and Antarctica peripheral glaciers, OGGM does not take into account the connectivity level between the Glaciers and the Ice sheets. We recommend to the users to think about this before they run the task: workflow.init_glacier_directories.

Comments

When creating a :ref:`glacierdir`, a suitable topographical data source is chosen automatically, depending on the glacier's location. OGGM supports a large number of datasets (almost all of the freely available ones, we hope). They are listed on the RGI-TOPO website.

The current default is to use the following datasets:

• NASADEM: 60°S-60°N
• COPDEM90: Global, with missing regions (islands, etc.)
• GIMP, REMA: Regional datasets
• TANDEM: Global, with artefacts / missing data
• MAPZEN: Global, when all other things failed

These data are chosen in the provided order. If a dataset is not available, the next on the list will be tested: if the tested dataset covers 75% of the glacier area, it is selected. In practice, NASADEM and COPDEM90 are sufficient for all but about 300 of the world's glaciers.

These data are downloaded only when needed (i.e. during an OGGM run) and they are stored in the dl_cache_dir directory. The gridded topography is then reprojected and resampled to the local glacier map. The local grid is defined on a Transverse Mercator projection centered over the glacier, and has a spatial resolution depending on the glacier size. The default in OGGM is to use the following rule:

\Delta x = d_1 \sqrt{S} + d_2

where \Delta x is the grid spatial resolution (in m), S the glacier area (in km^{2}) and d_1, d_2 some parameters (set to 14 and 10, respectively). If the chosen spatial resolution is larger than 200 m (S \ge 185 km^{2}) we clip it to this value.

.. ipython:: python
   :suppress:

    import json
    from oggm.utils import get_demo_file
    with open(get_demo_file('dem_sources.json'), 'r') as fr:
        DEM_SOURCE_INFO = json.loads(fr.read())
    # for k, v in DEM_SOURCE_INFO.items():
    #   print(v)

Important: when using these data sources for your OGGM runs, please refer to the original data provider of the data! OGGM adds a dem_source.txt file in each glacier directory specifying how to cite these data. We reproduce this information here.