Mass-balance¶
The mass-balance (MB) model implemented in OGGM is an extended version of the temperature index model presented by Marzeion et al., (2012). While the equation governing the mass-balance is that of a traditional temperature index model, our special approach to calibration requires that we spend some time describing it.
Climate data¶
The MB model implemented in OGGM needs monthly time series of temperature and precipitation. The current default is to download and use the CRU TS v3.24 data provided by the Climatic Research Unit of the University of East Anglia.
CRU (default)¶
If not specified otherwise, OGGM will automatically download and unpack the latest dataset from the CRU servers.
Warning
While the downloaded zip files are ~370mb in size, they are ~5.6Gb large after decompression!
The raw, coarse (0.5°) dataset is then downscaled to a higher resolution grid (CRU CL v2.0 at 10’ resolution) following the anomaly mapping approach described by Tim Mitchell in his CRU faq (Q25). Note that we don’t expect this downscaling to add any new information than already available at the original resolution, but this allows us to have an elevation-dependent dataset, from which we can compute the temperature at the elevation of the glacier.
User-provided dataset¶
You can provide any other dataset to OGGM by setting the climate_file
parameter in params.cfg
. See the HISTALP data file in the sample-data
folder for an example.
In [1]: example_plot_temp_ts() # the code for these examples is posted below
Elevation dependency¶
OGGM finally needs to compute the temperature and precipitation at the altitude of the glacier grid points. The default is to use a fixed gradient of -6.5K km \(^{-1}\) and no gradient for precipitation. However, OGGM implements a module which computes the local gradient by linear regression of the 9 surrounding grid points. This method requires that the near-surface temperature lapse-rates provided by the climate dataset are good (in most of the cases you should probably use a fixed gradient). The default config parameters are:
In [2]: cfg.PARAMS['temp_use_local_gradient'] # use the regression method?
Out[2]: False
In [3]: cfg.PARAMS['temp_default_gradient'] # constant gradiant