Monthly Archives: December 2013

Smooth

NASA’s Goddard Institute for Space Studies (GISS) has updated their global surface temperature estimate to include November 2013. It turns out that this most recent November was, globally, the hottest on record:

giss_nov

Greg Laden posted about it (and other things) recently in his continuing efforts to let people know what’s really happening to the globe (it’s still heating up) as well as spreading the word that “earth” includes a lot more than just the atmosphere. He featured this version of the graph (provided by “ThingsBreak” but prepared by Stefan Rahmstorf):

HottestNovemberOnRecord_2013

Of course this means that the fake skeptics must come out of the woordwork. Referring to the smooth (the red line on the graph), here’s what Paul Clark had to say about it:


It’s not clear how this red line was obtained. The red line is not described on the poster’s page. The graph comes from, what Laden describes as, “climate communicator” ThingsBreak. What on earth is a “climate communicator”?!

It seems to be some type of smoothed moving average. Five year spline perhaps?

Problem is, the red line is roughly in the middle of the blue line, except at the end. At the end, the red line is not in the middle at all, but is down at the beginning, and up at the end, of that final 10 year period. It’s shooting right up at the end!

How can that be? I therefore find this line to be completely made up, and a case of wishful thinking.


Here’s something about which every honest participant in the discussion of man-made global warming should think. Carefully. Namely, this: Paul Clark complains that it’s not clear how the red line (the smoothed version of the data) was obtained. Furthermore, it doesn’t seem right to him. How does he react?

Did he acquire in-depth knowledge of smoothing techniques? (I can tell you for a fact: no he didn’t.) Did he consult a disinterested expert? (Apparently not.) Did he, oh I don’t know, maybe ASK how it was obtained? (Nope.)

You see, those are some of the ways an actual scientist might proceed. The guiding principle being this: LEARN MORE ABOUT THE SUBJECT *BEFORE* YOU OPEN YOUR MOUTH.

It seems that’s not Paul Clark’s way. He doesn’t think the smooth (red line) looks right, but with little to no effort at all to find out about it, he declares that it is “completely made up, and a case of wishful thinking.” I declare that Paul Clark’s opinion is completely mistaken, and just about as clear a case of the Dunning-Kruger effect as you’re likely to find.

Here’s something else worth thinking about: suppose I wanted to make the slope at the end artificially large. What smoothing method — other than “force it by hand” — could do that?


Rahmstorf used a smoothing method based on MC-SSA (Monte Carlo singular spectrum analysis, Moore, J. C., et al., 2005. New Tools for Analyzing Time Series Relationships and Trends. Eos. 86, 226,232) with a filter half-width of 15 yr. I get a very similar result using my favorite method (a “modified lowess smooth”) with about the same time scale.

giss_nov2

My modified lowess smooth is in agreement with Rahmstorf’s MC-SSA smooth. Here’s just the modified lowess smooth (in red), a plain old plain-old lowess smooth (in green) for those who don’t trust me to modify anything, and a spline smooth (in blue):

giss_nov3

One of the things I like about my own smoothing program is that it also calculates the uncerainty of the result. Here are the three smooths I computed, together with dashed red lines to show the range 2 standard deviations above and below:

giss_nov4

The three methods are in agreement, within the limits of their uncertainty. Clearly.

Now let’s take the range of the modified lowess smooth which we plotted in the previous graph, and add some other smooths set to about the same time scale for smoothing: an ordinary moving average in black, a Gaussian smooth in green, and a 6th-degree polynomial (as used by Paul Clark himself) in blue:

giss_nov5

The moving-average line stays within the range indicated by the modified lowess smooth, but that’s easy because the moving averages don’t extend to the ends of the time series, we lose years at both the beginning and end. The Gaussian smooth stays within the range indicated by the modified lowess smooth except at the end, when the Gaussian smooth levels off. Is Paul Clark wondering why that might be? Does he know enough about smoothing in general, and about Gaussian smoothing specifically, to have expected that? I did.

Perhaps most interesting is the 6th-degree polynomial, which wanders outside the modified lowess range, not just at the beginning or end but in the middle as well. What’s really interesting is why it wanders outside the range, because it happens for different reasons at different times! The 6th-degree polynomial fit smooths too much in the middle of the time span, but smooths too little near the endpoints. Is Paul Clark wondering why that might be? Does he know enough about smoothing in general, and about polynomial fits specifically, to have expected that? I did.

Ordinarily, this is where I would launch into a technical discussion of smoothing. Why do certain methods tend to go one way more than another? What should one expect near the endpoints of the time span? How do smooths with longer time spans compare to those with shorter time spans? Why is the Gaussian smooth questionable near the endpoints? Why do high-degree (and 6 is a pretty high degree) polynomial fits really really suck as smoothing methods, especially near the endpoints of the time span. Yes, they really suck, and the reason is actually quite interesting.

But I’m not gonna. At least not yet. It’s not my job to educate ignorant Dunning-Kruger victims about smoothing techniques.

But here’s an offer for Paul Clark: Come to this blog, find this thread, and post a comment in which you admit — without a bunch of caveats or excuses or bullshit — just admit in no uncertain terms that you don’t know enough about smoothing to know how valid Rahmstorf’s MC-SSA smooth is or why your 6th-degree polynomial choice is a really really sucky choice. You don’t have to weep and moan, just simply admit that you don’t know enough about this topic to justify your opinion. You don’t have to admit anything else, just that you’re ignorant about smoothing methods. Don’t clutter the comment up with unrelated stuff, if you want to spew about other things put that in a separate comment. Just a single, simple admission of ignorance on this topic.

If you’ll do that, Paul Clark, then I’ll do a blog post on smoothing. Or maybe two. Maybe even three — it’s a topic of great interest for me. How ’bout it, Paul? All you have to do is admit that you’re ignorant of the subject, and I’ll educate you.

In case that offer isn’t acceptable, here’s another. Paul: I’ll blog about the topic and you don’t even have to admit anything. But if you want me to supply some lessons without you admitting your ignorance — pay me. Cash American.

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