inmcm4 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/cli_Amon_inmcm4_rcp85_r1i1p1.yaml")
ds=cat.netcdf.read()

Metadata

title	inmcm4 model output prepared for CMIP5 RCP8.5
location	/shared/cmip5/data/rcp85/atmos/mon/Amon/cli/INM.inmcm4/r1i1p1
tags	gridded,global,model,monthly
catalog_dir	https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/cli_Amon_inmcm4_rcp85_r1i1p1.yaml
last updated	2013-05-28

Dataset Contents

<xarray.Dataset>
Dimensions:    (bnds: 2, lat: 120, lev: 21, lon: 180, time: 1140)
Coordinates:
  * time       (time) float64 15.5 45.0 74.5 ... 3.46e+04 3.463e+04 3.466e+04
  * lev        (lev) float64 0.993 0.975 0.95 0.91 ... 0.041 0.0256 0.016 0.01
  * lat        (lat) float64 -89.25 -87.75 -86.25 -84.75 ... 86.25 87.75 89.25
  * lon        (lon) float64 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) float64 dask.array<chunksize=(120, 2), meta=np.ndarray>
    lev_bnds   (time, lev, bnds) float64 dask.array<chunksize=(120, 21, 2), meta=np.ndarray>
    p0         (time) float32 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
    a          (time, lev) float64 dask.array<chunksize=(120, 21), meta=np.ndarray>
    b          (time, lev) float64 dask.array<chunksize=(120, 21), meta=np.ndarray>
    ps         (time, lat, lon) float32 dask.array<chunksize=(120, 120, 180), meta=np.ndarray>
    a_bnds     (time, lev, bnds) float64 dask.array<chunksize=(120, 21, 2), meta=np.ndarray>
    b_bnds     (time, lev, bnds) float64 dask.array<chunksize=(120, 21, 2), meta=np.ndarray>
    lat_bnds   (time, lat, bnds) float64 dask.array<chunksize=(120, 120, 2), meta=np.ndarray>
    lon_bnds   (time, lon, bnds) float64 dask.array<chunksize=(120, 180, 2), meta=np.ndarray>
    cli        (time, lev, lat, lon) float32 dask.array<chunksize=(120, 21, 120, 180), meta=np.ndarray>
Attributes:
    institution:            INM (Institute for Numerical Mathematics,  Moscow...
    institute_id:           INM
    experiment_id:          rcp85
    source:                 inmcm4 (2009)
    model_id:               inmcm4
    forcing:                N/A
    parent_experiment_id:   historical
    branch_time:            56940.0
    contact:                Evgeny Volodin, volodin@inm.ras.ru,INM RAS, Gubki...
    history:                Output from /data5/volodin/RCP85 2010-10-22T12:52...
    comment:                no comments
    references:             Volodin, Diansky, Gusev 2010. Climate model INMCM...
    initialization_method:  1
    physics_version:        1
    tracking_id:            e3e8e1e6-e884-4743-9d21-c050987e7e5e
    product:                output
    experiment:             RCP8.5
    frequency:              mon
    creation_date:          2010-10-22T12:53:17Z
    Conventions:            CF-1.4
    project_id:             CMIP5
    table_id:               Table Amon (12 May 2010) f29a09de1d9a9f83de4bde5e...
    title:                  inmcm4 model output prepared for CMIP5 RCP8.5
    parent_experiment:      Historical
    modeling_realm:         atmos
    realization:            1
    cmor_version:           2.0.0

xarray.Dataset

Dimensions:
- bnds: 2
- lat: 120
- lev: 21
- lon: 180
- time: 1140

Coordinates: (4)

time
(time)
float64
15.5 45.0 ... 3.463e+04 3.466e+04
bounds :
time_bnds
units :
days since 2006-1-1
calendar :
365_day
axis :
T
long_name :
time
standard_name :
time
```
array([1.55000e+01, 4.50000e+01, 7.45000e+01, ..., 3.45985e+04, 3.46290e+04,
       3.46595e+04])
```
lev
(lev)
float64
0.993 0.975 0.95 ... 0.016 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(n,k,j,i) = a(k)*p0 + b(k)*ps(n,j,i)
formula_terms :
p0: p0 a: a b: b ps: ps
```
array([0.993 , 0.975 , 0.95  , 0.91  , 0.85  , 0.78  , 0.7   , 0.62  , 0.54  ,
       0.46  , 0.38  , 0.3   , 0.23  , 0.17  , 0.12  , 0.09  , 0.065 , 0.041 ,
       0.0256, 0.016 , 0.01  ])
```

lat

(lat)

float64

-89.25 -87.75 ... 87.75 89.25

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

array([-89.25, -87.75, -86.25, -84.75, -83.25, -81.75, -80.25, -78.75, -77.25,
       -75.75, -74.25, -72.75, -71.25, -69.75, -68.25, -66.75, -65.25, -63.75,
       -62.25, -60.75, -59.25, -57.75, -56.25, -54.75, -53.25, -51.75, -50.25,
       -48.75, -47.25, -45.75, -44.25, -42.75, -41.25, -39.75, -38.25, -36.75,
       -35.25, -33.75, -32.25, -30.75, -29.25, -27.75, -26.25, -24.75, -23.25,
       -21.75, -20.25, -18.75, -17.25, -15.75, -14.25, -12.75, -11.25,  -9.75,
        -8.25,  -6.75,  -5.25,  -3.75,  -2.25,  -0.75,   0.75,   2.25,   3.75,
         5.25,   6.75,   8.25,   9.75,  11.25,  12.75,  14.25,  15.75,  17.25,
        18.75,  20.25,  21.75,  23.25,  24.75,  26.25,  27.75,  29.25,  30.75,
        32.25,  33.75,  35.25,  36.75,  38.25,  39.75,  41.25,  42.75,  44.25,
        45.75,  47.25,  48.75,  50.25,  51.75,  53.25,  54.75,  56.25,  57.75,
        59.25,  60.75,  62.25,  63.75,  65.25,  66.75,  68.25,  69.75,  71.25,
        72.75,  74.25,  75.75,  77.25,  78.75,  80.25,  81.75,  83.25,  84.75,
        86.25,  87.75,  89.25])

lon

(lon)

float64

0.0 2.0 4.0 ... 354.0 356.0 358.0

bounds :: lon_bnds
units :: degrees_east
axis :: X
long_name :: longitude
standard_name :: longitude

array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,
        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,
        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,
        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,
        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,
       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,
       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,
       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,
       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,
       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,
       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,
       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,
       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,
       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,
       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.])

Data variables: (11)

time_bnds

(time, bnds)

float64

dask.array<chunksize=(120, 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=(120, 21, 2), meta=np.ndarray>

formula :: p(n,k,j,i) = a(k)*p0 + b(k)*ps(n,j,i)
standard_name :: atmosphere_hybrid_sigma_pressure_coordinate
units :: 1
formula_terms :: p0: p0 a: a_bnds b: b_bnds ps: ps

	Array	Chunk
Bytes	383.04 kB	40.32 kB
Shape	(1140, 21, 2)	(120, 21, 2)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

p0
(time)
float32
0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
long_name :
vertical coordinate formula term: reference pressure
units :
Pa
```
array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)
```

(time, lev)

float64

dask.array<chunksize=(120, 21), meta=np.ndarray>

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

	Array	Chunk
Bytes	191.52 kB	20.16 kB
Shape	(1140, 21)	(120, 21)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

(time, lev)

float64

dask.array<chunksize=(120, 21), meta=np.ndarray>

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

	Array	Chunk
Bytes	191.52 kB	20.16 kB
Shape	(1140, 21)	(120, 21)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

(time, lat, lon)

float32

dask.array<chunksize=(120, 120, 180), 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

	Array	Chunk
Bytes	98.50 MB	10.37 MB
Shape	(1140, 120, 180)	(120, 120, 180)
Count	30 Tasks	10 Chunks
Type	float32	numpy.ndarray

a_bnds

(time, lev, bnds)

float64

dask.array<chunksize=(120, 21, 2), meta=np.ndarray>

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

	Array	Chunk
Bytes	383.04 kB	40.32 kB
Shape	(1140, 21, 2)	(120, 21, 2)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

b_bnds

(time, lev, bnds)

float64

dask.array<chunksize=(120, 21, 2), meta=np.ndarray>

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

	Array	Chunk
Bytes	383.04 kB	40.32 kB
Shape	(1140, 21, 2)	(120, 21, 2)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

lat_bnds

(time, lat, bnds)

float64

dask.array<chunksize=(120, 120, 2), meta=np.ndarray>

	Array	Chunk
Bytes	2.19 MB	230.40 kB
Shape	(1140, 120, 2)	(120, 120, 2)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

lon_bnds

(time, lon, bnds)

float64

dask.array<chunksize=(120, 180, 2), meta=np.ndarray>

	Array	Chunk
Bytes	3.28 MB	345.60 kB
Shape	(1140, 180, 2)	(120, 180, 2)
Count	40 Tasks	10 Chunks
Type	float64	numpy.ndarray

cli

(time, lev, lat, lon)

float32

dask.array<chunksize=(120, 21, 120, 180), meta=np.ndarray>

standard_name :: mass_fraction_of_cloud_ice_in_air
long_name :: Mass Fraction of Cloud Ice
comment :: Report on model layers (not standard pressures). Include both large-scale and convective cloud. Calculate as the mass of cloud ice in the grid cell divided by the mass of air (including the water in all phases) in the grid cell. Include precipitating hydrometeors ONLY if the precipitating hydrometeor affects the calculation of radiative transfer in model.
units :: 1
original_name :: cli
cell_methods :: time: mean (interval: 1 month)
cell_measures :: area: areacella
history :: 2010-10-22T12:52:46Z altered by CMOR: Reordered dimensions, original order: time lat lon lev.
associated_files :: baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_fx_inmcm4_rcp85_r0i0p0.nc areacella: areacella_fx_inmcm4_rcp85_r0i0p0.nc

	Array	Chunk
Bytes	2.07 GB	217.73 MB
Shape	(1140, 21, 120, 180)	(120, 21, 120, 180)
Count	30 Tasks	10 Chunks
Type	float32	numpy.ndarray

Attributes: (27)
institution :
INM (Institute for Numerical Mathematics, Moscow, Russia)
institute_id :
INM
experiment_id :
rcp85
source :
inmcm4 (2009)
model_id :
inmcm4
forcing :
N/A
parent_experiment_id :
historical
branch_time :
56940.0
contact :
Evgeny Volodin, volodin@inm.ras.ru,INM RAS, Gubkina 8, Moscow, 119333 Russia,+7-495-9383904
history :
Output from /data5/volodin/RCP85 2010-10-22T12:52:47Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
comment :
no comments
references :
Volodin, Diansky, Gusev 2010. Climate model INMCM4.0. Izvestia RAS. Atmospheric and oceanic physics, V.46, N4, in print.
initialization_method :
1
physics_version :
1
tracking_id :
e3e8e1e6-e884-4743-9d21-c050987e7e5e
product :
output
experiment :
RCP8.5
frequency :
mon
creation_date :
2010-10-22T12:53:17Z
Conventions :
CF-1.4
project_id :
CMIP5
table_id :
Table Amon (12 May 2010) f29a09de1d9a9f83de4bde5e1b0bd83a
title :
inmcm4 model output prepared for CMIP5 RCP8.5
parent_experiment :
Historical
modeling_realm :
atmos
realization :
1
cmor_version :
2.0.0