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Zooplankton biomass

A copepod, a type of zooplankton. Art credit: Kristen Krumhardt

Overview

Zooplankton are tiny oceanic animals, making up the next step up after phytoplankton in the food web. Here we evaluate modeled zooplankton biomass and compare it to observational data.

  1. General setup

  2. Subsetting

  3. Processing - long-term mean

  4. Mapping zooplankton biomass at the surface

  5. Comparing mesozooplankton biomass to observations

  6. Making monthly climatology maps to compare to observations

Prerequisites

ConceptsImportanceNotes
MatplotlibNecessary
Intro to CartopyNecessary
Dask CookbookHelpful
Intro to XarrayHelpful

  • Time to learn: 30 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 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 44247 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(

Downloading file 'inputdata/ocn/pop/gx1v7/grid/region_mask_20151008.ieeei4' from 'https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/ocn/pop/gx1v7/grid/region_mask_20151008.ieeei4' 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(

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....
2025-09-09 01:47:35,542 - 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[4], line 14
     11 fileset = [s3.open(file) for file in remote_files]
     13 # Open with xarray
---> 14 ds = xr.open_mfdataset(fileset, data_vars="minimal", coords='minimal', compat="override", parallel=True,
     15                        drop_variables=["transport_components", "transport_regions", 'moc_components'], decode_times=True)
     17 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 =['mesozooC', 'microzooC']
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):
    """
    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 weights for each year 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, processing years and months separately

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

Plot mesozooplankton and microzooplankton biomass at the surface

fig = plt.figure(figsize=(8,5))

ax = fig.add_subplot(2,1,1, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('microzooC at surface', fontsize=12)
lon, lat, field = adjust_pop_grid(lons, lats,  ds_annual.microzooC.isel(z_t_150m=0))
pc=ax.pcolormesh(lon, lat, field, cmap='Blues',vmin=0,vmax=2,transform=ccrs.PlateCarree())
cbar1 = fig.colorbar(pc, ax=ax,extend='max',label='microzooC (mmol m$^{-3}$)')
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)


ax = fig.add_subplot(2,1,2, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('mesozooC at surface', fontsize=12)
lon, lat, field = adjust_pop_grid(lons, lats,  ds_annual.mesozooC.isel(z_t_150m=0))
pc=ax.pcolormesh(lon, lat, field, cmap='Oranges',vmin=0,vmax=4,transform=ccrs.PlateCarree())
cbar1 = fig.colorbar(pc, ax=ax,extend='max',label='mesozooC (mmol m$^{-3}$)')
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)

Compare mesozooplankton biomass to COPEPOD database

We use data compiled through the COPEPOD project (Moriarty & O’Brien, 2013). This data has been pre-processed, but the raw data is available on the COPEPOD website.

Read in COPEPOD data

copepod_obs_path = 's3://pythia/ocean-bgc/obs/copepod-2012__cmass-m00-qtr.zarr'

copepod_obs = s3fs.S3Map(root=copepod_obs_path, s3=s3)

ds_copepod = xr.open_dataset(copepod_obs, engine="zarr")

### converting grams to moles
ds_copepod['copepod_C']=ds_copepod.copepod_C/12.011
ds_copepod

Plot

fig = plt.figure(figsize=(12,3))

ax = fig.add_subplot(1,2,1, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('COPEPOD dataset', fontsize=12)
pc=ax.pcolormesh(ds_copepod.lon, ds_copepod.lat, ds_copepod.copepod_C, cmap='Reds',vmin=0,vmax=2,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(1,2,2, projection=ccrs.Robinson(central_longitude=305.0))
ax.set_title('CESM ${\it Mesozooplankton}$ biomass', fontsize=12)
lon, lat, field = adjust_pop_grid(lons, lats, ds_annual.mesozooC.mean(dim='z_t_150m'))
pc=ax.pcolormesh(lon, lat, field, cmap='Reds',vmin=0,vmax=2,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, 0.02, 0.7])
fig.colorbar(pc, cax=cbar_ax,extend='max', label='top 150m/200m mean (mmol m$^{-3}$)');

Making monthly climatology maps to compare to observations

Compare to observation-based GLMM (Generalized Linear Mixed Model) of global mesozooplankton biomass climatology

This data is from Heneghan et al., 2020, which includes the COPEPOD dataset we used previously as well as additional observations, with some pre-processing.

mesozoo_obs_path = 'data/obsglmm_zmeso_vint_200m_monthly_climatology.nc'

ds_copepod_clim = xr.open_dataset(mesozoo_obs_path)
ds_copepod_clim.zmeso200.attrs['units'] = 'mgC m-2'

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

Make our CESM data into a monthly climatology

mon_ds = ds.copy()
mon_ds = ds.groupby('time.month').mean('time')
### depth integrate and convert model to mg C/m2
mon_ds['mesozooC_zint'] = ((mon_ds.mesozooC) * 10.).sum(dim='z_t_150m') #in mmol/m2
mon_ds['mesozooC_zint'] = mon_ds['mesozooC_zint'] * 12.011 #convert to mgC/m2
mon_ds['mesozooC_zint'].attrs['units'] = 'mgC m-2'

Plot

fig = plt.figure(figsize=(5,18))

for row in np.arange(1,13):
    
    ts=row-1
    
    plot = row*2 - 1
    ax = fig.add_subplot(12,2,plot, projection=ccrs.Robinson(central_longitude=305.0))
    ax.set_title(months[ts]+' obs', fontsize=12)
    pc=ax.pcolormesh(ds_copepod_clim.Lon, ds_copepod_clim.Lat, ds_copepod_clim.zmeso200.isel(month=ts), 
                     cmap='Reds',vmin=0,vmax=4000,transform=ccrs.PlateCarree())
    land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
    ax.add_feature(land)
    
    plot = row*2
    ax = fig.add_subplot(12,2,plot, projection=ccrs.Robinson(central_longitude=305.0))
    ax.set_title(months[ts]+' CESM', fontsize=12)
    tmp = mon_ds.mesozooC_zint.isel(month=ts)
    lon, lat, field = adjust_pop_grid(lons, lats,  tmp)
    pc=ax.pcolormesh(lon, lat, field, cmap='Reds',vmin=0,vmax=4000,transform=ccrs.PlateCarree())
    land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
    ax.add_feature(land)

cbar_ax = fig.add_axes([0.92, 0.15, 0.03, 0.7])
fig.colorbar(pc, cax=cbar_ax,extend='max', label='Depth-integrated copepod biomass (mg m$^{-2}$)');

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

cluster.close()

Summary

You’ve learned how to evaluate zooplankton biomass modeled by CESM-MARBL and compare it to observations.

References
  1. Moriarty, R., & O’Brien, T. D. (2013). Distribution of mesozooplankton biomass in the global ocean. Earth System Science Data, 5(1), 45–55. 10.5194/essd-5-45-2013
  2. Heneghan, R. F., Everett, J. D., Sykes, P., Batten, S. D., Edwards, M., Takahashi, K., Suthers, I. M., Blanchard, J. L., & Richardson, A. J. (2020). A functional size-spectrum model of the global marine ecosystem that resolves zooplankton composition. Ecological Modelling, 435, 109265. 10.1016/j.ecolmodel.2020.109265
  3. Petrik, C. M., Luo, J. Y., Heneghan, R. F., Everett, J. D., Harrison, C. S., & Richardson, A. J. (2022). Assessment and Constraint of Mesozooplankton in CMIP6 Earth System Models. Global Biogeochemical Cycles, 36(11). 10.1029/2022gb007367
Plankton
Phytoplankton nutrient limitation