CMCC-CM model output prepared for CMIP5 RCP8.5

Load in Python

from intake import open_catalog

cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/cl_Amon_CMCC-CM_rcp85_r1i1p1.yaml")
ds=cat.netcdf.read()

Metadata

title	CMCC-CM model output prepared for CMIP5 RCP8.5
location	/shared/cmip5/data/rcp85/atmos/mon/Amon/cl/CMCC.CMCC-CM/r1i1p1
tags	gridded,global,model,monthly
catalog_dir	https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/cl_Amon_CMCC-CM_rcp85_r1i1p1.yaml
last updated	2013-06-14

Dataset Contents

<xarray.Dataset>
Dimensions:    (bnds: 2, lat: 240, lev: 31, lon: 480, time: 1140)
Coordinates:
  * time       (time) float64 15.5 45.0 74.5 ... 3.576e+03 3.606e+03 3.636e+03
  * lev        (lev) float64 0.9961 0.9826 0.959 0.9277 ... 0.07 0.05 0.03 0.01
  * lat        (lat) float64 -89.43 -88.68 -87.94 -87.19 ... 87.94 88.68 89.43
  * lon        (lon) float64 0.0 0.75 1.5 2.25 3.0 ... 357.0 357.8 358.5 359.2
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) float64 dask.array<chunksize=(60, 2), meta=np.ndarray>
    lev_bnds   (time, lev, bnds) float64 dask.array<chunksize=(60, 31, 2), meta=np.ndarray>
    p0         (time) float32 100000.0 100000.0 100000.0 ... 100000.0 100000.0
    a          (time, lev) float64 dask.array<chunksize=(60, 31), meta=np.ndarray>
    b          (time, lev) float64 dask.array<chunksize=(60, 31), meta=np.ndarray>
    ps         (time, lat, lon) float32 dask.array<chunksize=(60, 240, 480), meta=np.ndarray>
    a_bnds     (time, lev, bnds) float64 dask.array<chunksize=(60, 31, 2), meta=np.ndarray>
    b_bnds     (time, lev, bnds) float64 dask.array<chunksize=(60, 31, 2), meta=np.ndarray>
    lat_bnds   (time, lat, bnds) float64 dask.array<chunksize=(60, 240, 2), meta=np.ndarray>
    lon_bnds   (time, lon, bnds) float64 dask.array<chunksize=(60, 480, 2), meta=np.ndarray>
    cl         (time, lev, lat, lon) float32 dask.array<chunksize=(60, 31, 240, 480), meta=np.ndarray>
Attributes:
    institution:            CMCC - Centro Euro-Mediterraneo per i Cambiamenti
    institute_id:           CMCC
    experiment_id:          rcp85
    source:                 CMCC-CM
    model_id:               CMCC-CM
    forcing:                Nat,GHG,SA,TO,Sl
    parent_experiment_id:   historical
    parent_experiment_rip:  N/A
    branch_time:            56978.0
    contact:                Silvio Gualdi (gualdi@bo.ingv.it)
    history:                Model output postprocessed with CDO 2012-02-15T12...
    comment:                RCP8.5 simulation starting at the end of the hist...
    references:             model described in the documentation at http://ww...
    initialization_method:  1
    physics_version:        1
    tracking_id:            3ed0cd49-1bde-4743-aac7-2502ffbe50cd
    product:                output
    experiment:             RCP8.5
    frequency:              mon
    creation_date:          2012-02-15T12:36:27Z
    Conventions:            CF-1.4
    project_id:             CMIP5
    table_id:               Table Amon (27 April 2011) a5a1c518f52ae340313ba0...
    title:                  CMCC-CM model output prepared for CMIP5 RCP8.5
    parent_experiment:      historical
    modeling_realm:         atmos
    realization:            1
    cmor_version:           2.7.1

xarray.Dataset

Dimensions:
- bnds: 2
- lat: 240
- lev: 31
- lon: 480
- time: 1140
Coordinates: (4)
- time
  (time)
  float64
  15.5 45.0 ... 3.606e+03 3.636e+03
  bounds :
  time_bnds
  calendar :
  standard
  axis :
  T
  long_name :
  time
  standard_name :
  time
```
array([  15.5,   45. ,   74.5, ..., 3575.5, 3606. , 3636.5])
```
- lev
  (lev)
  float64
  0.9961 0.9826 0.959 ... 0.03 0.01
  bounds :
  lev_bnds
  units :
  1
  axis :
  Z
  positive :
  down
  long_name :
  hybrid sigma pressure coordinate
  standard_name :
  atmosphere_hybrid_sigma_pressure_coordinate
  formula :
  p = a*p0 + b*ps
  formula_terms :
  p0: p0 a: a b: b ps: ps
```
array([0.996141, 0.982633, 0.95896 , 0.927683, 0.890885, 0.850232, 0.80704 ,
       0.762332, 0.716891, 0.671308, 0.626022, 0.58136 , 0.537568, 0.494837,
       0.453323, 0.413166, 0.3745  , 0.337457, 0.302169, 0.268762, 0.237349,
       0.20801 , 0.180775, 0.155595, 0.132317, 0.110639, 0.090076, 0.07    ,
       0.05    , 0.03    , 0.01    ])
```
- lat
  (lat)
  float64
  -89.43 -88.68 ... 88.68 89.43
  bounds :
  lat_bnds
  units :
  degrees_north
  axis :
  Y
  long_name :
  latitude
  standard_name :
  latitude
```
array([-89.427084, -88.684919, -87.938371, ...,  87.938371,  88.684919,
        89.427084])
```
- lon
  (lon)
  float64
  0.0 0.75 1.5 ... 357.8 358.5 359.2
  bounds :
  lon_bnds
  units :
  degrees_east
  axis :
  X
  long_name :
  longitude
  standard_name :
  longitude
```
array([  0.  ,   0.75,   1.5 , ..., 357.75, 358.5 , 359.25])
```

Data variables: (11)

time_bnds

(time, bnds)

float64

dask.array<chunksize=(60, 2), meta=np.ndarray>

	Array	Chunk
Bytes	18.24 kB	1.92 kB
Shape	(1140, 2)	(120, 2)
Count	30 Tasks	10 Chunks
Type	float64	numpy.ndarray

lev_bnds

(time, lev, bnds)

float64

dask.array<chunksize=(60, 31, 2), meta=np.ndarray>

formula :: p = a*p0 + b*ps
standard_name :: atmosphere_hybrid_sigma_pressure_coordinate
units :: 1
formula_terms :: p0: p0 a: a_bnds b: b_bnds ps: ps

	Array	Chunk
Bytes	565.44 kB	59.52 kB
Shape	(1140, 31, 2)	(120, 31, 2)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

p0
(time)
float32
100000.0 100000.0 ... 100000.0
long_name :
vertical coordinate formula term: reference pressure
units :
Pa
```
array([100000., 100000., 100000., ..., 100000., 100000., 100000.],
      dtype=float32)
```

(time, lev)

float64

dask.array<chunksize=(60, 31), meta=np.ndarray>

long_name :: vertical coordinate formula term: a(k)

	Array	Chunk
Bytes	282.72 kB	29.76 kB
Shape	(1140, 31)	(120, 31)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

(time, lev)

float64

dask.array<chunksize=(60, 31), meta=np.ndarray>

long_name :: vertical coordinate formula term: b(k)

	Array	Chunk
Bytes	282.72 kB	29.76 kB
Shape	(1140, 31)	(120, 31)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

(time, lat, lon)

float32

dask.array<chunksize=(60, 240, 480), meta=np.ndarray>

standard_name :: surface_air_pressure
long_name :: Surface Air Pressure
comment :: not, in general, the same as mean sea-level pressure
units :: Pa
cell_methods :: time: mean
cell_measures :: area: areacella

	Array	Chunk
Bytes	525.31 MB	55.30 MB
Shape	(1140, 240, 480)	(120, 240, 480)
Count	30 Tasks	10 Chunks
Type	float32	numpy.ndarray

a_bnds

(time, lev, bnds)

float64

dask.array<chunksize=(60, 31, 2), meta=np.ndarray>

long_name :: vertical coordinate formula term: a(k+1/2)

	Array	Chunk
Bytes	565.44 kB	59.52 kB
Shape	(1140, 31, 2)	(120, 31, 2)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

b_bnds

(time, lev, bnds)

float64

dask.array<chunksize=(60, 31, 2), meta=np.ndarray>

long_name :: vertical coordinate formula term: b(k+1/2)

	Array	Chunk
Bytes	565.44 kB	59.52 kB
Shape	(1140, 31, 2)	(120, 31, 2)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

lat_bnds

(time, lat, bnds)

float64

dask.array<chunksize=(60, 240, 2), meta=np.ndarray>

	Array	Chunk
Bytes	4.38 MB	460.80 kB
Shape	(1140, 240, 2)	(120, 240, 2)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

lon_bnds

(time, lon, bnds)

float64

dask.array<chunksize=(60, 480, 2), meta=np.ndarray>

	Array	Chunk
Bytes	8.76 MB	921.60 kB
Shape	(1140, 480, 2)	(120, 480, 2)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

(time, lev, lat, lon)

float32

dask.array<chunksize=(60, 31, 240, 480), meta=np.ndarray>

standard_name :: cloud_area_fraction_in_atmosphere_layer
long_name :: Cloud Area Fraction
comment :: Includes both large-scale and convective cloud.
units :: %
original_name :: aclcac
original_units :: percent
history :: 2012-02-15T12:36:27Z altered by CMOR: Converted units from 'percent' to '%'. 2012-02-15T12:36:27Z altered by CMOR: Inverted axis: lev. 2012-02-15T12:36:27Z altered by CMOR: Inverted axis: lat.
cell_methods :: time: mean (interval: 1 month)
cell_measures :: area: areacella
associated_files :: baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_CMCC-CM_rcp85_r0i0p0.nc areacella: areacella_fx_CMCC-CM_rcp85_r0i0p0.nc

	Array	Chunk
Bytes	16.28 GB	1.71 GB
Shape	(1140, 31, 240, 480)	(120, 31, 240, 480)
Count	30 Tasks	10 Chunks
Type	float32	numpy.ndarray

Attributes: (28)
institution :
CMCC - Centro Euro-Mediterraneo per i Cambiamenti
institute_id :
CMCC
experiment_id :
rcp85
source :
CMCC-CM
model_id :
CMCC-CM
forcing :
Nat,GHG,SA,TO,Sl
parent_experiment_id :
historical
parent_experiment_rip :
N/A
branch_time :
56978.0
contact :
Silvio Gualdi (gualdi@bo.ingv.it)
history :
Model output postprocessed with CDO 2012-02-15T12:36:27Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
comment :
RCP8.5 simulation starting at the end of the historical run
references :
model described in the documentation at http://www.cmcc.it/data-models/models
initialization_method :
1
physics_version :
1
tracking_id :
3ed0cd49-1bde-4743-aac7-2502ffbe50cd
product :
output
experiment :
RCP8.5
frequency :
mon
creation_date :
2012-02-15T12:36:27Z
Conventions :
CF-1.4
project_id :
CMIP5
table_id :
Table Amon (27 April 2011) a5a1c518f52ae340313ba0aada03f862
title :
CMCC-CM model output prepared for CMIP5 RCP8.5
parent_experiment :
historical
modeling_realm :
atmos
realization :
1
cmor_version :
2.7.1