NASA GISS Version 3 vs. Version 2, using HadCRUT.4 Version differences as a baseline
Guest essay by David Dohbro
Recently the climate blogosphere has uncovered the effects of adjusting past and present United States’ land-temperature data as measured by the United States Historical Climatology Network (USHCN) and how that possible affects the temperature records as well as our understanding and knowledge of historic temperatures (References 1-3).
To that extend it is prudent to also look at the effects of temperature adjustments on a global scale. Here the National Aeronautics and Space Administration’s (NASA) Goddard Institute for Space Studies (GISS) GLOBAL Land-Ocean Temperature Index (GLOTI) previous Version 2 and the most recent Version 3, which includes the month of June 2014, are compared against each other (4, 5). GISS was updated for various reasons, which I won’t detail here.
In addition, early last year the collaborative product of the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia, HadCRUT.4 Global Surface Temperature Anomaly data set, was update from version 4.1.1.0 to 4.2.0.0 (6, 7). The difference between these two versions is negligible and shows no temporal trend (7). I also won’t elaborate on the changes between these two versions, as that’s a whole different discussion.
Namely, I simply wanted to assess if and how adjustments have affected the new NASA’s GLOTI data record (Version 3) versus the previous Version 2, and how those differences compare to the differences of the HadCRUT.4 versions’ update. However, since GISS Version 2 was replaced by Version 3 in November of 2011, the GISS comparison can only be made for data available till then. First, annual differences were assessed and then monthly differences. Differences are in this case simply the data from GISS Version 3 minus the data from GISS Version 2 (either annually or monthly). Then these differences in GISS are compared with the differences in HadCRUT.4
Below is a graphic showing the Version 3 GLOTI-values in red squares and the Version 2 GLOTI-values in blue diamonds.
On average, the difference between Version 3 and Version 2 GLOTI annual-averages data is 18% +/- 143%, ranging from -1280% (year 1947) to 629% (year 1960). However, since it is hard to discern the difference between the two Versions in an absolute value format, a better representation between Version 3 and Version 2 on an annual basis, is by plotting the actual annual differences (see below).
It follows from these two graphs that Version 3 and 2 do not produce the same annual GLOTI values; otherwise all annual differences would be 0. Instead, it can be observed that currently
1) From 1880 to the mid-1890s, Version 3 has higher annual GLOTI values (warmer) compared to Version 2 up to a difference of almost 0.10ºC.
2) From the mid-1890s to late-1960s, Version 3 has lower annual GLOTI values (colder) compared to Version 2.
a. In addition, from the mid-1890s to ~1910 the Version 3 annual GLOTI values become increasingly less compared to Version 2 (up to a difference of 0.12ºC)
b. While from ~1910 to late-1960s the difference –in general- becomes smaller
3) From the late-1960s to late-2011 Version 3 annual GLOTI values are higher (warmer) than Version 2’s reported values, with an increase till 1999 and a slight decrease since.
In simpler terms: the late 19th century is (made) warmer in Version 3, whereas the early to mid 20th century is (made) cooler in Version 3 especially at the beginning of the past century; compared to Version 2. In addition, the last quarter of the 20th century till the most recent past is also (made) warmer in Version 3 compared to Version 2.
These differences can also be assessed in more detail on a monthly scale. Below is the same plot as the previous graph but instead of annual-averages data, the actual monthly data values are used.
The above plot shows –of course- the same temporal patterns as the annual plot, but now it can be observed that some monthly values differ by over 0.20ºC, with most differences in monthly values between -0.10ºC and 0.10ºC. In addition, the green line shows the 0-value (no difference between both Versions); the blue, red and orange lines are simple linear regressions for the periods 1880-1910, 1910-2011, and for all data, respectively using Instat+ (8). The regression lines are plotted to exemplify the observations made from the annual differences.
As mentioned, the HadCRUT update from version 4.1.1.0 to 4.2.0.0 resulted in negligible differences and shows no temporal trend (7). As a check, I have plotted the HadCRUT.4 differences between versions myself below, which is identical to what is shown on their website, except for a few outliers that are not shown, but which don’t affect the general pattern (7).
It is obvious that the change from HadCRUT.4.1.1.0 to version 4.2.0.0 has not lead to any changes in reported GSTA or its temporal trend. This is in very stark contrast to the differences between GISS’ Version 2 and 3, which shows a strong cooling of the past and strong warming of the present in Version 3 vs. Version 2. These obvious differences in GLOTI-values require attention and understanding of their causes, especially in light of the fact that HadCRUT does not exhibit any significant differences in GSTA-values between versions.
The problem with trying to change the past is that we won’t understand the present and will be unable to predict the future correctly. Hence, policies and regulations based on analyses of adjusted past data and aimed at addressing supposed future climate will be ineffective.
References
3) http://notalotofpeopleknowthat.wordpress.com/2014/07/09/analysis-of-ushcn-dataset/
4) http://data.giss.nasa.gov/gistemp/tabledata_v2/GLB.Ts+dSST.txt
5) http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt
6) http://www.metoffice.gov.uk/hadobs/hadcrut4/
7) http://www.metoffice.gov.uk/hadobs/hadcrut4/data/versions/HadCRUT.4.2.0.0_release_notes.html
8) http://www.reading.ac.uk/ssc/n/n_instat.htm
=============================================================
NOTE: this essay as originally posted was missing figures 3 and 4, which somehow did not copy from the MS Word document, but the surrounding text did. I didn’t get notice of this error until after 7PM tonight due to being distracted by other issues. The figures are now updated. -Anthony
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
Missing the monthly graph, unless the intent was to point to a link?
The problem with trying to change the past is that we won’t understand the present and will be unable to predict the future correctly.
====
exactly….since the “best” they can do is extend a trend line……you can’t predict anything when your frame of reference is a wonky trend line to begin with
An editor is needed here. …..Lady in Red
“To that extend it is prudent …”
Uh, “To that extent…”
“The problem with trying to change the past is that we won’t understand the present and will be unable to predict the future correctly.”
——————————-
Not exactly….
The problem is that you can NOT predict the long term future of chaotic systems at all. It is no help to us that characterizing the past at best indicates the probable boundaries of the future. These futures possess a known probability of exceeding the past boundaries. For example, based on the past, we have the probability of any year exceeding it’s past max or min or whatever boundary of 1 divided by the observed period. Any year has a 1 in 100 possibility of exceeding the 100 year flood based on 100 years of observation.
So we have a non-zero probability of new record values, and near certainty that the future behavior will not behave as has the mean of past observations.
… models always forecast too warm
… historical data is always adjusted cooler in the past and warmer in the present
Are there any exceptions to this ? There must be somewhere.
This is slightly off topic but along the same line: The recently released data from the USRCN shows the US has cooled by 0.4 C in the last ten years. Is there a graph that shows the comparison with the USHCN over the same period?
So, best is to forget about the past and look only at recent history
There is no man made global warming, not even earthly influences
see graph below the minima table.
http://blogs.24.com/henryp/2013/02/21/henrys-pool-tables-on-global-warmingcooling/
I’m glad I did not live at the beginning of the 20th c. , it’s getting colder by the day 😉
Mary Brown says:
July 17, 2014 at 10:54 am
… models always forecast too warm
… historical data is always adjusted cooler in the past and warmer in the present
====
No Mary, you don’t understand. Older data was biased warm and has to be corrected.
Every year the old data is a bit older, so each year it needs correcting a bit more.
You’re new at climatology, aren’t you ? 😉
@ur momisugly Greg Goodman –
Believe what you described is “Climastrology”, isn’t it?
The corollary is also true:
Climate models are calibrated against the climate record. This means that they will necessarily bias the future warm. As we go forwards the this future warming bias will be reduced. So each year they will need to reduce the warming in the models to compensate.
By the time we get to 2100 the models will be bang on target.
I think you need read M. Mann’s new book: ” Climate whores, dispatches from the front line”, he explains how all this works.
Nice work. A good in-depth look at this issue.
Fleshing out a lot of what Steve Goddard does, for those who can’t read a simple graph!
Hence, policies and regulations based on analyses of adjusted past data and aimed at addressing supposed future climate will be ineffective.
Depends what is meant by “ineffective”. I’m sure with Gavin forcing an severe upward trend into the future from the 1900s the data will be really effective
Sorry about the unintended missing monthly GISS-value differences plot, and the missing HadCRUT.4 differences plot. You can find these two figures here :https://www.dropbox.com/s/rdw9ete3m4kf13j/figure%202%20and%20figure%203.pdf
I hope this helps!!
It is even worse than that, HADCRUT 4 is a manipulated data set, maing current warmer and past colder.
http://climate4you.com/images/HadCRUT4%20GlobalMonthlyTempSince1979%20With37monthRunningAverage.gif
Greg Goodman said:
“I’m glad I did not live at the beginning of the 20th c. , it’s getting colder by the day ;)”
To support UNFCCC they will have to cool the beginning of the 20th c by another 4 deg C by year 2100?
🙂
Who controls the past controls the future. Who controls the present controls the past.
George Orwell
For some Orwell’s 1984 is not warning about the nature of dictatorship but an instruction manual of how to behave.
And by ‘lucky chance ‘ all these adjustments always work in favour of ‘the cause’ and those that profit from it , never against. Climate ‘scientists’ are wasting their time , they should be at Vegas with that ‘luck’ they be multimillionaires .
Stop! My grandparents just froze to death before my parents were born!
now we know where Enron’s accounting department ended up.
When you adjust the data, it isn’t science.
Greg Goodman says, at 11:18:
“I think you need read M. Mann’s new book: ” Climate whores, dispatches from the front line”, he explains how all this works.”
I’m thrilled to hear about the new book.. He may get a Pulitzer for it, or even a second Nobel prize, this time for literature!
Rather than only comparing only versions of estimates of temperature data, you should compare estimates with actual measurements too.
Dave L. says:
July 17, 2014 at 12:37 pm
When you adjust the data, it isn’t science.
Not quite. When you adjust data it ceases to be data and becomes either information or results.
philip lee ,unfortunately climate science does not work like that .there would be no point making a comparison to the raw data,as in climate science the raw data is always incorrect .
you would be comparing raw data against the very finest adjusted data government money can buy, no raw data is worthy of comparison to that created by the finest minds in climate science 😉