Image Credit: WoodForTrees.org
Guest Post By Werner Brozek, Edited By Just The Facts
*At least April data was my intention. However as of June 8, HadCRUT3 for April is still not up! Could it be because as of the end of March, the slope of 0 lasted 16 years and 1 month and they do not want to add another month or two? What do you think? WoodForTrees (WFT) is up to date however, thank you very much Paul!
The graph above shows a few different things for three data sets where there has been no warming for at least 16 years. WFT only allows one to draw straight lines between two points, however climate does not go in straight lines. Often, temperatures vary in a sinusoidal fashion which cannot yet be shown using WFT. However we can do the next best thing and show what is happening over the first half of the 16 years and what is happening over the last half. As shown, the first half shows a small rise and the last half shows a small decline. Note that neither the rise in the first half nor the drop in the last half is statistically significant. However the lines do suggest that we are just continuing a 60 year sine wave that was started in 1880 according to the following graphic:
Do you agree? What are your views on the question in the title? Do you think we are presently in a pause or in a decline or neither?
In the sections below, we will present you with the latest facts. The information will be presented in three sections and an appendix. The first section will show the period that there has been no warming for various data sets. The second section will show the period that there has been no “significant” warming on several data sets. The third section will show how 2013 to date compares with 2012 and the warmest years and months on record. The appendix illustrate sections 1 and 2 in a different format. Graphs and a table will be used to illustrate the data.
Section 1
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. 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.
On all data sets below, the different times for a slope that is at least very slightly negative ranges from 8 years and 5 months to 16 years and 6 months.
1. For GISS, the slope is flat since January 2001 or 12 years, 4 months. (goes to April)
2. For Hadcrut3, the slope is flat since March 1, 1997 or 16 years, 1 month. (goes to March 31, 2013)
3. For a combination of GISS, Hadcrut3, UAH and RSS, the slope is flat since December 2000 or 12 years, 6 months. (This goes to May. I realize that Hadcrut3 is not up to date, but on the basis of its present slope and the latest numbers that I do have from the other three sets. I am confident that I can make this prediction.)
4. For Hadcrut4, the slope is flat since November 2000 or 12 years, 6 months. (goes to April)
5. For Hadsst2, the slope is flat from March 1, 1997 to April 30, 2013, or 16 years, 2 months.
6. For UAH, the slope is flat since January 2005 or 8 years, 5 months. (goes to May)
7. For RSS, the slope is flat since December 1996 or 16 years and 6 months. (goes to May) RSS is 198/204 or 97% of the way to Ben Santer’s 17 years. This 97% is real!
The next graph shows just the lines to illustrate the above for what can be shown. 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 sloped wiggly line shows how CO2 has increased over this period.
When two things are plotted as I have done, the left only shows a temperature anomaly. It goes from 0.1 C to 0.6 C. A change of 0.5 C over 16 years is about 3.0 C over 100 years. And 3.0 C is about the average of what the IPCC says may be the temperature increase by 2100.
So for this to be the case, the slope for all of the data sets would have to be as steep as the CO2 slope. Hopefully the graphs show that this is totally untenable.
The next graph shows the above, but this time, the actual plotted points are shown along with the slope lines and the CO2 is omitted.
Section 2
For this analysis, data was retrieved from SkepticalScience.com. This analysis indicates for how long there has not been significant warming according to their criteria. The numbers below start from January of the year indicated. Data go to their latest update for each set. In every case, note that the magnitude of the second number is larger than the first number so a slope of 0 cannot be ruled out. (To the best of my knowledge, SkS uses the same criteria that Phil Jones uses to determine significance.)
The situation with GISS, which used to have no statistically significant warming for 17 years, has now been changed with new data. GISS now has over 18 years of no statistically significant warming. As a result, we can now say the following: On six different data sets, there has been no statistically significant warming for between 18 and 23 years.
The details are below and are based on the SkS site:
For RSS the warming is not significant for over 23 years.
For RSS: +0.123 +/-0.131 C/decade at the two sigma level from 1990
For UAH the warming is not significant for over 19 years.
For UAH: 0.142 +/- 0.166 C/decade at the two sigma level from 1994
For Hadcrut3 the warming is not significant for over 19 years.
For Hadcrut3: 0.092 +/- 0.112 C/decade at the two sigma level from 1994
For Hadcrut4 the warming is not significant for over 18 years.
For Hadcrut4: 0.093 +/- 0.108 C/decade at the two sigma level from 1995
For GISS the warming is not significant for over 18 years.
For GISS: 0.103 +/- 0.111 C/decade at the two sigma level from 1995
For NOAA the warming is not significant for over 18 years.
For NOAA: 0.085 +/- 0.104 C/decade at the two sigma level from 1995
If you want to know the times to the nearest month that the warming is not significant for each set to their latest update, they are as follows:
RSS since August 1989;
UAH since June 1993;
Hadcrut3 since July 1993;
Hadcrut4 since July 1994;
GISS since October 1994 and
NOAA since May 1994.
Section 3
This section shows data about 2013 and other information in the form of a table. The table shows the six data sources along the top and bottom, namely UAH, RSS, Hadcrut4, Hadcrut3, Hadsst2, and GISS. Down the column, are the following:
1. 12ra: This is the final ranking for 2012 on each data set.
2. 12an: Here I give the average anomaly for 2012.
3. year: This indicates the warmest year on record so far for that particular data set. Note that two of the data sets have 2010 as the warmest year and four have 1998 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 two 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.
8. sig: This is the whole number of years for which warming is not significant according to the SkS criteria. The additional months are not added here, however for more details, see Section 2.
9. Jan: This is the January, 2013, anomaly for that particular data set.
10. Feb: This is the February, 2013, anomaly for that particular data set.
11. Mar: This is the March, 2013, anomaly for that particular data set.
12. Apr: This is the April, 2013, anomaly for that particular data set.
13. May: This is the May, 2013, anomaly for that particular data set.
21. ave: This is the average anomaly of all months to date taken by adding all numbers and dividing by the number of months. However if the data set itself gives that average, I use their number. Sometimes the number in the third decimal place differs by one, presumably due to all months not having the same number of days.
22. rnk: This is the rank that each particular data set would have if the anomaly above were to remain that way for the rest of the year. Of course it won’t, but think of it as an update 20 or 25 minutes into a game. Expect wild swings from month to month at the start of the year. As well, expect huge variations between data sets at the start. Due to different base periods, the rank may be more meaningful than the average anomaly.
| Source | UAH | RSS | Had4 | Had3 | Sst2 | GISS |
|---|---|---|---|---|---|---|
| 1. 12ra | 9th | 11th | 9th | 10th | 8th | 9th |
| 2. 12an | 0.161 | 0.192 | 0.448 | 0.405 | 0.342 | 0.56 |
| 3. year | 1998 | 1998 | 2010 | 1998 | 1998 | 2010 |
| 4. ano | 0.419 | 0.55 | 0.547 | 0.548 | 0.451 | 0.66 |
| 5. mon | Ap98 | Ap98 | Ja07 | Fe98 | Au98 | Ja07 |
| 6. ano | 0.66 | 0.857 | 0.829 | 0.756 | 0.555 | 0.93 |
| 7. y/m | 8/5 | 16/6 | 12/6 | 16/1 | 16/2 | 12/4 |
| 8. sig | 19 | 23 | 18 | 19 | 18 | |
| 9. Jan | 0.504 | 0.441 | 0.450 | 0.390 | 0.283 | 0.61 |
| 10.Feb | 0.175 | 0.194 | 0.479 | 0.424 | 0.308 | 0.52 |
| 11.Mar | 0.183 | 0.204 | 0.411 | 0.387 | 0.278 | 0.58 |
| 12.Apr | 0.103 | 0.219 | 0.425 | 0.353 | 0.50 | |
| 13.May | 0.074 | 0.139 | ||||
| 21.ave | 0.208 | 0.239 | 0.440 | 0.401 | 0.306 | 0.553 |
| 22.rnk | 6th | 8th | 11th | 12th | 11th | 10th |
| Source | UAH | RSS | Had4 | Had3 | Sst2 | GISS |
If you wish to verify all of the latest anomalies, go to the following links, UAH,
For RSS, Hadcrut4, Hadcrut3, Hadsst2,and GISS.
To see all points since January 2012 in the form of a graph, see the WFT graph below:
I wish to make a comment about this graph from WFT. It is right up to date. The only reason that both HadCRUT3 and WTI only go to March is because WTI uses 4 data sets, one of which is HadCRUT3, so if HadCRUT3 is not there for April, WTI cannot be there for April as well.
Appendix
In this part, we are summarizing data for each set separately.
RSS
The slope is flat since December 1996 or 16 years and 6 months. (goes to May) RSS is 198/204 or 97% of the way to Ben Santer’s 17 years.
For RSS the warming is not significant for over 23 years.
For RSS: +0.123 +/-0.131 C/decade at the two sigma level from 1990.
The RSS average anomaly so far for 2013 is 0.239. This would rank 8th if it stayed this way. 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 2012 was 0.192 and it came in 11th.
Following are two graphs via WFT. Both show all plotted points for RSS since 1990. Then two lines are shown on the first graph. The first upward sloping line is the line from where warming is not significant according to the SkS site criteria. The second straight line shows the point from where the slope is flat.
The second graph shows the above, but in addition, there are two extra lines. These show the upper and lower lines using the SkS site criteria. Note that the lower line is almost horizontal but slopes slightly downward. This indicates that there is a slight chance that cooling has occurred since 1990 according to RSS
UAH
The slope is flat since January 2005 or 8 years, 5 months. (goes to May)
For UAH, the warming is not significant for over 19 years.
For UAH: 0.142 +/- 0.166 C/decade at the two sigma level from 1994
The UAH average anomaly so far for 2013 is 0.208. This would rank 6th if it stayed this way. 1998 was the warmest at 0.419. The highest ever monthly anomaly was in April of 1998 when it reached 0.66. The anomaly in 2012 was 0.161 and it came in 9th.
Following are two graphs via WFT. Everything is identical as with RSS except the lines apply to UAH.
Hadcrut4
The slope is flat since November 2000 or 12 years, 6 months. (goes to April.)
For Hadcrut4, the warming is not significant for over 18 years.
For Hadcrut4: 0.093 +/- 0.108 C/decade at the two sigma level from 1995
The Hadcrut4 average anomaly so far for 2013 is 0.440. This would rank 11th if it stayed this way. 2010 was the warmest at 0.547. The highest ever monthly anomaly was in January of 2007 when it reached 0.829. The anomaly in 2012 was 0.448 and it came in 9th.
Following are two graphs via WFT. Everything is identical as with RSS except the lines apply to Hadcrut4.
Hadcrut3
The slope is flat since March 1 1997 or 16 years, 1 month (goes to March 31, 2013)
For Hadcrut3, the warming is not significant for over 19 years.
For Hadcrut3: 0.092 +/- 0.112 C/decade at the two sigma level from 1994
The Hadcrut3 average anomaly so far for 2013 is 0.401. This would rank 12th if it stayed this way. 1998 was the warmest at 0.548. The highest ever monthly anomaly was in February of 1998 when it reached 0.756. One has to go back to the 1940s to find the previous time that a Hadcrut3 record was not beaten in 10 years or less. The anomaly in 2012 was 0.405 and it came in 10th.
Following are two graphs via WFT. Everything is identical as with RSS except the lines apply to Hadcrut3.
Hadsst2
For Hadsst2, the slope is flat since March 1, 1997 or 16 years, 2 months. (goes to April 30, 2013).
The Hadsst2 average anomaly for the first four months for 2013 is 0.306. This would rank 11th if it stayed this way. 1998 was the warmest at 0.451. The highest ever monthly anomaly was in August of 1998 when it reached 0.555. The anomaly in 2012 was 0.342 and it came in 8th.
Sorry! The only graph available for Hadsst2 is the following
this.
GISS
The slope is flat since January 2001 or 12 years, 4 months. (goes to April)
For GISS, the warming is not significant for over 18 years.
For GISS: 0.103 +/- 0.111 C/decade at the two sigma level from 1995
The GISS average anomaly so far for 2013 is 0.553. This would rank 10th if it stayed this way. 2010 was the warmest at 0.66. The highest ever monthly anomaly was in January of 2007 when it reached 0.93. The anomaly in 2012 was 0.56 and it came in 9th.
Following are two graphs via WFT. Everything is identical as with RSS except the lines apply to GISS.
Conclusion
Above, various facts have been presented along with sources from where all facts were obtained. Keep in mind that no one is entitled to their own facts. It is only in the interpretation of the facts for which legitimate discussions can take place. After looking at the above facts, do you think that we should spend billions to prevent the claimed catastrophic anthropogenic global warming? Or do you think we should take a “wait and see” attitude for a few years to be sure that future warming will be as catastrophic as some claim it will be? Keep in mind that even the MET office felt the need to revise its forecasts. Look at the following and keep in mind that the MET office believes that the 1998 mark will be beaten by 2017. Do you agree?
By the way, here is an earlier prediction by the MET office:
“(H)alf of the years after 2009 are predicted to be hotter than the current record hot year, 1998.”
When this prediction was made, they had Hadcrut3 and so far, the 1998 mark has not been broken on Hadcrut3. 2013 is not starting well if they want a new record in 2013. Here are some relevant facts today: The sun is extremely quiet; ENSO has been between 0 and -0.5 since the start of the year; it takes at least 3 months for ENSO effects to kick in and the Hadcrut3 average anomaly after March was 0.401 which would rank it in 12th place. Granted, it is only 3 months, but you are not going to set any records starting the race in 12th place after three months. So even if a 1998 type El Nino started to set in tomorrow, it would be at least 4 or 5 months for the maximum ENSO reading to be reached. Then it would take at least 3 more months for the high ENSO to be reflected in Earth’s temperature. How hot would November and December then have to be to set a new record? In my opinion, the odds of setting a new record in 2013 are extremely remote.
RichardLH the eye is only too good at seeing signals that don’t actually exist in noise, which is why we have statistics. If you use moving average FILTERS, the output will accentuate any variability that is matched to that FILTER, it doesn’t necessarily mean anything. I said “statistically significant” for a good reason, it is a very useful sanity check that is absent from most discussions of trends and cycles in the blogsphere.
However, in this case I suspect the oscillation you are seeing there is a mixture of ENSO and volcanic activity, superimposed on a longer terms warming trend. Satelite temperature datasets are not the only evidence for ENSO or volcanic activity.
barry says:
June 10, 2013 at 9:19 am
If we get 25 years of flat or negative trend, then the models are definitely broken.
If we assume this is the new goal post, then in the meantime, are the world governments willing to not waste money on something that may not be a problem after all?
dikranmarsupial says:
June 10, 2013 at 9:47 am
“RichardLH the eye is only too good at seeing signals that don’t actually exist in noise, which is why we have statistics. If you use moving average FILTERS, the output will accentuate any variability that is matched to that FILTER, it doesn’t necessarily mean anything. I said “statistically significant” for a good reason, it is a very useful sanity check that is absent from most discussions of trends and cycles in the blogsphere.”
The definition of statistically significant is defined by the predictive model you use, and at the core of that (autoregressive forecasting) model you find a FILTER. And there has just been a debate between Keenan and the Met Office which filter to use. Keenan won.
http://suyts.wordpress.com/2013/06/09/keenan-confirmed-met-position-laid-to-utter-waste/
So when you say, don’t use filters, you are saying, do not make statements about statistical significance / do not use models.
dikranmarsupial says:
June 10, 2013 at 9:47 am
“RichardLH the eye is only too good at seeing signals that don’t actually exist in noise, which is why we have statistics.”
Hmmm. Centered moving average filters are quite good at removing most high pass components in their input. They do have a tendacy to add a ‘ring’ in the output (due to sampling with a square wave) but that can be minimised by multiplying the span value by 1.3371 at each stage which is what is being shown here. This creates a well defined low pass filter with minimal distortions in the output.
In any case what is being detected here is the nodal points. Those lead to the periodic signals. That can be shown with poorer filters as well.
Indeed if you want statistics then by all means distribute the measured values around the curves produced by the interaction of the 37 month, 4 year and ~60 year periodic features visible in the data.
wbrozek wrote: “If we assume this [If we get 25 years of flat or negative trend, then the models are definitely broken.] is the new goal post, …”
The goalposts are set by the credible interval of the model projections, when the observations stray outside those, then youcan claim the observations are inconsistent with the models. Nobody is shifting the goalposts, but it is worth checking out the playing field first to find out where they are before entering the game.
RichardLH, I note that your reply did not contain a statement about how the statistical significance of your oscillation was obtained.
Note I did point out that I accept that there is an oscillation there as there are other lines of physical evidence.
the fact that the only chart in this post that shows a timeline greater than 16 years has a prequalifier of “recovery from little ice age” (as if that is a scientific assertion) is very very telling.
reminds me of a book
The fact that both the politically liberal and politically conservative believe in anthropogenic climate change but the very conservative politically DON’T is also very very telling.
[Snip. Stop it. — mod.]
DirkH wrote “So when you say, don’t use filters, you are saying, do not make statements about statistical significance / do not use models.”
I didn’t say don’t use flters, I did say *do* use tests of statistical significance (if only as a sanity check) the two are not mutually exclusive.
Nothing short of a statistically significant drop in Global Mean Temperature occurring continuously over an extended period of time — probably no less than thirty years, and likely no less than fifty years — might cause the climate science community to reexamine their basic AGW narrative.
We have not seen anything in the way of a true public debate over the validity of current AGW theory, one that the average Joe and Jane might pay any real attention to.
A truly broadscope public examination of climate science will not begin until after politicians have imposed severe constraints on the public’s access to liquid carbon fuels, either through raising the price of carbon fuels artificially or through a program of outright fuel rationing as was done in World War II.
The late economist John Kenneth Galbraith, a commodity allocation administrator in World War II, noted that gasoline was far and away the most difficult commodity to ration, and even the government’s own administrators never did believe they were very effective at it.
“dikranmarsupial says:
June 10, 2013 at 10:03 am
RichardLH, I note that your reply did not contain a statement about how the statistical significance of your oscillation was obtained.”
Can i suggest that you apply this very, very simple statistical test to the referenced image.
Place your finger between red and black diamonds. Can you see more or less of the points locally if you choose red-black or back-red?
I’ll let you derive a more statiscal result for confirmation if you wish.
RichardLH, I note that you are still avoiding the topic of statistical significance. There is nothing wrong with not being able to assess the statistical significance of a finding, there is everything wrong with not being able to admit it and trying to evade the issue. Sorry, life is too short.
dikranmarsupial : Try this for a first pass statistical distribution.
http://s1291.photobucket.com/user/RichardLH/media/uahtrendsinflectionfuture_zps7451ccf9.png.html
According to über-warmist Phil Jones, there has been no statistically significant global warming for the past 16+ years.
RichardLH wrote “[something else that avoided the issue of statistical significance]”
Also according to Phil Jones, the most recent warming trend was no different from other past warming trends, which occurred when CO2 was much lower. Thus, the entire CO2=AGW conjecture is completely deconstructed. Sorry, life is too short to make that dog hunt.
dikranmarsupial : Try this for a first pass statistical distribution.
Sorry that should be the newer image not the older one.
http://i1291.photobucket.com/albums/b550/RichardLH/uahtrendsinflectionfuturedistibution_zps0232ae24.png
If you know a good statatician then I am sure they will confirm it with precise values.
jai mitchell says:
June 10, 2013 at 10:06 am
“book: http://en.wikipedia.org/wiki/File:Lies_and_the_lying_liars.jpg ”
Book: http://en.wikipedia.org/wiki/When_Prophecy_Fails
dbstealy yes, Phil Jones did say that, becuase scientists have a tendency to give direct answers to direct questions. He also went on to explain that this is pretty much what you would expect if you looked at a trend calculated over such a short period. It is clear that he at least understands the concept of the statistical power of a test and what “statistically significant” actually means (and what it doesn’t mean).
To be clear “no statistically significant warming” doesn’t mean “no warming”, it just means (loosely speaking) that the observations do not rule out the possibility that there had been no warming.
RichardLH, that STILL isn’t a test of statistical significance.
dikranmarsupial says:
“He also went on to explain that this is pretty much what you would expect if you looked at a trend calculated over such a short period.”
Thank you for giving your translation of Phil Jones’ statement. But I note that Jones has not corrected it — and I further note that the ‘no warming’ trend has been extended by a couple more years.
You cannot have it both ways. If global warming had continued, the alarmist crowd would be beating skeptics over the head with that fact. But global warming has stopped for at least a decade and a half, and you cannot credibly complain now that by parsing Jones’ words you can change that fact.
The endless and almost universal predictions of runaway global warming have failed. Those predictions turned out to be flat wrong, all of them, as any honest scientist will now acknowledge.
As a scientific skeptic I was fully prepared to accept the CO2=CAGW conjecture in the mid- to late-1990’s. But the real world intervened, and set me straight. The difference between alarmists and skeptics is that when real world facts and empirical observations falsify a conjecture, skeptics acceot reality — while alarmists attempt to make excuses for their failed predictions.
dbsteally wrote “Thank you for giving your translation of Phil Jones’ statement.”
actually I read the interview, so I know what he actually said, which was:
“Yes, but only just. I also calculated the trend for the period 1995 to 2009. This trend (0.12C per decade) is positive, but not significant at the 95% significance level. The positive trend is quite close to the significance level. Achieving statistical significance in scientific terms is much more likely for longer periods, and much less likely for shorter periods. ”
Note the part where he relates the difficulty of achieving statistical significance to the length of the period.
There is a good reason for the error bars: calibration is not perfect, equipment is not perfect, and humans are not perfect. The trend could easily be negative; we just don’t know. So we use error bars. And thus, there was neither warming nor cooling as far as we can tell. Again, you can’t have it both ways. Error bands are only statistical probabilities, not testable reality.
Also, the endless alarming predictions were for catastrophic runaway global warming. Even I suspected that was occurring. But Planet Earth showed me I was in error, so I adjusted my view accordingly.
Since the planet has warmed along the same long term trend line since the beginning of thermometer records, without any acceleration despite the large rise in CO2, it seems obvious that CO2 is not the cause of that warming.
Most likely the recent warming step is a continuing recovery from the depths of the LIA, and as such it will stall for a while, then resume. But CO2 — whether human-emitted or natural — has nothing measurable to do with global warming.
wbrozek says:
June 10, 2013 at 9:02 am
“”Henry Galt says:
June 10, 2013 at 8:14 am
This, folks, IS cognitive dissonance in action. “”
Sorry – I was unclear as you probably guessed from my next post.
‘barry’ is a welter of dissonance, some of it cognitive 😉
Your analysis, however, I use all the time and thank you for it here.
“dikranmarsupial says:
June 10, 2013 at 10:31 am
RichardLH, that STILL isn’t a test of statistical significance.”
I would have thought that a better than 90% chance that data points fall within an envelope dictated by the observed periodic feature is a pretty good correlation.