Global monthly instrumental temperatures since 1850

Global monthly temperatures since 1850 -- instrumental estimates


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This shows global average monthly temperature anomalies since 1850 from six sources based on instrumental records (historical meteorological measurements) plus one reanalysis series. The plotted values are temperature anomalies — the difference between a particular month’s temperature and the average for that month of the year across typically a 30-year reference interval. You may think that a standard reference interval would be used, but no, of course not — they’re all over the place. For plotting I’ve adjusted the series to a common ‘pre-industrial’ baseline (~late 19th century temperature, taken as 0.7 °C below the 1981-2010 average). The overlaid trend is a 4th order polynomial fit, intended as a visual guide based on the data, but in no sense claimed to be representative of underlying processes.

These are surface¹ air temperature estimates. The global values are area-weighted averages, meaning that the estimates attempt to give equal weight to each square kilometre of the earth’s surface when computing the global average. That requires rather more than just grabbing all the global weather station temperature records and average them. Most importantly, weather stations are nearly all on land, and land covers only about 30% of the earth’s surface, so more data is need. Fortunately seawater temperatures have been routinely recorded by ships at sea for well over a century, and that huge dataset provides a good estimate of near-surface air temperature for much of the other 70%. Buoy network measurements augment ship-based data in recent decades.

Ship-based sea surface temperature observation density (NOAA)

Current ARGO float oceanographic measurement network (Wikipedia)

The chief problems with the instrumental averaging approach are of course with the 70%, not the 30% (despite denialist noise about land-based weather station data quality). They have to do with things like the method of seawater sampling (engine intake water vs bucket over the side vs special insulated buckets) and the means of correcting for those. The biggest issue lies with the sea ice covered areas in the arctic and antarctic, where the air is insulated from the ocean by the floating ice — so seawater temperature is pretty much useless — and anyway water temperature measurements are sparse. Much attention has focused on the resulting arctic “data hole”, because that area is known to have been warming very rapidly. The series differ mostly in how they treat gaps in the data (the arctic, plus parts of the antarctic and north Africa).




The oldest of the instrumental estimates by the NASA Goddard Institute for Space Studies uses distance-weighted averaging within an equal-area grid, with relatively crude latitudinally-averaged interpolation to estimate temperatures across the data holes.

Data from:

2. HadCRUT

The most famous of the global temperature series is the one by the Climatic Research Unit at the University of East Anglia in cooperation with the Hadley Centre of the UK Met Office. It uses simple grid-based averaging but with different raw data correction. In HadCRUT, the data holes are just excluded from the average, resulting in slightly lower recent warming estimates than some other series because of exclusion of the rapidly warming arctic.

Data is from:

3. HadCRUT + Cowtan & Way

In 2014 a chemist from the UK and a geographer from Canada, Kevin Cowtan and Robert Way, published revisions to the the HadCRUT series that use a sophisticated interpolation scheme to fill the data holes (called kriging, a procedure originally developed for mineral ore estimation). The plotted trace shows a combination of their “long kriging” series, which just interpolates the gaps, and their “hybrid UAH” series, which cunningly incorporates satellite remote sensing data into the post-1979 interpretation to further improve gap filling.


4. JMA

The Japan Meteorological Agency also computes a global mean temperature estimate series using grid-based averaging. Like HadCRUT, the data holes are excluded from the average, with the effective coverage being even smaller (about 85%). They use their own sea surface temperature analysis (“COBE-SST“), which may be the source of their slightly spikier monthly estimates, particularly early in the record.

Data from:


The National Centers for Environmental Information of the US National Oceanic and Atmospheric Administration also computes a global mean temperature estimate series using a sophisticated grid-based averaging scheme.

Data from:

6. Berkeley Earth

Unlike the others, Berkeley Earth uses a full kriging approach to interpolation, allowing them to incorporate more data from more sources into the global average. I expect the Berkeley Earth products to eventually become the default, go to, estimates of global temperature.

Data from:

7. Copernicus ERA-Interim

‘Copernicus ERA-Interim’ is a high-quality reanalysis product rather an instrumental estimate, included here for comparison.

Data from:



These series use a variety of baselines to define temperature anomalies: 1981-2010 (Copernicus), 1961-1990 (HadCRUT), 1951-1980 (GISTEMP), 1971-2000 (NOAA, JMA), etc. They have been rebased for plotting as follows:

  • If a series is not stated to be 1981-2010 based, it is rebased to that interval using the difference between the (generally 30-year) all-months mean across its baseline and its all-months mean across 1981-2010. (Rebasing is annual not separately by calendar month, and is intrinsic — it does not depend at all on other series.)
  • All of the anomaly series are then rebased from 1981-2010 to ‘pre-industrial’ by adding a uniform 0.7 °C, following the plotting approach adopted by Copernicus, for which they cite support from Hawkins et al 2017.


Notes and references

  1. Meteorological surface air temperature is typically defined as the temperature at 1.2 – 2 m above ground level, measured in a standard meteorological screen.
  2. Hawkins, Ed, Pablo Ortega, Emma Suckling, Andrew Schurer, Gabi Hegerl, Phil Jones, Manoj Joshi et al. “Estimating changes in global temperature since the pre-industrial period.” Bulletin of the American Meteorological Society 2017.



The spreadsheet that produced this graph can be downloaded here:


Also see:

(click for links)


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