Final 2015 Statistics (Now Includes December Data)

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

One of the big stories from 2015 is the record shattering anomalies on the surface temperature data sets for 2015 versus the third ranked satellite anomalies for 2015. The discrepancy between 1998 and 2015 is now larger than it has ever been between the satellites and surface records as can be seen on the graphic above. Compare the blue end points with the red end points.

The table below shows the top ten years for the five data sets that I track. “1year” gives the year of the warmest anomaly for that data set and “1ano” gives the anomaly for that year, etc. However. I have included an extra column that I will also discuss, namely GIS5. GIS6 are the yearly anomalies from January 2016 for GISS (NASA Goddard Institute for Space Studies), but GIS5 are the yearly anomalies from January 2015.

First of all, I would like to draw your attention to the 2014 and 1998 anomalies as stated last year and as stated this year. Last year, the anomaly for 1998 was 61 and that of 2014 was 68 as can be seen on column GIS5. From column GIS6, it can be seen that 1998 jumped by 2 to 63, but 2014 jumped by 6 to 74. And 2013 jumped by 5, but 2002, which did not make the new cut of the top 10, only jumped by 3. And you know what this does to the “pause” from 1998 to 2015 when later years are bumped higher than earlier years.

Next, I will discuss some implications of GIS5. You may recall the controversy last year when GISS said that 2014 was a record warm year, but later said that there was only a 38% chance that it was really a record. The reason for this was that the error bar for each year’s anomaly is about 0.1.

Two things can be pointed out in this regard from GIS5. The 2010 anomaly was only 0.02 lower than that of 2014. This meant that 2010 had a pretty good chance of being the warmest, although by a lower percent than 38%. As well, the anomaly for 2014 was 0.68 whereas the anomaly for the 10th ranked year was 0.59. This difference of 0.09 was less than the error bar, so even the tenth ranked year had a statistical chance to be the warmest, although the probability would have been very low. So while 2014 had a greater chance to be the warmest year than any other year, the cumulative total of each of the next nine years being the warmest was about 62%.

Things are way different this year! Check out the 2015 anomalies for HadCRUT4, Hadsst3 and GIS6. In particular, note the difference between 1ano and 2ano for these three data sets. In all three cases, the difference is more than 0.1. This means that the probability that 2015 is a new record is over 90% in each case. It happens to be 94% for GISS.

In contrast, check out the anomalies for the two satellite data sets, UAH6.0beta4 and RSS. Specifically, compare the 2015 values at “3ano” with the 1998 values at “1ano”. The difference is way more than 0.1 indicating there is a much greater than a 90% chance that 2015 is NOT a record for the satellite data sets.

If we assume an error bar of 0.1, then even second place is outside this error bar for HadCRUT4, Hadsst3 and GISS. However it is different with the satellites. If we assume that the UAH anomaly of 0.266 could be as high as 0.366 and as low as 0.166, then its third place ranking could be as high as second or as low as seventh.

If we assume that the RSS anomaly of 0.358 could be as high as 0.458 and as low as 0.258, then its third place ranking still cannot be higher than third but it can be as low as sixth.

It is not shown anywhere either on the above table nor on the table in section 3, but the November and December anomalies on all five data sets showed records for November and December. As well, many other months showed monthly records for some of the data sets.

As can be seen in section 3, all time records were set by HadCRUT4, Hadsst3 and GISS. For HadCRUT4, its December anomaly of 1.005 beat the previous all time high mark of 0.832 set in January 2007. For GISS, the December anomaly of 1.12 beat the previous all time high January 2007 anomaly of 0.96. For GISS, October and November also beat the 0.96 mark with 1.06 and 1.05 respectively. For Hadsst3, its September anomaly of 0.725 beat the mark from August 2014 of 0.644.

As for the two satellite data sets, despite record warm monthly Novembers and Decembers, they were no where close to beating their all time high April 1998 anomaly. However if 2016 is patterned after 1998, the April 1998 record could fall.

What will happen to the present RSS pause of over 18 years? It all depends on how high and for how long the RSS anomalies stay high. If the RSS anomalies drop to 0.25 from the present 0.543 over the next three months, the pause will remain over 18 years. But if they stay high, then the pause may either drop to 15 years or to 7 years or it may disappear entirely. If the pause does disappear, it would take a La Nina to revive the pause again to over 18 years.

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 2015 so far compares with 2014 and the warmest years and months on record so far. For three of the data sets, 2014 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 July 1997 or 18 years and 6 months. (goes to December using version 6.0)

5. For RSS, the slope is flat since May 1997 or 18 years and 8 months. (goes to December)

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 6 and 22 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 -0.014 to 1.658

This is 22 years and 11 months.

For RSS: Since May 1993: Cl from -0.030 to 1.574

This is 22 years and 8 months.

For Hadcrut4.4: Since March 2001: Cl from -0.031 to 1.650

This is 14 years and 10 months.

For Hadsst3: Since January 1996: Cl from -0.021 to 2.082

This is an even 20 years.

For GISS: Since April 2009: Cl from -0.065 to 5.706

This is 6 years and 9 months.

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. 14ra: This is the final ranking for 2014 on each data set. NOTE: These are prior to 2015. See the first table in this post to see how 2015 has affected the 2014 rankings.

2. 14a: Here I give the average anomaly for 2014.

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 2014 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. NOTE: These numbers are all prior to 2015.

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. Depending on when the update was last done, the months may be off by one month.

10. Jan: This is the January 2015 anomaly for that particular data set.

11. Feb: This is the February 2015 anomaly for that particular data set, etc.

22. ave: This is the average anomaly of all months to date taken by adding all numbers and dividing by the number of months or by using the numbers in the data sets.

23. rnk: This is the rank that each particular data set has for 2015.

If you wish to verify all of the latest anomalies, go to the following:

For UAH, version 6.0beta4 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_6.0beta4.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 the last few 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.

RSS

The slope is flat since May 1997 or 18 years and 8 months. (goes to December)

For RSS: There is no statistically significant warming since May 1993: Cl from -0.030 to 1.574.

The RSS average anomaly for 2015 is 0.358. This puts it at 3rd place. 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 2014 was 0.254 and it was ranked 6th.

UAH6.0beta4

The slope is flat since July 1997 or 18 years and 6 months. (goes to December using version 6.0beta4)

For UAH: There is no statistically significant warming since February 1993: Cl from -0.014 to 1.658. (This is using version 6.0 according to Nick’s program.)

The UAH average anomaly for 2015 is 0.266. This would rank it at 3rd place. 1998 was the warmest at 0.482. The highest ever monthly anomaly was in April of 1998 when it reached 0.742. The anomaly in 2014 was 0.184 and it was ranked 5th.

Hadcrut4.4

The slope is not flat for any period that is worth mentioning.

For Hadcrut4: There is no statistically significant warming since March 2001: Cl from -0.031 to 1.650.

The Hadcrut4 average anomaly for 2015 is 0.745. This sets a new record. The highest ever monthly anomaly was in January of 2007 when it reached 0.832. This is prior to 2015. The anomaly in 2014 was 0.567 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 -0.021 to 2.082.

The Hadsst3 average anomaly for 2015 is 0.592. This sets a new record. The highest ever monthly anomaly was in August of 2014 when it reached 0.644. This is prior to 2015. The anomaly in 2014 was 0.477 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 since April 2009: Cl from -0.065 to 5.706.

The GISS average anomaly for 2015 is 0.87. This sets a new record. The highest ever monthly anomaly was in January of 2007 when it reached 0.96. This is prior to 2015. The anomaly in 2014 was 0.74 and it set a new record.

Conclusion

At the moment, the satellite data have not responded as in 1998. Do you think any 1998 satellite records will be beaten in 2016? In your opinion, how much of the terrestrial records in 2015 were real and how much was due to adjustments?

P.S. I thought Senator Cruz was very well informed on climate issues. I recently found out why.

Judith Curry says this:

“Senator Cruz seems very much into the Data, and generally knowledgable about the scientific process.  One of his staffers is an avid reader of CE, WUWT and apparently Steve Goddard’s blog.“