MRI-ESM2-0 output prepared for CMIP6

Load in Python

from intake import open_catalog

cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/thetao_Omon_MRI-ESM2-0_piControl_r1i1p1f1_gn.yaml")
ds=cat.netcdf.read()

Metadata

title	MRI-ESM2-0 output prepared for CMIP6
location	/shared/cmip6/data/piControl/ocean/mon/Omon/thetao/MRI-ESM2-0/r1i1p1
tags	gridded,global,model,monthly
catalog_dir	https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/thetao_Omon_MRI-ESM2-0_piControl_r1i1p1f1_gn.yaml
last updated	2020-10-22

Dataset Contents

<xarray.Dataset>
Dimensions:             (bnds: 2, lev: 61, time: 8412, vertices: 4, x: 360, y: 363)
Coordinates:
  * time                (time) float64 15.5 45.0 74.5 ... 2.56e+05 2.56e+05
  * lev                 (lev) float64 1.0 3.5 6.5 ... 5.5e+03 6.15e+03 6.525e+03
  * y                   (y) float64 -78.0 -77.5 -77.0 ... 149.7 151.8 154.0
  * x                   (x) float64 0.0 1.0 2.0 3.0 ... 356.0 357.0 358.0 359.0
    latitude            (y, x) float64 dask.array<chunksize=(363, 360), meta=np.ndarray>
    longitude           (y, x) float64 dask.array<chunksize=(363, 360), 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, 61, 2), meta=np.ndarray>
    y_bnds              (time, y, bnds) float64 dask.array<chunksize=(600, 363, 2), meta=np.ndarray>
    x_bnds              (time, x, bnds) float64 dask.array<chunksize=(600, 360, 2), meta=np.ndarray>
    vertices_latitude   (time, y, x, vertices) float64 dask.array<chunksize=(600, 363, 360, 4), meta=np.ndarray>
    vertices_longitude  (time, y, x, vertices) float64 dask.array<chunksize=(600, 363, 360, 4), meta=np.ndarray>
    thetao              (time, lev, y, x) float32 dask.array<chunksize=(600, 61, 363, 360), meta=np.ndarray>
Attributes:
    Conventions:            CF-1.7 CMIP-6.2
    activity_id:            CMIP
    branch_method:          standard
    branch_time_in_child:   0.0
    branch_time_in_parent:  365243.0
    creation_date:          2019-12-09T11:15:43Z
    data_specs_version:     01.00.31
    experiment:             pre-industrial control
    experiment_id:          piControl
    external_variables:     areacello volcello
    forcing_index:          1
    frequency:              mon
    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.MRI.MRI-ESM2...
    grid:                   native ocean tri-polar grid with 360x363 ocean cells
    grid_label:             gn
    history:                2019-12-09T11:15:43Z ; CMOR rewrote data to be co...
    initialization_index:   1
    institution:            Meteorological Research Institute, Tsukuba, Ibara...
    institution_id:         MRI
    mip_era:                CMIP6
    nominal_resolution:     100 km
    parent_activity_id:     CMIP
    parent_experiment_id:   piControl-spinup
    parent_mip_era:         CMIP6
    parent_source_id:       MRI-ESM2-0
    parent_time_units:      days since 1850-01-01
    parent_variant_label:   r1i1p1f1
    physics_index:          1
    product:                model-output
    realization_index:      1
    realm:                  ocean
    source:                 MRI-ESM2.0 (2017): 
aerosol: MASINGAR mk2r4 (TL9...
    source_id:              MRI-ESM2-0
    source_type:            AOGCM AER CHEM
    sub_experiment:         none
    sub_experiment_id:      none
    table_id:               Omon
    table_info:             Creation Date:(24 July 2019) MD5:c93735846d664589...
    title:                  MRI-ESM2-0 output prepared for CMIP6
    variable_id:            thetao
    variant_label:          r1i1p1f1
    license:                CMIP6 model data produced by MRI is licensed unde...
    cmor_version:           3.5.0
    tracking_id:            hdl:21.14100/9efbe1ca-8284-4062-a82e-12ed7d9e1181

xarray.Dataset

Dimensions:
- bnds: 2
- lev: 61
- time: 8412
- vertices: 4
- x: 360
- y: 363

Coordinates: (6)

time
(time)
float64
15.5 45.0 ... 2.56e+05 2.56e+05
bounds :
time_bnds
units :
days since 1850-01-01
calendar :
proleptic_gregorian
axis :
T
long_name :
time
standard_name :
time
```
array([1.550000e+01, 4.500000e+01, 7.450000e+01, ..., 2.559585e+05,
       2.559890e+05, 2.560195e+05])
```

lev

(lev)

float64

1.0 3.5 6.5 ... 6.15e+03 6.525e+03

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

array([1.0000e+00, 3.5000e+00, 6.5000e+00, 1.0000e+01, 1.5000e+01, 2.2000e+01,
       3.0500e+01, 4.0000e+01, 5.0000e+01, 6.0000e+01, 7.0000e+01, 8.0000e+01,
       9.0000e+01, 1.0000e+02, 1.1000e+02, 1.2000e+02, 1.3000e+02, 1.4000e+02,
       1.5000e+02, 1.6000e+02, 1.7000e+02, 1.8000e+02, 1.9000e+02, 2.0000e+02,
       2.1250e+02, 2.3000e+02, 2.5000e+02, 2.7250e+02, 3.0000e+02, 3.3000e+02,
       3.6250e+02, 4.0000e+02, 4.4000e+02, 4.8500e+02, 5.4000e+02, 6.0000e+02,
       6.6500e+02, 7.4000e+02, 8.2000e+02, 9.0500e+02, 1.0000e+03, 1.1000e+03,
       1.2125e+03, 1.3500e+03, 1.5000e+03, 1.6500e+03, 1.8125e+03, 2.0000e+03,
       2.2250e+03, 2.4750e+03, 2.7250e+03, 3.0000e+03, 3.3000e+03, 3.6000e+03,
       3.9000e+03, 4.2000e+03, 4.5500e+03, 4.9750e+03, 5.5000e+03, 6.1500e+03,
       6.5250e+03])

y
(y)
float64
-78.0 -77.5 -77.0 ... 151.8 154.0
bounds :
y_bnds
units :
degrees
axis :
Y
long_name :
y coordinate of projection
standard_name :
projection_y_coordinate
```
array([-78.      , -77.5     , -77.      , ..., 149.670841, 151.834689,
       154.      ])
```
x
(x)
float64
0.0 1.0 2.0 ... 357.0 358.0 359.0
bounds :
x_bnds
units :
degrees
axis :
X
long_name :
x coordinate of projection
standard_name :
projection_x_coordinate
```
array([  0.,   1.,   2., ..., 357., 358., 359.])
```

latitude

(y, x)

float64

dask.array<chunksize=(363, 360), meta=np.ndarray>

standard_name :: latitude
long_name :: latitude
units :: degrees_north
bounds :: vertices_latitude

	Array	Chunk
Bytes	1.05 MB	1.05 MB
Shape	(363, 360)	(363, 360)
Count	65 Tasks	1 Chunks
Type	float64	numpy.ndarray

longitude

(y, x)

float64

dask.array<chunksize=(363, 360), meta=np.ndarray>

standard_name :: longitude
long_name :: longitude
units :: degrees_east
bounds :: vertices_longitude

	Array	Chunk
Bytes	1.05 MB	1.05 MB
Shape	(363, 360)	(363, 360)
Count	65 Tasks	1 Chunks
Type	float64	numpy.ndarray

Data variables: (7)

time_bnds

(time, bnds)

float64

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

	Array	Chunk
Bytes	134.59 kB	9.79 kB
Shape	(8412, 2)	(612, 2)
Count	42 Tasks	14 Chunks
Type	float64	numpy.ndarray

lev_bnds

(time, lev, bnds)

float64

dask.array<chunksize=(600, 61, 2), meta=np.ndarray>

	Array	Chunk
Bytes	8.21 MB	597.31 kB
Shape	(8412, 61, 2)	(612, 61, 2)
Count	56 Tasks	14 Chunks
Type	float64	numpy.ndarray

y_bnds

(time, y, bnds)

float64

dask.array<chunksize=(600, 363, 2), meta=np.ndarray>

	Array	Chunk
Bytes	48.86 MB	3.55 MB
Shape	(8412, 363, 2)	(612, 363, 2)
Count	56 Tasks	14 Chunks
Type	float64	numpy.ndarray

x_bnds

(time, x, bnds)

float64

dask.array<chunksize=(600, 360, 2), meta=np.ndarray>

	Array	Chunk
Bytes	48.45 MB	3.53 MB
Shape	(8412, 360, 2)	(612, 360, 2)
Count	56 Tasks	14 Chunks
Type	float64	numpy.ndarray

vertices_latitude

(time, y, x, vertices)

float64

dask.array<chunksize=(600, 363, 360, 4), meta=np.ndarray>

units :: degrees_north

	Array	Chunk
Bytes	35.18 GB	2.56 GB
Shape	(8412, 363, 360, 4)	(612, 363, 360, 4)
Count	56 Tasks	14 Chunks
Type	float64	numpy.ndarray

vertices_longitude

(time, y, x, vertices)

float64

dask.array<chunksize=(600, 363, 360, 4), meta=np.ndarray>

units :: degrees_east

	Array	Chunk
Bytes	35.18 GB	2.56 GB
Shape	(8412, 363, 360, 4)	(612, 363, 360, 4)
Count	56 Tasks	14 Chunks
Type	float64	numpy.ndarray

thetao

(time, lev, y, x)

float32

dask.array<chunksize=(600, 61, 363, 360), meta=np.ndarray>

standard_name :: sea_water_potential_temperature
long_name :: Sea Water Potential Temperature
comment :: Diagnostic should be contributed even for models using conservative temperature as prognostic field.
units :: degC
cell_methods :: area: mean where sea time: mean
cell_measures :: area: areacello volume: volcello
history :: 2019-12-09T11:15:43Z altered by CMOR: replaced missing value flag (-9.99e+33) and corresponding data with standard missing value (1e+20).

	Array	Chunk
Bytes	268.22 GB	19.51 GB
Shape	(8412, 61, 363, 360)	(612, 61, 363, 360)
Count	42 Tasks	14 Chunks
Type	float32	numpy.ndarray

Attributes: (44)
Conventions :
CF-1.7 CMIP-6.2
activity_id :
CMIP
branch_method :
standard
branch_time_in_child :
0.0
branch_time_in_parent :
365243.0
creation_date :
2019-12-09T11:15:43Z
data_specs_version :
01.00.31
experiment :
pre-industrial control
experiment_id :
piControl
external_variables :
areacello volcello
forcing_index :
1
frequency :
mon
further_info_url :
https://furtherinfo.es-doc.org/CMIP6.MRI.MRI-ESM2-0.piControl.none.r1i1p1f1
grid :
native ocean tri-polar grid with 360x363 ocean cells
grid_label :
gn
history :
2019-12-09T11:15:43Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.
initialization_index :
1
institution :
Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan
institution_id :
MRI
mip_era :
CMIP6
nominal_resolution :
100 km
parent_activity_id :
CMIP
parent_experiment_id :
piControl-spinup
parent_mip_era :
CMIP6
parent_source_id :
MRI-ESM2-0
parent_time_units :
days since 1850-01-01
parent_variant_label :
r1i1p1f1
physics_index :
1
product :
model-output
realization_index :
1
realm :
ocean
source :
MRI-ESM2.0 (2017): aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa) atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa) atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa) land: HAL 1.0 landIce: none ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m) ocnBgchem: MRI.COM4.4 seaIce: MRI.COM4.4
source_id :
MRI-ESM2-0
source_type :
AOGCM AER CHEM
sub_experiment :
none
sub_experiment_id :
none
table_id :
Omon
table_info :
Creation Date:(24 July 2019) MD5:c93735846d66458966fc81f390b2d714
title :
MRI-ESM2-0 output prepared for CMIP6
variable_id :
thetao
variant_label :
r1i1p1f1
license :
CMIP6 model data produced by MRI 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.
cmor_version :
3.5.0
tracking_id :
hdl:21.14100/9efbe1ca-8284-4062-a82e-12ed7d9e1181