Question on modifying sea-ice restart fields in CESM2: remapping vsno to g37 points and consistency of enthalpy

J

jentus

H Y
New Member
Hello everyone,


I am planning to perform a set of ice–ocean coupled experiments based on CESM2. As part of the experiment design, I would like to modify the sea-ice restart file, specifically the snow volume field vsno.

My current situation is that the snow information I want to impose is available on a regular latitude–longitude grid, while the CESM2 sea-ice restart is on the model’s native sea-ice/ocean grid. Therefore, my first question is:

  1. What would be a recommended method to remap a lat–lon gridded field onto the CESM2 sea-ice grid (the 37-point/gx-type native grid used by the restart file)?
    I would especially appreciate suggestions on tools or workflows that are commonly used within the CESM community, for example ESMF/ESMPy, NCO, ncremap, SCRIP, or any existing CESM remapping utilities.
In addition, I understand that simply changing vsno may not be physically self-consistent. My understanding is that the snow/ice enthalpy field q should likely be modified at the same time, but I am not sure what standard or constraint should be followed.

So my second question is:
  1. If vsno is modified in the sea-ice restart, how should the enthalpy variable q be adjusted consistently?
    For example, should q be recalculated based on snow temperature, energy conservation, or some internal thermodynamic relationship used by CICE in CESM2? Any guidance on the correct physical or model-consistent treatment would be very helpful.

More generally, if anyone has experience with editing CICE/CESM2 restart variables for snow initialization, I would be very grateful for any advice on best practices or potential pitfalls.


Thank you very much for your help.
 
D

dbailey

CSEG and Liaisons
Staff member
The best way to do this is similar to what is done in ice_prescribed_mod.F90 under the drivers directory. This module reads in ice concentration and computes the rest of the state based on the concentration. The ESMF tools are the best way to go here. They are in NCL and also in python. There is an example of this going from a gx1 sea ice grid to a 1x1d grid in the CESM Tutorial here. Exercise 4.

 
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