The Pacemaker of the Chandler Wobble

Abstract: The Chandler Wobble is one of the largest circumannual periodic or quasi-periodic variations in the earth's orientation.  After over a century of searching for its forcing, it was found to be caused by atmospheric circulation and induced ocean circulation and pressure.  The question of why there should be such forcing from the atmosphere has remained open. I suggest that variations in earth-sun distance cause this forcing to the atmosphere and thence the ocean.  Analysis of earth-sun distance, earth's orientation, and atmospheric winds shows a coherent relationship between the atmosphere and earth orientation at just those periods expected from earth-sun distance variation.  As this is a general mechanism, it can be used in examining regular climatic variations on a wide range of periods and for climate parameters other than the earth's orientation.

-- -- -- -- -- -- -- 

That is the abstract for the paper I link to below.  It's not a peer-reviewed paper in the sense of being in a peer-reviewed journal.   But it has been reviewed by an expert in the field (William P. O'Connor), who was quite favorable.

I am posting the idea and paper here.  Long past time for the ideas to be discussed.  If they're shredded in the blogosphere, so be it.  I have quite a bit more than what I've put in the document. Over the next few days and weeks, I'll post more of those additional materials as well.

The Pacemaker of the Chandler Wobble, Grumbine 2014

AMS feeds for scientific articles and a sampling

Very few scientific articles get any splash in the media, even those parts which pay some attention to science.  Readers then can build a mistaken impression that there are only a few scientists, a few dozen, tops, working in any given area. 

The American Meteorological Society is one of very many professional societies which publish scientific journals.  They also have RSS feeds for their journals, and a relatively open access policy to their older (than 2 years) articles.  Below is a biased sampling (the bias being that I'm interested in these articles and will be pulling them down at work) of recent articles.  There's quite a lot more going on than you're liable to hear of in media, by quite a few more people than you'll ever hear of. 

Some of the papers will probably inspire a need to make use of my guide to science jabberwocky.  That's not a knock on the papers, just a reminder that fields do develop vocabularies to ensure that the professionals all know what each other means.


If you are fairly good with your technique, you can set up your own double-diffusive staircases.  (The first paper discusses observations in nature of the effect.)

Read more »

Climate and Computer Science

I'll pick up John Mashey's comment from the 'relevance' thread, as it illustrates in another way some of what I mean regarding relevance, and about who might know what. He wrote:

As a group, computer scientists are properly placed in the last tier.

Once upon a time, computer scientists often had early backgrounds in natural sciences, before shifting to CMPSC, especially when there were few undergraduate CMPSC degree programs.

This is less true these days, and people so inclined can get through CMPSC degrees with less physics, math, and statistics than one would expect.

Many computer scientists would fit B3 background, K2-K3 level of knowledge on that chart I linked earlier.

On that scale, I only rate myself a K4, which corresponds roughly to Robert's Tier 5. Many CMPSC PhDs would rate no higher than K2 (or even K1, I'm afraid, on climate science).

Of course John is one who has been spending serious effort at learning the science, so although our shortcut puts him on a low tier in this area (he's high for computer science!), the earned knowledge is higher. Best, of course, is to work from the actual knowledge of the individual. On the other hand, presented a list of 60 speakers at a meeting, and seeing few from fields in the upper levels (applicable to the topic at hand), it's not a bad bet that the meeting isn't really about the science (or whatever expertise is involved).

If we're talking specifically about climate modellers, we're talking about people who use computers a lot, and make the computers run for very long periods. So, does that mean that all climate modellers are experts about computers the way that computer scientists are? Absolutely not. Again, different matters. Some climate modellers, particularly those from the early days, are quite knowledgeable about gruesome details of computer science. But, as with computer scientists and climate models, that's not the way to bet.

I'll link again to John's K-scale. A computer scientist spends most time learning about computer science. At low levels, this means things like learning programming languages, how to write simple algorithms, and the like. Move up, and a computer scientist will be learning how to write the programs that turn a program in to something the computer can actually work with (compilers), how to write the system that keeps the computer doing all the sorts of processing you want it to (operating systems), interesting (to computer scientists, at least :-) things about data structures, data bases, syntactic analysis (how to invent programming languages, among other things), abstract algorithms, and ... well probably quite a few more things. It's a long time since I was an undergraduate rooming with the teaching assistant for the operating systems class. Things have changed, I'm sure.

Anyhow, on that scale of computer science knowledge, I probably sit in the K2-K3 level. I use computers a lot. And, on the scale of things in my field, am pretty good with the computer science end of things. But, considered as matters of computer science, things like numerical weather prediction models, ice sheet models, ocean models, climate models, etc., are just not that involved. The inputs take predictable paths through the program (clouds don't get to change their mind about how they behave, unlike what happens when you're making the computer work hard by making it do multiple different taxing operations at the same time and do what you like to the programs as they run). Our programs are very demanding in terms of it takes a lot of processing to get through to the answer. But in the computer science sense, it's fairly simple stuff -- beat on nail with hammer a billion times; here's your hammer and there's the nail, go to it.

The climate science, figuring out how to design the hammer, what exactly the nail looks like, and whether it's a billion times or a trillion you have to whack on it -- that part is quite complex. So, same as you can do well in my fields with only K2-K3 levels of knowledge of computer science, computer scientists can do well in theirs with only K2-K3 knowledge of climate science (or mechanical engineering, or Thai, or Shakespeare, ...).

Again, what the most relevant expertise is depends on what question you're trying to answer or problem you're trying to solve. If you want to write a climate model, you should study a lot of climate science, and a bit of computer science. To write the whole modern model yourself, you'll want to study meteorology, oceanography, glaciology, thermodynamics, radiative transfer, fluid dynamics, turbulence, cloud physics, and at least a bit (these days) of hydrology, limnology, and a good slug of mathematics. On the computer science side, you need to learn how to write in a programming language. That's it. It would be nice to know more, as for all things. But the only thing required, from a computer science standpoint, is a programming language. No need for syntactic analysis, operating system design, or the rest of the list I gave above. Not for climate model building, that is. If you want to solve a different problem, they can be vital. (I include numerical analysis in mathematics -- the field predated the existence of electronic computers. Arguably so did computer science. But the modern field, as with modern climatology, is different than 100 years ago.)

What fields are relevant?

I've never met someone who knew everything. Certainly I've met some very bright people, and people who knew quite a lot. But nobody has known everything. Conversely, I'm a bright guy, and know a lot of stuff, but I've never met anybody who didn't know things that I didn't. That including an 8 year old who was pointing out to me how to identify some animal tracks (they'd talked about this in her science class recently).

People know best what they've studied the most is my rule of thumb. That's why I go to a medical doctor when I'm sick, but take the dogs to a veterinarian when they're sick. I call up a plumber when the water heater needs replacing, and take my car to an auto mechanic when it needs work. And not vice versa on any of them. It might be true that the auto mechanic is also a good plumber. But, odds are, the person who focused on learning plumbing is the better plumber.

None of this should be a surprise to anybody, yet it seems in practice that it is once we come to climate. Let's be a little more specific in that -- make it the question of whether and how much human activity is affecting climate. There are many other climate questions, but it's this one that attracts the attention, and lists of people on declarations and petitions. If you look only at the people who have professionally studied the matter and contributed to our knowledge of the matter, then the answer to the question is an overwhelming 'yes', and a less overwhelming but substantial 'about half the warming of the last 50 years'.

I've tried to set up a graphic (you folks who have actual skills in graphics are invited to submit improved versions!) of 'the way to bet'. The idea is to provide a loose relative guide as to which fields most commonly have people who you can have the greatest expectations that they have studied material relevant to the question of global warming and human contributions to it from a standpoint of the natural science of the climate system.

Climatology, naturally, is on the top tier -- many people in that field will have relevant background. Not all, remember. Some climatologists look no further than their own forest (microclimatology of forests -- how the conditions in the forest differ locally from the larger scale averages) or other small area, or small time scale. Still, many will be relevant.

Second tier, fewer of the people will be climate-relevant, but still many. Oceanography, meteorology, glaciology.

Third tier, most people will not be climate-relevant. But some have made their way, at least, from those fields over to studying climate. That includes areas like Geomorphology (study of the shape of the surface of the earth) and quantum physics (the ones who come to climate were studying absorption of radiation).

Fourth tier, almost nobody is studying things relevant to the question I posed. The extremely rare exception does exist -- Judith Lean has come from astrophysics and done some good work (with David Rind, a more classically obvious climate scientist) regarding solar influence on climate. Milankovitch was an astronomer/mathematical analyst who developed an important theory of the ice ages.

Fifth tier, I don't think anybody has studied the question I posed directly. I do know a couple of nuclear physicists who have moved to climate-relevant studies. But they essentially started their careers over with some years of study to make the migration. In this, it's more a matter that they once were nuclear physicists. After some years of retraining, they finally were able to make contributions to weather and climate. At which point, really, they were meteorologists who happened to know surprisingly large amounts about nuclear physics.

Sixth tier, I wouldn't include at all except that they show up sometimes on the lists. My doctor is a good guy, bright, interested, and so on. But it takes a lot of work studying things other than climate to become a doctor, and more work after the degree is awarded to stay knowledgeable in that field. That doesn't leave a lot of time to become expert in some other highly unrelated field.

[Figure removed 14 September 2009 -- See Intro to Peer Review for details]


Suggestions of areas to add, or to move up or down, are welcome. I'm sure I have missed many fields and others are probably too high or low.

For now, though, if you're not an expert on climate yourself, I'll suggest that if the source is in the first two tiers, there's a fair chance that they've got some relevant background. If they're in the bottom 3, almost certainly not -- skip these. And the third level, is probably to skip but maybe pencil them in for later study, after you've developed more knowledge yourself from studying sources on the first two levels.

This ranking, of course, applies to the particular question asked. If the question is different, say "What are the medical effects of a warmer climate?", the pyramid would be quite different and MD's would be the top tier. Meteorology would move down one or two levels. Expertise exists only within some area. As I said, nobody knows everything.

Update:
frequent commenter jg has contributed the following graphic:


A general good change he's made is to split between general skills, that can transfer to studying climate, as well as what particular sorts of detailed skills or knowledge one might have. Almost everyone, for instance, involved in studying climate knows some statistics and mathematical analysis. Many fields also require such knowledge, so those would find it easier to move over to climate.

Different good change he made was to put the question directly into the graphic. This is important. As I said, but didn't illustrate, the priority list depends on exactly what question is at hand.

Life-saving science

Phil Plait, over at Bad Astronomy has a short note about a friend whose life was saved by science. (Ok, the technologies that scientific discovery enabled.)

My own story goes back quite a few years, to my childhood. I had a severe case of pneumonia. Two major contributors to the fact that I'm still around to blog at you were penicillin and the oxygen tent. Penicillin's story starts in 1928 with Alexander Fleming. He wasn't looking for it at the time. And it took another 12-15 years before years before other people, including Howard Florey and Earnest Chain (recipients, with Fleming, of the 1945 Nobel prize in Medicine for this work), were able to make enough penicillin for it to be used clinically in any significant amount.

Oxygen, on the other hand, owes its availability not to accidental discovery (plus over a decade of hard work), but to thermodynamics. James Dewar took an interest in liquifying gases, and in general, trying to reach absolute zero. Along the way, he invented the Dewar flask (which the Thermos company started selling, hence the name you probably know). I'm fairly confident he wasn't thinking about medicine. He was doing some interesting science, and learning more about how the universe worked. (Liquid oxygen is magnetic -- he was the first person to know this.) He also invented a way to produce industrial quantities of liquid oxygen. Once you've got that around, you have a chance to discover that, gee, it's a useful treatment to give high concentrations of oxygen to people whose lungs are seriously impaired -- like, say, me with my major pneumonia.

Two different technologies, one discovered by accident, and one not thought of for health. So it goes in science quite often. The ultimate uses can be unpredictable. But it's an awfully good bet that there will be a use down the road.

Unity of science and Turkey Vultures

One of the features that makes science fun to do, and to spectate at, is that the pieces of knowledge all fit together. There's this very large interlocking structure of knowledge that we each try to build more on to. If we're luckier, we find an area where the pieces don't fit together so well. Or, if we're very lucky, get to break open a large chunk and replace it with another of our own construction.

My recent personal illustration is the paper "Movement Ecology of Migration in Turkey Vultures" (link here, full citation and abstract below). The way I know about it is that it cites a paper I was involved in. Now, I know just shy of nothing about turkey vultures (though we seem to have a lot of them around here, that's about my extent of knowledge). Nor do I know about ecology to be publishing on that. So how do ecologists studying turkey vultures come to cite something I've done professionally? Turns out, they were relating the migration of turkey vultures to meteorological conditions. Now meteorology, I know something about, and contributed to a large project that they used to understand their turkey vultures.

The authors merely using the results of our work doesn't make so much for the picture of interlocking pieces. If all they do or can do is take our results, then, effectively, they're just accepting whatever shape piece we meteorologists hand them. But, in principle at least, this is not what they're doing. The authors look in to the migration patterns of the turkey vultures, and its dependance on meteorological conditions. If the analysis were wrong in some way that affected the birds, their data would be showing this. If the analysis winds were too calm (apparently the vultures like a turbulent atmosphere) in the analysis, then the tracking data would show vultures in places the analysis said they shouldn't be.

Here's the real fun. The ecologists would then send a note to the meteorologists who did the analysis and say that there was a problem. The meteorologists' data didn't fit the vulture data, so we have to talk and figure out where the problem was. Maybe it's the vulture data, maybe it's the meteorological analysis, maybe it's both. But the pieces have to fit together. In practice, we didn't get that call (I don't think, have to check with our lead author for certainty). Their results fit with our work. So we have a little bit more confidence that we got things right in ours -- and so do any other users of our work.


Movement ecology of migration in turkey vultures
Mandel, J. T.; Bildstein, K. L.; Bohrer, G.; Winkler, D. W.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 105 (49): 19102-19107 DEC 9 2008

Abstract:
We develop individual-based movement ecology models (MEM) to explore turkey vulture (Cathartes aura) migration decisions at both hourly and daily scales. Vulture movements in 10 migration events were recorded with satellite-reporting GPS sensors, and flight behavior was observed visually, aided by on-the-ground VHF radio-tracking. We used the North American Regional Reanalysis dataset to obtain values for wind speed, turbulent kinetic energy (TKE), and cloud height and used a digital elevation model for a measure of terrain ruggedness. A turkey vulture fitted with a heart-rate logger during 124 h of flight during 38 contiguous days showed only a small increase in mean heart rate as distance traveled per day increased, which suggests that, unlike flapping, soaring flight does not lead to greatly increased metabolic costs. Data from 10 migrations for 724 hourly segments and 152 daily segments showed that vultures depended heavily upon high levels of TKE in the atmospheric boundary layer to increase flight distances and maintain preferred bearings at both hourly and daily scales. We suggest how the MEM can be extended to other spatial and temporal scales of avian migration. Our success in relating model-derived atmospheric variables to migration indicates the potential of using regional reanalysis data, as here, and potentially other regional, higher-resolution, atmospheric models in predicting changing movement patterns of soaring birds under various scenarios of climate and land use change.

Reprint Address:
Mandel, JT, Cornell Univ, Dept Ecol & Evolutionary Biol, Corson Hall, Ithaca, NY 14853 USA.

Research Institution addresses:
[Mandel, J. T.; Winkler, D. W.] Cornell Univ, Dept Ecol & Evolutionary Biol, Ithaca, NY 14853 USA; [Mandel, J. T.; Bildstein, K. L.] Acopian Ctr Conservat Learning, Orwigsburg, PA 17961 USA; [Bohrer, G.] Ohio State Univ, Dept Civil & Environm Engn & Geodet Sci, Columbus, OH 43210 USA

National Academies Survey

The National Academies (US, for science and for engineering) are holding a survey to see what it is that people are interested in hearing about in science and engineering. The choices are limited, but that makes for an easy poll to answer. Less than 2 minutes for me. Maybe 30 seconds.

http://www.surveygizmo.com/s/75757/what-matters-most-to-you-iv

Science not politics

Continuing with Dave's comments about climate science:

Secondly you have a topic that's polarized many different groups, and many an individual will merely argue a point because a group they might relate to is arguing the same point.

Many people have vested interests relating to climate change and thoughts about what, if anything, to do about it. That does produce politics, in that groups of people with interests act politically.

But the science is the science, and respects no party, no nation, no religion, etc.

This does make for the problem that groups with interests other than explaining and discussing the best science also establish web sites, write editorials, produce shows, etc. to propagandize their views, distorting and lying about the science along the way. So if you're interested in the science, you have to work harder to find it than in something which doesn't scare people. You also have to work harder to disentangle the parts of an article that are science from those which are opinion, wishful thinking, and such.

One thing which I think is helpful in deciding about sources is to, first, hold your nose about their political viewpoints. This can be hard when the politics are greatly different from yours, but bear with it. As you read through, look for scientific claims, or claims which the author thinks are scientific. As you find them, go hit the literature on the topic and see if the author has represented the point correctly. It may sound like a lot of work, but in practice, most web sites which are more concerned about their politics than the science display this fairly quickly by lies and distortions, and some are at an extremely basic level. Basic enough that you can check the truth of it by looking at a textbook from 30 years ago (before the topic was getting nearly as much press, but well after the scientific basics were understood). If not an outright lie, very often what you'll see is a quote selected from a scientific article and removed from its context. Once you find the context, you see that the original author's intent was quite different than the bit quoted.

As you proceed with this, you'll find some sites are very prone to distortion or lying, and some are, if not doing it, then at least only doing it on such a subtle level that you can't detect it. Eliminating the most egregious (and those who quote the same lines -- there's very little originality, so learning a few facts suffices to eliminate a lot of the bad sites) narrows the field greatly, and productively. The difficult part is where you have to eliminate sites whose politics you generally like. On the other hand, if you want your car fixed, you go to your mechanic, not to your representative (aside from the times they're the same person :-). Scientists working in the area are better sources than political groups anyhow.

After you start to understand the science, you have separate questions. (for all 'you', including me). I don't think the responses are dictated by the science. Once you understand the science and the probable outcomes, now you have to ask the questions of what is morally appropriate, what is politically appropriate, what is economically appropriate, and so on, by way of responses (including 'do nothing'). Different people, even if agreed on the science, will (and should) disagree here. But, if the discussion were to be based on our best understanding of the science, I think we'd be far better off.

Math, Science, and Engineering, oh my

Since I've studied all three, and appreciate all three, it always surprises me when people try to claim that they're not different.  Usually this is a math or engineering person trying to say their area is science.  This is, well, just wrong, I think.

One area of confusion is that each is a matter of what you're doing at a given time.  One can easily have a job which carries one label, but be doing on of the other things.  My job title, for instance, says that I'm a scientist, but much of what I actually do is what I consider engineering.  No problem to my psyche as I respect engineering.  The only question then is whether I can do good engineering.  Conversely, some with a job title of engineer may at times be doing science.  What matters to me is not the title, but what one does.

The main divider between the three, as I see it, is how do you decide that you're right (or, have done good math, science, or engineering).  In mathematics, you work within a rigorous framework and prove that you are correct (that a theorem you or somebody else proposed is either true or false).  You might have made an error in your proof, and the decision on that is made by other mathematicians.

Science and engineering both appeal to nature, rather than scientists and engineers.  The scientists compare the predictions of their theory to the observations.  If they're close enough, then the theory is considered good enough.  It isn't proven.  Next year we might have more and better observations, and the theory might not agree well enough any more.  A different side is, scientists can think of highly idealized systems (frictionless surfaces, massless strings, point masses, ...) which don't actually exist in nature.  If the predictions are close enough, however, then the science is good.

Engineering, does appeal to nature (did the bridge hold up?), but has very limited degrees to which it can simplify its systems (real bridge beams vary in their properties, while ideal ones don't, and the difference can crash the bridge).  The more significant distinction from science goes back to the matter of how you decide you've done good engineering.  Engineering, I take it, is the application of a knowledge of science to achieve a useful end.  But who decides useful?   Somebody, somewhere, writing the checks to support the project.  It would be very easy to 

build a several mile long bridge, for instance, if your budget were unlimited and so were the time for construction.  But for real engineering, you have serious limits on time and money.  How do you build a bridge fast enough and cheap enough, while still carrying the required load?  That's an engineering challenge.