Pitfalls and limitations¶
As the OGGM project is gaining visibility and momentum, we also see an increase of potential misuse or misunderstandings about what OGGM can and cannot do. Hefer to our FAQ and Troubleshooting for a general introduction. Here, we discuss specific pitfalls in more details.
The default ice dynamics parameter “Glen A” is not calibrated¶
Out-of-the box OGGM will uses fixed values for the creep parameter \(A\) and the sliding parameter \(f_s\):
In : from oggm import cfg In : cfg.initialize() In : cfg.PARAMS['glen_a'] Out: 2.4e-24 In : cfg.PARAMS['fs'] Out: 0.0
That is, \(A\) is set to the standard value for temperate ice as given in [Cuffey_Paterson_2010], and sliding is set to zero. While these values are reasonable, they are unlikely to be the ones yielding the best results at the global scale, and even more unlikely at regional or local scales. In particular, in the absence of sliding parameter, it is recommended to set \(A\) to a larger value to compensate for this missing process.
There is a way to calibrate \(A\) for the ice thickness inversion procedure based on observations of ice thickness. This does not mean that this \(A\) can be applied unchanged to the forward model, unfortunately. At the global scale, a value in the range of [1.1-1.5] times the default value gives estimates close to [Farinotti_etal_2019]. At regional scale, these values can differ, with a value closer to a factor 3 e.g. for the Alps. Note that this depends on other variables as well, such as precipitation estimates (which affect the mass turnover).
Finally, note that a change in \(A\) has a very strong influence for values close to the default value, but this influences reduces to the power of 1/5 for large values of A (in other worlds, there is a big difference between values of 1 to 1.3 times the default \(A\), but a comparatively small difference for values between 3 to 5 times the default \(A\)). This is best shown by this figure from [Maussion_etal_2019]:
How to choose the “best A” for my application? Sorry, but we don’t know yet. We are working on it though!
The numerical model in OGGM is numerically unstable in some conditions¶
See this github issue for an ongoing discussion. We will post and update here soon!
The mass-balance model of OGGM is not calibrated with remote sensing data¶
Currently, the values for the mass-balance parameters such as the temperature sensitivity, the precipitation correction factor, etc. are calibrated based on the in-situ measurements provided by the WGMS (traditional mass-balance data). For more information about the procedure, see [Maussion_etal_2019] and our performance monitoring website.
We are looking for people to help us with this task: join us! See e.g. OEP-0003: Surface mass-balance enhancements for a discussion document.
|[Farinotti_etal_2019]||Farinotti, D., Huss, M., Fürst, J. J., Landmann, J., Machguth, H., Maussion, F. and Pandit, A.: A consensus estimate for the ice thickness distribution of all glaciers on Earth, Nat. Geosci., 12(3), 168–173, doi:10.1038/s41561-019-0300-3, 2019.|
|[Maussion_etal_2019]||(1, 2) Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B., Rothenpieler, T., Vlug, A., Wild, C. T. and Marzeion, B.: The Open Global Glacier Model (OGGM) v1.1, Geosci. Model Dev., 12(3), 909–931, doi:10.5194/gmd-12-909-2019, 2019.|
|[Huss_Hock_2015]||Huss, M. and Hock, R.: A new model for global glacier change and sea-level rise, Front. Earth Sci., 3(September), 1–22, doi:10.3389/feart.2015.00054, 2015.|
|[Zekollari_etal_2019]||Zekollari, H., Huss, M. and Farinotti, D.: Modelling the future evolution of glaciers in the European Alps under the EURO-CORDEX RCM ensemble, Cryosphere, 13(4), 1125–1146, doi:10.5194/tc-13-1125-2019, 2019.|