NOAA Future Aridity against Al Gore’s C20th Precipitation Graphic

Paul Homewood has taken a look at an article in yesterdays Daily Mail – A quarter of the world could become a DESERT if global warming increases by just 2ºC.

The article states

Aridity is a measure of the dryness of the land surface, obtained from combining precipitation and evaporation.  

‘Aridification would emerge over 20 to 30 per cent of the world’s land surface by the time the global temperature change reaches 2ºC (3.6ºF)’, said Dr Manoj Joshi from the University of East Anglia’s School of Environmental Sciences and one of the study’s co-authors.  

The research team studied projections from 27 global climate models and identified areas of the world where aridity will substantially change.  

The areas most affected areas are parts of South East Asia, Southern Europe, Southern Africa, Central America and Southern Australia.

Now, having read Al Gore’s authoritative book An Inconvenient Truth there are statements first about extreme flooding, and then about aridity (pages 108-113). The reason for flooding coming first is on a graphic of twentieth-century changes in precipitation on pages 114 & 115.

This graphic shows that, overall, the amount of precipitation has increased globally in the last century by almost 20%.

 However, the effects of climate change on precipitation is not uniform. Precipitation in the 20th century increased overall, as expected with global warming, but in some regions precipitation actually decreased.

The blue dots mark the areas with increased precipitation, the orange dots with decreases. The larger the dot, the larger the change. So, according to Nobel Laureate Al Gore, increased precipitation should be the far more common than increased aridity. If all warming is attributed to human-caused climate change (as the book seems to imply) then over a third of the dangerous 2ºC occurred in the 20th century. Therefore there should be considerable coherence between the recent arid areas and future arid areas.

The Daily Mail reproduces a map from the UEA, showing the high-risk areas.

There are a couple of areas with big differences.

Southern Australia

In the 20th century, much of Australia saw increased precipitation. Within the next two or three decades, the UEA projects it getting considerably arider. Could this change in forecast be the result of the extreme drought that broke in 2012 with extreme flooding? Certainly, the pictures of empty reservoirs taken a few years ago, alongside claims that they would never likely refill show the false predictions.

One such reservoir is Lake Eildon in Victoria. Below is a graphic of capacity levels in selected years. It is possible to compare other years by following the historical water levels for EILDON link.

Similarly, in the same post, I linked to a statement by re-insurer Munich Re stating increased forest fires in Southern Australia were due to human activity. Not by “anthropogenic climate change”, but by discarded fag ends, shards of glass and (most importantly) fires that were deliberately started.

Northern Africa

The UEA makes no claims about increased aridity in Northern Africa, particularly with respect to the Southern and Northern fringes of the Sahara. Increasing desertification of the Sahara used to be claimed as a major consequence of climate change. In the year following Al Gore’s movie and book, the UNIPCC produced its Fourth Climate Assessment Report. Working Group II report, Chapter 9 (Pg 448) on Africa made the following claim.

In other countries, additional risks that could be exacerbated by climate change include greater erosion, deficiencies in yields from rain-fed agriculture of up to 50% during the 2000-2020 period, and reductions in crop growth period (Agoumi, 2003).

Richard North took a detailed look at the background of this claim in 2010. The other African countries were Morocco, Algeria and Tunisia. Agoumi 2003 compiled three reports, only one of which – Morocco – had anything near a 50% claim. Yet Morocco seems, from Al Gore’s graphic to have had a modest increase in rainfall over the last century.


The UEA latest doom-laden prophesy of increased aridity flies in the face of the accepted wisdom that human-caused global warming will result in increased precipitation. In two major areas (Southern Australia and Northern Africa), increased aridity is at add odds with changes in precipitation claimed to have occurred in the 20th Century by Al Gore in An Inconvenient Truth. Yet over a third of the of the dangerous 2ºC warming limit occurred in the last century.

Kevin Marshall


Blighting of Fairbourne by flawed report and BBC reporting

The Telegraph is reporting (hattip Paul Homewood)

A Welsh village is to sue the government after a climate change report suggested their community would soon be washed away by rising sea levels.

The document says Fairbourne will soon be lost to the sea, and recommends that it is “decommissioned”.

However, I was not sure about some of the figures in the Telegraph report, so I checked for myself.

West of Wales Shoreline Management Plan 2(SMP2) is available in sections. Fairbourne is covered in file 4d3 – Section 4 Coastal Area D PDZ11.pdf under folder West of W…\Eng…\Coastal Area D

On page 16 is the following graphic.

Fairbourne is the grey area to the bottom left of the image. In 50 years about a third of the village will be submerged at high tide and in 100 years all of the village. This is without changes to flood defences. Even worse is this comment.

Over the 100 years with 2m SLR the area would be typically 1.5m below normal tidal levels.

Where would they have got this 1-2m of sea level rise from? In the Gwynedd council Cabinet Report 22/01/13 Topic : Shoreline Management Plan 2 it states

The WoWSMP2 was undertaken in defined stages as outlined in the Defra guidance published in March 2006.

And on sea level rise it states

There is a degree of uncertainty at present regarding the rate of sea level rise. There is an upper and lower estimate which produces a range of possibilities between 1m and 2m in the next 100 years. It will take another 10 to 20 years of data to determine where we are on the graph and what the projection for the future is.

Does the Defra guidance bear any resemblance to the expert opinion? In the UNIPCC AR5 Working Group 1 Summary for Policymakers page 21 is Table SPM.2

At the foot of the table is the RCP8.5 business as usual scenario for sea level rise.

The flood risk images produced in 2011 assume 0.36m of sea level rise in 50 years or about 2061. This is at the very top end of the RCP8.5 scenario estimates for 2046-2065. It is above the sea level rise projections with mitigation policies. Similarly a rise of 1m in 100 years is equivalent to the top end of the RCP8.5 scenario estimates for 2081-2100 of 0.82m. With any other mitigation scenario, the sea level rise is below that.

This means that the West of Wales Shoreline Management Plan 2 assumes that the Climate Change Act 2008 (which has increased electricity bills by at least 30% since it was passed, and blighted many rural areas with wind turbines) will have no impact at all. For added effect, it takes the most extreme estimate of sea level rise and doubles it.

It gets worse. The action group Fairbourne Facing Change has a website

The Fairbourne Facing Change Community Action Group (FFC) was established in direct response to the alarming way the West of Wales Shoreline Management Plan 2(SMP2) was publicised on national and local television. The BBC programme ‘Week in Week Out’ broadcast on Tuesday, 11th February 2014, did not present an accurate and balanced reporting of the situation. This, then followed with further inaccurate coverage culminating in unnecessary concern, anxiety, and panic for the community.

The BBC has long been the mouthpiece for an extremist view on climate change. The lack of balance has caused real distress and helped exacerbate the situation. Even, though the 2013 report was unduly alarmist in its forecasts, there is nothing in the figures to support the statement that

Fairbourne is expected to enter into “managed retreat” in 2025 when the council will stop maintaining defences due to rising sea levels.


More than 400 homes are expected to be abandoned in the village by 2055 as part of the council’s shoreline management plan (SMP) policy.

With sea level rise of about 3mm a year, and with forecast acceleration, the council is alleged to find it no longer worthwhile to maintain the sea defences when sea levels of have risen by one or two inches, and will have completely abandoned the village based on a sea level rise of less than 14 inches. With a shovel on my own I could construct an 18-inch high barrier out of the loose stone from the on a couple of mile front well before 2025.

Kevin Marshall



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.

Understanding GISS Temperature Adjustments

A couple of weeks ago something struck me as odd. Paul Homewood had been going on about all sorts of systematic temperature adjustments, showing clearly that the past has been cooled between the UHCN “raw data” and the GISS Homogenised data used in the data sets. When I looked at eight stations in Paraguay, at Reykjavik and at two stations on Spitzbergen I was able to corroborate this result. Yet Euan Mearns has looked at groups of stations in central Australia and Iceland, in both finding no warming trend between the raw and adjusted temperature data. I thought that Mearns must be wrong, so when he published on 26 stations in Southern Africa1, I set out to evaluate those results, to find the flaw. I have been unable to fully reconcile the differences, but the notes I have made on the Southern African stations may enable a greater understanding of temperature adjustments. What I do find is that clear trends in the data across a wide area have been largely removed, bringing the data into line with Southern Hemisphere trends. The most important point to remember is that looking at data in different ways can lead to different conclusions.

Net difference and temperature adjustments

I downloaded three lots of data – raw, GCHNv3 and GISS Homogenised (GISS H), then replicated Mearns’ method of calculating temperature anomalies. Using 5 year moving averages, in Chart 1 I have mapped the trends in the three data sets.

There is a large divergence prior to 1900, but for the twentieth century the warming trend is not excessively increased. Further, the warming trend from around 1900 is about half of that in the GISTEMP Southern Hemisphere or global anomalies. Looked in this way Mearns would appear to have a point. But there has been considerable downward adjustment of the early twentieth century warming, so Homewood’s claim of cooling the past is also substantiated. This might be the more important aspect, as the adjusted data makes the warming since the mid-1970s appear unusual.

Another feature is that the GCHNv3 data is very close to the GISS Homogenised data. So in looking the GISS H data used in the creation of the temperature data sets is very much the same as looking at GCHNv3 that forms the source data for GISS.

But why not mention the pre-1900 data where the divergence is huge?

The number of stations gives a clue in Chart 2.

It was only in the late 1890s that there are greater than five stations of raw data. The first year there are more data points left in than removed is 1909 (5 against 4).

Removed data would appear to have a role in the homogenisation process. But is it material? Chart 3 graphs five year moving averages of raw data anomalies, split between the raw data removed and retained in GISS H, along with the average for the 26 stations.

Where there are a large number of data points, it does not materially affect the larger picture, but does remove some of the extreme “anomalies” from the data set. But where there is very little data available the impact is much larger. That is particularly the case prior to 1910. But after 1910, any data deletions pale into insignificance next to the adjustments.

The Adjustments

I plotted the average difference between the Raw Data and the adjustment, along with the max and min values in Chart 4.

The max and min of net adjustments are consistent with Euan Mearns’ graph “safrica_deltaT” when flipped upside down and made back to front. It shows a difficulty of comparing adjusted, where all the data is shifted. For instance the maximum figures are dominated by Windhoek, which I looked at a couple of weeks ago. Between the raw data and the GISS Homogenised there was a 3.6oC uniform increase. There were a number of other lesser differences that I have listed in note 3. Chart 5 shows the impact of adjusting the adjustments is on both the range of the adjustments and the pattern of the average adjustments.

Comparing this with this average variance between the raw data and the GISS Homogenised shows the closer fit if the adjustments to the variance. Please note the difference in scale on Chart 6 from the above!

In the earlier period has by far the most deletions of data, hence the lack of closeness of fit between the average adjustment and average variance. After 1945, the consistent pattern of the average adjustment being slightly higher than the average variance is probably due to a light touch approach on adjustment corrections than due to other data deletions. The might be other reasons as well for the lack of fit, such as the impact of different length of data sets on the anomaly calculations.

Update 15/03/15

Of note is that the adjustments in the early 1890s and around 1930 is about three times the size of the change in trend. This might be partly due to zero net adjustments in 1903 and partly due to the small downward adjustments in post 2000.

The consequences of the adjustments

It should be remembered that GISS use this data to create the GISTEMP surface temperature anomalies. In Chart 7 I have amended Chart 1 to include Southern Hemisphere annual mean data on the same basis as the raw data and GISS H.

It seems fairly clear that the homogenisation process has achieved bringing the Southern Africa data sets into line with the wider data sets. Whether the early twentieth century warming and mid-century cooling are outliers that have been correctly cleansed is a subject for further study.

What has struck me in doing this analysis is that looking at individual surface temperature stations becomes nonsensical, as they are grid reference points. Thus comparing the station moves for Reykjavik with the adjustments will not achieve anything. The implications of this insight will have to wait upon another day.

Kevin Marshall


1. 26 Data sets

The temperature stations, with the periods for the raw data are below.








17.9 S

31.1 E



1897 – 2011


28.8 S

24.8 E



1897 – 2011


19.4 S

29.8 E



1898 – 1970


20.1 S

28.6 E



1897 – 2011


19.8 S

34.9 E



1913 – 1991


14.4 S

28.5 E



1925 – 2011


17.8 S

25.8 E



1918 – 2010


15.2 S

23.1 E


< 10,000

1923 – 2010


11.8 S

24.4 E


< 10,000

1923 – 1970


13.0 S

28.6 E



1923 – 1981

Capetown Safr

33.9 S

18.5 E



1880 – 2011


31.5 S

19.8 E


< 10,000

1941 – 2011

East London

33.0 S

27.8 E



1940 – 2011


22.6 S

17.1 E



1921 – 1991


26.5 S

18.1 E



1931 – 2010


29.1 S

26.3 E



1943 – 2011

De Aar

30.6 S

24.0 E



1940 – 2011


31.9 S

26.9 E



1940 – 1991


26.4 S

29.5 E



1940 – 1991


18.8 S

47.5 E



1889 – 2011


18.1 S

49.4 E



1951 – 2011

Porto Amelia

13.0 S

40.5 E


< 10,000

1947 – 1991


26.7 S

27.1 E



1940 – 1991


6.2 S

39.2 E



1880 – 1960


5.1 S

32.8 E



1893 – 2011

Dar Es Salaam

6.9 S

39.2 E



1895 – 2011

2. Temperature trends

To calculate the trends I used the OLS method, both from the formula and using the EXCEL “LINEST” function, getting the same answer each time. If you are able please check my calculations. The GISTEMP Southern Hemisphere and global data can be accessed direct from the NASA GISS website. The GISTEMP trends are from the skepticalscience trends tool. My figures are:-

3. Adjustments to the Adjustments


Recent adjustment

Other adjustment

Other Period






Mid-70s + inter-war



Dar Es Salaam






About 1999-2002
















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 Svalbard, (which he did as part of temperature reconstruction for Spitzbergen last year) with those of NASA 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 Svalbard 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 centered 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 Svalbard 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 conclusion to Trausti Jonsson of the Iceland Met Office. The adjustments made thousands of miles away in the United States by homogenization algorithms 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 Svalbard 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 Svalbad 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.

The Propaganda methods of ….and Then There’s Physics on Temperature Homogenisation

There has been a rash of blog articles about temperature homogenisations that is challenging the credibility of the NASS GISS temperature data. This has lead to attempts by anonymous blogger andthentheresphysics (ATTP) to crudely deflect from the issues identified. It is propagandist’s trick of turning people’s perspectives. Instead of a dispute about some scientific data, ATTP turns the affair into a dispute between those with authority and expertise in scientific analysis, against a few crackpot conspiracy theorists.

The issues on temperature homogenisation are to do with the raw surface temperature data and the adjustments made to remove anomalies or biases within the data. “Homogenisation” is a term used for process of adjusting the anomalous data into line with that from the surrounding data.

The blog articles can be split into three categories. The primary articles are those that make direct reference to the raw data set and the surrounding adjustments. The secondary articles refer to the primary articles, and comment upon them. The tertiary articles are directed at the secondary articles, making little or no reference to the primary articles. I perceive the two ATTP articles as fitting into the scheme below.

Primary Articles

The source of complaints about temperature homogenisations is Paul Homewood at his blog notalotofpeopleknowthat. The source of the articles is NASA’s Goddard Institute for Space Studies (GISS) database. For any weather station GISS provide nice graphs of the temperature data. The current after GISS homogeneity adjustment data is available here and the raw GHCN data + UHSHCN corrections is available here up until 2011 only. For any weather station GISS provide nice graphs of the temperature data. Homewood’s primary analysis was to show the “raw data” side by side.

20/01/15 Massive Tampering With Temperatures In South America

This looked at all three available rural stations in Paraguay. The data from all three at Puerto Casado, Mariscal and San Jan Buatista/Misiones had the same pattern of homogenization adjustments. That is, cooling of the past, so that instead of the raw data showing the 1960s being warmer than today, it was cooler. What could they have been homogenized to?

26/01/15 All Of Paraguay’s Temperature Record Has Been Tampered With

This checked the six available urban sites in Paraguay. Homewood’s conclusion was that

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

How can homogenization adjustments all go so same way? There is no valid reason for making such adjustments, as there is no reference point for the adjustments.

29/01/15Temperature Adjustments Around The World

Homewood details other examples from Southern Greenland, Iceland, Northern Russia, California, Central Australia and South-West Ireland. Instead of comparing the raw with the adjusted data, he compared the old adjusted data with the recent data. Adjustment decisions are changing over time, making the adjusted data sets give even more pronounced warming trends.

30/01/15 Cooling The Past In Bolivia

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.)

Why choose Paraguay in the first place? In the first post, Homewood explains that within a NOAA temperature map for the period 1981-2010 there appeared to be a warming hotspot around Paraguay. Being a former accountant he checked the underlying data to see if it existed in the data. Finding an anomaly in one area, he checked more widely.

The other primary articles are

26/01/15 Kevin Cowton NOAA Paraguay Data

This Youtube video was made in response to Christopher Booker’s article in the Telegraph, a secondary source of data. Cowton assumes Booker is the primary source, and is criticizing NOAA data. A screen shot of the first paragraph shows these are untrue.

Further, if you read down the article, Cowton’s highlighting of the data from one weather station is also misleading. Booker points to three, but just illustrates one.

Despite this, it still ranks as a primary source, as there are direct references to the temperature data and the adjustments. They are not GISS adjustments, but might be the same.

29/01/15 Shub Niggurath – The Puerto Casado Story

Shub looked at the station moves. He found that the metadata for the station data is a mess, so there is no actual evidence of the location changing. But, Shub reasons the fact that there was a step change in the data meant that it moved, and the fact that it moved meant there was a change. Shub is a primary source as he looks at the adjustment reason.


Secondary Articles

The three secondary articles by Christopher Booker, James Delingpole and BishopHill are just the connectors in this story.


Tertiary articles of “…and Then There’s Physics”

25/01/15 Puerto Cascado

This looked solely at Booker’s article. It starts

Christopher Booker has a new article in the The Telegraph called Climategate, the sequel: How we are STILL being tricked with flawed data on global warming. The title alone should be enough to convince anyone sensible that it isn’t really worth reading. I, however, not being sensible, read it and then called Booker an idiot on Twitter. It was suggested that rather than insulting him, I should show where he was wrong. Okay, this isn’t really right, as there’s only so much time and effort available, and it isn’t really worth spending it rebutting Booker’s nonsense.

However, thanks to a tweet from Ed Hawkins, it turns out that it is really easy to do. Booker shows data from a site in Paraguay (Puerto Casado) in which the data was adjusted from a trend of -1.37o C per century to +1.36o C per century. Shock, horror, a conspiracy?


ATTP is highlighting an article, but is strongly discouraging anybody from reading it. That is why the referral is a red line in the graphic above. He then says he is not going to provide a rebuttal. ATTP is good to his word and does not provide a rebuttal. Basically it is saying “Don’t look at that rubbish, look at the real authority“. But he is wrong for a number of reasons.

  1. ATTP provides misdirection to an alternative data source. Booker quite clearly states that the source of the data is the NASA GISS temperature set. ATTP cites Berkeley Earth.
  2. Booker clearly states that there are thee rural temperature stations spatially spread that show similar results. ATTP’s argument that a single site was homogenized with the others in the vicinity falls over.
  3. This was further undermined by Paul Homewood’s posting on the same day on the other 6 available sites in Paraguay, all giving similar adjustments.
  4. It was further undermined by Paul Homewood’s posting on 30th January on all 14 sites in Bolivia.

The story is not of a wizened old hack making some extremist claims without any foundation, but of a retired accountant seeing an anomaly, and exploring it. In audit, if there is an issue then you keep exploring it until you can bottom it out. Paul Homewood has found an issue, found it is extensive, but is still far from finding the full extent or depth. ATTP, when confronted by my summary of the 23 stations that corroborate each other chose to delete it. He has now issued an update.

Update 4/2/2015 : It’s come to my attention that some are claiming that this post is misleading my readers. I’m not quite sure why, but it appears to be related to me not having given proper credit for the information that Christopher Booker used in his article. I had thought that linking to his article would allow people to establish that for themselves, but – just to be clear – the idiotic, conspiracy-laden, nonsense originates from someone called Paul Homewood, and not from Chistopher Booker himself. Okay, everyone happy now? J

ATTP cannot accept that he is wrong. He has totally misrepresented the arguments. When confronted with alternative evidence ATTP resorts to vitriolic claims. If someone is on the side of truth and science, they will encourage people to compare and contrast the evidence. He seems to have forgotten the advice about when in a whole…..

Temperature homogenisation

ATTP’s article on Temperature Homogenisation starts

Amazing as it may seem, the whole tampering with temperature data conspiracy has managed to rear its ugly head once again. James Delingpole has a rather silly article that even Bishop Hill calls interesting (although, to be fair, I have a suspicion that in “skeptic” land, interesting sometimes means “I know this is complete bollocks, but I can’t bring myself to actually say so”). All of Delingpole’s evidence seems to come from “skeptic” bloggers, whose lack of understand of climate science seems – in my experience – to be only surpassed by their lack of understanding of the concept of censorship J.

ATPP starts with a presumption of being on the side of truth, with no fault possible on his side. Any objections are due to a conscious effort to deceive. The theory of cock-up or of people not checking their data does not seem to have occurred to him. Then there is a link to Delingpole’s secondary article, but calling it “silly” again deters readers from looking for themselves. If they did, the readers would be presented with flashing images of all the “before” and “after” GISS graphs from Paraguay, along with links to the 6 global sites and Shub’s claims that there is a lack of evidence for the Puerto Casado site being moved. Delingpole was not able the more recent evidence from Bolivia, that further corroborates the story.

He then makes a tangential reference to his deleting my previous comments, though I never once used the term “censorship”, nor did I tag the article “climate censorship”, as I have done to some others. Like on basic physics, ATTP claims to have a superior understanding of censorship.

There are then some misdirects.

  • The long explanation of temperature homogenisation makes some good points. But what it does not do is explain that the size and direction of any adjustment is an opinion, and as such be wrong. It a misdirection to say that the secondary sources are against any adjustments. They are against adjustments that create biases within the data.
  • Quoting Richard Betts’s comment on Booker’s article about negative adjustments in sea temperature data is a misdirection, as Booker (a secondary source) was talking about Paraguay, a land-locked country.
  • Referring to Cowton’s alternative analysis is another misdirect, as pointed out above. Upon reflection, ATTP may find it a tad embarrassing to have this as his major source of authority.


When I studied economics, many lecturers said that if you want to properly understand an argument or debate you need to look at the primary sources, and then compare and contrast the arguments. Although the secondary sources were useful background, particularly in a contentious issue, it is the primary sources on all sides that enable a rounded understanding. Personally, by being challenged by viewpoints that I disagreed with enhanced my overall understanding of the subject.

ATTP has managed to turn this on its head. He uses methods akin to crudest propagandists of last century. They started from deeply prejudiced positions; attacked an opponent’s integrity and intelligence; and then deflected away to what they wanted to say. There never gave the slightest hint that one side might be at fault, or any acknowledgement that the other may have a valid point. For ATTP, and similar modern propagandists, rather than have a debate about the quality of evidence and science, it becomes a war of words between “deniers“, “idiots” and “conspiracy theorists” against the basic physics and the overwhelming evidence that supports that science.

If there is any substance to these allegations concerning temperature adjustments, for any dogmatists like ATTP, it becomes a severe challenge to their view of the world. If temperature records have systematic adjustment biases then climate science loses its’ grip on reality. The climate models cease to be about understanding the real world, but conforming to people’s flawed opinions about the world.

The only way to properly understand the allegations is to examine the evidence. That is to look at the data behind the graphs Homewood presents. I have now done that for the nine Paraguayan weather stations. The story behind that will have to await another day. However, although I find Paul Homewood’s claims of systematic biases in the homogenisation process to be substantiated, I do not believe that it points to a conspiracy (in terms of a conscious and co-ordinated attempt to deceive) on the part of climate researchers.

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.