# July 1912 GISS Anomaly (-0.47) Was Warmer Than January 2007 GISS Anomaly (+0.94) – (Now Includes February Data)

John Kehr – The Inconvenient Skeptic – Click the pic to view at source

Image Credit: The above graphic was sourced from this article by John Kehr on his blog The Inconvenient Skeptic, and he also authored this interesting article on WUWT.

Guest Post By Werner Brozek, Edited By Just The Facts

It may appear as if there is a typo in the title, however some of you will know exactly what I am talking about. What we are given in various data sets are anomalies. So depending on the base period, new values for a given year or month are compared to values for 30 or more years in the past.

So let us suppose we have the noon temperature for July 15 for 30 consecutive years. We take the average. Then for the next year, we take the temperature, and if it is warmer than the average, the anomaly is positive and if it is colder than the average, the anomaly is negative. One definition of anomaly is “deviation from the normal”. However that is not our definition here. After all, what is normal? Is the average from 1901 to 1930 normal; or from 1981 to 2010; or from 1901 to 2000? Perhaps none of these are normal. Unlike a body temperature of 37.0 C that is considered normal, we do not have a temperature of Earth that is “normal”. Perhaps “normal” is 2 C warmer than it is today and we are below “normal” now.
As you can see from the graphic above, Earth’s temperature varies by 3.8 C throughout the year. On the average, July is 3.8 C warmer than January. This is despite the fact that Earth is closest to the sun in January and furthest in July. There have been many average temperatures quoted for Earth. Most are between 14.0 C to 15.0 C. One thing that I find troublesome is that anyone can come up with an anomaly if they do not know the temperature. If you use a thermometer, it will give you a temperature. It will not give an anomaly.

Then there is the argument about how meaningless an average temperature is. In addition, it takes much more energy to change a moist 39 C to 40 C than a dry -40 C to -39 C. So a change in anomaly must be taken with a grain of salt. Much can be discussed here, however I want to focus on the title and the graphic. As can be seen, the anomaly for GISS for July 1912 was -0.47. When combined with the graphic above, assuming it is true for the moment, the real average temperature for July 1912 was 15.8 – 0.47 = 15.3 rounded to a single decimal. On the other hand, the real average temperature for January 2007 was 12.0 + 0.94 = 12.9 rounded to a single decimal. Generally speaking, one can say that the coldest July since the start of the Hadcrut4 record in 1850 was several degrees warmer than the warmest January in the 2000s.

When we hear that the last several hundred months were all warmer than the average for the 1900 hundreds, that is only true when considering anomalies. However it is not true when talking about absolute temperatures. During the year, some places on Earth can vary by 60 C or more. During the day, some places on Earth can vary by 20 C or more. And it is even possible for a place in the far north to have a warm January temperature beat a cold July temperature. However that will not happen globally. With natural global swings of 3.8 C every year, and with higher daily and yearly swings, it is hard to imagine that an increase of 2 C over 100 years, even if it occurred, would be catastrophic.

In the sections below, we will present you with the latest facts. The information will be presented in three sections and an appendix:
The first section will show 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 2014 to date compares with 2013 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 9 years and 5 months to 17 years and 6 months.
1. For GISS, the slope is flat since July 2001 or 12 years, 8 months. (goes to February)
2. For Hadcrut3, the slope is flat since June 1997 or 16 years, 9 months. (goes to February)
3. For a combination of GISS, Hadcrut3, UAH and RSS, the slope is flat since December 2000 or 13 years, 3 months. (goes to February)
4. For Hadcrut4, the slope is flat since December 2000 or 13 years, 3 months. (goes to February)
5. For Hadsst3, the slope is flat since November 2000 or 13 years, 4 months. (goes to February)
6. For UAH, the slope is flat since October 2004 or 9 years, 5 months. (goes to February using version 5.5)
7. For RSS, the slope is flat since September 1996 or 17 years, 6 months (goes to February). So RSS has passed Ben Santer’s 17 years.

The next graph shows just the lines to illustrate the above. 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.

The actual numbers are meaningless since all slopes are essentially zero and the position of each line is merely a reflection of the base period from which anomalies are taken for each set. No numbers are given for CO2. Some have asked that the log of the concentration of CO2 be plotted. However WFT does not give this option. The upward sloping CO2 line only shows that while CO2 has been going up over the last 17 years, the temperatures have been flat for varying periods on various data sets.

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.

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

Section 2

For this analysis, data was retrieved from Nick Stokes’ Trendviewer available on his website. This analysis indicates for how long there has not been statistically significant warming according to Nick’s criteria. Data go to their latest update for each set. In every case, note that the lower error bar is negative so a slope of 0 cannot be ruled out from the month indicated.

On several different data sets, there has been no statistically significant warming for between 16 and 21 years.

The details for several sets are below.

For UAH: Since February 1996: CI from -0.041 to 2.392
For RSS: Since November 1992: CI from -0.022 to 1.900
For Hadcrut4: Since October 1996: CI from -0.027 to 1.234
For Hadsst3: Since January 1993: CI from -0.016 to 1.812
For GISS: Since June 1997: CI from -0.016 to 1.258

Section 3

This section shows data about 2014 and other information in the form of a table. The table shows the six data sources along the top and other places so they should be visible at all times. The sources are UAH, RSS, Hadcrut4, Hadcrut3, Hadsst3 and GISS.
Down the column, are the following:
1. 13ra: This is the final ranking for 2013 on each data set.
2. 13a: Here I give the average anomaly for 2013.
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 three 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 the first month for which warming is not statistically significant according to Nick’s criteria. The first three letters of the month is followed by the last two numbers of the year.
9. Jan: This is the January 2014 anomaly for that particular data set.
10.Feb: This is the February 2014 anomaly for that particular data set.
11.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 slightly, presumably due to all months not having the same number of days.
12. 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. It will not, but think of it as an update 10 minutes into a game. Due to different base periods, the rank is more meaningful than the average anomaly.

1. 13ra 7th 10th 8th 6th 6th 6th
2. 13a 0.197 0.218 0.486 0.459 0.376 0.61
3. year 1998 1998 2010 1998 1998 2010
4. ano 0.419 0.55 0.547 0.548 0.416 0.67
5. mon Apr98 Apr98 Jan07 Feb98 Jul98 Jan07
6. ano 0.662 0.857 0.829 0.756 0.526 0.94
7. y/m 9/5 17/6 13/3 16/9 13/4 12/8
8. sig Feb96 Nov92 Oct96 Jan93 Jun97
9.Jan 0.236 0.262 0.507 0.472 0.342 0.70
10.Feb 0.127 0.162 0.299 0.263 0.308 0.45
11.ave 0.182 0.212 0.403 0.367 0.325 0.575
12.rnk 10th 11th 14th 13th 12th 11th

If you wish to verify all of the latest anomalies, go to the following:
For UAH, version 5.5 was used since that is what WFT used, see: http://vortex.nsstc.uah.edu/public/msu/t2lt/tltglhmam_5.5.txt
For GISS, see: http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt
To see all points since January 2013 in the form of a graph, see the WFT graph below.

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

As you can see, all lines have been offset so they all start at the same place in January. This makes it easy to compare last January 2013 with the latest anomaly.

Appendix

In this part, we are summarizing data for each set separately.

The slope is flat since September 1996 or 17 years, 6 months. (goes to February) So RSS has passed Ben Santer’s 17 years.
For RSS: There is no statistically significant warming since November 1992: CI from -0.022 to 1.900.
The RSS average anomaly so far for 2014 is 0.212. This would rank it as 11th place 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 2013 was 0.218 and it is ranked 10th.

UAH
The slope is flat since October 2004 or 9 years, 5 months. (goes to February using version 5.5)
For UAH: There is no statistically significant warming since February 1996: CI from -0.041 to 2.392.
The UAH average anomaly so far for 2014 is 0.182. This would rank it as 10th place 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 0.662.
The anomaly in 2013 was 0.197 and it is ranked 7th.

The slope is flat since December 2000 or 13 years, 3 months. (goes to February)
For HadCRUT4: There is no statistically significant warming since October 1996: CI from -0.027 to 1.234.
The HadCRUT4 average anomaly so far for 2014 is 0.403. This would rank it as 14th place 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 2013 was 0.486 and it is ranked 8th.

The slope is flat since June 1997 or 16 years, 9 months. (goes to February)
The HadCRUT3 average anomaly so far for 2014 is 0.367. This would rank it as 13th place 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 2013 was 0.459 and it is ranked 6th.

The slope is flat since November 2000 or 13 years and 4 months. (goes to February).
For HadSST3: There is no statistically significant warming since January 1993: CI from -0.016 to 1.812.
The HadSST3 average anomaly so far for 2014 is 0.325. This would rank it as 12th place if it stayed this way. 1998 was the warmest at 0.416.
The highest ever monthly anomaly was in July of 1998 when it reached 0.526. The anomaly in 2013 was 0.376 and it is ranked 6th.

GISS
The slope is flat since July 2001 or 12 years, 8 months. (goes to February)
For GISS: There is no statistically significant warming since June 1997: CI from -0.016 to 1.258.
The GISS average anomaly so far for 2014 is 0.575. This would rank it as 11th place if it stayed this way. 2010 was the warmest at 0.67.
The highest ever monthly anomaly was in January of 2007 when it reached 0.94. The anomaly in 2013 was 0.60 and it is ranked 6th.

Conclusion

Anomalies only give the departure from an average. The base period for this average varies from one data set to the next. So if the base period was cooler, the newer anomalies will be positive and will have a larger magnitude. All anomalies since 1850 are rather small compared with the absolute temperature change for the earth during the year.

A positive anomaly does NOT mean the earth has a fever. It just means it is warmer than a long term average.

## 40 thoughts on “July 1912 GISS Anomaly (-0.47) Was Warmer Than January 2007 GISS Anomaly (+0.94) – (Now Includes February Data)”

1. Update for Section 2:
It says: For Hadcrut4: Since October 1996: CI from -0.027 to 1.234

Hadcrut4 had a large drop from 0.507 to 0.299. The latest value for 95% statistical significance which now includes February has been pushed back two months and is now:
Since August 1996: CI from -0.005 to 1.308.

2. Jim G says:

Excellent in pointing out the weakness of using anomalies both scientificly and in the sense of how they exagerate temperature changes over time. Using anomalies is similar to graphically fudging the x or y coordinate scales to make changes look more or less extreme. If people were regularly exposed to actual temperature graphs, over time they would be much more cognizant of reality and less likely to buy the coolaide being served to them.

3. Eliza says:

If earth was closer to sun in July we would certainly have a problem as 9/10 of land is in the NH!!!. It is precisely that is farther away in july that possibly life is possible on earth.

4. DirkH says:

Eliza says:
April 3, 2014 at 9:35 am
“If earth was closer to sun in July we would certainly have a problem as 9/10 of land is in the NH!!!. It is precisely that is farther away in july that possibly life is possible on earth.”

Life is much too hardy to be impressed by that, even though the Green Mob always tries to make it sound like it would perish the next moment.
Caveat: once created. Which is still a mystery for science.

5. Eliza says:
April 3, 2014 at 9:35 am
“If earth was closer to sun in July we would certainly have a problem as 9/10 of land is in the NH!!!. It is precisely that is farther away in july that possibly life is possible on earth.”
I believe the Australlians and New Zealanders will dispute the notion that life is hard/does not exist in places where the Sun is closest during the Summer. What temp differences exist
between equal atitude locations in the Northern and Southern Hemisphere? Presumably that would give you the extent to which being closer to the Sun matters. Besides, the Earth’s orbit appears almost circular – its elliptical orbit displays very small eccentricity. An elliptical orbit with a zero ecentricity is, in fact, circular.

6. Mike Maguire says:

Absurd to assume that the temperature 100 years ago based on human instrumentation was the perfect one for the ever changing planet.
Then, after a decade of no change in the temperature(which is supposed to be the objective) keep pushing for aggressive measures that will cost trillions to keep us from catastrophic warming from a greenhouse gas that has indisputably been the best thing to happen to our biosphere since we had the ability to accurately measure it.

7. Steven Mosher says:

“If you use a thermometer, it will give you a temperature. It will not give an anomaly.”

not really.

For example. bulb thermometer actually gives you a measure of the expansion of a liquid.
The instrument is calibrated to a zero point ( the “standard” temperature of water freezing)
and you measure an offset from this.

hmm try this

http://en.wikipedia.org/wiki/Mercury-in-glass_thermometer

So you can see the assumptions at play.
1. that there is a standard temperature at which water freezes
2. That the expansion is “roughly” linear
3. That the standard doesnt change (physical laws are immutable)
4. That the expansion characteristics of the liquid dont change over time (material properties are
immutable)
5. That the physical law governing expansion doesnt change. ( see #3)

So, based on the data ( the expansion of the liquid) AND the physical theory, we translate
the offset from zero into a “temperature”. The thermometer does not measure temperature.
It shows the amount of expansion from a calibrated zero point. When we accept the theory
behind the device we can them translate the expansion into a quantity which we call ‘temperature”

But the above is just a empirical based definition of temperature as opposed to the theoretical based view.

8. Col Mosby says:
April 3, 2014 at 10:08 am
Besides, the Earth’s orbit appears almost circular

True. For the exact numbers, see: http://spaceweather.com/glossary/aphelion.html
“In January when we’re closest to the Sun (perihelion), the distance is 147.5 million km. In July we’re 152.6 million km away–a five million kilometer difference.”

9. Steven Mosher says:
April 3, 2014 at 10:50 am
If you use a thermometer, it will give you a temperature. It will not give an anomaly.” not really.
When we accept the theory behind the device we can them translate the expansion into a quantity which we call ‘temperature

It seems as if we are in agreement since you did NOT say:
When we accept the theory behind the device we can then translate the expansion into a quantity which we call an anomaly.

10. Alan McIntire says:

There are even greater anomalies when you figure in that the temperature isn’t exactly the same everywhere on earth. For a blackbody

Watts/square meter = (5.67*/ 10^8) * T(K)^4

Plug in an average temperature of 15C = 288K , and you get
390.08 watts/square meter for a black body radiating at 15 C.

Suppose half the earth is at 25 C, and half at 5 C (Still a 15C average),
and for a blackbody you get half the earth radiating at 447.14 watts, half radiating at
338.66 watts, for an average of 392.9 watts, increased radiation of 2.82 watts with NO increase in average temperature anomaly. And with a DOUBLING of CO2 you’re looking at an increase of only 3.8 watts. Looking at anomalies is meaningless without knowing changes in VARIANCE.

To give credit where credit is due, I picked this up reading Lubos Motl’s website.

11. rgbatduke says:

The next graph shows just the lines to illustrate the above. 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.

No, it doesn’t. It is really difficult to convey this properly, impossible on the same scale, because you can set the y-axis scale independently for temperature and CO_2 concentration. It really drives me nuts when people try to do this — Mr. Monckton is often guilty of the same sin.

http://www.woodfortrees.org/plot/esrl-co2/plot/esrl-co2/scale:0.0001

shows CO_2, on an absolute scale. If one goes (way) back this curve will go down to 300 ppm and down as low as maybe 280 ppm in the fairly remote (century scale) past although the data from more than TOO far back starts to be proxy data or samples from populated areas possessing good science back no farther than the late 19th century. Basically, CO_2 varied by roughly 30% post 1950.

This:

is an absolute scale plot of HADCRUT4 over the same interval. It’s a bit difficult to tell, but the total variation from the left hand edge of this graph to the right hand edge is around 0.5 C. That is a 0.17% variation.

There is no way, ever to otherwise relate the two scales of CO_2 concentration and temperature. They have different units.

12. Alan McIntire says:
April 3, 2014 at 11:25 am

Looking at anomalies is meaningless without knowing changes in VARIANCE.

True enough. As well, when most of the warming is in the northern Arctic in the winter, then it is obvious that there is no 1:1 correlation with anomaly increase and energy increase.

13. Stephen Richards says:

Willis, there are anomalies, aren’t they? They are a totally meaningless comparison, surely ?

14. rgbatduke says:
April 3, 2014 at 11:33 am
“The next graph shows just the lines to illustrate the above. 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.”

No, it doesn’t. It is really difficult to convey this properly, impossible on the same scale
I will be the first to admit that my computer skills leave lots to be desired, so I have to use the tools I have. As well, I feel if it is good enough for Lord Monckton, then it is certainly good enough for me!
It was as a result of your comment several months ago that I revised my comment about the significance of the CO2 line. And I thank you for it. I obviously did not hit the nail on the head as I thought I did.

I said: “In addition, the sloped wiggly line shows how CO2 has increased over this period.”

You said: “No, it doesn’t.”

Would it improve things if I changed “how” to “that” so it reads:
“In addition, the sloped wiggly line shows
that CO2 has increased over this period.”

15. Stephen Richards says:
April 3, 2014 at 12:00 pm
Willis, there are anomalies, aren’t they? They are a totally meaningless comparison, surely?

I am not sure who you are addressing this to since I see no comment by a “Willis”.
But to try to answer your question, anomalies do serve a purpose, but to compare a GISS anomaly of 0.70 to a UAH anomaly of 0.236 is meaningless since they use different base periods to come up with their anomalies. That is why I feel that the rank number in row 12 of my table is much more meaningful than the anomaly average on row 11 of that table. Does this answer your question?

16. Werner Brozek says: April 3, 2014 at 12:10 pm
‘Would it improve things if I changed “how” to “that”…’

It’s more correct, but it illustrates the problem. You can say in words that CO2 is increased. Graphs are supposed to help us understand how much, and as RGB says, here that doesn’t work because there is an arbitrary degree of freedom in the scales.

You can see this on an active plot. This one shows HAD 4 and CO2 (to 2012) on the same plot, with T marked on the left axis and ppmv on the right. But you can click on the pink/blue bars to stretch and shift each axis independently. And you can make it look as if CO2 is roaring ahead and T is lagging (no warming?), or that T is blazing, and CO2 is lagging (driven out of the sea?). Because of the units difference, you can’t distinguish. The graph doesn’t help us understand that comparison.

17. Duster says:

Eliza says:
April 3, 2014 at 9:35 am

If earth was closer to sun in July we would certainly have a problem as 9/10 of land is in the NH!!!. It is precisely that is farther away in july that possibly life is possible on earth.

Unless you believe the earth is uncommonly young (e.g. <10 ky), the various periods of cyclic changes in the earth's relation with the sun need to be considered. Just one, the precession of the earth's axis is about 26,000 years. That means that about 13,000 years from now, we will nearest the sun in summer and farthest in winter. Australia will be a little more equitable. Look up Milankovitch Cyles, which are thought to be drivers of the glacial, interglacial cycle.

18. Nick Stokes says:
April 3, 2014 at 2:28 pm

Thank you!
Your “active plot” looks cool! Can it be used to show RSS from September 1996 to February 2014 and also show the slope is flat for this period of 17 years and 6 months? Thanks!

19. Werner Brozek says: April 3, 2014 at 3:48 pm
“Can it be used to show RSS from September 1996 to February 2014 and also show the slope is flat for this period of 17 years and 6 months?”

The plot I linked is version 2 which has annual data (and hasn’t been updated this year). There is an experimental new version, which allows monthly data, and also all sorts of paleo. Monthly data is up to Jan 2014, currently. Auto updating is coming.

You have to stretch the x-axis to the regression interval you want. Ctrl-click on the plot handle and it prints much data, including regression stats (if you have drawn a regression). It’s not smart on stat significance. You can select any of the usual indices.

Unfortunately, I don’t think the facility where I can link to a prepared plot is working there yet.

20. Nick Stokes says:
April 3, 2014 at 4:08 pm

Thank you. I guess at this point I need to wait for the new version to be ready. I basically need something like WFT, but that additionally allows both right and left y axis to be adjusted. In addition, it needs to be updated as soon as the data is released. WFT for example updates automatically at 8:00 PM mountain time any new data that appeared in the past 24 hours. Please let me know when you have what I need. Thanks!

21. Bruiser says:

Col Mosby says: “Besides, the Earth’s orbit appears almost circular – its elliptical orbit displays very small eccentricity. An elliptical orbit with a zero ecentricity is, in fact, circular.”
Whilst the Earth’s orbit may have a relatively small eccentricity it is significant in terms of solar radiation at the top of the atmosphere. The SOURCE full mission download gives about 90W/M^2 difference between the Perihelion and the Aphelion.
(http://lasp.colorado.edu/home/sorce/data/tsi-data/#summary_table) This annual variation is remarkably stable over the period of observation (2003 – present).

22. george e. smith says:

“””””…..Steven Mosher says:

April 3, 2014 at 10:50 am

“If you use a thermometer, it will give you a temperature. It will not give an anomaly.”

not really.

For example. bulb thermometer actually gives you a measure of the expansion of a liquid……”””””

Well that depends on where you buy your thermometers. If you buy good mercury in glass thermometers from a reputable industry standard supply house for such lab equipment, you can get thermometers that are individually calibrated. That is, the markings on the thermometer are individually applied at the Temperature they are reading. I have a very good -10 – +50 deg. C thermometer, that was made that way (got it for doing color film processing.)

Such calibration accounts for any variation in the capillary diameter along the scale, and also for any non uniformity of temperature along the scale. The calibration is specified for an immersion length (in water) that as I recall is 76 mm, which is about three inches, and is measured from the bottom of the mercury bulb. The higher the temperature, the longer the mercury column extends, and the more the extended part cools, and all that is allowed for in the marking of the scale on the glass. As I recall, this one is marked in 0.1 deg. C increments, but maybe it is 0.2, I’ll have to go look at it.

But I agree with your general premise; you can’t be sure just what it is whose temperature you are assuming corresponds to the reading..

My thermometer, would be no good for reading the air Temperature, because the immersion length would be wrong.

23. dalyplanet says:

Thank you Werner Brozek for an interesting post, quite well stated. The comment section has also been quite informative. I am especially enjoying Nick Stokes active plot. Well worth the time for a visit.

24. Reblogged this on The GOLDEN RULE and commented:
This is not for the non-academic readers but included as additional evidence that claims about trends in the so-called average global temperature are best left to the mathematical experts.
However, two things are clear –
It is virtually impossible to compute a meaningful global average temperature.
It is impossible to define a temperature trend that can be agreed to by a genuine spread of experts. Only those with a barrow to push can expect the world to accept a particular unsupported conclusion and unfortunately, much of the world does. This is not science, as it should be!
WUWT, with its many expert submissions, shows this quite clearly.
Then to use a conclusion that is impossible to ratify to create major global financial and political upheavals is, again, clearly unjustified.
Not just regarding temperatures but also about the incredible claims that are being propagated about extreme weather events and the need for carbon controls.
The risk of unwittingly becoming a willing participant in an extensive (and expensive) deception is real.

25. Richard Barraclough says:

Werner,

Your final chart shows the fallacy of trying to read too much into fluctuations from month to month. You adjusted all the anomalies to the same level in January 2013. By November there is a difference of about 0.5 deg C between UAH and GISS, with the other datasets scattered in between. Perhaps one of them is correct – and perhaps none of them?

26. Alan McIntire says:

“Richard Barraclough says:
April 4, 2014 at 3:19 am
Werner,

Your final chart shows the fallacy of trying to read too much into fluctuations from month to month. You adjusted all the anomalies to the same level in January 2013. By November there is a difference of about 0.5 deg C between UAH and GISS, with the other datasets scattered in between. Perhaps one of them is correct – and perhaps none of them?”

GiVEN a period of global warming due to changes in greenhouse gases, the COLDER months’ temperatures will increase faster than warmer months’ temperatures.

Likewise, with a period of global COOLING, the warmer months’ temperatures will cool faster than warmer months’ temperatures. So the answer to your question is not” perhaps none of them” ,
but ” temperature anomalies for warmer and cooler months AUTOMATICALLY change at different rates thanks to that 4th power black body rule and the fact that increased global warming tends to
BALANCE temperatures between colder and warmer regions- there IS no scale for which anomalies will change at the same rate for all seasons.

27. rgbatduke says:

Would it improve things if I changed “how” to “that” so it reads:
“In addition, the sloped wiggly line shows
that CO2 has increased over this period.”

But this information is not useful. The stock market increased over this period too.

If you are going to use the W4T interface, I’d strongly suggest just building two figures (or better yet, three figures, as it is always good to blow up the “temperature anomaly” right after showing people the actual absolute temperature to scale). Sadly, W4T kinda sucks for building arbitrary graphs (or else, its interface is non-intuitive and is missing things I’d like to be able to do such as define my own axis limits, apply filters that are better than the simple ones they provide, fit curves, generate Kolmogorov-Smirnov or Pearson statistics and so on). R is much better suited for doing real graphs with real statistical tools (and is free and pretty easy to learn — lots of books out there to help you as well as ample online resources and examples).

Or, if you want to do it in text why not simply state what I just stated: The total temperature change from 1950 to 2014 of roughly 0.5 C represents a variation of 0.17% of the absolute temperature (that is “seventeen hundredths of one percent”) which is itself only known within about 1 C. The total CO_2 concentration change from 1950 to 2014 of roughly 100 ppm represents a variation of 33% (that is “one third”) of the initial absolute concentration.

You can never go wrong stating simple fact.

rgbatduke says:
April 4, 2014 at 8:07 am
the only problem with presenting the facts as you do, is that it makes the crisis go away, which isnt in the interest of the industry….better to look at anomalies from an arbitrary reference point…a crisis is too important to waste, and all that.

29. rgbatduke says:
April 4, 2014 at 8:07 am

Or, if you want to do it in text why not simply state what I just stated:

The total temperature change from 1950 to 2014 of roughly 0.5 C represents a variation of 0.17% of the absolute temperature (that is “seventeen hundredths of one percent”) which is itself only known within about 1 C. The total CO_2 concentration change from 1950 to 2014 of roughly 100 ppm represents a variation of 33% (that is “one third”) of the initial absolute concentration.

And even that could (should be ?) simplified:

Yes, the earth is warming up – it has been steadily heating up since 1650. Now the actual temperature change from 1950 through 2014 is less than 1/2 of one degree C. That represents a change in absolute temperature of the earth of only 1/6 of one percent!

Since satellite measurements started in the 1970’s, it’s even less: Last month, the global average temperature was only 1/5 of one degree higher than it was in 1970. Since CO2 levels increased by 33% when global temperatures increased only 1/6 of one percent, CO2 cannot be the catastrophic threat that government-paid bureaucrats claim it is. Worse, over the past 17 years, satellite measurements of the entire planet prove that earth’s temperature has not increased at all. So, why do the governments and the politicians want to increase your energy prices and raise your taxes to prevent something that is not happening?

30. Keith A. Nonemaker says:

New RSS data now shows a 212 month cooling trend. This is more than half of the entire record (423 months).

RSS for March has just come out at 0.214. This makes the three month average equal to 0.213 so the rank remains at 11th if it were to stay this way.

P.S. Thank you Keith! So that is now 17 years and 8 months.

32. Cramer says:

The Section 1 analysis for slopes that are “at least very slightly negative” does not make much sense to me. First, all the slopes that turn negative turn positive again. When they do turn positive again, they peak at higher positive values than the lowest negative values reached (absolute values, of course). They tend to be trendless (slope=0) for significant periods around the period they turn negative.

UAH is probably the worse case example. It’s slope remains near zero for four to five years. It turns negative October 2004. However, it is only negative for four months (Oct 2004, Dec 2004, Jan 2005, Mar 2005). These four months have an average slope of -0.00019. This is less than 1% of the positive slope of 0.020 reached later in Nov 2007. After turning positive in Apr 2005, UAH doesn’t turn negative until over three years later (June 2008).

When the slopes of these data series are graphed, they all share something in common. The positives slopes have a peak in 2007 and the negative slopes have a peak in 2009.

A better method for analyzing the slopes might be to look at where the slopes peak rather than where they cross zero. Also, might want to look at where the R^2 values peak. An indication of robustness would be if all data series peak at the same time (rather than varying from 1996 to 2004). A good indication of robustness is if the same dates can be obtained by going in the opposite direction. In the backward direction (as in this blog), we start with the PRESENT date and go to the furthest month in the PAST where the slope peaks (NEGATIVE). In the forward direction, we would start with a PAST date (Jan 1976) and go to the furthest month in the FUTURE where the slope peaks (POSITIVE).

What we find in all the data series is that positive peaks occur in 2007 both forward and backward. In the forward analysis we see a dip after 2007 decreasing to a trough in 2009. These peaks and troughs are at the same time as in the backward analysis. Also, peaks in R^2 occur around these same 2007 and 2009 points.

It should also be noted that the backward analysis in this time period is more volatile than the forward analysis.

What’s interesting is if we compare all the previous positive and negative cycles going back to the 19th century. Here’s what we find:

cooling trend: 1878-1910 (32 yrs), slope = -0.0070
warming trend: 1910-1943 (33 yrs), slope = 0.0142
cooling trend: 1943-1976 (33 yrs), slope = -0.0028
warming trend: 1976-2008 (32 yrs), slope = 0.0193
[Note: 2008 cutoff could be in range from 2007 to 2009.]

Looks like an average cycle of approximately 32.5 years. If these cycles continue, we should be in a cooling trend that lasts to approximately 2040. How much cooling? If the cycles continue, it might be less than 0.1 degrees Celsius (-0.003*32).

33. Cramer says:
April 5, 2014 at 4:01 pm

You are correct that there are all kinds of ways to analyze data. Some would say that using straight lines is wrong since everything goes in cycles. In my Section 1, I simply go from the latest month and see how far back I can go and still get a negative slope. So in the case of RSS, I can go to August 1996 at the present time. Any month before August 1996 ALWAYS gives a positive slope. I do realize that there are many months after August 1996 that also give a positive slope. As a matter of fact, going from February 2014 to March 2014 gives a positive slope since March had a higher anomaly than February. However I was not interested in any shorter times with a positive slope.

34. Cramer says:

In reply to “Werner Brozek says: April 5, 2014 at 6:11 pm”

Yes, I know what you did. There was no need to repeat a description of it. I made no claims that you did not know that there were positive slopes after your dates or noise producing positive slopes in 2014 (in other words, I knew that the slope ALWAYS is positive before your calculated date; and I assumed you knew it too.). I did not see the point in your analysis because it did not provide any useful information in my opinion. Rather than just saying you were “not interested,” It would have been helpful if you explained why. That’s just the inquisitive scientist in me.

If I was to guess why, I believe you were attempting to prove that this is all chaos and it should all be taken with a grain of salt. Temperature (not just anomalies) is a meaningless property because it does not measure a change in energy. Average temperatures are meaningless because some places on Earth can vary by 60 degrees C or more. Temperature anomalies are meaningless because what is “normal.” And look at these slopes of the various temperature anomaly time series, one turns positive in 1996 and another in 2004, so those are meaningless too. And on and on. [Note: I know you did not use the word meaningless, but that seemed to be the message.]

As you said, “it is hard to imagine that an increase of 2 C over 100 years, even if it occurred, would be catastrophic.” It would have been a better article if you would have just explained why sea levels will not rise significantly, agriculture will not shift across boarders, extreme storms will not be more frequent, etc. Or why that won’t matter. Instead you disguise politics as science by attempting to bring a sense of confusion to where there should not be confusion. Concepts like 3.8 degrees C seasonality are simple fundamentals that should not be a source of contention.

Obviously I am not a “warmist” because I just made a case in my last comment why there might be global cooling until 2040. I came to wattsupwiththat.com because through word-of-mouth I heard there was excellent science here.

35. Cramer says:
April 6, 2014 at 1:54 am
Thank you for your post. Granted, there is much to discuss. You say:
It would have been a better article if you would have just explained why sea levels will not rise significantly, agriculture will not shift across boarders, extreme storms will not be more frequent, etc.

All of these are important and probably hundreds if not thousands of articles have been made over the years on all of these topics and more. I have no biology beyond high school so I will leave agriculture, for example, to others who have more expertise. I have decided that I like my little niche here at WUWT to focus on how long the pause is on 6 data sets each month. And believe me, this pause in warming is one very important topic. Many emails from climate gate commented on this fact.

“I did not see the point in your analysis because it did not provide any useful information in my opinion.”

Santer and NOAA have comments on this topic and I point out how well the different criteria such as 17 years and 15 years at 95% certainty are being met on six data sets.

36. Phil. says:

rgbatduke says:
April 4, 2014 at 8:07 am
Or, if you want to do it in text why not simply state what I just stated: The total temperature change from 1950 to 2014 of roughly 0.5 C represents a variation of 0.17% of the absolute temperature (that is “seventeen hundredths of one percent”) which is itself only known within about 1 C. The total CO_2 concentration change from 1950 to 2014 of roughly 100 ppm represents a variation of 33% (that is “one third”) of the initial absolute concentration.

You can never go wrong stating simple fact.

Nor by omitting salient facts either it seems!
Your remark would be more credible if you described Log([CO2}).