Climatic Temperature Variations

In the previous post I identified that the standard definition of temperature homogenisation assumes that there are little or no variations in climatic trends within the homogenisation area. I also highlighted specific instances of where this assumption has failed. However, the examples may be just isolated and extreme instances, or there might be other, offsetting instances so the failures could cancel each other out without a systematic bias globally. Here I explore why this assumption should not be expected to hold anywhere, and how it may have biased the picture of recent warming. After a couple of proposals to test for this bias, I look at alternative scenarios that could bias the global average temperature anomalies. I concentrate on the land surface temperatures, though my comments may also have application to the sea surface temperature data sets.


Comparing Two Recent Warming Phases

An area that I am particularly interested in is the relative size of the early twentieth century warming compared to the more recent warming phase. This relative size, along with the explanations for those warming periods gives a route into determining how much of the recent warming was human caused. Dana Nuccitelli tried such an explanation at skepticalscience blog in 20111. Figure 1 shows the NASA Gistemp global anomaly in black along with a split be eight bands of latitude. Of note are the polar extremes, each covering 5% of the surface area. For the Arctic, the trough to peak of 1885-1940 is pretty much the same as the trough to peak from 1965 to present. But in the earlier period it is effectively cancelled out by the cooling in the Antarctic. This cooling, I found was likely caused by use of inappropriate proxy data from a single weather station3.

Figure 1. Gistemp global temperature anomalies by band of latitude2.

For the current issue, of particular note is the huge variation in trends by latitude from the global average derived from the homogenised land and sea surface data. Delving further, GISS provide some very useful maps of their homogenised and extrapolated data4. I compare two identical time lengths – 1944 against 1906-1940 and 2014 against 1976-2010. The selection criteria for the maps are in figure 2.

Figure 2. Selection criteria for the Gistemp maps.

Figure 3. Gistemp map representing the early twentieth surface warming phase for land data only.

Figure 4. Gistemp map representing the recent surface warming phase for land data only.

The later warming phase is almost twice the magnitude of, and has much the better coverage than, the earlier warming. That is 0.43oC against 0.24oC. In both cases the range of warming in the 250km grid cells is between -2oC and +4oC, but the variations are not the same. For instance, the most extreme warming in both periods is at the higher latitudes. But, with the respect to North America in the earlier period the most extreme warming is over the Northwest Territories of Canada, whilst in the later period the most extreme warming is over Western Alaska, with the Northwest Territories showing near average warming. In the United States, in the earlier period there is cooling over Western USA, whilst in the later period there is cooling over much of Central USA, and strong warming in California. In the USA, the coverage of temperature stations is quite good, at least compared with much of the Southern Hemisphere. Euan Mearns has looked at a number of areas in the Southern Hemisphere4, which he summarised on the map in Figure 5

Figure 5. Euan Mearns says of the above “S Hemisphere map showing the distribution of areas sampled. These have in general been chosen to avoid large centres of human population and prosperity.

For the current analysis Figure 6 is most relevant.

Figure 6. Euan Mearns’ says of the above “The distribution of operational stations from the group of 174 selected stations.

The temperature data for the earlier period is much sparser than for later period. Even where there is data available in the earlier period the temperature data could be based on a fifth of the number of temperature stations as the later period. This may exaggerate slightly the issue, as the coasts of South America and Eastern Australia are avoided.

An Hypothesis on the Homogenisation Impact

Now consider again the description of homogenisation Venema et al 20125, quoted in the previous post.


The most commonly used method to detect and remove the effects of artificial changes is the relative homogenization approach, which assumes that nearby stations are exposed to almost the same climate signal and that thus the differences between nearby stations can be utilized to detect inhomogeneities. In relative homogeneity testing, a candidate time series is compared to multiple surrounding stations either in a pairwise fashion or to a single composite reference time series computed for multiple nearby stations. (Italics mine)


The assumption of the same climate signal over the homogenisation will not apply where the temperature stations are thin on the ground. The degree to which homogenisation eliminates real world variations in trend could be, to some extent, inversely related to the density. Given that the density of temperature data points diminishes in most areas of the world rapidly when one goes back in time beyond 1960, homogenisation in the early warming period far more likely to be between climatically different temperature stations than in the later period. My hypothesis is that, relatively, homogenisation will reduce the early twentieth century warming phase compared the recent warming phase as in earlier period homogenisation will be over much larger areas with larger real climate variations within the homogenisation area.

Testing the Hypothesis

There are at least two ways that my hypothesis can be evaluated. Direct testing of information deficits is not possible.

First is to conduct temperature homogenisations on similar levels of actual data for the entire twentieth century. If done for a region, the actual data used in global temperature anomalies should be run for a region as well. This should show that the recent warming phase is post homogenisation is reduced with less data.

Second is to examine the relative size of adjustments to the availability of comparative data. This can be done in various ways. For instance, I quite like the examination of the Manaus Grid block record Roger Andrews did in a post The Worst of BEST6.

Counter Hypotheses

There are two counter hypotheses on temperature bias. These may undermine my own hypothesis.

First is the urbanisation bias. Euan Mearns in looking at temperature data of the Southern Hemisphere tried to avoid centres of population due to the data being biased. It is easy to surmise the lack of warming Mearns found in central Australia7 was lack of an urbanisation bias from the large cities on the coast. However, the GISS maps do not support this. Ronan and Michael Connolly8 of Global Warming Solved claim that the urbanisation bias in the global temperature data is roughly equivalent to the entire warming of the recent epoch. I am not sure that the urbanisation bias is so large, but even if it were, it could be complementary to my hypothesis based on trends.

Second is that homogenisation adjustments could be greater the more distant in past that they occur. It has been noted (Steve Goddard in particular) that each new set of GISS adjustments adjusts past data. The same data set used to test my hypothesis above could also be utilized to test this hypothesis, by conducting homogenisations runs on the data to date, then only to 2000, then to 1990 etc. It could be that the earlier warming trend is somehow suppressed by homogenizing the most recent data, then working backwards through a number of iterations, each one using the results of the previous pass. The impact on trends that operate over different time periods, but converge over longer periods, could magnify the divergence and thus cause differences in trends decades in the past to be magnified. As such differences in trend appear to the algorithm to be more anomalous than in reality they actually are.

Kevin Marshall


  1. Dana Nuccitelli – What caused early 20th Century warming? 24.03.2011
  2. Source
  3. See my post Base Orcadas as a Proxy for early Twentieth Century Antarctic Temperature Trends 24.05.2015
  4. Euan Mearns – The Hunt For Global Warming: Southern Hemisphere Summary 14.03.2015. Area studies are referenced on this post.
  5. Venema et al 2012 – Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P., Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier, G., Lakatos, M., Williams, C. N., Menne, M. J., Lindau, R., Rasol, D., Rustemeier, E., Kolokythas, K., Marinova, T., Andresen, L., Acquaotta, F., Fratianni, S., Cheval, S., Klancar, M., Brunetti, M., Gruber, C., Prohom Duran, M., Likso, T., Esteban, P., and Brandsma, T.: Benchmarking homogenization algorithms for monthly data, Clim. Past, 8, 89-115, doi:10.5194/cp-8-89-2012, 2012.
  6. Roger Andrews – The Worst of BEST 23.03.2015
  7. Euan Mearns – Temperature Adjustments in Australia 22.02.2015
  8. Ronan and Michael Connolly – Summary: “Urbanization bias” – Papers 1-3 05.12.2013

Base Orcadas as a Proxy for early Twentieth Century Antarctic Temperature Trends

Temperature trends vary greatly across different parts of the globe, an aspect that is not recognized when homogenizing temperatures. At a top level NASA GISS usefully split their global temperature anomaly into eight bands of latitude. I have graphed the five year moving averages for each band, along with the Gistemp global anomaly in Figure 1.

Figure 1. Gistemp global temperature anomalies by band of latitude.

The biggest oddity is the 64S-90S band. This bottom slice of the globe roughly equates to Antarctica, which is South of 66°34′S. Not only was there massive cooling until 1930 – in contradiction to the global trend – but prior to the 1970 was very large volatility in temperatures, despite my using five year moving averages. Looking at the GHCN database of weather stations, there none listed in Antarctica until Rothera point started collecting data in 1946, as shown in Figure 21.

Figure 2. A selection of temperature anomalies in the Antarctica. The most numerous are either on the Antarctic Pennisula, or the islands just to the North.

The only long record is at Base Orcadas located at (60.8 S 44.7 W). I have graphed the GISS homogenised temperature anomaly data for station 701889680000 with the Gistemp 64S-90S band in Figure 3.

Figure 3. Gistemp 64S-90S annual temperature anomaly compared to Base Orcadas GISS homogenised data.

There is a remarkable similarity in the data sets until 1950, after which they appear unrelated. This suggests that in the absence of other data, Base Orcadas was the principle element in creating a proxy for the missing Antarctic data, despite it being located outside the area, and not being related to the actual data for well over half a century. The outcome is to bias the overall global temperature anomaly by suppressing the early twentieth century warming, making the late twentieth century warming appear relatively greater than is the underlying reality2. The error is due to assuming that temperature trends are the same at different latitudes are the same, an assumption that the homogenised data shows to be false.

Kevin Marshall



  1. Also in Antarctica (but not listed) there has been data collected at Amundsen-Scot base at the South Pole (90.0 S 0.0 E) since 1957, and at Vostok base (78.5 S 106.9 E) since 1958.
  2. Removing the Antarctic data would increase both the early twentieth century and post 1975 warming periods. But, given that 64S-90S is 5% of the global surface area, I estimate it would increase the earlier warming trends by 5-10% as against 1-3% for the later trend.

Using 15 year trends to replicate GISTEMP average surface temperature anomalies

At Jo Nova’s Unthreaded on 22/06/14, Philip Shehan posted some GISTEMP temperature trend figures that caused a good deal of controversy.

In 15 year steps they are

1924/39 Trend: 0.142 ±0.148 °C/decade (2σ)

1939/54 Trend: -0.088 ±0.144 °C/decade (2σ)

1954/69 Trend: 0.024 ±0.151 °C/decade (2σ)

1969/84 Trend: 0.165 ±0.162 °C/decade (2σ)

1984/99 Trend: 0.234 ±0.167 °C/decade (2σ)

1999- Trend: 0.099 ±0.138 °C/decade (2σ)

Two issues with the trends are

1. They do not really capture the trends in the data.

2. They slope of the 15 year OLS lines is sensitive to shifting the period by one year – for instance replacing 1999-2013 with 1998-2012.

I replicated Philip Shehan’s data (or a least tried to – he does not use J-D years) on a graph, along with shifting the periods a year backwards. Compare these slopes to the 5 year centred moving average curve in light blue.

NASA corrects errors in the GISTEMP data

In estimating global average temperatures there are a number of different measures to choose from. The UNIPCC tends to favour the British Hadley Centre HADCRUT data. Many of those who believe in the anthropogenic global warming hypothesis have a propensity to believe in the alternative NASA Goddard Institute for Space Studies data. Sceptics criticize GISTEMP due to its continual changes, often in the direction of supporting climate alarmism.

I had downloaded both sets of annual data in April 2011, and also last week. In comparing the two sets of data I noticed something remarkable. Over the last three years the two data sets have converged. The two most significant areas of convergence are in the early twentieth century warming phase (roughly 1910-1944) and the period 1998 to 2010. This convergence is mostly GISTEMP coming into line with HADCRUT. In doing so, it now diverges more from the rise in CO2.

In April 2011 I downloaded the HACRUT3 data, along with GISTEMP. The GISTEMP data carries the same name, but the Hadley centre now has replaced the HADCRUT3 data set with HADCRUT4. Between the two data sets and over just three years, one would expect the four sets of data to be broadly in agreement. To check this I plotted the annual average anomalies figures below.

The GISTEMP 2011 annual mean data, (in light blue) appears to be an outlier of the four data sets. This is especially for the periods 1890-1940 and post 2000.

To emphasise this, I found the difference between data sets, then plotted the five tear centred moving average of the data.

The light green dotted line shows the divergence in data sets three years ago. From 1890 to 1910 the divergence goes from zero to 0.3 degrees. This reduces to almost zero in the early 1940s, increases to 1950, reduces to the late 1960s. From 2000 to 2010 the divergence increases markedly. The current difference, shown by the dark green dotted line shows much greater similarities. The spike around 1910 has disappeared, as has the divergence in the last decade. These changes are more due to changes in GISTEMP (solid blue line) that HADCRUT (solid orange).

To see these changes more clearly, I applied OLS to the warming periods. The start of the period I took as the lowest year at the start, and the end point as the peak. The results of the early twentieth century were as follows:-

GISTEMP 2011 is the clear outlier for three reasons. First it has the most inconsistent measured warming, just 60-70% of the other figures. Second is that the beginning low point is the most inconsistent. Third is the only data set not to have 1944 as the peak of the warming cycle. The anomalies are below.

There were no such issues of start and end of the late twentieth century warming periods, shown below.

There is a great deal of conformity between these data sets. This is not the case for 1998-2010.

The GISTEMP 2011 figures seemed oblivious to the sharp deceleration in warming that occurred post 1998, which was also showing in satellite data. This has now been corrected in the latest figures.

The combined warming from 1976 to 2010 reported by the four data sets is as follows.

GISTEMP 2011 is the clear outlier here, this time being the highest of the four data sets. Different messages from the two warming periods can be gleaned by looking across the four data sets.

GISTEMP 2011 gives the impression of accelerating warming, consistent with the rise in atmospheric CO2 levels. HADCRUT3 suggests that rising CO2 has little influence on temperature, at least without demonstrating another warming element that was present in early part of the twentieth century and not in the latter part. The current data sets lean more towards HADCRUT3 2011 than GISTEMP 2011. Along with the clear pause from 1944 to 1976, it could explain why this is not examined too closely by the climate alarmists. The exception is by DANA1981 at, who tries to account for the early twentieth century warming by natural factors. As it is three years old, it would be interesting to see an update based on more recent data.

What is strongly apparent from recent changes, is that the GISTEMP global surface temperature record contained errors, or inferior methods, that have now been corrected. That does not necessarily mean that it is a more accurate representation of the real world, but that it is more consistent with the British data sets, and less consistent strong forms of the global warming hypothesis.

Kevin Marshall