Archive for September, 2011

Wiki Climate Lies Infect Non-Climate Articles

September 13, 2011

The bias of Wikipedia in dealing with climate issues has been well documented. I tend to find that the more controversy their is over any topic, actually, the less reliable wiki gets. But who would expect that should be mostly factual articles would be infected with climate bullshit? It would be one thing to expect unreliable, biased information on local politics to infect an article about a major city. It is quite surprising to look at an article about a major city and find a falsehood about climate being promoted, and referenced to “scientists” who ought to know better, given that they work for an organization that gathers data that contradicts their own claims.

But when, with the intention of merely identifying the coordinates of the City of Austin, Texas, to examine temperature records nearby (prompted to do so by this post), the above described situation is in fact what actually happened to me. I will use screen captures in case someone comes to their senses and realizes this is a really stupid, factually inaccurate thing to put in an article: here.

What is wrong with this statement? Well, let’s think carefully about it. What “these kinds of droughts will have effects that are even more extreme in the future, given a warming and drying regional climate” can be interpreted as meaning is pretty unambiguous. Droughts will get worse in the future, given regional warming and drying as facts. Now, weasels will say, that he is saying that, “if” we take such future trends as a “given” then “of course” that would be true. If that is what is being said, someone needs to go back to grade school, because the actual statement is saying that given the present tense trends, that is what will happen. So no weaseling for you fools. The facts are that this statement, that Texas is warming and drying is false both as two separate statements, and as a combined statement. The long term records for Texas annual temperature trend since 1895, according to the National Climate Data Center is 0.00 degF / Decade, precipitation 0.08 Inches / Decade, in other words, the numbers given by the NOAA organization that specifically monitors climate directly contradict the claims of an NOAA “scientist” who clearly should know better: The trends are pretty clearly not different from zero, if you look at the data themselves, although the temperature trend is technically slightly negative. So the claim that Texas is drying and is warming, is FALSE. If anything the long term trends are toward wetter and cooler. You can see the data for yourself here.

Anyway, I am glad I got that off my chest. I really can’t stand that “scientists” make claims that can be shown to be wrong by a mere amateur with a couple of mouse clicks. I am even more frustrated that such claims get parroted unquestioningly in “factual” articles about major cities. Thankfully it would appear that no such stupid statements are present on the West Palm Beach, Florida article (the nearest major weather station (the airport) to where I live, although that’s not particularly close), even though we have also had a bad drought (well, bad by our standards, and since we are subtropical, and very wet normally, “bad drought” here is nowhere near as bad as in Texas), although ours is safely over for now, with quite a lot of rain recently (it may return next year if La Niña persists). How does it come to be that climate idiocy finds its way into non-climate articles? Is nothing sacred?

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Critical Points

September 3, 2011

In calculus, a critical point of a function of a real variable is any value in the domain where either the function is not differentiable or its derivative is 0.

The above is from Wikipedia. As you all should know, I know Calculus. Now, technically, no climate data is a truly differentiable  function, since they are not provided in continuous form. Nevertheless, one can estimate the instantaneous rate of change of a function from slopes of lines between discrete points. So with timeseries that are not continuous in a mathematical sense, one can nevertheless identify points at which the rate of change abruptly switches sign. I am looking into this as a way to identify intervals of change for better estimating variance adjustments for LT versus surface temperatures. So my interest is in finding something analogous to “critical points” in the surface or LT data, specifically ones associated with changes in the sign of temperature change. So I will want to identify when, say, three month slopes change sign, and then identify those intervals between them that are useful for assessing short term amplification. I am just getting started, but will start with a plot of identified intervals in the surface data (here I will be using an average of GISS, HadCRUT, and NCDC over 1979-almost the present (deviations from the 1981-2010 mean), and three month averages to make the points of change stand out more. Note that later when finding the magnitude of temperature changes I will probably just use the unsmoothed data over identified intervals). The plot shows those intervals I chose to be sufficiently long, taking breaks in the intervals that were short as not seperate intervals put part of the longer intervals, and making them overlap on the maxima/minima that characterize the change in rate.

Now, having identified intervals, I examine the ratio of changes in the troposphere over said intervals: two problems occur: Near zero the ratios vary a little too much, and also a couple of times the rates of change are opposite in sign. This can be seen in a plot of the ratios with the the amount of atmospheric change as the independent variable:

See, now, a deceptive thing I could have done was not tell you that my new approach was failing to overcome the noise. But I am showing you this plot so you can see why I am going to say: I am disappointed, because this is not the kind of clean result I was hoping for, showing the short term amplification without a lot of noise. So anyway, a couple of things that might help: Eliminating ratios associated with changes too close to zero, and unphysical ratios less than zero. The result (eliminating ratios where the absolute value of the atmospheric change less than .1)  is that the mean ratio goes from about .64 to 1.04 and the standard deviation goes from .81 to .46, so while it does converge closer to be within the realm of my previous estimates (which were roughly from 1 to 1.5 with a best estimate of about 1.3, very close to the theoretical/model value of 1.2) it clearly is very uncertain. So I would consider this not a great method to get the right value for the amplification ratio for short term changes. I am still most satisfied with taking various different approaches and averaging them. So remember our previous estimates: 1.46, 1.07, 1.49, 1.32, 1.49, .954, 1.56, and 1.38, and add in the new estimate, the average is ~1.3 so a conservative estimate of the short term amplification remains about 1.2, with some uncertainty still to be figured out. (Excluding the estimates based on the smooth trend removal method, since it is a little difficult to explain and I am not sure it is a detrending method that is reliable anyway, the new mean is 1.28)

So I feel I have not made much progress in the variance adjustment estimate. Since I don’t yet have a good method to get it, I am going to be faced with the issue of continuing to search for a good method. Well, for now, I have the best I can do.