Guest Essay By Werner Brozek, Edited by Just The Facts
In comparing GISS with the other five data sets that I comment on, some of the points I raise below overlap, and others could be added. However, and in no particular order, the following are some things that I have come up with on why GISS is unique. Perhaps you may disagree on some points or you may come up with others.
Image Credit JoNova
1. GISS uses two decimals whereas all others use three. While I agree that we do not know anomalies to the nearest 1/1000 or 1/100 of a degree, I find it very inconvenient. In my table, I give the 2013 anomaly rank, but with GISS, I need to check it every month since 2003 is usually tied to two decimal places, however they may switch places to three decimal places. Of course I realize that depending on how you look at it, there may be a ten way tie for sixth place, however if I want the best single number for the table, it is just a nuisance.
2. For 95% statistical significance, all others are above 17 years, but according to GISS, it is just over 14 years. See the table for details.
3. Including May, GISS has the most months in 2014 above the average of its record year of 2010, namely four of the five months. All other data sets have either zero or one or two months in 2014 above the anomaly average for its highest year. See the table for details.
4. GISS has the highest ranking after five months at first place. I realize it is only by 0.001 C and that could change when China’s numbers come in, but at the same time, 2010 could revert back to 0.65 from 0.66 next month. By contrast, RSS is eighth after five months. So while it is very probable that GISS will set a record, there is no way that RSS will do so. At this point, each of the last seven months on RSS would need to have an average anomaly of 0.775 and thereby smash every monthly record to date for every month from now to December. That is just not going to happen with RSS. The other rankings are from 4th to 8th.
5. GISS has the coolest period as the base period causing it to have the highest anomalies. However this does not affect the warming rate.
6. 1998 is ranked 4th which is the lowest of all data sets. Hadcrut4 has it as third and the others as first.
7. This is the warmest May ever recorded by GISS. However on RSS it is sixth; on UAH, version 5.5, it is fourth; on Hadsst3 it is second; and on Hadcrut3 it is also second. In all of these cases, at least the 1998 anomaly was higher. However Hadcrut4 also had May 2014 in first place by beating its 2010 mark by 0.004 C. However this difference is certainly not statistically significant.
8. GISS is the most quoted by warmists.
9. GISS is the most volatile of all data sets. Like James Bond, GISS has a reputation that precedes it. Why further it? Who will read a long and possibly a perfectly logical explanation when the end result is that a previous record is now easier to beat? For example, the 1998 anomaly of 0.62 in January was lowered to 0.61 now. Why can they not leave a 16 year old anomaly alone like the rest of the world?
10. And last, but not least, per JoNova, as shown referenced at the top of this article, GISS progressively realigns and reinterprets the temperatures from decades long ago:
In the parts below, as in the 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 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 2014 to date compares with 2013 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.
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 9 years and 5 months to 17 years and 9 months.
1. For GISS, the slope is flat since September 2004 or 9 years, 9 months. (goes to May)
2. For Hadcrut3, the slope is flat since September 2000 or 13 years, 9 months. (goes to May)
3. For a combination of GISS, Hadcrut3, UAH and RSS, the slope is flat since January 2001 or 13 years, 5 months. (goes to May)
4. For Hadcrut4, the slope is flat since January 2001 or 13 years, 5 months. (goes to May)
5. For Hadsst3, the slope is flat since January 2001 or 13 years, 5 months. (goes to May)
6. For UAH, the slope is flat since January 2005 or 9 years, 5 months. (goes to May using version 5.5)
7. For RSS, the slope is flat since September 1996 or 17 years, 9 months (goes to May).
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 indicates that CO2 has steadily 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. As well, I have offset them so they are evenly spaced. 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 graph shows the above, but this time, the actual plotted points are shown along with the slope lines and the CO2 is omitted:
For this analysis, data was retrieved from Nick Stokes’ Trendviewer available on his website Nick Stokes’ Trendviewer. 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 14 and 21 years.
The details for several sets are below.
For UAH: Since February 1996: CI from -0.017 to 2.347
For RSS: Since November 1992: CI from -0.016 to 1.857
For Hadcrut4: Since October 1996: CI from -0.010 to 1.215
For Hadsst3: Since January 1993: CI from -0.016 to 1.813
For GISS: Since December 1999: CI from -0.004 to 1.413
This section shows data about 2014 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 areUAH, RSS, Hadcrut4, Hadcrut3, Hadsst3, and GISS.
Down the column, are the following:
1. 13ra: This is the final ranking for 2013 on each data set.
2. 13a: Here I give the average anomaly for 2013.
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 are followed by the last two numbers of the year.
9. Jan: This is the January 2014 anomaly for that particular data set.
10.Feb: This is the February 2014 anomaly for that particular data set, etc.
14.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.
15.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 will not, but think of it as an update 25 minutes into a game. Due to different base periods, the rank is more meaningful than the average anomaly.
If you wish to verify all of the latest anomalies, go to the following:
For UAH, version 5.5 was used since that is what WFT uses: http://vortex.nsstc.uah.edu/public/msu/t2lt/tltglhmam_5.5.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.126.96.36.199.monthly_ns_avg.txt
For Hadcrut3, see: http://www.cru.uea.ac.uk/cru/data/temperature/HadCRUT3-gl.dat
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 2013 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 2013. This makes it easy to compare January 2013 with the latest anomaly.
In this part, we are summarizing data for each set separately.
The slope is flat since September 1996 or 17 years, 9 months. (goes to May)
For RSS: There is no statistically significant warming since November 1992: CI from -0.016 to 1.857.
The RSS average anomaly so far for 2014 is 0.235. This would rank it as 8th 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 2013 was 0.218 and it is ranked 10th.
The slope is flat since January 2005 or 9 years, 5 months. (goes to May using version 5.5 according to WFT)
For UAH: There is no statistically significant warming since February 1996: CI from -0.017 to 2.347. (This is using version 5.6 according to Nick’s program.)
The UAH average anomaly so far for 2014 is 0.192. This would rank it as 8th place 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 0.662. The anomaly in 2013 was 0.197 and it is ranked 7th.
The slope is flat since January 2001 or 13 years, 5 months. (goes to May)
For Hadcrut4: There is no statistically significant warming since October 1996: CI from -0.010 to 1.215.
The Hadcrut4 average anomaly so far for 2014 is 0.515. This would rank it as 4th place 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 2013 was 0.486 and it is ranked 8th.
The slope is flat since September 2000 or 13 years, 9 months. (goes to May)
The Hadcrut3 average anomaly so far for 2014 is 0.472. This would rank it as 5th place 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 2013 was 0.459 and it is ranked 6th.
For Hadsst3, the slope is flat since January 2001 or 13 years and 5 months. (goes to May).
For Hadsst3: There is no statistically significant warming since January 1993: CI from -0.016 to 1.813.
The Hadsst3 average anomaly so far for 2014 is 0.392. This would rank it as 5th place 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 2013 was 0.376 and it is ranked 6th.
The slope is flat since September 2004 or 9 years, 9 months. (goes to May)
For GISS: There is no statistically significant warming since December 1999: CI from -0.004 to 1.413.
The GISS average anomaly so far for 2014 is 0.66. This would rank it as first place if it stayed this way. 2010 and 2005 were the warmest at 0.65 in April. But in May, 2010 was raised to 0.66, however to 3 digits, 2014 is very slightly warmer, although the difference is certainly not statistically significant. (By the way, 2010 was 0.67 in January.) The highest ever monthly anomaly was in January of 2007 when it reached 0.93. The anomaly in 2013 was 0.59 and it is ranked 7th.
GISS is unique in many ways. Can you think of other ways in which GISS is unique that I have missed? I seem to have the impression that most adjustments serve one of two purposes. With the odd exception, they either make the present warmer and the past cooler. However if this is not the case, then the adjustments make a new record easier to happen. Is this a fair assessment?
P.S. RSS came so fast for June and Hadcrut3 was so slow for May that the June value for RSS came in before I completed the report. As a result of the June value for RSS of 0.345, the average for RSS for the first six months is 0.253. If it stayed this way, it would rank 7th. However the time period for a slope of zero increased from 17 years and 9 months to 17 years and 10 months.
UAH, version 5.6 has also came out, although nothing shows on WFT yet. It was interesting, but not unexpected for me that UAH went down from 0.327 to 0.303. However RSS went up from 0.286 to 0.345.
Please correct me if I am wrong about the reason. It is my understanding that RSS only goes to 70 degrees south, whereas UAH goes to 85 degrees south.
According to this, it has been is cold in the Antarctic lately. Perhaps this cold anomaly has been captured by UAH but not by RSS. Does this make sense?