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Ocean carbon fluxes

Overview

The carbon cycle is a key part of ocean biogeochemistry and, more broadly, Earth’s climate system. Here we learn how to make maps of some key variables modeled by CESM related to the marine carbon cycle.

  1. General setup
  2. Subsetting
  3. Processing data
  4. Making maps

Prerequisites

ConceptsImportanceNotes
MatplotlibNecessary
Intro to CartopyNecessary
Dask CookbookHelpful
Intro to XarrayHelpful
  • Time to learn: 15 min

Imports

import xarray as xr
import glob
import numpy as np
import matplotlib.pyplot as plt
import cartopy
import cartopy.crs as ccrs
import pop_tools
from dask.distributed import LocalCluster
import dask
import distributed
import s3fs

from module import adjust_pop_grid
/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/pop_tools/__init__.py:4: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  from pkg_resources import DistributionNotFound, get_distribution

General setup (see intro notebooks for explanations)

Connect to cluster

cluster = LocalCluster()
client = cluster.get_client()
/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/node.py:187: UserWarning: Port 8787 is already in use.
Perhaps you already have a cluster running?
Hosting the HTTP server on port 45253 instead
  warnings.warn(

Bring in POP grid utilities

ds_grid = pop_tools.get_grid('POP_gx1v7')
lons = ds_grid.TLONG
lats = ds_grid.TLAT
depths = ds_grid.z_t * 0.01
Downloading file 'inputdata/ocn/pop/gx1v7/grid/horiz_grid_20010402.ieeer8' from 'https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/ocn/pop/gx1v7/grid/horiz_grid_20010402.ieeer8' to '/home/runner/.pop_tools'.

/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/connectionpool.py:1097: InsecureRequestWarning: Unverified HTTPS request is being made to host 'svn-ccsm-inputdata.cgd.ucar.edu'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings
  warnings.warn(

ds_grid
Loading...

Load the data

jetstream_url = 'https://js2.jetstream-cloud.org:8001/'

s3 = s3fs.S3FileSystem(anon=True, client_kwargs=dict(endpoint_url=jetstream_url))

# Generate a list of all files in CESM folder
s3path = 's3://pythia/ocean-bgc/cesm/g.e22.GOMIPECOIAF_JRA-1p4-2018.TL319_g17.4p2z.002branch/ocn/proc/tseries/month_1/*'
remote_files = s3.glob(s3path)
s3.invalidate_cache()
# Open all files from folder
fileset = [s3.open(file) for file in remote_files]

# Open with xarray
ds = xr.open_mfdataset(fileset, data_vars="minimal", coords='minimal', compat="override", parallel=True,
                       drop_variables=["transport_components", "transport_regions", 'moc_components'], decode_times=True)

ds
Fetching long content....
2025-09-07 01:51:07,222 - distributed.protocol.pickle - ERROR - Failed to serialize <xarray.Dataset> Size: 61MB
Dimensions:          (time: 120, nlat: 384, nlon: 320)
Coordinates:
    TLAT             (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
    TLONG            (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
  * time             (time) object 960B 2010-01-16 12:00:00 ... 2019-12-16 12...
Dimensions without coordinates: nlat, nlon
Data variables:
    diat_N_lim_surf  (time, nlat, nlon) float32 59MB dask.array<chunksize=(60, 192, 160), meta=np.ndarray>.
Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 63, in dumps
    result = pickle.dumps(x, **dump_kwargs)
TypeError: cannot pickle 'module' object

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 68, in dumps
    pickler.dump(x)
    ~~~~~~~~~~~~^^^
TypeError: cannot pickle 'module' object

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 80, in dumps
    result = cloudpickle.dumps(x, **dump_kwargs)
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/cloudpickle/cloudpickle.py", line 1537, in dumps
    cp.dump(obj)
    ~~~~~~~^^^^^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/cloudpickle/cloudpickle.py", line 1303, in dump
    return super().dump(obj)
           ~~~~~~~~~~~~^^^^^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/h5py/_hl/base.py", line 369, in __getnewargs__
    raise TypeError("h5py objects cannot be pickled")
TypeError: h5py objects cannot be pickled
2025-09-07 01:51:07,226 - distributed.protocol.pickle - ERROR - Failed to serialize <xarray.Dataset> Size: 61MB
Dimensions:           (time: 120, nlat: 384, nlon: 320)
Coordinates:
    TLAT              (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
    TLONG             (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
  * time              (time) object 960B 2010-01-16 12:00:00 ... 2019-12-16 1...
Dimensions without coordinates: nlat, nlon
Data variables:
    cocco_P_lim_surf  (time, nlat, nlon) float32 59MB dask.array<chunksize=(60, 192, 160), meta=np.ndarray>.
Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 63, in dumps
    result = pickle.dumps(x, **dump_kwargs)
TypeError: cannot pickle 'module' object

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 68, in dumps
    pickler.dump(x)
    ~~~~~~~~~~~~^^^
TypeError: cannot pickle 'module' object

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 80, in dumps
    result = cloudpickle.dumps(x, **dump_kwargs)
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/cloudpickle/cloudpickle.py", line 1537, in dumps
    cp.dump(obj)
    ~~~~~~~^^^^^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/cloudpickle/cloudpickle.py", line 1303, in dump
    return super().dump(obj)
           ~~~~~~~~~~~~^^^^^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/h5py/_hl/base.py", line 369, in __getnewargs__
    raise TypeError("h5py objects cannot be pickled")
TypeError: h5py objects cannot be pickled

2025-09-07 01:51:07,411 - distributed.protocol.core - CRITICAL - Failed to deserialize
Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/core.py", line 175, in loads
    return msgpack.loads(
           ~~~~~~~~~~~~~^
        frames[0], object_hook=_decode_default, use_list=False, **msgpack_opts
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "msgpack/_unpacker.pyx", line 194, in msgpack._cmsgpack.unpackb
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/core.py", line 159, in _decode_default
    return merge_and_deserialize(
        sub_header, sub_frames, deserializers=deserializers
    )
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/contextlib.py", line 85, in inner
    return func(*args, **kwds)
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/serialize.py", line 525, in merge_and_deserialize
    return deserialize(header, merged_frames, deserializers=deserializers)
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/serialize.py", line 452, in deserialize
    return loads(header, frames)
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/serialize.py", line 195, in serialization_error_loads
    raise TypeError(msg)
TypeError: Could not serialize object of type Dataset
Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 63, in dumps
    result = pickle.dumps(x, **dump_kwargs)
TypeError: cannot pickle 'module' object

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 68, in dumps
    pickler.dump(x)
    ~~~~~~~~~~~~^^^
TypeError: cannot pickle 'module' object

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/serialize.py", line 366, in serialize
    header, frames = dumps(x, context=context) if wants_context else dumps(x)
                     ~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/serialize.py", line 78, in pickle_dumps
    frames[0] = pickle.dumps(
                ~~~~~~~~~~~~^
        x,
        ^^
        buffer_callback=buffer_callback,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        protocol=context.get("pickle-protocol", None) if context else None,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 80, in dumps
    result = cloudpickle.dumps(x, **dump_kwargs)
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/cloudpickle/cloudpickle.py", line 1537, in dumps
    cp.dump(obj)
    ~~~~~~~^^^^^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/cloudpickle/cloudpickle.py", line 1303, in dump
    return super().dump(obj)
           ~~~~~~~~~~~~^^^^^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/h5py/_hl/base.py", line 369, in __getnewargs__
    raise TypeError("h5py objects cannot be pickled")
TypeError: h5py objects cannot be pickled


---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 13
     10 fileset = [s3.open(file) for file in remote_files]
     12 # Open with xarray
---> 13 ds = xr.open_mfdataset(fileset, data_vars="minimal", coords='minimal', compat="override", parallel=True,
     14                        drop_variables=["transport_components", "transport_regions", 'moc_components'], decode_times=True)
     16 ds

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/xarray/backends/api.py:1812, in open_mfdataset(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, errors, **kwargs)
   1807     datasets = [preprocess(ds) for ds in datasets]
   1809 if parallel:
   1810     # calling compute here will return the datasets/file_objs lists,
   1811     # the underlying datasets will still be stored as dask arrays
-> 1812     datasets, closers = dask.compute(datasets, closers)
   1814 # Combine all datasets, closing them in case of a ValueError
   1815 try:

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/dask/base.py:681, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
    678     expr = expr.optimize()
    679     keys = list(flatten(expr.__dask_keys__()))
--> 681     results = schedule(expr, keys, **kwargs)
    683 return repack(results)

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/utils_comm.py:416, in retry_operation(coro, operation, *args, **kwargs)
    410 retry_delay_min = parse_timedelta(
    411     dask.config.get("distributed.comm.retry.delay.min"), default="s"
    412 )
    413 retry_delay_max = parse_timedelta(
    414     dask.config.get("distributed.comm.retry.delay.max"), default="s"
    415 )
--> 416 return await retry(
    417     partial(coro, *args, **kwargs),
    418     count=retry_count,
    419     delay_min=retry_delay_min,
    420     delay_max=retry_delay_max,
    421     operation=operation,
    422 )

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/utils_comm.py:395, in retry(coro, count, delay_min, delay_max, jitter_fraction, retry_on_exceptions, operation)
    393             delay *= 1 + random.random() * jitter_fraction
    394         await asyncio.sleep(delay)
--> 395 return await coro()

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/core.py:1259, in PooledRPCCall.__getattr__.<locals>.send_recv_from_rpc(**kwargs)
   1257 prev_name, comm.name = comm.name, "ConnectionPool." + key
   1258 try:
-> 1259     return await send_recv(comm=comm, op=key, **kwargs)
   1260 finally:
   1261     self.pool.reuse(self.addr, comm)

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/core.py:1018, in send_recv(comm, reply, serializers, deserializers, **kwargs)
   1016 await comm.write(msg, serializers=serializers, on_error="raise")
   1017 if reply:
-> 1018     response = await comm.read(deserializers=deserializers)
   1019 else:
   1020     response = None

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/comm/tcp.py:248, in TCP.read(self, deserializers)
    246 else:
    247     try:
--> 248         msg = await from_frames(
    249             frames,
    250             deserialize=self.deserialize,
    251             deserializers=deserializers,
    252             allow_offload=self.allow_offload,
    253         )
    254     except EOFError:
    255         # Frames possibly garbled or truncated by communication error
    256         self.abort()

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/comm/utils.py:78, in from_frames(frames, deserialize, deserializers, allow_offload)
     76     res = await offload(_from_frames)
     77 else:
---> 78     res = _from_frames()
     80 return res

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/comm/utils.py:61, in from_frames.<locals>._from_frames()
     59 def _from_frames():
     60     try:
---> 61         return protocol.loads(
     62             frames, deserialize=deserialize, deserializers=deserializers
     63         )
     64     except EOFError:
     65         if size > 1000:

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/core.py:175, in loads(frames, deserialize, deserializers)
    172             return pickle.loads(sub_header["pickled-obj"], buffers=sub_frames)
    173         return msgpack_decode_default(obj)
--> 175     return msgpack.loads(
    176         frames[0], object_hook=_decode_default, use_list=False, **msgpack_opts
    177     )
    179 except Exception:
    180     logger.critical("Failed to deserialize", exc_info=True)

File msgpack/_unpacker.pyx:194, in msgpack._cmsgpack.unpackb()

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/core.py:159, in loads.<locals>._decode_default(obj)
    157     if "compression" in sub_header:
    158         sub_frames = decompress(sub_header, sub_frames)
--> 159     return merge_and_deserialize(
    160         sub_header, sub_frames, deserializers=deserializers
    161     )
    162 else:
    163     return Serialized(sub_header, sub_frames)

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/contextlib.py:85, in ContextDecorator.__call__.<locals>.inner(*args, **kwds)
     82 @wraps(func)
     83 def inner(*args, **kwds):
     84     with self._recreate_cm():
---> 85         return func(*args, **kwds)

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/serialize.py:525, in merge_and_deserialize(header, frames, deserializers)
    521             merged = host_array_from_buffers(subframes)
    523         merged_frames.append(merged)
--> 525 return deserialize(header, merged_frames, deserializers=deserializers)

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/serialize.py:452, in deserialize(header, frames, deserializers)
    447     raise TypeError(
    448         "Data serialized with %s but only able to deserialize "
    449         "data with %s" % (name, str(list(deserializers)))
    450     )
    451 dumps, loads, wants_context = families[name]
--> 452 return loads(header, frames)

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/serialize.py:195, in serialization_error_loads(header, frames)
    193 def serialization_error_loads(header, frames):
    194     msg = "\n".join([codecs.decode(frame, "utf8") for frame in frames])
--> 195     raise TypeError(msg)

TypeError: Could not serialize object of type Dataset
Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 63, in dumps
    result = pickle.dumps(x, **dump_kwargs)
TypeError: cannot pickle 'module' object

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 68, in dumps
    pickler.dump(x)
    ~~~~~~~~~~~~^^^
TypeError: cannot pickle 'module' object

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/serialize.py", line 366, in serialize
    header, frames = dumps(x, context=context) if wants_context else dumps(x)
                     ~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/serialize.py", line 78, in pickle_dumps
    frames[0] = pickle.dumps(
                ~~~~~~~~~~~~^
        x,
        ^^
        buffer_callback=buffer_callback,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        protocol=context.get("pickle-protocol", None) if context else None,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 80, in dumps
    result = cloudpickle.dumps(x, **dump_kwargs)
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/cloudpickle/cloudpickle.py", line 1537, in dumps
    cp.dump(obj)
    ~~~~~~~^^^^^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/cloudpickle/cloudpickle.py", line 1303, in dump
    return super().dump(obj)
           ~~~~~~~~~~~~^^^^^
  File "/home/runner/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/h5py/_hl/base.py", line 369, in __getnewargs__
    raise TypeError("h5py objects cannot be pickled")
TypeError: h5py objects cannot be pickled

Subsetting

variables =['FG_CO2','photoC_TOT_zint','POC_FLUX_100m']
keep_vars=['z_t','z_t_150m','dz','time_bound', 'time', 'TAREA','TLAT','TLONG'] + variables
ds = ds.drop_vars([v for v in ds.variables if v not in keep_vars])

Processing - means in time and space

Pull in the function we defined in the nutrients notebook...

def year_mean(ds):
    """
    Properly convert monthly data to annual means, taking into account month lengths.
    Source: https://ncar.github.io/esds/posts/2021/yearly-averages-xarray/
    """
    
    # Make a DataArray with the number of days in each month, size = len(time)
    month_length = ds.time.dt.days_in_month

    # Calculate the weights by grouping by 'time.year'
    weights = (
        month_length.groupby("time.year") / month_length.groupby("time.year").sum()
    )

    # Test that the sum of the year for each season is 1.0
    np.testing.assert_allclose(weights.groupby("time.year").sum().values, np.ones((len(ds.groupby("time.year")), )))

    # Calculate the weighted average
    return (ds * weights).groupby("time.year").sum(dim="time")

We also define a new function to take global mean in space.

def global_mean(ds, ds_grid, compute_vars, normalize=True, include_ms=False):
    """
    Compute the global mean on a POP dataset. 
    Return computed quantity in conventional units.
    """

    other_vars = list(set(ds.variables) - set(compute_vars))

    # note TAREA is in cm^2, which affects units
    
    if include_ms: # marginal seas!
        surface_mask = ds_grid.TAREA.where(ds_grid.KMT > 0).fillna(0.)
    else:
        surface_mask = ds_grid.TAREA.where(ds_grid.REGION_MASK > 0).fillna(0.)        
    
    masked_area = {
        v: surface_mask.where(ds[v].notnull()).fillna(0.) 
        for v in compute_vars
    }
    
    with xr.set_options(keep_attrs=True):
        
        dso = xr.Dataset({
            v: (ds[v] * masked_area[v]).sum(['nlat', 'nlon'])
            for v in compute_vars
        })
        
        if normalize:
            dso = xr.Dataset({
                v: dso[v] / masked_area[v].sum(['nlat', 'nlon'])
                for v in compute_vars
            })            
                
    return dso

Take the long-term mean of our data set. We process monthly to annual with our custom function, then use xarray’s built-in .mean() function to process from annual data to a single mean over time, since each year is the same length.

ds = year_mean(ds).mean("year")

Do some global integrals, to check if our values look reasonable

ds_glb = global_mean(ds, ds_grid, variables,normalize=False).compute()

# convert from nmol C/s to Pg C/yr
nmols_to_PgCyr = 1e-9 * 12. * 1e-15 * 365. * 86400.

for v in variables:
    ds_glb[v] = ds_glb[v] * nmols_to_PgCyr        
    ds_glb[v].attrs['units'] = 'Pg C yr$^{-1}$'
    
ds_glb

We can compare these values to some observationally derived values. Each of these is calculated in a different way with combinations of data and models--please reference each linked paper for detailed discussion. Takahashi et al., 2002 estimate global air-sea CO2_2 flux to be 2.2 (+22% or −19%) Pg C yr−1^{−1}. Our value (shown above as FG_CO2) is 2.779 Pg C yr−1^{−1}. This value is outside of these bounds, but still on the same order of magnitude. We note that these values are calculated over different time periods, so we also don’t expect them to be an exact comparison. photoC_TOT_zint represents global vertically-integrated NPP; Behrenfeld and Falkowski, 1997 estimate this value to be 43.5 Pg C yr−1^{−1}. Our value is 53.26 Pg C yr−1^{−1}, which is within 22% of the observationally derived value. POC_FLUX_100m represents the particulate organic carbon flux at 100 m depth. DeVries and Weber, 2017 calculated this flux integrated over the entire euphotic zone to be 9.1 ± 0.2 Pg C yr−1^{−1}. Since the depth ranges are different, this isn’t an exact comparison, but the orders of magnitude are similar. This first-pass analysis tells us that CESM is on the right track for these values.

Make some maps

First, convert from mmol/m3 cm/s to mmol/m2/day.

for var in variables:
    ds[var] = ds[var] * 0.01 * 86400.

Then, make a few maps of key carbon-related variables.

fig = plt.figure(figsize=(8,12))

ax = fig.add_subplot(3,1,1, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('a) Air-sea CO$_2$ flux', fontsize=12,loc='left')
lon, lat, field = adjust_pop_grid(lons, lats,  ds.FG_CO2)
pc=ax.pcolormesh(lon, lat, field, cmap='bwr',vmin=-5,vmax=5,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

cbar1 = fig.colorbar(pc, ax=ax,extend='both',label='mmol m$^{-2}$ d$^{-1}$')


ax = fig.add_subplot(3,1,2, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('b) NPP', fontsize=12,loc='left')
lon, lat, field = adjust_pop_grid(lons, lats,  ds.photoC_TOT_zint)
pc=ax.pcolormesh(lon, lat, field, cmap='Greens',vmin=0,vmax=100,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

cbar1 = fig.colorbar(pc, ax=ax,extend='max',label='mmol m$^{-2}$ d$^{-1}$')

ax = fig.add_subplot(3,1,3, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('c) POC flux at 100m', fontsize=12,loc='left')
lon, lat, field = adjust_pop_grid(lons, lats,  ds.POC_FLUX_100m)
pc=ax.pcolormesh(lon, lat, field, cmap='Oranges',vmin=0,vmax=10,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

cbar1 = fig.colorbar(pc, ax=ax,extend='max',label='mmol m$^{-2}$ d$^{-1}$');

And close the Dask cluster we spun up at the beginning.

cluster.close()

Summary

You’ve learned how to make maps of some key quantities related to oceanic carbon.

References
  1. Takahashi, T., Sutherland, S. C., Sweeney, C., Poisson, A., Metzl, N., Tilbrook, B., Bates, N., Wanninkhof, R., Feely, R. A., Sabine, C., Olafsson, J., & Nojiri, Y. (2002). Global sea–air CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects. Deep Sea Research Part II: Topical Studies in Oceanography, 49(9–10), 1601–1622. 10.1016/s0967-0645(02)00003-6
  2. Behrenfeld, M. J., & Falkowski, P. G. (1997). Photosynthetic rates derived from satellite‐based chlorophyll concentration. Limnology and Oceanography, 42(1), 1–20. 10.4319/lo.1997.42.1.0001
  3. DeVries, T., & Weber, T. (2017). The export and fate of organic matter in the ocean: New constraints from combining satellite and oceanographic tracer observations. Global Biogeochemical Cycles, 31(3), 535–555. 10.1002/2016gb005551
  4. (2013). In Ocean Biogeochemical Dynamics (pp. 318–358). Princeton University Press. 10.2307/j.ctt3fgxqx.12
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