Simple example for how to load and plot ice data

This script shows how to load and plot sea ice concentration from CICE output, while also indicating how to get around some of the pitfalls and foibles in CICE temporal and spatial gridding.

Firstly, load bare minimum modules:

import cosima_cookbook as cc
import matplotlib.pyplot as plt
from dask.distributed import Client
from datetime import timedelta

If we want, we can start a client - not strictly necessary for this small case but could help for longer timeseries:

client = Client()



  • Workers: 4
  • Cores: 12
  • Memory: 48.00 GiB

Start a database session:

session = cc.database.create_session()

Load sea ice area (aice_m) from the Repeat-Year forcing experiment. (We could, alternatively, try to load ice thickness (hi_m) or ice volume (vicen_m).) Note that we are just loading the last 10 years here.

Note also the decode_coords=False flag. This gets around some messy issues with the way xarray decides to load CICE grids:

variable = 'aice_m'
expt = '01deg_jra55v13_ryf9091'
var = cc.querying.getvar(expt, variable, session, start_time = '2090-02-01', decode_coords = False)

Another messy thing about CICE is that it thinks that monthly data for, say, January occurs at midnight on Jan 31 – while xarray interprets this as the first milllisecond of February.

To get around this, note that we loaded data from February above, and we now subtract 12 hours from the time dimension. This means that, at least data is sitting in the correct month, and really helps to compute monthly climatologies correctly.

var['time'] = var.time.to_pandas() - timedelta(hours = 12)

Note that aice_m is the monthly average of fractional ice area in each grid cell aka the concentration. To find the actual area of the ice we need to know the area of each cell. Unfortunately, CICE doesn’t save this for us … but the ocean model does. So, let’s load area_t from the ocean model, and rename the coordinates in our ice variable to match the ocean model. Then we can multiply the ice concentration with the cell area to get a total ice area.

area_t = cc.querying.getvar(expt, 'area_t',session,n = 1)
var.coords['ni'] = area_t['xt_ocean'].values
var.coords['nj'] = area_t['yt_ocean'].values
var = var.rename(({'ni':'xt_ocean', 'nj':'yt_ocean'}))
area = var*area_t

Let’s look at a timeseries of total SH sea ice extent. Note that to make this calculation less computationally intensive, we are first subsetting our dataset, so it goes from global coverage to roughly the total area of the Southern Ocean. This means that the sum function will only be applied to the Southern Ocean, making calculations a little faster.

SH_area = area.sel(yt_ocean = slice(-90, -45)).sum(('yt_ocean', 'xt_ocean'))
[<matplotlib.lines.Line2D at 0x1541f2af78e0>]

Or, we can look at the seasonal cycle of SH ice concentration, averaged over all 10 years.

seasonal_cycle = SH_area.groupby('time.month').mean('time')
[<matplotlib.lines.Line2D at 0x1541e7b0a460>]

We can plot a map of sea ice concentration for a selected month, as follows:

CN_Aug = var.sel(time='2095-08-31')
<matplotlib.collections.QuadMesh at 0x1542211343a0>
Once we are happy with your plot, we can save the plot to disk using plt.savefig('filepath/filename') function at the end of the cell containing the plot we want to save, as shown below. Note that your filename must contain the file (e.g., pdf, jpeg, png, etc.).
For more information on the options available to save figures refer to [].
plt.savefig('MyFirstPlot.png', dpi = 300)

Download python script:

Download Jupyter notebook: IcePlottingExample.ipynb