[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.
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 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.
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.
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.
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.
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.
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.
Nick Stokes says:
August 27, 2013 at 7:18 pm
The +- valies appear on the sidebar, where it says CI from …
So for UAH from January 2005 to August 2013 it says:
Rate: 0.243°C/Century;
CI from -2.769 to 3.255;
So if I add 2.769 to 3.225 and divide by 2 I get +/- 3.012. So does this mean the range for 95% certainty is 0.243 +/- 3.012?
That seems extremely high compared to SkS: +0.013/decade +/- 0.528.
As for the glitch with SST, I noticed that too since when I clicked Hadsst2, Hadsst3 showed up and then it only went to about 2007.
Werner,
I’ll work on it tonight – hope to have it checked by your morning. But I think the UAH may be 5,6 vs 5.5
Dan Pangburn says:
August 27, 2013 at 8:08 pm
The final R2 = 0.9 demonstrates that the hypothesis was valid. After correcting the error identified by Dr. S, R2 is still 0.9 and the graph is barely changed.
I don’t think this is true. Curve fitting often has a high R2 because the curve and the parameters were chosen to produce a good fit. Apart from the mathematical errors there is a much bigger physical error. You make a big deal out of ‘energy conservation’ and ‘thermodynamics’, but those are just words to impress [foremost yourself – Feynman also remarked that ‘the easiest person to fool is oneself’]. Here is the error: you say that energy IN minus energy OUT is the change. Fair enough, but then you assume that the sunspot number represents energy IN, and that is not correct. The yearly sunspot number varies by a factor of a hundred of more (and it should be obvious that energy IN does not vary by over a factor of a hundred – in fact, the variation is 0.1%) while the energy out represented by the temperature varies very little [of the order of one percent]. Integrating has nothing to do with it, because the balance between IN and OUT must hold for each year. You will find very nearly the same fit if you omit the T(i) bit altogether [try it]. You will find that you just have to adjust the value of D, and you get the same curve. So, change the sunspot term to (s(i) – AVERAGE(s)) and you will find the same curve [regardless of T(i)]. This is what I did in my curve http://www.leif.org/research/Roger-Integral-Comparison.png [the red curve is integrated s-AVERAGE(s); the Black area somebody else’s (Roger’s) curve]. Compare that to the result [the blue curve] using your formula http://www.leif.org/research/Dans-Folly.png . I can’t tell the difference. This shows that your idea of energy IN and energy OUT is wrong (which was obvious to me from the outset).
And please tell us which Journal you submitted the paper to. I have asked you now three times. Failure to respond will be taken as admission that no paper was submitted and peer reviewed.
Dan Pangburn says:
August 27, 2013 at 8:08 pm
The final R2 = 0.9 demonstrates that the hypothesis was valid.
A note on integration [of which you claim I know nothing] and my graph http://www.leif.org/research/Roger-Integral-Comparison.png . The blue and pink curves are two reconstructions of the sunspot number [they are hardly different, so I can just take the average]. Calculating the average sunspot number for the interval of integration [1749-today] and subtracting that average gives me the yellow curve, which I integrate from 1749 yielding the heavy red curve. Since the yellow curve has as much area above zero as below zero, the integral over the whole interval will by definition by zero [as is also evident from the figure] and will also be zero at the left edge [as there is nothing to integrate over yet], so any interval over which you integrate [s(i) – AVERAGE(s(i))] will always start at zero and end with zero, which BTW shows that integration as such [i.e. from a fixed point far back in time] is a meaningless thing to do. If you want to integrate you should use a sliding [fixed] window that you move along, the assumption here is that heat is stored for a while only.
Allan MacRae says: August 26, 2013 at 2:50 am: “…
I suggest that global warming hysteria will soon be fully discredited, and its advocates will be held responsible for our lack of preparedness should global cooling occur….”
JimF says: August 26, 2013 at 10:06 am
One hopes you are correct on two accounts (discredit and attribution of responsibility) but wrong about cooling of any significant sort occurring (if that is something you are in fact projecting).
Allan again:
Jim, in 2002 I wrote in a published newspaper article that global cooling would start by 2020-2030. At that time, SC24 was predicted (by Hathaway et al at NASA) to be robust (~150-180), and it now looks very weak (~60). My friend who made the informal global cooling prediction at my request was Paleoclimatologist Tim Patterson, and it was based on his research of natural climate cycles.
Based on more recent information including a very weak SC24 and ten more years of actual data, I suggest that global cooling could start sooner than 2020 (but we may not know this except in hindsight). In 2002 we did not predict the degree of severity of cooling, but SC24 and SC25 look so weak that I suggest significant cooling similar to that experienced during the Dalton or Maunder Minimums is a significant probability. SC25 was predicted by NASA in 2006 to be “one of the weakest in centuries”.
As I stated previously, I hope to be wrong on this informal prediction, since society is completely unprepared for significant global cooling..
On the other hand, I respect Leif’s informed opinion that solar variability is insufficient to drive significant warming or cooling. Let’s hope Leif is correct and my concerns of imminent global cooling are unfounded.
Regards, Allan
P.S. – More on SC25 at http://m.solarcycle25.com/index.php
Leif, In a post above you mentioned a decline in solar magnetic field intensities associated with sunspots that would cause them to become essentially invisible optically. Previously, you have stated that proxies indicated that the solar cycles continued through the Maunder minimum despite the lack of observable sunspots at the time. In the recent post, you also mentioned that TSI would be higher due to the lack of spots and be problematic for cooling.
Wouldn’t the lack of visible sunspots in the Maunder minimum most likely be caused by this same decline in magnetic field intensity and wouldn’t that have resulted in the Sun having greater TSI back then, just as it might in the near future? And, if one is to accept the Sun’s influence during the Maunder minimum was the driving force for the cooling, then the cause would not be reduced TSI? THen would that not suggest the cause was TSI-Albedo and that perhaps Albedo variation was due not to total TSI but rather to either magnet influence or to TSI spectral composition?
best regards.
cba
Werner,
I’ve tracked the reason for the glitches. The new update mechanism got the data right, but uploaded an older version of the javascript. I’ve fixed that.
The older version had another glitch – it had the months for UAH and RSS out by one (because they start in Dec 1978). That had been fixed. That’s why you were able to show Aug 2013. The correct slope to July 2013 (last available) is 0.14 °C/cen. SkS gets 0.13 °C/cen, so they are probably using UAH 5.6.
The discrepancy between my/SkS trend calc and WFT (-0.033°C/cen) is indeed just the difference between UAH 5.5 (WFT) and 5.6 (which I use). SkS has much larger confidence intervals which are a continuing puzzle. I checked using the amira() function in R recommended by Wayne above) which gave for Ar(1) CI’s similar to mine.
In short, SkS and I agree on slope but not CIs; WFT doesn’t AFAIK give CIs, but has the trend for Ver 5.5.
cba says:
August 28, 2013 at 4:31 am
And, if one is to accept the Sun’s influence during the Maunder minimum was the driving force for the cooling, then the cause would not be reduced TSI?
It seems more likely to me that the assumption that the Sun was the driving force for the cooling is wrong. The cosmic ray modulation during the Maunder Minimum was as strong as today so the magnetic field was still there.
Nick Stokes says:
August 28, 2013 at 5:10 am
In short, SkS and I agree on slope but not CIs; WFT doesn’t AFAIK give CIs, but has the trend for Ver 5.5.
Is it possible that you and SkS are talking about different percentages within the CI? As far as I know, if SkS says that if the slope is 0.013 +/- 0.528, then there is a 95% chance the real value is in that range. If your site were to say the same thing, is it also 95% or perhaps 90%? And if there is a discrepancy, which is the one Phil Jones uses?
The SkS site has very recently discontinued Hadcrut3 and I see that you also do not have Hadcrut3. SkS never did have Hadsst2 nor Hadsst3, however I see that you have Hadsst3 up to date now on your site. I expect my next report to be in about 2 months. I will consider switching from Hadsst2 to Hadsst3 and use your site, at least for Hadsst3 if I am satisfied your CIs are in the ball park. Thank you!
Hello Leif,
I hope you are well.
I recall our conversation of 2009 below, and had one further thought.
I said: “Climate change is natural and cyclical” to which you did not disagree.
Let’s assume you are correct, and solar variation is apparently too small to be the driver of global warming and cooling.
Let’s further assume you are correct and global warming and cooling cycles are just somewhat random cycles, and “it just goes up and down”.
Here is my thought:
Maybe in a natural cyclical system such as the global climate cycle that just goes up and down, the small influences of the Sun are enough to, on a somewhat irregular basis, influence the climate to move from a warming to cooling phase and vice-versa. This could explain the less-than-perfect relationship nature of observed global warming and cooling with respect to the solar cycles, for example the somewhat irregular cyclicity of the PDO, AMO etc.
This would seem to be consistent with the nature of chaotic systems – something causes them to change, but that cause can be apparently be quite small, and not always predictable, especially in time.
Best, Allan
http://wattsupwiththat.com/2009/01/10/polar-sea-ice-changes-are-having-a-net-cooling-effect-on-the-climate/#comment-74024
Allan M R MacRae (19:49:11) :
Climate change is natural and cyclical
Leif Svalgaard (19:57:40) :
I would not disagree with that, except for downplaying the ‘cyclic’ bit. I don’t think there is strict cyclicity, just that it ‘goes up and down’.
___________________
Allan again:
Agree the up-and-down cycles are less than perfect – although there is something of interest in the PDO and/or Gleissberg – and possibly also in longer cycles but I haven’t looked at them.
Allan MacRae says:
August 28, 2013 at 9:25 am
Maybe in a natural cyclical system such as the global climate cycle that just goes up and down, the small influences of the Sun are enough to, on a somewhat irregular basis, influence the climate to move from a warming to cooling phase and vice-versa.
Maybe, perhaps, possibly, etc…
Without a mechanism for such ‘nudges’ I would not attach much significance to hypotheticals. But, on the other hand, since we don’t know, anything is possible 🙂 [quoting Al Gore, I think]. For me, the issue boils down to ‘predictability’: can we use the nudge to successfully predict what the outcome will be? If not, we cannot react to it, mitigate it, do something about it, plan ahead, etc… and the practical aspects wane.
Werner,
“Is it possible that you and SkS are talking about different percentages within the CI?”
There is a very small difference. SkS uses 2σ, I use 95%. Both are common and often treated as the same, but 95% is actually 1.96σ, which is what I use. I don’t think that’s the reason.
SkS doesn’t get the same as Phil Jones in that much discussed calc. That’s discussed in the thread at SkS – this is a good starting point. It seems clear Jones was using Ar(1), probably via the Quenouille method, which I use. That process goes back to about 1953, and is widely used. Mine agrees with Jones.
I’ve left a comment as SkS on that same thread, and the response is that they are, following Tamino, using ARMA(1,1). So I checked that, and it does make a very large difference. It’s actually the c(1,0,1) case which Wayne mentioned above as a possibility. So I think that is the reason for the difference.
Hello Nick, I am not at all concerned about the differences between 1.96 and 2 sigma. I see you are working on things when I saw: CI from NaN to NaN
I will check your CI numbers out in about a month and take things from there. Thanks!
Leif,
Do we not have two (Maunder * Dalton) examples where Temperatures took a dive while visible sunspots ‘disappeared’? Are there any examples of the sunspots becoming invisible where we did not experience cooling? If these are correct, it would seem much more likely that there is a causality relationship between them. Granted that the magnetic field + cosmic ray flux affecting cloud cover – either fraction or reflectivity – would seem to be the most likely candidate for the cause and having no significant variation in the magnetic field might put a bit of a damper on that idea. There are still differences, such as the makeup of the higher energy component of the radiation curve which while tiny as TSI goes, still varies by quite a large fraction.
After all, two random variation events corresponding to a visible sunspot absence with no conflicting events would seem to be a much stronger case than a single event having a random correlation with the sunspots. It does offer a falsifiable hypothesis in that any loss of visible sunspot activity that is not associated with a cold spell would falsify this idea.
Cloud formation and cloud reflectivity provides the mechanism for a really powerful and really messy chaotic sort of driver – unlike co2. Many things both internal and external to the Earth can influence this also.
Leif,
Are there any examples of the sunspots becoming invisible where we did not experience cooling?
E.g. around 600 AD, see Slide 20 of http://www.leif.org/research/Does%20The%20Sun%20Vary%20Enough.pdf
Dear Dr. Svalgaard,
If you feel so inclined (perhaps, not until tomorrow, it’s late), your help is needed on this thread (http://wattsupwiththat.com/2013/08/28/another-paper-blames-enso-for-the-warming-hiatus/) starting after about here:
“Strong solar signal” seems a bit inaccurate to me… .
Thanks for taking a look at that thread and (if you do try to correct mistakes over there, THANKS!).
Your grateful student (in the cheap seats),
Janice
Janice Moore says:
August 28, 2013 at 10:05 pm
“Strong solar signal” seems a bit inaccurate to me… .
Worse. It is just hand waving without evidence by one of the ‘usual suspects’. I would not attach much significance to it.
Werner,
The NaN occurs for August, and happens because there’s no data (yet) for that month. July will work. It calculates a trend to August by regarding August as a missing value, but I will disable that, as it is correct but confusing.
I had further correspondence with SkS. Another difference is that they calculate their correlation coefficient for the period 1980-2010,rather than the period of trhe trend.
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lsvalgaard says:
August 28, 2013 at 9:24 pm
Leif,
Are there any examples of the sunspots becoming invisible where we did not experience cooling?
E.g. around 600 AD, see Slide 20 of http://www.leif.org/research/Does%20The%20Sun%20Vary%20Enough.pdf
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Leif,
Looks like a nice presentation slide set. I’m missing the relationship of my question to slide 20. It shows (as I see the wiggles) that there is a drop in TSI corresponding to an increase in T which seems to wiggle agree with C14 & Be10. Considering that 600AD shows a drop in TSI, would that not indicate visibile sunspots? What is the data there which would indicate the solar magnetic field has dropped to the level of invisible spots? Wouldn’t that drop in TSI be indicative of many visible spots? Also, what I’m not seeing concerning invisible spots versus some proxy would itself be an indication of some significant effect on Earth.
My hypothesis is that the driver is absorbed incoming solar power which equals the TSI*(1-albedofraction) or TSI-Albedo (incoming solar power – reflected power). This Albedo is a very dirty noisy signal subject to lots of things but it doesn’t require a high sensitivity to variations in absorbed power.
cba says:
August 29, 2013 at 4:36 am
It shows (as I see the wiggles) that there is a drop in TSI corresponding to an increase in T which seems to wiggle agree with C14 & Be10.
I was presuming too much. We have no measurements of TSI before 1978. Nevertheless it has become convenient [?] to express solar activity in tersm of ‘equivalent TSI’. The graph in question is actually based on cosmic ray proxies [10Be and 14C] ‘converted’ to TSI using a relationship derived from modern data since 1978. So, in a sense is just a plot of ‘solar activity’ in a general sense. The Maunder and Dalton periods [cold] then show up as minima, and does the [warm] period around 650 AD. This is the traditional, generally accepted view. I have been speculating [and note that this is just a ‘wild guess’ although it makes some sense, at least to me] that perhaps TSI was not at minima during the Maunder and Dalton periods, but rather a bit higher than today [because of the lack of dark spots].
This Albedo is a very dirty noisy signal
Agree that the albedo is important, but I consider the albedo to be rather a consequence instead of a cause of climate changes.
Leif,
The reflected power (I use here Albedo) is half the factor that matters – that of absorbed power or TSI-Albedo. That is our real determining factor of conservation of energy and what will or must be in the form of heat and in the change of temperature necessary for balance to exist on average. It doesn’t really matter what each does on their own – TSI up or down, Albedo up or down – only the difference is going to count and it’s got to balance on the moderately short time frame or there will be warming or cooling. It would appear that some large portion of Albedo is in fact a feedback (a negative feedback) so by those horrible ipcc definitions that I prefer to ignore to avoid confusion, it could be said that Albedo will vary as a consequence of other conditions.
Also, in reference to the solar ‘oven’, cooler spots result in less flux radiating from them. However, you have a very averaged generation of energy in the Sun that must also be dealt with on the rather short term. Just because you’re not radiating as much from the spots when present doesn’t mean the total solar luminosity can drop down. The Sun could shed produced energy in other ways, like by slightly expanding the surface to accommodate a slightly lower averaged surface T. If that is true, then the makeup of the radiation spectrum will vary and hence the TSI – Albedo absorbed by Earth can vary even if TSI actually doesn’t.
A last comment before class. Since the majority of Albedo is cloud cover generated at current times, things can work as a water cycle feedback mechanism. In the event of major glaciation or snowball Earth scenarios, the feedback is short circuited and Albedo is always high, limited recovery to typically very long periods.
cba says:
August 29, 2013 at 10:55 am
The Sun could shed produced energy in other ways
What is important is really not what it ‘could’ but what it actually does.
Nick Stokes says:
August 28, 2013 at 10:58 pm
Thank you! I will go all the way with your confidence numbers for my next article. Expect it between October 26 and November 8, depending on how fast Hadcrut3 and 4 come out. If they are really late, I may wait a few days more for the November RSS and UAH to come out. Please be prepared to answer all questions with regards to your method and why I made the correct move to switch.
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Leif Svalgaard says:
August 29, 2013 at 11:10 am
cba says:
August 29, 2013 at 10:55 am
The Sun could shed produced energy in other ways
What is important is really not what it ‘could’ but what it actually does.
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Well that’s rather obvious – as is my, relatively speaking, lack of knowledge concerning the Sun and these secondary factors.
Because the effects of Albedo variation due to cloud (and possibly other atmospheric effects) is so powerful, I don’t quite see how you can maintain the view that it is a consequence of something else. Were the EArth to have no atmospheric albedo contribution, the surface now would have something like an albedo fraction of 0.08, half of that typical of Mars or the Moon. That means 0.22 fraction is the combination of atmospheric effects giving a total of around 0.30. And, clouds are the major portion of that fraction due to the atmospheric factors. As I recall over the relatively short time frame, Palle & Goode found albedo fraction varied by as much as 10% with their efforts indicating that cloud cover is not assured to be some constant value or within a very narrow range of values. Lindzen’s efforts at one time promoted the idea that cloud reflectivity varied due to the nature of the nucleation particles suggesting that a great deal of variation in albedo can occur even with a fixed amount of cloud cover. The factor that the southern hemisphere receives a significantly greater amount of incoming solar power during a year than the northern hemisphere because of the current orbital parameters combined with the fact that most of the higher albedo fraction land mass is in the northern hemisphere and the lower albedo ocean surface is mostly in the southern hemisphere indicates that clouds are doing a tremendous effort in keeping that disparity down and the temperature difference between hemispheres minimized. Clouds, water cycle etc. has to be the 10 ton gorilla in the room.
cba says:
August 29, 2013 at 1:34 pm
Because the effects of Albedo variation due to cloud (and possibly other atmospheric effects) is so powerful, I don’t quite see how you can maintain the view that it is a consequence of something else.
Albedo variation is due to variations in cloud cover [ignoring for a moment show and ice cover] which in turn are due to variations of climate, so I don’t see any contradiction…Albedo is a consequence, not a driver.