Guest Post by Professor Robert Brown from Duke University and Werner Brozek, Edited by Just The Facts:
The above graphic shows RSS having a slope of zero from both January 1997 and March 2000. As well, GISS shows a positive slope of 0.012/year from both January 1997 and March 2000. This should put to rest the notion that the strong El Nino of 1998 had any lasting affect on anything. Why is there such a difference between GISS and RSS? That question will be explored further below.
The previous post had many gems in the comments. I would like to thank firetoice2014 for their comment that inspired the title of this article.
I would also like to thank sergeiMK for very good comments and questions here. Part of their comment is excerpted below:
So you are basically stating that all major providers of temperature series of either
1 being incompetent
2 purposefully changing the data to match their belief.”
Finally, I would like to thank Professor Brown for his response. With some changes and deletions, it is reproduced below and ends with rgb.
August 14, 2015 at 12:06 pm
Note well that all corrections used by USHCN boil down to (apparently biased) thermometric errors, errors that can be compared to the recently discovered failure to correctly correct for thermal coupling between the actual measuring apparatus in intake valves in ocean vessels and the incoming seawater that just happened to raise global temperatures enough to eliminate the unsightly and embarrassing global anomaly “Pause” in the latest round of corrections to the major global anomalies; they are errors introduced by changing the kind of thermometric sensors used, errors introduced by moving observation sites around, errors introduced by changes in the time of day observations are made, and so on. In general one would expect measurement errors in any
given thermometric time series, especially when they are from highly diverse causes, to be as likely to cool the past relative to the present as warm it, but somehow, that never happens. Indeed, one would usually expect them to be random, unbiased over all causes, and hence best ignored in statistical analysis of the time series.
Note well that the total correction is huge. The range is almost the entire warming reported in the form of an anomaly from 1850 to the present.
Would we expect the sum of all corrections to any good-faith dataset (not just the thermometric record, but say, the Dow Jones Industrial Average “DJIA”) to be correlated, with, say, the height of my grandson (who is growing fast at age 3)? No, because there is no reasonable causal connection between my grandson’s height and an error in thermometry. However, correlation is not causality, so both of them could be correlated with time. My grandson has a monotonic growth over time. So does (on average, over a long enough time) the Dow Jones Industrial Average. So does carbon dioxide. So does the temperature anomaly. So does (obviously) the USHCN correction to the temperature anomaly. We would then observe a similar correlation between carbon dioxide in the atmosphere and my grandson’s height that wouldn’t necessarily mean that increasing CO2 causes growth of children. We would observe a correlation between CO2 in the atmosphere and the DJIA that very likely would be at least partly causal in nature, as CO2 production produces energy as a side effect and energy produces economic prosperity and economic prosperity causes, among other things, a rise in the DJIA.
In Nicholas Nassim Taleb’s book The Black Swan, he describes the analysis of an unlikely set of coin flips by a naive statistician and Joe the Cab Driver. A coin is flipped some large number of times, and it always comes up heads. The statistician starts with a strong Bayesian prior that a coin, flipped should produce heads and tails roughly equal numbers of times. When in a game of chance played with a friendly stranger he flips the coin (say) ten times and it turns up heads every time (so that he loses) he says “Gee, the odds of that were only one in a thousand (or so). How unusual!” and continues to bet on tails as if the coin is an unbiased coin because sooner or later the laws of averages will kick in and tails will occur as often as heads or more so, things will balance out.
Joe the Cab Driver stopped at the fifth or sixth head. His analysis: “It’s a mug’s game. This joker slipped in a two headed coin, or a coin that it weighted to nearly always land heads”. He stops betting, looks very carefully at the coin in question, and takes “measures” to recover his money if he was betting tails all along. Or perhaps (if the game has many players) he quietly starts to bet on heads to take money from the rest of the suckers, including the naive statistician.
An alternative would be to do what any business would do when faced with an apparent linear correlation between the increasing monthly balance in the company presidents personal account and unexplained increasing shortfalls in total revenue. Sure, the latter have many possible causes — shoplifting, accounting errors, the fact that they changed accountants back in 1990 and changed accounting software back in 2005, theft on the manufacturing floor, inventory errors — but many of those changes (e.g. accounting or inventory) should be widely scattered and random, and while others might increase in time, an increase in time that matches the increase in time in the president’s personal account when the president’s actual salary plus bonuses went up and down according to how good a year the company had and so on seems unlikely.
So what do you do when you see this, and can no longer trust even the accountants and accounting that failed to observe the correlation? You bring in an outside auditor, one that is employed to be professionally skeptical of this amazing coincidence. They then check the books with a fine toothed comb and determine if there is evidence sufficient to fire and prosecute (smoking gun of provable embezzlement), fire only (probably embezzled, but can’t prove it beyond all doubt in a court of law, continue observing (probably embezzled, but there is enough doubt to give him the benefit of the doubt — for now), or exonerate him completely, all income can be accounted for and is disconnected from the shortfalls which really were coincidentally correlated with the president’s total net worth.
Until this is done, I have to side with Joe the Cab Driver. Up until the latest SST correction I was managing to convince myself of the general good faith of the keepers of the major anomalies. This correction, right before the November meeting, right when The Pause was becoming a major political embarrassment, was the straw that broke the p-value’s back. I no longer consider it remotely possible to accept the null hypothesis that the climate record has not been tampered with to increase the warming of the present and cooling of the past and thereby exaggerate warming into a deliberate better fit with the theory instead of letting the data speak for itself and hence be of some use to check the theory.
The bias doesn’t even have to be deliberate in the sense of people going “Mwahahahaha, I’m going to fool the world with this deliberate misrepresentation of the data”. Sadly, there is overwhelming evidence that confirmation bias doesn’t require anything like deliberate dishonesty. All it requires is a failure in applying double blind, placebo controlled reasoning in measurements. Ask any physician or medical researcher. It is almost impossible for the human mind not to select data in ways that confirm our biases if we don’t actively defeat it. It is as difficult as it is for humans to write down a random number sequence that is at all like an actual random number sequence (go on, try it, you’ll fail). There are a thousand small ways to make it so. Simply considering ten adjustments, trying out all of them on small subsets of the data, and consistently rejecting corrections that produce a change “with the wrong sign” compared to what you expect is enough. You can justify all six of the corrections you kept, but you couldn’t really justify not keeping the ones you reject. That will do it. In fact, if you truly believe that past temperatures are cooler than present ones, you will only look for hypotheses to test that lead to past cooling and won’t even try to think of those that might produce past warming (relative to the present).
Why was NCDC even looking at ocean intake temperatures? Because the global temperature wasn’t doing what it was supposed to do! Why did Cowtan and Way look at arctic anomalies? Because temperatures there weren’t doing what they were supposed to be doing! Is anyone looking into the possibility that phenomena like “The Blob” that are raising SSTs and hence global temperatures, and that apparently have occurred before in past times, might make estimates of the temperature back in the 19th century too cold compared to the present, as the existence of a hot spot covering much of the pacific would be almost impossible to infer from measurements made at the time? No, because that correction would have the wrong sign.
So even like this excellent discussion on Curry’s blog where each individual change made by USHCN can be justified in some way or another which pointed out — correctly, I believe — that the adjustments were made in a kind of good faith, that is not sufficient evidence that they are not made without bias towards a specific conclusion that might end up with correction error greater than the total error that would be made with no correction at all. One of the whole points about error analysis is that one expects a priori error from all sources to be random, not biased. One source of error might not be random, but another source of error might not be random as well, in the opposite direction. All it takes to introduce bias is to correct for all of the errors that are systematic in one direction, and not even notice sources of error that might work the other way. It is why correcting data before applying statistics to it, especially data correction by people who expect the data to point to some conclusion, is a place that angels rightfully fear to tread. Humans are greedy pattern matching engines, and it only takes one discovery of a four leaf clover correlated with winning the lottery to overwhelm all of the billions of four leaf clovers that exist but somehow don’t affect lottery odds in the minds of many individuals. We see fluffy sheep in the clouds, and Jesus on a burned piece of toast.
But they aren’t really there.
In the sections below, as in previous posts, we will present you with the latest facts. The information will be presented in three sections and an appendix. The first section will show for how long there has been no warming on some data sets. At the moment, only the satellite data have flat periods of longer than a year. The second section will show for how long there has been no statistically significant warming on several data sets. The third section will show how 2015 so far compares with 2014 and the warmest years and months on record so far. For three of the data sets, 2014 also happens to be the warmest year. The appendix will illustrate sections 1 and 2 in a different way. Graphs and a table will be used to illustrate the data.
This analysis uses the latest month for which data is available on WoodForTrees.com (WFT). All of the data on WFT is also available at the specific sources as outlined below. We start with the present date and go to the furthest month in the past where the slope is a least slightly negative on at least one calculation. So if the slope from September is 4 x 10^-4 but it is – 4 x 10^-4 from October, we give the time from October so no one can accuse us of being less than honest if we say the slope is flat from a certain month.
1. For GISS, the slope is not flat for any period that is worth mentioning.
2. For Hadcrut4, the slope is not flat for any period that is worth mentioning.
3. For Hadsst3, the slope is not flat for any period that is worth mentioning.
4. For UAH, the slope is flat since April 1997 or 18 years and 4 months. (goes to July using version 6.0)
5. For RSS, the slope is flat since January 1997 or 18 years and 7 months. (goes to July)
The next graph shows just the lines to illustrate the above. Think of it as a sideways bar graph where the lengths of the lines indicate the relative times where the slope is 0. In addition, the upward sloping blue line at the top indicates that CO2 has steadily increased over this period.
When two things are plotted as I have done, the left only shows a temperature anomaly.
The actual numbers are meaningless since the two slopes are essentially zero. No numbers are given for CO2. Some have asked that the log of the concentration of CO2 be plotted. However WFT does not give this option. The upward sloping CO2 line only shows that while CO2 has been going up over the last 18 years, the temperatures have been flat for varying periods on the two sets.
For this analysis, data was retrieved from Nick Stokes’ Trendviewer available on his website <a href=”http://moyhu.blogspot.com.au/p/temperature-trend-viewer.html”. This analysis indicates for how long there has not been statistically significant warming according to Nick’s criteria. Data go to their latest update for each set. In every case, note that the lower error bar is negative so a slope of 0 cannot be ruled out from the month indicated.
On several different data sets, there has been no statistically significant warming for between 11 and 22 years according to Nick’s criteria. Cl stands for the confidence limits at the 95% level.
The details for several sets are below.
For UAH6.0: Since November 1992: Cl from -0.007 to 1.723
This is 22 years and 9 months.
For RSS: Since February 1993: Cl from -0.023 to 1.630
This is 22 years and 6 months.
For Hadcrut4.4: Since November 2000: Cl from -0.008 to 1.360
This is 14 years and 9 months.
For Hadsst3: Since September 1995: Cl from -0.006 to 1.842
This is 19 years and 11 months.
For GISS: Since August 2004: Cl from -0.118 to 1.966
This is exactly 11 years.
This section shows data about 2015 and other information in the form of a table. The table shows the five data sources along the top and other places so they should be visible at all times. The sources are UAH, RSS, Hadcrut4, Hadsst3, and GISS.
Down the column, are the following:
1. 14ra: This is the final ranking for 2014 on each data set.
2. 14a: Here I give the average anomaly for 2014.
3. year: This indicates the warmest year on record so far for that particular data set. Note that the satellite data sets have 1998 as the warmest year and the others have 2014 as the warmest year.
4. ano: This is the average of the monthly anomalies of the warmest year just above.
5. mon: This is the month where that particular data set showed the highest anomaly. The months are identified by the first three letters of the month and the last two numbers of the year.
6. ano: This is the anomaly of the month just above.
7. y/m: This is the longest period of time where the slope is not positive given in years/months. So 16/2 means that for 16 years and 2 months the slope is essentially 0. Periods of under a year are not counted and are shown as “0”.
8. sig: This the first month for which warming is not statistically significant according to Nick’s criteria. The first three letters of the month are followed by the last two numbers of the year.
9. sy/m: This is the years and months for row 8. Depending on when the update was last done, the months may be off by one month.
10. Jan: This is the January 2015 anomaly for that particular data set.
11. Feb: This is the February 2015 anomaly for that particular data set, etc.
17. ave: This is the average anomaly of all months to date taken by adding all numbers and dividing by the number of months.
18. rnk: This is the rank that each particular data set would have for 2015 without regards to error bars and assuming no changes. Think of it as an update 35 minutes into a game.
If you wish to verify all of the latest anomalies, go to the following:
For UAH, version 6.0beta3 was used. Note that WFT uses version 5.6. So to verify the length of the pause on version 6.0, you need to use Nick’s program.
For Hadsst3, see: http://www.cru.uea.ac.uk/cru/data/temperature/HadSST3-gl.dat
For GISS, see:
To see all points since January 2015 in the form of a graph, see the WFT graph below. Note that UAH version 5.6 is shown. WFT does not show version 6.0 yet. Also note that Hadcrut4.3 is shown and not Hadcrut4.4, which is why the last few months are missing for Hadcrut.
As you can see, all lines have been offset so they all start at the same place in January 2015. This makes it easy to compare January 2015 with the latest anomaly.
In this part, we are summarizing data for each set separately.
The slope is flat since January 1997 or 18 years, 7 months. (goes to July)
For RSS: There is no statistically significant warming since February 1993: Cl from -0.023 to 1.630.
The RSS average anomaly so far for 2015 is 0.302. This would rank it as 6th place. 1998 was the warmest at 0.55. The highest ever monthly anomaly was in April of 1998 when it reached 0.857. The anomaly in 2014 was 0.255 and it was ranked 6th.
The slope is flat since April 1997 or 18 years and 4 months. (goes to July using version 6.0beta3)
For UAH: There is no statistically significant warming since November 1992: Cl from -0.007 to 1.723. (This is using version 6.0 according to Nick’s program.)
The UAH average anomaly so far for 2015 is 0.215. This would rank it as 3rd place, but just barely. 1998 was the warmest at 0.483. The highest ever monthly anomaly was in April of 1998 when it reached 0.742. The anomaly in 2014 was 0.170 and it was ranked 6th.
The slope is not flat for any period that is worth mentioning.
For Hadcrut4: There is no statistically significant warming since November 2000: Cl from -0.008 to 1.360.
The Hadcrut4 average anomaly so far for 2015 is 0.686. This would set a new record if it stayed this way. The highest ever monthly anomaly was in January of 2007 when it reached 0.832. The anomaly in 2014 was 0.564 and this set a new record.
For Hadsst3, the slope is not flat for any period that is worth mentioning. For Hadsst3: There is no statistically significant warming since September 1995: Cl from -0.006 to 1.842.
The Hadsst3 average anomaly so far for 2015 is 0.519. This would set a new record if it stayed this way. The highest ever monthly anomaly was in August of 2014 when it reached 0.644. The anomaly in 2014 was 0.479 and this set a new record.
The slope is not flat for any period that is worth mentioning.
For GISS: There is no statistically significant warming since August 2004: Cl from -0.118 to 1.966.
The GISS average anomaly so far for 2015 is 0.80. This would set a new record if it stayed this way. The highest ever monthly anomaly was in January of 2007 when it reached 0.96. The anomaly in 2014 was 0.74 and it set a new record.
There might be compelling reasons why each new version of a data set shows more warming than cooling over the most recent 15 years. But after so many of these instances, who can blame us if we are skeptical?