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.
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 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:
Section 2
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
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 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.
| 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 | 0.66 |
| 5.mon | Apr98 | Apr98 | Jan07 | Feb98 | Jul98 | Jan07 |
| 6. ano | 0.662 | 0.857 | 0.829 | 0.756 | 0.526 | 0.93 |
| 7. y/m | 9/5 | 17/9 | 13/5 | 13/9 | 13/5 | 9/9 |
| 8. sig | Feb96 | Nov92 | Oct96 | Jan93 | Dec99 | |
| Source | UAH | RSS | Had4 | Had3 | Sst3 | GISS |
| 9.Jan | 0.236 | 0.262 | 0.509 | 0.472 | 0.342 | 0.67 |
| 10.Feb | 0.127 | 0.162 | 0.304 | 0.264 | 0.314 | 0.43 |
| 11.Mar | 0.137 | 0.214 | 0.540 | 0.491 | 0.347 | 0.71 |
| 12.Apr | 0.184 | 0.251 | 0.641 | 0.592 | 0.478 | 0.73 |
| 13.May | 0.277 | 0.286 | 0.586 | 0.539 | 0.479 | 0.76 |
| Source | UAH | RSS | Had4 | Had3 | Sst3 | GISS |
| 14.ave | 0.192 | 0.235 | 0.515 | 0.472 | 0.392 | 0.66 |
| 15.rnk | 8th | 8th | 4th | 5th | 5th | 1st |
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.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 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.
UAH
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.
Hadcrut4
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.
Hadcrut3
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.
Hadsst3
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.
GISS
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.
Conclusion
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?
FYI I meant GISS… (in case one wondered if I meant RSS instead)
What is GISS?
GISS is the Goddard Institute of Space Studies, a division of NASA. GISS home office is in Manhattan, where they can be close to nature and the heavens above.
NCDC is the National Climatic Data Center and is a division of NOAA.
Acronyms are a part of government lingo. It takes time and lots of reading to become fully aware of who the players are as well as their interrelationships.
Nothing packs an emotional impact quite like the frequent monthly announcements of new all-time high record global temperatures.
And no other data set delivers these records quite as frequently as the political activists at GISS.
Coincidence?
I think not.
@ur momisugly Genghis. All their so called error changes always increase the warming trend, even converting marked cooling trends to warming trends. It all seems highly dubious.
Nick Stokes says:
July 5, 2014 at 11:04 am
GISS has been around a lot longer than any other index.
According to WFT, GISS starts at 1880 but HadCRUT4 starts at 1850.
On my calc it was below zero from Nov 2001 to April 2014. It’s possible May put it over the line, but these are very fine and basically random distinctions.
It was from November 2001 last month, but now WFT gives slope = 0.000422865 per year from November 2001. So to be negative, we need to go to September 2004. I agree, the jump is huge.
There was also a huge jump in the statistically significant times for GISS. It jumped from August 1997 last month to December 1999 this month.
P.S. Is your Hadsst3 up to date? The last 3 points do not match the last 3 months of 0.347, 0.478 and 0.479.
@ur momisugly Nick Stokes. Have you ever considered that you have a bad case of confirmation bias ?
How is converting a marked declining trend to a warming trend, good management ?
“…. however they may switch places to three decimal places. ”
whicih should tell you that your ranking is totally meaningless.
Long overdue analysis
GISS is unique in many ways. Can you think of other ways in which GISS is unique that I have missed?
As a user miked1947 posted:
http://suyts.wordpress.com/2014/07/03/breaking-watts-now-shows-giss-temp-adjustments-again/#comment-140516
For your reading pleasure:
http://data.giss.nasa.gov/gistemp/abs_temp.html
Excerpt:
Q. If SATs cannot be measured, how are SAT maps created ?
A. This can only be done with the help of computer models, the same models that are used to create the daily weather forecasts. We may start out the model with the few observed data that are available and fill in the rest with guesses (also called extrapolations) and then let the model run long enough so that the initial guesses no longer matter, but not too long in order to avoid that the inaccuracies of the model become relevant. This may be done starting from conditions from many years, so that the average (called a ‘climatology’) hopefully represents a typical map for the particular month or day of the year.
So GISS is using a computer model to generate the very “data” that is used for the graph.
Looking at the “evolution” of the graph where the red and blue lines realign I think the explanation is simple, more and more iterations of the same computer model, generating more and more “data”, lets us look at the computer generated data with some remnants of historical data, which gets diluted as the time pass.
In the end we will have 100% fit.
climategrog says:
July 5, 2014 at 12:48 pm
“…. however they may switch places to three decimal places. ”
whicih should tell you that your ranking is totally meaningless.
While I agree with your general sentiment, I would not say “totally meaningless” since there is obviously a difference between 8th and 1st on most data sets. I agree the divisions are sometimes extremely small and not significant. But that does not stop people from using these numbers as if they were without error. You have no doubt heard that May 2014 was the warmest May on record for GISS. It came in at 0.76. However 2010 and 2012 came in at 0.70. So statistically speaking, there could be a three way tie for the warmest May, right?
“8. GISS is the most quoted by warmists.”
Well, doh – see the other 9 items.
/grin
At least for the land-station averages, what GISS produces with its quaint methods is not the most egregious outlier in terms of maximizing the long-term trend and distorting local/regional temperature variations. That dubious distinction belongs to the whack-away-at-intact-records technique (sold under the “scalpel” rubric) that BEST uses to enforce physically unrealistic spatial homogeneity, so that kriging can be used to extrapolate to locations where no measurements were ever made. This over-ambitious approach severely diminishes the low-frequency spectral content in their manufactured time-series, thereby emphasizing linear trend at the expense of natural climatic variations over decadal and longer time-scales.
” That dubious distinction belongs to the whack-away-at-intact-records technique (sold under the “scalpel” rubric) that BEST uses to enforce physically unrealistic spatial homogeneity, so that kriging can be used to extrapolate to locations where no measurements were ever made. This over-ambitious approach severely diminishes the low-frequency spectral content in their manufactured time-series, thereby emphasizing linear trend at the expense of natural climatic variations over decadal and longer time-scales.”
Gross misunderstanding
Suppose you have a station named Grand Haven
Grand Haven Lat = 40, Lon = -97, Alt = 120
in 1967 the station is moved
Grand Haven Lat = 40.05, Lon = -97.02 alt = 400
In Ghcn Daily these two different stations will be listed as ONE STATION
But they are really two different stations. The only thing that is the same is the name and the station ID
Same thing for some stations that moved hundreds of miles.
Basically the SOURCE file has an error in it. They moved the station and didnt give it a new ID
Of course sometimes they do give it a new ID
Also, when a station gets a new sensor ( say MMTS) we refuse to apply the adjustment which would warm the station. We say its anew station. why, because the instrument changed.
basically we are restoring the data to its proper form. move the station from a city to the country?
no skeptic would say these were the same merely because they had the same NAME.
What are realistic accuracy, precision, and error numbers for any of these data sets? Are the data from the 1800s and most of the 1900s precise to even a tenth of a degree? Are any of these data sets of any value at all?
I wonder if employees would accept that they are getting more in their pay packets without any real monetary increase because their past wages have been regularly adjusted to lower values in the historical pay records.?
I know there is inflation that erodes the value of the current dollar and economists have ways of looking at that – also some economists are named by Main Stream Media as “climate experts” – are the climatologists just adopting the mantle of economic expert to inflate their own salaries by fiddling with automatic temperature adjustments and issuing alarming results?
“… we do not know anomalies to the nearest 1/1000 or 1/100 of a degree,…” Even 1/10 th of a degree is hard to believe. Temperatures were recorded to the nearest 1.0 degree until recently. How on Earth are measurements across a small sample of the Earth’s surface supposed to measure the “true” temperature accurately to 0.1 degree? I would love to see a full statistical justification of claimed accuracies and precision.
Read the end of page 11 of this NOAA document, regarding the accuracy of recording temperatures. Does anyone really believe an accuracy of 0.1 degrees?
http://www.srh.noaa.gov/srh/dad/coop/EQUIPMENT.pdf
Sal Minella says:
July 5, 2014 at 2:05 pm
What are realistic accuracy, precision, and error numbers for any of these data sets? Are the data from the 1800s and most of the 1900s precise to even a tenth of a degree? Are any of these data sets of any value at all?
If I recall correctly, measurements from around 1900 are only good to the nearest 0.5 C, but lately, they are expected to be within 0.1 C. In Section 2, I am using Nick Stokes’ site for the 95% confidence intervals. I will let him elaborate further.
Thanks Nick!
Dr Burns says:
July 5, 2014 at 2:21 pm
Read the end of page 11 of this NOAA document
Thank you! They must assume that the temperatures ending in 0.1 to 0.4 must balance the ones ending in 0.6 to 0.9. That would be reasonable. However in rounding a 0.5 to the next highest number makes 10% of the readings too high. It would have made more sense to ask that a 0.5 be rounded to the nearest even number if they wanted only whole numbers.
On the other hand, if 10% of the numbers were too high 50 years ago, and if 10% of the numbers are too high now, the trend should not be affected.
John Kennedy who helps compile the hadcrut4 figures pointed out there are many uncertainties in the May 2014 figures and it should be considered as certainly in the top ten of warm Mays, but they couldn’t be any more certain than that
Tonyb
Vern Cornell
July 5, 2014 at 11:20 am
‘What is GISS?…..it is not explained…it baffles me…’
First of all GISS stands for Goddard Institute for Space Studies. So, perhaps the next question that may come to mind is, who is Goddard? According to Wikipedia: ‘Robert Hutchings Goddard (October 5, 1882 – August 10, 1945) was an American professor, physicist, and inventor who is credited with creating and building the world’s first liquid-fueled rocket,..,successfully launched on March 16, 1926. Goddard and his team launched 34 rockets between 1926 and 1941, achieving altitudes as high as 2.6 km (1.6 mi) and speeds as high as 885 km/h (550 mph).’ Now, I do not wish to put Robert Goddard down. His accomplishments were substantial considering his funding. But he received very little, if any, funding from the US government at the time. His direct involvement with any US government space program was pretty much nonexistent. For that we have to turn to Wernher von Braun (March 23, 1912 – June 16, 1977) who developed the world’s first large scale liquid fueled rockets which were produced at Peenemünde on the Baltic in Germany. The Germans, during WWII, invested heavily in the development of rockets as long range weapons; Peenemünde is where they were produced, and Wernher von Braun was the German rocket scientist who headed up Peenemünde. At the end of WWII von Braun and some colleagues headed west to be taken captive by Allied troops advancing eastward. Other colleagues remained at Peenemünde to be captured by Soviet troops advancing westward. The large, liquid fueled V2 rockets developed at Peenemünde were split up as war booty between the Allies and the Soviets and it is from these V2s that the US conducted upper atmospheric research and acquired experience with a two stage rocket. Arguably, the space race of the 1950s through 70s between the US and USSR was driven by the German rocket scientists each side acquired. And our’s were better. Wernher von Braun was responsible for all the successful space program rockets up to and including the Moon launch. However, it wouldn’t do to name a space center after a former Nazi collaborator so we have no von Braun space centers today. So, instead we have GISS.
What does GISS do? Well, according to Wikipedia ‘GISS was established in May 1961 by Robert Jastrow to do basic research in space sciences in support of Goddard programs.’ Remember, this was all instigated as a result of WWII, the Cold War that followed, and the space race between the US’s and the USSR’s German rocket scientists that arose from that silent conflict. The Goddard programs were simply the programs of the GSFC, and GISS was an arm of it. And what is the GSFC? Again, according to Wikipedia; ‘The Goddard Space Flight Center (GSFC) is a major NASA space research laboratory established on May 1, 1959 as NASA’s first space flight center.’ Now, I don’t wish to insult anyone’s intelligence but it might be useful to reiterate what NASA (the parent to GSFC and GISS) really is, or at least is claimed to be, so, according to the US government’s own website; ‘NASA stands for National Aeronautics and Space Administration.’ Furthermore, on this website we learn NASA was initiated in 1958. It bears notice here that the world’s very first satellite, Sputnik I, was launched in 1957 by the USSR so the dates behind the creation of NASA, GSFC, and GISS become self explanatory. Prior to all this, the US military was responsible for the first rocket exploration, but after the embarrassment of Sputnik, the civilian agency NASA came into being and Wernher von Braun was enlisted.
Now, before there’s any misunderstandings as to what the word, ‘Space,’ present in NASA’s name means let us return to Wikipedia: ‘Space is the boundless three-dimensional extent in which objects and events have relative position and direction.’ But, let us be more precise. So, also, according to Wikipedia: ‘Outer space, or simply space, is the void that exists between celestial bodies, including the Earth…’; and; ‘There is no firm boundary where space begins. However the Kármán line, at an altitude of 100 km (62 mi) above sea level, is conventionally used as the start of outer space in space treaties and for aerospace records keeping.’
In conclusion, I have to apologize for my rather lengthy answer to your question. Especially since I don’t think I even began to answer it. You see, I don’t find anything whatsoever in the foregoing descriptions or explanations that points, ever so microscopically slightly, towards the idea that the entities above would possibly be involved in collecting land surface temperature measurements at all, let alone from anything less than 62 miles above the Earth. As we have seen, that purpose certainly wasn’t the motivation in creating NASA, GSFC, or GISS. And I can’t find it in their charters, mission statements, history, or even in their names. It pains me to tell you that GISS is a land surface temperature measurement when all my research tells me it shouldn’t be. Maybe it’s mission creep. Maybe, with the end of the space race (after all, we haven’t been back to the moon in almost 40 years) it’s a jobs preservation program. (If so, the only one the Obama administration has been successful with.) Perhaps it’s a change in direction to avoid the embarrassment of needing Russian rockets to now launch payloads.
In any case, maybe that’s why the numbers produced by it are so crappy.
“Werner Brozek says:
July 5, 2014 at 2:48 pm
Dr Burns says:
July 5, 2014 at 2:21 pm
Read the end of page 11 of this NOAA document
Thank you! They must assume that the temperatures ending in 0.1 to 0.4 must balance the ones ending in 0.6 to 0.9. That would be reasonable. However in rounding a 0.5 to the next highest number makes 10% of the readings too high. It would have made more sense to ask that a 0.5 be rounded to the nearest even number if they wanted only whole numbers.
On the other hand, if 10% of the numbers were too high 50 years ago, and if 10% of the numbers are too high now, the trend should not be affected.”
The way they round the numbers to the nearest whole degree is the standard way to round them…nothing new there. 0.5 to 0.9 is rounded up and 0.0 to 0.4 is rounded down…so 75.0 through 75.4 become 75 and 75.5 through 75.9 become 76.
Tonyb says:
July 5, 2014 at 2:50 pm
certainly in the top ten of warm Mays
Thank you for that. So the warmest May was 2014 at 0.586 and the 10th warmest was at 0.428. The difference is 0.158, so if we assume the low value could be 0.08 higher and the high value could be 0.08 lower, then we could say we have a 10 way tie for first assuming an error bar of +/- 0.08.
Something very similar could have been said for GISS in May.
Werner Brozek;
However in rounding a 0.5 to the next highest number makes 10% of the readings too high.
>>>>>>>>>>>
No it doesn’t. Everything up to 0.49999…. is rounded down, 0.5 and higher is rounded up. So the difference between 0.49999…. and 0.5 is effectively 0.0000… hence a perceived bias but from a purely math perspective, there actually isn’t one.
If I try to read GISS data in R (for example)
http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt
I am unable – it says “cannot open: HTTP status was ‘403 Forbidden'”
I’ve examined a great deal of data and the apparent blocking of R access by GISS is very unusual – unprecedented even. Is this experienced by anyone else?
I can manually copy the pages to files and then read them, but this creates pointless extra handling.
One small thing: “revert back” is a tautology. Revert means to go back to a previous state.