In [19]:

    

import iris
import iris.plot as iplt
import matplotlib.pyplot as plt

import numpy


    

In [2]:

    

wfo_hist_file = '/g/data/r87/dbi599/CMIP6/CMIP/CNRM-CERFACS/CNRM-CM6-1/historical/r1i1p1f2/Omon/wfo/gn/v20180917/wfo-tos-binned_Omon_CNRM-CM6-1_historical_r1i1p1f2_gn_185001-201412.nc'


    

In [3]:

    

wfo_hist_cube = iris.load_cube(wfo_hist_file, 'water_flux_into_sea_water')


    

In [4]:

    

wfo_hist_cube


    

    
Out[4]:







    

Water Flux Into Sea Water (kg s-1)
year
sea_surface_temperature
region

    

Shape
165
44
7

    

    
Dimension coordinates
    

    

    



    
	year
    
x
    
-
    
-


    
	sea_surface_temperature
    
-
    
x
    
-


    
	region
    
-
    
-
    
x


    
Attributes
    

    

    



    
	CMIP6_CV_version
    
cv=6.2.3.0-7-g2019642


    
	Conventions
    
CF-1.5


    
	EXPID
    
CNRM-CM6-1_historical_r1i1p1f2


    
	NCO
    
"4.5.5"


    
	activity_id
    
CMIP


    
	arpege_minor_version
    
6.3.2


    
	branch_method
    
standard


    
	branch_time_in_child
    
0.0


    
	branch_time_in_parent
    
0.0


    
	contact
    
contact.cmip@meteo.fr


    
	creation_date
    
2018-06-20T08:39:57Z


    
	data_specs_version
    
01.00.21


    
	description
    
computed as the water  flux into the ocean divided by the area of the ocean...


    
	dr2xml_md5sum
    
d6225e658d7de0912fca2a4293dbe2a7


    
	dr2xml_version
    
1.10


    
	experiment
    
all-forcing simulation of the recent past


    
	experiment_id
    
historical


    
	external_variables
    
areacello


    
	forcing_index
    
2


    
	frequency
    
mon


    
	further_info_url
    
https://furtherinfo.es-doc.org/CMIP6.CNRM-CERFACS.CNRM-CM6-1.historica...


    
	grid
    
native ocean tri-polar grid with 105 k ocean cells


    
	grid_label
    
gn


    
	history
    
Thu Dec 05 16:13:53 2019: /g/data/r87/dbi599/miniconda3/envs/ocean/bin/python...


    
	initialization_index
    
1


    
	institution
    
CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France),...


    
	institution_id
    
CNRM-CERFACS


    
	interval_operation
    
1800 s


    
	interval_write
    
1 month


    
	license
    
CMIP6 model data produced by CNRM-CERFACS is licensed under a Creative...


    
	mip_era
    
CMIP6


    
	name
    
/scratch/work/voldoire/outputs/CMIP6/DECK/CNRM-CM6-1_historical_r1i1p1...


    
	nemo_gelato_commit
    
49095b3accd5d4c_6524fe19b00467a


    
	nominal_resolution
    
100 km


    
	online_operation
    
average


    
	parent_activity_id
    
CMIP


    
	parent_experiment_id
    
piControl


    
	parent_mip_era
    
CMIP6


    
	parent_source_id
    
CNRM-CM6-1


    
	parent_time_units
    
days since 1850-01-01 00:00:00


    
	parent_variant_label
    
r1i1p1f2


    
	physics_index
    
1


    
	product
    
model-output


    
	realization_index
    
1


    
	realm
    
ocean


    
	references
    
http://www.umr-cnrm.fr/cmip6/references


    
	source
    
CNRM-CM6-1 (2017):  aerosol: prescribed monthly fields computed by TACTIC_v2...


    
	source_id
    
CNRM-CM6-1


    
	source_type
    
AOGCM


    
	sub_experiment
    
none


    
	sub_experiment_id
    
none


    
	table_id
    
Omon


    
	title
    
CNRM-CM6-1 model output prepared for CMIP6 / CMIP historical


    
	tracking_id
    
hdl:21.14100/54c0c5ce-1a58-47ae-9b55-4bc635c83226


    
	variable_id
    
wfo


    
	variant_label
    
r1i1p1f2


    
	xios_commit
    
1442-shuffle


        

In [9]:

    

fig = plt.figure(figsize=[10, 7])
plt.axhline(y=0, color='0.5')

iplt.plot(wfo_hist_cube[0, :, 0], label='North Atlantic')
iplt.plot(wfo_hist_cube[0, :, 1], label='South Atlantic')
iplt.plot(wfo_hist_cube[0, :, 2], label='North Pacific')
iplt.plot(wfo_hist_cube[0, :, 3], label='South Pacific')
iplt.plot(wfo_hist_cube[0, :, 4], label='Indian')

plt.legend()
plt.xlabel('sea surface temperature')
plt.ylabel('water flux into sea water (kg s-1)')
plt.title('CNRM-CM6-1 (year 1850)')
plt.show()


    

In [10]:

    

wmfile = '/g/data/r87/dbi599/CMIP6/CMIP/CCCma/CanESM5/historical/r1i1p1f1/Omon/water-mass/gn/v20190429/water-mass_Omon_CanESM5_historical_r1i1p1f1_gn_185001-186012.nc'


    

In [46]:

    

wmcube = iris.load_cube(wmfile, 'Ocean Grid-Cell Volume binned by salinity')


    

In [47]:

    

wmcube


    

    
Out[47]:







    

Ocean Grid-Cell Volume Binned By Salinity (m3)
year
sea_water_salinity
region

    

Shape
11
104
7

    

    
Dimension coordinates
    

    

    



    
	year
    
x
    
-
    
-


    
	sea_water_salinity
    
-
    
x
    
-


    
	region
    
-
    
-
    
x


    
Attributes
    

    

    



    
	CCCma_model_hash
    
3dedf95315d603326fde4f5340dc0519d80d10c0


    
	CCCma_parent_runid
    
rc3-pictrl


    
	CCCma_pycmor_hash
    
33c30511acc319a98240633965a04ca99c26427e


    
	CCCma_runid
    
rc3.1-his01


    
	Conventions
    
CF-1.5


    
	YMDH_branch_time_in_child
    
1850:01:01:00


    
	YMDH_branch_time_in_parent
    
5201:01:01:00


    
	activity_id
    
CMIP


    
	branch_method
    
Spin-up documentation


    
	branch_time_in_child
    
0.0


    
	branch_time_in_parent
    
1223115.0


    
	cmor_version
    
3.4.0


    
	contact
    
ec.cccma.info-info.ccmac.ec@canada.ca


    
	data_specs_version
    
01.00.29


    
	experiment
    
all-forcing simulation of the recent past


    
	experiment_id
    
historical


    
	external_variables
    
areacello volcello


    
	forcing_index
    
1


    
	frequency
    
mon


    
	further_info_url
    
https://furtherinfo.es-doc.org/CMIP6.CCCma.CanESM5.historical.none.r1i...


    
	grid
    
ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees...


    
	grid_label
    
gn


    
	history
    
Fri Dec 06 10:46:16 2019: /g/data/r87/dbi599/miniconda3/envs/ocean/bin/python...


    
	initialization_index
    
1


    
	institution
    
Canadian Centre for Climate Modelling and Analysis, Environment and Climate...


    
	institution_id
    
CCCma


    
	invalid_standard_name
    
ocean_volume_binned_by_salinity


    
	license
    
CMIP6 model data produced by The Government of Canada (Canadian Centre...


    
	mip_era
    
CMIP6


    
	nominal_resolution
    
100 km


    
	parent_activity_id
    
CMIP


    
	parent_experiment_id
    
piControl


    
	parent_mip_era
    
CMIP6


    
	parent_source_id
    
CanESM5


    
	parent_time_units
    
days since 1850-01-01 0:0:0.0


    
	parent_variant_label
    
r1i1p1f1


    
	physics_index
    
1


    
	product
    
model-output


    
	realization_index
    
1


    
	realm
    
ocean


    
	references
    
Geophysical Model Development Special issue on CanESM5 (https://www.ge...


    
	source
    
CanESM5 (2019):


    
	aerosol
    
interactive


    
	atmos
    
CanAM5 (T63L49 native atmosphere,...


    
	source_id
    
CanESM5


    
	source_type
    
AOGCM


    
	sub_experiment
    
none


    
	sub_experiment_id
    
none


    
	table_id
    
Omon


    
	table_info
    
Creation Date:(20 February 2019) MD5:374fbe5a2bcca535c40f7f23da271e49


    
	title
    
CanESM5 output prepared for CMIP6


    
	variant_label
    
r1i1p1f1


    
	version
    
v20190429


        

In [48]:

    

wmcube.coord('sea_water_salinity')


    

    
Out[48]:





DimCoord(array([ 5.  , 15.  , 25.  , 30.05, 30.15, 30.25, 30.35, 30.45, 30.55,
       30.65, 30.75, 30.85, 30.95, 31.05, 31.15, 31.25, 31.35, 31.45,
       31.55, 31.65, 31.75, 31.85, 31.95, 32.05, 32.15, 32.25, 32.35,
       32.45, 32.55, 32.65, 32.75, 32.85, 32.95, 33.05, 33.15, 33.25,
       33.35, 33.45, 33.55, 33.65, 33.75, 33.85, 33.95, 34.05, 34.15,
       34.25, 34.35, 34.45, 34.55, 34.65, 34.75, 34.85, 34.95, 35.05,
       35.15, 35.25, 35.35, 35.45, 35.55, 35.65, 35.75, 35.85, 35.95,
       36.05, 36.15, 36.25, 36.35, 36.45, 36.55, 36.65, 36.75, 36.85,
       36.95, 37.05, 37.15, 37.25, 37.35, 37.45, 37.55, 37.65, 37.75,
       37.85, 37.95, 38.05, 38.15, 38.25, 38.35, 38.45, 38.55, 38.65,
       38.75, 38.85, 38.95, 39.05, 39.15, 39.25, 39.35, 39.45, 39.55,
       39.65, 39.75, 39.85, 39.95, 45.  ]), bounds=array([[ 0. , 10. ],
       [10. , 20. ],
       [20. , 30. ],
       [30. , 30.1],
       [30.1, 30.2],
       [30.2, 30.3],
       [30.3, 30.4],
       [30.4, 30.5],
       [30.5, 30.6],
       [30.6, 30.7],
       [30.7, 30.8],
       [30.8, 30.9],
       [30.9, 31. ],
       [31. , 31.1],
       [31.1, 31.2],
       [31.2, 31.3],
       [31.3, 31.4],
       [31.4, 31.5],
       [31.5, 31.6],
       [31.6, 31.7],
       [31.7, 31.8],
       [31.8, 31.9],
       [31.9, 32. ],
       [32. , 32.1],
       [32.1, 32.2],
       [32.2, 32.3],
       [32.3, 32.4],
       [32.4, 32.5],
       [32.5, 32.6],
       [32.6, 32.7],
       [32.7, 32.8],
       [32.8, 32.9],
       [32.9, 33. ],
       [33. , 33.1],
       [33.1, 33.2],
       [33.2, 33.3],
       [33.3, 33.4],
       [33.4, 33.5],
       [33.5, 33.6],
       [33.6, 33.7],
       [33.7, 33.8],
       [33.8, 33.9],
       [33.9, 34. ],
       [34. , 34.1],
       [34.1, 34.2],
       [34.2, 34.3],
       [34.3, 34.4],
       [34.4, 34.5],
       [34.5, 34.6],
       [34.6, 34.7],
       [34.7, 34.8],
       [34.8, 34.9],
       [34.9, 35. ],
       [35. , 35.1],
       [35.1, 35.2],
       [35.2, 35.3],
       [35.3, 35.4],
       [35.4, 35.5],
       [35.5, 35.6],
       [35.6, 35.7],
       [35.7, 35.8],
       [35.8, 35.9],
       [35.9, 36. ],
       [36. , 36.1],
       [36.1, 36.2],
       [36.2, 36.3],
       [36.3, 36.4],
       [36.4, 36.5],
       [36.5, 36.6],
       [36.6, 36.7],
       [36.7, 36.8],
       [36.8, 36.9],
       [36.9, 37. ],
       [37. , 37.1],
       [37.1, 37.2],
       [37.2, 37.3],
       [37.3, 37.4],
       [37.4, 37.5],
       [37.5, 37.6],
       [37.6, 37.7],
       [37.7, 37.8],
       [37.8, 37.9],
       [37.9, 38. ],
       [38. , 38.1],
       [38.1, 38.2],
       [38.2, 38.3],
       [38.3, 38.4],
       [38.4, 38.5],
       [38.5, 38.6],
       [38.6, 38.7],
       [38.7, 38.8],
       [38.8, 38.9],
       [38.9, 39. ],
       [39. , 39.1],
       [39.1, 39.2],
       [39.2, 39.3],
       [39.3, 39.4],
       [39.4, 39.5],
       [39.5, 39.6],
       [39.6, 39.7],
       [39.7, 39.8],
       [39.8, 39.9],
       [39.9, 40. ],
       [40. , 50. ]]), standard_name='sea_water_salinity', units=Unit('g/kg'), long_name='Sea Water Salinity', var_name='so')

In [49]:

    

fig = plt.figure(figsize=[10, 7])
plt.axhline(y=0, color='0.5')

iplt.plot(wmcube[0, :, 0], label='North Atlantic')
iplt.plot(wmcube[0, :, 1], label='South Atlantic')
iplt.plot(wmcube[0, :, 2], label='North Pacific')
iplt.plot(wmcube[0, :, 3], label='South Pacific')
iplt.plot(wmcube[0, :, 4], label='Indian')

plt.legend()
plt.xlabel('sea water salinity')
plt.ylabel('volume (m3)')
plt.title('CanESM5 (year 1850)')
#plt.xlim(32, 38)
plt.show()


    

In [53]:

    

fig = plt.figure(figsize=[10, 7])
plt.axhline(y=0, color='0.5')

iplt.plot(wmcube[0, :, 0], label='North Atlantic')
iplt.plot(wmcube[0, :, 1], label='South Atlantic')
iplt.plot(wmcube[0, :, 2], label='North Pacific')
iplt.plot(wmcube[0, :, 3], label='South Pacific')
iplt.plot(wmcube[0, :, 4], label='Indian')

plt.legend()
plt.xlabel('sea water salinity')
plt.ylabel('volume (m3)')
plt.title('CanESM5 (year 1850)')
plt.xlim(33.5, 36.5)
plt.show()


    

In [ ]: