CNRM-CM5 model output prepared for CMIP5 pre-industrial control

Load in Python

from intake import open_catalog

cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/mrsos_Lmon_CNRM-CM5_piControl_r1i1p1.yaml")
ds=cat.netcdf.read()

Metadata

title	CNRM-CM5 model output prepared for CMIP5 pre-industrial control
location	/shared/cmip5/data/piControl/land/mon/Lmon/mrsos/CNRM-CERFACS.CNRM-CM5/r1i1p1
tags	gridded,global,model,monthly
catalog_dir	https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/mrsos_Lmon_CNRM-CM5_piControl_r1i1p1.yaml
last updated	2013-06-14

Dataset Contents

<xarray.Dataset>
Dimensions:     (bnds: 2, lat: 128, lon: 256, time: 10200)
Coordinates:
  * time        (time) float64 15.5 45.0 74.5 ... 3.104e+05 3.104e+05 3.104e+05
  * lat         (lat) float64 -88.93 -87.54 -86.14 -84.74 ... 86.14 87.54 88.93
  * lon         (lon) float64 0.0 1.406 2.812 4.219 ... 354.4 355.8 357.2 358.6
    depth       float64 0.005
Dimensions without coordinates: bnds
Data variables:
    time_bnds   (time, bnds) float64 dask.array<chunksize=(600, 2), meta=np.ndarray>
    lat_bnds    (time, lat, bnds) float64 dask.array<chunksize=(600, 128, 2), meta=np.ndarray>
    lon_bnds    (time, lon, bnds) float64 dask.array<chunksize=(600, 256, 2), meta=np.ndarray>
    depth_bnds  (time, bnds) float64 dask.array<chunksize=(600, 2), meta=np.ndarray>
    mrsos       (time, lat, lon) float32 dask.array<chunksize=(600, 128, 256), meta=np.ndarray>
Attributes:
    institution:            CNRM (Centre National de Recherches Meteorologiqu...
    institute_id:           CNRM-CERFACS
    experiment_id:          piControl
    source:                 CNRM-CM5 2010 Atmosphere: ARPEGE-Climat (V5.2.1, ...
    model_id:               CNRM-CM5
    forcing:                N/A
    parent_experiment_id:   N/A
    parent_experiment_rip:  r1i1p1
    branch_time:            0.0
    contact:                for all but decadal predictions : contact.CMIP5@m...
    comment:                Soil layers depth scheme is specific for mrlsl an...
    references:             See http://www.cnrm.meteo.fr/cmip5 - Follow model...
    initialization_method:  1
    physics_version:        1
    tracking_id:            5c554220-922a-4218-9ac1-b1ca310625d4
    product:                output
    experiment:             pre-industrial control
    frequency:              mon
    creation_date:          2011-04-01T08:28:58Z
    history:                2011-04-01T08:28:55Z CMOR rewrote data to comply ...
    Conventions:            CF-1.4
    project_id:             CMIP5
    table_id:               Table Lmon (31 January 2011) c511175fde1edd0ab481...
    title:                  CNRM-CM5 model output prepared for CMIP5 pre-indu...
    parent_experiment:      N/A
    modeling_realm:         land
    realization:            1
    cmor_version:           2.5.3

xarray.Dataset

Dimensions:
- bnds: 2
- lat: 128
- lon: 256
- time: 10200

Coordinates: (4)

time
(time)
float64
15.5 45.0 ... 3.104e+05 3.104e+05
bounds :
time_bnds
units :
days since 1850-1-1
calendar :
gregorian
axis :
T
long_name :
time
standard_name :
time
```
array([1.550000e+01, 4.500000e+01, 7.450000e+01, ..., 3.103795e+05,
       3.104100e+05, 3.104405e+05])
```

lat

(lat)

float64

-88.93 -87.54 ... 87.54 88.93

bounds :: lat_bnds
units :: degrees_north
axis :: Y
long_name :: latitude
standard_name :: latitude

array([-88.927734, -87.538704, -86.141472, -84.742386, -83.342598, -81.942467,
       -80.542145, -79.141708, -77.741196, -76.34063 , -74.940025, -73.539391,
       -72.138733, -70.73806 , -69.337372, -67.936676, -66.535965, -65.135254,
       -63.734531, -62.333801, -60.933071, -59.532337, -58.131599, -56.730858,
       -55.330112, -53.929367, -52.528618, -51.127865, -49.727116, -48.326363,
       -46.925606, -45.524849, -44.124092, -42.723335, -41.322575, -39.921818,
       -38.521057, -37.120293, -35.719532, -34.318771, -32.918007, -31.517244,
       -30.11648 , -28.715715, -27.314951, -25.914186, -24.51342 , -23.112656,
       -21.711889, -20.311123, -18.910357, -17.50959 , -16.108824, -14.708057,
       -13.30729 , -11.906524, -10.505756,  -9.104989,  -7.704221,  -6.303454,
        -4.902687,  -3.501919,  -2.101151,  -0.700384,   0.700384,   2.101151,
         3.501919,   4.902687,   6.303454,   7.704221,   9.104989,  10.505756,
        11.906524,  13.30729 ,  14.708057,  16.108824,  17.50959 ,  18.910357,
        20.311123,  21.711889,  23.112656,  24.51342 ,  25.914186,  27.314951,
        28.715715,  30.11648 ,  31.517244,  32.918007,  34.318771,  35.719532,
        37.120293,  38.521057,  39.921818,  41.322575,  42.723335,  44.124092,
        45.524849,  46.925606,  48.326363,  49.727116,  51.127865,  52.528618,
        53.929367,  55.330112,  56.730858,  58.131599,  59.532337,  60.933071,
        62.333801,  63.734531,  65.135254,  66.535965,  67.936676,  69.337372,
        70.73806 ,  72.138733,  73.539391,  74.940025,  76.34063 ,  77.741196,
        79.141708,  80.542145,  81.942467,  83.342598,  84.742386,  86.141472,
        87.538704,  88.927734])

lon
(lon)
float64
0.0 1.406 2.812 ... 357.2 358.6
bounds :
lon_bnds
units :
degrees_east
axis :
X
long_name :
longitude
standard_name :
longitude
```
array([  0.     ,   1.40625,   2.8125 , ..., 355.78125, 357.1875 , 358.59375])
```
depth
()
float64
0.005
units :
m
axis :
Z
positive :
down
long_name :
depth
standard_name :
depth
bounds :
depth_bnds
```
array(0.005)
```

Data variables: (5)

time_bnds

(time, bnds)

float64

dask.array<chunksize=(600, 2), meta=np.ndarray>

	Array	Chunk
Bytes	163.20 kB	9.60 kB
Shape	(10200, 2)	(600, 2)
Count	51 Tasks	17 Chunks
Type	float64	numpy.ndarray

lat_bnds

(time, lat, bnds)

float64

dask.array<chunksize=(600, 128, 2), meta=np.ndarray>

	Array	Chunk
Bytes	20.89 MB	1.23 MB
Shape	(10200, 128, 2)	(600, 128, 2)
Count	68 Tasks	17 Chunks
Type	float64	numpy.ndarray

lon_bnds

(time, lon, bnds)

float64

dask.array<chunksize=(600, 256, 2), meta=np.ndarray>

	Array	Chunk
Bytes	41.78 MB	2.46 MB
Shape	(10200, 256, 2)	(600, 256, 2)
Count	68 Tasks	17 Chunks
Type	float64	numpy.ndarray

depth_bnds

(time, bnds)

float64

dask.array<chunksize=(600, 2), meta=np.ndarray>

	Array	Chunk
Bytes	163.20 kB	9.60 kB
Shape	(10200, 2)	(600, 2)
Count	68 Tasks	17 Chunks
Type	float64	numpy.ndarray

mrsos

(time, lat, lon)

float32

dask.array<chunksize=(600, 128, 256), meta=np.ndarray>

standard_name :: moisture_content_of_soil_layer
long_name :: Moisture in Upper Portion of Soil Column
comment :: the mass of water in all phases in a thin surface soil layer; ACTUALLY 1cm deep AND NOT 10 cm as recommended
units :: kg m-2
original_name :: mrsos
cell_methods :: time: mean (interval: 24 hours) area: mean where land
cell_measures :: area: areacella
history :: 2011-04-01T08:28:55Z altered by CMOR: Treated scalar dimension: 'depth'.
associated_files :: baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_land_fx_CNRM-CM5_piControl_r0i0p0.nc areacella: areacella_fx_CNRM-CM5_piControl_r0i0p0.nc

	Array	Chunk
Bytes	1.34 GB	78.64 MB
Shape	(10200, 128, 256)	(600, 128, 256)
Count	51 Tasks	17 Chunks
Type	float32	numpy.ndarray

Attributes: (28)
institution :
CNRM (Centre National de Recherches Meteorologiques, Meteo-France, Toulouse,France) and CERFACS (Centre Europeen de Recherches et de Formation Avancee en Calcul Scientifique, Toulouse, France)
institute_id :
CNRM-CERFACS
experiment_id :
piControl
source :
CNRM-CM5 2010 Atmosphere: ARPEGE-Climat (V5.2.1, TL127L31); Ocean: NEMO (nemo3.3.v10.6.6P, ORCA1degL42); Sea Ice: GELATO (V5.30); River Routing: TRIP (v1); Land: SURFEX (v5.1.c); Coupler : OASIS 3
model_id :
CNRM-CM5
forcing :
N/A
parent_experiment_id :
N/A
parent_experiment_rip :
r1i1p1
branch_time :
0.0
contact :
for all but decadal predictions : contact.CMIP5@meteo.fr - METEO-FRANCE, CNRM/GMGEC/ASTER, CNRS URA 1357, 42 AV. Coriolis F-31057 TOULOUSE CEDEX 1 /for decadal predictions : contact.CMIP5@cerfacs.fr - CERFACS, Climate Modelling And Global Change, URA CERFACS/CNRS No1875, 42 Av. Coriolis F-31057 TOULOUSE CEDEX 1
comment :
Soil layers depth scheme is specific for mrlsl and tsl - see variable-level comments. Atmosphere vertical hybrid coordinate : a_bnds and b_bnds arrays are correct, but a and b values provided are mid-sum of a_bnds and b_bnds, which is a poor approximation compared to the hydrostatic approximation actually used in the model.
references :
See http://www.cnrm.meteo.fr/cmip5 - Follow model description link
initialization_method :
1
physics_version :
1
tracking_id :
5c554220-922a-4218-9ac1-b1ca310625d4
product :
output
experiment :
pre-industrial control
frequency :
mon
creation_date :
2011-04-01T08:28:58Z
history :
2011-04-01T08:28:55Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
Conventions :
CF-1.4
project_id :
CMIP5
table_id :
Table Lmon (31 January 2011) c511175fde1edd0ab4816b653c538655
title :
CNRM-CM5 model output prepared for CMIP5 pre-industrial control
parent_experiment :
N/A
modeling_realm :
land
realization :
1
cmor_version :
2.5.3