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.
dbstealy wrote “…Error bands are only statistical probabilities, not testable reality…”
actually there is a direct equivalence between “error bands” (i.e. confidence intervals) and statistical tests, see http://www.itl.nist.gov/div898/handbook/prc/section1/prc15.htm In this case, the test for the statistical significance of a warming trend is to see if the confidence interval excludes zero. However as I said, the lack of statistically significant warming does not imply that there has been no warming, just that the observations do not rule out the possibility that there has been no warming.
If you don’t undestand that, then I am not surprised that you misinterpreted Phil Jones’ statement.
jai mitchell says:
June 10, 2013 at 10:05 am
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.
Are you disputing the recovery from the LIA? FYI, the LIA was one of the coldest episodes of the entire Holocene. We are fortunate that the climate is rebounding.
And that recovery is happening despite — not because of — the rise in CO2. The long term global warming trend line has not accelerated, despite the ≈40% rise in [harmless, beneficial] CO2. Global warming has actually stopped for the past decade and a half, despite the large rise in CO2.
Thus, “carbon” is causing no measurable rise in global warming. None at all. That conjecture is being debunked by Planet Earth, to the great consternation of the alarmist crowd.
RichardLH a correllation is just a measure of the similarity of two signals, but how do you know if the correllation is high enough to be confident that the apparent correlation does not arise through random chance? That is determined by statistical significance testing. No amount of plotting data and smoothing of the data or visual inspection can determine that, it requeires formal statistical analysis.
dikranmarsupial,
Mile thick glaciers could once again cover Chicago, and you would still be a CAGW True Believer. I would ask you: how many more years of no global warming would it take for you to admit that your CAGW conjecture is wrong? But I’ve asked that many times, with no credible answer.
That demonstrates the difference between scientific skeptics and climate alarmists: skeptics pay attention to reality, while alarmists only pay attention to their belief system. It’s science vs religion; emotion vs logic. Reality vs Belief.
dikranmarsupial: I understand correlation which is why I used it. Correlation between the observed data and a peridoicity in that data.
I have no doubt that the distribution in the UAH data into the future will be matched by by a function that only requires white noise and 37 month, 4 year and ~60 year cycles with appropriate phase and magnitude.
@jai mitchell says:
June 10, 2013 at 10:05 am
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.
————————————-
Still awaiting your response to my question asking to what you imagine scientists attribute the recovery, 1850-1945, from low temperatures during the LIA, if not to natural causes, since CO2 didn’t really take off until after WWII.
On what possible can you doubt that temperatures recovered from the LIA when that’s what the data show? Granted that valid observation, what do you suppose explains about a century of net warming, without benefit of elevated carbon dioxide concentrations?
Please reply this time. Thanks.
Also, among the many other areas of your apparent abject databaselessness is the number of leftwing AGW skeptics, such as radical environmentalist Dr. Denis Rancourt or the co-founder of Greenpeace, Patrick Moore. Liberals who care about humans still mired in poverty support energy enrichment over the destructive, deadly fantasy of AGW.
Do you also imagine that the astronauts & NASA mission controllers who wrote in protest against Hansen’s lunacies are all “very conservative”, despite having spent their careers as federal employees?
RichardLH, you are missing the point, there is nothing wrong with using a correllation, it is a perfectly good statistic for many tasks. The question though is how high a correllation needs to be to be confident it isn’t spurious. This is why scientists go a step further and ask how likely we would see a correllation that high if the observations were simply noise (the usual null hypothesis, but this isn’t always the appropriate choice). They call this the p-value and “reject the null hypothesis” if this probability is below some threshold (commonly 0.05). They do this as a sanity check to avoid jumping to conclusions when they see an apparently good correllation between variables in an analysis (or equivalently the fit of a model). It is this step that you have missed out, but it is a step that involves a fair bit of statistical background. There is nothing wrong with not having this background, but it does mean that you need to be very cautious in interpreting your findings.
As I said, I have no problem agreeeing that there is an oscillation in the data that really does mean somthing about the climate, however the fact that real physical processes can be identified that explain it is much better evidence than a correllation. I am a statistcian and as such I am normally much more easily convinced by physics than I am by statistics, as I know how easy it is to be misled by statistics unless you take the proper steps, such as hypothesis testing (which is deeply flawed, by a useful sanity check nevertheless).
dikranmarsupial says:
June 10, 2013 at 10:01 am
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, …”
Nobody is shifting the goalposts
In that case, the following comment by Phil Jones seems very strange:
Phil Jones, July 5, 2005:
“The scientific community would come down on me in no uncertain terms if I said the world had cooled from 1998. Okay it has but it is only seven years of data and it isn’t statistically significant.”
http://mnichopolis.hubpages.com/hub/ClimateGate-The-Smoking-Gun-email
dikranmarsupial: Have you looked at the data analysis I provided? Does it represent a fair summary of the data so far? Is it reasonable to assume that it provides at least some clue as to the likely future?
dbstealy wrote “Mile thick glaciers could once again cover Chicago…” however this was simply evasion to distract from his errors regarding what Prof. Jones actually said and about the relationship between confidence intervals (“error bands”) and testing. It is this sort of thing that gives skeptics a bad name IMHO. Science is a search for the truth and if you have to indulge in evasion its an indication that your position isn’t too solid. Compare this with Phil Jones who was willing to give a completely straight answer about whether the trend was significant or not.
RichardLH, you are still missing the point. There is a good reason why scientists use statistical hypothesis testing. I’ve tried to explain it to you, but you don’t seem to be listening. It does provide a clue, but (a) why bother with a “clue” when there is an “physical explanation” and (b) why pay attention to a “clue” when no attempt has been made to establish whether the clue is likely to be spurious? Getting to grips with the data is a good thing, but don’t stop at plotting graphs, move on to the next step and you will have a really useful tool for spotting and refuting bogus arguments.
Dikran Marsupial,
See Werner Brozek’s comment @11:37 above.
I understand how and why scientists use statistics to demonstrate vadility in their conclusions.
You evaded my question, Does my data summary represent a fair view of the dtaa so far and its likely future?
jai mitchell says:
June 10, 2013 at 10:05 am
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.
*********************
What is telling is your use of “believe in”, since CACCA fits perfectly Feynman’s concept of a cult.
You’ve been repeatedly shown the valid science behind the non-controversial recovery from the LIA. Clearly, objective reality is not your bag.
Suggest you read your book about lying again, this time with more attention & comprehension.
dikranmarsupial says:
June 10, 2013 at 10:48 am
Note the part where he relates the difficulty of achieving statistical significance to the length of the period.
That is very true. But it is also true that if there were decent warming, then it could be seen in 16 years as the following shows. Note the second row.
Start of 1995 to end 2009: 0.135 +/- 0.147. Warming for 15 years is not significant.
Start of 1995 to end 2010: 0.138 +/- 0.132. Warming for 16 years is significant.
Start of 1995 to end 2011: 0.111 +/- 0.121. Warming for 17 years is not significant.
Start of 1995 to end 2012: 0.098 +/- 0.112. Warming for 18 years is not significant.
And the present situation is that since July 1994 the number is 0.102 ±0.103 °C/decade (2σ). That is almost 19 years.
In response to my saying “Nobody is shifting the goalposts…” wbrozek wrote
‘In that case, the following comment by Phil Jones seems very strange:
Phil Jones, July 5, 2005:
“The scientific community would come down on me in no uncertain terms if I said the world had cooled from 1998. Okay it has but it is only seven years of data and it isn’t statistically significant.”’
However it is worth looking at what was deleted from “”Nobody is shifting the goalposts…”, what I actually wrote was:
“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.”
Jones’s stament has precisely nothing to do with the credible interval on the model projections, so it is hardly an answer to my statement about the position of the goal posts. I’m sorry but this selective quoting is simply disingenuous, and I am not prepared to continue the discussion if this is the level to which you will stoop.
For reference, Jones’ comment isn’t strange at all, all he is saying is that you shouldn’t make claims SOLELY ON THE BASIS OF THE OBSERVATIONS unless there is statistically significant evidence to support your assertion. This isn’t rocket science, it is normal scientific practice 101.
Henry Galt says:
June 10, 2013 at 11:08 am
Sorry – I was unclear as you probably guessed from my next post.
Thank you for clarifying that.
JM VanWinkle says:
June 9, 2013 at 1:16 pm
Worse case is this is a start of the glaciation phase, perhaps triggered by obliquity, negative ocean oscillations confluence, and the solar minimum. In which case the 70′s worry of glaciation was just premature. Yeah, it is just a guess, but does anyone have a good handle on what initiates the glaciation phase?…
>>>>>>>>>>>>>>>>>>>
SEE:
In order of publication
http://wattsupwiththat.com/2011/01/05/on-“trap-speed-acc-and-the-snr/
http://wattsupwiththat.com/2010/12/30/the-antithesis/
http://wattsupwiththat.com/2012/10/02/can-we-predict-the-duration-of-an-interglacial/
dikranmarsupial says:
June 10, 2013 at 11:57 am
“This isn’t rocket science, it is normal scientific practice 101.”
Normal science is, if you observe patterns in a data set then you try and determine how those patterns arise and their causes.
wbrozek wrote “That is very true. But it is also true that if there were decent warming, then it could be seen in 16 years as the following shows. Note the second row.”
“could” yes, but that is not the same thing as “should”, for that you need to make sure the test has adequate statistical power by using a sufficiently long period (and you need to stop cherry picking the start date as this invalidates the test for the reasons I explained earlier).
Kelvin Vaughan says:
June 9, 2013 at 1:38 pm
Every month this year in Central England has been below the 136 year average and I’m freezing.
>>>>>>>>>>>>>>>>>>>
I am in the ‘sunny south’ aka mid North Carolina and we have yet to see 90F (32 C) at my local airport. Ten years ago we had 6 days above 90 in April and 17 days above 90 in May. (Summer what Summer?)
RichardLH wrote “Normal science is, if you observe patterns in a data set then you try and determine how those patterns arise and their causes.”
yes, and ironically it was me that pointed out that the most likely causes for the observed patterns were ENSO and volcanic activity!
Frank Mlinar says:
June 10, 2013 at 11:28 am
What I am saying is until one identifies and accounts for the contributors, one cannot say which way the temperatures are going long term.
Scientists have a pretty good idea what the sun might be doing 2 years from now, but they have no clue if we will have an El Nino or La Nina or neutral conditions 2 years from now. So while we do not know the future, it seems clear we should not spend huge amounts of money on something that may turn out not to be a problem.
I think you still miss my point. Yes temperatures have been stable over the last 16 (?) years, but it is bad science to try to extrapolate from that small amount of data. The temperature may actually go down for a few years, but the overall trend is increasingly up. So I can predict that the temperatures in the future will be higher. That is why looking at the signals and determining what can be done is important. Again: http://www.woodfortrees.org/plot/hadcrut3vgl/mean:50/plot/esrl-co2/offset:-315/scale:0.008/offset:-0.2/plot/crutem4vgl/mean:50/plot/hadsst2gl/mean:50
dikranmarsupial says:
June 10, 2013 at 12:10 pm
“yes, and ironically it was me that pointed out that the most likely causes for the observed patterns were ENSO and volcanic activity!”
All I am doing is pointing out that there is other periodicity in the data which needs explaining.
The analysis shows that there are strong 37 month, 4 year and partial ~60 year cycles beating in the data. It seems reasonable to conclude that this will continue into the future.
RichardLH wrote “The analysis shows that there are strong 37 month, 4 year and partial ~60 year cycles beating in the data. It seems reasonable to conclude that this will continue into the future.”
You didn’t establish the 60 year cycle as over such a short timespan there isn’t enough data to distinguish it from a linear trend. A statistical hypothesis test might have told you that as a model that involves a linear trend, ENSO and volcanoes would be the obvious null hypothesis, which is basically what Foster and Rahmstorf’s well discussed paper actually does.