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

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

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

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

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

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

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

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

Kevin Marshall

 

Notes

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


Temperature Homogenization at Puerto Casado

Summary

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

 

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

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

Cowtan explains at 6.40

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

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

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

The station move is after the fall in temperature.

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

IT MOVED BECAUSE THERE IS CHANGE AND THERE IS A CHANGE BECAUSE IT MOVED

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

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

GHCN Name

GHCN Location

BEST Ref

Break Type

Break Year

 

Concepcion

23.4 S,57.3 W

157453

Empirical

1969

 

Encarcion

27.3 S,55.8 W

157439

Empirical

1968

 

Mariscal

22.0 S,60.6 W

157456

Empirical

1970

 

Pilar

26.9 S,58.3 W

157441

Empirical

1967

 

Puerto Casado

22.3 S,57.9 W

157455

Station Move

1971

 

San Juan Baut

26.7 S,57.1 W

157442

Empirical

1970

 

Asuncion Aero

25.3 S,57.6 W

157448

Empirical

1969

 

  

  

  

Station Move

1972

 

  

  

  

Station Move

1973

 

San Juan Bautista

25.8 S,56.3 W

157444

Empirical

1965

 

  

  

  

Empirical

1967

 

  

  

  

Station Move

1971

 

Pedro Juan CA

22.6 S,55.6 W

19469

Empirical

1968

 

  

  

  

Empirical

3 in 1970s

 
           

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

 

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

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

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

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

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

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

Figure 5 – BEST adjustment for Encarnacion.

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

Kevin Marshall

Notes

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

ATTP on Lomborg’s Australian Funding

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

noun

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

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

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

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

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

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

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

Kevin Marshall

Royal Baby Names to Save the United Kingdom

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

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

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

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

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

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

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

Kevin Marshall

Declaration of Interest

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

 

Labour Manifesto is misleading the British Public

Today Ed Miliband formally launched the Labour Election Manifesto 2015. See the summary at the BBC.

David Cameron has called it a con trick. (Hattip Conservative Home)

This con trick claim can be substantiated by reading the Manifesto. Here are a few snippets.

 

The Economy

On the Economy, Labour realize they have an uphill struggle. A couple of examples

We will cut the deficit every year with a surplus on the current budget..”

The current budget deficit is the difference between tax revenue and current spending. To get the total deficit you need to add in (what used to be called) capital expenditure.

Remember Gordon Brown’s Golden Rule of only borrowing to invest?

Ed Miliband will return Britain to the days of 2001-2008, when Labour built a structural deficit of £50-£70bn. It is this reason that there is still a huge deficit, not the credit crunch. Labour still do not understand the public sector capital investment does not provide financial returns. New roads, schools and hospitals are not constructed to generate revenue like in a business but to provide social returns. Properly spent, overall welfare is increased, despite capital spending creating additional financial burdens in terms of staffing and maintenance.

There is not a single policy in this manifesto that is funded by additional borrowing.

This is grossly misleading. Labour are committed to at least maintaining current spending levels. When there is a deficit that means new additional borrowing is required, adding to the total debt. What Labour mean is that additional spending will be funded by additional taxes.

 

Discouraging entrepreneurship, jobs and growth

There is a subsection headed “We will back our entrepreneurs and businesses

The measures are tiny. Instead here are a scattering of policy initiatives which will likely damage British businesses and help undermine economic growth.

  1. We will reverse the Government’s top-rate tax cut.

    British Entrepreneurs will be discouraged from investing in Britain. They will go elsewhere.

     

  2. We will abolish the non-dom rules…”

    Ed Balls in January said

    “I think if you abolish the whole (nom-dom) status then probably it ends up costing Britain money”

    There on a lot of people who rely on the non-doms for jobs. Many invest money in Britain.

     

  3. We will close tax loopholes that cost the public billions of pounds a year,”

    The tax system will become even more complex, especially for small businesses. This could reduce revenues.

     

  4. We will end unfair tax breaks used by hedge funds and others

    A major part of Britain’s exports come from the financial services sector. Labour’s antipathy to this sector threatens hundreds of thousands of jobs and may demote the City of London to a second tier financial sector.

     

  5. “We will increase the National Minimum Wage

    We will ban exploitative zero-hours contracts

    We will promote the Living Wage”

    The cost of employing people will rise. Businesses who do not toe the official line on the living wage might be unable to sell to the State Sector. Start-up businesses will be reduced and small businesses will not expand as inflexible employment laws will increase the risks of taking people on. The unemployed will become locked out of jobs. Youth unemployment will rise.

     

  6. We will freeze gas and electricity prices until 2017

    Prices have been rising because of the Climate Change Act 2008 that Ed Miliband was responsible for steering through Parliament. There is huge investment needed in new sources of electricity. That ain’t going to happen if profit rates fall. This is a policy to ensure the lights go out in a couple of winters time.

     

  7. We will introduce a fairer deal for renters

    This will be at the expense of landlords, many of whom rent as a business.

     

  8. We will expand free childcare from 15 to 25 hours per week for parents of three- and four-year-olds, paid for with an increase in the bank levy.”

    See point 4 on the City of London

I am really concerned that a Labour Government will jeopardize the prosperity of this country, and my children’s future. Rather than learning from past Labour continue to deceive themselves through spin. Rather than and admitting that they got things wrong Labour blame others.

Kevin Marshall

Freeman Dyson on Climate Models

One of the leading physicists on the planet, Freeman Dyson, has given a video interview to the Vancouver Sun. Whilst the paper emphasizes Dyson’s statements about the impact of more CO2 greening the Earth, there is something more fundamental that can be gleaned.

Referring to a friend who constructed the first climate models, Dyson says at about 10.45

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

I believe that Climate Science has lost sight of what this understanding of what their climate models actually are literally attempts to understand the real world, but are not the real world at all. It reminds me of something another physicist spoke about fifty years ago. Richard Feynman, a contemporary that Dyson got to know well in the late 1940s and early 1950s said of theories:-

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

Complex mathematical models suffer from this vagueness in abundance. When I see supporters of climate arguing the critics of the models are wrong by stating some simple model, and using selective data they are doing what lesser scientists and pseudo-scientists have been doing for decades. How do you confront this problem? Climate is hugely complex, so simple models will always fail on the predictive front. However, unlike Dyson I do not think that all is lost. The climate models have had a very bad track record due to climatologists not being able to relate their models to the real world. There are a number of ways they could do this. A good starting point is to learn from others. Climatologists could draw upon the insights from varied sources. With respect to the complexity of the subject matter, the lack of detailed, accurate data and the problems of prediction, climate science has much in common with economics. There are insights that can be drawn on prediction. One of the first empirical methodologists was the preeminent (or notorious) economist of the late twentieth century – Milton Friedman. Even without his monetarism and free-market economics, he would be known for his 1953 Essay “The Methodology of Positive Economics”. Whilst not agreeing with the entirety of the views expressed (there is no satisfactory methodology of economics) Friedman does lay emphasis on making simple, precise and bold predictions. It is the exact opposite of the Cook et al. survey which claims a 97% consensus on climate, implying that it relates to a massive and strong relationship between greenhouse gases and catastrophic global warming when in fact it relates to circumstantial evidence for a minimal belief in (or assumption of) the most trivial form of human-caused global warming. In relation to climate science, Friedman would say that it does not matter about consistency with the basic physics, nor how elegantly the physics is stated. It could be you believe that the cause of warming comes from the hot air produced by the political classes. What matters that you make bold predictions based on the models that despite being simple and improbable to the non-expert, nevertheless turn out to be true. However, where bold predictions have been made that appear to be improbable (such as worsening hurricanes after Katrina or the effective disappearance of Arctic Sea ice in late 2013) they have turned out to be false.

Climatologists could also draw upon another insight, held by Friedman, but first clearly stated by John Neville Keynes (father of John Maynard Keynes). That is on the need to clearly distinguish between the positive (what is) and the normative (what ought to be). But that distinction was alienate the funders and political hangers-on. It would also mean a clear split of the science and policy.

Hattips to Hilary Ostrov, Bishop Hill, and Watts up with that.

 

Kevin Marshall

Massive Exaggeration on Southern Alaskan Glacial ice melt

Paul Homewood has a lovely example of gross exaggeration on climate change. He has found the following quote from a University of Oregon study

Incessant mountain rain, snow and melting glaciers in a comparatively small region of land that hugs the southern Alaska coast and empties fresh water into the Gulf of Alaska would create the sixth largest coastal river in the world if it emerged as a single stream, a recent study shows.

Since it’s broken into literally thousands of small drainages pouring off mountains that rise quickly from sea level over a short distance, the totality of this runoff has received less attention, scientists say. But research that’s more precise than ever before is making clear the magnitude and importance of the runoff, which can affect everything from marine life to global sea level.

The collective fresh water discharge of this region is more than four times greater than the mighty Yukon River of Alaska and Canada, and half again as much as the Mississippi River, which drains all or part of 31 states and a land mass more than six times as large.

“Freshwater runoff of this magnitude can influence marine biology, near shore oceanographic studies of temperature and salinity, ocean currents, sea level and other issues,” said David Hill, lead author of the research and an associate professor in the College of Engineering at Oregon State University.

“This is an area of considerable interest, with its many retreating glaciers,” Hill added, “and with this data as a baseline we’ll now be able to better monitor how it changes in the future.” (Bold mine)

This implies that melting glaciers are a significant portion of the run-off. I thought I would check this out. From the yukoninfo website I find

The watershed’s total drainage area is 840 000 sq. km (323 800 sq. km in Canada) and it discharges 195 cubic kilometres of water per year.

Therefore the runoff is about 780 cubic kilometres per year.

From Wikipedia I find that the Mississippi River has an average annual discharge of 16,792 m3/s. This implies the average discharge into the Gulf of Alaska is about 25,000 m3/s. This equates to 90,000,000 m3 per hour or 2,160,000,000 m3 per day. That is 2.16 cubic kilometres per day, or 788 cubic kilometres per year. If this gross runoff was net, it would account for two thirds of the 3.2mm sea level rise recorded by the satellites. How much of this might be from glacial ice melt? This is quite difficult to estimate. From the UNIPCC AR5 WGI SPM of Sept-13 we have the following statement.

Since the early 1970s, glacier mass loss and ocean thermal expansion from warming together explain about 75% of the observed global mean sea level rise (high confidence). Over the period 1993 to 2010, global mean sea level rise is, with high confidence, consistent with the sum of the observed contributions from ocean thermal expansion due to warming (1.1 [0.8 to 1.4] mm yr–1), from changes in glaciers (0.76 [0.39 to 1.13] mm yr–1), Greenland ice sheet (0.33 [0.25 to 0.41] mm yr–1), Antarctic ice sheet (0.27 [0.16 to 0.38] mm yr–1), and land water storage (0.38 [0.26 to 0.49] mm yr–1). The sum of these contributions is 2.8 [2.3 to 3.4] mm yr–1. {13.3}

How much of this 0.76 mm yr–1 (around 275 cubic kilometres) is accounted for by Southern Alaska?

The author of the Oregon study goes onto say.

This is one of the first studies to accurately document the amount of water being contributed by melting glaciers, which add about 57 cubic kilometers of water a year to the estimated 792 cubic kilometers produced by annual precipitation in this region.

That is 20% (range 14-40%) of the global glacial ice melt outside of Greenland and Iceland is accounted for by Southern Alaska. Northern and Central Alaska, along with Northern Canada are probably far more significant. The Himalayan glaciers are huge, especially compared to the Alps or the Andes which are also meant to be melting. There might be glaciers in Northern Russia as well. Maybe 1%-10% of the global total comes from Southern Alaska, or 3 to 30 cubic kilometres per annum, not 14-40%. The Oregon Article points to two photographs on Flikr (1 & 2) which together seem less than a single cubic kilometre of loss per year. From Homewood’s descriptions of the area, most of the glacial retreat in the area may have been in the nineteenth and early twentieth centuries.

Maybe someone can provide a reconciliation that will make the figures stack up. Maybe the 57 cubic kilometres is a short-term tend – a sibgle year even?

Kevin Marshall

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

 

 

Windhoek Temperature adjustments

At Euan Mean’s blog I made reference to my findings, posted in full last night, that in the Isfjord Radio weather station had adjustments that varied between +4.0oC in 1917 to -1.7oC in the 1950s. I challenged anyone to find bigger adjustments than that. Euan came back with the example of Windhoek in South Africa, claiming 5oC of adjustments between the “raw” and GISS homogenised data.

I cry foul, as the adjustments are throughout the data set. J

That is the whole of the data set has been adjusted up by about 4 oC!

However, comparing the “raw” with the GISS homogenised data, with 5 year moving averages, (alongside the net adjustments) there are some interesting features.

The overall temperatures have been adjusted up by around 4oC, but

  • From the start of the record in 1920 to 1939 the cooling has been retained, if not slightly amplified.
  • The warming from 1938 to 1947 of 1.5oC has been erased by a combination of deleting the 1940 to 1944 data and reducing the 1945-1948 adjustments by 1.4oC.
  • The 1945-1948 adjustments, along with random adjustments and deletion of data mostly remove the near 1.5oC of cooling from the late 1940s to mid-1950s and the slight rebound through to the early 1960s.
  • The early 1970s cooling and the warming to the end of the series in the mid-1980s is largely untouched.

The overall adjustments leave a peculiar picture that cannot be explained by a homogenisation algorithm. The cooling in the 1920s offsets the global trend. Deletion of data and the adjustments in the data counter the peak of warming in the early 1940s in the global data. Natural variations in the raw data between the late 1940s and 1970 appear to have been removed, then the slight early 1970s cooling and the subsequent warming in the raw data left alone. However, the raw data shows average temperatures in the 1980s to be around 0.8oC higher than in the early 1920s. The adjustments seem to have removed this.

This removal of the warming trend tends to disprove something else. There appears to be no clever conspiracy, with a secret set of true figures. Rather, there are a lot of people dipping in to adjusting adjusted data to their view of the world, but nobody really questioning the results. They have totally lost sight of what the real data actually is. If they have compared the final adjusted data with the raw data, then they realised that the adjustments had managed to have eliminated a warming trend of over 1 oC per century.

Kevin Marshall

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