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?

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.

And Now El Nino Versus La Nina Precipitation:

So now let me go out on a limb here and say that, what with us  just having come out one La Nina and going into another one, having had a warm summer across most of the Central and Eastern US and dry conditions in most of the South Central US as a result, it looks like we will be in for more of the same for this next La Nina. This is bad news for Texas and Florida, as it means that we will have a repeat of the drought conditions just when we thought we were out of the woods.

Of course, one can be sure that typical, predictable weather patterns will be played for all they are worth in the media as somehow evidence of our evil human ways, even though years just like the last one and what will likely happen in the coming year, have happened throughout the recorded history of climate in the US, and undoubtedly longer than that. The observational record offers us many opportunities to identify past patterns of weather and the conditions that gave rise to them. What the record tells us is often that the weather we can expect looks a lot like weather we have had in the past. Sometimes that past weather was quite nasty. We better listen and be ready.

Anyway, after getting daily data averaged over this whole region, I separated data out into calendar years, and ranked each day in each year from coldest to warmest. Subsequently, for leap years, I copied the 183 coldest day (183 warmest in those cases, as well) to be a fake 184 coldest day, so that leap years would have the same number of days as non leap years. I am not entirely satisfied with doing things this way, but it matters very little, from what I’ve been able to tell. I will try to redo the analysis with any other method people suggest if they are interested. Anyway, I then calculated trends for day ranks over the last 32 years (approximately one half the length of the dataset) from 1979-2010 as this corresponds to the satellite period, the period when anthropogenic warming is supposed to be strongest, and in the thirty previous years the global climate appears to have changed little and temperatures cooled in the US. I will also calculate the trends for the first thirty two years for anyone interested if they ask (of course, those trends will have the central year in common in this dataset) but my primary interest is in the satellite period for now. So what did I find? Well, if the temperatures were getting more extreme in both cold and warm days, the coldest days should be slightly cooling and the warmest days strongly warming. If there is even just a net increase in extremes of warmth with no loss of cold extremes, then there should be more hot day warming than cold day, and if there is no change in over all extremes, then all days should show about the same trend. In fact, what I’ve found is that the most warming occurs on the very coldest days of the year, indicating that any increase in warm extremes is more than offset by much larger loss of cold extremes!

The X axis the rank of temperature within each year, y axis the slope of the linear trend line for that set of days from 1979-2010, in degrees Celsius per year.

This finding is consistent with earlier work by Knappenberger et al. Who came to the same conclusion examining different data (temperature stations, as opposed to mainly radiosonde derived products from the reanalysis) and slightly different periods of time. If this cold day warming is a signature of anthropogenic warming, it seems like a pretty nice thing to me. It certainly doesn’t support ideas about the US climate getting more extreme, at least in terms of temperatures. Once again, the results of testing a hypothesis about climate doom and gloom result in the reality being revealed: the world isn’t coming to an end. That’s information worth knowing, I think.

UAH residuals are in blue, HadCRUT in red. Now my first test was to see what the ratio their standard deviations  is, and it turns out to be about 1.46 (0.179/0.123) of UAH residual standard deviation and HadCRUT residual standard deviation. I expect that this overestimates the ratio of their actual co-varying behavior, due to the presence of un-related noise at from month to month. At any rate, a best fit of a regression of HadCRUT residuals on the UAH residuals yields a slope of about 1.07 and an r squared of about .54. So clearly there is a lot of noise still, but the satellite data clearly varies slightly more than the surface data in the same changes. Next, I do the same thing with twelve months smooths:

Now the ratio of standard deviations is about 1.49 (0.135/0.091) the slope is about 1.32 and the r squared about .78. At this point I think it is pretty clear that variations, at least in the short term, of surface temperatures are amplified in the troposphere. So now we come to the interesting bit (to me anyway): while this behavior is seen in the short term variations of surface and tropospheric temperatures, it is distinctly absent from the long term trends, which suggests something different is going on with the data’s long term trends than in the fluctuations: our ratios for the short term range from 1.07 to 1.49, while the trend ratio is about 0.898. Clearly there is something different going on with the long term trend.

Now personally, I think that the satellite data are pretty well supported by careful analyses done by John  Christy, so I think there are essentially three possible explanations (not necessarily mutually exclusive) of what is going on here:

1. The surface data contain a spurious warm bias due to contamination from nonclimatic effects and data quality (see for example this)

2. This a real feature of climate behavior due to different physical processes controlling the long term relationship between surface and tropospheric temperatures.

3. The surface contains additional real, climatic long term warming due to an effect which does not extend into the troposphere as a whole. This process must be pretty different from greenhouse gases, which should create warming throughout the troposphere, not isolated to the surface. Landuse seems a probable candidate.

Note that if either 2 or 3 are correct, then there is an important factor which present climate models must be missing or getting quite wrong, since they do not decouple the surface and tropospheric rates this way, but rather amplify the long term trends, too. If 1 is correct, then current models are overestimating climate sensitivity by being fit to surface temperatures with a non-climatic warming. Whatever the explanation, current mainstream interpretations of surface warming need to be re-examined, since they do not account for this feature of the data.

Oh, and I am of course going to check on how a different method detrending (my smoothing, for example) effects the numbers, but that will take a little more time. So I’ll get back to you.

UPDATE: As promised, I have tried looking at amplification with a more nonlinear detrending method, using the smoothing method I described here, (the approach that does not average all the smooths). No plots this time since you can hardly tell the difference, I think. First, for the monthly detrended, the standard deviation ratio was about 1.49, the slope about .954 and r squared of .409, the twelve month averages standard deviation ratio of about 1.56, a slope of 1.38 r squared of .781, finally comparing the relationship between the non-linear trends, it’s a slope of .928 and r squared of .919; So the final result is a range of short term amplification factors from .954 to 1.56, and long term from .898 to .919, clearly while short term fluctuations at the surface tend to be amplified in the troposphere, this is not the case over the long term.

Someone should see what effect these changes to the sea surface temperature data have on the calculations of various so called “indices” of “variability”. There could be quite a lot of mis-interpretation of “PDO” and “AMO” etc “signatures” or “patterns” in the data if these things are taken into account. Seeing if it changes the “AMO” will be relatively easy, just using the data available on climate explorer. I’ll see what happens, and perhaps get back to everyone.

Well of course, that clever commenter was me, and I am happy to oblige myself by examining this issue. The data, along with many climate datasets, is conveniently available here. Which is good because I have a hard time using the Hadley website some times. The region of Atlantic Sea Surface Temperatures involve is from the equator to 70 degrees North latitude, and 80 to zero West longitude (-80 East to zero East). I got these SST for both the old HADSST2 and new HADSST3 data so I am comparing old apples to new apples. I calculated the differences (ie, the “corrections” that had been applied) I then calculated the AMO from the old data (ie detrended) and also another AMO which was the same data, but with the differences added in (zero after 2006 since no corrected data are yet available, and the differences towards the end are very small anyway. The three (HADSST2 AMO, HADSST3-HADSST2 data from the region, and HADSST2 AMO + “correction”) were then calculated as 12 month moving averages to eliminate the monthly noise that obscures the ability to discern what is going on. Here is what you get:

It looks to me like this may make the AMO data if HADSST is used more closely resemble the AMO calculated using Kaplan’s dataset, the differences previously looked like this, according to Bob Tisdale.

Anyway, make of it what you will, it looks to me like the “AMO” is indeed somewhat altered by this, but not hugely. The “PDO” will be another story, something for someone else to look at, I think.

The AO data from here:

http://www.esrl.noaa.gov/psd/data/correlation/ao.data

Are the average of the eleven and thirteen month centered averages. I realize this is slightly different from my method of averaging the Snow Cover data, which is part of why I have not yet determined the exact relationship between these two series. More later, maybe!

Make of it what you will.

The NH snowcover data is unfortunately missing several months in the earlier part of the data set. My solution for dealing with this was to identify the missing months in separate time series, and estimate the missing values based on the same months in surrounding years. This doesn’t really have an influence on the identification of the step shift, as the missing months are mainly in the late sixties, and the apparent shift is in the late eighties.

The next step was to remove the seasonal effects by taking a twelve month moving average of the complete time series. This is the result, with the average line for 1967-2010, here:

I have split the series at the point at which values go from predominantly above average to predominantly below average.

Curiously, this step shift, while quite apparent when the data are averaged in this way. But except for the summer seasonal graph, which is not available for some reason, it is not clear to me that such a step shift can be seen in the seasonal plots. The winter actually shows an increasing trend, and the spring trend might possibly look step-ish but appears more continuous after.