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

Bad philosophy 1

Different people are good at different things, which is no real surprise; but one of the common situations where some people suddenly become blind to this is scientists regarding philosophy.  Plus, well, most non-philosophers regarding philosophy.  I've had the good fortune to know a couple of serious philosophers of science, enough to appreciate that they've developed some understandings more profoundly than I have.  And, I'm immodest enough to extend that to 'more profoundly than most non-philosophers'.

One path of bad philosophy, the one which causes this post, follows from mistakes on the matter of certainty.  Or, naming it by way of the error it leads to, intellectual nihilism.  Certainty is a problematic concept for science, and science versus philosophy.  Errors come from both sides, so beware of throwing rocks.  From my philosophical vantage point, science is intrinsically uncertain.  My scientific excuse for that philosophical assumption is to consider the Uncertainty Principle.  It's enough for here to understand that you cannot, simultaneously, observe everything about a complex system (like an electron, an atom, or the climate system) exactly.  You can do pretty well, but there's always some uncertainty in the observations.

A different line of philosophy regards how and how well you can consider yourself to know something (epistomology).  One view of this derives from Karl Popper, under the label 'falsification'.  For here, it's enough to note that one can really only be confident about your knowledge to the extent to which you've tested it.  (Do, of course read further!)  Since you can only be confident about your knowledge to the degree to which you've tested the idea/hypothesis/theory/..., and any test of an idea (etc.) is intrinsically uncertain (uncertainty principle again), you can never be entirely certain that you have the right answer, idea, hypothesis, theory.  So some humility is in order -- for everybody.

Enter the bad philosophy.

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Chandler and the Chandler Wobble

The fact that the earth wobbles was expected/predicted long before it was observed, which makes for a couple stories about the nature of science and the people who do it.  The story of the Chandler wobble starts up almost a century before Chandler was born.  In 1765, the Swiss mathematician Leonhard Euler, whose name appears throughout mathematics and physics, was examining the mathematics for conservation of angular momentum on a rotating spheroid -- i.e., something like the earth.  It turned out that such a body could have a wobble on top of its regular rotation.  Given what was known about the shape of the earth then, Euler predicted a period for his wobble of about 305 days.  (Modern information doesn't change this number much.)

The magnitude of such a wobble could also be estimated from the law of conservation of angular momentum, and was somewhere around 0.1 seconds of arc.  That made for a difficult observation in 1765, and it wasn't until 1841 that astronomers started trying to observe this 10 month (305 day) wobble.  Given data analysis methods of the day -- pencil and paper -- analyses were done looking to see if there was a signal with a 10 month period.  No such signal was found, even though several people looked.  Side note being that one of the astronomers who looked in to this was Friedrich Bessel, who was the first person to measure the parallax (thence distance) to a star.  Quality of observing skill was not an issue in his failure to detect the wobble. 

Enter, then, in the late 1880s the next start of our story, and some options of how to tell it.  I could tell the story about a 'lone genius, toiling in obscurity until his great moment'.  It would be doing some fair amount of violence to reality, but that hasn't stopped all story tellers.  Chandler was indeed not being paid to do science.  He made his living as a merchant.  But keep in mind, though, that in the 1800s, 'scientist' even as a label was fairly new, and very few people who were scientists, or rather, who were doing science, did it full time.  Many were men of independent means who used their free time to try to understand the world.

Chandler was one such, but his roots in astronomy extended far back -- to high school.

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Multiple Working Hypotheses

In exploring Arctic ice minima I was not so much trying to reach conclusions as to find hypotheses for further testing and exploration.  Let's pick up the hypotheses side now, as I think it gets much too little attention in science education and science student practice.  In saying that, I'm projecting my bias, of course.

Part of that bias comes from having read and agreed with T. C. Chamberlin's Method of Multiple Hypotheses (1890).  Or at least liked my take on it.  It also has some correspondence to John Stuart Mill's ideas in On Liberty about a marketplace of ideas (1859), which I also liked.  The crux is, if we consider only one idea/hypothesis we are liable to be overly protective of it, or overly hostile to it.  Either way, we do not arrive at the best hypothesis for continued work.  Chances of us having started by selecting the best of all possible hypotheses, out of the infinity which could be generated, are essentially zero.

So, instead of starting with:

• Observe
• Make a hypothesis about those observations
• Make a prediction from that hypothesis
• Run an experiment to test the hypothesis

We try something more like:

• Observe
• Make multiple hypotheses that explain the observations
• Examine the hypotheses for how/where/when they lead to different predictions
• Run an experiment to distinguish between stronger and weaker hypotheses

A different take, or at least a different discussion, of the method of multiple working hypotheses is by L. Bruce Railsback

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How many links does it take?

How many links does it take to go from one part of science to another?  To be a little more concrete, how many steps do you have to take to get from a paper on exercise physiology to a paper on black holes?

This was the question my son and I discussed some Sunday night.  It arose because I'd suggested PubMed as a good place for him to get information about exercise (what's good, or not, for you).  PubMet is a great resource.  At least the abstract of every paper (within some range of biology) is available there.  If you want to know how much protein is too much, and just why that's too much (my last use of it), they've got the research.  Now, PubMed works great for me.  I go in, find what I'm looking for, and get out.

My son, however, has the problem that I do with research in my field.  Namely, in reading the first paper on a topic, he sees how it references several others that are also very interesting.  So read one, find 3 more that have to be read.  (I'm being conservative here.)  Read those three, and each shows you three more that are also very interesting.  So now we have nine to read.  And so on. 

He mentioned that he could start out reading about exercise physiology and wind up with a paper on black holes.  I agreed (he is my son after all) and started wondering about how many steps it would take.  The only thing which keeps me from having the same problem is that I reserve this inclination for my professional field.  But I do approach satisfying it there.  (Eventually, namely after the first couple thousand papers I read, the interesting papers I found from reading one paper were papers I'd already read.)

My guess is maybe 20 steps between exercise physiology and black holes.  I know that it's only 1 step between turkey vultures and sea ice.  Keep in mind, turkey vultures are not polar creatures, and do not like it to be especially cold.  You don't find them closer to the Arctic than southern Canada.  But I was involved in a project, which definitely did need knowledge of sea ice, and that project was then used by people studying turkey vultures.  This is part of what I call the range and unity of science.  I also know, though never wrote it up for the blog, that it's only 1 step between trying to observe gravitational waves (LIGO) and predicting waves on the ocean.  My source being one of the LIGO people asking for information about the ocean's waves.

Might be only two steps between exercise physiology and black holes.  1) Exercise physiology paper looking at swimming or kayaking in the ocean, and how waves affect that. 2) waves and LIGO (I'm sure some LIGO paper cites both waves and black holes at this point).

Since I've put forward two unlikely connections, each only 1 step, I'll turn the table over to you all.  Can you make a connection -- in the professional literature, no fair using something like 'Guide to all science' -- between exercise physiology and black holes?  How short a chain can you make it?  Feel free to change the targets to other things you're interested in (kumquats and functional MRI imaging of the brain?).

Science Fair Participants

First: Congratulations to Elliott Rebello, winning his category in the Eleanor Roosevelt HS Science Fair.  (The reason I single him out -- he's my intern.  Be sure, though: the work he presented was his.  And it was his presentation and understanding that earned him his place.  Yay Elliott!)

Having judged another year's science fair at ERHS, I'll share some thoughts for participants.  I'm a little emboldened that maybe I know something since Elliott did well.  On the other hand, maybe he did well in spite of me.  Use your own judgement on what ideas to make use of, and how to make use of them.

One note: I never did very well in science fairs when I was growing up.  You don't have to do well in science fairs to do well in science, even more true than you don't have to be good at math to do well in science.  One failing in most of my projects: I was setting about learning what was already known, rather than striking out my own path.  This is an excellent way to learn more, but not to get science fair points.

My base suggestion for any age: try to learn more about the universe, know what you did and why you did it.  Maybe there are points in it, maybe not.  But you'll definitely learn something, which is always good.

For science fairs, the major categories on the official judge's score sheet are: 'Scientific Thought', 'Creative Ability', 'Thoroughness/Clarity', and 'Exhibit Presentation'.  They have some connection to usual professional proposal or paper review criteria (except, mostly, for exhibit presentation).
But we all, and it's interesting that it's all of us given that we come from different backgrounds, even judges in my rather small niche, think differently than this.  We start more like journalists:

• What did you do?
• Why did you do it?
• Why did you do it this way?
• What did you learn?
• How would you do it differently?  (given what you've learned)

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Science Fair Judges

I'll write about and to science fair judges before a note to the students.  A joke I made today got its due chuckle, but there's a real point to it.  I observed of judges that "We're very scary people."

Now, we know ourselves, and scientists in general are not scary people at all.  Even more so, if anything, those of us who do science fair judging.  We tend to be parents with school age kids ourselves, or at least not too long since we were (and, in my case, I'm still an uncle to kids this age).  And to like talking with kids and have a certain degree of understanding of (in today's case) 14-18 year olds. 

On the other hand, I can recall ages back, when I was a 26 year old finishing his PhD and presenting at an international scientific meeting.  Only about 200 people in the room (on the other hand: 200 people in the room!).  And I was 26 and nearly done with a PhD, not a 14-18 year old in perhaps my first talk with a scientist.  But I was seriously nervous, before, during, and after.  Most of that was unnecessary, as, again, scientists aren't actually a very scary bunch.  (It did work out in my accidental favor, more in a moment.) 

It was a great relief to survive the talk (nobody threw anything!  er, ok, that didn't happen to anyone, and I'd never seen it happen before.  But ... I was nervous).  And it was thrilling when, unforced, one of the 'Big Name in Field' people present said they'd liked my presentation.

I try to pass this along (not the big name in field aspect, which I'm not, but at least a good word somewhere).  And try to de-scarify for the students I talk to about their work.  We're still pretty scary to the students.  But I enjoyed my chats with students, and hope they came away with a bit more understanding of doing science.

The 'more in a moment':  The later postscript on my presentation was about my nerves.  Back then, when I was nervous, I spoke slower.  Opposite of most people, but it worked in my favor.  The thing was, at an international meeting, many people (in this case, about 2/3rds) are not native English speakers.  A speed that a nervous native is capable of racing through can be all but impossible for a non-native to follow.  Since I slowed down, I was more understandable to the group.  Several folks thanked me for my consideration.  They didn't know it was terror :-)

Which way is up?

Simple questions sometimes have subtle answers.  Of course, some answers are also pretty simple.  Which way is up starts out simple and then gets pretty subtle. (Note on scientist-speak: subtle = complicated and/or difficult).  This winds up being related to What is a day? as we get a little more complex.  But, while we can, let's go with simple.  Up is the opposite of down.  Slightly less simple, down is the direction a ball falls.

Even less simple: hang a weight on a string.  Hold it still.  This is difficult, so maybe hang it from a nail or off a board.  There's probably still a little swinging back and forth.  So either wait (it'll come to a halt eventually, but who says scientists are always patient?!) or get a large (larger than your weight) cup or bucket of water and bring that up underneath the weight.  Make sure the weight is made of something that doesn't float if you use this approach!  Once the weight comes to a halt, the string gives you a line which points up and down.  The weight is the 'down' side of the line.

By the way -- not only do you not have to be good at math to be good at science, you also don't have to be good at drawing. For me, this is pretty good artwork. Some people are great at drawing, same as some are great at math. Some of us, well, you see my caliber of artwork.

 Now for getting subtle ... which also explains why the earth isn't exactly a sphere.

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Scientist mutual criticism

I've been active on twitter lately (@rgrumbine).  The 140 character limit poses the problems to me that regular readers would expect.  140 words is pretty short for me.  Still, there are some good things out there (I'll be posting a raft of links from my twitter feeds Real Soon Now).  And sometimes a short comment is sufficient, but reminds me of things worth more than 140 characters.

One short comment, bizarre to me, was that scientists don't criticize each other's work.  On one narrow aspect, there's some truth to this.  That aspect being that, for example, pretty much all the people studying sea ice think that sea ice is something worth studying.  Within any given niche of science, occupants of the niche think it's important.  The thing is, each niche is very, very, small.  Occupants of every other niche are more than happy to tell the sea ice people that sea ice isn't nearly as important as their own niche.  At length and volume.  Of course the sea ice people argue back.  And so it goes.  Every multidisciplinary meeting I'm at, this is routine conversation.  Partly it's just a game.  Partly it can lead to something interesting -- say when the sea ice person (finally :-) persuades the ... let's say boundary layer theorist ... that there really is something interesting -- to a boundary layer theorist -- about sea ice.  It's for this latter prospect that I play the game (sometimes being the persuaded rather than persuader).

Yet, even within a relatively small niche like sea ice, there are sub-niches, and sub-sub-niches.  Each of these divisions, even while agreeing that sea ice is important and important to study, is in disagreement about the how, why, what about studying sea ice.  One has in any natural science a certain amount of division between observation/modeling/theory.  The observers think what's really needed is more and better observations, modellers think you need bigger and better models, theorists think we need better theories.  All are right, to some degree.  All are wrong, to some other degree.  But one thing this guarantees is that the sub-niches are ready to criticize each other.  And do so.

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Peer review and Wagner Resignation over Spencer and Braswell

"It's peer review, not God review"    My wife's comment about peer review seems particularly apt for the current tempest regarding the resignation of Wolfgang Wagner from his post as editor in chief of the journal Remote Sensing.  It regards a paper I mentioned in July, and related to the one that prompted Barry Bickmore to suggest "Just Put the Model Down, Roy". 

I won't be taking the usual line of consideration here (surprise!).  Rather, let's go back to talking about peer review.  As my wife said, it is not God review -- reviewers and editors are human, and therefore make mistakes.  At the end of peer review, therefore, you don't have gospel, you have something that has a fairly good chance of being worth your time to read.  To rephrase Wagner, papers that pass peer review should at least not contain fundamental errors of method or false claims.  And it now seems likely to him that this paper (Spencer and Braswell) may well not pass that standard.

Richard Feynman's comment about fooling yourself is commonly quoted:
We've learned from experience that the truth will come out. Other experimenters will repeat your experiment and find out whether you were wrong or right. Nature's phenomena will agree or they'll disagree with your theory. And, although you may gain some temporary fame and excitement, you will not gain a good reputation as a scientist if you haven't tried to be very careful in this kind of work. And it's this type of integrity, this kind of care not to fool yourself, that is missing to a large extent in much of the research in cargo cult science. "Cargo Cult Science", adapted from a commencement address given at Caltech (1974)

This underlies some parts, I think, of the failure in Spencer and Braswell's work, and the subsequent failure in the review and editorial process.  Namely, it is assumed by reviewers and editors that authors have already done some work doubting themselves and checking to see how it is they might have fooled themselves -- and to take action against such possibilities.  Further, the review process is based on the presumption that your purpose in publishing is to advance our understanding of science.

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Is it really normal?

Is my previous post about finding a climate normal really ok, or fatally flawed for statistical reasons?  This wasn't how I was planning on doing so, but it provides a good chance to discuss some ideas about doing science and statistical versus physical significance.

In my note, I took a look to see if it was possible to find a period in which climate (as defined by the HadCRU temperatures) behaved in a way that we think of climate as doing -- some warming, some cooling, and totalling to no real change.  That put me in mind of a cumulative sum, and the result was that it was indeed possible to find such a period -- 1850-1940.  It could well have been that it was not possible to find such a period, or at least not one long enough to be interesting for climate.  That would have told us that our notion of climate was not something that the climate system respected -- time to learn more about climate and update our thinking. 

A second part of that post was my conclusion that something changed around 1940.  This is a statistical conclusion, made by eyeball inspection.  Quite a hazardous thing to do and Tamino shows ample reason to be leery of that conclusion, given the statistical nature of cumulative sums.  On the other hand, the deviations he shows from his noise simulation reach only about 50, versus the 300+ of mine using real data. 

So where are we? 

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Hiatus

Granted there's something odd about writing two posts the day you are mentioning that you're on blog hiatus.  Still, a bit of updating here in promise of things to come.

I haven't been napping the entire time that I've been silent here.  Quite the opposite, really.  One part being the running that I just posted.  Another, and more interesting to the usual content of this blog is that I've been doing some science at home.  Even some at work as well.  The thing is, it takes enough concentration and concerted effort to do something novel at home, after a full day at work, that I don't get over to the blog.

The good news is that, knock wood, I'll be submitting an interesting paper that I've been working on at home for professional publication shortly.  There are at least 3 stories involved in it.  Unlike most scientific work I've done or been involved with, this piece has a well-defined starting point and history.  The three will be the origin of the idea, carrying out the research at home, and what happened in/with the submission process.  Note that I'm talking about submitting the paper; whether it gets published there or elsewhere, and what happens afterwards, will be a different story still.

A couple of interesting-to-here notes are also getting started at work.  We'll see what happens with them.  In both cases, the 'what is climate?' question is major. 

The Invention of Air

It isn't often that I wind up able to talk about a book, science, a scientist, and my genealogy in the same post, but Steven Johnson's The Invention of Air manages that feat.

The book is a pleasure to read.  Johnson's linchpin is Joseph Priestley's life and science.  I'd always thought of him as an English scientist, which turns out to be only partly true.  He finished his life in the USA, corresponding particularly with Thomas Jefferson both in revolutionary and post revolutionary days.  The Jefferson connection (and before that, Franklin) make for some interesting reading and historical insight outside of science as well as inside.

In his writing on Priestley's science, Johnson captures some of my themes about scientists being people, having lives, and those having some influence on what work they do and how they do it.   Also nice to see was that Johnson did not take the oversimple telling of 'good guy / bad guy' for Priestley's advancing the phlogiston theory and holding on to it longer than most.

To back up, as not everybody already knows, Joseph Priestley was one of the major chemists of the 1700s, most known perhaps for 'discovering' oxygen, but also (and Johnson makes a good case that this was the more significant) that plants release oxygen and consume carbon dioxide.  His approach to his research, though, was not the stereotypical one step leading to the next with some ultimate conclusion drawing ever closer.  It was more the 'try many things and see a) what happens or b) what works'.  And he then was active in describing how it is he did his experiments, as often the method itself was the important aspect of the work.

If you know a young scientist, I'll suggest you get this for them as well and not just yourself.

The genealogy I'll put below the fold.  For here, it suffices that I'm not a descendant of Priestley's.

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Wrestling with data

I'll suggest those who haven't been, join me in keeping an eye on a series of posts that Ron Broberg is doing over at The Whiteboard.  As befits a whiteboard, he's showing a lot of the details that get cleaned out of most final publications, even on blogs.  The topic at hand is looking at the temperature records since 1880 and testing ideas on fitting curves to the data.  The series is now to #9 and it's apparent that there will be several more:


http://rhinohide.wordpress.com/2011/01/07/lines-sines-and-curve-fitting-1-oh-my/ (Starts out more on the issue of testing ideas on what we can conclude about temperature trends)
http://rhinohide.wordpress.com/2011/01/08/lines-sines-and-curve-fitting-2-r/ (try fitting the sine and then a line)
http://rhinohide.wordpress.com/2011/01/09/lines-sines-and-curve-fitting-3-double-down/ (Try fitting 2 sine waves)
http://rhinohide.wordpress.com/2011/01/10/lines-sines-and-curve-fittings-4-walk-and-chew-gum/ (Simultaneous line and sine fit.)
http://rhinohide.wordpress.com/2011/01/12/lines-sines-and-curve-fitting-5-a-growth/ (Trying an exponential curve)

http://rhinohide.wordpress.com/2011/01/14/lines-sines-and-curve-fitting-6-backcast-and-forecast/
http://rhinohide.wordpress.com/2011/01/15/lines-sines-and-curve-fitting-7-normal/  (Testing Normality 1)
http://rhinohide.wordpress.com/2011/01/16/lines-sines-and-curve-fitting-8-dagostino/ (Testing Normality 2)
http://rhinohide.wordpress.com/2011/01/17/lines-sines-and-curve-fitting-9-girma/

A sine is a standard oscillation.  It would be a pure tone (rather flute-like) in music.  For a bit more about oscillations and data series, and the language of time series analysis, take a look at my Introduction to Time Series Analysis.

Was Easterbrook talking science?

A friend suggested that I take up this article by Don Easterbrook, on comparing 'present' temperatures to those of the past 10,000 years or so.  The article is severely flawed, as has been discussed at Hot Topic and In it For the Gold.  Since that set of flaws has already been discussed at some length, I'll look to different issues with it.  (I'll add that you can check out a couple comments I made over at Hot Topic.)

The most fundamental issue, I think, is one I've posted about previously -- But is it science?.  There's a strongly related side trip to cherry picking.  Good science doesn't engage in cherry picking, so that becomes related to questions about whether we're reading a science article or something aimed at politics or other.

That fundamental issue shows up with the title: 2010 -- where does it fit in the warmest year list?  That isn't really even a question for or of science.

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Verifying forecasts 1

I already discussed my earlier sea ice estimates and how they came out, but a few things have happened since then to occasion a two part look at forecast verification.  As usual, it's prompted by seeing someone do it wrong.

One of the errors, which I have to remedy on my own part, is that you should verify (compare to reality) all your forecasts.  I think that the end of May ice estimates are the most interesting and important, rather than later in the year.  Partly this is because of how I think the sea ice pack behaves.  Partly it is because the practical uses of sea ice information I know of require that kind of lead time.  It takes a long time to get a tanker up to Barrow from Seattle, for instance.

Xingren and I did submit a later estimate, for the August Sea ice outlook.  That estimated 4.60 million km^2 for the September average sea ice cover.  An excellent approximation to the NSIDC's reported minimum (4.60) but not as good compared to the observed average extent of 4.90.  Actually a touch worse than our May (30th, even though not reported by SEARCH until June) estimate of 5.13 from the model.  Both estimates were well within 1 standard deviation of the natural variability (errors of +0.23 and -0.30 for May and August's predictions, respectively, versus about 0.5 for the natural variability).  So, on the whole, pretty reasonable.  Just that we'd have expected better from the later estimate.   But ... there's more to that story ...

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Sea Ice Predictions vs Reality

Ok, I didn't jump on the end of the ice season.  But, the good thing about doing science is that being right or wrong, or learning from your mistakes (or learning from your right answers, even if that's harder to do) is not a matter of a 'news cycle' or what is currently 'hot' in the blogosphere.

The observed ice extent for September 2010, monthly average, from the National Snow and Ice Data Center was 4.90 million km^2.  One thing about making your predictions and deciding how well you did is that you also have do be specific about what you're going to compare against.  You'll find somewhat different figures if you look at other places. 

If you were dishonest, or just not careful, you might select whichever observation was closest to your  prediction.  The problem with that is that it then becomes easy to claim an accurate prediction -- with little regard for the quality of the prediction itself.  Just select the most favorable observation, or process the data yourself in your own way.  (By changing how you do your land masking, you can change your ice areas or extents by upwards of 1 million square km.  ... he said with no tinge of annoying experience.)

It turns out that my May predictions did pretty well. 

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Knight anoles and science writing

What Are Knight Anoles?

By: Kristen Martinet
December 15, 2008
Liberty Middle School
Science/ Period 2

Abstract
Knight anoles are very interesting lizards. They are the largest anoles in the world and have very distinct features such as their speckled backs and striped sides. These reptiles are an invasive species in Florida and originate from Cuba. People like to keep knight anoles as pets, but then release them into the wild without knowing the consequences for the lizard. This makes them more abundant in urban areas. They eat insects and other lizards in the wild and in captivity. When fighting off a predator, the lizard bluffs to scare it away. While fighting with other males, the anole bobs its head up and down and extends the dewlap to look tough.  In the summer, knight anoles breed to create at least eight new baby knight anoles in five-seven weeks.     Knight anoles (anolis equestris) are a very interesting species of lizard that are also called the Cuban anole. This reptile is part of the order squamata, the sub-order iguanidae, and the family polychroidae. The knight anole is part of the genus anolis, which has about 250 species (Crowther, 1999). A researcher from Centralpets.com stated that the common name “knight” is derived from the Latin species name “equestris” which is derived from “equester,” a Latin word for knight. The other common name, Cuban anole, is probably used because its first home is in Cuba.

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Unity of science and reaching decisons

The next two paragraphs were in a private email list where there was then a request that I make the comments public.  The situation was my response to another scientist, the topic at hand being the scope of the conspiracy that would be involved in pulling of a hoax that CO2 is a greenhouse gas, etc..  I also talked here a while back about the unity of science:

I think a crucial part of that error is a failure to understand how science works.  While you and I (and others) look at it and see masses of scientists from different areas and reach a conclusion, others don't.  The extra piece of knowledge we have is that science has to hang together as a coherent picture.  If climate people were seriously wrong about the radiative properties of CO2, then CO2 lasers would not work.  And so on through a very, very long list.  Conversely, if climate types were seriously wrong about CO2's radiative properties, laser specialists would look at the climate work and point to the errors and that'd be the end of the wrong climate CO2 work.

Instead, they take the view that science is story-telling.  Laser physicists go along with the climate people because the climate folks are telling a story that the laser folks like, not because there's any particular evidence in favor of it.  The "It's a liberal conspiracy", or "They only say this because they want to impose one world government" responses are part of this.  The he said -- she said journlistic line is exactly this, as the science is presented as two stories the reader is chosing between.  They think the scientists are doing the same thing.  (How would they know differently?)

Back to the present:
I'll also mention, in terms of how people could tell what scientists actually do, that John Wilkins is taking ideas on sources for describing how it is that scientists reach conclusions:
How Scientists Think: A Book Proposal
The Scientists Operating Manual
While I'm mentioning John, h/t also to this xkcd cartoon, which captures a certain crowd (an attitude I've occasionally borrowed at least part of) quite well:

Bad Astronomy: The Wonders of the Universe

Somewhat in the vein of asking about links that you-all think might be good to add to the blogroll (I'll get there, honest!), I'll mention a blog that I read and isn't on the blogroll.
One such is Phil Plait's Bad Astronomy. Not that he needs the advertising, but I do read and enjoy his blog for reasons relevant to my own aims here. Namely, he regularly has articles (I'll list a few below; apparently 'dozen' should follow the 'few') that illustrate my own feeling -- that the universe is a wonderful and interesting place, and doing science is a way to embrace that wonder.

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