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

A new HadCRUT4.5 data set came out last month. HadCRUT4.5 replaces HadCRUT4.4. In an earlier post, I commented on how virtually all changes over the past 16 years showed increases. I cannot say that this time. Of the last 6 complete years, the last 3 showed increases but the previous 3 showed decreases. Changes in earlier years are relatively minor for the most part.

Last month, I was happy to say that HadCRUT4 and WTI had finally been updated on WFT. Normally, when a new anomaly comes out, it automatically appears on WFT within 24 hours. But when the title changes to 4.5 instead of 4.4, it takes a manual adjustment which has not been done yet, but hopefully will be done soon. If not, you will just be able to get slopes and all monthly values to July with the 4.4 version using WFT. If you want slopes and all monthly values to August using the latest 4.5 version, you can go to Nick Stokes Trend Viewer Site.

In the table below the graph above I provide the final average values for the calendar year 2013 using various versions of HadCRUT. You may have values that differ by 1 or 2 thousandths, depending on when the anomaly was taken, however the general trend is clear. I could not get a HadCRUT4.1 value since it ended in February of 2013.

According to this article by Eric Worral, “The first six months were a sweltering 1.3°C above pre-industrial times.” The graph above compares UAH with Hadcrut4. The mean of 6 months was set at the same maximum height for 1998, and as a result, the 6 month mean in 2016 was about 0.3°C higher for HadCRUT4 than for UAH. Does that imply that if it were not for pause busting adjustments and other adjustments, that HadCRUT4 would be 0.5°C away from the 1.5°C tipping point?

Other questions also arise. For example, which pre-industrial times period was chosen and how accurate are the numbers from that period? In a 2009 article Lubos Motl noted that, “What the “pre-industrial temperature” actually means is an arbitrarily chosen and mostly unknown temperature at a random year during the feudal era.”

Does any one have a better answer? And what would the error bars look like? Why did they not pick a more modern time period where we have a better idea as to what the anomaly was like? For example, why not set a goal to be no more than 1.3 °C above the GISS average for 1951 to 1980? But how well do we know this value?

Bob Tisdale noted in a 2014 article that “[T]he average global mean surface temperature during their base period of 1951 to 1980 (their climatology) is roughly 14 deg C +/- 0.6 deg C.” Well that does not sound too good. If we only know the temperature to the nearest 0.6°C, can the anomaly be better known? And I assume the HadCRUT error bars for their base period are no better.

Why not use something from this century with the latest technology to get the best temperature and go from there? For example, why not suggest that we should not go higher than a certain amount above the 2013 HadCRUT anomaly? But as shown above, how well is that known? Furthermore, is it possible that with HadCRUT8.3 in 2028 we will discover that 2016 had already averaged above 1.5°C but we did not know it at the time?

To paraphrase Mark Steyn, they apparently know the pre-industrial anomaly, but they do not know the 2013 anomaly. Another question to be raised is if this 1.5°C is to be measured during an extremely large El Niño or whether it should be measured during an ENSO neutral year. As well, should a 6 month average be taken or should it be for a calendar year? An entirely different question to be addressed is if the 1.5°C is at all meaningful. I would argue that it is not, but that is for a another post.

NOTE: There are also major changes to all RSS numbers as compared to last month as this recent article on WUWT illustrated.

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 2016 so far 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. All data sets except Hadcrut4 go to September.

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 1993: Cl from -0.002 to 1.801

This is 23 years and 1 month.

For RSS: Since July 1994: Cl from -0.033 to 1.800 This is 22 years and 3 months.

For Hadcrut4.4: The warming is statistically significant for all periods above three years.

For Hadsst3: Since February 1997: Cl from -0.029 to 2.124 This is 19 years and 8 months.

For GISS: The warming is statistically significant for all periods above three years.

Section 2

This section shows data about 2016 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 prior to 2016. 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. 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.

8. sy/m: This is the years and months for row 7.

9. Jan: This is the January 2016 anomaly for that particular data set.

10. Feb: This is the February 2016 anomaly for that particular data set, etc.

18. ave: This is the average anomaly of all months to date.

19. rnk: This is the rank that each particular data set would have for 2016 without regards to error bars and assuming no changes to the current average anomaly. Think of it as an update 45 minutes into a game.

Source UAH RSS Had4 Sst3 GISS 1.15ra 3rd 3rd 1st 1st 1st 2.15a 0.261 0.381 0.760 0.592 0.87 3.year 1998 1998 2015 2015 2015 4.ano 0.484 0.550 0.760 0.592 0.87 5.mon Apr98 Apr98 Dec15 Sep15 Dec15 6.ano 0.743 0.857 1.024 0.725 1.11 7.sig Sep93 Jul94 Feb97 8.sy/m 23/1 22/3 19/8 Source UAH RSS Had4 Sst3 GISS 9.Jan 0.540 0.679 0.906 0.732 1.16 10.Feb 0.832 0.989 1.068 0.611 1.34 11.Mar 0.734 0.866 1.069 0.690 1.30 12.Apr 0.714 0.783 0.915 0.654 1.09 13.May 0.545 0.543 0.688 0.595 0.93 14.Jun 0.338 0.485 0.733 0.622 0.75 15.Jul 0.389 0.492 0.734 0.670 0.84 16.Aug 0.435 0.471 0.775 0.654 0.97 17.Sep 0.441 0.576 0.607 0.91 18.ave 0.552 0.654 0.859 0.646 1.03 19.rnk 1st 1st 1st 1st 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://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.4.5.0.0.monthly_ns_avg.txt

For Hadsst3, see: https://crudata.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 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, UAH6.0beta5, Hadcrut4.4 and GISS. Unfortunately, WTI will not be updated until HadCRUT4.5 appears.

Appendix

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

UAH6.0beta5

For UAH: There is no statistically significant warming since September 1993: Cl from -0.002 to 1.801. (This is using version 6.0 according to Nick’s program.)

The UAH average anomaly so far for 2016 is 0.552. This would set a record if it stayed this way. 1998 was the warmest at 0.484. Prior to 2016, the highest ever monthly anomaly was in April of 1998 when it reached 0.743. The average anomaly in 2015 was 0.261 and it was ranked 3rd.

RSS

NOTE: As noted earlier, there are major changes to all RSS numbers as compared to last month.

Presently, for RSS: There is no statistically significant warming since July 1994: Cl from -0.033 to 1.800.

The RSS average anomaly so far for 2016 is 0.654. This would set a record if it stayed this way. 1998 was the warmest at 0.550. Prior to 2016, the highest ever monthly anomaly was in April of 1998 when it reached 0.857. The average anomaly in 2015 was 0.381 and it was ranked 3rd.

For comparison, here is what I said last month:

For RSS: There is no statistically significant warming since December 1993: Cl from -0.008 to 1.746.

The RSS average anomaly so far for 2016 is 0.645. This would set a record if it stayed this way. 1998 was the warmest at 0.550. Prior to 2016, 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.

Hadcrut4.5

For Hadcrut4: The warming is significant for all periods above three years.

The Hadcrut4 average anomaly so far is 0.859. This would set a record if it stayed this way. Prior to 2016, the highest ever monthly anomaly was in December of 2015 when it reached 1.024. The average anomaly in 2015 was 0.760 and this set a new record.

For comparison, here are the last two lines from last month:

2015 when it reached 1.010. The average anomaly in 2015 was 0.746 and this set a new record.

Hadsst3

For Hadsst3: There is no statistically significant warming since February 1997: Cl from -0.029 to 2.124.

The Hadsst3 average anomaly so far for 2016 is 0.646. This would set a record if it stayed this way. Prior to 2016, the highest ever monthly anomaly was in September of 2015 when it reached 0.725. The average anomaly in 2015 was 0.592 and this set a new record.

GISS

For GISS: The warming is significant for all periods above three years.

The GISS average anomaly so far for 2016 is 1.03. This would set a record if it stayed this way. Prior to 2016, the highest ever monthly anomaly was in December of 2015 when it reached 1.11. The average anomaly in 2015 was 0.87 and it set a new record.

Conclusion

How close are we really to the 1.5°C tipping point? If we consider that we were 0.2°C short to begin with, and if we then subtract 0.3°C to account for the difference between the HadCRUT and UAH peaks, and if we then subtract a further 0.5°C to account for the difference between very strong El Niño and neutral conditions, it seems as if we might only a third of the way there. Do you agree?

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