Corrupted Data-Sets Due to Equipment Failure/Placement

EQUIPMENT FAIL

Here are some examples of fudged data because of sub-standard equipment:

You’d think the answer would be obvious, but here we have a NOAA operated USHCN climate station of record providing a live experiment. It always helps to illustrate with photos. Today I surveyed a sewage treatment plant, one of 4 stations surveyed today (though I tried for 5) and found that for convenience, they had made a nice concrete walkway to allow servicing the Fisher-Porter rain gauge, which needs a paper punch tape replaced one a month.

Here is what you see in visible light:

Here is what the infrared camera sees:

Note that the concrete surface is around 22-24°C, while the grassy areas are between 12-19°C

This station will be rated a CRN5 by this definition from the NOAA Climate Reference Network handbook, section 2.2.1:

Class 5 (error >~= 5C) – Temperature sensor located next to/above an artificial heating source, such a building, roof top, parking lot, or concrete surface.”

More than half of the stations the NOAA use are tainted or wrongly placed equipment.

CHANGING DATA-SETS

Another example of changing averages was noted by Steve Goddard and others — even the NOAA have acknowledge it — have been discussing recently is exemplified in Dr. Judith Carry’s post on the matter (from a larger post of mine):


Even the Wall Street Journal chose the higher temperature reading to say that July of 2012 was July was the “hottest month in the contiguous U.S. since records began in 1895.” WUWT found this on accident and it has led to quite a few other revelations as we will see. Here is description in part of what we looking at:

Glaring inconsistencies found between State of the Climate (SOTC) reports sent to the press and public and the “official” climate database record for the United States. Using NCDC’s own data, July 2012 can no longer be claimed to be the “hottest month on record”.

[….]

I initially thought this was just some simple arithmetic error or reporting error, a one-off event, but then I began to find it in other months when I compared the output from the NCDC climate database plotter. Here is a table of the differences I found for the last two years between claims made in the SOTC report and the NCDC database output.

[….]

In almost every instance dating back to the inception of the CONUS Tavg value being reported in the SOTC report, there’s a difference. Some are quite significant. In most cases, the database value is cooler than the claim made in the SOTC report. Clearly, it is a systemic issue that spans over two years of reporting to the press and to the public.

It suggests that claims made by NCDC when they send out these SOTC reports aren’t credible because there are such differences between the data. Clearly, NCDC means for the plotter output they link to, to be an official representation to the public, so there cannot be a claim of me using some “not fit for purpose” method to get that data….

The Wall Street Journal made a graph showing this record setting month (below-left). The more accurate temperature for July likewise is shown in the same graph (below-right):

This looking at the data sets chosen and what is used and isn’t used to support an idea that fails in every way. Combine this obvious cherry-picking with the bias, collusion, and charges against the report that the President used to route Congress, all show we have a problem Houston! But this is only the tip of the proverbial iceberg. It seems the NOAA has been skewing these temps for some time. Why? Because the left uses this as a way to promote an ever growing government and the scientists get more-and-more funding. This data fudging story is newer, and it is evolving quickley, including this newest post via Real Science where Steve Goddard notes that More Than 40% Of USHCN Station Data Is Fabricated. Here is Dr. Judith carry’s synopsis (excerpted), in which she critiques a bit Goddard’s post… but then bows to the evidence:

OK, acknowledging that Goddard made some analysis errors, I am still left with some uneasiness about the actual data, and why it keeps changing. For example, Jennifer Marohasy has been writing about Corrupting Australian’s temperature record.

In the midst of preparing this blog post, I received an email from Anthony Watts, suggesting that I hold off on my post since there is some breaking news. Watts pointed me to a post by Paul Homewood entitled Massive Temperature Adjustments At Luling, Texas. Excerpt:

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.

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.

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.

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.

Watts appears in the comments, stating that he has contacted John Nielsen-Gammon (Texas State Climatologist) about this issue. Nick Stokes also appears in the comments, and one commenter finds a similar problem for another Texas station.

Homewood’s post sheds light on Goddard’s original claim regarding the data drop out (not just stations that are no longer reporting, but reporting stations that are ‘estimated’). I infer from this that there seems to be a real problem with the USHCN data set, or at least with some of the stations. Maybe it is a tempest in a teacup, but it looks like something that requires NOAA’s attention. As far as I can tell, NOAA has not responded to Goddard’s allegations. Now, with Homewood’s explanation/clarification, NOAA really needs to respond….

(H/T to Climate Realist ~ See WUWT and Hockey Schtick for more)


So we see in the above, that temperatures can be changed years later as the totality of the data is included. What was considered the hottest falls to just an average month in the heat index.

And this has — within the past few months — turned into a very large debate.

EQUIPMENT FAIL II

Here is another example of older/faulty equipment:

A Quick Note about the Difference between RSS and UAH TLT data

There is a noticeable difference between the RSS and UAH lower troposphere temperature anomaly data. Dr. Roy Spencer discussed this in his July 2011 blog post On the Divergence Between the UAH and RSS Global Temperature Records. In summary, John Christy and Roy Spencer believe the divergence is caused by the use of data from different satellites. UAH has used the NASA Aqua AMSU satellite in recent years, while as Dr. Spencer writes:

…RSS is still using the old NOAA-15 satellite which has a decaying orbit, to which they are then applying a diurnal cycle drift correction based upon a climate model, which does not quite match reality.

I updated the graphs in Roy Spencer’s post in On the Differences and Similarities between Global Surface Temperature and Lower Troposphere Temperature Anomaly Datasets.

While the two lower troposphere temperature datasets are different in recent years, UAH believes their data are correct, and, likewise, RSS believes their TLT data are correct. Does the UAH data have a warming bias in recent years or does the RSS data have cooling bias? Until the two suppliers can account for and agree on the differences, both are available for presentation.