Guest Post by Werner Brozek, Excerpts from Des and Edited by Just The Facts

At Dr. Roy Spencer’s site, regular commenter Des posted a very interesting analysis with respect to September 2017 on UAH6 and the Top 10 first-9-months-of-the-year. Des has graciously allowed me to use their work. Everything that appears below is from Des until you see the statement “Written by Des.” below:

Top 10 Septembers on the record:

1. 2017 (+0.54)
2. 2016 (+0.45) … EL NINO
3. 1998 (+0.44) … EL NINO
4. 2010 (+0.37) … EL NINO
5. 2009 (+0.27) … EL NINO
6. 2005 (+0.25) … EL NINO
7. 2015 (+0.25) … EL NINO
8. 1995 (+0.22) … EL NINO
9. 2012 (+0.22)
10. 2013 (+0.22)

2017 0.32 above 2nd highest non-El-Nino-affected September.

Top 10 first-9-months-of-the-year:

1. 1998 (+0.558) … EL NINO
2. 2016 (+0.554) … EL NINO
3. 2010 (+0.394) … EL NINO
4. 2017 (+0.342)
5. 2002 (+0.241) … EL NINO
6. 2015 (+0.217) … EL NINO
7. 2005 (+0.204) … EL NINO
8. 2007 (+0.199)
9. 2014 (+0.159)
10. 2003 (0.157)

Highest non-El-Nino-affected year by 0.143.

Average for last 5 years (Oct 2012 – Sep 2017): +0.278
Average for “last 5 years” at same point after 97-98 El Nino
(Oct 1994 – Sep 1999): +0.106

When I wrote “EL NINO”, it was not necessarily an El Nino month. There is a 4-6 month lag between ENSO events and their associated anomalies. The months marked “EL NINO” are either an El Nino month or they fall within that lag period.

Written by Des
———

The general expectation is that La Nina years are cooler than average; El Nino years are warmer than average; and that ENSO neutral years are in between. The year 2017 has been an ENSO neutral year all year. On top of that, the last five months of 2016 were week La Nina months, so there is no carry over from 2016 to help explain 2017. A single hot month may be just a fluke, however as Des showed above, the first nine months of 2017 were also much higher than expected for a neutral ENSO. The numbers are puzzling to me. Do you have any thoughts as to why September was so warm and/or why the first nine months of 2017 were so warm?

In the sections below, we will present you with the latest facts. The information will be presented in two sections and an appendix. The first section will show for how long there has been no statistically significant warming on several data sets. The second section will show how 2017 compares with 2016, the warmest year so far, and the warmest 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

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 0 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 September 1994: Cl from -0.010 to 1.778
This is 23 years and 1 month.
For RSS4: Since May 2009: Cl from -0.037 to 7.997 This is 8 years and 4 months.
For Hadcrut4.5: The warming is statistically significant for all periods above five years.
For Hadsst3: Since May 2001: Cl from -0.002 to 2.563 This is 16 years and 4 months.
For GISS: The warming is statistically significant for all periods above five years.

Section 2

This section shows data about 2017 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. 16ra: This is the final ranking for 2016 on each data set. On all data sets, 2016 set a new record. How statistically significant the records were was covered in an earlier post here: http://ift.tt/2onIVUi
2. 16a: Here I give the average anomaly for 2016.
3. 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.
4. ano: This is the anomaly of the month just above.
5. 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.
6. sy/m: This is the years and months for row 5.
7. Jan: This is the January 2017 anomaly for that particular data set.
8. Feb: This is the February 2017 anomaly for that particular data set, etc.
16. ave: This is the average anomaly of all available months.
17. rnk: This is the 2017 rank for each particular data set assuming the average of the anomalies stays that way the rest of the year. Of course they may not, but think of it as an update 45 minutes into a game.

Source	UAH	RSS4	Had4	Sst3	GISS
1.16ra	1st	1st	1st	1st	1st
2.16a	0.511	0.737	0.798	0.613	0.99
3.mon	Feb16	Feb16	Feb16	Jan16	Feb16
4.ano	0.851	1.157	1.111	0.732	1.34
5.sig	Sep94	May09		May01
6.sy/m	23/1	8/4		16/4
Source	UAH	RSS4	Had4	Sst3	GISS
7.Jan	0.325	0.578	0.739	0.484	0.97
8.Feb	0.382	0.661	0.845	0.520	1.12
9.Mar	0.225	0.563	0.873	0.550	1.13
10.Apr	0.272	0.544	0.737	0.598	0.93
11.May	0.441	0.628	0.659	0.564	0.88
12.Jun	0.213	0.486	0.640	0.540	0.70
13.Jul	0.286	0.594	0.653	0.540	0.81
14.Aug	0.407	0.713	0.715	0.606	0.84
15.Sep	0.540	0.841	0.561	0.436	0.80
16.ave	0.343	0.623	0.711	0.535	0.91
17.rnk	3rd	2nd	3rd	3rd	2nd
Source	UAH	RSS4	Had4	Sst3	GISS

If you wish to verify all of the latest anomalies, go to the following:
For UAH, version 6.0beta5 was used.
http://ift.tt/2p3iaCA
For RSS, see: http://ift.tt/1p2UGKO
For Hadcrut4, see: http://ift.tt/2p3jEN9
For Hadsst3, see: http://ift.tt/2onRPkq
For GISS, see:
http://ift.tt/y8yEne

To see all points since January 2016 in the form of a graph, see the WFT graph below. Note that it shows RSS3.

As you can see, all lines have been offset so they all start at the same place in January 2016. This makes it easy to compare January 2016 with the latest anomaly.
The thick double line is the WTI which shows the average of RSS, UAH, HadCRUT4.5 and GISS.

Appendix

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

UAH6.0beta5

For UAH: There is no statistically significant warming since September 1994: Cl from -0.010 to 1.778. (This is using version 6.0 according to Nick’s program.)
The UAH average anomaly so far is 0.343. This would rank in third place if it stayed this way. 2016 was the warmest year at 0.511. The highest ever monthly anomaly was in February of 2016 when it reached 0.851.

RSS4

For RSS4: There is no statistically significant warming since May 2009: Cl from -0.037 to 7.997.
The RSS average anomaly so far is 0.623. This would rank in second place if it stayed this way. 2016 was the warmest year at 0.737. The highest ever monthly anomaly was in February of 2016 when it reached 1.157. (NOTE: In my last report, I used TTT by mistake. I apologize for that.)

Hadcrut4.5

For Hadcrut4.5: The warming is significant for all periods above five years.
The Hadcrut4.5 average anomaly for 2016 was 0.798. This set a new record. The highest ever monthly anomaly was in February of 2016 when it reached 1.111. The HadCRUT4.5 average so far is 0.711 which would rank 2017 in third place if it stayed this way.

Hadsst3

For Hadsst3: There is no statistically significant warming since May 2001: Cl from -0.002 to 2.563.
The Hadsst3 average so far is 0.535 which would rank 2017 in third place if it stayed this way. The highest ever monthly anomaly was in January of 2016 when it reached 0.732.

GISS

For GISS: The warming is significant for all periods above five years.
The GISS average anomaly for 2016 was 0.99. This set a new record. The highest ever monthly anomaly was in February of 2016 when it reached 1.34. The GISS average so far is 0.91 which would rank 2017 in second place if it stayed this way.

Conclusion

The RSS4 numbers are very close to the UAH6 numbers in terms of the September ranking and yearly ranking. To have the warmest September in an ENSO neutral year that is warmer than all El Nino years seems odd. Do you have any reasons why this has occured?

(P.S. Thank you very much for all well wishes on my last post!)