RSS Flat For 200 Months (Now Includes July Data)

WoodForTrees.org – Paul Clark – Click the pic to view at source

[NOTE: RSS is a satellite temperature data set much like the UAH dataset from Dr. Roy Spencer and John Christy – Anthony]

Image Credit: WoodForTrees.org

Guest Post By Werner Brozek, Edited By Just The Facts

The graphic above shows 3 lines. The long line shows that RSS has been flat from December 1996 to July 2013, which is a period of 16 years and 8 months or 200 months. The other slightly higher flat line in the middle is the latest complete decade of 120 months from January 2001 to December 2010. The other slightly downward sloping line is the latest 120 months prior from present. It very clearly shows it has been cooling lately, however this cooling is not statistically significant.

In my opinion, if you want to find out what the temperatures are doing over the last 10 or 16 years on any data set, you should find the slope of the line for the years in question. However some people insist on saying global warming is accelerating by comparing the decade from 2001 to 2010 to the previous decade. They conveniently ignore what has happened since January 2011. However, when one compares the average anomaly from January 2011 to the present with the average anomaly from January 2001 to December 2010, the latest quarter decade has the lower number on all six data sets that I have been discussing. Global warming is not even decelerating. In fact, on all six data sets, cooling is actually taking place.

The numbers for RSS for example are as follows: From January 2001 to December 2010, the average anomaly was 0.265. For the last 31 months from January 2011 to July 2013, the average anomaly is 0.184. The difference between these is -0.081. I realize that it is only for a short time, but it is long enough that there is no way that RSS, for example, will show a positive difference before the end of the year. In order for that to happen, we can use the numbers indicated to calculate what is required. Our equation would be (0.184)(31) + 5x = (0.265)(36). Solving for x gives 0.767. This is close to the highest anomaly ever recorded on RSS, which is 0.857 from April 1998. With the present ENSO conditions, there is no way that will happen.

A word to the wise: do not even mention accelerated global warming until the difference is positive on all data sets.

I have added rows 23 to 25 to the table in Section 3 with the intention of updating it with every post. This table shows the numbers that I have given for RSS above as well as the corresponding numbers on the other five data sets I have been discussing. Do you feel this would be a valuable addition to my posts?

(Note: If you read my last article and just wish to know what is new with the July data, you will find the most important new things from lines 7 to the end of the table.)

Below we will present you with the latest fact, 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 several data sets. 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 2013 to date compares with 2012 and the warmest years and months on record so far. The appendix will illustrate sections 1 and 2 in a different way. 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 7 months to 16 years and 8 months.

1. For GISS, the slope is flat since February 2001 or 12 years, 6 months. (goes to July)

2. For Hadcrut3, the slope is flat since April 1997 or 16 years, 4 months. (goes to July)

3. For a combination of GISS, Hadcrut3, UAH and RSS, the slope is flat since December 2000 or 12 years, 8 months. (goes to July)

4. For Hadcrut4, the slope is flat since December 2000 or 12 years, 8 months. (goes to July)

5. For Hadsst2, the slope is flat since March 1997 or 16 years, 4 months. (goes to June) (The July anomaly is out, but it is not on WFT yet.)

6. For UAH, the slope is flat since January 2005 or 8 years, 7 months. (goes to July using version 5.5)

7. For RSS, the slope is flat since December 1996 or 16 years and 8 months. (goes to July) RSS is 200/204 or 98% of the way to Ben Santer’s 17 years.

The next link 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.

WoodForTrees.org – Paul Clark – Click the pic to view at source

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.

Trend1B
Source: WoodForTrees – Paul Clark – click to view at source

Section 2

For this analysis, data was retrieved from SkepticalScience.com. This analysis indicates for how long there has not been statistically 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 statistical 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 Temperature Trend Calculator:

For RSS the warming is not statistically significant for over 23 years.

For RSS: +0.120 +/-0.129 C/decade at the two sigma level from 1990

For UAH the warming is not statistically significant for over 19 years.

For UAH: 0.141 +/- 0.163 C/decade at the two sigma level from 1994

For Hadcrut3 the warming is not statistically significant for over 19 years.

For Hadcrut3: 0.091 +/- 0.110 C/decade at the two sigma level from 1994

For Hadcrut4 the warming is not statistically significant for over 18 years.

For Hadcrut4: 0.092 +/- 0.106 C/decade at the two sigma level from 1995

For GISS the warming is not statistically significant for over 18 years.

For GISS: 0.104 +/- 0.106 C/decade at the two sigma level from 1995

For NOAA the warming is not statistically significant for over 18 years.

For NOAA: 0.085 +/- 0.102 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 statistically significant for each set to their latest update, they are as follows:

RSS since August 1989;

UAH since June 1993;

Hadcrut3 since August 1993;

Hadcrut4 since July 1994;

GISS since January 1995 and

NOAA since June 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. 12a: 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 statistically 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, etc.

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 may 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 30 or 35 minutes into a game. Due to different base periods, the rank may be more meaningful than the average anomaly.

23.new: This gives the average anomaly of the last 31 months on the six data sets I have been discussing, namely from January 2011 to the latest number available.

24.old: This gives the average anomaly of the 120 months before that on the six data sets I have been discussing. The time goes from January 2001 to December 2010.

25.dif: This gives the difference between these two numbers.

Note that in every single case, the difference is negative. In other words, from the previous decade to this present one, global warming is NOT accelerating. As a matter of fact, cooling is taking place.

Source UAH RSS Had4 Had3 Sst2 GISS
1. 12ra 9th 11th 9th 10th 8th 9th
2. 12a 0.161 0.192 0.448 0.406 0.342 0.57
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 8/7 16/8 12/8 16/4 16/4 12/6
8. sig 19 23 18 19 18
Source UAH RSS Had4 Had3 Sst2 GISS
9. Jan 0.504 0.441 0.450 0.390 0.283 0.63
10.Feb 0.175 0.194 0.479 0.424 0.308 0.50
11.Mar 0.183 0.205 0.405 0.384 0.278 0.58
12.Apr 0.103 0.219 0.427 0.400 0.354 0.48
13.May 0.077 0.139 0.498 0.472 0.377 0.56
14.Jun 0.269 0.291 0.451 0.426 0.304 0.66
15.Jul 0.118 0.222 0.514 0.490 0.468 0.54
Source UAH RSS Had4 Had3 Sst2 GISS
21.ave 0.204 0.244 0.459 0.427 0.339 0.564
22.rnk 6th 8th 9th 8th 10th 10th
23.new 0.158 0.184 0.436 0.385 0.314 0.562
24.old 0.187 0.265 0.483 0.435 0.352 0.591
25.dif -.029 -.081 -.047 -.050 -.038 -.029

If you wish to verify all of the latest anomalies, go to the following links, For UAH, version 5.5 was used since that is what WFT used,, RSS, Hadcrut4, Hadcrut3, Hadsst2,and GISS.

To see all points since January 2012 in the form of a graph, see the WFT graph below.

WoodForTrees.org – Paul Clark – Click the pic to view at source

Appendix

In this section, we summarize the data for each set separately.

RSS

The slope is flat since December 1996 or 16 years and 7 months. (goes to June) RSS is 199/204 or 97.5% of the way to Ben Santer’s 17 years.

For RSS the warming is not statistically significant for over 23 years.

For RSS: +0.122 +/-0.131 C/decade at the two sigma level from 1990.

The RSS average anomaly so far for 2013 is 0.248. This would rank 7th 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 statistically 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.

Graph 1 and graph 2.

UAH

The slope is flat since July 2008 or 5 years, 0 months. (goes to June)

For UAH, the warming is not statistically significant for over 19 years.

For UAH: 0.139 +/- 0.165 C/decade at the two sigma level from 1994

The UAH average anomaly so far for 2013 is 0.219. This would rank 4th 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.

Graph 1 and Graph 2.

Hadcrut4

The slope is flat since November 2000 or 12 years, 7 months. (goes to May.)

For Hadcrut4, the warming is not statistically significant for over 18 years.

For Hadcrut4: 0.093 +/- 0.107 C/decade at the two sigma level from 1995

The Hadcrut4 average anomaly so far for 2013 is 0.450. This would rank 9th 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.

Graph 1 and Graph 2.

Hadcrut3

The slope is flat since April 1997 or 16 years, 2 months (goes to May, 2013)

For Hadcrut3, the warming is not statistically significant for over 19 years.

For Hadcrut3: 0.091 +/- 0.110 C/decade at the two sigma level from 1994

The Hadcrut3 average anomaly so far for 2013 is 0.414. This would rank 9th 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.

Graph 1 and Graph 2

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 this.

GISS

The slope is flat since February 2001 or 12 years, 5 months. (goes to June)

For GISS, the warming is not statistically significant for over 18 years.

For GISS: 0.105 +/- 0.110 C/decade at the two sigma level from 1995

The GISS average anomaly so far for 2013 is 0.57. This would rank 9th 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. Graph 1 and Graph 2

Conclusion

So far in 2013, there is no evidence that the pause in global warming has ended. As well, all indications are that RSS will reach Santer’s 17 years in three or four months. The average rank so far is 8.5 on the six data sets discussed here. ENSO has been neutral all year so far and shows no signs of changing. The sun has been in a slump all year and also shows no sign of changing. As far as polar ice is concerned, the area that the north is losing is close to what the south is gaining. So the net effect is that there is little overall change and this also shows no sign of changing.

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RACookPE1978
Editor
August 26, 2013 10:20 am

Dr Svalgaard:
I “think” what these latest writers are trying to propose is the following:
They believe, or think that they have observed, or have actually found that the number of solar cycles have increased recently, and that the maximum number of sunspots in these “faster” cycles has changed from the previous period when temperatures were declining or rising before.
-> Which means they believe there is are more cycles recently, so each cycle with a shorter cycle length than the normal 11 year length. This would mean that, for example, 17-18-19 are slower (longer period) and lower (fewer sunspots) than the group 20-21-22. Now, in the last 15 years, the 23-24 cycles in the period we are in now are even longer and much lower than 20-21-22.
Thus, their theory would hold that: if you integrate several different a series of sunspot cycles, then divide that integral over the same number of years each time, you’d find that the “average integral” – if you want o coin that term – of sunspots per time is changing, and that change corresponds to the past century’s changes in global temperatures: increasing at times, steady at times, and decreasing at times.
Now, I don’t know if this works or not: Maybe it works if you use 33 years (over three normal cycles), or if you use 22 or 11 as the length. Alternately, a shorter cycle with the same peak number of spots means that, over the same length of time, you get more sunspots total. If – BIG IF THERE – sunspots do relate to global temperatures, the latter seems logical, but not necessarily correct!
After all, is not the entire CAGW dogma based on extrapolated “logical” thoughts “projected” into the fat future with “simplistic” straight lines?

August 26, 2013 10:25 am

Isva – The physics is correct and has been peer reviewed.
An earlier version of the equation, with more-detailed description of its derivation can be seen by searching ‘Verification of natural climate change’. I can’t determine what Leif did but the correct graph of the equation is shown as Figure 1 in the climatechange90 link.

August 26, 2013 10:45 am

Dan Pangburn says:
August 26, 2013 at 10:25 am
The physics is correct and has been peer reviewed.
How many peer-reviewed papers are there promoting AGW?
The ‘equation’ is just curve fitting with many parameters [enough to wiggle the elephant’s trunk] and is not ‘physics-based’. The ‘conservation of energy’ bit is the ‘wool’.

Pamela Gray
August 26, 2013 11:45 am

I found lots of published peer-reviewed papers on AGW. I have not found a single peer reviewed paper authored by Dan Pangburn. A blog post is not an author-vetted, peer reviewed research article. I want to see an online journal or paper journal that has a standard vetting and peer-review process of a paper submitted by Dan Pangburn. Is there such a thing? If you make the claim back it up.
I have published in an accepted journal. Been vetted and my work has been peer reviewed. In fact it was rejected by the first journal we submitted to. It was accepted by the second after an improved re-write. But I would certainly not put that stamp of approval out there as some kind of green card that says I know what I am talking about. Having had only 1 article successfully published in a peer-reviewed paper journal is not any kind of seal of approval that I am right, even though I might say, occasionally, and only on this website, and then very rarely, indeed hardly ever, that I am right (right?).
But let’s use Dan’s measuring stick. Heck, I’ve commented enough on WUWT to say that if you put all my comments together I have blogged in public and therefore what I have said has been peer reviewed. Seems pretty thin doesn’t it. Dan are you saying that your blogs have been peer reviewed and therefore demonstrate that your equation is right? That also seems rather thin to me.

August 26, 2013 12:06 pm

Dan Pangburn says:
August 26, 2013 at 10:25 am
The physics is correct and has been peer reviewed.
Well, let us look at the sunspot term: s(i)-43.97*(T(i)/286.8)^4. The first member, s(i), varies from 1.4 to 190.2 while the second member [with T(i)] varies between 44.5 and 45.2 [both for 1895-2013]. That small variation is smaller than the error with which we can determine s(i), so already there the physics goes out the window.

JackT
August 26, 2013 12:11 pm

Werner, I have been playing with SkS trend calculator using your RSS example, from 1990 to 2013 that you have as +0.120/decade with +- 0.129/decade. Your figures do not match what the SkS calculates.
I must be entering the wrong dates. What fractional (decimal) dates did you enter into the ‘Start’ and ‘End’ fields at SkS?
Thanks.

JustAnother
August 26, 2013 12:37 pm

I think rgbs mini essays are utterly brilliant

Tom in Florida
August 26, 2013 1:28 pm

RACookPE1978 says:
August 26, 2013 at 10:20 am
” Which means they believe there is are more cycles recently, so each cycle with a shorter cycle length than the normal 11 year length. This would mean that, for example, 17-18-19 are slower (longer period) and lower (fewer sunspots) than the group 20-21-22. Now, in the last 15 years, the 23-24 cycles in the period we are in now are even longer and much lower than 20-21-22. ”
First of all, there is no “normal” 11 year cycle. Second of all, I have no idea what the rest means. Have a look:
No Length SSN
1 11.0 87
2 9.0 106
3 9.2 154
4 13.6 131
5 12.1 47
6 12.9 46
7 10.6 71
8 9.6 138
9 12.5 125
10 11.2 96
11 11.7 139
12 10.7 64
13 12.1 85
14 11.9 66
15 10.0 104
16 10.2 78
17 10.4 110
18 10.1 152
19 10.6 190
20 11.6 105
21 10.3 155
22 9.8 158
23 12.1 120

August 26, 2013 1:50 pm

Pam – Congratulations on getting all the way through the process.
As I said, the physics has been peer reviewed (it is a straight-forward application of the first law of thermodynamics and some calculations using readily available data). The paper has not yet been published.
I have yet to see a single paper that actually demonstrates AGW without using the results from GCMs. There are lots of things wrong with the GCMs. I discuss some of them in http://consensusmistakes.blogspot.com/. There is also a plethora of published papers that describe various catastrophes IF the planet warms much more.
Hundreds of billions of dollars have been wasted in failed attempts using super computers to demonstrate that added atmospheric CO2 is a primary cause of global warming and in misguided activities to try to do something about it. A lot of reputations are at stake. It is not going down easy.
The CO2 level continues to go up while the average global temperature doesn’t. Apparently, the separation between the rising CO2 level and not-rising agt will need to get much wider for the AGW mistake to become evident to the deniers of natural climate change.

August 26, 2013 1:54 pm

JackT says:
August 26, 2013 at 12:11 pm
Werner, I have been playing with SkS trend calculator using your RSS example, from 1990 to 2013 that you have as +0.120/decade with +- 0.129/decade. Your figures do not match what the SkS calculates.
I must be entering the wrong dates. What fractional (decimal) dates did you enter into the ‘Start’ and ‘End’ fields at SkS?
Thanks.

If you want to start with 1990 and end with the latest date, do not put in an end date but leave it blank. By putting in 2013 as an end date, you are really only going to December 31, 2012.
In order to calculate the month where the warming is not significant, I plot the following for January to December respectively: 1989.00, 1989.08, 1989.17, 1989.25, 1989.33, 1989.42, 1989.50, 1989.58, 1989.67, 1989.75, 1989.83, and 1989.92. It turns out that 1989.50 gives: Trend: 0.124 ±0.124 °C/decade (2σ) (July). And 1989.58 gives: Trend: 0.123 ±0.125 °C/decade (2σ) (August). So I made the claim that warming was not significant since August. You may ask why not July? The reason is that I cannot be sure about July. The numbers are given to 3 significant digits and 0.124 ±0.124 could in fact be 0.1242 ±0.1238 for example. This would not include 0 so I play it safe.

August 26, 2013 2:01 pm

Isv – Apparently you do not understand how numerical integration works. Your statement is not relevant.

RACookPE1978
Editor
August 26, 2013 2:11 pm

Tom in Florida says:
August 26, 2013 at 1:28 pm
Remember, I have no “side” or position in this discussion. I’m simply trying to determine what one possible interpretation of “integral of sunspot count” might mean.
Maybe it matters, maybe it doesn’t matter. Don’t know.
But, note that your value for “sunspot count” is only for the peak of the cycle, at its peak.
How many spots – total! were in each cycle, and how long did the sunspots last in each cycle? Perhaps the spots in 20-21-22 lasted substantially longer than did those appearing now? Perhaps we have more small spots now, fewer cluster than before? We do know we are seeing a lower visible sunspots. Perhaps some other change is occurring as well.
If I looked only at reflected light in the Arctic, but didn’t know that there were many times less cloud cover in Feb and March than in August and July, I would generate the wrong heat balance for all 4 months. Can you say we know everything about the sun’s long-term cycles today? Can you tell WHY the present cycle is so much lower than 20-21-22? We do know it IS lower, but why is it lower now, instead of for example, being lower cycle 25 or 27 instead of 24?

August 26, 2013 2:16 pm

Dan Pangburn says:
August 26, 2013 at 2:01 pm
Isv – Apparently you do not understand how numerical integration works. Your statement is not relevant.
Regardless, to show that you understand your own equation, give us here for the first five years [1895, 1896, …] the values of s(i) and of 43.97*(T(i)/286.8)^4 and of T(i), as your reply was not responsive. It is a common subterfuge of pseudo-scientists to claim that nobody understands their methods and that everybody else is a moron, so show us that you do not fall in that category.

Gail Combs
August 26, 2013 2:43 pm

thingadonta says:
August 25, 2013 at 7:41 pm
Unfortunately, some years ago the data became too readily available, and they couldnt make adjustments anymore, strange that the warming stopped at about the same time.
>>>>>>>>>>>>>>>>>>>>>>
Also at about the same time the satellite data became available and the difference in trends was noted and remarked on. GISS Divergence with satellite temperatures since the start of 2003

August 26, 2013 2:49 pm

Isv – Apparently you don’t see that conservation of energy is applied. In the equation, energy IN is proportional to the integral of S(i). Energy OUT is proportional to the integral of T(i)^4. The difference between them, ENERGY CHANGE, is proportional to the temperature anomaly. The excellent correlation (R2=0.9) is demonstration that the initial hypothesis (energy IN is proportional to sunspot number time-integral) was valid.
An earlier version of the equation, with more-detailed description of its derivation can be seen by searching “Verification of natural climate change”. That might help.

Jurgen
August 26, 2013 2:53 pm

Werner Brozek says:
August 25, 2013 at 2:49 pm
(…) RSS is a satellite data set (…)

Thanks for the reply, Werner. I was off for a while, so am a bit late in responding.

Pamela Gray
August 26, 2013 2:57 pm

Well, somebody must have entered the data calculations to get the graph you have Dan. If you were the one to enter this information for each variable and calculate the results, you should be able to pound out those first 5 data sets in about 5 minutes (and don’t be like the warmers who say they have lost the hard copies). I guess if you don’t have the first 5 years of the values for each part of the variables, who does?

August 26, 2013 3:15 pm

Dan Pangburn says:
August 26, 2013 at 2:49 pm
Isv – Apparently you don’t see that conservation of energy is applied. In the equation, energy IN is proportional to the integral of S(i). Energy OUT is proportional to the integral of T(i)^4. The difference between them, ENERGY CHANGE, is proportional to the temperature anomaly.
Apart from what you just said being nonsense, you are not responsive. Please post the s(i) and T(i) for the first five years.

August 26, 2013 3:19 pm

The tired line that “this 10 warmest years were all in the last 11 years” is very irrelevant. Let’s use an analogy of a flooding river (river level compared to temperature anomaly). Once the river crests, residance don’t care that it is still higher than usual, they only care that the water level is going down. Allegidly, 2012 was the 8th or 11th warmest year on record. Well, that is great, that is well down from the peak in 1998 or 2010.

August 26, 2013 4:03 pm

Isv – I am not sure how the formatting works so here are the values that you asked for in sequence 1895, 1896…:
S(i)
63.9667
41.8083
26.2417
26.7167
12.1083
T(i) (This is an average of the 4 reporting agencies normalized to HadCRUT4)
286.741
286.896
286.872
286.709
286.825
43.97*(T(i)/286.8)^4
43.9337
44.0290
44.0139
43.9144
43.9854
After applying the effective capacitance factor and the coefficient, B, the resulting influence of sunspot number on average global temperature is
0.03622
0.03558
0.03046
0.02550
0.01631
To this must be added the ocean oscillation effect as determined using coefficient A,
0.175
0.1625
0.15
0.1375
0.125
and the offset, D giving the final result
-0.2088
-0.2219
-0.2395
-0.2570
-0.2787
As shown in the Figure 1 graph at the climatechange90 link.
It is an EXCEL file. I had combined the variables a bit differently. EXCEL does the heavy lifting (calculating R2 for non-linear data is especially comput intensive).

August 26, 2013 4:11 pm

rgbatduke: some exelent posts. An error I noted in your second large post. Although fresh water is most dense at 4°C, sea water acts like other compounds. It gets more and more dense until it freezes at aroung -1.7°C. I was courious about this last week, and did a bit of googling. It was kind of supprising. I figured the temperature of maximum density would very, but to find out that the anomaly compleatly dissapears was not expected.

August 26, 2013 6:36 pm

rgbatduke: Two things:
(1) Great explanation of why I (and so many others) know without inspecting them that those models have to be wrong.
(2) But throwing things in like “In a linear system, perhaps, although it would bother me to linearize any system that has a thermal oscillation in precise counterphase with the primary driver” is bound to make people scratch their heads. What does that mean? Surely you’re not saying a linear system couldn’t respond 180 degrees out of phase with its stimulus.

August 26, 2013 7:35 pm

Dan Pangburn says:
August 26, 2013 at 4:03 pm
Lsv – I am not sure how the formatting works so here are the values that you asked for in sequence 1895, 1896…:
S(i) 63.9667 41.8083 26.2417 26.7167 12.1083
T(i) 286.741 286.896 286.872 286.709 286.825
2nd mbr 43.9337 44.0290 44.0139 43.9144 43.9854
After applying the effective capacitance factor and the coefficient, B, the resulting influence of sunspot number on average global temperature is
0.03622 0.03558 0.03046 0.02550 0.01631

Using your numbers I get
0.00519 -0.00058 -0.00461 -0.00446 -0.00994
Let me do that in steps for the first number for 1895:
1) T(1895)/286.8 = 286.741/286.8 = 0.999794
2) to fourth power 0.999177
3) multiply by 43.97 = 43.9338, close to yours 43.9337
4) subtract from S(1895) 63.9667 – 43.9338 = 20.0329
5) apply capacitance factor 20.0329/17 = 1.1784
6) multiply by B 0.004407 * 1.1784 = 0.00519
and so on for the other years.
But then you forgot to integrate [doesn’t matter for the first year]. If I integrate, I get:
0.00519 0.00462 0.00001 -0.00445 -0.01439
Adding the A and the D has nothing to do with the sunspots, but we can add in D [=-0.4145, as it just moves the curve a bit], to get -0.40931 -0.40988 -0.41449 -0.41895 -0.42889
Plotting everything gives me this:
http://www.leif.org/research/Dans-Folly.png
I would not call that “astoundingly well”. Actually, I would say it is “crummy” instead.
Now, your good correlation comes from the A term which does not depend on the sunspot number, but is actually a bit of circular ‘logic’ as you inject knowledge of the climate into your formula.

Janice Moore
August 26, 2013 9:57 pm

Well done, Dr. Svalgaard.
I throw you a rose for your persevering patience with Mr. Pangborn. LOL, first he calls you “Isva,” then, “Isv”. To your glory, you overlooked his insults. Leif Svalgaard knows nothing of integrals. (head shake)
“Rock is heavy and sand a burden,
but the provocation of a fool who can bear?”
WAY TO GO, LEIF SVALGAARD! (yes, I SHOUT it, #(:))
(and Pamela Gray and others, too!)
P.S. I was so relieved to read your comment at 4:29pm on August 25th. For months, I have wanted very much for you to be one of the “good guys” (defined: one NOT promoting CAGW). Until this evening, I was not sure; you only seemed to be adamant to not let us anti-CAGWers use the Sun to argue against CAGW and that implied (I did not conclude) you were pro-CAGW. I’m so glad to discover that you who were not (overtly) for us were not against us.
.
.
.
“Dan’s Folly” lol. Sounds like a name for a race horse. Hm.
OKAY, OKAY!
Back to science!

policycritic
August 27, 2013 4:00 am

JustAnother says:
August 26, 2013 at 12:37 pm
I think rgbs mini essays are utterly brilliant

Me too.