HadCRUT4 is From Venus, GISS is From Mars (Now Includes November Data)

Image Credit: WoodForTrees.org

Guest Post By Werner Brozek, Edited By Just The Facts

With apologies to John Gray, it can be seen from the above graph that the two data sets often either go in opposite directions or are different in other ways. Looking at the first 11 months, there are three months where the jumps are similar, namely May, October and November. However even for November, where both went in the same direction, there is a small discrepancy with respect to the ranking of their respective November anomalies. As we know, November on GISS at 0.77 was the warmest November ever. However the HadCRUT4 November at 0.596 was the third warmest November ever.

Here are the November 2013 rankings on the following other data sets with respect to all other Novembers: HadCRUT3 (1), Hadsst3 (2), UAH (8), and RSS (13). An earlier post on WUWT asked “Claim: November 2013 is the ‘warmest ever’ – but will the real November 2013 temperature please stand up?”

In my opinion, the best answer would be how WTI ranks this November. (WTI is a combination of Hadcrut3, UAHversion5.5, RSS, and GISS. It could be argued that WTI would be more meaningful with UAH version 5.6 as well as Hadcrut4 data instead of Hadcrut3. However until that change is made, I have to go with what I have.) WTI gives the November 2013 average as 0.212. This would rank it 7th. It is below the following years, from highest to lowest: 2009 (0.296), 2005, 2010, 2001, 2012, and 2004 (0.219).

One thing to keep in mind is that we are talking about anomalies in November. In terms of actual temperatures, the global change varies from 12.0 C in January to 15.8 C in July. An excellent explanation of this is given at this site. So regardless how high the anomaly was in November, the earth was not sizzling hot. The coldest July since 1850 was still way warmer than this November, as far as actual temperatures are concerned.

As well, as davidmhoffer has often noted, it takes very little energy to raise the temperature of dry, cold air by a certain amount versus raising hot moist air by the same amount. “For easy figuring, it takes about 1.8 w/m2 to raise the temperature in the Antarctic from 200K to 201K, or 1 degree. But that same 1.8 w/m2 in the tropics at 303K only raises the temperature by less than 0.3 degrees!”

Apparently all anomalies are only accurate to 0.1 degrees if I am not mistaken. If that is the case, then some months are really pushing the limits. For example, for the month of March, the difference between Hadcrut4 and GISS is 0.195. However for the month of July, there is no difference. In September, the difference is 0.198. While these differences are technically just within the error bars, they do not inspire confidence in their accuracy.

To put these numbers into perspective, the warmest year in HadCRUT4 is 2010 where the anomaly was 0.547. Subtracting 0.198 from this gives 0.349. An anomaly of 0.349 would rank only 15th! Would you trust GISS or HadCRUT4 or neither if you had to make trillion dollar decisions?

In the parts below, 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 several data sets. 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 2013 to date compares with 2012 and the warmest years and 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

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. 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.

On all data sets below, the different times for a slope that is at least very slightly negative ranges from 8 years and 11 months to 17 years and 3 months.

1. For GISS, the slope is flat since September 2001 or 12 years, 3 months. (goes to November)

2. For Hadcrut3, the slope is flat since June 1997 or 16 years, 6 months. (goes to November)

3. For a combination of GISS, Hadcrut3, UAH and RSS, the slope is flat since December 2000 or exactly 13 years. (goes to November)

4. For Hadcrut4, the slope is flat since December 2000 or exactly 13 years. (goes to November)

5. For Hadsst3, the slope is flat since December 2000 or exactly 13 years. (goes to November)

6. For UAH, the slope is flat since January 2005 or 8 years, 11 months. (goes to November using version 5.5)

7. For RSS, the slope is flat since September 1996 or 17 years, 3 months (goes to November). RSS has passed Ben Santer’s 17 years.

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 sloped wiggly line shows how CO2 has increased over this period.

When two things are plotted as I have done, the left only shows a temperature anomaly.

The actual numbers are meaningless since all slopes are essentially zero and the position of each line is merely a reflection of the base period from which anomalies are taken for each set. 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 17 years, the temperatures have been flat for varying periods on various data sets.

The next graphs shows the above, but this time, the actual plotted points are shown along with the slope lines and the CO2 is omitted.

Section 2

For this analysis, data was retrieved from Nick Stokes moyhu.blogspot.com. 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 16 and 21 years.

The details for several sets are below.

For UAH: Since January 1996: CI from -0.024 to 2.445

For RSS: Since November 1992: CI from -0.008 to 1.959

For Hadcrut4: Since August 1996: CI from -0.005 to 1.345

For Hadsst3: Since January 1994: CI from -0.029 to 1.697

For GISS: Since June 1997: CI from -0.007 to 1.298

Section 3

This section shows data about 2013 and other information in the form of a table. The table shows the six data sources along the top and other places so they should be visible at all times. The sources are UAH, RSS, Hadcrut4, Hadcrut3, Hadsst3, and GISS. Down the column, are the following:

1. 12ra: This is the final ranking for 2012 on each data set.

2. 12a: Here I give the average anomaly for 2012.

3. year: This indicates the warmest year on record so far for that particular data set. Note that two of the data sets have 2010 as the warmest year and four have 1998 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.

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 is followed by the last two numbers of the year.

9. Jan: This is the January, 2013, anomaly for that particular data set.

10. Feb: This is the February, 2013, anomaly for that particular data set, etc.

21. ave: This is the average anomaly of all months to date taken by adding all numbers and dividing by the number of months. However if the data set itself gives that average, I may use their number. Sometimes the number in the third decimal place differs slightly, presumably due to all months not having the same number of days.

22. rnk: This is the rank that each particular data set would have if the anomaly above were to remain that way for the rest of the year. It may not, but think of it as an update 55 minutes into a game. Due to different base periods, the rank is more meaningful than the average anomaly. A “!” indicates a tie for that rank.

If you wish to verify all of the latest anomalies, go to the following links, For UAH, version 5.5 was used since that is what WFT used, RSS, Hadcrut4, Hadcrut3, Hadsst3,and GISS

To see all points since January 2013 in the form of a graph, see the WFT graph below.

Note that the satellite data sets often go in the opposite direction to the others. Can you think of any reason for this? As you can see, all lines have been offset so they all start at the same place in January.

Appendix

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

RSS

The slope is flat since September 1996 or 17 years, 3 months. (goes to November) RSS has passed Ben Santer’s 17 years.

For RSS: There is no statistically significant warming since November 1992: CI from -0.008 to 1.959.

The RSS average anomaly so far for 2013 is 0.224. This would rank as a two way tie for 9th place if it stayed this way. 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 2012 was 0.192 and it came in 11th.

UAH

The slope is flat since January 2005 or 8 years, 11 months. (goes to November using version 5.5)

For UAH: There is no statistically significant warming since January 1996: CI from -0.024 to 2.445.

The UAH average anomaly so far for 2013 is 0.198. This would rank 7th if it stayed this way. 1998 was the warmest at 0.419. The highest ever monthly anomaly was in April of 1998 when it reached 0.66. The anomaly in 2012 was 0.161 and it came in 9th.

Hadcrut4

The slope is flat since December 2000 or exactly 13 years. (goes to November)

For HadCRUT4: There is no statistically significant warming since August 1996: CI from -0.005 to 1.345.

The Hadcrut4 average anomaly so far for 2013 is 0.486. This would rank 8th if it stayed this way. 2010 was the warmest at 0.547. The highest ever monthly anomaly was in January of 2007 when it reached 0.829. The anomaly in 2012 was 0.448 and it came in 9th.

Hadcrut3

The slope is flat since June 1997 or 16 years, 6 months. (goes to November)

The Hadcrut3 average anomaly so far for 2013 is 0.455. This would rank 6th if it stayed this way. 1998 was the warmest at 0.548. The highest ever monthly anomaly was in February of 1998 when it reached 0.756. One has to go back to the 1940s to find the previous time that a Hadcrut3 record was not beaten in 10 years or less. The anomaly in 2012 was 0.403 and it came in 10th.

Hadsst3

For Hadsst3, the slope is flat since December 2000 or exactly 13 years. (goes to November).

For Hadsst3: There is no statistically significant warming since January 1994: CI from -0.029 to 1.697.

The Hadsst3 average anomaly so far for 2013 is 0.380. This would rank 6th if it stayed this way. 1998 was the warmest at 0.416. The highest ever monthly anomaly was in July of 1998 when it reached 0.526. The anomaly in 2012 was 0.346 and it came in 9th.

GISS

The slope is flat since September 2001 or 12 years, 3 months. (goes to November)

For GISS: There is no statistically significant warming since June 1997: CI from -0.007 to 1.298.

The GISS average anomaly so far for 2013 is 0.60. This would rank as a 3 way tie for 6th place if it stayed this way. 2010 was the warmest at 0.67. The highest ever monthly anomaly was in January of 2007 when it reached 0.93. The anomaly in 2012 was 0.57 and it came in 9th.

Conclusion

Different data sets can give very different anomalies for any given month. As well, there can be spikes in any given month from some data sets, but not in others. Surface data sets have all kinds of issues such as UHI and poor stations that the satellite data sets do not have. On the other hand, satellites measure slightly different things.

One cannot lose perspective either. While this November was very warm on some data sets, the rankings for 2013 on the six data sets I discuss varies from 6 to 9 after 11 months. Some of these rankings are ties or very close to other years so a departure in December from the current average could easily change these rankings by a small amount. However a record warm year for 2013 is totally out of reach on all data sets.

Just for the fun of it, if you want to know what it would take to set a record, take the value in row 4 of the table, (0.67 for GISS), and subtract the value in row 21 of the table (0.60 for GISS). This gives 0.07 for GISS. Multiply this by 12 and add to the number in row 21 of the table. So GISS would need a December anomaly of 0.07 x 12 + 0.60 = 1.44 to tie a record.