Massive Temperature Adjustments At Luling, Texas
By Paul Homewood
As most will be aware, Steve Goddard has been running a series of posts about the large and unexplained adjustments being made to the US temperature record by NOAA.
For instance, his latest post is here.
So, I thought it might be worth looking in more detail at a few stations, to see what is going on. In Steve’s post, mentioned above, he links to the USHCN Final dataset for monthly temperatures, making the point that approx 40% of these monthly readings are “estimated”, as there is no raw data.
From this dataset, I picked the one at the top of the list, (which appears to be totally random), Station number 415429, which is Luling, Texas.
(The file can be opened by Zip File).
Taking last year as an example, we can see that ten of the twelve months are tagged as “E”, i.e estimated. It is understandable that a station might be a month, or even two, late in reporting, but it is not conceivable that readings from last year are late. (The other two months, Jan/Feb are marked “a”, indicating missing days).
But, the mystery thickens. Each state produces a monthly and annual State Climatological Report, which among other things includes a list of monthly mean temperatures by station. If we look at the 2013 annual report for Texas, we can see these monthly temperatures for Luling.
(The table is split into two, to make it readable).
Where an “M” appears after the temperature, this indicates some days are missing, i.e Jan, Feb, Oct and Nov. (Detailed daily data shows just one missing day’s minimum temperature for each of these months).
Yet, according to the USHCN dataset, all ten months from March to December are “Estimated”. Why, when there is full data available?
But it gets worse. The table below compares the actual station data with what USHCN describe as “the bias-adjusted temperature”. The results are shocking.
In other words, the adjustments have added an astonishing 1.35C to the annual temperature for 2013. Note also that I have included the same figures for 1934, which show that the adjustment has reduced temperatures that year by 0.91C. So, the net effect of the adjustments between 1934 and 2013 has been to add 2.26C of warming.
Note as well, that the largest adjustments are for the estimated months of March – December. This is something that Steve Goddard has been emphasising.
The figures for 1934 are below.
(Again, note that eleven out of twelve months are estimated for 1934, yet the State Climatological Report indicates there is data for every month. (See page 101 of the report).
How accurate are the State Climatological Reports? Well, they tally exactly with the Monthly Station Reports of daily data, for instance March 2013, (which you will recall has been estimated by USHCN).
So what possible justification can USHCN have for making such large adjustments? Their usual answer is TOBS, or Time of Observation Bias, which arises because temperatures are now monitored in the early morning rather than the late afternoon, which tended to be the practice before. But by their own admittance, TOBS adjustments should only account for about 0.2C.
What about station location? Has this changed? Well, not since 1949 according to the official Station Metadata.
Luling is a small town of about 5000 people, and the station is situated at the Foundation Farm, 0.7 miles outside town. In other words, a fairly rural site, that should not need adjusting for urban influences.
It is plain that these adjustments made are not justifiable in any way. It is also clear that the number of “Estimated” measurements made are not justified either, as the real data is there, present and correct.
Luling is only one case. But, coming at the top of the list, as it does, it is inconceivable that this is a rogue sample. If it is, though, it should be easy enough for USHCN to check and correct.
Whenever the question of adjustments comes up, there is usually a lot of arm waving, and talk of TOBS, station mix and so on. This manages to divert attention from what is going on, as these things cannot be easily quantified, or challenged. We are told, in effect, to “trust the computer”.
Well, we now have a specific example here with Luling, which cannot be so easily explained away.
Could it be that this rural station is being homogenised to match nearby urban stations? Or is something else to blame?
I don’t know, but what I do know is that the time for arm waving is past, and we deserve serious answers.