Calculate Surface Ocean Heat using CESM2 LENS data

Overview¶

This notebook is an adpation of a workflow in the NCAR gallery of the Pangeo collection
This notebook illustrates how to compute surface ocean heat content using potential temperature data from CESM2 Large Ensemble Dataset (Community Earth System Model 2) hosted on NCAR’s GDEX.
This data is open access and is accessed via OSDF using the pelicanFS package and demonstrates how you can stream data from NCAR’s GDEX
Please refer to the first chapter of this cookbook to learn more about OSDF, pelican or pelicanFS

Prerequisites¶

Concepts	Importance	Notes
Intro to Intake-ESM	Necessary	Used for searching CMIP6 data
Understanding of Zarr	Helpful	Familiarity with metadata structure
Matplotlib	Helpful	Package used for plotting
PelicanFS	Necessary	The python package used to stream data in this notebook
OSDF	Helpful	OSDF is used to stream data in this notebook

Time to learn: 20 mins

Imports¶

import intake
import numpy as np
import pandas as pd
import xarray as xr
import seaborn as sns
import re
import matplotlib.pyplot as plt
import dask
from dask.distributed import LocalCluster
import pelicanfs 
import cf_units as cf

# Load Catalog URL
cat_url = 'https://osdata.gdex.ucar.edu/d010092/catalogs/d010092-osdf.json'

Set up local dask cluster¶

Before we do any computation let us first set up a local cluster using dask

cluster = LocalCluster()          
client = cluster.get_client()

# Scale the cluster
n_workers = 5
cluster.scale(n_workers)
cluster

Data Loading¶

Load CESM2 LENS data from NCAR’s GDEX¶

Load CESM2 LENS zarr data from GDEx using an intake-ESM catalog that has OSDF links
For more details regarding the dataset. See, https://gdex.ucar.edu/datasets/d010092/#

col = intake.open_esm_datastore(cat_url)
col

# Uncomment this line to see all the variables
# cesm_cat.df['variable'].values

cesm_temp = col.search(variable ='TEMP', frequency ='monthly')
cesm_temp

cesm_temp.df['path'].values

array(['osdf:///ncar-gdex/d010092/ocn/monthly/cesm2LE-historical-cmip6-TEMP.zarr',
       'osdf:///ncar-gdex/d010092/ocn/monthly/cesm2LE-ssp370-cmip6-TEMP.zarr',
       'osdf:///ncar-gdex/d010092/ocn/monthly/cesm2LE-ssp370-smbb-TEMP.zarr'],
      dtype=object)

dsets_cesm = cesm_temp.to_dataset_dict()


--> The keys in the returned dictionary of datasets are constructed as follows:
	'component.experiment.frequency.forcing_variant'

dsets_cesm.keys()

dict_keys(['ocn.historical.monthly.cmip6', 'ocn.ssp370.monthly.smbb', 'ocn.ssp370.monthly.cmip6'])

historical       = dsets_cesm['ocn.historical.monthly.cmip6']
future_smbb      = dsets_cesm['ocn.ssp370.monthly.smbb']
future_cmip6     = dsets_cesm['ocn.ssp370.monthly.cmip6']

Change units¶

orig_units = cf.Unit(historical.z_t.attrs['units'])
orig_units

Unit('centimeters')

def change_units(ds, variable_str, variable_bounds_str, target_unit_str):
    orig_units = cf.Unit(ds[variable_str].attrs['units'])
    target_units = cf.Unit(target_unit_str)
    variable_in_new_units = xr.apply_ufunc(orig_units.convert, ds[variable_bounds_str], target_units, dask='parallelized', output_dtypes=[ds[variable_bounds_str].dtype])
    return variable_in_new_units

depth_levels_in_m = change_units(historical, 'z_t', 'z_t', 'm')
hist_temp_in_degK = change_units(historical, 'TEMP', 'TEMP', 'degK')
fut_cmip6_temp_in_degK = change_units(future_cmip6, 'TEMP', 'TEMP', 'degK')
fut_smbb_temp_in_degK = change_units(future_smbb, 'TEMP', 'TEMP', 'degK')
#
hist_temp_in_degK  = hist_temp_in_degK.assign_coords(z_t=("z_t", depth_levels_in_m['z_t'].data))
hist_temp_in_degK["z_t"].attrs["units"] = "m"
hist_temp_in_degK

depth_levels_in_m.isel(z_t=slice(0, -1))

Compute depth level deltas using the difference of z_t levels

depth_level_deltas = depth_levels_in_m.isel(z_t=slice(1, None)).values - depth_levels_in_m.isel(z_t=slice(0, -1)).values
# Optionally, if you want to keep it as an xarray DataArray, re-wrap the result
depth_level_deltas = xr.DataArray(depth_level_deltas, dims=["z_t"], coords={"z_t": depth_levels_in_m.z_t.isel(z_t=slice(0, -1))})
depth_level_deltas

Compute Ocean Heat content for ocean surface¶

Ocean surface is considered to be the top 100m
The formula for this is:
$H = \rho C \int_0^z T(z) dz$
(1)

Where H is ocean heat content, the value we are trying to calculate,

$\rho$ is the density of sea water, $1026 kg/m^3$ ,

$C$ is the specific heat of sea water, $3990 J/(kg K)$ ,

$z$ is the depth limit of the calculation in meters,

and $T(z)$ is the temperature at each depth in degrees Kelvin.

def calc_ocean_heat(delta_level, temperature):
    rho = 1026 #kg/m^3
    c_p = 3990 #J/(kg K)
    weighted_temperature = delta_level * temperature
    heat = weighted_temperature.sum(dim="z_t")*rho*c_p
    return heat

# Remember that the coordinate z_t still has values in cm
hist_temp_ocean_surface = hist_temp_in_degK.where(hist_temp_in_degK['z_t'] < 1e4,drop=True)
hist_temp_ocean_surface

depth_level_deltas_surface = depth_level_deltas.where(depth_level_deltas['z_t'] <1e4, drop= True)
depth_level_deltas_surface

hist_ocean_heat = calc_ocean_heat(depth_level_deltas_surface,hist_temp_ocean_surface)
hist_ocean_heat

Plot Ocean Heat¶

%%time
# Compute annual and ensemble mean
hist_oceanheat_ann_mean = hist_ocean_heat.mean('member_id').groupby('time.year').mean()
hist_oceanheat_ann_mean

CPU times: user 443 ms, sys: 28.6 ms, total: 471 ms
Wall time: 466 ms

hist_oceanheat_ano = \
hist_oceanheat_ann_mean.sel(year=2014) - hist_oceanheat_ann_mean.sel(year=1850)

%%time
hist_oceanheat_ano.plot()

2026-10-22 03:48:40,213 - distributed.worker - ERROR - Compute Failed
Key:       ('convert-convert_0-transpose-e6fb9c836fd2c42c20255265d80299c0', 27, 1, 0, 0, 0)
State:     executing
Task:  <Task ('convert-convert_0-transpose-e6fb9c836fd2c42c20255265d80299c0', 27, 1, 0, 0, 0) _execute_subgraph(...)>
Exception: 'ClientPayloadError("Response payload is not completed: <ContentLengthError: 400, message=\'Not enough data to satisfy content length header (received 14680064 of 85437122 bytes).\'>")'
Traceback: '  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/dask/array/core.py", line 140, in getter\n    c = np.asarray(c)\n        ^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/indexing.py", line 691, in __array__\n    return np.asarray(to_numpy(self.get_duck_array()), dtype=dtype, copy=copy)\n                               ^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/indexing.py", line 697, in get_duck_array\n    return self.array.get_duck_array()\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/indexing.py", line 930, in get_duck_array\n    return self.array.get_duck_array()\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/coding/common.py", line 80, in get_duck_array\n    return self.func(self.array.get_duck_array())\n                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/indexing.py", line 770, in get_duck_array\n    array = self.array[self.key]\n            ~~~~~~~~~~^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/backends/zarr.py", line 316, in __getitem__\n    return indexing.explicit_indexing_adapter(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/indexing.py", line 1162, in explicit_indexing_adapter\n    result = raw_indexing_method(raw_key.tuple)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/backends/zarr.py", line 279, in _getitem\n    return self._array[key]\n           ~~~~~~~~~~~^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/zarr/core.py", line 798, in __getitem__\n    result = self.get_orthogonal_selection(pure_selection, fields=fields)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/zarr/core.py", line 1080, in get_orthogonal_selection\n    return self._get_selection(indexer=indexer, out=out, fields=fields)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/zarr/core.py", line 1343, in _get_selection\n    self._chunk_getitems(\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/zarr/core.py", line 2179, in _chunk_getitems\n    cdatas = self.chunk_store.getitems(ckeys, contexts=contexts)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/zarr/storage.py", line 1435, in getitems\n    raise v\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/fsspec/asyn.py", line 359, in _run_coro\n    return await asyncio.wait_for(coro, timeout=timeout), i\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/asyncio/tasks.py", line 520, in wait_for\n    return await fut\n           ^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/fsspec/implementations/http.py", line 249, in _cat_file\n    out = await r.read()\n          ^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/aiohttp/client_reqrep.py", line 902, in read\n    self._body = await self.content.read()\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/aiohttp/streams.py", line 448, in read\n    block = await self.readany()\n            ^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/aiohttp/streams.py", line 471, in readany\n    await self._wait("readany")\n  File "/home/runner/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/aiohttp/streams.py", line 365, in _wait\n    await waiter\n'

CPU times: user 11.7 s, sys: 2.11 s, total: 13.8 s
Wall time: 3min 8s

---------------------------------------------------------------------------
ContentLengthError                        Traceback (most recent call last)
File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/aiohttp/client_proto.py:145, in connection_lost()
    144 try:
--> 145     uncompleted = self._parser.feed_eof()
    146 except Exception as underlying_exc:

File aiohttp/_http_parser.pyx:625
--> 625     def feed_eof(self):
    626         cdef bytes desc

ContentLengthError: 400, message:
  Not enough data to satisfy content length header (received 14680064 of 85437122 bytes).

The above exception was the direct cause of the following exception:

ClientPayloadError                        Traceback (most recent call last)
Cell In[23], line 1
----> 1 get_ipython().run_cell_magic('time', '', 'hist_oceanheat_ano.plot()\n')

File <timed eval>:1
----> 1 'Could not get source, probably due dynamically evaluated source code.'

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/plot/accessor.py:48, in DataArrayPlotAccessor.__call__(self, **kwargs)
     46 @functools.wraps(dataarray_plot.plot, assigned=("__doc__", "__annotations__"))
     47 def __call__(self, **kwargs) -> Any:
---> 48     return dataarray_plot.plot(self._da, **kwargs)

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/plot/dataarray_plot.py:279, in plot(darray, row, col, col_wrap, ax, hue, subplot_kws, **kwargs)
    226 def plot(
    227     darray: DataArray,
    228     *,
   (...)    235     **kwargs: Any,
    236 ) -> Any:
    237     """
    238     Default plot of DataArray using :py:mod:`matplotlib:matplotlib.pyplot`.
    239 
   (...)    275     xarray.DataArray.squeeze
    276     """
    277     darray = darray.squeeze(
    278         d for d, s in darray.sizes.items() if s == 1 and d not in (row, col, hue)
--> 279     ).compute()
    281     plot_dims = set(darray.dims)
    282     plot_dims.discard(row)

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/dataarray.py:1254, in DataArray.compute(self, **kwargs)
   1224 """Trigger loading data into memory and return a new dataarray.
   1225 
   1226 Data will be computed and/or loaded from disk or a remote source.
   (...)   1251 Variable.compute
   1252 """
   1253 new = self.copy(deep=False)
-> 1254 return new.load(**kwargs)

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/dataarray.py:1180, in DataArray.load(self, **kwargs)
   1150 def load(self, **kwargs) -> Self:
   1151     """Trigger loading data into memory and return this dataarray.
   1152 
   1153     Data will be computed and/or loaded from disk or a remote source.
   (...)   1178     Variable.load
   1179     """
-> 1180     ds = self._to_temp_dataset().load(**kwargs)
   1181     new = self._from_temp_dataset(ds)
   1182     self._variable = new._variable

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/dataset.py:569, in Dataset.load(self, **kwargs)
    565         if chunked_data:
    566             chunkmanager = get_chunked_array_type(*chunked_data.values())
    567 
    568             # evaluate all the chunked arrays simultaneously
--> 569             evaluated_data: tuple[np.ndarray[Any, Any], ...] = chunkmanager.compute(
    570                 *chunked_data.values(), **kwargs
    571             )
    572 

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/namedarray/daskmanager.py:85, in DaskManager.compute(self, *data, **kwargs)
     80 def compute(
     81     self, *data: Any, **kwargs: Any
     82 ) -> tuple[np.ndarray[Any, _DType_co], ...]:
     83     from dask.array import compute
---> 85     return compute(*data, **kwargs)

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/dask/base.py:769, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
    766     expr = expr.optimize()
    767     keys = list(flatten(expr.__dask_keys__()))
--> 769     results = schedule(expr, keys, **kwargs)
    771 return repack(results)

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/indexing.py:691, in __array__()
    687 def __array__(
    688     self, dtype: DTypeLike | None = None, /, *, copy: bool | None = None
    689 ) -> np.ndarray:
    690     if Version(np.__version__) >= Version("2.0.0"):
--> 691         return np.asarray(to_numpy(self.get_duck_array()), dtype=dtype, copy=copy)
    693     else:
    694         return np.asarray(to_numpy(self.get_duck_array()), dtype=dtype)

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/indexing.py:697, in get_duck_array()
    696 def get_duck_array(self):
--> 697     return self.array.get_duck_array()

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/indexing.py:930, in get_duck_array()
    929 def get_duck_array(self):
--> 930     return self.array.get_duck_array()

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/coding/common.py:80, in get_duck_array()
     79 def get_duck_array(self):
---> 80     return self.func(self.array.get_duck_array())

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/indexing.py:770, in get_duck_array()
    767 from xarray.backends.common import BackendArray
    769 if isinstance(self.array, BackendArray):
--> 770     array = self.array[self.key]
    771 else:
    772     array = apply_indexer(self.array, self.key)

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/backends/zarr.py:316, in __getitem__()
    314 elif isinstance(key, indexing.OuterIndexer):
    315     method = self._oindex
--> 316 return indexing.explicit_indexing_adapter(
    317     key, array.shape, indexing.IndexingSupport.VECTORIZED, method
    318 )

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/core/indexing.py:1162, in explicit_indexing_adapter()
   1140 """Support explicit indexing by delegating to a raw indexing method.
   1141 
   1142 Outer and/or vectorized indexers are supported by indexing a second time
   (...)   1159 Indexing result, in the form of a duck numpy-array.
   1160 """
   1161 raw_key, numpy_indices = decompose_indexer(key, shape, indexing_support)
-> 1162 result = raw_indexing_method(raw_key.tuple)
   1163 if numpy_indices.tuple:
   1164     # index the loaded duck array
   1165     indexable = as_indexable(result)

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/xarray/backends/zarr.py:279, in _getitem()
    278 def _getitem(self, key):
--> 279     return self._array[key]

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/zarr/core.py:798, in __getitem__()
    796     result = self.vindex[selection]
    797 elif is_pure_orthogonal_indexing(pure_selection, self.ndim):
--> 798     result = self.get_orthogonal_selection(pure_selection, fields=fields)
    799 else:
    800     result = self.get_basic_selection(pure_selection, fields=fields)

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/zarr/core.py:1080, in get_orthogonal_selection()
   1077 # setup indexer
   1078 indexer = OrthogonalIndexer(selection, self)
-> 1080 return self._get_selection(indexer=indexer, out=out, fields=fields)

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/zarr/core.py:1343, in _get_selection()
   1340 if math.prod(out_shape) > 0:
   1341     # allow storage to get multiple items at once
   1342     lchunk_coords, lchunk_selection, lout_selection = zip(*indexer)
-> 1343     self._chunk_getitems(
   1344         lchunk_coords,
   1345         lchunk_selection,
   1346         out,
   1347         lout_selection,
   1348         drop_axes=indexer.drop_axes,
   1349         fields=fields,
   1350     )
   1351 if out.shape:
   1352     return out

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/zarr/core.py:2179, in _chunk_getitems()
   2177     if not isinstance(self._meta_array, np.ndarray):
   2178         contexts = ConstantMap(ckeys, constant=Context(meta_array=self._meta_array))
-> 2179     cdatas = self.chunk_store.getitems(ckeys, contexts=contexts)
   2181 for ckey, chunk_select, out_select in zip(ckeys, lchunk_selection, lout_selection):
   2182     if ckey in cdatas:

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/zarr/storage.py:1435, in getitems()
   1432     continue
   1433 elif isinstance(v, Exception):
   1434     # Raise any other exception
-> 1435     raise v
   1436 else:
   1437     # The function calling this method may not recognize the transformed
   1438     # keys, so we send the values returned by self.map.getitems back into
   1439     # the original key space.
   1440     results[keys_transformed[k]] = v

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/fsspec/asyn.py:359, in _run_coro()
    357 async def _run_coro(coro, i):
    358     try:
--> 359         return await asyncio.wait_for(coro, timeout=timeout), i
    360     except Exception as e:
    361         if not return_exceptions:

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/asyncio/tasks.py:520, in wait_for()
    517         raise TimeoutError from exc
    519 async with timeouts.timeout(timeout):
--> 520     return await fut

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/fsspec/implementations/http.py:249, in _cat_file()
    247 session = await self.set_session()
    248 async with session.get(self.encode_url(url), **kw) as r:
--> 249     out = await r.read()
    250     self._raise_not_found_for_status(r, url)
    251 return out

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/aiohttp/client_reqrep.py:902, in read()
    900 if self._body is None:
    901     try:
--> 902         self._body = await self.content.read()
    903         for trace in self._traces:
    904             await trace.send_response_chunk_received(
    905                 self.method, self.url, self._body
    906             )

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/aiohttp/streams.py:448, in read()
    446 blocks = []
    447 while True:
--> 448     block = await self.readany()
    449     if not block:
    450         break

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/aiohttp/streams.py:471, in readany()
    467 # TODO: should be `if` instead of `while`
    468 # because waiter maybe triggered on chunk end,
    469 # without feeding any data
    470 while not self._buffer and not self._eof:
--> 471     await self._wait("readany")
    473 return self._read_nowait(-1)

File ~/micromamba/envs/osdf-cookbook/lib/python3.12/site-packages/aiohttp/streams.py:365, in _wait()
    363 try:
    364     with self._timer:
--> 365         await waiter
    366 finally:
    367     self._waiter = None

ClientPayloadError: Response payload is not completed: <ContentLengthError: 400, message='Not enough data to satisfy content length header (received 14680064 of 85437122 bytes).'>

Indeed! The surface ocean is trapping heat as the globe warms!

Summary¶

In this notebook we used sea temperature data from the Community Earth System Model (CESM2) Large Ensemble dataset to compute surface ocean heat and convince ourselves that the surface ocean is trapping extra heat as the globe warms. We used an intake-ESM catalog backed by pelican links to stream data from NCAR’s Geoscience Data Exchange via NCAR’s OSDF origin!

What’s next?¶

In the next notebook, we will see how to load data from multiple OSDF origins into a workflow. We will stream CMIP6 model data from AWS and observational data from NCAR’s GDEX.

Resources and references¶

Original notebook on the pangeo NCAR gallery
CESM2 Large Ensemble Dataset (Community Earth System Model 2) hosted on NCAR’s GDEX.