Registering New Diagnostic Functions

%load_ext autoreload
%autoreload 2

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

2025.6.22

dirpath = '/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005'
case = x4c.Timeseries(dirpath, grid_dict={'atm': 'ne16np4'})

>>> 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/*/casename.hstr.vn.timespan.nc
>>> case.grid_dict: {'atm': 'ne16np4', 'ocn': 'g16', 'lnd': 'ne16np4', 'rof': 'ne16np4', 'ice': 'g16'}
>>> case.casename: b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005
>>> case.vars_info created

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

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

>>> Spell `TS` is already calculated and the calculation is skipped.

<xarray.DataArray 'TS' (time: 600, ncol: 13826)> Size: 33MB
array([[30.723846, 30.15387 , 29.340027, ..., 20.610352, 17.8833  ,
        18.761658],
       [31.421326, 31.069   , 30.561584, ..., 19.588074, 17.21759 ,
        18.28897 ],
       [31.425201, 31.07251 , 30.54779 , ..., 19.613098, 18.46637 ,
        19.39978 ],
       ...,
       [25.155579, 24.095459, 22.823944, ..., 29.183075, 27.860046,
        28.439423],
       [27.179352, 26.204254, 24.977203, ..., 25.29721 , 22.786072,
        23.526276],
       [29.186981, 28.433838, 27.424072, ..., 21.56839 , 19.447754,
        20.136566]], shape=(600, 13826), dtype=float32)
Coordinates:
  * time     (time) object 5kB 8951-01-31 00:00:00 ... 9000-12-31 00:00:00
    lat      (ncol) float64 111kB -35.26 -35.98 -37.07 ... 37.91 37.91 36.74
    lon      (ncol) float64 111kB 315.0 316.6 319.1 320.6 ... 132.5 137.5 135.0
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 'area' (ncol: 13826)> Size: 111kB\narray...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16np4

xarray.DataArray

'TS'

• time: 600
• ncol: 13826

• 30.72 30.15 29.34 28.77 31.63 30.99 ... 21.87 20.14 21.57 19.45 20.14

  array([[30.723846, 30.15387 , 29.340027, ..., 20.610352, 17.8833  ,
          18.761658],
         [31.421326, 31.069   , 30.561584, ..., 19.588074, 17.21759 ,
          18.28897 ],
         [31.425201, 31.07251 , 30.54779 , ..., 19.613098, 18.46637 ,
          19.39978 ],
         ...,
         [25.155579, 24.095459, 22.823944, ..., 29.183075, 27.860046,
          28.439423],
         [27.179352, 26.204254, 24.977203, ..., 25.29721 , 22.786072,
          23.526276],
         [29.186981, 28.433838, 27.424072, ..., 21.56839 , 19.447754,
          20.136566]], shape=(600, 13826), dtype=float32)

• Coordinates: (3)
  • time
    (time)
    object
    8951-01-31 00:00:00 ... 9000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8951, 1, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 3, 31, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(9000, 10, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 11, 30, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          shape=(600,), dtype=object)

  • lat
    (ncol)
    float64
    -35.26 -35.98 ... 37.91 36.74

    long_name :

    latitude

    units :

    degrees_north

    array([-35.26438968, -35.98082588, -37.07155175, ...,  37.90897632,
            37.90897632,  36.74360278], shape=(13826,))

  • lon
    (ncol)
    float64
    315.0 316.6 319.1 ... 137.5 135.0

    long_name :

    longitude

    units :

    degrees_east

    array([315.        , 316.55471176, 319.07028824, ..., 132.47470023,
           137.52529977, 135.        ], shape=(13826,))

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8951-01-31 00:00:00, 8951-02-28 00:00:00, 8951-03-31 00:00:00,
                 8951-04-30 00:00:00, 8951-05-31 00:00:00, 8951-06-30 00:00:00,
                 8951-07-31 00:00:00, 8951-08-31 00:00:00, 8951-09-30 00:00:00,
                 8951-10-31 00:00:00,
                 ...
                 9000-03-31 00:00:00, 9000-04-30 00:00:00, 9000-05-31 00:00:00,
                 9000-06-30 00:00:00, 9000-07-31 00:00:00, 9000-08-31 00:00:00,
                 9000-09-30 00:00:00, 9000-10-31 00:00:00, 9000-11-30 00:00:00,
                 9000-12-31 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.895101-900012.nc

  gw :

  <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB array([0.00015457, 0.00051501, 0.00051372, ..., 0.00128989, 0.00128989, 0.00128935], shape=(13826,)) 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 :

  ne16np4

A deduced variable based on 1st order variables

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

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

<xarray.DataArray 'SST' (time: 600, nlat: 384, nlon: 320)> Size: 295MB
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]]],
      shape=(600, 384, 320), dtype=float32)
Coordinates:
    TLAT     (nlat, nlon) float64 983kB -87.73 -87.72 -87.7 ... 72.52 72.52
    TLONG    (nlat, nlon) float64 983kB 341.6 342.9 344.2 ... 326.2 326.5 326.8
    ULAT     (nlat, nlon) float64 983kB -87.53 -87.52 -87.5 ... 72.64 72.64
    ULONG    (nlat, nlon) float64 983kB 343.5 344.8 346.1 ... 326.4 326.7 327.0
  * time     (time) object 5kB 8951-01-31 00:00:00 ... 9000-12-31 00:00:00
Dimensions without coordinates: nlat, nlon
Attributes:
    long_name:     Sea Surface Temperature
    units:         °C
    grid_loc:      3111
    cell_methods:  time: mean z_t: mean
    path:          /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseri...
    gw:            <xarray.DataArray 'TAREA' (nlat: 384, nlon: 320)> Size: 98...
    lat:           <xarray.DataArray 'TLAT' (nlat: 384, nlon: 320)> Size: 983...
    lon:           <xarray.DataArray 'TLONG' (nlat: 384, nlon: 320)> Size: 98...
    dz:            <xarray.DataArray 'dz' ()> Size: 4B\n[1 values with dtype=...
    comp:          ocn
    grid:          g16

xarray.DataArray

'SST'

• time: 600
• 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]]],
        shape=(600, 384, 320), dtype=float32)

• Coordinates: (5)
  • TLAT
    (nlat, nlon)
    float64
    -87.73 -87.72 -87.7 ... 72.52 72.52

    long_name :

    array of t-grid latitudes

    units :

    degrees_north

    array([[-87.73386371, -87.71671455, -87.69937608, ..., -87.78407581,
            -87.76755438, -87.75081365],
           [-87.3506397 , -87.33384401, -87.31684841, ..., -87.39973296,
            -87.38359367, -87.36722608],
           [-86.9674157 , -86.95097347, -86.93432074, ..., -87.01539011,
            -86.99963296, -86.98363851],
           ...,
           [ 72.04218407,  72.04533769,  72.0516375 , ...,  72.0516375 ,
             72.04533769,  72.04218407],
           [ 72.27887742,  72.28185793,  72.28781167, ...,  72.28781167,
             72.28185793,  72.27887742],
           [ 72.51788946,  72.5206839 ,  72.5262657 , ...,  72.5262657 ,
             72.5206839 ,  72.51788946]], shape=(384, 320))

  • TLONG
    (nlat, nlon)
    float64
    341.6 342.9 344.2 ... 326.5 326.8

    long_name :

    array of t-grid longitudes

    units :

    degrees_east

    array([[341.64972662, 342.93514694, 344.21196801, ..., 337.74124774,
            339.05283196, 340.35564121],
           [341.64972662, 342.93514694, 344.21196801, ..., 337.74124774,
            339.05283196, 340.35564121],
           [339.63298627, 340.90594116, 342.17174082, ..., 335.77093448,
            337.0655021 , 338.35284741],
           ...,
           [327.17676287, 327.53009726, 327.88285816, ..., 326.11714184,
            326.46990274, 326.82323713],
           [327.16544883, 327.49615817, 327.82630323, ..., 326.17369677,
            326.50384183, 326.83455117],
           [327.1536079 , 327.46063974, 327.76712034, ..., 326.23287966,
            326.53936026, 326.8463921 ]], shape=(384, 320))

  • ULAT
    (nlat, nlon)
    float64
    -87.53 -87.52 -87.5 ... 72.64 72.64

    long_name :

    array of u-grid latitudes

    units :

    degrees_north

    array([[-87.53219215, -87.51509609, -87.49781204, ..., -87.58225575,
            -87.56578173, -87.5490906 ],
           [-87.15096924, -87.13425409, -87.11733774, ..., -87.19981607,
            -87.18375942, -87.16747408],
           [-86.76642971, -86.75003397, -86.7334281 , ..., -86.81426926,
            -86.79855605, -86.78260663],
           ...,
           [ 72.16065859,  72.16526223,  72.17292267, ...,  72.16526223,
             72.16065859,  72.15912282],
           [ 72.39844829,  72.40278283,  72.40999509, ...,  72.40278283,
             72.39844829,  72.39700224],
           [ 72.63861007,  72.64265531,  72.6493858 , ...,  72.64265531,
             72.63861007,  72.6372605 ]], shape=(384, 320))

  • ULONG
    (nlat, nlon)
    float64
    343.5 344.8 346.1 ... 326.7 327.0

    long_name :

    array of u-grid longitudes

    units :

    degrees_east

    array([[343.54038726, 344.8268904 , 346.1039482 , ..., 339.62326414,
            340.93866105, 342.24434105],
           [341.21162274, 342.48690786, 343.75439307, ..., 337.33852291,
            338.63746813, 339.92848993],
           [339.43939522, 340.702884  , 341.95981255, ..., 335.60940096,
            336.89266458, 338.1693267 ],
           ...,
           [327.34240762, 327.68443505, 328.02570331, ..., 326.31556495,
            326.65759238, 327.        ],
           [327.31926434, 327.63815582, 327.95630284, ..., 326.36184418,
            326.68073566, 327.        ],
           [327.29502978, 327.58969684, 327.88363973, ..., 326.41030316,
            326.70497022, 327.        ]], shape=(384, 320))

  • time
    (time)
    object
    8951-01-31 00:00:00 ... 9000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8951, 1, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 3, 31, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(9000, 10, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 11, 30, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          shape=(600,), dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8951-01-31 00:00:00, 8951-02-28 00:00:00, 8951-03-31 00:00:00,
                 8951-04-30 00:00:00, 8951-05-31 00:00:00, 8951-06-30 00:00:00,
                 8951-07-31 00:00:00, 8951-08-31 00:00:00, 8951-09-30 00:00:00,
                 8951-10-31 00:00:00,
                 ...
                 9000-03-31 00:00:00, 9000-04-30 00:00:00, 9000-05-31 00:00:00,
                 9000-06-30 00:00:00, 9000-07-31 00:00:00, 9000-08-31 00:00:00,
                 9000-09-30 00:00:00, 9000-10-31 00:00:00, 9000-11-30 00:00:00,
                 9000-12-31 00:00:00],
                dtype='object', length=600, calendar='noleap', freq='ME'))

• Attributes: (11)

  long_name :

  Sea Surface Temperature

  units :

  °C

  grid_loc :

  3111

  cell_methods :

  time: mean z_t: 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.SST.895101-900012.nc

  gw :

  <xarray.DataArray 'TAREA' (nlat: 384, nlon: 320)> Size: 983kB array([[3.58324052e+12, 3.58275618e+12, 3.58133194e+12, ..., 3.57903907e+12, 3.58138059e+12, 3.58278201e+12], [3.03286780e+12, 3.03419089e+12, 3.03553795e+12, ..., 3.02904677e+12, 3.03029530e+12, 3.03156912e+12], [3.40099516e+12, 3.40241807e+12, 3.40386668e+12, ..., 3.39688554e+12, 3.39822843e+12, 3.39959846e+12], ..., [3.17630203e+12, 3.17539484e+12, 3.17358204e+12, ..., 3.17358204e+12, 3.17539484e+12, 3.17630203e+12], [2.96328714e+12, 2.96239074e+12, 2.96059964e+12, ..., 2.96059964e+12, 2.96239074e+12, 2.96328714e+12], [2.74242741e+12, 2.74154913e+12, 2.73979435e+12, ..., 2.73979435e+12, 2.74154913e+12, 2.74242741e+12]], shape=(384, 320)) Coordinates: TLAT (nlat, nlon) float64 983kB ... TLONG (nlat, nlon) float64 983kB ... ULAT (nlat, nlon) float64 983kB ... ULONG (nlat, nlon) float64 983kB ... Dimensions without coordinates: nlat, nlon Attributes: long_name: area of T cells units: centimeter^2

  lat :

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

  lon :

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

  dz :

  <xarray.DataArray 'dz' ()> Size: 4B [1 values with dtype=float32] Attributes: long_name: thickness of layer k units: centimeters cell_methods: z_t: mean

  comp :

  ocn

  grid :

  g16

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

>>> d18Op is a supported derived variable.
>>> 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([[-3.9242506, -4.0639043, -4.1208863, ..., -9.632945 , -8.070588 ,
        -8.230329 ],
       [-5.675137 , -5.547166 , -5.905688 , ..., -8.86035  , -8.010567 ,
        -7.663846 ],
       [-4.2343736, -4.4196844, -4.8832297, ..., -7.989228 , -7.1674585,
        -6.616235 ],
       ...,
       [-4.006505 , -4.380405 , -5.3446293, ..., -5.651295 , -5.3805113,
        -5.291581 ],
       [-3.416419 , -4.7590733, -4.9126744, ..., -8.602322 , -6.9465637,
        -6.7592263],
       [-3.531158 , -3.5579205, -3.6706924, ..., -8.005201 , -7.1768165,
        -6.878078 ]], shape=(600, 13826), dtype=float32)
Coordinates:
  * time     (time) object 5kB 8951-01-31 00:00:00 ... 9000-12-31 00:00:00
    lat      (ncol) float64 111kB -35.26 -35.98 -37.07 ... 37.91 37.91 36.74
    lon      (ncol) float64 111kB 315.0 316.6 319.1 320.6 ... 132.5 137.5 135.0
Dimensions without coordinates: ncol
Attributes:
    units:         permil
    long_name:     Precipitation d18O
    cell_methods:  time: mean
    path:          /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseri...
    gw:            <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB\narray...
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\n[13826...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\n[13826...
    comp:          atm
    grid:          ne16np4

xarray.DataArray

'd18Op'

• time: 600
• ncol: 13826

• -3.924 -4.064 -4.121 -4.052 -3.643 ... -8.267 -8.005 -7.177 -6.878

  array([[-3.9242506, -4.0639043, -4.1208863, ..., -9.632945 , -8.070588 ,
          -8.230329 ],
         [-5.675137 , -5.547166 , -5.905688 , ..., -8.86035  , -8.010567 ,
          -7.663846 ],
         [-4.2343736, -4.4196844, -4.8832297, ..., -7.989228 , -7.1674585,
          -6.616235 ],
         ...,
         [-4.006505 , -4.380405 , -5.3446293, ..., -5.651295 , -5.3805113,
          -5.291581 ],
         [-3.416419 , -4.7590733, -4.9126744, ..., -8.602322 , -6.9465637,
          -6.7592263],
         [-3.531158 , -3.5579205, -3.6706924, ..., -8.005201 , -7.1768165,
          -6.878078 ]], shape=(600, 13826), dtype=float32)

• Coordinates: (3)
  • time
    (time)
    object
    8951-01-31 00:00:00 ... 9000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8951, 1, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 3, 31, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(9000, 10, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 11, 30, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          shape=(600,), dtype=object)

  • lat
    (ncol)
    float64
    -35.26 -35.98 ... 37.91 36.74

    long_name :

    latitude

    units :

    degrees_north

    array([-35.26438968, -35.98082588, -37.07155175, ...,  37.90897632,
            37.90897632,  36.74360278], shape=(13826,))

  • lon
    (ncol)
    float64
    315.0 316.6 319.1 ... 137.5 135.0

    long_name :

    longitude

    units :

    degrees_east

    array([315.        , 316.55471176, 319.07028824, ..., 132.47470023,
           137.52529977, 135.        ], shape=(13826,))

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8951-01-31 00:00:00, 8951-02-28 00:00:00, 8951-03-31 00:00:00,
                 8951-04-30 00:00:00, 8951-05-31 00:00:00, 8951-06-30 00:00:00,
                 8951-07-31 00:00:00, 8951-08-31 00:00:00, 8951-09-30 00:00:00,
                 8951-10-31 00:00:00,
                 ...
                 9000-03-31 00:00:00, 9000-04-30 00:00:00, 9000-05-31 00:00:00,
                 9000-06-30 00:00:00, 9000-07-31 00:00:00, 9000-08-31 00:00:00,
                 9000-09-30 00:00:00, 9000-10-31 00:00:00, 9000-11-30 00:00:00,
                 9000-12-31 00:00:00],
                dtype='object', length=600, calendar='noleap', freq='ME'))

• Attributes: (9)

  units :

  permil

  long_name :

  Precipitation d18O

  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.PRECRC_H218Or.895101-900012.nc

  gw :

  <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB array([0.00015457, 0.00051501, 0.00051372, ..., 0.00128989, 0.00128989, 0.00128935], shape=(13826,)) 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 :

  ne16np4

User-defined derived variables

x4c.diags.Registry.funcs

{'SST': <function x4c.diags.DiagCalc.get_SST(case, **kws)>,
 'SSS': <function x4c.diags.DiagCalc.get_SSS(case, **kws)>,
 'LST': <function x4c.diags.DiagCalc.get_LST(case, **kws)>,
 'MLD': <function x4c.diags.DiagCalc.get_MLD(case, **kws)>,
 'PRECT': <function x4c.diags.DiagCalc.get_PRECT(case, **kws)>,
 'dD': <function x4c.diags.DiagCalc.get_dD(case, **kws)>,
 'd18Op': <function x4c.diags.DiagCalc.get_d18Op(case, **kws)>,
 'd18Osw': <function x4c.diags.DiagCalc.get_d18Osw(case, **kws)>,
 'd18Oc': <function x4c.diags.DiagCalc.get_d18Oc(case, **kws)>,
 'RESTOM': <function x4c.diags.DiagCalc.get_RESTOM(case, **kws)>,
 'MOC': <function x4c.diags.DiagCalc.get_MOC(case, **kws)>,
 'ICEFRAC': <function x4c.diags.DiagCalc.get_ICEFRAC(case, **kws)>,
 'H2O': <function __main__.get_H2O(case, **kws)>}

@x4c.diags.F
def get_H2O(case, **kws):
    case.load('PRECRC_H2Or', **kws)
    case.load('PRECSC_H2Os', **kws)
    case.load('PRECRL_H2OR', **kws)
    case.load('PRECSL_H2OS', **kws)
    h2o = case.ds['PRECRC_H2Or'].x.da + case.ds['PRECSC_H2Os'].x.da + case.ds['PRECRL_H2OR'].x.da + case.ds['PRECSL_H2OS'].x.da
    h2o = h2o.where(h2o > 1e-18, 1e-18)
    h2o.name = 'H2O'
    h2o.attrs['long_name'] = 'H2O'
    h2o.attrs['units'] = 'kg m-2 s-1'
    return h2o

x4c.diags.Registry.funcs

{'SST': <function x4c.diags.DiagCalc.get_SST(case, **kws)>,
 'SSS': <function x4c.diags.DiagCalc.get_SSS(case, **kws)>,
 'LST': <function x4c.diags.DiagCalc.get_LST(case, **kws)>,
 'MLD': <function x4c.diags.DiagCalc.get_MLD(case, **kws)>,
 'PRECT': <function x4c.diags.DiagCalc.get_PRECT(case, **kws)>,
 'dD': <function x4c.diags.DiagCalc.get_dD(case, **kws)>,
 'd18Op': <function x4c.diags.DiagCalc.get_d18Op(case, **kws)>,
 'd18Osw': <function x4c.diags.DiagCalc.get_d18Osw(case, **kws)>,
 'd18Oc': <function x4c.diags.DiagCalc.get_d18Oc(case, **kws)>,
 'RESTOM': <function x4c.diags.DiagCalc.get_RESTOM(case, **kws)>,
 'MOC': <function x4c.diags.DiagCalc.get_MOC(case, **kws)>,
 'ICEFRAC': <function x4c.diags.DiagCalc.get_ICEFRAC(case, **kws)>,
 'H2O': <function __main__.get_H2O(case, **kws)>}

spell = 'H2O:ann'
case.calc(spell, timespan=(8901, 9000), recalculate=True)
case.diags[spell]

>>> H2O is a supported derived variable.
>>> case.ds["PRECRC_H2Or"] will be reloaded due to different paths.
>>> case.ds["PRECRC_H2Or"] created
>>> case.ds["PRECSC_H2Os"] will be reloaded due to different paths.
>>> case.ds["PRECSC_H2Os"] created
>>> case.ds["PRECRL_H2OR"] will be reloaded due to different paths.
>>> case.ds["PRECRL_H2OR"] created
>>> case.ds["PRECSL_H2OS"] will be reloaded due to different paths.
>>> case.ds["PRECSL_H2OS"] created
>>> Timespan: [8901-01-31 00:00:00, 9000-12-31 00:00:00]
>>> case.diags["H2O:ann"] created

<xarray.DataArray 'H2O' (time: 100, ncol: 13826)> Size: 6MB
array([[3.2436031e-08, 3.2029504e-08, 3.3239736e-08, ..., 6.1915671e-08,
        7.3360034e-08, 7.6831476e-08],
       [4.3094449e-08, 3.9509558e-08, 4.0895586e-08, ..., 7.6069632e-08,
        7.6563857e-08, 8.0979220e-08],
       [4.8572193e-08, 4.6255469e-08, 4.6900130e-08, ..., 6.2611662e-08,
        6.3687779e-08, 7.1505802e-08],
       ...,
       [4.9972602e-08, 4.8664834e-08, 4.9744781e-08, ..., 6.6670054e-08,
        7.4954293e-08, 8.1129713e-08],
       [4.0223568e-08, 3.6252263e-08, 3.7049467e-08, ..., 6.4540522e-08,
        8.0180683e-08, 8.5970662e-08],
       [4.0655792e-08, 3.9256889e-08, 3.9505519e-08, ..., 7.3763481e-08,
        7.8176519e-08, 8.6220005e-08]], shape=(100, 13826), dtype=float32)
Coordinates:
    lat      (ncol) float64 111kB -35.26 -35.98 -37.07 ... 37.91 37.91 36.74
    lon      (ncol) float64 111kB 315.0 316.6 319.1 320.6 ... 132.5 137.5 135.0
  * time     (time) object 800B 8901-12-31 00:00:00 ... 9000-12-31 00:00:00
Dimensions without coordinates: ncol
Attributes:
    units:         kg m-2 s-1
    long_name:     H2O (Annual)
    cell_methods:  time: mean
    path:          ['/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timese...
    gw:            <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB\ndask....
    lat:           <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB\ndask.a...
    lon:           <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB\ndask.a...
    comp:          atm
    grid:          ne16np4

xarray.DataArray

'H2O'

• time: 100
• ncol: 13826

• 3.244e-08 3.203e-08 3.324e-08 ... 7.376e-08 7.818e-08 8.622e-08

  array([[3.2436031e-08, 3.2029504e-08, 3.3239736e-08, ..., 6.1915671e-08,
          7.3360034e-08, 7.6831476e-08],
         [4.3094449e-08, 3.9509558e-08, 4.0895586e-08, ..., 7.6069632e-08,
          7.6563857e-08, 8.0979220e-08],
         [4.8572193e-08, 4.6255469e-08, 4.6900130e-08, ..., 6.2611662e-08,
          6.3687779e-08, 7.1505802e-08],
         ...,
         [4.9972602e-08, 4.8664834e-08, 4.9744781e-08, ..., 6.6670054e-08,
          7.4954293e-08, 8.1129713e-08],
         [4.0223568e-08, 3.6252263e-08, 3.7049467e-08, ..., 6.4540522e-08,
          8.0180683e-08, 8.5970662e-08],
         [4.0655792e-08, 3.9256889e-08, 3.9505519e-08, ..., 7.3763481e-08,
          7.8176519e-08, 8.6220005e-08]], shape=(100, 13826), dtype=float32)

• Coordinates: (3)
  • lat
    (ncol)
    float64
    -35.26 -35.98 ... 37.91 36.74

    long_name :

    latitude

    units :

    degrees_north

    array([-35.26438968, -35.98082588, -37.07155175, ...,  37.90897632,
            37.90897632,  36.74360278], shape=(13826,))

  • lon
    (ncol)
    float64
    315.0 316.6 319.1 ... 137.5 135.0

    long_name :

    longitude

    units :

    degrees_east

    array([315.        , 316.55471176, 319.07028824, ..., 132.47470023,
           137.52529977, 135.        ], shape=(13826,))

  • time
    (time)
    object
    8901-12-31 00:00:00 ... 9000-12-...

    long_name :

    Model Year

    array([cftime.DatetimeNoLeap(8901, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8902, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8903, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8904, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8905, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8906, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8907, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8908, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8909, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8910, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8911, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8912, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8913, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8914, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8915, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8916, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8917, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8918, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8919, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8920, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8921, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8922, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8923, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8924, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8925, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8926, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8927, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8928, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8929, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8930, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8931, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8932, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8933, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8934, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8935, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8936, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8937, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8938, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8939, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8940, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8941, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8942, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8943, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8944, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8945, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8946, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8947, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8948, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8949, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8950, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8951, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8952, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8953, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8954, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8955, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8956, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8957, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8958, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8959, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8960, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8961, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8962, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8963, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8964, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8965, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8966, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8967, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8968, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8969, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8970, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8971, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8972, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8973, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8974, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8975, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8976, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8977, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8978, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8979, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8980, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8981, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8982, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8983, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8984, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8985, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8986, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8987, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8988, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8989, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8990, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8991, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8992, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8993, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8994, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8995, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8996, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8997, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8998, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(8999, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(9000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([8901-12-31 00:00:00, 8902-12-31 00:00:00, 8903-12-31 00:00:00,
                 8904-12-31 00:00:00, 8905-12-31 00:00:00, 8906-12-31 00:00:00,
                 8907-12-31 00:00:00, 8908-12-31 00:00:00, 8909-12-31 00:00:00,
                 8910-12-31 00:00:00, 8911-12-31 00:00:00, 8912-12-31 00:00:00,
                 8913-12-31 00:00:00, 8914-12-31 00:00:00, 8915-12-31 00:00:00,
                 8916-12-31 00:00:00, 8917-12-31 00:00:00, 8918-12-31 00:00:00,
                 8919-12-31 00:00:00, 8920-12-31 00:00:00, 8921-12-31 00:00:00,
                 8922-12-31 00:00:00, 8923-12-31 00:00:00, 8924-12-31 00:00:00,
                 8925-12-31 00:00:00, 8926-12-31 00:00:00, 8927-12-31 00:00:00,
                 8928-12-31 00:00:00, 8929-12-31 00:00:00, 8930-12-31 00:00:00,
                 8931-12-31 00:00:00, 8932-12-31 00:00:00, 8933-12-31 00:00:00,
                 8934-12-31 00:00:00, 8935-12-31 00:00:00, 8936-12-31 00:00:00,
                 8937-12-31 00:00:00, 8938-12-31 00:00:00, 8939-12-31 00:00:00,
                 8940-12-31 00:00:00, 8941-12-31 00:00:00, 8942-12-31 00:00:00,
                 8943-12-31 00:00:00, 8944-12-31 00:00:00, 8945-12-31 00:00:00,
                 8946-12-31 00:00:00, 8947-12-31 00:00:00, 8948-12-31 00:00:00,
                 8949-12-31 00:00:00, 8950-12-31 00:00:00, 8951-12-31 00:00:00,
                 8952-12-31 00:00:00, 8953-12-31 00:00:00, 8954-12-31 00:00:00,
                 8955-12-31 00:00:00, 8956-12-31 00:00:00, 8957-12-31 00:00:00,
                 8958-12-31 00:00:00, 8959-12-31 00:00:00, 8960-12-31 00:00:00,
                 8961-12-31 00:00:00, 8962-12-31 00:00:00, 8963-12-31 00:00:00,
                 8964-12-31 00:00:00, 8965-12-31 00:00:00, 8966-12-31 00:00:00,
                 8967-12-31 00:00:00, 8968-12-31 00:00:00, 8969-12-31 00:00:00,
                 8970-12-31 00:00:00, 8971-12-31 00:00:00, 8972-12-31 00:00:00,
                 8973-12-31 00:00:00, 8974-12-31 00:00:00, 8975-12-31 00:00:00,
                 8976-12-31 00:00:00, 8977-12-31 00:00:00, 8978-12-31 00:00:00,
                 8979-12-31 00:00:00, 8980-12-31 00:00:00, 8981-12-31 00:00:00,
                 8982-12-31 00:00:00, 8983-12-31 00:00:00, 8984-12-31 00:00:00,
                 8985-12-31 00:00:00, 8986-12-31 00:00:00, 8987-12-31 00:00:00,
                 8988-12-31 00:00:00, 8989-12-31 00:00:00, 8990-12-31 00:00:00,
                 8991-12-31 00:00:00, 8992-12-31 00:00:00, 8993-12-31 00:00:00,
                 8994-12-31 00:00:00, 8995-12-31 00:00:00, 8996-12-31 00:00:00,
                 8997-12-31 00:00:00, 8998-12-31 00:00:00, 8999-12-31 00:00:00,
                 9000-12-31 00:00:00],
                dtype='object', length=100, calendar='noleap', freq='YE-DEC'))

• Attributes: (9)

  units :

  kg m-2 s-1

  long_name :

  H2O (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.offset.d18O.branch.cam.h0.PRECRC_H2Or.890101-895012.nc', '/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.PRECRC_H2Or.895101-900012.nc']

  gw :

  <xarray.DataArray 'area' (ncol: 13826)> Size: 111kB dask.array<where, shape=(13826,), dtype=float64, chunksize=(13826,), chunktype=numpy.ndarray> Dimensions without coordinates: ncol

  lat :

  <xarray.DataArray 'lat' (ncol: 13826)> Size: 111kB dask.array<open_dataset-lat, shape=(13826,), dtype=float64, chunksize=(13826,), chunktype=numpy.ndarray> Dimensions without coordinates: ncol Attributes: long_name: latitude units: degrees_north

  lon :

  <xarray.DataArray 'lon' (ncol: 13826)> Size: 111kB dask.array<open_dataset-lon, shape=(13826,), dtype=float64, chunksize=(13826,), chunktype=numpy.ndarray> Dimensions without coordinates: ncol Attributes: long_name: longitude units: degrees_east

  comp :

  atm

  grid :

  ne16np4

fig, ax = case.plot(spell, regrid=True)