القائمة
Download and analyze oceanographic data from Copernicus Marine Service and Argo floats using copernicusmarine, gsw, and xarray.
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| name | ocean-data |
| description | Download and analyze oceanographic data from Copernicus Marine Service and Argo floats using copernicusmarine, gsw, and xarray. |
| tags | ["oceanography","copernicus","argo","xarray","earth-science"] |
| version | 1.0.0 |
| authors | [{"name":"Rosetta Skills Contributors","github":"@xjtulyc"}] |
| license | MIT |
| platforms | ["claude-code","codex","gemini-cli","cursor"] |
| dependencies | ["copernicusmarine>=1.0","gsw>=3.6","xarray>=2023.6","pandas>=2.0","matplotlib>=3.7","numpy>=1.24","cartopy>=0.22"] |
| last_updated | 2026-03-17 |
| status | stable |
Retrieve, process, and visualize ocean physical and biogeochemical data using the Copernicus Marine Service (CMEMS) Python client, Argo float profiles, and the TEOS-10 Gibbs SeaWater (GSW) toolbox.
The Copernicus Marine Environment Monitoring Service provides free,
quality-controlled ocean datasets spanning physical, biogeochemical, and sea-ice
variables. Products include reanalysis, near-real-time, and forecast datasets.
The copernicusmarine Python package (v1+) replaced the legacy motuclient
and ftplib workflows.
Argo is a global array of ~4000 autonomous profiling floats that measure
temperature and salinity from 2000 m to the surface every ~10 days. Data are
freely available via GDAC servers (Ifremer / BODC) and through the
copernicusmarine catalog.
The Thermodynamic Equation of Seawater 2010 (TEOS-10) defines Absolute
Salinity (SA) and Conservative Temperature (CT) as the preferred variables.
The gsw Python library converts Practical Salinity and in-situ temperature to
TEOS-10 variables and computes derived quantities such as potential density,
buoyancy frequency, and mixed layer depth.
MLD is commonly estimated using a density threshold criterion: the depth at which potential density exceeds the surface value by 0.03 kg/m³ (de Boyer Montégut et al., 2004).
EKE quantifies mesoscale variability: EKE = 0.5 * (u'^2 + v'^2), where u' and v' are anomalies of eastward and northward geostrophic currents relative to a long-term mean.
pip install "copernicusmarine>=1.0" "gsw>=3.6" "xarray>=2023.6" \
"pandas>=2.0"
Register for a free account at https://marine.copernicus.eu/ then store your credentials:
# Option 1 – interactive login (writes ~/.copernicusmarine/credentials)
copernicusmarine login
# Option 2 – environment variables (CI/CD friendly)
export COPERNICUSMARINE_USERNAME="<your-username>"
export COPERNICUSMARINE_PASSWORD=$(cat ~/.cmems_passwd) # read from file, never hardcode
import os
import copernicusmarine as cm
username = os.getenv("COPERNICUSMARINE_USERNAME", "")
password = os.getenv("COPERNICUSMARINE_PASSWORD", "")
# Verify login works
cm.login(username=username, password=password, overwrite_configuration_file=True)
import copernicusmarine as cm
import xarray as xr
# List available datasets matching a keyword
catalog = cm.describe(contains=["SST", "Mediterranean"])
for entry in catalog.products[:5]:
print(entry.product_id, "-", entry.title)
# Download a subset of the CMEMS global SST L4 product
ds = cm.open_dataset(
dataset_id="cmems_obs-sst_glo_phy_l4_my_0.25deg",
variables=["analysed_sst", "analysis_error"],
minimum_longitude=-20.0,
maximum_longitude=40.0,
minimum_latitude=25.0,
maximum_latitude=50.0,
start_datetime="2023-01-01T00:00:00",
end_datetime="2023-03-31T23:59:59",
)
print(ds)
# Convert from Kelvin to Celsius
ds["sst_celsius"] = ds["analysed_sst"] - 273.15
ds["sst_celsius"].attrs["units"] = "degC"
import gsw
import numpy as np
import xarray as xr
import copernicusmarine as cm
# Download Argo BGC profiles for the North Atlantic (float WMO 6902880)
argo = cm.open_dataset(
dataset_id="cmems_obs-ins_glo_phy-temp-sal_nrt_argo_P1D-m",
variables=["TEMP", "PSAL", "PRES", "LATITUDE", "LONGITUDE", "TIME"],
minimum_longitude=-40.0,
maximum_longitude=-20.0,
minimum_latitude=40.0,
maximum_latitude=60.0,
start_datetime="2023-06-01T00:00:00",
end_datetime="2023-08-31T23:59:59",
)
def compute_mld(temp, psal, pres, lat, threshold=0.03):
"""
Compute mixed layer depth using a density threshold criterion.
Parameters
----------
temp : array-like, in-situ temperature (ITS-90, °C)
psal : array-like, Practical Salinity (PSS-78)
pres : array-like, sea pressure (dbar)
lat : float, latitude (degrees N)
threshold : float, density difference criterion (kg/m³), default 0.03
Returns
-------
mld : float, mixed layer depth (m)
"""
SA = gsw.SA_from_SP(psal, pres, 0.0, lat)
CT = gsw.CT_from_t(SA, temp, pres)
sigma0 = gsw.sigma0(SA, CT) # potential density anomaly (kg/m³)
# Reference density at shallowest valid level
valid = np.isfinite(sigma0)
if valid.sum() < 3:
return np.nan
ref_density = sigma0[valid][0]
# Find first depth where density exceeds reference + threshold
exceeds = np.where((sigma0 - ref_density) > threshold)[0]
if len(exceeds) == 0:
return float(pres[valid][-1]) # whole profile is mixed
return float(pres[exceeds[0]])
# Apply to each profile
mld_values = []
for i in range(min(50, argo.dims.get("N_PROF", 0))):
profile = argo.isel(N_PROF=i)
mld = compute_mld(
profile["TEMP"].values,
profile["PSAL"].values,
profile["PRES"].values,
float(profile["LATITUDE"].values),
)
mld_values.append(mld)
print(f"Mean MLD (N=50 profiles): {np.nanmean(mld_values):.1f} dbar")
import copernicusmarine as cm
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
# Download gridded surface current anomalies (AVISO altimetry)
alt = cm.open_dataset(
dataset_id="cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.25deg_P1D",
variables=["ugosa", "vgosa"], # geostrophic current anomalies
minimum_longitude=-80.0,
maximum_longitude=-40.0,
minimum_latitude=25.0,
maximum_latitude=55.0,
start_datetime="2023-01-01T00:00:00",
end_datetime="2023-12-31T23:59:59",
)
# Compute time-mean EKE (m²/s²)
eke = 0.5 * (alt["ugosa"] ** 2 + alt["vgosa"] ** 2)
eke_mean = eke.mean(dim="time")
# Plot
fig, ax = plt.subplots(
subplot_kw={"projection": ccrs.PlateCarree()}, figsize=(10, 7)
)
ax.add_feature(cfeature.LAND, facecolor="lightgray")
ax.add_feature(cfeature.COASTLINE, linewidth=0.5)
ax.gridlines(draw_labels=True, linewidth=0.3, linestyle="--")
pcm = ax.pcolormesh(
eke_mean.longitude,
eke_mean.latitude,
eke_mean.values * 1e4, # convert to cm²/s²
cmap="plasma",
transform=ccrs.PlateCarree(),
vmin=0,
vmax=500,
)
plt.colorbar(pcm, ax=ax, label="EKE (cm²/s²)", shrink=0.8)
ax.set_title("Annual Mean Eddy Kinetic Energy – North Atlantic 2023")
plt.tight_layout()
plt.savefig("eke_north_atlantic_2023.png", dpi=150)
plt.show()
import gsw
import numpy as np
import matplotlib.pyplot as plt
def ts_diagram(temp_array, psal_array, pres_array, lat, color_by=None,
title="T-S Diagram"):
"""
Plot a Temperature-Salinity diagram with TEOS-10 potential density contours.
Parameters
----------
temp_array : 2-D array (N_PROF, N_LEVELS)
psal_array : 2-D array (N_PROF, N_LEVELS)
pres_array : 2-D array (N_PROF, N_LEVELS)
lat : float, representative latitude
color_by : 1-D array (N_PROF,) used to color-code profiles, optional
title : str
"""
SA_all = gsw.SA_from_SP(psal_array, pres_array, 0.0, lat)
CT_all = gsw.CT_from_t(SA_all, temp_array, pres_array)
# Density contour grid
sa_grid = np.linspace(SA_all[np.isfinite(SA_all)].min() - 0.5,
SA_all[np.isfinite(SA_all)].max() + 0.5, 100)
ct_grid = np.linspace(CT_all[np.isfinite(CT_all)].min() - 1,
CT_all[np.isfinite(CT_all)].max() + 1, 100)
SA_g, CT_g = np.meshgrid(sa_grid, ct_grid)
sigma0_grid = gsw.sigma0(SA_g, CT_g)
fig, ax = plt.subplots(figsize=(8, 7))
cs = ax.contour(sa_grid, ct_grid, sigma0_grid,
levels=np.arange(22, 30, 0.5), colors="gray",
linewidths=0.6, linestyles="--")
ax.clabel(cs, fmt="%.1f", fontsize=8)
sc = ax.scatter(SA_all.ravel(), CT_all.ravel(),
c=color_by if color_by is not None else "steelblue",
s=1, alpha=0.4, cmap="viridis")
if color_by is not None:
plt.colorbar(sc, ax=ax, label="Depth (m)")
ax.set_xlabel("Absolute Salinity SA (g/kg)")
ax.set_ylabel("Conservative Temperature CT (°C)")
ax.set_title(title)
plt.tight_layout()
plt.savefig("ts_diagram.png", dpi=150)
plt.show()
# Example usage with synthetic data
rng = np.random.default_rng(42)
n_prof, n_lev = 100, 50
pres_syn = np.tile(np.linspace(5, 500, n_lev), (n_prof, 1))
psal_syn = 35.0 + rng.normal(0, 0.3, (n_prof, n_lev))
temp_syn = 15.0 - pres_syn / 50 + rng.normal(0, 0.5, (n_prof, n_lev))
ts_diagram(temp_syn, psal_syn, pres_syn, lat=45.0,
color_by=np.repeat(np.arange(n_prof), n_lev),
title="Synthetic T-S Diagram – North Atlantic")
import copernicusmarine as cm
import xarray as xr
import matplotlib.pyplot as plt
import numpy as np
ds = cm.open_dataset(
dataset_id="cmems_mod_glo_phy_my_0.083deg_P1D-m",
variables=["thetao"],
minimum_longitude=-10.0,
maximum_longitude=36.0,
minimum_latitude=30.0,
maximum_latitude=46.0,
minimum_depth=0.0,
maximum_depth=1.0,
start_datetime="2010-01-01T00:00:00",
end_datetime="2020-12-31T23:59:59",
)
sst = ds["thetao"].isel(depth=0)
# Compute climatological monthly mean
sst_clim = sst.groupby("time.month").mean(dim="time")
# Spatial mean over the basin
sst_basin = sst_clim.mean(dim=["latitude", "longitude"])
fig, ax = plt.subplots(figsize=(9, 4))
ax.plot(np.arange(1, 13), sst_basin.values, "o-", color="firebrick", lw=2)
ax.set_xticks(np.arange(1, 13))
ax.set_xticklabels(["J","F","M","A","M","J","J","A","S","O","N","D"])
ax.set_xlabel("Month")
ax.set_ylabel("SST (°C)")
ax.set_title("Mediterranean Sea – 2010–2020 SST Climatological Cycle")
ax.grid(True, linestyle="--", alpha=0.5)
plt.tight_layout()
plt.savefig("mediterranean_sst_climatology.png", dpi=150)
plt.show()
| Symptom | Likely Cause | Fix |
|---|---|---|
Missing credentials error | COPERNICUSMARINE_USERNAME / PASSWORD not set | Run copernicusmarine login or export env vars |
TimeoutError on large downloads | Requesting too large a spatial/temporal domain | Split into smaller chunks; use cm.subset() to download files locally |
KeyError: 'N_PROF' | Argo dataset dimension name differs by product | Inspect ds.dims and adapt index variable |
gsw returns NaN arrays | Pressure must be in dbar and non-negative; salinity must be > 0 | Filter out fill values (typically 99999) before calling gsw functions |
| Cartopy install fails on Windows | Binary wheel not available | Use conda install -c conda-forge cartopy instead of pip |
404 Not Found for dataset_id | Product retired or renamed in CMEMS catalog | Run cm.describe(contains=["keyword"]) to find current product ID |
copernicusmarine Python package docs: https://toolbox-docs.marine.copernicus.eu/import copernicusmarine as cm
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
from scipy import stats
# Download monthly mean surface salinity
ds = cm.open_dataset(
dataset_id="cmems_mod_blk_phy_my_2.5km_P1M-m",
variables=["so"],
minimum_depth=0.0,
maximum_depth=1.0,
start_datetime="2015-01-01T00:00:00",
end_datetime="2023-12-31T23:59:59",
)
sal = ds["so"].isel(depth=0)
basin_mean = sal.mean(dim=["latitude", "longitude"])
# Convert time to decimal year for linear regression
times = basin_mean["time"].values
decimal_years = [
float(str(t)[:4]) + float(str(t)[5:7]) / 12
for t in basin_mean["time"].dt.strftime("%Y-%m").values
]
slope, intercept, r, p, se = stats.linregress(decimal_years, basin_mean.values)
trend_line = slope * np.array(decimal_years) + intercept
fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(decimal_years, basin_mean.values, color="steelblue", lw=1.5,
label="Monthly mean salinity")
ax.plot(decimal_years, trend_line, "r--", lw=2,
label=f"Trend: {slope*10:.3f} PSU/decade (p={p:.3f})")
ax.set_xlabel("Year")
ax.set_ylabel("Surface Salinity (PSU)")
ax.set_title("Black Sea Basin-Mean Surface Salinity 2015–2023")
ax.legend()
ax.grid(True, linestyle="--", alpha=0.4)
plt.tight_layout()
plt.savefig("black_sea_salinity_trend.png", dpi=150)
plt.show()
print(f"Trend: {slope*10:.4f} PSU/decade, R²={r**2:.3f}, p={p:.4f}")
import gsw
import numpy as np
import matplotlib.pyplot as plt
import copernicusmarine as cm
# Download a single float's profiles
argo = cm.open_dataset(
dataset_id="cmems_obs-ins_glo_phy-temp-sal_nrt_argo_P1D-m",
variables=["TEMP", "PSAL", "PRES", "TIME", "LATITUDE", "LONGITUDE"],
minimum_longitude=-35.0,
maximum_longitude=-30.0,
minimum_latitude=45.0,
maximum_latitude=50.0,
start_datetime="2023-07-01T00:00:00",
end_datetime="2023-09-30T23:59:59",
)
# Select first valid profile
i_prof = 0
pres = argo["PRES"].isel(N_PROF=i_prof).values
temp = argo["TEMP"].isel(N_PROF=i_prof).values
psal = argo["PSAL"].isel(N_PROF=i_prof).values
lat = float(argo["LATITUDE"].isel(N_PROF=i_prof).values)
# Mask fill values
valid = (pres > 0) & (psal > 0) & np.isfinite(temp)
pres, temp, psal = pres[valid], temp[valid], psal[valid]
# Compute derived quantities
SA = gsw.SA_from_SP(psal, pres, 0.0, lat)
CT = gsw.CT_from_t(SA, temp, pres)
sigma0 = gsw.sigma0(SA, CT)
# MLD (density threshold 0.03 kg/m³)
ref = sigma0[0]
mld_idx = np.where(sigma0 - ref > 0.03)[0]
mld = pres[mld_idx[0]] if len(mld_idx) > 0 else pres[-1]
# Plot
fig, axes = plt.subplots(1, 3, figsize=(12, 6), sharey=True)
ax_t, ax_s, ax_d = axes
ax_t.plot(CT, pres, "b-", lw=1.5)
ax_t.axhline(mld, color="red", ls="--", label=f"MLD={mld:.0f} m")
ax_t.set_xlabel("Conservative Temp CT (°C)")
ax_t.set_ylabel("Pressure (dbar)")
ax_t.invert_yaxis()
ax_t.legend(fontsize=9)
ax_t.grid(True, linestyle="--", alpha=0.4)
ax_t.set_title("Temperature")
ax_s.plot(SA, pres, "g-", lw=1.5)
ax_s.axhline(mld, color="red", ls="--")
ax_s.set_xlabel("Absolute Salinity SA (g/kg)")
ax_s.grid(True, linestyle="--", alpha=0.4)
ax_s.set_title("Salinity")
ax_d.plot(sigma0, pres, "k-", lw=1.5)
ax_d.axhline(mld, color="red", ls="--")
ax_d.set_xlabel("Potential Density σ₀ (kg/m³)")
ax_d.grid(True, linestyle="--", alpha=0.4)
ax_d.set_title("Potential Density")
plt.suptitle(f"Argo Profile – Lat {lat:.2f}°N", fontsize=12)
plt.tight_layout()
plt.savefig("argo_profile_mld.png", dpi=150)
plt.show()