[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.
Werner,
Using RSS 1990 & ‘blank’ @ur momisugly SkS: .120 +-.129
Using RSS 1990 & 2013.5 @ur momisugly SkS: .121 +-.130
Using RSS 1990 & 2013.42 @ur momisugly SkS: .121 +-.131
Then tried the following:
Using 1990 & 2013: .123 +-.125
Using 1990 & 2012.92: .127 +-.135
Obviously the SkS trend calculator is interpreting the ‘End’ date differently if it is ‘blank’. And entering just ‘2013’ with no decimal produces a number that’s neither for July (2013.5) nor for December (2012.92) – my guess is that ‘2013’ represents end of month January 2013.
Anyways, based on this tiny experiment, I’m not confident what time period ‘blank’ really represents when used as ‘End’ and SkS provides no confirmation of what time periods are being used in the calculations (not sure if that’s a bug in the design or a feature).
For future users of SkS, I would caution against using ‘blank’ and instead rely exclusively on the decimal dates you supplied for each month.
But, our “camp” is not trying to hijack the world economy, virtually shut down western industry, transfer wealth from producing countries to failed countries, and silence real discussion about the climate. That is the difference, so it might have been more worthwhile if you had said ” thus, now warming”.
oops- meant to say “no warming”. At a minimum, couldn’t we agree that the science is not settled?
JackT says:
August 27, 2013 at 5:23 am
Werner,
Using RSS 1990 & ‘blank’ @ur momisugly SkS: .120 +-.129
This is the number I gave. To prove it is correct, you can put in any date after July 2013 and you will get the same answer. For example put in 2013.58 or any higher number such as 2014, and you will get the above number. The way to prove to yourself exactly what numbers are being used, you need to know what the numbers for RSS are. The last 3 months in RSS are 0.139, 0.291 and 0.222. So if you see an uptick followed by a down tick at the end, you know you have the latest. As for just putting in 2013, that is just to January 1, 2013. To prove this, 2013 gives the same number as 2012.99.
In terms of research, when you want to consider the effects of a tiny variable, don’t include the big variable in the recipe. So your first step is ALWAYS to consider how to exclude the big variable, in this case the very thing that predominantly determines land temperatures. The big cahoona is the oceanic/atmospheric sourced driver of weather pattern variations.
The CO2 crowd and Dan make the same mistake but in different ways. They fail to properly remove the effects of the big variable so they can study a tiny little variable. The CO2 crowd thinks that oceanic/atmospheric variables are random and will cancel out if you run the model enough times (we do this with brainwaves). But oceanic/atmospheric variables are not random and they do not cancel out. Dan actually keeps the big variable in his calculation by incorporating a value based on actual observations that is different for each observed year, thus burying his tiny gnat of a variable in a room he has purposely filled with very large elephants. To make matters worse, he then proclaims that the poop in the room is being influenced by his gnat.
Weather, and thus climate, is a savory soup you cannot undo once it is cooked. Anyone who tries to pick apart the soup, aka weather data, into its original separate driving components is nuts.
Dr. I – Thanks for checking EXCEL’s arithmetic. I looked back to see why you got different numbers. My bad. I failed to mention that I started the integration from 1850. My feeble excuse is that I did the work a few months ago. The integral 1850-1894 (including the 43.97 & T^4 factors) is 105.6212 so add this to 20.0329 = 125.6541.
125.6541/17 = 7.3914.
I used the factor for no CO2
7.3914 * .0049 = 0.03622 as stated.
If I had used the factor including the CO2 effect it would be
7.3914 * 0.004407 = 0.0326 but the difference on the graph would be barely detectable.
The equation and graph at the climatechange90 link are correct.
Sorry about the missing information and wasting your time. If I could figure out how to publish the EXCEL file I would do it. That might reduce this kind of foolishness.
Dan Pangburn says:
August 27, 2013 at 9:22 am
My bad. I failed to mention that I started the integration from 1850. My feeble excuse is that I did the work a few months ago.
So much for so-called peer review…
BTW, which journal did you submit the paper to?
The equation and graph at the climatechange90 link are correct.
If you mean this link:
http://climatechange90.blogspot.com/2013/05/natural-climate-change-has-been.html
The equation states that the integration started in 1895. The graph starts the ‘calculated’ curve in 1880. So much for ‘correct’.
Your link says:
“43.97 = average sunspot number for 1850-1940.
286.8 = global mean surface temperature for 1850-1940, °K.
294.8 = ppmv atmospheric CO2 in 1895”
To be ‘correct’ all these things should be consistent.
And why use 1850-1940 averages. One would expect 1850-2013. The 1940 is too arbitrary.
Nick Stokes says:
August 25, 2013 at 5:12 pm
I tried it out, but the numbers did not match for what I knew to be true for UAH from 2005 to date from WFT and SkS. But when I realized the following, it made sense.
“Update – data has been updated to Jan 2013 (where available), and Hadcrut 4 replaces Hadcrut 3.”
With my monthly updates, I have to have the very latest available so I cannot use your product for my purposes.
Dr S – Sorry about the name tag errors. I misread the first character in your earlier posts as an upper case ‘I’ instead of a lower case ‘l’. I also apologize for being unfamiliar with your work. That will change.
Thanks for checking MY work. It looks like you caught something that was missed in peer review. I agree that the ‘integration’ should have been zero in 1894 to be consistent with the equation. I fixed it in one of the EXCEL files and it appears to make a tiny bit of difference. The most noticeable difference is a prediction of the trend to be approximately 0.05 K cooler in 2020. The coefficients shifted a bit but R2 is still 0.9 (very tedious to walk the coefficients up to max R2). I will be sure to correct the paper before it gets published.
The coefficients, A, B, C, D, were determined for max coefficient-of-determination for the equation compared to the data from 1895-2012. After the coefficients are determined, the results of the equation can be plotted for any time period. Starting the plot in 1880 is interesting but arbitrary.
1940 was picked because that is where the sunspot number time-integral starts its rapid climb (which ended about a decade ago). Graphs that show this can be seen at http://hockeyschtick.blogspot.com/2010/01/blog-post_23.html or at http://climaterealists.com/attachments/ftp/Verification%20Dan%20P.pdf (this shows an earlier version of the equation and HadCRUT4 data was not used. This also shows CO2 data. Mauna Loa data was used when available.)
It appears from the equation that using a different time range would change the values of the coefficients but not influence the value of R2 or the anomaly ‘prediction’ or the graph trace. The prediction is in quotes because of uncertainties in the future sunspot time-integral and uncertainty that ocean cycles will continue as they have for more than a century. I expect the ocean cycle factor to fade eventually.
It is the influence of the change in CO2 that is under investigation. Since 1895 is the start point, it is the change from 1895 that is needed. I rechecked to make sure that I did this correctly in EXCEL.
Dan Pangburn says:
August 27, 2013 at 2:07 pm
1940 was picked because that is where the sunspot number time-integral starts its rapid climb (which ended about a decade ago).
Cherry picking 1940 because of some property of the data invalidates the whole analysis. There is no way to ‘save’ the analysis except doing it right, that is: having the same begin and end times for all data and for all averages.
Dan Pangburn says:
August 27, 2013 at 2:07 pm
It looks like you caught something that was missed in peer review.
which journal did you submit your work to?
Nick Stokes says:
August 25, 2013 at 10:01 pm
RGB
“I agree that the GISS document is remarkable and subversive. It is also wrong, rather horribly wrong. “
It’s neither remarkable nor subsersive. It is something Hansen has been saying for over 30 years. The NOAA has a very similar statement (see para 7).
———————-
You are aware are you not that Hansen is a raving lunatic?
The loon imagines that the oceans of Earth could boil & our planet become another Venus at 500 to 600 ppm (or less), even though the Cenozoic high for CO2 was around 2500 ppm, without any such dire consequences having happened then. I might add that solar radiance was practically the same as now when our planet last experienced that level of carbon dioxide (about 42 Ma; the sun gains strength at about one percent per 110 M years).
Nor of course did Earth become Venus when CO2 was 7000 ppm or 90,000 ppm (or more) in the past five to seven hundred million years.
Dan, trust me, been there, done that. If you want to submit that pdf for publication with just a tweak here and there, you are in for a very sad experience. However, on the bright side, because it is so far away from any kind of serious consideration, it will be a very short, sad experience. Mine was more than a year’s worth of rewrites for journal publication AFTER a year’s worth of rewrites just for the University’s archive publication. That it eventually got considered at all for professional journals is likely due to the addition of a very talented and well-respected researcher who made the manuscript sing with discussion. It was a humbling experience. While he said my data was a well-controlled “gold-mine”, I had a long way to go in interpreting the results. How right he was.
By the way, one of the hallmarks of a true researcher, let alone a Ph.D., is that you get to say on a regular basis that you don’t know something and therefore you need to study it. Else why would any of us ever do research? You seem convinced a priori that the Sun is the deciding factor, so you came up with a calculation that in your mind shows it. That makes you not a researcher. And you would be joining the ranks of several AGW researchers who have done the same thing.
Spare yourself the embarrassment. Do not let your manuscripts see the light of day.
Dear Pamela Gray,
If you would ever care to share, I’m sure many of us would enjoy hearing about your research (in summarized, layperson’s language if I, at least, am to understand it). Perhaps, when this thread goes essentially defunct (if you don’t mind going OT, go for it, now!), you might feel comfortable posting about yourself, here? Please forgive me if this comes off as too nosy.
An admirer from the “cheap seats,”
Janice
******************************
@ur momisugly Dan Pangburn — I apologize for accusing you above of deliberately misusing Dr. Svalgaard’s name. Good for you to tell him that you did not mean to do that. You may be mistaken in your research, but, at least you are (it appears, anyway) willing to learn. (Your initial arrogant tone created a bad impression). GOOD LUCK!
*****************
@ur momisugly Leif Svalgaard — your silence tells me you thought little of my praise above. If I offended you, please forgive an overenthusiastic encourager (sometimes, it is a blessing, sometimes, not).
Janice Moore says:
August 27, 2013 at 3:06 pm
@ur momisugly Leif Svalgaard — your silence tells me you thought little of my praise above. If I offended you, please forgive an overenthusiastic encourager (sometimes, it is a blessing, sometimes, not).
On the contrary, it was appreciated, but my experience is that it is better just to humbly bow one’s head when praise is heaped upon it.
Well, good for you, Dr. Svalgaard. But, LOL, I can’t “hear” you bowing, you stoic, stalwart, Dane, you! #(:))
Thanks for taking the time to respond.
Janice
Janice Moore says:
August 27, 2013 at 3:19 pm
I can’t “hear” you bowing, you stoic, stalwart, Dane, you! #(:))
You can’t hear me cringe either at times, when that is called for.
Janice I am nothing but a flash in the pan. I decided to do research for my Master’s thesis. It gave me quite more than I wanted to learn, including leaving me jaded about the political nature of Ivory Tower research. Maybe I was just too young and idealistic to be able to survive that cauldron. Those who do have my respect.
Fausti was Head of the Audiology department at the VA medical center I was employed at. He chose to put his name first, I don’t know why. Frey was a colleague who taught me lab techniques, and Rappaport helped me with my manuscripts. Oregon State University has the original thesis archived in its library.
Fausti SA, Gray PS, Frey RH and Rappaport BZ. (1991). Rise Time and Center-Frequency Effects on Auditory Brainstem Responses to High-Frequency Tone Bursts. J Am Acad Audiolo 2:24-31.
Gray PS. (1986). Rise-Time and Center Frequency Effects on the Auditory Brainstem Response. A Masters Thesis, Oregon State University, Corvallis, OR.
Werner Brozek says: August 27, 2013 at 1:41 pm
“I tried it out, but the numbers did not match for what I knew to be true for UAH from 2005 to date from WFT and SkS. But when I realized the following, it made sense.
“Update – data has been updated to Jan 2013 (where available), and Hadcrut 4 replaces Hadcrut 3.”
With my monthly updates, I have to have the very latest available so I cannot use your product for my purposes.”
My apologies – that update needs changing. I have recently instituted a continuous data updating scheme, described here, so that is no longer true.
I think my trends generally match those of WFT and SkS. The CI’s are often narrower than SkS. As I mentioned above, SkS uses a somewhat novel method, but I don’t thin k that should be the reason. I’ve checked mine carefully, and I think they are right.
Pamela,
At least you flashed. I didn’t even make it into the research pan! Wow! Your research (from the only closely related article I could find, a Fausti one from 2003, yup, his name was first on that one, too….) is helping prevent the SAME hearing loss (from ototoxicity) that our wonderful host has had to live with nearly all his life! How cool that you are a pillar of WUWT.
Medical research is a golden chain; if your link had not been there, there would be people with severely impaired hearing walking around today who, instead, are singing to the music on the radio. Your link mattered. And YOU are a flash of pure golden character, intelligence, and wit.
Thanks, so much, for responding.
Happy fishing!
Janice
My mother’s hearing was destroyed by the very same process. She tool copious amounts of Gentamycin. Had to. Fabulous antibiotic but sometimes the cure is worse than the disease. It can harm kidneys too. And her’s were bad to begin with. By the time she hit her 20’s she had profound bilateral hearing loss.
On the subject of the political nature of research. If you want a very good read, pick up a copy (if you can find it) of “Molecules of Emotion” by Candace Pert. She chronicles her journey as a woman in a man’s world of medical research. The last couple of chapters are a little “out there” but the understory related to the political nature of Ivory Tower research is fascinating. Brilliant woman, no doubt. Reminded me of Rosalind Franklin.
Nick Stokes says:
August 27, 2013 at 3:47 pm
Here are the numbers I am getting. For UAH from 2005 to date, WFT gives -0.00033/year and I know they are still using the 5.5 version.
SkS gives +0.013/decade +/- 0.528 and I know they are using the 5.6 version.
For yours, when I click UAH from January 2005 to August 2013, and the 1989-now and the trend button, I get 0.243/Century. What am I doing wrong? I assume you are using version 5.6 so it should be like SkS, unless of course SkS is either wrong or using a different program. And how do I get the +/- values with your program? Thanks!
Dear Pamela,
Thank you for sharing about your mom. So, you are bi-lingual (at least), I would guess, for you likely are fluent in ASL (?). I took a beginning ASL class (just in case I ever needed it — still haven’t!) and the hearing teacher’s parents were both completely deaf all his life. It really does a number on the family dynamics, at times! You were, no doubt, highly motivated (like Alexander Graham Bell!) and that must have helped you with all those long hours of often tedious measuring and recording and on and on and on…. . I hope she is still with us. If not, I’m sorry that you have had to say “Good bye” to her, too (yes, I remember what you wrote a few months ago and you’ve been in my prayers — big holes in the heart never completely heal, you just forget about them for awhile). She certainly raised a girl who can stand on her own two feet. Go, Mom!
Thanks for the book recommendation. If I have the opportunity, I’ll read it. I hope things, as far as the sexism, anyway, have improved. We (you and I) owe so much to the pioneers. Even though there were always some good, fair-minded, men in academia (Marie Curie’s husband, for instance), it was not easy.
Thanks again for honoring me with a response!
Take care,
Janice
Werner Brozek says: August 27, 2013 at 5:21 pm
I get the same as you. I am using UAH 5.6 now; the automatic update (which is new) ran last night. There are actually some glitches; some of the later options, like SST aren’t getting the new data. I’ll fix that. But the datasets you’re using should be OK.
I’ll look into the reason for the discrepancy. The +- valies appear on the sidebar, where it says CI from … Below that, it gives the t-value – ie in sigma units. You can also click for a color plot of the upper and lower CI values.
Pam – Thanks for the advice. The paper is substantially more comprehensive than the pdf and has been through peer review (although with a dumb error as Dr. S helped uncover which needs to be fixed). I won’t know for sure whether it will be published until it happens.
You might wonder why an old, retired, unfunded engineer has been doing research in global warming (for over 6 years now). Initially I was just curious about the truth. Now I’m concerned about some politicians needlessly destroying the economy.
Many fail to accept that I did not start with the assumption that it was the sun. Instead I started with the energy equation. I suppose the misconception happens because I didn’t talk about the energy equation much in the pdf. I talked about it a bit more in an earlier work at http://climaterealists.com/attachments/ftp/Verification%20Dan%20P.pdf I noticed that declining temperatures in the past were associated with fewer sunspots. I made the hypothesis (Feynman, in one of his lectures, would have called it a guess) that the time-integral of sunspot numbers was proportional to energy in. This established the form of the equation.
The coefficients were adjusted to maximize R2. 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 realize that this is way different from what anyone else has done. Many seem to be convinced that CO2 has a significant effect. I demonstrated over five years ago that noncondensing ghg have had no significant influence on average global temperatures in a paper made public at http://www.middlebury.net/op-ed/pangburn.html .
Some may be appalled by the simplicity of the concept but with an R2 of 0.9 with only one external forcing it is definitely not going away. Everything not explicitly considered must find room in that unexplained 10%.