The graphic above, show the “GMST Trend Temperature” milestones passing in the following years

- 1985 – The “GMST Trend Temperature” passes 0.50C
- 2000 – The “GMST Trend Temperature” passes 0.75C
- 2012 – The “GMST Trend Temperature” passes 1.00C
- 2021 – The “GMST Trend Temperature” passes 1.25C
- 2024 – The “GMST Trend Temperature” can reasonably be claimed to be:
- January 2024: Using Monthly average values of all the GMST Data Sets (NOAA, HadCRUT, Copernicus ERA-5, GISSTemp, Berkeley Earth)
- … applying a 10-year linear smoother: 1.30C
- … applying a 10-year quadratic smoother: 1.35C

- April 2024 – Using only the Copernicus ERA-5 data (see Climate Milestones Copernicus – ERA5)
- … applying a 10-year linear smoother: 1.30C
- … applying a 10-year quadratic smoother: 1.42C

- January 2024: Using Monthly average values of all the GMST Data Sets (NOAA, HadCRUT, Copernicus ERA-5, GISSTemp, Berkeley Earth)

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” (This is one area I intend to improve). I don’t want to use a 30-year-running-average trendline because it will simply run underneath all the data points, and will effectively be 15 years / 0.3C behind / below what we are experiencing and unlikely to be representative of what we should expect going forwards (as per Key Climate Indicators).

The “GMST Trend Temperature” dates I have given above (1985, 200, 2021, 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 0.07C 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.

## How the Graphic was Created

The Monthly “GMST Trend Temperature” values plotted on the graph were calculated using a smoothing process:

- Get the Monthly Anomaly data from GMST Data Sets
- Calculate a “Raw Average Monthly GMST Temperature”, by taking the average (mean average) of the monthly GMST values for each of the institutions: Berkeley Earth, NOAA, HadCRUT, GISSTEMP, Copernicus
- Average of Berkeley Earth, NOAA, HadCRUT for 1850-1879
- Average of Berkeley Earth, NOAA, HadCRUT, GISTEMP for 1880-1939
- Average of Berkeley Earth, NOAA, HadCRUT, GISTEMP, Copernicus for 1940-2023

- For each “Raw Average Monthly GMST Temperature” value, take 120 months either side, and calculate the “Best Linear Fit” and a “Quadratic Regression” for those 241 months, and use this function to find the best fit temperature for the current month
- For the first and last 120 months, simply take the most months (up to 120 months) either side, and use the “Best Linear Fit” method. E.g. October 2023 smoothing function uses October 2023, 120 preceding months and 2 following months (November, December) and then finds the best linear fit and best quadratic regression value for October 2023 using this dataset.

- This gives a 20-year-centred smoothing effect for the mid data and at the last 10 years + best fit for the end data.
- The Linear fit is less jumpy, holding a very steady upwards rise.
- The Quadratic fit jumps about a bit more, but still gives very justifiable and believable values for “GMST Trend Temperature” on any given year.