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:

(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.
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)

25 July 2014

Yabba -- Building your own stream gauge

Katherine Monroe*, the author/inventor of this stream gauge, is a graduate of Eleanor Roosevelt High School, in the same class as Elliott Rebello.  Her senior project was quite different, and you'll get to see the details in her own words.  Part 1 is today, the narrative.  Part 2 will be on Monday -- the full parts list and construction instructions.

Engineering the “Yabba Dabba Doo”

By: Katherine Monroe
June 2014
Eleanor Roosevelt High School

One year ago, as a rising senior at Eleanor Roosevelt High School in Greenbelt, MD, I was faced with the same grueling task that all students in the Science and Technology program were: RP- that is Research Practicum. This is what we had been leading up to for the past three years and now, here it was. 

RP is the year long research project that all seniors in the Science and Technology program at Roosevelt are required to complete. By the end of the year we had to have completed a science fair backboard, a laminated poster, a power point, and a five chapter paper. We had a whole class dedicated to working on all the different aspects of the project and to learning how to analyze data quantitatively and statistically. We were told to come up with a project that was interesting to us because we would be spending the entire year working on it. Some students applied for internships with NASA, USDA, NIH, the University of Maryland, the National Zoo, Walter Reed Hospital and more. Other students applied for programs established by and within the school and other students worked separately from any structured programs. 

I chose to apply to a program started by one of our school’s AP Chemistry teachers called WISP (Watershed Integrated Study Program.) It was a program which emphasized local water quality studies. Students in WISP formed groups and measured chemical and physical properties of local waterways at a bunch of sites across the county. We measured nitrate and phosphate concentrations, dissolved oxygen levels, alkalinity, ph, turbidity, total dissolved solids, temperature and took seasonal macroinvertebrate data. We then added our values to an ongoing database which students could draw from for all sorts of studies which require long term data collection.

I applied to WISP because out of the endless ocean of things I was unsure of I was sure of at least one thing and that was my love for the environment and for being outdoors. After having been accepted to WISP I began the process of deciding what to do for my project. In the end the basis of my project came from the one other thing I was sure of which was that I enjoyed building things. So I knew I wanted to build something and I knew it should relate to the local water quality movement that WISP was promoting. I looked at what we did in WISP and thought about what we measured. One aspect of water quality that I found important to a gaining a comprehensive understanding of a stream or river’s health (that we did not measure in WISP) was the speed of the water in the stream.

The water speed can provide insight into the types of organisms that can live in a stream or river, to the flow of sediment down a river, and sometimes to the oxygen levels of a river. The greater the speed of a river, the more aerated it typically is, and the higher the dissolved oxygen level. All of these can greatly affect the health of a stream or river. Stream speed can also help in understanding volume flow rate of a stream and in identifying storm water runoff patterns near and around the stream or river and in developing flood models. Overall stream speed seemed like an important factor that we did not account for in WISP due to what I believe to be a range of reasons, the expense of the necessary equipment, the complicated nature of taking stream speed measurements at a variety of points along a stream and still getting inaccurate results due to the variability of speed along an uneven stream bed, and maybe more. 
I decided that I wanted to design and build something that would measure stream speed; something that would be cost effective and accurate, and something that would be easy for anyone who wanted to do research, like the kind we do in WISP, to build for their own purposes. The point was to encourage citizen science by going through all the steps independently and then showing people what I had done so that they could do it too. 
In the end what I came up with consisted of an open track along which a light and neutrally buoyant ball was pushed by the flowing water. On either end of the track there were magnetic sensors which timed how long it took for the ball to move from one end of the track to the other. From this the speed was computed. This is where the name of the device comes in. I decided to call it the “Yabba Dabba Doo” because it looked like something out of the Flintstones (or maybe like an old-fashioned push lawn mower.) 
Next I had to figure out if my design actually worked. In order to do that, I compared my device to an already existing speed measurement device by the company Vernier. I assumed that the Vernier data was accurate. My null hypothesis was that the average of the speeds taken with my device would be statistically equivalent to the average of the data taken with the Vernier device. Strangely enough, I wanted to FAIL to reject the null hypothesis. Statistics are weird. I collected data with each of the devices within a half an hour period of each other (assuming that the stream speed would not change in that amount of time.) Then I analyzed the data through a statistical t-test which looked for a significant difference between the two sets of speeds and their averages. 
After multiple trials and readjustments to the design I got what looked like a pretty accurate result. Initially (before reaching my final design), the object moving along the track of the Yabba Dabba Doo was a metal disk attached to the metal rods of the track with metal rings. All that metal caused for a lot of friction between the disk and the track which prevented the disk from reaching the speed of the water and gave me slower averages than the Vernier averages. This also yielded a significant difference in the statistics which I did not want. In order to minimize the coefficient of friction there, I changed my design to one which consisted of that light weight, neutrally buoyant foosball (which I mentioned earlier,) that was attached to the metal track with small sections of plastic drinking straw. The foosball had no tendency to float or sink in the water and caused less friction on the track. Furthermore, the coefficient of friction between the plastic straw and the metal track was much less than between the original metal rings and the metal track. After making this design change I got averages that were much closer together between the Yabba Dabba Doo and the Vernier and in a majority of my statistical t-tests there was no significant difference. In the end I had a device that seemed to be working pretty accurately and cost $350 less to build than to buy the Vernier. 
Going through that process, of trial and error and trial and error and trial and then success!!! was extremely gratifying. I got to experience the life of an engineer first hand and to learn about the plethora of unforeseen problems that can arise. 
This entire year was a great learning experience for me. I learned what a null hypothesis was and how to go about trying to reject it (or in my case, fail to reject it.) I learned all sorts of things about the materials that I used to build my device. I learned how to bear standing out in winter weather water up to my waist, wearing my mom’s baptismal waders, for the good of science! I learned about all the things that can go wrong and need to be accounted for in a field study like this one. I learned how to access all sorts of functions on excel, power point, and word. And I learned something about myself. I learned that engineering, and I think in particular environmental engineering, is something that I could easily be passionate about and be satisfied with in the future. And for a chronically confused and disoriented teenager about to go to college, that is reassuring.

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. 

[Back to your host: Directions on Monday ; The * is that Ms. Monroe normally goes by a more informal version of her name and I've gone with the formal here.  Formal for publication is a rule I use myself (I'm not usually Robert), and one which I've learned is helpful for women to be taken seriously.] 

23 July 2014

Data are ugly

Current news about whether there really is an increase in Antarctic sea ice cover is reinforcing my belief, shared by most people who deal with data, that data are ugly.  This work argues that the trend that some have seen in some trend analyses has more to do with the data processing than with nature.  I encourage you to read the article in full itself.  It is freely available.

From the abstract:
Although our analysis does not definitively identify whether this change introduced an error or removed one, the resulting difference in the trends suggests that a substantial error exists in either the current data set or the version that was used prior to the mid- 2000s, and numerous studies that have relied on these observations should be reexamined to determine the sensitivity of their results to this change in the data set.
One of the obnoxious things about data sources is that they don't remain the same forever.  This is not so much a problem for my concerns about weather prediction, since the atmosphere forgets what you said you observed in a few days.  But for a climate trend, the entire record is important.  For the data set being discussed, the Bootstrap Algorithm (Comiso) applied to passive microwave, we immediately run in to data obnoxing.  Since 1978, there have been several passive microwave instruments -- SMMR, SSMI F-8, SSMI-F11, SSMI-F13, 14, 15, AMSR-E, SSMI-S F16, 17, 18, and AMSR-2.  They didn't all fly at the same time, and they don't have exactly the same methods of observation.  And none of them exactly observe 'sea ice', which leads to a universal problem which we (people who want to use these instruments to say something about sea ice) all have to deal with.

So a few considerations of what all is behind the scenes of this paper and the earlier Screen, 2011.  The latter paper involved some of my work (read deep in to the acknowledgements).  This one doesn't, but the fundamental issues are the same ...

24 June 2014

Ice Science Cafe

This Thursday (June 26th) I'll be talking about ice, and, better, yet, answering questions about ice at the Annapolis Cafe Scientifique.  The time will 6:30 PM.  Same location as usual -- Cafe 49 West.  Local folks are invited, and non-local are welcome to pose questions here. 
I'll also invite folks to suggest topics for me to prepare for.  My sources of information on sea ice are pretty different than my audience's, so it's hard for me to tell what people have been hearing about.

Our Sea Ice Outlook guesses this year are wildly different -- 4.8 million km^2 and 6.3 million km^2.  The former is above the median of contributions.  The latter is the highest of all, even higher than from Watts and company.  We have a third, unpublished, guess in between the two.  I need to check out a couple of things before writing it up.  Given the spread we're encountering ourselves, I think we're going to learn a lot this year.

21 May 2014

A challenge and offer

The challenge is for a science teacher to incorporate Science and/or Nature in to their teaching.  The offer is that I'll pay for the subscription(s) for at least the first year.  US High School teachers only (sorry others, but I'll exercise provincialism here).  First come first served.

The prompting here is that I've been reading some of my backlogged Science and Nature issues.  Some articles are past almost all K-12 students (though not some I was talking to at Eleanor Roosevelt High School's Research Practicum celebration, so even the most rarefied will be useful in some institutions).  But there are research article summaries which don't require such a high level of background.  And I think a talented enough teacher can make good use of the wealth of material in each issue of Science and Nature.

The third leg of the tripod, so to speak, is that I'll invite some discussion as to exactly how a (US) grade 9-12 teacher can make use of professional journals like Science and Nature.  I know I have a teacher or two in the readership, and look forward to their ideas.

Update 5/27/2014: I've now got a taker, @ragbag01 on twitter.  But discussion of how to make use of the subscription is still very welcome (per anonymous1). 

Anonymous2 notes http://scienceintheclassroom.org/ has some free materials for the classroom on selected papers.

An article of mine on language and reading science may also be useful -- Science Jabberwocky

20 May 2014

Agriculture in changing climate

If you're one of the people who thinks that food grows in grocery stores, all the talk about climate change affecting agriculture is passing you by.  You'd be wrong to think so, but most modern industrial country people are not involved in agriculture.  Having grown up in the corn belt I'm perhaps a little sensitized to the fact that farming is hard work.  And that farming is extremely sensitive to details of the weather.  Anything sensitive to weather is sensitive to climate.

Many foods depend on extremely specific climates.  Not just current climates, but the history of climate for thousands of years -- soils to grow a good crop in develop over that time span.  The corn belt is where it is not just because of current (well, 1950-1980) climate but because in the thousands of years before that, the soil improved and developed to the point of being able to support such farming.  For something like corn, which is grown across a huge area, climate change can be an issue.  But someone, somewhere, will probably be able to grow corn 30 years from now.

But many items grow in relatively small areas, subject to the whims of local change.  Some of these are:
Such specialized crops are sensitive, to the point of perhaps being eliminated, to climate changes.

I invite readers to check the sources linked to above.  And to contribute their own crop types that are either sensitive to climate change, those which are insensitive, and those which would even benefit from expected changes.  Please do include links to your examples.

02 May 2014


"Kids are born learners.  Job of parents and teachers is to avoid killing that drive." @rgrumbine

That tweet from yesterday has gotten picked up more, and by wider group, than usual.  No doubt this was aided by the fact that it was in midst of tweets between @louisck (I'm a fan) and @alexnazaryan.  The twitter-storm is driven by 'common core', whatever that is these days, Alex making some surprising-to-me defenses, and Louis being peeved about the cc (by way of his daughters).  I have some biases I'll discuss at the bottom

Alex replied to my tweet: "Yes.  But learning is a hungry beast.  You have to supply it with good material daily."  I agree with that, too, though we may not agree about what constitutes good material.

I'm going to start very much smaller than writing my own comprehensive national program for education.  Start with a tweet I made earlier today, answering @dougmcneal about how I got in to science -- "Permanent interest in learning, and being better at that than applying it practically (i.e. engineering)."

As a permanent learner, what do I do? Well, let's start with one end result: it means I know a lot of stuff*.  That's probably not the end to emphasize in design and testing of schooling, though.  Two of the things I know, for instance, are that the atomic mass of hydrogen is right about 1, and the atomic mass of helium is a bit less than 4 times hydrogen's.  It would be easy to write a test that asked students for the name and atomic mass of every element (#117 was recently confirmed).  This would be worse than useless, though this approach is common.  Also common to be asking whether the atomic mass of Helium is 4.00, or 3.98, or 3.998, etc.

26 March 2014

AABW in the news!

It only took 25 years, but my thesis topic is now becoming newsworthy!  Gluttons for punishment can see at least the abstract at A model of the formation of high-salinity shelf water on polar continental shelves.  Which is aimed at one of the important ingredients for AABW (Antarctic Bottom Water).

I've been reluctant to blog about the topic because it is, after all, my baby and I'm sorely tempted to post at excruciating length and detail.  (Not that there aren't other people who have studied the topic before or since, but I'm one of the people who has.)

I'll take this note as opportunity to get in to some detail about the weirdness that is sea water, and come to the climate change, carbon dioxide burial, and heat burial, aspects later.  The story of AABW turns on some odd facts about how sea water behaves in Antarctic conditions.  Not least, it can go below freezing.

24 March 2014

Harry Bulkeley: A few questions about global warming -- Answered

An opinion writer (a retired judge) asked a few questions in his Galesburg, IL local paper, and I'll provide some answers here.  As always, I encourage you to read the original.

The good judge, like the usually informative Mr. Krauthammer, starts off on a very wrong foot, with bad philosophy of science.  There are many facts in science -- the earth is round, the sun is hot, there is a greenhouse effect, and CO2 is a greenhouse gas.  All can be questioned -- but not in the trivial way that Bulkeley and Krauthammer seem to think.  'I question it' is trivial, and pointless.  If you have a _scientific_ question about these things, or any other, it is because, and only because, you have scientific evidence that the 'fact' is false.

Climate change, as even commenters in agreement with Bulkeley note, is indeed a fact.  Climate changes, that's a fact.  One of the tasks of science is to try to understand the hows and whys of that fact.

Let's see about the questions:
1) Average temperature has indeed gone up the past 15 years.  This is a question, apparently, because the author didn't bother to look at the data. One can experiment with time periods and trends at NOAA/NCDC.
It's worth paying attention to the fact that climate trends are defined on 30 year periods, not 15.  Some discussion of why this is the case is at

05 March 2014

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)

04 March 2014

Hurricane Control?

Reader Bayesian Bouffant raised the question of whether we might be able to take some of the strength out of hurricanes, if not to really control them -- by way of an article Massive offshore turbine arrays would help us harness hurricanes.  The article is more positive about chances for weather modification than I, but let's take a look at some details and issues.

By way of background, hurricanes are heat engines.  The heat source is partly warm oceans, but mostly the latent heat released when water vapor condenses in the atmosphere.  The source of that water vapor is, again, warm oceans -- but by way of evaporating water vapor from the ocean surface in to the atmosphere.  When the water condenses, it releases heat, which drives the circulation, and makes for clouds.  The rising air draws more air in, again along the ocean surface. 

Over the years, there have been many suggestions for trying to weaken hurricanes.  One of the least reasonable is to throw a nuclear bomb at the hurricane.  Somehow this is supposed to 'disrupt' the hurricane.  But, hurricanes are heat engines, and nuclear bombs supply heat.  They also don't really have much energy compared to a hurricane!  In one day, a hurricane releases about 52*10^18 Joules (see Christopher Landsea's estimation of the wattage of a hurricane).  The Hiroshima bomb released about 67*10^12 Joules.  Roundly speaking, 1 day of a hurricane is a million Hiroshima bombs.

Since frontal assault is pretty well doomed by the fact that hurricanes are vastly, overwhelmingly, more energetic than anything humans have to wield, any attempt to control, or affect in any meaningful way, has relied on more indirect means.

13 February 2014

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 :-)

11 February 2014

PIOMAS ice volume anomaly q

Q: I've got a question that you may be able to answer with your sea ice hat on.

Why is it that the minimum volume anomaly of ice appears in mid summer and not in September when the minimum volume happens? See piomas.
(From Alastair)

A: I'm hard-pressed to tell what season the anomaly extremes are occurring from this graph, but can say that your reading isn't surprising to me.  At the seasonal minimum of ice volume, you're at the minimum -- so it is relatively difficult to get even lower.  Late winter, towards the maximum doesn't have that problem -- since it's maximum, there's lots of room to go lower.  But the Arctic is cold in winter, so it's going to pile up a lot of additional mass (from the prior minimum) pretty consistently.  The place/time that there's the most room for decreasing the ice volume is a time of year when the ice volume hasn't already declined a lot, but it is melting rapidly -- early- to mid- summer.

As you also see from the figure, the main trend is the year-round decline.  For your question, we're looking at the timing of largest excursions below the line. 

The converse question, for largest excursions above the trend, is that I'd expect those in late fall - early winter, during the freeze-up season.  Then, there isn't much volume, and it's freezing fast.  A couple weeks earlier start to freeze-up makes a comparatively large difference.

10 February 2014

Evaluating 2013 sea ice outlook estimates

Very late wrap on my 2013 sea ice season guesses, but, as I was thinking in early August, the straight statistical one busted pretty badly.  The observed (NSIDC) September Average sea ice extent was 5.35 million km^2.  The guesses were:

The original outlooks (end of May) were:
3.9 million km^2 -- Grumbine, Wu, Wang statistical
4.1 million km^2 -- Wu, Grumbine, Wang model-based
4.4 million km^2 -- Wang, Grumbine, Wu model-based statistical
The end-June Wu et al. estimate was 4.7 million km^2.
The end-July Wu et al. estimate is 4.57 million km^2.
As I suggested then, I'm going to get back in to the statistical approach to try to fix it up.  While we don't expect excellent estimates from it, if it's using a sound basis, it shouldn't be wrong by this much.  I have some ideas on how to do it. 

Still, better estimates than Stephen Goddard's end of August (the 26th) prediction -- Doubling of Arctic Ice in 2013.  Doubling September 2012 would have meant 7.26 million km^2.  (I was just touring web sites, and saw this article, which reminded me that I hadn't posted the evaluation of my forecasts.)

29 January 2014

Science Tweeters to follow?

Yesterday I mentioned a few science tweeters.  Today I'll ask you for your favorites on science.  Any science.

28 January 2014

Old links still of interest

I'm something of a pack rat -- keeping things eternally, or close to it.  I still have, for instance, almost every program I wrote in college, plus almost everything ever since.  I also have preserved links of interest from my blog reading back to ... well, not quite that long.  Part of my getting back up to speed is to look through my old noted links of interest, and I'll share them out.  I'm more or less arbitrarily diving them in to links of interest, and links to follow up.  Everything actually fits in both categories, but a bit of a matter of emphasis between them.  

The items to follow up are old enough that you and I can do some searching to see how well they've held up over time.  The papers are interesting and good, but many interesting and good papers turn out not to stand up without important additions or modifications over the next few years.  These links are at least 3, and some over 4, years old, so there's been some time to see the evolution of thinking the the fields.  Some papers' conclusions get stronger over time, some weaker.  Pick a topic you're interested in and see what happened through time.

I'm also noting twitter identities for the blogs/bloggers I link to.  I'll post a separate note tomorrow asking for your suggestions.

Items of interest

Open Source Climate Education
TB is also on twitter at 

FAQ on climate models -- part 1
FAQ on climate models -- part 2

Science is something people do

computing before electronic computers

A Google Earth Explosion! -- Geology layers for Google Earth
Kim is on twitter at

 Dan Moutal: Why I accept the scientific consensus on global warming, and what would change my mind
-- I also have a number of comments there.
Dan is on twitter at and

Naomi Oreskes video: American Denial of Global Warming

Items to follow up to see how well they've held up over time:

Sea Level may rise 1 meter by 2100
21 Meter sea level rise 400,000 years ago
West Antarctic Ice Sheet and sea level last 5 million years

Reconstructing and estimating sea level 200 to 2100 AD
You can also follow Aslak on twitter at

New studies disprove cosmic ray driver for climate
Post 1850 global temperature increase not driven by sun
IPY sea ice model -- Arctic Ice probably will not recover
Coastal Erosion doubles in parts of Alaska

27 January 2014

Happy New Years's

Been away for a while, I now realize.  A longer while than expected.  So in the mean time, we've passed the winter solstice, Gregorian new year, and closest approach to the sun (perihelion), and are about to reach (January 31st) the Chinese new year.  Hope all the astronomical and other dates of interest to you have been good.

In the mean time, I've been making progress on writing at work, publishing a couple notes and finally finishing a draft of another.  That's a part of the quiet here -- looks like I can only put down so many words per day in writing.

Science reading has been mostly from the professional literature, which I'll take up in more detail article by article (with, I hope, proper flags for researchblogging.org).  What good books on science (any realm) have you read in the past year?