4. Additional information and examples

Tutorial at the 2024 paleoCAMP | June 18–July 1, 2024

Jiang Zhu
jiangzhu@ucar.edu
Climate & Global Dynamics Laboratory
NSF National Center for Atmospheric Research

---

More information and examples to demonstrate model data access and analysis using the NCAR JupyterHub

• Further Info 1: Guidance on using climate data

• Further Info 2: Access ESM output

• Example 1: Access CESM output on NCAR’s Campaign Storage and perform model-data comparison

• Example 2: Access ERA5 Reanalysis on NSF NCAR’s Research Data Archive

• Example 3: Plot AMOC from TraCE and compare with McManus et al. 2004

• Example 4. Compute and plot precipitation δ18O

Time to go through: 10 minutes

---

4.1. Further Info 1: Guidance on using climate data

• NCAR Climate Data Guide: Key strength, Key limitations, Expert User Guidance, etc.

  • Atmospheric Reanalysis: Overview & Comparison Tables

  • Paleoclimate (You could contribute!)

  • …

---

4.2. Further Info 2: Access ESM output

• Earth System Grid Federation (ESGF), e.g., the LLNL Node

• NCAR Climate Data Gateway, e.g., the iTRACE

• NCAR Research Data Archive: e.g., EAR5 Reanalysis (1.77 PB!)

• Other portals, such as the DeepMIP Model Database

• NCAR JupyterHub is your one-stop shop for CESM data and analysis!

  • Multiple PB of CESM Paleoclimate simulation data

  • Other CESM simulation data

  • Preinstalled Python environment

Note: variable names may be different depending on the portals (IPCC Standard; CESM Standard)

---

Load Python packages

import os
import glob
from datetime import timedelta

import xarray as xr
import numpy as np
import pandas as pd

import matplotlib as mpl
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from cartopy.util import add_cyclic_point

# xesmf is used for regridding ocean output
import xesmf

import warnings
warnings.filterwarnings("ignore")

---

4.3. Example 1: Access CESM paleoclimate output and perform model-data comparison on NCAR’s Campaign Storage

• We try to reproduce Figure 2a of Jiang’s paper to use the LGM ΔSST to assess CESM2’s climate sensitivity.

• Whole set of model output is shared at: /glade/campaign/cesm/development/palwg

• You can find additional simulation data here: /glade/campaign/cgd/ppc/jiangzhu

• We are in the process of organizing available data on the Paleoclimate Working Group webiste

• As for now, prior knowledge of the experiments and file structure are needed. We hope to develop a Simulation Catalog for this!

!ls /glade/campaign/cesm/development/palwg/

cesm                        Idealized           old.setups
CMIP_DECK                   inputdata           paleoweather
ctsm_glacier_gridfiles_LGM  LastGlacialMaximum  pliocene
datasets                    LastInterglacial    Pliocene
EXTRA                       LastMillennium      raw_boundary_data
holocene                    lig-H11             Tabor_paleo
Holocene                    LIGtransient        temp
Holocene-9ka                mvr

!ls -l /glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2
!ls /glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2/b.e21.B1850CLM50SP.f09_g17.PI.01/ocn/proc/tseries/month_1/*.TEMP.*
!ls /glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2/b.e21.B1850CLM50SP.f09_g17.21ka.01/ocn/proc/tseries/month_1/*.TEMP.*

total 3
drwxr-sr-x+ 7 jiangzhu cesm 4096 Feb 24  2023 b.e21.B1850CLM50SP.f09_g17.21ka.01
drwxr-sr-x+ 8 jiangzhu cesm 4096 Feb 24  2023 b.e21.B1850CLM50SP.f09_g17.PI.01
-rw-r-----+ 1 jiangzhu cesm  413 Aug 14  2023 please_cite_zhu_etal_2021

/glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2/b.e21.B1850CLM50SP.f09_g17.PI.01/ocn/proc/tseries/month_1/b.e21.B1850CLM50SP.f09_g17.PI.01.pop.h.TEMP.000101-010012.nc
/glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2/b.e21.B1850CLM50SP.f09_g17.PI.01/ocn/proc/tseries/month_1/b.e21.B1850CLM50SP.f09_g17.PI.01.pop.h.TEMP.010101-020012.nc
/glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2/b.e21.B1850CLM50SP.f09_g17.PI.01/ocn/proc/tseries/month_1/b.e21.B1850CLM50SP.f09_g17.PI.01.pop.h.TEMP.020101-030012.nc

/glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2/b.e21.B1850CLM50SP.f09_g17.21ka.01/ocn/proc/tseries/month_1/b.e21.B1850CLM50SP.f09_g17.21ka.01.pop.h.TEMP.000101-010012.nc
/glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2/b.e21.B1850CLM50SP.f09_g17.21ka.01/ocn/proc/tseries/month_1/b.e21.B1850CLM50SP.f09_g17.21ka.01.pop.h.TEMP.010101-020012.nc
/glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2/b.e21.B1850CLM50SP.f09_g17.21ka.01/ocn/proc/tseries/month_1/b.e21.B1850CLM50SP.f09_g17.21ka.01.pop.h.TEMP.020101-030012.nc
/glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2/b.e21.B1850CLM50SP.f09_g17.21ka.01/ocn/proc/tseries/month_1/b.e21.B1850CLM50SP.f09_g17.21ka.01.pop.h.TEMP.030101-040012.nc
/glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2/b.e21.B1850CLM50SP.f09_g17.21ka.01/ocn/proc/tseries/month_1/b.e21.B1850CLM50SP.f09_g17.21ka.01.pop.h.TEMP.040101-050012.nc

campaign_dir = '/glade/campaign/cesm/development/palwg/LastGlacialMaximum/CESM2'
comp = 'ocn/proc/tseries/month_1'

4.3.1. Read preindustrial SST

case = 'b.e21.B1850CLM50SP.f09_g17.PI.01'
fname = 'b.e21.B1850CLM50SP.f09_g17.PI.01.pop.h.TEMP.020101-030012.nc'

file = os.path.join(campaign_dir, case, comp, fname)

# Open the file and select the last 10 years of data
ds_pre = xr.open_dataset(file).isel(time=slice(-120, None))
sst_pre = ds_pre.TEMP.isel(z_t=0).mean('time')
sst_pre

<xarray.DataArray 'TEMP' (nlat: 384, nlon: 320)>
array([[       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [-1.9042568, -1.9034731, -1.9024575, ...,        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 500.0
    ULONG    (nlat, nlon) float64 ...
    ULAT     (nlat, nlon) float64 ...
    TLONG    (nlat, nlon) float64 ...
    TLAT     (nlat, nlon) float64 ...
Dimensions without coordinates: nlat, nlon

xarray.DataArray

'TEMP'

• 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],
         [-1.9042568, -1.9034731, -1.9024575, ...,        nan,        nan,
                 nan],
         ...,
         [       nan,        nan,        nan, ...,        nan,        nan,
                 nan],
         [       nan,        nan,        nan, ...,        nan,        nan,
                 nan],
         [       nan,        nan,        nan, ...,        nan,        nan,
                 nan]], dtype=float32)

• Coordinates: (5)
  • 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]

• Indexes: (0)
• Attributes: (0)

4.3.2. Read LGM SST

case = 'b.e21.B1850CLM50SP.f09_g17.21ka.01'
fname = 'b.e21.B1850CLM50SP.f09_g17.21ka.01.pop.h.TEMP.040101-050012.nc'

file = os.path.join(campaign_dir, case, comp, fname)

# Open the file and select the last 10 years of data
ds_lgm = xr.open_dataset(file).isel(time=slice(-120, None))
sst_lgm = ds_lgm.TEMP.isel(z_t=0).mean('time')
sst_lgm

<xarray.DataArray 'TEMP' (nlat: 384, nlon: 320)>
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]], dtype=float32)
Coordinates:
    z_t      float32 500.0
    ULONG    (nlat, nlon) float64 ...
    ULAT     (nlat, nlon) float64 ...
    TLONG    (nlat, nlon) float64 ...
    TLAT     (nlat, nlon) float64 ...
Dimensions without coordinates: nlat, nlon

xarray.DataArray

'TEMP'

• 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]], dtype=float32)

• Coordinates: (5)
  • 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]

• Indexes: (0)
• Attributes: (0)

4.3.3. Regrid into the 1° × 1° grid using xesmf

%%time

ds_pre['lat'] = ds_pre.TLAT
ds_pre['lon'] = ds_pre.TLONG

regridder = xesmf.Regridder(
    ds_in=ds_pre,
    ds_out=xesmf.util.grid_global(1, 1, cf=True, lon1=360),
    method='bilinear',
    periodic=True)

dsst_1x1 = regridder(sst_lgm - sst_pre)
dsst_1x1

CPU times: user 6.62 s, sys: 244 ms, total: 6.86 s
Wall time: 7.2 s

<xarray.DataArray (lat: 180, lon: 360)>
array([[        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       ...,
       [-0.17081444, -0.17075008, -0.17069343, ..., -0.17104828,
        -0.17096627, -0.1708865 ],
       [-0.178788  , -0.17878205, -0.17877994, ..., -0.17882909,
        -0.17881154, -0.17879783],
       [-0.18547155, -0.18547687, -0.18548317, ..., -0.18546143,
        -0.18546383, -0.1854672 ]], dtype=float32)
Coordinates:
    z_t                 float32 500.0
  * lon                 (lon) float64 0.5 1.5 2.5 3.5 ... 357.5 358.5 359.5
    latitude_longitude  float64 nan
  * lat                 (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5
Attributes:
    regrid_method:  bilinear

xarray.DataArray

• lat: 180
• lon: 360

• nan nan nan nan nan nan ... -0.1855 -0.1855 -0.1855 -0.1855 -0.1855

  array([[        nan,         nan,         nan, ...,         nan,
                  nan,         nan],
         [        nan,         nan,         nan, ...,         nan,
                  nan,         nan],
         [        nan,         nan,         nan, ...,         nan,
                  nan,         nan],
         ...,
         [-0.17081444, -0.17075008, -0.17069343, ..., -0.17104828,
          -0.17096627, -0.1708865 ],
         [-0.178788  , -0.17878205, -0.17877994, ..., -0.17882909,
          -0.17881154, -0.17879783],
         [-0.18547155, -0.18547687, -0.18548317, ..., -0.18546143,
          -0.18546383, -0.1854672 ]], dtype=float32)

• Coordinates: (4)
  • z_t
    ()
    float32
    500.0

    array(500., dtype=float32)

  • lon
    (lon)
    float64
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    bounds :

    lon_bounds

    standard_name :

    longitude

    units :

    degrees_east

    array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])

  • latitude_longitude
    ()
    float64
    nan

    array(nan)

  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    bounds :

    lat_bounds

    standard_name :

    latitude

    units :

    degrees_north

    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])

• Indexes: (2)
  • lon
    PandasIndex

    PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
           ...
           350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
          dtype='float64', name='lon', length=360))

  • 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))

• Attributes: (1)

  regrid_method :

  bilinear

4.3.4. Read proxy ΔSST in csv from Jess’s github

url = 'https://raw.githubusercontent.com/jesstierney/lgmDA/master/proxyData/Tierney2020_ProxyDataPaired.csv'
proxy_dsst = pd.read_csv(url)
proxy_dsst.head()

	Latitude	Longitude	Lower2s	Median	Upper2s	ProxyType	Species
0	-55.0	73.3	-2.927296	-1.379339	0.302709	delo	pachy
1	-53.0	-58.0	1.426191	2.773898	4.253553	uk	NaN
2	-51.1	67.7	-4.810437	-3.192364	-0.570439	delo	pachy
3	-48.1	146.9	-4.784523	-3.370838	-1.886123	uk	NaN
4	-46.1	90.1	-4.949764	-3.470513	-1.919271	delo	bulloides

4.3.5. Plot the LGM ΔSST in the model and proxy records

cmap = plt.get_cmap('YlGnBu').reversed()
norm = mpl.colors.Normalize(-12, 0)

fig, ax = plt.subplots(nrows=1, ncols=1,
                       figsize=(3, 1.5),
                       subplot_kw={'projection': ccrs.Robinson(central_longitude=210)},
                       constrained_layout=True)

# Plot model results using contourf
dsst_1x1_new, lon_new = add_cyclic_point(dsst_1x1, dsst_1x1.lon)
p0 = ax.contourf(lon_new, dsst_1x1.lat, dsst_1x1_new,
                 levels=np.linspace(-12, 0, 13),
                 cmap=cmap, norm=norm, extend='both',
                 transform=ccrs.PlateCarree())
plt.colorbar(p0, ax=ax)

# Create a land-sea mask and plot the LGM coastal line
lmask = xr.where(dsst_1x1.isnull(), 0, 1)
ax.contour(lmask.lon, lmask.lat, lmask,
           levels=[0.5],
           linewidths=0.5,
           colors='black',
           transform=ccrs.PlateCarree())

# Plot proxy SST using markers
ax.scatter(proxy_dsst['Longitude'],
           proxy_dsst['Latitude'],
           c=proxy_dsst['Median'],
           marker='o',
           s=15,
           cmap=cmap,
           edgecolors='black',
           lw=0.35,
           norm=norm,
           zorder=3,
           transform=ccrs.PlateCarree())

ax.set_title("LGM ΔSST: CESM2 vs proxy")

Text(0.5, 1.0, 'LGM ΔSST: CESM2 vs proxy')

---

4.4. Example 2: Access ERA5 Reanalysis data on NSF NCAR’s Research Data Archive

Browse the RDA website and figure out the file structure

• Search era5

• Click the Monthly Mean product (ds633.1)

• Click the tab DATA ACCESS

• Total precipitation is in ERA5 monthly mean atmospheric surface forecast (accumulated) [netCDF4]

• Click GLADE File Listing to see all the files stored on the NCAR Campaign Storage

data_dir = '/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/'

files_tp = glob.glob(data_dir + '*/*_tp.*.nc')
print(*files_tp, sep='\n')

/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2003/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2003010100_2003120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2018/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2018010100_2018120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2004/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2004010100_2004120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2019/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2019010100_2019120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2005/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2005010100_2005120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2006/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2006010100_2006120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1979/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1979010100_1979120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2014/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2014010100_2014120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2000/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2000010100_2000120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1987/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1987010100_1987120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2015/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2015010100_2015120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2001/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2001010100_2001120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1988/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1988010100_1988120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2016/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2016010100_2016120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2002/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2002010100_2002120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1989/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1989010100_1989120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2017/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2017010100_2017120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2010/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2010010100_2010120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1997/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1997010100_1997120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1983/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1983010100_1983120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2011/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2011010100_2011120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1998/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1998010100_1998120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1984/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1984010100_1984120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2012/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2012010100_2012120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1999/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1999010100_1999120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1985/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1985010100_1985120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2013/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2013010100_2013120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2020/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2020010100_2020120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1986/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1986010100_1986120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1993/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1993010100_1993120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2021/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2021010100_2021120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1994/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1994010100_1994120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2022/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2022010100_2022120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1980/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1980010100_1980120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1995/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1995010100_1995120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1981/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1981010100_1981120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1996/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1996010100_1996120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1982/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1982010100_1982120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2007/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2007010100_2007120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1990/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1990010100_1990120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2008/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2008010100_2008120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1991/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1991010100_1991120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/2009/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.2009010100_2009120100.nc
/glade/campaign/collections/rda/data/ds633.1/e5.moda.fc.sfc.accumu/1992/e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1992010100_1992120100.nc

ds = xr.open_mfdataset(files_tp)
ds = ds.reindex(latitude=sorted(ds.latitude.values))
ds

<xarray.Dataset>
Dimensions:    (latitude: 721, longitude: 1440, time: 528)
Coordinates:
  * latitude   (latitude) float64 -90.0 -89.75 -89.5 -89.25 ... 89.5 89.75 90.0
  * longitude  (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8
  * time       (time) datetime64[ns] 1979-01-01 1979-02-01 ... 2022-12-01
Data variables:
    TP         (time, latitude, longitude) float32 dask.array<chunksize=(3, 332, 776), meta=np.ndarray>
    utc_date   (time) int32 dask.array<chunksize=(12,), meta=np.ndarray>
Attributes:
    DATA_SOURCE:          ECMWF: https://cds.climate.copernicus.eu, Copernicu...
    NETCDF_CONVERSION:    CISL RDA: Conversion from ECMWF GRIB 1 data to netC...
    NETCDF_VERSION:       4.6.1
    CONVERSION_PLATFORM:  Linux casper02 3.10.0-693.21.1.el7.x86_64 #1 SMP We...
    CONVERSION_DATE:      Mon Nov 11 08:45:33 MST 2019
    Conventions:          CF-1.6
    NETCDF_COMPRESSION:   NCO: Precision-preserving compression to netCDF4/HD...
    history:              Mon Nov 11 08:45:34 2019: ncks -4 --ppc default=7 e...
    NCO:                  netCDF Operators version 4.7.9 (Homepage = http://n...

xarray.Dataset

• Dimensions:
  • latitude: 721
  • longitude: 1440
  • time: 528
• Coordinates: (3)
  • latitude
    (latitude)
    float64
    -90.0 -89.75 -89.5 ... 89.75 90.0

    long_name :

    latitude

    short_name :

    lat

    units :

    degrees_north

    array([-90.  , -89.75, -89.5 , ...,  89.5 ,  89.75,  90.  ])

  • longitude
    (longitude)
    float64
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    long_name :

    longitude

    short_name :

    lon

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02])

  • time
    (time)
    datetime64[ns]
    1979-01-01 ... 2022-12-01

    long_name :

    time

    array(['1979-01-01T00:00:00.000000000', '1979-02-01T00:00:00.000000000',
           '1979-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (2)
  • TP
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(3, 332, 776), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    original_format :

    WMO GRIB 1 with ECMWF local table

    ecmwf_local_table :

    128

    ecmwf_parameter :

    228

    minimum_value :

    0.0

    maximum_value :

    0.11752701

    grid_specification :

    0.25 degree x 0.25 degree from 90N to 90S and 0E to 359.75E (721 x 1440 Latitude/Longitude)

    rda_dataset :

    ds633.1

    rda_dataset_url :

    https:/rda.ucar.edu/datasets/ds633.1/

    rda_dataset_doi :

    DOI: 10.5065/P8GT-0R61

    rda_dataset_group :

    ERA5 monthly mean atmospheric surface forecast (accumulated) [netCDF4]

    number_of_significant_digits :

    7

    Array Chunk Bytes 2.04 GiB 3.45 MiB Shape (528, 721, 1440) (3, 389, 776) Dask graph 704 chunks in 90 graph layers Data type float32 numpy.ndarray

  • utc_date
    (time)
    int32
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    UTC date yyyy-mm-dd hh:00:00 as yyyymmddhh

    units :

    Gregorian_year month day hour

    Array Chunk Bytes 2.06 kiB 48 B Shape (528,) (12,) Dask graph 44 chunks in 89 graph layers Data type int32 numpy.ndarray

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([ -90.0, -89.75,  -89.5, -89.25,  -89.0, -88.75,  -88.5, -88.25,  -88.0,
           -87.75,
           ...
            87.75,   88.0,  88.25,   88.5,  88.75,   89.0,  89.25,   89.5,  89.75,
             90.0],
          dtype='float64', name='latitude', length=721))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float64', name='longitude', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-01', '1979-02-01', '1979-03-01', '1979-04-01',
                   '1979-05-01', '1979-06-01', '1979-07-01', '1979-08-01',
                   '1979-09-01', '1979-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=528, freq=None))

• Attributes: (9)

  DATA_SOURCE :

  ECMWF: https://cds.climate.copernicus.eu, Copernicus Climate Data Store

  NETCDF_CONVERSION :

  CISL RDA: Conversion from ECMWF GRIB 1 data to netCDF4.

  NETCDF_VERSION :

  4.6.1

  CONVERSION_PLATFORM :

  Linux casper02 3.10.0-693.21.1.el7.x86_64 #1 SMP Wed Mar 7 19:03:37 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux

  CONVERSION_DATE :

  Mon Nov 11 08:45:33 MST 2019

  Conventions :

  CF-1.6

  NETCDF_COMPRESSION :

  NCO: Precision-preserving compression to netCDF4/HDF5 (see "history" and "NCO" global attributes below for specifics).

  history :

  Mon Nov 11 08:45:34 2019: ncks -4 --ppc default=7 e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1979010100_1979120100.unc.nc e5.moda.fc.sfc.accumu.128_228_tp.ll025sc.1979010100_1979120100.nc

  NCO :

  netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

4.4.1. Change the units from m per day to mm per day and make a plot

See here for clarification of units.

tp = ds.TP.mean('time') * 1000.0
tp = tp.compute()
tp

<xarray.DataArray 'TP' (latitude: 721, longitude: 1440)>
array([[0.18871191, 0.18871191, 0.18871191, ..., 0.18871191, 0.18871191,
        0.18871191],
       [0.1763593 , 0.17630874, 0.17631234, ..., 0.17640808, 0.17640086,
        0.17640446],
       [0.17613353, 0.17612088, 0.17608838, ..., 0.17611367, 0.17612632,
        0.17613533],
       ...,
       [0.6987416 , 0.69886446, 0.6989421 , ..., 0.6986495 , 0.69864047,
        0.69868386],
       [0.7080526 , 0.7081086 , 0.70814294, ..., 0.70803094, 0.7080472 ,
        0.70808154],
       [0.7012306 , 0.7012306 , 0.7012306 , ..., 0.7012306 , 0.7012306 ,
        0.7012306 ]], dtype=float32)
Coordinates:
  * latitude   (latitude) float64 -90.0 -89.75 -89.5 -89.25 ... 89.5 89.75 90.0
  * longitude  (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8

xarray.DataArray

'TP'

• latitude: 721
• longitude: 1440

• 0.1887 0.1887 0.1887 0.1887 0.1887 ... 0.7012 0.7012 0.7012 0.7012

  array([[0.18871191, 0.18871191, 0.18871191, ..., 0.18871191, 0.18871191,
          0.18871191],
         [0.1763593 , 0.17630874, 0.17631234, ..., 0.17640808, 0.17640086,
          0.17640446],
         [0.17613353, 0.17612088, 0.17608838, ..., 0.17611367, 0.17612632,
          0.17613533],
         ...,
         [0.6987416 , 0.69886446, 0.6989421 , ..., 0.6986495 , 0.69864047,
          0.69868386],
         [0.7080526 , 0.7081086 , 0.70814294, ..., 0.70803094, 0.7080472 ,
          0.70808154],
         [0.7012306 , 0.7012306 , 0.7012306 , ..., 0.7012306 , 0.7012306 ,
          0.7012306 ]], dtype=float32)

• Coordinates: (2)
  • latitude
    (latitude)
    float64
    -90.0 -89.75 -89.5 ... 89.75 90.0

    long_name :

    latitude

    short_name :

    lat

    units :

    degrees_north

    array([-90.  , -89.75, -89.5 , ...,  89.5 ,  89.75,  90.  ])

  • longitude
    (longitude)
    float64
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    long_name :

    longitude

    short_name :

    lon

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02])

• Indexes: (2)
  • latitude
    PandasIndex

    PandasIndex(Index([ -90.0, -89.75,  -89.5, -89.25,  -89.0, -88.75,  -88.5, -88.25,  -88.0,
           -87.75,
           ...
            87.75,   88.0,  88.25,   88.5,  88.75,   89.0,  89.25,   89.5,  89.75,
             90.0],
          dtype='float64', name='latitude', length=721))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float64', name='longitude', length=1440))

• Attributes: (0)

fig, ax = plt.subplots(
    nrows=1, ncols=1,
    figsize=(3, 1.5),
    subplot_kw={'projection': ccrs.Robinson(central_longitude=210)},
    constrained_layout=True)

# Plot model results using contourf
p0 = ax.contourf(tp.longitude, tp.latitude, tp,
                 levels=np.linspace(0, 16, 17),
                 cmap='YlGnBu', extend='both',
                 transform=ccrs.PlateCarree())
plt.colorbar(p0, ax=ax)
ax.coastlines(linewidth=0.5)

<cartopy.mpl.feature_artist.FeatureArtist at 0x1479a0bd03d0>

---

4.5. Example 3: Plot AMOC from TraCE and compare with McManus et al. 2004

• TraCE data is directly accessible on NCAR machines: /glade/campaign/cesm/collections/TraCE

file = '/glade/campaign/cesm/collections/TraCE/ocn/proc/tavg/decadal/trace.01-36.22000BP.pop.MOC.22000BP_decavg_400BCE.nc'

ds = xr.open_dataset(file)
ds

<xarray.Dataset>
Dimensions:               (nlat: 116, nlon: 100, time: 2204, transport_reg: 2,
                           moc_comp: 1, moc_z: 26, lat_aux_grid: 105, z_t: 25,
                           z_w: 25, transport_comp: 3)
Coordinates:
    TLAT                  (nlat, nlon) float32 ...
    TLONG                 (nlat, nlon) float32 ...
    ULAT                  (nlat, nlon) float32 ...
    ULONG                 (nlat, nlon) float32 ...
  * lat_aux_grid          (lat_aux_grid) float32 -80.26 -78.73 ... 88.38 90.0
    moc_components        (moc_comp) |S256 ...
  * moc_z                 (moc_z) float32 0.0 800.0 ... 4.503e+05 5e+05
  * time                  (time) float64 -22.0 -21.99 -21.98 ... 0.01 0.02 0.03
    transport_regions     (transport_reg) |S256 ...
  * z_t                   (z_t) float32 400.0 1.222e+03 ... 4.255e+05 4.751e+05
  * z_w                   (z_w) float32 0.0 800.0 ... 4.007e+05 4.503e+05
Dimensions without coordinates: nlat, nlon, transport_reg, moc_comp,
                                transport_comp
Data variables: (12/51)
    ANGLE                 (nlat, nlon) float32 ...
    ANGLET                (nlat, nlon) float32 ...
    DXT                   (nlat, nlon) float32 ...
    DXU                   (nlat, nlon) float32 ...
    DYT                   (nlat, nlon) float32 ...
    DYU                   (nlat, nlon) float32 ...
    ...                    ...
    sea_ice_salinity      float64 ...
    sflux_factor          float64 ...
    sound                 float64 ...
    stefan_boltzmann      float64 ...
    transport_components  (transport_comp) |S256 ...
    vonkar                float64 ...
Attributes:
    title:                     b30.22_0kaDVT
    contents:                  Diagnostic and Prognostic Variables
    source:                    POP, the NCAR/CSM Ocean Component
    revision:                   $Name: ccsm3_0_1_beta22 $
    calendar:                  All years have exactly  365 days.
    conventions:               CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netc...
    start_time:                This dataset was created on 2007-07-29 at 23:1...
    cell_methods:              cell_methods = time: mean ==> the variable val...
    history:                   Fri Nov  8 22:51:40 2013: /glade/apps/opt/nco/...
    nco_openmp_thread_number:  1
    NCO:                       4.3.2

xarray.Dataset

• Dimensions:
  • nlat: 116
  • nlon: 100
  • time: 2204
  • transport_reg: 2
  • moc_comp: 1
  • moc_z: 26
  • lat_aux_grid: 105
  • z_t: 25
  • z_w: 25
  • transport_comp: 3
• Coordinates: (11)
  • TLAT
    (nlat, nlon)
    float32
    ...

    long_name :

    array of t-grid latitudes

    units :

    degrees_north

    [11600 values with dtype=float32]

  • TLONG
    (nlat, nlon)
    float32
    ...

    long_name :

    array of t-grid longitudes

    units :

    degrees_east

    [11600 values with dtype=float32]

  • ULAT
    (nlat, nlon)
    float32
    ...

    long_name :

    array of u-grid latitudes

    units :

    degrees_north

    [11600 values with dtype=float32]

  • ULONG
    (nlat, nlon)
    float32
    ...

    long_name :

    array of u-grid longitudes

    units :

    degrees_east

    [11600 values with dtype=float32]

  • lat_aux_grid
    (lat_aux_grid)
    float32
    -80.26 -78.73 -77.22 ... 88.38 90.0

    long_name :

    latitude grid for transport diagnostics

    units :

    degrees_north

    valid_min :

    -9.96921e+36

    array([-80.26017 , -78.73382 , -77.217636, -75.70146 , -74.18529 , -72.66911 ,
           -71.15295 , -69.636795, -68.12066 , -66.60454 , -65.08846 , -63.481495,
           -61.779697, -59.9793  , -58.07679 , -56.069073, -53.953655, -51.728966,
           -49.39478 , -46.9529  , -44.40814 , -41.769688, -39.052826, -36.280697,
           -33.48543 , -30.707624, -27.993225, -25.388052, -22.931374, -20.650919,
           -18.560844, -16.662758, -14.948642, -13.404314, -12.012482, -10.754977,
            -9.614184,  -8.573812,  -7.619225,  -6.737495,  -5.917311,  -5.148796,
            -4.423321,  -3.733296,  -3.071989,  -2.43335 ,  -1.811855,  -1.202367,
            -0.600001,   0.      ,   0.600001,   1.202367,   1.811855,   2.43335 ,
             3.071989,   3.733296,   4.423321,   5.148796,   5.917311,   6.737495,
             7.619225,   8.573812,   9.614184,  10.754977,  12.012482,  13.404314,
            14.948642,  16.662758,  18.560844,  20.650919,  22.931374,  25.388052,
            27.993225,  30.707624,  33.48543 ,  36.280697,  39.052826,  41.769688,
            44.40814 ,  46.9529  ,  49.39478 ,  51.728966,  53.953655,  56.069073,
            58.07679 ,  59.9793  ,  61.779697,  63.481495,  65.08846 ,  66.60454 ,
            68.12066 ,  69.636795,  71.15295 ,  72.66911 ,  74.18529 ,  75.70146 ,
            77.217636,  78.73382 ,  80.26017 ,  81.88347 ,  83.506775,  85.13008 ,
            86.75339 ,  88.37669 ,  90.      ], dtype=float32)

  • moc_components
    (moc_comp)
    |S256
    ...

    long_name :

    MOC component names

    [1 values with dtype=|S256]

  • moc_z
    (moc_z)
    float32
    0.0 800.0 ... 4.503e+05 5e+05

    long_name :

    depth from surface to top of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    0.0

    valid_max :

    499997.1

    array([     0.    ,    800.    ,   1644.0491,   2573.7122,   3627.362 ,
             4851.0317,   6304.715 ,   8070.1416,  10262.088 ,  13045.74  ,
            16663.955 ,  21480.236 ,  28045.176 ,  37192.684 ,  50154.63  ,
            68615.055 ,  94462.67  , 128039.875 , 166920.86  , 209842.53  ,
           255543.1   , 303014.53  , 351557.7   , 400728.75  , 450260.5   ,
           499997.1   ], dtype=float32)

  • time
    (time)
    float64
    -22.0 -21.99 -21.98 ... 0.02 0.03

    long_name :

    time

    units :

    ka BP

    bounds :

    time_bound

    calendar :

    noleap

    array([-2.200000e+01, -2.199000e+01, -2.198000e+01, ...,  1.000023e-02,
            2.000046e-02,  2.999878e-02])

  • transport_regions
    (transport_reg)
    |S256
    ...

    long_name :

    regions for all transport diagnostics

    [2 values with dtype=|S256]

  • z_t
    (z_t)
    float32
    400.0 1.222e+03 ... 4.751e+05

    long_name :

    depth from surface to midpoint of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    400.0

    valid_max :

    475128.78

    array([4.000000e+02, 1.222025e+03, 2.108881e+03, 3.100537e+03, 4.239197e+03,
           5.577873e+03, 7.187428e+03, 9.166115e+03, 1.165391e+04, 1.485485e+04,
           1.907210e+04, 2.476271e+04, 3.261893e+04, 4.367366e+04, 5.938484e+04,
           8.153886e+04, 1.112513e+05, 1.474804e+05, 1.883817e+05, 2.326928e+05,
           2.792788e+05, 3.272861e+05, 3.761432e+05, 4.254946e+05, 4.751288e+05],
          dtype=float32)

  • z_w
    (z_w)
    float32
    0.0 800.0 ... 4.007e+05 4.503e+05

    long_name :

    depth from surface to top of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    0.0

    valid_max :

    450260.5

    array([     0.    ,    800.    ,   1644.0491,   2573.7122,   3627.362 ,
             4851.0317,   6304.715 ,   8070.1416,  10262.088 ,  13045.74  ,
            16663.955 ,  21480.236 ,  28045.176 ,  37192.684 ,  50154.63  ,
            68615.055 ,  94462.67  , 128039.875 , 166920.86  , 209842.53  ,
           255543.1   , 303014.53  , 351557.7   , 400728.75  , 450260.5   ],
          dtype=float32)

• Data variables: (51)
  • ANGLE
    (nlat, nlon)
    float32
    ...

    long_name :

    angle grid makes with latitude line

    units :

    radians

    [11600 values with dtype=float32]

  • ANGLET
    (nlat, nlon)
    float32
    ...

    long_name :

    angle grid makes with latitude line on T grid

    units :

    radians

    [11600 values with dtype=float32]

  • DXT
    (nlat, nlon)
    float32
    ...

    long_name :

    x-spacing centered at T points

    units :

    centimeters

    [11600 values with dtype=float32]

  • DXU
    (nlat, nlon)
    float32
    ...

    long_name :

    x-spacing centered at U points

    units :

    centimeters

    [11600 values with dtype=float32]

  • DYT
    (nlat, nlon)
    float32
    ...

    long_name :

    y-spacing centered at T points

    units :

    centimeters

    [11600 values with dtype=float32]

  • DYU
    (nlat, nlon)
    float32
    ...

    long_name :

    y-spacing centered at U points

    units :

    centimeters

    [11600 values with dtype=float32]

  • HT
    (nlat, nlon)
    float32
    ...

    long_name :

    ocean depth at T points

    units :

    centimeter

    [11600 values with dtype=float32]

  • HTE
    (nlat, nlon)
    float32
    ...

    long_name :

    cell widths on East sides of T cell

    units :

    centimeters

    [11600 values with dtype=float32]

  • HTN
    (nlat, nlon)
    float32
    ...

    long_name :

    cell widths on North sides of T cell

    units :

    centimeters

    [11600 values with dtype=float32]

  • HU
    (nlat, nlon)
    float32
    ...

    long_name :

    ocean depth at U points

    units :

    centimeter

    [11600 values with dtype=float32]

  • HUS
    (nlat, nlon)
    float32
    ...

    long_name :

    cell widths on South sides of U cell

    units :

    centimeters

    [11600 values with dtype=float32]

  • HUW
    (nlat, nlon)
    float32
    ...

    long_name :

    cell widths on West sides of U cell

    units :

    centimeters

    [11600 values with dtype=float32]

  • KMT
    (nlat, nlon)
    float64
    ...

    long_name :

    k Index of Deepest Grid Cell on T Grid

    [11600 values with dtype=float64]

  • KMU
    (nlat, nlon)
    float64
    ...

    long_name :

    k Index of Deepest Grid Cell on U Grid

    [11600 values with dtype=float64]

  • MOC
    (time, transport_reg, moc_comp, moc_z, lat_aux_grid)
    float32
    ...

    long_name :

    Meridional Overturning Circulation

    units :

    Sverdrups

    [12033840 values with dtype=float32]

  • REGION_MASK
    (nlat, nlon)
    float64
    ...

    long_name :

    basin index number (signed integers)

    [11600 values with dtype=float64]

  • T0_Kelvin
    ()
    float64
    ...

    long_name :

    Zero Point for Celsius

    units :

    degK

    [1 values with dtype=float64]

  • TAREA
    (nlat, nlon)
    float32
    ...

    long_name :

    area of T cells

    units :

    centimeter^2

    [11600 values with dtype=float32]

  • UAREA
    (nlat, nlon)
    float32
    ...

    long_name :

    area of U cells

    units :

    centimeter^2

    [11600 values with dtype=float32]

  • cp_air
    ()
    float64
    ...

    long_name :

    Heat Capacity of Air

    units :

    joule/kg/degK

    [1 values with dtype=float64]

  • cp_sw
    ()
    float64
    ...

    long_name :

    Specific Heat of Sea Water

    units :

    erg/g/K

    [1 values with dtype=float64]

  • days_in_norm_year
    ()
    timedelta64[ns]
    ...

    long_name :

    Calendar Length

    [1 values with dtype=timedelta64[ns]]

  • dz
    (z_t)
    float32
    ...

    long_name :

    thickness of layer k

    units :

    centimeters

    [25 values with dtype=float32]

  • dzw
    (z_w)
    float32
    ...

    long_name :

    midpoint of k to midpoint of k+1

    units :

    centimeters

    [25 values with dtype=float32]

  • fwflux_factor
    ()
    float64
    ...

    long_name :

    Convert Net Fresh Water Flux to Salt Flux (in model units)

    [1 values with dtype=float64]

  • grav
    ()
    float64
    ...

    long_name :

    Acceleration Due to Gravity

    units :

    centimeter/s^2

    [1 values with dtype=float64]

  • heat_to_PW
    ()
    float64
    ...

    long_name :

    Convert Heat Flux to Petawatts

    [1 values with dtype=float64]

  • hflux_factor
    ()
    float64
    ...

    long_name :

    Convert Heat and Solar Flux to Temperature Flux

    [1 values with dtype=float64]

  • latent_heat_fusion
    ()
    float64
    ...

    long_name :

    Latent Heat of Fusion

    units :

    erg/g

    [1 values with dtype=float64]

  • latent_heat_vapor
    ()
    float64
    ...

    long_name :

    Latent Heat of Vaporization

    units :

    erg/g

    [1 values with dtype=float64]

  • mass_to_Sv
    ()
    float64
    ...

    long_name :

    Convert Mass Flux to Sverdrups

    [1 values with dtype=float64]

  • momentum_factor
    ()
    float64
    ...

    long_name :

    Convert Windstress to Velocity Flux

    [1 values with dtype=float64]

  • nsurface_t
    ()
    float64
    ...

    long_name :

    Number of Ocean T Points at Surface

    [1 values with dtype=float64]

  • nsurface_u
    ()
    float64
    ...

    long_name :

    Number of Ocean U Points at Surface

    [1 values with dtype=float64]

  • ocn_ref_salinity
    ()
    float64
    ...

    long_name :

    Ocean Reference Salinity

    units :

    g/kg

    [1 values with dtype=float64]

  • omega
    ()
    float64
    ...

    long_name :

    Earths Angular Velocity

    units :

    1/second

    [1 values with dtype=float64]

  • ppt_to_salt
    ()
    float64
    ...

    long_name :

    Convert Salt in g/kg to gram/gram

    [1 values with dtype=float64]

  • radius
    ()
    float64
    ...

    long_name :

    Earths Radius

    units :

    centimeters

    [1 values with dtype=float64]

  • rho_air
    ()
    float64
    ...

    long_name :

    Ambient Air Density

    units :

    kg/m^3

    [1 values with dtype=float64]

  • rho_fw
    ()
    float64
    ...

    long_name :

    Density of Fresh Water

    units :

    gram/centimeter^3

    [1 values with dtype=float64]

  • rho_sw
    ()
    float64
    ...

    long_name :

    Density of Sea Water

    units :

    gram/centimeter^3

    [1 values with dtype=float64]

  • salinity_factor
    ()
    float64
    ...

    [1 values with dtype=float64]

  • salt_to_Svppt
    ()
    float64
    ...

    long_name :

    Convert Salt Flux to Sverdrups*g/kg

    [1 values with dtype=float64]

  • salt_to_mmday
    ()
    float64
    ...

    long_name :

    Convert Salt to Water (millimeters/day)

    [1 values with dtype=float64]

  • salt_to_ppt
    ()
    float64
    ...

    long_name :

    Convert Salt in gram/gram to g/kg

    [1 values with dtype=float64]

  • sea_ice_salinity
    ()
    float64
    ...

    long_name :

    Salinity of Sea Ice

    units :

    g/kg

    [1 values with dtype=float64]

  • sflux_factor
    ()
    float64
    ...

    long_name :

    Convert Salt Flux to Salt Flux (in model units)

    [1 values with dtype=float64]

  • sound
    ()
    float64
    ...

    long_name :

    Speed of Sound

    units :

    centimeter/s

    [1 values with dtype=float64]

  • stefan_boltzmann
    ()
    float64
    ...

    long_name :

    Stefan-Boltzman Constant

    units :

    watt/m^2/degK^4

    [1 values with dtype=float64]

  • transport_components
    (transport_comp)
    |S256
    ...

    long_name :

    T,S transport components

    [3 values with dtype=|S256]

  • vonkar
    ()
    float64
    ...

    long_name :

    von Karman Constant

    [1 values with dtype=float64]

• Indexes: (5)
  • lat_aux_grid
    PandasIndex

    PandasIndex(Index([-80.26016998291016, -78.73381805419922, -77.21763610839844,
           -75.70146179199219, -74.18528747558594, -72.66911315917969,
           -71.15294647216797, -69.63679504394531, -68.12065887451172,
           -66.60453796386719,
           ...
            75.70146179199219,  77.21763610839844,  78.73381805419922,
            80.26016998291016,  81.88346862792969,  83.50677490234375,
            85.13008117675781,  86.75338745117188,  88.37669372558594,
                         90.0],
          dtype='float32', name='lat_aux_grid', length=105))

  • moc_z
    PandasIndex

    PandasIndex(Index([              0.0,             800.0, 1644.049072265625,
           2573.712158203125, 3627.362060546875,  4851.03173828125,
               6304.71484375,   8070.1416015625,   10262.087890625,
             13045.740234375,   16663.955078125,   21480.236328125,
              28045.17578125,    37192.68359375,    50154.62890625,
               68615.0546875,      94462.671875,        128039.875,
               166920.859375,      209842.53125,      255543.09375,
                303014.53125,       351557.6875,         400728.75,
                    450260.5,      499997.09375],
          dtype='float32', name='moc_z'))

  • time
    PandasIndex

    PandasIndex(Index([                -22.0,   -21.989999771118164,   -21.979999542236328,
             -21.969999313354492,   -21.959999084472656,   -21.950000762939453,
             -21.940000534057617,    -21.93000030517578,   -21.920000076293945,
              -21.90999984741211,
           ...
           -0.060001373291015625,  -0.05000114440917969,  -0.04000091552734375,
           -0.030000686645507812, -0.020000457763671875, -0.010000228881835938,
                             0.0,  0.010000228881835938,  0.020000457763671875,
               0.029998779296875],
          dtype='float64', name='time', length=2204))

  • z_t
    PandasIndex

    PandasIndex(Index([             400.0, 1222.0245361328125,  2108.880615234375,
               3100.537109375,   4239.19677734375,     5577.873046875,
             7187.42822265625,     9166.115234375,      11653.9140625,
               14854.84765625,    19072.095703125,     24762.70703125,
                32618.9296875,        43673.65625,     59384.83984375,
                 81538.859375,     111251.2734375,      147480.359375,
                  188381.6875,        232692.8125,        279278.8125,
                 327286.09375,       376143.21875,         425494.625,
                 475128.78125],
          dtype='float32', name='z_t'))

  • z_w
    PandasIndex

    PandasIndex(Index([              0.0,             800.0, 1644.049072265625,
           2573.712158203125, 3627.362060546875,  4851.03173828125,
               6304.71484375,   8070.1416015625,   10262.087890625,
             13045.740234375,   16663.955078125,   21480.236328125,
              28045.17578125,    37192.68359375,    50154.62890625,
               68615.0546875,      94462.671875,        128039.875,
               166920.859375,      209842.53125,      255543.09375,
                303014.53125,       351557.6875,         400728.75,
                    450260.5],
          dtype='float32', name='z_w'))

• Attributes: (11)

  title :

  b30.22_0kaDVT

  contents :

  Diagnostic and Prognostic Variables

  source :

  POP, the NCAR/CSM Ocean Component

  revision :

  $Name: ccsm3_0_1_beta22 $

  calendar :

  All years have exactly 365 days.

  conventions :

  CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-current.htm

  start_time :

  This dataset was created on 2007-07-29 at 23:13:09.2

  cell_methods :

  cell_methods = time: mean ==> the variable values are averaged over the time interval between the previous time coordinate and the current one. cell_methods absent ==> the variable values are at the time given by the current time coordinate.

  history :

  Fri Nov 8 22:51:40 2013: /glade/apps/opt/nco/4.3.2/gnu/4.7.2/bin/ncks -O -v time,ANGLE,ANGLET,DXT,DXU,DYT,DYU,HT,HTE,HTN,HU,HUS,HUW,KMT,KMU,REGION_MASK,TAREA,TLAT,TLONG,UAREA,ULAT,ULONG,dz,dzw,lat_aux_grid,moc_components,moc_z,transport_components,transport_regions,z_t,z_w,T0_Kelvin,cp_air,cp_sw,days_in_norm_year,fwflux_factor,grav,heat_to_PW,hflux_factor,latent_heat_fusion,latent_heat_vapor,mass_to_Sv,momentum_factor,nsurface_t,nsurface_u,ocn_ref_salinity,omega,ppt_to_salt,radius,rho_air,rho_fw,rho_sw,salinity_factor,salt_to_Svppt,salt_to_mmday,salt_to_ppt,sea_ice_salinity,sflux_factor,sound,stefan_boltzmann,vonkar,MOC b30.00_4kaDVTd.pop.ncrcat.ann.nc to_archive/trace.01-36.22000BP.pop.MOC.22000BP_decavg_400BCE.nc Fri Jan 22 13:46:58 2010: ncrcat -O -x -v QREFHT b30.01_4kaDVTc/ocn/hist/b30.01_4kaDVTc.pop.ncrcat.ann.nc b30.00_4kaDVTd/ocn/hist/b30.00_4kaDVTd.pop.decade.ann.nc b30.00_4kaDVTd/ocn/hist/b30.00_4kaDVTd.pop.ncrcat.ann.nc Fri Jan 22 11:41:23 2010: ncrcat -O -x -v QREFHT b30.02_4kaDVT/ocn/hist/b30.02_4kaDVT.pop.ncrcat.ann.nc b30.01_4kaDVTc/ocn/hist/b30.01_4kaDVTc.pop.decade.ann.nc b30.01_4kaDVTc/ocn/hist/b30.01_4kaDVTc.pop.ncrcat.ann.nc Fri Jan 22 06:34:33 2010: ncrcat -O -x -v QREFHT b30.03_2kaDVT/ocn/hist/b30.03_2kaDVT.pop.ncrcat.ann.nc b30.02_4kaDVT/ocn/hist/b30.02_4kaDVT.pop.decade.ann.nc b30.02_4kaDVT/ocn/hist/b30.02_4kaDVT.pop.ncrcat.ann.nc Fri Jan 22 04:41:31 2010: ncrcat -O -x -v QREFHT b30.04_0kaDVT/ocn/hist/b30.04_0kaDVT.pop.ncrcat.ann.nc b30.03_2kaDVT/ocn/hist/b30.03_2kaDVT.pop.decade.ann.nc b30.03_2kaDVT/ocn/hist/b30.03_2kaDVT.pop.ncrcat.ann.nc Fri Jan 22 01:31:11 2010: ncrcat -O -x -v QREFHT b30.05_0kaDVT/ocn/hist/b30.05_0kaDVT.pop.ncrcat.ann.nc b30.04_0kaDVT/ocn/hist/b30.04_0kaDVT.pop.decade.ann.nc b30.04_0kaDVT/ocn/hist/b30.04_0kaDVT.pop.ncrcat.ann.nc Thu Jan 21 23:57:37 2010: ncrcat -O -x -v QREFHT b30.05_7kaDVT/ocn/hist/b30.05_7kaDVT.pop.ncrcat.ann.nc b30.05_0kaDVT/ocn/hist/b30.05_0kaDVT.pop.decade.ann.nc b30.05_0kaDVT/ocn/hist/b30.05_0kaDVT.pop.ncrcat.ann.nc Thu Jan 21 20:32:43 2010: ncrcat -O -x -v QREFHT b30.06_2kaDVT/ocn/hist/b30.06_2kaDVT.pop.ncrcat.ann.nc b30.05_7kaDVT/ocn/hist/b30.05_7kaDVT.pop.decade.ann.nc b30.05_7kaDVT/ocn/hist/b30.05_7kaDVT.pop.ncrcat.ann.nc Thu Jan 21 16:46:02 2010: ncrcat -O -x -v QREFHT b30.06_7kaDVT/ocn/hist/b30.06_7kaDVT.pop.ncrcat.ann.nc b30.06_2kaDVT/ocn/hist/b30.06_2kaDVT.pop.decade.ann.nc b30.06_2kaDVT/ocn/hist/b30.06_2kaDVT.pop.ncrcat.ann.nc Thu Jan 21 02:28:44 2010: ncrcat -O -x -v QREFHT b30.07_2kaDVT/ocn/hist/b30.07_2kaDVT.pop.ncrcat.ann.nc b30.06_7kaDVT/ocn/hist/b30.06_7kaDVT.pop.decade.ann.nc b30.06_7kaDVT/ocn/hist/b30.06_7kaDVT.pop.ncrcat.ann.nc Wed Jan 20 22:06:27 2010: ncrcat -O -x -v QREFHT b30.07_6kaDVT/ocn/hist/b30.07_6kaDVT.pop.ncrcat.ann.nc b30.07_2kaDVT/ocn/hist/b30.07_2kaDVT.pop.decade.ann.nc b30.07_2kaDVT/ocn/hist/b30.07_2kaDVT.pop.ncrcat.ann.nc Wed Jan 20 17:51:15 2010: ncrcat -O -x -v QREFHT b30.08_0kaDVT/ocn/hist/b30.08_0kaDVT.pop.ncrcat.ann.nc b30.07_6kaDVT/ocn/hist/b30.07_6kaDVT.pop.decade.ann.nc b30.07_6kaDVT/ocn/hist/b30.07_6kaDVT.pop.ncrcat.ann.nc Wed Jan 20 13:48:56 2010: ncrcat -O -x -v QREFHT b30.08_5kaDVT/ocn/hist/b30.08_5kaDVT.pop.ncrcat.ann.nc b30.08_0kaDVT/ocn/hist/b30.08_0kaDVT.pop.decade.ann.nc b30.08_0kaDVT/ocn/hist/b30.08_0kaDVT.pop.ncrcat.ann.nc Wed Jan 20 05:42:42 2010: ncrcat -O -x -v QREFHT b30.08_7kaDVT/ocn/hist/b30.08_7kaDVT.pop.ncrcat.ann.nc b30.08_5kaDVT/ocn/hist/b30.08_5kaDVT.pop.decade.ann.nc b30.08_5kaDVT/ocn/hist/b30.08_5kaDVT.pop.ncrcat.ann.nc Mon Jan 18 03:04:03 2010: ncrcat -O -x -v QREFHT b30.09_2kaDVT/ocn/hist/b30.09_2kaDVT.pop.ncrcat.ann.nc b30.08_7kaDVT/ocn/hist/b30.08_7kaDVT.pop.decade.ann.nc b30.08_7kaDVT/ocn/hist/b30.08_7kaDVT.pop.ncrcat.ann.nc Thu Oct 15 22:45:50 2009: ncrcat -O -x -v QREFHT b30.09_7kaDVT/ocn/hist/b30.09_7kaDVT.pop.ncrcat.ann.nc b30.09_2kaDVT/ocn/hist/b30.09_2kaDVT.pop.decade.ann.nc b30.09_2kaDVT/ocn/hist/b30.09_2kaDVT.pop.ncrcat.ann.nc Thu Oct 15 17:35:41 2009: ncrcat -O -x -v QREFHT b30.10_2kaDVT/ocn/hist/b30.10_2kaDVT.pop.ncrcat.ann.nc b30.09_7kaDVT/ocn/hist/b30.09_7kaDVT.pop.decade.ann.nc b30.09_7kaDVT/ocn/hist/b30.09_7kaDVT.pop.ncrcat.ann.nc Thu Oct 15 09:20:26 2009: ncrcat -O -x -v QREFHT b30.10_8kaDVT/ocn/hist/b30.10_8kaDVT.pop.ncrcat.ann.nc b30.10_2kaDVT/ocn/hist/b30.10_2kaDVT.pop.decade.ann.nc b30.10_2kaDVT/ocn/hist/b30.10_2kaDVT.pop.ncrcat.ann.nc Thu Oct 15 04:39:08 2009: ncrcat -O -x -v QREFHT b30.11_3kaDVT/ocn/hist/b30.11_3kaDVT.pop.ncrcat.ann.nc b30.10_8kaDVT/ocn/hist/b30.10_8kaDVT.pop.decade.ann.nc b30.10_8kaDVT/ocn/hist/b30.10_8kaDVT.pop.ncrcat.ann.nc Thu Sep 3 16:10:21 2009: ncrcat -O b30.11_3kaDVT.pop.ncrcat.ann.part1.nc b30.11_3kaDVT.pop.ncrcat.ann.part2.nc b30.11_3kaDVT.pop.ncrcat.ann.part3.nc b30.11_3kaDVT.pop.ncrcat.ann.nc Thu Sep 3 15:43:00 2009: ncks -F -d time,1,514,1 b30.11_3kaDVT.pop.ncrcat.ann.nc b30.11_3kaDVT.pop.ncrcat.ann.part1.nc Wed Sep 2 04:35:54 2009: ncrcat -O -x -v QREFHT b30.11_7kaDVTa/ocn/hist/b30.11_7kaDVTa.pop.ncrcat.ann.nc b30.11_3kaDVT/ocn/hist/b30.11_3kaDVT.pop.decade.ann.nc b30.11_3kaDVT/ocn/hist/b30.11_3kaDVT.pop.ncrcat.ann.nc Wed Sep 2 03:11:11 2009: ncrcat -O -x -v QREFHT b30.12_0kaDVTn/ocn/hist/b30.12_0kaDVTn.pop.ncrcat.ann.nc b30.11_7kaDVTa/ocn/hist/b30.11_7kaDVTa.pop.decade.ann.nc b30.11_7kaDVTa/ocn/hist/b30.11_7kaDVTa.pop.ncrcat.ann.nc Wed Sep 2 02:55:22 2009: ncrcat -O -x -v QREFHT b30.12_5kaDVTn/ocn/hist/b30.12_5kaDVTn.pop.ncrcat.ann.nc b30.12_0kaDVTn/ocn/hist/b30.12_0kaDVTn.pop.decade.ann.nc b30.12_0kaDVTn/ocn/hist/b30.12_0kaDVTn.pop.ncrcat.ann.nc Wed Sep 2 01:44:02 2009: ncrcat -O -x -v QREFHT b30.12_9kaDVTn/ocn/hist/b30.12_9kaDVTn.pop.ncrcat.ann.nc b30.12_5kaDVTn/ocn/hist/b30.12_5kaDVTn.pop.decade.ann.nc b30.12_5kaDVTn/ocn/hist/b30.12_5kaDVTn.pop.ncrcat.ann.nc Tue Sep 1 22:13:26 2009: ncrcat -O -x -v QREFHT b30.13_1kaDVT/ocn/hist/b30.13_1kaDVT.pop.ncrcat.ann.nc b30.12_9kaDVTn/ocn/hist/b30.12_9kaDVTn.pop.decade.ann.nc b30.12_9kaDVTn/ocn/hist/b30.12_9kaDVTn.pop.ncrcat.ann.nc Sun Aug 9 14:14:53 2009: ncrcat -O -x -v QREFHT b30.13_87kaDVT/ocn/hist/b30.13_87kaDVT.pop.ncrcat.ann.nc b30.13_1kaDVT/ocn/hist/b30.13_1kaDVT.pop.decade.ann.nc b30.13_1kaDVT/ocn/hist/b30.13_1kaDVT.pop.ncrcat.ann.nc Sun Aug 9 08:30:56 2009: ncrcat -O -x -v QREFHT b30.14_35kaDVT/ocn/hist/b30.14_35kaDVT.pop.ncrcat.ann.nc b30.13_87kaDVT/ocn/hist/b30.13_87kaDVT.pop.decade.ann.nc b30.13_87kaDVT/ocn/hist/b30.13_87kaDVT.pop.ncrcat.ann.nc Sun Aug 9 04:04:49 2009: ncrcat -O -x -v QREFHT b30.14_9kaDVT/ocn/hist/b30.14_9kaDVT.pop.ncrcat.ann.nc b30.14_35kaDVT/ocn/hist/b30.14_35kaDVT.pop.decade.ann.nc b30.14_35kaDVT/ocn/hist/b30.14_35kaDVT.pop.ncrcat.ann.nc Sun Aug 9 03:52:12 2009: ncrcat -O -x -v QREFHT b30.15_0kaDVTd/ocn/hist/b30.15_0kaDVTd.pop.ncrcat.ann.nc b30.14_9kaDVT/ocn/hist/b30.14_9kaDVT.pop.decade.ann.nc b30.14_9kaDVT/ocn/hist/b30.14_9kaDVT.pop.ncrcat.ann.nc Tue Oct 14 14:07:19 2008: ncrcat -O b30.16_0kaDVTd/ocn/hist/b30.16_0kaDVTd.pop.ncrcat.ann.nc b30.15_0kaDVTd/ocn/hist/b30.15_0kaDVTd.pop.decade.ann.nc b30.15_0kaDVTd/ocn/hist/b30.15_0kaDVTd.pop.ncrcat.ann.nc Tue Oct 14 01:49:03 2008: ncrcat -O b30.17_0kaDVTd/ocn/hist/b30.17_0kaDVTd.pop.ncrcat.ann.nc b30.16_0kaDVTd/ocn/hist/b30.16_0kaDVTd.pop.decade.ann.nc b30.16_0kaDVTd/ocn/hist/b30.16_0kaDVTd.pop.ncrcat.ann.nc Mon Oct 13 03:19:43 2008: ncrcat -O b30.17_5kaDVT/ocn/hist/b30.17_5kaDVT.pop.ncrcat.ann.nc b30.17_0kaDVTd/ocn/hist/b30.17_0kaDVTd.pop.decade.ann.nc b30.17_0kaDVTd/ocn/hist/b30.17_0kaDVTd.pop.ncrcat.ann.nc Sun Oct 12 17:10:11 2008: ncrcat -O b30.18_4kaDVT/ocn/hist/b30.18_4kaDVT.pop.ncrcat.ann.nc b30.17_5kaDVT/ocn/hist/b30.17_5kaDVT.pop.decade.ann.nc b30.17_5kaDVT/ocn/hist/b30.17_5kaDVT.pop.ncrcat.ann.nc Sun Oct 12 03:28:15 2008: ncrcat -O b30.18_5kaDVTa/ocn/hist/b30.18_5kaDVTa.pop.ncrcat.ann.nc b30.18_4kaDVT/ocn/hist/b30.18_4kaDVT.pop.decade.ann.nc b30.18_4kaDVT/ocn/hist/b30.18_4kaDVT.pop.ncrcat.ann.nc Sat Oct 11 16:11:08 2008: ncrcat -O b30.19_0kaDVTa/ocn/hist/b30.19_0kaDVTa.pop.ncrcat.ann.nc b30.18_5kaDVTa/ocn/hist/b30.18_5kaDVTa.pop.decade.ann.nc b30.18_5kaDVTa/ocn/hist/b30.18_5kaDVTa.pop.ncrcat.ann.nc Sat Oct 11 14:09:11 2008: ncrcat -O b30.20_0kaDVT/ocn/hist/b30.20_0kaDVT.pop.ncrcat.ann.nc b30.19_0kaDVTa/ocn/hist/b30.19_0kaDVTa.pop.decade.ann.nc b30.19_0kaDVTa/ocn/hist/b30.19_0kaDVTa.pop.ncrcat.ann.nc Sat Oct 11 12:58:54 2008: ncks -O -x -v time_bound b30.20_0kaDVT/ocn/hist/b30.20_0kaDVT.pop.ncrcat.ann.nc b30.20_0kaDVT/ocn/hist/b30.20_0kaDVT.pop.ncrcat.ann.nc Fri Oct 10 19:05:15 2008: ncrcat -O b30.22_0kaDVT/ocn/hist/b30.22_0kaDVT.pop.decade.ann.nc b30.20_0kaDVT/ocn/hist/b30.20_0kaDVT.pop.decade.ann.nc b30.20_0kaDVT/ocn/hist/b30.20_0kaDVT.pop.ncrcat.ann.nc Wed Sep 3 18:58:20 2008: ncrcat -O -n 200,3,1 b30.22_0kaDVT.pop.decade.ann.001.nc b30.22_0kaDVT.pop.decade.ann.nc Wed Sep 3 17:21:14 2008: ncea -O b30.22_0kaDVT.pop.decade.jan.001.nc b30.22_0kaDVT.pop.decade.feb.001.nc b30.22_0kaDVT.pop.decade.mar.001.nc b30.22_0kaDVT.pop.decade.apr.001.nc b30.22_0kaDVT.pop.decade.may.001.nc b30.22_0kaDVT.pop.decade.jun.001.nc b30.22_0kaDVT.pop.decade.jul.001.nc b30.22_0kaDVT.pop.decade.aug.001.nc b30.22_0kaDVT.pop.decade.sep.001.nc b30.22_0kaDVT.pop.decade.oct.001.nc b30.22_0kaDVT.pop.decade.nov.001.nc b30.22_0kaDVT.pop.decade.dec.001.nc b30.22_0kaDVT.pop.decade.ann.001.nc Wed Sep 3 17:21:00 2008: ncra -O b30.22_0kaDVT.pop.h.0001-01.nc b30.22_0kaDVT.pop.h.0002-01.nc b30.22_0kaDVT.pop.h.0003-01.nc b30.22_0kaDVT.pop.h.0004-01.nc b30.22_0kaDVT.pop.h.0005-01.nc b30.22_0kaDVT.pop.h.0006-01.nc b30.22_0kaDVT.pop.h.0007-01.nc b30.22_0kaDVT.pop.h.0008-01.nc b30.22_0kaDVT.pop.h.0009-01.nc b30.22_0kaDVT.pop.decade.jan.001.nc

  nco_openmp_thread_number :

  1

  NCO :

  4.3.2

4.5.1. Meridional Overturning Circulation is MOC, a 5-dimentional variable

• transport_components has three components: (1) Total, (2) Eulerian-Mean Advection, and (3) Eddy-Induced Advection (bolus) + Diffusion

• moc_comp of MOC is 0, which means the MOC caculation is the (1) total transport

• transport_regions has two parts: (1) Global Ocean - Marginal Seas and (2) Atlantic Ocean + Labrador Sea + GIN Sea + Arctic Ocean

• transport_reg of MOC are 0 and 1, which indicate MOC values for Global Ocean and the Atlantic Ocean, respectively

• Note that the depth, moc_z, is in centimeter

ds.MOC

<xarray.DataArray 'MOC' (time: 2204, transport_reg: 2, moc_comp: 1, moc_z: 26,
                         lat_aux_grid: 105)>
[12033840 values with dtype=float32]
Coordinates:
  * lat_aux_grid       (lat_aux_grid) float32 -80.26 -78.73 ... 88.38 90.0
    moc_components     (moc_comp) |S256 ...
  * moc_z              (moc_z) float32 0.0 800.0 1.644e+03 ... 4.503e+05 5e+05
  * time               (time) float64 -22.0 -21.99 -21.98 ... 0.01 0.02 0.03
    transport_regions  (transport_reg) |S256 ...
Dimensions without coordinates: transport_reg, moc_comp
Attributes:
    long_name:  Meridional Overturning Circulation
    units:      Sverdrups

xarray.DataArray

'MOC'

• time: 2204
• transport_reg: 2
• moc_comp: 1
• moc_z: 26
• lat_aux_grid: 105

• ...

  [12033840 values with dtype=float32]

• Coordinates: (5)
  • lat_aux_grid
    (lat_aux_grid)
    float32
    -80.26 -78.73 -77.22 ... 88.38 90.0

    long_name :

    latitude grid for transport diagnostics

    units :

    degrees_north

    valid_min :

    -9.96921e+36

    array([-80.26017 , -78.73382 , -77.217636, -75.70146 , -74.18529 , -72.66911 ,
           -71.15295 , -69.636795, -68.12066 , -66.60454 , -65.08846 , -63.481495,
           -61.779697, -59.9793  , -58.07679 , -56.069073, -53.953655, -51.728966,
           -49.39478 , -46.9529  , -44.40814 , -41.769688, -39.052826, -36.280697,
           -33.48543 , -30.707624, -27.993225, -25.388052, -22.931374, -20.650919,
           -18.560844, -16.662758, -14.948642, -13.404314, -12.012482, -10.754977,
            -9.614184,  -8.573812,  -7.619225,  -6.737495,  -5.917311,  -5.148796,
            -4.423321,  -3.733296,  -3.071989,  -2.43335 ,  -1.811855,  -1.202367,
            -0.600001,   0.      ,   0.600001,   1.202367,   1.811855,   2.43335 ,
             3.071989,   3.733296,   4.423321,   5.148796,   5.917311,   6.737495,
             7.619225,   8.573812,   9.614184,  10.754977,  12.012482,  13.404314,
            14.948642,  16.662758,  18.560844,  20.650919,  22.931374,  25.388052,
            27.993225,  30.707624,  33.48543 ,  36.280697,  39.052826,  41.769688,
            44.40814 ,  46.9529  ,  49.39478 ,  51.728966,  53.953655,  56.069073,
            58.07679 ,  59.9793  ,  61.779697,  63.481495,  65.08846 ,  66.60454 ,
            68.12066 ,  69.636795,  71.15295 ,  72.66911 ,  74.18529 ,  75.70146 ,
            77.217636,  78.73382 ,  80.26017 ,  81.88347 ,  83.506775,  85.13008 ,
            86.75339 ,  88.37669 ,  90.      ], dtype=float32)

  • moc_components
    (moc_comp)
    |S256
    ...

    long_name :

    MOC component names

    [1 values with dtype=|S256]

  • moc_z
    (moc_z)
    float32
    0.0 800.0 ... 4.503e+05 5e+05

    long_name :

    depth from surface to top of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    0.0

    valid_max :

    499997.1

    array([     0.    ,    800.    ,   1644.0491,   2573.7122,   3627.362 ,
             4851.0317,   6304.715 ,   8070.1416,  10262.088 ,  13045.74  ,
            16663.955 ,  21480.236 ,  28045.176 ,  37192.684 ,  50154.63  ,
            68615.055 ,  94462.67  , 128039.875 , 166920.86  , 209842.53  ,
           255543.1   , 303014.53  , 351557.7   , 400728.75  , 450260.5   ,
           499997.1   ], dtype=float32)

  • time
    (time)
    float64
    -22.0 -21.99 -21.98 ... 0.02 0.03

    long_name :

    time

    units :

    ka BP

    bounds :

    time_bound

    calendar :

    noleap

    array([-2.200000e+01, -2.199000e+01, -2.198000e+01, ...,  1.000023e-02,
            2.000046e-02,  2.999878e-02])

  • transport_regions
    (transport_reg)
    |S256
    ...

    long_name :

    regions for all transport diagnostics

    [2 values with dtype=|S256]

• Indexes: (3)
  • lat_aux_grid
    PandasIndex

    PandasIndex(Index([-80.26016998291016, -78.73381805419922, -77.21763610839844,
           -75.70146179199219, -74.18528747558594, -72.66911315917969,
           -71.15294647216797, -69.63679504394531, -68.12065887451172,
           -66.60453796386719,
           ...
            75.70146179199219,  77.21763610839844,  78.73381805419922,
            80.26016998291016,  81.88346862792969,  83.50677490234375,
            85.13008117675781,  86.75338745117188,  88.37669372558594,
                         90.0],
          dtype='float32', name='lat_aux_grid', length=105))

  • moc_z
    PandasIndex

    PandasIndex(Index([              0.0,             800.0, 1644.049072265625,
           2573.712158203125, 3627.362060546875,  4851.03173828125,
               6304.71484375,   8070.1416015625,   10262.087890625,
             13045.740234375,   16663.955078125,   21480.236328125,
              28045.17578125,    37192.68359375,    50154.62890625,
               68615.0546875,      94462.671875,        128039.875,
               166920.859375,      209842.53125,      255543.09375,
                303014.53125,       351557.6875,         400728.75,
                    450260.5,      499997.09375],
          dtype='float32', name='moc_z'))

  • time
    PandasIndex

    PandasIndex(Index([                -22.0,   -21.989999771118164,   -21.979999542236328,
             -21.969999313354492,   -21.959999084472656,   -21.950000762939453,
             -21.940000534057617,    -21.93000030517578,   -21.920000076293945,
              -21.90999984741211,
           ...
           -0.060001373291015625,  -0.05000114440917969,  -0.04000091552734375,
           -0.030000686645507812, -0.020000457763671875, -0.010000228881835938,
                             0.0,  0.010000228881835938,  0.020000457763671875,
               0.029998779296875],
          dtype='float64', name='time', length=2204))

• Attributes: (2)

  long_name :

  Meridional Overturning Circulation

  units :

  Sverdrups

ds.transport_regions.values

array([b'Global Ocean - Marginal Seas',
       b'Atlantic Ocean + Labrador Sea + GIN Sea + Arctic Ocean'],
      dtype='|S256')

ds.MOC.transport_reg.values

array([0, 1])

ds.transport_components.values

array([b'Total', b'Eulerian-Mean Advection',
       b'Eddy-Induced Advection (bolus) + Diffusion'], dtype='|S256')

ds.MOC.moc_comp.values

array([0])

4.5.2. The left and right plots are the global mean and Atlantic MOC, respectively

ds.MOC.isel(time=slice(0, 10), moc_comp=0).mean('time').plot.contourf(
    size=1.5, x='lat_aux_grid', y='moc_z', col='transport_reg',
    levels=np.linspace(-20, 20, 21))
plt.gca().invert_yaxis()

4.5.3. Again, transport_reg=1 means Atlantic and moc_comp=0 means the total transport

amoc = ds.MOC.isel(transport_reg=1, moc_comp=0)
amoc

<xarray.DataArray 'MOC' (time: 2204, moc_z: 26, lat_aux_grid: 105)>
[6016920 values with dtype=float32]
Coordinates:
  * lat_aux_grid       (lat_aux_grid) float32 -80.26 -78.73 ... 88.38 90.0
    moc_components     |S256 ...
  * moc_z              (moc_z) float32 0.0 800.0 1.644e+03 ... 4.503e+05 5e+05
  * time               (time) float64 -22.0 -21.99 -21.98 ... 0.01 0.02 0.03
    transport_regions  |S256 b'Atlantic Ocean + Labrador Sea + GIN Sea + Arct...
Attributes:
    long_name:  Meridional Overturning Circulation
    units:      Sverdrups

xarray.DataArray

'MOC'

• time: 2204
• moc_z: 26
• lat_aux_grid: 105

• ...

  [6016920 values with dtype=float32]

• Coordinates: (5)
  • lat_aux_grid
    (lat_aux_grid)
    float32
    -80.26 -78.73 -77.22 ... 88.38 90.0

    long_name :

    latitude grid for transport diagnostics

    units :

    degrees_north

    valid_min :

    -9.96921e+36

    array([-80.26017 , -78.73382 , -77.217636, -75.70146 , -74.18529 , -72.66911 ,
           -71.15295 , -69.636795, -68.12066 , -66.60454 , -65.08846 , -63.481495,
           -61.779697, -59.9793  , -58.07679 , -56.069073, -53.953655, -51.728966,
           -49.39478 , -46.9529  , -44.40814 , -41.769688, -39.052826, -36.280697,
           -33.48543 , -30.707624, -27.993225, -25.388052, -22.931374, -20.650919,
           -18.560844, -16.662758, -14.948642, -13.404314, -12.012482, -10.754977,
            -9.614184,  -8.573812,  -7.619225,  -6.737495,  -5.917311,  -5.148796,
            -4.423321,  -3.733296,  -3.071989,  -2.43335 ,  -1.811855,  -1.202367,
            -0.600001,   0.      ,   0.600001,   1.202367,   1.811855,   2.43335 ,
             3.071989,   3.733296,   4.423321,   5.148796,   5.917311,   6.737495,
             7.619225,   8.573812,   9.614184,  10.754977,  12.012482,  13.404314,
            14.948642,  16.662758,  18.560844,  20.650919,  22.931374,  25.388052,
            27.993225,  30.707624,  33.48543 ,  36.280697,  39.052826,  41.769688,
            44.40814 ,  46.9529  ,  49.39478 ,  51.728966,  53.953655,  56.069073,
            58.07679 ,  59.9793  ,  61.779697,  63.481495,  65.08846 ,  66.60454 ,
            68.12066 ,  69.636795,  71.15295 ,  72.66911 ,  74.18529 ,  75.70146 ,
            77.217636,  78.73382 ,  80.26017 ,  81.88347 ,  83.506775,  85.13008 ,
            86.75339 ,  88.37669 ,  90.      ], dtype=float32)

  • moc_components
    ()
    |S256
    ...

    long_name :

    MOC component names

    [1 values with dtype=|S256]

  • moc_z
    (moc_z)
    float32
    0.0 800.0 ... 4.503e+05 5e+05

    long_name :

    depth from surface to top of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    0.0

    valid_max :

    499997.1

    array([     0.    ,    800.    ,   1644.0491,   2573.7122,   3627.362 ,
             4851.0317,   6304.715 ,   8070.1416,  10262.088 ,  13045.74  ,
            16663.955 ,  21480.236 ,  28045.176 ,  37192.684 ,  50154.63  ,
            68615.055 ,  94462.67  , 128039.875 , 166920.86  , 209842.53  ,
           255543.1   , 303014.53  , 351557.7   , 400728.75  , 450260.5   ,
           499997.1   ], dtype=float32)

  • time
    (time)
    float64
    -22.0 -21.99 -21.98 ... 0.02 0.03

    long_name :

    time

    units :

    ka BP

    bounds :

    time_bound

    calendar :

    noleap

    array([-2.200000e+01, -2.199000e+01, -2.198000e+01, ...,  1.000023e-02,
            2.000046e-02,  2.999878e-02])

  • transport_regions
    ()
    |S256
    b'Atlantic Ocean + Labrador Sea ...

    long_name :

    regions for all transport diagnostics

    array(b'Atlantic Ocean + Labrador Sea + GIN Sea + Arctic Ocean', dtype='|S256')

• Indexes: (3)
  • lat_aux_grid
    PandasIndex

    PandasIndex(Index([-80.26016998291016, -78.73381805419922, -77.21763610839844,
           -75.70146179199219, -74.18528747558594, -72.66911315917969,
           -71.15294647216797, -69.63679504394531, -68.12065887451172,
           -66.60453796386719,
           ...
            75.70146179199219,  77.21763610839844,  78.73381805419922,
            80.26016998291016,  81.88346862792969,  83.50677490234375,
            85.13008117675781,  86.75338745117188,  88.37669372558594,
                         90.0],
          dtype='float32', name='lat_aux_grid', length=105))

  • moc_z
    PandasIndex

    PandasIndex(Index([              0.0,             800.0, 1644.049072265625,
           2573.712158203125, 3627.362060546875,  4851.03173828125,
               6304.71484375,   8070.1416015625,   10262.087890625,
             13045.740234375,   16663.955078125,   21480.236328125,
              28045.17578125,    37192.68359375,    50154.62890625,
               68615.0546875,      94462.671875,        128039.875,
               166920.859375,      209842.53125,      255543.09375,
                303014.53125,       351557.6875,         400728.75,
                    450260.5,      499997.09375],
          dtype='float32', name='moc_z'))

  • time
    PandasIndex

    PandasIndex(Index([                -22.0,   -21.989999771118164,   -21.979999542236328,
             -21.969999313354492,   -21.959999084472656,   -21.950000762939453,
             -21.940000534057617,    -21.93000030517578,   -21.920000076293945,
              -21.90999984741211,
           ...
           -0.060001373291015625,  -0.05000114440917969,  -0.04000091552734375,
           -0.030000686645507812, -0.020000457763671875, -0.010000228881835938,
                             0.0,  0.010000228881835938,  0.020000457763671875,
               0.029998779296875],
          dtype='float64', name='time', length=2204))

• Attributes: (2)

  long_name :

  Meridional Overturning Circulation

  units :

  Sverdrups

4.5.4. We define AMOC time series as the maximum over the North Atlantic and beneath a depth of 500 m (to avoid the wind-drive cell)

amoc_ts = amoc.sel(moc_z=slice(50000, 500000),
                   lat_aux_grid=slice(0, 90)).max(('moc_z', 'lat_aux_grid'))
amoc_ts.plot(size=1.5)

[<matplotlib.lines.Line2D at 0x147993801750>]

4.5.5. Load the McManus et al. (2004) data from NOAA

McManus04 = 'https://www.ncei.noaa.gov/pub/data/paleo/paleocean/sediment_files/complete/o326-gc5-tab.txt'
pa_th = pd.read_table(McManus04, header=37)

# Replace missing values
pa_th = pa_th[pa_th["pa/th232"] != -999].reset_index(drop=True)
pa_th.head()

	depth	calyrBP	pa/th232	pa/th232.err	pa/th238	pa/th238.err	d18Og.infla	Unnamed: 7
0	-999	100	0.054	0.002	0.055	0.002	-999.00	NaN
1	-999	480	0.054	0.002	0.054	0.002	-999.00	NaN
2	-999	960	0.057	0.004	0.057	0.004	0.66	NaN
3	-999	1430	0.054	0.002	0.054	0.002	-999.00	NaN
4	-999	1910	0.055	0.002	0.055	0.002	0.63	NaN

# Change the time to be consistent with TraCE
pa_th['time'] = pa_th['calyrBP '] / -1000.0
pa_th.head()

	depth	calyrBP	pa/th232	pa/th232.err	pa/th238	pa/th238.err	d18Og.infla	Unnamed: 7	time
0	-999	100	0.054	0.002	0.055	0.002	-999.00	NaN	-0.10
1	-999	480	0.054	0.002	0.054	0.002	-999.00	NaN	-0.48
2	-999	960	0.057	0.004	0.057	0.004	0.66	NaN	-0.96
3	-999	1430	0.054	0.002	0.054	0.002	-999.00	NaN	-1.43
4	-999	1910	0.055	0.002	0.055	0.002	0.63	NaN	-1.91

4.5.6. Plot time series

fig, ax = plt.subplots(figsize=(4, 2))

# Plot the AMOC time series in TraCE
ax.plot(amoc_ts.time, amoc_ts, 'red',
        label='TraCE')
ax.set_xlim([-21, -11])
ax.set_ylim([2, 20])
ax.set_xlabel('time (ka BP)')
ax.set_ylabel('AMOC (Sv)')
ax.xaxis.set_major_locator(plt.MultipleLocator(2))
ax.xaxis.set_minor_locator(plt.MultipleLocator(1))
ax.yaxis.set_major_locator(plt.MultipleLocator(5))
ax.yaxis.set_minor_locator(plt.MultipleLocator(1))

# Plot the Pa/Th time series in McManus et al. (2004)
ax2 = ax.twinx()
ax2.plot(pa_th['time'], pa_th['pa/th238']*-1,
         'blue',
         marker='s', markersize=3,
         label='McManus2004')
ax2.set_ylabel('Pa/Th')
ax2.yaxis.set_major_locator(plt.MultipleLocator(0.01))
ax2.yaxis.set_minor_locator(plt.MultipleLocator(0.002))

# Add a legend
lh1, ll1 = ax.get_legend_handles_labels()
lh2, ll2 = ax2.get_legend_handles_labels()
leg = ax.legend(lh1+lh2, ll1+ll2, frameon=False,
                loc='upper left',  ncol=2, fontsize=8)

---

4.6. Example 4. Compute and plot precipitation δ18O

• We define a function to compute precipitation δ18O

def calculate_d18Op(ds):
    """
    Compute precipitation δ18O with iCESM output

    Parameters
    ds: xarray.Dataset contains necessary variables

    Returns
    ds: xarray.Dataset with δ18O added
    """

    # convective & large-scale rain and snow, respectively
    p16O = ds.PRECRC_H216Or + ds.PRECSC_H216Os + ds.PRECRL_H216OR + ds.PRECSL_H216OS
    p18O = ds.PRECRC_H218Or + ds.PRECSC_H218Os + ds.PRECRL_H218OR + ds.PRECSL_H218OS

    # avoid dividing by small number here
    p18O = p18O.where(p16O > 1.E-18, 1.E-18)
    p16O = p16O.where(p16O > 1.E-18, 1.E-18)
    d18O = (p18O / p16O - 1.0) * 1000.0

    ds['p16O'] = p16O
    ds['p18O'] = p18O
    ds['d18O'] = d18O

    return ds

• Figure out the file names

!ls /glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/*PRECRC_H218Or*
!ls /glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/*PRECRC_H218Or*

/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECRC_H218Or.0001-0900.cal_adj.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECRC_H218Or.0001-0900.nc

/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECRC_H218Or.0001-0900.cal_adj.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECRC_H218Or.0001-0900.nc

• We need to read in all these variables

vnames = ['PRECC', 'PRECL',
          'PRECRC_H216Or', 'PRECSC_H216Os', 'PRECRL_H216OR', 'PRECSL_H216OS',
          'PRECRC_H218Or', 'PRECSC_H218Os', 'PRECRL_H218OR', 'PRECSL_H218OS']

storage_dir = '/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/'
hist_dir = '/atm/proc/tseries/month_1/'

case_pre = 'b.e12.B1850C5.f19_g16.iPI.01'
case_lgm = 'b.e12.B1850C5.f19_g16.i21ka.03'

fnames_pre = []
fnames_lgm = []

for vname in vnames:

    fname = glob.glob(storage_dir + case_pre + hist_dir + '*' + vname + '.0001-0900.nc')
    fnames_pre.extend(fname)

    fname = glob.glob(storage_dir + case_lgm + hist_dir + '*' + vname + '.0001-0900.nc')
    fnames_lgm.extend(fname)

print(*fnames_pre, sep='\n')
print(*fnames_lgm, sep='\n')

/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECC.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECL.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECRC_H216Or.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECSC_H216Os.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECRL_H216OR.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECSL_H216OS.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECRC_H218Or.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECSC_H218Os.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECRL_H218OR.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.iPI.01/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.iPI.01.cam.h0.PRECSL_H218OS.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECC.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECL.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECRC_H216Or.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECSC_H216Os.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECRL_H216OR.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECSL_H216OS.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECRC_H218Or.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECSC_H218Os.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECRL_H218OR.0001-0900.nc
/glade/campaign/cgd/ppc/jiangzhu/iCESM1.2/b.e12.B1850C5.f19_g16.i21ka.03/atm/proc/tseries/month_1/b.e12.B1850C5.f19_g16.i21ka.03.cam.h0.PRECSL_H218OS.0001-0900.nc

%%time

ds_pre = xr.open_mfdataset(fnames_pre, parallel=True,
                           data_vars='minimal',
                           coords='minimal',
                           compat='override',
                           chunks={'time':12}).isel(time=slice(-120, None))

ds_lgm = xr.open_mfdataset(fnames_lgm, parallel=True,
                           data_vars='minimal',
                           coords='minimal',
                           compat='override',
                           chunks={'time':12}).isel(time=slice(-120, None))

CPU times: user 1.33 s, sys: 184 ms, total: 1.51 s
Wall time: 5.13 s

ds_pre = calculate_d18Op(ds_pre)
ds_lgm = calculate_d18Op(ds_lgm)
ds_pre

<xarray.Dataset>
Dimensions:        (lev: 30, ilev: 31, lat: 96, lon: 144, slat: 95, slon: 144,
                    time: 120, nbnd: 2)
Coordinates:
  * lev            (lev) float64 3.643 7.595 14.36 24.61 ... 957.5 976.3 992.6
  * ilev           (ilev) float64 2.255 5.032 10.16 18.56 ... 967.5 985.1 1e+03
  * lat            (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0
  * lon            (lon) float64 0.0 2.5 5.0 7.5 ... 350.0 352.5 355.0 357.5
  * slat           (slat) float64 -89.05 -87.16 -85.26 ... 85.26 87.16 89.05
  * slon           (slon) float64 -1.25 1.25 3.75 6.25 ... 351.2 353.8 356.2
  * time           (time) object 0891-02-01 00:00:00 ... 0901-01-01 00:00:00
Dimensions without coordinates: nbnd
Data variables: (12/44)
    hyam           (lev) float64 dask.array<chunksize=(30,), meta=np.ndarray>
    hybm           (lev) float64 dask.array<chunksize=(30,), meta=np.ndarray>
    hyai           (ilev) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    hybi           (ilev) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    P0             float64 ...
    w_stag         (slat) float64 dask.array<chunksize=(95,), meta=np.ndarray>
    ...             ...
    PRECSC_H218Os  (time, lat, lon) float32 dask.array<chunksize=(12, 96, 144), meta=np.ndarray>
    PRECSL_H216OS  (time, lat, lon) float32 dask.array<chunksize=(12, 96, 144), meta=np.ndarray>
    PRECSL_H218OS  (time, lat, lon) float32 dask.array<chunksize=(12, 96, 144), meta=np.ndarray>
    p16O           (time, lat, lon) float32 dask.array<chunksize=(12, 96, 144), meta=np.ndarray>
    p18O           (time, lat, lon) float32 dask.array<chunksize=(12, 96, 144), meta=np.ndarray>
    d18O           (time, lat, lon) float32 dask.array<chunksize=(12, 96, 144), meta=np.ndarray>
Attributes:
    Conventions:      CF-1.0
    source:           CAM
    case:             b.e12.B1850C5.f19_g16.iPI.01
    title:            UNSET
    logname:          jiangzhu
    host:             r5i1n15
    Version:          $Name$
    revision_Id:      $Id$
    initial_file:     b.ie12.B1850C5CN.f19_g16.09.cam.i.0401-01-01-00000.nc
    topography_file:  /glade/p/cesmdata/cseg/inputdata/atm/cam/topo/USGS-gtop...

xarray.Dataset

• Dimensions:
  • lev: 30
  • ilev: 31
  • lat: 96
  • lon: 144
  • slat: 95
  • slon: 144
  • time: 120
  • nbnd: 2
• Coordinates: (7)
  • lev
    (lev)
    float64
    3.643 7.595 14.36 ... 976.3 992.6

    long_name :

    hybrid level at midpoints (1000*(A+B))

    units :

    level

    positive :

    down

    standard_name :

    atmosphere_hybrid_sigma_pressure_coordinate

    formula_terms :

    a: hyam b: hybm p0: P0 ps: PS

    array([  3.643466,   7.59482 ,  14.356632,  24.61222 ,  38.2683  ,  54.59548 ,
            72.012451,  87.82123 , 103.317127, 121.547241, 142.994039, 168.22508 ,
           197.908087, 232.828619, 273.910817, 322.241902, 379.100904, 445.992574,
           524.687175, 609.778695, 691.38943 , 763.404481, 820.858369, 859.534767,
           887.020249, 912.644547, 936.198398, 957.48548 , 976.325407, 992.556095])

  • ilev
    (ilev)
    float64
    2.255 5.032 10.16 ... 985.1 1e+03

    long_name :

    hybrid level at interfaces (1000*(A+B))

    units :

    level

    positive :

    down

    standard_name :

    atmosphere_hybrid_sigma_pressure_coordinate

    formula_terms :

    a: hyai b: hybi p0: P0 ps: PS

    array([   2.25524 ,    5.031692,   10.157947,   18.555317,   30.669123,
             45.867477,   63.323483,   80.701418,   94.941042,  111.693211,
            131.401271,  154.586807,  181.863353,  213.952821,  251.704417,
            296.117216,  348.366588,  409.835219,  482.149929,  567.224421,
            652.332969,  730.445892,  796.363071,  845.353667,  873.715866,
            900.324631,  924.964462,  947.432335,  967.538625,  985.11219 ,
           1000.      ])

  • lat
    (lat)
    float64
    -90.0 -88.11 -86.21 ... 88.11 90.0

    long_name :

    latitude

    units :

    degrees_north

    array([-90.      , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316,
           -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895,
           -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474,
           -55.894737, -54.      , -52.105263, -50.210526, -48.315789, -46.421053,
           -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632,
           -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211,
           -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,
           -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,
             0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,
            12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,
            23.684211,  25.578947,  27.473684,  29.368421,  31.263158,  33.157895,
            35.052632,  36.947368,  38.842105,  40.736842,  42.631579,  44.526316,
            46.421053,  48.315789,  50.210526,  52.105263,  54.      ,  55.894737,
            57.789474,  59.684211,  61.578947,  63.473684,  65.368421,  67.263158,
            69.157895,  71.052632,  72.947368,  74.842105,  76.736842,  78.631579,
            80.526316,  82.421053,  84.315789,  86.210526,  88.105263,  90.      ])

  • lon
    (lon)
    float64
    0.0 2.5 5.0 ... 352.5 355.0 357.5

    long_name :

    longitude

    units :

    degrees_east

    array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,
            25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,
            50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,
            75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,
           100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,
           125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,
           150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,
           175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,
           200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,
           350. , 352.5, 355. , 357.5])

  • slat
    (slat)
    float64
    -89.05 -87.16 ... 87.16 89.05

    long_name :

    staggered latitude

    units :

    degrees_north

    array([-89.052632, -87.157895, -85.263158, -83.368421, -81.473684, -79.578947,
           -77.684211, -75.789474, -73.894737, -72.      , -70.105263, -68.210526,
           -66.315789, -64.421053, -62.526316, -60.631579, -58.736842, -56.842105,
           -54.947368, -53.052632, -51.157895, -49.263158, -47.368421, -45.473684,
           -43.578947, -41.684211, -39.789474, -37.894737, -36.      , -34.105263,
           -32.210526, -30.315789, -28.421053, -26.526316, -24.631579, -22.736842,
           -20.842105, -18.947368, -17.052632, -15.157895, -13.263158, -11.368421,
            -9.473684,  -7.578947,  -5.684211,  -3.789474,  -1.894737,   0.      ,
             1.894737,   3.789474,   5.684211,   7.578947,   9.473684,  11.368421,
            13.263158,  15.157895,  17.052632,  18.947368,  20.842105,  22.736842,
            24.631579,  26.526316,  28.421053,  30.315789,  32.210526,  34.105263,
            36.      ,  37.894737,  39.789474,  41.684211,  43.578947,  45.473684,
            47.368421,  49.263158,  51.157895,  53.052632,  54.947368,  56.842105,
            58.736842,  60.631579,  62.526316,  64.421053,  66.315789,  68.210526,
            70.105263,  72.      ,  73.894737,  75.789474,  77.684211,  79.578947,
            81.473684,  83.368421,  85.263158,  87.157895,  89.052632])

  • slon
    (slon)
    float64
    -1.25 1.25 3.75 ... 353.8 356.2

    long_name :

    staggered longitude

    units :

    degrees_east

    array([ -1.25,   1.25,   3.75,   6.25,   8.75,  11.25,  13.75,  16.25,  18.75,
            21.25,  23.75,  26.25,  28.75,  31.25,  33.75,  36.25,  38.75,  41.25,
            43.75,  46.25,  48.75,  51.25,  53.75,  56.25,  58.75,  61.25,  63.75,
            66.25,  68.75,  71.25,  73.75,  76.25,  78.75,  81.25,  83.75,  86.25,
            88.75,  91.25,  93.75,  96.25,  98.75, 101.25, 103.75, 106.25, 108.75,
           111.25, 113.75, 116.25, 118.75, 121.25, 123.75, 126.25, 128.75, 131.25,
           133.75, 136.25, 138.75, 141.25, 143.75, 146.25, 148.75, 151.25, 153.75,
           156.25, 158.75, 161.25, 163.75, 166.25, 168.75, 171.25, 173.75, 176.25,
           178.75, 181.25, 183.75, 186.25, 188.75, 191.25, 193.75, 196.25, 198.75,
           201.25, 203.75, 206.25, 208.75, 211.25, 213.75, 216.25, 218.75, 221.25,
           223.75, 226.25, 228.75, 231.25, 233.75, 236.25, 238.75, 241.25, 243.75,
           246.25, 248.75, 251.25, 253.75, 256.25, 258.75, 261.25, 263.75, 266.25,
           268.75, 271.25, 273.75, 276.25, 278.75, 281.25, 283.75, 286.25, 288.75,
           291.25, 293.75, 296.25, 298.75, 301.25, 303.75, 306.25, 308.75, 311.25,
           313.75, 316.25, 318.75, 321.25, 323.75, 326.25, 328.75, 331.25, 333.75,
           336.25, 338.75, 341.25, 343.75, 346.25, 348.75, 351.25, 353.75, 356.25])

  • time
    (time)
    object
    0891-02-01 00:00:00 ... 0901-01-...

    long_name :

    time

    bounds :

    time_bnds

    array([cftime.DatetimeNoLeap(891, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(891, 3, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(891, 4, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(891, 5, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(891, 6, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(891, 7, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(891, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(891, 9, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(891, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(891, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(891, 12, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 3, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 4, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 5, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 6, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 7, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 9, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(892, 12, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 3, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 4, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 5, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 6, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 7, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 9, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(893, 12, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 3, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 4, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 5, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 6, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 7, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 9, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(894, 12, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 3, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 4, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 5, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 6, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 7, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 9, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(895, 12, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 3, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 4, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 5, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 6, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 7, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 9, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(896, 12, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 3, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 4, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 5, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 6, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 7, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 9, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(897, 12, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 3, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 4, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 5, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 6, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 7, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 9, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(898, 12, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 3, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 4, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 5, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 6, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 7, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 9, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(899, 12, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 3, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 4, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 5, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 6, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 7, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 9, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(900, 12, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(901, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Data variables: (44)
  • hyam
    (lev)
    float64
    dask.array<chunksize=(30,), meta=np.ndarray>

    long_name :

    hybrid A coefficient at layer midpoints

    Array Chunk Bytes 240 B 240 B Shape (30,) (30,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • hybm
    (lev)
    float64
    dask.array<chunksize=(30,), meta=np.ndarray>

    long_name :

    hybrid B coefficient at layer midpoints

    Array Chunk Bytes 240 B 240 B Shape (30,) (30,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • hyai
    (ilev)
    float64
    dask.array<chunksize=(31,), meta=np.ndarray>

    long_name :

    hybrid A coefficient at layer interfaces

    Array Chunk Bytes 248 B 248 B Shape (31,) (31,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • hybi
    (ilev)
    float64
    dask.array<chunksize=(31,), meta=np.ndarray>

    long_name :

    hybrid B coefficient at layer interfaces

    Array Chunk Bytes 248 B 248 B Shape (31,) (31,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • P0
    ()
    float64
    ...

    long_name :

    reference pressure

    units :

    Pa

    [1 values with dtype=float64]

  • w_stag
    (slat)
    float64
    dask.array<chunksize=(95,), meta=np.ndarray>

    long_name :

    staggered latitude weights

    Array Chunk Bytes 760 B 760 B Shape (95,) (95,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ntrm
    ()
    int32
    ...

    long_name :

    spectral truncation parameter M

    [1 values with dtype=int32]

  • ntrn
    ()
    int32
    ...

    long_name :

    spectral truncation parameter N

    [1 values with dtype=int32]

  • ntrk
    ()
    int32
    ...

    long_name :

    spectral truncation parameter K

    [1 values with dtype=int32]

  • ndbase
    ()
    int32
    ...

    long_name :

    base day

    [1 values with dtype=int32]

  • nsbase
    ()
    int32
    ...

    long_name :

    seconds of base day

    [1 values with dtype=int32]

  • nbdate
    ()
    int32
    ...

    long_name :

    base date (YYYYMMDD)

    [1 values with dtype=int32]

  • nbsec
    ()
    int32
    ...

    long_name :

    seconds of base date

    [1 values with dtype=int32]

  • mdt
    ()
    int32
    ...

    long_name :

    timestep

    units :

    s

    [1 values with dtype=int32]

  • nlon
    (lat)
    int32
    dask.array<chunksize=(96,), meta=np.ndarray>

    long_name :

    number of longitudes

    Array Chunk Bytes 384 B 384 B Shape (96,) (96,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • wnummax
    (lat)
    int32
    dask.array<chunksize=(96,), meta=np.ndarray>

    long_name :

    cutoff Fourier wavenumber

    Array Chunk Bytes 384 B 384 B Shape (96,) (96,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • gw
    (lat)
    float64
    dask.array<chunksize=(96,), meta=np.ndarray>

    long_name :

    gauss weights

    Array Chunk Bytes 768 B 768 B Shape (96,) (96,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • date
    (time)
    int32
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    current date (YYYYMMDD)

    Array Chunk Bytes 480 B 48 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type int32 numpy.ndarray

  • datesec
    (time)
    int32
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    current seconds of current date

    Array Chunk Bytes 480 B 48 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type int32 numpy.ndarray

  • time_bnds
    (time, nbnd)
    object
    dask.array<chunksize=(12, 2), meta=np.ndarray>

    long_name :

    time interval endpoints

    Array Chunk Bytes 1.88 kiB 192 B Shape (120, 2) (12, 2) Dask graph 10 chunks in 3 graph layers Data type object numpy.ndarray

  • date_written
    (time)
    |S8
    dask.array<chunksize=(12,), meta=np.ndarray>

    Array Chunk Bytes 0.94 kiB 96 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type |S8 numpy.ndarray

  • time_written
    (time)
    |S8
    dask.array<chunksize=(12,), meta=np.ndarray>

    Array Chunk Bytes 0.94 kiB 96 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type |S8 numpy.ndarray

  • ndcur
    (time)
    int32
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    current day (from base day)

    Array Chunk Bytes 480 B 48 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type int32 numpy.ndarray

  • nscur
    (time)
    int32
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    current seconds of current day

    Array Chunk Bytes 480 B 48 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type int32 numpy.ndarray

  • co2vmr
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    co2 volume mixing ratio

    Array Chunk Bytes 0.94 kiB 96 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type float64 numpy.ndarray

  • ch4vmr
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    ch4 volume mixing ratio

    Array Chunk Bytes 0.94 kiB 96 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type float64 numpy.ndarray

  • n2ovmr
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    n2o volume mixing ratio

    Array Chunk Bytes 0.94 kiB 96 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type float64 numpy.ndarray

  • f11vmr
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    f11 volume mixing ratio

    Array Chunk Bytes 0.94 kiB 96 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type float64 numpy.ndarray

  • f12vmr
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    f12 volume mixing ratio

    Array Chunk Bytes 0.94 kiB 96 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type float64 numpy.ndarray

  • sol_tsi
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    total solar irradiance

    units :

    W/m2

    Array Chunk Bytes 0.94 kiB 96 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type float64 numpy.ndarray

  • nsteph
    (time)
    int32
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    current timestep

    Array Chunk Bytes 480 B 48 B Shape (120,) (12,) Dask graph 10 chunks in 3 graph layers Data type int32 numpy.ndarray

  • PRECC
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    units :

    m/s

    long_name :

    Convective precipitation rate (liq + ice)

    cell_methods :

    time: mean

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 3 graph layers Data type float32 numpy.ndarray

  • PRECL
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    units :

    m/s

    long_name :

    Large-scale (stable) precipitation rate (liq + ice)

    cell_methods :

    time: mean

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 3 graph layers Data type float32 numpy.ndarray

  • PRECRC_H216Or
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    units :

    m/s

    long_name :

    Convective rain rate for H216Or

    cell_methods :

    time: mean

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 3 graph layers Data type float32 numpy.ndarray

  • PRECRC_H218Or
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    units :

    m/s

    long_name :

    Convective rain rate for H218Or

    cell_methods :

    time: mean

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 3 graph layers Data type float32 numpy.ndarray

  • PRECRL_H216OR
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    units :

    m/s

    long_name :

    Large-scale (stable) rain rate for H216OR

    cell_methods :

    time: mean

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 3 graph layers Data type float32 numpy.ndarray

  • PRECRL_H218OR
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    units :

    m/s

    long_name :

    Large-scale (stable) rain rate for H218OR

    cell_methods :

    time: mean

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 3 graph layers Data type float32 numpy.ndarray

  • PRECSC_H216Os
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    units :

    m/s

    long_name :

    Convective snow rate (water equivalent) for H216Os

    cell_methods :

    time: mean

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 3 graph layers Data type float32 numpy.ndarray

  • PRECSC_H218Os
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    units :

    m/s

    long_name :

    Convective snow rate (water equivalent) for H218Os

    cell_methods :

    time: mean

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 3 graph layers Data type float32 numpy.ndarray

  • PRECSL_H216OS
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    units :

    m/s

    long_name :

    Large-scale (stable) snow rate (water equivalent) for H216OS

    cell_methods :

    time: mean

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 3 graph layers Data type float32 numpy.ndarray

  • PRECSL_H218OS
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    units :

    m/s

    long_name :

    Large-scale (stable) snow rate (water equivalent) for H218OS

    cell_methods :

    time: mean

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 3 graph layers Data type float32 numpy.ndarray

  • p16O
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 17 graph layers Data type float32 numpy.ndarray

  • p18O
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 32 graph layers Data type float32 numpy.ndarray

  • d18O
    (time, lat, lon)
    float32
    dask.array<chunksize=(12, 96, 144), meta=np.ndarray>

    Array Chunk Bytes 6.33 MiB 648.00 kiB Shape (120, 96, 144) (12, 96, 144) Dask graph 10 chunks in 36 graph layers Data type float32 numpy.ndarray

• Indexes: (7)
  • lev
    PandasIndex

    PandasIndex(Index([  3.64346569404006,  7.594819646328688, 14.356632251292467,
           24.612220004200935,  38.26829977333546,  54.59547974169254,
            72.01245054602623,  87.82123029232025, 103.31712663173676,
           121.54724076390266, 142.99403876066208, 168.22507977485657,
            197.9080867022276, 232.82861895859241,  273.9108167588711,
            322.2419023513794, 379.10090386867523,   445.992574095726,
            524.6871747076511,  609.7786948084831,  691.3894303143024,
             763.404481112957,  820.8583686500788,  859.5347665250301,
            887.0202489197254,   912.644546944648,  936.1983984708786,
             957.485479535535,   976.325407391414,   992.556095123291],
          dtype='float64', name='lev'))

  • ilev
    PandasIndex

    PandasIndex(Index([ 2.255239523947239,  5.031691864132881,  10.15794742852449,
            18.55531707406044,  30.66912293434143,  45.86747661232948,
             63.3234828710556,  80.70141822099686,  94.94104236364365,
           111.69321089982991,  131.4012706279755,  154.5868068933487,
            181.8633526563644, 213.95282074809072, 251.70441716909409,
           296.11721634864807,  348.3665883541107,  409.8352193832398,
           482.14992880821234,    567.22442060709,  652.3329690098763,
            730.4458916187286,  796.3630706071854,  845.3536666929722,
            873.7158663570881,  900.3246314823627,  924.9644624069333,
            947.4323345348239,  967.5386245362461,   985.112190246582,
                       1000.0],
          dtype='float64', name='ilev'))

  • lat
    PandasIndex

    PandasIndex(Index([              -90.0,  -88.10526315789474,  -86.21052631578948,
             -84.3157894736842,  -82.42105263157895,  -80.52631578947368,
            -78.63157894736842,  -76.73684210526316,  -74.84210526315789,
            -72.94736842105263,  -71.05263157894737,  -69.15789473684211,
            -67.26315789473685,  -65.36842105263158, -63.473684210526315,
           -61.578947368421055, -59.684210526315795,  -57.78947368421053,
            -55.89473684210527,               -54.0,  -52.10526315789474,
            -50.21052631578947,  -48.31578947368421,  -46.42105263157895,
           -44.526315789473685, -42.631578947368425,  -40.73684210526316,
             -38.8421052631579,  -36.94736842105264,  -35.05263157894737,
            -33.15789473684211, -31.263157894736842, -29.368421052631582,
           -27.473684210526322, -25.578947368421055, -23.684210526315795,
           -21.789473684210535,  -19.89473684210526,               -18.0,
            -16.10526315789474,  -14.21052631578948,  -12.31578947368422,
           -10.421052631578945,  -8.526315789473685,  -6.631578947368425,
            -4.736842105263165, -2.8421052631579045, -0.9473684210526301,
            0.9473684210526301,  2.8421052631578902,    4.73684210526315,
             6.631578947368411,   8.526315789473685,  10.421052631578945,
            12.315789473684205,  14.210526315789465,  16.105263157894726,
                          18.0,   19.89473684210526,   21.78947368421052,
             23.68421052631578,   25.57894736842104,  27.473684210526315,
            29.368421052631575,  31.263157894736835,  33.157894736842096,
            35.052631578947356,   36.94736842105263,   38.84210526315789,
             40.73684210526315,   42.63157894736841,   44.52631578947367,
             46.42105263157893,   48.31578947368419,   50.21052631578948,
             52.10526315789474,                54.0,   55.89473684210526,
             57.78947368421052,   59.68421052631578,   61.57894736842104,
              63.4736842105263,   65.36842105263156,   67.26315789473682,
             69.15789473684211,   71.05263157894737,   72.94736842105263,
             74.84210526315789,   76.73684210526315,   78.63157894736841,
             80.52631578947367,   82.42105263157893,   84.31578947368419,
             86.21052631578945,   88.10526315789474,                90.0],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,  22.5,
           ...
           335.0, 337.5, 340.0, 342.5, 345.0, 347.5, 350.0, 352.5, 355.0, 357.5],
          dtype='float64', name='lon', length=144))

  • slat
    PandasIndex

    PandasIndex(Index([ -89.05263157894737,  -87.15789473684211,  -85.26315789473685,
            -83.36842105263158,  -81.47368421052632,  -79.57894736842104,
            -77.68421052631581,  -75.78947368421053,  -73.89473684210526,
                         -72.0,  -70.10526315789474,  -68.21052631578948,
            -66.31578947368422,  -64.42105263157896, -62.526315789473685,
            -60.63157894736841,  -58.73684210526316,  -56.84210526315789,
            -54.94736842105263,  -53.05263157894736,  -51.15789473684211,
            -49.26315789473684, -47.368421052631575, -45.473684210526315,
           -43.578947368421055, -41.684210526315795,  -39.78947368421052,
            -37.89473684210527,  -36.00000000000001,  -34.10526315789474,
            -32.21052631578948, -30.315789473684205, -28.421052631578952,
           -26.526315789473692, -24.631578947368425, -22.736842105263165,
           -20.842105263157897,  -18.94736842105263,  -17.05263157894737,
            -15.15789473684211,  -13.26315789473685, -11.368421052631582,
            -9.473684210526315,  -7.578947368421055,  -5.684210526315794,
           -3.7894736842105337, -1.8947368421052675,                 0.0,
              1.89473684210526,    3.78947368421052,  5.6842105263157805,
             7.578947368421047,   9.473684210526315,  11.368421052631575,
            13.263157894736834,  15.157894736842094,  17.052631578947363,
             18.94736842105263,  20.842105263157887,   22.73684210526315,
            24.631578947368407,  26.526315789473678,  28.421052631578945,
            30.315789473684205,  32.210526315789465,   34.10526315789473,
             35.99999999999999,   37.89473684210526,   39.78947368421052,
             41.68421052631577,  43.578947368421034,    45.4736842105263,
            47.368421052631554,  49.263157894736835,   51.15789473684211,
             53.05263157894736,   54.94736842105263,   56.84210526315789,
             58.73684210526314,   60.63157894736841,  62.526315789473685,
             64.42105263157893,   66.31578947368419,   68.21052631578947,
             70.10526315789474,                72.0,   73.89473684210526,
             75.78947368421052,   77.68421052631578,   79.57894736842104,
              81.4736842105263,   83.36842105263155,   85.26315789473682,
             87.15789473684211,   89.05263157894737],
          dtype='float64', name='slat'))

  • slon
    PandasIndex

    PandasIndex(Index([ -1.25,   1.25,   3.75,   6.25,   8.75,  11.25,  13.75,  16.25,  18.75,
            21.25,
           ...
           333.75, 336.25, 338.75, 341.25, 343.75, 346.25, 348.75, 351.25, 353.75,
           356.25],
          dtype='float64', name='slon', length=144))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([0891-02-01 00:00:00, 0891-03-01 00:00:00, 0891-04-01 00:00:00,
                 0891-05-01 00:00:00, 0891-06-01 00:00:00, 0891-07-01 00:00:00,
                 0891-08-01 00:00:00, 0891-09-01 00:00:00, 0891-10-01 00:00:00,
                 0891-11-01 00:00:00,
                 ...
                 0900-04-01 00:00:00, 0900-05-01 00:00:00, 0900-06-01 00:00:00,
                 0900-07-01 00:00:00, 0900-08-01 00:00:00, 0900-09-01 00:00:00,
                 0900-10-01 00:00:00, 0900-11-01 00:00:00, 0900-12-01 00:00:00,
                 0901-01-01 00:00:00],
                dtype='object', length=120, calendar='noleap', freq='MS'))

• Attributes: (10)

  Conventions :

  CF-1.0

  source :

  CAM

  case :

  b.e12.B1850C5.f19_g16.iPI.01

  title :

  UNSET

  logname :

  jiangzhu

  host :

  r5i1n15

  Version :

  $Name$

  revision_Id :

  $Id$

  initial_file :

  b.ie12.B1850C5CN.f19_g16.09.cam.i.0401-01-01-00000.nc

  topography_file :

  /glade/p/cesmdata/cseg/inputdata/atm/cam/topo/USGS-gtopo30_1.9x2.5_remap_c050602.nc

# Save netcdf data
# ds_pre.to_netcdf('dataset_with_d18Op.nc')

d18Op_pre = ds_pre.d18O.mean('time')
d18Op_lgm = ds_lgm.d18O.mean('time')

# Sometimes, it is preferred to use the precipitation-amount weighted d18O
#d18Op_pre = ds_pre.d18O.weighted(ds_pre.p16O).mean('time')

d18Op_pre

<xarray.DataArray 'd18O' (lat: 96, lon: 144)>
dask.array<mean_agg-aggregate, shape=(96, 144), dtype=float32, chunksize=(96, 144), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0
  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5

xarray.DataArray

'd18O'

• lat: 96
• lon: 144

• dask.array<chunksize=(96, 144), meta=np.ndarray>

  Array Chunk Bytes 54.00 kiB 54.00 kiB Shape (96, 144) (96, 144) Dask graph 1 chunks in 39 graph layers Data type float32 numpy.ndarray

• Coordinates: (2)
  • lat
    (lat)
    float64
    -90.0 -88.11 -86.21 ... 88.11 90.0

    long_name :

    latitude

    units :

    degrees_north

    array([-90.      , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316,
           -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895,
           -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474,
           -55.894737, -54.      , -52.105263, -50.210526, -48.315789, -46.421053,
           -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632,
           -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211,
           -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,
           -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,
             0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,
            12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,
            23.684211,  25.578947,  27.473684,  29.368421,  31.263158,  33.157895,
            35.052632,  36.947368,  38.842105,  40.736842,  42.631579,  44.526316,
            46.421053,  48.315789,  50.210526,  52.105263,  54.      ,  55.894737,
            57.789474,  59.684211,  61.578947,  63.473684,  65.368421,  67.263158,
            69.157895,  71.052632,  72.947368,  74.842105,  76.736842,  78.631579,
            80.526316,  82.421053,  84.315789,  86.210526,  88.105263,  90.      ])

  • lon
    (lon)
    float64
    0.0 2.5 5.0 ... 352.5 355.0 357.5

    long_name :

    longitude

    units :

    degrees_east

    array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,
            25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,
            50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,
            75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,
           100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,
           125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,
           150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,
           175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,
           200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,
           350. , 352.5, 355. , 357.5])

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([              -90.0,  -88.10526315789474,  -86.21052631578948,
             -84.3157894736842,  -82.42105263157895,  -80.52631578947368,
            -78.63157894736842,  -76.73684210526316,  -74.84210526315789,
            -72.94736842105263,  -71.05263157894737,  -69.15789473684211,
            -67.26315789473685,  -65.36842105263158, -63.473684210526315,
           -61.578947368421055, -59.684210526315795,  -57.78947368421053,
            -55.89473684210527,               -54.0,  -52.10526315789474,
            -50.21052631578947,  -48.31578947368421,  -46.42105263157895,
           -44.526315789473685, -42.631578947368425,  -40.73684210526316,
             -38.8421052631579,  -36.94736842105264,  -35.05263157894737,
            -33.15789473684211, -31.263157894736842, -29.368421052631582,
           -27.473684210526322, -25.578947368421055, -23.684210526315795,
           -21.789473684210535,  -19.89473684210526,               -18.0,
            -16.10526315789474,  -14.21052631578948,  -12.31578947368422,
           -10.421052631578945,  -8.526315789473685,  -6.631578947368425,
            -4.736842105263165, -2.8421052631579045, -0.9473684210526301,
            0.9473684210526301,  2.8421052631578902,    4.73684210526315,
             6.631578947368411,   8.526315789473685,  10.421052631578945,
            12.315789473684205,  14.210526315789465,  16.105263157894726,
                          18.0,   19.89473684210526,   21.78947368421052,
             23.68421052631578,   25.57894736842104,  27.473684210526315,
            29.368421052631575,  31.263157894736835,  33.157894736842096,
            35.052631578947356,   36.94736842105263,   38.84210526315789,
             40.73684210526315,   42.63157894736841,   44.52631578947367,
             46.42105263157893,   48.31578947368419,   50.21052631578948,
             52.10526315789474,                54.0,   55.89473684210526,
             57.78947368421052,   59.68421052631578,   61.57894736842104,
              63.4736842105263,   65.36842105263156,   67.26315789473682,
             69.15789473684211,   71.05263157894737,   72.94736842105263,
             74.84210526315789,   76.73684210526315,   78.63157894736841,
             80.52631578947367,   82.42105263157893,   84.31578947368419,
             86.21052631578945,   88.10526315789474,                90.0],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,  22.5,
           ...
           335.0, 337.5, 340.0, 342.5, 345.0, 347.5, 350.0, 352.5, 355.0, 357.5],
          dtype='float64', name='lon', length=144))

• Attributes: (0)

fig, axes = plt.subplots(1, 3,
                         figsize=(10, 2),
                         subplot_kw={'projection': ccrs.Robinson(central_longitude=210)},
                         constrained_layout=True)
axes = axes.ravel()

lon = d18Op_pre.lon
lat = d18Op_pre.lat

ax = axes[0]
var_new, lon_new = add_cyclic_point(d18Op_pre, lon)
p0 = ax.contourf(lon_new, lat, var_new,
                 levels=np.linspace(-40, 0, 21),
                 extend='both',
                 transform=ccrs.PlateCarree())
plt.colorbar(p0, ax=ax)
ax.set_title("δ18Op of PI")

ax = axes[1]
var_new, lon_new = add_cyclic_point(d18Op_lgm, lon)
p1 = ax.contourf(lon_new, lat, var_new,
                 levels=np.linspace(-40, 0, 21),
                 extend='both',
                 transform=ccrs.PlateCarree())
plt.colorbar(p1, ax=ax)
ax.set_title("δ18Op of LGM")

ax = axes[2]
var_diff = d18Op_lgm - d18Op_pre
var_new, lon_new = add_cyclic_point(var_diff, lon)

p2 = ax.contourf(lon_new, lat, var_new,
                 cmap='RdBu_r',
                 levels=np.linspace(-8, 8, 17),
                 extend='both',
                 transform=ccrs.PlateCarree())
plt.colorbar(p2, ax=ax)
ax.set_title("δ18Op of LGM ‒ PI")

for ax in axes:
    ax.set_global()
    ax.coastlines(linewidth=0.5)