Guest Post by Werner Brozek (Edited By Just The Facts)
GISS and other sets are poised to set new records in 2014, even without an El Nino. The present GISS record is a two way tie for first place with 2005 and 2010 both showing an average anomaly of 0.65. (By the way, in January of this year, it was stated that the 2010 anomaly was 0.67. I do not understand how 2010 lost 0.02 C over the last four months.) The present average over the first four months of 2014 is 0.64, so it is only 0.01 lower. However of greater significance is that the April anomaly was 0.73. If this anomaly were to continue for the rest of the year, the old GISS record would be shattered.
Below, I will provide the corresponding information for the other five data sets that I am following. All differences will be provided to the nearest 1/100 degree.
The current Hadsst3 average is 0.370, which is only 0.05 below its record of 0.416. And as is the case with GISS, the April anomaly was a huge 0.478, so if this anomaly were to continue for the rest of 2014, Hadsst3 would also set a new record.
The current Hadcrut3 average is 0.455, which is only 0.09 below its record of 0.548. And as is the case with GISS, the April anomaly was a huge 0.592, so if this anomaly were to continue for the rest of 2014, Hadcrut3 would virtually tie its record.
The current Hadcrut4 average is 0.500, which is only 0.05 below its record of 0.547. And as is the case with GISS, the April anomaly was a huge 0.641, so if this anomaly were to continue for the rest of 2014, Hadcrut4 would also set a new record.
The current RSS average is 0.222. This is 0.33 below the 1998 record of 0.550. This record seems safe for this year. Even the April anomaly of 0.251 would not challenge the record if it continued for the rest of the year.
The current UAH average is 0.171. This is 0.25 below the 1998 record of 0.419. This record seems safe for this year. Even the April anomaly of 0.184 would not challenge the record if it continued for the rest of the year. (Note: This applies to version 5.5.)
In the table, I have added a row 15 which I have labelled 15.dif and here I give the above differences between rows 4 and 13. Since no rank is first at this point, all numbers have the same sign indicating the present record is still higher than the present average in all cases.
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.
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 9 years and 8 months to 17 years and 9 months
1. For GISS, the slope is flat since November 2001 or 12 years, 6 months. (goes to April)
2. For Hadcrut3, the slope is flat since August 2000 or 13 years, 9 months. (goes to April) The latest spike caused the time to start after the 1998 El Nino.
3. For a combination of GISS, Hadcrut3, UAH and RSS, the slope is flat since December 2000 or 13 years, 5 months. (goes to April)
4. For Hadcrut4, the slope is flat since January 2001 or 13 years, 4 months. (goes to April)
5. For Hadsst3, the slope is flat since December 2000 or 13 years, 5 months. (goes to April)
6. For UAH, the slope is flat since September 2004 or 9 years, 8 months. (goes to April using version 5.5)
7. For RSS, the slope is flat since August 1996 or 17 years, 9 months (goes to April).
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.
Section 2
For this analysis, data was retrieved from Nick Stokes’ Trendviewer page. 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 February 1996: CI from -0.043 to 2.349
For RSS: Since November 1992: CI from -0.022 to 1.867
For Hadcrut4: Since October 1996: CI from -0.033 to 1.192
For Hadsst3: Since January 1993: CI from -0.016 to 1.813
For GISS: Since August 1997: CI from -0.008 to 1.233
Section 3
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 are UAH, 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.
13.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.
14.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 15 minutes into a game. Due to different base periods, the rank is more meaningful than the average anomaly.
15.dif: This is row 4 minus row 13. A number of less than 0.10 at this point in time means that a record is possible for 2014 for four of the data sets. Both of the satellite data would need a miracle to set a record this year in my opinion.
| Source | UAH | RSS | Had4 | Had3 | Sst3 | GISS |
|---|---|---|---|---|---|---|
| 1. 13ra | 7th | 10th | 8th | 6th | 6th | 7th |
| 2. 13a | 0.197 | 0.218 | 0.486 | 0.459 | 0.376 | 0.59 |
| 3. year | 1998 | 1998 | 2010 | 1998 | 1998 | 2010 |
| 4. ano | 0.419 | 0.55 | 0.547 | 0.548 | 0.416 | 0.65 |
| 5.mon | Apr98 | Apr98 | Jan07 | Feb98 | Jul98 | Jan07 |
| 6. ano | 0.662 | 0.857 | 0.829 | 0.756 | 0.526 | 0.92 |
| 7. y/m | 9/8 | 17/9 | 13/4 | 13/9 | 13/5 | 12/6 |
| 8. sig | Feb96 | Nov92 | Oct96 | Jan93 | Aug97 | |
| Source | UAH | RSS | Had4 | Had3 | Sst3 | GISS |
| 9.Jan | 0.236 | 0.262 | 0.507 | 0.472 | 0.342 | 0.68 |
| 10.Feb | 0.127 | 0.161 | 0.304 | 0.264 | 0.314 | 0.44 |
| 11.Mar | 0.137 | 0.214 | 0.544 | 0.491 | 0.347 | 0.70 |
| 12.Apr | 0.184 | 0.251 | 0.641 | 0.592 | 0.478 | 0.73 |
| Source | UAH | RSS | Had4 | Had3 | Sst3 | GISS |
| 13.ave | 0.171 | 0.222 | 0.500 | 0.455 | 0.370 | 0.64 |
| 14.rnk | 10th | 9th | 5th | 7th | 7th | 3rd |
| 15.dif | 0.25 | 0.33 | 0.05 | 0.09 | 0.05 | 0.01 |
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 used.
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.4.2.0.0.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.
Appendix
In this part, we are summarizing data for each set separately.
RSS
The slope is flat since August 1996 or 17 years, 9 months. (goes to April)
For RSS: There is no statistically significant warming since November 1992: CI from -0.022 to 1.867.
The RSS average anomaly so far for 2014 is 0.222. This would rank it as 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 2013 was 0.218 and it is ranked 10th.
UAH
The slope is flat since September 2004 or 9 years, 8 months. (goes to April using version 5.5 according to WFT)
For UAH: There is no statistically significant warming since February 1996: CI from -0.043 to 2.349. (This is using version 5.6 according to Nick’s program.)
The UAH average anomaly so far for 2014 is 0.171. This would rank it as 10th 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.
Hadcrut4
The slope is flat since January 2001 or 13 years, 4 months. (goes to April)
For Hadcrut4: There is no statistically significant warming since October 1996: CI from -0.033 to 1.192.
The Hadcrut4 average anomaly so far for 2014 is 0.500. This would rank it as 5th 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.
Hadcrut3
The slope is flat since August 2000 or 13 years, 9 months. (goes to April)
The Hadcrut3 average anomaly so far for 2014 is 0.455. This would rank it as 7th 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.
Hadsst3
For Hadsst3, the slope is flat since December 2000 or 13 years and 5 months. (goes to April).
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 0.370. This would rank it as 7th 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.
GISS
The slope is flat since November 2001 or 12 years, 6 months. (goes to April)
For GISS: There is no statistically significant warming since August 1997: CI from -0.008 to 1.233.
The GISS average anomaly so far for 2014 is 0.64. This would rank it as 3rd place if it stayed this way. 2010 and 2005 were the warmest at 0.65. The highest ever monthly anomaly was in January of 2007 when it reached 0.92. The anomaly in 2013 was 0.59 and it is ranked 7th.
Conclusion
Even without an El Nino, it appears likely that some records will be set on at least some surface data sets, however not on the satellite data sets.
(By the way, in January of this year, it was stated that the 2010 anomaly was 0.67. I do not understand how 2010 lost 0.02 C over the last four months.)
http://stevengoddard.wordpress.com/2014/03/27/settled-science-update-at-giss/
http://stevengoddard.wordpress.com/2014/01/15/almost-23-of-giss-warming-is-fake/
Even without an El Nino, it appears likely that some records will be set on at least some surface data sets, however not on the satellite data sets.
Perhaps the surface temperature datasets will show a drop if Obama succeeds in driving up the price of electricity… 🙂
Adjust the past down, adjust the present up. Look, a trend!
Latitude says:
May 25, 2014 at 6:08 pm
Thank you!
I can understand that there could possibly be good reasons why things could be adjusted from a hundred years ago, but why would 2010 be adjusted in the last 4 months? And why would the all time record month of January 2007 go down from 0.94 to 0.92 over the last 4 months?
One hundredth of a degree C… What instrument is so carefully calibrated and placed around the world so quickly that we are talking this small a change?
I am all out of patience with these anomalies of 0.001 accuracy.
Pure fiction.
In audio this noise is called what it is, noise.
The temperature data lacks both duration and detail(accurate to =/- 1or2 degrees C).
The entrails of a fish would give information as useful as these imaginary changes and the fish might be edible.
Now supposedly, the satellite record is the very best, precision measurement, we have managed to date, being a mere 30-40 years old, its should be throwing up “record” temperatures regularly.
Where are they?
Bill H says:
May 25, 2014 at 6:44 pm
One hundredth of a degree C… What instrument is so carefully calibrated and placed around the world so quickly that we are talking this small a change?
Good question! We must realize a couple of things. If we average a thousand readings that are only accurate to the nearest degree, we can easily get an average to the nearest 1/1000 degree. And we need to realize that numbers within about 0.1 C could be considered a statistical tie. So with GISS, the top ten range from 0.56 to 0.65. So the top ten could be considered to be statistically tied for first.
wbrozek says:
May 25, 2014 at 6:26 pm
Latitude says:
May 25, 2014 at 6:08 pm
Thank you!
I can understand that there could possibly be good reasons why things could be adjusted from a hundred years ago, but why would 2010 be adjusted in the last 4 months? And why would the all time record month of January 2007 go down from 0.94 to 0.92 over the last 4 months?
_________________________________________________________________________________________________
Exactly!
Sand through the hourglass.
Who looks at that 2 years from now?
Nobody.
Why did it happen? Somebody knows…….. Why don’t we?
It’s simple ideology mathematics taught in Climatology school, artificially adjust temps 100 years ago way down, then artificially adjust current temps up. Then after the current hottest year ever sets the record then the process of dropping those temps can begin so the new current year sets the record as hottest ever. This is how you turn a flat line into a upward slope. Climatology mathematics 101.
“GISS global temperature estimates should be treated with considerable caution”
http://oprj.net/oprj-archive/climate-science/31/oprj-article-climate-science-31.pdf
The real accuracy of the datasets listed is in the 10-of-degree range (i.e. from -10 to +10), not the 10th nor hundredth (laughable) of a degree range.
Sorry old boy. Epic fail.
wbrozek says:
May 25, 2014 at 6:26 pm
Thank you!
I can understand that there could possibly be good reasons why things could be adjusted from a hundred years ago, but why would 2010 be adjusted in the last 4 months? And why would the all time record month of January 2007 go down from 0.94 to 0.92 over the last 4 months?
—
Maybe you should check out the FAQ on the GISS site? Looks like there are a few papers cited that explain the adjustment process. http://data.giss.nasa.gov/gistemp/FAQ.html
Bill 2 says:
Maybe you should check out the FAQ on the GISS site? Looks like there are a few papers cited that explain the adjustment process. http://data.giss.nasa.gov/gistemp/FAQ.html
Given the magnitude of the adjustment, its a real dog ate my homework effort, regardless of their excuses.
If you add a hockey stick shaped adjustment to otherwise flat temperatures, don’t be surprised if the result is a hockey stick.
But calling the result of this operation a “measurement” is a bit of a stretch. More like water boarding the data until it confesses.
From the NOAA site – the hockey stick shaped adjustment.
http://www.ncdc.noaa.gov/img/climate/research/ushcn/ts.ushcn_anom25_diffs_urb-raw_pg.gif
It’s routine for NOAA to adjust data, and adjustments tend to add in a warming trend. I complained to NASA, a few years ago in an email, about how older temps on their website for my hometown had been adjusted downward, while newer were adjusted upward (I had save a couple of snapshots of the data from their website comparing one timeshot to another) and Reto Ruedy wrote back. Here is the text of his email:
“The station you are referring to is part of USHCN. It is not quite clear to me whether your question concerns the difference between the data that we download from USHCN and the data after our homogenization, or whether it concerns the difference between the data presented by USHCN today as compared to their data from 2007. So I’ll address both instances.
The effect of our homogenization can easily be determined since we present for each station the data before and after that homogenization.
We use the same method to modify all non-rural stations, and averaged over all stations that procedure reduces the global warming trend slightly. For individual stations, the result may well be an increase or a decrease of the long term trend, since the effect is so small that it is easily dwarfed by local natural variations.
What we do essentially is make non-rural stations look like their rural neighbors, which has the same effect as basing our analysis only on rural stations. The adjusted data represent the region rather than the individual station.
We download the NOAA’s adjusted USHCN data; they also make available the unadjusted data; however, many of their adjustments are based on extra information, like documented changes in the location of the thermometer, changes in the way daily/monthly mean temperatures are derived, etc. Not adjusting for such documented changes, i.e. going back to the raw data, preserves artificial inaccuracies in the time series; these may have a substantial impact particularly if they are the result of a regional coordination effort. NOAA, who spent a lot more time and effort to investigate US station data than we did, found that in the US, such systematic changes usually tended to underestimate the warming trend.
See e.g. http://www.ncdc.noaa.gov/oa/climate/research/ushcn and the papers listed at the end of that web site.
Let me know if you have any further questions.
Reto Ruedy”
I can also explain GISS adjustments – they’re plain old scientific fraud.
Comparisons of individual stations used by GISS to IMO originals: sources given in the comments section.
Stykkisholmur, Iceland
https://www.flickr.com/photos/7360644@N07/11244518635/in/photostream/
Keflavik, Iceland
https://www.flickr.com/photos/7360644@N07/11244423904/in/photostream/
Akureyri, Iceland
https://www.flickr.com/photos/7360644@N07/11244417413/in/photostream/
Teigarhorn, Iceland
https://www.flickr.com/photos/7360644@N07/11244199504/in/photostream/
Holar Hornafirdi, Iceland
https://www.flickr.com/photos/7360644@N07/11243965324/in/photostream/
I am a lay person so excuse me if I am missing something. Is there something in climate science that dispenses with the entire discipline of measurement accuracy and confidence and application of significant figures?
Bill 2, further to my point, from the GISS site:-
Q. Does GISS deal directly with raw (observed) data?
A. No. GISS has neither the personnel nor the funding to visit weather stations or deal directly with data observations from weather stations. GISS relies on data collected by other organizations, specifically, NOAA/NCDC’s Global Historical Climatology Network (GHCN) v3 adjusted monthly mean data as augmented by Antarctic data collated by UK Scientific Committee on Antarctic Research (SCAR) and also NOAA/NCDC’s Extended Reconstructed Sea Surface Temperature (ERSST) v3b data.
So GISS uses NOAA’s figures, with the incorporated NOAA hockey stick shaped adjustment. Both GISS and NOAA figures are contaminated with this joke size adjustment. Then GISS applies further adjustments…
Ebeni
… Is there something in climate science that dispenses with the entire discipline of measurement accuracy and confidence and application of significant figures?
Yes – lack of expertise in statistics. Their statistics weaknesses were laid bare, by publication of a hilarious paper written by McIntyre, who is a real statistics expert, which demonstrated that Michael Mann’s hockey stick algorithm produced a hockey stick when it was fed with random data (“red noise”).
http://climateaudit.files.wordpress.com/2009/12/mcintyre-grl-2005.pdf
So, if the satellite data cannot be trusted, and GISS cannot be trusted, and NOAA NCDC presumably is too untrustworthy even to be considered, that just leaves HadCRUT4 (trend since 1850 = 0.47 C / century) ?
Bill 2 says:
May 25, 2014 at 7:29 pm
Maybe you should check out the FAQ on the GISS site?
It is very understandable why Hadcrut would adjust things for the previous month or even two months because they did not have some numbers from the middle of China in a timely manner. But if GISS needs to adjust things way back every month, something seems very wrong.
And if we assume for argument sake that GISS is indeed doing the right thing with all of these adjustments for years in the past, then we can only assume the Hadcrut people are not doing it right. Would this be a fair assumption?
Why do dogs lick their gonads?
Because they can.
Ebeni says:
May 25, 2014 at 7:53 pm
“Is there something in climate science …”
Actually, no there isn’t. That is why the term is always in quotes: “climate science”
This keeps everyone aware of the nothingness therein.
Ebeni says:
May 25, 2014 at 7:53 pm
I am a lay person so excuse me if I am missing something. Is there something in climate science that dispenses with the entire discipline of measurement accuracy and confidence and application of significant figures?
Climate science has its own rules regarding the above. So while we know that we cannot know the anomaly to the nearest 1/1000 degree, we give that number and say it is only to +/- 0.1 C or something like that.
Then climate science requires 95% confidence to say whether warming or cooling is occurring. So measurement uncertainties, etc are presumably dealt with and incorporated in the site found here:
http://moyhu.blogspot.com.au/p/temperature-trend-viewer.html?Xxdat=%5B0,1,4,48,92%5D
Sleepalot said:
“I can also explain GISS adjustments – they’re plain old scientific fraud.”
You haven’t explained anything. All you have done is made a statement, and linked a few graphs.
Eric Worrall says: May 25, 2014 at 8:01 pm
“Yes – lack of expertise in statistics. Their statistics weaknesses were laid bare, by publication of a hilarious paper written by McIntyre, who is a real statistics expert, which demonstrated that Michael Mann’s hockey stick algorithm produced a hockey stick when it was fed with random data (“red noise”).”
Yes. Here is how it was expertly done.