Warming Bias in Temperature Data due to Consensus Belief not Conspiracy

In a Cliscep article Science: One Damned Adjustment After Another? Geoff Chambers wrote:-

So is the theory of catastrophic climate change a conspiracy? According to the strict dictionary definition, it is, in that the people concerned clearly conferred together to do something wrong – namely introduce a consistent bias in the scientific research, and then cover it up.

This was in response to last the David Rose article in the Mail on Sunday, about claims the infamous the Karl et al 2015 breached America’s National Oceanic and Atmospheric Administration (NOAA) own rules on scientific intergrity.

I would counter this claim about conspiracy in respect of temperature records, even in the strict dictionary definition. Still less does it conform to a conspiracy theory in the sense of some group with a grasp of what they believe to be the real truth, act together to provide an alternative to that truth. or divert attention and resources away from that understanding of that truth. like an internet troll. A clue as to know why this is the case comes from on of the most notorious Climategate emails. Kevin Trenberth to Micheal Mann on Mon, 12 Oct 2009 and copied to most of the leading academics in the “team” (including Thomas R. Karl).

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

It is the first sentence that was commonly quoted, but it is the last part is the most relevant for temperatures anomalies. There is inevitably a number of homogenisation runs to get a single set of anomalies. For example the Reykjavik temperature data was (a) adjusted by the Iceland Met office by standard procedures to allow for known locals biases (b) adjusted for GHCNv2 (the “raw data”) (c) adjusted again in GHCNv3 (d) homogenized by NASA to be included in Gistemp.

There are steps that I have missed. Certainly Gistemp homogenize the data quite frequently for new sets of data. As Paul Matthews notes, adjustments are unstable. Although one data set might on average be pretty much the same as previous ones, there will be quite large anomalies thrown out every time the algorithms are re-run for new data. What is more, due to the nature of the computer algorithms, there is no audit trail, therefore the adjustments are largely unexplainable with reference to the data before, let alone with reference to the original thermometer readings. So how does one know whether the adjustments are reasonable or not, except through a belief in how the results ought to look? In the case of the climatologists like Kevin Trenberth and Thomas R. Karl, variations that show warmer than the previous run will be more readily accepted as correct rather than variations that show cooler. That is, they will find reasons why a particular temperature data set now shows greater higher warming than before. but will reject as outliers results that show less warming than before. It is the same when choosing techniques, or adjusting for biases in the data. This is exacerbated when a number of different bodies with similar belief systems try to seek a consensus of results, like  Zeke Hausfather alludes to in his article at the CarbonBrief. Rather than verifying results in the real world, temperature data seeks to conform to the opinions of others with similar beliefs about the world.

Kevin Marshall

How strong is the Consensus Evidence for human-caused global warming?

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.

Richard Feynman – 1964 Lecture on the Scientific Method

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

The Debunking Handbook 2011 – John Cook and Stephan Lewandowsky

My previous post looked at the attacks on David Rose for daring to suggest that the rapid fall in global land temperatures at the El Nino event were strong evidence that the record highs in global temperatures were not due to human greenhouse gas emissions. The technique used was to look at long-term linear trends. The main problems with this argument were
(a) according to AGW theory warming rates from CO2 alone should be accelerating and at a higher rate than the estimated linear warming rates from HADCRUT4.
(b) HADCRUT4 shows warming stopped from 2002 to 2014, yet in theory the warming from CO2 should have accelerated.

Now there are at least two ways to view my arguments. First is to look at Feynman’s approach. The climatologists and associated academics attacking journalist David Rose chose to do so from a perspective of a very blurred specification of AGW theory. That is human emissions will cause greenhouse gas levels to rise, which will cause global average temperatures to rise. Global average temperature clearly have risen from all long-term (>40 year) data sets, so theory is confirmed. On a rising trend, with large variations due to natural variability, then any new records will be primarily “human-caused”. But making the theory and data slightly less vague reveals an opposite conclusion. Around the turn of the century the annual percentage increase in CO2 emissions went from 0.4% to 0.5% a year (figure 1), which should have lead to an acceleration in the rate of warming. In reality warming stalled.

The reaction was to come up with a load of ad hoc excuses. Hockey Schtick blog reached 66 separate excuses for the “pause” by November 2014, from the peer-reviewed to a comment in the UK Parliament.  This could be because climate is highly complex, with many variables, the presence of each contributing can only be guessed at, let alone the magnitude of each factor and the interrelationships with all factors. So how do you tell which statements are valid information and which are misinformation? I agree with Cook and Lewandowsky that misinformation is pernicious, and difficult to get rid of once it becomes entrenched. So how does one evaluate distinguish between the good information and the bad, misleading or even pernicious?

The Lewandowsky / Cook answer is to follow the consensus of opinion. But what is the consensus of opinion? In climate one variation is to follow a small subset of academics in the area who answer in the affirmative to

1. When compared with pre-1800s levels, do you think that mean global temperatures have generally risen, fallen, or remained relatively constant?

2. Do you think human activity is a significant contributing factor in changing mean global temperatures?

Problem is that the first question is just reading a graph and the second could be is a belief statement will no precision. Anthropogenic global warming has been a hot topic for over 25 years now. Yet these two very vague empirically-based questions, forming the foundations of the subject, should be able to be formulated more precisely. On the second it is a case of having pretty clear and unambiguous estimates as to the percentage of warming, so far, that is human caused. On that the consensus of leading experts are unable to say whether it is 50% or 200% of the warming so far. (There are meant to be time lags and factors like aerosols that might suppress the warming). This from the 2013 UNIPCC AR5 WG1 SPM section D3:-

It is extremely likely that more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings together.

The IPCC, encapsulating the state-of-the-art knowledge, cannot provide firm evidence in the form of a percentage, or even a fairly broad range even with over 60 years of data to work on..  It is even worse than it appears. The extremely likely phrase is a Bayesian probability statement. Ron Clutz’s simple definition from earlier this year was:-

Here’s the most dumbed-down description: Initial belief plus new evidence = new and improved belief.

For the IPCC claim that their statement was extremely likely, at the fifth attempt, they should be able to show some sort of progress in updating their beliefs to new evidence. That would mean narrowing the estimate of the magnitude of impact of a doubling of CO2 on global average temperatures. As Clive Best documented in a cliscep comment in October, the IPCC reports, from 1990 to 2013 failed to change the estimate range of 1.5°C to 4.5°C. Looking up Climate Sensitivity in Wikipedia we get the origin of the range estimate.

A committee on anthropogenic global warming convened in 1979 by the National Academy of Sciences and chaired by Jule Charney estimated climate sensitivity to be 3 °C, plus or minus 1.5 °C. Only two sets of models were available; one, due to Syukuro Manabe, exhibited a climate sensitivity of 2 °C, the other, due to James E. Hansen, exhibited a climate sensitivity of 4 °C. “According to Manabe, Charney chose 0.5 °C as a not-unreasonable margin of error, subtracted it from Manabe’s number, and added it to Hansen’s. Thus was born the 1.5 °C-to-4.5 °C range of likely climate sensitivity that has appeared in every greenhouse assessment since…

It is revealing that quote is under the subheading Consensus Estimates. The climate community have collectively failed to update the original beliefs, based on a very rough estimate. The emphasis on referring to consensus beliefs about the world, rather than looking outward for evidence in the real world, I would suggest is the primary reason for this failure. Yet such community-based beliefs completely undermines the integrity of the Bayesian estimates, making its use in statements about climate clear misinformation in Cook and Lewandowsky’s use of the term. What is more, those in the climate community who look primarily to these consensus beliefs rather than the data of the real world will endeavour to dismiss the evidence, or make up ad hoc excuses, or smear those who try to disagree. A caricature of these perspectives with respect to global average temperature anomalies is available in the form of a flickering widget at John Cooks’ skepticalscience website. This purports to show the difference between “realist” consensus and “contrarian” non-consensus views. Figure 2 is a screenshot of the consensus views, interpreting warming as a linear trend. Figure 3 is a screenshot of the non-consensus or contrarian views. They is supposed to interpret warming as a series of short, disconnected,  periods of no warming. Over time, each period just happens to be at a higher level than the previous. There are a number of things that this indicates.

(a) The “realist” view is of a linear trend throughout any data series. Yet the period from around 1940 to 1975 has no warming or slight cooling depending on the data set. Therefore any linear trend line derived for a longer period than 1970 to 1975 and ending in 2015 will show a lower rate of warming. This would be consistent the rate of CO2 increasing over time, as shown in figure 1. But for shorten the period, again ending in 2015, and once the period becomes less than 30 years, the warming trend will also decrease. This contracts the theory, unless ad hoc excuses are used, as shown in my previous post using the HADCRUT4 data set.

(b) Those who agree with the consensus are called “Realist”, despite looking inwards towards common beliefs. Those who disagree with warming are labelled “Contrarian”. This is not inaccurate when there is a dogmatic consensus. But it utterly false to lump all those who disagree with the same views, especially when no examples are provided of those who hold such views.

(c) The linear trend appears as a more plausible fit than the series of “contrarian” lines. By implication, those who disagree with the consensus are viewed as as having a distinctly more blinkered and distorted perspective than those who follow the consensus. Yet even using gistemp data set (which is gives greatest support to the consensus views) there is a clear break in the linear trend. The less partisan HADCRUT4 data shows an even greater break.

Those who spot the obvious – that around the turn of the century warming stopped or slowed down, when in theory it should have accelerated – are given a clear choice. They can conform to the scientific consensus, denying the discrepancy between theory and data. Or they can act as scientists, denying the false and empirically empty scientific consensus, receiving the full weight of all the false and career-damaging opprobrium that accompanies it.

fig2-sks-realists

 

 

fig3-sks-contras

Kevin Marshall

 

Climate Experts Attacking a Journalist by Misinformation on Global Warming

Summary

Journalist David Rose was attacked for pointing out in a Daily Mail article that the strong El Nino event, that resulted in record temperatures, was reversing rapidly. He claimed record highs may be not down to human emissions. The Climate Feedback attack article claimed that the El Nino event did not affect the long-term human-caused trend. My analysis shows

  • CO2 levels have been rising at increasing rates since 1950.
  • In theory this should translate in warming at increasing rates. That is a non-linear warming rate.
  • HADCRUT4 temperature data shows warming stopped in 2002, only resuming with the El Nino event in 2015 and 2016.
  • At the central climate sensitivity estimate of doubling of CO2 leads to 3C of global warming, HADCRUT4 was already falling short of theoretical warming in 2000. This is without the impact of other greenhouse gases.
  • Putting a linear trend lines through the last 35 to 65 years of data will show very little impact of El Nino, but has a very large visual impact on the divergence between theoretical human-caused warming and the temperature data. It reduces the apparent impact of the divergence between theory and data, but does not eliminate it.

Claiming that the large El Nino does not affect long-term linear trends is correct. But a linear trend neither describes warming in theory or in the leading temperature set. To say, as experts in their field, that the long-term warming trend is even principally human-caused needs a lot of circumspection. This is lacking in the attack article.

 

Introduction

Journalist David Rose recently wrote a couple of articles in the Daily Mail on the plummeting global average temperatures.
The first on 26th November was under the headline

Stunning new data indicates El Nino drove record highs in global temperatures suggesting rise may not be down to man-made emissions

With the summary

• Global average temperatures over land have plummeted by more than 1C
• Comes amid mounting evidence run of record temperatures about to end
• The fall, revealed by Nasa satellites, has been caused by the end of El Nino

Rose’s second article used the Met Offices’ HADCRUT4 data set, whereas the first used satellite data. Rose was a little more circumspect when he said.

El Nino is not caused by greenhouse gases and has nothing to do with climate change. It is true that the massive 2015-16 El Nino – probably the strongest ever seen – took place against a steady warming trend, most of which scientists believe has been caused by human emissions.

But when El Nino was triggering new records earlier this year, some downplayed its effects. For example, the Met Office said it contributed ‘only a few hundredths of a degree’ to the record heat. The size of the current fall suggests that this minimised its impact.

There was a massive reaction to the first article, as discussed by Jaime Jessop at Cliscep. She particularly noted that earlier in the year there were articles on the dramatically higher temperature record of 2015, such as in a Guardian article in January.There was also a follow-up video conversation between David Rose and Dr David Whitehouse of the GWPF commenting on the reactions. One key feature of the reactions was claiming the contribution to global warming trend of the El Nino effect was just a few hundredths of a degree. I find particularly interesting the Climate Feedback article, as it emphasizes trend over short-run blips. Some examples

Zeke Hausfather, Research Scientist, Berkeley Earth:
In reality, 2014, 2015, and 2016 have been the three warmest years on record not just because of a large El Niño, but primarily because of a long-term warming trend driven by human emissions of greenhouse gases.

….
Kyle Armour, Assistant Professor, University of Washington:
It is well known that global temperature falls after receiving a temporary boost from El Niño. The author cherry-picks the slight cooling at the end of the current El Niño to suggest that the long-term global warming trend has ended. It has not.

…..
KEY TAKE-AWAYS
1.Recent record global surface temperatures are primarily the result of the long-term, human-caused warming trend. A smaller boost from El Niño conditions has helped set new records in 2015 and 2016.

…….

2. The article makes its case by relying only on cherry-picked data from specific datasets on short periods.

To understand what was said, I will try to take the broader perspective. That is to see whether the evidence points conclusively to a single long-term warming trend being primarily human caused. This will point to the real reason(or reasons) for downplaying the impact of an extreme El Nino event on record global average temperatures. There are a number of steps in this process.

Firstly to look at the data of rising CO2 levels. Secondly to relate that to predicted global average temperature rise, and then expected warming trends. Thirdly to compare those trends to global data trends using the actual estimates of HADCRUT4, taking note of the consequences of including other greenhouse gases. Fourthly to put the calculated trends in the context of the statements made above.

 

1. The recent data of rising CO2 levels
CO2 accounts for a significant majority of the alleged warming from increases in greenhouse gas levels. Since 1958 CO2 (when accurate measures started to be taken at Mauna Loa) levels have risen significantly. Whilst I could produce a simple graph either the CO2 level rising from 316 to 401 ppm in 2015, or the year-on-year increases CO2 rising from 0.8ppm in the 1960s to over 2ppm in in the last few years, Figure 1 is more illustrative.

CO2 is not just rising, but the rate of rise has been increasing as well, from 0.25% a year in the 1960s to over 0.50% a year in the current century.

 

2. Rising CO2 should be causing accelerating temperature rises

The impact of CO2 on temperatures is not linear, but is believed to approximate to a fixed temperature rise for each doubling of CO2 levels. That means if CO2 levels were rising arithmetically, the impact on the rate of warming would fall over time. If CO2 levels were rising by the same percentage amount year-on-year, then the consequential rate of warming would be constant over time.  But figure 1 shows that percentage rise in CO2 has increased over the last two-thirds of a century.  The best way to evaluate the combination of CO2 increasing at an accelerating rate and a diminishing impact of each unit rise on warming is to crunch some numbers. The central estimate used by the IPCC is that a doubling of CO2 levels will result in an eventual rise of 3C in global average temperatures. Dana1981 at Skepticalscience used a formula that produces a rise of 2.967 for any doubling. After adjusting the formula, plugging the Mauna Loa annual average CO2 levels into values in produces Figure 2.

In computing the data I estimated the level of CO2 in 1949 (based roughly on CO2 estimates from Law Dome ice core data) and then assumed a linear increased through the 1950s. Another assumption was that the full impact of the CO2 rise on temperatures would take place in the year following that rise.

The annual CO2 induced temperature change is highly variable, corresponding to the fluctuations in annual CO2 rise. The 11 year average – placed at the end of the series to give an indication of the lagged impact that CO2 is supposed to have on temperatures – shows the acceleration in the expected rate of CO2-induced warming from the acceleration in rate of increase in CO2 levels. Most critically there is some acceleration in warming around the turn of the century.

I have also included the impact of linear trend (by simply dividing the total CO2 increase in the period by the number of years) along with a steady increase of .396% a year, producing a constant rate of temperature rise.

Figure 3 puts the calculations into the context of the current issue.

This gives the expected temperature linear temperature trends from various start dates up until 2014 and 2016, assuming a one year lag in the impact of changes in CO2 levels on temperatures. These are the same sort of linear trends that the climate experts used in criticizing David Rose. The difference in warming by more two years produces very little difference – about 0.054C of temperature rise, and an increase in trend of less than 0.01 C per decade. More importantly, the rate of temperature rise from CO2 alone should be accelerating.

 

3. HADCRUT4 warming

How does one compare this to the actual temperature data? A major issue is that there is a very indeterminate lag between the rise in CO2 levels and the rise in average temperature. Another issue is that CO2 is not the only greenhouse gas. More minor greenhouse gases may have different patterns if increases in the last few decades. However, the change the trends of the resultant warming, but only but the impact should be additional to the warming caused by CO2. That is, in the long term, CO2 warming should account for less than the total observed.
There is no need to do actual calculations of trends from the surface temperature data. The Skeptical Science website has a trend calculator, where one can just plug in the values. Figure 4 shows an example of the graph, which shows that the dataset currently ends in an El Nino peak.

The trend results for HADCRUT4 are shown in Figure 5 for periods up to 2014 and 2016 and compared to the CO2 induced warming.

There are a number of things to observe from the trend data.

The most visual difference between the two tables is the first has a pause in global warming after 2002, whilst the second has a warming trend. This is attributable to the impact of El Nino. These experts are also right in that it makes very little difference to the long term trend. If the long term is over 40 years, then it is like adding 0.04C per century that long term trend.

But there is far more within the tables than this observations. Concentrate first on the three “Trend in °C/decade” columns. The first is of the CO2 warming impact from figure 3. For a given end year, the shorter the period the higher is the warming trend. Next to this are Skeptical Science trends for the HADCRUT4 data set. Start Year 1960 has a higher trend than Start Year 1950 and Start Year 1970 has a higher trend than Start Year 1960. But then each later Start Year has a lower trend the previous Start Years. There is one exception. The period 2010 to 2016 has a much higher trend than for any other period – a consequence of the extreme El Nino event. Excluding this there are now over three decades where the actual warming trend has been diverging from the theory.

The third of the “Trend in °C/decade” columns is simply the difference between the HADCRUT4 temperature trend and the expected trend from rising CO2 levels. If a doubling of CO2 levels did produce around 3C of warming, and other greenhouse gases were also contributing to warming then one would expect that CO2 would eventually start explaining less than the observed warming. That is the variance would be positive. But CO2 levels accelerated, actual warming stalled, increasing the negative variance.

 

4. Putting the claims into context

Compare David Rose

Stunning new data indicates El Nino drove record highs in global temperatures suggesting rise may not be down to man-made emissions

With Climate Feedback KEY TAKE-AWAY

1.Recent record global surface temperatures are primarily the result of the long-term, human-caused warming trend. A smaller boost from El Niño conditions has helped set new records in 2015 and 2016.

The HADCRUT4 temperature data shows that there had been no warming for over a decade, following a warming trend. This is in direct contradiction to theory which would predict that CO2-based warming would be at a higher rate than previously. Given that a record temperatures following this hiatus come as part of a naturally-occurring El Nino event it is fair to say that record highs in global temperatures ….. may not be down to man-made emissions.

The so-called long-term warming trend encompasses both the late twentieth century warming and the twenty-first century hiatus. As the later flatly contradicts theory it is incorrect to describe the long-term warming trend as “human-caused”. There needs to be a more circumspect description, such as the vast majority of academics working in climate-related areas believe that the long-term (last 50+ years) warming  is mostly “human-caused”. This would be in line with the first bullet point from the UNIPCC AR5 WG1 SPM section D3:-

It is extremely likely that more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings together.

When the IPCC’s summary opinion, and the actual data are taken into account Zeke Hausfather’s comment that the records “are primarily because of a long-term warming trend driven by human emissions of greenhouse gases” is dogmatic.

Now consider what David Rose said in the second article

El Nino is not caused by greenhouse gases and has nothing to do with climate change. It is true that the massive 2015-16 El Nino – probably the strongest ever seen – took place against a steady warming trend, most of which scientists believe has been caused by human emissions.

Compare this to Kyle Armour’s statement about the first article.

It is well known that global temperature falls after receiving a temporary boost from El Niño. The author cherry-picks the slight cooling at the end of the current El Niño to suggest that the long-term global warming trend has ended. It has not.

This time Rose seems to have responded to the pressure by stating that there is a long-term warming trend, despite the data clearly showing that this is untrue, except in the vaguest sense. There data does not show a single warming trend. Going back to the skeptical science trends we can break down the data from 1950 into four periods.

1950-1976 -0.014 ±0.072 °C/decade (2σ)

1976-2002 0.180 ±0.068 °C/decade (2σ)

2002-2014 -0.014 ±0.166 °C/decade (2σ)

2014-2016 1.889 ±1.882 °C/decade (2σ)

There was warming for about a quarter of a century sandwiched between two periods of no warming. At the end is an uptick. Only very loosely can anyone speak of a long-term warming trend in the data. But basic theory hypotheses a continuous, non-linear, warming trend. Journalists can be excused failing to make the distinctions. As non-experts they will reference opinion that appears sensibly expressed, especially when the alleged experts in the field are united in using such language. But those in academia, who should have a demonstrable understanding of theory and data, should be more circumspect in their statements when speaking as experts in their field. (Kyle Armour’s comment is an extreme example of what happens when academics completely suspend drawing on their expertise.)  This is particularly true when there are strong divergences between the theory and the data. The consequence is plain to see. Expert academic opinion tries to bring the real world into line with the theory by authoritative but banal statements about trends.

Kevin Marshall