NOAA GFDL GFDL-ESM2G, pre-industrial control (run 1) experiment output for CMIP5 AR5
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/uo_Omon_GFDL-ESM2G_piControl_r1i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | NOAA GFDL GFDL-ESM2G, pre-industrial control (run 1) experiment output for CMIP5 AR5 |
| location | /shared/cmip5/data/piControl/ocean/mon/Omon/uo/NOAA-GFDL.GFDL-ESM2G/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/uo_Omon_GFDL-ESM2G_piControl_r1i1p1.yaml |
| last updated | 2015-01-17 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, lev: 50, rlat: 210, rlon: 360, time: 6000, vertices: 4)
Coordinates:
* bnds (bnds) float64 1.0 2.0
* time (time) float64 15.5 45.0 74.5 ... 1.825e+05 1.825e+05
* rlon (rlon) float64 -279.0 -278.0 -277.0 -276.0 ... 78.0 79.0 80.0
* rlat (rlat) float64 -76.75 -76.25 -75.75 ... 88.75 89.25 89.75
* lev (lev) float64 5.0 15.0 25.0 ... 4.588e+03 4.95e+03 5.316e+03
lat (rlat, rlon) float64 dask.array<chunksize=(210, 360), meta=np.ndarray>
lon (rlat, rlon) float64 dask.array<chunksize=(210, 360), meta=np.ndarray>
* vertices (vertices) int32 0 1 2 3
Data variables:
average_DT (time) float64 dask.array<chunksize=(60,), meta=np.ndarray>
average_T1 (time) float64 dask.array<chunksize=(60,), meta=np.ndarray>
average_T2 (time) float64 dask.array<chunksize=(60,), meta=np.ndarray>
time_bnds (time, bnds) float64 dask.array<chunksize=(60, 2), meta=np.ndarray>
uo (time, lev, rlat, rlon) float32 dask.array<chunksize=(60, 50, 210, 360), meta=np.ndarray>
lat_vertices (time, rlat, rlon, vertices) float64 dask.array<chunksize=(60, 210, 360, 4), meta=np.ndarray>
lev_bnds (time, lev, bnds) float64 dask.array<chunksize=(60, 50, 2), meta=np.ndarray>
lon_vertices (time, rlat, rlon, vertices) float64 dask.array<chunksize=(60, 210, 360, 4), meta=np.ndarray>
Attributes:
title: NOAA GFDL GFDL-ESM2G, pre-industrial control (run...
institute_id: NOAA GFDL
source: GFDL-ESM2G 2010 ocean: TOPAZ (TOPAZ1p2,Tripolar36...
contact: gfdl.climate.model.info@noaa.gov
project_id: CMIP5
table_id: Table Omon (31 Jan 2011)
experiment_id: piControl
realization: 1
modeling_realm: ocean
tracking_id: 769e694d-1a49-4a3b-8cd0-09c8cf212242
Conventions: CF-1.4
references: The GFDL Data Portal (http://nomads.gfdl.noaa.gov...
comment: GFDL experiment name = ESM2G_pi-control_C2. PCMDI...
gfdl_experiment_name: ESM2G_pi-control_C2
creation_date: 2012-08-27T14:11:01Z
model_id: GFDL-ESM2G
branch_time: 0.0
experiment: pre-industrial control
forcing: N/A
frequency: mon
initialization_method: 1
parent_experiment_id: N/A
physics_version: 1
product: output1
institution: NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540)
history: File was processed by fremetar (GFDL analog of CM...
parent_experiment_rip: N/Axarray.Dataset
- bnds: 2
- lev: 50
- rlat: 210
- rlon: 360
- time: 6000
- vertices: 4
- bnds(bnds)float641.0 2.0
- long_name :
- vertex number
- cartesian_axis :
- N
array([1., 2.])
- time(time)float6415.5 45.0 ... 1.825e+05 1.825e+05
- long_name :
- time
- units :
- days since 0001-01-01 00:00:00
- cartesian_axis :
- T
- calendar_type :
- noleap
- calendar :
- noleap
- bounds :
- time_bnds
- standard_name :
- time
- axis :
- T
array([1.550000e+01, 4.500000e+01, 7.450000e+01, ..., 1.824235e+05, 1.824540e+05, 1.824845e+05]) - rlon(rlon)float64-279.0 -278.0 -277.0 ... 79.0 80.0
- long_name :
- longitude in rotated pole grid
- units :
- degrees
- axis :
- X
- standard_name :
- grid_longitude
array([-279., -278., -277., ..., 78., 79., 80.])
- rlat(rlat)float64-76.75 -76.25 ... 89.25 89.75
- long_name :
- latitude in rotated pole grid
- units :
- degrees
- axis :
- Y
- standard_name :
- grid_latitude
array([-76.75, -76.25, -75.75, ..., 88.75, 89.25, 89.75])
- lev(lev)float645.0 15.0 ... 4.95e+03 5.316e+03
- long_name :
- ocean depth coordinate
- units :
- m
- cartesian_axis :
- Z
- positive :
- down
- bounds :
- lev_bnds
- standard_name :
- depth
- axis :
- Z
- description :
- generic ocean model vertical coordinate (nondimensional or dimensional)
array([5.000000e+00, 1.500000e+01, 2.500000e+01, 3.500000e+01, 4.500000e+01, 5.500000e+01, 6.500000e+01, 7.500000e+01, 8.500000e+01, 9.500000e+01, 1.050000e+02, 1.150000e+02, 1.250000e+02, 1.350000e+02, 1.450000e+02, 1.550000e+02, 1.650000e+02, 1.750000e+02, 1.850000e+02, 1.950000e+02, 2.050000e+02, 2.150000e+02, 2.250000e+02, 2.361228e+02, 2.506000e+02, 2.706208e+02, 2.983049e+02, 3.356756e+02, 3.846343e+02, 4.469366e+02, 5.241706e+02, 6.177363e+02, 7.288285e+02, 8.584215e+02, 1.007257e+03, 1.175835e+03, 1.364406e+03, 1.572971e+03, 1.801279e+03, 2.048829e+03, 2.314879e+03, 2.598456e+03, 2.898365e+03, 3.213206e+03, 3.541390e+03, 3.881162e+03, 4.230621e+03, 4.587743e+03, 4.950409e+03, 5.316429e+03]) - lat(rlat, rlon)float64dask.array<chunksize=(210, 360), meta=np.ndarray>
- bounds :
- lat_vertices
- long_name :
- latitude coordinate
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 604.80 kB 604.80 kB Shape (210, 360) (210, 360) Count 495 Tasks 1 Chunks Type float64 numpy.ndarray - lon(rlat, rlon)float64dask.array<chunksize=(210, 360), meta=np.ndarray>
- bounds :
- lon_vertices
- long_name :
- longitude coordinate
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 604.80 kB 604.80 kB Shape (210, 360) (210, 360) Count 495 Tasks 1 Chunks Type float64 numpy.ndarray - vertices(vertices)int320 1 2 3
array([0, 1, 2, 3], dtype=int32)
- average_DT(time)float64dask.array<chunksize=(60,), meta=np.ndarray>
- long_name :
- Length of average period
- units :
- days
Array Chunk Bytes 48.00 kB 480 B Shape (6000,) (60,) Count 300 Tasks 100 Chunks Type float64 numpy.ndarray - average_T1(time)float64dask.array<chunksize=(60,), meta=np.ndarray>
- long_name :
- Start time for average period
- units :
- days since 0001-01-01 00:00:00
Array Chunk Bytes 48.00 kB 480 B Shape (6000,) (60,) Count 300 Tasks 100 Chunks Type float64 numpy.ndarray - average_T2(time)float64dask.array<chunksize=(60,), meta=np.ndarray>
- long_name :
- End time for average period
- units :
- days since 0001-01-01 00:00:00
Array Chunk Bytes 48.00 kB 480 B Shape (6000,) (60,) Count 300 Tasks 100 Chunks Type float64 numpy.ndarray - time_bnds(time, bnds)float64dask.array<chunksize=(60, 2), meta=np.ndarray>
- long_name :
- time axis boundaries
- units :
- days since 0001-01-01 00:00:00
Array Chunk Bytes 96.00 kB 960 B Shape (6000, 2) (60, 2) Count 300 Tasks 100 Chunks Type float64 numpy.ndarray - uo(time, lev, rlat, rlon)float32dask.array<chunksize=(60, 50, 210, 360), meta=np.ndarray>
- long_name :
- Sea Water X Velocity
- units :
- m s-1
- cell_methods :
- time: mean
- standard_name :
- sea_water_x_velocity
- cell_measures :
- area: areacello
- comments :
- Note: This model output is presented on the model's tripolar grid. The North Pole singularity is avoided by using this nonregular grid north of 65N. More information about the ocean and sea ice model's grid can be found at http://nomads.gfdl.noaa.gov/. Warning: any volume-weighted calculations will not be consistent with the model formulation.
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_ocean_fx_GFDL-ESM2G_piControl_r0i0p0.nc areacello: areacello_fx_GFDL-ESM2G_piControl_r0i0p0.nc
- original_name :
- u
- original_units :
- <undefined>
Array Chunk Bytes 90.72 GB 907.20 MB Shape (6000, 50, 210, 360) (60, 50, 210, 360) Count 300 Tasks 100 Chunks Type float32 numpy.ndarray - lat_vertices(time, rlat, rlon, vertices)float64dask.array<chunksize=(60, 210, 360, 4), meta=np.ndarray>
- units :
- degrees_north
Array Chunk Bytes 14.52 GB 145.15 MB Shape (6000, 210, 360, 4) (60, 210, 360, 4) Count 400 Tasks 100 Chunks Type float64 numpy.ndarray - lev_bnds(time, lev, bnds)float64dask.array<chunksize=(60, 50, 2), meta=np.ndarray>
Array Chunk Bytes 4.80 MB 48.00 kB Shape (6000, 50, 2) (60, 50, 2) Count 400 Tasks 100 Chunks Type float64 numpy.ndarray - lon_vertices(time, rlat, rlon, vertices)float64dask.array<chunksize=(60, 210, 360, 4), meta=np.ndarray>
- units :
- degrees_east
Array Chunk Bytes 14.52 GB 145.15 MB Shape (6000, 210, 360, 4) (60, 210, 360, 4) Count 400 Tasks 100 Chunks Type float64 numpy.ndarray
- title :
- NOAA GFDL GFDL-ESM2G, pre-industrial control (run 1) experiment output for CMIP5 AR5
- institute_id :
- NOAA GFDL
- source :
- GFDL-ESM2G 2010 ocean: TOPAZ (TOPAZ1p2,Tripolar360x210L63); atmosphere: AM2 (AM2p14,M45L24); sea ice: SIS (SISp2,Tripolar360x210L63); land: LM3 (LM3p7_cESM,M45)
- contact :
- gfdl.climate.model.info@noaa.gov
- project_id :
- CMIP5
- table_id :
- Table Omon (31 Jan 2011)
- experiment_id :
- piControl
- realization :
- 1
- modeling_realm :
- ocean
- tracking_id :
- 769e694d-1a49-4a3b-8cd0-09c8cf212242
- Conventions :
- CF-1.4
- references :
- The GFDL Data Portal (http://nomads.gfdl.noaa.gov/) provides access to NOAA/GFDL's publicly available model input and output data sets. From this web site one can view and download data sets and documentation, including those related to the GFDL coupled models experiments run for the IPCC's 5th Assessment Report and the US CCSP.
- comment :
- GFDL experiment name = ESM2G_pi-control_C2. PCMDI experiment name = pre-industrial control (run1). Initial conditions for this experiment were derived from a multi-step process involving a multi-century spin-up integration to allow both the physical climate system and the carbon cycle to come into a quasi-dynamic equilibrium with year 1860 radiative forcing. First, a dynamic vegetation and carbon cycle land only model was spun up with forcing agents obtained from a previous CM2.1 control integration at levels representative of 1860, and potential vegetation (i.e., no land use). Second, the spun up dynamic vegetation and carbon cycle land only model was coupled to an atmosphere model using Atmospheric Model Intercomparison Project (AMIP) protocols and spun up with using the monthly climatology of observed sea surface temperatures and sea ice concentrations averaged over the AMIP period. Third, an ocean + sea ice model was initialized from a zero-velocity state with temperature and salinity profiles derived from late 20th century observations (Levitus et al. 2005). The ocean + ice model was integrated for one year using atmospheric forcing also obtained from a previous CM2.1 control integration representative of 1860. The ESM was coupled to the ocean biogeochemistry component which uses observed fields of various chemically-active species. The fully-coupled ESM was initialized from the three steps above, and the radiative forcing was set back to 1860 conditions. The model was run for several centuries to adjust to those conditions. For the first 248 years of this spin-up integration, the physical climate was allowed to achieve a quasi-radiative equilibrium with the constant 1860 radiative forcing using potential, static vegetation and a fixed pCO2 value of 286 ppm, while a sponge was applied to the actual atmospheric CO2 values, restoring them back to 286 ppm to avoid drift. The criteria used to determine equilibrium for the physical climate was a TOA net radiative flux of less than +/- 0.1 W/m-2. At year 249 of the spin-up integration, dynamic vegetation was activated and the model was integrated for an additional 200 years after which time the land photosynthesis routine was allowed to feel the model's own atmospheric CO2. The atmospheric pCO2 was still restored to 286 ppm with a restoring time scale of about 1 year. After another ~150 years, the soil carbon was set to a quasi-equilibrium state using an offline analytic procedure and the ocean. At year 601, the temperature, salinity, and biogeochemical tracers in the ocean were reset to observations (Levitus et al. 2005) and changes to the mixing and diffusion parameterizations were introduced in order to address a deep-ocean cold bias. At years 1101 and 1401, changes were incorporated into the land vegetation model that resulted in an equilibrium total global biomass on land of ~850 PgC. The model integration was continued until the land and ocean carbon stores reached equilibrium. A net carbon flux of each component of less than 20 PgC per century and leading to net atmospheric response of less than 10 ppm per century was the criteria used for determining carbon equilibrium. Year 1 of the archived ESM2G_pi-control_C2 experiment data begins at the end of this adjustment period. ESM2G_pi-control_C2 forcing agents representative of conditions circa-1860 include the well-mixed greenhouse gases (CO2, CH4, N2O), tropospheric and stratospheric O3, tropospheric sulfates, black and organic carbon, dust, sea salt and solar irradiance. This integration did not include land use change (i.e., a "potential vegetation" integration). The direct effect of tropospheric aerosols is calculated by the model, but the model simulated no indirect aerosol effects.
- gfdl_experiment_name :
- ESM2G_pi-control_C2
- creation_date :
- 2012-08-27T14:11:01Z
- model_id :
- GFDL-ESM2G
- branch_time :
- 0.0
- experiment :
- pre-industrial control
- forcing :
- N/A
- frequency :
- mon
- initialization_method :
- 1
- parent_experiment_id :
- N/A
- physics_version :
- 1
- product :
- output1
- institution :
- NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540)
- history :
- File was processed by fremetar (GFDL analog of CMOR). TripleID: [exper_id_bUomECD3dz,realiz_id_G7zr17eNlL,run_id_ShwLw7E1g6]
- parent_experiment_rip :
- N/A