Display glacier area and thickness changes on a grid

from oggm import cfg
from oggm import tasks, utils, workflow, graphics, DEFAULT_BASE_URL
import salem
import xarray as xr
import numpy as np
import glob
import os
import matplotlib.pyplot as plt

This notebook proposes a method for redistributing glacier ice that has been simulated along the flowline after a glacier retreat simulation. Extrapolating the glacier ice onto a map involves certain assumptions and trade-offs. Depending on the purpose, different choices may be preferred. For example, higher resolution may be desired for visualization compared to using the output for a hydrological model. It is possible to add different options to the final function to allow users to select the option that best suits their needs.

This notebook demonstrates the redistribution process using a single glacier. Its purpose is to initiate further discussion before incorporating it into the main OGGM code base (currently, it is in the sandbox).

Pick a glacier

# Initialize OGGM and set up the default run parameters
cfg.initialize(logging_level='WARNING')

# Local working directory (where OGGM will write its output)
# WORKING_DIR = utils.gettempdir('OGGM_distr4')
cfg.PATHS['working_dir'] = utils.get_temp_dir('OGGM_distributed', reset=True)

2024-08-25 21:08:52: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2024-08-25 21:08:52: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2024-08-25 21:08:52: oggm.cfg: Multiprocessing: using all available processors (N=4)

rgi_ids = ['RGI60-11.01450',  # Aletsch
           'RGI60-11.01478']  # Fieschergletscher
gdirs = workflow.init_glacier_directories(rgi_ids, prepro_base_url=DEFAULT_BASE_URL, from_prepro_level=4, prepro_border=80)

2024-08-25 21:08:54: oggm.workflow: init_glacier_directories from prepro level 4 on 2 glaciers.
2024-08-25 21:08:54: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 2 glaciers

Experiment: a random warming simulation

Here we use a random climate, but you can use any GCM, as long as glaciers are getting smaller, not bigger!

# Do a random run with a bit of warming
workflow.execute_entity_task(
    tasks.run_random_climate,
    gdirs,
    ys=2020, ye=2100,  # Although the simulation is idealised, lets use real dates for the animation
    y0=2009, halfsize=10,  # Random climate of 1999-2019
    seed=1,  # Random number generator seed 
    temperature_bias=1.5,  # additional warming - change for other scenarios
    store_fl_diagnostics=True,  # important! This will be needed for the redistribution
    init_model_filesuffix='_spinup_historical',  # start from the spinup run
    output_filesuffix='_random_s1',  # optional - here I just want to make things explicit as to which run we are using afterwards
);

2024-08-25 21:08:54: oggm.workflow: Execute entity tasks [run_random_climate] on 2 glaciers

Redistribute: preprocessing

The required tasks can be found in the distribute_2d module of the sandbox:

from oggm.sandbox import distribute_2d

# This is to add a new topography to the file (smoothed differently)
workflow.execute_entity_task(distribute_2d.add_smoothed_glacier_topo, gdirs)
# This is to get the bed map at the start of the simulation
workflow.execute_entity_task(tasks.distribute_thickness_per_altitude, gdirs)
# This is to prepare the glacier directory for the interpolation (needs to be done only once)
workflow.execute_entity_task(distribute_2d.assign_points_to_band, gdirs);

2024-08-25 21:08:55: oggm.workflow: Execute entity tasks [add_smoothed_glacier_topo] on 2 glaciers
2024-08-25 21:08:55: oggm.workflow: Execute entity tasks [distribute_thickness_per_altitude] on 2 glaciers
2024-08-25 21:08:56: oggm.workflow: Execute entity tasks [assign_points_to_band] on 2 glaciers

Let’s have a look at what we just did:

gdir = gdirs[0]  # here for Aletsch
#gdir = gdirs[1]  # uncomment for Fieschergletscher

with xr.open_dataset(gdir.get_filepath('gridded_data')) as ds:
    ds = ds.load()

# Inititial glacier thickness
f, ax = plt.subplots()
ds.distributed_thickness.plot(ax=ax);
ax.axis('equal');

# Which points belongs to which band, and then within one band which are the first to melt
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
ds.band_index.plot.contourf(ax=ax1);
ds.rank_per_band.plot(ax=ax2);
ax1.axis('equal'); ax2.axis('equal'); plt.tight_layout();

Redistribute simulation

The tasks above need to be run only once. The next one however should be done for each simulation:

ds = workflow.execute_entity_task(
    distribute_2d.distribute_thickness_from_simulation,
    gdirs, 
    input_filesuffix='_random_s1',  # Use the simulation we just did
    concat_input_filesuffix='_spinup_historical',  # Concatenate with the historical spinup
    output_filesuffix='',  # filesuffix added to the output filename gridded_simulation.nc, if empty input_filesuffix is used
)

2024-08-25 21:08:57: oggm.workflow: Execute entity tasks [distribute_thickness_from_simulation] on 2 glaciers

Plot

Let’s have a look!

# # This below is only to open the file again later if needed
# with xr.open_dataset(gdir.get_filepath('gridded_simulation', filesuffix='_random_s1')) as ds:
#     ds = ds.load()

def plot_distributed_thickness(ds, title):
    f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 4))
    ds.simulated_thickness.sel(time=2005).plot(ax=ax1, vmax=400);
    ds.simulated_thickness.sel(time=2050).plot(ax=ax2, vmax=400);
    ds.simulated_thickness.sel(time=2100).plot(ax=ax3, vmax=400);
    ax1.axis('equal'); ax2.axis('equal'); f.suptitle(title, fontsize=20);
    plt.tight_layout();

plot_distributed_thickness(ds[0], 'Aletsch')
# plot_distributed_thickness(ds[1], 'Fieschergletscher')  

Note: the simulation before the RGI date cannot be trusted - it is the result of the dynamical spinup. Furthermore, if the area is larger than the RGI glacier, the redistribution algorithm will put all mass in the glacier mask, which is not what we want:

def plot_area(ds, gdir, title):
    area = (ds.simulated_thickness > 0).sum(dim=['x', 'y']) * gdir.grid.dx**2 * 1e-6
    area.plot(label='Distributed area');
    plt.hlines(gdir.rgi_area_km2, gdir.rgi_date, 2100, color='C3', linestyles='--', label='RGI Area');
    plt.legend(loc='lower left'); plt.ylabel('Area [km2]'); plt.title(title, fontsize=20); plt.show();


plot_area(ds[0], gdirs[0], 'Aletsch')
# plot_area(ds[1], gdirs[1], 'Fieschergletscher')

Volume however is conserved:

def plot_volume(ds, gdir, title):
    vol = ds.simulated_thickness.sum(dim=['x', 'y']) * gdir.grid.dx**2 * 1e-9
    vol.plot(label='Distributed volume'); plt.ylabel('Distributed volume [km3]');
    plt.title(title, fontsize=20); plt.show();


plot_volume(ds[0], gdirs[0], 'Aletsch')
# plot_volume(ds[1], gdirs[1], 'Fieschergletscher')

Therefore, lets just keep all data after the RGI year only:

for i, (ds_single, gdir) in enumerate(zip(ds, gdirs)):
    ds[i] = ds_single.sel(time=slice(gdir.rgi_date, None))

Animation!

from matplotlib import animation
from IPython.display import HTML, display

# Get a handle on the figure and the axes
fig, ax = plt.subplots()

thk = ds[0]['simulated_thickness']  # Aletsch
# thk = ds[1]['simulated_thickness']  # Fieschergletscher

# Plot the initial frame. 
cax = thk.isel(time=0).plot(ax=ax,
    add_colorbar=True,
    cmap='viridis',
    vmin=0, vmax=thk.max(),
    cbar_kwargs={
        'extend':'neither'
    }
)
ax.axis('equal')

def animate(frame):
    ax.set_title(f'Year {int(thk.time[frame])}')
    cax.set_array(thk.values[frame, :].flatten())

ani_glacier = animation.FuncAnimation(fig, animate, frames=len(thk.time), interval=100);

HTML(ani_glacier.to_jshtml())

Once Loop Reflect