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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Exploring Arctic Ice Minima

Every year 2007-2013 had a lower Arctic sea ice extent than every year before 2007.  2014 seems likely to continue this record.  I'll also suggest below that maybe the Arctic has entered a 'new normal', with September ice extents bouncing around 4.7 million km^2. 

For some data to work with further, I pulled the NSIDC September figures.  It's a small, simple text file, so you can check yourself what follows.  First up, let's draw a figure of what we're looking at -- but don't connect the observation dots.  Our eyes tend to be led to conclusions by the superposed lines.

You can check some of the sources for ice before 1979 and see that figures below 5.5 million km^2 are unprecedented in the longer records as well.  To have data precision and consistency, though, I'll stay with the 1979-present.

What else can we say from eyeballing the data?  Since the 1979 starting point:

• There have been 2 record highs (1980 and 1996)
• There have been 8 record lows (1984, 1985, 1990, 1995, 2002, 2005, 2007, 2012)
• There have been more record lows in the last 10 years (3) than record highs in the full 35 year record
• 1996 is about the last year one could say there was no trend in the data
• Versus eyeball curve fitting, 1996 is the most exceptionally high year (not just an absolute record, but even higher above smooth curves we'd try to fit to the data than any other year).
• More recent years look like they have more scatter than the earlier years
• It looks like we might want to divide the period in to 3 intervals -- 1979-1996 (the longest arguably trendless span), 1997-2006 (an intermediate with at least some overlap on the earlier figures) and 2007-present (entirely outside the range of the previous years)

But maybe your eyeballs disagree with mine, and perhaps the appearances are deceiving.  So on to working with numbers, which will also lead us to some additional ideas.

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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?).

Arctic Ice Guesses 2014

Have to bite the bullet here and discuss my guesses for the September 2014 Arctic sea ice extent average.  The thing which has made them so difficult is that they're so different from each other.  Now, one method I've retired.  It was simply so bad last year that there's no point in continuing it.  That is the one I did based on a population growth (of ice-free area) curve.

That leaves, however, two different model-based guessers.  The first one, which appears at the Sea ice prediction network as 'Wang', is based on doing a statistical regression between what the CFSv2 (climate forecast system, version 2) predicts for September ice area and what is observed.  The second also uses CFSv2, but in a different way.  Namely, we know that the model is biased towards ice being too extensive (which the Wang method addresses statistically) and to being too thick.  The Wu method is based on thinning the ice and seeing what the extent is thicker than a critical limit (60 cm it turns out).  (Both Wang and Wu work with me, or vice versa, and we discuss how to work on these guesses.)

The guesses are:
June -- Wang -- 6.3 million km^2 0.47 stdev
July -- Wang -- 5.9 million km^2 0.47 stdev
July -- Wu -- 5.1 million km^2 0.56 stdev
June -- Wu -- 4.8 million km^2 0.65 stdev

One of the things to notice is that the two estimates moved towards each other from June to July.  Wu rose, the higher Wang declined.  The second is, the Wu method has a standard deviation (variability of its estimate) that is double what it was last year.  Whatever is going on in the model, it is much less self-consistent in previous years.  Much more uncertain.  This is one of the reasons for ensemble modeling (part of the Wu approach).

You can also see that the Wang estimate is the highest of all -- even higher than the Watts up with that group estimate.  This is true in both June and July.

So, what's up?  Well, I'm not sure.  Some of it is certainly related to sea ice thickness estimates.  Xingren (Wu) did a different approach based on thickness for June, which we didn't submit, but which landed in between the official June estimates from Wang and Wu.  With the step towards convergence from June to July between Wu and Wang methods, I'm inclined to guess (a meta-guess) 5.5 million km^2 for September.  If this were to occur in reality, it probably suggests something important.  What, exactly, I'm still pondering.

Yabba2 -- Construction

Katherine Monroe:

Below are the full instructions on how to build exactly what I built. There is so much that could be done to improve the design. I know it is not anywhere close to perfect. The materials I used were makeshift, whatever was lying around the house or wasn’t too expensive. But that was the point. I like spontaneity. It doesn’t have to be extremely elaborate to work and to be useful. This is for anyone who wants to do anything with it or for anyone who is just interested.
  
Materials
1. Vernier Flow Rate Sensor, Order Code: FLO-BTA/FLO-CBL
2. Vernier Lab Quest by Vernier Software and Technology.13979 SW Millikan Way, Beaverton, Or 97005. 888-837-6437. (for transmitting and collecting data from the Flow Rate Sensor.)
3. 3 22” steel dowel rods
4. Compressed fiber board
5. Minwax Polyurethane Varnish
6. 24 Gauge- 100 ft. Green Floral Wire Twister
7. Small foosball
8. 2 IDEC Sensors, Magnetic Proximity Switches. Type: DPRI-019. Premium Waterproof Clear Silicone Sealant (without Acetic Acid)
10. Plugable USB to RS-232 DB9 Serial Adapter (Prolific PL2303HX Rev D Chipset)
11. RS232 Breakout - DB9 Female to Terminal Block Adapter
12. Xnote stop watch, version 1.66 (downloadable at http://www.xnotestopwatch.com/)
13. Loctite Epoxy glue
14. Drill
15. Hammer
16. 2 Brass quarter inch Phillips Head screws
17. Electric hand held reciprocating saw
18. Electrical tape
19. 4” by 3/4” strip of thin steel (cut from a can)
20. Twisted Nylon string
21. 2’ long wooden slat (to be used as a handle for carrying and placing the designed device in the water.)
22. Study Site: United States Geological Survey (0164900), Northeast Branch of Anacostia River at Riverdale, MD. (Test site was just next to the USGS data collection gauge.) (-38.961,  -76.626)

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