Plot climatology of sea ice volume tendency¶

Questions:

1. How does OM3 compare with OM2?
2. Does the sum of freeze + melt match the dvidtt diagnostic?

In [1]:

parameters

# These first two cells must be in all notebooks!
# It allows us to run all the notebooks at once, this cell has a tag "parameters" which allows us to pass in 
# arguments externally using papermill (see mkfigs.sh for details)

# Set esm_file to the datastore for the main experiment of interest
esm_file = "/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf+wombatlite-test3v2-00532b88/datastore.json"

# papermill settings. *No need to modify these if running interactively.* 
papermill = False                      # `cwd` and `nbname` will be populated by papermill.
cwd = None                             # current working directory 
nbname = None                          # notebook name

# These first two cells must be in all notebooks! # It allows us to run all the notebooks at once, this cell has a tag "parameters" which allows us to pass in # arguments externally using papermill (see mkfigs.sh for details) # Set esm_file to the datastore for the main experiment of interest esm_file = "/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf+wombatlite-test3v2-00532b88/datastore.json" # papermill settings. *No need to modify these if running interactively.* papermill = False # `cwd` and `nbname` will be populated by papermill. cwd = None # current working directory nbname = None # notebook name

In [2]:

injected-parameters

# Parameters
esm_file = "/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_ryf+wombatlite-test3-f4d79e82/experiment_datastore.json"
papermill = True
cwd = "/g/data/tm70/cyb561/access-om3-paper-1-runs/MC_25km_jra_ryf+wombatlite-test3-f4d79e82/notebooks/mkfigs_output_MC_25km_jra_ryf+wombatlite-test3-f4d79e82/"
nbname = "SeaIce_mass_budget_climatology.ipynb"

# Parameters esm_file = "/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_ryf+wombatlite-test3-f4d79e82/experiment_datastore.json" papermill = True cwd = "/g/data/tm70/cyb561/access-om3-paper-1-runs/MC_25km_jra_ryf+wombatlite-test3-f4d79e82/notebooks/mkfigs_output_MC_25km_jra_ryf+wombatlite-test3-f4d79e82/" nbname = "SeaIce_mass_budget_climatology.ipynb"

In [3]:

import os
if not papermill: 
    import nci_ipynb  # requires conda/analysis3-26.03 or later
    cwd = nci_ipynb.dir()
    nbname = nci_ipynb.name()
    os.chdir(cwd)
import mkfigs_bootstrap  # noqa: adds external/access-model-mkfigs/src to sys.path (stop-gap)
from mkfigs import MkmdWriter
mkmd = MkmdWriter(esm_file, nbname, str(cwd), pm=papermill)
plotfolder = str(cwd) + "/" if cwd else "./"

import os if not papermill: import nci_ipynb # requires conda/analysis3-26.03 or later cwd = nci_ipynb.dir() nbname = nci_ipynb.name() os.chdir(cwd) import mkfigs_bootstrap # noqa: adds external/access-model-mkfigs/src to sys.path (stop-gap) from mkfigs import MkmdWriter mkmd = MkmdWriter(esm_file, nbname, str(cwd), pm=papermill) plotfolder = str(cwd) + "/" if cwd else "./"

In [4]:

import xarray as xr
import numpy as np
import cf_xarray
from datetime import timedelta
import intake
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from distributed import Client

import pandas as pd
pd.set_option('display.max_rows', 500)

import xarray as xr import numpy as np import cf_xarray from datetime import timedelta import intake import cartopy.crs as ccrs import matplotlib.pyplot as plt from distributed import Client import pandas as pd pd.set_option('display.max_rows', 500)

In [5]:

IAF = esm_file.find('iaf') > 0
IAF

IAF = esm_file.find('iaf') > 0 IAF

Out[5]:

False

In [6]:

if IAF:
    om2_exp = '025deg_jra55_iaf_omip2_cycle6'
else:
    om2_exp = '025deg_jra55_ryf9091_gadi'

if IAF: om2_exp = '025deg_jra55_iaf_omip2_cycle6' else: om2_exp = '025deg_jra55_ryf9091_gadi'

In [7]:

client = Client(threads_per_worker=1)
client.dashboard_link

client = Client(threads_per_worker=1) client.dashboard_link

Out[7]:

'http://127.0.0.1:8787/status'

Open the intake-esm datastore¶

In [8]:

COLUMNS_WITH_ITERABLES = [
        "variable",
        "variable_long_name",
        "variable_standard_name",
        "variable_cell_methods",
        "variable_units"
]

datastore = intake.open_esm_datastore(
    esm_file,
    columns_with_iterables=COLUMNS_WITH_ITERABLES
)

COLUMNS_WITH_ITERABLES = [ "variable", "variable_long_name", "variable_standard_name", "variable_cell_methods", "variable_units" ] datastore = intake.open_esm_datastore( esm_file, columns_with_iterables=COLUMNS_WITH_ITERABLES )

What ocean variables are available at monthly frequency?¶

In [9]:

def available_variables(datastore):
    """Return a pandas dataframe summarising the variables in a datastore"""
    variable_columns = [col for col in datastore.df.columns if "variable" in col]
    return (
        datastore.df[variable_columns]
        .explode(variable_columns)
        .drop_duplicates()
        .set_index("variable")
        .sort_index()
    )

def available_variables(datastore): """Return a pandas dataframe summarising the variables in a datastore""" variable_columns = [col for col in datastore.df.columns if "variable" in col] return ( datastore.df[variable_columns] .explode(variable_columns) .drop_duplicates() .set_index("variable") .sort_index() )

In [10]:

datastore_filtered = datastore.search(realm="seaIce", frequency="1mon")
datastore_filtered_grid = datastore.search(realm="seaIce", frequency="fx")

available_variables(datastore_filtered)

datastore_filtered = datastore.search(realm="seaIce", frequency="1mon") datastore_filtered_grid = datastore.search(realm="seaIce", frequency="fx") available_variables(datastore_filtered)

Out[10]:

	variable_long_name	variable_standard_name	variable_cell_methods	variable_units
variable
ANGLE	angle grid makes with latitude line on U grid			radians
ANGLET	angle grid makes with latitude line on T grid			radians
NCAT	category maximum thickness			m
Tair_m	air temperature		time: mean	C
Tsfc_m	snow/ice surface temperature		time: mean	C
VGRDa	vertical snow-ice-bio levels			1
VGRDb	vertical ice-bio levels			1
VGRDi	vertical ice levels			1
VGRDs	vertical snow levels			1
aice_m	ice area (aggregate)		time: mean	1
aicen_m	ice area, categories		time: mean	1
albice_m	bare ice albedo		time: mean	%
albsni_m	snow/ice broad band albedo		time: mean	%
albsno_m	snow albedo		time: mean	%
alidf_ai_m	near IR diffuse albedo		time: mean	%
alidr_ai_m	near IR direct albedo		time: mean	%
alvdf_ai_m	visible diffuse albedo		time: mean	%
alvdr_ai_m	visible direct albedo		time: mean	%
blkmask	block id of T grid cells, mytask + iblk/100			1
congel_m	congelation ice growth		time: mean	cm/day
daidtd_m	area tendency dynamics		time: mean	%/day
daidtt_m	area tendency thermo		time: mean	%/day
divu_m	strain rate (divergence)			%/day
dvidtd_m	volume tendency dynamics		time: mean	cm/day
dvidtt_m	volume tendency thermo		time: mean	cm/day
evap_ai_m	evaporative water flux		time: mean	cm/day
fcondtop_ai_m	top surface conductive heat flux		time: mean	W/m^2
fcondtopn_ai_m	top sfc conductive heat flux, cat		time: mean	W/m^2
fhocn_ai_m	heat flux ice to ocean (fhocn_ai)		time: mean	W/m^2
flat_ai_m	latent heat flux		time: mean	W/m^2
flatn_ai_m	latent heat flux, category		time: mean	W/m^2
flwdn_m	down longwave flux		time: mean	W/m^2
flwup_ai_m	upward longwave flux		time: mean	W/m^2
fmeltt_ai_m	net surface heat flux causing melt		time: mean	W/m^2
fmelttn_ai_m	net sfc heat flux causing melt, cat		time: mean	W/m^2
frazil_m	frazil ice growth		time: mean	cm/day
fresh_ai_m	freshwtr flx ice to ocn		time: mean	cm/day
frzmlt_m	freeze/melt potential		time: mean	W/m^2
fsalt_ai_m	salt flux ice to ocean		time: mean	kg/m^2/s
fsalt_m	salt flux ice to ocn (cpl)		time: mean	kg/m^2/s
fsens_ai_m	sensible heat flux		time: mean	W/m^2
fsensn_ai_m	sensible heat flux, category		time: mean	W/m^2
fsurfn_ai_m	net surface heat flux, categories		time: mean	W/m^2
fswabs_ai_m	snow/ice/ocn absorbed solar flux		time: mean	W/m^2
fswdn_m	down solar flux		time: mean	W/m^2
fswthru_ai_m	SW flux thru ice to ocean		time: mean	W/m^2
fswup_m	upward solar flux		time: mean	W/m^2
hi_m	grid cell mean ice thickness		time: mean	m
hs_m	grid cell mean snow thickness		time: mean	m
ice_present_m	fraction of time-avg interval that ice is present		time: mean	1
meltb_m	basal ice melt		time: mean	cm/day
meltl_m	lateral ice melt		time: mean	cm/day
melts_m	top snow melt		time: mean	cm/day
meltt_m	top ice melt		time: mean	cm/day
opening_m	lead area opening rate		time: mean	%/day
rain_ai_m	rainfall rate		time: mean	cm/day
scale_factor_m	shortwave scaling factor		time: mean	1
shear_m	strain rate (shear)			%/day
sice_m	bulk ice salinity		time: mean	ppt
sifb_m	sea-ice freeboard		area: time: mean where sea ice (mask=siconc)	m
snoice_m	snow-ice formation		time: mean	cm/day
snow_ai_m	snowfall rate		time: mean	cm/day
strairx_m	atm/ice stress (x)		time: mean	N/m^2
strairy_m	atm/ice stress (y)		time: mean	N/m^2
strcorx_m	coriolis stress (x)		time: mean	N/m^2
strcory_m	coriolis stress (y)		time: mean	N/m^2
strength_m	compressive ice strength		time: mean	N/m
strintx_m	internal ice stress (x)		time: mean	N/m^2
strinty_m	internal ice stress (y)		time: mean	N/m^2
strocnx_m	ocean/ice stress (x)		time: mean	N/m^2
strocny_m	ocean/ice stress (y)		time: mean	N/m^2
strtltx_m	sea sfc tilt stress (x)		time: mean	N/m^2
strtlty_m	sea sfc tilt stress (y)		time: mean	N/m^2
time	time			days since 1900-01-01 00:00:00
time_bounds	time interval bounds			days since 1900-01-01 00:00:00
trsig_m	internal stress tensor trace			N/m
uatm_m	atm velocity (x)		time: mean	m/s
uvel_m	ice velocity (x)		time: mean	m/s
vatm_m	atm velocity (y)		time: mean	m/s
vicen_m	ice volume, categories		time: mean	m
vort_m	strain rate (vorticity)			%/day
vvel_m	ice velocity (y)		time: mean	m/s

In [11]:

available_variables(datastore_filtered_grid)

available_variables(datastore_filtered_grid)

Out[11]:

	variable_long_name	variable_standard_name	variable_cell_methods	variable_units
variable
ANGLE	angle grid makes with latitude line on U grid			radians
ANGLET	angle grid makes with latitude line on T grid			radians
ELAT	E grid center latitude			degrees_north
ELON	E grid center longitude			degrees_east
HTE	T cell width on East side			m
HTN	T cell width on North side			m
NCAT	category maximum thickness			m
NFSD	category floe size (center)			m
NLAT	N grid center latitude			degrees_north
NLON	N grid center longitude			degrees_east
TLAT	T grid center latitude			degrees_north
TLON	T grid center longitude			degrees_east
ULAT	U grid center latitude			degrees_north
ULON	U grid center longitude			degrees_east
VGRDa	vertical snow-ice-bio levels			1
VGRDb	vertical ice-bio levels			1
VGRDi	vertical ice levels			1
VGRDs	vertical snow levels			1
blkmask	block id of T grid cells, mytask + iblk/100			1
dxe	E cell width through middle			m
dxn	N cell width through middle			m
dxt	T cell width through middle			m
dxu	U cell width through middle			m
dye	E cell height through middle			m
dyn	N cell height through middle			m
dyt	T cell height through middle			m
dyu	U cell height through middle			m
earea	area of E grid cells			m^2
emask	mask of E grid cells, 0 = land, 1 = ocean			1
late_bounds	latitude bounds (E-cell)			degrees_north
latn_bounds	latitude bounds (N-cell)			degrees_north
latt_bounds	latitude bounds (T-cell)			degrees_north
latu_bounds	latitude bounds (U-cell)			degrees_north
lone_bounds	longitude bounds (E-cell)			degrees_east
lonn_bounds	longitude bounds (N-cell)			degrees_east
lont_bounds	longitude bounds (T-cell)			degrees_east
lonu_bounds	longitude bounds (U-cell)			degrees_east
narea	area of N grid cells			m^2
nmask	mask of N grid cells, 0 = land, 1 = ocean			1
tarea	area of T grid cells			m^2
tmask	mask of T grid cells, 0 = land, 1 = ocean			1
uarea	area of U grid cells			m^2
umask	mask of U grid cells, 0 = land, 1 = ocean			1

Load ACCESS-OM3 sea ice mass budget diagnostics using the ESM datastore¶

In [12]:

variables = ["aice_m", "congel_m", "frazil_m", "snoice_m", "meltt_m", "meltb_m", "meltl_m", "evap_ai_m", "dvidtt_m"]
ds = datastore.search(variable=variables, frequency="1mon").to_dask(
    xarray_open_kwargs = dict(chunks={"yh": -1, "xh": -1}, # Good for spatial operations, but not temporal
                              decode_timedelta=True,),
)

variables = ["aice_m", "congel_m", "frazil_m", "snoice_m", "meltt_m", "meltb_m", "meltl_m", "evap_ai_m", "dvidtt_m"] ds = datastore.search(variable=variables, frequency="1mon").to_dask( xarray_open_kwargs = dict(chunks={"yh": -1, "xh": -1}, # Good for spatial operations, but not temporal decode_timedelta=True,), )

Pick the last 10 years

In [13]:

if IAF:
    t_slice = slice('2009','2018') #manually pick some years in both om2 & 3 experiments
else:
    years = np.unique(ds.time.dt.year)[-10:]
    t_slice = slice(str(years[0]),str(years[-1]))
    t_slice

if IAF: t_slice = slice('2009','2018') #manually pick some years in both om2 & 3 experiments else: years = np.unique(ds.time.dt.year)[-10:] t_slice = slice(str(years[0]),str(years[-1])) t_slice

In [14]:

ds = ds.sel(time=t_slice)
ds

ds = ds.sel(time=t_slice) ds

Out[14]:

<xarray.Dataset> Size: 14GB
Dimensions:    (time: 120, nj: 1152, ni: 1440)
Coordinates:
  * time       (time) object 960B 1921-01-16 12:00:00 ... 1930-12-16 12:00:00
Dimensions without coordinates: nj, ni
Data variables:
    frazil_m   (time, nj, ni) float64 2GB dask.array<chunksize=(1, 384, 720), meta=np.ndarray>
    dvidtt_m   (time, nj, ni) float64 2GB dask.array<chunksize=(1, 384, 720), meta=np.ndarray>
    meltl_m    (time, nj, ni) float64 2GB dask.array<chunksize=(1, 384, 720), meta=np.ndarray>
    snoice_m   (time, nj, ni) float64 2GB dask.array<chunksize=(1, 384, 720), meta=np.ndarray>
    congel_m   (time, nj, ni) float64 2GB dask.array<chunksize=(1, 384, 720), meta=np.ndarray>
    evap_ai_m  (time, nj, ni) float64 2GB dask.array<chunksize=(1, 384, 720), meta=np.ndarray>
    meltb_m    (time, nj, ni) float64 2GB dask.array<chunksize=(1, 384, 720), meta=np.ndarray>
    aice_m     (time, nj, ni) float64 2GB dask.array<chunksize=(1, 384, 720), meta=np.ndarray>
    meltt_m    (time, nj, ni) float64 2GB dask.array<chunksize=(1, 384, 720), meta=np.ndarray>
Attributes: (12/22)
    title:                                    access-om3
    contents:                                 Diagnostic and Prognostic Varia...
    source:                                   CICE Sea Ice Model, unknown_ver...
    comment:                                  All years have exactly 365 days
    comment3:                                 seconds elapsed into model date...
    time_period_freq:                         month_1
    ...                                       ...
    intake_esm_attrs:variable_cell_methods:   ,,,time: mean,time: mean,,,,,ti...
    intake_esm_attrs:variable_units:          radians,radians,m,C,C,1,1,1,1,1...
    intake_esm_attrs:realm:                   seaIce
    intake_esm_attrs:temporal_label:          mean
    intake_esm_attrs:_data_format_:           netcdf
    intake_esm_dataset_key:                   seaIce.1mon.nbnd:2.nc:5.ni:1440...

xarray.Dataset

• Dimensions:
  • time: 120
  • nj: 1152
  • ni: 1440
• Coordinates: (1)
  • time
    (time)
    object
    1921-01-16 12:00:00 ... 1930-12-...

    long_name :

    time

    axis :

    T

    bounds :

    time_bounds

    array([cftime.DatetimeNoLeap(1921, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1921, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1921, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1921, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1921, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1921, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1921, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1921, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1921, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1921, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1921, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1921, 12, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1922, 12, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1923, 12, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1924, 12, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1925, 12, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1926, 12, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1927, 12, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1928, 12, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1929, 12, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1930, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Data variables: (9)
  • frazil_m
    (time, nj, ni)
    float64
    dask.array<chunksize=(1, 384, 720), meta=np.ndarray>

    units :

    cm/day

    long_name :

    frazil ice growth

    cell_measures :

    area: tarea

    cell_methods :

    time: mean

    time_rep :

    averaged

    Array Chunk Bytes 1.48 GiB 2.11 MiB Shape (120, 1152, 1440) (1, 384, 720) Dask graph 720 chunks in 746 graph layers Data type float64 numpy.ndarray

  • dvidtt_m
    (time, nj, ni)
    float64
    dask.array<chunksize=(1, 384, 720), meta=np.ndarray>

    units :

    cm/day

    long_name :

    volume tendency thermo

    cell_measures :

    area: tarea

    cell_methods :

    time: mean

    time_rep :

    averaged

    Array Chunk Bytes 1.48 GiB 2.11 MiB Shape (120, 1152, 1440) (1, 384, 720) Dask graph 720 chunks in 746 graph layers Data type float64 numpy.ndarray

  • meltl_m
    (time, nj, ni)
    float64
    dask.array<chunksize=(1, 384, 720), meta=np.ndarray>

    units :

    cm/day

    long_name :

    lateral ice melt

    cell_measures :

    area: tarea

    cell_methods :

    time: mean

    time_rep :

    averaged

    Array Chunk Bytes 1.48 GiB 2.11 MiB Shape (120, 1152, 1440) (1, 384, 720) Dask graph 720 chunks in 746 graph layers Data type float64 numpy.ndarray

  • snoice_m
    (time, nj, ni)
    float64
    dask.array<chunksize=(1, 384, 720), meta=np.ndarray>

    units :

    cm/day

    long_name :

    snow-ice formation

    cell_measures :

    area: tarea

    cell_methods :

    time: mean

    time_rep :

    averaged

    Array Chunk Bytes 1.48 GiB 2.11 MiB Shape (120, 1152, 1440) (1, 384, 720) Dask graph 720 chunks in 746 graph layers Data type float64 numpy.ndarray

  • congel_m
    (time, nj, ni)
    float64
    dask.array<chunksize=(1, 384, 720), meta=np.ndarray>

    units :

    cm/day

    long_name :

    congelation ice growth

    cell_measures :

    area: tarea

    cell_methods :

    time: mean

    time_rep :

    averaged

    Array Chunk Bytes 1.48 GiB 2.11 MiB Shape (120, 1152, 1440) (1, 384, 720) Dask graph 720 chunks in 746 graph layers Data type float64 numpy.ndarray

  • evap_ai_m
    (time, nj, ni)
    float64
    dask.array<chunksize=(1, 384, 720), meta=np.ndarray>

    units :

    cm/day

    long_name :

    evaporative water flux

    cell_measures :

    area: tarea

    comment :

    weighted by ice area

    cell_methods :

    time: mean

    time_rep :

    averaged

    Array Chunk Bytes 1.48 GiB 2.11 MiB Shape (120, 1152, 1440) (1, 384, 720) Dask graph 720 chunks in 746 graph layers Data type float64 numpy.ndarray

  • meltb_m
    (time, nj, ni)
    float64
    dask.array<chunksize=(1, 384, 720), meta=np.ndarray>

    units :

    cm/day

    long_name :

    basal ice melt

    cell_measures :

    area: tarea

    cell_methods :

    time: mean

    time_rep :

    averaged

    Array Chunk Bytes 1.48 GiB 2.11 MiB Shape (120, 1152, 1440) (1, 384, 720) Dask graph 720 chunks in 746 graph layers Data type float64 numpy.ndarray

  • aice_m
    (time, nj, ni)
    float64
    dask.array<chunksize=(1, 384, 720), meta=np.ndarray>

    units :

    1

    long_name :

    ice area (aggregate)

    cell_measures :

    area: tarea

    cell_methods :

    time: mean

    time_rep :

    averaged

    Array Chunk Bytes 1.48 GiB 2.11 MiB Shape (120, 1152, 1440) (1, 384, 720) Dask graph 720 chunks in 746 graph layers Data type float64 numpy.ndarray

  • meltt_m
    (time, nj, ni)
    float64
    dask.array<chunksize=(1, 384, 720), meta=np.ndarray>

    units :

    cm/day

    long_name :

    top ice melt

    cell_measures :

    area: tarea

    cell_methods :

    time: mean

    time_rep :

    averaged

    Array Chunk Bytes 1.48 GiB 2.11 MiB Shape (120, 1152, 1440) (1, 384, 720) Dask graph 720 chunks in 746 graph layers Data type float64 numpy.ndarray

• Attributes: (22)

  title :

  access-om3

  contents :

  Diagnostic and Prognostic Variables

  source :

  CICE Sea Ice Model, unknown_version_name

  comment :

  All years have exactly 365 days

  comment3 :

  seconds elapsed into model date: 0

  time_period_freq :

  month_1

  time_axis_position :

  middle

  Conventions :

  CF-1.8

  external_variables :

  TLON TLAT ULON ULAT NLON NLAT ELON ELAT NFSD tmask umask nmask emask tarea uarea narea earea dxt dyt dxu dyu dxn dyn dxe dye HTN HTE

  io_flavor :

  io_pio2 hdf5

  intake_esm_vars :

  ['frazil_m', 'dvidtt_m', 'meltl_m', 'snoice_m', 'congel_m', 'evap_ai_m', 'meltb_m', 'aice_m', 'meltt_m']

  intake_esm_attrs:file_id :

  seaIce.1mon.nbnd:2.nc:5.ni:1440.nj:1152.nkaer:5.nkbio:3.nkice:4.nksnow:1

  intake_esm_attrs:frequency :

  1mon

  intake_esm_attrs:variable :

  ANGLE,ANGLET,NCAT,Tair_m,Tsfc_m,VGRDa,VGRDb,VGRDi,VGRDs,aice_m,aicen_m,albice_m,albsni_m,albsno_m,alidf_ai_m,alidr_ai_m,alvdf_ai_m,alvdr_ai_m,blkmask,congel_m,daidtd_m,daidtt_m,divu_m,dvidtd_m,dvidtt_m,evap_ai_m,fcondtop_ai_m,fcondtopn_ai_m,fhocn_ai_m,flat_ai_m,flatn_ai_m,flwdn_m,flwup_ai_m,fmeltt_ai_m,fmelttn_ai_m,frazil_m,fresh_ai_m,frzmlt_m,fsalt_ai_m,fsalt_m,fsens_ai_m,fsensn_ai_m,fsurfn_ai_m,fswabs_ai_m,fswdn_m,fswthru_ai_m,fswup_m,hi_m,hs_m,ice_present_m,meltb_m,meltl_m,melts_m,meltt_m,opening_m,rain_ai_m,scale_factor_m,shear_m,sice_m,sifb_m,snoice_m,snow_ai_m,strairx_m,strairy_m,strcorx_m,strcory_m,strength_m,strintx_m,strinty_m,strocnx_m,strocny_m,strtltx_m,strtlty_m,time,time_bounds,trsig_m,uatm_m,uvel_m,vatm_m,vicen_m,vort_m,vvel_m

  intake_esm_attrs:variable_long_name :

  angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,category maximum thickness,air temperature,snow/ice surface temperature,vertical snow-ice-bio levels,vertical ice-bio levels,vertical ice levels,vertical snow levels,ice area (aggregate),ice area, categories,bare ice albedo,snow/ice broad band albedo,snow albedo,near IR diffuse albedo,near IR direct albedo,visible diffuse albedo,visible direct albedo,block id of T grid cells, mytask + iblk/100,congelation ice growth,area tendency dynamics,area tendency thermo,strain rate (divergence),volume tendency dynamics,volume tendency thermo,evaporative water flux,top surface conductive heat flux,top sfc conductive heat flux, cat,heat flux ice to ocean (fhocn_ai),latent heat flux,latent heat flux, category,down longwave flux,upward longwave flux,net surface heat flux causing melt,net sfc heat flux causing melt, cat,frazil ice growth,freshwtr flx ice to ocn,freeze/melt potential,salt flux ice to ocean,salt flux ice to ocn (cpl),sensible heat flux,sensible heat flux, category,net surface heat flux, categories,snow/ice/ocn absorbed solar flux,down solar flux,SW flux thru ice to ocean,upward solar flux,grid cell mean ice thickness,grid cell mean snow thickness,fraction of time-avg interval that ice is present,basal ice melt,lateral ice melt,top snow melt,top ice melt,lead area opening rate,rainfall rate,shortwave scaling factor,strain rate (shear),bulk ice salinity,sea-ice freeboard,snow-ice formation,snowfall rate,atm/ice stress (x),atm/ice stress (y),coriolis stress (x),coriolis stress (y),compressive ice strength,internal ice stress (x),internal ice stress (y),ocean/ice stress (x),ocean/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),time,time interval bounds,internal stress tensor trace,atm velocity (x),ice velocity (x),atm velocity (y),ice volume, categories,strain rate (vorticity),ice velocity (y)

  intake_esm_attrs:variable_standard_name :

  ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

  intake_esm_attrs:variable_cell_methods :

  ,,,time: mean,time: mean,,,,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,,time: mean,time: mean,time: mean,,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,,time: mean,area: time: mean where sea ice (mask=siconc),time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,time: mean,,,,time: mean,time: mean,time: mean,time: mean,,time: mean

  intake_esm_attrs:variable_units :

  radians,radians,m,C,C,1,1,1,1,1,1,%,%,%,%,%,%,%,1,cm/day,%/day,%/day,%/day,cm/day,cm/day,cm/day,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,m,m,1,cm/day,cm/day,cm/day,cm/day,%/day,cm/day,1,%/day,ppt,m,cm/day,cm/day,N/m^2,N/m^2,N/m^2,N/m^2,N/m,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,days since 1900-01-01 00:00:00,days since 1900-01-01 00:00:00,N/m,m/s,m/s,m/s,m,%/day,m/s

  intake_esm_attrs:realm :

  seaIce

  intake_esm_attrs:temporal_label :

  mean

  intake_esm_attrs:_data_format_ :

  netcdf

  intake_esm_dataset_key :

  seaIce.1mon.nbnd:2.nc:5.ni:1440.nj:1152.nkaer:5.nkbio:3.nkice:4.nksnow:1.mean

Calculate the monthly climatology for these last 10 years.

In [15]:

ds_clim = ds.groupby('time.month').mean('time').compute()

ds_clim = ds.groupby('time.month').mean('time').compute()

Load grid cell area from cice variable

In [16]:

_search = datastore.search(variable="tarea", frequency="fx", realm="seaIce")
_path = _search.df.loc[0, "path"]
tarea = datastore.search(variable="tarea", frequency="fx", realm="seaIce", path=_path).to_dask().compute()

_search = datastore.search(variable="tarea", frequency="fx", realm="seaIce") _path = _search.df.loc[0, "path"] tarea = datastore.search(variable="tarea", frequency="fx", realm="seaIce", path=_path).to_dask().compute()

Integrate terms for both poles. The terms are currently in cm/day, so convert to m/day

In [17]:

antarctic_seaice = (ds_clim * tarea.tarea / 100).sel(nj=slice(0,int(len(ds.nj)/2))).sum(['nj','ni'])
arctic_seaice = (ds_clim * tarea.tarea / 100).sel(nj=slice(int(len(ds.nj)/2), None)).sum(['nj','ni'])

antarctic_seaice = (ds_clim * tarea.tarea / 100).sel(nj=slice(0,int(len(ds.nj)/2))).sum(['nj','ni']) arctic_seaice = (ds_clim * tarea.tarea / 100).sel(nj=slice(int(len(ds.nj)/2), None)).sum(['nj','ni'])

Load ACCESS-OM2 sea ice mass budget diagnostics using the intake catalog¶

In [18]:

catalog = intake.cat.access_nri

catalog = intake.cat.access_nri

In [19]:

var = catalog[om2_exp].search(variable=variables,frequency="1mon")
var.esmcat.aggregation_control.groupby_attrs = ['frequency']  ## Not sure why I have to do this!!
ds_om2=var.to_dask(xarray_open_kwargs=dict(chunks={"time": -1},
                                                   decode_timedelta=True,
                                                   use_cftime=True), 
                           xarray_combine_by_coords_kwargs = dict( # These kwargs can make things faster
        compat="override", data_vars="minimal", coords="minimal",),                          
)

# shift time to correct for OM2 calendar strangeness
def shift_time(ds):
    ds["time"] = ds.time.to_pandas() - timedelta(minutes=1)
    return ds

ds_om2 = shift_time(ds_om2)

# select out the same 10 years as for OM3
ds_om2 = ds_om2.sel(time=t_slice)

var = catalog[om2_exp].search(variable=variables,frequency="1mon") var.esmcat.aggregation_control.groupby_attrs = ['frequency'] ## Not sure why I have to do this!! ds_om2=var.to_dask(xarray_open_kwargs=dict(chunks={"time": -1}, decode_timedelta=True, use_cftime=True), xarray_combine_by_coords_kwargs = dict( # These kwargs can make things faster compat="override", data_vars="minimal", coords="minimal",), ) # shift time to correct for OM2 calendar strangeness def shift_time(ds): ds["time"] = ds.time.to_pandas() - timedelta(minutes=1) return ds ds_om2 = shift_time(ds_om2) # select out the same 10 years as for OM3 ds_om2 = ds_om2.sel(time=t_slice)

/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/access_nri_intake/aliases.py:192: UserWarning: Value aliasing: variable='aice_m' → variable=['aice_m','aice_m']
  norm: dict[str, Any] = self._normalise_kwargs(kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/access_nri_intake/aliases.py:192: UserWarning: Value aliasing: variable='congel_m' → variable=['congel_m','congel_m']
  norm: dict[str, Any] = self._normalise_kwargs(kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/access_nri_intake/aliases.py:192: UserWarning: Value aliasing: variable='frazil_m' → variable=['frazil_m','frazil_m']
  norm: dict[str, Any] = self._normalise_kwargs(kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/access_nri_intake/aliases.py:192: UserWarning: Value aliasing: variable='snoice_m' → variable=['snoice_m','snoice_m']
  norm: dict[str, Any] = self._normalise_kwargs(kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/access_nri_intake/aliases.py:192: UserWarning: Value aliasing: variable='meltt_m' → variable=['meltt_m','meltt_m']
  norm: dict[str, Any] = self._normalise_kwargs(kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/access_nri_intake/aliases.py:192: UserWarning: Value aliasing: variable='meltb_m' → variable=['meltb_m','meltb_m']
  norm: dict[str, Any] = self._normalise_kwargs(kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/access_nri_intake/aliases.py:192: UserWarning: Value aliasing: variable='meltl_m' → variable=['meltl_m','meltl_m']
  norm: dict[str, Any] = self._normalise_kwargs(kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/access_nri_intake/aliases.py:192: UserWarning: Value aliasing: variable='evap_ai_m' → variable=['evap_ai_m','evap_ai_m']
  norm: dict[str, Any] = self._normalise_kwargs(kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/access_nri_intake/aliases.py:192: UserWarning: Value aliasing: variable='dvidtt_m' → variable=['dvidtt_m','dvidtt_m']
  norm: dict[str, Any] = self._normalise_kwargs(kwargs)

/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/distributed/client.py:3387: UserWarning: Sending large graph of size 20.87 MiB.
This may cause some slowdown.
Consider loading the data with Dask directly
 or using futures or delayed objects to embed the data into the graph without repetition.
See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.
  warnings.warn(

In [20]:

print(ds_om2.time.min().values, ds_om2.time.max().values)

print(ds_om2.time.min().values, ds_om2.time.max().values)

1921-01-31 23:59:00 1930-12-31 23:59:00

Calculate the monthly climatology for these last 10 years.

In [21]:

ds_clim_om2 = ds_om2.groupby('time.month').mean('time').compute()

ds_clim_om2 = ds_om2.groupby('time.month').mean('time').compute()

/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/distributed/client.py:3387: UserWarning: Sending large graph of size 82.29 MiB.
This may cause some slowdown.
Consider loading the data with Dask directly
 or using futures or delayed objects to embed the data into the graph without repetition.
See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.
  warnings.warn(

Load grid cell area variable

In [22]:

var = catalog['025deg_jra55_ryf9091_gadi'].search(variable='tarea', frequency="1mon", start_date='1940-01-01.*')
ds_om2_tarea = var.to_dask()

var = catalog['025deg_jra55_ryf9091_gadi'].search(variable='tarea', frequency="1mon", start_date='1940-01-01.*') ds_om2_tarea = var.to_dask()

Integrate terms for both poles. The terms are currently in cm/day.

In [23]:

antarctic_seaice_om2 = (ds_clim_om2 * ds_om2_tarea.tarea / 100).sel(nj=slice(0,int(len(ds_om2.nj)/2))).sum(['nj','ni']).compute()
arctic_seaice_om2 = (ds_clim_om2 * ds_om2_tarea.tarea / 100).sel(nj=slice(int(len(ds_om2.nj)/2), None)).sum(['nj','ni']).compute()

antarctic_seaice_om2 = (ds_clim_om2 * ds_om2_tarea.tarea / 100).sel(nj=slice(0,int(len(ds_om2.nj)/2))).sum(['nj','ni']).compute() arctic_seaice_om2 = (ds_clim_om2 * ds_om2_tarea.tarea / 100).sel(nj=slice(int(len(ds_om2.nj)/2), None)).sum(['nj','ni']).compute()

/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/distributed/client.py:3387: UserWarning: Sending large graph of size 640.75 MiB.
This may cause some slowdown.
Consider loading the data with Dask directly
 or using futures or delayed objects to embed the data into the graph without repetition.
See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.
  warnings.warn(

/g/data/xp65/public/apps/med_conda/envs/analysis3-26.07/lib/python3.12/site-packages/distributed/client.py:3387: UserWarning: Sending large graph of size 640.75 MiB.
This may cause some slowdown.
Consider loading the data with Dask directly
 or using futures or delayed objects to embed the data into the graph without repetition.
See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.
  warnings.warn(

Plot Antarctic sea ice climatology¶

In [24]:

def figure(region):
    plt.rcParams['font.size'] = 12
    fig, axes = plt.subplots(ncols=3, figsize=(21,4), sharex=True)
    plt.subplots_adjust(wspace=0.12)

    axes[0].text(0.0, 1.03, 'a) ACCESS-OM3', fontsize=12, fontweight='bold', transform=axes[0].transAxes)
    axes[1].text(0.0, 1.03, 'b) ACCESS-OM2', fontsize=12, fontweight='bold', transform=axes[1].transAxes)
    axes[2].text(0.0, 1.03, 'c) ACCESS-OM3 - ACCESS-OM2', fontsize=12, fontweight='bold', transform=axes[2].transAxes)

    for i, ax in enumerate(axes):
        ax.text(0.99, 1.03, f'{region}', ha='right', fontsize=12, transform=ax.transAxes)
        ax.plot(np.arange(1,13,1), np.full(12,0), lw=0.5, c='k')
        ax.set_xlim([1,12])
        ax.set_xticks(np.arange(1,13,1))
        ax.set_xlabel('Month')
    
    axes[0].set_ylabel('Sea ice volume tendency\n(x1000 km$^3$ day$^{-1}$)')
        
    return fig, axes

def figure(region): plt.rcParams['font.size'] = 12 fig, axes = plt.subplots(ncols=3, figsize=(21,4), sharex=True) plt.subplots_adjust(wspace=0.12) axes[0].text(0.0, 1.03, 'a) ACCESS-OM3', fontsize=12, fontweight='bold', transform=axes[0].transAxes) axes[1].text(0.0, 1.03, 'b) ACCESS-OM2', fontsize=12, fontweight='bold', transform=axes[1].transAxes) axes[2].text(0.0, 1.03, 'c) ACCESS-OM3 - ACCESS-OM2', fontsize=12, fontweight='bold', transform=axes[2].transAxes) for i, ax in enumerate(axes): ax.text(0.99, 1.03, f'{region}', ha='right', fontsize=12, transform=ax.transAxes) ax.plot(np.arange(1,13,1), np.full(12,0), lw=0.5, c='k') ax.set_xlim([1,12]) ax.set_xticks(np.arange(1,13,1)) ax.set_xlabel('Month') axes[0].set_ylabel('Sea ice volume tendency\n(x1000 km$^3$ day$^{-1}$)') return fig, axes

Sum of freeze and melt versus dvidtt diagnostic

Note: Not using evap_ai_m because this includes evaporation from snow. There is no diagnostic (currently) for evap_ice.

In [25]:

region = 'ANTARCTICA'
fig, axes = figure(region)

x = np.arange(1, 13, 1)

### OM3 ###
freeze =  antarctic_seaice['frazil_m'] + antarctic_seaice['congel_m'] + antarctic_seaice['snoice_m']
melt = antarctic_seaice['meltb_m'] + antarctic_seaice['meltt_m'] + antarctic_seaice['meltl_m']
axes[0].plot(x, freeze/1e12, c='blue', label='Total freeze')
axes[0].plot(x, -melt/1e12, c='orangered', label='Total melt')
axes[0].plot(x, (freeze - melt)/1e12, c='grey', lw=2, label='Net (freeze + melt)')
axes[0].plot(x, antarctic_seaice['dvidtt_m']/1e12, c='k', ls=':', lw=2, label='dvidtt')

axes[0].legend(frameon=False, ncols=2, fontsize=12)

### OM2 ###
freeze_om2 =  antarctic_seaice_om2['frazil_m'] + antarctic_seaice_om2['congel_m'] + antarctic_seaice_om2['snoice_m']
melt_om2 = antarctic_seaice_om2['meltb_m'] + antarctic_seaice_om2['meltt_m'] + antarctic_seaice_om2['meltl_m']
axes[1].plot(x, freeze_om2/1e12, c='blue', label='Total freeze')
axes[1].plot(x, -melt_om2/1e12, c='orangered', label='Total melt')
# axes[0].plot(x, -melt_om2/1e12, c='firebrick', label='Total melt')
axes[1].plot(x, (freeze_om2 - melt_om2)/1e12, c='grey', lw=2, label='Net (freeze + melt)')
axes[1].plot(x, antarctic_seaice_om2['dvidtt_m']/1e12, c='k', ls=':', lw=2, label='dvidtt')

### Difference (OM3 - OM2) ###
axes[2].plot(x, (freeze - freeze_om2)/1e12, c='blue', label='Total freeze')
axes[2].plot(x, (-melt - -melt_om2)/1e12, c='orangered', label='Total melt')
axes[2].plot(x, ((freeze - melt) - (freeze_om2 - melt_om2))/1e12, c='grey', lw=2, label='Net (freeze + melt)')
axes[2].plot(x, (antarctic_seaice['dvidtt_m'] - antarctic_seaice_om2['dvidtt_m'])/1e12, c='k', ls=':', lw=2, label='dvidtt')

axes[0].set_ylim([-0.25, 0.15])
axes[1].set_ylim([-0.25, 0.15])
axes[2].set_ylim([-0.02, 0.02])

figfile = plotfolder + f'seaice_vol_budget_{region}.jpg'
plt.savefig(figfile, dpi=300, bbox_inches='tight') 

plt.show()
mkmd.savefig(fig, "Sea Ice Mass Budget", "Antarctic sea ice volume budget climatology comparing ACCESS-OM3 and ACCESS-OM2. [GitHub issue: Sea ice mass budget climatology](https://github.com/ACCESS-Community-Hub/access-om3-paper-1/issues/52)")

region = 'ANTARCTICA' fig, axes = figure(region) x = np.arange(1, 13, 1) ### OM3 ### freeze = antarctic_seaice['frazil_m'] + antarctic_seaice['congel_m'] + antarctic_seaice['snoice_m'] melt = antarctic_seaice['meltb_m'] + antarctic_seaice['meltt_m'] + antarctic_seaice['meltl_m'] axes[0].plot(x, freeze/1e12, c='blue', label='Total freeze') axes[0].plot(x, -melt/1e12, c='orangered', label='Total melt') axes[0].plot(x, (freeze - melt)/1e12, c='grey', lw=2, label='Net (freeze + melt)') axes[0].plot(x, antarctic_seaice['dvidtt_m']/1e12, c='k', ls=':', lw=2, label='dvidtt') axes[0].legend(frameon=False, ncols=2, fontsize=12) ### OM2 ### freeze_om2 = antarctic_seaice_om2['frazil_m'] + antarctic_seaice_om2['congel_m'] + antarctic_seaice_om2['snoice_m'] melt_om2 = antarctic_seaice_om2['meltb_m'] + antarctic_seaice_om2['meltt_m'] + antarctic_seaice_om2['meltl_m'] axes[1].plot(x, freeze_om2/1e12, c='blue', label='Total freeze') axes[1].plot(x, -melt_om2/1e12, c='orangered', label='Total melt') # axes[0].plot(x, -melt_om2/1e12, c='firebrick', label='Total melt') axes[1].plot(x, (freeze_om2 - melt_om2)/1e12, c='grey', lw=2, label='Net (freeze + melt)') axes[1].plot(x, antarctic_seaice_om2['dvidtt_m']/1e12, c='k', ls=':', lw=2, label='dvidtt') ### Difference (OM3 - OM2) ### axes[2].plot(x, (freeze - freeze_om2)/1e12, c='blue', label='Total freeze') axes[2].plot(x, (-melt - -melt_om2)/1e12, c='orangered', label='Total melt') axes[2].plot(x, ((freeze - melt) - (freeze_om2 - melt_om2))/1e12, c='grey', lw=2, label='Net (freeze + melt)') axes[2].plot(x, (antarctic_seaice['dvidtt_m'] - antarctic_seaice_om2['dvidtt_m'])/1e12, c='k', ls=':', lw=2, label='dvidtt') axes[0].set_ylim([-0.25, 0.15]) axes[1].set_ylim([-0.25, 0.15]) axes[2].set_ylim([-0.02, 0.02]) figfile = plotfolder + f'seaice_vol_budget_{region}.jpg' plt.savefig(figfile, dpi=300, bbox_inches='tight') plt.show() mkmd.savefig(fig, "Sea Ice Mass Budget", "Antarctic sea ice volume budget climatology comparing ACCESS-OM3 and ACCESS-OM2. [GitHub issue: Sea ice mass budget climatology](https://github.com/ACCESS-Community-Hub/access-om3-paper-1/issues/52)")

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Plot Arctic sea ice climatology¶

In [26]:

region = 'ARCTIC'
fig, axes = figure(region)

x = np.arange(1, 13, 1)

### OM3 ###
freeze =  arctic_seaice['frazil_m'] + arctic_seaice['congel_m'] + arctic_seaice['snoice_m']
melt = arctic_seaice['meltb_m'] + arctic_seaice['meltt_m'] + arctic_seaice['meltl_m']
axes[0].plot(x, freeze/1e12, c='blue', label='Total freeze')
axes[0].plot(x, -melt/1e12, c='orangered', label='Total melt')
axes[0].plot(x, (freeze - melt)/1e12, c='grey', lw=2, label='Net (freeze + melt)')
axes[0].plot(x, arctic_seaice['dvidtt_m']/1e12, c='k', ls=':', lw=2, label='dvidtt')

axes[0].legend(frameon=False, ncols=1, fontsize=12, loc='lower left')

### OM2 ###
freeze_om2 =  arctic_seaice_om2['frazil_m'] + arctic_seaice_om2['congel_m'] + arctic_seaice_om2['snoice_m']
melt_om2 = arctic_seaice_om2['meltb_m'] + arctic_seaice_om2['meltt_m'] + arctic_seaice_om2['meltl_m']
axes[1].plot(x, freeze_om2/1e12, c='blue', label='Total freeze')
# axes[0].plot(x, freeze_om2/1e12, c='cyan', label='Total freeze')
axes[1].plot(x, -melt_om2/1e12, c='orangered', label='Total melt')
# axes[0].plot(x, -melt_om2/1e12, c='firebrick', label='Total melt')
axes[1].plot(x, (freeze_om2 - melt_om2)/1e12, c='grey', lw=2, label='Net (freeze + melt)')
axes[1].plot(x, arctic_seaice_om2['dvidtt_m']/1e12, c='k', ls=':', lw=2, label='dvidtt')

### Difference (OM3 - OM2) ###
axes[2].plot(x, (freeze - freeze_om2)/1e12, c='blue', label='Total freeze')
axes[2].plot(x, (-melt - -melt_om2)/1e12, c='orangered', label='Total melt')
axes[2].plot(x, ((freeze - melt) - (freeze_om2 - melt_om2))/1e12, c='grey', lw=2, label='Net (freeze + melt)')
axes[2].plot(x, (arctic_seaice['dvidtt_m'] - arctic_seaice_om2['dvidtt_m'])/1e12, c='k', ls=':', lw=2, label='dvidtt')

axes[0].set_ylim([-0.3, 0.2])
axes[1].set_ylim([-0.3, 0.2])
axes[2].set_ylim([-0.02, 0.035])

figfile = plotfolder + f'seaice_vol_budget_{region}.jpg'
plt.savefig(figfile, dpi=300, bbox_inches='tight') 

plt.show()
mkmd.savefig(fig, "Sea Ice Mass Budget", "Arctic sea ice volume budget climatology comparing ACCESS-OM3 and ACCESS-OM2. [GitHub issue: Sea ice mass budget climatology](https://github.com/ACCESS-Community-Hub/access-om3-paper-1/issues/52)")

region = 'ARCTIC' fig, axes = figure(region) x = np.arange(1, 13, 1) ### OM3 ### freeze = arctic_seaice['frazil_m'] + arctic_seaice['congel_m'] + arctic_seaice['snoice_m'] melt = arctic_seaice['meltb_m'] + arctic_seaice['meltt_m'] + arctic_seaice['meltl_m'] axes[0].plot(x, freeze/1e12, c='blue', label='Total freeze') axes[0].plot(x, -melt/1e12, c='orangered', label='Total melt') axes[0].plot(x, (freeze - melt)/1e12, c='grey', lw=2, label='Net (freeze + melt)') axes[0].plot(x, arctic_seaice['dvidtt_m']/1e12, c='k', ls=':', lw=2, label='dvidtt') axes[0].legend(frameon=False, ncols=1, fontsize=12, loc='lower left') ### OM2 ### freeze_om2 = arctic_seaice_om2['frazil_m'] + arctic_seaice_om2['congel_m'] + arctic_seaice_om2['snoice_m'] melt_om2 = arctic_seaice_om2['meltb_m'] + arctic_seaice_om2['meltt_m'] + arctic_seaice_om2['meltl_m'] axes[1].plot(x, freeze_om2/1e12, c='blue', label='Total freeze') # axes[0].plot(x, freeze_om2/1e12, c='cyan', label='Total freeze') axes[1].plot(x, -melt_om2/1e12, c='orangered', label='Total melt') # axes[0].plot(x, -melt_om2/1e12, c='firebrick', label='Total melt') axes[1].plot(x, (freeze_om2 - melt_om2)/1e12, c='grey', lw=2, label='Net (freeze + melt)') axes[1].plot(x, arctic_seaice_om2['dvidtt_m']/1e12, c='k', ls=':', lw=2, label='dvidtt') ### Difference (OM3 - OM2) ### axes[2].plot(x, (freeze - freeze_om2)/1e12, c='blue', label='Total freeze') axes[2].plot(x, (-melt - -melt_om2)/1e12, c='orangered', label='Total melt') axes[2].plot(x, ((freeze - melt) - (freeze_om2 - melt_om2))/1e12, c='grey', lw=2, label='Net (freeze + melt)') axes[2].plot(x, (arctic_seaice['dvidtt_m'] - arctic_seaice_om2['dvidtt_m'])/1e12, c='k', ls=':', lw=2, label='dvidtt') axes[0].set_ylim([-0.3, 0.2]) axes[1].set_ylim([-0.3, 0.2]) axes[2].set_ylim([-0.02, 0.035]) figfile = plotfolder + f'seaice_vol_budget_{region}.jpg' plt.savefig(figfile, dpi=300, bbox_inches='tight') plt.show() mkmd.savefig(fig, "Sea Ice Mass Budget", "Arctic sea ice volume budget climatology comparing ACCESS-OM3 and ACCESS-OM2. [GitHub issue: Sea ice mass budget climatology](https://github.com/ACCESS-Community-Hub/access-om3-paper-1/issues/52)")

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Check freeze - melt and dvidtt residuals¶

In [27]:

def figure():
    plt.rcParams['font.size'] = 12
    fig, axes = plt.subplots(ncols=2, figsize=(14,4), sharex=True, sharey=True)
    plt.subplots_adjust(wspace=0.08)

    axes[0].text(0.0, 1.03, 'a) ACCESS-OM3', fontsize=12, fontweight='bold', transform=axes[0].transAxes)
    axes[1].text(0.0, 1.03, 'b) ACCESS-OM2', fontsize=12, fontweight='bold', transform=axes[1].transAxes)

    for i, ax in enumerate(axes):
        ax.plot(np.arange(1,13,1), np.full(12,0), lw=0.5, c='k')
        ax.set_xlim([1,12])
        ax.set_xticks(np.arange(1,13,1))
        ax.set_xlabel('Month')
    
    axes[0].set_ylabel('Sea ice volume tendency\n(x1000 km$^3$ day$^{-1}$)')
        
    return fig, axes

def figure(): plt.rcParams['font.size'] = 12 fig, axes = plt.subplots(ncols=2, figsize=(14,4), sharex=True, sharey=True) plt.subplots_adjust(wspace=0.08) axes[0].text(0.0, 1.03, 'a) ACCESS-OM3', fontsize=12, fontweight='bold', transform=axes[0].transAxes) axes[1].text(0.0, 1.03, 'b) ACCESS-OM2', fontsize=12, fontweight='bold', transform=axes[1].transAxes) for i, ax in enumerate(axes): ax.plot(np.arange(1,13,1), np.full(12,0), lw=0.5, c='k') ax.set_xlim([1,12]) ax.set_xticks(np.arange(1,13,1)) ax.set_xlabel('Month') axes[0].set_ylabel('Sea ice volume tendency\n(x1000 km$^3$ day$^{-1}$)') return fig, axes

In [28]:

fig, axes = figure()

x = np.arange(1, 13, 1)

freeze =  antarctic_seaice['frazil_m'] + antarctic_seaice['congel_m'] + antarctic_seaice['snoice_m']
melt = antarctic_seaice['meltb_m'] + antarctic_seaice['meltt_m'] + antarctic_seaice['meltl_m']
freeze_om2 =  antarctic_seaice_om2['frazil_m'] + antarctic_seaice_om2['congel_m'] + antarctic_seaice_om2['snoice_m']
melt_om2 = antarctic_seaice_om2['meltb_m'] + antarctic_seaice_om2['meltt_m'] + antarctic_seaice_om2['meltl_m']
axes[0].plot(x, ((freeze - melt) - antarctic_seaice['dvidtt_m'])/1e12, c='darkviolet', label='Antarctic: Net - dvidtt')
axes[1].plot(x, ((freeze_om2 - melt_om2) - antarctic_seaice_om2['dvidtt_m'])/1e12, c='darkviolet', label='Antarctic: Net - dvidtt')


freeze =  arctic_seaice['frazil_m'] + arctic_seaice['congel_m'] + arctic_seaice['snoice_m']
melt = arctic_seaice['meltb_m'] + arctic_seaice['meltt_m'] + arctic_seaice['meltl_m']
freeze_om2 =  arctic_seaice_om2['frazil_m'] + arctic_seaice_om2['congel_m'] + arctic_seaice_om2['snoice_m']
melt_om2 = arctic_seaice_om2['meltb_m'] + arctic_seaice_om2['meltt_m'] + arctic_seaice_om2['meltl_m']
axes[0].plot(x, ((freeze - melt) - arctic_seaice['dvidtt_m'])/1e12, c='green', label='Arctic: Net - dvidtt')
axes[1].plot(x, ((freeze_om2 - melt_om2) - arctic_seaice_om2['dvidtt_m'])/1e12, c='green', label='Arctic: Net - dvidtt')

axes[0].legend(frameon=False, ncols=1, fontsize=12, loc='lower left')

figfile = plotfolder + f'seaice_vol_budget_net-dvidtt_differences.jpg'
plt.savefig(figfile, dpi=300, bbox_inches='tight') 

plt.show()
mkmd.savefig(fig, "Sea Ice Mass Budget", "Sea ice volume budget residual comparing ACCESS-OM3 and ACCESS-OM2. [GitHub issue: Sea ice mass budget climatology](https://github.com/ACCESS-Community-Hub/access-om3-paper-1/issues/52)")

fig, axes = figure() x = np.arange(1, 13, 1) freeze = antarctic_seaice['frazil_m'] + antarctic_seaice['congel_m'] + antarctic_seaice['snoice_m'] melt = antarctic_seaice['meltb_m'] + antarctic_seaice['meltt_m'] + antarctic_seaice['meltl_m'] freeze_om2 = antarctic_seaice_om2['frazil_m'] + antarctic_seaice_om2['congel_m'] + antarctic_seaice_om2['snoice_m'] melt_om2 = antarctic_seaice_om2['meltb_m'] + antarctic_seaice_om2['meltt_m'] + antarctic_seaice_om2['meltl_m'] axes[0].plot(x, ((freeze - melt) - antarctic_seaice['dvidtt_m'])/1e12, c='darkviolet', label='Antarctic: Net - dvidtt') axes[1].plot(x, ((freeze_om2 - melt_om2) - antarctic_seaice_om2['dvidtt_m'])/1e12, c='darkviolet', label='Antarctic: Net - dvidtt') freeze = arctic_seaice['frazil_m'] + arctic_seaice['congel_m'] + arctic_seaice['snoice_m'] melt = arctic_seaice['meltb_m'] + arctic_seaice['meltt_m'] + arctic_seaice['meltl_m'] freeze_om2 = arctic_seaice_om2['frazil_m'] + arctic_seaice_om2['congel_m'] + arctic_seaice_om2['snoice_m'] melt_om2 = arctic_seaice_om2['meltb_m'] + arctic_seaice_om2['meltt_m'] + arctic_seaice_om2['meltl_m'] axes[0].plot(x, ((freeze - melt) - arctic_seaice['dvidtt_m'])/1e12, c='green', label='Arctic: Net - dvidtt') axes[1].plot(x, ((freeze_om2 - melt_om2) - arctic_seaice_om2['dvidtt_m'])/1e12, c='green', label='Arctic: Net - dvidtt') axes[0].legend(frameon=False, ncols=1, fontsize=12, loc='lower left') figfile = plotfolder + f'seaice_vol_budget_net-dvidtt_differences.jpg' plt.savefig(figfile, dpi=300, bbox_inches='tight') plt.show() mkmd.savefig(fig, "Sea Ice Mass Budget", "Sea ice volume budget residual comparing ACCESS-OM3 and ACCESS-OM2. [GitHub issue: Sea ice mass budget climatology](https://github.com/ACCESS-Community-Hub/access-om3-paper-1/issues/52)")

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client.close()

client.close()

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