Ocean iron concentration - Ocean Biogeochemistry Cookbook

Overview¶

Iron is one of the most important micronutrients in the ocean, limiting production in some areas. In this notebook, we make a map of iron concentration and compare it to observational data.

General setup
Subsetting
Processing - long-term mean
Comparing to observational data

Prerequisites¶

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

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 pandas as pd
import s3fs

from module import adjust_pop_grid

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 7
      3 import numpy as np
      4 import matplotlib.pyplot as plt
      5 import cartopy
      6 import cartopy.crs as ccrs
----> 7 import pop_tools
      8 from dask.distributed import LocalCluster
      9 import pandas as pd
     10 import s3fs

File ~/micromamba/envs/ocean-bgc-cookbook-dev/lib/python3.14/site-packages/pop_tools/__init__.py:4
      1 """Top-level module for pop_tools"""
----> 4 from pkg_resources import DistributionNotFound, get_distribution
      6 from .calc import cfc11sol, cfc12sol
      7 from .datasets import DATASETS

ModuleNotFoundError: No module named 'pkg_resources'

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)
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 =['Fe']

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])

ds.Fe.isel(time=0,z_t=0).plot()

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.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")

Take the long-term mean of our data set. We process monthly to annual data 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")

nmolcm3_to_nM = 1.e3
ds['Fe'] = ds['Fe'] * nmolcm3_to_nM
ds['Fe'].attrs['units'] = 'nM'

Compare to observational Fe database¶

This dataset includes observations from the following papers:

as collected by Long et al., 2021 -- see this paper for details.

fe_obs_path = 's3://pythia/ocean-bgc/obs/dFe-database-2021-05-20.csv'

fe_obs = s3.open(fe_obs_path)

df = pd.read_csv(fe_obs, na_values=-999.).dropna(axis=0, how='all')

df

fig = plt.figure(figsize=(16,5))

fig.suptitle("Surface iron concentration comparison")

### CESM
ax = fig.add_subplot(1,2,1, projection=ccrs.Robinson(central_longitude=305.0))
lon, lat, field = adjust_pop_grid(lons, lats,  ds.Fe.isel(z_t=0))
pc=ax.pcolormesh(lon, lat, field, cmap='plasma',vmin=0,vmax=3,transform=ccrs.PlateCarree())
land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='110m', edgecolor='k', facecolor='white', linewidth=0.5)
ax.add_feature(land)
ax.set_title('CESM Fe at surface', fontsize=10)


### obs
ax = fig.add_subplot(1,2,2, projection=ccrs.Robinson(central_longitude=305.0))
ax.coastlines('10m',linewidth=0.5)
ax.set_title('Obs Fe at surface', fontsize=10)


df_sub = df.loc[(df.depth <= 2.5)]
sc = ax.scatter(df_sub.lon, df_sub.lat, c=df_sub.dFe_obs.values,
                cmap='plasma',
                vmin=0, vmax=3, 
                transform=ccrs.PlateCarree())

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, label='Fe (nM)')

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

cluster.close()

Summary¶

You’ve learned how to make a map of ocean iron and compare it to observations.

Resources and references¶

References¶

Schlitzer, R., Anderson, R. F., Dodas, E. M., Lohan, M., Geibert, W., Tagliabue, A., Bowie, A., Jeandel, C., Maldonado, M. T., Landing, W. M., Cockwell, D., Abadie, C., Abouchami, W., Achterberg, E. P., Agather, A., Aguliar-Islas, A., van Aken, H. M., Andersen, M., Archer, C., … Zurbrick, C. (2018). The GEOTRACES Intermediate Data Product 2017. Chemical Geology, 493, 210–223. 10.1016/j.chemgeo.2018.05.040
Moore, J. K., & Braucher, O. (2008). Sedimentary and mineral dust sources of dissolved iron to the world ocean. Biogeosciences, 5(3), 631–656. 10.5194/bg-5-631-2008
Tagliabue, A., Mtshali, T., Aumont, O., Bowie, A. R., Klunder, M. B., Roychoudhury, A. N., & Swart, S. (2012). A global compilation of dissolved iron measurements: focus on distributions and processes in the Southern Ocean. Biogeosciences, 9(6), 2333–2349. 10.5194/bg-9-2333-2012
Long, M. C., Moore, J. K., Lindsay, K., Levy, M., Doney, S. C., Luo, J. Y., Krumhardt, K. M., Letscher, R. T., Grover, M., & Sylvester, Z. T. (2021). Simulations With the Marine Biogeochemistry Library (MARBL). Journal of Advances in Modeling Earth Systems, 13(12). 10.1029/2021ms002647