Basic search and load

Google Cloud CMIP6 Public Data: Basic Python Example

This notebooks shows how to query the catalog and load the data using python

from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr
import zarr
import gcsfs

xr.set_options(display_style='html')
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import cftime

plt.rcParams['figure.figsize'] = 12, 6

Browse Catalog

The data catatalog is stored as a CSV file. Here we read it with Pandas.

df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')
df.head()

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year
0	AerChemMIP	BCC	BCC-ESM1	histSST	r1i1p1f1	AERmon	mmrbc	gn	gs://cmip6/AerChemMIP/BCC/BCC-ESM1/histSST/r1i...	NaN
1	AerChemMIP	BCC	BCC-ESM1	histSST	r1i1p1f1	AERmon	mmrdust	gn	gs://cmip6/AerChemMIP/BCC/BCC-ESM1/histSST/r1i...	NaN
2	AerChemMIP	BCC	BCC-ESM1	histSST	r1i1p1f1	AERmon	mmroa	gn	gs://cmip6/AerChemMIP/BCC/BCC-ESM1/histSST/r1i...	NaN
3	AerChemMIP	BCC	BCC-ESM1	histSST	r1i1p1f1	AERmon	mmrso4	gn	gs://cmip6/AerChemMIP/BCC/BCC-ESM1/histSST/r1i...	NaN
4	AerChemMIP	BCC	BCC-ESM1	histSST	r1i1p1f1	AERmon	mmrss	gn	gs://cmip6/AerChemMIP/BCC/BCC-ESM1/histSST/r1i...	NaN

The columns of the dataframe correspond to the CMI6 controlled vocabulary. A beginners' guide to these terms is available in this document.

Here we filter the data to find monthly surface air temperature for historical experiments.

df_ta = df.query("activity_id=='CMIP' & table_id == 'Amon' & variable_id == 'tas' & experiment_id == 'historical'")
df_ta

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year
4896	CMIP	AS-RCEC	TaiESM1	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/AS-RCEC/TaiESM1/historical/r1i...	NaN
5674	CMIP	BCC	BCC-CSM2-MR	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/BCC/BCC-CSM2-MR/historical/r1i...	NaN
5778	CMIP	BCC	BCC-CSM2-MR	historical	r2i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/BCC/BCC-CSM2-MR/historical/r2i...	NaN
5870	CMIP	BCC	BCC-CSM2-MR	historical	r3i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/BCC/BCC-CSM2-MR/historical/r3i...	NaN
6180	CMIP	BCC	BCC-ESM1	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/BCC/BCC-ESM1/historical/r1i1p1...	NaN
...	...	...	...	...	...	...	...	...	...	...
50318	CMIP	SNU	SAM0-UNICON	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/SNU/SAM0-UNICON/historical/r1i...	NaN
50474	CMIP	THU	CIESM	historical	r1i1p1f1	Amon	tas	gr	gs://cmip6/CMIP/THU/CIESM/historical/r1i1p1f1/...	NaN
50494	CMIP	THU	CIESM	historical	r2i1p1f1	Amon	tas	gr	gs://cmip6/CMIP/THU/CIESM/historical/r2i1p1f1/...	NaN
50579	CMIP	UA	MCM-UA-1-0	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/UA/MCM-UA-1-0/historical/r1i1p...	NaN
50620	CMIP	UA	MCM-UA-1-0	historical	r1i1p1f2	Amon	tas	gn	gs://cmip6/CMIP/UA/MCM-UA-1-0/historical/r1i1p...	NaN

311 rows × 10 columns

Now we do further filtering to find just the models from NCAR.

df_ta_ncar = df_ta.query('institution_id == "NCAR"')
df_ta_ncar

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year
41761	CMIP	NCAR	CESM2-FV2	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2-FV2/historical/r1i1...	NaN
41923	CMIP	NCAR	CESM2-FV2	historical	r2i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2-FV2/historical/r2i1...	NaN
41934	CMIP	NCAR	CESM2-FV2	historical	r3i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2-FV2/historical/r3i1...	NaN
42102	CMIP	NCAR	CESM2-WACCM-FV2	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2-WACCM-FV2/historica...	NaN
42263	CMIP	NCAR	CESM2-WACCM-FV2	historical	r2i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2-WACCM-FV2/historica...	NaN
42274	CMIP	NCAR	CESM2-WACCM-FV2	historical	r3i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2-WACCM-FV2/historica...	NaN
42980	CMIP	NCAR	CESM2-WACCM	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2-WACCM/historical/r1...	NaN
43255	CMIP	NCAR	CESM2-WACCM	historical	r2i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2-WACCM/historical/r2...	NaN
43487	CMIP	NCAR	CESM2-WACCM	historical	r3i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2-WACCM/historical/r3...	NaN
44529	CMIP	NCAR	CESM2	historical	r10i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2/historical/r10i1p1f...	NaN
44756	CMIP	NCAR	CESM2	historical	r11i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2/historical/r11i1p1f...	NaN
45003	CMIP	NCAR	CESM2	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2/historical/r1i1p1f1...	NaN
45280	CMIP	NCAR	CESM2	historical	r2i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2/historical/r2i1p1f1...	NaN
45557	CMIP	NCAR	CESM2	historical	r3i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2/historical/r3i1p1f1...	NaN
45834	CMIP	NCAR	CESM2	historical	r4i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2/historical/r4i1p1f1...	NaN
46077	CMIP	NCAR	CESM2	historical	r5i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2/historical/r5i1p1f1...	NaN
46320	CMIP	NCAR	CESM2	historical	r6i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2/historical/r6i1p1f1...	NaN
46564	CMIP	NCAR	CESM2	historical	r7i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2/historical/r7i1p1f1...	NaN
46806	CMIP	NCAR	CESM2	historical	r8i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2/historical/r8i1p1f1...	NaN
47048	CMIP	NCAR	CESM2	historical	r9i1p1f1	Amon	tas	gn	gs://cmip6/CMIP/NCAR/CESM2/historical/r9i1p1f1...	NaN

Load Data

Now we will load a single store using gcsfs, zarr, and xarray.

# this only needs to be created once
gcs = gcsfs.GCSFileSystem(token='anon')

# get the path to a specific zarr store (the first one from the dataframe above)
zstore = df_ta_ncar.zstore.values[-1]

# create a mutable-mapping-style interface to the store
mapper = gcs.get_mapper(zstore)

# open it using xarray and zarr
ds = xr.open_zarr(mapper, consolidated=True)
ds

xarray.Dataset

Dimensions:
- lat: 192
- lon: 288
- nbnd: 2
- time: 1980

Coordinates: (6)

lat

(lat)

float64

-90.0 -89.06 -88.12 ... 89.06 90.0

axis :: Y
bounds :: lat_bnds
standard_name :: latitude
title :: Latitude
type :: double
units :: degrees_north
valid_max :: 90.0
valid_min :: -90.0

array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,
       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,
       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,
       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,
       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,
       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,
       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,
       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,
       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,
       -16.492147, -15.549738, -14.60733 , -13.664921, -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,  13.664921,  14.60733 ,  15.549738,  16.492147,
        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,
        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,
        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,
        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,
        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,
        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,
        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,
        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,
        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,
        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])

lat_bnds

(lat, nbnd)

float32

dask.array<chunksize=(192, 2), meta=np.ndarray>

units :: degrees_north





  
      Array  Chunk 
  
  
     Bytes  1.54 kB   1.54 kB 
     Shape  (192, 2)   (192, 2) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

lon
(lon)
float64
0.0 1.25 2.5 ... 356.2 357.5 358.8
axis :
X
bounds :
lon_bnds
standard_name :
longitude
title :
Longitude
type :
double
units :
degrees_east
valid_max :
360.0
valid_min :
0.0
```
array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])
```

lon_bnds

(lon, nbnd)

float32

dask.array<chunksize=(288, 2), meta=np.ndarray>

units :: degrees_east





  
      Array  Chunk 
  
  
     Bytes  2.30 kB   2.30 kB 
     Shape  (288, 2)   (288, 2) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

time

(time)

object

1850-01-15 12:00:00 ... 2014-12-15 12:00:00

axis :: T
bounds :: time_bnds
standard_name :: time
title :: time
type :: double

array([cftime.DatetimeNoLeap(1850-01-15 12:00:00),
       cftime.DatetimeNoLeap(1850-02-14 00:00:00),
       cftime.DatetimeNoLeap(1850-03-15 12:00:00), ...,
       cftime.DatetimeNoLeap(2014-10-15 12:00:00),
       cftime.DatetimeNoLeap(2014-11-15 00:00:00),
       cftime.DatetimeNoLeap(2014-12-15 12:00:00)], dtype=object)

time_bnds

(time, nbnd)

object

dask.array<chunksize=(1980, 2), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  31.68 kB   31.68 kB 
     Shape  (1980, 2)   (1980, 2) 
     Count  2 Tasks  1 Chunks 
     Type  object  numpy.ndarray

Data variables: (1)

tas

(time, lat, lon)

float32

dask.array<chunksize=(600, 192, 288), meta=np.ndarray>

cell_measures :: area: areacella
cell_methods :: area: time: mean
comment :: near-surface (usually, 2 meter) air temperature
description :: near-surface (usually, 2 meter) air temperature
frequency :: mon
id :: tas
long_name :: Near-Surface Air Temperature
mipTable :: Amon
out_name :: tas
prov :: Amon ((isd.003))
realm :: atmos
standard_name :: air_temperature
time :: time
time_label :: time-mean
time_title :: Temporal mean
title :: Near-Surface Air Temperature
type :: real
units :: K
variable_id :: tas





  
      Array  Chunk 
  
  
     Bytes  437.94 MB   132.71 MB 
     Shape  (1980, 192, 288)   (600, 192, 288) 
     Count  5 Tasks  4 Chunks 
     Type  float32  numpy.ndarray

Attributes: (47)
Conventions :
CF-1.7 CMIP-6.2
activity_id :
CMIP
branch_method :
standard
branch_time_in_child :
674885.0
branch_time_in_parent :
295650.0
case_id :
23
cesm_casename :
b.e21.BHIST.f09_g17.CMIP6-historical.009
contact :
cesm_cmip6@ucar.edu
creation_date :
2019-01-27T10:42:54Z
data_specs_version :
01.00.29
experiment :
all-forcing simulation of the recent past
experiment_id :
historical
external_variables :
areacella
forcing_index :
1
frequency :
mon
further_info_url :
https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.historical.none.r9i1p1f1
grid :
native 0.9x1.25 finite volume grid (192x288 latxlon)
grid_label :
gn
history :
none
initialization_index :
1
institution :
National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA
institution_id :
NCAR
license :
CMIP6 model data produced by <The National Center for Atmospheric Research> is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.
mip_era :
CMIP6
model_doi_url :
https://doi.org/10.5065/D67H1H0V
nominal_resolution :
100 km
parent_activity_id :
CMIP
parent_experiment_id :
piControl
parent_mip_era :
CMIP6
parent_source_id :
CESM2
parent_time_units :
days since 0001-01-01 00:00:00
parent_variant_label :
r1i1p1f1
physics_index :
1
product :
model-output
realization_index :
9
realm :
atmos
source :
CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)
source_id :
CESM2
source_type :
AOGCM BGC
sub_experiment :
none
sub_experiment_id :
none
table_id :
Amon
tracking_id :
hdl:21.14100/4b164514-1627-4deb-a8d5-93d4c5166d41 hdl:21.14100/4b164514-1627-4deb-a8d5-93d4c5166d41 hdl:21.14100/4b164514-1627-4deb-a8d5-93d4c5166d41 hdl:21.14100/4b164514-1627-4deb-a8d5-93d4c5166d41
variable_id :
tas
variant_info :
CMIP6 20th century experiments (1850-2014) with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)
variant_label :
r9i1p1f1
status :
2019-10-25;created;by nhn2@columbia.edu

Plot a map from a specific date.

ds.tas.sel(time='1950-01').squeeze().plot()

<matplotlib.collections.QuadMesh at 0x7fb7a627e650>

Create a timeseries of global-average surface air temperature. For this we need the area weighting factor for each gridpoint.

df_area = df.query("variable_id == 'areacella' & source_id == 'CESM2'")
ds_area = xr.open_zarr(gcs.get_mapper(df_area.zstore.values[0]), consolidated=True)
ds_area

xarray.Dataset

Dimensions:
- lat: 192
- lon: 288
- nbnd: 2

Coordinates: (4)

lat

(lat)

float64

-90.0 -89.06 -88.12 ... 89.06 90.0

axis :: Y
bounds :: lat_bnds
standard_name :: latitude
title :: Latitude
type :: double
units :: degrees_north
valid_max :: 90.0
valid_min :: -90.0

array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,
       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,
       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,
       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,
       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,
       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,
       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,
       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,
       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,
       -16.492147, -15.549738, -14.60733 , -13.664921, -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,  13.664921,  14.60733 ,  15.549738,  16.492147,
        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,
        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,
        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,
        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,
        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,
        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,
        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,
        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,
        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,
        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])

lat_bnds

(lat, nbnd)

float64

dask.array<chunksize=(192, 2), meta=np.ndarray>

units :: degrees_north





  
      Array  Chunk 
  
  
     Bytes  3.07 kB   3.07 kB 
     Shape  (192, 2)   (192, 2) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

lon
(lon)
float64
0.0 1.25 2.5 ... 356.2 357.5 358.8
axis :
X
bounds :
lon_bnds
standard_name :
longitude
title :
Longitude
type :
double
units :
degrees_east
valid_max :
360.0
valid_min :
0.0
```
array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])
```

lon_bnds

(lon, nbnd)

float64

dask.array<chunksize=(288, 2), meta=np.ndarray>

units :: degrees_east





  
      Array  Chunk 
  
  
     Bytes  4.61 kB   4.61 kB 
     Shape  (288, 2)   (288, 2) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

Data variables: (1)

areacella

(lat, lon)

float32

dask.array<chunksize=(192, 288), meta=np.ndarray>

cell_methods :: area: sum
comment :: AREA[0,:,:]
description :: Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).
frequency :: fx
id :: areacella
long_name :: Grid-Cell Area for Atmospheric Grid Variables
mipTable :: fx
out_name :: areacella
prov :: fx ((isd.003))
realm :: atmos land
standard_name :: cell_area
time_label :: None
time_title :: No temporal dimensions ... fixed field
title :: Grid-Cell Area for Atmospheric Grid Variables
type :: real
units :: m2
variable_id :: areacella





  
      Array  Chunk 
  
  
     Bytes  221.18 kB   221.18 kB 
     Shape  (192, 288)   (192, 288) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

Attributes: (45)
Conventions :
CF-1.7 CMIP-6.2
activity_id :
CFMIP
branch_method :
no parent
branch_time_in_child :
721970.0
branch_time_in_parent :
0.0
case_id :
965
cesm_casename :
f.e21.FHIST_BGC.f09_f09_mg17.CFMIP-amip-4xCO2.001
contact :
cesm_cmip6@ucar.edu
creation_date :
2019-03-13T22:41:31Z
data_specs_version :
01.00.29
experiment :
Continuation of CFMIP-2 AMIP experiments and CMIP5 experiment 6.5. AMIP conditions are imposed but the radiation code sees quadrupled CO2, relative to the AMIP. If the carbon cycle remains active, it should continue to "see" AMIP CO2, while the radiation should see 4xCO2 with respect to the AMIP experiment.
experiment_id :
amip-4xCO2
forcing_index :
1
frequency :
fx
further_info_url :
https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.amip-4xCO2.none.r1i1p1f1
grid :
native 0.9x1.25 finite volume grid (192x288 latxlon)
grid_label :
gn
initialization_index :
1
institution :
National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA
institution_id :
NCAR
license :
CMIP6 model data produced by <The National Center for Atmospheric Research> is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.
mip_era :
CMIP6
model_doi_url :
https://doi.org/10.5065/D67H1H0V
nominal_resolution :
100 km
parent_activity_id :
no parent
parent_experiment_id :
no parent
parent_mip_era :
no parent
parent_source_id :
no parent
parent_time_units :
no parent
parent_variant_label :
no parent
physics_index :
1
product :
model-output
realization_index :
1
realm :
atmos land
source :
CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)
source_id :
CESM2
source_type :
AGCM AER
sub_experiment :
none
sub_experiment_id :
none
table_id :
fx
tracking_id :
hdl:21.14100/35b69461-eb69-4a58-b2f7-413dbf2cd8fe
variable_id :
areacella
variant_info :
The same as the amip experiment within DECK, except that the CO2 concentration seen by the radiation scheme is quadrupled.
variant_label :
r1i1p1f1
status :
2019-11-04;created;by nhn2@columbia.edu

total_area = ds_area.areacella.sum(dim=['lon', 'lat'])
ta_timeseries = (ds.tas * ds_area.areacella).sum(dim=['lon', 'lat']) / total_area
ta_timeseries

xarray.DataArray

time: 1980

dask.array<chunksize=(600,), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  7.92 kB   2.40 kB 
     Shape  (1980,)   (600,) 
     Count  37 Tasks  4 Chunks 
     Type  float32  numpy.ndarray

Coordinates: (1)

time

(time)

object

1850-01-15 12:00:00 ... 2014-12-15 12:00:00

axis :: T
bounds :: time_bnds
standard_name :: time
title :: time
type :: double

array([cftime.DatetimeNoLeap(1850-01-15 12:00:00),
       cftime.DatetimeNoLeap(1850-02-14 00:00:00),
       cftime.DatetimeNoLeap(1850-03-15 12:00:00), ...,
       cftime.DatetimeNoLeap(2014-10-15 12:00:00),
       cftime.DatetimeNoLeap(2014-11-15 00:00:00),
       cftime.DatetimeNoLeap(2014-12-15 12:00:00)], dtype=object)

Attributes: (0)

By default the data are loaded lazily, as Dask arrays. Here we trigger computation explicitly.

%time ta_timeseries.load()

CPU times: user 2.87 s, sys: 1.57 s, total: 4.44 s
Wall time: 4.41 s

xarray.DataArray

time: 1980

285.6408 285.57397 286.33856 287.15988 ... 288.6992 287.76303 287.0621

array([285.6408 , 285.57397, 286.33856, ..., 288.6992 , 287.76303,
       287.0621 ], dtype=float32)

Coordinates: (1)

time

(time)

object

1850-01-15 12:00:00 ... 2014-12-15 12:00:00

axis :: T
bounds :: time_bnds
standard_name :: time
title :: time
type :: double

array([cftime.DatetimeNoLeap(1850-01-15 12:00:00),
       cftime.DatetimeNoLeap(1850-02-14 00:00:00),
       cftime.DatetimeNoLeap(1850-03-15 12:00:00), ...,
       cftime.DatetimeNoLeap(2014-10-15 12:00:00),
       cftime.DatetimeNoLeap(2014-11-15 00:00:00),
       cftime.DatetimeNoLeap(2014-12-15 12:00:00)], dtype=object)

Attributes: (0)

ta_timeseries.plot(label='monthly')
ta_timeseries.rolling(time=12).mean().plot(label='12 month rolling mean')
plt.legend()
plt.title('Global Mean Surface Air Temperature')

Text(0.5, 1.0, 'Global Mean Surface Air Temperature')