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Category Archives: climate change
In the last post we looked at the extent, area, and volume of Arctic sea ice. We also mentioned that we can derive other quantities from these, namely the average thickness as the volume divided by area, and what I called the “spread” which is the extent divided by the area. I’ve also been looking at the difference (rather than ratio) of sea ice extent and area, which I’ve dubbed the “split” (for lack of a better term). I’ll take up spread and split in another post, at the moment let’s see how thickness has changed over time.
Although it’s useful and sometimes interesting to refute silly ideas about Arctic sea ice loss (such as the claim that it is “stabilizing” or even in “recovery”), it’s far more interesting scientifically to consider what available data actually tell us about the changes of the ice pack in the frozen north.
NOTE: for a brief, non-technical summary of this post see the UPDATE at the end. To get there, go to the full post (not just the blog’s home page), then click here.
Real data are the combination of signal and noise. By noise I don’t just mean measurement error. I mean the stochastic part of the process. That includes naturally occuring noise in the system itself — those ubiquitous wiggles up and down and up and down and down and up, that never cease but never really get anywhere. They’re not part of the trend, they’re noise. If you want to know what the trend is then you have to account for the noise.
If you claim that “ice cover is stabilising,” then you better be talking about the trend. You damn well better not be basing that conclusion on the effect of those ubiquitous wiggles that never cease but never get anywhere.
Much of what’s wrong with the online discussion of global warming is revealed by a recent reader comment on RealClimate.
Greg Goodman thinks that he’s taking climate scientists to school — he actually “lectures” the RealClimate readership about their supposed need to “dig a bit deeper” into the data on Arctic sea ice (both extent and area). He shows a graph based on some analysis which — unbeknownst to him — actually reveals that he doesn’t know what the hell he’s doing. He thinks he has established the presence of “cyclic variations” of which the climate science community is ignorant, and concludes that climate scientists are missing “important clues” about “internal fluctuations” which, of course, those inadequate computer models just can’t handle.
One would be hard pressed to find a more clear-cut example of hubris.
Climate scientists who study sea ice have been all over the data, every piece of it, but instead of making the mistakes Goodman makes they’ve been as careful and rigorous as their expertise and experience allow. They have certainly dug a whole helluva lot deeper than Greg Goodman has, or probably is capable of. It’s Goodman who needs to go back to school.
Anybody can do it.
Fake “skeptics” of global warming do it all the time. One of the latest and most extreme — this one is a real doozy — comes from John Coleman. Of course it’s regurgitated by Anthony Watts.
A reader recently inquired about using the Theil-Sen slope to estimate trends in temperature data, rather than the more usual least-squares regression. The Theil-Sen estimator is a non-parametric method to estimate a slope (perhaps more properly, a “distribution-free” method) which is robust, i.e., it is resistant to the presence of outliers (extremely variant data values) which can wreak havoc with least-squares regression. It also doesn’t rely on the noise following the normal distribution, it’s truly a distribution-free method. Even when the data are normally distributed and outliers are absent, it’s still competitive with least-squares regression.
A recently published paper by Ludecke et al. in Climate of the Past claims, as its main result, that “the climate dynamics is governed at present by periodic oscillations.”
In the past I’ve explored simple energy balance models for the evolution of global average temperature. One of the important things to note is that a “1-box” energy balance model — in which the entire climate system is considered to have a single time constant — isn’t really sufficient. It can give a pretty good fit, but for a more realistic estimate you need at least two boxes. One represents rapid response to climate forcing — think of it as the “atmosphere” if you wish. The other is for slower response — think of it as “ocean” if you wish, or as “upper ocean,” or as “everything else.” One could be even more realistic with more than two boxes, after all the deep ocean certainly effects climate but with a longer time scale still, and there’s the cryosphere on top of it all, but rather than go the way of the full-blown computer simulation model, let’s see what happens if we just use a 2-box model with two time constants. We’ll think of box 1 as the atmosphere, so that it should correspond to the surface temperature we’re all familiar with.
As ClimateProgress and others report, initial results are in from Cryosat-2. Since 2010, this European Space Agency satellite has surveyed polar ice to estimate its thickness, and by extension, its volume. It was a replacement for Cryosat-1, which was unfortunately destroyed in a launch failure. But the European Space Agency (ESA) considered this mission important enough to construct and deploy a replacement promptly, approving Cryosat-2 less than five months after the failure of Cryosat-1.