20 January 2016

Earth-Sun distance and Chandler Wobble

Continuing from The Pacemaker of the Chandler Wobble, Grumbine 2014:

The Chandler Wobble (CW) is a small variation in the orientation of the earth’s rotational axis [Chandler, 1891]. It has a period near 433 days [Liao and Zhou, 2004] (0.8435cycles per year, 0.0023095 cycles per day). Some source of energy for the Chandler Wobble  must exist because it dies out on a time scale of decades [Munk and MacDonald, 1960] if energy is not continuingly added. Gross [2000] found that atmosphere-ocean forcing on the earth’s rotation, computed in an ocean general circulation model driven by observed  meteorological parameters, provided that forcing. [O’Connor et al., 2000] also found wind forcing of the ocean to drive the pole tide. This source was questioned [Wunsch, 2001] partly on the grounds that the ocean was displaying a very narrow band response, but there was no reason to believe that the forcing itself was narrow band.

I suggest that the atmosphere-ocean variability near the Chandler Wobble period, among others, is paced by variation in earth-sun distance. The earth-sun distance, in addition to annual and semi-annual variations due to the elliptical shape of the earth’s orbit, varies due to perturbations from the moon (29.53 day period and others), Venus (292, 584, 417, 1455, ... days), and Jupiter (399, 199, 439, 489, ... days). The size of these variations is small, the largest being the 29.53 day lunar synodic period (31*106 Astronomical Units), amounting to approximately 0.08 W/m2 on a plane perpendicular to the sun at the top of the atmosphere. See Table 1 for more precise periods and the amplitudes of distance variations corresponding to them.

Horizons [Giorgini et al., 1996] was used to provided 6-hourly earth-sun distance and osculating elements for 1 Jan 1962 00 UTC through 31 Dec 2008 18 UTC. Table 1 was derived by harmonic analysis of those data at precise frequencies to determine purely cyclic variations in the earth-sun distance. The leading terms are, of course, the annual and semi-annual cycles from the elliptical orbit. Following this, however, are perturbations in Earth-Sun distance due to the moon, Venus, and Jupiter. Note that the orbital elements are not precisely locked to the periods given. The osculating (instantaneous) orbital elements vary; the osculating year varies from 364 to 366 days, for instance [Giorgini et al., 1996]. Consequently, there are residuals near the annual period. But they are far smaller than the main line. The anomalistic year, 365.259635 days [Observatory and Observatory, 2001], is the period between successive perihelia. This has been found to be the appropriate period for climate temperature analysis rather than the tropical (vernal equinox to vernal equinox) year [Thomson, 1995]. As we will be drawing the conclusion that earth-sun distance is important, even for small variations, the anomalistic year is the self-consistent one to use here. 

Previous analyses of orbital variation at relatively high frequency (high compared to, e.g., Milankovitch periods [Milankovich, 1941]) have used annual average orbital parameters [Borisenkov et al., 1985; Loutre et al., 1992], precluding them from examining periods shorter than 2 years and aliasing some of the periods examined here. Also, those works were examining the earth’s tilt, rather than earth-sun distance. Gravitational torques have been examined previously as the main driver of the Chandler Wobble and rejected [Munk and MacDonald , 1960; Lambeck , 1980], which means only non-gravitational external forces, such as earth-sun distance, force Chandler Wobble at these periods, if any external sources do. 

19 January 2016

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

26 October 2015

Been a while hasn't it

Didn't mean to disappear quite that long, 2.5 months it turns out.  Well, I'll be picking up my posting again.  In the interim, I've been on vacation in the Peruvian Amazon, picture below, been a manager at work, and generally running around.

You probably think of piranha when thinking about the Amazon river.   We were on a fishing expedition for piranha.  My wife, the pilot, and our guide all caught piranha -- mostly red bellied, but a couple white bellied.  Above is my one catch.  It is a sardine (about 10", 25 cm).  I'm amused, or puzzled, or something.  I'm happy about it.  It's a reminder of the fact that the world is more involved and weirder than you might think.  And a reminder than if there's weirdness to be found, I'll be the one to find it.

Managing, well, I'll go back to a story from college.  A friend of mine was a highly talented computer science major who went from starting his bachelor's degree to finishing his master's in 4 years.  While we were room mates, he did a group project with the other two top students in the class.  If technical skill were the only issue in doing a technical project, this group would have done by far the best.  Instead, it was a mediocre project.  That's when he, and I by contact, developed an appreciation for good managers.  One of their skills is to get the best out of a group of people.  So that's my aim.

23 July 2015

Data Horrors

"The great tragedy of science -- the slaying of a beautiful hypothesis by an ugly fact."  Thomas H. Huxley.

Sometimes, though, you have to pay attention to just how ugly the observation (fact) is.  And even more to how ugly a collection of observations is.  Science fair project I judged a couple of years ago, the student mentioned his methods for keeping the experiment, which had to be untouched while going, out of reach of his young brother.  This student has a firm grasp of the ugliness of data and trying to collect it.  I gave him high marks.

I also mentioned a story or two I knew of data collection challenges.  I'll share them and some others here, and invite you to add your own.

One family of ocean data comes from buoys floating on top of the ocean.  A lot of the ocean is far from land, therefore far from perches for birds.  Sea gulls and other birds are often grateful for the lovely perches we're putting out for them.  Unfortunately, it does not help the accuracy of your wind speed measurements to have a bird sitting on your gauge.  Birds sitting on the solar panel reduce your energy available/recharge rate, and thence maybe lead to data outages while waiting for recharging. Guano is great for fertilizer, but wrecks havoc on the accuracy of your temperature, pressure, and moisture readings.

Walrus don't mind taking a rest every now and then either.  They're not normally a threat to wind speed measurement (which is at the top of the buoy).  But we also want to get wave measurements -- how high are they, how fast are they, what direction are they going.  Having a walrus or two on your buoy slows its ability to respond, and may suppress the peaks of the measured waves.

On land, your instrument enclosures (the Stevenson Screen for instance) provide a nice place for bees, wasps, small birds to nest.  Squirrels like to play with them too.  A beehive next to your thermometer does not help its accuracy.

Back at sea, I once got a call about a problem buoy.  It was reporting extremely high temperatures near noon because the paint had been stripped during a storm, and the now-bare metal was reflecting sunlight onto the marine thermometer.

That should get you started for remembering your own horror stories about data collection.

Recently saw someone on the web taking the line that if data wasn't perfect, you should throw out everything from that instrument or site.  Well, no.  If you did that, you'd never have any data to work with.  For my examples, you mostly just ignore the data during the period you've got a walrus infestation.  But there are other kinds of things which affect your observing, and which you might be able to compensate for.

08 June 2015

Spectating on Science: Length of the Game

Science doesn't move as fast as basketball, so spectators need to adjust their expectations.  The 'game' plays out over a period of years.  The first play of the game is that someone publishes their work in the peer reviewed professional literature.  But that's something like the first pass in football/basketball/hockey -- it might _eventually_ turn in to a score.  But it isn't the score itself.

The short-hand for this is 'single study syndrome'.  All sorts of things show up in the media, or scientific literature, as being interesting and perhaps revolutionary.  But almost no revolutions follow from the very first study.  Few of the potentially interesting ideas, from the first publication, really hold up for any length of time.  Something worked out to be interesting _once_.  But, chances are good it won't hold up in the long run -- the previous consensus or state of knowledge is more likely correct than the new idea with just a single supporting piece of research.

For the spectator of science, which also includes me most of the time, we can, and have to, sit back a little and wait for the confirming evidence or studies.  One area which is an active area of discussion in science now is whether the recent US weather extremes (Eastern US has been far to the cold end of the historical distribution in winters of 2014 and 2015, but the Western US has been far to the hot end -- including setting several all time records) is due to the reduced Arctic sea ice pack.

02 June 2015

How to build a climate model?

How is it that we go about building climate models?  One thing is, that we would like to build our model to represent everything that we know happens.  If we could actually do so -- mainly meaning if the computers were fast enough -- life would be simple.  As usual, life is not simple.

I'll take one feature as a poster child.  We know the laws of motion pretty well.  I could write them down pretty easily and with only a moderate amount more effort write a computer program to solve them.  These are the Navier-Stokes equations.  On one hand, they're surprisingly complex (from them comes dynamical chaos), but on the other, they're no problem -- we know how to write the computer programs to do conservation of momentum.  Ok entire books have been written on even a single portion of the problem.  Still, the books have already been written.

The problem is, if you want to run your climate model using what we know is a representation sufficient to capture everything we need to do, in order to represent everything we know is going on, you need to have your grid points only 1 millimeter apart.  That's ok, but it means something like 10^30 times as much computing power as the world's most powerful computer today. (A million trillion trillion times as much computing power.)

What do we do in the mean time?

01 June 2015

What is a model?

In the blogospheric talk about climate change 'model' gets mentioned a lot.  Sometimes it's merely descriptive, and often it is perjorative.  But it is mostly never really defined.  Like or loath them, nobody says just what models are.  Except for me, here and now.  (And probably a number of other people at other times and places -- but still, few and far between. :-)

'Obviously' a model is a particularly attractive human.  Right?  I've actually received email at my workplace (a 'modelling branch') from people who were trying to advance the careers of their models, in this sense of model.  We don't deal with that kind of model.

'Obviously' a model is to take the original (the Apollo Saturn V rocket that took people to the moon, for example) and duplicate everything about it, but at 1/32 the original size  Right?  Perhaps.  I know people tho like this sort of thing.  But again that's not what we mean either if we are discussing climate (or atmosphere, ocean, sea ice, land, glacier, ...) models.

For my purposes, a model is an idealized, and/or simplified, representation of the real world.  When we are interested in something as big and complex as climate, or even just the Arctic sea ice pack, we really can't cope with the whole thing in all of its glorious complexity.  We have to simplify the reality somehow.  That simplification is the model.

In this sense of 'model', models are everywhere.  We use a model for human behavior when we decide what somebody else means when they raise their hand in a certain way.  (is it open hand, or a fist?  did they just say 'hello', or 'I'm going to kill you'.  and so on)  Weather has also been modelled by using 'dishpans' -- Raymond Hide and David Fultz being two of the best examples of people taking this approach*.

22 May 2015

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.