ACCESS-OM3 and ACCESS-OM2 horizontal mean temperature and salinity Hovmollers: drift from initial condition, vs depth and time¶

Related issue: https://github.com/ACCESS-Community-Hub/access-om3-25km-paper-1/issues/8

Run with a lot of cores (say, 28).

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"
#esm_file = "/g/data/zv30/non-cmip/ACCESS-CM3/cm3-run-03-06-2026/cm3-datastore/cm3-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" #esm_file = "/g/data/zv30/non-cmip/ACCESS-CM3/cm3-run-03-06-2026/cm3-datastore/cm3-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_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/datastore.json"
papermill = True
cwd = "/g/data/tm70/cyb561/access-om3-paper-1-runs/MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/notebooks/mkfigs_output_MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/"
nbname = "temp-salt-vs-depth-time.ipynb"

# Parameters esm_file = "/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/datastore.json" papermill = True cwd = "/g/data/tm70/cyb561/access-om3-paper-1-runs/MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/notebooks/mkfigs_output_MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/" nbname = "temp-salt-vs-depth-time.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)
from exptdata_access import guess_experiment_from_esm_file
# Infer model information from the datastore path
expt_key, info = guess_experiment_from_esm_file(esm_file)
model_name = info["model"]             # OM3 or CM3
print(f"Running notebook for model: {model_name}")
print("ESM datastore path:", esm_file)

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) from exptdata_access import guess_experiment_from_esm_file # Infer model information from the datastore path expt_key, info = guess_experiment_from_esm_file(esm_file) model_name = info["model"] # OM3 or CM3 print(f"Running notebook for model: {model_name}") print("ESM datastore path:", esm_file)

Running notebook for model: ACCESS-OM3
ESM datastore path: /g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/datastore.json

In [4]:

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

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

Out[4]:

True

In [5]:

import os
import numpy as np
import xarray as xr
import cftime
import cf_xarray as cfxr
import cf_xarray.units
import pint_xarray
from pint import application_registry as ureg
import intake
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
import matplotlib as mpl
from distributed import Client
import cmocean as cm
import cartopy.crs as ccrs
import cartopy.feature as cft
from shapely import geometry
from textwrap import wrap
from tqdm.notebook import tqdm
xr.set_options(keep_attrs=True);  # cf_xarray works best when xarray keeps attributes by default

import os import numpy as np import xarray as xr import cftime import cf_xarray as cfxr import cf_xarray.units import pint_xarray from pint import application_registry as ureg import intake import matplotlib.pyplot as plt from matplotlib.patches import Patch import matplotlib as mpl from distributed import Client import cmocean as cm import cartopy.crs as ccrs import cartopy.feature as cft from shapely import geometry from textwrap import wrap from tqdm.notebook import tqdm xr.set_options(keep_attrs=True); # cf_xarray works best when xarray keeps attributes by default

In [6]:

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

import warnings warnings.simplefilter(action='ignore', category=FutureWarning)

In [7]:

from model_agnostic import patch_broken_conda_env, patch_dask_workers
patch_broken_conda_env()   # no-op on healthy conda envs
client = Client(threads_per_worker=1)
patch_dask_workers(client) # patch the dask workers too
client

from model_agnostic import patch_broken_conda_env, patch_dask_workers patch_broken_conda_env() # no-op on healthy conda envs client = Client(threads_per_worker=1) patch_dask_workers(client) # patch the dask workers too client

Out[7]:

Client

Client-d371c23d-8667-11f1-b8b6-000001f7fe80

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Launch dashboard in JupyterLab

Cluster Info

LocalCluster

926bd067

Dashboard: http://127.0.0.1:8787/status	Workers: 16
Total threads: 16	Total memory: 188.56 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-8581573f-6e28-499f-b5d3-faaaf25e2651

Comm: tcp://127.0.0.1:41047	Workers: 0
Dashboard: http://127.0.0.1:8787/status	Total threads: 0
Started: Just now	Total memory: 0 B

Workers

Worker: 0

Comm: tcp://127.0.0.1:42901	Total threads: 1
Dashboard: http://127.0.0.1:35847/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:43001
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-35r_2ipn

Worker: 1

Comm: tcp://127.0.0.1:42287	Total threads: 1
Dashboard: http://127.0.0.1:46871/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:44845
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-e4lrm1k1

Worker: 2

Comm: tcp://127.0.0.1:43233	Total threads: 1
Dashboard: http://127.0.0.1:41315/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:34387
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-jee_pr2a

Worker: 3

Comm: tcp://127.0.0.1:41551	Total threads: 1
Dashboard: http://127.0.0.1:41983/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:44339
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-d7_8k50m

Worker: 4

Comm: tcp://127.0.0.1:36907	Total threads: 1
Dashboard: http://127.0.0.1:45181/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:39629
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-qy0n2t7t

Worker: 5

Comm: tcp://127.0.0.1:38987	Total threads: 1
Dashboard: http://127.0.0.1:36269/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:40433
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-86ro9wrc

Worker: 6

Comm: tcp://127.0.0.1:36423	Total threads: 1
Dashboard: http://127.0.0.1:40927/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:38521
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-9jz0tx2_

Worker: 7

Comm: tcp://127.0.0.1:40475	Total threads: 1
Dashboard: http://127.0.0.1:42173/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:38819
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-124b6lz9

Worker: 8

Comm: tcp://127.0.0.1:33967	Total threads: 1
Dashboard: http://127.0.0.1:36811/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:40309
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-fi2c6ibt

Worker: 9

Comm: tcp://127.0.0.1:36673	Total threads: 1
Dashboard: http://127.0.0.1:37369/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:37211
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-df99zbj8

Worker: 10

Comm: tcp://127.0.0.1:40125	Total threads: 1
Dashboard: http://127.0.0.1:42753/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:36581
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-8nbkwgcx

Worker: 11

Comm: tcp://127.0.0.1:44735	Total threads: 1
Dashboard: http://127.0.0.1:36289/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:34883
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-z8wvvyd9

Worker: 12

Comm: tcp://127.0.0.1:36197	Total threads: 1
Dashboard: http://127.0.0.1:42325/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:41249
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-om26wogj

Worker: 13

Comm: tcp://127.0.0.1:37761	Total threads: 1
Dashboard: http://127.0.0.1:35897/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:38999
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-sqnsh6sw

Worker: 14

Comm: tcp://127.0.0.1:44277	Total threads: 1
Dashboard: http://127.0.0.1:43951/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:41845
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-wmva7mhk

Worker: 15

Comm: tcp://127.0.0.1:42615	Total threads: 1
Dashboard: http://127.0.0.1:36633/status	Memory: 11.78 GiB
Nanny: tcp://127.0.0.1:39605
Local directory: /jobfs/174557274.gadi-pbs/dask-scratch-space/worker-1ktwkozh

Define regions¶

In [8]:

regions = { # [minx, maxx, miny, maxy], using model longitude range (-280 to 80)
    "Global": [-280, 80, -90, 90],
    # "Arctic": [-280, 80, 65, 90],
    # "Southern Ocean": [-280, 80, -82, -63],
    # "ACC": [-280, 80, -63, -45],
    # "Southern Pacific": [-210, -70, -45, -20],
    # "Tropical Pacific": [-240, -100, -20, 20],
    # "North Pacific": [-240, -100, 20, 65],
    # "South Atlantic": [-60, 20, -45, -20],
    # "Tropical Atlantic": [-70, 20, -20, 20],
    "North Atlantic": [-100, 0, 20, 65],
    # "Indian": [30, 120, -45, 20],
    # "Aegean Sea": [18, 27.5, 34, 44],
    "Black Sea": [27.5, 43, 40.5, 48],
    # "Baltic Sea": [13, 30, 53, 58],
    # "Mediterranean Sea": [0, 35, 31, 41],
    # "Red Sea": [33, 44, 12, 29],
    # "Persian Gulf": [47, 56, 24, 31],
    # "White Sea": [31, 41, 63, 68],
}
regions = {k: dict(zip(["minx", "maxx", "miny", "maxy"], v)) for k, v in regions.items()}  # convert to dicts

for r, d in regions.items():
    for k, x in d.items():
        if k in ["minx", "maxx"] and x != max(-280, min(x, 80)):
            raise ValueError(f"{r} {k} = {x} is outside the range -280 to 80")

fig = plt.figure(figsize=(10, 4))

colors = mpl.color_sequences['tab20']
try:  # set Global color to white 
    colors[list(regions.keys()).index("Global")] = (1, 1, 1)
except ValueError:
    pass

ax = plt.axes(position=[0.05,0.05,0.7,0.9], projection=ccrs.Robinson())
ax.coastlines(resolution="110m")
ax.gridlines(draw_labels=False)

legend_elements = []
for i, (region, limits) in enumerate(regions.items()):
    ax.add_geometries([geometry.box(**limits)], crs=ccrs.PlateCarree(), color=colors[i], alpha=0.3)
    legend_elements.append(Patch(color=colors[i], alpha=0.3, label=region))

ax.legend(handles=legend_elements, bbox_to_anchor=(1.2, 1.00))
plt.title("Regions (BUG: incorrect shapes north of 65°N)")

regions = { # [minx, maxx, miny, maxy], using model longitude range (-280 to 80) "Global": [-280, 80, -90, 90], # "Arctic": [-280, 80, 65, 90], # "Southern Ocean": [-280, 80, -82, -63], # "ACC": [-280, 80, -63, -45], # "Southern Pacific": [-210, -70, -45, -20], # "Tropical Pacific": [-240, -100, -20, 20], # "North Pacific": [-240, -100, 20, 65], # "South Atlantic": [-60, 20, -45, -20], # "Tropical Atlantic": [-70, 20, -20, 20], "North Atlantic": [-100, 0, 20, 65], # "Indian": [30, 120, -45, 20], # "Aegean Sea": [18, 27.5, 34, 44], "Black Sea": [27.5, 43, 40.5, 48], # "Baltic Sea": [13, 30, 53, 58], # "Mediterranean Sea": [0, 35, 31, 41], # "Red Sea": [33, 44, 12, 29], # "Persian Gulf": [47, 56, 24, 31], # "White Sea": [31, 41, 63, 68], } regions = {k: dict(zip(["minx", "maxx", "miny", "maxy"], v)) for k, v in regions.items()} # convert to dicts for r, d in regions.items(): for k, x in d.items(): if k in ["minx", "maxx"] and x != max(-280, min(x, 80)): raise ValueError(f"{r} {k} = {x} is outside the range -280 to 80") fig = plt.figure(figsize=(10, 4)) colors = mpl.color_sequences['tab20'] try: # set Global color to white colors[list(regions.keys()).index("Global")] = (1, 1, 1) except ValueError: pass ax = plt.axes(position=[0.05,0.05,0.7,0.9], projection=ccrs.Robinson()) ax.coastlines(resolution="110m") ax.gridlines(draw_labels=False) legend_elements = [] for i, (region, limits) in enumerate(regions.items()): ax.add_geometries([geometry.box(**limits)], crs=ccrs.PlateCarree(), color=colors[i], alpha=0.3) legend_elements.append(Patch(color=colors[i], alpha=0.3, label=region)) ax.legend(handles=legend_elements, bbox_to_anchor=(1.2, 1.00)) plt.title("Regions (BUG: incorrect shapes north of 65°N)")

Out[8]:

Text(0.5, 1.0, 'Regions (BUG: incorrect shapes north of 65°N)')

Load data from ACCESS-OM3¶

In [9]:

if model_name == "ACCESS-CM3":
    catalogs = [esm_file]
else:
    catalogs = [
                '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-5165c0f8/datastore.json',
                esm_file,
                # '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-gm1-d968c801/datastore.json',
                # '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-gm2-5dc49da6/datastore.json',
                # '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-gm3-da330542/datastore.json',
                # '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-gm4-9fd08880/datastore.json',
                # '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-gm5-9b5dbfa9/datastore.json',
                # '/g/data/ol01/access-om3-output/access-om3-025/25km-iaf-test-for-AK-expt-7df5ef4c/datastore.json',
            ]
catalogs

if model_name == "ACCESS-CM3": catalogs = [esm_file] else: catalogs = [ '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-5165c0f8/datastore.json', esm_file, # '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-gm1-d968c801/datastore.json', # '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-gm2-5dc49da6/datastore.json', # '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-gm3-da330542/datastore.json', # '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-gm4-9fd08880/datastore.json', # '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-gm5-9b5dbfa9/datastore.json', # '/g/data/ol01/access-om3-output/access-om3-025/25km-iaf-test-for-AK-expt-7df5ef4c/datastore.json', ] catalogs

Out[9]:

['/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf-1.0-beta-5165c0f8/datastore.json',
 '/g/data/ol01/outputs/access-om3-25km/MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/datastore.json']

In [10]:

om3datastores = { os.path.normpath(c).split(os.sep)[-2]:
               intake.open_esm_datastore(c,
                                         columns_with_iterables=[
                                            "variable",
                                            "variable_long_name",
                                            "variable_standard_name",
                                            "variable_cell_methods",
                                            "variable_units"]
                                        )
              for c in catalogs }
om3datastores

om3datastores = { os.path.normpath(c).split(os.sep)[-2]: intake.open_esm_datastore(c, columns_with_iterables=[ "variable", "variable_long_name", "variable_standard_name", "variable_cell_methods", "variable_units"] ) for c in catalogs } om3datastores

Out[10]:

{'MC_25km_jra_iaf-1.0-beta-5165c0f8': <datastore catalog with 22 dataset(s) from 6042 asset(s)>,
 'MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36': <datastore catalog with 16 dataset(s) from 2190 asset(s)>}

In [11]:

om3varnames = [ 'thetao', 'so' ]

om3chunks = {"xh": -1, "yh": -1, "z_l": -1}  # remove "yh": -1 for resolution finer than 25km
if model_name == "ACCESS-CM3":
    om3chunks["time"] = 1  # CM3 stores yearly files; avoid ~6 GB chunks

om3vars = {
    vname: {expt: ds.search(variable=vname).to_dask(
                xarray_open_kwargs = dict(
                    chunks=om3chunks,
                    decode_timedelta=True
                )
            )[vname]
            for expt, ds in om3datastores.items()
          }
    for vname in om3varnames
    }

# omit latitudes with grid bug in this run https://github.com/ACCESS-NRI/ocean_model_grid_generator/issues/7
for vname, d in om3vars.items():
    try:
        d['25km-iaf-test-for-AK-expt-7df5ef4c'] = d['25km-iaf-test-for-AK-expt-7df5ef4c'].isel(yh=slice(10, None))
    except ValueError:
        d['25km-iaf-test-for-AK-expt-7df5ef4c'] = d['25km-iaf-test-for-AK-expt-7df5ef4c'].isel(yq=slice(10, None))
    except KeyError:
        pass

if IAF:
    for vname, d in om3vars.items():
        for expt in d:
            try:
                d[expt] = d[expt].convert_calendar("proleptic_gregorian", use_cftime=True)
            except KeyError:
                pass

om3varnames = [ 'thetao', 'so' ] om3chunks = {"xh": -1, "yh": -1, "z_l": -1} # remove "yh": -1 for resolution finer than 25km if model_name == "ACCESS-CM3": om3chunks["time"] = 1 # CM3 stores yearly files; avoid ~6 GB chunks om3vars = { vname: {expt: ds.search(variable=vname).to_dask( xarray_open_kwargs = dict( chunks=om3chunks, decode_timedelta=True ) )[vname] for expt, ds in om3datastores.items() } for vname in om3varnames } # omit latitudes with grid bug in this run https://github.com/ACCESS-NRI/ocean_model_grid_generator/issues/7 for vname, d in om3vars.items(): try: d['25km-iaf-test-for-AK-expt-7df5ef4c'] = d['25km-iaf-test-for-AK-expt-7df5ef4c'].isel(yh=slice(10, None)) except ValueError: d['25km-iaf-test-for-AK-expt-7df5ef4c'] = d['25km-iaf-test-for-AK-expt-7df5ef4c'].isel(yq=slice(10, None)) except KeyError: pass if IAF: for vname, d in om3vars.items(): for expt in d: try: d[expt] = d[expt].convert_calendar("proleptic_gregorian", use_cftime=True) except KeyError: pass

Load data from ACCESS-OM2¶

use control - see https://forum.access-hive.org.au/t/access-om2-control-experiments/258#p-747-ryf-7

In [12]:

if model_name == "ACCESS-CM3":
    data_name = 'ACCESS-CM2'
    om2exptname = 'cj877'  # CM2 control archive on /g/data/p73 (same reference as pPV.ipynb)
else:
    data_name = 'ACCESS-OM2'
    if IAF:
        om2exptname = '025deg_jra55_iaf_omip2_cycle1'
    else:
        om2exptname = '025deg_jra55_ryf9091_gadi'  # monthly temp only for 1900-1903, and 2300-, with annual data in the gap
    om2datastores = {om2exptname: intake.cat.access_nri[om2exptname]}

if model_name == "ACCESS-CM3": data_name = 'ACCESS-CM2' om2exptname = 'cj877' # CM2 control archive on /g/data/p73 (same reference as pPV.ipynb) else: data_name = 'ACCESS-OM2' if IAF: om2exptname = '025deg_jra55_iaf_omip2_cycle1' else: om2exptname = '025deg_jra55_ryf9091_gadi' # monthly temp only for 1900-1903, and 2300-, with annual data in the gap om2datastores = {om2exptname: intake.cat.access_nri[om2exptname]}

In [13]:

om2varnames = [ 'temp', 'salt' ]

if model_name == "ACCESS-CM3":
    # Reference: ACCESS-CM2 cj877 control archive (same source as pPV.ipynb and
    # Overturning_in_ACCESS_OM3.ipynb). Limit to the last 600 months to match
    # the CM3 record length; each cj877 file holds 6 months.
    from pathlib import Path
    cm2_dir = Path("/g/data/p73/archive/non-CMIP/ACCESS-CM2/cj877/history/ocn")
    n_months = 600

    def _load_cm2(varname):
        files = sorted(cm2_dir.glob(f"ocean-3d-{varname}-1-monthly-mean-ym_*.nc"))
        if not files:
            raise FileNotFoundError(f"No cj877 monthly files found for {varname!r}")
        files = files[-((n_months + 5) // 6):]
        ds = xr.open_mfdataset(
            files,
            combine="by_coords",
            parallel=True,
            chunks={"time": 1, "xt_ocean": -1, "yt_ocean": -1, "st_ocean": -1},
            decode_timedelta=True,
            use_cftime=True,  # cj877 model years (~453-503) can't be datetime64
            data_vars="minimal", coords="minimal", compat="override",
        )
        return ds[varname]

    om2vars = { vname: {om2exptname: _load_cm2(vname)} for vname in om2varnames }

    # Relabel the CM2 control-run times onto the CM3 time axis so the panels can
    # share the x-axis (same approach as Overturning_in_ACCESS_OM3.ipynb) — this
    # also avoids mixing cftime and datetime64 axes, which matplotlib can't share.
    cm3_time = next(iter(om3vars[om3varnames[0]].values())).time
    for vname, dd in om2vars.items():
        for expt, da in dd.items():
            n = min(da.sizes["time"], cm3_time.sizes["time"])
            if da.sizes["time"] != cm3_time.sizes["time"]:
                print(f"Aligning {expt} {vname} with last {n} time steps")
            dd[expt] = (da.isel(time=slice(-n, None))
                          .assign_coords(time=("time", cm3_time.isel(time=slice(-n, None)).values)))

else:
    om2vars = {
        vname: {expt: ds.search(variable=vname, frequency="1mon").to_dask(
                    xarray_open_kwargs = dict(
                        chunks={"xt_ocean": -1, "yt_ocean": -1, "st_ocean" : -1},  # remove "yt_ocean": -1 for resolution finer than 25km
                        decode_timedelta=True
                    )
                )[vname]
                for expt, ds in om2datastores.items()
              }
        for vname in om2varnames
        }

    if IAF:
        for vname, d in om2vars.items():
            for expt in d:
                try:
                    d[expt] = d[expt].convert_calendar("proleptic_gregorian", use_cftime=True)
                except KeyError:
                    pass

om2varnames = [ 'temp', 'salt' ] if model_name == "ACCESS-CM3": # Reference: ACCESS-CM2 cj877 control archive (same source as pPV.ipynb and # Overturning_in_ACCESS_OM3.ipynb). Limit to the last 600 months to match # the CM3 record length; each cj877 file holds 6 months. from pathlib import Path cm2_dir = Path("/g/data/p73/archive/non-CMIP/ACCESS-CM2/cj877/history/ocn") n_months = 600 def _load_cm2(varname): files = sorted(cm2_dir.glob(f"ocean-3d-{varname}-1-monthly-mean-ym_*.nc")) if not files: raise FileNotFoundError(f"No cj877 monthly files found for {varname!r}") files = files[-((n_months + 5) // 6):] ds = xr.open_mfdataset( files, combine="by_coords", parallel=True, chunks={"time": 1, "xt_ocean": -1, "yt_ocean": -1, "st_ocean": -1}, decode_timedelta=True, use_cftime=True, # cj877 model years (~453-503) can't be datetime64 data_vars="minimal", coords="minimal", compat="override", ) return ds[varname] om2vars = { vname: {om2exptname: _load_cm2(vname)} for vname in om2varnames } # Relabel the CM2 control-run times onto the CM3 time axis so the panels can # share the x-axis (same approach as Overturning_in_ACCESS_OM3.ipynb) — this # also avoids mixing cftime and datetime64 axes, which matplotlib can't share. cm3_time = next(iter(om3vars[om3varnames[0]].values())).time for vname, dd in om2vars.items(): for expt, da in dd.items(): n = min(da.sizes["time"], cm3_time.sizes["time"]) if da.sizes["time"] != cm3_time.sizes["time"]: print(f"Aligning {expt} {vname} with last {n} time steps") dd[expt] = (da.isel(time=slice(-n, None)) .assign_coords(time=("time", cm3_time.isel(time=slice(-n, None)).values))) else: om2vars = { vname: {expt: ds.search(variable=vname, frequency="1mon").to_dask( xarray_open_kwargs = dict( chunks={"xt_ocean": -1, "yt_ocean": -1, "st_ocean" : -1}, # remove "yt_ocean": -1 for resolution finer than 25km decode_timedelta=True ) )[vname] for expt, ds in om2datastores.items() } for vname in om2varnames } if IAF: for vname, d in om2vars.items(): for expt in d: try: d[expt] = d[expt].convert_calendar("proleptic_gregorian", use_cftime=True) except KeyError: pass

Calculate Hovmollers for each region - SLOW!¶

BUG: horizontal mean needs to be area-weighted - use xgcm?¶

In [14]:

%%time
om3vars_hov = {
    region:
    {
        vname: {
                expt: da.sel(yh=slice(b['miny'], b['maxy']))\
                        .sel(xh=slice(b['minx'], b['maxx']))\
                        .mean('yh').mean('xh').load()
                for expt, da in tqdm(vdatadict.items(), desc=f'      {vname} experiments', )
              }
        for vname, vdatadict in tqdm(om3vars.items(), desc=f'   {region} variables')
    }
    for region, b in tqdm(regions.items(), desc='regions')
}

%%time om3vars_hov = { region: { vname: { expt: da.sel(yh=slice(b['miny'], b['maxy']))\ .sel(xh=slice(b['minx'], b['maxx']))\ .mean('yh').mean('xh').load() for expt, da in tqdm(vdatadict.items(), desc=f' {vname} experiments', ) } for vname, vdatadict in tqdm(om3vars.items(), desc=f' {region} variables') } for region, b in tqdm(regions.items(), desc='regions') }

CPU times: user 5min 46s, sys: 2min 14s, total: 8min
Wall time: 27min 44s

BUG: horizontal mean needs to be area-weighted - use xgcm?¶

In [15]:

%%time
om2vars_hov = {
    region:
    {
        vname: {
                expt: da.sel(yt_ocean=slice(b['miny'], b['maxy']))\
                        .sel(xt_ocean=slice(b['minx'], b['maxx']))\
                        .mean('yt_ocean').mean('xt_ocean').load()
                for expt, da in tqdm(vdatadict.items(), desc=f'      {vname} experiments', )
              }
        for vname, vdatadict in tqdm(om2vars.items(), desc=f'   {region} variables')
    }
    for region, b in tqdm(regions.items(), desc='regions')
}

%%time om2vars_hov = { region: { vname: { expt: da.sel(yt_ocean=slice(b['miny'], b['maxy']))\ .sel(xt_ocean=slice(b['minx'], b['maxx']))\ .mean('yt_ocean').mean('xt_ocean').load() for expt, da in tqdm(vdatadict.items(), desc=f' {vname} experiments', ) } for vname, vdatadict in tqdm(om2vars.items(), desc=f' {region} variables') } for region, b in tqdm(regions.items(), desc='regions') }

CPU times: user 1min 48s, sys: 38.4 s, total: 2min 26s
Wall time: 10min 15s

Plot Hovmollers for each region¶

In [16]:

def doplots(om3variable = "thetao",
            om2variable = "temp",
            vmax = None, mkmdfig=True):
    for region, d in om3vars_hov.items():
        npanels = len(d[om3variable]) + len(om2vars_hov[region][om2variable])
        nrows = (npanels+1)//2
        fig, axs = plt.subplots(nrows, 2,
                                figsize=(9, 3*nrows),
                                sharex=True, sharey=True)
        fig.suptitle(f'{region} depth-time Hovmollers (BUG: not weighted by cell area)')

        if vmax == None: # calculate max abs over all panels (different for each region)
            vmax = 0
            for i, (expt, da) in enumerate(d[om3variable].items(), start=1):
                if i == 1:
                    ref = da.isel(time=0)  # use the same reference state for all om3 expts in this region
                vmax = max(vmax, abs(da - ref).max().data)
            for i2, (expt, da) in enumerate(om2vars_hov[region][om2variable].items(), start=i+1):
                if i2 == i+1:
                    ref = da.isel(time=0)  # use the same reference state for all om2 expts in this region
                vmax = max(vmax, abs(da - ref).max().data)

        # ACCESS-OM3 (or ACCESS-CM3)
        for i, (expt, da) in enumerate(d[om3variable].items(), start=1):
            plt.subplot(nrows, 2, i)
            if i == 1:
                ref = da.isel(time=0)  # use the same reference state for all om3 expts in this region
            p = (da - ref).transpose().plot.contourf(
                levels=101, vmin=-vmax, vmax=vmax, extend="both", cmap="seismic", add_colorbar=False,
            )
            (da - ref).transpose().plot.contour(
                levels=25, vmin=-vmax, vmax=vmax, add_colorbar=False, colors=["k"], linewidths=[0.2], linestyles=["-"]
            )
            try:
                zmax = da["z_l"][np.where(np.isnan(da.isel(time=0)))[0][0] - 1]
                plt.gca().set_ylim([0, zmax])  # crop to deepest wet cell
            except IndexError:
                plt.gca().set_ylim([0, None])  # no dry cells so show all
            plt.gca().invert_yaxis()
            plt.title(f"{model_name} {expt} {om3variable}", fontsize=8)
            plt.ylabel('Depth [m]')
            label = f"{model_name}: {da.attrs['long_name']} ({om3variable}) difference [{da.attrs['units']}]"
    
        # ACCESS-OM2 (or ACCESS-CM2)
        for i2, (expt, da) in enumerate(om2vars_hov[region][om2variable].items(), start=i+1):
            plt.subplot(nrows, 2, i2)
            if i2 == i+1:
                ref = da.isel(time=0)  # use the same reference state for all om2 expts in this region
            (da - ref).transpose().plot.contourf(
                levels=101, vmin=-vmax, vmax=vmax, extend="both", cmap="seismic", add_colorbar=False,
            )
            (da - ref).transpose().plot.contour(
                levels=25, vmin=-vmax, vmax=vmax, add_colorbar=False, colors=["k"], linewidths=[0.2], linestyles=["-"]
            )
            try:
                zmax = da["st_ocean"][np.where(np.isnan(da.isel(time=0)))[0][0] - 1]
                plt.gca().set_ylim([0, zmax])  # crop to deepest wet cell
            except IndexError:
                plt.gca().set_ylim([0, None])  # no dry cells so show all
            plt.gca().invert_yaxis()
            plt.title(f"{data_name} {expt} {om2variable}", fontsize=8)
            plt.ylabel('Depth [m]')
            label += f"\n{data_name}: {da.attrs['long_name']} ({om2variable}) difference [{da.attrs['units']}]"
    
        try:  # drop empty subfigs (if any)
            fig.delaxes(axs.flatten()[i2])
        except IndexError:
            pass
        for ax in fig.get_axes():
            ax.label_outer()
        plt.tight_layout()
        fig.colorbar(p, ax=axs, orientation='horizontal',
                     fraction=.1, shrink=0.7, aspect=40,
                     label=label)
        if mkmdfig:
            mkmd.savefig(fig, f"Horizontal mean temperature and salinity Hovmollers: {region} ({om3variable})", 
                         f"{model_name} and {data_name} horizontal mean "+om3variable+" Hovmollers: drift from initial condition, vs depth and time. [GitHub issue: Hovmoller temperature anomaly (depth-time)](https://github.com/ACCESS-Community-Hub/access-om3-paper-1/issues/8)")            

def doplots(om3variable = "thetao", om2variable = "temp", vmax = None, mkmdfig=True): for region, d in om3vars_hov.items(): npanels = len(d[om3variable]) + len(om2vars_hov[region][om2variable]) nrows = (npanels+1)//2 fig, axs = plt.subplots(nrows, 2, figsize=(9, 3*nrows), sharex=True, sharey=True) fig.suptitle(f'{region} depth-time Hovmollers (BUG: not weighted by cell area)') if vmax == None: # calculate max abs over all panels (different for each region) vmax = 0 for i, (expt, da) in enumerate(d[om3variable].items(), start=1): if i == 1: ref = da.isel(time=0) # use the same reference state for all om3 expts in this region vmax = max(vmax, abs(da - ref).max().data) for i2, (expt, da) in enumerate(om2vars_hov[region][om2variable].items(), start=i+1): if i2 == i+1: ref = da.isel(time=0) # use the same reference state for all om2 expts in this region vmax = max(vmax, abs(da - ref).max().data) # ACCESS-OM3 (or ACCESS-CM3) for i, (expt, da) in enumerate(d[om3variable].items(), start=1): plt.subplot(nrows, 2, i) if i == 1: ref = da.isel(time=0) # use the same reference state for all om3 expts in this region p = (da - ref).transpose().plot.contourf( levels=101, vmin=-vmax, vmax=vmax, extend="both", cmap="seismic", add_colorbar=False, ) (da - ref).transpose().plot.contour( levels=25, vmin=-vmax, vmax=vmax, add_colorbar=False, colors=["k"], linewidths=[0.2], linestyles=["-"] ) try: zmax = da["z_l"][np.where(np.isnan(da.isel(time=0)))[0][0] - 1] plt.gca().set_ylim([0, zmax]) # crop to deepest wet cell except IndexError: plt.gca().set_ylim([0, None]) # no dry cells so show all plt.gca().invert_yaxis() plt.title(f"{model_name} {expt} {om3variable}", fontsize=8) plt.ylabel('Depth [m]') label = f"{model_name}: {da.attrs['long_name']} ({om3variable}) difference [{da.attrs['units']}]" # ACCESS-OM2 (or ACCESS-CM2) for i2, (expt, da) in enumerate(om2vars_hov[region][om2variable].items(), start=i+1): plt.subplot(nrows, 2, i2) if i2 == i+1: ref = da.isel(time=0) # use the same reference state for all om2 expts in this region (da - ref).transpose().plot.contourf( levels=101, vmin=-vmax, vmax=vmax, extend="both", cmap="seismic", add_colorbar=False, ) (da - ref).transpose().plot.contour( levels=25, vmin=-vmax, vmax=vmax, add_colorbar=False, colors=["k"], linewidths=[0.2], linestyles=["-"] ) try: zmax = da["st_ocean"][np.where(np.isnan(da.isel(time=0)))[0][0] - 1] plt.gca().set_ylim([0, zmax]) # crop to deepest wet cell except IndexError: plt.gca().set_ylim([0, None]) # no dry cells so show all plt.gca().invert_yaxis() plt.title(f"{data_name} {expt} {om2variable}", fontsize=8) plt.ylabel('Depth [m]') label += f"\n{data_name}: {da.attrs['long_name']} ({om2variable}) difference [{da.attrs['units']}]" try: # drop empty subfigs (if any) fig.delaxes(axs.flatten()[i2]) except IndexError: pass for ax in fig.get_axes(): ax.label_outer() plt.tight_layout() fig.colorbar(p, ax=axs, orientation='horizontal', fraction=.1, shrink=0.7, aspect=40, label=label) if mkmdfig: mkmd.savefig(fig, f"Horizontal mean temperature and salinity Hovmollers: {region} ({om3variable})", f"{model_name} and {data_name} horizontal mean "+om3variable+" Hovmollers: drift from initial condition, vs depth and time. [GitHub issue: Hovmoller temperature anomaly (depth-time)](https://github.com/ACCESS-Community-Hub/access-om3-paper-1/issues/8)")

In [17]:

doplots(om3variable = "thetao",
        om2variable = "temp",
        mkmdfig=True)

doplots(om3variable = "thetao", om2variable = "temp", mkmdfig=True)

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In [18]:

#cb: only exporting the one with a vmax specified -- temp
doplots(om3variable = "thetao",
        om2variable = "temp",
        vmax = 1)

#cb: only exporting the one with a vmax specified -- temp doplots(om3variable = "thetao", om2variable = "temp", vmax = 1)

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In [19]:

doplots(om3variable = "so",
        om2variable = "salt",
        mkmdfig=True)

doplots(om3variable = "so", om2variable = "salt", mkmdfig=True)

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In [20]:

#cb: only exporting the one with a vmax specified -- salinity
doplots(om3variable = "so",
        om2variable = "salt",
        vmax = 0.2)

#cb: only exporting the one with a vmax specified -- salinity doplots(om3variable = "so", om2variable = "salt", vmax = 0.2)

Saved /g/data/tm70/cyb561/access-om3-paper-1-runs/MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/notebooks/mkfigs_output_MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/mkmd/temp-salt-vs-depth-time_10.png
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This title already exists in the notebook markdown – appending an additional figure.
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Adding entry to per-notebook markdown: /g/data/tm70/cyb561/access-om3-paper-1-runs/MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/notebooks/mkfigs_output_MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/mkmd/temp-salt-vs-depth-time.md
This title already exists in the notebook markdown – appending an additional figure.
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Adding entry to per-notebook markdown: /g/data/tm70/cyb561/access-om3-paper-1-runs/MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/notebooks/mkfigs_output_MC_25km_jra_iaf+wombatlite_bs2-mpudig-backscat2-0578cc36/mkmd/temp-salt-vs-depth-time.md
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In [21]:

client.close()

client.close()