inmcm4 model output prepared for CMIP5 abrupt 4XCO2

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_inmcm4_abrupt4xCO2_r1i1p1.yaml")
ds=cat.netcdf.read()

Metadata

title	inmcm4 model output prepared for CMIP5 abrupt 4XCO2
location	/shared/cmip5/data/abrupt4xCO2/atmos/mon/Amon/cl/INM.inmcm4/r1i1p1
tags	gridded,global,model,monthly
catalog_dir	https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/cl_Amon_inmcm4_abrupt4xCO2_r1i1p1.yaml
last updated	2013-06-14

Dataset Contents

<xarray.Dataset>
Dimensions:    (bnds: 2, lat: 120, lev: 21, lon: 180, time: 1800)
Coordinates:
  * time       (time) float64 15.5 45.0 74.5 ... 5.467e+04 5.47e+04 5.473e+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>
    cl         (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:          abrupt4xCO2
    source:                 inmcm4 (2009)
    model_id:               inmcm4
    forcing:                N/A
    parent_experiment_id:   piControl
    branch_time:            87600.0
    contact:                Evgeny Volodin, volodin@inm.ras.ru,INM RAS, Gubki...
    history:                Output from /data5/volodin/4CO2B 2010-06-23T13:05...
    comment:                no comments
    references:             Volodin, Diansky, Gusev 2010. Climate model INMCM...
    initialization_method:  1
    physics_version:        1
    tracking_id:            f3992b10-91cb-43fd-8f9d-72176e1b5f44
    product:                output
    experiment:             abrupt 4XCO2
    frequency:              mon
    creation_date:          2010-06-23T13:05:01Z
    Conventions:            CF-1.4
    project_id:             CMIP5
    table_id:               Table Amon (12 May 2010) 12e195f2fdc87c907b38b38e...
    title:                  inmcm4 model output prepared for CMIP5 abrupt 4XCO2
    parent_experiment:      pre-industrial control
    modeling_realm:         atmos
    realization:            1
    cmor_version:           2.0.0

xarray.Dataset

Dimensions:
- bnds: 2
- lat: 120
- lev: 21
- lon: 180
- time: 1800

Coordinates: (4)

time
(time)
float64
15.5 45.0 ... 5.47e+04 5.473e+04
bounds :
time_bnds
units :
days since 2090-1-1
calendar :
365_day
axis :
T
long_name :
time
standard_name :
time
```
array([1.55000e+01, 4.50000e+01, 7.45000e+01, ..., 5.46735e+04, 5.47040e+04,
       5.47345e+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	28.80 kB	1.92 kB
Shape	(1800, 2)	(120, 2)
Count	45 Tasks	15 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	604.80 kB	40.32 kB
Shape	(1800, 21, 2)	(120, 21, 2)
Count	60 Tasks	15 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	302.40 kB	20.16 kB
Shape	(1800, 21)	(120, 21)
Count	60 Tasks	15 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	302.40 kB	20.16 kB
Shape	(1800, 21)	(120, 21)
Count	60 Tasks	15 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	155.52 MB	10.37 MB
Shape	(1800, 120, 180)	(120, 120, 180)
Count	45 Tasks	15 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	604.80 kB	40.32 kB
Shape	(1800, 21, 2)	(120, 21, 2)
Count	60 Tasks	15 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	604.80 kB	40.32 kB
Shape	(1800, 21, 2)	(120, 21, 2)
Count	60 Tasks	15 Chunks
Type	float64	numpy.ndarray

lat_bnds

(time, lat, bnds)

float64

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

	Array	Chunk
Bytes	3.46 MB	230.40 kB
Shape	(1800, 120, 2)	(120, 120, 2)
Count	60 Tasks	15 Chunks
Type	float64	numpy.ndarray

lon_bnds

(time, lon, bnds)

float64

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

	Array	Chunk
Bytes	5.18 MB	345.60 kB
Shape	(1800, 180, 2)	(120, 180, 2)
Count	60 Tasks	15 Chunks
Type	float64	numpy.ndarray

(time, lev, lat, lon)

float32

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

standard_name :: cloud_area_fraction_in_atmosphere_layer
long_name :: Cloud Area Fraction
comment :: Report on model layers (not standard pressures). Include both large-scale and convective cloud.
units :: %
original_name :: cl
cell_methods :: time: mean (interval: 1 month)
cell_measures :: area: areacella
history :: 2010-06-23T13:05:01Z 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_abrupt4xCO2_r0i0p0.nc areacella: areacella_fx_inmcm4_abrupt4xCO2_r0i0p0.nc

	Array	Chunk
Bytes	3.27 GB	217.73 MB
Shape	(1800, 21, 120, 180)	(120, 21, 120, 180)
Count	45 Tasks	15 Chunks
Type	float32	numpy.ndarray

Attributes: (27)
institution :
INM (Institute for Numerical Mathematics, Moscow, Russia)
institute_id :
INM
experiment_id :
abrupt4xCO2
source :
inmcm4 (2009)
model_id :
inmcm4
forcing :
N/A
parent_experiment_id :
piControl
branch_time :
87600.0
contact :
Evgeny Volodin, volodin@inm.ras.ru,INM RAS, Gubkina 8, Moscow, 119333 Russia,+7-495-9383904
history :
Output from /data5/volodin/4CO2B 2010-06-23T13:05:01Z 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 :
f3992b10-91cb-43fd-8f9d-72176e1b5f44
product :
output
experiment :
abrupt 4XCO2
frequency :
mon
creation_date :
2010-06-23T13:05:01Z
Conventions :
CF-1.4
project_id :
CMIP5
table_id :
Table Amon (12 May 2010) 12e195f2fdc87c907b38b38e15851337
title :
inmcm4 model output prepared for CMIP5 abrupt 4XCO2
parent_experiment :
pre-industrial control
modeling_realm :
atmos
realization :
1
cmor_version :
2.0.0