02 December 2014

Girls on Ice 2015 Expeditions

A chance to go climb and sleep on glaciers in either Alaska or Washington State, plus learn and do science.

Supported in part by the NSF and Alaska Climate Science Center.
Application window opens 10 December 2014, closes 31 January 2015.

From Girls on Ice Web Site:

Girls on Ice is a unique, FREE, wilderness science education program for high school girls. Each year two teams of 9 teenage girls and 3 instructors spend 12 days exploring and learning about mountain glaciers and the alpine landscape through scientific field studies with professional glaciologists, ecologists, artists, and mountaineers. One team explores Mount Baker, an ice-covered volcano in the North Cascades of Washington State. The other team sleeps under the midnight sun exploring an Alaskan glacier.

“Girls on Ice is not a reward for past good grades or academic achievement, it is an inspiration for future success.”

The application period for the 2015 Girls on Ice Teams will begin December 10, 2014 and end on January 31, 2015 at 11:59 p.m. Alaska time.

Alaska program: June 19 – 30, 2015

North Cascades program: July 13 – 24, 2015

To be eligible, girls must be at least 16 years old by June 19, and no older than 18 on July 24.


01 December 2014

High School Educational Program on Greenland

For US High School Students -- a chance to work in Greenland doing science.  Application deadline 9 January 2015

More information, including the application, is available at:
http://www.arcus.org/jsep

(From the web site:)


In this successful summer science and culture opportunity, students and teachers from the United States, Denmark, and Greenland come together to learn about the research conducted in Greenland and the logistics involved in supporting the research. They conduct experiments first-hand and participate in inquiry-based educational activities.
The JSEP format has evolved over the years into its current state, which consists of two field-based subprograms on-site in Greenland: the Greenland-led Kangerlussuaq Science Field School and the U.S.-led Science Education Week.
Program Dates and Descriptions
Kangerlussuaq Field School (2 weeks) and Science Education Week (1 week): Tentative dates for JSEP 2015 are June 29th through July 20th.
Kangerlussuaq Science Field School: Students learn about and participate in polar science alongside researchers and teachers at field stations around Kangerlussuaq, Greenland. This area is a rural region with limited amenities. Participants live in dormitory style housing and share in cooking and cleaning responsibilities. This part of the JSEP Program is supported by the government of Greenland.

29 November 2014

Still living

Still living, just been away from the blogosphere on other things.  Some of them will find their way back here as posts. 

In the mean time, Kevin O'Neill, who has introduced an interesting idea in one of his comments at Multiple Working Hypotheses, has encountered one of the annoying things about to do science.  Namely, a data set he has been using was discontinued.  I sympathize.  At work, a satellite I was about to make use of in our operations died the week before our implementation.  A resource for looking in to climate (at least as long as you don't need to go before 1979) is the The NCEP Climate Forecast System Reanalysis.  The numerical results of which do include the outgoing longwave radiation.  There are slightly differing (in resolution and time spans) archives at NCDC and NCAR.  The NCDC archive mentions that it is 500 Terabytes.  That sounds about right.  It'll take a while to download.  Or you can use the NOMADS subsetting capabilities to extract just the fields and regions that you're interested in.

I'll also be getting back to my sea ice guesses for ARCUS, and evaluating them.  This time around, as I prepare at work to do some more substantial sea ice things, I'll do a general survey of how they all performed, in all years.  Then to focus on the remaining method.  A point related to the method of multiple working hypotheses is that you have to be active in weeding them down.  You'll generate more and better ones to take their place.  But you have to make room first.

Kicking around in the 'almost done' bin is a post on how long it takes to detect an acceleration in global mean temperature.  Acceleration being a change in the trend.  This was prompted by some todo at the Washington Post Capital Weather Gang, when someone claimed that he'd found a negative acceleration (i.e., a decrease in trend, which would, if continued, turn to a cooling trend).  I'll give away the answer here -- it takes about 40 years (at least 40 years) to define the acceleration. 

Another 'nearly done' is to revisit Does CO2 correlate with Temperature?.  It's almost 6 years since the original, and for all 6 years, there's been talk of 'hiatus', 'pause', and 'climate hasn't changed in N years'.  N varies a lot by who is talking.  Perhaps the additional data will break the correlation, since CO2 has certainly been rising.

I'm also going, at some point, to play on the blog with Bayesian statistics.  Readers who like Bayes, please do correct me as I (inevitably) make mistakes.

Plus in January, when I'm done with the meetings, holidays, and other things, of December, I'll hang back out the question place shingle.  Probably some minor notes before then.

29 September 2014

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

28 August 2014

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.

04 August 2014

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?).

29 July 2014

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.

28 July 2014

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)