CAMS-CSM1-0 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/mlotst_Omon_CAMS-CSM1-0_piControl_r1i1p1f1_gn.yaml")
ds=cat.netcdf.read()

Metadata

title	CAMS-CSM1-0 output prepared for CMIP6
location	/shared/cmip6/data/piControl/ocean/mon/Omon/mlotst/CAMS-CSM1-0/r1i1p1
tags	gridded,global,model,monthly
catalog_dir	https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/mlotst_Omon_CAMS-CSM1-0_piControl_r1i1p1f1_gn.yaml
last updated	2020-10-08

Dataset Contents

<xarray.Dataset>
Dimensions:             (bnds: 2, i: 360, j: 200, time: 6000, vertices: 4)
Coordinates:
  * time                (time) float64 15.5 45.0 74.5 ... 9.12e+04 9.123e+04
  * j                   (j) int32 1 2 3 4 5 6 7 ... 194 195 196 197 198 199 200
  * i                   (i) int32 1 2 3 4 5 6 7 ... 354 355 356 357 358 359 360
    latitude            (j, i) float32 dask.array<chunksize=(200, 360), meta=np.ndarray>
    longitude           (j, i) float32 dask.array<chunksize=(200, 360), meta=np.ndarray>
Dimensions without coordinates: bnds, vertices
Data variables:
    time_bnds           (time, bnds) float64 dask.array<chunksize=(3000, 2), meta=np.ndarray>
    vertices_latitude   (time, j, i, vertices) float32 dask.array<chunksize=(3000, 200, 360, 4), meta=np.ndarray>
    vertices_longitude  (time, j, i, vertices) float32 dask.array<chunksize=(3000, 200, 360, 4), meta=np.ndarray>
    mlotst              (time, j, i) float32 dask.array<chunksize=(3000, 200, 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:  0.0
    contact:                Dr. Xinyao Rong (rongxy@cma.gov.cn)
    creation_date:          2019-07-05T12:35:10Z
    data_specs_version:     01.00.30
    description:            DECK: piControl
    experiment:             pre-industrial control
    experiment_id:          piControl
    external_variables:     areacello
    forcing_index:          1
    frequency:              mon
    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.CAMS.CAMS-CS...
    grid:                   native ocean tri-polar grid
    grid_label:             gn
    history:                2019-07-05T12:24:14Z ;rewrote data to be consiste...
    initialization_index:   1
    institution:            Chinese Academy of Meteorological Sciences, Beiji...
    institution_id:         CAMS
    mip_era:                CMIP6
    nominal_resolution:     100 km
    parent_activity_id:     CMIP
    parent_experiment_id:   piControl-spinup
    parent_mip_era:         CMIP6
    parent_source_id:       CAMS-CSM1-0
    parent_time_units:      days since 2900-01-01
    parent_variant_label:   r1i1p1f1
    physics_index:          1
    product:                model-output
    realization_index:      1
    realm:                  ocean
    references:             Model described by Rong et al (J. Meteoro. Res., ...
    run_variant:            forcing: N/A
    source:                 CAMS_CSM 1.0 (2016): 
aerosol: none
atmos: ECHA...
    source_id:              CAMS-CSM1-0
    source_type:            AOGCM
    sub_experiment:         none
    sub_experiment_id:      none
    table_id:               Omon
    table_info:             Creation Date:(30 April 2019) MD5:cc2ae51c23960ce...
    title:                  CAMS-CSM1-0 output prepared for CMIP6
    tracking_id:            hdl:21.14100/72811503-08a9-4c7b-80ac-e0143d7ca0a4
    variable_id:            mlotst
    variant_label:          r1i1p1f1
    license:                CMIP6 model data produced by Lawrence Livermore P...
    cmor_version:           3.4.0

xarray.Dataset

Dimensions:
- bnds: 2
- i: 360
- j: 200
- time: 6000
- vertices: 4

Coordinates: (5)

time
(time)
float64
15.5 45.0 ... 9.12e+04 9.123e+04
bounds :
time_bnds
calendar :
365_day
axis :
T
long_name :
time
standard_name :
time
```
array([1.55000e+01, 4.50000e+01, 7.45000e+01, ..., 9.11735e+04, 9.12040e+04,
       9.12345e+04])
```

(j)

int32

1 2 3 4 5 6 ... 196 197 198 199 200

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, 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,
       183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,
       197, 198, 199, 200], dtype=int32)

i
(i)
int32
1 2 3 4 5 6 ... 356 357 358 359 360
units :
1
long_name :
cell index along first dimension
```
array([  1,   2,   3, ..., 358, 359, 360], dtype=int32)
```

latitude

(j, i)

float32

dask.array<chunksize=(200, 360), meta=np.ndarray>

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

	Array	Chunk
Bytes	288.00 kB	288.00 kB
Shape	(200, 360)	(200, 360)
Count	5 Tasks	1 Chunks
Type	float32	numpy.ndarray

longitude

(j, i)

float32

dask.array<chunksize=(200, 360), meta=np.ndarray>

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

	Array	Chunk
Bytes	288.00 kB	288.00 kB
Shape	(200, 360)	(200, 360)
Count	5 Tasks	1 Chunks
Type	float32	numpy.ndarray

Data variables: (4)

time_bnds

(time, bnds)

float64

dask.array<chunksize=(3000, 2), meta=np.ndarray>

	Array	Chunk
Bytes	96.00 kB	48.00 kB
Shape	(6000, 2)	(3000, 2)
Count	6 Tasks	2 Chunks
Type	float64	numpy.ndarray

vertices_latitude

(time, j, i, vertices)

float32

dask.array<chunksize=(3000, 200, 360, 4), meta=np.ndarray>

units :: degrees_north

	Array	Chunk
Bytes	6.91 GB	3.46 GB
Shape	(6000, 200, 360, 4)	(3000, 200, 360, 4)
Count	8 Tasks	2 Chunks
Type	float32	numpy.ndarray

vertices_longitude

(time, j, i, vertices)

float32

dask.array<chunksize=(3000, 200, 360, 4), meta=np.ndarray>

units :: degrees_east

	Array	Chunk
Bytes	6.91 GB	3.46 GB
Shape	(6000, 200, 360, 4)	(3000, 200, 360, 4)
Count	8 Tasks	2 Chunks
Type	float32	numpy.ndarray

mlotst

(time, j, i)

float32

dask.array<chunksize=(3000, 200, 360), meta=np.ndarray>

standard_name :: ocean_mixed_layer_thickness_defined_by_sigma_t
long_name :: Ocean Mixed Layer Thickness Defined by Sigma T
comment :: Sigma T is potential density referenced to ocean surface.
units :: m
original_name :: mld
cell_methods :: area: mean where sea time: mean
cell_measures :: area: areacello
history :: 2019-07-05T12:24:14Z altered by CMOR: replaced missing value flag (0) with standard missing value (1e+20).

	Array	Chunk
Bytes	1.73 GB	864.00 MB
Shape	(6000, 200, 360)	(3000, 200, 360)
Count	6 Tasks	2 Chunks
Type	float32	numpy.ndarray

Attributes: (48)
Conventions :
CF-1.7 CMIP-6.2
activity_id :
CMIP
branch_method :
Standard
branch_time_in_child :
0.0
branch_time_in_parent :
0.0
contact :
Dr. Xinyao Rong (rongxy@cma.gov.cn)
creation_date :
2019-07-05T12:35:10Z
data_specs_version :
01.00.30
description :
DECK: piControl
experiment :
pre-industrial control
experiment_id :
piControl
external_variables :
areacello
forcing_index :
1
frequency :
mon
further_info_url :
https://furtherinfo.es-doc.org/CMIP6.CAMS.CAMS-CSM1-0.piControl.none.r1i1p1f1
grid :
native ocean tri-polar grid
grid_label :
gn
history :
2019-07-05T12:24:14Z ;rewrote data to be consistent with CMIP for variable tos found in table Omon.; Output from CAMS-CSM1-0
initialization_index :
1
institution :
Chinese Academy of Meteorological Sciences, Beijing 100081, China
institution_id :
CAMS
mip_era :
CMIP6
nominal_resolution :
100 km
parent_activity_id :
CMIP
parent_experiment_id :
piControl-spinup
parent_mip_era :
CMIP6
parent_source_id :
CAMS-CSM1-0
parent_time_units :
days since 2900-01-01
parent_variant_label :
r1i1p1f1
physics_index :
1
product :
model-output
realization_index :
1
realm :
ocean
references :
Model described by Rong et al (J. Meteoro. Res., 2019, 576-591).
run_variant :
forcing: N/A
source :
CAMS_CSM 1.0 (2016): aerosol: none atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb) atmosChem: none land: CoLM 1.0 landIce: none ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m) ocnBgchem: none seaIce: SIS 1.0
source_id :
CAMS-CSM1-0
source_type :
AOGCM
sub_experiment :
none
sub_experiment_id :
none
table_id :
Omon
table_info :
Creation Date:(30 April 2019) MD5:cc2ae51c23960ce127b4d68ab3d8ac87
title :
CAMS-CSM1-0 output prepared for CMIP6
tracking_id :
hdl:21.14100/72811503-08a9-4c7b-80ac-e0143d7ca0a4
variable_id :
mlotst
variant_label :
r1i1p1f1
license :
CMIP6 model data produced by Lawrence Livermore PCMDI 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) and at https:///pcmdi.llnl.gov/. 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 :
3.4.0