GISS is Unique (Now Includes May Data)

This one post says it all….
http://stevengoddard.wordpress.com/2014/07/03/giss-data-tampering-worse-than-it-seems/

J Martin says:
July 5, 2014 at 9:16 am
“What possible justification is there for perpetual adjustments ?”
To fix errors made by previous adjustments.

What possible justification is there for perpetual adjustments
============
start here…..
http://wattsupwiththat.com/2014/06/26/on-denying-hockey-sticks-ushcn-data-and-all-that-part-2/#comment-1670422

Calculate from the anomaly data the real global temperature and compare then the different data sets. I guess that you will find that they differ more than 0.1 °C. (The weather stations always measure temperatures not anomalies.) That’ s a simple method to find the error of measurement. I guess most of your statements will disappear in the sea of error.

GISS is the worst of all of the surface series. And I’m not saying that the others are correct.

Only thing GISS manage to prove is lack in usage of Theories of Science choosing Fallacies and “corrections”….. mind you trying to hunt one’s own tail doesn’t come cheap….

What is GISS?…..it is not explained…it baffles me…

To most of us who are skeptical of CAGW, the consistency of the GISS temp adjustments comes as no surprise. Putting Hansen or Schmidt in charge of temperatures gives them a unique opportunity to perpetuate the world’s greatest science hoax.
I am eagerly waiting the day NASA or NOAA whistleblowers expose the fraud being foisted on the world’s scientific community. The adherence to the scientific method has been violated and a large portion of the public are aware of the corruption. Following the end of the manmade climate change era, it will be a long time until the scientific community regains public trust and an even longer time (if ever) for politicians, who jumped on the CAGW bandwagon in its hay day.

werner, thanks for a great, comprehensive, clear and concise update and comparison of the different GSTA data-sets. GSS is clearly in a world of its own… unfortunately…
One of the main problems with all these adjustments is that if one tries to change the past, one is doomed to make the same mistakes again in the future (since one isn’t learning from the past). Hence, policies to affect the future that are based on data that changes the past will fail and be likely counter productive. This actually warrants a whole essay by itself.

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.

@ 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. 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 ?

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.

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?

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

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.

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.

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.

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.

davidmhoffer says:
July 5, 2014 at 3:37 pm
No it doesn’t. Everything up to 0.49999…. is rounded down
Keep in mind we are talking about temperature readings to the nearest tenth of a degree, or so I thought.

Mike T says:
July 5, 2014 at 3:57 pm
One small thing: “revert back” is a tautology. Revert means to go back to a previous state.
==========================
It is with the deepest regret that I must point out that “go back to a previous point” is a tautology, since you can’t go forward or sideways to a previous state.

As a statistician and meteorologist, much of what is going on here makes me feel like I am watching a horror show. The errors occurring on all sides here are frightening – here are a few of them:
1) If the measuring instrument changes, no amount of “infilling” or “adjustments” can ever change the fact that the data are bad – you cannot treat new and old as the same data series. If they are used in calculating a statistic, then the proper degrees of freedom of any adjustment method require deflation. On the flip side, if the changing data are used in a model as a proxy of an unobserved “true” state (such as often used in econometrics), then all statistics must be calculated with respect to this model, but usually model specification errors dominate to the point of making the inferences useless. In other words, one cannot use fake data from which to calculate an inference and not acknowledge that the data have been tampered with in the corresponding inferences.
2) With respect to the first point, the other option would be to throw out data series that have a changing measurement or introduced bias, but this then means that survivorship bias will dominate. In other words, are surviving “good” series in rural areas, urban areas, country/regional specific, etc?
3) The bigger horror is that average temperature anomalies are not really even measuring what has to be measured, namely, the combined total heat content found in the atmosphere/ocean system. Since even small relative changes in latent heat content dominate small changes in average temperature, this is not even measuring the right thing, much less handling distributional issues (e.g., net boundary layer heat content as opposed to a surface station temperature that will be influenced by land-use change effects on boundary layer decoupling, etc).
4) Point 3 was the tip of the “iceberg”, as the heat capacity and storage of the oceans is so massive compared to the atmosphere that even accounting for water vapor and latent heat roles in the atmosphere would still succumb to the much more massive and still mostly unmeasured oceanic heat content. In a nutshell, we do not even have a valid historical series in which we can measure such changes on the scale being attempted.
We do “know” that the earth has been both much warmer on average and much colder on average than present, but the ugly truth is that we do not yet have any usable data to give us a good estimate of total heat content changes in the modern era. Satellite measurements of total upwelling/down-welling radiation fluxes are a definite step in the right direction, as are deployment of much more extensive ocean buoy/temp sensors, but the rest of this looks like bad science and statistics to me. It’s nice to be able to ask if things are getting warmer or colder, but the real question is what is happening to the heat content and distribution. And that is not going to appear in any of these single number series, even ignoring their issues.

What if they do it the this way ?
I may be 0.001 to 0.444 is rounded down and 0.445 to 0.999 is rounded up.
That will give a bias too …
Worth checking ….

With my sincere apologies to our American friends.
Maybe this constant exaggeration of data as seen in GISS is an inherent characteristic of the always optimistic American psyche.
I have flown gliders, those big mostly German and Polish built FRP ones for just over 50 years now .
A number of American outfits over those 50 years have had a go at building gliders with the performance curves that are claimed to match or exceed the German and Polish built gliders and their performance curves, that is the sink rates and glide angles at different speeds, the “polar curves” as they are called which on paper the American designed and built gliders can match the best of the German and Polish aircraft .
[ Those best German gliders depending on class models now have best glide ratios of between 50 and 60 to one; ie 50 to 60 kilometres gliding distance in calm air for each kilometre of height; ie One kilometre = 3281.4 feet
A late model jet airliner has a glide ration close to and around 20 to one ]
But in the air it is a different matter altogether as the American designed and built gliders, despite their claimed performances derived from their “polar curves”, have never matched the German and Polish machines in performance.
And the reason is quite simple.
The Americans as do the European manufacturers of gliders design the glider using all the aerodynamic inputs required for best performance as well as for good handling in the air, a vital characteristic whether you spend six or eight hours driving hard over long distances of hundreds of kilometres or just lazing around for an hour or so casually enjoying flying the local airfield thermals .
When the glider’s claimed glide angles at different speeds, the performance characteristics seen in the “polar curve” of the glider is published, the Americans in the past have always use the modeled, the maximum theoretical performance curves in their advertising literature and in their technical specifications for the glider type.
As we all know there can be a lot of quite large differences between modeled and calculated performance and the real world performance in just about any field we would consider, especially climate and now temperature data manipulation..
The Germans use the measured in air performance of the very carefully built and finished prototype or preproduction models of a type for their performance curves.
The actual performance of their commercially built gliders are usually not quite up to their claimed performance simply because the precision built molds, built to less than a few tenths of a millimetre in accuracy in the wing aerodynamic profile which the wing is then molded in, deteriorate over a few tens of the numbers of wings produced and have to be re-profiled to regain the accurate aerodynamic wing profile.
So a glider type off the German and European production lines will rarely match the performance of the preproduction gliders but will generally come very close.
And those flown performance curves from the preproduction gliders are what are used in the advertising and technical information of the glider type to sell those gliders.
The Poles apparently grab a glider off the production line and measure the performance curves of the real world glider with all it’s small imperfections from a series built production, the glider the customers actually buy so you generally get the advertised performance you pay for from the Poles.
And so with the American GISS and the European HADCRU.
The Americans as usual figure that it is advertised world beating performance that sells and that has certainly been the case with the exaggerations of GISS in it’s temperature data .
And maybe that tendency to exaggerate somewhat in the American psyche is also seen in the gross corruption of temperature data by the American based NCDC before it ever reaches GISS and HADCRU for further severe massaging before being released for public consumption.
Mind you, if NCDC and either or both GISS and HADCRU were calculating and advertising the performance specifications for a glider, given their known ability to “adjust” global temperature performance and advertise those grossly hyped figures as the real deal I would now just assume that they were into advertising and selling gliders that had real world performances somewhat akin to that of a “concrete” glider as an alternative to selling the odd bridge or two.

Reminds us again which data set ‘Dr Doom’ hand his sticky little fingers all over , that is when they where not busy shutting windows and turning off ?

Steve McIntyre:

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?

Yes. You need to use cURL instead. A bit of discussion here.
Here is a simple example for command line applications:
curl -o GLB.Ts+dSST.txt http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt
See for example rcurl for packages that implement this in R.

Regarding rounding used in ASOS observations, for a number of years, the instructions
have been to ALWAYS round UP a 0.5 degree reading. This means that if the reading
is below zero, you round the reading to the nearest higher whole degree, rather than
the nearest higher number absolute with the sign appended.
In other words, if the temperature reads -1.5 degrees, it would be rounded to -1
degree, rather than -2 degrees.
This is from the ASOS manual regarding measurements from before 2010.

Steve McIntyre says:
July 5, 2014 at 9:53 am
When I looked at GISS in 2007, they used a weird “two-legged” adjustment to individual stations that is not used by anyone else. I haven’t checked their recent methods to determine whether it’s still being used, but it probably is.

Yes, they still use the “two-legged” adjustment, where each of the legs can either go down (to remove “urban warming”) or up (to remove “urban cooling”). I think the rationale was as follows:
1. Urbanization bias is not necessarily “linear”, e.g., an area could undergo exponential growth during some period, while during other periods the rate of development might “slow down”. Therefore, Hansen et al. decided to allow a bit more flexibility in their adjustments than a single “linear” adjustment per station. Hence, the “bi-linear” adjustments…
2. They heard that, under certain conditions, urban development could introduce a slight “cooling” bias. So, they decided to let their automated adjustment program calculate adjustments that had either a negative or a positive slope.
Unfortunately, because of a number of serious flaws in their automated adjustment algorithm and the poor quality of the GHCN dataset, these adjustments are frequently unrealistic and/or nonsensical.
We provide a detailed discussion & analysis of these adjustments in our “Urbanization bias II. An assessment of the NASA GISS urbanization adjustment method” paper, which we have submitted for open peer review at our OPRJ forum: http://oprj.net/articles/climate-science/31
For our paper, we carried out a series of five comprehensive surveys of the adjustments over the period August 2010 until November 2011, when GISS switched to GHCN V3 and stopped publishing their intermediate calculations on their public ftp site. We also used some of the GISS files you have in your ClimateAudit data folder to calculate the adjustments for February 2008.
For each survey, we divided the station adjustments into 4 types, based on the slopes of the two “legs”:
Type 1 – Both slopes negative, i.e., remove “urban warming”
Type 2 – Both slopes positive, i.e., remove “urban cooling”
Type 3 – First slope negative; second slope positive
Type 4 – First slope positive; second slope negative
Types 3 & 4 comprise the majority of their adjustments. We call these “tag-team” adjustments. I think this is similar to the adjustments you referred to as “bi-polar”, although as far as I recall you found less “bi-polar” adjustments than we found “tag team” adjustments.
Here are the results of our surveys (see Table 1 of our paper):
Survey date | Type 1 | Type 2 | Type 3 | Type 4 |Total | Rural | Skipped |
Feb 2008 | 447 | 300 | 1342 | 1440 | 3529 | 2488 |250 |
Aug 2010 | 457 | 265 | 1191 | 1098 | 3011 | 3124 |164 |
Jan 2011 | 459 | 260 | 1184 | 1108 | 3011 | 3127 |176 |
Jul 2011 | 451 | 266 | 1182 | 1112 | 3011 | 3131 |180 |
Oct 2011 | 461 | 261 | 1181 | 1113 | 3016 | 3132 |177 |
Nov 2011 | 455 | 265 | 1177 | 1117 | 3014 | 3136 |177 |
After November 2011, NASA GISS stopped publishing the intermediate calculations (presumably as part of NASA’s “Open Data” project??? 😉 ). So, we were unable to check exactly how the breakdown of the four types changed when they switched to using GHCN V3.
But, as Nick Stokes points out, when they switched to using V3, they started using the homogenized version of GHCN.
As we discuss in our papers, the Menne & Williams, 2009 homogenization algorithm used for GHCN causes “urban blending”. That is, the trends of the most heavily urbanized stations are slightly reduced to match their less urbanized neighbours… but, the rural stations have their trends increased to better match their urban neighbours.
This means that, in the homogenized GHCN stations, the urbanization bias is blended (or “homogenized”) amongst all the stations – “rural” and “urban”.
The GISS adjustments explicitly assume that the “rural” stations have no urbanization bias. So, whatever the problems with the pre-2012 GISS adjustments, their post-2011 adjustments are likely nonsensical.

Let me fix that headline for you.
GISS is unique( May include Data)

You list how far back in the data sets the regression line is flat. Actually, you can push it back a bit further than that. If the slope of the regression line is less than the Standard Error, then the slope is not statistically significant and you can consider that line to be flat.

GISS reached some sort of a tipping point with its record May anomaly which pushed its statistically significant warming date forward by over two years from last month.
Hadcrut4 also set a record for May, however since it came so late, the statistically significant date had not been updated when I sent this off. It has now been updated and the time for statistically significance for Hadcrut4 only went up by one month to November 1996 where the CI is now -0.023 to 1.167. So Hadcrut4 still shows no statistically significant warming for over 17 years.

” however if I want the best single number for the table, it is just a nuisance.”
This is not the best number. Those numbers have error bars dude. The third dp is likely to be random at best. Do not try to rank based on that. If there is a draw within statistical significance then you must declare a draw. There is no other way to do it. Sorry about that.

Adam says:
July 7, 2014 at 6:30 pm
If there is a draw within statistical significance then you must declare a draw.
I am not disputing what you say. I give ranks in row 1 and in row 15 of the table. In addition, I mention ranks continuously in the Appendix. In almost all cases, I could probably give a rank and say +/- 5, unless the rank is less than 5.
And with every single temperature anomaly on the table, as well as elsewhere, I could probably say +/- 0.1.
I will just keep things simple, although we all realize the limitations. The given data sets also do not have a +/- 0.1 behind every one of the hundreds of numbers.