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.



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.

RealClimate’s Mis-directions on Arctic Temperatures


Real Climate attempted to rebut the claims that the GISS temperature data is corrupted with unjustified adjustments by

  • Attacking the commentary of Christopher Booker, not the primary source of the allegations.
  • Referring readers instead to a dogmatic source who claims that only 3 stations are affected, something clearly contradicted by Booker and the primary source.
  • Alleging that the complaints are solely about cooling the past, uses a single counter example for Svarlbard of a GISS adjustment excessively warming the past compared to the author’s own adjustments.
  • However, compared to the raw data, the author’s adjustments, based on local knowledge were smaller than GISS, showing the GISS adjustments to be unjustified. But the adjustments bring the massive warming trend into line with (the still large) Reykjavik trend.
  • Examination of the site reveals that the Stevenson screen at Svarlbard airport is right beside the tarmac of the runway, with the heat from planes and the heat from snow-clearing likely affecting measurements. With increasing use of the airport over the last twenty years, it is likely the raw data trend should be reduced, but at an increasing adjustment trend, not decreasing.
  • Further, data from a nearby temperature station at Isfjord Radio reveals that the early twentieth century warming on Spitzbergen may have been more rapid and of greater magnitude. GISS Adjustments reduce that trend by up to 4 degrees, compared with just 1.7 degrees for the late twentieth century warming.
  • Questions arise how raw data for Isfjord Radio could be available for 22 years before the station was established, and how the weather station managed to keep on recording “raw data” between the weather station being destroyed and abandoned in 1941 and being re-opened in 1946.


In climate I am used to mis-directions and turning, but in this post I may have found the largest temperature adjustments to date.

In early February, RealClimate – the blog of the climate science consensus – had an article attacking Christopher Booker in the Telegraph. It had strong similarities the methods used by anonymous blogger ….andthentheresphysics. In a previous post I provided a diagram to illustrate ATTP’s methods.

One would expect that a blog supported by the core of the climate scientific consensus would provide a superior defence than an anonymous blogger who censors views that challenge his beliefs. However, RealClimate may have dug an even deeper hole. Paul Homewood covered the article on February 12th, but I feel it only scratched the surface. Using the procedures outlined above I note similarities include:-

  • Attacking the secondary commentary, and not mentioning the primary sources.
  • Misleading statements that understate the extent of the problem.
  • Avoiding comparison of the raw and adjusted data.
  • Single counter examples that do not stand up.

Attacking the secondary commentary

Like ATTP, RealClimate attacked the same secondary source – Christopher Booker – but another article. True academics would have referred Paul Homewood, the source of the allegations.

Misleading statement about number of weather stations

The article referred to was by Victor Venema of Variable Variability. The revised title is “Climatologists have manipulated data to REDUCE global warming“, but the original title can be found from the link address –

It was published on 10th February and only refers to Christopher Booker’s original article in the Telegraph article of 24th January without mentioning the author or linking. After quoting from the article Venema states:-

Three, I repeat: 3 stations. For comparison, global temperature collections contain thousands of stations. ……

Booker’s follow-up article of 7th February states:-

Following my last article, Homewood checked a swathe of other South American weather stations around the original three. ……

Homewood has now turned his attention to the weather stations across much of the Arctic, between Canada (51 degrees W) and the heart of Siberia (87 degrees E). Again, in nearly every case, the same one-way adjustments have been made, to show warming up to 1 degree C or more higher than was indicated by the data that was actually recorded.

My diagram above was published on the 8th February, and counted 29 stations. Paul Homewood’s original article on the Arctic of 4th February lists 19 adjusted sites. If RealClimate had actually read the cited article, they would have known that quotation was false in connection to the Arctic. Any undergraduate who made this mistake in an essay would be failed.

Misleading Counter-arguments

Øyvind Nordli – the Real Climate author – provides a counter example from his own research. He compares his adjustments of the Svarlbard, (which is did as part of temperature reconstruction for Spitzbergen last year) with those of GISS.

Clearly he is right in pointing out that his adjustments created a lower warming trend than those of GISS.

I checked the “raw data” with the “GISS Homogenised” for Svarlbard and compare with the Reykjavik data I looked at last week, as the raw data is not part of the comparison. To make them comparable, I created anomalies based on the raw data average of 2000-2009. I have also used a 5 year centred moving average.

The raw data is in dark, the adjusted data in light. For Reykjavik prior to 1970 the peaks in the data have been clearly constrained, making the warming since 1980 appear far more significant. For the much shorter Svarlbard data the total adjustments from GHCN and GISS reduce the warming trend by a full 1.7oC, bringing the warming trend into line with the largely unadjusted Reykjavik. The GHCN & GISS seem to be adjusted to a pre-conceived view of what the data should look like. What Nordli et. al have effectively done is to restore the trend present in the raw data. So Nordli et al, using data on the ground, has effectively reached a similar to conclusion to Trausti Jonsson of the Iceland Met Office. The adjustments made thousands of miles away in the United States by homogenisation bots are massive and unjustified. It just so happens that in this case it is in the opposite direction to cooling the past. I find it somewhat odd Øyvind Nordli, an expert on local conditions, should not challenge these adjustments but choose to give the opposite impression.

What is even worse is that there might be a legitimate reason to adjust downwards the recent warming. In 2010, Anthony Watts looked at the citing of the weather station at Svarlbard Airport. Photographs show it to right beside the runway. With frequent snow, steam de-icers will regularly pass, along with planes with hot exhausts. The case is there for a downward adjustment over the whole of the series, with an increasing trend to reflect the increasing aircraft movements. Tourism quintupled between 1991 and 2008. In addition, the University Centre in Svarlbad founded in 1993 now has 500 students.

Older data for Spitzbergen

Maybe the phenomenal warming in the raw data for Svarlbard is unprecedented, despite some doubts about the adjustments. Nordli et al 2014 is titled Long-term temperature trends and variability on Spitsbergen: the extended Svalbard Airport temperature series, 1898-2012. Is a study that gathers together all the available data from Spitzbergen, aiming to create a composite temperature record from fragmentary records from a number of places around the Islands. From NASA GISS, I can only find Isfjord Radio for the earlier period. It is about 50km west of Svarlbard, so should give a similar shape of temperature anomaly. According to Nordli et al

Isfjord Radio. The station was established on 1 September 1934 and situated on Kapp Linne´ at the mouth of Isfjorden (Fig. 1). It was destroyed by actions of war in September 1941 but re-established at the same place in July 1946. From 30 June 1976 onwards, the station was no longer used for climatological purposes.

But NASA GISS has data from 1912, twenty-two years prior to the station citing, as does Berkeley Earth. I calculated a relative anomaly to Reykjavik based on 1930-1939 averages, and added the Isfjord Radio figures to the graph.

The portion of the raw data for Isfjord Radio, which seems to have been recorded before any thermometer was available, shows a full 5oC rise in the 5 year moving average temperature. The anomaly for 1917 was -7.8oC, compared with 0.6 oC in 1934 and 1.0 oC in 1938. For Svarlbard Airport lowest anomalies are -4.5 oC in 1976 and -4.7 oC in 1988. The peak year is 2.4 oC in 2006, followed by 1.5 oC in 2007. The total GHCNv3 and GISS adjustments are also of a different order. At the start of the Svarlbard series every month was adjusted up by 1.7. The Isfjord Radio 1917 data was adjusted up by 4.0 oC on average, and 1918 by 3.5 oC. February of 1916 & 1918 have been adjusted upwards by 5.4 oC.

So the Spitzbergen warming the trough to peak warming of 1917 to 1934 may have been more rapid and greater than in magnitude that the similar warming from 1976 to 2006. But from the adjusted data one gets the opposite conclusion.

Also we find from Nordli at al

During the Second World War, and also during five winters in the period 18981911, no observations were made in Svalbard, so the only possibility for filling data gaps is by interpolation.

The latest any data recording could have been made was mid-1941, and the island was not reoccupied for peaceful purposes until 1946. The “raw” GHCN data is actually infill. If it followed the pattern of Reykjavik – likely the nearest recording station – temperatures would have peaked during the Second World War, not fallen.


Real Climate should review their articles better. You cannot rebut an enlarging problem by referring to out-of-date and dogmatic sources. You cannot pretend that unjustified temperature adjustments in one direction are somehow made right by unjustified temperature adjustments in another direction. Spitzbergen is not only cold, it clearly experiences vast and rapid fluctuations in average temperatures. Any trend is tiny compared to these fluctuations.

Is there a Homogenisation Bias in Paraguay’s Temperature Data?

Last month Paul Homewood at Notalotofpeopleknowthat looked at the temperature data for Paraguay. His original aim was to explain the GISS claims of 2014 being the hottest year.

One of the regions that has contributed to GISS’ “hottest ever year” is South America, particularly Brazil, Paraguay and the northern part of Argentina. In reality, much of this is fabricated, as they have no stations anywhere near much of this area…

….there does appear to be a warm patch covering Paraguay and its close environs. However, when we look more closely, we find things are not quite as they seem.

In “Massive Tampering With Temperatures In South America“, Homewood looked at the “three genuinely rural stations in Paraguay that are currently operating – Puerto Casado, Mariscal and San Juan.” A few days later in “All Of Paraguay’s Temperature Record Has Been Tampered With“, he looked at remaining six stations.

After identifying that all of the three rural stations currently operational in Paraguay had had huge warming adjustments made to their data since the 1950’s, I tended to assume that they had been homogenised against some of the nearby urban stations. Ones like Asuncion Airport, which shows steady warming since the mid 20thC. When I went back to check the raw data, it turns out all of the urban sites had been tampered with in just the same way as the rural ones.

What Homewood does not do is to check the data behind the graphs, to quantify the extent of the adjustment. This is the aim of the current post.

Warning – This post includes a lot of graphs to explain how I obtained my results.

Homewood uses comparisons of two graphs, which he helpful provides the links to. The raw GHCN data + UHSHCN corrections is available here up until 2011 only. The current after GISS homogeneity adjustment data is available here.

For all nine data sets that I downloaded both the raw and homogenised data. By simple subtraction I found the differences. In any one year, they are mostly the same for each month. But for clarity I selected a single month – October – the month of my wife’s birthday.

For the Encarnacion (27.3 S,55.8 W) data sets the adjustments are as follows.

In 1967 the adjustment was -1.3C, in 1968 +0.1C. There is cooling of the past.

The average adjustments for all nine data sets is as follows.

This pattern is broadly consistent across all data sets. These are the maximum and minimum adjustments.

However, this issue is clouded by the special adjustments required for the Pedro Juan CA data set. The raw data set has been patched from four separate files,

Removing does not affect the average picture.

But does affect the maximum and minimum adjustments. This is shows the consistency in the adjustment pattern.

The data sets are incomplete. Before 1941 there is only one data set – Ascuncion Aero. The count for October each year is as follows.

In recent years there are huge gaps in the data, but for the late 1960s when the massive switch in adjustments took place, there are six or seven pairs of raw and adjusted data.

Paul Homewood’s allegation that the past has been cooled is confirmed. However, it does not give a full understanding of the impact on the reported data. To assist, for the full year mean data, I have created temperature anomalies based on the average anomaly in that year.

The raw data shows a significant cooling of up to 1oC in the late 1960s. If anything there has been over-compensation in the adjustments. Since 1970, any warming in the adjusted data has been through further adjustments.

Is this evidence of a conspiracy to “hide a decline” in Paraguayan temperatures? I think not. My alternative hypothesis is that this decline, consistent over a number of thermometers is unexpected. Anybody looking at just one of these data sets recently, would assume that the step change in 40-year-old data from a distant third world country is bound to be incorrect. (Shub has a valid point) That change goes against the known warming trend for over a century from the global temperature data sets and the near stationary temperatures from 1950-1975. More importantly cooling goes against the “known” major driver of temperature recent change – rises in greenhouse gas levels. Do you trust some likely ropey instrument data, or trust your accumulated knowledge of the world? The clear answer is that the instruments are wrong. Homogenisation is then not to local instruments in the surrounding areas, but to the established expert wisdom of the world. The consequent adjustment cools past temperatures by one degree. The twentieth century warming is enhanced as a consequence of not believing what the instruments are telling you. The problem is that this step change is replicated over a number of stations. Paul Homewood had shown that it probably extends into Bolivia as well.

But what happens if the converse happens? What if there is a step rise in some ropey data set from the 1970s and 1980s? This might be large, but not inconsitent with what is known about the world. It is unlikely to be adjusted downwards. So if there have been local or regional step changes in average temperature over time both up and down, the impact will be to increase the rate of warming if the data analysts believe that the world is warming and human beings are the cause of it.

Further analysis is required to determine the extent of the problem – but not from this unpaid blogger giving up my weekends and evenings.

Kevin Marshall

All first time comments are moderated. Please also use the comments as a point of contact, stating clearly that this is the case and I will not click the publish button, subject to it not being abusive. I welcome other points of view, though may give a robust answer.

AndThenTheresPhysics on Paraguayan Temperature Data

The blog andthentheresphysics is a particularly dogmatic and extremist website. Most of the time it provides extremely partisan opinion pieces on climate science, but last week the anonymous blogger had a post “Puerto Casado” concerning an article in the Telegraph about Paraguayan temperature by Christopher Booker. I posted the following comment

The post only looks at one station in isolation, and does not reference original source of the claims.

Paul Homewood at notalotofpeopleknowthat looked at all three available rural stations in Paraguay. The data from Mariscal and San Jan Buatista/Misiones had the same pattern of homogenization adjustments as Puerto Casado. That is, cooling of the past, so that instead of the raw data showing the 1960s being warmer than today, it was cooler.

Using his accountancy mind set, Homewood then (after Booker’s article was published) checked the six available urban sites in Paraguay. His conclusion was that

warming adjustments have taken place at every single, currently operational site in Paraguay.

Then he looked at all 14 available stations in neighbouring Bolivia. His conclusion

At every station, bar one, we find the ….. past is cooled and the present warmed.”

(The exception was La Paz, where the cooling trend in the raw data had been reduced.)

Homogenization of data means correcting for biases. For a 580,000 sq mile area of Central South America it would appears strong adjustment biases to have been introduced in a single direction.

Homewood references every single site. Anyone can easily debunk my summary by searching the following:-

Jan-20 Massive Tampering With Temperatures In South America

Jan-26 All Of Paraguay’s Temperature Record Has Been Tampered With

Jan-30 Cooling The Past In Bolivia

My comment did not contain the hyperlinks or italics. It has been deleted without passing through moderation. The only bit of the moderation policy I believe that I fall foul of is the last.

This blog is also turning out to be both more time consuming and more stressful than anticipated. Some moderation may be based purely on whether or not I/we can face dealing with how a particular comment thread is evolving. This is not a public service and so, in general, any moderation decision is final.

The counter-argument from ATTP is

If you look again at the information for this station the trend before adjustments was -1.37oC per century, after quality control it was -0.89 oC per century, and after adjusting for the station moves was +1.36 oC per century. Also, if you consider the same region for the same months, the trend is +1.37 oC per century, and for the country for the same months it is +1.28 oC per century. So, not only can one justify the adjustments, the result of the adjustments is consistent with what would be expected for that region and for the country.

Paul Homewood has investigated all the other stations in Paraguay or in neighbouring Bolivia and found similar ad hoc adjustments. It completely undermines ATTP’s arguments. This anonymous individual is wrong. Rather than face dealing that he is wrong, ATTP has deleted my comment. He is entitled to his beliefs, and in a free society can proselytize to his heart’s content. But there are boundaries. One of them is in suppressing evidence that undermines the justification for costly and harmful public policies. That is policies that are harming the poor here in Britain, but (and more importantly) can only be remotely successful by destroying the prospect of increasing living standards for over half the world’s population. Paul Homewood and others are increasingly uncovering similar biases in the temperature record in other parts of the world. The underlying data for the global surface temperature sets is in need of a proper, independent audit, to determine the extent of the biases within it. But when the accusation that the Paraguayan temperature data set is corrupted, people will point to ATTP’s blog post as evidence that there is but a single instance, and that instance has been debunked. Another boundary is a value that that many in the criminal justice system also hold dear. The more emotive the subject, the greater all concerned must go out of their way to compare and contrast the arguments. That way, the influence of our very human prejudices will be minimized. Again, independent audits will help eliminate this. If ATTP thinks he has all the answers then he will not be afraid to encourage people to look at both sides, evaluate by independent standards, and make up their own minds.

Kevin Marshall

Comment ATTP 310115

Instances of biases in the temperature sets

This will be added to when I get time.

Paul Homewood on San Diego data 30-01-15

Shub Niggareth looks into the Puerto Casado story 29-01-15

Paul Homewood on Reykjavik, Iceland 30-01-15

Jennifer Marohasy letter on Australian data 15-01-15

Update 01-02-15

I have invited a response from ATTP, by posting #comment-46021.


You have deleted two of my comments in the last 24 hours that meet all of your moderation criteria except one – that you cannot face dealing with a challenge. That is your prerogative. However, the first comment, (now posted on my blog) I believe completely undermines your argument. Paul Homewood has shown that the Puerto Casado dataset homogenization did not make it consistent with neighbouring non-homogenized surface temperature stations, but that all the Paraguayan and neighbouring Bolivian surface temperature stations were “homogenized” in the same way. That is, rather than eliminating the biases that local factors can create, the homogenizations, by people far removed from the local situations, effectively corrupted the data set, in a way that fits reality to the data.

I might be wrong in this. But based on your arguments so far I believe that my analysis is better than yours. I also believe that who has the better argument will only be resolved by an independent audit of the adjustments. If you are on the side of truth you would welcome that, just as a prosecutor would welcome the chance to prove their case in court, or a pharmaceutical company would welcome independent testing of their new wonder-drug that could save millions of lives. Even if I am wrong, I will be glad at being refuted by superior arguments, as I will know that to refute my claims will require you to up your game. Humanity will be served by my challenging a weak case and making it stronger. You have generated over 500 comments to your post, so an appeal for help via email should generate some response. If that does not work there are many well-funded organisations that I am sure will rush to your assistance.

There are at least seven options I think you can take.

  1. Ignore me, and pretend nothing has happened. Bad idea. I will start analysing your posts, as you did with Wattsupwiththat, only rather than your pea-shooters firing blanks, I have the heavy artillery with HE shells.
  2. Do an attack post – like desmogblog or Bob Ward of the Grantham Institute might do. Bad idea, I will take that as perverting or suppressing the evidence, and things will get rather rough. After all, I am but a (slightly) manic ex-beancounter, and you have the consensus of science on your side, so why is should sending in the PR thugs be necessary unless you are on the losing side?
  3. Get together a response that genuinely ups the game. Win or lose you will have served humanity as I and others will have to rebut you. Engage and all will gain through greater understanding.
  4. Admit that there are other valid points of view. A start would be to release this comment, which will get posted on my blog anyway. I quite accept that you cannot come up with a rebuttal at the drop-of-a-hat. A simple comment that a response will be made sometime this year is fine by me.
  5. Also call for a truly independent audit of the surface temperature set. It could be for your own reasons, and if truly independent, I will support it. If a whitewash, like the enquiries that Gordon Brown ordered into Climategate, an audit will do more harm than good.
  6. Close down your blog and do something else instead. You choose to be anonymous, and I respect that. Walking away is easy.
  7. Admit that you got this one wrong. You will take some flack, but not from me.

Why no country should sign up to Climate Mitigation at Paris 2015

The blog “the eco experts“, has produced a map of the countries most likely to survive climate change.

The most populous country with a high risk is India. In fact it has more people than the 50+ nations of Africa, or nearly twice the population of the OECD – the rich nations club. It is determined not to constrain the rapid growth in emissions if it means sacrificing the rapid economic growth that is pulling people out of poverty. Is this sensible when rapidly increasing its emissions create the prospect of dangerous climate change?

Look at the pattern of vulnerability.

Why is Mongolia more vulnerable than Russia or China?

Why is Haiti more vulnerable than Guatemala & El Salvador, which in turn are more vulnerable than Mexico, which in turn is more vulnerable than the USA?

Why are Syria and Iraq more vulnerable than Iran, which in turn is more vulnerable than Saudi Arabia, which is in turn more vulnerable than the UAE?

Why is Madagascar more vulnerable than Tanzania, which in turn is more vulnerable than South Africa, which is in turn more vulnerable than Botswana?

The answer does not lie in the local climate system but in the level of economic development. As with natural extreme weather events, any adverse consequences of climate change will impact on the poorest disproportionately.

In the light of this, should India

  1. Agree to sacrifice economic growth to constrain emissions, having a significant impact on global emissions and maybe encouraging others to do likewise?


  2. Continue with the high economic growth (and hence emission growth) strategy knowing that if catastrophic climate change is real the population will be better able to cope with it, and if inconsequential they will have sacrificed future generations to a trivial problem?


  3. Continue with the high economic growth (and hence emission growth) strategy and invest in more accurately identifying the nature and extent of climate change?

Now consider that any Government should be first and foremost responsible for the people of that country. If that can be best progressed by international agreements (such as in trade and keeping global peace) then it is the interests of that country to enter those agreements, and encourage other nations to do likewise. Global peace and globalisation are win-win strategies. But climate change is fundamentally different. It is a prospective future problem, the prospective harms from which are here clearly linked to stage of economic development. Combating the future problem means incurring costs, the biggest of which is economic growth. Technologically, there low-cost solutions are in place, and there is no example of any country aggressively weeding out ineffectual policies. Even if there were effective policies in in theory, for costs to exceed benefits would mean every major country either drastically cutting emissions (e.g. the OECD, China, Russia, Saudi Arabia, South Africa) or drastically constraining future emissions growth (India, Brazil, Indonesia, Vietnam, Thailand, plus dozens of other countries). If some countries fail to sign up then policy countries will be burdened with the certain actual costs of policy AND any residual possible costs of policy. Responsible countries will duck the issue, and, behind the scenes, help scupper the climate talks in Paris 2015.

Kevin Marshall

Veritasium Misinforms on Global Warming

Bishop Hill posts on a You-tube video “13 Misconceptions About Global Warming” from Veritasium (Dr Derek Muller), inviting readers to play a sort of bingo to “spot all the strawmen arguments, cherrypicking, out of date data, and plain old mistakes”. Here is my attempt, restricted to just 13 points.

  1. “Global warming” / “climate change” naming. It might be true that people can deny global warming by pointing to a localized cold weather snap. But it is also true that using the term “climate change” can result in any unusual weather event or short-term trend being blamed on anthropogenic global warming, along with natural global fluctuations. The term “global warming” reminds us that the adverse effects on climate are as a result of rising greenhouse gas levels warming the atmosphere. More importantly the use of the term “global” reminds us those changes in climate due to changes in greenhouse gases is a global issue requiring global solutions. Any mitigation policy that excludes 80% of the global population and two-thirds of global carbon emissions, will not work.


  2. Veritasium claims climate change is also about more extreme weather and ocean acidification, not just the average surface temperature is warming. But there is nothing in the greenhouse gas hypothesis that says a rise in temperatures will result in more extreme weather, nor does Veritasium provide the evidence of this happening. At Wattupwiththat there is a page that demonstrates weather is not getting more extreme from a number of different measures.


  3. Claim that it has not stopped warming as 13 of the 14 hottest years are in this century. This is a strawman, as there was significant warming in the last quarter of the twentieth century. We would only fail to have hottest years if global average temperatures had taken a sharp step decrease.


  4. Claims that taking the satellite data of global temperature anomalies into account shows that warming has not stopped. From Kevin Cowtan’s page (copied by Skeptical Science) we can calculate linear trends. It is the RSS satellite data that shows the longest period of no warming – 18 years from 1997-2014 based on the linear trend. It is just 13 years for GISTEMP and 14 years for HADCRUT4. The other satellite data is UAH, where there is just 6 years of no warming.



  5. What he is doing is comparing UAH satellite data that only shows the pause from 2009. There is now 35 years of satellite data, with the total recorded trend of 0.48oC. The RSS data shows 0.51oC of warming. The surface thermometer measures vary between 0.59 and 0.63 oC of warming. This is data cherry-picking.


  6. There is a claim that climate sensitivity is lower than thought in the 1980s. Not according to Nicholas Lewis, who found that the range of sensitivities is unchanged from the Charney Report 1979 through to AR5 WG1 of Sept-13


  7. Claims the central estimate for warming from a doubling of CO2 is 3.0oC of warming. Based on this from 2001 from HADCRUT4 shows no warming there would be 0.30oC of warming, when the trend from HADCRUT4 is zero. In a longer period from 1979 for which we have satellite data, an increase in CO2 from 336.8 to 398.5 ppm (Mauna Loa data) implies an increase in temperatures of 0.72oC – between 1.14 on 1.5 times greater than that measured by the temperature series. Even this is misleading, as there was no warming from 1944 to the late 1970s. In 1944 I estimate that CO2 levels were 308ppm, indicating a total warming in the last 70 years of 1.1oC, respectively 1.7 and 2.1 times greater than the trend in GISTEMP and HADCRUT4.


  8. This would appear to contradict this graph, which has no proper labelling showing have 3.0oC of doubling affects temperatures.

    Specifically from 1958 to 1980 CO2 rose from 315 to 339ppm, indicating warming of about 0.31 oC, but there was no warming in the IPCC projections. A rise in CO2 of 315 to 398.5 ppm from 1958 to 2014 would predict 1.0 oC in warming, almost double the actual data and the IPCC projections. Another point is with the “observed temperature”. It is not identified (probably GISTEMP) and ends on the high of 2010.


  9. Completely ignores the other greenhouse gases that contribute to warming, such as methane and halocarbons.


  10. Claims that sea level rise is another indication of global warming, through thermal expansion. This is not necessarily the case. The average temperature of the ocean is 3.9oC. A rise of to 4.0 oC will have zero expansion. If the rise in sea temperatures is confined to the Arctic or in the deep oceans where temperatures are below 4.0 oC, a rise in temperatures would mean a fall in sea levels. Below I have compiled a graph to show the expansion of a 100metre column of water by 0.1 oC from various starting temperatures.


  11. On Arctic Sea ice, is correct in saying that the 40% uptick in the last two years ignores the longer period of data. But in turn, Veritasium ignores evidence pre-satellites that were fluctuations in sea ice. Further, the uptick occurred at precisely the year when previous experts had predicted that summer sea ice cover would disappear. As a consequence, contraction of the sea ice is both less severe and less likely to be linked to human-caused warming than previously thought.


  12. Correctly points out that water vapour is the major greenhouse gas, but incorrectly claims to have evidence that water vapour is increasing in the atmosphere. The evidence is from a graphic from a 2007 PNAS paper.

    The evidence from 1900 is the average of 12 models. The confidence intervals are utter rubbish, appearing to be related to the magnitude of the average modelled anomaly. The actual (estimated) data in black does not have a confidence interval. It would appear that this estimated data has a step increase at roughly the time, or slightly before, when the warming stopped in the surface temperature records.


  13. Policy justification is totally wrong.

Veritasium says at 5.35

I’m not claiming it’s going to be some sort of crazy catastrophe, but we are going to get more intense storms, more droughts and floods, the oceans will become more acidic, sea levels will rise and my point is it would be better for all species on this planet and probably cheaper for us if we just started reducing emissions now than if we wait and pay the consequences later.

Every economic justification of policy projects “some sort of crazy catastrophe” that human being and other species will not be able to adapt to. Further they project that global emissions reductions will be both effective and relatively costless, which is contradicted by the evidence. But most of all, there is no political proposal in the climate talks that will reduce global emissions in the next twenty years. The proposals may only constrain the rate of increase.

Kevin Marshall

Climategate : The greatest quote is from Kevin Trenberth

As Paul Matthews at IPCC Report and Anthony Watts at Wattsupwiththat are pointing out, 17th November marked the 5th Anniversary of Climategate1. Paul Matthews has his pick of the most significant quotes. But I believe he misses the most important. Kevin Trenberth to Micheal Mann on Mon, 12 Oct 2009 and copied to most of the leading academics2

The fact is that we can’t account for the lack of warming at the moment and it is a travesty that we can’t. The CERES data published in the August BAMS 09 supplement on 2008 shows there should be even more warming: but the data are surely wrong. Our observing system is inadequate. (emphasis mine)

The first sentence is the mostly widely quoted. It is an admission that we, the experts, cannot explain what is happening. The end of the quote is even more important. There is a clear divergence between the predictions from the climate models – the theoretical understanding of the world – and the real world data. Trenberth’s reaction is that the data is wrong, not the theory. His later excuse for continuing belief in the climate models was coined a few months later. The truth is lurking in the murky depths. As with the mythical Loch Ness Monster, the believers in climate catastrophism hold that the evidence will be found, but we are not able to access it yet. This has created a new branch of climatology – the excuses for the pause. At the time of writing there are 65 excuses and new cases are appearing at more than two a week.

Kevin Marshall


  1. The term Climategate was coined by James Delingpole on 20th November 2009.
  2. Cc: Stephen H Schneider , Myles Allen , peter stott , “Philip D. Jones” , Benjamin Santer , Tom Wigley , Thomas R Karl , Gavin Schmidt , James Hansen , Michael Oppenheimer. This was an email between the high priests of the global warming movement.

The Climate Policy Issue Crystallized

There is a huge amount of nonsense made about how the rich industrialized countries need to cut carbon emissions to save the world from catastrophic global warming. Just about every climate activist group is gearing up to Paris 2015 where at last they feel that world agreement will be reach on restraining the growth of greenhouse gas emissions. Barak Obama will be pushing for a monumental deal in the dying days of his Presidency. There is a graphic that points out, whatever agreement is signed attempts to cut global emissions will be a monumental failure. It comes from the blandly named “Trends in global CO2 emissions: 2013 report” from the PBL Netherlands Environmental Assessment Agency. In the interactive presentation, there is a comparison between the industrialised countries in 1990 and 2012.

In over two decades the emissions of the industrialised countries have fallen slightly, almost entirely due to the large falls in emission in the ex-Warsaw Pact countries consequent on the collapse in the energy-inefficient communist system. In the countries formerly known as the “First World” the emissions have stayed roughly the same. It is the developing countries that account for more than 100% of the emissions increase since 1990. Two-thirds of the entire increase is accounted for by China where in less than a generation emissions quadrupled. Yet still China has half the emissions per capita of United States, Australia or Canada. It emissions growth will slow and stop in the next couple of decades, not because population will peak, or because of any agreement to stop emissions growth. China’s emissions will peak, like with other developed countries, as heavy industry shifts abroad and the country becomes more energy efficient. In the next 30-40 years India is likely to contribute more towards global emissions growth than China. But the “remaining developing countries” is the real elephant in the room. It includes 1050 million people in Africa (excluding South Africa); 185m in South America (excluding Brazil); 182m in Pakistan; 167m in Bangladesh, 98m in Philippines and 90m in Vietnam. The is over 2000 million people, or 30% of the global population that do not currently register on the global emissions scale, but by mid-century could have emissions equivalent to half of the 1990 global emissions. To the end of the century most of the global population increase will be in these countries. As half the countries of the world are in this group any attempt to undermine their potential economic growth through capping emissions would derail any chance of a global agreement.

Hattip Michel of trustyetverify

Kevin Marshall


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