Can Climatology Ever Be Considered a Science?

Can climatology ever be considered a science? My favourite Richard Feynman quote.

You cannot prove a vague theory wrong. If the guess that you make is poorly expressed and the method you have for computing the consequences is a little vague then ….. you see that the theory is good as it can’t be proved wrong. If the process of computing the consequences is indefinite, then with a little skill any experimental result can be made to look like an expected consequence.

I would maintain that by its nature climatology will always be a vague theory. Climate consists of an infinite number of interrelationships that can only be loosely modelled by empirical generalisations. These can only ever be imperfectly measured, although that is improving both in scope and period of observations. Tweaking the models can always produce a desired outcome. In this sense climatology is never going to be a science way that physics and chemistry have become. But this does not mean that climatology cannot become more scientific. A step forward might be to classify empirical statements according to the part of the global warming theory they support, and the empirical content of those statements.

Catastrophic Anthropogenic Global Warming (CAGW) is a subset of AGW. The other elements of AGW are trivial, or positive. I would also include the benign impacts of aerosols in reducing the warming impacts. So AGW’ is not an empty set.

AGW is a subset of GW, where GW is the hypothesis that an increase in greenhouse gas levels will cause temperatures to rise. There could be natural causes of the rise in greenhouse gases as well, so GW’ is not an empty set.

GW is a subset of Climate Change CC. That is all causes of changing climate, both known and unknown, including entirely random causes.

In summary


Or diagrammatically the sets can be represented by a series of concentric rings.

To become more scientific, climatology as an academic discipline should be moving on two complementary fronts. Firstly, through generating clearer empirical confirmations, as against banal statements or conditional forecasts. Secondly, for the statements to become more unambiguous in being ascribable solely to the CAGW hypothesis in particular rather being just as easily be ascribed to vague and causeless climate change in general. These twin aims are shown in the diagram below, where the discipline should be aiming in the direction of the red progressing arrow towards science, rather the green degenerating arrow.

Nullis in verba on a recent Bishop Hill discussion forum rightly points out the statement

“you acknowledge that scientists predicted warming. And warming is what we observed”

commits the fallacy of “confirming the consequent”.

If your definition of climate change is loose enough, the observed rise could be a member the CC set. But to infer it is not part of GW’ (outside of the GW set) requires more empirical content. As Nullis has shown in his tightly worded comment to prove this is impossible. But the greater empirical content will give more confidence that the scientists did not just strike lucky. Two years ago Roy Spencer did attempt just that. From 73 climate models the prediction was that between 1979 and 2012 average global temperatures would rise by between 0.3 and 1.5C, with an average estimate of 0.8C. Most were within the 0.6 to 1.2C, so any actual rise in that range, which is pretty unusual historically, would be a fairly strong confirmation of a significant AGW impact. The actual satellite and weather balloon data showed a rise of about 0.2C. The scientists got it wrong on the basis of their current models. At a minimum the models are running too hot, at a minimum failing to confirm the CAGW hypothesis.

By more clearly specifying the empirical content of statements the scope of alternative explanations is narrowed. In this case we have an explanation for someone using a more banal statement.

I would contend that to obtain confirmation of CAGW requires a combination of the warming and the adverse consequences. So even if the hurricanes had got worse after Katrina in 2005, with zero warming on its own it is just that an observation climate has changed. But together they form a more empirically rich story that is explained by CAGW theory. Still better is a number of catastrophic consequences.

In the next post I shall show some further examples of the discipline moving in the direction of degenerating climatology.

Kevin Marshall

John Cook undermining democracy through misinformation

It seems that John Cook was posting comments in 2011 under the pseudonym Lubos Motl. The year before physicist and blogger Luboš Motl had posted a rebuttal of Cook’s then 104 Global Warming & Climate Change Myths. When someone counters your beliefs point for point, then most people would naturally feel some anger. Taking the online identity of Motl is potentially more than identity theft. It can be viewed as an attempt to damage the reputation of someone you oppose.

However, there is a wider issue here. In 2011 John Cook co-authored with Stephan Lewandowsky The Debunking Handbook, that is still featured prominently on the This short tract starts with the following paragraphs:-

It’s self-evident that democratic societies should base their decisions on accurate information. On many issues, however, misinformation can become entrenched in parts of the community, particularly when vested interests are involved. Reducing the influence of misinformation is a difficult and complex challenge.

A common misconception about myths is the notion that removing its influence is as simple as packing more information into people’s heads. This approach assumes that public misperceptions are due to a lack of knowledge and that the solution is more information – in science communication, it’s known as the “information deficit model”. But that model is wrong: people don’t process information as simply as a hard drive downloading data.

If Cook was indeed using the pseudonym Lubos Motl then he was knowingly putting out into the public arena misinformation in a malicious form. If he misrepresented Motl’s beliefs, then the public may not know who to trust. Targeted against one effective critic, it could trash their reputation. At a wider scale it could allow morally and scientifically inferior views to gain prominence over superior viewpoints. If the alarmist beliefs were superior it what be necessary to misrepresent alternative opinions. Open debate would soon reveal which side had the better views. But in debating and disputing, all sides would sharpen their arguments. What would quickly disappear is the reliance on opinion surveys and rewriting of dictionaries. Instead, proper academics would be distinguishing between quality, relevant evidence from dogmatic statements based on junk sociology and psychology. They would start defining the boundaries of expertise between the basic physics, computer modelling, results analysis, public policy-making, policy-implementation, economics, ethics and the philosophy of science. They may then start to draw on the understanding that has been achieved in these subject areas.

Kevin Marshall

A Great and Humble Man Dies

Sir Nicholas Winton died today at 106 Years Old. A true hero of mine.

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 relate 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

Defining “Temperature Homogenisation”


The standard definition of temperature homogenisation is of a process that cleanses the temperature data of measurement biases to only leave only variations caused by real climatic or weather variations. This is at odds with GHCN & GISS adjustments which delete some data and add in other data as part of the homogenisation process. A more general definition is to make the data more homogenous, for the purposes of creating regional and global average temperatures. This is only compatible with the standard definition if assume that there are no real data trends existing within the homogenisation area. From various studies it is clear that there are cases where this assumption does not hold good. The likely impacts include:-

  • Homogenised data for a particular temperature station will not be the cleansed data for that location. Instead it becomes a grid reference point, encompassing data from the surrounding area.
  • Different densities of temperature data may lead to different degrees to which homogenisation results in smoothing of real climatic fluctuations.

Whether or not this failure of understanding is limited to a number of isolated instances with a near zero impact on global temperature anomalies is an empirical matter that will be the subject of my next post.



A common feature of many concepts involved with climatology, the associated policies and sociological analyses of non-believers, is a failure to clearly understand of the terms used. In the past few months it has become evident to me that this failure of understanding extends to term temperature homogenisation. In this post I look at the ambiguity of the standard definition against the actual practice of homogenising temperature data.


The Ambiguity of the Homogenisation Definition

The World Meteorological Organisation in its’ 2004 Guidelines on Climate Metadata and Homogenization1 wrote this explanation.

Climate data can provide a great deal of information about the atmospheric environment that impacts almost all aspects of human endeavour. For example, these data have been used to determine where to build homes by calculating the return periods of large floods, whether the length of the frost-free growing season in a region is increasing or decreasing, and the potential variability in demand for heating fuels. However, for these and other long-term climate analyses –particularly climate change analyses– to be accurate, the climate data used must be as homogeneous as possible. A homogeneous climate time series is defined as one where variations are caused only by variations in climate.

Unfortunately, most long-term climatological time series have been affected by a number of nonclimatic factors that make these data unrepresentative of the actual climate variation occurring over time. These factors include changes in: instruments, observing practices, station locations, formulae used to calculate means, and station environment. Some changes cause sharp discontinuities while other changes, particularly change in the environment around the station, can cause gradual biases in the data. All of these inhomogeneities can bias a time series and lead to misinterpretations of the studied climate. It is important, therefore, to remove the inhomogeneities or at least determine the possible error they may cause.


That is temperature homogenisation is necessary to isolate and remove what Steven Mosher has termed measurement biases2, from the real climate signal. But how does this isolation occur?

Venema et al 20123 states the issue more succinctly.


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 (Conrad and Pollak, 1950). 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)


Blogger …and Then There’s Physics (ATTP) partly recognizes these issues may exist in his stab at explaining temperature homogenisation4.

So, it all sounds easy. The problem is, we didn’t do this and – since we don’t have a time machine – we can’t go back and do it again properly. What we have is data from different countries and regions, of different qualities, covering different time periods, and with different amounts of accompanying information. It’s all we have, and we can’t do anything about this. What one has to do is look at the data for each site and see if there’s anything that doesn’t look right. We don’t expect the typical/average temperature at a given location at a given time of day to suddenly change. There’s no climatic reason why this should happen. Therefore, we’d expect the temperature data for a particular site to be continuous. If there is some discontinuity, you need to consider what to do. Ideally you look through the records to see if something happened. Maybe the sensor was moved. Maybe it was changed. Maybe the time of observation changed. If so, you can be confident that this explains the discontinuity, and so you adjust the data to make it continuous.

What if there isn’t a full record, or you can’t find any reason why the data may have been influenced by something non-climatic? Do you just leave it as is? Well, no, that would be silly. We don’t know of any climatic influence that can suddenly cause typical temperatures at a given location to suddenly increase or decrease. It’s much more likely that something non-climatic has influenced the data and, hence, the sensible thing to do is to adjust it to make the data continuous. (Italics mine)

The assumption of a nearby temperature stations have the same (or very similar) climatic signal, if true would mean that homogenisation would cleanse the data of the impurities of measurement biases. But there is only a cursory glance given to the data. For instance, when Kevin Cowtan gave an explanation of the fall in average temperatures at Puerto Casado neither he, nor anyone else, checked to see if the explanation stacked up beyond checking to see if there had been a documented station move at roughly that time. Yet the station move is at the end of the drop in temperatures, and a few minutes checking would have confirmed that other nearby stations exhibit very similar temperature falls5. If you have a preconceived view of how the data should be, then a superficial explanation that conforms to that preconception will be sufficient. If you accept the authority of experts over personally checking for yourself, then the claim by experts that there is not a problem is sufficient. Those with no experience of checking the outputs following processing of complex data will not appreciate the issues involved.


However, this definition of homogenisation appears to be different from that used by GHCN and NASA GISS. When Euan Mearns looked at temperature adjustments in the Southern Hemisphere and in the Arctic6, he found numerous examples in the GHCN and GISS homogenisations of infilling of some missing data and, to a greater extent, deleted huge chunks of temperature data. For example this graphic is Mearns’ spreadsheet of adjustments between GHCNv2 (raw data + adjustments) and the GHCNv3 (homogenised data) for 25 stations in Southern South America. The yellow cells are where V2 data exist V3 not; the greens cells V3 data exist where V2 data do not.



Definition of temperature homogenisation

A more general definition that encompasses the GHCN / GISS adjustments is of broadly making the
data homogenous. It is not done by simply blending the data together and smoothing out the data. Homogenisation also adjusts anomalous data as a result of pairwise comparisons between local temperature stations, or in the case of extreme differences in the GHCN / GISS deletes the most anomalous data. This is a much looser and broader process than homogenisation of milk, or putting some food through a blender.

The definition I cover in more depth in the appendix.



The Consequences of Making Data Homogeneous

A consequence of cleansing the data in order to make it more homogenous gives a distinction that is missed by many. This is due to making the strong assumption that there are no climatic differences between the temperature stations in the homogenisation area.

Homogenisation is aimed at adjusting for the measurement biases to give a climatic reading for the location where the temperature station is located that is a closer approximation to what that reading would be without those biases. With the strong assumption, making the data homogenous is identical to removing the non-climatic inhomogeneities. Cleansed of these measurement biases the temperature data is then both the average temperature readings that would have been generated if the temperature station had been free of biases and a representative location for the area. This latter aspect is necessary to build up a global temperature anomaly, which is constructed through dividing the surface into a grid. Homogenisation, in the sense of making the data more homogenous by blending is an inappropriate term. All what is happening is adjusting for anomalies within the through comparisons with local temperature stations (the GHCN / GISS method) or comparisons with an expected regional average (the Berkeley Earth method).


But if the strong assumption does not hold, homogenisation will adjust these climate differences, and will to some extent fail to eliminate the measurement biases. Homogenisation is in fact made more necessary if movements in average temperatures are not the same and the spread of temperature data is spatially uneven. Then homogenisation needs to not only remove the anomalous data, but also make specific locations more representative of the surrounding area. This enables any imposed grid structure to create an estimated average for that area through averaging the homogenized temperature data sets within the grid area. As a consequence, the homogenised data for a temperature station will cease to be a closer approximation to what the thermometers would have read free of any measurement biases. As homogenisation is calculated by comparisons of temperature stations beyond those immediately adjacent, there will be, to some extent, influences of climatic changes beyond the local temperature stations. The consequences of climatic differences within the homogenisation area include the following.


  • The homogenised temperature data for a location could appear largely unrelated to the original data or to the data adjusted for known biases. This could explain the homogenised Reykjavik temperature, where Trausti Jonsson of the Icelandic Met Office, who had been working with the data for decades, could not understand the GHCN/GISS adjustments7.
  • The greater the density of temperature stations in relation to the climatic variations, the less that climatic variations will impact on the homogenisations, and the greater will be the removal of actual measurement biases. Climate variations are unlikely to be much of an issue with the Western European and United States data. But on the vast majority of the earth’s surface, whether land or sea, coverage is much sparser.
  • If the climatic variation at a location is of different magnitude to that of other locations in the homogenisation area, but over the same time periods and direction, then the data trends will be largely retained. For instance, in Svarlbard the warming temperature trends of the early twentieth century and from the late 1970s were much greater than elsewhere, so were adjusted downwards8.
  • If there are differences in the rate of temperature change, or the time periods for similar changes, then any “anomalous” data due to climatic differences at the location will be eliminated or severely adjusted, on the same basis as “anomalous” data due to measurement biases. For instance in large part of Paraguay at the end of the 1960s average temperatures by around 1oC. Due to this phenomena not occurring in the surrounding areas both the GHCN and Berkeley Earth homogenisation processes adjusted out this trend. As a consequence of this adjustment, a mid-twentieth century cooling in the area was effectively adjusted to out of the data9.
  • If a large proportion of temperature stations in a particular area have consistent measurement biases, then homogenisation will retain those biases, as it will not appear anomalous within the data. For instance, much of the extreme warming post 1950 in South Korea is likely to have been as a result of urbanization10.


Other Comments

Homogenisation is just part of the process of adjusting data for the twin purposes of attempting to correct for biases and building a regional and global temperature anomalies. It cannot, for instance, correct for time of observation biases (TOBS). This needs to be done prior to homogenisation. Neither will homogenisation build a global temperature anomaly. Extrapolating from the limited data coverage is a further process, whether for fixed temperature stations on land or the ship measurements used to calculate the ocean surface temperature anomalies. This extrapolation has further difficulties. For instance, in a previous post11 I covered a potential issue with the Gistemp proxy data for Antarctica prior to permanent bases being established on the continent in the 1950s. Making the data homogenous is but the middle part of a wider process.

Homogenisation is a complex process. The Venema et al 20123 paper on the benchmarking of homogenisation algorithms demonstrates that different algorithms produce significantly different results. What is clear from the original posts on the subject by Paul Homewood and the more detailed studies by Euan Mearns and Roger Andrews at Energy Matters, is that the whole process of going from the raw monthly temperature readings to the final global land surface average trends has thrown up some peculiarities. In order to determine whether they are isolated instances that have near zero impact on the overall picture, or point to more systematic biases that result from the points made above, it is necessary to understand the data available in relation to the overall global picture. That will be the subject of my next post.


Kevin Marshall



  1. GUIDELINES ON CLIMATE METADATA AND HOMOGENIZATION by Enric Aguilar, Inge Auer, Manola Brunet, Thomas C. Peterson and Jon Wieringa
  2. Steven Mosher – Guest post : Skeptics demand adjustments 09.02.2015
  3. 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.
  4. …and Then There’s Physics – Temperature homogenisation 01.02.2015
  5. See my post Temperature Homogenization at Puerto Casado 03.05.2015
  6. For example

    The Hunt For Global Warming: Southern Hemisphere Summary

    Record Arctic Warmth – in 1937

  7. See my post Reykjavik Temperature Adjustments – a comparison 23.02.2015
  8. See my post RealClimate’s Mis-directions on Arctic Temperatures 03.03.2015
  9. See my post Is there a Homogenisation Bias in Paraguay’s Temperature Data? 02.08.2015
  10. NOT A LOT OF PEOPLE KNOW THAT (Paul Homewood) – UHI In South Korea Ignored By GISS 14.02.2015



Appendix – Definition of Temperature Homogenisation

When discussing temperature homogenisations, nobody asks what the term actual means. In my house we consume homogenised milk. This is the same as the pasteurized milk I drank as a child except for one aspect. As a child I used to compete with my siblings to be the first to open a new pint bottle, as it had the cream on top. The milk now does not have this cream, as it is blended in, or homogenized, with the rest of the milk. Temperature homogenizations are different, involving changes to figures, along with (at least with the GHCN/GISS data) filling the gaps in some places and removing data in others1.

But rather than note the differences, it is better to consult an authoritative source. From, the definitions of homogenize are:-

verb (used with object), homogenized, homogenizing.

  1. to form by blending unlike elements; make homogeneous.
  2. to prepare an emulsion, as by reducing the size of the fat globules in (milk or cream) in order to distribute them equally throughout.
  3. to make uniform or similar, as in composition or function:

    to homogenize school systems.

  4. Metallurgy. to subject (metal) to high temperature to ensure uniform diffusion of components.

Applying the dictionary definitions, data homogenization in science is not about blending various elements together, nor about additions or subtractions from the data set, or adjusting the data. This is particularly true in chemistry.

For UHCN and NASA GISS temperature data homogenization involves removing or adjusting elements in the data that are markedly dissimilar from the rest. It can also mean infilling data that was never measured. The verb homogenize does not fit the processes at work here. This has led to some, like Paul Homewood, to refer to the process as data tampering or worse. A better idea is to look further at the dictionary.

Again from, the first two definitions of the adjective homogeneous are:-

  1. composed of parts or elements that are all of the same kind; not heterogeneous:

a homogeneous population.

  1. of the same kind or nature; essentially alike.

I would suggest that temperature homogenization is a loose term for describing the process of making the data more homogeneous. That is for smoothing out the data in some way. A false analogy is when I make a vegetable soup. After cooking I end up with a stock containing lumps of potato, carrot, leeks etc. I put it through the blender to get an even constituency. I end up with the same weight of soup before and after. A similar process of getting the same after homogenization as before is clearly not what is happening to temperatures. The aim of making the data homogenous is both to remove anomalous data and blend the data together.



Ivanpah Solar Project Still Failing to Achieve Potential

Paul Homewood yesterday referred to a Marketwatch report titled “High-tech solar projects fail to deliver.” This was reposted at Tallbloke.

Marketwatch looks at the Ivanpah solar project. They comment

The $2.2 billion Ivanpah solar power project in California’s Mojave Desert is supposed to be generating more than a million megawatt-hours of electricity each year. But 15 months after starting up, the plant is producing just 40% of that, according to data from the U.S. Energy Department.

I looked at the Ivanpah solar project last fall, when the investors applied for a $539million federal grant to help pay off a $1.5 billion federal loan. One of the largest investors was Google, who at the end of 2013 had Cash, Cash Equivalents & Marketable Securities of $58,717million, $10,000million than the year before.

Technologically the Ivanpah plant seems impressive. It is worth taking a look at the website.

That might have been the problem. The original projections were for 1065,000 MWh annually from a 392 MW nameplate implying a planned output of 31% of capacity. When I look at the costings on Which? for solar panels on the roof of a house, they assume just under 10% of capacity. Another site, Wind and Sun UK, say

1 kWp of well sited PV array in the UK will produce 700-800 kWh of electricity per year.

That is around 8-9.5% of capacity. Even considering the technological superiority of the project and the climatic differences, three times is a bit steep, although 12.5% (40% of 31%) is very low. From Marketwatch some of the difference is can be explained by

  • Complex equipment constantly breaking down
  • Optimization of complex new technologies
  • Steam pipes leaking due to vibrations
  • Generating the initial steam takes longer than expected
  • It is cloudier than expected

However, even all of this cannot account for the output only being at 40% of expected. With the strong sun of the desert I would expect daily output to never exceed 40% of theoretical, as it is only daylight for 50% of the time, and just after sunrise and before sunset the sun is less strong than at midday. As well as the teething problems with complex technology, it appears that the engineers were over optimistic. A lack of due diligence in appraising the scheme – a factor common to many large scale Government backed initiatives – will have let the engineers have the finance for a fully scaled-up version of what should have been a small-scale project to prove the technology.


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.

Temperature Homogenization at Puerto Casado


The temperature homogenizations for the Paraguay data within both the BEST and UHCN/Gistemp surface temperature data sets points to a potential flaw within the temperature homogenization process. It removes real, but localized, temperature variations, creating incorrect temperature trends. In the case of Paraguay from 1955 to 1980, a cooling trend is turned into a warming trend. Whether this biases the overall temperature anomalies, or our understanding of climate variation, remains to be explored.


A small place in Mid-Paraguay, on the Brazil/Paraguay border has become the centre of focus of the argument on temperature homogenizations.

For instance here is Dr Kevin Cowtan, of the Department of Chemistry at the University of York, explaining the BEST adjustments at Puerto Casado.

Cowtan explains at 6.40

In a previous video we looked at a station in Paraguay, Puerto Casado. Here is the Berkeley Earth data for that station. Again the difference between the station record and the regional average shows very clear jumps. In this case there are documented station moves corresponding to the two jumps. There may be another small change here that wasn’t picked up. The picture for this station is actually fairly clear.

The first of these “jumps” was a fall in the late 1960s of about 1oC. Figure 1 expands the section of the Berkeley Earth graph from the video, to emphasise this change.

Figure 1 – Berkeley Earth Temperature Anomaly graph for Puerto Casado, with expanded section showing the fall in temperature and against the estimated mean station bias.

The station move is after the fall in temperature.

Shub Niggareth looked at the metadata on the actual station move concluding


That is the evidence of the station move was vague. The major evidence was the fall in temperatures. Alternative evidence is that there were a number of other stations in the area exhibiting similar patterns.

But maybe there was some, unknown, measurement bias (to use Steven Mosher’s term) that would make this data stand out from the rest? I have previously looked eight temperature stations in Paraguay with respect to the NASA Gistemp and UHCN adjustments. The BEST adjustments for the stations, along another in Paul Homewood’s original post, are summarized in Figure 2 for the late 1960s and early 1970s. All eight have similar downward adjustment that I estimate as being between 0.8 to 1.2oC. The first six have a single adjustment. Asuncion Airport and San Juan Bautista have multiple adjustments in the period. Pedro Juan CA was of very poor data quality due to many gaps (see GHCNv2 graph of the raw data) hence the reason for exclusion.


GHCN Location


Break Type

Break Year



23.4 S,57.3 W






27.3 S,55.8 W






22.0 S,60.6 W






26.9 S,58.3 W





Puerto Casado

22.3 S,57.9 W


Station Move



San Juan Baut

26.7 S,57.1 W





Asuncion Aero

25.3 S,57.6 W








Station Move






Station Move



San Juan Bautista

25.8 S,56.3 W














Station Move



Pedro Juan CA

22.6 S,55.6 W









3 in 1970s


Figure 2 – Temperature stations used in previous post on Paraguayan Temperature Homogenisations


Why would both BEST and UHCN remove a consistent pattern covering and area of around 200,000 km2? The first reason, as Roger Andrews has found, the temperature fall was confined to Paraguay. The second reason is suggested by the UHCNv2 raw data1 shown in figure 3.

Figure 3 – UHCNv2 “raw data” mean annual temperature anomalies for eight Paraguayan temperature stations, with mean of 1970-1979=0.

There was an average temperature fall across these eight temperature stations of about half a degree from 1967 to 1970, and over one degree by the mid-1970s. But it was not at the same time. The consistency is only show by the periods before and after as the data sets do not diverge. Any homogenisation program would see that for each year or month for every data set, the readings were out of line with all the other data sets. Now maybe it was simply data noise, or maybe there is some unknown change, but it is clearly present in the data. But temperature homogenisation should just smooth this out. Instead it cools the past. Figure 4 shows the impact average change resulting from the UHCN and NASA GISS homogenisations.

Figure 4 – UHCNv2 “raw data” and NASA GISS Homogenized average temperature anomalies, with the net adjustment.

A cooling trend for the period 1955-1980 has been turned into a warming trend due to the flaw in homogenization procedures.

The Paraguayan data on its own does not impact on the global land surface temperature as it is a tiny area. Further it might be an isolated incident or offset by incidences of understating the warming trend. But what if there are smaller micro climates that are only picked up by one or two temperature stations? Consider figure 5 which looks at the BEST adjustments for Encarnacion, one of the eight Paraguayan stations.

Figure 5 – BEST adjustment for Encarnacion.

There is the empirical break in 1968 from the table above, but also empirical breaks in the 1981 and 1991 that look to be exactly opposite. What Berkeley earth call the “estimated station mean bias” is as a result of actual deviations in the real data. Homogenisation eliminates much of the richness and diversity in the real world data. The question is whether this happens consistently. First we need to understand the term “temperature homogenization“.

Kevin Marshall


  1. The UHCNv2 “raw” data is more accurately pre-homogenized data. That is the raw data with some adjustments.

ATTP on Lomborg’s Australian Funding

Blogger …and then there’s physics (ATTP) joins in the hullabaloo about Bjorn Lomberg’s Lomborg’s Consensus Centre is getting A$4m of funding to set up a branch at the University of Western Australia. He says

However, ignoring that Lomborg appears to have a rather tenuous grasp on the basics of climate science, my main issue with what he says is its simplicity. Take all the problems in the world, determine some kind of priority ordering, and then start at the top and work your way down – climate change, obviously, being well down the list. It’s as if Lomborg doesn’t realise that the world is a complex place and that many of the problems we face are related. We can’t necessarily solve something if we don’t also try to address many of the other issues at the same time. It’s this kind of simplistic linear thinking – and that some seem to take it seriously – that irritates me most.

The comment about climatology is just a lead in. ATTP is expressing a normative view about the interrelationship of problems, along with beliefs about the solution. What he is rejecting as simplistic is the method of identifying the interrelated issues separately, understanding the relative size of the problems along with the effectiveness and availability of possible solutions and then prioritizing them.

This this errant notion is exacerbated when ATTP implies that Lomborg has received the funding. Lomborg heads up the Copenhagen Consensus Centre and it is they who have received the funding to set up a branch in Australia. This description is from their website

We work with some of the world’s top economists (including 7 Nobel Laureates) to research and publish the smartest solutions to global challenges. Through social, economic and environmental benefit-cost research, we show policymakers and philanthropists how to do the most good for each dollar spent.

It is about bringing together some of the best minds available to understand the problems of the world. It is then to persuade those who are able to do something about the issues. It is not Lomborg’s personal views that are present here, but people with different views and from different specialisms coming together to argue and debate. Anyone who has properly studied economics will soon learn that there are a whole range of different views, many of them plausible. Some glimpse that economic systems are highly interrelated in ways that cannot be remotely specified, leading to the conclusion that any attempt to create a computer model of an economic system will be a highly distorted simplification. At a more basic level they will have learnt that in the real world there are 200 separate countries, all with different priorities. In many there is a whole range of different voiced opinions about what the priorities should be at national, regional and local levels. To address all these interrelated issues together would require the modeller of be omniscient and omnipresent. To actually enact the modeller’s preferred policies over seven billion people would require a level of omnipotence that Stalin could only dream of.

This lack of understanding of economics and policy making is symptomatic of those who believe in climate science. They fail to realize that models are only an attempted abstraction of the real world. Academic economists have long recognized the abstract nature of the subject along with the presence of strong beliefs about the subject. As a result, in the last century many drew upon the rapidly developing philosophy of science to distinguish whether theories were imparting knowledge about the world or confirming beliefs. The most influential by some distance was Milton Friedman. In his seminal essay The Methodology of Positive Economics he suggested the way round this problem was to develop bold yet simple predictions from the theory that, despite being unlikely, are nevertheless come true. I would suggest that you do not need to be too dogmatic in the application. The bold predictions do not need to be right 100% of the time, but an entire research programme should be establishing a good track record over a sustained period. In climatology the bold predictions, that would show a large and increasing problem, have been almost uniformly wrong. For instance:-

  • The rate of melting of the polar ice caps has not accelerated.
  • The rate of sea level rise has not accelerated in the era of satellite measurements.
  • Arctic sea ice did not disappear in the summer of 2013.
  • Hurricanes did not get worse following Katrina. Instead there followed the quietest period on record.
  • Snow has not become a thing of the past in England, nor in Germany.

Other examples have been compiled by Pierre Gosselin at Notrickszone, as part of his list of climate scandals.

Maybe it is different in climatology. The standard response is that the reliability of the models is based on the strength of the consensus in support. This view is not proclaimed by ATTP. Instead from the name it would appear he believes the reliability can be obtained from the basic physics. I have not done any physics since high school and have forgotten most of what I learnt. So in discerning what is reality in that area I have to rely on the opinions of physicists themselves. One of the greatest physicists since Einstein was Richard Feynman. He said fifty years ago in a lecture on the Scientific Method

You cannot prove a vague theory wrong. If the guess that you make is poorly expressed and the method you have for computing the consequences is a little vague then ….. you see that the theory is good as it can’t be proved wrong. If the process of computing the consequences is indefinite, then with a little skill any experimental result can be made to look like an expected consequence.

Climate models, like economic models, will always be vague. This is not due to being poorly expressed (though they often are) but due to the nature of the subject. Short of rejecting climate models as utter nonsense, I would suggest the major way of evaluating whether they say something distinctive about the real world is on the predictive ability. But a consequence of theories always being vague in both economics and climate is you will not be able to use the models as a forecasting tool. As Freeman Dyson (who narrowly missed sharing a Nobel Prize with Feynman) recently said of climate models:-

These climate models are excellent tools for understanding climate, but that they are very bad tools for predicting climate. The reason is simple – that they are models which have very few of the factors that may be important, so you can vary one thing at a time ……. to see what happens – particularly carbon dioxide. But there are a whole lot of things that they leave out. ….. The real world is far more complicated than the models.

This implies that when ATTP is criticizing somebody else’s work with a simple model, or a third person’s work, he is likely criticizing them for looking at a highly complex issue in another way. Whether his way is better, worse or just different we have no way of knowing. All we can infer from his total rejection of ideas of experts in a field to which he lacks even a basic understanding, is that he has no basis of knowing either.

To be fair, I have not looked at the earlier part of ATTP’s article. For instance he says:-

If you want to read a defense of Lomborg, you could read Roger Pielke Jr’s. Roger’s article makes the perfectly reasonable suggestion that we shouldn’t demonise academics, but fails to acknowledge that Lomborg is not an academic by any standard definition…….

The place to look for a “standard definition” of a word is a dictionary. The noun definitions are


8. a student or teacher at a college or university.

9. a person who is academic in background, attitudes, methods, etc.:

He was by temperament an academic, concerned with books and the arts.

10. (initial capital letter) a person who supports or advocates the Platonic school of philosophy.

This is Bjorn Lomborg’s biography from the Copenhagen Consensus website:-

Dr. Bjorn Lomborg is Director of the Copenhagen Consensus Center and Adjunct Professor at University of Western Australia and Visiting Professor at Copenhagen Business School. He researches the smartest ways to help the world, for which he was named one of TIME magazine’s 100 most influential people in the world. His numerous books include The Skeptical Environmentalist, Cool It, How to Spend $75 Billion to Make the World a Better Place and The Nobel Laureates’ Guide to the Smartest Targets for the World 2016-2030.

Lomborg meets both definitions 8 & 9, which seem to be pretty standard. Like with John Cook and William Connolley defining the word sceptic, it would appear that ATTP rejects the authority of those who write the dictionary. Or more accurately does not even to bother to look. Like with rejecting the authority of those who understand economics it suggests ATTP uses the authority of his own dogmatic beliefs as the standard by which to evaluate others.

Kevin Marshall

Royal Baby Names to Save the United Kingdom

In a complete break from my normal posts on climate, after discussions with my daughter, I am going to speculate on Royal baby names.

In less than two weeks’ time there will be a General Election. Given that the betting is that a Labour/ Scots Nat Coalition is likely there is a strong possibility that the incoming Government could lead to the breakup of the United Kingdom. The Scottish Nationalists have very similar left-of-centre policies , there is which will in turn lead to the breakup of the United Kingdom. The Royal Family, particularly The Queen, firmly believes in the United Kingdom, has long been proud of its Scottish routes (the Queen is half Scots), but at the same time does not directly intercede in politics, except in the most tangential ways. Naming of a Royal Baby who is fourth in line to the throne is one of the few methods open to the Royal Family of sending a political message. The naming cannot offend the Scots, but at the same time will satisfy the far more numerous English. It must also be a name seen to be reasonably modern, but also in keeping with royal traditions.

If the Katherine, Duchess of Cambridge gives birth to a baby boy there will be a number of names that could be chosen. Prince George Alexander Louis set a precedent. George is the names of six British Monarchs, but identified as very English. It was in the reign of George II (1727-1760) for instance that the Young Pretender Bonnie Prince Charlie was finally defeated at Culloden in 1745. The second name, whilst being highly international was also the name of three medieval Scottish Kings, and the Gaelic form is Alistair. Louis is from Prince William’s great uncle Lord Louis Mountbatten, who was very close to the Prince of Wales. There was both traditional English and Scottish elements in the name, without seeming too old fashioned. Alexander has already been used, so is counted out. Some names of Scottish Kings cannot be considered. “Kenneth” and “Duncan” are very old fashioned. Macbeth was trashed as a plausible name by William Shakespeare. Lulach, Amlaíb, Cuilén, Dub and Indulf as too lost in time to inflict on any child, and would need explaining. This leaves James, David and Robert. In Scotland James is currently fourth most popular, behind Jack, Lewis and Riley. At Befair it is the most popular Boys’ name. So this might be a strong contender. However, the Royal Family will want to make an imprint less than two weeks before a General Election that could destroy the United Kingdom that the Queen pledged to defend. James is both Scottish and English. We have the King James Bible of 1611 that helped unite the factions in the Church of England for a while. But King James VI of Scotland (and James I of England) was an anomaly. He was a strong Scottish Presbyterian, who in commissioning this great work sought to bring together both the Puritan and Catholic elements of the Anglican Church. His grandson, James II almost caused a second Civil War through his Catholic tendencies, resulting in the current inability of the heir to the throne to marry a Roman Catholic. The betting markets, along with my daughter, may favour such a name, but the Queen may advise against.

So what is an appropriate boys’ name for a possible (but unlikely) future monarch, whose only role may be to save the Union by being born?

There are two courses that the Royal Family may take. I believe that they will take the safe course, and call the boy David. It has both strong Scottish routes, and David is the patron saint of Wales. But the option to save the United Kingdom is Robert. On 13th June 1214, Robert the Bruce defeated the English forces of Edward I (“Hammer of the Scots”) at Bannockburn near Stirling. Less than 150 years earlier William of Normandy had defeated the Anglo Saxon (English) at the Battle of Hastings. Although “Edward” was Anglo Saxon in origin the “English Kings” still spoke French at Court. Most fighting on the side of Edward could as little understand their Sovereigns’ words as the Gaelic-speaking Scots. If Robert is chosen, a second name cannot be Edward. But the older Anglo Saxon form of Edward (and still used today) is Edmund. How better for the Royal Family to remember the subjected of 800 years ago, whilst uniting both the downtrodden of both Scotland and England, whilst reconciling ancient enmities, whilst remembering the ancient Kings of both countries. A third name could be David, or one than avers to the Irish, such as Kevin J

Girls names are more difficult. The most famous Scottish girls name is Margaret, and until the 1960s was easily the most popular name. I have an Auntie Margaret and have fond memories of my Great Aunt Margaret, and had (by all accounts) a formidable Great Grandmother Margaret Ross, who died at the age of 93 when I was 3 years old. Many will remember the Queen’s Sister, Princess Margaret. But the name is now not in the top 100 of girl names in Scotland, and (due to the Royal connection) will not be viewed as particularly Scottish. In left-of-centre Scottish minds, it is also the Christian name of one of twentieth century’s greatest Prime Ministers.

There are not many Queens of Scotland. The most famous is Mary Queen of Scots, who, being a French-speaking Catholic, was hardly a figurehead for an increasingly Presbyterian Scotland of the time, nor for the a British Monarchy who has defended the middle-of-the-road Anglican Communion for well over 300 hundred years. Scottish Queens consorts were undistinguished and with names such as Maud, Joan, Sybilla, Ethelreda and Grouoch are hardly able to capture the imagination of the Scottish public. Margaret is again the most popular name, followed by Elizabeth. Looking at current most popular Scottish Girls names in 2012, they are Sophie followed by Emily, Olivia, Ava and Lucy. Hardly Royal, and not much different from England. A statement cannot be easily be made. The last truly Scottish Royal was Queen Elizabeth the Queen Mother, so Elizabeth might be a family option. The policy might be to play safe, or in a thorough break with tradition, let the parents decide.

Kevin Marshall

Declaration of Interest

I was born and bred in England, but my Mother is, and three of my grandparents were, Scottish. I named my son Edmund Alexander. The latter name was after a Great Grandfather and an Uncle who was always known as Alistair. I consider myself British, and am proud of both my Derbyshire and North Scottish ancestry.



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