Warming in the historic record is not going away.  It may be different by a few tenths, but I am not sure its going to change arguments one way or another.  Even the (what skeptics consider) exaggerated current global temperature metrics fall far short of the historic warming that would be consistent with current catastrophic high-CO2-sensitivity models.  So a few tenths higher or lower will not change this – heroic assumptions of tipping points and cooling aerosols will still be needed either way to reconcile aggressive warming forecasts with history.

What can be changed, however, is the stupid amount of time we spend arguing about a topic that should be fixable.  It is great to see a group trying to honestly create such a fix so we can move on to more compelling topics.  Some of the problems, though, are hard to fix — for example, there simply has been a huge decrease in the last 20 years of stations without urban biases, and it will be interesting to see how the team works around this.

For this reason I want to cite an article by Anthony Watt in which he criticizes, rightly I think, a skeptic for pushing a fraud/cover-up story that simply does not exist.  Ironically, the article occurs just days after Joe Romm, whose site would never tolerate the dissenting opinions in its comments section that Watt’s allows, generally equates Watt’s past work with the 10:10 video blowing up children.  (more comments on the Romm post here).

Here is what I do not like about his work, at least as I understand it — I would greatly prefer to see this work done on some sort of double-blind system.  One group, without any knowledge of station temperature numbers, sorts the stations while another works on the temperature trends.  This way there is no danger of the sorting decisions being pre-biased by knowledge of their characteristics (something that arguably happens all the time in dendro-climatology).

I am reminded of this seeing how skeptics finally convinced the NOAA that one of its satellites had gone wonky, producing absurd data (e.g. Great Lakes temperatures in the 400-600F range).  Absolutely typically, the NOAA initially blamed skeptics for fabricating the data

NOAA’s Chuck Pistis went into whitewash mode on first hearing the story about the worst affected location, Egg Harbor, set by his instruments onto fast boil. On Tuesday morning Pistis loftily declared, “I looked in the archives and I find no image with that time stamp. Also we don’t typically post completely cloudy images at all, let alone with temperatures. This image appears to be manufactured for someone’s entertainment.”

Later he went on to own up to the problem, but not before implying at various times that the data is a) trustworthy  b) not trustworthy  c) placed online by hand with verification and d) posted online automatically with no human intervention.

This was the final NOAA position, which is absurd to me:

“NOTICE: Due to degradation of a satellite sensor used by this mapping product, some images have exhibited extreme high and low surface temperatures. “Please disregard these images as anomalies. Future images will not include data from the degraded satellite and images caused by the faulty satellite sensor will be/have been removed from the image archive.”

OK, so 600F readings will be thrown out, but how do we have any confidence the rest of the readings are OK.  Just because they may read in a reasonable range, e.g, 59F, the NOAA is just going to assume those readings are OK?

GISS has made a straight-line adjustment of 1.1°C in twenty years, or 5.5°C per century. They have changed a cooling trend to a strong warming trend … I’m sorry, but I see absolutely no scientific basis for that massive adjustment. I don’t care if it was done by a human using their best judgement, done by a computer algorithm utilizing comparison temperatures in India and China, or done by monkeys with typewriters. I don’t buy that adjustment, it is without scientific foundation or credible physical explanation.

At best that is shoddy quality control of an off-the-rails computer algorithm. At worst, the aforesaid monkeys were having a really bad hair day. Either way I say adjusting the Kathmandu temperature record in that manner has no scientific underpinnings at all. We have one stinking record for the whole country of Nepal, which shows cooling. GISS homogenizes the data and claims it wasn’t really cooling at all, it really was warming, and warming at four degrees per century at that

In updates to the post, Eschenbach and his readers track down what is likely driving this bizarre adjustment in the GISS methodology.

This is a hilariously bad siting.  It demonstrates how small things can sometimes have big effects.  The MMTS sensor has a very limited cable length.  This does not mean that it only comes with a short cable (begging the question of why they can’t just buy a longer one), but that it can only have a short cable due to signal amplification issues.  As a result, we get this terrible siting because it needs to be close to the building, whereas even a hundred yards away there were much better locations

Carefree is a fairly rural (at least suburban) low density town with lots of undeveloped land.  They had to work to get a siting this bad.  A monkey throwing darts at a map of the area would have gotten a better siting.

The natural environment for millions of acres around this site in Russia Norway is reflective snow cover, so the temperature station is on black asphalt.

With NASA GISS admitting that missing minus signs contributed to the hot anomaly over Finland in March, and with the many METAR coding error events I’ve demonstrated on opposite sides of the globe, it seems reasonable to conclude that our METAR data from cold places might very well be systemically corrupted with instances of coding errors.

A new SPPI paper examines the raw and adjusted historical temperature records for Pennsylvania and finds the mean temperature trend from 1895 to 2009 to be minus .08°C/century, but after unexplained adjustments the official trend becomes positive .7°C/century. The difference between the raw and adjusted data exceeds the .6°C/century in global warming claimed for the 20th century.

I think people are too quick to jump onto the conspiracy bandwagon and paint these adjustments as scientists forcing the outcome they want.  In fact, as I have written before, some of these adjustments (such as adjustments for changes in time of observation) are essential.  Some, such as how the urbanization adjustments are done (or not done) are deeply flawed.  But the essential point is that the signal to noise ratio here is really really low.  The signal we are trying to measure (0.6C or so of warming) is smaller than the noise, even ignoring measurement and other errors.

[For] example, we extrapolate station measurements as much as 1200 km. This allows us to include results for the full Arctic. In 2005 this turned out to be important, as the Arctic had a large positive temperature anomaly. We thus found 2005 to be the warmest year in the record, while the British did not and initially NOAA also did not. …

So he is trumpeting this approach as an innovation?  Does he really think he has a better answer because he has extrapolated station measurement by 1200km (746 miles)?  This is roughly equivalent, in distance, to extrapolating the temperature in Fargo to Oklahoma City.  This just represents for me the kind of false precision, the over-estimation of knowledge about a process, that so characterizes climate research.  If we don’t have a thermometer near Oklahoma City then we don’t know the temperature in Oklahoma City and lets not fool ourselves that we do.

I had a call from a WaPo reporter today about modeling and modeling errors.  We talked about a lot of things, but my main point was that whether in finance or in climate, computer models typically perform what I call knowledge laundering.   These models, whether forecasting tools or global temperature models like Hansen’s, take poorly understood descriptors of a complex system in the front end and wash them through a computer model to create apparent certainty and precision.  In the financial world, people who fool themselves with their models are called bankrupt (or bailed out, I guess).  In the climate world, they are Oscar and Nobel Prize winners.

Update: To the 1200 km issue, this is somewhat related.