IPSL-CM5B-LR 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/rhopoto_Omon_IPSL-CM5B-LR_piControl_r1i1p1.yaml")
ds=cat.netcdf.read()

Metadata

title	IPSL-CM5B-LR model output prepared for CMIP5 pre-industrial control
location	/shared/cmip5/data/piControl/ocean/mon/Omon/rhopoto/IPSL.IPSL-CM5B-LR/r1i1p1
tags	gridded,global,model,monthly
catalog_dir	https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/rhopoto_Omon_IPSL-CM5B-LR_piControl_r1i1p1.yaml
last updated	2013-05-26

Dataset Contents

<xarray.Dataset>
Dimensions:       (bnds: 2, i: 182, j: 149, lev: 31, time: 3600, vertices: 4)
Coordinates:
  * time          (time) float64 15.5 45.0 74.5 ... 1.095e+05 1.095e+05
  * lev           (lev) float64 5.0 15.0 25.0 ... 4.25e+03 4.75e+03 5.25e+03
  * j             (j) int32 1 2 3 4 5 6 7 8 ... 142 143 144 145 146 147 148 149
  * i             (i) int32 1 2 3 4 5 6 7 8 ... 175 176 177 178 179 180 181 182
    lat           (j, i) float32 dask.array<chunksize=(149, 182), meta=np.ndarray>
    lon           (j, i) float32 dask.array<chunksize=(149, 182), meta=np.ndarray>
Dimensions without coordinates: bnds, vertices
Data variables:
    time_bnds     (time, bnds) float64 dask.array<chunksize=(600, 2), meta=np.ndarray>
    lev_bnds      (time, lev, bnds) float64 dask.array<chunksize=(600, 31, 2), meta=np.ndarray>
    lat_vertices  (time, j, i, vertices) float32 dask.array<chunksize=(600, 149, 182, 4), meta=np.ndarray>
    lon_vertices  (time, j, i, vertices) float32 dask.array<chunksize=(600, 149, 182, 4), meta=np.ndarray>
    rhopoto       (time, lev, j, i) float32 dask.array<chunksize=(600, 31, 149, 182), meta=np.ndarray>
Attributes:
    institution:            IPSL (Institut Pierre Simon Laplace, Paris, France)
    institute_id:           IPSL
    experiment_id:          piControl
    source:                 IPSL-CM5B-LR (2011) : atmos : LMDZ5 (LMDZ5_NPv3.1...
    model_id:               IPSL-CM5B-LR
    forcing:                N/A
    parent_experiment_id:   N/A
    parent_experiment_rip:  N/A
    branch_time:            0.0
    contact:                ipsl-cmip5 _at_ ipsl.jussieu.fr Data manager : Se...
    comment:                This is the preindustrial control simulation. Thi...
    references:             Model documentation and further reference availab...
    initialization_method:  1
    physics_version:        1
    tracking_id:            3e8a1a71-903c-402b-b2ac-427e7d67a786
    product:                output
    experiment:             pre-industrial control
    frequency:              mon
    creation_date:          2012-01-26T02:34:05Z
    history:                2012-01-26T02:34:05Z CMOR rewrote data to comply ...
    Conventions:            CF-1.4
    project_id:             CMIP5
    table_id:               Table Omon (31 January 2011) d2d6beec2b8fea5bbed3...
    title:                  IPSL-CM5B-LR model output prepared for CMIP5 pre-...
    parent_experiment:      N/A
    modeling_realm:         ocean
    realization:            1
    cmor_version:           2.7.1

xarray.Dataset

Dimensions:
- bnds: 2
- i: 182
- j: 149
- lev: 31
- time: 3600
- vertices: 4

Coordinates: (6)

time
(time)
float64
15.5 45.0 ... 1.095e+05 1.095e+05
bounds :
time_bnds
units :
days since 1830-01-01 00:00:00
calendar :
noleap
axis :
T
long_name :
time
standard_name :
time
```
array([1.550000e+01, 4.500000e+01, 7.450000e+01, ..., 1.094235e+05,
       1.094540e+05, 1.094845e+05])
```

lev

(lev)

float64

5.0 15.0 25.0 ... 4.75e+03 5.25e+03

bounds :: lev_bnds
units :: m
axis :: Z
positive :: down
long_name :: ocean depth coordinate
standard_name :: depth

array([4.999938e+00, 1.500029e+01, 2.500176e+01, 3.500541e+01, 4.501332e+01,
       5.502950e+01, 6.506181e+01, 7.512551e+01, 8.525037e+01, 9.549429e+01,
       1.059699e+02, 1.168962e+02, 1.286979e+02, 1.421953e+02, 1.589606e+02,
       1.819628e+02, 2.166479e+02, 2.724767e+02, 3.643030e+02, 5.115348e+02,
       7.322009e+02, 1.033217e+03, 1.405698e+03, 1.830885e+03, 2.289768e+03,
       2.768242e+03, 3.257479e+03, 3.752442e+03, 4.250401e+03, 4.749913e+03,
       5.250227e+03])

(j)

int32

1 2 3 4 5 6 ... 145 146 147 148 149

units :: 1
long_name :: cell index along second dimension

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,  36,  37,  38,  39,  40,  41,  42,
        43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
        57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
        71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
        85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
       113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
       127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140,
       141, 142, 143, 144, 145, 146, 147, 148, 149], dtype=int32)

(i)

int32

1 2 3 4 5 6 ... 178 179 180 181 182

units :: 1
long_name :: cell index along first dimension

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,  36,  37,  38,  39,  40,  41,  42,
        43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
        57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
        71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
        85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
       113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
       127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140,
       141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,
       155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182],
      dtype=int32)

lat

(j, i)

float32

dask.array<chunksize=(149, 182), meta=np.ndarray>

standard_name :: latitude
long_name :: latitude coordinate
units :: degrees_north
bounds :: lat_vertices

	Array	Chunk
Bytes	108.47 kB	108.47 kB
Shape	(149, 182)	(149, 182)
Count	25 Tasks	1 Chunks
Type	float32	numpy.ndarray

lon

(j, i)

float32

dask.array<chunksize=(149, 182), meta=np.ndarray>

standard_name :: longitude
long_name :: longitude coordinate
units :: degrees_east
bounds :: lon_vertices

	Array	Chunk
Bytes	108.47 kB	108.47 kB
Shape	(149, 182)	(149, 182)
Count	25 Tasks	1 Chunks
Type	float32	numpy.ndarray

Data variables: (5)

time_bnds

(time, bnds)

float64

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

	Array	Chunk
Bytes	57.60 kB	9.60 kB
Shape	(3600, 2)	(600, 2)
Count	18 Tasks	6 Chunks
Type	float64	numpy.ndarray

lev_bnds

(time, lev, bnds)

float64

dask.array<chunksize=(600, 31, 2), meta=np.ndarray>

	Array	Chunk
Bytes	1.79 MB	297.60 kB
Shape	(3600, 31, 2)	(600, 31, 2)
Count	24 Tasks	6 Chunks
Type	float64	numpy.ndarray

lat_vertices

(time, j, i, vertices)

float32

dask.array<chunksize=(600, 149, 182, 4), meta=np.ndarray>

units :: degrees_north

	Array	Chunk
Bytes	1.56 GB	260.33 MB
Shape	(3600, 149, 182, 4)	(600, 149, 182, 4)
Count	24 Tasks	6 Chunks
Type	float32	numpy.ndarray

lon_vertices

(time, j, i, vertices)

float32

dask.array<chunksize=(600, 149, 182, 4), meta=np.ndarray>

units :: degrees_east

	Array	Chunk
Bytes	1.56 GB	260.33 MB
Shape	(3600, 149, 182, 4)	(600, 149, 182, 4)
Count	24 Tasks	6 Chunks
Type	float32	numpy.ndarray

rhopoto

(time, lev, j, i)

float32

dask.array<chunksize=(600, 31, 149, 182), meta=np.ndarray>

standard_name :: sea_water_potential_density
long_name :: Sea Water Potential Density
units :: kg m-3
original_name :: rhopoto
original_units :: kg/m3
history :: 2012-01-26T02:33:49Z altered by CMOR: Converted units from 'kg/m3' to 'kg m-3'. 2012-01-26T02:33:49Z altered by CMOR: replaced missing value flag (9.96921e+36) with standard missing value (1e+20).
cell_methods :: time: mean (interval: 30 minutes)
cell_measures :: area: areacello volume: volcello
associated_files :: baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_ocean_fx_IPSL-CM5B-LR_piControl_r0i0p0.nc areacello: areacello_fx_IPSL-CM5B-LR_piControl_r0i0p0.nc volcello: volcello_fx_IPSL-CM5B-LR_piControl_r0i0p0.nc

	Array	Chunk
Bytes	12.11 GB	2.02 GB
Shape	(3600, 31, 149, 182)	(600, 31, 149, 182)
Count	18 Tasks	6 Chunks
Type	float32	numpy.ndarray

Attributes: (28)
institution :
IPSL (Institut Pierre Simon Laplace, Paris, France)
institute_id :
IPSL
experiment_id :
piControl
source :
IPSL-CM5B-LR (2011) : atmos : LMDZ5 (LMDZ5_NPv3.1, 96x95x39); ocean : ORCA2 (NEMOV2_3, 2x2L31); seaIce : LIM2 (NEMOV2_3); ocnBgchem : PISCES (NEMOV2_3); land : ORCHIDEE (orchidee_1_9_4_AR5)
model_id :
IPSL-CM5B-LR
forcing :
N/A
parent_experiment_id :
N/A
parent_experiment_rip :
N/A
branch_time :
0.0
contact :
ipsl-cmip5 _at_ ipsl.jussieu.fr Data manager : Sebastien Denvil
comment :
This is the preindustrial control simulation. This simulation was initiated from a preindustrial spinup simulation when equilibrium was reached.
references :
Model documentation and further reference available here : http://icmc.ipsl.fr
initialization_method :
1
physics_version :
1
tracking_id :
3e8a1a71-903c-402b-b2ac-427e7d67a786
product :
output
experiment :
pre-industrial control
frequency :
mon
creation_date :
2012-01-26T02:34:05Z
history :
2012-01-26T02:34:05Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
Conventions :
CF-1.4
project_id :
CMIP5
table_id :
Table Omon (31 January 2011) d2d6beec2b8fea5bbed33920a6e08bbe
title :
IPSL-CM5B-LR model output prepared for CMIP5 pre-industrial control
parent_experiment :
N/A
modeling_realm :
ocean
realization :
1
cmor_version :
2.7.1