Climate change — temperature anomalies vs a baseline¶

Climate-change communication usually centres on anomalies — how much warmer or cooler a recent period is than a long-term reference. ERA5 covers 1940–present at monthly resolution, plenty for the WMO 30-year baseline.

Domain context. The standard WMO climatological normal period is 1991–2020 (or 1961–1990 for older comparisons). To evaluate recent warming we'll compute monthly 2 m temperature averaged over Europe for two windows — a 1991–2000 decade and a 2014–2023 decade — and show the difference.

Step 1 — pull two decades, monthly¶

Box: Europe (35°–60°N, 10°W–30°E). Two retrievals — one per decade — to keep the per-call queue request small. Each retrieval covers 120 monthly samples.

In [1]:

from pathlib import Path
from earthlens import EarthLens, AggregationConfig

EUR = {"lat_lim": [35.0, 60.0], "lon_lim": [-10.0, 30.0]}
VAR = {"reanalysis-era5-single-levels-monthly-means": ["2m-temperature"]}

OUT_OLD = Path("data/era5-europe-1991-2000")
OUT_NEW = Path("data/era5-europe-2014-2023")

for path, start, end in [
    (OUT_OLD, "1991-01-01", "2000-12-01"),
    (OUT_NEW, "2014-01-01", "2023-12-01"),
]:
    path.mkdir(parents=True, exist_ok=True)
    earthlens = EarthLens(
        data_source="ecmwf",
        temporal_resolution="monthly",
        start=start, end=end,
        variables=VAR, **EUR,
        path=str(path),
    )
    earthlens.download(aggregate=AggregationConfig(freq="1MS", op="mean"))

from pathlib import Path from earthlens import EarthLens, AggregationConfig EUR = {"lat_lim": [35.0, 60.0], "lon_lim": [-10.0, 30.0]} VAR = {"reanalysis-era5-single-levels-monthly-means": ["2m-temperature"]} OUT_OLD = Path("data/era5-europe-1991-2000") OUT_NEW = Path("data/era5-europe-2014-2023") for path, start, end in [ (OUT_OLD, "1991-01-01", "2000-12-01"), (OUT_NEW, "2014-01-01", "2023-12-01"), ]: path.mkdir(parents=True, exist_ok=True) earthlens = EarthLens( data_source="ecmwf", temporal_resolution="monthly", start=start, end=end, variables=VAR, **EUR, path=str(path), ) earthlens.download(aggregate=AggregationConfig(freq="1MS", op="mean"))

2026-05-10 01:47:11.959 | INFO     | earthlens.ecmwf.backend:download:536 - Download ECMWF reanalysis-era5-single-levels-monthly-means/2m-temperature data for period 1991-01-01 00:00:00 till 2000-12-01 00:00:00

2026-05-10 01:47:12.789 | INFO     | earthlens.ecmwf.backend:_api:724 - Requesting reanalysis-era5-single-levels-monthly-means from CDS; this may take several minutes

2026-05-10 01:47:13,380 INFO Request ID is 6cb768fc-05d1-4670-a31d-a4cc379aee83

2026-05-10 01:47:13,458 INFO status has been updated to accepted

2026-05-10 01:47:46,653 INFO status has been updated to successful

2026-05-10 01:47:48 | INFO | pyramids.base.config | Logging is configured.

2026-05-10 01:47:49.661 | INFO     | earthlens.ecmwf.backend:download:575 - ECMWF download summary: all 1 variables succeeded ([('reanalysis-era5-single-levels-monthly-means', '2m-temperature')])

2026-05-10 01:47:50.376 | INFO     | earthlens.ecmwf.backend:download:536 - Download ECMWF reanalysis-era5-single-levels-monthly-means/2m-temperature data for period 2014-01-01 00:00:00 till 2023-12-01 00:00:00

2026-05-10 01:47:50.378 | INFO     | earthlens.ecmwf.backend:_api:724 - Requesting reanalysis-era5-single-levels-monthly-means from CDS; this may take several minutes

2026-05-10 01:47:50,625 INFO Request ID is efbe8417-abbf-41cd-82bc-963c555aac82

2026-05-10 01:47:50 | INFO | ecmwf.datastores.legacy_client | Request ID is efbe8417-abbf-41cd-82bc-963c555aac82

2026-05-10 01:47:50,686 INFO status has been updated to accepted

2026-05-10 01:47:50 | INFO | ecmwf.datastores.legacy_client | status has been updated to accepted

2026-05-10 01:48:23,444 INFO status has been updated to successful

2026-05-10 01:48:23 | INFO | ecmwf.datastores.legacy_client | status has been updated to successful

2026-05-10 01:48:23 | INFO | multiurl.base | Downloading https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-cache-1/2026-05-09/e4a5eff474c75bbc96dc4d2def5860b.nc

2026-05-10 01:48:25.645 | INFO     | earthlens.ecmwf.backend:download:575 - ECMWF download summary: all 1 variables succeeded ([('reanalysis-era5-single-levels-monthly-means', '2m-temperature')])

Step 2 — compute the climatology of each decade¶

Group the 120 monthly samples in each decade by calendar month and average. Result: a 12-month seasonal cycle per decade.

In [2]:

import numpy as np
import pandas as pd
from pyramids.dataset import Dataset

def decade_climatology(out_dir: Path, start_year: int) -> pd.Series:
    paths = sorted((out_dir / "aggregated").glob("2m_temperature_1MS_*.tif"))
    cube = np.stack([Dataset.read_file(str(p)).read_array() for p in paths])
    site_mean = np.nanmean(cube, axis=(1, 2)) - 273.15  # to °C
    months = pd.date_range(f"{start_year}-01-01", periods=len(site_mean), freq="MS")
    s = pd.Series(site_mean, index=months)
    return s.groupby(s.index.month).mean()

old = decade_climatology(OUT_OLD, 1991)
new = decade_climatology(OUT_NEW, 2014)
anom = new - old

names = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
df = pd.DataFrame(
    {"1991–2000 [°C]": old.round(2).to_numpy(),
     "2014–2023 [°C]": new.round(2).to_numpy(),
     "Δ [°C]":          anom.round(2).to_numpy()},
    index=names,
)
df

import numpy as np import pandas as pd from pyramids.dataset import Dataset def decade_climatology(out_dir: Path, start_year: int) -> pd.Series: paths = sorted((out_dir / "aggregated").glob("2m_temperature_1MS_*.tif")) cube = np.stack([Dataset.read_file(str(p)).read_array() for p in paths]) site_mean = np.nanmean(cube, axis=(1, 2)) - 273.15 # to °C months = pd.date_range(f"{start_year}-01-01", periods=len(site_mean), freq="MS") s = pd.Series(site_mean, index=months) return s.groupby(s.index.month).mean() old = decade_climatology(OUT_OLD, 1991) new = decade_climatology(OUT_NEW, 2014) anom = new - old names = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] df = pd.DataFrame( {"1991–2000 [°C]": old.round(2).to_numpy(), "2014–2023 [°C]": new.round(2).to_numpy(), "Δ [°C]": anom.round(2).to_numpy()}, index=names, ) df

Out[2]:

	1991–2000 [°C]	2014–2023 [°C]	Δ [°C]
Jan	4.620000	5.210000	0.59
Feb	4.900000	6.070000	1.17
Mar	7.000000	7.750000	0.75
Apr	9.830000	10.560000	0.73
May	13.710000	14.250000	0.53
Jun	17.040001	18.379999	1.35
Jul	19.360001	20.469999	1.11
Aug	19.740000	20.490000	0.75
Sep	16.360001	17.549999	1.20
Oct	12.470000	13.690000	1.22
Nov	8.270000	9.600000	1.33
Dec	5.470000	6.850000	1.38

Step 3 — plot the seasonal cycles and anomaly¶

European summer warming has accelerated more than winter — visible as a larger anomaly in JJA than DJF on the right panel.

In [3]:

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 2, figsize=(11, 4))
axes[0].plot(names, old, marker="o", label="1991–2000", color="tab:blue")
axes[0].plot(names, new, marker="s", label="2014–2023", color="tab:red")
axes[0].set_ylabel("T_2m [°C]")
axes[0].set_title("European seasonal cycle, two decades")
axes[0].legend()
axes[0].grid(alpha=0.3)

axes[1].bar(names, anom, color=["tab:red" if v > 0 else "tab:blue" for v in anom])
axes[1].axhline(0, color="gray", lw=0.5)
axes[1].set_ylabel("Anomaly Δ [°C]")
axes[1].set_title("2014–2023 minus 1991–2000")
axes[1].grid(alpha=0.3)

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt fig, axes = plt.subplots(1, 2, figsize=(11, 4)) axes[0].plot(names, old, marker="o", label="1991–2000", color="tab:blue") axes[0].plot(names, new, marker="s", label="2014–2023", color="tab:red") axes[0].set_ylabel("T_2m [°C]") axes[0].set_title("European seasonal cycle, two decades") axes[0].legend() axes[0].grid(alpha=0.3) axes[1].bar(names, anom, color=["tab:red" if v > 0 else "tab:blue" for v in anom]) axes[1].axhline(0, color="gray", lw=0.5) axes[1].set_ylabel("Anomaly Δ [°C]") axes[1].set_title("2014–2023 minus 1991–2000") axes[1].grid(alpha=0.3) plt.tight_layout() plt.show()

Notes¶

• Pick the right baseline. WMO 1991–2020 is the current standard; IPCC reports use 1850–1900 (pre-industrial). The choice changes the numerical anomaly substantially.
• Spatial vs temporal averaging order matters. This notebook computes the per-month spatial mean first, then averages across years per calendar month. For a true climatological grid, do it the other way around — average each pixel through time first, then display the spatial pattern.
• CDS-Beta data assimilation upgrades. ERA5 was reprocessed in CDS-Beta with consistent satellite assimilation. The single-levels product has a known small jump at 1979 (start of ERA5 satellite era) that you should be aware of for cross-decadal comparisons.
• For higher resolution. ERA5-Land is 0.1° but only available from 1950. ERA5 single-levels at 0.25° goes back to 1940 — choose based on whether the spatial detail or the time depth matters more.