The big problem with the monthly global temperature estimates from the world’s Met Services, such as the UKMOUKMO The Meteorological Office is the United Kingdom's national weather service. It is an executive agency and trading fund of the Department for Business, Energy and Industrial Strategy, is that they are all quoted in anomalies rather than as monthly mean temperatures. Anomalies are calculated by taking the LTALTA Long Term Average. This is usually defined as a 30 year period by the WMO. from that mean temperature, and the LTA used to do this aren’t all using the same 30 year period. Ideally that period should now be from 1991 to 2020, but only two of the big six temperature series use it, in fact the GISTempGISTemp v4 The GISS Surface Temperature Analysis version 4 is an estimate of global surface temperature change using data from NOAA GHCN v4 (land stations) and ERSST v5 (ocean areas). series still uses the period from 1951 to 1980. My global temperature application allows you to compare anomalies from the big six series, and it also allows you to ‘zero’ the anomalies of all of them, and that’s what I’ve done in the chart above for December 1992.
As you can see there is now quite a spread in the estimated anomalies close to 0.12°C between the highest and lowest, which as far as global temperatures goes is a pretty large deviation. The UAH series can get pretty wild with its estimates, but in recent years it’s the ERA5ERA ERA stands for 'ECMWF Re-Analysis' and refers to a series of research projects at ECMWF which produced various datasets (ERA-Interim, ERA-40, etcetera). series from the ECMWFECMWF The European Centre for Medium-Range Weather Forecasts is an independent intergovernmental organisation supported by most of the nations of Europe. It is based at three sites: Shinfield Park, Reading, United Kingdom; Bologna, Italy; and Bonn, Germany. It operates one of the largest supercomputer complexes in Europe and the world's largest archive of numerical weather prediction data. that’s running the hottest of the six.
The dashed line represents values from my own DIYDIY Do It Yourself series. As you can see the shape of the curve follows the other series faithfully but the magnitude of the anomaly in that time is around 0.25°C lower. The DIY series is based on pretty coarse grid (2.5° x 2.5°) of six hourly surface temperatures, but is obviously lacking a certain slowly increasing (fudge) factor that my series doesn’t seem to have. This maybe because of differences in the LTA they use, more probably the complexity of the algorithms they use to produce a global estimate, and how they cope with temperatures over land, sea and ice.