Pycnocline tutorial

A demonstration of pycnocline depth and thickness diagnostics. The first and second depth moments of stratification are computed as proxies for pycnocline depth and thickness, suitable for a nearly two-layer fluid.

Note that in the AMM7 example data the plots are not particularly spectacular as the internal tide is poorly resolved at 7km.

Relevant imports and filepath configuration

import coast
import numpy as np
import os
import matplotlib.pyplot as plt
import matplotlib.colors as colors  # colormap fiddling

/usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pydap/lib.py:5: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
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# set some paths
root = "./"
dn_files = root + "./example_files/"
fn_nemo_grid_t_dat = dn_files + "nemo_data_T_grid_Aug2015.nc"
fn_nemo_dom = dn_files + "coast_example_nemo_domain.nc"
config_t = root + "./config/example_nemo_grid_t.json"
config_w = root + "./config/example_nemo_grid_w.json"

Loading data

# Create a Gridded object and load in the data:

nemo_t = coast.Gridded(fn_nemo_grid_t_dat, fn_nemo_dom, config=config_t)

#nemo_t.dataset # uncomment to print data object summary

# The stratification variables are computed as centred differences of the t-grid variables. 
# These will become w-grid variables. So, create an empty w-grid object, to store stratification. 
# Note how we do not pass a NEMO data file for this load.
nemo_w = coast.Gridded(fn_domain=fn_nemo_dom, config=config_w)

Subset the domain

We are not interested in the whole doman so it is computationally efficient to subset the data for the region of interest. Here we will look at the North Sea between (51N: 62N) and (-4E:15E). We will great subset objects for both the t- and w-grids:

ind_2d = nemo_t.subset_indices(start=[51,-4], end=[62,15])
nemo_nwes_t = nemo_t.isel(y_dim=ind_2d[0], x_dim=ind_2d[1]) #nwes = northwest european shelf
ind_2d = nemo_w.subset_indices(start=[51,-4], end=[62,15])
nemo_nwes_w = nemo_w.isel(y_dim=ind_2d[0], x_dim=ind_2d[1]) #nwes = northwest european shelf
#nemo_nwes_t.dataset # uncomment to print data object summary

Diagnostic calculations and plotting

We can use a COAsT method to construct the in-situ density:

nemo_nwes_t.construct_density( eos='EOS10' )

Then we construct stratification using a COAsT method to take the vertical derivative. Noting that the inputs are on t-pts and the outputs are on w-pt

nemo_nwes_w = nemo_nwes_t.differentiate( 'density', dim='z_dim', out_var_str='rho_dz', out_obj=nemo_nwes_w ) # --> sci_nwes_w.rho_dz

This has created a variable called nemo_nwes_w.rho_dz.

Create internal tide diagnostics

We can now use the GriddedStratification class to construct the first and second moments (over depth) of density. In the limit of an idealised two-layer fluid these converge to the depth and thickness of the interface. I.e. the pycnocline depth and thickness respectively.

strat = coast.GriddedStratification(nemo_nwes_t)

#%%  Construct pycnocline variables: depth and thickness
strat.construct_pycnocline_vars( nemo_nwes_t, nemo_nwes_w )

/usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
/usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/computation.py:822: RuntimeWarning: invalid value encountered in sqrt

Plotting data

Finally we plot pycnocline variables (depth and thickness) using an GriddedStratification method:

strat.quick_plot()

(<Figure size 1000x1000 with 2 Axes>,
 <Axes: title={'center': '01 Aug 2015: masked pycnocline thickness (m)'}, xlabel='longitude', ylabel='latitude'>)