Guest Post by Werner Brozek, and Edited by Just The Facts

Source	UAH	RSS	Had4	Sst3	GISS
JFM17ave	0.277	0.399		0.517
JFM15ave	0.215	0.334		0.423
JF17ave			0.793		1.01
JF15ave			0.697		0.85
diff	0.062	0.065	0.096	0.094	0.16
months	Aug	Sep	Oct	Nov	Dec
ENSO16	-0.6	-0.8	-0.8	-0.8	-0.7
ENSO14	0.0	0.1	0.4	0.5	0.6
diff	-0.6	-0.9	-1.2	-1.3	-1.3

ENSO data above from NOAA.

Based upon NOAA’s methodology of defining La Niñas and El Niños, they have concluded that the last five months of 2016 were La Niña months. My understanding is that following on the heels of five La Niña months, anomalies should be below average as there is a certain lag between ENSO numbers and global anomalies. The bottom part of the above table provides ENSO numbers for the five months from August to December of 2016 as well as the ENSO numbers for the five months from August to December of 2014.

Additionally, the differences are provided for each month. Note that all numbers are negative, so the ENSO numbers for the last five months of 2016 were significantly below those of 2014. The top part of the table provides January to March averages for the two satellite data sets and HadSST3 that followed on the heels of the 2014 and 2016 ENSO numbers. As well, the January and February averages are given for two surface data sets. So these numbers are for the years 2015 and 2017. Then the differences are given. Note that all differences are positive! This is exactly the opposite that is expected to happen with the ENSO differences being negative for the preceding five months! What is going on?

Analyzing the numbers further gives more unusual information. The HadCRUT4.5 average for January and February is 0.793. This would set a new record if it stayed this way as it would beat the 0.773 mark for 2016. That is of course ignoring statistical significances for now. As well, the January and February anomalies are the second highest on record. These numbers would normally be expected to follow a very strong El Niño. The 2017 numbers for January and February are even higher than those for 1998 for HadCRUT4.5.

For GISS, the situation is similar. The GISS average for January and February is 1.01. This would set a new record if it stayed this way as it would beat the 0.98 mark for 2016. As well, the January and February anomalies are the second highest on record. The 2017 numbers for January and February are also even higher than those for 1998 for GISS.

The sea surface numbers are a bit more reasonable since 0.517 would put HadSST3 in third place. But even this would normally be expected after a few El Niño months.

The numbers for the satellite data sets are still a bit high, but much better than for the surface data sets. UAH6.0 would rank in fourth place if its three month average were to hold for the whole year. The 2017 numbers for January, February and March are lower than those for 1998, which is what one would expect. RSS would also rank in fourth place if its three month average were to hold for the whole year. The 2017 numbers for January, February and March are also lower than those for 1998, which is again also what one would expect.
In my mind, this brings up the question as to whether or not the criteria for deciding when to declare a La Nina is correct. Should a different part of the ocean be considered? Or should the threshold be lowered to -1.0 C rather than -0.5 C? If you wish, please give your answer to the multiple choice question in the conclusion.

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 December 1993: Cl from -0.009 to 1.776
This is 23 years and 3 months.
For RSS: Since October 1994: Cl from -0.006 to 1.768 This is 22 years and 5 months.
For Hadcrut4.5: The warming is statistically significant for all periods above four years.
For Hadsst3: Since May 1997: Cl from -0.015 to 2.078 This is 19 years and 10 months.
For GISS: The warming is statistically significant for all periods above four 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.:
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 if available, etc.
10. ave: This is the average anomaly of all available months.
11. rnk: This is the 2017 rank for each particular data set assuming the average of the anomalies stay that way all year. Of course they won’t, but think of it as an update 10 minutes into a game.

Source	UAH	RSS	Had4	Sst3	GISS
1.16ra	1st	1st	1st	1st	1st
2.16a	0.503	0.574	0.773	0.613	0.98
3.mon	Feb16	Feb16	Feb16	Jan16	Feb16
4.ano	0.828	0.995	1.070	0.732	1.32
5.sig	Dec93	Oct94		May97
6.sy/m	23/3	22/5		19/10
7.Jan	0.298	0.409	0.738	0.484	0.92
8.Feb	0.348	0.440	0.851	0.520	1.10
9.Mar	0.185	0.349		0.550
10.ave	0.277	0.399	0.793	0.517	1.01
11.rnk	4th	4th	1st	3rd	1st
Source	UAH	RSS	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.

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 December 1993: Cl from -0.009 to 1.776. (This is using version 6.0 according to Nick’s program.)
The UAH average anomaly so far is 0.277. This would rank in fourth place if it stayed this way. 2016 was the warmest year at 0.503. The highest ever monthly anomaly was in February of 2016 when it reached 0.828.

RSS

For RSS: There is no statistically significant warming since October 1994: Cl from -0.006 to 1.768.
The RSS average anomaly so far is 0.399. This would rank in fourth place if it stayed this way. 2016 was the warmest year at 0.574. The highest ever monthly anomaly was in February of 2016 when it reached 0.995.

Hadcrut4.5

For Hadcrut4.5: The warming is significant for all periods above four years.
The Hadcrut4.5 average anomaly for 2016 was 0.773. This set a new record. The highest ever monthly anomaly was in February of 2016 when it reached 1.070. The HadCRUT4.5 average so far is 0.793 which would rank 2017 in first place if it stayed this way.

Hadsst3

For Hadsst3: There is no statistically significant warming since May 1997: Cl from -0.015 to 2.078.
The Hadsst3 average so far is 0.517 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 four years.
The GISS average anomaly for 2016 was 0.98. This set a new record. The highest ever monthly anomaly was in February of 2016 when it reached 1.32. The GISS average so far is 1.01 which would rank 2017 in first place if it stayed this way.

Conclusion

What is the most important thing that determines anomalies?
A. Changes in ENSO numbers
B. Changes in the sun
C. Changes in CO2
D. A thumb on the scale
E. Other (Please specify)