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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Nick Stokes
August 29, 2013 4:13 pm

Werner,
There’s isn’t really one “correct” method for calculating CI’s here. Tamino/SkS have quite a good argument to support what they do, but it may overshoot a bit on se. I’ve adopted what has been a long-time standard (AR(1) with Quenouille, which may undershoot) – there’s also the practical issue that it’s simple enough for me to program in Javascript.
I’m planning to write a blog post on the issue.

August 29, 2013 4:40 pm

Nick Stokes says:
“Tamino/SkS have quite a good argument to support what they do…”
As if.
Yo, Nick, why don’t you submit your blog post here? Then you can see what it’s like to have your argument ripped to shreds.
So there’s your challenge. Post your article here. Find out what peer review is like in the real world.

Werner Brozek
August 29, 2013 5:08 pm

Nick Stokes says:
August 29, 2013 at 4:13 pm
Thank you. I have compared some numbers and the earliest date with SkS for RSS was August 1989, but yours was December 1992. And for Hadcrut4, SkS had July 1994 and yours had July 1996. That spread of two or more years is a bit more than I would have liked to have seen.
You say one method may overshoot and the other may undershoot. Are you suggesting the ideal is somewhere in between? On the other hand, as far as I know, all of your numbers show more than 15 years at the 95% level as per NOAA’s criteria, so I will point that out in my next article. So if a method that may undershoot shows climate models to be in trouble, that makes a strong point.

cba
August 29, 2013 7:45 pm


Leif Svalgaard says:
August 29, 2013 at 1:46 pm
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.

Agreed that Albedo variation is primarily cloud cover. Cloud covers are affected by many factors – or at least hypothesized to be affected by many factors which are not variations of climate. I would think that variations of climate would be simply a difference in temperature over time.
That I have a problem thinking about as being a factor that determines cloud cover or reflectivity which is what will change the Albedo. For one thing, the consequences of the change will render essentially a massive immediate response. A drop in T will lower the amount of cloud cover due to less h2o vapor present. More energy will come thru and where ever there is liquid h2o, you will get evaporation, heat transfer up into the atmosphere and almost assuredly, added cloud cover which will reduce the incoming power – a strong negative feedback – or more aptly in the engineering venacular – a feedback stableized setpoint control system. Too much Albedo and you’ve got a reduction of absorbed solar power and hence less power to be absorbed to drive h2o evaporation and cloud formation.
Basically, I’m having trouble conceptualizing some sort of long term cloud cover / reflectivity shifts as being caused by some climate change. However, I can conceive of a long term change in cloud cover/reflectivity shifting the climate or rather the T to another average value. There are too many factors affecting clouds in the shorter term for there to be an actual fixed value that isn’t bouncing all over the place. It should be obvious that the cloud cover tends to have a negative feedback. There should also be a causality. Either long term shifts in Albedo cause the T shift and hence the climate change or the climate change – the T shift?- causes the Albedo long term change.
What long term Albedo control factor do you think is controlled by the climate change?

August 29, 2013 7:58 pm

cba says:
August 29, 2013 at 7:45 pm
What long term Albedo control factor do you think is controlled by the climate change?
Evaporation [for clouds]. Snow/Ice cover [glaciation]. Volcanoes [nuclear winter]. You can probably think of others.

August 29, 2013 8:20 pm

Werner Brozek says: August 29, 2013 at 5:08 pm
“You say one method may overshoot and the other may undershoot. Are you suggesting the ideal is somewhere in between?”

I’m basically saying there is no ideal, although if there were, then “in between” is a reasonable guess.
I think you’ll find that it’s mainly skeptics who want to talk about statistical significance of temperature trend relative to zero, and this ambiguity is part of the reason scientists don’t focus on it. It’s really up to the people who want to talk about it to say what their version means and why they think it’s important. I quote the standard measure because I think that’s what people want.
Normally you use stat sig when you are deducing something from observation; it saves you from misinterpreting randomness. It’s not the right consideration when you’re looking for agreement with some other theory. You can see that with Willis’ claim that UAH should no warming since Aug 94 (I think it was); he says he meant stat sig warming. But it in fact showed about 1.38°C/cen. Now it’s maybe not significant, but it’s also not so far from what AGW theory predicts. A theory can’t do better than get it right, whatever stat sig says.
Where stat sig can be important is in establishing a discrepancy between some prediction and observations,

cba
August 29, 2013 8:32 pm


Leif Svalgaard says:
August 29, 2013 at 7:58 pm
cba says:
August 29, 2013 at 7:45 pm
What long term Albedo control factor do you think is controlled by the climate change?
Evaporation [for clouds]. Snow/Ice cover [glaciation]. Volcanoes [nuclear winter]. You can probably think of others.

Whoa! Volcanoes can affect cloud covers and potentially generate a nuclear winter, affecting the climate but volcanoes are not the climate. Snow & Ice covers as in glaciation are going to short circuit the cloud feedback system by providing high albedo reflectivity at the surface negating any benefit of cloud cover reflectivity. This is important only during the glaciation phase of the climate as the cryosphere of today has a rather minimal effect due to the high latitudes. That leaves potentially evaporation and cloud formation as a climate determined (or controlled) factor. Cloud cover itself will have a significant effect on evaporation and admitedly, lower T means lower h2o vapor content. However, in the non glaciation state, a lower T with less h2o vapor and fewer clouds will result in higher absorbed power which will try to correct the situation by providing warming.
In short, volcanoes are a factor not caused by climate change which can cause climate change. Snow & Ice as in glaciation is a short circuit of the climate setpoint system we have between glaciation periods and it is not currently in operation. Evaporation, cloud formation etc. is part of the system that will logically attempt to regulate temperature as a feedback control system and try to maintain the climate as it is despite variations in power absorption.
Do you see how or where I might be missing your point or why we seem to be a bit at odds over the interpretations?
best regards,
cba
my regards to Vera.

Werner Brozek
August 29, 2013 8:46 pm

Nick Stokes says:
August 29, 2013 at 8:20 pm
You can see that with Willis’ claim that UAH should no warming since Aug 94 (I think it was)
UAH is a real problem.
Version 5.5 has no warming since January 2005 according to WFT.
Version 5.6 has no warming since July 2008 if I am not mistaken.
Version 5.6 has no statistically significant warming since Jun 1993 using SkS.
Version 5.6 has no statistically significant warming since Jun 1995 using your method.
So Willis’ number is between yours and SkS.

August 29, 2013 8:51 pm

cba says:
August 29, 2013 at 8:32 pm
Do you see how or where I might be missing your point or why we seem to be a bit at odds over the interpretations?
this comes from not being precise and specific. Let me be specific for one of the causes: assume the climate turns colder [not glaciation]. That makes the snow cover last longer and increases the albedo [at least as long as there is snow on the ground]. In fact, there is a feedback there: the higher albedo will make it colder yet, etc. To really deal with this, one needs to model the whole shebang: evaporation, precipitation, radiation, etc. Hand waving is just that.
But going back and forth over such details detracts from the main point: was the low solar activity during the LIA a chance coincidence or not. I pointed to an example of very low activity during a warm period, and there are others. Now, you can say [I have heard that argument] that even if some events are random coincidences, others are not. That argument doesn’t do much in my book [and I think not in yours, either]. Now, can one change the albedo by changing the cloud cover by external [extra terrestrial] means such as cosmic rays? The model calculations say no, experimental verification is inconclusive, and direct measurements the past 30 years say no, so one will have to have strong faith to maintain there is an effect. And I concede that I have no such faith, because the case is not strong enough. Others may have a lower bar. That is their problem. not mine.

cba
August 30, 2013 6:32 am

Leif,
Your snow on the ground will reduce the absorbed energy and so the overall total will be less and the temperature will be lower because of it regardless why or how the snow got there in the first place. Precipitation was required which means there were clouds earlier. Colder surfaces may lead to lower amounts of clouds there and hence less precipitation but Albedo has still affected the immediate area for the immediate time and potentially that my extend out into the realm of climate. I would expect clouds are way more complex to model in total than the most advanced modeling ever done on a gcm and even those are way too course geographically to deal with individual clouds.
My response is more engineering oriented. That is we have cloud cover and we have a myriad of inputs that control or affect cloud cover. In other words we have lots of noise. Some of this noise we can maybe identify as originating from other sources, some perhaps can be quantified and hence filtered out. Unfortunately, it’s quite a coincidence that our two big cold events align with the same sort of solar event and that is compelling and makes many say they must be related. The problem is what associated with the solar event actually affected clouds sufficiently for the significant change in Albedo – or worse what combination of factors was involved – perhaps not all due to the Sun.
Personally, after seeing your presentations concerning the lack of TSI variation, I started ignoring TSI variations as a viable alternative. The mixed results or lack of results from the cosmic ray experiments and the problems with proxies indications have me a bit frustrated over that too.
Have you any familiarity with what pilots refer to as Cloud Glory? It’s a phenomenon occurring from backscattering off of cloud vapor droplets. It means that cloud reflectivity is a serious function of light angle and it points to the complexity of even accurately measuring the stuff.

August 30, 2013 7:01 am

cba says:
August 30, 2013 at 6:32 am
Have you any familiarity with what pilots refer to as Cloud Glory?
I have often seen a glory. From the air and even from mountain tops.
A good friend of mine, Enric Palle has an ongoing program of measuring the albedo by observing Earthshine on the Moon. That method avoids many of the problems.

cba
August 30, 2013 2:36 pm

doubt it could accomodate cloud glory but I check Palle out everynow and then. It’s a horrible way to try to measure Albedo but it’s probably the only actual measurements that have ever been done. Every time I’ve heard of a satellite being launched that includes serious Albedo measurements, it never achieves orbit. I can recall two over the last few years.
The reason I mentioned cloud glory was since I’m not a pilot or frequent flyer, I ‘discovered’ it driving to work one morning. There was a lot of fog out in the fields I was driving by and the Sun had just risen in the other direction. As I drove along, I noticed that the fog always appeared the thickest as I passed by and was viewing it from the same direction as the Sun was shining from. The first person I asked about the phenomena was a private pilot who was familiar with cloud glory and told me where to find out about it on the web. Whle I was not seeing the actual cloud glory, I was seeing the backscattering as it changed while passing through 180 degrees of incidence. It was apparent that the optical thickness appeared thickest (becoming totally opaque) at 180 deg from incidence and thinner as one got further away ( permitting transmission from the other side of the point where it appeared opaque before and after passing through the 180 deg point).

August 30, 2013 2:39 pm

cba says:
August 30, 2013 at 2:36 pm
<i.I check Palle out everynow and then. It’s a horrible way to try to measure Albedo but it’s probably the only actual measurements that have ever been done
Yeah, we need a satellite at L1 looking back at the Earth…

August 30, 2013 3:04 pm

Dr. S – Instead of attempting to explain it again, I will respond to your comments.
“…R2 is still 0.9 and the graph is barely changed. I don’t think this is true.” I just finished it; it is true. R2 = 0.8982 (unchanged) for no CO2 and 0.9006 if CO2 is included.
“Curve fitting often has a high R2…” Perhaps we differ on what ‘curve fitting’ means. To me, curve fitting means mathematically finding the polynomial (or other function) that fits the data. It is what EXCEL does and the user selects the order to use. It requires a fifth order polynomial to fit the data set with an R2 of 0.899. I did not curve fit.
As you probably already know, curve fits have no predictive ability. That is a main reason why I did not do curve fitting. What I did do is write the physical equation using the simple concept of conservation of energy and then determine the coefficients to best match the equation to the data set. This way, the equation has predictive ability.
“…‘energy conservation’ and ‘thermodynamics’, but those are just words to impress…” perhaps some who are technologically incompetent might be impressed but they are used because they have exact meaning.
‘the easiest person to fool is oneself’ I am fully aware of that and it applies to us both; well, actually to everyone.
“…you assume that the sunspot number represents energy IN, and that is not correct” I didn’t assume anything. I made the hypothesis that the net energy IN, above (or below) the break-even energy, is proportional to the sunspot time-integral. The high R2, with only one external forcing, demonstrates that the hypothesis was correct. You may also notice that the energy OUT in the expression is that above (or below) the break-even energy.
“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%)” No offense intended but this clearly shows that you do not yet grasp the concept. The 0.1% is variation of TSI which isn’t enough to have the observed effect on average global temperature. But the sunspot number time integral with proxy factor, as included in the equation, does as shown in the graph. Others have looked at just magnitude or just duration and got poor correlation. The time-integral accounts for both magnitude and duration and produces the excellent fit.
“You will find very nearly the same fit if you omit the T(i) bit altogether [try it].” In my first work on this about 4 years ago I used a constant value for T(i) which amounts to about the same thing and yes, it produces very nearly the same curve. T(i) is included to avoid the complaint of not using actual temperatures.
“This is what I did in my curve…” You must have done something wrong because the correct curve looks very much like Figure 1 in http://climatechange90.blogspot.com/2013/05/natural-climate-change-has-been.html Fixing the error that you helped discover required new coefficients which compensated so there was no noticeable effect on the graph.
“…the result [the blue curve] using your formula…” This looks something like what you would get if you did not account for ocean oscillation. I show this with different scale factor back to 1700 on page 3 in a paper made public 11/24/11 at http://climaterealists.com/attachments/ftp/Verification%20Dan%20P.pdf The graph on page 4 of that paper has a scale factor applied and closely matches your blue curve.
“…your idea of energy IN and energy OUT is wrong (which was obvious to me from the outset).” The first law of thermodynamics was discovered way before my time. I have explained it to you but I cannot understand it for you.
“Failure to respond will be taken as admission that no paper was submitted and peer reviewed.” I told you the status. Whether you believe it is not important to me.
“A note on integration [of which you claim I know nothing] and my graph…” It doesn’t work to use the average sunspot number to today. Prior to 1940, the sunspot number time-integral (reduced by the time integral of radiation from the planet) had a fairly level trend and from 1940 to about 2005 it rises sharply. The important number in the equation is 43.97. If that number is much bigger the coefficients cannot be adjusted to make the calculated line fit the data. R2 would be lower.
It appears that here also you did not include the effects of ocean oscillations.
“…so any interval over which you integrate [s(i) – AVERAGE(s(i))] will always start at zero and end with zero…” That would be true if you integrated over the same range that you averaged over. Don’t do that.
“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.” My first thought is that this is nonsense but perhaps you have an explanation.
It is obvious that you are antagonistic to my assessment. You made graphs which you claimed proved that I was wrong when in fact your graphs are done wrong. Thus as the carbon dioxide continues to go up and the average global temperature doesn’t perhaps you will return to this and begin to understand why that is happening.

August 30, 2013 3:35 pm

Dan Pangburn says:
August 30, 2013 at 3:04 pm
You did not understand much, but let me note one of the biggest error:
“It doesn’t work to use the average sunspot number to today. Prior to 1940, the sunspot number time-integral (reduced by the time integral of radiation from the planet) had a fairly level trend and from 1940 to about 2005 it rises sharply. The important number in the equation is 43.97. If that number is much bigger the coefficients cannot be adjusted to make the calculated line fit the data”
That it ‘doesn’t work’ simply means that it is not correct what you are doing. So you are injecting into your fit the knowledge that there is a sharp rise in 1940. That invalidates the analysis and makes it circular.
The important number in the equation is 43.97
So you find that if you use another number things don’t work. That is precisely what curve fiddling is.
It appears that here also you did not include the effects of ocean oscillations.
Of course not, because that injects the observed climate into the fit and makes the argument circular.
I told you the status. Whether you believe it is not important to me.
You did not tell me the name of the Journal. And that is important to me.
It is obvious that you are antagonistic to my assessment
Not at all, I’m trying to help you avoid embarrassing yourself too much.

Werner Brozek
August 30, 2013 9:15 pm

Nick, If you are still reading here, I was working on an article and I noticed that UAH only goes to June, but RSS is up to date to July. Can this please be updated? Thanks!

Nick Stokes
August 31, 2013 12:28 am

Werner,
Yes, I’m still looking in. I’ll check that – thanks for the warning. I ran the automatic update mechanism again, and thought everything was OK, but…
I’ve written a post here talking about some of these issues. I think some of the graphs there may help.

Nick Stokes
August 31, 2013 3:22 am

Werner,
It seemed to me to be the other way around – UAH OK but MSU lagging. I’ve fixed that, and also put in a more explanatory error message if data is not up to date,

Werner Brozek
August 31, 2013 7:11 am

Nick Stokes says:
August 31, 2013 at 3:22 am
Werner,
It seemed to me to be the other way around – UAH OK but MSU lagging.

Nick, I am still seeing what I saw last night. Do I need some sort of refresh button or something? For RSS, I see a large uptick followed by a small down tick at the end. This is consistent with the last 3 values of RSS, namely 0.139, 0.291 and 0.222.
However for UAH, I just see a large uptick at the end. This is NOT consistent with its last 3 values of 0.083, 0.295 and 0.174.
(P.S. I know I said I was planning an article in about 2 months, but I may have something on RSS much sooner, depending on anomalies.)

August 31, 2013 7:28 am

Nick Stokes says:
August 31, 2013 at 12:28 am
In the article that you reference, you say:
“Conclusion
I don’t think scouting around for a period free of significant trend is a useful activity, because it doesn’t actually prove that the theory made a bad prediction. For that you have to test the deviation from the prediction.”
I agree with you to a certain extent, but it seems as if NOAA does not.
”The simulations rule out (at the 95% level) zero trends for intervals of 15 yr or more, suggesting that an observed absence of warming of this duration is needed to create a discrepancy with the expected present-day warming rate.”

August 31, 2013 7:40 am

Nick, RSS starts in January 1979, but UAH starts December 1978. Could this be the problem?

August 31, 2013 11:24 am

Dr. S – Thanks for your help anyway.
I wonder if either of us will live long enough to see who should be embarrassed.
I need to go fix some things.

August 31, 2013 11:28 am

Dan Pangburn says:
August 31, 2013 at 11:24 am
I wonder if either of us will live long enough to see who should be embarrassed.
For some papers embarrassment is immediate. Which Journal did you submit to?

Nick Stokes
August 31, 2013 1:17 pm

Werner,
Yes, that was it.

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