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Introduction to Intake-ESM

Max Grover

01 October 2022

---

Overview

1. What is Intake-ESM?

2. How can we use it to read in data?

3. How do we work with dictionaries of datasets?

4. How do I write my analysis to work with Intake-ESM?

Prerequisites

Concepts	Importance	Notes
Introduction to Xarray	Necessary	-
Introduction to Matplotlib	Helpful	-

• Time to learn: 50 minutes.

---

Setup

Imports

Here, we will import intake, and dask.distributed (distributed)

Remember, Intake-ESM is a plugin within the Intake project, so we do not need to explicitly call

import intake_esm

import intake
from distributed import Client, LocalCluster
import xarray as xr
import matplotlib.pyplot as plt
import fsspec

Spin up a Dask Cluster

We will go ahead and spin up a Dask Cluster

cluster = LocalCluster()
client = Client(cluster)
client

/Users/mgrover/miniforge3/envs/mscar-python-tutorial-dev/lib/python3.10/site-packages/distributed/node.py:183: UserWarning: Port 8787 is already in use.
Perhaps you already have a cluster running?
Hosting the HTTP server on port 57407 instead
  warnings.warn(

Client

Client-c8ff070e-420f-11ed-a074-520a01803a93

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:57407/status

Cluster Info

LocalCluster

7c72d1a8

Dashboard: http://127.0.0.1:57407/status	Workers: 5
Total threads: 10	Total memory: 32.00 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-9e871823-4c3b-4f19-9efa-61d916417a76

Comm: tcp://127.0.0.1:57408	Workers: 5
Dashboard: http://127.0.0.1:57407/status	Total threads: 10
Started: Just now	Total memory: 32.00 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:57432	Total threads: 2
Dashboard: http://127.0.0.1:57433/status	Memory: 6.40 GiB
Nanny: tcp://127.0.0.1:57413
Local directory: /var/folders/bw/c9j8z20x45s2y20vv6528qjc0000gq/T/dask-worker-space/worker-c789voz8

Worker: 1

Comm: tcp://127.0.0.1:57427	Total threads: 2
Dashboard: http://127.0.0.1:57429/status	Memory: 6.40 GiB
Nanny: tcp://127.0.0.1:57412
Local directory: /var/folders/bw/c9j8z20x45s2y20vv6528qjc0000gq/T/dask-worker-space/worker-yiv0bk22

Worker: 2

Comm: tcp://127.0.0.1:57435	Total threads: 2
Dashboard: http://127.0.0.1:57437/status	Memory: 6.40 GiB
Nanny: tcp://127.0.0.1:57415
Local directory: /var/folders/bw/c9j8z20x45s2y20vv6528qjc0000gq/T/dask-worker-space/worker-hetk3utz

Worker: 3

Comm: tcp://127.0.0.1:57426	Total threads: 2
Dashboard: http://127.0.0.1:57428/status	Memory: 6.40 GiB
Nanny: tcp://127.0.0.1:57411
Local directory: /var/folders/bw/c9j8z20x45s2y20vv6528qjc0000gq/T/dask-worker-space/worker-ef2rd9n4

Worker: 4

Comm: tcp://127.0.0.1:57436	Total threads: 2
Dashboard: http://127.0.0.1:57439/status	Memory: 6.40 GiB
Nanny: tcp://127.0.0.1:57414
Local directory: /var/folders/bw/c9j8z20x45s2y20vv6528qjc0000gq/T/dask-worker-space/worker-ivhltpnd

How can I use Intake-ESM to read in my data?

“Traditional” Workflow

Say, for example, wanted to take a look at data from the AWS hosted Community Earth System Model Large Ensemble (CESM-LENS)…

We want to take a look at:

• Atmospheric temperature (T)

• Atmospheric moisture (Q)

As well as:

• Ocean temperature (TEMP)

• Ocean salinity (SALT)

Investigate the files on AWS

We use fsspec here to take a look at what files are available; this would be similar to listing files on your local system (ex. glob)

fs = fsspec.filesystem('s3', anon=True)
bucket = 'ncar-cesm-lens'
fs.ls(bucket)

['ncar-cesm-lens/atm',
 'ncar-cesm-lens/catalogs',
 'ncar-cesm-lens/ice_nh',
 'ncar-cesm-lens/ice_sh',
 'ncar-cesm-lens/lnd',
 'ncar-cesm-lens/ocn']

You’ll notice we have a few directories in this bucket, corresponding to each component:

• Atmosphere (atm)

• Ice Northern Hemisphere (ice_nh)

• Ice Southern Hemisphere (ice_sh)

• Land (lnd)

• Ocean (ocn)

If we go into the atm directory, we see there are various frequencies which each component as well

bucket = 'ncar-cesm-lens/atm'
fs.ls(bucket)

['ncar-cesm-lens/atm/',
 'ncar-cesm-lens/atm/daily',
 'ncar-cesm-lens/atm/hourly6-1990-2005',
 'ncar-cesm-lens/atm/hourly6-2026-2035',
 'ncar-cesm-lens/atm/hourly6-2071-2080',
 'ncar-cesm-lens/atm/monthly',
 'ncar-cesm-lens/atm/static']

If we go one level further, we see the actual data, separated by cesmLE-{experiment}-{variable}.zarr

bucket = 'ncar-cesm-lens/atm/monthly'
fs.ls(bucket)

['ncar-cesm-lens/atm/monthly/',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-FLNS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-FLNSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-FLUT.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-FSNS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-FSNSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-FSNTOA.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-ICEFRAC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-LHFLX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-PRECC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-PRECL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-PRECSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-PRECSL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-PSL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-Q.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-SHFLX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-T.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-TMQ.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-TREFHT.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-TREFHTMN.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-TREFHTMX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-TS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-U.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-V.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-20C-Z3.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-FLNS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-FLNSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-FLUT.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-FSNS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-FSNSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-FSNTOA.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-ICEFRAC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-LHFLX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-PRECC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-PRECL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-PRECSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-PRECSL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-PS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-PSL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-Q.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-SHFLX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-T.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-TMQ.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-TREFHT.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-TREFHTMN.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-TREFHTMX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-TS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-U.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-V.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-CTRL-Z3.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-FLNS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-FLNSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-FLUT.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-FSNS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-FSNSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-FSNTOA.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-ICEFRAC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-LHFLX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-PRECC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-PRECL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-PRECSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-PRECSL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-PSL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-Q.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-SHFLX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-T.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-TMQ.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-TREFHT.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-TREFHTMN.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-TREFHTMX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-TS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-U.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-V.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-HIST-Z3.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-FLNS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-FLNSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-FLUT.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-FSNS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-FSNSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-FSNTOA.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-ICEFRAC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-LHFLX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-PRECC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-PRECL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-PRECSC.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-PRECSL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-PS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-PSL.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-Q.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-SHFLX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-T.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-TMQ.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-TREFHT.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-TREFHTMN.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-TREFHTMX.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-TS.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-U.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-V.zarr',
 'ncar-cesm-lens/atm/monthly/cesmLE-RCP85-Z3.zarr']

Loading in Data Using Xarray

Let’s say we wanted to look at data from the historical scenario (HIST)… We could load the data using the following syntax!

var = 'T'
atmosphere_ds = xr.open_zarr(f's3://ncar-cesm-lens/atm/monthly/cesmLE-RCP85-{var}.zarr', storage_options={'anon':True})
atmosphere_ds

<xarray.Dataset>
Dimensions:    (member_id: 40, time: 1140, lev: 30, lat: 192, lon: 288, nbnd: 2)
Coordinates:
  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
  * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
  * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
  * time       (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00
    time_bnds  (time, nbnd) object dask.array<chunksize=(1140, 2), meta=np.ndarray>
Dimensions without coordinates: nbnd
Data variables:
    T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
Attributes:
    Conventions:               CF-1.0
    NCO:                       4.3.4
    Version:                   $Name$
    host:                      tcs-f02n07
    important_note:            This data is part of the project 'Blind Evalua...
    initial_file:              b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i.2006-01-...
    logname:                   mudryk
    nco_openmp_thread_number:  1
    revision_Id:               $Id$
    source:                    CAM
    title:                     UNSET
    topography_file:           /scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata...

xarray.Dataset

• Dimensions:
  • member_id: 40
  • time: 1140
  • lev: 30
  • lat: 192
  • lon: 288
  • nbnd: 2
• Coordinates: (6)
  • lat
    (lat)
    float64
    -90.0 -89.06 -88.12 ... 89.06 90.0

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -89.057594, -88.115181, -87.172775, -86.23037 , -85.287956,
           -84.345551, -83.403145, -82.460732, -81.518326, -80.575912, -79.633507,
           -78.691101, -77.748688, -76.806282, -75.863876, -74.921463, -73.979057,
           -73.036652, -72.094238, -71.151833, -70.209427, -69.267014, -68.324608,
           -67.382202, -66.439789, -65.497383, -64.554977, -63.612564, -62.670158,
           -61.727749, -60.785339, -59.842934, -58.900524, -57.958115, -57.015705,
           -56.073299, -55.13089 , -54.18848 , -53.246075, -52.303665, -51.361256,
           -50.41885 , -49.47644 , -48.534031, -47.591621, -46.649216, -45.706806,
           -44.764397, -43.821991, -42.879581, -41.937172, -40.994766, -40.052357,
           -39.109947, -38.167538, -37.225132, -36.282722, -35.340313, -34.397907,
           -33.455498, -32.513088, -31.570681, -30.628273, -29.685863, -28.743456,
           -27.801046, -26.858639, -25.916231, -24.973822, -24.031414, -23.089005,
           -22.146597, -21.204189, -20.26178 , -19.319372, -18.376963, -17.434555,
           -16.492147, -15.549738, -14.607329, -13.664922, -12.722513, -11.780105,
           -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
            -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
             0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
             6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
            11.780105,  12.722513,  13.664922,  14.607329,  15.549738,  16.492147,
            17.434555,  18.376963,  19.319372,  20.26178 ,  21.204189,  22.146597,
            23.089005,  24.031414,  24.973822,  25.916231,  26.858639,  27.801046,
            28.743456,  29.685863,  30.628273,  31.570681,  32.513088,  33.455498,
            34.397907,  35.340313,  36.282722,  37.225132,  38.167538,  39.109947,
            40.052357,  40.994766,  41.937172,  42.879581,  43.821991,  44.764397,
            45.706806,  46.649216,  47.591621,  48.534031,  49.47644 ,  50.41885 ,
            51.361256,  52.303665,  53.246075,  54.18848 ,  55.13089 ,  56.073299,
            57.015705,  57.958115,  58.900524,  59.842934,  60.785339,  61.727749,
            62.670158,  63.612564,  64.554977,  65.497383,  66.439789,  67.382202,
            68.324608,  69.267014,  70.209427,  71.151833,  72.094238,  73.036652,
            73.979057,  74.921463,  75.863876,  76.806282,  77.748688,  78.691101,
            79.633507,  80.575912,  81.518326,  82.460732,  83.403145,  84.345551,
            85.287956,  86.23037 ,  87.172775,  88.115181,  89.057594,  90.      ])

  • lev
    (lev)
    float64
    3.643 7.595 14.36 ... 976.3 992.6

    formula_terms :

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

    long_name :

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

    positive :

    down

    standard_name :

    atmosphere_hybrid_sigma_pressure_coordinate

    units :

    level

    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])

  • lon
    (lon)
    float64
    0.0 1.25 2.5 ... 356.2 357.5 358.8

    long_name :

    longitude

    units :

    degrees_east

    array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

  • member_id
    (member_id)
    int64
    1 2 3 4 5 6 ... 101 102 103 104 105

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])

  • time
    (time)
    object
    2006-01-16 12:00:00 ... 2100-12-...

    bounds :

    time_bnds

    long_name :

    time

    array([cftime.DatetimeNoLeap(2006, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2006, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2006, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bnds
    (time, nbnd)
    object
    dask.array<chunksize=(1140, 2), meta=np.ndarray>

    long_name :

    time interval endpoints

    Array Chunk Bytes 17.81 kiB 17.81 kiB Shape (1140, 2) (1140, 2) Count 2 Graph Layers 1 Chunks Type object numpy.ndarray

• Data variables: (1)
  • T
    (member_id, time, lev, lat, lon)
    float32
    dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Temperature

    mdims :

    1

    units :

    K

    Array Chunk Bytes 281.80 GiB 113.91 MiB Shape (40, 1140, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2560 Chunks Type float32 numpy.ndarray

• Attributes: (12)

  Conventions :

  CF-1.0

  NCO :

  4.3.4

  Version :

  $Name$

  host :

  tcs-f02n07

  important_note :

  This data is part of the project 'Blind Evaluation of Lossy Data-Compression in LENS'. Please exercise caution before using this data for other purposes.

  initial_file :

  b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i.2006-01-01-00000.nc

  logname :

  mudryk

  nco_openmp_thread_number :

  1

  revision_Id :

  $Id$

  source :

  CAM

  title :

  UNSET

  topography_file :

  /scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc

If we wanted all of these in the same dataset, we would need to write the merging ourselves

variables = ['T', 'Q']
ds_list = []

# Loop through the different files and add them to the list of datasets
for var in variables:
    ds_list.append(xr.open_zarr(f's3://ncar-cesm-lens/atm/monthly/cesmLE-RCP85-{var}.zarr', storage_options={'anon':True}))
    
atmosphere_merged = xr.merge(ds_list)
atmosphere_merged

<xarray.Dataset>
Dimensions:    (member_id: 40, time: 1140, lev: 30, lat: 192, lon: 288, nbnd: 2)
Coordinates:
  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
  * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
  * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
  * time       (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00
    time_bnds  (time, nbnd) object dask.array<chunksize=(1140, 2), meta=np.ndarray>
Dimensions without coordinates: nbnd
Data variables:
    T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
    Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
Attributes:
    Conventions:               CF-1.0
    NCO:                       4.3.4
    Version:                   $Name$
    host:                      tcs-f02n07
    important_note:            This data is part of the project 'Blind Evalua...
    initial_file:              b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i.2006-01-...
    logname:                   mudryk
    nco_openmp_thread_number:  1
    revision_Id:               $Id$
    source:                    CAM
    title:                     UNSET
    topography_file:           /scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata...

xarray.Dataset

• Dimensions:
  • member_id: 40
  • time: 1140
  • lev: 30
  • lat: 192
  • lon: 288
  • nbnd: 2
• Coordinates: (6)
  • lat
    (lat)
    float64
    -90.0 -89.06 -88.12 ... 89.06 90.0

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -89.057594, -88.115181, -87.172775, -86.23037 , -85.287956,
           -84.345551, -83.403145, -82.460732, -81.518326, -80.575912, -79.633507,
           -78.691101, -77.748688, -76.806282, -75.863876, -74.921463, -73.979057,
           -73.036652, -72.094238, -71.151833, -70.209427, -69.267014, -68.324608,
           -67.382202, -66.439789, -65.497383, -64.554977, -63.612564, -62.670158,
           -61.727749, -60.785339, -59.842934, -58.900524, -57.958115, -57.015705,
           -56.073299, -55.13089 , -54.18848 , -53.246075, -52.303665, -51.361256,
           -50.41885 , -49.47644 , -48.534031, -47.591621, -46.649216, -45.706806,
           -44.764397, -43.821991, -42.879581, -41.937172, -40.994766, -40.052357,
           -39.109947, -38.167538, -37.225132, -36.282722, -35.340313, -34.397907,
           -33.455498, -32.513088, -31.570681, -30.628273, -29.685863, -28.743456,
           -27.801046, -26.858639, -25.916231, -24.973822, -24.031414, -23.089005,
           -22.146597, -21.204189, -20.26178 , -19.319372, -18.376963, -17.434555,
           -16.492147, -15.549738, -14.607329, -13.664922, -12.722513, -11.780105,
           -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
            -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
             0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
             6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
            11.780105,  12.722513,  13.664922,  14.607329,  15.549738,  16.492147,
            17.434555,  18.376963,  19.319372,  20.26178 ,  21.204189,  22.146597,
            23.089005,  24.031414,  24.973822,  25.916231,  26.858639,  27.801046,
            28.743456,  29.685863,  30.628273,  31.570681,  32.513088,  33.455498,
            34.397907,  35.340313,  36.282722,  37.225132,  38.167538,  39.109947,
            40.052357,  40.994766,  41.937172,  42.879581,  43.821991,  44.764397,
            45.706806,  46.649216,  47.591621,  48.534031,  49.47644 ,  50.41885 ,
            51.361256,  52.303665,  53.246075,  54.18848 ,  55.13089 ,  56.073299,
            57.015705,  57.958115,  58.900524,  59.842934,  60.785339,  61.727749,
            62.670158,  63.612564,  64.554977,  65.497383,  66.439789,  67.382202,
            68.324608,  69.267014,  70.209427,  71.151833,  72.094238,  73.036652,
            73.979057,  74.921463,  75.863876,  76.806282,  77.748688,  78.691101,
            79.633507,  80.575912,  81.518326,  82.460732,  83.403145,  84.345551,
            85.287956,  86.23037 ,  87.172775,  88.115181,  89.057594,  90.      ])

  • lev
    (lev)
    float64
    3.643 7.595 14.36 ... 976.3 992.6

    formula_terms :

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

    long_name :

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

    positive :

    down

    standard_name :

    atmosphere_hybrid_sigma_pressure_coordinate

    units :

    level

    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])

  • lon
    (lon)
    float64
    0.0 1.25 2.5 ... 356.2 357.5 358.8

    long_name :

    longitude

    units :

    degrees_east

    array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

  • member_id
    (member_id)
    int64
    1 2 3 4 5 6 ... 101 102 103 104 105

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])

  • time
    (time)
    object
    2006-01-16 12:00:00 ... 2100-12-...

    bounds :

    time_bnds

    long_name :

    time

    array([cftime.DatetimeNoLeap(2006, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2006, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2006, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bnds
    (time, nbnd)
    object
    dask.array<chunksize=(1140, 2), meta=np.ndarray>

    long_name :

    time interval endpoints

    Array Chunk Bytes 17.81 kiB 17.81 kiB Shape (1140, 2) (1140, 2) Count 5 Graph Layers 1 Chunks Type object numpy.ndarray

• Data variables: (2)
  • T
    (member_id, time, lev, lat, lon)
    float32
    dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Temperature

    mdims :

    1

    units :

    K

    Array Chunk Bytes 281.80 GiB 113.91 MiB Shape (40, 1140, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2560 Chunks Type float32 numpy.ndarray

  • Q
    (member_id, time, lev, lat, lon)
    float32
    dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Specific humidity

    mdims :

    1

    units :

    kg/kg

    Array Chunk Bytes 281.80 GiB 113.91 MiB Shape (40, 1140, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2560 Chunks Type float32 numpy.ndarray

• Attributes: (12)

  Conventions :

  CF-1.0

  NCO :

  4.3.4

  Version :

  $Name$

  host :

  tcs-f02n07

  important_note :

  This data is part of the project 'Blind Evaluation of Lossy Data-Compression in LENS'. Please exercise caution before using this data for other purposes.

  initial_file :

  b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i.2006-01-01-00000.nc

  logname :

  mudryk

  nco_openmp_thread_number :

  1

  revision_Id :

  $Id$

  source :

  CAM

  title :

  UNSET

  topography_file :

  /scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc

That wasn’t too bad, what if we wanted to look look at other experiments? Or other components, such as the ocean? It gets tricky…

• Experiments have different time ranges

• Components have different grids

Let’s load in an ocean temperature dataset…

ocean_ds = xr.open_zarr(f's3://ncar-cesm-lens/ocn/monthly/cesmLE-RCP85-TEMP.zarr', storage_options={'anon':True})
ocean_ds

<xarray.Dataset>
Dimensions:     (member_id: 40, time: 1140, z_t: 60, nlat: 384, nlon: 320, d2: 2)
Coordinates:
  * member_id   (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
  * time        (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00
    time_bound  (time, d2) object dask.array<chunksize=(1140, 2), meta=np.ndarray>
  * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05
Dimensions without coordinates: nlat, nlon, d2
Data variables:
    TEMP        (member_id, time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>
Attributes:
    Conventions:               CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netc...
    NCO:                       4.3.4
    calendar:                  All years have exactly  365 days.
    cell_methods:              cell_methods = time: mean ==> the variable val...
    contents:                  Diagnostic and Prognostic Variables
    nco_openmp_thread_number:  1
    nsteps_total:              750
    revision:                  $Id: tavg.F90 41939 2012-11-14 16:37:23Z mlevy...
    source:                    CCSM POP2, the CCSM Ocean Component
    start_time:                This dataset was created on 2014-12-26 at 15:5...
    tavg_sum:                  2592000.0
    tavg_sum_qflux:            2592000.0

xarray.Dataset

• Dimensions:
  • member_id: 40
  • time: 1140
  • z_t: 60
  • nlat: 384
  • nlon: 320
  • d2: 2
• Coordinates: (4)
  • member_id
    (member_id)
    int64
    1 2 3 4 5 6 ... 101 102 103 104 105

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])

  • time
    (time)
    object
    2006-01-16 12:00:00 ... 2100-12-...

    bounds :

    time_bound

    long_name :

    time

    array([cftime.DatetimeNoLeap(2006, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2006, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2006, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bound
    (time, d2)
    object
    dask.array<chunksize=(1140, 2), meta=np.ndarray>

    long_name :

    boundaries for time-averaging interval

    Array Chunk Bytes 17.81 kiB 17.81 kiB Shape (1140, 2) (1140, 2) Count 2 Graph Layers 1 Chunks Type object numpy.ndarray

  • z_t
    (z_t)
    float32
    500.0 1.5e+03 ... 5.375e+05

    long_name :

    depth from surface to midpoint of layer

    positive :

    down

    units :

    centimeters

    valid_max :

    537500.0

    valid_min :

    500.0

    array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,
           5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,
           1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,
           1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,
           2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,
           2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,
           4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,
           6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,
           1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,
           1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,
           3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,
           4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],
          dtype=float32)

• Data variables: (1)
  • TEMP
    (member_id, time, z_t, nlat, nlon)
    float32
    dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>

    cell_methods :

    time: mean

    coordinates :

    TLONG TLAT z_t time

    grid_loc :

    3111

    long_name :

    Potential Temperature

    units :

    degC

    Array Chunk Bytes 1.22 TiB 168.75 MiB Shape (40, 1140, 60, 384, 320) (1, 6, 60, 384, 320) Count 2 Graph Layers 7600 Chunks Type float32 numpy.ndarray

• Attributes: (12)

  Conventions :

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

  NCO :

  4.3.4

  calendar :

  All years have exactly 365 days.

  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.

  contents :

  Diagnostic and Prognostic Variables

  nco_openmp_thread_number :

  1

  nsteps_total :

  750

  revision :

  $Id: tavg.F90 41939 2012-11-14 16:37:23Z mlevy@ucar.edu $

  source :

  CCSM POP2, the CCSM Ocean Component

  start_time :

  This dataset was created on 2014-12-26 at 15:59:10.7

  tavg_sum :

  2592000.0

  tavg_sum_qflux :

  2592000.0

Notice how we now have dimensions (time, z_t, nlat, nlon), which differs from the atmosphere dataset (time, lev, lat, lon)

atmosphere_ds.isel(member_id=0, time=0, lev=-1).T.plot();

ocean_ds.isel(member_id=0, time=0, z_t=0).TEMP.plot();

What if there were an easier way of searching for available data, as well as loading the data into your analysis to make it easier to generalize? This is where Intake-ESM comes in!

Intake-ESM Method

We can use the Intake-ESM catalog from the CESM-LENS dataset (https://raw.githubusercontent.com/NCAR/cesm-lens-aws/master/intake-catalogs/aws-cesm1-le.json) to work with these data!

data_catalog = intake.open_esm_datastore('https://raw.githubusercontent.com/NCAR/cesm-lens-aws/master/intake-catalogs/aws-cesm1-le.json')
data_catalog

aws-cesm1-le catalog with 56 dataset(s) from 442 asset(s):

	unique
variable	78
long_name	75
component	5
experiment	4
frequency	6
vertical_levels	3
spatial_domain	5
units	25
start_time	12
end_time	13
path	427
derived_variable	0

You’ll notice that this catalog has 5 components, and six frequencies as we mentioned earlier. If we call .df on this catalog, we can see the table of metadata!

data_catalog.df

	variable	long_name	component	experiment	frequency	vertical_levels	spatial_domain	units	start_time	end_time	path
0	FLNS	net longwave flux at surface	atm	20C	daily	1.0	global	W/m2	1920-01-01 12:00:00	2005-12-31 12:00:00	s3://ncar-cesm-lens/atm/daily/cesmLE-20C-FLNS....
1	FLNSC	clearsky net longwave flux at surface	atm	20C	daily	1.0	global	W/m2	1920-01-01 12:00:00	2005-12-31 12:00:00	s3://ncar-cesm-lens/atm/daily/cesmLE-20C-FLNSC...
2	FLUT	upwelling longwave flux at top of model	atm	20C	daily	1.0	global	W/m2	1920-01-01 12:00:00	2005-12-31 12:00:00	s3://ncar-cesm-lens/atm/daily/cesmLE-20C-FLUT....
3	FSNS	net solar flux at surface	atm	20C	daily	1.0	global	W/m2	1920-01-01 12:00:00	2005-12-31 12:00:00	s3://ncar-cesm-lens/atm/daily/cesmLE-20C-FSNS....
4	FSNSC	clearsky net solar flux at surface	atm	20C	daily	1.0	global	W/m2	1920-01-01 12:00:00	2005-12-31 12:00:00	s3://ncar-cesm-lens/atm/daily/cesmLE-20C-FSNSC...
...	...	...	...	...	...	...	...	...	...	...	...
437	WVEL	vertical velocity	ocn	RCP85	monthly	60.0	global_ocean	centimeter/s	2006-01-16 12:00:00	2100-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-RCP85-W...
438	NaN	NaN	ocn	CTRL	static	NaN	global_ocean	NaN	NaN	NaN	s3://ncar-cesm-lens/ocn/static/grid.zarr
439	NaN	NaN	ocn	HIST	static	NaN	global_ocean	NaN	NaN	NaN	s3://ncar-cesm-lens/ocn/static/grid.zarr
440	NaN	NaN	ocn	RCP85	static	NaN	global_ocean	NaN	NaN	NaN	s3://ncar-cesm-lens/ocn/static/grid.zarr
441	NaN	NaN	ocn	20C	static	NaN	global_ocean	NaN	NaN	NaN	s3://ncar-cesm-lens/ocn/static/grid.zarr

442 rows × 11 columns

Searching for your Data

We can use the .search API to search for the data we are interested in. In this case, as mentioned before, we are interested in future data (RCP85) from the atmosphere and ocean

data_catalog_subset = data_catalog.search(variable=['T', 'Q', 'TEMP', 'SALT'],
                                          frequency='monthly',)
data_catalog_subset.df

	variable	long_name	component	experiment	frequency	vertical_levels	spatial_domain	units	start_time	end_time	path
0	Q	specific humidity	atm	20C	monthly	30.0	global	kg/kg	1920-01-16 12:00:00	2005-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-20C-Q.zarr
1	T	temperature	atm	20C	monthly	30.0	global	K	1920-01-16 12:00:00	2005-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-20C-T.zarr
2	Q	specific humidity	atm	CTRL	monthly	30.0	global	kg/kg	0400-01-16 12:00:00	2200-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-CTRL-Q....
3	T	temperature	atm	CTRL	monthly	30.0	global	K	0400-01-16 12:00:00	2200-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-CTRL-T....
4	Q	specific humidity	atm	HIST	monthly	30.0	global	kg/kg	1850-01-16 12:00:00	1919-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-HIST-Q....
5	T	temperature	atm	HIST	monthly	30.0	global	K	1850-01-16 12:00:00	1919-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-HIST-T....
6	Q	specific humidity	atm	RCP85	monthly	30.0	global	kg/kg	2006-01-16 12:00:00	2100-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-RCP85-Q...
7	T	temperature	atm	RCP85	monthly	30.0	global	K	2006-01-16 12:00:00	2100-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-RCP85-T...
8	SALT	salinity	ocn	20C	monthly	60.0	global_ocean	gram/kilogram	1920-01-16 12:00:00	2005-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-20C-SAL...
9	TEMP	potential temperature	ocn	20C	monthly	60.0	global_ocean	degC	1920-01-16 12:00:00	2005-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-20C-TEM...
10	SALT	salinity	ocn	CTRL	monthly	60.0	global_ocean	gram/kilogram	0400-01-16 12:00:00	2200-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-CTRL-SA...
11	TEMP	potential temperature	ocn	CTRL	monthly	60.0	global_ocean	degC	0400-01-16 12:00:00	2200-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-CTRL-TE...
12	SALT	salinity	ocn	HIST	monthly	60.0	global_ocean	gram/kilogram	1850-01-16 12:00:00	1919-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-HIST-SA...
13	TEMP	potential temperature	ocn	HIST	monthly	60.0	global_ocean	degC	1850-01-16 12:00:00	1919-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-HIST-TE...
14	SALT	salinity	ocn	RCP85	monthly	60.0	global_ocean	gram/kilogram	2006-01-16 12:00:00	2100-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-RCP85-S...
15	TEMP	potential temperature	ocn	RCP85	monthly	60.0	global_ocean	degC	2006-01-16 12:00:00	2100-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-RCP85-T...

We can take a look at our catalog dataframe again, to verify we have the datasets we are looking for!

data_catalog_subset.df

	variable	long_name	component	experiment	frequency	vertical_levels	spatial_domain	units	start_time	end_time	path
0	Q	specific humidity	atm	20C	monthly	30.0	global	kg/kg	1920-01-16 12:00:00	2005-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-20C-Q.zarr
1	T	temperature	atm	20C	monthly	30.0	global	K	1920-01-16 12:00:00	2005-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-20C-T.zarr
2	Q	specific humidity	atm	CTRL	monthly	30.0	global	kg/kg	0400-01-16 12:00:00	2200-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-CTRL-Q....
3	T	temperature	atm	CTRL	monthly	30.0	global	K	0400-01-16 12:00:00	2200-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-CTRL-T....
4	Q	specific humidity	atm	HIST	monthly	30.0	global	kg/kg	1850-01-16 12:00:00	1919-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-HIST-Q....
5	T	temperature	atm	HIST	monthly	30.0	global	K	1850-01-16 12:00:00	1919-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-HIST-T....
6	Q	specific humidity	atm	RCP85	monthly	30.0	global	kg/kg	2006-01-16 12:00:00	2100-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-RCP85-Q...
7	T	temperature	atm	RCP85	monthly	30.0	global	K	2006-01-16 12:00:00	2100-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-RCP85-T...
8	SALT	salinity	ocn	20C	monthly	60.0	global_ocean	gram/kilogram	1920-01-16 12:00:00	2005-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-20C-SAL...
9	TEMP	potential temperature	ocn	20C	monthly	60.0	global_ocean	degC	1920-01-16 12:00:00	2005-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-20C-TEM...
10	SALT	salinity	ocn	CTRL	monthly	60.0	global_ocean	gram/kilogram	0400-01-16 12:00:00	2200-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-CTRL-SA...
11	TEMP	potential temperature	ocn	CTRL	monthly	60.0	global_ocean	degC	0400-01-16 12:00:00	2200-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-CTRL-TE...
12	SALT	salinity	ocn	HIST	monthly	60.0	global_ocean	gram/kilogram	1850-01-16 12:00:00	1919-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-HIST-SA...
13	TEMP	potential temperature	ocn	HIST	monthly	60.0	global_ocean	degC	1850-01-16 12:00:00	1919-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-HIST-TE...
14	SALT	salinity	ocn	RCP85	monthly	60.0	global_ocean	gram/kilogram	2006-01-16 12:00:00	2100-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-RCP85-S...
15	TEMP	potential temperature	ocn	RCP85	monthly	60.0	global_ocean	degC	2006-01-16 12:00:00	2100-12-16 12:00:00	s3://ncar-cesm-lens/ocn/monthly/cesmLE-RCP85-T...

Load in the Data

Now that we have subset our catalog, we can load the data into our notebook using .to_dataset_dict() which is short for “to dataset dictionary”

Intake-ESM aggregates these datasets by component, experiment, and frequency, and provides a nice progress bar to check in on the data access process!

dsets = data_catalog_subset.to_dataset_dict(storage_options={'anon':True})
dsets

--> The keys in the returned dictionary of datasets are constructed as follows:
	'component.experiment.frequency'

100.00% [8/8 00:03<00:00]

{'ocn.20C.monthly': <xarray.Dataset>
 Dimensions:     (member_id: 40, time: 1032, z_t: 60, nlat: 384, nlon: 320, d2: 2)
 Coordinates:
   * member_id   (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
   * time        (time) object 1920-01-16 12:00:00 ... 2005-12-16 12:00:00
     time_bound  (time, d2) object dask.array<chunksize=(1032, 2), meta=np.ndarray>
   * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05
 Dimensions without coordinates: nlat, nlon, d2
 Data variables:
     SALT        (member_id, time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>
     TEMP        (member_id, time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>
 Attributes: (12/19)
     Conventions:                       CF-1.0; http://www.cgd.ucar.edu/cms/ea...
     calendar:                          All years have exactly  365 days.
     cell_methods:                      cell_methods = time: mean ==> the vari...
     contents:                          Diagnostic and Prognostic Variables
     nco_openmp_thread_number:          1
     nsteps_total:                      750
     ...                                ...
     intake_esm_attrs:vertical_levels:  60.0
     intake_esm_attrs:spatial_domain:   global_ocean
     intake_esm_attrs:start_time:       1920-01-16 12:00:00
     intake_esm_attrs:end_time:         2005-12-16 12:00:00
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            ocn.20C.monthly,
 'ocn.RCP85.monthly': <xarray.Dataset>
 Dimensions:     (member_id: 40, time: 1140, z_t: 60, nlat: 384, nlon: 320, d2: 2)
 Coordinates:
   * member_id   (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
   * time        (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00
     time_bound  (time, d2) object dask.array<chunksize=(1140, 2), meta=np.ndarray>
   * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05
 Dimensions without coordinates: nlat, nlon, d2
 Data variables:
     SALT        (member_id, time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>
     TEMP        (member_id, time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>
 Attributes: (12/21)
     Conventions:                       CF-1.0; http://www.cgd.ucar.edu/cms/ea...
     NCO:                               4.3.4
     calendar:                          All years have exactly  365 days.
     cell_methods:                      cell_methods = time: mean ==> the vari...
     contents:                          Diagnostic and Prognostic Variables
     nco_openmp_thread_number:          1
     ...                                ...
     intake_esm_attrs:vertical_levels:  60.0
     intake_esm_attrs:spatial_domain:   global_ocean
     intake_esm_attrs:start_time:       2006-01-16 12:00:00
     intake_esm_attrs:end_time:         2100-12-16 12:00:00
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            ocn.RCP85.monthly,
 'atm.RCP85.monthly': <xarray.Dataset>
 Dimensions:    (member_id: 40, time: 1140, lev: 30, lat: 192, lon: 288, nbnd: 2)
 Coordinates:
   * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
   * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
   * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
   * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
   * time       (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00
     time_bnds  (time, nbnd) object dask.array<chunksize=(1140, 2), meta=np.ndarray>
 Dimensions without coordinates: nbnd
 Data variables:
     Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
     T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
 Attributes: (12/21)
     Conventions:                       CF-1.0
     NCO:                               4.3.4
     Version:                           $Name$
     host:                              tcs-f02n07
     important_note:                    This data is part of the project 'Blin...
     initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i....
     ...                                ...
     intake_esm_attrs:vertical_levels:  30.0
     intake_esm_attrs:spatial_domain:   global
     intake_esm_attrs:start_time:       2006-01-16 12:00:00
     intake_esm_attrs:end_time:         2100-12-16 12:00:00
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            atm.RCP85.monthly,
 'ocn.HIST.monthly': <xarray.Dataset>
 Dimensions:     (time: 840, z_t: 60, nlat: 384, nlon: 320, d2: 2)
 Coordinates:
     member_id   int64 1
   * time        (time) object 1850-01-16 12:00:00 ... 1919-12-16 12:00:00
     time_bound  (time, d2) object dask.array<chunksize=(840, 2), meta=np.ndarray>
   * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05
 Dimensions without coordinates: nlat, nlon, d2
 Data variables:
     SALT        (time, z_t, nlat, nlon) float32 dask.array<chunksize=(6, 60, 384, 320), meta=np.ndarray>
     TEMP        (time, z_t, nlat, nlon) float32 dask.array<chunksize=(6, 60, 384, 320), meta=np.ndarray>
 Attributes: (12/19)
     Conventions:                       CF-1.0; http://www.cgd.ucar.edu/cms/ea...
     calendar:                          All years have exactly  365 days.
     cell_methods:                      cell_methods = time: mean ==> the vari...
     contents:                          Diagnostic and Prognostic Variables
     nco_openmp_thread_number:          1
     nsteps_total:                      750
     ...                                ...
     intake_esm_attrs:vertical_levels:  60.0
     intake_esm_attrs:spatial_domain:   global_ocean
     intake_esm_attrs:start_time:       1850-01-16 12:00:00
     intake_esm_attrs:end_time:         1919-12-16 12:00:00
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            ocn.HIST.monthly,
 'atm.HIST.monthly': <xarray.Dataset>
 Dimensions:    (time: 840, lev: 30, lat: 192, lon: 288, nbnd: 2)
 Coordinates:
   * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
   * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
   * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
     member_id  int64 1
   * time       (time) object 1850-01-16 12:00:00 ... 1919-12-16 12:00:00
     time_bnds  (time, nbnd) object dask.array<chunksize=(840, 2), meta=np.ndarray>
 Dimensions without coordinates: nbnd
 Data variables:
     Q          (time, lev, lat, lon) float32 dask.array<chunksize=(18, 30, 192, 288), meta=np.ndarray>
     T          (time, lev, lat, lon) float32 dask.array<chunksize=(18, 30, 192, 288), meta=np.ndarray>
 Attributes: (12/20)
     Conventions:                       CF-1.0
     NCO:                               4.3.4
     Version:                           $Name$
     important_note:                    This data is part of the project 'Blin...
     initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i....
     logname:                           mudryk
     ...                                ...
     intake_esm_attrs:vertical_levels:  30.0
     intake_esm_attrs:spatial_domain:   global
     intake_esm_attrs:start_time:       1850-01-16 12:00:00
     intake_esm_attrs:end_time:         1919-12-16 12:00:00
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            atm.HIST.monthly,
 'atm.20C.monthly': <xarray.Dataset>
 Dimensions:    (member_id: 40, time: 1032, lev: 30, lat: 192, lon: 288, nbnd: 2)
 Coordinates:
   * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
   * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
   * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
   * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
   * time       (time) object 1920-01-16 12:00:00 ... 2005-12-16 12:00:00
     time_bnds  (time, nbnd) object dask.array<chunksize=(1032, 2), meta=np.ndarray>
 Dimensions without coordinates: nbnd
 Data variables:
     Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
     T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
 Attributes: (12/20)
     Conventions:                       CF-1.0
     NCO:                               4.3.4
     Version:                           $Name$
     important_note:                    This data is part of the project 'Blin...
     initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i....
     logname:                           mudryk
     ...                                ...
     intake_esm_attrs:vertical_levels:  30.0
     intake_esm_attrs:spatial_domain:   global
     intake_esm_attrs:start_time:       1920-01-16 12:00:00
     intake_esm_attrs:end_time:         2005-12-16 12:00:00
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            atm.20C.monthly,
 'ocn.CTRL.monthly': <xarray.Dataset>
 Dimensions:     (member_id: 1, time: 21612, z_t: 60, nlat: 384, nlon: 320, d2: 2)
 Coordinates:
   * member_id   (member_id) int64 1
   * time        (time) object 0400-01-16 12:00:00 ... 2200-12-16 12:00:00
     time_bound  (time, d2) object dask.array<chunksize=(10806, 2), meta=np.ndarray>
   * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05
 Dimensions without coordinates: nlat, nlon, d2
 Data variables:
     SALT        (member_id, time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>
     TEMP        (member_id, time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>
 Attributes: (12/20)
     Conventions:                       CF-1.0; http://www.cgd.ucar.edu/cms/ea...
     NCO:                               4.3.4
     calendar:                          All years have exactly  365 days.
     cell_methods:                      cell_methods = time: mean ==> the vari...
     contents:                          Diagnostic and Prognostic Variables
     nco_openmp_thread_number:          1
     ...                                ...
     intake_esm_attrs:vertical_levels:  60.0
     intake_esm_attrs:spatial_domain:   global_ocean
     intake_esm_attrs:start_time:       0400-01-16 12:00:00
     intake_esm_attrs:end_time:         2200-12-16 12:00:00
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            ocn.CTRL.monthly,
 'atm.CTRL.monthly': <xarray.Dataset>
 Dimensions:    (member_id: 1, time: 21612, lev: 30, lat: 192, lon: 288, nbnd: 2)
 Coordinates:
   * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
   * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
   * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
   * member_id  (member_id) int64 1
   * time       (time) object 0400-01-16 12:00:00 ... 2200-12-16 12:00:00
     time_bnds  (time, nbnd) object dask.array<chunksize=(10806, 2), meta=np.ndarray>
 Dimensions without coordinates: nbnd
 Data variables:
     Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
     T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
 Attributes: (12/20)
     Conventions:                       CF-1.0
     NCO:                               4.3.4
     Version:                           $Name$
     case:                              b.e11.B1850C5CN.f09_g16.005
     initial_file:                      /glade/p/cesm/cseg//inputdata/atm/cam/...
     logname:                           mai
     ...                                ...
     intake_esm_attrs:vertical_levels:  30.0
     intake_esm_attrs:spatial_domain:   global
     intake_esm_attrs:start_time:       0400-01-16 12:00:00
     intake_esm_attrs:end_time:         2200-12-16 12:00:00
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            atm.CTRL.monthly}

How do we work with dictionaries of datasets?

The result of .to_dataset_dict() is a dictionary of datasets, organized via key:xarray.Dataset

For example, we see that our dictionary of datasets has two keys, corresponding to atmospheric, and oceanic components respectively

dsets.keys()

dict_keys(['ocn.20C.monthly', 'ocn.RCP85.monthly', 'atm.RCP85.monthly', 'ocn.HIST.monthly', 'atm.HIST.monthly', 'atm.20C.monthly', 'ocn.CTRL.monthly', 'atm.CTRL.monthly'])

We can access the xarray.Datasets within our dictionary by using these keys! For example, let’s take a look at the atmospheric data.

atmosphere_dset = dsets['atm.RCP85.monthly']
atmosphere_dset

<xarray.Dataset>
Dimensions:    (member_id: 40, time: 1140, lev: 30, lat: 192, lon: 288, nbnd: 2)
Coordinates:
  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
  * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
  * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
  * time       (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00
    time_bnds  (time, nbnd) object dask.array<chunksize=(1140, 2), meta=np.ndarray>
Dimensions without coordinates: nbnd
Data variables:
    Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
    T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
Attributes: (12/21)
    Conventions:                       CF-1.0
    NCO:                               4.3.4
    Version:                           $Name$
    host:                              tcs-f02n07
    important_note:                    This data is part of the project 'Blin...
    initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i....
    ...                                ...
    intake_esm_attrs:vertical_levels:  30.0
    intake_esm_attrs:spatial_domain:   global
    intake_esm_attrs:start_time:       2006-01-16 12:00:00
    intake_esm_attrs:end_time:         2100-12-16 12:00:00
    intake_esm_attrs:_data_format_:    zarr
    intake_esm_dataset_key:            atm.RCP85.monthly

xarray.Dataset

• Dimensions:
  • member_id: 40
  • time: 1140
  • lev: 30
  • lat: 192
  • lon: 288
  • nbnd: 2
• Coordinates: (6)
  • lat
    (lat)
    float64
    -90.0 -89.06 -88.12 ... 89.06 90.0

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -89.057594, -88.115181, -87.172775, -86.23037 , -85.287956,
           -84.345551, -83.403145, -82.460732, -81.518326, -80.575912, -79.633507,
           -78.691101, -77.748688, -76.806282, -75.863876, -74.921463, -73.979057,
           -73.036652, -72.094238, -71.151833, -70.209427, -69.267014, -68.324608,
           -67.382202, -66.439789, -65.497383, -64.554977, -63.612564, -62.670158,
           -61.727749, -60.785339, -59.842934, -58.900524, -57.958115, -57.015705,
           -56.073299, -55.13089 , -54.18848 , -53.246075, -52.303665, -51.361256,
           -50.41885 , -49.47644 , -48.534031, -47.591621, -46.649216, -45.706806,
           -44.764397, -43.821991, -42.879581, -41.937172, -40.994766, -40.052357,
           -39.109947, -38.167538, -37.225132, -36.282722, -35.340313, -34.397907,
           -33.455498, -32.513088, -31.570681, -30.628273, -29.685863, -28.743456,
           -27.801046, -26.858639, -25.916231, -24.973822, -24.031414, -23.089005,
           -22.146597, -21.204189, -20.26178 , -19.319372, -18.376963, -17.434555,
           -16.492147, -15.549738, -14.607329, -13.664922, -12.722513, -11.780105,
           -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
            -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
             0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
             6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
            11.780105,  12.722513,  13.664922,  14.607329,  15.549738,  16.492147,
            17.434555,  18.376963,  19.319372,  20.26178 ,  21.204189,  22.146597,
            23.089005,  24.031414,  24.973822,  25.916231,  26.858639,  27.801046,
            28.743456,  29.685863,  30.628273,  31.570681,  32.513088,  33.455498,
            34.397907,  35.340313,  36.282722,  37.225132,  38.167538,  39.109947,
            40.052357,  40.994766,  41.937172,  42.879581,  43.821991,  44.764397,
            45.706806,  46.649216,  47.591621,  48.534031,  49.47644 ,  50.41885 ,
            51.361256,  52.303665,  53.246075,  54.18848 ,  55.13089 ,  56.073299,
            57.015705,  57.958115,  58.900524,  59.842934,  60.785339,  61.727749,
            62.670158,  63.612564,  64.554977,  65.497383,  66.439789,  67.382202,
            68.324608,  69.267014,  70.209427,  71.151833,  72.094238,  73.036652,
            73.979057,  74.921463,  75.863876,  76.806282,  77.748688,  78.691101,
            79.633507,  80.575912,  81.518326,  82.460732,  83.403145,  84.345551,
            85.287956,  86.23037 ,  87.172775,  88.115181,  89.057594,  90.      ])

  • lev
    (lev)
    float64
    3.643 7.595 14.36 ... 976.3 992.6

    formula_terms :

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

    long_name :

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

    positive :

    down

    standard_name :

    atmosphere_hybrid_sigma_pressure_coordinate

    units :

    level

    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])

  • lon
    (lon)
    float64
    0.0 1.25 2.5 ... 356.2 357.5 358.8

    long_name :

    longitude

    units :

    degrees_east

    array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

  • member_id
    (member_id)
    int64
    1 2 3 4 5 6 ... 101 102 103 104 105

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])

  • time
    (time)
    object
    2006-01-16 12:00:00 ... 2100-12-...

    bounds :

    time_bnds

    long_name :

    time

    array([cftime.DatetimeNoLeap(2006, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2006, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2006, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bnds
    (time, nbnd)
    object
    dask.array<chunksize=(1140, 2), meta=np.ndarray>

    long_name :

    time interval endpoints

    Array Chunk Bytes 17.81 kiB 17.81 kiB Shape (1140, 2) (1140, 2) Count 5 Graph Layers 1 Chunks Type object numpy.ndarray

• Data variables: (2)
  • Q
    (member_id, time, lev, lat, lon)
    float32
    dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Specific humidity

    mdims :

    1

    units :

    kg/kg

    Array Chunk Bytes 281.80 GiB 113.91 MiB Shape (40, 1140, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2560 Chunks Type float32 numpy.ndarray

  • T
    (member_id, time, lev, lat, lon)
    float32
    dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Temperature

    mdims :

    1

    units :

    K

    Array Chunk Bytes 281.80 GiB 113.91 MiB Shape (40, 1140, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2560 Chunks Type float32 numpy.ndarray

• Attributes: (21)

  Conventions :

  CF-1.0

  NCO :

  4.3.4

  Version :

  $Name$

  host :

  tcs-f02n07

  important_note :

  This data is part of the project 'Blind Evaluation of Lossy Data-Compression in LENS'. Please exercise caution before using this data for other purposes.

  initial_file :

  b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i.2006-01-01-00000.nc

  logname :

  mudryk

  nco_openmp_thread_number :

  1

  revision_Id :

  $Id$

  source :

  CAM

  title :

  UNSET

  topography_file :

  /scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc

  intake_esm_attrs:component :

  atm

  intake_esm_attrs:experiment :

  RCP85

  intake_esm_attrs:frequency :

  monthly

  intake_esm_attrs:vertical_levels :

  30.0

  intake_esm_attrs:spatial_domain :

  global

  intake_esm_attrs:start_time :

  2006-01-16 12:00:00

  intake_esm_attrs:end_time :

  2100-12-16 12:00:00

  intake_esm_attrs:_data_format_ :

  zarr

  intake_esm_dataset_key :

  atm.RCP85.monthly

Our dataset has both temperature (T), and specific humidity (Q) within it! We can follow the same process for the ocean data…

ocean_dset = dsets['ocn.RCP85.monthly']
ocean_dset

<xarray.Dataset>
Dimensions:     (member_id: 40, time: 1140, z_t: 60, nlat: 384, nlon: 320, d2: 2)
Coordinates:
  * member_id   (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
  * time        (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00
    time_bound  (time, d2) object dask.array<chunksize=(1140, 2), meta=np.ndarray>
  * z_t         (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05
Dimensions without coordinates: nlat, nlon, d2
Data variables:
    SALT        (member_id, time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>
    TEMP        (member_id, time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>
Attributes: (12/21)
    Conventions:                       CF-1.0; http://www.cgd.ucar.edu/cms/ea...
    NCO:                               4.3.4
    calendar:                          All years have exactly  365 days.
    cell_methods:                      cell_methods = time: mean ==> the vari...
    contents:                          Diagnostic and Prognostic Variables
    nco_openmp_thread_number:          1
    ...                                ...
    intake_esm_attrs:vertical_levels:  60.0
    intake_esm_attrs:spatial_domain:   global_ocean
    intake_esm_attrs:start_time:       2006-01-16 12:00:00
    intake_esm_attrs:end_time:         2100-12-16 12:00:00
    intake_esm_attrs:_data_format_:    zarr
    intake_esm_dataset_key:            ocn.RCP85.monthly

xarray.Dataset

• Dimensions:
  • member_id: 40
  • time: 1140
  • z_t: 60
  • nlat: 384
  • nlon: 320
  • d2: 2
• Coordinates: (4)
  • member_id
    (member_id)
    int64
    1 2 3 4 5 6 ... 101 102 103 104 105

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])

  • time
    (time)
    object
    2006-01-16 12:00:00 ... 2100-12-...

    bounds :

    time_bound

    long_name :

    time

    array([cftime.DatetimeNoLeap(2006, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2006, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2006, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bound
    (time, d2)
    object
    dask.array<chunksize=(1140, 2), meta=np.ndarray>

    long_name :

    boundaries for time-averaging interval

    Array Chunk Bytes 17.81 kiB 17.81 kiB Shape (1140, 2) (1140, 2) Count 5 Graph Layers 1 Chunks Type object numpy.ndarray

  • z_t
    (z_t)
    float32
    500.0 1.5e+03 ... 5.375e+05

    long_name :

    depth from surface to midpoint of layer

    positive :

    down

    units :

    centimeters

    valid_max :

    537500.0

    valid_min :

    500.0

    array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,
           5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,
           1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,
           1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,
           2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,
           2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,
           4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,
           6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,
           1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,
           1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,
           3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,
           4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],
          dtype=float32)

• Data variables: (2)
  • SALT
    (member_id, time, z_t, nlat, nlon)
    float32
    dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>

    cell_methods :

    time: mean

    coordinates :

    TLONG TLAT z_t time

    grid_loc :

    3111

    long_name :

    Salinity

    units :

    gram/kilogram

    Array Chunk Bytes 1.22 TiB 168.75 MiB Shape (40, 1140, 60, 384, 320) (1, 6, 60, 384, 320) Count 2 Graph Layers 7600 Chunks Type float32 numpy.ndarray

  • TEMP
    (member_id, time, z_t, nlat, nlon)
    float32
    dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>

    cell_methods :

    time: mean

    coordinates :

    TLONG TLAT z_t time

    grid_loc :

    3111

    long_name :

    Potential Temperature

    units :

    degC

    Array Chunk Bytes 1.22 TiB 168.75 MiB Shape (40, 1140, 60, 384, 320) (1, 6, 60, 384, 320) Count 2 Graph Layers 7600 Chunks Type float32 numpy.ndarray

• Attributes: (21)

  Conventions :

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

  NCO :

  4.3.4

  calendar :

  All years have exactly 365 days.

  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.

  contents :

  Diagnostic and Prognostic Variables

  nco_openmp_thread_number :

  1

  nsteps_total :

  750

  revision :

  $Id: tavg.F90 41939 2012-11-14 16:37:23Z mlevy@ucar.edu $

  source :

  CCSM POP2, the CCSM Ocean Component

  start_time :

  This dataset was created on 2014-12-26 at 15:59:10.7

  tavg_sum :

  2592000.0

  tavg_sum_qflux :

  2592000.0

  intake_esm_attrs:component :

  ocn

  intake_esm_attrs:experiment :

  RCP85

  intake_esm_attrs:frequency :

  monthly

  intake_esm_attrs:vertical_levels :

  60.0

  intake_esm_attrs:spatial_domain :

  global_ocean

  intake_esm_attrs:start_time :

  2006-01-16 12:00:00

  intake_esm_attrs:end_time :

  2100-12-16 12:00:00

  intake_esm_attrs:_data_format_ :

  zarr

  intake_esm_dataset_key :

  ocn.RCP85.monthly

How do I write analysis code to work with Intake-ESM?

What if we wanted to look at a timeseries of atmospheric data, including both the historical and future scenarios… We need to follow the same few steps

1. Read in the catalog

2. Search for our data using .search()

3. Load in the data using .to_dataset_dict()

Search for Monthly Atmospheric Data

We restrict the search to atmospheric temperature and specific humidity

data_catalog_subset = data_catalog.search(component='atm',
                                          variable=['T', 'Q'],
                                          frequency='monthly',
                                          experiment=['RCP85', '20C'])
data_catalog_subset.df

	variable	long_name	component	experiment	frequency	vertical_levels	spatial_domain	units	start_time	end_time	path
0	Q	specific humidity	atm	20C	monthly	30.0	global	kg/kg	1920-01-16 12:00:00	2005-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-20C-Q.zarr
1	T	temperature	atm	20C	monthly	30.0	global	K	1920-01-16 12:00:00	2005-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-20C-T.zarr
2	Q	specific humidity	atm	RCP85	monthly	30.0	global	kg/kg	2006-01-16 12:00:00	2100-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-RCP85-Q...
3	T	temperature	atm	RCP85	monthly	30.0	global	K	2006-01-16 12:00:00	2100-12-16 12:00:00	s3://ncar-cesm-lens/atm/monthly/cesmLE-RCP85-T...

Load the Data using .to_dataset_dict()

Similar to before, we load in our dictionary of datasets!

dsets = data_catalog_subset.to_dataset_dict(storage_options={'anon':True})
dsets

--> The keys in the returned dictionary of datasets are constructed as follows:
	'component.experiment.frequency'

100.00% [2/2 00:02<00:00]

{'atm.RCP85.monthly': <xarray.Dataset>
 Dimensions:    (member_id: 40, time: 1140, lev: 30, lat: 192, lon: 288, nbnd: 2)
 Coordinates:
   * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
   * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
   * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
   * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
   * time       (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00
     time_bnds  (time, nbnd) object dask.array<chunksize=(1140, 2), meta=np.ndarray>
 Dimensions without coordinates: nbnd
 Data variables:
     Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
     T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
 Attributes: (12/21)
     Conventions:                       CF-1.0
     NCO:                               4.3.4
     Version:                           $Name$
     host:                              tcs-f02n07
     important_note:                    This data is part of the project 'Blin...
     initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i....
     ...                                ...
     intake_esm_attrs:vertical_levels:  30.0
     intake_esm_attrs:spatial_domain:   global
     intake_esm_attrs:start_time:       2006-01-16 12:00:00
     intake_esm_attrs:end_time:         2100-12-16 12:00:00
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            atm.RCP85.monthly,
 'atm.20C.monthly': <xarray.Dataset>
 Dimensions:    (member_id: 40, time: 1032, lev: 30, lat: 192, lon: 288, nbnd: 2)
 Coordinates:
   * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
   * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
   * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
   * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
   * time       (time) object 1920-01-16 12:00:00 ... 2005-12-16 12:00:00
     time_bnds  (time, nbnd) object dask.array<chunksize=(1032, 2), meta=np.ndarray>
 Dimensions without coordinates: nbnd
 Data variables:
     Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
     T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
 Attributes: (12/20)
     Conventions:                       CF-1.0
     NCO:                               4.3.4
     Version:                           $Name$
     important_note:                    This data is part of the project 'Blin...
     initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i....
     logname:                           mudryk
     ...                                ...
     intake_esm_attrs:vertical_levels:  30.0
     intake_esm_attrs:spatial_domain:   global
     intake_esm_attrs:start_time:       1920-01-16 12:00:00
     intake_esm_attrs:end_time:         2005-12-16 12:00:00
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            atm.20C.monthly}

Build a Function to Operator on a Single Dataset

A good practice is to write a function which works with a single dataset. Let’s work on an example.

historical_ds = dsets['atm.20C.monthly']
historical_ds

<xarray.Dataset>
Dimensions:    (member_id: 40, time: 1032, lev: 30, lat: 192, lon: 288, nbnd: 2)
Coordinates:
  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
  * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
  * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
  * time       (time) object 1920-01-16 12:00:00 ... 2005-12-16 12:00:00
    time_bnds  (time, nbnd) object dask.array<chunksize=(1032, 2), meta=np.ndarray>
Dimensions without coordinates: nbnd
Data variables:
    Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
    T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
Attributes: (12/20)
    Conventions:                       CF-1.0
    NCO:                               4.3.4
    Version:                           $Name$
    important_note:                    This data is part of the project 'Blin...
    initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i....
    logname:                           mudryk
    ...                                ...
    intake_esm_attrs:vertical_levels:  30.0
    intake_esm_attrs:spatial_domain:   global
    intake_esm_attrs:start_time:       1920-01-16 12:00:00
    intake_esm_attrs:end_time:         2005-12-16 12:00:00
    intake_esm_attrs:_data_format_:    zarr
    intake_esm_dataset_key:            atm.20C.monthly

xarray.Dataset

• Dimensions:
  • member_id: 40
  • time: 1032
  • lev: 30
  • lat: 192
  • lon: 288
  • nbnd: 2
• Coordinates: (6)
  • lat
    (lat)
    float64
    -90.0 -89.06 -88.12 ... 89.06 90.0

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -89.057594, -88.115181, -87.172775, -86.23037 , -85.287956,
           -84.345551, -83.403145, -82.460732, -81.518326, -80.575912, -79.633507,
           -78.691101, -77.748688, -76.806282, -75.863876, -74.921463, -73.979057,
           -73.036652, -72.094238, -71.151833, -70.209427, -69.267014, -68.324608,
           -67.382202, -66.439789, -65.497383, -64.554977, -63.612564, -62.670158,
           -61.727749, -60.785339, -59.842934, -58.900524, -57.958115, -57.015705,
           -56.073299, -55.13089 , -54.18848 , -53.246075, -52.303665, -51.361256,
           -50.41885 , -49.47644 , -48.534031, -47.591621, -46.649216, -45.706806,
           -44.764397, -43.821991, -42.879581, -41.937172, -40.994766, -40.052357,
           -39.109947, -38.167538, -37.225132, -36.282722, -35.340313, -34.397907,
           -33.455498, -32.513088, -31.570681, -30.628273, -29.685863, -28.743456,
           -27.801046, -26.858639, -25.916231, -24.973822, -24.031414, -23.089005,
           -22.146597, -21.204189, -20.26178 , -19.319372, -18.376963, -17.434555,
           -16.492147, -15.549738, -14.607329, -13.664922, -12.722513, -11.780105,
           -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
            -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
             0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
             6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
            11.780105,  12.722513,  13.664922,  14.607329,  15.549738,  16.492147,
            17.434555,  18.376963,  19.319372,  20.26178 ,  21.204189,  22.146597,
            23.089005,  24.031414,  24.973822,  25.916231,  26.858639,  27.801046,
            28.743456,  29.685863,  30.628273,  31.570681,  32.513088,  33.455498,
            34.397907,  35.340313,  36.282722,  37.225132,  38.167538,  39.109947,
            40.052357,  40.994766,  41.937172,  42.879581,  43.821991,  44.764397,
            45.706806,  46.649216,  47.591621,  48.534031,  49.47644 ,  50.41885 ,
            51.361256,  52.303665,  53.246075,  54.18848 ,  55.13089 ,  56.073299,
            57.015705,  57.958115,  58.900524,  59.842934,  60.785339,  61.727749,
            62.670158,  63.612564,  64.554977,  65.497383,  66.439789,  67.382202,
            68.324608,  69.267014,  70.209427,  71.151833,  72.094238,  73.036652,
            73.979057,  74.921463,  75.863876,  76.806282,  77.748688,  78.691101,
            79.633507,  80.575912,  81.518326,  82.460732,  83.403145,  84.345551,
            85.287956,  86.23037 ,  87.172775,  88.115181,  89.057594,  90.      ])

  • lev
    (lev)
    float64
    3.643 7.595 14.36 ... 976.3 992.6

    formula_terms :

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

    long_name :

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

    positive :

    down

    standard_name :

    atmosphere_hybrid_sigma_pressure_coordinate

    units :

    level

    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])

  • lon
    (lon)
    float64
    0.0 1.25 2.5 ... 356.2 357.5 358.8

    long_name :

    longitude

    units :

    degrees_east

    array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

  • member_id
    (member_id)
    int64
    1 2 3 4 5 6 ... 101 102 103 104 105

    array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
            15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
            29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])

  • time
    (time)
    object
    1920-01-16 12:00:00 ... 2005-12-...

    bounds :

    time_bnds

    long_name :

    time

    array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2005, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2005, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2005, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bnds
    (time, nbnd)
    object
    dask.array<chunksize=(1032, 2), meta=np.ndarray>

    long_name :

    time interval endpoints

    Array Chunk Bytes 16.12 kiB 16.12 kiB Shape (1032, 2) (1032, 2) Count 5 Graph Layers 1 Chunks Type object numpy.ndarray

• Data variables: (2)
  • Q
    (member_id, time, lev, lat, lon)
    float32
    dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Specific humidity

    mdims :

    1

    units :

    kg/kg

    Array Chunk Bytes 255.10 GiB 113.91 MiB Shape (40, 1032, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2320 Chunks Type float32 numpy.ndarray

  • T
    (member_id, time, lev, lat, lon)
    float32
    dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    Temperature

    mdims :

    1

    units :

    K

    Array Chunk Bytes 255.10 GiB 113.91 MiB Shape (40, 1032, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2320 Chunks Type float32 numpy.ndarray

• Attributes: (20)

  Conventions :

  CF-1.0

  NCO :

  4.3.4

  Version :

  $Name$

  important_note :

  This data is part of the project 'Blind Evaluation of Lossy Data-Compression in LENS'. Please exercise caution before using this data for other purposes.

  initial_file :

  b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i.1920-01-01-00000.nc

  logname :

  mudryk

  nco_openmp_thread_number :

  1

  revision_Id :

  $Id$

  source :

  CAM

  title :

  UNSET

  topography_file :

  /scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc

  intake_esm_attrs:component :

  atm

  intake_esm_attrs:experiment :

  20C

  intake_esm_attrs:frequency :

  monthly

  intake_esm_attrs:vertical_levels :

  30.0

  intake_esm_attrs:spatial_domain :

  global

  intake_esm_attrs:start_time :

  1920-01-16 12:00:00

  intake_esm_attrs:end_time :

  2005-12-16 12:00:00

  intake_esm_attrs:_data_format_ :

  zarr

  intake_esm_dataset_key :

  atm.20C.monthly

Let’s write a simple function to subset for the lowest level (.isel(lev=-1)), and select a lat/lon point of our choosing.

We can do this with only this with the following syntax:

lat = 40.0150
lon = 105.2705
variable = 'T'

ds = historical_ds.isel(member_id=0, lev=-1).sel(lat=lat, lon=lon, method='nearest')[variable]
ds.isel(time=range(10)).plot()

[<matplotlib.lines.Line2D at 0x164f78610>]

As a function, this would accept a dataset, with a few parameters. We will also subset for the first 5 years (range(60)) for the sake of time.

def plot_point_timeseries(ds, variable, lat=40.015, lon = 105.2705):
    ds_subset = ds.isel(member_id=0, lev=-1, time=range(60)).sel(lat=lat, lon=lon, method='nearest')[variable]
    ds_subset['time'] = ds_subset.indexes['time'].to_datetimeindex()
    return ds_subset.plot(figsize=(10,8))

plot_point_timeseries(historical_ds, variable='Q');

/Users/mgrover/miniforge3/envs/mscar-python-tutorial-dev/lib/python3.10/site-packages/xarray/coding/times.py:360: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.
  sample = dates.ravel()[0]
/var/folders/bw/c9j8z20x45s2y20vv6528qjc0000gq/T/ipykernel_84367/3553229457.py:3: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.
  ds_subset['time'] = ds_subset.indexes['time'].to_datetimeindex()

Loop through the Dictionary of Datasets to Apply this Function

# Loop through the different keys in the dictionary of datasets
for key in dsets.keys():
    plot = plot_point_timeseries(dsets[key], variable='T')
    plt.show()
    plt.close()

/Users/mgrover/miniforge3/envs/mscar-python-tutorial-dev/lib/python3.10/site-packages/xarray/coding/times.py:360: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.
  sample = dates.ravel()[0]
/var/folders/bw/c9j8z20x45s2y20vv6528qjc0000gq/T/ipykernel_84367/3553229457.py:3: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.
  ds_subset['time'] = ds_subset.indexes['time'].to_datetimeindex()

/Users/mgrover/miniforge3/envs/mscar-python-tutorial-dev/lib/python3.10/site-packages/xarray/coding/times.py:360: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.
  sample = dates.ravel()[0]
/var/folders/bw/c9j8z20x45s2y20vv6528qjc0000gq/T/ipykernel_84367/3553229457.py:3: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.
  ds_subset['time'] = ds_subset.indexes['time'].to_datetimeindex()

Use Datatree instead

dt = data_catalog_subset.to_datatree(storage_options={"anon":True})
dt

--> The keys in the returned dictionary of datasets are constructed as follows:
	'component/experiment/frequency'

100.00% [4/4 00:02<00:00]

<xarray.DatasetView>
Dimensions:  ()
Data variables:
    *empty*

datatree.DataTree

• Groups: (3)
  <xarray.DatasetView>
  Dimensions:  ()
  Data variables:
      *empty*

  atm

  • Groups: (2)
    <xarray.DatasetView>
    Dimensions:  ()
    Data variables:
        *empty*

    20C

    • Groups: (1)
      <xarray.DatasetView>
      Dimensions:    (member_id: 40, time: 1032, lev: 30, lat: 192, lon: 288, nbnd: 2)
      Coordinates:
        * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
        * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
        * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
        * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
        * time       (time) object 1920-01-16 12:00:00 ... 2005-12-16 12:00:00
          time_bnds  (time, nbnd) object dask.array<chunksize=(1032, 2), meta=np.ndarray>
      Dimensions without coordinates: nbnd
      Data variables:
          Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
          T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
      Attributes: (12/20)
          Conventions:                       CF-1.0
          NCO:                               4.3.4
          Version:                           $Name$
          important_note:                    This data is part of the project 'Blin...
          initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i....
          logname:                           mudryk
          ...                                ...
          intake_esm_attrs:vertical_levels:  30.0
          intake_esm_attrs:spatial_domain:   global
          intake_esm_attrs:start_time:       1920-01-16 12:00:00
          intake_esm_attrs:end_time:         2005-12-16 12:00:00
          intake_esm_attrs:_data_format_:    zarr
          intake_esm_dataset_key:            atm/20C/monthly

      monthly

      • Groups: (0)
      • Dimensions:
        • member_id: 40
        • time: 1032
        • lev: 30
        • lat: 192
        • lon: 288
        • nbnd: 2
      • Coordinates: (6)
        • lat
          (lat)
          float64
          -90.0 -89.06 -88.12 ... 89.06 90.0

          axis :

          Y

          bounds :

          lat_bnds

          long_name :

          latitude

          standard_name :

          latitude

          units :

          degrees_north

          array([-90.      , -89.057594, -88.115181, -87.172775, -86.23037 , -85.287956,
                 -84.345551, -83.403145, -82.460732, -81.518326, -80.575912, -79.633507,
                 -78.691101, -77.748688, -76.806282, -75.863876, -74.921463, -73.979057,
                 -73.036652, -72.094238, -71.151833, -70.209427, -69.267014, -68.324608,
                 -67.382202, -66.439789, -65.497383, -64.554977, -63.612564, -62.670158,
                 -61.727749, -60.785339, -59.842934, -58.900524, -57.958115, -57.015705,
                 -56.073299, -55.13089 , -54.18848 , -53.246075, -52.303665, -51.361256,
                 -50.41885 , -49.47644 , -48.534031, -47.591621, -46.649216, -45.706806,
                 -44.764397, -43.821991, -42.879581, -41.937172, -40.994766, -40.052357,
                 -39.109947, -38.167538, -37.225132, -36.282722, -35.340313, -34.397907,
                 -33.455498, -32.513088, -31.570681, -30.628273, -29.685863, -28.743456,
                 -27.801046, -26.858639, -25.916231, -24.973822, -24.031414, -23.089005,
                 -22.146597, -21.204189, -20.26178 , -19.319372, -18.376963, -17.434555,
                 -16.492147, -15.549738, -14.607329, -13.664922, -12.722513, -11.780105,
                 -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
                  -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
                   0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
                   6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
                  11.780105,  12.722513,  13.664922,  14.607329,  15.549738,  16.492147,
                  17.434555,  18.376963,  19.319372,  20.26178 ,  21.204189,  22.146597,
                  23.089005,  24.031414,  24.973822,  25.916231,  26.858639,  27.801046,
                  28.743456,  29.685863,  30.628273,  31.570681,  32.513088,  33.455498,
                  34.397907,  35.340313,  36.282722,  37.225132,  38.167538,  39.109947,
                  40.052357,  40.994766,  41.937172,  42.879581,  43.821991,  44.764397,
                  45.706806,  46.649216,  47.591621,  48.534031,  49.47644 ,  50.41885 ,
                  51.361256,  52.303665,  53.246075,  54.18848 ,  55.13089 ,  56.073299,
                  57.015705,  57.958115,  58.900524,  59.842934,  60.785339,  61.727749,
                  62.670158,  63.612564,  64.554977,  65.497383,  66.439789,  67.382202,
                  68.324608,  69.267014,  70.209427,  71.151833,  72.094238,  73.036652,
                  73.979057,  74.921463,  75.863876,  76.806282,  77.748688,  78.691101,
                  79.633507,  80.575912,  81.518326,  82.460732,  83.403145,  84.345551,
                  85.287956,  86.23037 ,  87.172775,  88.115181,  89.057594,  90.      ])

        • lev
          (lev)
          float64
          3.643 7.595 14.36 ... 976.3 992.6

          formula_terms :

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

          long_name :

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

          positive :

          down

          standard_name :

          atmosphere_hybrid_sigma_pressure_coordinate

          units :

          level

          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])

        • lon
          (lon)
          float64
          0.0 1.25 2.5 ... 356.2 357.5 358.8

          long_name :

          longitude

          units :

          degrees_east

          array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

        • member_id
          (member_id)
          int64
          1 2 3 4 5 6 ... 101 102 103 104 105

          array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
                  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
                  29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])

        • time
          (time)
          object
          1920-01-16 12:00:00 ... 2005-12-...

          bounds :

          time_bnds

          long_name :

          time

          array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0, has_year_zero=True),
                 cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0, has_year_zero=True),
                 cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0, has_year_zero=True),
                 ...,
                 cftime.DatetimeNoLeap(2005, 10, 16, 12, 0, 0, 0, has_year_zero=True),
                 cftime.DatetimeNoLeap(2005, 11, 16, 0, 0, 0, 0, has_year_zero=True),
                 cftime.DatetimeNoLeap(2005, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
                dtype=object)

        • time_bnds
          (time, nbnd)
          object
          dask.array<chunksize=(1032, 2), meta=np.ndarray>

          long_name :

          time interval endpoints

          Array Chunk Bytes 16.12 kiB 16.12 kiB Shape (1032, 2) (1032, 2) Count 5 Graph Layers 1 Chunks Type object numpy.ndarray

      • Data variables: (2)
        • Q
          (member_id, time, lev, lat, lon)
          float32
          dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

          cell_methods :

          time: mean

          long_name :

          Specific humidity

          mdims :

          1

          units :

          kg/kg

          Array Chunk Bytes 255.10 GiB 113.91 MiB Shape (40, 1032, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2320 Chunks Type float32 numpy.ndarray

        • T
          (member_id, time, lev, lat, lon)
          float32
          dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

          cell_methods :

          time: mean

          long_name :

          Temperature

          mdims :

          1

          units :

          K

          Array Chunk Bytes 255.10 GiB 113.91 MiB Shape (40, 1032, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2320 Chunks Type float32 numpy.ndarray

      • Attributes: (20)

        Conventions :

        CF-1.0

        NCO :

        4.3.4

        Version :

        $Name$

        important_note :

        This data is part of the project 'Blind Evaluation of Lossy Data-Compression in LENS'. Please exercise caution before using this data for other purposes.

        initial_file :

        b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i.1920-01-01-00000.nc

        logname :

        mudryk

        nco_openmp_thread_number :

        1

        revision_Id :

        $Id$

        source :

        CAM

        title :

        UNSET

        topography_file :

        /scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc

        intake_esm_attrs:component :

        atm

        intake_esm_attrs:experiment :

        20C

        intake_esm_attrs:frequency :

        monthly

        intake_esm_attrs:vertical_levels :

        30.0

        intake_esm_attrs:spatial_domain :

        global

        intake_esm_attrs:start_time :

        1920-01-16 12:00:00

        intake_esm_attrs:end_time :

        2005-12-16 12:00:00

        intake_esm_attrs:_data_format_ :

        zarr

        intake_esm_dataset_key :

        atm/20C/monthly

    • Dimensions:
    • Coordinates: (0)
    • Data variables: (0)
    • Attributes: (0)

    <xarray.DatasetView>
    Dimensions:  ()
    Data variables:
        *empty*

    RCP85

    • Groups: (1)
      <xarray.DatasetView>
      Dimensions:    (member_id: 40, time: 1140, lev: 30, lat: 192, lon: 288, nbnd: 2)
      Coordinates:
        * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
        * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
        * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
        * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
        * time       (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00
          time_bnds  (time, nbnd) object dask.array<chunksize=(1140, 2), meta=np.ndarray>
      Dimensions without coordinates: nbnd
      Data variables:
          Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
          T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
      Attributes: (12/21)
          Conventions:                       CF-1.0
          NCO:                               4.3.4
          Version:                           $Name$
          host:                              tcs-f02n07
          important_note:                    This data is part of the project 'Blin...
          initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i....
          ...                                ...
          intake_esm_attrs:vertical_levels:  30.0
          intake_esm_attrs:spatial_domain:   global
          intake_esm_attrs:start_time:       2006-01-16 12:00:00
          intake_esm_attrs:end_time:         2100-12-16 12:00:00
          intake_esm_attrs:_data_format_:    zarr
          intake_esm_dataset_key:            atm/RCP85/monthly

      monthly

      • Groups: (0)
      • Dimensions:
        • member_id: 40
        • time: 1140
        • lev: 30
        • lat: 192
        • lon: 288
        • nbnd: 2
      • Coordinates: (6)
        • lat
          (lat)
          float64
          -90.0 -89.06 -88.12 ... 89.06 90.0

          axis :

          Y

          bounds :

          lat_bnds

          long_name :

          latitude

          standard_name :

          latitude

          units :

          degrees_north

          array([-90.      , -89.057594, -88.115181, -87.172775, -86.23037 , -85.287956,
                 -84.345551, -83.403145, -82.460732, -81.518326, -80.575912, -79.633507,
                 -78.691101, -77.748688, -76.806282, -75.863876, -74.921463, -73.979057,
                 -73.036652, -72.094238, -71.151833, -70.209427, -69.267014, -68.324608,
                 -67.382202, -66.439789, -65.497383, -64.554977, -63.612564, -62.670158,
                 -61.727749, -60.785339, -59.842934, -58.900524, -57.958115, -57.015705,
                 -56.073299, -55.13089 , -54.18848 , -53.246075, -52.303665, -51.361256,
                 -50.41885 , -49.47644 , -48.534031, -47.591621, -46.649216, -45.706806,
                 -44.764397, -43.821991, -42.879581, -41.937172, -40.994766, -40.052357,
                 -39.109947, -38.167538, -37.225132, -36.282722, -35.340313, -34.397907,
                 -33.455498, -32.513088, -31.570681, -30.628273, -29.685863, -28.743456,
                 -27.801046, -26.858639, -25.916231, -24.973822, -24.031414, -23.089005,
                 -22.146597, -21.204189, -20.26178 , -19.319372, -18.376963, -17.434555,
                 -16.492147, -15.549738, -14.607329, -13.664922, -12.722513, -11.780105,
                 -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
                  -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
                   0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
                   6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
                  11.780105,  12.722513,  13.664922,  14.607329,  15.549738,  16.492147,
                  17.434555,  18.376963,  19.319372,  20.26178 ,  21.204189,  22.146597,
                  23.089005,  24.031414,  24.973822,  25.916231,  26.858639,  27.801046,
                  28.743456,  29.685863,  30.628273,  31.570681,  32.513088,  33.455498,
                  34.397907,  35.340313,  36.282722,  37.225132,  38.167538,  39.109947,
                  40.052357,  40.994766,  41.937172,  42.879581,  43.821991,  44.764397,
                  45.706806,  46.649216,  47.591621,  48.534031,  49.47644 ,  50.41885 ,
                  51.361256,  52.303665,  53.246075,  54.18848 ,  55.13089 ,  56.073299,
                  57.015705,  57.958115,  58.900524,  59.842934,  60.785339,  61.727749,
                  62.670158,  63.612564,  64.554977,  65.497383,  66.439789,  67.382202,
                  68.324608,  69.267014,  70.209427,  71.151833,  72.094238,  73.036652,
                  73.979057,  74.921463,  75.863876,  76.806282,  77.748688,  78.691101,
                  79.633507,  80.575912,  81.518326,  82.460732,  83.403145,  84.345551,
                  85.287956,  86.23037 ,  87.172775,  88.115181,  89.057594,  90.      ])

        • lev
          (lev)
          float64
          3.643 7.595 14.36 ... 976.3 992.6

          formula_terms :

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

          long_name :

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

          positive :

          down

          standard_name :

          atmosphere_hybrid_sigma_pressure_coordinate

          units :

          level

          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])

        • lon
          (lon)
          float64
          0.0 1.25 2.5 ... 356.2 357.5 358.8

          long_name :

          longitude

          units :

          degrees_east

          array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

        • member_id
          (member_id)
          int64
          1 2 3 4 5 6 ... 101 102 103 104 105

          array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
                  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
                  29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])

        • time
          (time)
          object
          2006-01-16 12:00:00 ... 2100-12-...

          bounds :

          time_bnds

          long_name :

          time

          array([cftime.DatetimeNoLeap(2006, 1, 16, 12, 0, 0, 0, has_year_zero=True),
                 cftime.DatetimeNoLeap(2006, 2, 15, 0, 0, 0, 0, has_year_zero=True),
                 cftime.DatetimeNoLeap(2006, 3, 16, 12, 0, 0, 0, has_year_zero=True),
                 ...,
                 cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0, has_year_zero=True),
                 cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0, has_year_zero=True),
                 cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
                dtype=object)

        • time_bnds
          (time, nbnd)
          object
          dask.array<chunksize=(1140, 2), meta=np.ndarray>

          long_name :

          time interval endpoints

          Array Chunk Bytes 17.81 kiB 17.81 kiB Shape (1140, 2) (1140, 2) Count 5 Graph Layers 1 Chunks Type object numpy.ndarray

      • Data variables: (2)
        • Q
          (member_id, time, lev, lat, lon)
          float32
          dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

          cell_methods :

          time: mean

          long_name :

          Specific humidity

          mdims :

          1

          units :

          kg/kg

          Array Chunk Bytes 281.80 GiB 113.91 MiB Shape (40, 1140, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2560 Chunks Type float32 numpy.ndarray

        • T
          (member_id, time, lev, lat, lon)
          float32
          dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

          cell_methods :

          time: mean

          long_name :

          Temperature

          mdims :

          1

          units :

          K

          Array Chunk Bytes 281.80 GiB 113.91 MiB Shape (40, 1140, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2560 Chunks Type float32 numpy.ndarray

      • Attributes: (21)

        Conventions :

        CF-1.0

        NCO :

        4.3.4

        Version :

        $Name$

        host :

        tcs-f02n07

        important_note :

        This data is part of the project 'Blind Evaluation of Lossy Data-Compression in LENS'. Please exercise caution before using this data for other purposes.

        initial_file :

        b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i.2006-01-01-00000.nc

        logname :

        mudryk

        nco_openmp_thread_number :

        1

        revision_Id :

        $Id$

        source :

        CAM

        title :

        UNSET

        topography_file :

        /scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc

        intake_esm_attrs:component :

        atm

        intake_esm_attrs:experiment :

        RCP85

        intake_esm_attrs:frequency :

        monthly

        intake_esm_attrs:vertical_levels :

        30.0

        intake_esm_attrs:spatial_domain :

        global

        intake_esm_attrs:start_time :

        2006-01-16 12:00:00

        intake_esm_attrs:end_time :

        2100-12-16 12:00:00

        intake_esm_attrs:_data_format_ :

        zarr

        intake_esm_dataset_key :

        atm/RCP85/monthly

    • Dimensions:
    • Coordinates: (0)
    • Data variables: (0)
    • Attributes: (0)

  • Dimensions:
  • Coordinates: (0)
  • Data variables: (0)
  • Attributes: (0)

  <xarray.DatasetView>
  Dimensions:    (member_id: 40, time: 1140, lev: 30, lat: 192, lon: 288, nbnd: 2)
  Coordinates:
    * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
    * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
    * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
    * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
    * time       (time) object 2006-01-16 12:00:00 ... 2100-12-16 12:00:00
      time_bnds  (time, nbnd) object dask.array<chunksize=(1140, 2), meta=np.ndarray>
  Dimensions without coordinates: nbnd
  Data variables:
      Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
      T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
  Attributes: (12/21)
      Conventions:                       CF-1.0
      NCO:                               4.3.4
      Version:                           $Name$
      host:                              tcs-f02n07
      important_note:                    This data is part of the project 'Blin...
      initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i....
      ...                                ...
      intake_esm_attrs:vertical_levels:  30.0
      intake_esm_attrs:spatial_domain:   global
      intake_esm_attrs:start_time:       2006-01-16 12:00:00
      intake_esm_attrs:end_time:         2100-12-16 12:00:00
      intake_esm_attrs:_data_format_:    zarr
      intake_esm_dataset_key:            atm.RCP85.monthly

  atm.RCP85.monthly

  • Groups: (0)
  • Dimensions:
    • member_id: 40
    • time: 1140
    • lev: 30
    • lat: 192
    • lon: 288
    • nbnd: 2
  • Coordinates: (6)
    • lat
      (lat)
      float64
      -90.0 -89.06 -88.12 ... 89.06 90.0

      axis :

      Y

      bounds :

      lat_bnds

      long_name :

      latitude

      standard_name :

      latitude

      units :

      degrees_north

      array([-90.      , -89.057594, -88.115181, -87.172775, -86.23037 , -85.287956,
             -84.345551, -83.403145, -82.460732, -81.518326, -80.575912, -79.633507,
             -78.691101, -77.748688, -76.806282, -75.863876, -74.921463, -73.979057,
             -73.036652, -72.094238, -71.151833, -70.209427, -69.267014, -68.324608,
             -67.382202, -66.439789, -65.497383, -64.554977, -63.612564, -62.670158,
             -61.727749, -60.785339, -59.842934, -58.900524, -57.958115, -57.015705,
             -56.073299, -55.13089 , -54.18848 , -53.246075, -52.303665, -51.361256,
             -50.41885 , -49.47644 , -48.534031, -47.591621, -46.649216, -45.706806,
             -44.764397, -43.821991, -42.879581, -41.937172, -40.994766, -40.052357,
             -39.109947, -38.167538, -37.225132, -36.282722, -35.340313, -34.397907,
             -33.455498, -32.513088, -31.570681, -30.628273, -29.685863, -28.743456,
             -27.801046, -26.858639, -25.916231, -24.973822, -24.031414, -23.089005,
             -22.146597, -21.204189, -20.26178 , -19.319372, -18.376963, -17.434555,
             -16.492147, -15.549738, -14.607329, -13.664922, -12.722513, -11.780105,
             -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
              -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
               0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
               6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
              11.780105,  12.722513,  13.664922,  14.607329,  15.549738,  16.492147,
              17.434555,  18.376963,  19.319372,  20.26178 ,  21.204189,  22.146597,
              23.089005,  24.031414,  24.973822,  25.916231,  26.858639,  27.801046,
              28.743456,  29.685863,  30.628273,  31.570681,  32.513088,  33.455498,
              34.397907,  35.340313,  36.282722,  37.225132,  38.167538,  39.109947,
              40.052357,  40.994766,  41.937172,  42.879581,  43.821991,  44.764397,
              45.706806,  46.649216,  47.591621,  48.534031,  49.47644 ,  50.41885 ,
              51.361256,  52.303665,  53.246075,  54.18848 ,  55.13089 ,  56.073299,
              57.015705,  57.958115,  58.900524,  59.842934,  60.785339,  61.727749,
              62.670158,  63.612564,  64.554977,  65.497383,  66.439789,  67.382202,
              68.324608,  69.267014,  70.209427,  71.151833,  72.094238,  73.036652,
              73.979057,  74.921463,  75.863876,  76.806282,  77.748688,  78.691101,
              79.633507,  80.575912,  81.518326,  82.460732,  83.403145,  84.345551,
              85.287956,  86.23037 ,  87.172775,  88.115181,  89.057594,  90.      ])

    • lev
      (lev)
      float64
      3.643 7.595 14.36 ... 976.3 992.6

      formula_terms :

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

      long_name :

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

      positive :

      down

      standard_name :

      atmosphere_hybrid_sigma_pressure_coordinate

      units :

      level

      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])

    • lon
      (lon)
      float64
      0.0 1.25 2.5 ... 356.2 357.5 358.8

      long_name :

      longitude

      units :

      degrees_east

      array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

    • member_id
      (member_id)
      int64
      1 2 3 4 5 6 ... 101 102 103 104 105

      array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
              15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
              29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])

    • time
      (time)
      object
      2006-01-16 12:00:00 ... 2100-12-...

      bounds :

      time_bnds

      long_name :

      time

      array([cftime.DatetimeNoLeap(2006, 1, 16, 12, 0, 0, 0, has_year_zero=True),
             cftime.DatetimeNoLeap(2006, 2, 15, 0, 0, 0, 0, has_year_zero=True),
             cftime.DatetimeNoLeap(2006, 3, 16, 12, 0, 0, 0, has_year_zero=True),
             ...,
             cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0, has_year_zero=True),
             cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0, has_year_zero=True),
             cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
            dtype=object)

    • time_bnds
      (time, nbnd)
      object
      dask.array<chunksize=(1140, 2), meta=np.ndarray>

      long_name :

      time interval endpoints

      Array Chunk Bytes 17.81 kiB 17.81 kiB Shape (1140, 2) (1140, 2) Count 5 Graph Layers 1 Chunks Type object numpy.ndarray

  • Data variables: (2)
    • Q
      (member_id, time, lev, lat, lon)
      float32
      dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

      cell_methods :

      time: mean

      long_name :

      Specific humidity

      mdims :

      1

      units :

      kg/kg

      Array Chunk Bytes 281.80 GiB 113.91 MiB Shape (40, 1140, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2560 Chunks Type float32 numpy.ndarray

    • T
      (member_id, time, lev, lat, lon)
      float32
      dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

      cell_methods :

      time: mean

      long_name :

      Temperature

      mdims :

      1

      units :

      K

      Array Chunk Bytes 281.80 GiB 113.91 MiB Shape (40, 1140, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2560 Chunks Type float32 numpy.ndarray

  • Attributes: (21)

    Conventions :

    CF-1.0

    NCO :

    4.3.4

    Version :

    $Name$

    host :

    tcs-f02n07

    important_note :

    This data is part of the project 'Blind Evaluation of Lossy Data-Compression in LENS'. Please exercise caution before using this data for other purposes.

    initial_file :

    b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i.2006-01-01-00000.nc

    logname :

    mudryk

    nco_openmp_thread_number :

    1

    revision_Id :

    $Id$

    source :

    CAM

    title :

    UNSET

    topography_file :

    /scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc

    intake_esm_attrs:component :

    atm

    intake_esm_attrs:experiment :

    RCP85

    intake_esm_attrs:frequency :

    monthly

    intake_esm_attrs:vertical_levels :

    30.0

    intake_esm_attrs:spatial_domain :

    global

    intake_esm_attrs:start_time :

    2006-01-16 12:00:00

    intake_esm_attrs:end_time :

    2100-12-16 12:00:00

    intake_esm_attrs:_data_format_ :

    zarr

    intake_esm_dataset_key :

    atm.RCP85.monthly

  <xarray.DatasetView>
  Dimensions:    (member_id: 40, time: 1032, lev: 30, lat: 192, lon: 288, nbnd: 2)
  Coordinates:
    * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
    * lev        (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
    * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
    * member_id  (member_id) int64 1 2 3 4 5 6 7 8 ... 34 35 101 102 103 104 105
    * time       (time) object 1920-01-16 12:00:00 ... 2005-12-16 12:00:00
      time_bnds  (time, nbnd) object dask.array<chunksize=(1032, 2), meta=np.ndarray>
  Dimensions without coordinates: nbnd
  Data variables:
      Q          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
      T          (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>
  Attributes: (12/20)
      Conventions:                       CF-1.0
      NCO:                               4.3.4
      Version:                           $Name$
      important_note:                    This data is part of the project 'Blin...
      initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i....
      logname:                           mudryk
      ...                                ...
      intake_esm_attrs:vertical_levels:  30.0
      intake_esm_attrs:spatial_domain:   global
      intake_esm_attrs:start_time:       1920-01-16 12:00:00
      intake_esm_attrs:end_time:         2005-12-16 12:00:00
      intake_esm_attrs:_data_format_:    zarr
      intake_esm_dataset_key:            atm.20C.monthly

  atm.20C.monthly

  • Groups: (0)
  • Dimensions:
    • member_id: 40
    • time: 1032
    • lev: 30
    • lat: 192
    • lon: 288
    • nbnd: 2
  • Coordinates: (6)
    • lat
      (lat)
      float64
      -90.0 -89.06 -88.12 ... 89.06 90.0

      axis :

      Y

      bounds :

      lat_bnds

      long_name :

      latitude

      standard_name :

      latitude

      units :

      degrees_north

      array([-90.      , -89.057594, -88.115181, -87.172775, -86.23037 , -85.287956,
             -84.345551, -83.403145, -82.460732, -81.518326, -80.575912, -79.633507,
             -78.691101, -77.748688, -76.806282, -75.863876, -74.921463, -73.979057,
             -73.036652, -72.094238, -71.151833, -70.209427, -69.267014, -68.324608,
             -67.382202, -66.439789, -65.497383, -64.554977, -63.612564, -62.670158,
             -61.727749, -60.785339, -59.842934, -58.900524, -57.958115, -57.015705,
             -56.073299, -55.13089 , -54.18848 , -53.246075, -52.303665, -51.361256,
             -50.41885 , -49.47644 , -48.534031, -47.591621, -46.649216, -45.706806,
             -44.764397, -43.821991, -42.879581, -41.937172, -40.994766, -40.052357,
             -39.109947, -38.167538, -37.225132, -36.282722, -35.340313, -34.397907,
             -33.455498, -32.513088, -31.570681, -30.628273, -29.685863, -28.743456,
             -27.801046, -26.858639, -25.916231, -24.973822, -24.031414, -23.089005,
             -22.146597, -21.204189, -20.26178 , -19.319372, -18.376963, -17.434555,
             -16.492147, -15.549738, -14.607329, -13.664922, -12.722513, -11.780105,
             -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
              -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
               0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
               6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
              11.780105,  12.722513,  13.664922,  14.607329,  15.549738,  16.492147,
              17.434555,  18.376963,  19.319372,  20.26178 ,  21.204189,  22.146597,
              23.089005,  24.031414,  24.973822,  25.916231,  26.858639,  27.801046,
              28.743456,  29.685863,  30.628273,  31.570681,  32.513088,  33.455498,
              34.397907,  35.340313,  36.282722,  37.225132,  38.167538,  39.109947,
              40.052357,  40.994766,  41.937172,  42.879581,  43.821991,  44.764397,
              45.706806,  46.649216,  47.591621,  48.534031,  49.47644 ,  50.41885 ,
              51.361256,  52.303665,  53.246075,  54.18848 ,  55.13089 ,  56.073299,
              57.015705,  57.958115,  58.900524,  59.842934,  60.785339,  61.727749,
              62.670158,  63.612564,  64.554977,  65.497383,  66.439789,  67.382202,
              68.324608,  69.267014,  70.209427,  71.151833,  72.094238,  73.036652,
              73.979057,  74.921463,  75.863876,  76.806282,  77.748688,  78.691101,
              79.633507,  80.575912,  81.518326,  82.460732,  83.403145,  84.345551,
              85.287956,  86.23037 ,  87.172775,  88.115181,  89.057594,  90.      ])

    • lev
      (lev)
      float64
      3.643 7.595 14.36 ... 976.3 992.6

      formula_terms :

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

      long_name :

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

      positive :

      down

      standard_name :

      atmosphere_hybrid_sigma_pressure_coordinate

      units :

      level

      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])

    • lon
      (lon)
      float64
      0.0 1.25 2.5 ... 356.2 357.5 358.8

      long_name :

      longitude

      units :

      degrees_east

      array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

    • member_id
      (member_id)
      int64
      1 2 3 4 5 6 ... 101 102 103 104 105

      array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,
              15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,
              29,  30,  31,  32,  33,  34,  35, 101, 102, 103, 104, 105])

    • time
      (time)
      object
      1920-01-16 12:00:00 ... 2005-12-...

      bounds :

      time_bnds

      long_name :

      time

      array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0, has_year_zero=True),
             cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0, has_year_zero=True),
             cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0, has_year_zero=True),
             ...,
             cftime.DatetimeNoLeap(2005, 10, 16, 12, 0, 0, 0, has_year_zero=True),
             cftime.DatetimeNoLeap(2005, 11, 16, 0, 0, 0, 0, has_year_zero=True),
             cftime.DatetimeNoLeap(2005, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
            dtype=object)

    • time_bnds
      (time, nbnd)
      object
      dask.array<chunksize=(1032, 2), meta=np.ndarray>

      long_name :

      time interval endpoints

      Array Chunk Bytes 16.12 kiB 16.12 kiB Shape (1032, 2) (1032, 2) Count 5 Graph Layers 1 Chunks Type object numpy.ndarray

  • Data variables: (2)
    • Q
      (member_id, time, lev, lat, lon)
      float32
      dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

      cell_methods :

      time: mean

      long_name :

      Specific humidity

      mdims :

      1

      units :

      kg/kg

      Array Chunk Bytes 255.10 GiB 113.91 MiB Shape (40, 1032, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2320 Chunks Type float32 numpy.ndarray

    • T
      (member_id, time, lev, lat, lon)
      float32
      dask.array<chunksize=(1, 18, 30, 192, 288), meta=np.ndarray>

      cell_methods :

      time: mean

      long_name :

      Temperature

      mdims :

      1

      units :

      K

      Array Chunk Bytes 255.10 GiB 113.91 MiB Shape (40, 1032, 30, 192, 288) (1, 18, 30, 192, 288) Count 2 Graph Layers 2320 Chunks Type float32 numpy.ndarray

  • Attributes: (20)

    Conventions :

    CF-1.0

    NCO :

    4.3.4

    Version :

    $Name$

    important_note :

    This data is part of the project 'Blind Evaluation of Lossy Data-Compression in LENS'. Please exercise caution before using this data for other purposes.

    initial_file :

    b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i.1920-01-01-00000.nc

    logname :

    mudryk

    nco_openmp_thread_number :

    1

    revision_Id :

    $Id$

    source :

    CAM

    title :

    UNSET

    topography_file :

    /scratch/p/pjk/mudryk/cesm1_1_2_LENS/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc

    intake_esm_attrs:component :

    atm

    intake_esm_attrs:experiment :

    20C

    intake_esm_attrs:frequency :

    monthly

    intake_esm_attrs:vertical_levels :

    30.0

    intake_esm_attrs:spatial_domain :

    global

    intake_esm_attrs:start_time :

    1920-01-16 12:00:00

    intake_esm_attrs:end_time :

    2005-12-16 12:00:00

    intake_esm_attrs:_data_format_ :

    zarr

    intake_esm_dataset_key :

    atm.20C.monthly

• Dimensions:
• Coordinates: (0)
• Data variables: (0)
• Attributes: (0)

In a similar fashion to what we used before, we can apply functions that work at the dataset level across our tree of datasets!

timeseries = dt.sel(lat = 40.0150, lon = 105.2705, method='nearest').isel(member_id=0, lev=-1, time=range(60),)

for model_name, model in timeseries.children.items():
    try:
        timeseries[model_name].ds.T.plot()
        plt.show()
        plt.close()
    except AttributeError:
        pass

---

Summary

Within this tutorial, we covered how Intake-ESM is useful, how to search for data, load your data into a dictionary of datasets, and write functions to plot your output.

Resources and references

• Intake-ESM Documentation

• List of Available Catalogs

• ESDS Blog Posts Related to Intake-ESM

• Pangeo Gallery Intake-ESM

• CMIP6 Pre-Processing

Intake-ESM Citation

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Introduction to Xarray