The graphic above, show the “All-Dataset Global Mean Surface Temperature (GMST) Trendline” milestones reaching:

  • 1985 November: Trend line passes 0.50°C
  • 2000 April: Trend line passes 0.75°C
  • 2012 June: Trend line passes 1.00°C
  • 2022 May: Trend line passes 1.25°C
  • 2024 September: Trend line reaches 1.32°C

Note The “All-Dataset Global Mean Surface Temperature (GMST) Trendline” is calculated by

  • Using all datasets (Noaa, HadCRUT, GISS, Berkeley Earth, Copernicus) – See GMST Data Sets
  • Re-baseline all the datasets to be relative to 1850-1900
  • For each month take the average value of all the datasets
    • Note not all datasets have all the months from 1850
      • E.g. Copernicus starts in 1940 and GISS starts in 1880
  • Use Loess Smoothing, configured to use a 30 year window of data
    • Using python library “loess.loess_1d”
    • Use a factor of 0.1717
      • 30 years is 360 months of data
      • Jan 1850-> Sep 2024 is 2097 months
      • Factor 0.1717 = 360 / 2097

Copernicus Only – Value

Note: The Copernicus Loess-30-year-window value for September 2024 is also listed as 1.35°C. This uses the same technique as the “add data” technique, but just uses the Copernicus data, and a factor of 0.354 (360 months / 1017 months).

Trend Line Smoothing options

As per Climate Reporting – Why so many different values – There are multiple global GMST Data Sets sets with slightly different values. The global temperature does jump around year-to-year and therefore it makes sense to apply smoothing techniques to allow the trend to shine through. There are many different “smoothing techniques” available to determine “GMST Trend Temperature”. I am just looking for a value which is calculated deterministically and appears “about right”

The “GMST Trend Temperature” dates I have given above (1985, 2000, 2012, 2022, etc…) are subjective, although after trying various smoothing techniques (using average values over all 5 data sets), the results were always within 1 or 2 years of the values I have put on the graph … for any smoothing techniques that appeared to believably overlap the data points. If you do use a 30-year-running-average and just NOAA / GISSTemp (which run about 0.07°C lower than the average since the 1970s), then everything will show up as running around 15 years slower.

Below is the same data, but showing the full 1850-2023 dataset.