✨ climate: change baseline period in the MET temperature anomalies ch…

…art (#4024)

etl/steps/data/garden/met_office_hadley_centre/2025-01-21/near_surface_temperature.meta.yml

@@ -1,12 +1,21 @@
 definitions:
   common:
     description_key:
-      - Temperature anomalies are given in degrees Celsius relative to the average temperature over the period 1961-1990.
-      - Temperature anomalies are available for the Northern Hemisphere and the Southern Hemisphere.
-      - The global mean is calculated by averaging anomalies for northern and southern hemispheres.
+      - Temperature anomalies show how many degrees Celsius temperatures have changed compared to the 1861-1890 period. This baseline period is commonly used to highlight the changes in temperature since pre-industrial times, prior to major human impacts.
+      - The data includes separate measurements for the Northern and Southern Hemispheres, which helps researchers analyze regional differences.
+      - The global temperature anomaly is the average of both hemisphere measurements.
+    description_from_producer: |-
+       The 1961-90 period is most often used as a baseline because it is the period recommended by the World Meteorological Organisation. In some cases other periods are used. For global average temperatures, an 1861-1890 period is sometimes used to show the warming since the "pre-industrial" period.
+    description_processing: |-
+      We switch from using 1961-1990 to using 1861-1890 as our baseline to better show how temperatures have changed since pre-industrial times. For each region, we calculate the mean temperature anomaly for 1961–1990 and for 1861–1890. The difference between these two means serves as the adjustment factor. This factor is applied uniformly to both the temperature anomalies and the confidence intervals to ensure that both the central values and the associated uncertainty bounds are correctly shifted relative to the new 1861–1890 baseline.
+    processing_level: major
+    display:
+      numDecimalPlaces: 1
   presentation:
     topic_tags:
     - Climate Change
+    grapher_config:
+      note: The period between 1861–1890 is used as a baseline to better reflect temperature changes since pre-industrial times, [as recommended by the source](https://www.metoffice.gov.uk/hadobs/indicators/index.html#:~:text=The%201961%2D90%20period%20is,other%20parts%20of%20the%20world.).
 
 dataset:
   update_period_days: 90
@@ -15,9 +24,11 @@ tables:
   near_surface_temperature:
     variables:
       temperature_anomaly:
-        title: Global average temperature anomaly relative to 1961-1990
+        title: Global average temperature anomaly relative to 1861-1890
         short_unit: °C
         unit: degrees Celsius
+        description_short: Global average land-sea temperature anomaly relative to the 1861-1890 average temperature baseline.
+
       upper_limit:
         title: Upper bound of the annual temperature anomaly (95% confidence interval)
         short_unit: °C

etl/steps/data/garden/met_office_hadley_centre/2025-01-21/near_surface_temperature.py

@@ -12,8 +12,23 @@ def run(dest_dir: str) -> None:
     #
     # Load meadow dataset and read its main table.
     ds_meadow = paths.load_dataset("near_surface_temperature")
-    tb_meadow = ds_meadow["near_surface_temperature"]
+    tb_meadow = ds_meadow.read("near_surface_temperature")
+    # Switch from using 1961-1990 to using 1861-1890 as our baseline to better show how temperatures have changed since pre-industrial times.
+    # Calculate the adjustment factors based only on temperature_anomaly
+    adjustment_factors = (
+        tb_meadow[tb_meadow["year"].between(1961, 1990)].groupby("region")["temperature_anomaly"].mean()
+        - tb_meadow[tb_meadow["year"].between(1861, 1890)].groupby("region")["temperature_anomaly"].mean()
+    )
 
+    columns_to_adjust = ["temperature_anomaly", "lower_limit", "upper_limit"]  # Add any other columns as needed
+
+    # Apply the temperature_anomaly adjustment factor
+    # The adjustment factor is applied uniformly to the temperature anomalies and their confidence intervals to ensure that both the central values and the associated uncertainty bounds are correctly shifted relative to the new 1861–1890 baseline.
+    for region in adjustment_factors.index:
+        for column in columns_to_adjust:
+            tb_meadow.loc[tb_meadow["region"] == region, column] += adjustment_factors[region]
+
+    tb_meadow = tb_meadow.format(["region", "year"])
     #
     # Save outputs.
     #