ISMIP-HOM test suite exercise
In this exercise, we will test out Glimmer/CISM's higher-order stress balance subroutines by running the model through a few of the ISMIP-HOM test suite problems. The tests we'll run are for 3d models, so the domain and boundary conditions vary in the x and y directions (i.e. in map plane). For test A, the topography varies periodically in x and y, and for test C, the basal traction varies periodically in x and y. While the amplitude of the variations is the same for all tests, the wavelength is decreased by a factor of two for each successive test. For λ=160 km, the velocities solutions essentially look like that from a shallow ice model. Halving λ to 80 km, then to 40, 20, 10, and finally 5 km, the higher-order components of the stress balance become successively more important to the velocity solution. Figures 1 and 2 below shows relevant input data for each of the two experiments for λ = 80km. Here, in the interest of time, we will only run tests for the first three wavelengths in the series (160, 80, and 40 km).
Figure 1: ISMIP-HOM test A input (periodic basal roughness with no sliding); ice thickness, basal topography, and surface elevation. The basal boundary condition is no slip and the lateral boundary conditions are periodic velocities in x and y.
Figure 2: ISMIP-HOM test C input (sliding according to periodic basal traction); ice thickness,bBetasquared, and surface elevation. Sliding takes place along the basal boundary according to a "betasquared" (traction) type sliding law. The lateral boundary conditions are periodic velocities in x and y (NOTE: In this experiment we have a slab of constant thickness on an inclined plane with only the sliding properties changing along/across the domain. Also, the surface slope is ~10x smaller than for experiment A).
Running the test cases
To set up the experiments, we will use some configuration files and python scripts developed by Tim Bocek and Jesse Johnson (also, see this link). These set the correct flags, so that Glimmer/CISM calls the necessary subroutines, and construct the necessary input netCDF files.
First, we need to change into the correct directory where the test scripts and configuration files live. Assuming that you are starting in the directory from the directory glimmer/src/fortran/, type
cd ../tests/ISMIP-HOM/; ls -l
to change into the appropriate directory and list its contents. The files with a .config extension are read by the appropriate python scripts to construct the appropriate fields for the input netCDF file. The .config files are also read by the model at run time, as they specify the values for various flags (including calls to the HO subroutines rather than the shallow ice dynamics routines). The files with a .py extension are the relevant python scripts.
Let's set up test cases A and C for a domain length of 160 km. First check the configuration file to make sure that the domain length, number of grid spaces, and the grid spacing give the correct input values.
[grid] upn = 21 ewn = 51 nsn = 51 dew = 3200 dns = 3200
for the grid variables. Note that 51 x 3200 = 16.32 km, so that our domain will actually have one 3.2 km grid space extra in each of the x and y directions. This is necessary in order to implement the periodic boundary conditions at the lateral domain boundaries. If we now type
python ismip_hom_a.py ishom.a.config
we will generate the necessary netCDF input file for the experiment. Using NCVIEW, you can look at the input data fields and make sure that they are the correct lateral dimensions. To run the model using these input data, we need to execute simple_glide. If that file is not in your path you can copy it into the test directory as follows
cp ../../srf/fortran/simple_glide ./
and then execute it by typing
You will be prompted for the relevant configuration file, which is of course ishom.a.config. Another way to do this would have been to use the linux/unix pipe command
echo ishom.a.config | ./simple_glide - or - echo ishom.a.config | simple_glide (the latter if "simple_glide" is already in your path)
Either way, after responding to the prompt, you will see model output that looks something like this:
Running Payne/Price higher-order dynamics solver iter # uvel resid vvel resid target resid 2 1.00000 1.00000 0.100000E-04 3 0.143388 0.215689E-02 0.100000E-04 4 0.392197E-01 0.902012E-03 0.100000E-04 5 0.655156E-01 0.745786E-03 0.100000E-04 6 0.503367E-01 0.465037E-03 0.100000E-04 7 0.344782E-02 0.329053E-03 0.100000E-04 8 0.100065E-01 0.256138E-03 0.100000E-04 9 0.163779E-01 0.190435E-03 0.100000E-04 10 0.866196E-02 0.136968E-03 0.100000E-04 11 0.549490E-02 0.104118E-03 0.100000E-04 12 0.546429E-02 0.798739E-04 0.100000E-04
At this point, you know that the higher-order dynamics routine is working on a solution. The 1st column tells you which "outer loop" iteration you are on (that is, iteration on the effective viscosity - the "inner loop" iteration is the conjugate gradient iterative solution to the matrix inversion, and that output is normally suppressed). The 2nd and 3rd columns display the x (uvel) and y (vvel) residuals (the normalized, maximum change in the velocity field between the current and previous iterations) and the last column shows the target residual, at which point the solution is considered to be converged.
When the model stops iterating it will create an output netCDF file that we can evaluate. In this case, the file name is ishom.a.out.nc.
Plotting model output
We can do a quick evaluation of ishom.a.out.nc using NCVIEW or some other netCDF file viewing utility. However, what we really want to do is compare our model solutions with the ISMIP-HOM benchmark solutions, so we can see how are model is doing relative to other models that took place in the benchmark exercise (see Pattyn et. al (2008) for a detailed discussion of the results). To do that, we'll use some of handy python test-suite scripts we mentioned above.
First, we need to generate a text file of output date, from our model result, which will be compared with other model results from the benchmarking study. To do that, we type
./formatData.py a ishom.a.out.nc glm1a160.txt
The script formatData.py reads the netCDF output file and generates the text file glm1a160.txt (the "...a160..." denotes test A for a domain length of 160 km). We then type
./createVisuals.py -a --160km
which will use some python modules to make a nice Matlab style figure, that should look something like this
Figure 3: Higher-order model output (solid line) for ISMIP-HOM test A with a domain length of 160 km, compared to the same output from other models participating in the benchmarking study.
Now, do the same set of steps but decrease the domain wavelength to 80 and then 40 km. To make it easy for you, some grid parameters that work for this are
[grid] upn = 21 ewn = 51 nsn = 51 dew = 1600 dns = 1600
for the 80 km test and
[grid] upn = 21 ewn = 51 nsn = 51 dew = 800 dns = 800
for the 40 km test. Output files for these tests will be generated in the same way as above,
./formatData.py a ishom.a.out.nc glm1a080.txt - and - ./formatData.py a ishom.a.out.nc glm1a040.txt
To plot all of the three ouputs for test A on one plot, use
./createVisuals.py -a --160km --80km --40km
which should give you something that looks similar to
Figure 4: Higher-order model output (solid line) for ISMIP-HOM test A with domain lengths of 160, 80, and 40 km, compared to the same output from other models participating in the benchmarking study.
Now go through the same set of steps for test case C (again, with wavelengths of 160, 80, and 40 km).
Ask some clever questions about what one learns from this exercise ...
Some other things to try if you have time ...
Have them play w/ the grid spacing to see how that affects results?
Can we get a 0-order solution for these as well?