Phytoplankton nutrient limitation - Ocean Biogeochemistry Cookbook

A centric diatom, another type of phytoplankton. Art credit: Kristen Krumhardt

Overview¶

In previous notebooks, we explored the distribution of different nutrients in the ocean. Here we examine how the growth of phytoplankton communities is limited by these nutrient distributions.

General setup
Subsetting
Processing - long-term mean
Mapping nutrient limitation at the surface
Mapping biomass-weighted nutrient limitation in the top 100m
Making monthly climatology maps

Prerequisites¶

Concepts	Importance	Notes
Matplotlib	Necessary
Intro to Cartopy	Necessary
Dask Cookbook	Helpful
Intro to Xarray	Helpful

Time to learn: 30 min

Imports¶

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

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 0x7f98bfdf0050>: 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 0x7f98bfdf0050>: 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 0x7f98bfdf0050>: Failed to establish a new connection: [Errno 111] Connection refused'))

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 =['sp_Fe_lim_Cweight_avg_100m','sp_P_lim_Cweight_avg_100m','sp_N_lim_Cweight_avg_100m',
             'diat_Fe_lim_Cweight_avg_100m', 'diat_P_lim_Cweight_avg_100m','diat_N_lim_Cweight_avg_100m',
             'diat_SiO3_lim_Cweight_avg_100m','diaz_P_lim_Cweight_avg_100m',
             'diaz_Fe_lim_Cweight_avg_100m','cocco_Fe_lim_Cweight_avg_100m','cocco_C_lim_Cweight_avg_100m','cocco_N_lim_Cweight_avg_100m',
             'cocco_P_lim_Cweight_avg_100m','sp_Fe_lim_surf','sp_P_lim_surf','sp_N_lim_surf',
             'diat_Fe_lim_surf', 'diat_P_lim_surf','diat_N_lim_surf','diat_SiO3_lim_surf',
             'diaz_P_lim_surf','cocco_Fe_lim_surf','cocco_C_lim_surf','cocco_N_lim_surf',
             'cocco_P_lim_surf','diaz_Fe_lim_surf']
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 - long-term mean¶

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.season'
    weights = (
        month_length.groupby("time.year") / month_length.groupby("time.year").sum()
    )

    # Test that the sum of the weights 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")

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

Mapping nutrient limitation at the surface¶

Phytoplankton need a specific ratio of nutrients to grow and produce organic matter. In general, this is known as the Redfield ratio, first proposed by Redfield et al., 1963, and is approximately 106 C:16 N:1 P. Micronutrients like silicate and iron are also needed in more variable amounts depending on plankton type. To learn more about nutrient limitation, see Sarmiento and Gruber Chapter 4: Organic Matter Production. Our dataset uses a numerical notation to specify which nutrient is limiting in each area for each phytoplankton functional type, as specified below:

0 = PO4
1 = Fe
2 = NO3 (only for small phytoplankton and diatoms)
3 = Si (only for diatoms)
3 = C (only for coccolithophores)

To turn this information into a single array, we concatenate the limitation terms along the nutrient dimension for each phytoplankton functional type.

limarray_sp=xr.concat((ds_year.sp_P_lim_surf, ds_year.sp_Fe_lim_surf,ds_year.sp_N_lim_surf),dim='nutrient')
limarray_diat=xr.concat((ds_year.diat_P_lim_surf, ds_year.diat_Fe_lim_surf, ds_year.diat_N_lim_surf, ds_year.diat_SiO3_lim_surf),dim='nutrient')
limarray_diaz=xr.concat((ds_year.diaz_P_lim_surf, ds_year.diaz_Fe_lim_surf),dim='nutrient')
limarray_cocco=xr.concat((ds_year.cocco_P_lim_surf, ds_year.cocco_Fe_lim_surf, ds_year.cocco_N_lim_surf, ds_year.cocco_C_lim_surf),dim='nutrient')
                          
most_lim_sp=limarray_sp.argmin(dim='nutrient', skipna=False).squeeze()
most_lim_diat=limarray_diat.argmin(dim='nutrient', skipna=False).squeeze()
most_lim_diaz=limarray_diaz.argmin(dim='nutrient', skipna=False).squeeze()
most_lim_cocco=limarray_cocco.argmin(dim='nutrient', skipna=False).squeeze()
mask = np.isnan(ds_year.sp_N_lim_surf.squeeze())

fig = plt.figure(figsize=(8,13))
colorbar_specs = {'ticks' : np.arange(0,4,1)}

ax = fig.add_subplot(4,1,2, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('Diat surface nutrient limitation', fontsize=12)
lon, lat, field = adjust_pop_grid(lons, lats,  most_lim_diat.where(~mask))
pc=ax.pcolormesh(lon, lat, field, cmap=matplotlib.colormaps['viridis'].resampled(4),vmin=-0.5,vmax=3.5,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

ax = fig.add_subplot(4,1,1, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('SP surface nutrient limitation', fontsize=12)
lon, lat, field = adjust_pop_grid(lons, lats,  most_lim_sp.where(~mask))
pc=ax.pcolormesh(lon, lat, field, cmap=matplotlib.colormaps['viridis'].resampled(4),vmin=-0.5,vmax=3.5,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

ax = fig.add_subplot(4,1,3, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('Cocco surface nutrient limitation', fontsize=12)
lon, lat, field = adjust_pop_grid(lons, lats,  most_lim_cocco.where(~mask))
pc=ax.pcolormesh(lon, lat, field, cmap=matplotlib.colormaps['viridis'].resampled(4),vmin=-0.5,vmax=3.5,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

ax = fig.add_subplot(4,1,4, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('Diaz surface nutrient limitation', fontsize=12)
lon, lat, field = adjust_pop_grid(lons, lats,  most_lim_diaz.where(~mask))
pc=ax.pcolormesh(lon, lat, field, cmap=matplotlib.colormaps['viridis'].resampled(4),vmin=-0.5,vmax=3.5,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
cbar = fig.colorbar(pc, cax=cbar_ax,**colorbar_specs)
cbar.ax.set_yticklabels(['P lim', 'Fe lim', 'N lim','SiO3/C lim']);

Mapping biomass-weighted nutrient limitation in the top 100m¶

limarray_sp=xr.concat((ds_year.sp_P_lim_Cweight_avg_100m, ds_year.sp_Fe_lim_Cweight_avg_100m,ds_year.sp_N_lim_Cweight_avg_100m),dim='nutrient')
limarray_diat=xr.concat((ds_year.diat_P_lim_Cweight_avg_100m, ds_year.diat_Fe_lim_Cweight_avg_100m, ds_year.diat_N_lim_Cweight_avg_100m, ds_year.diat_SiO3_lim_Cweight_avg_100m),dim='nutrient')
limarray_diaz=xr.concat((ds_year.diaz_P_lim_Cweight_avg_100m, ds_year.diaz_Fe_lim_Cweight_avg_100m),dim='nutrient')
limarray_cocco=xr.concat((ds_year.cocco_P_lim_Cweight_avg_100m, ds_year.cocco_Fe_lim_Cweight_avg_100m, ds_year.cocco_N_lim_Cweight_avg_100m, ds_year.cocco_C_lim_Cweight_avg_100m),dim='nutrient')
                          
most_lim_sp=limarray_sp.argmin(dim='nutrient', skipna=False).squeeze()
most_lim_diat=limarray_diat.argmin(dim='nutrient', skipna=False).squeeze()
most_lim_diaz=limarray_diaz.argmin(dim='nutrient', skipna=False).squeeze()
most_lim_cocco=limarray_cocco.argmin(dim='nutrient', skipna=False).squeeze()
mask = np.isnan(ds_year.sp_N_lim_surf.squeeze())

fig = plt.figure(figsize=(8,13))
colorbar_specs = {'ticks' : np.arange(0,4,1)}

ax = fig.add_subplot(4,1,2, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('Diat biomass-weighted nutrient limitation', fontsize=12)
lon, lat, field = adjust_pop_grid(lons, lats,  most_lim_diat.where(~mask))
pc=ax.pcolormesh(lon, lat, field, cmap=matplotlib.colormaps['viridis'].resampled(4),vmin=-0.5,vmax=3.5,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

ax = fig.add_subplot(4,1,1, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('SP biomass-weighted nutrient limitation', fontsize=12)
lon, lat, field = adjust_pop_grid(lons, lats,  most_lim_sp.where(~mask))
pc=ax.pcolormesh(lon, lat, field, cmap=matplotlib.colormaps['viridis'].resampled(4),vmin=-0.5,vmax=3.5,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

ax = fig.add_subplot(4,1,3, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('Cocco biomass-weighted nutrient limitation', fontsize=12)
lon, lat, field = adjust_pop_grid(lons, lats,  most_lim_cocco.where(~mask))
pc=ax.pcolormesh(lon, lat, field, cmap=matplotlib.colormaps['viridis'].resampled(4),vmin=-0.5,vmax=3.5,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

ax = fig.add_subplot(4,1,4, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('Diaz biomass-weighted nutrient limitation', fontsize=12)
lon, lat, field = adjust_pop_grid(lons, lats,  most_lim_diaz.where(~mask))
pc=ax.pcolormesh(lon, lat, field, cmap=matplotlib.colormaps['viridis'].resampled(4),vmin=-0.5,vmax=3.5,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
cbar = fig.colorbar(pc, cax=cbar_ax,**colorbar_specs)
cbar.ax.set_yticklabels(['P lim', 'Fe lim', 'N lim','SiO3/C lim']);

Making monthly climatology maps¶

Make a monthly climatology dataset¶

A monthly climatology is a dataset where data from each month, including over different years, is averaged together. So for our dataset, the groups would include the average of all Januaries 2010-2019, all Februaries 2010-2019, and so on. This would usually be over a longer time period such as 30 or more years, but we use our shorter dataset so that it processes faster. This technique is helpful for looking at seasonal phenomena while averaging out year-to-year fluctuations.

mon_ds = ds.copy()
mon_ds = ds.groupby('time.month').mean('time')

limarray_sp=xr.concat((mon_ds.sp_P_lim_surf, mon_ds.sp_Fe_lim_surf,mon_ds.sp_N_lim_surf),dim='nutrient')
limarray_diat=xr.concat((mon_ds.diat_P_lim_surf, mon_ds.diat_Fe_lim_surf, mon_ds.diat_N_lim_surf, mon_ds.diat_SiO3_lim_surf),dim='nutrient')
limarray_diaz=xr.concat((mon_ds.diaz_P_lim_surf, mon_ds.diaz_Fe_lim_surf),dim='nutrient')
limarray_cocco=xr.concat((mon_ds.cocco_P_lim_surf, mon_ds.cocco_Fe_lim_surf, mon_ds.cocco_N_lim_surf, mon_ds.cocco_C_lim_surf),dim='nutrient')

most_lim_sp=limarray_sp.argmin(dim='nutrient', skipna=False)
most_lim_diat=limarray_diat.argmin(dim='nutrient', skipna=False)
most_lim_diaz=limarray_diaz.argmin(dim='nutrient', skipna=False)
most_lim_cocco=limarray_cocco.argmin(dim='nutrient', skipna=False)
mask = np.isnan(ds_year.sp_N_lim_surf.squeeze())

fig = plt.figure(figsize=(12,23))

month_list = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]

for row in np.arange(1,13):
    ts=row-1
    
    plot = row*3 - 2
    
    #row 1 Jan
    ax = fig.add_subplot(12,3,plot, projection=ccrs.Robinson(central_longitude=305.0))
    ax.set_title('Diat surface nutrient limitation', fontsize=12)
    lon, lat, field = adjust_pop_grid(lons, lats,  most_lim_diat.isel(month=ts).where(~mask))
    pc=ax.pcolormesh(lon, lat, field, cmap=matplotlib.colormaps['viridis'].resampled(4),vmin=-0.5,vmax=3.5,transform=ccrs.PlateCarree())
    land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
    ax.add_feature(land)
    ax.set_ylabel(month_list[ts])
    ax.set_yticks([]) # necessary to get ylabel to show up
    colorbar_specs = {'ticks' : np.arange(0,4,1)}
    
    plot = row*3 - 1
    ax = fig.add_subplot(12,3,plot, projection=ccrs.Robinson(central_longitude=305.0))
    ax.set_title('SP surface nutrient limitation', fontsize=12)
    lon, lat, field = adjust_pop_grid(lons, lats,  most_lim_sp.isel(month=ts).where(~mask))
    pc=ax.pcolormesh(lon, lat, field, cmap=matplotlib.colormaps['viridis'].resampled(4),vmin=-0.5,vmax=3.5,transform=ccrs.PlateCarree())
    land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
    ax.add_feature(land)
    colorbar_specs = {'ticks' : np.arange(0,4,1)}
    
    plot = row*3
    ax = fig.add_subplot(12,3,plot, projection=ccrs.Robinson(central_longitude=305.0))
    ax.set_title('Cocco surface nutrient limitation', fontsize=12)
    lon, lat, field = adjust_pop_grid(lons, lats,  most_lim_cocco.isel(month=ts).where(~mask))
    pc=ax.pcolormesh(lon, lat, field, cmap=matplotlib.colormaps['viridis'].resampled(4),vmin=-0.5,vmax=3.5,transform=ccrs.PlateCarree())
    land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
    ax.add_feature(land)
    colorbar_specs = {'ticks' : np.arange(0,4,1)}

fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
cbar = fig.colorbar(pc, cax=cbar_ax,**colorbar_specs)
cbar.ax.set_yticklabels(['P lim', 'Fe lim', 'N lim','SiO3/C lim']);

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

cluster.close()

Summary¶

You’ve learned how to evaluate phytoplankton nutrient limitation.

Resources and references¶

Sarmiento and Gruber Chapter 4: Organic Matter Production (see Limiting Nutrient in Section 4.2)
Redfield et al., 1963

References¶

(2013). In Ocean Biogeochemical Dynamics (pp. 102–172). Princeton University Press. 10.2307/j.ctt3fgxqx.7

Plankton

Phytoplankton biomass

Plankton

Zooplankton biomass