October temperatures rise 0.18C

October saw a large increase of 0.18C in the global average temperature (anomaly) since September.

NH Temperature anomaly distribution for October 2019

These results use a spherical triangulation of land (GHCN-V4) and ocean (HadSST3) temperature data with a baseline of 1961-1990. The methodology is described in this post

Here are the monthly trends since 1998.

Monthly global temperature anomalies since 1998

2019 is also set to be the second warmest year based on the partial average for the first 10 months.

Annual values of global temperature anomalies. 2019 is based on the first 10 months of data.

As remarked previously GHCN are no longer updating V3, which showed a significantly lower warming trend than V4.

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A comparison of HadCRUT4.6 using Icosahedral binning

The classic HadCRUT processing calculates average surface temperatures on a 5×5 grid in Latitude and Longitude. To calculate the global average a spatial weighting of cos(Lat) must be applied. The main advantage of using a more complex 3D Icosahedral grid is the resultant regular bin spacing over the earth’s surface. The global average is then simply the average over all occupied bins. In this post I look at differences between the two methods. First here are the monthly global temperature averages compared.

Figure 1. Monthly average temperatures from 1880 to September 2019 calculated by both methods. The two orange curves show the differences between the two against the right hand y-axis

In the early years there is no significant difference because there are less stations outside Europe/North America and very few stations at high latitudes. After 1970 however Icosahedral averaging gives slightly warmer global averages extending up to 0.05C warmer than classic result. This is because relatively more bins near the poles become occupied.

Figure 2. Detailed differences between Icosahedral grids and lat/lon grids after 1970

The global average is slightly larger for an Icosahedral grid, reaching 0.06C warmer by 2019 . Figure 3 shows an Arctic  view of the  spatial distribution for September 2019.

Figure 3: Spatial temperature distribution showing 2562 element Icosahedral grid.

This can be compared with  the classic (lat,lon) distribution (taken from Hadley’s website).

Figure 4. Spatial Distribution HadCRUT4.6 for September 2019

The same features are clearly present in both.

Finally we can compare icosahedral binning  to the Cowtan & Way (C&W) data. C&W use the same (lat,lon) binning as classic HadCRUT4 but in addition extrapolate the measured data into empty bins using a kriging technique. They justify this procedure  by arguing that it “corrects” a coverage bias in temperature anomalies,  particularly in the Arctic which is  warming relatively faster than elsewhere. My results show that this is not strictly even necessary. Using equal area icosahedral binning avoids this spatial bias without any need to extrapolate temperature measurements into empty cells. Figure 5 compares the 3 methods and shows that icosahedral binning inherently agrees with the C&W simply because it works in 3D.

Figure 5: Compare the 3 methods methods of averaging HadSST4 data. The deltas are differences relative to classic HadCRUT4. Click to expand.

We can see in figure 5. that there is hardly any difference between C&W and Icosahedral, whereas both diverge from HadCRUT4 by about 0.06C between  1998 and  2019. However I think  the most important  issue is that you can accurately use just the measured temperature values on a 3D surface, rather  than extrapolate into empty regions in 2D. Experimental results should only depend on the measured data. My only remaining problem  now is to speed up the algorithm because  to calculate all of H4 from scratch currently takes over 12 hours CPU!

The time series can be downloaded here



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Icosahedral binning

A recent post by Nick Stokes has started me thinking again about the use of 3D Icosahedral binning of temperature data. Their major advantage is that all bins are of equal size on the earth’s surface thereby removing artificial spatial biases, as demonstrated in Figure 1. This method when applied to HadCRUT4 in my opinion gives a more natural result than their standard (lat,lon) binning in 2D. especially at the poles..

Figure 1. A level 4 Icosahedral grid with 2562 nodes showing equal coverage over both poles. The data shown is HadCRUT4.6 averaged over all 12 months of 2017.

The final result gives almost the same as Cowtan & Way, but  without the need for any kriging into empty regions. It also agrees rather well with the spherical triangulation method, but also avoids its implicit triangular extrapolation across sparse regions.

Fig 2. HadCRUT4 calculated 3 different ways. a) Classic CRU method, b) Icosahedral binning and, c) Spherical triangulation. The bottom curve shows differences to classic.

I have used Icosahedral binning to analyse GHCN-Daily as I couldn’t think of any other way top handle it. GHCN-Daily consists of 114,786 daily records of weather station data from around the world, the bulk of which are precipitation records. 27181 stations contain sufficient temperature data for normalisation. The monthly averages of these stations also form the bulk of GHCN-V4. The daily records however are the nearest we can get to the original raw measurement data. Each consists of a daily Tmax and Tmin value. I process them as follows.
1.Basic bins are a level 5 icosahedral grid with 10224 equilateral triangles. Each station is located within one triangle.
2. I calculate the 12 monthly average temperatures for Tmax, Tmin and Tav for each occupied triangle and all days in the month, using a baseline of 1961-1990. A minimum of 10 years worth of values are required.
3. I also calculate the offsets for each station relative to the overall average in each triangle. This is the difference between the cell average and the station average
4. Based on these results I calculate the average anomaly for each cell and for each month from 1761 to 2019. The station offsets ensure that the cell average anomaly corrects for changing station population with time.

Global land average temperature anomalies for GHCN-Daily, CRUTEM4.6 and Berkeley Earth (their rolling 12 month average smooths out spikes)

In general there is good agreement between all datasets although CRUTEM4 seems too warm around 1850 corresponding to the coolest period in both GCHN-Daily and BEST.  The sudden temperature drop in temperature around 1815 coincides with the eruption of Tambora – the largest in recorded history.

It is problematic estimating global averages temperatures much before 1850 because these tend to be just  Europe and US centric, with  a scattering of ex-colonial outposts elsewhere. Despite this it does appear that the 18th century was likely  ~ 0.5C cooler than the late 19th century.

Icosahedral binning is the optimum method for averaging temperatures anomalies which avoids fitting and extrapolation.

I can’t help feeling that HadCRUT should be looking into it.




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