Dixon and Jones confirm a result on the Stephan Lewandowsky Surveys

Congratulations to Ruth Dixon and Jonathan Jones on managing to get a commentary on the two Stephan Lewandowsky, Gilles Gignac & Klaus Oberauer surveys published in Psychological Science. Entitled “Conspiracist Ideation as a Predictor of Climate Science Rejection: An Alternative Analysis” it took two years to get published. Ruth Dixon gives a fuller description on her blog, My Garden Pond. It confirms something that I have stated independently, with the use of pivot tables instead of advanced statistical techniques. In April last year I compared the two surveys in a couple of posts – Conspiracist Ideation Falsified? (CIF) & Extreme Socialist-Environmentalist Ideation as Motivation for belief in “Climate Science” (ESEI).

The major conclusion through their analysis of the survey

All the data really shows is that people who have no opinion about one fairly technical matter (conspiracy theories) also have no opinion about another fairly technical matter (climate change). Complex models mask this obvious (and trivial) finding.

In CIF my summary was

A recent paper, based on an internet survey of American people, claimed that “conspiracist ideation, is associated with the rejection of all scientific propositions tested“. Analysis of the data reveals something quite different. Strong opinions with regard to conspiracy theories, whether for or against, suggest strong support for strongly-supported scientific hypotheses, and strong, but divided, opinions on climate science.

In the concluding comments I said

The results of the internet survey confirm something about people in the United States that I and many others have suspected – they are a substantial minority who love their conspiracy theories. For me, it seemed quite a reasonable hypothesis that these conspiracy lovers should be both suspicious of science and have a propensity to reject climate science. Analysis of the survey results has over-turned those views. Instead I propose something more mundane – that people with strong opinions in one area are very likely to have strong opinions in others. (Italics added)

Dixon and Jones have a far superior means of getting to the results. My method is to input the data into a table, find groupings or classifications, then analyse the results via pivot tables or graphs. This mostly leads up blind alleys, but can develop further ideas. For every graph or table in my posts, there can be a number of others stashed on my hard drive. To call it “trial and error” misses out the understanding to be gained from analysis. Their method (through rejecting linear OLS) is loess local regression. They derive the following plot.

This compares with my pivot table for the same data.

The shows in the Grand Total row that the strongest Climate (band 5) comprise 12% of the total responses. For the smallest group of beliefs about conspiracy theories with just 60/5005 responses, 27% had the strongest beliefs in about climate. The biggest percentage figure is the group who averaged a middle “3” score on both climate and conspiracy theories. That is those with no opinion on either subject.

The more fundamental area that I found is that in the blog survey between strong beliefs in climate science and extreme left-environmentalist political views. It is a separate topic, and its inclusion by Dixon and Jones would have both left much less space for the above insight in 1,000 words, and been much more difficult to publish. The survey data is clear.

The blog survey (which was held on strongly alarmist blogs) shows that most of the responses were highly skewed to anti-free market views (that is lower response score) along with being strongly pro-climate.

The internet survey of the US population allowed 5 responses instead of 4. The fifth was a neutral. This shows a more normal distribution of political beliefs, with over half of the responses in the middle ground.

This shows what many sceptics have long suspected, but I resisted. Belief in “climate science” is driven by leftish world views. Stephan Lewandowsky can only see the link between the “climate denial” beliefs and free-market, because he views left-environmentalist perspectives and “climate science” as a priori truths. This is the reality that everything is to be measured. From this perspective climate science has not failed due to being falsified by the evidence, but because scientists have yet to find the evidence; the models need refining; and there is a motivated PR campaign to undermine these efforts.

Kevin Marshall

 

 

 

 

 

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

Notes

1. 26 Data sets

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

Location

Lat

Lon

ID

Pop.

Years

Harare

17.9 S

31.1 E

156677750005

601,000

1897 – 2011

Kimberley

28.8 S

24.8 E

141684380004

105,000

1897 – 2011

Gwelo

19.4 S

29.8 E

156678670010

68,000

1898 – 1970

Bulawayo

20.1 S

28.6 E

156679640005

359,000

1897 – 2011

Beira

19.8 S

34.9 E

131672970000

46,000

1913 – 1991

Kabwe

14.4 S

28.5 E

155676630004

144,000

1925 – 2011

Livingstone

17.8 S

25.8 E

155677430003

72,000

1918 – 2010

Mongu

15.2 S

23.1 E

155676330003

< 10,000

1923 – 2010

Mwinilunga

11.8 S

24.4 E

155674410000

< 10,000

1923 – 1970

Ndola

13.0 S

28.6 E

155675610000

282,000

1923 – 1981

Capetown Safr

33.9 S

18.5 E

141688160000

834,000

1880 – 2011

Calvinia

31.5 S

19.8 E

141686180000

< 10,000

1941 – 2011

East London

33.0 S

27.8 E

141688580005

127,000

1940 – 2011

Windhoek

22.6 S

17.1 E

132681100000

61,000

1921 – 1991

Keetmanshoop

26.5 S

18.1 E

132683120000

10,000

1931 – 2010

Bloemfontein

29.1 S

26.3 E

141684420002

182,000

1943 – 2011

De Aar

30.6 S

24.0 E

141685380000

18,000

1940 – 2011

Queenstown

31.9 S

26.9 E

141686480000

39,000

1940 – 1991

Bethal

26.4 S

29.5 E

141683700000

30,000

1940 – 1991

Antananarivo

18.8 S

47.5 E

125670830002

452,000

1889 – 2011

Tamatave

18.1 S

49.4 E

125670950003

77,000

1951 – 2011

Porto Amelia

13.0 S

40.5 E

131672150000

< 10,000

1947 – 1991

Potchefstroom

26.7 S

27.1 E

141683500000

57,000

1940 – 1991

Zanzibar

6.2 S

39.2 E

149638700000

111,000

1880 – 1960

Tabora

5.1 S

32.8 E

149638320000

67,000

1893 – 2011

Dar Es Salaam

6.9 S

39.2 E

149638940003

757,000

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

Location

Recent adjustment

Other adjustment

Other Period
Antananarivo

0.50

 

 
Beira

 

0.10

Mid-70s + inter-war
Bloemfontein

0.70

 

 
Dar Es Salaam

0.10

 

 
Harare

 

1.10

About 1999-2002
Keetmanshoop

1.57

 

 
Potchefstroom

-0.10

 

 
Tamatave

0.39

 

 
Windhoek

3.60

 

 
Zanzibar

-0.80

 

 

RealClimate’s Mis-directions on Arctic Temperatures

Summary

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.

Introduction

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 – http://variable-variability.blogspot.de/2015/02/evil-nazi-communist-world-government.html

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.

Conclusion

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.

Reykjavik Temperature Adjustments – a comparison

Summary

On 20th February, Paul Homewood made some allegations that the temperature adjustments for Reykjavík were not supported by any known reasons. The analysis was somewhat vague. I have looked into the adjustments by both the GHCN v3 and NASA GISS. The major findings, which support Homewood’s view, are:-

  • The GHCN v3 adjustments appear entirely arbitrary. They do not correspond to the frequent temperature relocations. Much of the period from 1901-1965 is cooled by a full one degree centigrade.
  • Even more arbitrary was the adjustments for the period 1939-1942. In years where there was no anomalous spike in the data, a large cool period was created.
  • Also, despite there being complete raw data, the GHCN adjusters decided to dismiss the data from 1926 and 1946.
  • The NASA GISS homogenisation adjustments were much smaller in magnitude, and to some extent partly offset the GHCN adjustments. The greatest warming was of the 1929-51 period.

The combined impact of the adjustments is to change the storyline from the data, suppressing the early twentieth century warming and massively reducing the mid-century cooling. As a result an impression is created that the significant warming since the 1980s is unprecedented.

 

Analysis of the adjustments

There are a number of data sets to consider. There is the raw data available from 1901 to 2011 at NASA GISS. Nick Stokes has confirmed that this is the same raw data issued by the Iceland Met Office, baring a few roundings. The adjustments made by the Iceland Met Office are unfortunately only available from 1948. Quite separate, is the Global Historical Climatology Network dataset (GHCN v3) from the US National Oceanic and Atmospheric Administration (NOAA) I accessed from NASA GISS, along with the GISS’s own homogenised data used to compile the GISTEMP global temperature anomaly.

The impact of the adjustments from the raw data is as follows

The adjustments by the Icelandic Met Office professionals with a detailed knowledge of the instruments and the local conditions, is quite varied from year-to-year and appears to impose no trend in the data. The impact of GCHN is to massively cool the data prior to 1965. Most years are by about a degree, more than the 0.7oC total twentieth century global average surface temperature increase. The pattern of adjustments has long periods of adjustments that are the same. The major reason could be relocations. Trausti Jonsson, Senior Meteorologist with the Iceland Met Office, has looked at the relocations. He has summarized in the graphic below, along with gaps in the data.

I have matched these relocations with the adjustments.

The relocation dates appear to have no impact on the adjustments. If it does affect the data, the wrong data must be used.

Maybe the adjustments reflect the methods of calculation? Trausti Jonsson says:-

I would again like to make the point that there are two distinct types of adjustments:

1. An absolutely necessary recalculation of the mean because of changes in the observing hours or new information regarding the diurnal cycle of the temperature. For Reykjavík this mainly applies to the period before 1924.

2. Adjustments for relocations. In this case these are mainly based on comparative measurements made before the last relocation in 1973 and supported by comparisons with stations in the vicinity. Most of these are really cosmetic (only 0.1 or 0.2 deg C). There is a rather large adjustment during the 1931 to 1945 period (- 0.4 deg C, see my blog on the matter – you should read it again:http://icelandweather.blog.is/blog/icelandweather/entry/1230185/). 
I am not very comfortable with this large adjustment – it is supposed to be constant throughout the year, but it should probably be seasonally dependent. The location of the station was very bad (on a balcony/rooftop).

So maybe there can be some adjustment prior to 1924, but nothing major after. There is also nothing in the this account, or in the more detailed history, that indicates a reason for the reduction in adjustments in 1917-1925, or the massive increase in negative adjustments in the period 1939-1942.

Further, there is nothing in the local conditions that I can see to then justify GISS imposing an artificial early twentieth century warming period. There are two possible non-data reasons. The first is due to software which homogenizes to the global pattern. The second is human intervention. The adjusters at GISS realised the folks at NOAA had been conspicuously over-zealous in their adjustments, so were trying to restore a bit of credibility to the data story.

 

The change in the Reykjavík data story

When we compare graphs of raw data to adjusted data, it is difficult to see the impact of adjustments on the trends. The average temperatures vary widely from year to year, masking the underlying patterns. As a rough indication I have therefore taken the average temperature anomaly per decade. The decades are as in common usage, so the 1970s is from 1970-1979. The first decade is actually 1901-1909, and for the adjusted data there are some years missing. The decade of 2000-2009 had no adjustments. The average temperature of 5.35oC was set to zero, to become the anomaly.

The warmest decade was the last decade of 2000-2009. Further, both the raw data (black) and the GISS Homogenised data (orange) show the 1930s to be the second warmest decade. However, whilst the raw data shows the 1930s to be just 0.05oC cooler than the 2000s, GISS estimates it to be 0.75oC cooler. The coolest decades are also different. The raw data shows the 1980s to be the coolest decade, whilst GISS shows the 1900s and the 1910s to be about 0.40oC cooler. The GHCN adjustments (green) virtually eliminate the mid-century cooling.

But adjustments still need to be made. Trausti Jonsson believes that the data prior to 1924 needs to be adjusted downwards to allow for biases in the time of day when readings were taken. This would bring the 1900s and the 1910s more into line with the 1980s, along with lowering the 1920s. The leap in temperatures from the 1910s to the 1930s becomes very similar to that from 1980s to the 2000s, instead of half the magnitude in the GHCNv3 data and two-thirds the magnitude in the GISS Homogenised data.

The raw data tell us there were two similar-sized fluctuations in temperature since 1900 of 1920s-1940s and from 1980s-2010s. In between there was a period cooling that almost entirely cancelled out the earlier warming period. The massive warming since the 1980s is not exceptional, though there might be some minor human influence if patterns are replicated elsewhere.

The adjusted data reduces the earlier warming period and the subsequent cooling that bottomed out in the 1980s. Using the GISS Homogenised data we get the impression of unprecedented warming closely aligned to the rise in greenhouse gas levels. As there is no reason for the adjustments from relocations, or from changes to the method of calculation, the adjustments would appear to be made to fit reality to the adjuster’s beliefs about the world.

Kevin Marshall

 

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.

DECC’s Dumb Global Calculator Model

On the 28th January 2015, the DECC launched a new policy emissions tool, so everyone can design policies to save the world from dangerous climate change. I thought I would try it out. By simply changing the parameters one-by-one, I found that the model is both massively over-sensitive to small changes in input parameters and is based on British data. From the model, it is possible to entirely eliminate CO2 emissions by 2100 by a combination of three things – reducing the percentage travel in urban areas by car from 43% to 29%; reducing the average size of homes to 95m2 from 110m2 today; and for everyone to go vegetarian.

The DECC website says

Cutting carbon emissions to limit global temperatures to a 2°C rise can be achieved while improving living standards, a new online tool shows.

The world can eat well, travel more, live in more comfortable homes, and meet international carbon reduction commitments according to the Global Calculator tool, a project led by the UK’s Department of Energy and Climate Change and co-funded by Climate-KIC.

Built in collaboration with a number of international organisations from US, China, India and Europe, the calculator is an interactive tool for businesses, NGOs and governments to consider the options for cutting carbon emissions and the trade-offs for energy and land use to 2050.

Energy and Climate Change Secretary Edward Davey said:

“For the first time this Global Calculator shows that everyone in the world can prosper while limiting global temperature rises to 2°C, preventing the most serious impacts of climate change.

“Yet the calculator is also very clear that we must act now to change how we use and generate energy and how we use our land if we are going to achieve this green growth.

“The UK is leading on climate change both at home and abroad. Britain’s global calculator can help the world’s crucial climate debate this year. Along with the many country-based 2050 calculators we pioneered, we are working hard to demonstrate to the global family that climate action benefits people.”

Upon entering the calculator I was presented with some default settings. Starting from a baseline emissions in 2011 of 49.9 GT/CO2e, this would give predicted emissions of 48.5 GT/CO2e in 2050 and 47.9 GT/CO2e in 2100 – virtually unchanged. Cumulative emissions to 2100 would be 5248 GT/CO2e, compared with 3010 GT/CO2e target to give a 50% chance of limiting warming to a 2°C rise. So the game is on to save the world.

I only dealt with the TRAVEL, HOMES and DIET sections on the left.

I went through each of the parameters, noting the results and then resetting back to the baseline.

The TRAVEL section seems to be based on British data, and concentrated on urban people. Extrapolating for the rest of the world seems a bit of a stretch, particularly when over 80% of the world is poorer. I was struck first by changing the mode of travel. If car usage in urban areas fell from 43% to 29%, global emissions from all sources in 2050 would be 13% lower. If car usage in urban areas increased from 43% to 65%, global emissions from all sources in 2050 would be 7% higher. The proportions are wrong (-14% gives -13%, but +22% gives +7%) along with urban travel being too high a proportion of global emissions.

The HOMES section has similar anomalies. Reducing the average home area by 2050 to 95m2 from 110m2 today reduces total global emissions in 2050 by 20%. Independently decreasing average urban house temperature in 2050 from 17oC in Winter & 27oC in Summer, instead of 20oC & 24oC reduces total global emissions in 2050 by 7%. Both seem to be based on British-based data, and highly implausible in a global context.

In the DIET section things get really silly. Cutting the average calorie consumption globally by 10% reduces total global emissions in 2050 by 7%. I never realised that saving the planet required some literal belt tightening. Then we move onto meat consumption. The baseline for 2050 is 220 Kcal per person per day, against the current European average of 281 Kcal. Reducing that to 14 Kcal reduces global emissions from all sources in 2050 by 73%. Alternatively, plugging in the “worst case” 281 Kcal, increases global emissions from all sources in 2050 by 71%. That is, if the world becomes as carnivorous in 2050 as the average European in 2011, global emissions from all sources at 82.7 GT/CO2e will be over six times higher the 13.0 GT/CO2e. For comparison, OECD and Chinese emissions from fossil fuels in 2013 were respectively 10.7 and 10.0 GT/CO2e. It seems it will be nut cutlets all round at the climate talks in Paris later this year. No need for China, India and Germany to scrap all their shiny new coal-fired power stations.

Below is the before and after of the increase in meat consumption.

Things get really interesting if I take the three most sensitive, yet independent, scenarios together. That is, reducing urban car use from 43% to 29% of journeys in 2050; reducing the average home area by 2050 to 95m2 from 110m2; and effectively making a sirloin steak (medium rare) and venison in redcurrant sauce things of the past. Adding them together gives global emissions of -2.8 GT/CO2e in 2050 and -7.1 GT/CO2e in 2100, with cumulative emissions to 2100 of 2111 GT/CO2e. The model does have some combination effect. It gives global emissions of 3.2 GT/CO2e in 2050 and -0.2 GT/CO2e in 2100, with cumulative emissions to 2100 of 2453 GT/CO2e. Below is the screenshot of the combined elements, along with a full table of my results.

It might be great to laugh at the DECC for not sense-checking the outputs of its glitzy bit of software. But it concerns me that it is more than likely the same people who are responsible for this nonsense are also responsible for the glossy plans to cut Britain’s emissions by 80% by 2050 without destroying hundreds of thousands of jobs; eviscerating the countryside; and reducing living standards, especially of the poor. Independent and critical review and audit of DECC output is long overdue.

Kevin Marshall

 

A spreadsheet model is also available, but I used the online tool, with its’ excellent graphics. The calculator is built by a number of organisations.

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?

    OR

  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?

    OR

  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

Has NASA distorted the data on global warming?

The Daily Mail has published some nice graphics from NASA on how the Earth’s climate has changed in recent years. The Mail says

Twenty years ago world leaders met for the first ever climate change summit but new figures show that since then the globe has become hotter and weather has become more weird.

Numbers show that carbon dioxide emissions are up, the global temperature has increased, sea levels are rising along with the earth’s population.

The statistics come as more than 190 nations opened talks on Monday at a United Nations global warming conference in Lima, Peru.

Read more: http://www.dailymail.co.uk/news/article-2857093/Hotter-weirder-How-climate-changed-Earth.html#ixzz3KyaTz1j9

Follow us: @MailOnline on Twitter | DailyMail on Facebook

http://www.dailymail.co.uk/news/article-2857093/Hotter-weirder-How-climate-changed-Earth.html

See if anyone can find a reason for the following.

  1. A nice graphic compares the minimum sea ice extent in 1980 with 2012 – nearly three month after the 2014 minimum. Why not use the latest data?

  2. There is a nice graphic showing the rise in global carbon emissions from 1960 to the present. Notice gradient is quite steep until the mid-70s; there is much shallower gradient to around 2000 when the gradient increases. Why do NASA not produce their temperature anomaly graph to show us all how these emissions are heating up the world?

    Data from http://cdiac.ornl.gov/GCP/.

     

  3. There is a simple graphic on sea level rise, derived from the satellite data. Why does the NASA graph start in 1997, when the University of Colorado data, that is available free to download, starts in 1993? http://sealevel.colorado.edu/

     

     

Some Clues

Sea Ice extent

COI | Centre for Ocean and Ice | Danmarks Meteorologiske Institut

Warming trends – GISTEMP & HADCRUT4

The black lines are an approximate fit of the warming trends.

Sea Level Rise

Graph can be obtained from the University of Colorado.

 

NB. This is in response to a post by Steve Goddard on Arctic Sea Ice.

Kevin Marshall

Have 250.000 Spanish jobs been sacrificed for the folly of saving the planet?

Spain is one of the leading countries in Europe for Renewables. In 2013 output broke new records, with renewables accounting for 21.1% of Spanish electricity demand, with wind and hydroelectric power production increasing by 12% and 16%, respectively on 2012.

This is to the detriment of the Spanish economy for three financial reasons.

First is the huge amount now likely being spent on wind power subsidies. In 2013 output from wind farms was about 54GWh, or 12% higher than the 48.5GWh produced in 2012. Assuming an average subsidy of €54MWh (the rate for onshore wind turbines in the UK) that would be €2.9billion in subsidies.

Second, there is the huge amount now likely being spent on solar power. Spain is home to the massive Anadasol Solar Power Station. The three sections are expected to produce 495GWh per year, which at 38% of capacity seems a tad high. This will have a guaranteed price of €270 per megawatt. In the UK, the wholesale price is about £45 or €60 a megawatt. The excess cost (or subsidy) is therefore €210MWh, or €100million a year. At this rate, the total 8.2GWh produced by photovoltaics would have attracted a subsidy of €1.7bn in subsidies.

The combined estimated subsidy is worth €4.6bn is equivalent to 0.3% of GDP. Total subsidies are likely to be much more.

Third is the disastrous foray in solar panels lead to huge amounts of investments in solar schemes. In 2008 there were an estimated 30,000 jobs supported in the boom years. These jobs disappeared with the bust. With this sudden boom, caused by extremely generous subsidies, the quality of the panels was poor and overpriced. Many investors would not have got their money back even if the subsidies had remained. Now they will be saddled in debt, with no income. These borrowing were often state-backed. According to Bloomberg this fund was €24bn at the end of 2011. If some of this has to be written off, then there could be a material impact on deficit reduction plans, and thus the levels of unemployment. Government backing loss-making projects costs jobs.

This claim can be cross-checked. In the same Bloomberg article the Renewable Energy Producers Association (Asociación de productores de energías renovables or APPA) was quoted as saying that the renewables industry sustains about 110,000 Spanish jobs. In 2011 Verso Economics, a Kirkcaldy-based outfit, wrote a report about the effect of renewables jobs in Scotland and the impact on the wider UK. Whilst the report found that the jobs in renewables were largely neutral with Scotland – one job lost in the wider economy for each gained in renewables – in the wider UK economy for each job gained in Scottish renewables 3.7 jobs were lost in the wider UK economy. (report here, and reported at Caledonian Mercury, BBC and Scottish Sceptic) If this were replicated in Spain, the net impact of 110,000 jobs in renewables would be 400,000 jobs less jobs in the wider Spanish economy. Without renewables more than 250,000 people could be in work, or over 1% of the labor force.

Why I call Spain’s attempt to save the planet a folly, are the same reasons for calling Britain’s attempts a folly. Any emissions reductions in Europe will be more than offset by many times over from the emerging economies elsewhere. In reducing emissions, Spain will increase unemployment and reduce growth. But future generations will still bear over 80% of any consequences of warming than if no rich country did anything. In the current situation, I believe that a lot of Spanish people might object to their country being called “rich” anyway.

Update 20/11/14 – minor editing.

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