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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 37911 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'.

---------------------------------------------------------------------------
ConnectionRefusedError                    Traceback (most recent call last)
File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/connection.py:198, in HTTPConnection._new_conn(self)
    197 try:
--> 198     sock = connection.create_connection(
    199         (self._dns_host, self.port),
    200         self.timeout,
    201         source_address=self.source_address,
    202         socket_options=self.socket_options,
    203     )
    204 except socket.gaierror as e:

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/util/connection.py:85, in create_connection(address, timeout, source_address, socket_options)
     84 try:
---> 85     raise err
     86 finally:
     87     # Break explicitly a reference cycle

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/util/connection.py:73, in create_connection(address, timeout, source_address, socket_options)
     72     sock.bind(source_address)
---> 73 sock.connect(sa)
     74 # Break explicitly a reference cycle

ConnectionRefusedError: [Errno 111] Connection refused

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

NewConnectionError                        Traceback (most recent call last)
File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/connectionpool.py:787, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
    786 # Make the request on the HTTPConnection object
--> 787 response = self._make_request(
    788     conn,
    789     method,
    790     url,
    791     timeout=timeout_obj,
    792     body=body,
    793     headers=headers,
    794     chunked=chunked,
    795     retries=retries,
    796     response_conn=response_conn,
    797     preload_content=preload_content,
    798     decode_content=decode_content,
    799     **response_kw,
    800 )
    802 # Everything went great!

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/connectionpool.py:488, in HTTPConnectionPool._make_request(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)
    487         new_e = _wrap_proxy_error(new_e, conn.proxy.scheme)
--> 488     raise new_e
    490 # conn.request() calls http.client.*.request, not the method in
    491 # urllib3.request. It also calls makefile (recv) on the socket.

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/connectionpool.py:464, in HTTPConnectionPool._make_request(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)
    463 try:
--> 464     self._validate_conn(conn)
    465 except (SocketTimeout, BaseSSLError) as e:

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/connectionpool.py:1093, in HTTPSConnectionPool._validate_conn(self, conn)
   1092 if conn.is_closed:
-> 1093     conn.connect()
   1095 # TODO revise this, see https://github.com/urllib3/urllib3/issues/2791

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/connection.py:753, in HTTPSConnection.connect(self)
    752 sock: socket.socket | ssl.SSLSocket
--> 753 self.sock = sock = self._new_conn()
    754 server_hostname: str = self.host

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/connection.py:213, in HTTPConnection._new_conn(self)
    212 except OSError as e:
--> 213     raise NewConnectionError(
    214         self, f"Failed to establish a new connection: {e}"
    215     ) from e
    217 sys.audit("http.client.connect", self, self.host, self.port)

NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f0b8412c050>: Failed to establish a new connection: [Errno 111] Connection refused

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

MaxRetryError                             Traceback (most recent call last)
File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/requests/adapters.py:644, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
    643 try:
--> 644     resp = conn.urlopen(
    645         method=request.method,
    646         url=url,
    647         body=request.body,
    648         headers=request.headers,
    649         redirect=False,
    650         assert_same_host=False,
    651         preload_content=False,
    652         decode_content=False,
    653         retries=self.max_retries,
    654         timeout=timeout,
    655         chunked=chunked,
    656     )
    658 except (ProtocolError, OSError) as err:

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/connectionpool.py:841, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
    839     new_e = ProtocolError("Connection aborted.", new_e)
--> 841 retries = retries.increment(
    842     method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]
    843 )
    844 retries.sleep()

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/urllib3/util/retry.py:519, in Retry.increment(self, method, url, response, error, _pool, _stacktrace)
    518     reason = error or ResponseError(cause)
--> 519     raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
    521 log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)

MaxRetryError: HTTPSConnectionPool(host='svn-ccsm-inputdata.cgd.ucar.edu', port=443): Max retries exceeded with url: /trunk/inputdata/ocn/pop/gx1v7/grid/horiz_grid_20010402.ieeer8 (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f0b8412c050>: Failed to establish a new connection: [Errno 111] Connection refused'))

During handling of the above exception, another exception occurred:

ConnectionError                           Traceback (most recent call last)
Cell In[3], line 1
----> 1 ds_grid = pop_tools.get_grid('POP_gx1v7')
      2 lons = ds_grid.TLONG
      3 lats = ds_grid.TLAT

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/pop_tools/grid.py:137, in get_grid(grid_name, scrip)
    134 nlon = grid_attrs['lateral_dims'][1]
    136 # read horizontal grid
--> 137 horiz_grid_fname = INPUTDATA.fetch(grid_attrs['horiz_grid_fname'], downloader=downloader)
    138 grid_file_data = np.fromfile(horiz_grid_fname, dtype='>f8', count=-1)
    139 grid_file_data = grid_file_data.reshape((7, nlat, nlon))

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/pop_tools/grid.py:92, in fetch(self, fname, processor, downloader)
     89     if downloader is None:
     90         downloader = pooch.downloaders.choose_downloader(url)
---> 92     pooch.core.stream_download(url, full_path, known_hash, downloader, pooch=self)
     94 if processor is not None:
     95     return processor(str(full_path), action, self)

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/pooch/core.py:807, in stream_download(url, fname, known_hash, downloader, pooch, retry_if_failed)
    803 try:
    804     # Stream the file to a temporary so that we can safely check its
    805     # hash before overwriting the original.
    806     with temporary_file(path=str(fname.parent)) as tmp:
--> 807         downloader(url, tmp, pooch)
    808         hash_matches(tmp, known_hash, strict=True, source=str(fname.name))
    809         shutil.move(tmp, str(fname))

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/pooch/downloaders.py:220, in HTTPDownloader.__call__(self, url, output_file, pooch, check_only)
    218     # pylint: enable=consider-using-with
    219 try:
--> 220     response = requests.get(url, timeout=timeout, **kwargs)
    221     response.raise_for_status()
    222     content = response.iter_content(chunk_size=self.chunk_size)

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/requests/api.py:73, in get(url, params, **kwargs)
     62 def get(url, params=None, **kwargs):
     63     r"""Sends a GET request.
     64 
     65     :param url: URL for the new :class:`Request` object.
   (...)     70     :rtype: requests.Response
     71     """
---> 73     return request("get", url, params=params, **kwargs)

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/requests/api.py:59, in request(method, url, **kwargs)
     55 # By using the 'with' statement we are sure the session is closed, thus we
     56 # avoid leaving sockets open which can trigger a ResourceWarning in some
     57 # cases, and look like a memory leak in others.
     58 with sessions.Session() as session:
---> 59     return session.request(method=method, url=url, **kwargs)

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/requests/sessions.py:589, in Session.request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
    584 send_kwargs = {
    585     "timeout": timeout,
    586     "allow_redirects": allow_redirects,
    587 }
    588 send_kwargs.update(settings)
--> 589 resp = self.send(prep, **send_kwargs)
    591 return resp

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/requests/sessions.py:703, in Session.send(self, request, **kwargs)
    700 start = preferred_clock()
    702 # Send the request
--> 703 r = adapter.send(request, **kwargs)
    705 # Total elapsed time of the request (approximately)
    706 elapsed = preferred_clock() - start

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.13/site-packages/requests/adapters.py:677, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
    673     if isinstance(e.reason, _SSLError):
    674         # This branch is for urllib3 v1.22 and later.
    675         raise SSLError(e, request=request)
--> 677     raise ConnectionError(e, request=request)
    679 except ClosedPoolError as e:
    680     raise ConnectionError(e, request=request)

ConnectionError: HTTPSConnectionPool(host='svn-ccsm-inputdata.cgd.ucar.edu', port=443): Max retries exceeded with url: /trunk/inputdata/ocn/pop/gx1v7/grid/horiz_grid_20010402.ieeer8 (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f0b8412c050>: Failed to establish a new connection: [Errno 111] Connection refused'))
ds_grid

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

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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