A “spell” system

%load_ext autoreload
%autoreload 2

import os
os.chdir('/glade/u/home/fengzhu/Github/x4c/docsrc/notebooks')
import x4c
print(x4c.__version__)

2024.5.15

dirpath = '/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005'
case = x4c.Timeseries(dirpath)

>>> case.root_dir: /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005
>>> case.path_pattern: comp/proc/tseries/month_1/casename.mdl.h_str.vn.timespan.nc
>>> case.grid_dict: {'atm': 'ne16', 'ocn': 'g16', 'lnd': 'ne16', 'rof': 'ne16', 'ice': 'g16'}
>>> case.vars_info created

A 1st order variable that exists under the CESM Postprocessing Timeseries directory

spell = 'TS'
case.calc(spell)
case.diags[spell]

>>> case.ds["TS"] created
>>> case.diags["TS"] created

<xarray.DataArray 'TS' (time: 600, ncol: 13826)> Size: 33MB
array([[30.115295, 29.491943, 28.593048, ..., 20.259125, 17.343262,
        18.36267 ],
       [31.816986, 31.327759, 30.49112 , ..., 19.40506 , 17.631165,
        18.420319],
       [30.922668, 30.435669, 29.57724 , ..., 19.17099 , 17.414886,
        18.323761],
       ...,
       [24.949066, 23.907959, 22.722198, ..., 29.804321, 27.803436,
        28.56009 ],
       [26.115387, 25.092682, 23.751007, ..., 25.909088, 23.512573,
        24.234802],
       [28.901093, 28.040283, 26.766968, ..., 22.911407, 20.540161,
        21.184845]], dtype=float32)
Coordinates:
  * time     (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-31 00:00:00
Dimensions without coordinates: ncol
Attributes:
    units:         °C
    long_name:     Surface temperature (radiative)
    cell_methods:  time: mean
    path:          /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseri...
    gw:            <xarray.DataArray 'gw' (ncol: 13826)> Size: 111kB\n[13826 ...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16

xarray.DataArray

'TS'

• time: 600
• ncol: 13826

• 30.12 29.49 28.59 28.03 30.98 30.23 ... 23.11 21.35 22.91 20.54 21.18

  array([[30.115295, 29.491943, 28.593048, ..., 20.259125, 17.343262,
          18.36267 ],
         [31.816986, 31.327759, 30.49112 , ..., 19.40506 , 17.631165,
          18.420319],
         [30.922668, 30.435669, 29.57724 , ..., 19.17099 , 17.414886,
          18.323761],
         ...,
         [24.949066, 23.907959, 22.722198, ..., 29.804321, 27.803436,
          28.56009 ],
         [26.115387, 25.092682, 23.751007, ..., 25.909088, 23.512573,
          24.234802],
         [28.901093, 28.040283, 26.766968, ..., 22.911407, 20.540161,
          21.184845]], dtype=float32)

• Coordinates: (1)
  • time
    (time)
    object
    6951-01-31 00:00:00 ... 7000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(6951, 1, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6951, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6951, 3, 31, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(7000, 10, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 11, 30, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6951-01-31 00:00:00, 6951-02-28 00:00:00, 6951-03-31 00:00:00,
                 6951-04-30 00:00:00, 6951-05-31 00:00:00, 6951-06-30 00:00:00,
                 6951-07-31 00:00:00, 6951-08-31 00:00:00, 6951-09-30 00:00:00,
                 6951-10-31 00:00:00,
                 ...
                 7000-03-31 00:00:00, 7000-04-30 00:00:00, 7000-05-31 00:00:00,
                 7000-06-30 00:00:00, 7000-07-31 00:00:00, 7000-08-31 00:00:00,
                 7000-09-30 00:00:00, 7000-10-31 00:00:00, 7000-11-30 00:00:00,
                 7000-12-31 00:00:00],
                dtype='object', length=600, calendar='noleap', freq='ME'))

• Attributes: (9)

  units :

  °C

  long_name :

  Surface temperature (radiative)

  cell_methods :

  time: mean

  path :

  /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.TS.695101-700012.nc

  gw :

  <xarray.DataArray 'gw' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol

  lat :

  <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: latitude units: degrees_north

  lon :

  <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: longitude units: degrees_east

  comp :

  atm

  grid :

  ne16

A deduced variable based on 1st order variables

spell = 'SST'
case.calc(spell)
case.diags[spell]

>>> case.ds["TEMP"] created
>>> case.diags["SST"] created

<xarray.DataArray 'SST' (time: 600, nlat: 384, nlon: 320)> Size: 295MB
[73728000 values with dtype=float32]
Coordinates:
    z_t      float32 4B 500.0
    ULONG    (nlat, nlon) float64 983kB ...
    ULAT     (nlat, nlon) float64 983kB ...
    TLONG    (nlat, nlon) float64 983kB ...
    TLAT     (nlat, nlon) float64 983kB ...
  * time     (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-31 00:00:00
Dimensions without coordinates: nlat, nlon
Attributes:
    long_name:     Sea Surface Temperature
    units:         °C
    grid_loc:      3111
    cell_methods:  time: mean
    path:          /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseri...
    gw:            <xarray.DataArray 'gw' (nlat: 384, nlon: 320)> Size: 983kB...
    lat:           <xarray.DataArray 'lat' (nlat: 384, nlon: 320)> Size: 983k...
    lon:           <xarray.DataArray 'lon' (nlat: 384, nlon: 320)> Size: 983k...
    comp:          ocn
    grid:          g16

xarray.DataArray

'SST'

• time: 600
• nlat: 384
• nlon: 320

• ...

  [73728000 values with dtype=float32]

• Coordinates: (6)
  • z_t
    ()
    float32
    500.0

    long_name :

    depth from surface to midpoint of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    500.0

    valid_max :

    537500.0

    array(500., dtype=float32)

  • ULONG
    (nlat, nlon)
    float64
    ...

    long_name :

    array of u-grid longitudes

    units :

    degrees_east

    [122880 values with dtype=float64]

  • ULAT
    (nlat, nlon)
    float64
    ...

    long_name :

    array of u-grid latitudes

    units :

    degrees_north

    [122880 values with dtype=float64]

  • TLONG
    (nlat, nlon)
    float64
    ...

    long_name :

    array of t-grid longitudes

    units :

    degrees_east

    [122880 values with dtype=float64]

  • TLAT
    (nlat, nlon)
    float64
    ...

    long_name :

    array of t-grid latitudes

    units :

    degrees_north

    [122880 values with dtype=float64]

  • time
    (time)
    object
    6951-01-31 00:00:00 ... 7000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(6951, 1, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6951, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6951, 3, 31, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(7000, 10, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 11, 30, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6951-01-31 00:00:00, 6951-02-28 00:00:00, 6951-03-31 00:00:00,
                 6951-04-30 00:00:00, 6951-05-31 00:00:00, 6951-06-30 00:00:00,
                 6951-07-31 00:00:00, 6951-08-31 00:00:00, 6951-09-30 00:00:00,
                 6951-10-31 00:00:00,
                 ...
                 7000-03-31 00:00:00, 7000-04-30 00:00:00, 7000-05-31 00:00:00,
                 7000-06-30 00:00:00, 7000-07-31 00:00:00, 7000-08-31 00:00:00,
                 7000-09-30 00:00:00, 7000-10-31 00:00:00, 7000-11-30 00:00:00,
                 7000-12-31 00:00:00],
                dtype='object', length=600, calendar='noleap', freq='ME'))

• Attributes: (10)

  long_name :

  Sea Surface Temperature

  units :

  °C

  grid_loc :

  3111

  cell_methods :

  time: mean

  path :

  /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.TEMP.695101-700012.nc

  gw :

  <xarray.DataArray 'gw' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: area of T cells units: centimeter^2

  lat :

  <xarray.DataArray 'lat' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid latitudes units: degrees_north

  lon :

  <xarray.DataArray 'lon' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid longitudes units: degrees_east

  comp :

  ocn

  grid :

  g16

spell = 'd18Op'
case.calc(spell)
case.diags[spell]

>>> case.ds["PRECRC_H216Or"] created
>>> case.ds["PRECSC_H216Os"] created
>>> case.ds["PRECRL_H216OR"] created
>>> case.ds["PRECSL_H216OS"] created
>>> case.ds["PRECRC_H218Or"] created
>>> case.ds["PRECSC_H218Os"] created
>>> case.ds["PRECRL_H218OR"] created
>>> case.ds["PRECSL_H218OS"] created
>>> case.diags["d18Op"] created

<xarray.DataArray 'd18Op' (time: 600, ncol: 13826)> Size: 33MB
array([[-1.7427206, -1.7643571, -2.1771193, ..., -8.510709 , -7.7379346,
        -7.486999 ],
       [-2.3528934, -2.1284223, -1.8881559, ..., -7.032275 , -6.7304373,
        -6.397307 ],
       [-2.2357106, -2.3323298, -2.5407076, ..., -7.622123 , -6.967664 ,
        -6.7536235],
       ...,
       [-2.425313 , -3.3228397, -4.793942 , ..., -3.848195 , -4.269719 ,
        -3.5235286],
       [-1.9090176, -2.6127696, -3.4157038, ..., -6.644845 , -6.4725876,
        -6.0330033],
       [-1.0784268, -1.4600754, -6.1579943, ..., -6.125927 , -4.7311783,
        -4.798174 ]], dtype=float32)
Coordinates:
  * time     (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-31 00:00:00
Dimensions without coordinates: ncol
Attributes:
    units:         m/s
    long_name:     Convective rain rate for H218Or
    cell_methods:  time: mean
    path:          /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseri...
    gw:            <xarray.DataArray 'gw' (ncol: 13826)> Size: 111kB\n[13826 ...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16

xarray.DataArray

'd18Op'

• time: 600
• ncol: 13826

• -1.743 -1.764 -2.177 -2.441 -1.713 ... -5.962 -6.126 -4.731 -4.798

  array([[-1.7427206, -1.7643571, -2.1771193, ..., -8.510709 , -7.7379346,
          -7.486999 ],
         [-2.3528934, -2.1284223, -1.8881559, ..., -7.032275 , -6.7304373,
          -6.397307 ],
         [-2.2357106, -2.3323298, -2.5407076, ..., -7.622123 , -6.967664 ,
          -6.7536235],
         ...,
         [-2.425313 , -3.3228397, -4.793942 , ..., -3.848195 , -4.269719 ,
          -3.5235286],
         [-1.9090176, -2.6127696, -3.4157038, ..., -6.644845 , -6.4725876,
          -6.0330033],
         [-1.0784268, -1.4600754, -6.1579943, ..., -6.125927 , -4.7311783,
          -4.798174 ]], dtype=float32)

• Coordinates: (1)
  • time
    (time)
    object
    6951-01-31 00:00:00 ... 7000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(6951, 1, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6951, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6951, 3, 31, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(7000, 10, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 11, 30, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6951-01-31 00:00:00, 6951-02-28 00:00:00, 6951-03-31 00:00:00,
                 6951-04-30 00:00:00, 6951-05-31 00:00:00, 6951-06-30 00:00:00,
                 6951-07-31 00:00:00, 6951-08-31 00:00:00, 6951-09-30 00:00:00,
                 6951-10-31 00:00:00,
                 ...
                 7000-03-31 00:00:00, 7000-04-30 00:00:00, 7000-05-31 00:00:00,
                 7000-06-30 00:00:00, 7000-07-31 00:00:00, 7000-08-31 00:00:00,
                 7000-09-30 00:00:00, 7000-10-31 00:00:00, 7000-11-30 00:00:00,
                 7000-12-31 00:00:00],
                dtype='object', length=600, calendar='noleap', freq='ME'))

• Attributes: (9)

  units :

  m/s

  long_name :

  Convective rain rate for H218Or

  cell_methods :

  time: mean

  path :

  /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.PRECRC_H218Or.695101-700012.nc

  gw :

  <xarray.DataArray 'gw' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol

  lat :

  <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: latitude units: degrees_north

  lon :

  <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: longitude units: degrees_east

  comp :

  atm

  grid :

  ne16

Climatology

spell = 'TS:climo:gm'
case.calc(spell)
case.diags[spell]

>>> case.ds["TS"] created
>>> case.diags["TS:climo:gm"] created

<xarray.DataArray 'TS' (time: 12)>
array([17.132423  , 17.17904783, 16.67184088, 16.56906268, 17.24778526,
       18.35244407, 19.17750972, 18.95686033, 17.87159633, 16.73436938,
       16.24615771, 16.48020455])
Coordinates:
  * time     (time) int64 1 2 3 4 5 6 7 8 9 10 11 12
Attributes:
    units:         °C
    long_name:     Global Mean Surface temperature (radiative) (Climatology)
    cell_methods:  time: mean
    path:          /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseri...
    gw:            <xarray.DataArray 'gw' (lat: 180, lon: 360)>\narray([[0.00...
    comp:          atm
    grid:          ne16
    climo_period:  (7951, 8000)

xarray.DataArray

'TS'

• time: 12

• 17.13 17.18 16.67 16.57 17.25 18.35 ... 18.96 17.87 16.73 16.25 16.48

  array([17.132423  , 17.17904783, 16.67184088, 16.56906268, 17.24778526,
         18.35244407, 19.17750972, 18.95686033, 17.87159633, 16.73436938,
         16.24615771, 16.48020455])

• Coordinates: (1)
  • time
    (time)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    long_name :

    Model Year

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='time'))

• Attributes: (8)

  units :

  °C

  long_name :

  Global Mean Surface temperature (radiative) (Climatology)

  cell_methods :

  time: mean

  path :

  /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.offset.d18O.branch.cam.h0.TS.795101-800012.nc

  gw :

  <xarray.DataArray 'gw' (lat: 180, lon: 360)> array([[0.00058087, 0.00058082, 0.00058071, ..., 0.00058071, 0.00058082, 0.00058087], [0.00066908, 0.00066908, 0.00066908, ..., 0.00066908, 0.00066908, 0.00066908], [0.00066877, 0.00066877, 0.00066877, ..., 0.00066877, 0.00066877, 0.00066877], ..., [0.00066877, 0.00066877, 0.00066877, ..., 0.00066877, 0.00066877, 0.00066877], [0.00066908, 0.00066908, 0.00066908, ..., 0.00066908, 0.00066908, 0.00066908], [0.00058087, 0.00058082, 0.00058071, ..., 0.00058071, 0.00058082, 0.00058087]]) Coordinates: * lat (lat) float64 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5 * lon (lon) float64 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5

  comp :

  atm

  grid :

  ne16

  climo_period :

  (7951, 8000)

x4c.set_style('web', font_scale=1)
fig, ax = case.plot(spell)

A horizontal 2D map

spell = 'TS:ann'
case.calc(spell)
case.diags[spell]

>>> case.ds["TS"] created
>>> case.diags["TS:ann"] created

<xarray.DataArray 'TS' (time: 50, ncol: 13826)> Size: 3MB
array([[27.255615, 26.59375 , 25.732697, ..., 24.627686, 23.223907,
        24.14569 ],
       [27.369781, 26.764069, 25.98816 , ..., 24.485626, 23.12378 ,
        24.04718 ],
       [27.426819, 26.78302 , 25.943298, ..., 24.74707 , 23.381744,
        24.290405],
       ...,
       [27.305878, 26.622803, 25.72229 , ..., 25.120941, 23.717438,
        24.671234],
       [27.353516, 26.718628, 25.887177, ..., 25.187317, 23.84436 ,
        24.80011 ],
       [27.750732, 27.057007, 26.139557, ..., 25.670074, 24.444519,
        25.14618 ]], dtype=float32)
Coordinates:
  * time     (time) object 400B 6951-12-31 00:00:00 ... 7000-12-31 00:00:00
Dimensions without coordinates: ncol
Attributes:
    units:         °C
    long_name:     Surface temperature (radiative) (Annual)
    cell_methods:  time: mean
    path:          /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseri...
    gw:            <xarray.DataArray 'gw' (ncol: 13826)> Size: 111kB\n[13826 ...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16

xarray.DataArray

'TS'

• time: 50
• ncol: 13826

• 27.26 26.59 25.73 25.24 28.19 27.47 ... 25.38 24.65 25.67 24.44 25.15

  array([[27.255615, 26.59375 , 25.732697, ..., 24.627686, 23.223907,
          24.14569 ],
         [27.369781, 26.764069, 25.98816 , ..., 24.485626, 23.12378 ,
          24.04718 ],
         [27.426819, 26.78302 , 25.943298, ..., 24.74707 , 23.381744,
          24.290405],
         ...,
         [27.305878, 26.622803, 25.72229 , ..., 25.120941, 23.717438,
          24.671234],
         [27.353516, 26.718628, 25.887177, ..., 25.187317, 23.84436 ,
          24.80011 ],
         [27.750732, 27.057007, 26.139557, ..., 25.670074, 24.444519,
          25.14618 ]], dtype=float32)

• Coordinates: (1)
  • time
    (time)
    object
    6951-12-31 00:00:00 ... 7000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(6951, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6952, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6953, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6954, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6955, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6956, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6957, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6958, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6959, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6960, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6961, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6962, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6963, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6964, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6965, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6966, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6967, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6968, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6969, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6970, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6971, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6972, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6973, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6974, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6975, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6976, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6977, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6978, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6979, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6980, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6981, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6982, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6983, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6984, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6985, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6986, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6987, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6988, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6989, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6990, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6991, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6992, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6993, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6994, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6995, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6996, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6997, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6998, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6999, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6951-12-31 00:00:00, 6952-12-31 00:00:00, 6953-12-31 00:00:00,
                 6954-12-31 00:00:00, 6955-12-31 00:00:00, 6956-12-31 00:00:00,
                 6957-12-31 00:00:00, 6958-12-31 00:00:00, 6959-12-31 00:00:00,
                 6960-12-31 00:00:00, 6961-12-31 00:00:00, 6962-12-31 00:00:00,
                 6963-12-31 00:00:00, 6964-12-31 00:00:00, 6965-12-31 00:00:00,
                 6966-12-31 00:00:00, 6967-12-31 00:00:00, 6968-12-31 00:00:00,
                 6969-12-31 00:00:00, 6970-12-31 00:00:00, 6971-12-31 00:00:00,
                 6972-12-31 00:00:00, 6973-12-31 00:00:00, 6974-12-31 00:00:00,
                 6975-12-31 00:00:00, 6976-12-31 00:00:00, 6977-12-31 00:00:00,
                 6978-12-31 00:00:00, 6979-12-31 00:00:00, 6980-12-31 00:00:00,
                 6981-12-31 00:00:00, 6982-12-31 00:00:00, 6983-12-31 00:00:00,
                 6984-12-31 00:00:00, 6985-12-31 00:00:00, 6986-12-31 00:00:00,
                 6987-12-31 00:00:00, 6988-12-31 00:00:00, 6989-12-31 00:00:00,
                 6990-12-31 00:00:00, 6991-12-31 00:00:00, 6992-12-31 00:00:00,
                 6993-12-31 00:00:00, 6994-12-31 00:00:00, 6995-12-31 00:00:00,
                 6996-12-31 00:00:00, 6997-12-31 00:00:00, 6998-12-31 00:00:00,
                 6999-12-31 00:00:00, 7000-12-31 00:00:00],
                dtype='object', length=50, calendar='noleap', freq='YE-DEC'))

• Attributes: (9)

  units :

  °C

  long_name :

  Surface temperature (radiative) (Annual)

  cell_methods :

  time: mean

  path :

  /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.TS.695101-700012.nc

  gw :

  <xarray.DataArray 'gw' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol

  lat :

  <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: latitude units: degrees_north

  lon :

  <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB [13826 values with dtype=float64] Dimensions without coordinates: ncol Attributes: long_name: longitude units: degrees_east

  comp :

  atm

  grid :

  ne16

x4c.set_style('journal_spines', font_scale=1.2)
fig, ax = case.plot(spell)

Regridding from [1, 13826] to [180, 360]
>>> case.ds["SSH"] created

spell = 'SST:-12,1,2'
case.calc(spell)
case.diags[spell]

>>> case.ds["TEMP"] created
>>> case.diags["SST:-12,1,2"] created

<xarray.DataArray 'SST' (time: 51, nlat: 384, nlon: 320)> Size: 25MB
array([[[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
...
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
Coordinates:
    z_t      float32 4B 500.0
    ULONG    (nlat, nlon) float64 983kB ...
    ULAT     (nlat, nlon) float64 983kB ...
    TLONG    (nlat, nlon) float64 983kB ...
    TLAT     (nlat, nlon) float64 983kB ...
  * time     (time) object 408B 6951-02-28 00:00:00 ... 7001-02-28 00:00:00
Dimensions without coordinates: nlat, nlon
Attributes:
    long_name:     Sea Surface Temperature (DJF)
    units:         °C
    grid_loc:      3111
    cell_methods:  time: mean
    path:          /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseri...
    gw:            <xarray.DataArray 'gw' (nlat: 384, nlon: 320)> Size: 983kB...
    lat:           <xarray.DataArray 'lat' (nlat: 384, nlon: 320)> Size: 983k...
    lon:           <xarray.DataArray 'lon' (nlat: 384, nlon: 320)> Size: 983k...
    comp:          ocn
    grid:          g16

xarray.DataArray

'SST'

• time: 51
• nlat: 384
• nlon: 320

• nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan

  array([[[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
  ...
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)

• Coordinates: (6)
  • z_t
    ()
    float32
    500.0

    long_name :

    depth from surface to midpoint of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    500.0

    valid_max :

    537500.0

    array(500., dtype=float32)

  • ULONG
    (nlat, nlon)
    float64
    ...

    long_name :

    array of u-grid longitudes

    units :

    degrees_east

    [122880 values with dtype=float64]

  • ULAT
    (nlat, nlon)
    float64
    ...

    long_name :

    array of u-grid latitudes

    units :

    degrees_north

    [122880 values with dtype=float64]

  • TLONG
    (nlat, nlon)
    float64
    ...

    long_name :

    array of t-grid longitudes

    units :

    degrees_east

    [122880 values with dtype=float64]

  • TLAT
    (nlat, nlon)
    float64
    ...

    long_name :

    array of t-grid latitudes

    units :

    degrees_north

    [122880 values with dtype=float64]

  • time
    (time)
    object
    6951-02-28 00:00:00 ... 7001-02-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(6951, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6952, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6953, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6954, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6955, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6956, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6957, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6958, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6959, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6960, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6961, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6962, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6963, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6964, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6965, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6966, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6967, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6968, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6969, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6970, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6971, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6972, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6973, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6974, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6975, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6976, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6977, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6978, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6979, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6980, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6981, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6982, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6983, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6984, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6985, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6986, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6987, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6988, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6989, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6990, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6991, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6992, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6993, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6994, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6995, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6996, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6997, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6998, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6999, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7001, 2, 28, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6951-02-28 00:00:00, 6952-02-28 00:00:00, 6953-02-28 00:00:00,
                 6954-02-28 00:00:00, 6955-02-28 00:00:00, 6956-02-28 00:00:00,
                 6957-02-28 00:00:00, 6958-02-28 00:00:00, 6959-02-28 00:00:00,
                 6960-02-28 00:00:00, 6961-02-28 00:00:00, 6962-02-28 00:00:00,
                 6963-02-28 00:00:00, 6964-02-28 00:00:00, 6965-02-28 00:00:00,
                 6966-02-28 00:00:00, 6967-02-28 00:00:00, 6968-02-28 00:00:00,
                 6969-02-28 00:00:00, 6970-02-28 00:00:00, 6971-02-28 00:00:00,
                 6972-02-28 00:00:00, 6973-02-28 00:00:00, 6974-02-28 00:00:00,
                 6975-02-28 00:00:00, 6976-02-28 00:00:00, 6977-02-28 00:00:00,
                 6978-02-28 00:00:00, 6979-02-28 00:00:00, 6980-02-28 00:00:00,
                 6981-02-28 00:00:00, 6982-02-28 00:00:00, 6983-02-28 00:00:00,
                 6984-02-28 00:00:00, 6985-02-28 00:00:00, 6986-02-28 00:00:00,
                 6987-02-28 00:00:00, 6988-02-28 00:00:00, 6989-02-28 00:00:00,
                 6990-02-28 00:00:00, 6991-02-28 00:00:00, 6992-02-28 00:00:00,
                 6993-02-28 00:00:00, 6994-02-28 00:00:00, 6995-02-28 00:00:00,
                 6996-02-28 00:00:00, 6997-02-28 00:00:00, 6998-02-28 00:00:00,
                 6999-02-28 00:00:00, 7000-02-28 00:00:00, 7001-02-28 00:00:00],
                dtype='object', length=51, calendar='noleap', freq='YE-FEB'))

• Attributes: (10)

  long_name :

  Sea Surface Temperature (DJF)

  units :

  °C

  grid_loc :

  3111

  cell_methods :

  time: mean

  path :

  /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.TEMP.695101-700012.nc

  gw :

  <xarray.DataArray 'gw' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: area of T cells units: centimeter^2

  lat :

  <xarray.DataArray 'lat' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid latitudes units: degrees_north

  lon :

  <xarray.DataArray 'lon' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid longitudes units: degrees_east

  comp :

  ocn

  grid :

  g16

fig, ax = case.plot(spell)

case.calc('MLD:3')
case.calc('MLD:9')

mask = case.diags['MLD:3'].lat > 0
case.diags['MLD:mix'] = case.diags['MLD:3'].mean('time').where(mask, other=case.diags['MLD:9'].mean('time'))

>>> case.ds["XMXL"] created
>>> case.diags["MLD:3"] created
>>> case.ds["XMXL"] already loaded; to reload, run case.clear_ds("XMXL") before case.load("XMXL")
>>> case.diags["MLD:9"] created

fig, ax = case.plot(
    'MLD:mix',
    figsize=(6, 6),
    cbar_kwargs={'orientation': 'horizontal', 'aspect': 20, 'pad': 0.05},
)
ax.set_title('Mixed Layer Depth (NH: Mar; SH: Sept)', weight='bold')
x4c.showfig(fig)
x4c.savefig(fig, './figs/map_MLD_mix.pdf')

Figure saved at: "figs/map_MLD_mix.pdf"

[WARNING] yaksa: 10 leaked handle pool objects

A timeseries

spell = 'TS:ann:gm'
case.calc(spell)
case.diags[spell]

>>> case.ds["TS"] created
Regridding from [1, 13826] to [180, 360]
>>> case.diags["TS:ann:gm"] created

<xarray.DataArray 'TS' (time: 50)> Size: 400B
array([17.42720653, 17.48905824, 17.44967605, 17.47774547, 17.51654981,
       17.24848253, 17.39658188, 17.59053861, 17.48456964, 17.43645915,
       17.4322128 , 17.3081354 , 17.48215638, 17.76362381, 17.3122794 ,
       17.21187245, 17.20908332, 17.54942076, 17.72846092, 17.33755901,
       17.43767921, 17.57137876, 17.52678597, 17.32145085, 17.38547791,
       17.56191536, 17.36943034, 17.33152329, 17.24494713, 17.52045722,
       17.34284225, 17.21382635, 17.36316737, 17.39722006, 17.38297636,
       17.3732631 , 17.3726002 , 17.46888324, 17.66213172, 17.29246269,
       17.14775584, 17.40703955, 17.62723439, 17.50499981, 17.37590867,
       17.34382487, 17.40328731, 17.49603029, 17.37513375, 17.16387423])
Coordinates:
  * time     (time) object 400B 6951-12-31 00:00:00 ... 7000-12-31 00:00:00
Attributes:
    units:         °C
    long_name:     Global Mean Surface temperature (radiative) (Annual)
    cell_methods:  time: mean
    path:          /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseri...
    gw:            <xarray.DataArray 'gw' (lat: 180, lon: 360)> Size: 518kB\n...
    lat:           <xarray.DataArray 'lat' (lat: 180)> Size: 1kB\narray([-89....
    lon:           <xarray.DataArray 'lon' (lon: 360)> Size: 3kB\narray([  0....
    comp:          atm
    grid:          ne16

xarray.DataArray

'TS'

• time: 50

• 17.43 17.49 17.45 17.48 17.52 17.25 ... 17.34 17.4 17.5 17.38 17.16

  array([17.42720653, 17.48905824, 17.44967605, 17.47774547, 17.51654981,
         17.24848253, 17.39658188, 17.59053861, 17.48456964, 17.43645915,
         17.4322128 , 17.3081354 , 17.48215638, 17.76362381, 17.3122794 ,
         17.21187245, 17.20908332, 17.54942076, 17.72846092, 17.33755901,
         17.43767921, 17.57137876, 17.52678597, 17.32145085, 17.38547791,
         17.56191536, 17.36943034, 17.33152329, 17.24494713, 17.52045722,
         17.34284225, 17.21382635, 17.36316737, 17.39722006, 17.38297636,
         17.3732631 , 17.3726002 , 17.46888324, 17.66213172, 17.29246269,
         17.14775584, 17.40703955, 17.62723439, 17.50499981, 17.37590867,
         17.34382487, 17.40328731, 17.49603029, 17.37513375, 17.16387423])

• Coordinates: (1)
  • time
    (time)
    object
    6951-12-31 00:00:00 ... 7000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(6951, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6952, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6953, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6954, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6955, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6956, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6957, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6958, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6959, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6960, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6961, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6962, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6963, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6964, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6965, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6966, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6967, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6968, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6969, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6970, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6971, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6972, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6973, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6974, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6975, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6976, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6977, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6978, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6979, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6980, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6981, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6982, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6983, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6984, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6985, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6986, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6987, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6988, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6989, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6990, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6991, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6992, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6993, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6994, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6995, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6996, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6997, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6998, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6999, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6951-12-31 00:00:00, 6952-12-31 00:00:00, 6953-12-31 00:00:00,
                 6954-12-31 00:00:00, 6955-12-31 00:00:00, 6956-12-31 00:00:00,
                 6957-12-31 00:00:00, 6958-12-31 00:00:00, 6959-12-31 00:00:00,
                 6960-12-31 00:00:00, 6961-12-31 00:00:00, 6962-12-31 00:00:00,
                 6963-12-31 00:00:00, 6964-12-31 00:00:00, 6965-12-31 00:00:00,
                 6966-12-31 00:00:00, 6967-12-31 00:00:00, 6968-12-31 00:00:00,
                 6969-12-31 00:00:00, 6970-12-31 00:00:00, 6971-12-31 00:00:00,
                 6972-12-31 00:00:00, 6973-12-31 00:00:00, 6974-12-31 00:00:00,
                 6975-12-31 00:00:00, 6976-12-31 00:00:00, 6977-12-31 00:00:00,
                 6978-12-31 00:00:00, 6979-12-31 00:00:00, 6980-12-31 00:00:00,
                 6981-12-31 00:00:00, 6982-12-31 00:00:00, 6983-12-31 00:00:00,
                 6984-12-31 00:00:00, 6985-12-31 00:00:00, 6986-12-31 00:00:00,
                 6987-12-31 00:00:00, 6988-12-31 00:00:00, 6989-12-31 00:00:00,
                 6990-12-31 00:00:00, 6991-12-31 00:00:00, 6992-12-31 00:00:00,
                 6993-12-31 00:00:00, 6994-12-31 00:00:00, 6995-12-31 00:00:00,
                 6996-12-31 00:00:00, 6997-12-31 00:00:00, 6998-12-31 00:00:00,
                 6999-12-31 00:00:00, 7000-12-31 00:00:00],
                dtype='object', length=50, calendar='noleap', freq='YE-DEC'))

• Attributes: (9)

  units :

  °C

  long_name :

  Global Mean Surface temperature (radiative) (Annual)

  cell_methods :

  time: mean

  path :

  /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.TS.695101-700012.nc

  gw :

  <xarray.DataArray 'gw' (lat: 180, lon: 360)> Size: 518kB array([[0.00058087, 0.00058082, 0.00058071, ..., 0.00058071, 0.00058082, 0.00058087], [0.00066908, 0.00066908, 0.00066908, ..., 0.00066908, 0.00066908, 0.00066908], [0.00066877, 0.00066877, 0.00066877, ..., 0.00066877, 0.00066877, 0.00066877], ..., [0.00066877, 0.00066877, 0.00066877, ..., 0.00066877, 0.00066877, 0.00066877], [0.00066908, 0.00066908, 0.00066908, ..., 0.00066908, 0.00066908, 0.00066908], [0.00058087, 0.00058082, 0.00058071, ..., 0.00058071, 0.00058082, 0.00058087]]) Coordinates: * lat (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5 * lon (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5

  lat :

  <xarray.DataArray 'lat' (lat: 180)> Size: 1kB array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5]) Coordinates: * lat (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5

  lon :

  <xarray.DataArray 'lon' (lon: 360)> Size: 3kB array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5]) Coordinates: * lon (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5

  comp :

  atm

  grid :

  ne16

x4c.set_style('web', font_scale=1.2)
fig, ax = case.plot(spell)

A zonal mean plot

spell = 'LST:ann:zm'
case.calc(spell)
case.diags[spell]

>>> case.ds["TS"] created
>>> case.ds["LANDFRAC"] created
Regridding from [1, 13826] to [180, 360]
>>> case.diags["LST:ann:zm"] created

<xarray.DataArray 'LST' (time: 50, lat: 180)> Size: 36kB
array([[-17.61438 , -18.295929, -18.382156, ...,        nan,        nan,
               nan],
       [-17.989594, -18.596909, -18.620834, ...,        nan,        nan,
               nan],
       [-17.135468, -18.006424, -18.126862, ...,        nan,        nan,
               nan],
       ...,
       [-17.056915, -17.642578, -17.642654, ...,        nan,        nan,
               nan],
       [-17.3172  , -17.903625, -17.959457, ...,        nan,        nan,
               nan],
       [-17.90384 , -18.5457  , -18.590149, ...,        nan,        nan,
               nan]], dtype=float32)
Coordinates:
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * time     (time) object 400B 6951-12-31 00:00:00 ... 7000-12-31 00:00:00
Attributes:
    units:         °C
    long_name:     Zonal Mean Land Surface Temperature (Annual)
    cell_methods:  time: mean
    path:          /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseri...
    gw:            <xarray.DataArray 'gw' (lat: 180, lon: 360)> Size: 518kB\n...
    lat:           <xarray.DataArray 'lat' (lat: 180)> Size: 1kB\narray([-89....
    lon:           <xarray.DataArray 'lon' (lon: 360)> Size: 3kB\narray([  0....
    comp:          atm
    grid:          ne16

xarray.DataArray

'LST'

• time: 50
• lat: 180

• -17.61 -18.3 -18.38 -18.2 -17.18 -14.58 ... nan nan nan nan nan nan

  array([[-17.61438 , -18.295929, -18.382156, ...,        nan,        nan,
                 nan],
         [-17.989594, -18.596909, -18.620834, ...,        nan,        nan,
                 nan],
         [-17.135468, -18.006424, -18.126862, ...,        nan,        nan,
                 nan],
         ...,
         [-17.056915, -17.642578, -17.642654, ...,        nan,        nan,
                 nan],
         [-17.3172  , -17.903625, -17.959457, ...,        nan,        nan,
                 nan],
         [-17.90384 , -18.5457  , -18.590149, ...,        nan,        nan,
                 nan]], dtype=float32)

• Coordinates: (2)
  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • time
    (time)
    object
    6951-12-31 00:00:00 ... 7000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(6951, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6952, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6953, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6954, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6955, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6956, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6957, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6958, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6959, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6960, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6961, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6962, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6963, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6964, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6965, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6966, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6967, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6968, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6969, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6970, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6971, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6972, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6973, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6974, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6975, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6976, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6977, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6978, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6979, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6980, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6981, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6982, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6983, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6984, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6985, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6986, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6987, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6988, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6989, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6990, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6991, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6992, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6993, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6994, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6995, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6996, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6997, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6998, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6999, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6951-12-31 00:00:00, 6952-12-31 00:00:00, 6953-12-31 00:00:00,
                 6954-12-31 00:00:00, 6955-12-31 00:00:00, 6956-12-31 00:00:00,
                 6957-12-31 00:00:00, 6958-12-31 00:00:00, 6959-12-31 00:00:00,
                 6960-12-31 00:00:00, 6961-12-31 00:00:00, 6962-12-31 00:00:00,
                 6963-12-31 00:00:00, 6964-12-31 00:00:00, 6965-12-31 00:00:00,
                 6966-12-31 00:00:00, 6967-12-31 00:00:00, 6968-12-31 00:00:00,
                 6969-12-31 00:00:00, 6970-12-31 00:00:00, 6971-12-31 00:00:00,
                 6972-12-31 00:00:00, 6973-12-31 00:00:00, 6974-12-31 00:00:00,
                 6975-12-31 00:00:00, 6976-12-31 00:00:00, 6977-12-31 00:00:00,
                 6978-12-31 00:00:00, 6979-12-31 00:00:00, 6980-12-31 00:00:00,
                 6981-12-31 00:00:00, 6982-12-31 00:00:00, 6983-12-31 00:00:00,
                 6984-12-31 00:00:00, 6985-12-31 00:00:00, 6986-12-31 00:00:00,
                 6987-12-31 00:00:00, 6988-12-31 00:00:00, 6989-12-31 00:00:00,
                 6990-12-31 00:00:00, 6991-12-31 00:00:00, 6992-12-31 00:00:00,
                 6993-12-31 00:00:00, 6994-12-31 00:00:00, 6995-12-31 00:00:00,
                 6996-12-31 00:00:00, 6997-12-31 00:00:00, 6998-12-31 00:00:00,
                 6999-12-31 00:00:00, 7000-12-31 00:00:00],
                dtype='object', length=50, calendar='noleap', freq='YE-DEC'))

• Attributes: (9)

  units :

  °C

  long_name :

  Zonal Mean Land Surface Temperature (Annual)

  cell_methods :

  time: mean

  path :

  /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.TS.695101-700012.nc

  gw :

  <xarray.DataArray 'gw' (lat: 180, lon: 360)> Size: 518kB array([[0.00058087, 0.00058082, 0.00058071, ..., 0.00058071, 0.00058082, 0.00058087], [0.00066908, 0.00066908, 0.00066908, ..., 0.00066908, 0.00066908, 0.00066908], [0.00066877, 0.00066877, 0.00066877, ..., 0.00066877, 0.00066877, 0.00066877], ..., [0.00066877, 0.00066877, 0.00066877, ..., 0.00066877, 0.00066877, 0.00066877], [0.00066908, 0.00066908, 0.00066908, ..., 0.00066908, 0.00066908, 0.00066908], [0.00058087, 0.00058082, 0.00058071, ..., 0.00058071, 0.00058082, 0.00058087]]) Coordinates: * lat (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5 * lon (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5

  lat :

  <xarray.DataArray 'lat' (lat: 180)> Size: 1kB array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5]) Coordinates: * lat (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5

  lon :

  <xarray.DataArray 'lon' (lon: 360)> Size: 3kB array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5]) Coordinates: * lon (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5

  comp :

  atm

  grid :

  ne16

fig, ax = case.plot(spell)

A vertical 2D plot

spell = 'MOC:ann:yz'
case.calc(spell)
case.diags[spell]

>>> case.ds["MOC"] created
>>> case.diags["MOC:ann:yz"] created

<xarray.DataArray 'MOC' (time: 50, moc_z: 61, lat_aux_grid: 91)> Size: 1MB
array([[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          2.36021541e-03,  8.10216938e-04,  3.16757913e-04],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          8.38778690e-02,  7.99491182e-02,  7.37172067e-02],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          1.06880188e-01,  9.84256342e-02,  8.74157026e-02],
        ...,
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          2.72689181e-06,  2.72689181e-06,  2.72689181e-06],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          7.71718305e-07,  7.71718305e-07,  7.71718305e-07],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],

       [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
         -7.30628439e-04, -8.62350353e-05,  1.25391569e-04],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          7.74025843e-02,  6.72006086e-02,  6.18883558e-02],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          8.21162239e-02,  8.27210248e-02,  7.43025541e-02],
...
         -6.49816911e-07, -6.49816911e-07, -6.49816911e-07],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
         -3.18197493e-07, -3.18197493e-07, -3.18197493e-07],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],

       [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
         -4.25623963e-04, -1.90087245e-04, -1.15045346e-04],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          4.39730100e-02,  4.85592298e-02,  4.60209064e-02],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          6.86945692e-02,  7.88548887e-02,  7.23393336e-02],
        ...,
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          3.27784420e-07,  3.27784420e-07,  3.27784420e-07],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
         -1.13812291e-06, -1.13812291e-06, -1.13812291e-06],
        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]],
      dtype=float32)
Coordinates:
    moc_components  |S256 256B ...
  * lat_aux_grid    (lat_aux_grid) float32 364B -90.0 -88.0 -86.0 ... 88.0 90.0
  * moc_z           (moc_z) float32 244B 0.0 0.01 0.02 0.03 ... 5.0 5.25 5.5
  * time            (time) object 400B 6951-12-31 00:00:00 ... 7000-12-31 00:...
Attributes:
    long_name:     Meridional Overturning Circulation (Annual)
    units:         Sverdrups
    cell_methods:  time: mean
    path:          /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseri...
    gw:            <xarray.DataArray 'gw' (nlat: 384, nlon: 320)> Size: 983kB...
    lat:           <xarray.DataArray 'lat' (nlat: 384, nlon: 320)> Size: 983k...
    lon:           <xarray.DataArray 'lon' (nlat: 384, nlon: 320)> Size: 983k...
    comp:          ocn
    grid:          g16

xarray.DataArray

'MOC'

• time: 50
• moc_z: 61
• lat_aux_grid: 91

• 0.0 0.0 0.0 3.517e-06 -4.179e-06 -3.038e-05 ... 0.0 0.0 0.0 0.0 0.0

  array([[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            2.36021541e-03,  8.10216938e-04,  3.16757913e-04],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            8.38778690e-02,  7.99491182e-02,  7.37172067e-02],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            1.06880188e-01,  9.84256342e-02,  8.74157026e-02],
          ...,
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            2.72689181e-06,  2.72689181e-06,  2.72689181e-06],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            7.71718305e-07,  7.71718305e-07,  7.71718305e-07],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],
  
         [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
           -7.30628439e-04, -8.62350353e-05,  1.25391569e-04],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            7.74025843e-02,  6.72006086e-02,  6.18883558e-02],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            8.21162239e-02,  8.27210248e-02,  7.43025541e-02],
  ...
           -6.49816911e-07, -6.49816911e-07, -6.49816911e-07],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
           -3.18197493e-07, -3.18197493e-07, -3.18197493e-07],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],
  
         [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
           -4.25623963e-04, -1.90087245e-04, -1.15045346e-04],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            4.39730100e-02,  4.85592298e-02,  4.60209064e-02],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            6.86945692e-02,  7.88548887e-02,  7.23393336e-02],
          ...,
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            3.27784420e-07,  3.27784420e-07,  3.27784420e-07],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
           -1.13812291e-06, -1.13812291e-06, -1.13812291e-06],
          [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
            0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]],
        dtype=float32)

• Coordinates: (4)
  • moc_components
    ()
    |S256
    ...

    long_name :

    MOC component names

    units :

    [1 values with dtype=|S256]

  • lat_aux_grid
    (lat_aux_grid)
    float32
    -90.0 -88.0 -86.0 ... 88.0 90.0

    long_name :

    latitude grid for transport diagnostics

    units :

    degrees_north

    valid_min :

    -90.0

    valid_max :

    90.0

    array([-90., -88., -86., -84., -82., -80., -78., -76., -74., -72., -70., -68.,
           -66., -64., -62., -60., -58., -56., -54., -52., -50., -48., -46., -44.,
           -42., -40., -38., -36., -34., -32., -30., -28., -26., -24., -22., -20.,
           -18., -16., -14., -12., -10.,  -8.,  -6.,  -4.,  -2.,   0.,   2.,   4.,
             6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,  24.,  26.,  28.,
            30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,  48.,  50.,  52.,
            54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,  72.,  74.,  76.,
            78.,  80.,  82.,  84.,  86.,  88.,  90.], dtype=float32)

  • moc_z
    (moc_z)
    float32
    0.0 0.01 0.02 0.03 ... 5.0 5.25 5.5

    long_name :

    depth from surface to top of layer

    units :

    km

    positive :

    down

    valid_min :

    0.0

    valid_max :

    549999.06

    array([0.      , 0.01    , 0.02    , 0.03    , 0.04    , 0.05    , 0.06    ,
           0.07    , 0.08    , 0.09    , 0.1     , 0.11    , 0.12    , 0.13    ,
           0.14    , 0.15    , 0.16    , 0.170197, 0.180761, 0.191821, 0.203499,
           0.215923, 0.229233, 0.243585, 0.259156, 0.276153, 0.294815, 0.315424,
           0.338312, 0.363875, 0.39258 , 0.424989, 0.461767, 0.503707, 0.551749,
           0.606997, 0.670729, 0.744398, 0.829607, 0.928044, 1.041368, 1.17104 ,
           1.318094, 1.482901, 1.664992, 1.863014, 2.074874, 2.298039, 2.529904,
           2.768098, 3.010671, 3.256138, 3.503449, 3.751892, 4.001011, 4.250525,
           4.500261, 4.75012 , 5.000047, 5.25001 , 5.49999 ], dtype=float32)

  • time
    (time)
    object
    6951-12-31 00:00:00 ... 7000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(6951, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6952, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6953, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6954, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6955, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6956, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6957, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6958, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6959, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6960, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6961, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6962, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6963, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6964, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6965, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6966, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6967, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6968, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6969, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6970, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6971, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6972, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6973, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6974, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6975, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6976, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6977, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6978, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6979, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6980, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6981, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6982, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6983, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6984, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6985, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6986, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6987, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6988, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6989, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6990, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6991, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6992, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6993, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6994, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6995, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6996, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6997, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6998, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6999, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (3)
  • lat_aux_grid
    PandasIndex

    PandasIndex(Index([-90.0, -88.0, -86.0, -84.0, -82.0, -80.0, -78.0, -76.0, -74.0, -72.0,
           -70.0, -68.0, -66.0, -64.0, -62.0, -60.0, -58.0, -56.0, -54.0, -52.0,
           -50.0, -48.0, -46.0, -44.0, -42.0, -40.0, -38.0, -36.0, -34.0, -32.0,
           -30.0, -28.0, -26.0, -24.0, -22.0, -20.0, -18.0, -16.0, -14.0, -12.0,
           -10.0,  -8.0,  -6.0,  -4.0,  -2.0,   0.0,   2.0,   4.0,   6.0,   8.0,
            10.0,  12.0,  14.0,  16.0,  18.0,  20.0,  22.0,  24.0,  26.0,  28.0,
            30.0,  32.0,  34.0,  36.0,  38.0,  40.0,  42.0,  44.0,  46.0,  48.0,
            50.0,  52.0,  54.0,  56.0,  58.0,  60.0,  62.0,  64.0,  66.0,  68.0,
            70.0,  72.0,  74.0,  76.0,  78.0,  80.0,  82.0,  84.0,  86.0,  88.0,
            90.0],
          dtype='float32', name='lat_aux_grid'))

  • moc_z
    PandasIndex

    PandasIndex(Index([                 0.0, 0.009999999776482582, 0.019999999552965164,
           0.029999999329447746,  0.03999999910593033,  0.05000000074505806,
            0.05999999865889549,  0.07000000029802322,  0.07999999821186066,
            0.09000000357627869,  0.10000000149011612,  0.10999999940395355,
            0.11999999731779099,  0.12999999523162842,  0.14000000059604645,
            0.15000000596046448,   0.1599999964237213,  0.17019681632518768,
            0.18076129257678986,  0.19182124733924866,  0.20349931716918945,
            0.21592344343662262,  0.22923313081264496,   0.2435845285654068,
              0.259155809879303,   0.2761526107788086,  0.29481470584869385,
            0.31542372703552246,   0.3383122682571411,  0.36387473344802856,
            0.39258047938346863,  0.42498886585235596,   0.4617665708065033,
             0.5037068724632263,   0.5517491102218628,   0.6069966554641724,
             0.6707285642623901,   0.7443980574607849,   0.8296069502830505,
               0.92804354429245,   1.0413682460784912,   1.1710401773452759,
             1.3180935382843018,   1.4829007387161255,   1.6649919748306274,
             1.8630143404006958,    2.074873924255371,    2.298038959503174,
             2.5299041271209717,   2.7680983543395996,   3.0106706619262695,
              3.256138324737549,    3.503448724746704,    3.751891851425171,
              4.001011371612549,    4.250524520874023,    4.500260829925537,
              4.750120162963867,    5.000046730041504,    5.250009536743164,
              5.499990463256836],
          dtype='float32', name='moc_z'))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6951-12-31 00:00:00, 6952-12-31 00:00:00, 6953-12-31 00:00:00,
                 6954-12-31 00:00:00, 6955-12-31 00:00:00, 6956-12-31 00:00:00,
                 6957-12-31 00:00:00, 6958-12-31 00:00:00, 6959-12-31 00:00:00,
                 6960-12-31 00:00:00, 6961-12-31 00:00:00, 6962-12-31 00:00:00,
                 6963-12-31 00:00:00, 6964-12-31 00:00:00, 6965-12-31 00:00:00,
                 6966-12-31 00:00:00, 6967-12-31 00:00:00, 6968-12-31 00:00:00,
                 6969-12-31 00:00:00, 6970-12-31 00:00:00, 6971-12-31 00:00:00,
                 6972-12-31 00:00:00, 6973-12-31 00:00:00, 6974-12-31 00:00:00,
                 6975-12-31 00:00:00, 6976-12-31 00:00:00, 6977-12-31 00:00:00,
                 6978-12-31 00:00:00, 6979-12-31 00:00:00, 6980-12-31 00:00:00,
                 6981-12-31 00:00:00, 6982-12-31 00:00:00, 6983-12-31 00:00:00,
                 6984-12-31 00:00:00, 6985-12-31 00:00:00, 6986-12-31 00:00:00,
                 6987-12-31 00:00:00, 6988-12-31 00:00:00, 6989-12-31 00:00:00,
                 6990-12-31 00:00:00, 6991-12-31 00:00:00, 6992-12-31 00:00:00,
                 6993-12-31 00:00:00, 6994-12-31 00:00:00, 6995-12-31 00:00:00,
                 6996-12-31 00:00:00, 6997-12-31 00:00:00, 6998-12-31 00:00:00,
                 6999-12-31 00:00:00, 7000-12-31 00:00:00],
                dtype='object', length=50, calendar='noleap', freq='YE-DEC'))

• Attributes: (9)

  long_name :

  Meridional Overturning Circulation (Annual)

  units :

  Sverdrups

  cell_methods :

  time: mean

  path :

  /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.MOC.695101-700012.nc

  gw :

  <xarray.DataArray 'gw' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: area of T cells units: centimeter^2

  lat :

  <xarray.DataArray 'lat' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid latitudes units: degrees_north

  lon :

  <xarray.DataArray 'lon' (nlat: 384, nlon: 320)> Size: 983kB [122880 values with dtype=float64] Coordinates: ULONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... TLAT (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: array of t-grid longitudes units: degrees_east

  comp :

  ocn

  grid :

  g16

x4c.set_style('journal_spines', font_scale=1.2)
fig, ax = case.plot(spell)