PISCES — chlorophyll seasonal cycle off the Iberian upwelling¶

Demonstrates the multi-variable subset pattern: pull two PISCES biogeochem variables (chl chlorophyll-a, o2 dissolved oxygen) over a coastal box off Iberia for a multi-year window, group by month, and plot the seasonal cycle.

PISCES is cmems_mod_glo_bgc_my_0.25deg_P1D-m — daily, 1/4° grid, global. Variables are 4-D (time, depth, lat, lon); we clip to the surface 0-10 m to keep the request small.

Setup¶

Consolidate the imports up front. pyramids provides NetCDF (reading the downloaded file); earthlens provides the unified EarthLens entry point and the CMEMS Catalog. The monthly climatology below is a manual per-day/ per-month average over pandas/numpy, and matplotlib draws the final plot.

In [ ]:

import os
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pyramids.netcdf import NetCDF

from earthlens.cmems import Catalog
from earthlens.core import EarthLens

import os from pathlib import Path import matplotlib.pyplot as plt import numpy as np import pandas as pd from pyramids.netcdf import NetCDF from earthlens.cmems import Catalog from earthlens.core import EarthLens

The request¶

PISCES is cmems_mod_glo_bgc_my_0.25deg_P1D-m — daily, 1/4° grid, global. We pull two biogeochem variables (chl chlorophyll-a, o2 dissolved oxygen) over a coastal box off Iberia, and create the output directory.

In [ ]:

OUT_DIR = Path('data/cmems-pisces')
OUT_DIR.mkdir(parents=True, exist_ok=True)

DATASET_ID = 'cmems_mod_glo_bgc_my_0.25deg_P1D-m'
VARIABLES = ['chl', 'o2']

OUT_DIR = Path('data/cmems-pisces') OUT_DIR.mkdir(parents=True, exist_ok=True) DATASET_ID = 'cmems_mod_glo_bgc_my_0.25deg_P1D-m' VARIABLES = ['chl', 'o2']

Inspect the dataset metadata¶

The CMEMS Catalog resolves the dataset id to its cadence, domain, and per- variable units / long names — a quick sanity check before downloading.

In [ ]:

ds = Catalog().get_dataset(DATASET_ID)
print(ds)
print(f'domain: {ds.domain}')
for v in VARIABLES:
    print(f'  {ds.variables[v]}')

ds = Catalog().get_dataset(DATASET_ID) print(ds) print(f'domain: {ds.domain}') for v in VARIABLES: print(f' {ds.variables[v]}')

Download — three years of daily PISCES at the surface¶

Multi-year × multi-variable × surface-clipped, in one server-side subset call. We build the EarthLens request first — variables are 4-D (time, depth, lat, lon), so we clip to the surface 0-10 m to keep it small.

In [ ]:

earthlens = EarthLens(
    data_source='cmems',
    start='2018-01-01',
    end='2020-12-31',
    cadence='daily',
    dataset=DATASET_ID,
    variables=VARIABLES,
    aoi=[-12.0, 40.0, -8.0, 44.0],  # Iberian upwelling box (W, S, E, N)
    path=OUT_DIR,
    minimum_depth=0.0,
    maximum_depth=10.0,
    service_username=os.environ.get('COPERNICUSMARINE_SERVICE_USERNAME'),
    service_password=os.environ.get('COPERNICUSMARINE_SERVICE_PASSWORD'),
)

earthlens = EarthLens( data_source='cmems', start='2018-01-01', end='2020-12-31', cadence='daily', dataset=DATASET_ID, variables=VARIABLES, aoi=[-12.0, 40.0, -8.0, 44.0], # Iberian upwelling box (W, S, E, N) path=OUT_DIR, minimum_depth=0.0, maximum_depth=10.0, service_username=os.environ.get('COPERNICUSMARINE_SERVICE_USERNAME'), service_password=os.environ.get('COPERNICUSMARINE_SERVICE_PASSWORD'), )

Run the subset download on its own line so the request build and the network call read as separate steps. It returns the list of written NetCDF paths.

In [ ]:

paths = earthlens.download()
print(paths)

paths = earthlens.download() print(paths)

Compute the box-averaged monthly climatology for each variable¶

Read the NetCDF into pyramids¶

pd.to_datetime(nc.get_time_variable()) turns the CF "hours since 1950" time axis into datetime64 timestamps, which the next cell groups into a monthly climatology with a manual per-day/per-month average (not a groupby). The cube itself is (time, depth, latitude, longitude), reshaped by hand from the band-major layout read_array returns.

In [ ]:

nc = NetCDF.read_file(paths[0], read_only=True)
print('variables :', nc.variable_names)
print('dimensions:', nc.dimension_sizes)

nc = NetCDF.read_file(paths[0], read_only=True) print('variables :', nc.variable_names) print('dimensions:', nc.dimension_sizes)

Reduce to a 12-month climatology¶

Average across (depth, lat, lon) per day, then group by calendar month across the three years. Both arrays end up as 12-element vectors keyed on month-of-year (1-12).

In [ ]:

# A 4-D cube arrives as bands ordered time-major: band = time_index * n_depth + depth.
n_time = nc.dimension_sizes['time']
n_depth = nc.dimension_sizes['depth']
months = pd.to_datetime(nc.get_time_variable()).month

climatology = {}
for name in VARIABLES:
    var = nc.get_variable(name)
    cube = var.read_array(masked=True).reshape(n_time, n_depth, var.rows, var.columns)
    per_day = cube.mean(axis=(1, 2, 3))  # surface box mean for each day
    climatology[name] = np.array(
        [float(per_day[months == m].mean()) for m in range(1, 13)]
    )
nc.close()
for v in VARIABLES:
    print(f'{v}: {climatology[v].round(4)}')

# A 4-D cube arrives as bands ordered time-major: band = time_index * n_depth + depth. n_time = nc.dimension_sizes['time'] n_depth = nc.dimension_sizes['depth'] months = pd.to_datetime(nc.get_time_variable()).month climatology = {} for name in VARIABLES: var = nc.get_variable(name) cube = var.read_array(masked=True).reshape(n_time, n_depth, var.rows, var.columns) per_day = cube.mean(axis=(1, 2, 3)) # surface box mean for each day climatology[name] = np.array( [float(per_day[months == m].mean()) for m in range(1, 13)] ) nc.close() for v in VARIABLES: print(f'{v}: {climatology[v].round(4)}')

Plot the seasonal cycle¶

Iberian upwelling chlorophyll peaks in spring / late summer with the wind-driven upwelling events; dissolved oxygen tracks the same pattern inversely (warmer water holds less O2).

In [ ]:

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))
months = np.arange(1, 13)
ax1.bar(months, climatology['chl'], color='tab:green')
ax1.set_xlabel('Month')
ax1.set_ylabel('chl (mg m-3)')
ax1.set_title('PISCES chlorophyll-a climatology')
ax1.set_xticks(months)
ax2.bar(months, climatology['o2'], color='tab:blue')
ax2.set_xlabel('Month')
ax2.set_ylabel('o2 (mmol m-3)')
ax2.set_title('PISCES dissolved oxygen climatology')
ax2.set_xticks(months)
fig.suptitle('Iberian upwelling box, 2018-2020 surface (0-10 m) mean')
fig.tight_layout()

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4)) months = np.arange(1, 13) ax1.bar(months, climatology['chl'], color='tab:green') ax1.set_xlabel('Month') ax1.set_ylabel('chl (mg m-3)') ax1.set_title('PISCES chlorophyll-a climatology') ax1.set_xticks(months) ax2.bar(months, climatology['o2'], color='tab:blue') ax2.set_xlabel('Month') ax2.set_ylabel('o2 (mmol m-3)') ax2.set_title('PISCES dissolved oxygen climatology') ax2.set_xticks(months) fig.suptitle('Iberian upwelling box, 2018-2020 surface (0-10 m) mean') fig.tight_layout()