multimelt 0.5

Regroupment of the main existing ice shelf basal melt parameterisations

docs

Multimelt contains a large amount of existing basal melt parameterisations for Antarctic ice shelves. The functions are written as such that the main input needed are temperature and salinity profiles in front of the ice shelf, the rest happens in the functions.

Also, multimelt contains functions to create masks of the Antarctic continent and the different ice shelves, and other geographical parameters on a circum-Antarctic scale, for input on a stereographic grid.

To use this package, you can now go through pypi

pip install multimelt

or clone the repository and do

pip install .

It contains four example python notebooks:

• prepare_mask_example.ipynb : a script using geometric circum-Antarctic input to produce masks of the ice shelves, and the needed box characteristics and plume characteristics

• conversion_CTtoPT_SAtoSP.ipynb : a script to convert 3D fields of conservative temperature to potential temperature and 3D fields of absolute salinity to practical salinity

• T_S_profiles_per_ice_shelf.ipynb : a script to created averaged temperature and salinity profiles in front of the different ice shelves

• compute_melt_example.ipynb : a script showing how to apply the melting functions

The documentation can be found here: http://multimelt.readthedocs.io/

Don’t hesitate to contact me if any questions arise: [e-mail]

How to cite multimelt

The detailed description of the application of the functions in multimelt is found in Burgard et al., 2022 and should therefore, when used, be cited as follows:

Burgard, C., Jourdain, N. C., Reese, R., Jenkins, A., and Mathiot, P. (2022): An assessment of basal melt parameterisations for Antarctic ice shelves, The Cryosphere, https://doi.org/10.5194/tc-16-4931-2022.

multimelt now also includes DeepMelt, the neural network introduced in the following:

Burgard, C., Jourdain, N. C., Mathiot, P., Smith, R. S., Schäfer, R., Caillet, J., Finn, T.S., and Johnson, J.E. (2023): Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks. Journal of Advances in Modeling Earth Systems, 15, e2023MS003829. https://doi.org/10.1029/2023MS003829