Time-based operations

In this notebook, we show how to work with operations along the time axis. The sample files used here are small enough so it’s not mandatory to use a DASK cluster but compute cells can typically take 30s/1min. Feel free to add a dask cluster using the templates.

[1]:

%matplotlib inline
import warnings
warnings.filterwarnings("ignore")

[2]:

import subprocess as sp

[3]:

import xarray as xr

Open the dataset and inspect it:

[4]:

dataurl = 'http://35.188.34.63:8080/thredds/dodsC/OM4p5/'

ds = xr.open_dataset(f'{dataurl}/ocean_monthly_z.200301-200712.nc4',
                     chunks={'time':1, 'z_l': 1}, engine='pydap')

[5]:

ds

[5]:

<xarray.Dataset>
Dimensions:       (nv: 2, time: 60, xh: 720, xq: 720, yh: 576, yq: 576, z_i: 36, z_l: 35)
Coordinates:
  * nv            (nv) float64 1.0 2.0
  * xh            (xh) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75
  * xq            (xq) float64 -299.5 -299.0 -298.5 -298.0 ... 59.0 59.5 60.0
  * yh            (yh) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89
  * yq            (yq) float64 -77.82 -77.63 -77.45 -77.26 ... 89.58 89.79 90.0
  * z_i           (z_i) float64 0.0 5.0 15.0 25.0 ... 5.75e+03 6.25e+03 6.75e+03
  * z_l           (z_l) float64 2.5 10.0 20.0 32.5 ... 5.5e+03 6e+03 6.5e+03
  * time          (time) object 2003-01-16 12:00:00 ... 2007-12-16 12:00:00
Data variables:
    Coriolis      (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello     (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello_bu  (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello_cu  (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello_cv  (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    deptho        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dxCu          (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dxCv          (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dxt           (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dyCu          (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dyCv          (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dyt           (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat_c      (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat_u      (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat_v      (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon_c      (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon_u      (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon_v      (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    hfgeou        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    sftof         (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    thkcello      (z_l, yh, xh) float32 dask.array<chunksize=(1, 576, 720), meta=np.ndarray>
    wet           (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    wet_c         (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    wet_u         (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    wet_v         (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    average_DT    (time) timedelta64[ns] dask.array<chunksize=(1,), meta=np.ndarray>
    average_T1    (time) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>
    average_T2    (time) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>
    so            (time, z_l, yh, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    time_bnds     (time, nv) timedelta64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
    thetao        (time, z_l, yh, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    umo           (time, z_l, yh, xq) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    uo            (time, z_l, yh, xq) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    vmo           (time, z_l, yq, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    vo            (time, z_l, yq, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    volcello      (time, z_l, yh, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    zos           (time, yh, xh) float32 dask.array<chunksize=(1, 576, 720), meta=np.ndarray>
Attributes:
    filename:                        ocean_monthly.200301-200712.zos.nc
    title:                           OM4p5_IAF_BLING_CFC_abio_csf_mle200
    associated_files:                areacello: 20030101.ocean_static.nc
    grid_type:                       regular
    grid_tile:                       N/A
    external_variables:              areacello
    DODS_EXTRA.Unlimited_Dimension:  time

1. Computing climatologies for SST

1.1 Annual climatology

We have a 5 years monthly dataset that we can use to compute a climatology:

[6]:

climato_sst = ds['thetao'].sel(z_l=2.5).mean(dim='time')

[7]:

climato_sst

[7]:

<xarray.DataArray 'thetao' (yh: 576, xh: 720)>
dask.array<mean_agg-aggregate, shape=(576, 720), dtype=float32, chunksize=(576, 720), chunktype=numpy.ndarray>
Coordinates:
  * xh       (xh) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75
  * yh       (yh) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89
    z_l      float64 2.5

The result is instaneous because none of the computation actually happened. This is called lazy evaluation, xarray only returns the structure of the resulting data array. Calling plot will explicitly trigger compute:

[8]:

climato_sst.plot(figsize=[10,8], vmin=-2, vmax=38, cmap='gist_ncar')

[8]:

<matplotlib.collections.QuadMesh at 0x1023ab198>

../_images/notebooks_time_operations_14_1.png

1.2 Monthly climatology

Using groupby, we can group data by month and then average over the years, resulting in a monthly climatology:

[9]:

sst = ds['thetao'].sel(z_l=2.5)
monthly_clim_sst = sst.groupby(sst['time'].dt.month).mean(dim='time')

[10]:

monthly_clim_sst

[10]:

<xarray.DataArray 'thetao' (month: 12, yh: 576, xh: 720)>
dask.array<stack, shape=(12, 576, 720), dtype=float32, chunksize=(1, 576, 720), chunktype=numpy.ndarray>
Coordinates:
  * xh       (xh) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75
  * yh       (yh) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89
    z_l      float64 2.5
  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12

Let’s look at the climatological seasonal cycle in zonal average.

NB: For an irregular grid such as this one, the x-mean is not a correct zonal mean in the arctic. See tutorial on spatial averages for correct computation.

[11]:

monthly_clim_sst.mean(dim='xh').plot(vmin=-2, vmax=35, cmap='jet', x='month')

[11]:

<matplotlib.collections.QuadMesh at 0x115bb15f8>

../_images/notebooks_time_operations_20_1.png

1.3 Anomaly to seasonal cycle

[12]:

anom = sst.groupby(sst['time'].dt.month) - monthly_clim_sst

[13]:

anom

[13]:

<xarray.DataArray 'thetao' (time: 60, yh: 576, xh: 720)>
dask.array<getitem, shape=(60, 576, 720), dtype=float32, chunksize=(1, 576, 720), chunktype=numpy.ndarray>
Coordinates:
    z_l      float64 2.5
  * xh       (xh) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75
  * yh       (yh) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89
  * time     (time) object 2003-01-16 12:00:00 ... 2007-12-16 12:00:00
    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12

[14]:

anom.mean(dim='xh').plot(vmin=-1, vmax=1, cmap='bwr', x='time')

[14]:

<matplotlib.collections.QuadMesh at 0x115bee5c0>

../_images/notebooks_time_operations_24_1.png

It’s a bit noisy but We can make it annual with a new groupby:

[15]:

yearly_anom = anom.groupby(anom['time'].dt.year).mean(dim='time')

[16]:

yearly_anom

[16]:

<xarray.DataArray 'thetao' (year: 5, yh: 576, xh: 720)>
dask.array<stack, shape=(5, 576, 720), dtype=float32, chunksize=(1, 576, 720), chunktype=numpy.ndarray>
Coordinates:
    z_l      float64 2.5
  * xh       (xh) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75
  * yh       (yh) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89
  * year     (year) int64 2003 2004 2005 2006 2007

[17]:

yearly_anom.mean(dim='xh').plot(vmin=-1, vmax=1, cmap='bwr', x='year')

[17]:

<matplotlib.collections.QuadMesh at 0x116397438>

../_images/notebooks_time_operations_28_1.png

2. Selecting based on dates

Dates in the dataset are interpreted as time objects, which makes it easy to select a range of dates once you are familiar with how to write the query.

[18]:

ds['time']

[18]:

<xarray.DataArray 'time' (time: 60)>
array([cftime.DatetimeNoLeap(2003, 1, 16, 12, 0, 0, 0, 2, 16),
       cftime.DatetimeNoLeap(2003, 2, 15, 0, 0, 0, 0, 4, 46),
       cftime.DatetimeNoLeap(2003, 3, 16, 12, 0, 0, 0, 5, 75),
       cftime.DatetimeNoLeap(2003, 4, 16, 0, 0, 0, 0, 1, 106),
       cftime.DatetimeNoLeap(2003, 5, 16, 12, 0, 0, 0, 3, 136),
       cftime.DatetimeNoLeap(2003, 6, 16, 0, 0, 0, 0, 6, 167),
       cftime.DatetimeNoLeap(2003, 7, 16, 12, 0, 0, 0, 1, 197),
       cftime.DatetimeNoLeap(2003, 8, 16, 12, 0, 0, 0, 4, 228),
       cftime.DatetimeNoLeap(2003, 9, 16, 0, 0, 0, 0, 0, 259),
       cftime.DatetimeNoLeap(2003, 10, 16, 12, 0, 0, 0, 2, 289),
       cftime.DatetimeNoLeap(2003, 11, 16, 0, 0, 0, 0, 5, 320),
       cftime.DatetimeNoLeap(2003, 12, 16, 12, 0, 0, 0, 0, 350),
       cftime.DatetimeNoLeap(2004, 1, 16, 12, 0, 0, 0, 3, 16),
       cftime.DatetimeNoLeap(2004, 2, 15, 0, 0, 0, 0, 5, 46),
       cftime.DatetimeNoLeap(2004, 3, 16, 12, 0, 0, 0, 6, 75),
       cftime.DatetimeNoLeap(2004, 4, 16, 0, 0, 0, 0, 2, 106),
       cftime.DatetimeNoLeap(2004, 5, 16, 12, 0, 0, 0, 4, 136),
       cftime.DatetimeNoLeap(2004, 6, 16, 0, 0, 0, 0, 0, 167),
       cftime.DatetimeNoLeap(2004, 7, 16, 12, 0, 0, 0, 2, 197),
       cftime.DatetimeNoLeap(2004, 8, 16, 12, 0, 0, 0, 5, 228),
       cftime.DatetimeNoLeap(2004, 9, 16, 0, 0, 0, 0, 1, 259),
       cftime.DatetimeNoLeap(2004, 10, 16, 12, 0, 0, 0, 3, 289),
       cftime.DatetimeNoLeap(2004, 11, 16, 0, 0, 0, 0, 6, 320),
       cftime.DatetimeNoLeap(2004, 12, 16, 12, 0, 0, 0, 1, 350),
       cftime.DatetimeNoLeap(2005, 1, 16, 12, 0, 0, 0, 4, 16),
       cftime.DatetimeNoLeap(2005, 2, 15, 0, 0, 0, 0, 6, 46),
       cftime.DatetimeNoLeap(2005, 3, 16, 12, 0, 0, 0, 0, 75),
       cftime.DatetimeNoLeap(2005, 4, 16, 0, 0, 0, 0, 3, 106),
       cftime.DatetimeNoLeap(2005, 5, 16, 12, 0, 0, 0, 5, 136),
       cftime.DatetimeNoLeap(2005, 6, 16, 0, 0, 0, 0, 1, 167),
       cftime.DatetimeNoLeap(2005, 7, 16, 12, 0, 0, 0, 3, 197),
       cftime.DatetimeNoLeap(2005, 8, 16, 12, 0, 0, 0, 6, 228),
       cftime.DatetimeNoLeap(2005, 9, 16, 0, 0, 0, 0, 2, 259),
       cftime.DatetimeNoLeap(2005, 10, 16, 12, 0, 0, 0, 4, 289),
       cftime.DatetimeNoLeap(2005, 11, 16, 0, 0, 0, 0, 0, 320),
       cftime.DatetimeNoLeap(2005, 12, 16, 12, 0, 0, 0, 2, 350),
       cftime.DatetimeNoLeap(2006, 1, 16, 12, 0, 0, 0, 5, 16),
       cftime.DatetimeNoLeap(2006, 2, 15, 0, 0, 0, 0, 0, 46),
       cftime.DatetimeNoLeap(2006, 3, 16, 12, 0, 0, 0, 1, 75),
       cftime.DatetimeNoLeap(2006, 4, 16, 0, 0, 0, 0, 4, 106),
       cftime.DatetimeNoLeap(2006, 5, 16, 12, 0, 0, 0, 6, 136),
       cftime.DatetimeNoLeap(2006, 6, 16, 0, 0, 0, 0, 2, 167),
       cftime.DatetimeNoLeap(2006, 7, 16, 12, 0, 0, 0, 4, 197),
       cftime.DatetimeNoLeap(2006, 8, 16, 12, 0, 0, 0, 0, 228),
       cftime.DatetimeNoLeap(2006, 9, 16, 0, 0, 0, 0, 3, 259),
       cftime.DatetimeNoLeap(2006, 10, 16, 12, 0, 0, 0, 5, 289),
       cftime.DatetimeNoLeap(2006, 11, 16, 0, 0, 0, 0, 1, 320),
       cftime.DatetimeNoLeap(2006, 12, 16, 12, 0, 0, 0, 3, 350),
       cftime.DatetimeNoLeap(2007, 1, 16, 12, 0, 0, 0, 6, 16),
       cftime.DatetimeNoLeap(2007, 2, 15, 0, 0, 0, 0, 1, 46),
       cftime.DatetimeNoLeap(2007, 3, 16, 12, 0, 0, 0, 2, 75),
       cftime.DatetimeNoLeap(2007, 4, 16, 0, 0, 0, 0, 5, 106),
       cftime.DatetimeNoLeap(2007, 5, 16, 12, 0, 0, 0, 0, 136),
       cftime.DatetimeNoLeap(2007, 6, 16, 0, 0, 0, 0, 3, 167),
       cftime.DatetimeNoLeap(2007, 7, 16, 12, 0, 0, 0, 5, 197),
       cftime.DatetimeNoLeap(2007, 8, 16, 12, 0, 0, 0, 1, 228),
       cftime.DatetimeNoLeap(2007, 9, 16, 0, 0, 0, 0, 4, 259),
       cftime.DatetimeNoLeap(2007, 10, 16, 12, 0, 0, 0, 6, 289),
       cftime.DatetimeNoLeap(2007, 11, 16, 0, 0, 0, 0, 2, 320),
       cftime.DatetimeNoLeap(2007, 12, 16, 12, 0, 0, 0, 4, 350)], dtype=object)
Coordinates:
  * time     (time) object 2003-01-16 12:00:00 ... 2007-12-16 12:00:00
Attributes:
    long_name:       time
    cartesian_axis:  T
    calendar_type:   NOLEAP
    bounds:          time_bnds
    _ChunkSizes:     512

For example, selecting a range of years (think time slice):

[19]:

my_selection = ds.sel(time=slice('2003', '2005'))

[20]:

my_selection

[20]:

<xarray.Dataset>
Dimensions:       (nv: 2, time: 36, xh: 720, xq: 720, yh: 576, yq: 576, z_i: 36, z_l: 35)
Coordinates:
  * nv            (nv) float64 1.0 2.0
  * xh            (xh) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75
  * xq            (xq) float64 -299.5 -299.0 -298.5 -298.0 ... 59.0 59.5 60.0
  * yh            (yh) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89
  * yq            (yq) float64 -77.82 -77.63 -77.45 -77.26 ... 89.58 89.79 90.0
  * z_i           (z_i) float64 0.0 5.0 15.0 25.0 ... 5.75e+03 6.25e+03 6.75e+03
  * z_l           (z_l) float64 2.5 10.0 20.0 32.5 ... 5.5e+03 6e+03 6.5e+03
  * time          (time) object 2003-01-16 12:00:00 ... 2005-12-16 12:00:00
Data variables:
    Coriolis      (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello     (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello_bu  (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello_cu  (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello_cv  (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    deptho        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dxCu          (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dxCv          (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dxt           (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dyCu          (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dyCv          (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dyt           (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat_c      (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat_u      (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat_v      (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon_c      (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon_u      (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon_v      (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    hfgeou        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    sftof         (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    thkcello      (z_l, yh, xh) float32 dask.array<chunksize=(1, 576, 720), meta=np.ndarray>
    wet           (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    wet_c         (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    wet_u         (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    wet_v         (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    average_DT    (time) timedelta64[ns] dask.array<chunksize=(1,), meta=np.ndarray>
    average_T1    (time) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>
    average_T2    (time) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>
    so            (time, z_l, yh, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    time_bnds     (time, nv) timedelta64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
    thetao        (time, z_l, yh, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    umo           (time, z_l, yh, xq) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    uo            (time, z_l, yh, xq) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    vmo           (time, z_l, yq, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    vo            (time, z_l, yq, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    volcello      (time, z_l, yh, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    zos           (time, yh, xh) float32 dask.array<chunksize=(1, 576, 720), meta=np.ndarray>
Attributes:
    filename:                        ocean_monthly.200301-200712.zos.nc
    title:                           OM4p5_IAF_BLING_CFC_abio_csf_mle200
    associated_files:                areacello: 20030101.ocean_static.nc
    grid_type:                       regular
    grid_tile:                       N/A
    external_variables:              areacello
    DODS_EXTRA.Unlimited_Dimension:  time

Or just a few months:

[21]:

my_selection2 = ds.sel(time=slice('2004-04', '2005-07'))

[22]:

my_selection2

[22]:

<xarray.Dataset>
Dimensions:       (nv: 2, time: 16, xh: 720, xq: 720, yh: 576, yq: 576, z_i: 36, z_l: 35)
Coordinates:
  * nv            (nv) float64 1.0 2.0
  * xh            (xh) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75
  * xq            (xq) float64 -299.5 -299.0 -298.5 -298.0 ... 59.0 59.5 60.0
  * yh            (yh) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89
  * yq            (yq) float64 -77.82 -77.63 -77.45 -77.26 ... 89.58 89.79 90.0
  * z_i           (z_i) float64 0.0 5.0 15.0 25.0 ... 5.75e+03 6.25e+03 6.75e+03
  * z_l           (z_l) float64 2.5 10.0 20.0 32.5 ... 5.5e+03 6e+03 6.5e+03
  * time          (time) object 2004-04-16 00:00:00 ... 2005-07-16 12:00:00
Data variables:
    Coriolis      (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello     (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello_bu  (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello_cu  (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    areacello_cv  (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    deptho        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dxCu          (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dxCv          (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dxt           (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dyCu          (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dyCv          (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    dyt           (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat_c      (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat_u      (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolat_v      (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon_c      (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon_u      (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    geolon_v      (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    hfgeou        (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    sftof         (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    thkcello      (z_l, yh, xh) float32 dask.array<chunksize=(1, 576, 720), meta=np.ndarray>
    wet           (yh, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    wet_c         (yq, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    wet_u         (yh, xq) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    wet_v         (yq, xh) float32 dask.array<chunksize=(576, 720), meta=np.ndarray>
    average_DT    (time) timedelta64[ns] dask.array<chunksize=(1,), meta=np.ndarray>
    average_T1    (time) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>
    average_T2    (time) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>
    so            (time, z_l, yh, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    time_bnds     (time, nv) timedelta64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
    thetao        (time, z_l, yh, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    umo           (time, z_l, yh, xq) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    uo            (time, z_l, yh, xq) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    vmo           (time, z_l, yq, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    vo            (time, z_l, yq, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    volcello      (time, z_l, yh, xh) float32 dask.array<chunksize=(1, 1, 576, 720), meta=np.ndarray>
    zos           (time, yh, xh) float32 dask.array<chunksize=(1, 576, 720), meta=np.ndarray>
Attributes:
    filename:                        ocean_monthly.200301-200712.zos.nc
    title:                           OM4p5_IAF_BLING_CFC_abio_csf_mle200
    associated_files:                areacello: 20030101.ocean_static.nc
    grid_type:                       regular
    grid_tile:                       N/A
    external_variables:              areacello
    DODS_EXTRA.Unlimited_Dimension:  time