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

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 cartopy
import cartopy.crs as ccrs
import pop_tools
from dask.distributed import LocalCluster
import s3fs
import netCDF4

from module import adjust_pop_grid

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

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)

# 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

<xarray.Dataset> Size: 28GB
Dimensions:                         (nlat: 384, nlon: 320, time: 120, z_t: 60,
                                     z_t_150m: 15)
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 .....
  * z_t                             (z_t) float32 240B 500.0 ... 5.375e+05
  * z_t_150m                        (z_t_150m) float32 60B 500.0 ... 1.45e+04
Dimensions without coordinates: nlat, nlon
Data variables: (12/45)
    FG_CO2                          (time, nlat, nlon) float32 59MB dask.array<chunksize=(60, 192, 160), meta=np.ndarray>
    Fe                              (time, z_t, nlat, nlon) float32 4GB dask.array<chunksize=(30, 15, 96, 80), meta=np.ndarray>
    NO3                             (time, z_t, nlat, nlon) float32 4GB dask.array<chunksize=(30, 15, 96, 80), meta=np.ndarray>
    PO4                             (time, z_t, nlat, nlon) float32 4GB dask.array<chunksize=(30, 15, 96, 80), meta=np.ndarray>
    POC_FLUX_100m                   (time, nlat, nlon) float32 59MB dask.array<chunksize=(60, 192, 160), meta=np.ndarray>
    SALT                            (time, z_t, nlat, nlon) float32 4GB dask.array<chunksize=(30, 15, 96, 80), meta=np.ndarray>
    ...                              ...
    sp_Fe_lim_Cweight_avg_100m      (time, nlat, nlon) float32 59MB dask.array<chunksize=(60, 192, 160), meta=np.ndarray>
    sp_Fe_lim_surf                  (time, nlat, nlon) float32 59MB dask.array<chunksize=(60, 192, 160), meta=np.ndarray>
    sp_N_lim_Cweight_avg_100m       (time, nlat, nlon) float32 59MB dask.array<chunksize=(60, 192, 160), meta=np.ndarray>
    sp_N_lim_surf                   (time, nlat, nlon) float32 59MB dask.array<chunksize=(60, 192, 160), meta=np.ndarray>
    sp_P_lim_Cweight_avg_100m       (time, nlat, nlon) float32 59MB dask.array<chunksize=(60, 192, 160), meta=np.ndarray>
    sp_P_lim_surf                   (time, nlat, nlon) float32 59MB dask.array<chunksize=(60, 192, 160), meta=np.ndarray>

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)

<cartopy.mpl.feature_artist.FeatureArtist at 0x7f282832b080>
../_images/0bd5856a2faf0428aaaab31e6ed8ac10a4d78ae831f498e1e4edc4c2001a65ca.png

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

<xarray.Dataset> Size: 17MB
Dimensions:    (lat: 720, lon: 1440)
Coordinates:
  * lat        (lat) float64 6kB -89.88 -89.62 -89.38 ... 89.38 89.62 89.88
  * lon        (lon) float64 12kB -179.9 -179.6 -179.4 ... 179.4 179.6 179.9
Data variables:
    copepod_C  (lat, lon) float64 8MB nan nan nan nan nan ... nan nan nan nan
    n_obs      (lat, lon) float64 8MB ...
Attributes:
    file_in:  data/orig/copepod-2012__biomass-fields/data/copepod-2012__cmass...

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$^{-3}$)');

<>:10: SyntaxWarning: invalid escape sequence '\i'
<>:10: SyntaxWarning: invalid escape sequence '\i'
/tmp/ipykernel_3468/3097243711.py:10: SyntaxWarning: invalid escape sequence '\i'
  ax.set_title('CESM ${\it Mesozooplankton}$ biomass', fontsize=12)
../_images/9a2e30e1301652283c830565666a0e3b9caf9b1e1b8e28f901ab362a649ee910.png

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$^{-2}$)');
../_images/ee07d0145072b5597c45870e772cf85ca13f3f0fb3d0b44a99e92b2726f6d866.png

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.

Resources and references

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