copernicus-climate

Copernicus Climate Data Store (CDS)

Access ERA5 reanalysis, climate projections, and satellite climate records through the Copernicus CDS API. Covers global gridded climate data from 1940 to present.

Prerequisites

Install the CDS API client and configure credentials:

pip install cdsapi

Create ~/.cdsapirc with your CDS credentials:

url: https://cds.climate.copernicus.eu/api
key: <your-uid>:<your-api-key>

Register at https://cds.climate.copernicus.eu to obtain credentials.

API Base URL

https://cds.climate.copernicus.eu/api

Basic Python Retrieval Pattern

import cdsapi

c = cdsapi.Client()

c.retrieve(
    "reanalysis-era5-single-levels",
    {
        "product_type": "reanalysis",
        "variable": "2m_temperature",
        "year": "2023",
        "month": "07",
        "day": "15",
        "time": "12:00",
        "area": [60, -10, 35, 30],  # N, W, S, E bounding box
        "format": "netcdf",
    },
    "era5_temperature.nc",
)

ERA5 Pressure-Level Variables

Retrieve upper-air data on pressure levels:

c.retrieve(
    "reanalysis-era5-pressure-levels",
    {
        "product_type": "reanalysis",
        "variable": ["temperature", "geopotential", "relative_humidity"],
        "pressure_level": ["500", "700", "850", "925"],
        "year": "2023",
        "month": "01",
        "day": "15",
        "time": "12:00",
        "format": "netcdf",
    },
    "era5_pressure_levels.nc",
)

Key Dataset Identifiers

Dataset ID	Description
reanalysis-era5-single-levels	Surface and single-level hourly fields
reanalysis-era5-pressure-levels	Upper-air on 37 pressure levels
reanalysis-era5-single-levels-monthly	Monthly-averaged surface fields
reanalysis-era5-land	ERA5-Land (enhanced land, 9 km)
satellite-sea-level-global	Satellite altimetry sea level

Common Variables

Single level: 2m_temperature, total_precipitation, 10m_u_component_of_wind, 10m_v_component_of_wind, mean_sea_level_pressure, surface_solar_radiation_downwards.

Pressure level: temperature, geopotential, relative_humidity, specific_humidity.

Processing Downloaded NetCDF

import xarray as xr
ds = xr.open_dataset("era5_temperature.nc")
temp_celsius = ds["t2m"] - 273.15  # Kelvin to Celsius
print(f"Mean temperature: {float(temp_celsius.mean()):.1f} C")

Area Selection (N, W, S, E bounding box)

Global: [90, -180, -90, 180], Europe: [72, -25, 33, 45], Continental US: [50, -125, 25, -65], East Asia: [55, 70, 5, 145].

Best Practices

1. Specify the smallest area and fewest variables needed to reduce download time.
2. Use monthly-averaged datasets when daily resolution is not required.
3. Request data in NetCDF format for analysis; GRIB for operational workflows.
4. CDS queues requests; large jobs may take hours. Check status via the web dashboard.
5. ERA5 data is available from 1940 to present with ~5-day latency.
6. For multi-year bulk downloads, split requests by year to avoid timeouts.
7. Install xarray and netCDF4 for reading downloaded files in Python.