The Past Keeps Getting Cooler
By Paul Homewood
h/t Eliza
Roy Spencer has spotted another example of how the past keeps getting miraculously cooler.
I was updating a U.S. Corn Belt summer temperature and precipitation dataset from the NCDC website, and all of a sudden the no-warming-trend-since-1900 turned into a significant warming trend. (Clarification: the new warming trend for 1900-2013 is still not significantly different from zero at the 90% confidence level. H/T, Pat Michaels)
As can be seen in the following chart, the largest adjustments were to earlier years in the dataset, which were made colder. The change in the linear trend goes from 0.2 deg F/century to 0.6 deg. F/century.
I know others have commented on the tendency of thermometer data adjustments by NOAA always leading to greater warming.
As Dick Lindzen has noted, it seems highly improbable that successive revisions to the very same data would lead to ever greater warming trends. Being the co-developer of a climate dataset (UAH satellite temperatures) I understand the need to make adjustments for known errors in the data…when you can quantitatively demonstrate an error exists.
But a variety of errors in data measurement and collection would typically have both positive and negative signs. For example, orbit decay causes a spurious cooling trend in the satellite lower tropospheric temperatures (discovered by RSS), while the instrument body temperature effect causes a spurious warming trend (discovered by us). The two effects approximately cancel out over the long term, but we (and RSS) make corrections for them anyway since they affect different years differently.
Also, the drift in satellite local observation time associated with orbit decay causes spurious cooling in the 1:30 satellites, but spurious warming in the 7:30 satellites. Again this shows that a variety of errors typically have positive and negative signs.
In contrast, the thermometer data apparently need to be adjusted in such a way that almost always leads to greater and greater warming trends.
How odd.
http://www.drroyspencer.com/2015/03/even-though-warming-has-stopped-it-keeps-getting-worse/
There is a bit of armwaving in the comments, telling us it is all down to TOBS and how we should pay attention to Zeke. However, as Roy makes clear, the adjustment of 0.4F/C,that he has discovered, is just the change since the chart was run a year ago, which, of course, already included adjustments for TOBS and the like.
( This is confirmed by NCDC in their Readme for the nClimdiv dataset, which includes these adjustments, and which was introduced last March).
So these latest changes are adjustments to already adjusted data.
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NOT A LOT OF PEOPLE KNOW THAT 
Reblogged this on eliquidassets.
Paul, reports of altered temperature data ended the AGW debate.
The data were altered to hide effects of a secret solar force that produced, in solar cycle #24, the lowest sunspot number recorded since 1750. (Sunspots are produced when powerful, deep-seated magnetic fields emerge through the photosphere.)
The unacknowledged solar force was publicly pointed out in 2002.
See: “Super-fluidity in the solar interior: Implications for solar eruptions and climate”, Journal of Fusion Energy 21, 193-198 (2002)]: http://www.springerlink.com/content/r2352635vv166363/
It may depend on when Roy grabbed the data. The switch in 2014 to nClimDiv from the previous Drd964x introduced warming or additional warming in 40 out of 48 CONUS states, near doubling the decadal trend. Plainly nClimDiv is suspect. California, Maine, and Michigan are illustrated in essay When Data Isn’t. If both 2014 and 2015 are nClimDiv, then further tampering is unequivocal.
Rud,
Government lies are almost always credible.
For example, after WWII most nuclear physics textbooks replaced Aston’s rigorously valid “nuclear packing fraction” with Weizsacker’s invalid “nuclear binding energy”.
That change kept nuclear physicists from seeing The secret energy source NEUTRON REPULSION.
Global temperatures were altered to hide effects of a secret solar force that produced the lowest sunspot number recorded in solar cycle #24.
I looked at some of the GISS Raw (version 2) and Homogenized South American Temperatures. Applying a linear trend to the data, I found the rate of temperature change. The following lists the location, then the slope values. The first slope value, in degrees C per decade, is from the raw data, the second slope value is based on the Homogeneous (adjusted) data.
Asuncion, Paraguay, -0.015, +0.17
Mariscal, Paraguay, -0.38, +0.22
San Juan Bautista, Paraguay, -0.36, +0.18
Pilar, Paraguay, -0.26, +0.21
With the exception of Pilar, the raw Paraguay data shows temperatures decreasing with time (negative slope), but the revised data shows temperatures are increasing.
I also looked at another 16 randomly selected (latitude range: 4N to 46 S) South American sites and found most of them showed no significant data adjustments. However, these 3 were adjusted significantly.
La Paz Alto, Bolivia, -0.19, +0.18
Tacna, Peru, -0.14, +0.28
Pudahuel, Chile, +0.07, +0.11
These temperature adjustments obviously have the effect of increasing the rate of temperature increase. But lowering the past temperatures can be done only once; from now on (unless they find a new reason to increase the future measured temperatures) the new raw data will control. Expect temperatures to remain static, continuing the present trend of no warming.
But lowering the past temperatures can be done only once; from now on (unless they find a new reason to increase the future measured temperatures) the new raw data will control.
I think you’ll find that successive versions of surface data record have lowered past temps further and further. As for present raw data, who in their right mind thinks that there’s any reason why it should need any adjustment, yet in the graph, seven of the last eight years are adjusted upwards.
Rather than make the past cooler, they should have adjusted more recent temps down to adjust for the UHI. What is with these people?
In my days as a test engineer seeing something like this would immediately raise the question what’s gone wrong the the software? Why is this batch getting progressively slower/faster when the reverse or nothing should be happening?
Usually it would be down to repetitively adding the adjustment to the already adjusted data. More often than not a problem like this would appear after a test programme had been modified or improved.
Great article!
Oops, sorry. Error: “With the exception of Pilar, the raw Paraguay data shows temperatures decreasing with time (negative slope)”, Corrrection: All Paraguay raw data slopes are negative.
Paul,
If there is something wrong with data, it should be pretty easy to show; and these are adjustments made on adjustments… Something just doesn’t add up.
people still having to shove snow in ate spring, will soon begin to doubt the “official data”
sorry, that should read
people still having to shove snow in late spring, will soon begin to doubt the “official data”
Society will continue to live under tyranny until we address the past:
Falsehoods inserted into the very foundations of nuclear and solar physics in 1945 to hide NEUTRON REPULSION.
Reblogged this on 4timesayear's Blog.
“The past is a foreign country; they do things differently there.”
Or at least that is the current theory when it comes to reading a thermometer.
Oh, oh. Infestation by the internet’s premier thread bomber! Where’s the RAID?