Guest Post by Werner Brozek, Excerpted From Nick Stokes, Edited by Just The Facts:

Image Credit: Nick Stokes
Before beginning the discussion, I just want to comment on the authors. It is said that you are entitled to your interpretation, but not to your facts. Werner Brozek and Nick Stokes are presenting you with just the facts. You cannot argue with the facts. However you may attach completely different significances to those same facts. Please let us know what significance you attach to the facts. I will reserve the right to include selected replies as part of the introduction to my next post.
The top diagram shows the monthly changes to RSS during the last large El Nino in 1997 and 1998. As well, it shows where 2016 is starting from, which is much higher than where 1998 started from. If similar changes occur in 2016 as in 1998, the present pause of over 18 years on the satellite data sets will soon be gone.
The following table gives some information to compare 1997/1998 with 2015/2016 on the five data sets I have been tracking. In addition, there are two different average values where the average of the five differences is given. As well, four monthly ENSO values are given.
| Source | UAH | RSS | Had4 | Sst3 | GISS | ave | ENSO |
|---|---|---|---|---|---|---|---|
| 1.1997 | -0.007 | 0.102 | 0.389 | 0.318 | 0.47 | ||
| 2.1998 | 0.484 | 0.550 | 0.536 | 0.416 | 0.63 | ||
| 3.diff | 0.491 | 0.448 | 0.147 | 0.098 | 0.16 | 0.269 | |
| 4.D97 | 0.250 | 0.302 | 0.505 | 0.477 | 0.59 | 2.3 | |
| 5.J98 | 0.479 | 0.550 | 0.483 | 0.419 | 0.61 | 2.1 | |
| 6.F98 | 0.653 | 0.736 | 0.763 | 0.478 | 0.88 | 1.8 | |
| 7.DFd | 0.403 | 0.434 | 0.258 | 0.001 | 0.29 | 0.277 | |
| 8.2015 | 0.264 | 0.358 | 0.745 | 0.592 | 0.86 | ||
| 9.D15 | 0.453 | 0.543 | 1.005 | 0.717 | 1.11 | 2.3 | |
| 10.J16 | 0.543 | 0.663 | 0.894 | 0.728 | 1.13 | ||
| 11.(F16) | (0.856 ) | (0.977) | (1.263) | (0.718) | (1.40) | ||
| Source | UAH | RSS | Had4 | Sst3 | GISS | ave | ENSO |
Row 1 gives the 1997 average anomaly for each of the five data sets. (Please see section 3 for all URLs.)
Row 2 gives the 1998 average anomaly for each of the five data sets.
Row 3 gives the difference between these anomalies for each data set along with the average of these five numbers.
Row 4 gives the December 1997 anomaly for each of the five data sets along with the ENSO value for that month.
Row 5 gives the January 1998 anomaly for each of the five data sets along with the ENSO value for that month.
Row 6 gives the February 1998 anomaly for each of the five data sets along with the ENSO value for that month.
Row 7 gives the difference between the December 1997 and February 1998 anomalies for each data set along with the average of these five numbers. Note the difference between December and January for the satellites versus the others. However by February 1998, all had made significant jumps from December 1997 except for Hadsst3.
Row 8 gives the 2015 average anomaly for each of the five data sets.
Row 9 gives the December 2015 anomaly for each of the five data sets along with the ENSO value for that month.
Row 10 gives the January 2016 anomaly for each of the five data sets.
Row 11 has all February anomalies in ( ). These numbers were obtained by adding the December 2015 anomalies (row 9) to the difference between the December 1997 and February 1998 anomaly (row 7). It will be interesting to compare these values to what will actually happen in February.
ENSO values were taken from here. It should be noted that with an error margin of 0.3 C, the ENSO values from May 1997 to December were the same as those from May 2015 to December.
Nick Stokes’ post from a month ago is well worth reading. I will excerpt the following from that post and then comment on the implications now that we have the January data. It applies to RSS:
“If the January anomaly exceeds about 1.3°C, the Pause is gone. This is unlikely.
If the Jan and Feb anomalies exceed on average about 0.77°C, the curve will be above the axis. For reference, the Dec anomaly was 0.543°C. I think this is quite likely.
If the first three months exceed 0.59°C on average, that would suffice to extinguish the pause. That is barely above the December value, and I think very likely indeed.
If Jan-April exceed 0.5°C, that will also suffice.”
The January anomaly was 0.66 C for RSS. So to reach an average of 0.77 C for January and February, the February anomaly needs to be 0.88 C. So if the February anomaly is under 0.88 C, the pause of over 18 years will still remain for at least another month. How likely is a jump of 0.22 C? The jump from January 1998 to February 1998 was 0.186. The jump from March 1998 to April 1998 was 0.272. So a jump of 0.22 cannot be ruled out. As well, the projection in the above table gives a value of 0.977, so the pause can certainly end in February.
What happens if the February anomaly is under 0.88? According to Nick’s numbers above, the average for the first three months needs to be 0.59 to extinguish the pause. In other words, J + F + M = 0.59(3). Since January was 0.66, the equation reduces to
March = 1.11 – February. This sets the maximum value that the March anomaly can be to keep the pause intact should the RSS pause not end in February. Therefore the RSS anomalies do not even have to increase over the next two months from January in order for the pause to disappear with the March anomaly.
Suppose February comes in at 0.882? If you are curious whether this is enough to kill the pause, go to Nick’s site here, click RSS, then using the blue >, move the blue ball to February 2016. If you see numbers and not “Some data not yet available”, it has been updated. Then use the red > to move the red ball to the earliest date where you know the pause started in January which would be June 1997. If the rate is negative, the pause still exists from June. If the rate is positive, advance a month at a time and see if the rate becomes negative over the next several months. Should the rate not become negative soon, you may wish to try from 2001.
If you want the latest slopes and times of no statistically significant warming for other data sets such as UAH6.0beta5, NOAA, BEST, etc, Nick Stokes’ site is excellent! This is in contrast to WFT that has not updated BEST since 2010 and which still uses UAH5.6 and which has not updated Hadcrut4 since May, 2015.
What about UAH6.0beta5? When the January number for 2016 came in and applied to UAH6.0beta4, it looked like the pause was over. However changes to other earlier values allowed the pause to hang on from October 1997. But unless there is a huge drop in the February anomaly to 0.315 or lower, the UAH pause will be over. Based on the projection in the table above, it will not even be close.
A rather interesting coincidence on all five data sets is that the January 1998 anomaly (row 5) was close to or equal to the 1998 average (row 2). Should this also be the case for 2016, then 2016 would set a new record on all five data sets.
Another interesting coincidence is that the average difference between 1997 and 1998 anomalies (row 3) for the five data sets is very close to the average difference between the December 1997 anomalies and the February 1998 anomalies (row 7).
All January anomalies on all five data sets are record highs for the month of January. In addition, for GISS and HadSST3, the January anomalies are the highest ever compared to any month in the past.
(P.S. Typing the letters “Stokes” puts your comment into moderation.)
In the sections below, as in previous posts, 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 some data sets. At the moment, only the satellite data have flat periods of longer than a year. 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 January 2016 compares with 2015 and the warmest years and months on record so far. For three of the data sets, 2015 also happens to be the warmest year. 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 on at least one calculation. 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.
1. For GISS, the slope is not flat for any period that is worth mentioning.
2. For Hadcrut4, the slope is not flat for any period that is worth mentioning.
3. For Hadsst3, the slope is not flat for any period that is worth mentioning.
4. For UAH, the slope is flat since October 1997 or 18 years and 4 months. (goes to January using version 6.0beta5)
5. For RSS, the slope is flat since June 1997 or 18 years and 8 months. (goes to January)
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 upward sloping blue line at the top indicates that CO2 has steadily increased over this period.
Note that the UAH5.6 from WFT needed a detrend to show the slope is zero for UAH6.0.

When two things are plotted as I have done, the left only shows a temperature anomaly.
The actual numbers are meaningless since the two slopes are essentially zero. 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 18 years, the temperatures have been flat for varying periods on the two sets.
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 1 and 23 years according to Nick’s criteria. Cl stands for the confidence limits at the 95% level.
The details for several sets are below.
For UAH6.0: Since February 1993: Cl from -0.031 to 1.685
This is exactly 23 years.
For RSS: Since May 1993: Cl from -0.012 to 1.625
This is 22 years and 9 months.
For Hadcrut4.4: Since October 2001: Cl from -0.016 to 1.812
This is 14 years and 4 months.
For Hadsst3: Since January 1996: Cl from -0.013 to 2.142
This is 20 years and 1 month.
For GISS: There is no statistically significant warming for any period worth mentioning.
Section 3
This section shows data about 2015 and other information in the form of a table. The table shows the five data sources along the top and other places so they should be visible at all times. The sources are UAH, RSS, Hadcrut4, Hadsst3, and GISS.
Down the column, are the following:
1. 15ra: This is the final ranking for 2015 on each data set.
2. 15a: Here I give the average anomaly for 2015.
3. year: This indicates the warmest year on record so far for that particular data set. Note that the satellite data sets have 1998 as the warmest year and the others have 2015 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. Periods of under a year are not counted and are shown as “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 are followed by the last two numbers of the year.
9. sy/m: This is the years and months for row 8.
10. Jan: This is the January 2016 anomaly for that particular data set.
11. rnk: This is the rank that each particular data set would have if the January anomaly was also the average anomaly at the end of the year.
SourceUAHRSSHad4Sst3GISS
| Source | UAH | RSS | Had4 | Sst3 | GISS |
|---|---|---|---|---|---|
| 1.15ra | 3rd | 3rd | 1st | 1st | 1st |
| 2.15a | 0.264 | 0.358 | 0.745 | 0.592 | 0.86 |
| 3.year | 1998 | 1998 | 2015 | 2015 | 2015 |
| 4.ano | 0.484 | 0.550 | 0.745 | 0.592 | 0.86 |
| 5.mon | Apr98 | Apr98 | Dec15 | Sep15 | Dec15 |
| 6.ano | 0.742 | 0.857 | 1.005 | 0.725 | 1.11 |
| 7.y/m | 18/4 | 18/8 | 0 | 0 | 0 |
| 8.sig | Feb93 | May93 | Oct01 | Jan96 | Mar14 |
| 9.sy/m | 23/0 | 22/9 | 14/4 | 20/1 | 1/11 |
| 10.Jan | 0.543 | 0.663 | 0.894 | 0.728 | 1.13 |
| 11.rnk | 1st | 1st | 1st | 1st | 1st |
If you wish to verify all of the latest anomalies, go to the following:
For UAH, version 6.0beta5 was used. Note that WFT uses version 5.6. So to verify the length of the pause on version 6.0, you need to use Nick’s program.
http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tlt/tltglhmam_6.0beta5.txt
For RSS, see: ftp://ftp.ssmi.com/msu/monthly_time_series/rss_monthly_msu_amsu_channel_tlt_anomalies_land_and_ocean_v03_3.txt
For Hadcrut4, see: http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.4.0.0.monthly_ns_avg.txt
For Hadsst3, see: http://www.cru.uea.ac.uk/cru/data/temperature/HadSST3-gl.dat
For GISS, see:
http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt
To see all points since January 2015 in the form of a graph, see the WFT graph below. Note that UAH version 5.6 is shown. WFT does not show version 6.0 yet. Also note that Hadcrut4.3 is shown and not Hadcrut4.4, which is why many months are missing for Hadcrut.

As you can see, all lines have been offset so they all start at the same place in January 2015. This makes it easy to compare January 2015 with the latest anomaly.
Appendix
In this part, we are summarizing data for each set separately.
UAH6.0beta5
The slope is flat since October 1997 or 18 years and 4 months. (goes to January using version 6.0beta5)
For UAH: There is no statistically significant warming since February 1993: Cl from -0.031 to 1.685. (This is using version 6.0 according to Nick’s program.)
The UAH anomaly for January is 0.543. This would set a record if it stayed this way. 1998 was the warmest at 0.484. The highest ever monthly anomaly was in April of 1998 when it reached 0.742. The average anomaly in 2015 was 0.264 and it was ranked 3rd.
RSS
The slope is flat since June 1997 or 18 years and 8 months. (goes to January)
For RSS: There is no statistically significant warming since May 1993: Cl from -0.012 to 1.625.
The RSS anomaly for January is 0.663. This would set a record if it stayed this way. 1998 was the warmest at 0.550. The highest ever monthly anomaly was in April of 1998 when it reached 0.857. The average anomaly in 2015 was 0.358 and it was ranked 3rd.
Hadcrut4.4
The slope is not flat for any period that is worth mentioning.
For Hadcrut4: There is no statistically significant warming since October 2001: Cl from -0.016 to 1.812.
The Hadcrut4 anomaly for January is 0.894. This would set a record if it stayed this way. The highest ever monthly anomaly was in December of 2015 when it reached 1.005. The average anomaly in 2015 was 0.745 and this set a new record.
Hadsst3
For Hadsst3, the slope is not flat for any period that is worth mentioning. For Hadsst3: There is no statistically significant warming since January 1996: Cl from -0.013 to 2.142.
The Hadsst3 anomaly for January is 0.728. This would set a record if it stayed this way. The highest ever monthly anomaly was in September of 2015 when it reached 0.725. This is prior to 2016. The average anomaly in 2015 was 0.592 and this set a new record.
GISS
The slope is not flat for any period that is worth mentioning.
For GISS: There is no statistically significant warming for any period worth mentioning.
The GISS anomaly for January is 1.13. This would set a record if it stayed this way. The highest ever monthly anomaly was in December of 2015 when it reached 1.11. This is prior to 2016. The average anomaly in 2015 was 0.86 and it set a new record.
Conclusion
Using the definition of the longest time with a negative slope, the pauses on all data sets have either ended or will end soon. What significance should be attached to this fact? Should it be considered as just an El Nino blip that is best ignored or should a huge amount of importance be attached to this fact?
“Science is experiment, not debate.”
I agree. The hysteria surrounding CO2 is completely irrational. But hysteria about the atmosphere didn’t start with CO2. It started with H2O. And it didn’t start recently, it started way back in 1840 with the birth of meteorology, by a guy named Walter James Espy, the father of meteorology:
https://youtu.be/pl-GOPq8aA0
“Long Satellite Pauses
Endingerased (Now Includes January Data)”Werner Brozek and Nick Stokes are presenting you with just the facts. You cannot argue with the facts
Are you really suggesting that you have discovered a ‘fact’ in climate science? Your piece will be out of date in a month. Flexible data, flexible ‘facts’.
The facts that I was alluding to were that the February anomaly needed to be over 0.88 in order for the long RSS pause to cease to exist. The anomaly came in at 0.974 and I have verified it using Nick’s program that the pause is indeed gone for any time period. A month from now will still show no long pause.
“Why in the world would anyone release a new data set now that ends the pause? The pause is ending anyway with the old data set.”
Actually, that’s because some people are interested just in the science and not in the politics.
“We start with the present date and go to the furthest month in the past where the slope is a least slightly negative on at least one calculation.”
This statistical methodology is mathematical masturbation.
How about common sense?
All we know NOW is there was a flat trend between two El Nino peaks ( 1998 and 2015/2016 ).
Way in the future we may look back and see it as something else.
The only way to end that flat trend is to “adjust” the data to make it disappears.
NOAA will probably do that little at a time!
Use your eyeballs and a chart.
There was a flat trend between two El Nino peaks.
You don’t need statistics to see that, and know that.
The article title is misleading, and counterproductive for the skeptic’s cause (good science).
People need to know there was a flat temperature trend for many years.
They don’t need to be told it was there, and now it’s gone.
It is not gone.
You have applied inappropriate statistics to obscure an important average temperature trend — a flat trend that contradicts the greenhouse gas theory — and you have done so for no logical reason, or maybe to show off your skill with statistics.
I am using the same definition as Lord Monckton when he said this last month:
“The hiatus period of 18 years 8 months is the farthest back one can go in the RSS satellite temperature record and still show a sub-zero trend.”
I have used this definition for the last three years, and by this definition, the pause has ended for now. I am certainly not thrilled by that news. On the other hand, it is not up to me to arbitrarily change a definition because of the latest blip in temperature. I feel that if we are honest and admit the pause is over for now, then we will be more believable when a La Nina resumes the pause later.
But I could be wrong. Feel free to express your perspective when Lord Monckton writes his next report on this matter.
However just because the slope from December 1997 is now 0.000106212 per year, that does not mean the cause is lost.
YOU WROTE:
“I feel that if we are honest and admit the pause is over for now, then we will be more believable when a La Nina resumes the pause later.”
MY COMMENT:
The flat trend IS over because of the El Nino temperature spike.
So what.
The only important point is there was a flat trend for 15 years AND IT WILL NEVER DISAPPEAR.
The statistic you used to define a “pause” was just as stupid when Monckton used it.
Even more stupid if it is used while ignoring reasonable margins of error.
Include margins of error, and the visible flat trend could be a slight rise, or a slight decline.
But that’s not important now.
The statistics you and Monckton used violate common sense.
You are wrong to defend them by “blaming” Monckton for being first, as if he could never make a mistake, and then you seem to defend his mistake by saying you’ve done the same thing for three years.
WELL, THEN, YOU”VE BEEN WRONG FOR THREE YEARS !
It’s not too late to change!
I’ll tell Monckton the same thing when he resorts to this mathematical masturbation.
A child ten years old could see a “flat trend” on an average temperature chart, surrounded by two (El Nino) peaks in 1998 and 2015 / 2016.
There is no need for statistical games to define the obvious.
It’s important to show people there was a flat trend for over 15 years.
No statistics are needed.
Just a clear chart.
And a clear explanation.
The skeptic’s cause IS lost if we can not clearly communicate climate history to the general public.
I started a climate blog in late 2014 because of that communication problem.
The warmunists have bad science and good communications.
We skeptical deniers need both good science and good communications.
Based on your poor choice of statistics, and your misleading article title, you have failed at both the science (statistical analysis) and the communication.
The real message for the general public is there was a 15 year flat trend while CO2 levels went up — that flat trend was not predicted … because a flat trend was not supposed to happen, according to the “CO2 is the climate controller” theory.
Anyone who knows a subject well should be able to communicate an important message about that subject in clear, simple English.
You failed to do that, and did not help the skeptic’s cause because you created an article that’s worthless for the general public.
To say it is poorly written, and tedious to read, is me being kind.
I bet you could do a lot better with your next article.
Of course you are right about that and I pointed out exactly that in this article:
http://wattsupwiththat.com/2014/12/02/on-the-difference-between-lord-moncktons-18-years-for-rss-and-dr-mckitricks-26-years-now-includes-october-data/
I am fully aware of the fact that every slope and every measurement has error bars. However my table would be extremely cluttered if I included every error range for every number.
Another blogger decided that a slope is “flat” if the slope is less than 0.1 C/century or something like that. That is certainly reasonable and up to him.
We all have different expectations, but any post that generates over 200 comments in 17 hours and that even gets mentioned in the “Weekly Climate and Energy News Roundup #217” is a very successful post in my opinion. If our host does not appreciate my posts, he can cancel them any time.
I think it is invalid to cite measurements to support a position that the collectors (NASA, NOAA) say are invalid interpretations.
I think you need to launch your own satellites – or buy time on existing ones capable of relevant measurements – then amass a commensurate set of historical data (1,600 months will do, I should think), and only then claim that the science of the authoritative sources is wrong.
Meanwhile, 2014 was the “hottest year on record”, 2015 beat that record for an unprecedented 2-in-a-row, January 2016 was the warmest global average for 1,600 months, and now February 2016 was warmer than January.
Many places on earth were 10C above “long term average” in January and February. What the hell happens if that occurs in June, July, and August? (I lived in Phoenix AZ when it got to 121F. You do NOT want to live in a world in which that happens with Cleveland OH summer humidity).
Keep in mind that for satellites, 1998 was still the warmest. However that could be beaten in 2016.
If you’re going to use the Ted Cruz name, you need to be smart at about climate change.
Earth’s climate is always changing.
There has been a warming trend for about 15,000 years since the last glaciation peak.
That warming was not started by SUVs or coal burning, and is likely to be at least +5 degrees C. so far
In more recent times, there has been a warming trend since about 1850.
There was actually a very cool period in the last 1600s to early 1700s, during a period of unusually low solar activity, called the Maunder Minimum — it is possible the average temperture has already increased +2 degree C. since that trough.
All the average temperature measurements you refer to were made DURING the post-1850 warming trend, so “record highs” are to be expected regularly, perhaps not every year, until that warming trend ends, and a cooling trend begins.
Ice core studies identified hundreds of mild warming/cooling cycles in the past one million years.
There is no indication in climate history studies that CO2 level changes CAUSE average temperature changes, and much evidence of the opposite: Average temperature changes cause CO2 level changes with a multi-hundred year lag.
In addition, greenhouse gases are invisible to sunlight and barely affect daytime high temperatures — their primary effect would be a slightly warmer nighttime low temperature, assuming any effect at all, given the the current CO2 level of 400 ppmv.
Even the IPCC agrees that most of the alleged greenhouse effect is from the first 40 ppmv of CO2 in the air, and the direct effect of CO2 will be tiny when CO2 increases above the current CO2 concentration of 400 ppmv.
I spent my time typing in the hope of sharing my climate history knowledge with you, assuming you would want to learn.
There is no doubt the climate in 2015 is better than it has been for humans and green plants, in at least 500 years, and you should be enjoying it.
The climate models have been making grossly inaccurate predictions for 40 years, and have been a waste of taxpayer money.
The coming climate change catastrophe has been a fantasy for 40 years so far.