CEDA/ESGF¶

CEDA+ESGF - an easy CMIP6 wrapper¶

---

After the lab meeting:¶

• We discussed everyones different ways of getting at CMIP6 data
• The two prevailing camps are CEDA and intake ESGF
• CEDA is incomplete however does contain useful datasets
• intake ESGF methods are easy to use but relies on unlimited storage capacity
• Bridging the gap between these two/ identifiying where CEDA resouces start and end may lead to better data management practices

---

Goals:¶

• ESGF Web browser like search interface (auto complete?)
• CEDA first comprehensive search
• Indentification of missing variables
• Estimation of data download requirements
• Easy download to cache/ repeatable access
• Storage statistics

---

Potential Issues/ Planned fixes:¶

• Different grids (gn, gr, gr1...) - could mess with things
• No way of combining timelines at the moment - ie historical into SSPXXX
• No way of grabbing land masks - relies on importing external ones
• Would like a storage demand estimator/ way of managing storage
• View of pivot table to visualize data avaliability

Note that this notebook uses functions from the ceda_esgf section of the climdyn_tools package.

In [1]:

import os, intake_esgf
import glob
import xarray as xr
import numpy as np
import cftime
import pandas as pd
from intake_esgf import ESGFCatalog
from importlib import reload  # Python 3.4+
import matplotlib.pyplot as plt
import climdyn_tools.ceda_esgf.base as CEFunc
# import CEDAESGF_Funcs as CEFunc

reload(CEFunc)

import os, intake_esgf import glob import xarray as xr import numpy as np import cftime import pandas as pd from intake_esgf import ESGFCatalog from importlib import reload # Python 3.4+ import matplotlib.pyplot as plt import climdyn_tools.ceda_esgf.base as CEFunc # import CEDAESGF_Funcs as CEFunc reload(CEFunc)

Out[1]:

<module 'climdyn_tools.ceda_esgf.base' from '/Users/joshduffield/Documents/StAndrews/Wiki/climdyn_tools/ceda_esgf/base.py'>

In [250]:

#### Config
activity_id = 'CMIP'
experiment_id = 'abrupt-4xCO2'
source_id = "GFDL-CM4"
do1member = True

# CMIP variables of interest
variableList = ['tas', 'huss', 'rsds', 'rsus', 'rlds', 'rlus', 'hfls', 'hfss']
table_id = 'Amon'  ## Time step

### Change to personal cache's
intake_esgf.conf.set(local_cache="/gws/nopw/j04/global_ex/chingosa/cache")
intake_esgf.conf.set(indices={ ### Sets up which nodes it looks at - can be fiddly
    "esgf-node.llnl.gov": False,
    "esgf-node.ornl.gov": True,
    "esgf.ceda.ac.uk": True,
    "anl-dev": True,
    "ornl-dev": True,
    "ESGF2-US-1.5-Catalog": True,
    "esgf-data.dkrz.de": True,
    "esgf-node.ipsl.upmc.fr": True,
    "esg-dn1.nsc.liu.se": True,
    "esgf.nci.org.au": True,
})

### Meta Data
CMIP6Meta = CEFunc.load_cmip6_source_id()                             #JSON of CMIP6 Meta Data
source_id_list = CEFunc.source_id_in_activity(activity_id, CMIP6Meta) # Source_id_list of models participatingin activity_id - use this for looping over source_ids
M2I = CEFunc.getModel_to_inst(CMIP6Meta)                              # Dictionary linking institution name (for CEDA) to source_id

# Figure out what CEDA/ESGF have
CEDA_res = CEFunc.checkCEDA(source_id, activity_id, experiment_id, M2I, variableList, table_id, member_id = '*')
ESGF_res = CEFunc.checkESGF(source_id, activity_id, experiment_id, M2I, variableList, table_id, member_id = '*')

# Find Overlapping Variants and variables that make sense to grab
pivot = CEFunc.compare_cat_res_pivot(ESGF_res.df, CEDA_res)
ranking = CEFunc.rank_members_with_vars(pivot)
sensible_members = ranking[ranking.CEDA_CHOICE_count == ranking.CEDA_CHOICE_count.iloc[0]]

# Which variables are we getting from each source
ESGF_vars = sensible_members.ESGF_vars.iloc[0]

print(f'There are like {len(sensible_members)} that would make sense to analyse but will have to download {len(ESGF_vars)} of {len(variableList)} variables from ESGF')

# If looping through the sensible members heres where we decide which ones we are doing
if do1member: member_ids = [sensible_members.member_id.iloc[0]]
else:         member_ids = sensible_members.member_id

### Loop through Members
for member_id in member_ids:
    print(f'Starting Analysis for {member_id}')
    
    row = sensible_members[sensible_members.member_id == member_id].reset_index(drop=True)
    # Which variables are we getting from each source
    CEDA_vars = row.CEDA_vars.item()
    ESGF_vars = row.ESGF_vars.item()

    # if there are multiple sources - finds and divies them up and combines into one thing...
    ds = CEFunc.getCombinedData(source_id, activity_id, experiment_id, M2I,CEDA_vars, ESGF_vars, table_id, member_id, doReadOut = True)

    ## do whatever you need to do with ds

#### Config activity_id = 'CMIP' experiment_id = 'abrupt-4xCO2' source_id = "GFDL-CM4" do1member = True # CMIP variables of interest variableList = ['tas', 'huss', 'rsds', 'rsus', 'rlds', 'rlus', 'hfls', 'hfss'] table_id = 'Amon' ## Time step ### Change to personal cache's intake_esgf.conf.set(local_cache="/gws/nopw/j04/global_ex/chingosa/cache") intake_esgf.conf.set(indices={ ### Sets up which nodes it looks at - can be fiddly "esgf-node.llnl.gov": False, "esgf-node.ornl.gov": True, "esgf.ceda.ac.uk": True, "anl-dev": True, "ornl-dev": True, "ESGF2-US-1.5-Catalog": True, "esgf-data.dkrz.de": True, "esgf-node.ipsl.upmc.fr": True, "esg-dn1.nsc.liu.se": True, "esgf.nci.org.au": True, }) ### Meta Data CMIP6Meta = CEFunc.load_cmip6_source_id() #JSON of CMIP6 Meta Data source_id_list = CEFunc.source_id_in_activity(activity_id, CMIP6Meta) # Source_id_list of models participatingin activity_id - use this for looping over source_ids M2I = CEFunc.getModel_to_inst(CMIP6Meta) # Dictionary linking institution name (for CEDA) to source_id # Figure out what CEDA/ESGF have CEDA_res = CEFunc.checkCEDA(source_id, activity_id, experiment_id, M2I, variableList, table_id, member_id = '*') ESGF_res = CEFunc.checkESGF(source_id, activity_id, experiment_id, M2I, variableList, table_id, member_id = '*') # Find Overlapping Variants and variables that make sense to grab pivot = CEFunc.compare_cat_res_pivot(ESGF_res.df, CEDA_res) ranking = CEFunc.rank_members_with_vars(pivot) sensible_members = ranking[ranking.CEDA_CHOICE_count == ranking.CEDA_CHOICE_count.iloc[0]] # Which variables are we getting from each source ESGF_vars = sensible_members.ESGF_vars.iloc[0] print(f'There are like {len(sensible_members)} that would make sense to analyse but will have to download {len(ESGF_vars)} of {len(variableList)} variables from ESGF') # If looping through the sensible members heres where we decide which ones we are doing if do1member: member_ids = [sensible_members.member_id.iloc[0]] else: member_ids = sensible_members.member_id ### Loop through Members for member_id in member_ids: print(f'Starting Analysis for {member_id}') row = sensible_members[sensible_members.member_id == member_id].reset_index(drop=True) # Which variables are we getting from each source CEDA_vars = row.CEDA_vars.item() ESGF_vars = row.ESGF_vars.item() # if there are multiple sources - finds and divies them up and combines into one thing... ds = CEFunc.getCombinedData(source_id, activity_id, experiment_id, M2I,CEDA_vars, ESGF_vars, table_id, member_id, doReadOut = True) ## do whatever you need to do with ds

Attempt 1 to initialize ESGFCatalog...
ESGFCatalog successfully initialized.

   Searching indices:   0%|          |0/9 [       ?index/s]

/home/users/chingosa/.local/lib/python3.11/site-packages/intake_esgf/catalog.py:316: UserWarning: SolrESGFIndex('esgf-node.ornl.gov') failed to return a response, results may be incomplete
  warnings.warn(

There are like 1 that would make sense to analyse but will have to download 2 of 8 variables from ESGF
Starting Analysis for r1i1p1f1
Attempt 1 to initialize ESGFCatalog...
ESGFCatalog successfully initialized.

   Searching indices:   0%|          |0/9 [       ?index/s]

/home/users/chingosa/.local/lib/python3.11/site-packages/intake_esgf/catalog.py:316: UserWarning: SolrESGFIndex('esgf-node.ornl.gov') failed to return a response, results may be incomplete
  warnings.warn(

Get file information:   0%|          |0/9 [       ?index/s]

/home/users/chingosa/.local/lib/python3.11/site-packages/intake_esgf/catalog.py:468: UserWarning: SolrESGFIndex('esgf-node.ornl.gov') failed to return a response, info may be incomplete
  warnings.warn(

Downloading 499.5 [Mb]...

rlus_Amon_GFDL-CM4_abrupt-4xCO2_r1i1p...:   0%|          |0.00/160M [?B/s]

rlus_Amon_GFDL-CM4_abrupt-4xCO2_r1i1p...:   0%|          |0.00/79.8M [?B/s]

rsus_Amon_GFDL-CM4_abrupt-4xCO2_r1i1p...:   0%|          |0.00/173M [?B/s]

rsus_Amon_GFDL-CM4_abrupt-4xCO2_r1i1p...:   0%|          |0.00/86.7M [?B/s]

In [251]:

ds

ds

Out[251]:

<xarray.Dataset> Size: 3GB
Dimensions:    (time: 1800, lat: 180, lon: 288, bnds: 2)
Coordinates:
  * bnds       (bnds) float64 16B 1.0 2.0
  * lat        (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon        (lon) float64 2kB 0.625 1.875 3.125 4.375 ... 356.9 358.1 359.4
  * time       (time) object 14kB 0001-01-16 12:00:00 ... 0150-12-16 12:00:00
    height     float64 8B 2.0
Data variables:
    hfls       (time, lat, lon) float32 373MB dask.array<chunksize=(1, 180, 288), meta=np.ndarray>
    lat_bnds   (time, lat, bnds) float64 5MB dask.array<chunksize=(1200, 180, 2), meta=np.ndarray>
    lon_bnds   (time, lon, bnds) float64 8MB dask.array<chunksize=(1200, 288, 2), meta=np.ndarray>
    time_bnds  (time, bnds) object 29kB dask.array<chunksize=(1, 2), meta=np.ndarray>
    hfss       (time, lat, lon) float32 373MB dask.array<chunksize=(1, 180, 288), meta=np.ndarray>
    huss       (time, lat, lon) float32 373MB dask.array<chunksize=(1, 180, 288), meta=np.ndarray>
    rlds       (time, lat, lon) float32 373MB dask.array<chunksize=(1, 180, 288), meta=np.ndarray>
    rsds       (time, lat, lon) float32 373MB dask.array<chunksize=(1, 180, 288), meta=np.ndarray>
    tas        (time, lat, lon) float32 373MB dask.array<chunksize=(1, 180, 288), meta=np.ndarray>
    rlus       (time, lat, lon) float32 373MB dask.array<chunksize=(1, 180, 288), meta=np.ndarray>
    rsus       (time, lat, lon) float32 373MB dask.array<chunksize=(1, 180, 288), meta=np.ndarray>
Attributes: (12/46)
    external_variables:     areacella
    history:                File was processed by fremetar (GFDL analog of CM...
    table_id:               Amon
    activity_id:            CMIP
    branch_method:          standard
    branch_time_in_child:   0.0
    ...                     ...
    variable_id:            hfls
    variant_info:           N/A
    references:             see further_info_url attribute
    variant_label:          r1i1p1f1
    branch_time_in_parent:  36500.0
    parent_time_units:      days since 0001-1-1

xarray.Dataset

• Dimensions:
  • time: 1800
  • lat: 180
  • lon: 288
  • bnds: 2
• Coordinates: (5)
  • bnds
    (bnds)
    float64
    1.0 2.0

    long_name :

    vertex number

    array([1., 2.])

  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    bounds :

    lat_bnds

    standard_name :

    latitude

    cell_methods :

    time: point

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lon
    (lon)
    float64
    0.625 1.875 3.125 ... 358.1 359.4

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    bounds :

    lon_bnds

    standard_name :

    longitude

    cell_methods :

    time: point

    array([  0.625,   1.875,   3.125, ..., 356.875, 358.125, 359.375])

  • time
    (time)
    object
    0001-01-16 12:00:00 ... 0150-12-...

    long_name :

    time

    axis :

    T

    calendar_type :

    noleap

    bounds :

    time_bnds

    standard_name :

    time

    description :

    Temporal mean

    array([cftime.DatetimeNoLeap(1, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 3, 16, 12, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(150, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(150, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(150, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • height
    ()
    float64
    2.0

    long_name :

    height

    units :

    m

    cell_methods :

    time: point

    axis :

    Z

    positive :

    up

    standard_name :

    height

    description :

    ~2 m standard surface air temperature and surface humidity height

    array(2.)

• Data variables: (11)
  • hfls
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 180, 288), meta=np.ndarray>

    long_name :

    Surface Upward Latent Heat Flux

    units :

    W m-2

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    comment :

    Lv*evap

    standard_name :

    surface_upward_latent_heat_flux

    interp_method :

    conserve_order2

    original_name :

    hfls

    Array Chunk Bytes 355.96 MiB 202.50 kiB Shape (1800, 180, 288) (1, 180, 288) Dask graph 1800 chunks in 5 graph layers Data type float32 numpy.ndarray

  • lat_bnds
    (time, lat, bnds)
    float64
    dask.array<chunksize=(1200, 180, 2), meta=np.ndarray>

    long_name :

    latitude bounds

    units :

    degrees_north

    axis :

    Y

    Array Chunk Bytes 4.94 MiB 3.30 MiB Shape (1800, 180, 2) (1200, 180, 2) Dask graph 2 chunks in 52 graph layers Data type float64 numpy.ndarray

  • lon_bnds
    (time, lon, bnds)
    float64
    dask.array<chunksize=(1200, 288, 2), meta=np.ndarray>

    long_name :

    longitude bounds

    units :

    degrees_east

    axis :

    X

    Array Chunk Bytes 7.91 MiB 5.27 MiB Shape (1800, 288, 2) (1200, 288, 2) Dask graph 2 chunks in 52 graph layers Data type float64 numpy.ndarray

  • time_bnds
    (time, bnds)
    object
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    long_name :

    time axis boundaries

    Array Chunk Bytes 28.12 kiB 16 B Shape (1800, 2) (1, 2) Dask graph 1800 chunks in 42 graph layers Data type object numpy.ndarray

  • hfss
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 180, 288), meta=np.ndarray>

    long_name :

    Surface Upward Sensible Heat Flux

    units :

    W m-2

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    standard_name :

    surface_upward_sensible_heat_flux

    interp_method :

    conserve_order2

    original_name :

    hfss

    Array Chunk Bytes 355.96 MiB 202.50 kiB Shape (1800, 180, 288) (1, 180, 288) Dask graph 1800 chunks in 5 graph layers Data type float32 numpy.ndarray

  • huss
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 180, 288), meta=np.ndarray>

    long_name :

    Near-Surface Specific Humidity

    units :

    1

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    standard_name :

    specific_humidity

    interp_method :

    conserve_order2

    original_name :

    huss

    Array Chunk Bytes 355.96 MiB 202.50 kiB Shape (1800, 180, 288) (1, 180, 288) Dask graph 1800 chunks in 5 graph layers Data type float32 numpy.ndarray

  • rlds
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 180, 288), meta=np.ndarray>

    long_name :

    Surface Downwelling Longwave Radiation

    units :

    W m-2

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    standard_name :

    surface_downwelling_longwave_flux_in_air

    interp_method :

    conserve_order2

    original_name :

    rlds

    Array Chunk Bytes 355.96 MiB 202.50 kiB Shape (1800, 180, 288) (1, 180, 288) Dask graph 1800 chunks in 5 graph layers Data type float32 numpy.ndarray

  • rsds
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 180, 288), meta=np.ndarray>

    long_name :

    Surface Downwelling Shortwave Radiation

    units :

    W m-2

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    standard_name :

    surface_downwelling_shortwave_flux_in_air

    interp_method :

    conserve_order1

    original_name :

    rsds

    Array Chunk Bytes 355.96 MiB 202.50 kiB Shape (1800, 180, 288) (1, 180, 288) Dask graph 1800 chunks in 5 graph layers Data type float32 numpy.ndarray

  • tas
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 180, 288), meta=np.ndarray>

    long_name :

    Near-Surface Air Temperature

    units :

    K

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    standard_name :

    air_temperature

    interp_method :

    conserve_order2

    original_name :

    tas

    Array Chunk Bytes 355.96 MiB 202.50 kiB Shape (1800, 180, 288) (1, 180, 288) Dask graph 1800 chunks in 5 graph layers Data type float32 numpy.ndarray

  • rlus
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 180, 288), meta=np.ndarray>

    long_name :

    Surface Upwelling Longwave Radiation

    units :

    W m-2

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    standard_name :

    surface_upwelling_longwave_flux_in_air

    interp_method :

    conserve_order2

    original_name :

    rlus

    Array Chunk Bytes 355.96 MiB 202.50 kiB Shape (1800, 180, 288) (1, 180, 288) Dask graph 1800 chunks in 5 graph layers Data type float32 numpy.ndarray

  • rsus
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 180, 288), meta=np.ndarray>

    long_name :

    Surface Upwelling Shortwave Radiation

    units :

    W m-2

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    standard_name :

    surface_upwelling_shortwave_flux_in_air

    interp_method :

    conserve_order1

    original_name :

    rsus

    Array Chunk Bytes 355.96 MiB 202.50 kiB Shape (1800, 180, 288) (1, 180, 288) Dask graph 1800 chunks in 5 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • bnds
    PandasIndex

    PandasIndex(Index([1.0, 2.0], dtype='float64', name='bnds'))

  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([             0.625, 1.8749999999999998,              3.125,
                        4.375,              5.625,              6.875,
                        8.125,              9.375,             10.625,
                       11.875,
           ...
                      348.125,            349.375,            350.625,
                      351.875,            353.125,            354.375,
                      355.625,            356.875,            358.125,
                      359.375],
          dtype='float64', name='lon', length=288))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([0001-01-16 12:00:00, 0001-02-15 00:00:00, 0001-03-16 12:00:00,
                 0001-04-16 00:00:00, 0001-05-16 12:00:00, 0001-06-16 00:00:00,
                 0001-07-16 12:00:00, 0001-08-16 12:00:00, 0001-09-16 00:00:00,
                 0001-10-16 12:00:00,
                 ...
                 0150-03-16 12:00:00, 0150-04-16 00:00:00, 0150-05-16 12:00:00,
                 0150-06-16 00:00:00, 0150-07-16 12:00:00, 0150-08-16 12:00:00,
                 0150-09-16 00:00:00, 0150-10-16 12:00:00, 0150-11-16 00:00:00,
                 0150-12-16 12:00:00],
                dtype='object', length=1800, calendar='noleap', freq=None))

• Attributes: (46)

  external_variables :

  areacella

  history :

  File was processed by fremetar (GFDL analog of CMOR). TripleID: [exper_id_qVYgCix5ML,realiz_id_c2oVUWxeGl,run_id_aUlk27asiH]

  table_id :

  Amon

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  0.0

  comment :

  <null ref>

  contact :

  gfdl.climate.model.info@noaa.gov

  Conventions :

  CF-1.7 CMIP-6.0 UGRID-1.0

  creation_date :

  2019-03-09T21:37:53Z

  data_specs_version :

  01.00.27

  experiment :

  abrupt quadrupling of CO2

  experiment_id :

  abrupt-4xCO2

  forcing_index :

  1

  frequency :

  monC

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.NOAA-GFDL.GFDL-CM4.abrupt-4xCO2.none.r1i1p1f1

  grid :

  atmos data regridded from Cubed-sphere (c96) to 180,288; interpolation method: conserve_order2

  grid_label :

  gr1

  initialization_index :

  1

  institution :

  National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA

  institution_id :

  NOAA-GFDL

  license :

  CMIP6 model data produced by NOAA-GFDL 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). 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.

  mip_era :

  CMIP6

  nominal_resolution :

  100 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  GFDL-CM4

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  atmos

  source :

  GFDL-CM4 (2018): aerosol: interactive atmos: GFDL-AM4.0.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 33 levels; top level 1 hPa) atmosChem: fast chemistry, aerosol only land: GFDL-LM4.0.1 (1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 20 levels; bot level 10m); land:Veg:unnamed (dynamic vegetation, dynamic land use); land:Hydro:unnamed (soil water and ice, multi-layer snow, rivers and lakes) landIce: GFDL-LM4.0.1 ocean: GFDL-OM4p25 (GFDL-MOM6, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 75 levels; top grid cell 0-2 m) ocnBgchem: GFDL-BLINGv2 seaIce: GFDL-SIM4p25 (GFDL-SIS2.0, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 5 layers; 5 thickness categories) (GFDL ID: 2019_0193)

  source_id :

  GFDL-CM4

  source_type :

  AOGCM

  sub_experiment :

  none

  sub_experiment_id :

  none

  title :

  NOAA GFDL GFDL-CM4 model output prepared for CMIP6 abrupt quadrupling of CO2

  tracking_id :

  hdl:21.14100/336b54c7-e1ae-408d-a1d8-1c53a0eebabe

  variable_id :

  hfls

  variant_info :

  N/A

  references :

  see further_info_url attribute

  variant_label :

  r1i1p1f1

  branch_time_in_parent :

  36500.0

  parent_time_units :

  days since 0001-1-1