More Grumbine Science

Starting a bestiary of oscillations and cycles

2012-01-30T06:00:00.032-05:00

A bestiary originally was originally a book of pictures and descriptions, often with morals attached, of animals. Well, that was the middle ages. The version I've got in mind is one describing the more or less regular oscillations or cycles in the earth system, its orbit, and the sun. For now, I'll describe just the period and its name and invite you to add to the list. Also, I won't worry about whether the named thing is a proper oscillation (such as tides) or more of an index that may not have any particular period (PNA). The later rendition will have some discussion of what happens in each and concern about whether the variation is a real thing or just an artefact of how people looked at the data. For those who'd like to jump straight to discussion of weather cycles directly, I'll suggest William Burroughs' Weather Cycles, Real or Imaginary

As always, you're encouraged to add your own suggestions!

Around a day

12h 25 min (12:25) -- Lunar semidiurnal tide
23:56 -- Sidereal day
24:00 -- Mean Solar day (1 dy)
24:50 -- Lunar diurnal tide

Around a month

13.66 dy -- Lunar fortnightly tide
27.32 dy -- Lunar monthly tide
40-60 dy -- Madden-Julian Oscillation (MJO)

Around a year

365.2422 dy -- Tropical Year (equinox to equinox) (1 year for later)
365.259 dy -- Anomalistic Year (perihelion to perihelion)
~433 dy -- Chandler Wobble

A few years

~26 months -- Quasi-biennial Oscillation (QBO)
2-7 years -- El-Nino/Southern Oscillation (ENSO), Southern Oscillation Index (SOI)
8.85 years -- Lunar perigee
18.6 years -- Precession of the Lunar Node

Many years

(See http://http://www.arctic.noaa.gov/essay_bond.html for some discussion)
--- -- Arctic Oscillation (AO)
--- -- Antarctic Oscillation (AAO)
--- -- North Atlantic Oscillation (NAO)
--- -- Pacific/North America Pattern (PNA)
20-30 years -- Pacific Decadal Oscillation (PDO)

~11 years -- Sunspot cycle
~22 years -- Solar cycle (each sunspot cycle is opposite magnetic polarity)
~88 years -- Gleissberg cycle (clumping of solar cycles)

Long period

19-23,000 years -- Milankovitch Cycle -- Precession of the equinoxes
~41,000 years --Milankovitch Cycle -- Tilt of the earth
~100,000 years -- Milankovitch Cycle -- Eccentricity of the earth's orbit
~400,000 years -- Milankovitch Cycle -- Eccentricity of the earth's orbit

Very long period

400 Million years -- Supercontinent cycle

National Center for Science Education now also defending science on climate change

2012-01-24T05:30:00.009-05:00

The National Center for Science Education is now also engaging on teaching good science on climate change. I've long been a member, because they've been for even longer helping ensure that science is taught in biology classes. I'm not quick on the announcement, it was originally made on the 16th. But if you haven't seen the mention yet, it's new to you :-)

I'll add a few thoughts of my own as a long-time member, and one who had suggested some time back to the director, Eugenie C. Scott, that they take this step. One of the things I like about the NCSE is that their focus is on the science. They're not the place to go if, say, you want someone to lobby for your idea for solving climate change. They're a good place for parents, teachers, school boards, to go with questions and concerns about whether the science in your school's textbook is good, or is even science. NCSE is also a good place to go to find out what is happening in your state regarding attempts to change the science curriculum away from science. The main page address is http://ncse.com/

It was partly the tracking of attempts to remove teaching evolution in biology classes and other such anti-scientific moves that made me suggest also covering climate change science. Increasingly, over the last 10 years, bills opposed to teaching good science in biology classrooms have been including directives opposed to teaching good science in earth science classrooms. Bills to deny that CO2 is a greenhouse gas, or deny that there's a greenhouse effect, or to deny that CO2 is increasing and this is due to human activity, and other versions of denial.

High School Student Science Opportunity in the Arctic

2012-01-16T05:22:00.010-05:00

Call for High School Applicants
Joint Science Education Project
Kangerlussuaq, Greenland

The Joint Science Education Project (JSEP) announces a call for applications from high school students interested in participating in field research in Greenland.

A limited number of high school students from the United States will join peers from Denmark and Greenland to spend three weeks during summer 2012 doing field science in and around Kangerlussuaq, Greenland, and visiting a research station on the Greenland ice sheet. Students will work with arctic scientists and with their peers on research projects in a wide variety of fields including biology, geology, climatology, chemistry, and engineering.

The program is sponsored by NSF's Office of Polar Programs in collaboration with the Joint Committee.

Application deadline: Friday, 17 February 2012.

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

For questions, please contact:
Shelly Hynes
Email: shynes@nsf.gov

Parts per million

2012-01-12T05:08:00.026-05:00

One of the sillier arguments against climate change, especially human-affected climate change, is to claim that since CO2 is a trace gas, it can't have any significant effect. It's now about 395 parts per million in the atmosphere.

It happens that I'm taking some medicine at the moment. 100 mg of one, and 10 mg of another. To make the math easy, let's say I weigh 100 kg. mg is milligram, so there are 1000 of them per gram. 1000 grams is 1 kg.

That first medication represents 1 part per million. The second is a mere 0.1 parts per million. The body is a complex system. Small amounts of things can be very important. Climate is also a complex system.

Let's continue a little in this vein. Daily nutrition requirements, which I'll take from http://en.wikipedia.org/wiki/Reference_Daily_Intake , include:

1000 mg Calcium --> 10 ppm
1000 mg Phosphorous --> 10 ppm
60 mg Vitamin C --> 0.6 ppm
15 mg Zinc --> 0.15 ppm
2 mg Manganese --> 0.02 ppm
80 micrograms Vitamin K --> 0.008 ppm
6 micrograms Vitamin B-12 --> 0.0006 ppm

If you'd like to avoid scurvy, rickets, pellagra, and a host of other illnesses, you have to treat concentrations far, far lower than 400 ppm as being important.

Happy New Year

2012-01-11T08:08:00.000-05:00

Happy new year folks. Couple of months there when I was off to other things. One of which is that my oldest son introduced me to World of Warcraft. Not a whole lot of science content there, but I don't do science all the time.

It looks like some of the comments you made during my disappearance didn't come up even though I approved them, I thought. They should be there now.

During my blog-vacation, I did have a number of ideas for articles, and made some progress on background work for some. So things will be livening up.

Is it science?

2011-11-14T06:26:00.002-05:00

One of the things I like to ponder is how to decide whether something is science or not. An attempt to come up with a clear demarcation criterion is Karl Popper's, which gets more widely distributed as being "If it isn't falsifiable, it isn't science." I'm not sure what he said himself, but philosophers tend to write books on these topics, rather than short sentences, so I'll guess that some details are lost in this version.

The question arises here because a recent question at the question place (yes, Robert, that's exactly what it's for) mentioned Popper. I'll give a different response and discussion here. (Same conclusion*).

For some cases, Popper's falsifiability criterion works well. Religion is not science. There is no observation, experiment, or test that will tell someone that their religion is wrong. No matter what you observe, the religion can accommodate it. Same thing for mathematics, actually, as it isn't necessarily concerned with observations. Unfortunately, those (theology and mathematics) are the only two areas which can lay claim to absolute Truth (of a sort -- mathematical truth is only about mathematical things). Science is left with only approximate truth -- the theory seems to work pretty well, the observations are pretty reliable. But not absolutely reliable, and not absolutely perfectly.

For others, though, it's more difficult. In the later 1800s, astronomers observed that the planet Mercury wasn't where it was supposed to be according to Newton's laws. Its point of closest approach to the sun (perihelion) was moving by 43 seconds of arc per century too much$. If Popper's criterion were correct, astronomers and physicists should have immediately thrown out Newton's laws and gone looking for something else. Instead, some patches were suggested -- like a planet 'Vulcan', orbiting even closer to the Sun than Mercury, in just such a way to cause Mercury to behave as observed. But it was never observed. Eventually, Einstein proposed his theories of relativity to expand on Newton's laws. Among other things, they explained why Mercury wasn't where Newton expected it to be.

For climatology, Popper is not so much relevant, or at least doesn't pose very much difficulty.

Popper's criterion is mostly concerned with theories, not observations. And climatology has few theories of its own. It borrows theories from other areas of science -- the laws of conservation of mass, energy (first law of thermodynamics), and momentum (Newton's laws), the second law of thermodynamics, Planck's law for blackbody radiation, quantum mechanics (to get emission and absorption of radiation by gases), and a few more subtle matters. Climatology also makes some use of observations -- where are the absorption lines of CO2 and how strong are they, what is the saturation vapor pressure of H2O, what is the freezing point of water, and so on.

To the extent that people complain about climatology, and climate models, the theory (theories) that have to be wrong are from somewhere else (usually physics). The climate models are just trying to carry out the laws and theories from physics.

Popper's criterion, to come back to it, doesn't have much to say about observations. I am 6'1" (185 cm) tall. At least I claim that this is true. Suppose you come to my house and measure my height and see that I'm, instead, 184 cm tall. Maybe you've 'falsified' my claim. But what has changed in the world of science? Nothing. No portion of science relied on me being 185 cm tall rather than 184 cm tall. Certainly far less depended on that than depended on Mercury's orbit, and that didn't, on its own, cause the downfall of Newton's mechanics and gravity.

But the law(?), theory of conservation of energy is something to falsify. It _could_ be falsified. If you observed the amount of energy in a system at one time, and then some time later saw that it had less (or more), you'd be on your way to a Nobel prize. Or at least a new revolution in science. One of the major importances of Einstein's E = mc^2 was that it showed there was a new place to find energy in the universe. Odds are good, given the 100+ years that people have been at it, that you've just made an error in your observation process. Still ... maybe you've got something.

Rather than drive on to a firmer conclusion, I'll stop here and invite discussion. What theory or theories do you think climatology has that couldn't be falsified? Which ones do you think have already been falsified? If you think climatology is making claims which could not, even in principle, be falsified, what are they? Is Popper's criterion even a good one to use? And so on.

* There's an old story that goes: A student who was not doing very well in a class discovered that their professor always gave the same test every year. Even better, it was multiple choice. So they tracked down an old, corrected, copy of the test and memorized the answers. Come the day of the final, they checked off all the previously-memorized answers. Then was extremely surprised to have gotten an F (failing grade). After some internal debate, the student went to the professor and said what they had done. The professor answered "Yes, I give the same questions every year. But I change which answers are correct!"

$ 43 seconds of arc is a seriously small number. A circle has 360 degrees. Each degree has 60 minutes of arc. Each minute of arc has 60 seconds of arc. The difference was 43 / (360*60*60) of a circle -- 0.00003318 of a circle (about 33 parts per million, far less than the fraction of the atmosphere that is CO2, and small enough that the thickness of the line when you draw a circle is much larger than this).

Veteran's Day

2011-11-11T11:11:00.007-05:00

I'll take this chance to thank Veterans for their efforts to preserve our civil liberties and freedoms. That including one of my sons.
I'll also take the opportunity to thank and encourage another of my sons in his exercise of his civil liberties and freedoms -- in spite of the slander campaign now being waged against him. Or, really, because of it. You don't get that kind of response unless you're doing something right.

I've never really done anything that would peeve people of that kind of power. But, in their different ways, all three of my sons are doing so.

AMSR-E failure and fallout

2011-10-07T07:28:00.038-04:00

Update 17 October: The meeting last Tuesday gives little hope. There will definitely be no data for weeks. I don't know what prevents a conclusion of never.
original:
AMSR-E has failed and is probably permanently out of commission. For most of you, that's merely news. Perhaps a source of amusement and interest is now gone. For me, since I use(d) it in my day job, AMSR-E failing means some real work. Most of that work was already planned, but now it needs to be done more speedily.

As I've often said here, and even more often in 3d, data are messy and ugly. One sort of ugliness is that instruments do not last forever. When (not if) they fail, you have to turn to a different instrument. Ideally, you already have the replacement in hand and have been running it regularly and intercomparing its results with your current main system and ensured that there are no differences other than those you wanted -- like better resolution on the new instrument. The present situation is not ideal, so, as we usually do in science, I'm making the best of it that I can. And making notes for what to do when I have a chance to rework the immediate fixes.

Step 1 was to bring back in to service an (even) older satellite data source that I used to use. I stopped using it because it was hearing voices, which degraded the quality of the work I did. On the other hand, using it is far better than having no data at all. This is in hand, and will be officially operational tomorrow.

Step 2 is to develop a 'voice filter' for the data, to get around that problem. Fingers crossed, tests today look promising.

Step 3 is to bring a new instrument in to use. It's newer than AMSRE, but not as high resolution. Plus there are some issues with biases as compared to the older record. To bring it on line, these need to be reduced (possibly substantially), or, if we're very unlucky, characterized and my downstream users warned of the change (they already know about what's happening tomorrow, at least the immediately-to-hand folks).

AMSR-E did a good job. Its designed life span was 5 years (maybe 6), and it gave data for about 9.5. The real problem, for me at work, was not AMSR-E, but the fact that no successor was launched in that 9.5 years. A launch of AMSR-2 is currently (last I heard from my spies) planned for February 2012, and the spies report that there's discussion of maybe moving up the launch date. Fingers crossed. On the other hand, for my work, even a launch tomorrow doesn't save me from steps 2 and 3 above and the work involved. It takes time for a satellite to reach its working orbit, to be brought up to operational status for collecting data, for the data flow system on earth to start passing out the data to some locations, and some more for the data to come down to me in a form that I can use. These are a matter of months (optimistically) to years (pessimistically). So, again, cross your fingers. If we're lucky, by this time next year, we'll have AMSR-2 in my work's operations.

Irrespective of my working life, the gap between AMSR-E and AMSR-2 just by its existence degrades the quality of the climate data record. It makes impossible the direct inter-comparison and inter-calibration between the two instruments. A few years down the road, we're looking at a larger scale example of that, as the next set of polar-orbiting satellites (JPSS is the acronym there, now, used to be NPOESS) was delayed by the 2011 budget, and looks likely to be delayed in the 2012 budget as well.

Bottom line message:
It'll probably be a bit before I'm back to writing normally, including how my sea ice outlooks did (pretty well, actually; the high and low, I thought were high and low (5.0 and 4.4 vs. the observed 4.6), and the two model predictions were 4.8 and 4.6, vs. the observed 4.6).

If people are really interested in the gruesome details of what I'm doing at work, that I can keep writing about. Any takers? (bwah hah hah!)

Climate change science history

2011-10-04T21:25:00.000-04:00

A question at the question place regarded this history of climate change science. See that link for the full question. Here's my response, which over-ran the blog comment length limit there.

On the historical link side, the best single source is Spencer Weart's _The Discovery of Global Warming_ http://www.aip.org/history/climate/index.htm I think he under-rates the significance of G. S. Callendar's work in the 1930s-50s, but that's my take and I haven't yet written it up formally. It's an idea I've had, to do so. Unfortunately, a lot of Weart's work will probably pass your students' level. But you should be fine with it yourself and translate suitably to your students.

A different matter for your middle schoolers is the time scale of the history. Perhaps combine this with a project to collect family history?

I'll take your students as being about 11. Let's go with 30 years per generation, which is, at least, fairly accurate for my family. That means your kids born in about 2000, parents about 1970, grandparents in 1940, great-grandparents in 1910, great-great grandparents in 1880, great-great-great grandparents (g3 grandparents) in 1850, and g4 grandparents in 1820.

The history is:
When your students' g4 grandparents were pre-school age, Fourier realized and documented that the greenhouse effect (the name for it now, the name didn't exist then) existed. That the earth was warmer than it would be if the atmosphere didn't do anything to retain heat to the earth's surface. 1824-1827.

When your students' g3 grandparents were middle school age, Tyndall showed that water vapor, carbon dioxide, and some other gases, were greenhouse gases. 1859-1861.

When their great-great grandparents were about to graduate from high school, Arrhenius showed that doubling the atmospheric CO2 levels could lead to about a 4 C warming, and that the warming would be stronger towards the poles than the equator. Both are still considered reasonable predictions, and the latter is now well-observed. 1896.

Around the time their great-grandparents were being born, Angstrom made his error regarding saturation. The error was to think that because the band centers saturated, that increased CO2 and water vapor could not increase the earth's temperature. This was wrong because he ignored the fact that there is more than band centers involved. The off-center parts can give continued warming, even while the centers are saturated.

During their grandparents' early lives (and somewhat before), Callendar was at work, documenting that CO2 levels had risen significantly (he was right, but the objection to his work was also correct -- this is one of the things I want to document properly), that the rise was from human activity, and that meaningful-to-humans climate change can be expected to result. (1938 and following).

As your students' grandparents were graduating from high school and starting to work, Ralph Keeling started observing atmospheric CO2 levels on a regular and reliable basis. Plass conducted his acceptable-to-modern-scientists computation of CO2-induced climate changes. The international geophysical year was held and first earth-observing satellites were launched. (1956-1958)

When your children's parents were younger than your children, the earth experienced its last month that was colder than the 20th century average (in 1976). Your children have never experienced a year when the global mean temperature was colder than the 20th century average. The younger parents never have either.

1988 was something of a watershed year in climate policy, if not the science. 1987-8 was a particularly hot year in the US, and a major drought across the US corn belt (for that time, these days it would be unremarkable, or even a rather cool year). In 1988, Jim Hansen testified to Congress about climate change. One part of that even was that the Whitehouse edited his testimony about the science. The second being that Hansen was predicting that the signature of human-induced climate change would soon come out of the noise. I thought he was ahead of the evidence at the time. He turned out to be correct. But the precedent of politicians trying to (and often being successful) in editing what science was presented was set.

Since 1988, there has (I would say) been little change in the public knowledge of the science in the US on climate change. Your middle schoolers don't really hear much more than their parents did. I have some late 80's, early 90's general press articles on climate, and they could all still be published as-is today.

Another thing I'll point out from the history: The existence of a greenhouse effect was shown long before we knew what gases were greenhouse gases. We knew what gases were greenhouse gases long before we knew that humans could significantly change climate by changing greenhouse gas levels. And (not mentioned above) at the time it was discovered, it was considered (Arrhenius) a beneficial thing. He lived in a very cold climate.

Happy Huskies

2011-10-03T21:51:00.000-04:00

Not a post about the U. Washington, Seattle teams, who I'm sure do fine. Instead Alaskan Huskies I visited on my vacation:

This was at the Chena River Village, which I'll be talking more about later. The huskies are, of course, adorable in their own way. That's sufficient, of course, but there's also some science involved.

These are Alaskan Huskies, of course. The major distinction between Alaskan and Siberian, to borrow a description from an Alaskan musher (not the one above with her lead dog) is that the Siberians are 'Disney Dogs' -- selected for prettyness. There might be some regional bias involved.
The Alaskan Huskies are selected for their ability to run for long periods in Alaskan winter. To that end, they're not uniform in size or appearance. In size, they're from 35-85 pounds (16-40 kg). Surprisingly to me, a 35 pound dog can be hitched with an 85 and both pull well.

A different thing they're selected for is enjoying running. The two dogs in front of the lower photo are absolutely thrilled -- they're about to get to run! The white dog in the foreground is now retired. I didn't catch it in this photo, but both he and the dogs in the pen are very excited. Even though they're not the ones getting to run, they're all excited. Running will be happening!

The downside for the dogs was that the day we visited, it was about 50 F (10 C). The dogs are happiest running at about -10 F (-23 C). So the dogs were only allowed to run a little and then released to the river. Here's a video of the tail end of their run:

Question Place

2011-09-27T11:42:00.000-04:00

I'm now back from my hiatus from the net, and will be putting up pictures and science comments on them over the next few days/weeks.

But first, here's a place and chance to ask questions and make suggestions. I've been pretty thoroughly off-net since the 8th, so please be sure to provide links if you're asking about recent events. (See the link policy; links to science content are good things whether I've been off-net or not.)

Off net

2011-09-08T07:59:00.000-04:00

I'll be off net for a while. When I get back, I'll finally hang out the shingle for a question place. Arctic ice will have pretty much revealed by then which, if any, of my guesses was best.

Peer review and Wagner Resignation over Spencer and Braswell

2011-09-03T11:49:00.000-04:00

"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.

Wagner mentions one of those non-scientific aspects to the paper -- the public exaggeration of the paper's conclusions by the author himself. What the university press releases and certain media outlets do in exaggerating is pretty much outside the author's control. But Spencer's own exaggerations indicate (my thought) that his purpose was to make those grand claims, rather than to make an incremental improvement to our knowledge of how the universe works. As Wagner mentions, no single paper looking at a single data source, comparing with a single model, is capable of refuting all science on global warming.

A different non-scientific point, endemic to any of Spencer's work, is that he doesn't fundamentally view himself as engaging in science and an effort to understand the universe; he says "I view my job a little like a legislator, supported by the taxpayer, to protect the interests of the taxpayer and to minimize the role of government." Note that -- his job. There are scientists who pursue political goals, whether to minimize government, or prevent what they think are bad political decisions -- but it's on their own time, as private citizens -- not as their job. The particularly active ones I know have always had a clear distinction between their job and their activities as a private citizen.

With Spencer, though, he views is job as being to achieve a political end. As such, science takes a back seat. This paper of his, then, is less a matter of trying to advance our understanding of the world, and more (as he views it as his job) a matter of trying to achieve a political result. Peer review was not developed for such things, and does not manage them well. When science is not the primary purpose of a paper, that presumption reviewers and editors make that the author has done his due effort at avoiding fooling himself is false.

Some comments have been made hither and thither about how the criticisms, like Bickmore's, of Spencer's work are not in the peer-reviewed literature. They then conclude that this means that Spencer is right and Bickmore (and others) are wrong, or the criticisms can, at least be ignored. If the authors have done their due efforts to not fool themselves, this may well be true. In such efforts the fundamental errors Wagner mentions would not be present.

Instead, as was the case for McLean et al., some errors were so elementary that they could be analyzed and discussed at blog-level. Granted I may often leave some of the middle-school kids behind, but Bickmore's analysis, for instance, should have been manageable by anybody with an undergraduate major in a math/science/engineering field.

There's a flip side to that. You seldom can publish things at that level in scientific journals. They're 'too obvious', or 'uninteresting', and the like. Consequently, if the errors in a paper are elementary enough, there will be no peer-reviewed literature to cite regarding the error. Authors are supposed to have done that checking themselves, and if they fail to, the reviewers are supposed to mention them. If both fail, well, now we do have the possibility of writing blog comments on the failings of a paper.

For comments and discussion here, I will encourage you to focus on the peer review process itself. I give links below for the science in the paper, and others for the story itself if you're more interested in those things.

Some links on the science:
http://www.realclimate.org/index.php/archives/2011/07/misdiagnosis-of-surface-temperature-feedback/
http://profmandia.wordpress.com/2011/08/03/spencer-braswell-2011-proof-that-global-warming-is-exaggerated-or-just-bad-science/
http://bbickmore.wordpress.com/2011/07/26/just-put-the-model-down-roy/

Some additional links on the story:
http://initforthegold.blogspot.com/2011/09/editor-apologizes-for-spencer-paper.html
http://www.skepticalscience.com/news.php?n=982
http://scienceblogs.com/deltoid/2011/09/editor_of_remote_sensing_agree.php?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+scienceblogs%2Fdeltoid+%28Deltoid%29
http://www.forbes.com/sites/petergleick/2011/09/02/paper-disputing-basic-science-of-climate-change-is-fundamentally-flawed-editor-resigns-apologizes/
http://retractionwatch.wordpress.com/2011/09/02/editor-of-remote-sensing-resigns-over-controversial-climate-paper-co-author-stands-by-it/
http://scienceblogs.com/stoat/2011/09/holy_editor_resignation_batman.php
http://rabett.blogspot.com/2011/09/honor-and-respect.html
http://blog.chron.com/climateabyss/2011/09/spencer-braswell-and-the-review-process/
http://bigcitylib.blogspot.com/2011/09/editor-steps-down-for-publishing.html
http://scienceblogs.com/gregladen/2011/09/cloudgate_denialism_gets_dirty.php

Spencer's blog and his comments on the news

Too-early consideration of sea ice estimates

2011-08-31T05:02:00.018-04:00

I won't let it stop me, but considering that it is still August as I write, it's premature to evaluate estimates of September's sea ice extent. On the other hand, it's fun and easy, and some parts make sense even at this early date.

Last May I made, with colleagues, some estimates for September 2011's extent. As usual, I also included some other forecasts for comparison. The NSIDC figure for August 29th is:

Where I've added lines in red for the 30 year mean climatology of September extents (6.67 million km^2), the mean monthly September extents if we take a 30 year linear trend (5.31), and lines at 5.0 and 4.5 million km^2.

While not always true, it's generally the case that the September average extent is lower than the end of August level. Our end (well, 29th) of August value is about 4.8 million km^2. So we can be fairly confident that the September figure will come in not much higher than 4.8 (5.0 would be high, my intuition says), and probably some tenths below 4.8. But probably not many -- look how little the curve changes across late August through September.

Some estimates:
~~30 Year Mean -- 6.67~~
~~Joe Bastardi -- 5.5~~
~~30 year Trend -- 5.31~~
Wang, Wu, Grumbine May -- 5.0
Wu, Grumbine, Wang May (December 2010) -- 4.8
Wu, Grumbine, Wang August (June 2011) -- 4.6
Grumbine, Wu, Wang May -- 4.4

... where I've struck through the estimates that are already (very likely) busted. The 5.0 of our first model estimate is probably too high. The 4.4 of our statistical guess is probably too low. Since I said that I thought this was their probable sense of error, I'm not too mortified. The two estimates which Wu took the lead on making both look plausible. 4.8 might be a touch high if the current figure is this. The 4.6 looks pretty good. The parenthetical dates are when the initial conditions for the model runs came from; we're not entirely up to real time for making our experiments.

The striking thing about the estimates that have already busted is that they're climatology (or someone known for making dramatic statements and doesn't like to see them checked, which is not striking but is worth noting if only because forecasts should be verified). Normally, you expect climatology to be fairly good, not among the first forecasters eliminated. Also, you expect, if climate is stable, that the mean will be a better forecaster than a trend (which results only from noise if climate is stable). Instead, the mean is a horrible predictor. And even the trend is very bad -- even assuming that ice is declining steadily is insufficient. The ice extent is down even more than a linear trend.

Update: For looks at the Arctic, including 3 days of sea ice drift forecast from NOAA/NWS/NCEP, see http://www.arctic.io/observations/520//

Earthquake followup

2011-08-30T19:48:00.000-04:00

Phil Plait showed the spectacular animation of seismic waves propagating across the US from the 5.8 Virginia earthquake last week, but left out part of the story. A commenter, davenquinn, picked up some details. If you look at the video:

you see an enormous number of sensors in the Midwest and Great Plains. These are areas not known for seismic activity, so what are they doing with so many sensors?
They are part of a travelling array (the 'transportable array') of seismometers, part of the Earthscope initative from the National Science Foundation.

The idea is to have a substantial number of seismometers moving stepwise across the US every few years. Then, having a dense array of seismometers, particularly to have them in places that we don't normally, will show us things that we don't normally see. That spectacular wave propagating across the country is one of those things. Get in to the data and you start seeing that the seismic waves didn't travel the same speed in all directions. And that tells us something about what the earth is made of. No idea what is up, but take a look at the Texas-Louisiana area. Early in the propagation of the waves, they follow along with the rest of the US. But later in the animation, that area shows much larger amplitude variations. Why?

In other words, more fun science to be done!

Now twittering @rgrumbine

2011-08-27T17:40:00.000-04:00

My thoughts, between earthquake, stolen wallet, and hurricane, are now more in line with twitter than blogging. I'm @rgrumbine there.

For tracking the hurricane and forecasts, I like the Weather Underground. Specifically on the hurricane, the WunderMap (r).

But, speaking of the wallet, it was returned this morning. Minus cash, but plus all the plastic and photos.

My first earthquake

2011-08-23T17:24:00.000-04:00

Yes, I was one of the many who felt the 5.8 to 5.9 earthquake centered in Virginia. The final scoring and location will come from the US Geological Survey -- http://www.usgs.gov/ This is the first time I've noticed an earthquake in my life. Growing up around Chicago and then living around here doesn't give you a lot of exposure. The strongest here that I've been present for was 4.3 to 4.4, as I recall it. I didn't notice it at all, not even in the sense of 'oh, so that's what was happening'.

This time, it was noticeable. The initial stage was some fairly minor, fairly high frequency vibration -- similar to having some heavy trucks drive past, or some kinds of construction drilling. Since there is construction going on in my area, I figured this was it. For the first few seconds. Then there was a rapid increase in the size of the vibrations, and a decrease in their frequency. Rather than having some vibrations passing through the building, we moved to having the building itself swaying back and forth. Earthquake!

By my count, I felt the quake for about 30 seconds. (If that's too long compared to the USGS figures, blame my inner ears.) Call it 20-30. One friend estimated the swaying as being 4 cycles per second in the real quake phase. My guess is closer to 2. I'll have to see what the USGS has. There are probably confounders from the engineering of my building. So far, all seems ok at work and home. Some of the more precariously perched items fell off book cases at home, but that's about it. Fingers crossed that this is true for everybody.

A question popular in our parking lot at work (we'd been evacuated post-quake) was to wonder what fault line caused the quake. My early answer, which was supported by a snippet on the radio (i.e., don't place a lot of confidence here) is that there was no fault line involved. This is simply a much stronger version of the usual earthquake for this region -- adjusting to the fact that the Laurentide ice sheet is gone. In areas that the ice sheet occupied -- down to southern Illinois or in to Pennsylvania -- the land sank under the weight of the ice sheet. This squeezed some of the more fluid parts of the mantle out from there, and over to the areas in front (south) of the ice sheet. That elevated the areas around, say, Virginia. Once the ice sheet was removed, the fluid started oozing back to its original location. As it does so, the crust creaks its way back in to position. Creaking = earthquake.

Is climate a random walk?

2011-08-08T09:45:00.000-04:00

Let's pick up again the discussion between Tamino and me. He has objected to my use of cumulative sums on the grounds that cumulative sums of random numbers have bad behavior statistically. He's correct about that statistical point, naturally, which means caution is needed regarding the statistical part of my post on finding a climate normal.

But how concerned should we be as climate scientists? Crucial to that concern is that climate be, to a fair degree, experiencing random variation. It isn't, strictly. As Tamino mentioned, there is certainly a trend in more recent years -- not purely random variation. His criticism is more one against the cumulative sums method. As Jim Bouldin mentioned in the comments recently, we do routinely transform variables in order to study topics of scientific interest. In his case, plants.

I'll start by showing you an illustration of why Tamino is concerned. This is a plot of the cumulative sum -- of a purely random variable, with uniformly random numbers in the range plus or minus 1 degree. This is actually about 10 times too large for climate. So the late period value of 40 really means 4 degrees.

Each tick mark is 1 month, but assumes that each month's random number is completely independent of each other. That isn't the case, as Tamino has documented. This curve manages to accumulate its 4 degrees in about 200 years.

Er, it accumulates that climate wandering in only 200 years!. That and the smooth curves, suggest why we cannot take climate to be random wandering over long periods. Volcanoes suggest why we cannot do so for short periods either. Intermediate periods might be ok physically.

The upper and lower curves are growing proportional to the square root of the number of months. Random walks are known to do this. We can't tell whether such an accumulation will head for the warming or the cooling side, but we can be confident that over time, the cumulative sum will move away from zero. I've loaded my spreadsheet for this in Open Office and Excel formats. I do encourage you to pull one of these down, or write your own, and look at a few dozen examples of what can happen with cumulative sums of random numbers. As Tamino said, you can find all kinds of interesting results -- that have nothing to do with anything meaningful.

Let's go back to that 4 degree accumulation in 200 years. That's gargantuan compared to real climate changes. The 200 years comes out partly because of the nature of randomness used in my spreadsheet, so don't be too wedded to it. It could easily be more like 2000 years with a better approximation to climate's random nature. I'll hope that Tamino or one of my more mathematical readers take this part up. The other thing is, the accumulation is unbounded -- wait long enough and any limit you put, 4 C, 20 C, 100 C, ..., will be passed. It will take about 25 times as long to pass 20 C warming or cooling, so maybe as fast as 5000 years!?

Regardless, however, of the exact period, the random walk is quite happy to move away from a reference climate by 4 C in a pretty short period. Ok, long compared to how long I expect to live. But quite short compared to the 100,000 years or so of an ice age cycle. Global mean temperatures change by about 5 C in an ice age cycle. So this 200 (or 2000?) year accumulation of 4 C is enormous, and fast.

But we don't see that in the ice age records. Temperatures cool by about 5 C going in to an ice age. It takes a while. The thing is, we don't see global warmings and coolings of several degrees occurring routinely each few hundred or few thousand years. We don't see 20 C changes at all. What we see instead is that there's a more or less smooth cooling going in to an ice age, and a more or less smooth warming coming out of one. There are occasional large shifts in regional temperatures (the Younger Dryas, for instance, coming out of the last ice age). But there are never such large temperature shifts as the random walk looks to expect.

The reason is, climate variation is not a purely random process. This is no surprise, of course, and no surprise to Tamino either. The climate system has an idea of what its reference temperature is. That is, if temperatures are a bit higher than normal, it's more likely that the next change will be a cooling. Not necessarily the next month, due to the autocorrelation that Tamino has mentioned. But sooner than later. -- Unless there's something pushing the climate to a new reference temperature. I'll illustrate this, from data, in a post to follow. It provides, by the way, a different way to look for a period of 'normal climate'. The random walk has no idea whether it is currently warmer or cooler than reference, because there is no real reference.

This gives a sense that over long periods (even if we're not exactly sure of what 'long' means -- 200 years in this example) climate isn't really a random walk.

Volcanoes let us see this return to reference temperature over short periods. When a major volcanic eruption throws a lot of dust in to the upper atmosphere, the earth cools. This, itself, is highly non-random. Throw junk into the upper atmosphere, cool the earth. Never do we see a warming from it. No surprise there. What follows is the interesting part. Over time, the earth warms back up to about where it was before the eruption. That time is a few years. If climate were just a random walk, after that eruption the earth would be as likely to cool as warm. And a few years after the eruption, the earth could just as easily be markedly cooler as back to its normal. Yet what we see is the earth returns to its pre-eruption temperature. Those old enough to remember Pinatubo (1991) experienced this fact themselves.

There certainly is randomness to global climate. If there weren't, we would be able to make perfect climate predictions. On the other hand, the randomness is limited somehow. The climate system does have an idea of what temperature it would be at if it weren't being disturbed by something (volcanoes, El Niño, greenhouse gas releases, etc.) and tends to return towards that.

For the discussion between Tamino and me, I'll suggest:

We do want to be cautious about using cumulative sums. They have bad statistical behavior against random processes.
We do not have to give up on cumulative sums in studying climate. Real climate is not a purely random process.
Since climate does seem to have the ability to tell where its 'normal' is, at least over very short (few year) and long (few hundred? few thousand? years) periods, we still have a chance at locating a 'normal' climate.
In respect to that, it is reassuring that the length of time over which the normal climate was found in the original post is 90 years -- not just 20-30.
"More research is needed"

#5 is the almost universal conclusion for scientific discussions. I'll hope that Tamino or others take up the question of what more realistic noise would tell us about climate as a random walk processes. I'll take up a different novel approach to climate myself shortly. Instead of sums, it will be differences. They're subject to problems, just as sums are, but they're different problems. If we get to the same conclusions by both methods, we can be more confident of our conclusions.

Endogenous Retroviruses and a Different Writing Style

2011-08-08T05:48:00.000-04:00

One of the blogs I keep an eye on is erv, Endogenous RetroViruses, by Abbie Smith. Not the usual language you'll see here (some over PG-13), and certainly different style (Abbie doesn't believe in the ', for instance). But for some discussion of biology, particular parts involving viruses and especially endogenous retroviruses (surprise), it's a good place to go.

I'll note that back when I was thinking about blogging, Abbie was one of the people who gave me some ideas on approach.

Some articles to take a look at for a sample of the blog:

What is normal, and how it matters in examining bees
Open Access Publishing -- and some limits
How immunization works
Antivaccinationism and death by measles
AIDS and CD4+ T-cells
Blood groups and viruses
Antibodies and dengue
Pursuing science and results
Zinc and the common cold
The Timetree of life
Scientists being slandered
Scientists and media

Is it really normal?

2011-08-04T09:18:00.000-04:00

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?
Well, I certainly did not prove that there was a fundamental change in the climate system in about 1940. On the other hand, Tamino only gives good reason to be skeptical of that conclusion, but not a statistically measured reason. That is, he doesn't show the statistical probability of arriving at as large a cumulative deviation as is found in the real data. If that probability is quite low, then the conclusion of change remains pretty good. Since the 1940 date for change, rather than 1960 or 1970, say, is a surprise, it would be interesting to see the probabilities for getting behavior like what is found from my analysis method on the real data. Meaning there's some science or statistics still to learn or do.

Where we really are is that we're doing some science out in public. It's a messy process with some back and forths, as you're seeing. Also in the comments here, some further suggestions of issues that should be addressed -- Tim Curtin's on HadCRU being unsuitable for the purpose, and Rick Baartman somewhere between Tamino and me -- noting that even with the problems that cumulative sums have, the magnitudes look to be significant, something for which he provides a quantitative argument. These, too, are points to follow up. Tamino is the better one for the statistical part. And I had already suggested that it would be a good idea to follow my analysis from HadCRU with analysis of other temperature reconstructions.

Something else to notice is that neither Tamino nor I are calling each other names, I'm not offended that he's criticized me (rather, gratified -- one comment talked about me being 'schooled', well, I like learning things so that's to the good), and so forth. Nor, as Tamino noted, is there a suggestion that he disagrees with the conclusion of there being, currently, a warming trend. He questions this as an approach, and gives reason for that. I disagree with him about the lack of reason to look in this way, which I'll take up tomorrow as this is getting long.

How to find climate normals?

2011-08-02T05:58:00.194-04:00

It's that time of the decade when the official 'climate normals' are computed -- according to the rules of the WMO and NOAA (in the US). But can we find a better way of deciding 'normal'? I'll do some thinking out loud here, and invite you to play too. Could we even be so lucky as to find a way of defining climate normal in such a way that we don't need to worry about an early period of 'coming out of the Little Ice Age', or a later period of Anthropogenic Global Warming.

In previous posts, I illustrated that there are principles which lead to the requirement of 20-30 years to define a climate average and, separately, 20-30 years to define a climate trend.

In those approaches, we were able to make good use of the adage that climate is what you expect. It was only after 20-30 years of data that our expectations for the mean or trend would be stable -- would not depend sensitively on how long a period we chose to be our data period.

This time, I'll pick up with a different notion or description. A common description of climate is also that sometimes its warmer and sometimes it's cooler, but it all averages out in the end. Let's start by looking at the Hadley-CRU temperatures, back to 1850, month by month. I'll start with looking at temperatures relative to the average over the entire data set:

For the earlier part of the record, we see the deviations being both above and below the reference value. On the other hand, the last 26+ years, every month since December 1984 -- 317 consecutive months, has been above the reference value. That's not a sign of sometimes warmer, sometimes cooler. That's the recent period being different from 'normal'. For instance, because of a warming trend. If you're younger than 26, you have never seen a month where the global mean was as cold as the 161 year average. On the other hand, looking at the early part of the record, we see some years warmer than 'normal' even though most are cooler. Further (look directly at the original data, don't take my word) there are no periods as long as even 20 years of continual below reference temperatures. In other words, the Little Ice Age, insofar as it was global, had ended by 1850.

But, can we do something else to explore the notion of 'normal'? Those wiggles in the curves are not all the same size. If we were to add them up, would they sum up to zero? They have to do so over the whole record -- that's what the average means; you have just as much above as below average, by just as much, observations. If the numbers are mostly just wandering around, then the sums from the start of the record to a given month should also wander, sum times totalling more than zero, sometimes less. Here's what happens for using the 161 year average as the reference:

That's clearly not a matter of climate wandering around the reference point we chose! The first 80 years are almost uninterrupted decline in total temperature anomaly, while the last 30 are an aggressive warming -- in 30 years compensating for the 80 years of cumulative cooling at the start of the record.

A word about what this sum means. It is an accumulation of heat or cold compared to our reference number. It is the same concept as heating degree days, frost degree days, and cooling degree days. Since it is summer here, I'll go with cooling degree days. (I'll actually describe it in hours.) Suppose you're ok with temperatures as warm as 77 F (25 C), but will want to run the air conditioner if it is warmer than that. Of course the air conditioner has to run harder if it is hotter. So hour by hour (day by day) what you do is add up how many degrees warmer than 77 you are. A day with 5 cooling degree hours means you don't run the air conditioner much. A day like today here will have something like 200 cooling degree hours. You appreciate very much the invention of air conditioning on a day like this!

In the case of global mean temperatures, month by month, what we have above is cooling degree-months. Let's say that glaciers all experienced global mean (which, of course they don't, but I'll use for the sake of description). Further, let's suppose that glaciers were all in balance in 1850. And (again not exactly true) let's assume that colder temperatures mean glacial growth. That large accumulation of cooling degree months suggests that glaciers should have been growing from 1850 through 1930, then retreating until the present -- just now retreating to their position in 1850. We actually observe that glaciers have retreated behind their 1850 locations, so, again this doesn't look like a good reference to choose.

The good reference temperature, and good definition of climate 'normal' will show us some temperature accumulations that cross back and forth across zero. And about half the months should be above normal, or below normal.Given the original figure, we don't want to include the last 30 years at least. So I took 1850-1979. Well, again, didn't behave very well. I backed it up a decade at a time until I finally found a period which gave a well 'behaved' in these senses 'normal'. It is 1850-1939:

Now this looks like what we've been told climate is like -- sometimes warmer, sometimes cooler, and on average not going anywhere. The sums hit zero 3 times in these 90 years -- about 1856, 1880, 1892, come close around 1902. The zeroes in 1850 and 1940 are because that's how we constructed the curve, so they don't count. Having found a period that behaves like a 'normal', now let's look at the rest of the resulting curve to the present:

Whoa! Not only are there no more zero crossings, meaning that climate's accumulated tendency for the last 70+ years is one of warming, but it's hard to even notice any declines in the sums. They're there, but you have to look very carefully on this scale.

The scale is another thing to look at carefully. In the first 90 years, the largest accumulation we see is a net cooling of 30 degree-months. It took about 20 years to get there (about 240 months), and about 20 years to warm back out of it. Those mean a cooling or warming bias of about 1/8th (0.125) degree per month being a normal number in climate. Numbers of that magnitude have appeared elsewhere (if you've got a memory like mine, you might recognize figures like this from earlier posts, otherwise, don't worry) which grants a certain degree of comfort.

But the more recent period, 70 years of it, shows 10 times as large an accumulation as the 'normal' period! 328 degree-months accumulating in 70 years. This averages to a warming bias of 0.39 degrees per month, triple what happened in the normal period.

(Update) You can see the scale issue more easily in this figure, with the whole period on the same scale:

I confess I was quite surprised to see this result. One response to that is to invite you all to find my mistake (if you can't find it, maybe I didn't make one). My spreadsheet is available in Open Office and Excel formats. The other thing I'll invite you to do is to use different climate data sources. Maybe this conclusion depends on having used the HadCRU data? Let us know what you get if you use the NCDC, NASA, JMA, and so forth data sets instead.

In the mean time, it looks like there has been a fundamental change in climate since 1940. Something has been active since 1940 that wasn't active before 1940. The good news being that we actually arrive at a period when we can call climate to be 'normal' -- 1850-1940. Further, since the NOAA and GISS data start in 1880 rather than 1850, it's reassuring that our reference choice here shows 1850-1880 to be a span of zero accumulated cooling/heating. So we could reference 1880-1940 instead.

Something else we can do is take a look at the reference value again, and how long it has been since we've been below that. Using the anomaly values as given by HadCRU, the normal value for climate is -0.334. The normal period they chose was quite a bit warmer than the 'normal' period of the climate system it turns out. The last time the global mean was below the climate normal was March, 1976. If you're 35 or younger, you have never seen a global mean below climate's real normal*. The most recent month in the data, May 2011, is 0.667 K above the climate normal, 1.2 F warmer than normal.

*Assuming, of course, that I didn't make a mistake somewhere.

Update2: It seems that blogger is having problems with comments. That's particularly unfortunate here as I think there are likely many good comments getting choked by the system. If you've had this problem, please email them to me at bobg at radix dot net. 3: Problems seem to be resolved now.

Update4: Tamino has some illustration and discussion of hazards of using cumulative sums. It's a statistical argument, naturally, so this affects portions of this post which are statistical in nature. On the other hand, as one of the commenters there noted, there's a well-known and important paper on climate by http://www.aos.princeton.edu/WWWPUBLIC/gkv/history/Hasselmann76.pdf Hasselmann, 1976 which points out the physical importance of cumulative sums. I'll take up these and related ideas in a full post.

Update 5 (8/8/2011): Let's also take a look at how close climate is to being random

Giant Mutant Imperial Moths

2011-08-01T07:44:00.000-04:00

Here's the title-bearer:

He's about 5", 8 cm from wing tip to wing tip. The reasons I call him a mutant are a little more obvious in this top view:

He's got 5 legs. You can see the front left leg, and the doubled front right legs. The photo couldn't capture it, but I did verify two more back legs. He also seems to not have antennae, and moths do normally have some, including members of this family.

For completeness, here's the bottom view:

For more technical information, see http://www.butterfliesandmoths.org/species/Eacles-imperialis

It was my first time seeing one of these. I had seen two other moths of this size -- a Luna moth many years ago, and a Polyphemus a few years ago while I was running in a 24 hour relay. Turns out they're all in the same family, the Saturniidae.

He was extremely patient with my photography and my wife going in and out of the sliding doors. Stayed put for several hours.

Like other members of this family, the adults don't eat. Go back to the top view -- no mouth parts. The adults try to reproduce, the females lay their eggs, and a few days later they die. Their main period of life is as a caterpillar. I've been calling this a male because it seems (the top link and further checking around the net) that the females are a more straightforwardly yellow. It is the males who have fair amounts of purple/brown.

Odds and Ends -- July 2011

2011-07-29T05:42:00.000-04:00

A number of interesting items that are a little more time-related than I normally talk about.

Some sociology for amusement:
Nation's Climatologists Exhibiting Strange Behavior h/t Michael Tobis.

Regarding some of Roy Spencer's latest Well, give me more than 30 parameters, and I can fit a trans-dimensional lizard-goat ... by Barry Bickmore. I've downloaded the other recent paper and will take it up as my time and interest permits. A couple people have already asked about this, so read Barry's notes in the mean time.

Some fun science, and a reminder to beware of gifts bearing Greeks:
Phil Plait on Earth's first Trojan Asteroid
... and the NASA press release on it.

Trojan asteroids do not, it turns out, contain Greeks. Apparently that is limited to a horse in the Iliad. What happens is that if you have two bodies that are very much more massive than a third, like, say, the Sun and Earth compared to an asteroid, you can park an asteroid on the earth's orbit, but 60 degrees ahead or behind. And it will pretty much stay there. The 60 degrees ahead or behind are called the 'Trojan points'. We've long known of bunches of Trojan asteroids for Jupiter. As Phil's title suggests, this is the first time we've found one for the earth.

That's a bit about the doing of science: There was every reason to believe that the Earth had trojan asteroids. It would actually have been quite remarkable if we didn't -- gravity is supposed to work the same way for us as for Jupiter (allowing for the fact that we're so much less massive). Still, we're happier to see what we expected.

Best Frenemies

2011-07-28T05:36:00.004-04:00

A friend refers to another scientist as his best enemy. The important thing about this is, he is not angry or upset about the other scientist. I'll call them John and Jane, John being the one I know. John and Jane are both outspoken people. Consequently, at meetings the two of them spend a fair amount of time disagreeing with each other. And they disagree vigorously.

That vigor is part of what makes Jane a best enemy for John. John's not a quiet person himself. So it would be easy for him to vigorously say what he thinks is true and other people to quietly agree, because no other ideas were presented, or to quietly disagree. Quiet disagreement would be worse. It would mean that John would not have a chance to explain the parts of his thinking that would persuade those people that he was right after all. With a vigorous enemy, however, John can be confident that Jane will bring up those points that aren't clear to other people. And then John can explain them. After this, if anyone disagrees with him, there's a fair chance that it's because he doesn't really have things right himself. And he also has a chance to change his thinking, to arrive at something even better than what either he or Jane thought were the case when the two started their discussion.

The even larger bonus is that whatever conclusion John and Jane reach personally, they're confident that the entire audience knows what is the real topic of discussion, and why they each think as they do. This puts enough substance on the table for the audience to be making good decisions. If John's position isn't the one that some in the audience walk away with, that's fine. He's going to keep thinking about the topic himself and maybe decide that something closer to Jane's original position is more correct. Or maybe he realizes that there's a better way of describing why he thinks as he does. Either way, some scientific progress is made.

Another part of what makes it work is that their discussions, regardless of how vigorous (an uninformed observer might say 'violent'), are technical. Both of them have serious professional reasons for their conclusions. And it is those professional reasons they turn to, not cherry picking starting points for time series trend analysis and other dishonest or ignorant methods.

A final matter that makes it work is that neither of them is personally upset by the fact that they have professional disagreement. Both apparently rather relish it. After spending 8 hours at the meeting disagreeing with each other about almost everything under the sun, they go out to dinner together and chat pleasantly about other topics.

Strictly speaking, I only have 'John's view of matters. I don't know 'Jane'. Still, they've been doing it for decades now, and I think even of 'John' were extremely clueless about other people (and my observation is that he is fairly clueful), he'd have picked up on 'Jane's differing viewpoint.

Names and genders may well have been changed for the purpose of the story telling. The people and descriptions are otherwise accurate.

How not to compute trends

2011-07-26T06:13:00.066-04:00

Did you know that scientists are lying about the trend in sea ice extent? That's the conclusion if you apply the popular trend analysis technique those who claim that the earth has cooled, or 'not warmed', since 1998, or 2005. The probable reason you don't hear about this is that the 'lie' would be that scientists are grossly underestimating how drastic the trend to less ice is if you believe that method.

The method used to claim that there's a cooling trend, or no warming trend, is to cherry-pick a too-recent start year that is exceptionally high and compute the difference between that and a particular recent year (any one, repeat the 'no cooling trend for the last decade' for years afterwards, even if more recent years are warmer).

So I'll take a recent year that had large sea ice extent -- 1996, and compute the trend between there and a recent year that had a low extent -- 2007. Here's the straight line computed that way, plotted against the observations between 1996 and present. These data are September ice extents from the NSIDC:

And from eye-inspection of it, it even looks like the average error is about 0. Sometimes high, sometimes low. This trend is for ice pack extent to lose about 330,000 km^2 per year, against the about 78,000 km^2 that is computed for a linear trend by climate scientists. Clearly climate scientists are trying to hide the decline!

I've already done some things more honest than what the 'no warming since 1998' folks do, not least is, I showed you the trend line and the data. But this is far from sufficient to have a reasonable trend analysis.
Not least of the flaws is, most of the data were ignored. Related is, this was too short a period to establish a climate trend. A third is that business about 'average error'.

If I continue the pseudo-skeptics' cherry-picking method, but show you the 'trend' that comes from such an analysis against 30 years of data (1979-2008, this is also the reason for 2008 being the last year shown), the result is:

Now it's clear that this trend computation has nothing to do with representing what's going on with the sea ice extent. If you use a long enough period to be analyzing climate trends, you're protected against several varieties of silly results.

But suppose you are insisting, for some reason, that the climate system changed fundamentally in 1996, so that nothing before then matters. (1998 for those cherry-picking on global temperatures.) Is this trend meaningful then? Well, no.

The problem is the story of the statisticians target shooting. One fired and hit the target a foot to the left of center. The other hit a foot to the right. They then promptly congratulated each other on the fact that on average they had made bullseyes. Here is an example of two lines, both of which have zero average error compared to the observations. If the line is above the data, that's a positive error. If it's below, that's a negative error. We compute the error for every year and add them up. For both lines (the yellow 'least square' line and the orange 'slope-intercept' line) the average is zero.

I repeat: The average is zero. This is why we don't compute trends by looking for a line with zero average error. In fact, there are an infinite number of lines that would have zero average error. Whatever slope you want, I can find an intercept that would give zero average error. Conversely, this is a cherry-picker's dream come true. If they even mention actual numbers, which is rare, they can honestly say that their trend has zero average error. As long as the listeners are sufficient lazy or uninformed, the cherry-pickers are safe.

What is actually done almost all the time in science is to look at the total of the square of the errors. In that case, the fit line being 3 million km^2 too low (a value of -3) becomes and error of 9 (-3 * -3). Later, when the line is 3 million km^2 too high, (a value of +3) the error we add is, again, 9 (3 * 3). The average error for these two is zero. The sum of the squared errors is 18. Big difference! What is normally done is to find the line that has the least squared errors. Also mentioned as 'least squares method', 'least squares fit', 'ordinary least squares', and probably a number of other names. You can get quite a bit more elaborate than this, and at times need to. For such cases, I'll recommend taking a look over at Tamino's place. For instance, his recent article on How not to analyze tide gauge data.

Still, this will give you a decent start in reviewing arguments.

Trend analysis must be done over a long enough period
All relevant data should be considered.
If this results in a period shorter than normally considered long enough, a specific and strong explanation must be made
Trends cannot be computed by picking a single year to start from and a single end year and then ignore all years in between
Average error is not sufficient. Squared errors should be used, or some other method which avoids the problems that 'average error' has.

Regarding that last point, I'll suggest some looking at the numbers yourself. I've made up a spreadsheet available in both Excel and OpenOffice formats for you to play with. In the spreadsheet, page 1, is something called 'power'. It is just says what power the error is raised to. For 'average error', the power is 1. For squared error, it is 2. You can experiment some looking at lines for power = 3, 4, 5, .... For all odd powers, you can create a line where the error totals to zero (just keep juggling the 'slope' and 'intercept' until you get there). For even powers, you cannot get it to zero. But at some point, some combination of slope and intercept, you'll arrive at the minimum error. This won't be quite the same line for power = 2 as for 4, 6, 8, .... See for yourself how different the lines are. A more advance method, or at least a little more challenging to enter, is to look for the least absolute deviation. For this you count 3 million km^2 too low as an error of 3 (rather than -3) and an error of 3 million km^2 too high is also 3.

Since I've been mentioning the temperature trends, let's take a look at those. I'll use the Hadley - CRU temperatures because those are favored by the cherry-pickers for this purpose. Others don't show 1998 as the warmest year, which defeats the cherry-picking. Since one of the favorite lines of the cherry-pickers is 'cooling for the last decade', which they've repeated since long after then end of 2007, I'll take the decade 1998-2007. The cherry-pickers' method gives a trend of -0.0147 K per year. If it were meaningful, which a decade isn't for temperature, then that'd be a pretty large figure. Warming over the last century was only around 0.8 K per century, and this would be a cooling of 1.5 K. Least squares over that period shows a warming trend of 0.0046 K per year. Over the century that's about 0.5 K, which is a bit less than the 0.8, but markedly closer. Here's what the two lines look like compared to the data for the 'last decade' of the cherry-pickers:

And here's what it looks like over the whole period of record:

The cherry-pickers' line illustrates two different thing. 1) Why it is we don't use only 10 years to define climate and 2) Why we don't use the cherry pickers' method. Even though 10 years is too short to define climate, if we use least squares, the results are at least not insane.

As usual, don't take my word for any of this. If what I've said is already obvious, I hope you found the writing interesting. If the content is not obvious, get hold of some data yourself, whether the NSIDC (link to the right) ice extents for September, or the HadCRUT (link above) or some other climate data type and start experimenting with the data yourself. Look, yourself, at what kinds of lines you can run through the data, and how long a period you need before the lines start to stabilize -- that one year more or less doesn't mean you need a very different line. Also take a look at methods for finding a line through the data. Average error, I claim, is a very bad method. But try it out yourself. See what happens for least square, least 4th power, least 18th, and so forth.

And, of course, if what you find disagrees with what I've said here, post a comment explaining what you did and how what you saw differs from what I discuss here.

In the mean time, I'll suggest that people applying the cherry-picker method are either not honest -- they're trying to mislead you -- or they just don't know much about how to analyze data. Either way, these aren't the people to learn about climate from, no matter how much you might like their thoughts and conclusions. That's one of the drawbacks to doing science -- the people doing the best work aren't always the ones you like the most.

Update (3 Aug 2011): It seems that blogger is having problems with comments. That's particularly unfortunate here as I think there are likely many good comments getting choked by the system. If you've had this problem, please email them to me at bobg at radix dot net.

Reconsidering forecasts and wagers

2011-07-22T05:44:00.074-04:00

Comments in two different threads suggest that there's some good room for more discussion. Yesterday, I noted that if someone would only take a bet at 50 to 1 odds, he wasn't very confident about his side of the bet. Certainly not the 'just as likely to warm as cool' that was the statement which prompted the bet. (Nobody here, by the way.) As M commented, and I assume that he mean in terms of real money, he'd want 5:1 odds even on an even money situation. I wouldn't mind that myself (again, I don't bet real money, but in examining the mathematics of expectation, that's how it goes).

For illustration of a concept that was in my mind, but not in the prior post, I'll pick up with Alastair's comments about his predictions and preferences for odds. The thing is, the odds that you're willing to accept also describe what you think is really the case. At least it's much closer than in the case that your lunch money is riding on the bet and you're already hungry. Our situation here is betting something that doesn't exist (quatloos) and presuming that we can make a lot of bets, so that we can average out the wins and losses over time -- converging towards the mathematical situation.

I make use of this in group settings when lunch place selection is being discussed. In a group of, say, 6, all will claim to have no preference between places A and B. I pull out a coin and say, fine, heads it's A, tails it's B. An amazing number of people suddenly develop a preference for one or the other.

So it goes with Alastair's estimate of 3.9 million km^2 against mine of 4.4. His original estimate for uncertainty was 0.1 million km^2, while mine was 0.5 million. I'll add a curve for him, where the uncertainty is increased to match mine; that'll be the 'Alastair-2' curve:

Here begins the fun and games of the mathematics. My expectation, if I thought my statistical guess were seriously good, which I don't (hence the 3 different methods for estimating the September cover) is the blue curve. Alastair's original is the orangish -- the one with the very narrow peak. And the curve for less confidence is the yellow -- same peak, but broader spread.

To the extent that we believe any of our estimates, and estimates of uncertainty, the probability that we're predicting for a given range of extents is the area under the curve between those. You see essentially no area under Alastair's original curve for extents over 4.2 million km^2. That represents a high confidence in a new record. My statistical guess shows some area under the blue curve for extents under 4.2, meaning that I wouldn't be shocked if a new record were set this year. I would, on the other hand, be shocked if the ice extent were over 5.5 million km^2.

In his second comment, Alastair mentioned a good science point -- he had changed his forecast method because it hadn't worked in previous years. That's key to doing science. Being right is the goal, and the way you learn how to be right is to change your methods as reality disagrees with your expectation. Mistakes are fine -- learning from them is where you're doing the science.

Alastair also mentioned that he thinks there's a 50% chance of a new record this year. The old record was 4.3 million km^2. His original estimates give essentially 100% chance for that. My modification shows an 86% chance for his less confident estimate. To get it down to 50%, we have to increase the uncertainty (the standard deviation of the curve) to 4! That's ... extraordinary, to say the least. The difference between highest and lowest years is not even that large. The other way to get the probability of a new record to 50% is to make the center of his expectation to be 4.3 million km^2 -- practically the same as my prediction of 4.4.

Taking my own numbers, the guess translates to a 37.5% chance of a new record this year. Let's look at that. Does my intuition complain when it sees that number? Not really. If it were to be 98%, or even 86%, I'd be concerned. But somewhat less than half ... doesn't seem outrageous. 2% would also be unreasonable. We set the record in only 30 years, which means a 3% chance in each year. And there's a downward trend in the extent, so the chances of a new record should be increasing year by year.

It looks like I misplaced a decimal point in figuring things the other day. Here's a table of areas (probabilities) of various outcomes with respect to my curve and the two I'm using for Alastair:

Outcome	Bob	Alastair-1	Alastair-2
Extent below 4.15	0.25	0.9999	0.77
Extent below 4.30	0.375	1.0	0.86
Extent between 3.8 and 4.0	0.080	0.838	0.223

So our expectations for the ice cover being below 4.15 are really more like 4:1, Alastair. Does that sound more attractive? 3:1 for the less confident version of your prediction. For setting a new record, your 50% chance translates to 4:3 odds between us -- you win 3 quatloos if it is a new record, pay 4 if it isn't.

Sea Ice Wagers

2011-07-21T05:18:00.002-04:00

I'll hang out the shingle here for folks who want to talk about sea ice wagers with me. Keep in mind that I don't bet real money, quatloos instead. Call them points of honor.

I don't take any of this very seriously. Seriously enough to mention that so far, I have won all my wagers of quatloos.

The serious part is how rapidly pseudoskeptics leave the room if one raises even a casual honor bet that takes their statements seriously. People who claim that temperatures are just as likely to go down as up demand 50:1 odds if challenged to a bet on whether temperatures will go up or down. If they believed what they were saying, they'd take 1:1 odds, and be happy with either side of the bet.

Last fall as I was evaluating the seaice ... statements from Wattsupwiththat , Joe Bastardi declared (over there) his belief that September 2011 ice would be at least 5.5 million km^2. If anyone can find Bastardi's address, I would be happy to bet quite a few quatloos with him. If he believed what he said, he should pay off infinity to my 1 if ice is less than 5.5 million km^2. I'll pay him 1 if it's over.

Making your own sea ice estimates

2011-07-20T06:42:00.045-04:00

The Sea Ice Outlook does accept estimates from outside the professional community. Maybe not everybody involved is thrilled by this, but I do think it's a good idea from my distant vantage. And Jim Overland, one of the people behind the SIO, is strongly in favor of it. (I had a chance to talk with him about the outlook and other ice matters a few weeks ago.)

In the most recent report, there are 3 submissions from 'outsiders' -- Chris Randles (you've seen him comment here as crandles) and Larry Hamilton, both at Neven's Arctic Sea Ice Blog, and one from Wattsupwiththat.

Watts' entry was a poll of readers. While perfectly legitimate as an entry, it's also perfectly useless scientifically. One goal of science is to gain understanding of the system in order to spread the knowledge around. Polling can't be spread.

Much more interesting are Chris's and Larry's methods. Both are obviously methods of great brilliance, as they currently have the same estimate as I do from my statistical method -- 4.4 million km^2 for this September. Beyond that, you can read their method descriptions in the Sea Ice Outlook report and start constructing your own method by not making the mistake they and I have made. Whatever those turn out to be. Larry Hamilton's write ups (one for ice extent, one for volume) are:
http://neven1.typepad.com/blog/2011/04/trends-in-arctic-sea-ice-extent.html
http://neven1.typepad.com/blog/2011/04/trends-in-arctic-sea-ice-volume.html

And you can also examine model output from the PIPS replacement model ACNFS, PIOMAS, and CFS as a basis for making your own estimates. (And please do cite others that you know of.)

All are welcome to post your methods here in addition to (or instead of) at the SIO.

Adding to the blogroll

2011-07-19T07:20:00.002-04:00

Finally got around, after broken wrist and other hiatus, to adding http://blogs.chron.com/climateabyss/ to the blogroll. Thanks -M for the suggestion.

Other suggestions welcome. As always, I aim for sources that lean to the educational -- science content rather than political policy.

2011 Sea Ice Outlooks

2011-07-18T06:25:00.017-04:00

For 2011, I added a third method of estimation. Or, rather, I talked with someone who was using a third method and helped refine it some. Our guesses are 4.4, 4.8, 5.0 million km^2 for September monthly average sea ice extent as computed by NSIDC.

Again, I'll put our estimates in context of some other estimation methods.

Climatology 1979-2000: 7.03 million km^2
Climatology 1979-2008: 6.67 million km^2
Linear Trend Climatology 1979-2008: 5.31 million km^2
Wang, Wu, Grumbine model: 5.0 million km^2
Wu, Grumbine, Wang model: 4.8 million km^2
Grumbine, Wu, Wang statistical ensemble: 4.4 million km^2

We normally like 30 years for deciding a trend, but 20 years can be enough. Since it's far from obvious how to decide what is 'climatology' when climate is changing, taking a few different approaches seems a good idea. I include a climatology which has a (declining) linear trend on the grounds that there clearly is a declining trend to the sea ice extent, so we expect this year to be lower than last year to some degree (on average).

The two climatology means (22 and 30 years) are relatively close to each other, and are far away from anything we've seen in years. Taking the 30 year trend, from the first 30 years of the satellite record, gives 5.31 million km^2, which is close to a figure seen in recent years (5.36 in 2009), but well above any of our estimates or the 4.9 seen last year.

Below the fold for a few more words about our 3 estimates:
The new model method is Wanqiu Wang's. For this, he took the sea ice output from the CFS 2.0 model (Coupled Forecast System) and compared the model's extent for September to the observed. For all Septembers. Then derive a relationship between what the model thought would happen and what did. The model is biased towards too much ice cover, something we've known for a long time, so his correction is to reduce the model's estimate. Take a look at the link, as you can see more detail on the sea ice -- monthly to 9 months lead -- estimated from the model. The estimate's variability is 0.5 million km^2 vs. observation ('1 sigma'). And the estimate is 5.0 million km^2.

Last fall, I mentioned that Xingren Wu and I were thinking of some
new experiments on the CFS to see if we could improve the model's raw sea ice estimates. Well, so far it looks good, in that we were able to use a 30 cm cutoff thickness, instead of the 60 cm we used last year. Not as good as we'd hoped -- 10 cm -- but a big improvement. Or at least it's a big improvement if this year's estimate turns out as good or better than last year's! Again we used an ensemble of estimates from the model. The '1 sigma' spread is 0.22 million km^2.

The statistical estimate's method is the same one as last year. Just that we now have another year of data to use in making the regressions. The '1 sigma' spread is still 0.5 million km^2. The estimate is 4.41 million km^2 -- well below last year's 4.78. The reason for the large change is not that last year was such a low ice cover year; the statistical estimator was actually too low last year. The reason for the decline is that the curve I used is now entering the period where year by year the changes will be relatively large. If this approach is reasonably correct, then this year should indeed see a large drop. If we don't see it, I'll have to go back to think of a different pretty simple statistical method.

'1 sigma':
This may look strange to some. What I mean is that if you drew a normal, 'bell', curve, the central 2/3rds of the area would be this close (whether higher or lower) to the mean. 1/6th of the time, the observation would be higher than the mean plus '1 sigma', and 1/6th of the time it would be lower than mean minus '1 sigma'. A few percent of the time you'd see something more than 2 times as far away from the mean. And, if you collected enough observations, you'd see some that are more than 3 times as far.

I mentioned last fall and described in a bit of detail how you can combine different estimates of the same quantity. It relies not only on the estimate, but the variability (the 1 sigma again) of the estimates. Applied here, the joint estimate is 4.77 million km^2. Not terribly surprising to see it come in close to the Wu model estimate since it is close to the middle of the other two and has the lowest variability.

update:
See also Larry Hamilton's estimates at:
http://neven1.typepad.com/blog/2011/04/trends-in-arctic-sea-ice-extent.html
http://neven1.typepad.com/blog/2011/04/trends-in-arctic-sea-ice-volume.html
(more to come on Wednesday)

How large a conspiracy?

2011-07-17T06:58:00.022-04:00

Did you know that the observed decline in Arctic sea ice cover is all just a fake? I didn't, but encountered folks who thought so. Since I'm one of the people who would have to be involved in the conspiracy or at least being duped by the masterminds, I'll take a minute to ponder the matter and how someone who does not personally know many of the people involved could go about deciding whether there was indeed such a conspiracy.

One of the tools I find useful in considering issues is to follow a common mathematician's approach -- called the reductio ad absurdum. That means reduction to absurdity. What you do is assume the thing at hand to be true, and then pursue it logically to see where you go -- whether it leads you to absurdity. This is also the starting point for a proof by contradiction. Again, assume the thing to be true and see if it leads logically to a conclusion that contradicts what you know to be true.

So let's assume that there is indeed a fake involved in the decline in Arctic sea ice. The people involved were a little particular -- the passive microwave sea ice record.

Some questions to pursue, then:

How long would the conspiracy have to have lasted?
How much data would have to be faked?
Who all would have to be involved in the fakery?
Are there other sources of data to confirm or refute the passive microwave observations?

* The period of the passive microwave data is 1979 (actually late October 1978) to the present. Much of the time, only a single instrument was flying (easier to fake?). SMMR from 1978 to 1987, DMSP SSMI F-8 for a few years, F-11 for some more, then F-13 1995 to 2009. As we get more recent, though, there are multiple satellites, F-14 until late 2008, F-15 until 2008/present (the different dates depending on what algorithm you're using. And a different passive microwave instrument, AMSRE from 2002 to present

So when does the fakery have to be? The trend from start passed significance in the mid-1990s, and has only gotten moreso since then. If the trend were faked, it had to start from some time around then. Since the declining trend has only gotten larger since then, the conspiracy must have been getting bolder.

Probably at least 15 years -- perhaps nature produced that marginally significant trend back then, but then the conspiracy took the chance to intervene?

Anyone familiar with scientists has already rejected the conspiracy as reductio ad absurdum. Scientists are very fond of talking about what they're up to. You may have noticed some of that here. The lifetime of a conspiracy of scientists is probably measured in seconds for that reason alone.

Let's continue, though. Maybe (chortle) there are some wily scientists who are great conspirators.

* The scale of data involved is, well, every passive microwave instrument flying since 1979. They're not terribly high-density instruments by modern standards. About 0.08 Gb/day for SMMR and SSMI. AMSRE is more demanding, but still only about 1 Gb/day. Now you wouldn't have to fake every byte. The Arctic ice pack at its maximum only covers (well, covered, wintertime maximum has also declined) about 15 million km^2 -- about 3% of the globe. But only the decrease in area itself would need to be faked, something like 2.5 million km^2. 0.5% of the data, so a mere 50 Mb/day even for AMSRE.

On the other hand, it would have to be skillful fakery. You can't just throw in random numbers. The algorithms for finding sea ice concentration and ocean wind speed, among others would go nuts -- and be detected thereby. The fake has to make sense as sea ice observations (of no ice) and as wind observations, and for each other the others as well.

But who has to be doing the faking?

* Who? The SSMI are on US Department of Defense satellites. So either the DoD are the original fakers, or somebody or some group has managed to hack the DoD satellite program. And, for some reason known only to them, they mess only with the sea ice concentration observations.

With the AMSRE, the instrument is from Japan and is flying on a NASA platform. So either the Japanese instrument makers decided to fake the observations carefully (only that 0.5% or so in the Arctic periphery), or NASA decided to fake what it relayed to ground -- and the Japanese team never noticed.

I confess that the numbers don't have to be very large here -- a handful of people in the DoD (or hacking DoD) and another handful in Japan and NASA (or hacking NASA). A single group hacking both DoD and NASA would make the smaller number. It might also make it easier to figure why the conspiracy would be faking a decline in sea ice cover. Why Japan, NASA, and the DoD would want to fake a decline in sea ice is a bit of a mystery to me even assuming that there were a conspiracy. NASA has rather different goals than DoD, and why Japan would want the same as either ...? Numbers get bigger if you consider the next point. Quite a lot larger.

* Are there other data sources? Yes. Quite a few, and this is where and why things really blow up for the conspiracy-minded folks.

Instruments that use visible wavelengths -- MODIS, AVHRR, GOES, OLS, and so on. These instruments are on platforms from the US -- NOAA, NASA, DoD, Europe (EUMETSAT), India, China, Japan, and probably several more.
Flight observations from the US, Canada, Russia, Norway, and others. Anybody flying a plane can see that there's no ice for hundreds of km that the conspirators are trying to lie about. Anybody
Ship observations from the US, Canada, Russia, Norway, and, again, others. China, for instance, has started taking ships to the Arctic recently.
Fishermen -- See The Deadliest Catch for some visuals and an appreciation of what they're doing. In any case, fishermen pursue the ice edge because the fishing, at least for some things, is best near the ice edge. If the ice edge were a lie, the fishermen would either not be catching what they need, or they'd be dead. The location of the ice edge is a matter of life and death.
Traditional cultures in the Arctic -- many different tribes in different part of the Arctic hunt from the ice. They do use satellite information (something I know first hand). If the satellite information were a lie, the tribes would be going to the wrong place, or I'd assume that they'd notice that they hadn't fallen in to the ocean when the satellite types said they should.

* Summing up -- The idea of conspiracy is absurd. Too many different groups would have to be involved, and be involved for a long time.

Ok, you knew I was going to get to this conclusion. After all, I'm one of the people who would have to be involved in the conspiracy, or be too embarrassed to admit that I'd been duped. So the thing to do instead or in addition is to examine a point or three yourself. Whether the visible satellites observations match up with the passive microwave is something you can check out yourself fairly easily. Neven, for instance, routinely shows both types of observations.

Back in 2007, when the ice cover was behaving so strangely compared to prior years, that was one of the things I did myself. Not that I was thinking conspiracy, but that maybe the sensor was going bad. So I looked for some visible observations as a check against the passive microwave. They agreed, something amazing really was going on.

Happy Anniversary ...

2011-07-16T18:58:00.000-04:00

... to me and my wife. Part of the recent hiatus is due to me taking some time off to celebrate my 5th wedding anniversary with my wife. I've marked this as a 'being a scientist' post since being a spouse is another thing that scientists do. I enjoy that, and others of my roles -- being a son, father, uncle, brother. Science is another, of course, and I enjoy that too, and is the point of the blog. But a secondary point is that scientists are people.

We took the time, among other things, to think about what we have done in the 5 years so far -- good ideas we've carried out, storms we've weathered, and so forth. And to think about what kinds of things we might like to try in the next 5. There's a bit of science there -- experiments. We try things, and some we like and keep doing, and some we don't, and quit. Probably none of these experiments are publishable, but that's not the point. We learn what we're trying to learn. And have a lot of fun along the way!

Hiatus to end shortly

2011-06-29T14:39:00.000-04:00

A heads up, warning, or promise of good things to come. Depends on your viewpoint. Still, my hiatus from blogging will be ending shortly, probably this weekend. Many things to come, including the longer write up of my September sea ice guesses (3 of them this time, and I'll constructed a combined guess), getting back to some things from the 'what is a day' and 'where is north' posts, and some things from the articles I'm finishing in the next few days for journal publication. That includes sea ice drift, evolutionary computing (two papers), and a topic to be named later.

Hiatus

2011-05-25T18:59:00.003-04:00

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.

Setting Goals in Running

2011-05-25T13:59:00.000-04:00

One of the things I've been doing while on hiatus (which will, barring a couple of exceptions, continue for a while yet) is getting back to my running. (Yes, yet again I am getting back to running.)

Now that, knock wood, I am in reasonable health, it's time to be improving on that by getting active. Running is my prime way of getting active. Even though there's no logical or physical requirement, if I'm not running some, I also don't do other good things like lift some weights, do some rehabilitation exercises, do some core strengthening, and so on. So it's more important for me to run than if I were otherwise more virtuous.

At the end of April, my youngest son gave me an excuse that will help me with my goal setting process. Some people do fine with 'just do it'. Many people, and I'm one, do better with having a goal. My major goal is to complete that half marathon race next year the same day as my son. I'd say with him, but with little training he ran a time that I'll probably need a couple years of steady training to reach! So that's a nice goal itself, but how to get from here -- almost no running or other aerobic exercise in the previous 6 months -- to there -- running 13.1 miles (21.1 km) straight through?

That's where intermediate goals come in. Athletes, I'm told, make much use of this, and it seems like a generally good idea. A big goal a year out is the major motivator. But it doesn't say much about the roadmap. And without a decent roadmap, it's easy to do nothing for the first 9 months and then exclaim "Oh no, how am I going to run that race in only 3 months!?"

My plan is to break the major goal in to quarters. 3 months at a time, each ending with a goal race. It will be 5k at the end of July, 10k at the end of October, 15k at the end of January, and the half marathon iteself at the end of April 2012. I know that there are couch to marathon in 4 month plans, but I dislike that approach greatly. I've heard from too many people who never ran again, or who got injured (and probably never ran again) after that approach. Since I plan on keeping running, and am doing it for my health, such an approach is out.

The first 3 months take me from couch to 5k. In this period, I'll be working towards running 30 minutes straight through. Right now, I'm at run 2 minutes, walk 1 minute. You can see my suggestion for how to choose run:walk proportions on my running progression note. The basic idea is to start with walking for 30 minutes as a comfortable activity and then toss in some comfortable running as part of your 30 minutes. What's happening in my body (and yours if you go through a similar program) in this phase is that the cardiovascular system is improving at pulling in oxygen and distributing it around to the muscles.

Going from 5k to 10k involves a couple different things. The obvious one is that I have to increase my running duration from 30 minutes to 60 minutes. Regardless of your pace, by the way, those times are good durations to work with. 30 and 60 minutes seem to be important durations for your body's response to the training. The other training consideration is that even though by this time my cardiovascular system will be in relatively good shape, the muscles will still be adapting to the new activity of running. They 'forget' that running is normal in something like 6 months. It takes 3-6 months for them to relearn how to run, rebuild to be strong enough to run easily, and so forth. This doesn't mean that you can't run at all; but it does mean that running is still taxing the muscles and they need more time to rebuild after each workout than will be the case later. It also suggests that this is not the time to run on particularly hilly courses, or with extreme speed (sprinting 100 meters). They're good things to do, in principle, but first the muscles need to be trained and ready.

After these 6 months, the muscles and cardiovascular system will be in decent shape, and ready for the demands of running good long distances and even with pretty good speed. Comments about speed are always compared to the person. Compared to an elite runner, I have never run with speed. My fastest 1 mile race is far slower than an elite marathoner averages for 26.2 miles. Still, for me that is speed. More importantly, in terms of health and training, it is speed to my body -- challenging and requiring extra time for recovery.

From 6-9 months, I'll be working on going from 10 k to 15 k. This will also be when the last major system in the body will be starting to finish up its adaptation to running. That's the tendons, ligaments, and bones. One of the major types of injury in runners, perhaps especially in the first year, is to inflame the tendons. Even though the muscles are fine by this point, and perfectly happy to contract many, many times, and do so with speed, the tendons they're pulling are still adjusting. Those 30-60 minute runs at moderate speed of the first 6 months aren't terribly taxing (if they are -- slow down, run shorter, or both) on the tendons and such. But in this quarter, I'll be taking the duration up to 90 minutes, and starting to introduce some more challenging running (more significant hills, running pretty hard for 100-400 meters). So, to permit the adjustment, my pace will be pretty relaxed on those long runs, and I won't be running as many of those hard/fast stretches as I will a year later. Enough to encourage the body to adapt, but not enough, knock wood, to encourage injury.

The last stretch will be 9-12 months in to training -- working on going from being able to run a 15k race to being able to run a half marathon. In some respects, this is the easiest stage. There is a rule of thumb, which has worked for me also, that if you can run some distance, you can survive a run 1.5 times as long. It won't be pretty, and you're somewhat asking for injury, but you can probably complete it. What I'll be training for is to complete it in reasonable comfort and without injury. In this phase, the body should be pretty much finished with adapting to running. So it is here that I'll be less restrictive about how fast I run, or how hilly the routes are. This last is particularly good, because I really like running on trails, and they tend not to be flat around here. I'll also work on increasing my longest run of the week from 90 minutes to 120 minutes. Maybe that'll be every other week for the runs longer than 90 minutes. 90 is really my longest 'do it every week' kind of duration.

Along the way, I'll have an eye on numbers. (You're shocked that I look at numbers, I know.) There are two sets, paces and equivalents. I have a conservative goal of 2:15 (hours:minutes) for the half marathon. That means a certain pace per mile (km). So one thing I'll have an eye on is how far I can jog the required pace. At the moment, with my walk:run approach, that's about 1 mile. The other thing are the equivalents. The farther you race, the slower you run. When I was in shape last, I could run a 4 minute mile (pace) -- for about 100 meters. For most people, if they're equally well-trained at both distances, they'll take 4.67 times as long to run a half marathon as a 5k. And I have rules for other distances as well. The positive part of this is, I don't need to run very fast by my standards to meet these numbers. The only thing I 'need' to do is meet the 100 meter time that is equivalent to the 2:15 half marathon*, which I did last week and which did not require me to strain the muscles and tendons. The equivalent for an optimistic time (1:45, let's say) would require serious strain, and, at this point, probably get me injured. This way, I get to check something off -- mark off a milestone, another good thing for training plans -- without having to risk much. Positive feedback.

And then celebrate the year back to running by doing that half marathon race with my son.

* There's more than a little fiction involved in extrapolating from 100 meters to 21.1 km. And that qualifier of 'equally well trained' is important. It takes much longer to get to a given performance level for the half marathon than the 100 meters. Still, for my own amusement, and for having things to check off for my progress, it works. If I were already in reasonable shape, I wouldn't look at equivalents for anything shorter than 5k.

Starting to work with data

2011-04-05T05:26:00.001-04:00

Let's suppose you have no particular axe to grind and are wondering about what 2011's average temperature will look like. What are some things you would do? First, of course, is try to get some data to work with. I'm choosing the NCDC global land and sea temperatures, annual average. If you prefer some other source, go with that. And if you're interested in something entirely different than this, have a go with that data source. I'll be talking about methods of dealing with data, not anything unique to annual average global mean 2 meter air temperatures.

Next is to do some basic looking around the data. That includes plotting it, finding the maximum, the minimum, the average, and the standard deviation. Since you're thinking about making a prediction of 2011, you also want to take a look at how things change from year to year. What does the plot of year to year change look like? What are the maximum, minimum, average, and standard deviations of change?

	Anomaly	Change
Maximum	0.6183	0.2493
Minimum	-0.4146	-0.2595
Average	0.0086	0.0058
Standard Deviation	0.252	0.098

The bulk statistics, as usual, aren't terribly informative, but they do give us a sense of what is going on and that is important for getting started with data. The record high temperature was 0.6183 (in 2005, statistically indistinguishable from 2010's 0.6158). The record fastest year to year warming was 0.2493 degrees, from 1976 to 1977. 0.2405 from 1956 to 1957, and 0.2024 from 1996 to 1997. On the cold side, the record coldest year was -0.4146 degrees (below the 1901-2000 average) in 1911. Record cooling was -0.2595 from 1963-1964; with -0.2482 from 1973-1974 and -0.2217 from 1953-1954.

The standard deviation tells us something about how much spread there is in the values away from their means. We don't expect to see many that are more than 2 standard deviations away from the mean.

For all that I don't show many plots here, I do think it's important to look at them yourself. Without looking at the plots to know better, you'd be tempted to forecast this year to be about the average, plus or minus a standard deviation. So you'd call for an anomaly of 0.0086 plus or minus 0.252. At that point, I'd be happy to take up a bet against you! Because I've looked at the plot and done a little more math. Here's the plot of temperatures:

One thing we see is that the temperatures are not random from year to year. A cold year tends to be followed by a cold year, and a warm year tends to be followed by another warm year. The easier way to be quantitative about this is to look at the year to year changes:

Although our table above assures us that the average change is not zero -- being +0.0058 degrees/year -- I defy anybody to pick that up from eyeball examination of the figure. Likewise, it's hard to say by eye that there's a trend in these numbers. The good part to that is that, unlike the temperatures themselves, the changes are (to eyeball inspection) pretty random. They go up, they go down. We see few changes more than 2 standard deviations away from the average, as expected for random numbers. And negative values occur throughout the time period, as do positive values.

That means we can make a better-informed guess about 2011's temperatures -- that they'll be the same as last year, plus the average change (0.0058 K/year), and plus or minus the annual variability (0.098 K). So, for 2011, our simple prediction is 0.6216 plus or minus 0.098. That's both quite a bit warmer than the previous, even more simple, prediction of 0.0086, and quite a bit more confident -- variation of 0.098 vs. 0.252.

To make a serious prediction, of course, we'd want something more intelligent than the present simple methods. For instance, you'd probably want to consider whether the current changes are represented well by the 130 year average. You'd also want to include some knowledge about how La Nina and El Nino behave -- we're currently in La Nina conditions, which tend to be cold. And you'd want to include in your uncertainty the possibility of a major volcanic eruption -- such as occurred with Mount Agung in 1963-1964. You remember those years from above -- they're when the record year to year cooling occurred.

The simple methods give us a start to understanding the variable, and a bit about how it changes. In this case, in looking at extreme changes tells us more than extreme values. We do get a sense that the extreme warm values are all recent, and the extreme colds are some time back, which argues for a change of climate but not much beyond that. The extreme changes, however, point us to some climate events -- volcanoes, El Nino, and La Nina. The typical changes tell us that it's hard to change the earth's temperature by much very quickly. 0.1 degrees is a large change year to year, and 0.2 is very large.

Messy Science

2011-04-04T05:20:00.001-04:00

The type of science I like is what I call 'messy science'. 'Neat science' is the sort where you are looking at only one or maybe a few (easily counted) objects, which can be described by one or only a few number, like mass and momentum. Celestial mechanics is a 'neat science' in this way, at least for the solar system. Also, I hope, obvious is that being neat does not mean that it's easy.

Messy science is things like organismal biology or, for my own professional work, climate. In climate, you can take something simple, like the rotation of the earth, and wind up with a lengthy list of things which affect it. Conversely, you have a long list of things affected by it -- including the climate. In What is a Day?, I mentioned a few things which affect the earth's rotation.

Another thing that is involved is the fact that the earth's inner core -- sitting about 5000 km below us -- rotates at a different rate than the crust. In between the two is the liquid outer core, which has its own angular momentum, and whose top is about 3000 km below us. More recent work (that paper was published in 2000) is now suggesting that one can learn about climate by studying the earth's core's rotation -- here for the press release, or here for the Dickey, Marcus, and deViron, 2011 paper: Air Temperature and Anthropogenic Forcing: Insights from the Solid Earth.

What a lovely, messy, situation! We can look to the earth's core for signs about what is happening in climate!

I'll come back to this later for a discussion of the research paper itself. Maybe, like many new ideas, it won't hold up. If not, somebody else will get to write the paper which shows why not. In the mean time, here's another candidate for the messiness of climate. There's also another datum for how small the scientific community is. I've met the lead author; it was she who explained to me how it was possible to measure length of day variations to such very high precision.

Says who?

2011-04-01T05:05:00.007-04:00

I think citations are a greatly underappreciated part of scientific works. They also, for some of the same reasons, provide a way of assessing the strength of a source even if you don't know the topic that's involved.

My first real introduction to citations as being important was when a history teacher of mine in college was concerned that I'd committed academic dishonesty -- failed to cite a source for something she felt was obscure. After a nervous couple of minutes for me, we had a nice chat. What I'd done was to mention, without citation, Newton's prism experiment. I hadn't cited it because it was something I'd been seeing mentioned for years without citation, so figured counted as 'common knowledge' and not in need of a citation. My history teacher, on the other hand, had never heard of it before, so was looking for the citation to the person who had discovered the experiment (perhaps a citation to Newton himself; I now have the right book -- Newton's Opticks).

So that's one use of citations -- avoid annoying your teacher. Somewhat more generally, credit people for the work they do. That's an important thing in being a scientist, as the people you're giving credit to are your colleagues. Conversely, your colleagues will be peeved, to put it mildly, if you fail to credit them for their work.

The use at hand, as the title suggests, is to provide the backup for your claims. You could avoid some of that by providing full descriptions yourself, but then your article becomes impossibly long. Instead you can write something like "The earth is round[1] and rotates[2].", where you then give the full address to 1 and 2 somewhere later in the document (in print media days) or hyperlink the words directly. An alternate that I prefer is to provide the direct 'who' and 'when', such as "The earth is round [~~c.f.~~ e.g. Aristotle, ca. 322 BC*] and rotates [Foucault, 1851]." In this way the reader immediately sees something about who your source is, and how old it is, and retains some merit even in a hyperlinking medium.

If you could read infinitely fast, it might be doable to simply read everything from everywhere. But for us humans, some means of trimming the candidates to manageable volumes is needed. So, for myself at least, if I'm trying to learn about a scientific topic, I head for scientific sources, or as close to the original as I can understand.

The bibliography/citation list is a quick way to figure this out. Places that are citing wikipedia articles, newspaper editorials, and so forth, for most of what they have to say are not strong sources. If the topic has scientific merit, there will be scientific papers on it. If I couldn't read, or would have a hard time finding and reading, the original scientific papers (which is true in most fields), then I want to be learning from someone who could and did. The strong source is one which is providing me the ability to go in to the literature and start learning about the particular part of the article which caught my attention.

This last is another important purpose of citation: It helps readers learn more. I would rather be learning the science from an author who is trying to help me learn it.

Now for the mirror test: How do my own postings hold up to that standard? In this post, it does ok, in the sense that this isn't about the content of science; it's my opinion of some things to consider in looking for sources from which to learn the science. In the science posts, not always as well as I'd like. So I'll take this post as a reminder to myself to include more references and links.

In my blogroll, two that are particularly good with their citations are Skeptical Science and RealClimate, though I think almost all are pretty good -- at least better than I.

*
c.f., I translate to myself as meaning 'See, for example'. It means that there's more than one source, and this is either the one that I used (though I know there are more), or that for some reason I prefer it.
Update: my self-translation is incorrect, see Nick and Peter's comments. What I really want is 'e.g.', for exempli gratia (free example is my translation here, unfortunately, it's my son who is the latinist.)

ca means 'about' (circa).

Internationality of Science

2011-03-18T07:49:00.000-04:00

Comments here and at Serendipity by Kooiti Masuda remind me yet again of the internationality of science. Not news to people in the field, but perhaps for younger readers. And the small world that science is.

Here, Masuda observed: Precise description of the polar motion by Hisashi Kimura (who led observations at Mizusawa) was a moment of demonstration that the Japanese can substantially contribute to the international scientific enterprise.

Today, of course, it's no surprise. But in 1899, when this was happening, Japan was new to the world science scene. The US wasn't exactly an old hand itself. While we'd had some individual excellent scientists before then (Ben Franklin, for instance), it wasn't until after the land grant universities (founded in 1850s and 1860s) had been at work for some decades that the US was noticeable in international science. Japan had an even later start and more rapid run up. Today, there are other countries going through the process of building their science infrastructures to the point of making significant contributions internationally.

Over at Serendipity, part of Masuda's comment is:

It reminded me another thought. There was a great development of computational geophysics in the latter half of the 20th century, including both climate modeling (Manabe, Arakawa, Kasahara), meteorological data assimilation (Sasaki, Miyakoda) and quantitative seismology (Aki, Kanamori), largely contributed by Japanese-American (born in Japan and emigrated to the USA) scientists. They made innovation by amalgamating the oriental tradition of precise numerical computation and the western tradition of rigorous logical mathematics. (I have not yet substantiated this interpretation, though.)

The very small world effect involved -- I have a connection with almost every person he names. Manabe would probably even remember me :-) after our chats in the 1990s, where I'd tell him how bad the sea ice was in his model and he'd cheerfully agree and then tell me about how good his results were anyhow. We were both right. Miyakoda, I've never met, but he's the reason that I've had sushi. My graduate advisor knew Miyakoda and apparently Miyakoda had a comment that nobody could be an oceanographer who hadn't had sushi. So after I'd successfully defended my thesis, my advisor took me out to a sushi place, thereby finishing my qualifications. Kanamori I wouldn't count except for some jr. high students. Namely, I'd attended a presentation of Kanamori's when I was in graduate school. Quiet a few years later, I went to talk to a jr. high science class. It turned out they were studying earthquakes, and their textbook had a personal profile of Kanamori. The kids were shocked/amazed/bewildered when I mentioned his sense of humor coming through in his presentation. The notion of a scientist having a sense of humor was pretty strange to them.

Kooiti Masuda: Do you know of any English language histories of Japanese mathematics and science? Your comment about the numerical computation tradition is interesting to me.

I also knew a Japanese-born and -educated scientist who was no great fan of mathematics -- Ted (Tetsuya) Fujita, who liked to be known as 'Mr. Tornado', and was down the hall from me at the University of Chicago. He had phenomenal physical insight, and prided himself on using a minimum of mathematics.

Where is north?

2011-03-17T06:44:00.005-04:00

Where is north is actually intensely tied to the question of What is a day?. At least we wind up defining it in much the same way(s) as we define the day. In the previous post, I gave a definition for north/south. Namely, the line of a shadow cast by the sun at solar noon (itself define by the fact that it's the shortest shadow of the day) is north/south.

As happened for the day, we find our most accurate definition from examining the stars other than the sun. The 'pole star' isn't actually one that we use for this. It's almost a full degree away from the actual pole of the earth's rotation.

What we do instead is look for day to day differences in the location of stars passing overhead or nearly so. This approach dates farther back than Seth Carlo Chandler, in the 1890s. But we'll be coming back to Chandler. If the earth has wobbled a little to the north, then the star will pass the zenith a little to the north of where it did yesterday. If you've got a good telescope and other instruments, you can observe this to pretty good precision. Chandler was working with accuracy of 1 second of arc or somewhat better for single measurements. Because of the power of using multiple measurements he was able to examine earth wobbles that were less than 0.1 seconds of arc.

This turned out to be quite useful, as the earth wobbles by about 0.3 seconds of arc. It's how he discovered what was promptly called the Chandler Wobble. This translates to about 3 meters motion in where the pole is.

The orientation of the earth is, as with the rotation rate, tied to where the mass is and where it moves to. The earth's orientation is believed to have changed by some millionths of an arc second due to the earthquake. The variations of a few tenths of a second of arc are caused by ... other things. Atmosphere and ocean circulations are what I'm most concerned about, but also the earth's inner core, and the moon, and .... It's a messy business.

As for the length of day, your scientific source for observations is the International Earth Rotation Service. Which, itself, owes something to Chandler.

What is a day?

2011-03-16T06:27:00.007-04:00

Some friends have been puzzled about how an earthquake or a tsunami could change the length of a day. This question comes, of course, from the tremendous earthquake in Japan. I trust you're all aware of it, the severity, and are doing what you can. Given how late I am to comment at all, I'll take up my friends' puzzlement.

As is common in science, once you get detailed about just what you are talking about, you also understand much about the thing. So: What is a day? There are really at least 4 different definitions of 'day' that we can fairly easily point to. Only two of them are still in serious scientific use. One is commonly used, kind of. And the one with the longest history is no longer in use.

What we need, in order to define a day, is something that takes 1 day to happen. The longest history for a meaning of 'day' is: "The time between maximum elevations of the sun." Almost equivalent would be time between sunsets or sunrises. Maximum elevation of the sun is a much easier and accurate measurement to make. Don't look at the sun! You also don't need to. Get yourself a stick and put it straight in to the ground (on a desk, sheet of paper, ...). Be sure that the ground is flat and the stick is vertical. Every so often through the day, mark where the shadow ends. At some point, the shadow will reach its ~~greatest~~ shortest length. That's solar noon. The direction of the shadow (if you're in the northern hemisphere mid or high latitudes) is north. (There's more fun to be had by repeating this exercise many days through the year.)

This notion of 'day' is affected by earthquakes, since it depends on how fast the earth is rotating. (This also makes it one of the more obscure ways of showing that the earth does rotate.)
As we got increasingly accurate mechanical time pieces, and our understanding of astronomy improved, we realized that this definition of 'day' had problems. Even if we consider the earth as rotating at an absolutely constant rate (which turns out to be a pretty good assumption), the motion of the sun through the sky is not nearly as constant. The thing is, the earth's orbit around the sun is not a perfect circle. When we're closer to the sun than usual, we move faster than usual. When we're farther away, we slow down. This shows up in the motion of the sun through the sky. The time between successive solar noons is then not very constant. The excess accumulates to several minutes each way. Consequently, we invented something called the 'mean solar day', declaring it to be exactly 24 hours long, and the hour was defined out of how clocks measured time. It's variable by something like 3 seconds per day. 3 parts in 100,000. For a long time, this was pretty good.

Contemporary with those increasingly accurate time pieces was inventing the telescope. Even before the telescope, this principle was understood. Namely, we could look to the motion of something other than the sun through the sky. The wanderers (planets) were not useful. But the fixed stars could also observed. Once we knew where north/south was -- and you can find many ancient observatories which are laid out with obvious knowledge of north/south -- we could observe the time between successive passings of the star across the north/south line (meridian). This is slightly shorter than passages of the sun, about 23.934 hours. It defines the sidereal day -- the star day. The difference between the mean solar day and the sidereal day is because of the motion of the earth around the sun. (Which means that if you have a very good watch, you can observe the earth's orbit by comparing the solar day to the sidereal day, day by day.)

With the telescope, we could be very precise about just how long it was between successive passages of a star across the meridian. Even a modest telescope (60 mm lens) can observe something that is 2 seconds of arc fairly easily. The earth rotates through 15 seconds of arc in 1 second of clock time. So with a good clock, and even modest telescope, you can measure the sidereal day to better than 0.2 seconds (2 parts per million) . Without the telescope, the limit was about 2 minutes of arc, so about 12 seconds (1 part in 100,000, not much better than mean solar day). The people who do this professionally, the International Earth Rotation Service (IERS), measure the earth's length of day (sidereal day) to 0.001 seconds, for an precision of about 1 part per hundred million. Again, this notion of day is affected by the rotation of the earth -- the passage of stars through the sky.

Our fourth, and by far most accurate, notion of day is based from atomic clocks. For them a second is some number of vibrations of some atom between particular atomic states. Last I recall, the atomic clocks are more precise than 1 part per trillion (10^12). Perhaps that's quadrillion (10^15). A day is then 86400 atomic seconds. This notion of day is completely unaffected by the earth's rotation.

The fact that the high precision day as observed by the IERS is independent from the atomic clock day is what leads to the fact that we have both UTC and UTC1 time, and the occasional 'leap second'. Some parts of society are tied to the sun and stars, still, and some go with the atomic clocks. The leap seconds bring things back together. (Most of this is due to the moon, rather than earthquakes, but that's another post.)

So 3 of our days -- the solar day, the mean solar day, and the sidereal day -- are affected by the earth's rotation. The atomic day is not. Anything that can affect the earth's rotation affects the length of day, for those 3 definitions.

The thing we need to complete our understanding is to answer: "How could an earthquake change the earth's rotation rate?" Now that we understand what a 'day' is, we see why the earth's rotation rate matters. The cryptic answer is 'conservation of angular momentum'. The visual answer is:

The skater starts out with her mass mostly away from the center of her body. As she brings her arms and legs in, she speeds up her spin. Conservation of angular momentum is the principle involved. Angular momentum is mass times how far from the line of rotation times how fast she's rotating. The arms and legs have some mass, and that isn't changing. But when she pulls her arms or legs in towards her torso, she decreases the distance. A conservation law means that the thing can't change -- in total. Since the 'how far' is decreasing, the 'how fast' has to increase. And as you see at the end, when she puts her arms out, she slows right back down.

For the Japanese earthquake, a part of the earth moved towards or away from the pole. Mass that is at the pole has zero distance from the line of rotation, so doesn't contribute to the earth's angular momentum. Something at the equator has the greatest distance from the pole. As a plate moves towards the north or south, then, it is decreasing (or increasing) its distance from the earth's line of rotation. The earth's rotation rate increases (or decreases) correspondingly.

The amount is very small, a matter of a microsecond or so for this earthquake (which means 10 parts per trillion in the length of day). Several microseconds for the Sumatran earthquake a few years ago. You see how drastic to humans something that is small to the earth can be.

Running in the rain

2011-03-14T04:51:00.001-04:00

It was raining, of course, this past Sunday when I went to the track for my second run since breaking my wrist while running last fall. (First having been Thursday.) I've previously talked about running with lightning (i.e., don't!). But my negativity about being electrocuted* should not be taken to apply against running in the rain. A number of my favorite runs have been in the rain. Probably a higher fraction than in 'nice' weather.

Running in the rain becomes pleasant once you make some attitude adjustments, and perhaps do some physical preparations. Bonuses are that you can congratulate yourself for your tremendous virtue in being out in spite of the unpleasant weather. And if you race, it's a plus because even though you could arrange to do all your training in nice weather, you cannot ensure that all races will have nice weather.

The main attitude adjustment is one suggested by a fellow trail runner. We were going to run on trails during moderately heavy rain, after 2 days of steady, heavy rain. Our path was all dirt except for a stream crossing. In other words, after all that rain, no question that we were going to be soaked, and muddy, and soaked again (by the stream). As he said, it was time to let our inner child out to play -- splash through the puddles (which were numerous) and enjoy the mud. I won the contest for having the mud highest on my body (up near the shoulder blades) without having fallen.

Physical preparation starts with the fact that you are going to get soaked. So relax about that part. During the run, get wet and don't worry about it -- assuming that you're not running with too little clothing on, and temperatures are low enough to make hypothermia an issue. (If that sort of thing is a consideration, get out of the rain immediately.) But a spring rain, like I had Sunday, with a warm house or car nearby, and adequate clothing ... enjoy. The thing is, have two sets of clothes. One for running in, and one for immediately after you finish running.

Your clothes for the running should be what will soak up the least water. Go for your shortest socks, shorts, shirts, ... that will still be warm enough. You'll need a little warmer clothing than usual because the rain will be cooling you down. If you have clothes that are rain resistant, such as a waterproof nylon shell, they're good ones to go for. Regardless of that, avoid cotton. It will soak up water by the pound. Rainy days are your days to appreciate technology -- go for the nylon, polyester, tech fabrics for your tops and bottoms. Be sure, though, in reaching for your nylon shell that it does breathe, otherwise you'll boil. (Or at least I would; I generate a _lot_ of heat when I run. Corresponding amounts of sweat once temperatures break 20C / 70 F.)

The longer your run, the more expert you should be in what to wear before you take up the rain running. 30 minutes in moderate temperatures doesn't require particular expertise. 3 hours in near-sleet conditions (yes, I've done that) requires a fair amount of previous practice.

After the run, it's fairly obvious what to do -- change in to dry clothes. But apparently not so obvious that some hundreds of runners at a race of a couple thousand managed to remember it. This was a 10k race a number of years back that I finished ahead of a lot of people that I normally didn't. My rain training was current, and I knew I had a checked bag of dry clothing, and a towel or two, waiting for me at the finish line. So I relaxed in the race, cruised down the highway, and actually ran my fastest 10k ever, in spite (because?) of the rain. At the end of the run, have a sheltered area to change, and change in to those nice, dry, clothes after towelling yourself down.

The only novelty I'd add is that you want to aim for layers of clothes so you can pull on more as you cool off from the run, and to have more layers available than you ordinarily would for the temperature. This latter is because you will have lost more energy in the run than your normal -- all that rain will have its effect at sucking out your body heat. As your metabolism relaxes from the during-run furnace levels, you'll welcome that extra shirt or sweats.

One thing I've never worked out is what to do for/with glasses. I'm fortunate in being able to run without mine. If I'm surprised by rain on a run, I just (and have to) take them off. Anyone have good solutions here?

*Nobody will get credit for puns, or complaints about them, for/about that comment of mine. Only punsters with low standards would count that one.

Science Fairs

2011-02-23T05:38:00.070-05:00

Last week I had the pleasure of judging at the Eleanor Roosevelt High School science fair. The pleasure was only added to by the breakfast, snacks, and beverages provided by the ERHS PTSA. Program organized by Jennifer Massagli

The main fun, as always, was talking to the students. But I'll also make some comments here for students who are thinking about next year's science fair projects. One part of the fun (for judges) being to talk to the students, I'll advise that students act like they're interested in their projects. "Here is something I slapped together because the school made me." even if true, is just not the way to your judge's heart. I also make this comment to graduate students and scientists about their presentations. Many people don't act interested in their own work. Trust me, if you aren't interested, we won't be either.

Fun parts of the talk include finding out what prompted the student to do their project and where they might take it in the future. Also an important part of a professional presentation. One student I spoke with was looking at the output of solar cells, how they depended on light sources and filters. This is sufficient reason for the science fair project, and he explored that question ok. But it became much more interesting to me when I discovered that he was using the solar cells as proxies for plant photosynthesis. Plants do rely on the sun, as do solar cells, and there are degrees to which you can indeed use solar cells to map out plant responses.

A different line of interest for me is to see what the students think of to investigate, and how. Many different sorts of things investigation, and many ingenious ideas on how to get the measurements. Both are good areas to use and show your creativity, which is one of the areas on the official scoresheet.

The judge's worksheet we had is a fairly typical one (I've judged science fairs at several different levels and areas). General areas are scientific thought (15 points), Creative ability (10), thoroughness/clarity (15), exhibit presentation (10). Total of 50.

For scientific thought, the areas are:
1) The problem/hypothesis is stated clearly
2) Variables are clearly recognized and defined by the experiment
3) There is a procedural plan for obtaining a solution and the plan covered the problem completel

For Creativity:
4) The approach showed creativity in solving the problem.
5) The analysis of the data showed creativity

Thoroughness/Clarity:
6) Data and results are clearly presented and there are adequate data to support the conclusions, such as replication of experiments
7) The conclusion is justified based on the data and results
8) Project shows evidence of laboratory, observational, and computational skill needed to obtain and analyze the data

Exhibit Presentation
9) Backboard is well-designed and effective in presenting the project
10) Student is able to discuss the project clearly and concisely; demonstrating and understanding of principles involved in the research

Standard checklists don't always cover vitally important things. For instance, though it only officially shows up as 5 points (#10), in practice it is about a million points that if you don't know what you did or why, you're not going to do well. The 5 points is to distinguish between students who did do their own project, not to distinguish between students who did, and those who didn't. This is seldom an issue, but it does show up from time to time. I usually feel sorry for these students, because it's usually a matter of an over-helpful or over-involved advisor or parent. Unfortunately, the outcome is that the student doesn't know why they were doing one thing rather than another, or even that there might have been other things to try.

Students are also usually very good at the straightforward parts of the list. Almost everybody states their hypothesis clearly (#1) and backboards (#9) are usually very good, and several others. An outcome of this is that simply adding up the item scores tends to be extremely close, including a lot of ties. We judges then have to use tiebreakers not on the list, which takes us back to those issues of the student knowing why they were doing what they were doing, connecting it to other parts of science, seeing a path forward in to other researches, how to improve their own work if they were to be restarting it now, and so forth.

Regardless of all the preceding, I'll say to pursue figuring out something that's interesting to you about the universe. Do it in a way you find interesting. This may or may not turn out to be something that gets a lot of points. But it will be how you learn the most. My own science fair projects were generally not very good by the standards above, nor would I as a judge today give my projects back then a high rating. They were usually a matter of reading about what other people had found out, rather than doing my own experiments to find out things. Not what the judges looked for then or now, but something that I did learn a lot from.

For schools and teachers in my area: I'd be happy to come to your school for judging, or to talk with you about science and science fairs. Comment here, or use my email -- bobg at radix dot net. Allow some time for me to find your real email amidst the dreck I get there.

The Invention of Air

2011-02-21T06:52:00.005-05:00

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.

As I got late in to the book, I found that when Priestley came to the USA, he moved to Northumberland County, Pennsylvania. This is about 100 miles, as the crow flies, outside Philadelphia. Being that far away from an intellectual center, Johnson noted, greatly slowed Priestley's communications and discussions. Which, given how Priestley worked, was a great barrier. His reason for being so inconveniently far away was the Yellow Fever and Smallpox that were rife in Philadelphia.

At that time (call it 1800, he arrived before and died after, and shows up in the 1800 census) I have several ancestors in Pennsylvania. One particular family is Peter White and Elizabeth Brittain, who lived in what was then Northumberland County (now Columbia). As I make it by Google earth, about 20 miles from Priestley's location in Pilot township.

Elizabeth's father, Zeboeth Brittain (or Zeboath) died in 1790, in Northampton county -- about 50 miles outside Philadelphia -- of smallpox. I'd have thought 50 miles was enough to be safe, but Priestley had some fair reason to be even farther away.

Alas, no prospect that Peter and Elizabeth, or Peter's father John, who also lived in Northumberland, ever met up with Priestley. Peter and John were farmers. John was about Priestley's age. But 20 miles in 1800 was a 2 day trip, possibly more then due to the roads versus mountains problem (i.e. roads tended not to cross mountains, and Priestley and the Whites were in different valleys).

Still, I find it helpful to think about the notable figures, such as Priestley, versus ancestry. Saying that he was contemporary with my 5 times great grandfather (John White/Johann Weisz) helps bring things in to a perspective better than observing that 1800 was 210 years ago.

The Way Things Break

2011-02-05T04:53:00.001-05:00

The Way Things Break is the blog of thingsbreak, who comments here as well. As you'd expect, content isn't a perfect overlap of mine, which is one reason to suggest it. The articles I mention below will lean to climate, but take your own look. Especially, check out the Books of Interest and Open Source Climate Science Education.

Cosmic Rays 1
Reliable sources and ocean acidification
Geoengineering and ocean acidification
(2009) Arctic summer sea ice
Lonnie Thompson viceo
Cosmic rays and climate 2a
Cosmic rays and climate 2b
Reducing greenhouse emissions through household actions
Who is really alarming?
Tropical cyclones and consensus
Climate communication and public health
What was really predicted about ocean circulation?
Media vs. scientific description of climate report -- See also the comments, especially John Fleck's.

Question Place

2011-02-01T05:12:00.001-05:00

Finally back and in some shape to answer questions and perhaps even do something useful with suggestions, so bring 'em on and let's have some fun!

Rabett on History of Radiation

2011-01-26T04:52:00.001-05:00

No surprise to you that I'm interested in the history of science and of knowledge, but perhaps a little surprising that I'm not the only one. Eli Rabett has recently taken up the history of our knowledge on atmospheric infrared radiation.

http://rabett.blogspot.com/2011/01/required-reading.html Ångström observing infrared radiation from the atmosphere

http://rabett.blogspot.com/2011/01/angstrom-effect.html Arguing for 'Ångström effect' as the name rather than greenhouse effect

but then joining many of the rest of us in 'Callendar effect' in http://rabett.blogspot.com/2011/01/well-damn-it-all-its-callendar-effect.html

http://rabett.blogspot.com/2011/01/fourier-and-greenhouse.html More about Fourier and the term 'greenhouse effect'

I'll also take this chance to recommend The Callendar Effect as being a readable introduction both to the biography of the engineer/scientist who did the work, and to the science that he did on carbon dioxide as an important driver of climate change. I also have the complete papers, one of which and its response have some interesting, to me at least, illumination regarding the difference between being skeptical and being in denial.

Whiteboard on the end of global warming

2011-01-24T04:41:00.000-05:00

The Whiteboard also has a series (now looks to be finished) on whether global warming has 'stopped'. That's what prompted the series I linked to last week. The question is pursued statistically, following the lead of Tamino at Open Mind. The brief summary of the series is 'no'. But it is well worth looking in to the series for the how and why we can say this, and how strongly we can say it.

Did Global Warming Stop After 1998?
Did Global Warming Stop After 2000?
Did Global Warming Stop in 1940?
Did Global Warming Stop After 2007?

For looking at the converse, global cooling:
Did Global Cooling Stop in 1970?

These are more mathematical takes than my old What cooling trend? They come to the same conclusion, so those who'd like more math behind their conclusions can get it. Since I did that post over a year ago, it's probably time for an update. One of these days ...

Wrestling with data

2011-01-21T04:02:00.001-05:00

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.

Help save data

2011-01-20T05:36:00.002-05:00

You've noticed by now the frequent pleas from people studying climate for more data, particularly from the past; here's your chance to help: Data Rescue at Home is working to retrieve hand-written climate records to make them useful for modern science. The catch being exactly the fact that they are hand-written. It is best to have a person looking at the writing to decide whether that squiggle is a 9 or 7 (to name one that confuses others who try to read my writing). You can be that person.

Penguins

2011-01-19T05:22:00.003-05:00

A couple of people have guessed where my 'penguindreams' id comes from, but there's a little more to it. The obvious part is the collection of Bloom County cartoons which were collected as Penguin Dreams and Stranger Things. There can be strange things here, so it seemed appropriate. The penguin part has less to do with Opus, and more to do with a platypus.

The platypus is itself a story. When I was younger (not that I'm not currently young, but back then I was less practiced at it), my sisters each had an animal they collected. Somehow it was deemed that I should also have an animal that I should collect. I didn't want to do so, so I spent some time trying to think up the most unlikely animal possible. Platypus was what I finally hit on. That served me well. No platypi found their way to me until I was well in to college, when my grandmother had found a pattern and made one.

I was safe again until I reached graduate school, when I started working on the Antarctic. Immediately it was decided that penguins were now my animal. So I've got quite a few penguins, of one sort and another. They did give my (now) wife pause back when we were dating and she saw the penguin collection. Fortunately she believed this story. And I've grown to like the penguins.

Was Easterbrook talking science?

2011-01-18T04:55:00.001-05:00

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.

One part is, to be a question of science, it has to be something that you'll accept either (any) answer for from the examination of the data. And that you will conclude something different if the answer is yes rather than no. Or, for Easterbrook's question, if the answer is 1 rather than 20,000, or any other answer. As the article continued, though, (read it for yourself, of course), it was quite irrelevant whether the answer was #900, #9000, or anything else except for #1 -- as long as this year was not the warmest 'ever', Easterbrook's conclusion(s) were (are) immovable. That's not science. Nor is it remotely clear what it would have meant to Easterbook if this year had been ranked #1. Some year has to be, after all.

We do often start investigations in science without having a 'therefore' attached. More typical would be "What have global temperatures been like over the last 10,000 years?" Obviously, some years will be warmer and some colder. But a single year? Does that tell us about climate? probably not. But we could go ahead and collect as much data as we can and start trying to come to grips with what we can know about global temperatures, and how far back.

That takes us to two more problems with Easterbrook's note. Already commented elsewhere is that he's using only a single point -- the GISP2 core from Greenland -- and treating that as if it were global mean temperature, comparable to what is obtained by the surface (or satellite) observing networks. That's wrong, of course. Points are not the globe. Even averages over large countries, like the US, are not comparable to global averages. The US is only 2% of the globe.

The second problem is one that remained even in my rephrased version of Easterbrook's question. Namely, who said, and on what basis, that 10,000 years is the longest duration we care about climate change? Sometimes we are limited by data sources. No matter how much we'd like to have thermometer-based temperatures for the past 1000 years, we just can't do it. The thermometers would not permit it. They didn't exist, nor, until the last 100-150 years, were there enough to make global reconstructions.

So, was Easterbrook limited to 10,000 years by the data set? He cites Alley, 2000. A brief search lead me to the NCDC which has archive of the data. The data begin 95 years "before present", and run to 49,981 years "before present".

"before present" is one of those hazardous bits of scientific language -- it looks like something normal, but actually has a precise technical meaning. Two facts that many people know turn out to be involved. One is, in the late 1940s through early 1960s, the nuclear powers did above ground testing of their nuclear bombs. The other is, geologists and archeologists use radioactive material (especially carbon-14) to date things that have happened in the last 50,000 years or so. It turns out that the bomb tests put enough carbon-14 into the atmosphere that it affects dating of anything modern. Modern means 'more recent than the bomb tests'. Consequently for geological use, as done by Alley, "present" means 1950 -- when the bomb tests started to be numerous enough to affect the atmosphere's carbon-14 levels. It's also useful to define 'present' to be a fixed date (1950), so that you can always refer to a 'present', without needing to check the date a paper was submitted, among other things.

So, although Easterbrook labels 'present' to be 2000 AD, it is actually 1950. And the first data in the GISP2 ice core, which is what he says he's plotting, is 95 years before that -- 1855. Even though his most recent data is for 155 years ago, he draws his conclusions as if it were for 2010.

And, to get back to cherry-picking, he presents his statistics for the last 10,500 years. Is his data source limited to that period? No, as we saw above, it goes back to 49,981 years before present. He's selected only the most recent portion. Let's plot the whole period from his data source (you can get the data yourself and produce your own plots and analyses at the NCDC link above):

He's ignoring 79% of the data that is available from the source he used. (I'll invite you to check out the NCDC link above, and the NGDC for other ice core temperature records, to see if maybe there are more data even than this.*) As befits cherry-picking, all of the ignored data run counter to the conclusion he wants drawn. I'll also invite readers to plot the period around 10,500 years before present to examine why one would cut off the examination there, rather than 1000 years before or after. The figure above gives you an idea. As always, though, conduct your own examination.

Hot topic has a more recent follow-up. It confirms that my article here is still relevant.

To back up some to my point about accepting all answers, and on those answers affecting your conclusions, let's go to Easterbrook's original scientific point and the conclusion he draws -- that 2010 ranked as 9,099th warmest of 10,500 years. As we see above, he's badly wrong about both parts of the statement. Following Hot Topic's discussion, 2010 may well be the warmest year in the past 10,500 on greenland. In particular, see this figure. But let's proceed; given his conclusion on the science, he then concludes "It’s really much to do about nothing." ('It' being reaction to 2010's warmth.)

Based on his original conclusion that 90% of the past 10,500 years were warmer than 2010, he concludes (not a scientific conclusion, but a conclusion) that "It's really much to do about nothing." Since it turns out that more than 90% of the past 50,000 years were colder, his new conclusion should be that 2010 is indeed exceptional, or at least warrants 'much to do'. If his discussion were for the science, that would be his new conclusion. Per the followup, it seems he hasn't even corrected his elementary errors like the 'before present'.

* Hint: There is. By quite a lot.

Back and becoming active

2011-01-17T13:55:00.000-05:00

I'm now back to checking out the blog -- at least comments people have submitted. So, please take a look at:
Stephen L. at Verifying Forecasts 1
William at Verifying Forecasts 2 (two comments)
Horatio Algernon on Kids are Scientists
Bayesian Bouffant, FCD on Kids are Scientists (giving us a link to a 10 year old who discovered a supernova!)
jyyh on Evolving Thoughts

I'll be posting original material myself shortly. William's discussion is prompting a separate post or two. There is an interesting to me point involved, which is helpful for many things in pursuing science regardless of topic. Namely: What is your object of study? How do you know?

Hope everybody had a good winter solstice (December 21), holiday season, new year, and perihelion (January 3). We're now moving away from the sun and those of us in the northern hemisphere are seeing more light. (Itself a point important to long term -- tens of thousands of years -- climate.)

My wrist has been recovering pretty well, as these things go. Unfortunate, that does not mean complete recovery 2 months after surgery. But the physical therapy is going ok (as these things go) and I can now type fairly normally.

Kids are scientists

2010-12-23T11:50:00.000-05:00

It's something of an article of faith in my family that children are natural scientists. Yet another illustration is from a classroom of 8 year olds, who recently published in a professional scientific journal. Yay!

In their case, it was a study of bees and how they identified food. The full paper is here. A nice journalistic coverage is here.

They did have a professional scientist helping figure out things, and doing the writing, etc.. But the fundamental ideas came from the kids.

Off-blog and Happy New Year

2010-12-23T11:39:00.000-05:00

I'm going to be (even more) off-blog into the New Year. My apologies to William, who did have some good comments over in http://moregrumbinescience.blogspot.com/2010/11/verifying-forecasts-2.html that got hung up for a couple weeks as I hadn't been checking in, and hadn't told anybody about this. I'll be checking in today for comments and to post them, but will be back to off-blog from the 24th through 3rd of January.

Not that this is major to anybody, but I'll mention that my cast came off 2 weeks ago and I've been doing physical therapy to rehabilitate the broken wrist. Fingers are doing pretty well, the thumb is so so, and the wrist is awful -- compared to my hopes. All are doing ok to pretty well compared to what you might expect for someone over 20 with this kind of break. The therapy amounts to a second job -- about 2 hours a day, every day. That's been one of the time sinks w.r.t. getting blogging.

I hope everybody is doing well, enjoying the season, and has a happy, healthy, 2011.

Evolving Thoughts

2010-12-02T06:40:00.001-05:00

For a more philosophical take on science, and leaning to biology rather than climate for examples, you should take a look at John Wilkins' Evolving Thoughts. John's a philosopher of science. We've known each other electronically for about 20 years.

Some posts which struck me at the time to save for later:
Sausages and science (the practice of science isn't as pretty as I tend to paint)
It was 150 years ago tomorrow on the 150th anniversary of the publication of Darwn's Origin.
Lazy Manager Theory (ok, aimed a bit older than I usually try, but having encountered managers, and now being one, sort of, I like this.)
Science eats its seed corn (I blog about the ideals of science; the realities are seldom as nice.)
Jorge Cham is following me (PhDcomics.com) Looks pretty much like my time line, though 350 messages in my in-box says 'weekend' more than 'vacation'.
Apes and evolution in the news
Linnaeus: The founder of databases
A code of conduct for effective rational discussion
Plagiarism, citations, and fact checking
John's own list of his better posts in 2009

talkorigins.org (not his blog, but a site that he helps administer; it was by way of the newsgroup talk.origins that we met)

Note that here down are at his old blogging locale, you want the above for his current site and material:

What is the 'humanities'?
Early Modern Philosophy Texts for Students
Again, uncannily accurate -- actually a link to an excellent cartoon by Jorge Cham at www.phdcomics.com
How to learn about science

How to make sanity checks

2010-12-01T05:49:00.001-05:00

In late September, I wrote a note on whether Lake Superior still remembers the last ice age. The answer was no (read that post for why). But along the way I illustrated a simple sanity check that would have given the author I was responding to a heads up that he was seriously wrong.

The check I used was to compare volumes. If something the volume of Lake Superior remembered conditions for 10,000 years, then something with 100,000 times the volume would (could/should/...) take 100,000 times as long to adjust. The ocean is that much bigger, so would take that much longer. Yet we know (sanity) that the ocean's circulation time is only a few hundred to a few thousand years.

This doesn't prove that the original 10,000 year estimate was wrong. That's not the purpose of a sanity check. Rather, the sanity check alerts us to examine the system more carefully. Maybe there's something fundamentally wrong about using volume for comparison, maybe there's something fundamentally wrong about what lead that author to saying 10,000 year memory for Lake Superior. As we found out, it is the original claim of 10,000 years that was severely wrong. (Turned out to be about 6 months.)

In the comments, though, there were some noting that my approach to sanity check wasn't right. Or at least that I could have made a better estimate than I did. Since I take sanity checking to be a heads up process rather than a proof, I'm not very concerned with whether I chose the most accurate (I did choose one of the simplest) method. But it is worth its own discussion how you might make better estimates.
One route to estimates is simple size comparisons. I did volume, but you could also easily do area, or depth. With Lake Superior being 1 unit (we then measure by Lake Superiors), the ocean is 100,000 for volume, 3750 for area, and 25 for depth. I've rounded all of them. The only part that's important is that 25 is much smaller than 3750, and so on.

You could also consider some general processes. One is the diffusion of heat. The thing about diffusion is that if it takes 1 time period (second, day, year, ...) do diffuse 1 distance unit (whether that's 1 foot, meter, Lake Superior average depth), it takes 4 time units to diffuse 2 distance units. Time needed is proportional to the square of distance. T ~ d^2 If it takes over 10,000 years to diffuse heat through Lake Superior, then it takes 25^2 (25*25, 625) times as long, 6.25 million years, to diffuse heat through the depth of the ocean.

One commentator suggested treating Lake Superior as a sphere and examine surface area to volume ratios. There's a good physical principle involved, but first I'll digress to mention a book I've heard good things about: Consider a Spherical Cow. (The subtitle turns out to be 'A course in environmental problem solving'). This would give you either 10 times longer to change the ocean's temperature (assuming same energy per square meter of surface area is lost) to 100 times longer (diffusion from the core of the ocean-sphere has 10 times farther to go, so takes 100 times as long).

The thing is, Lake Superior, the ocean, the atmosphere, and most large climate system components are not very 3 dimensional, making a sphere a poor model. I have myself used the 'spherical cow' approximation, for cows, for people, and quite a few other things. But Lake Superior (and so forth) are very much larger in 2 dimensions than in the third. As one commenter responded to the original suggestion, Superior is a thin film, not spheroidal. 563,000 meters long, 257,000 meters wide, 147 meters average depth. (Hank Roberts, Oct 22). In other words, more than 1000 times long or wide than it is deep. That's also true of the ocean, atmosphere, and the Antarctic ice sheet, among many others. They're all much closer in shape to a piece of paper than a sphere.

Depending on which route we chose for the sanity check, then, we'd have gotten the ocean taking longer, by all routes, to adjust to climate change than Lake Superior. 10 times, 25 times, 100, 625, 3750, and up to 100,000 times longer. But if we add 1 piece of knowledge (go look for it) -- that the ocean's adjustment time is only several hundred to a few thousand years -- we have our check that the original estimate of 10,000 years for Lake Superior was (perhaps) far, far too long. It turned out to be 20,000 times too long, though for reasons that had nothing to do with the volume or area comparisons between the lake and the ocean.

In making these estimates, and particularly the way in which we've been going about them, we're using (if not naming) concepts from dimensional analysis, nondimensionalization, and scale analysis. I'll be returning to these concepts more directly. On nondimensionalization, perhaps the best-known non-dimensional number is the Mach number for speed. It's the ratio between the object's speed and the local speed of sound. 'breaking the sound barrier' meaning to go faster than Mach 1.

Verifying forecasts 2

2010-11-29T05:42:00.011-05:00

As I said last week, verifying predictions is difficult, and was prompted in to looking again at the matter by someone doing it wrong. Of course the standard of 'wrongness' involved is mine. Forecast verification is something of an art as well as mathematics and science. But some points I think I'll get little argument from Allan Murphy* and his intellectual colleagues and descendants for are:

You have to be clear what you're forecasting

what variable
at what time (or time span)
for what place or area

You have to be clear how the forecast is going to be evaluated
You should evaluate all forecasts
Forecast must be public
Forecasts must be verifiable

That last might seem a little strange. I hope not. Suppose I said next July 20th at 3:34 PM at Washington National Airport the official temperature would be hot. Very specific about what I'm forecasting and what it will be evaluated against. But what is 'hot'? To me, anything over 80 F (27 C). As such, it's a near certainty that my forecast will be correct. It's also awfully easy for me, on July 21st, to say, regardless of the temperature, that it was 'hot'. This is one reason that we prefer numbers in science. You can, and we do, work with qualitative predictions. But it takes more work, as you have to find some way of making 'hot' objective, so that we can all agree that such a forecast was correct or not.

In general, if not as universal, we add a couple more items, at least desirable if not mandatory:

Forecasts should specify their degree/nature of confidence
It's a good idea to compare the quality of your prediction against an null forecast method (not a personal comment, means any method that doesn't know any of the science -- like straight line regression, or persistence; also goes by the principle of 'check how wrong you could be', which I'll illustrate later this week).
A trivial matter (except that it comes up in Watts' Nov 23 2010 response to greenman3610) is that all predictions depend on what really happens. Of course if it's colder, there'll be more ice, and if it's warmer there'll be less. That's what you're supposed to be predicting!

Now, what prompted this was a video by greenman3610 video and the response from Watts up With That. greenman observed a very bad forecast coming from WUWT, Watts said it was really rather good. Figuring out good vs. bad isn't really a scientific question, and those aren't really the words used by either, so be fair to both. Those are my words, but I think capture fairly the sense of their respective comments.

This is all regarding sea ice. You can check my original comments from June on my May estimates -- that they were for September average, Arctic, sea ice extent, as measured by NSIDC. Further, at least in what we submitted to the sea ice outlook, we mentioned what the standard errors in the predictions were. Don't want it said that I have higher standards for others than I live by myself.

So what was Goddard's prediction? That turns out to be hard to track down. Tamino and Neven have also looked in to the matter, Neven getting back to February (from his June check). My selection:

1) On June 6th it is that "Conclusion : Based on current ice thickness, we should expect September extent/area to come in near the top of the JAXA rankings (near 2003 and 2006.) However, unusual weather conditions like those from the summer of 2007 could dramatically change this. There is no guarantee, because weather is very variable."
-- this does tell us what the verification data source is supposed to be, but not whether it is monthly average or daily minimum. Fairly clearly it is September. September's minimum and average for 2003, from JAXA, were (6.03, and 6.13) million square km. September 2006 showed (5.78, 5.91). The nearest to both would be their average, giving his June 6, 2010 forecast(s) as 5.905, 6.02 million square km for minimum day and monthly average, respectively.

It is not until comments at his personal, separate from WUWT, blog in September that it becomes clear to me that Goddard means the minimum day, not the monthly average. JAXA's minimum September 2010 day is 4.81 million square km. So Goddard's June 6 forecast is off by over 1 million km^2. He gave no sense of variability at this point, but I'll observe my own prior estimate of 0.5 million km^2 for natural variability. So 2 standard deviations errors. (Aside: that others were off by as much or more does not affect our evaluation of Goddard's predictions. n.b., I was not one of those others.)

2) On June 14th, the forecast has changed to "Conclusion : 2010 minimum extent is on track to come in just below 2006. With the cold temperatures the Arctic is experiencing, the likelihood of a big melt is diminishing."
Ok, what does 'just below' mean? About the same as my 'hot', perhaps. 2006's minimum day at JAXA was 5.78 million km^2, September average of 5.91. Observed 2010 was (4.81, 5.10). I'm hard-pressed to call errors of (+0.97, +0.81) million square km 'just below', but the Goddard never defined the term. (Hence that guide on verifying forecasts!)

3) On June 23rd the forecast becomes:
"I’m forecasting a summer minimum of 5.5 million km², based on JAXA. i.e. higher than 2009, lower than 2006."
The first time he directly names a specific number for the ice (well, one assumes extent, but he doesn't say here whether it's extent or area he means; nor whether it's minimum day or monthly average). 2009's JAXA numbers are (5.25, 5.38) for minimum day and monthly average extent, respectively. 2006 are (5.78, 5.91). 5.5 is between either the minimum day or the monthly average, so this didn't help clarify which he meant. His September comments did (minimum day), and this comment is also more clearly consistent with minimum day. (0.25 above 2009, 0.28 below 2006, versus being much closer to the 2009 monthly average than 2006 monthly average). This also gives us a sense of his level of uncertainty -- 0.25 million km^2. If he were more uncertain than that, he would give a wider range of extents. Whether that's one or two 'sigma' is also not clear, and, again, points to why we like these things specified.

I'll note that in following this up, I read every one of the WUWT 'sea ice news' posts from #2 to #30, as well as an August midweek update, and all 'verification' posts at Goddard's. Plus some, but not all, comments in August's posts. This matters some for what follows.

From July 4th through a comment of his on his own WUWT post August 24th, Goddard continues with 5.5 million square km being his prediction. Quoting his comment (with date and time so you can find it; I've never figured how to link straight to comments):
"
stevengoddard says:
August 24, 2010 at 9:46 am
Scott,
Remember that NSIDC took a mulligan, changing their forecast in July. They started at 5.5 million.
I haven’t taken my mulligan yet ;^)
"

So at least as late as that the 24th, 5.5 is his prediction and he's taking pride in having not changed his forecast, when talking to WUWT readers. That's odd, because in the August Sea Ice Outlook, whose due date for submission was mid-month (I did submit to it myself, on time), his prediction was 5.1 million km^2 for September monthly average at NSIDC. As I mentioned before JAXA runs about 0.2 million above NSIDC, so a 0.4 million square km drop doesn't make sense. On top of which is monthly average (which, at JAXA, runs about 0.15 million km^2 above minimum day, and more between NSIDC's monthly average and minimum day -- about 0.30 million km^2 this year).

To back that out: If the prediction for minimum day was 5.5 according to JAXA for minimum day, subtract 0.2 to get NSIDC's minimum day, and then add 0.3 to get the September (NSIDC) average extent. If his prediction hadn't fundamentally changed, the SEARCH submission should have been 5.6 million km^2. Since it was 5.1 instead, there's a rather large change. Surely worthy of a post of its own at either WUWT or his own blog. In any case, given his August 24th comment, it had to be between then and the 31st. (At least if it's going to be called an August prediction, which he does.) That, or he was telling WUWT readers different things on the 24th than he was telling the Sea Ice Outlook. Or Outlook let him submit late, or ... -- the point being, this shows why it is we want our forecasts to be clearly public.

August 29th Goddard is still referring only to his 'June' forecast of 5.5 million km^2. No mention of an August prediction.

August 30th, Steven Goddard started blogging regularly at his own blog rather than WUWT.

On August 31st he seems to still like his 'June' forecast (actually, the at least 3rd forecast from June, the one on June 23rd) as he says:
"The video below shows current ice (thin red line) my June forecast (dashed line) and NSIDC’s forecast summer minimum (red horizontal line.) Who do you think is going to be closest?"
-- and there is no mention of an August prediction. Note, too, he doesn't mention that NSIDC is predicting a different thing than he is.

The first I see Goddard directly referring to his 'August forecast' is September 7th. It is mentioned at WUWT the the day before by Watts. Can you really call something that doesn't deserve a main-post mention until the end of the first week of September a prediction of September? Ok, maybe I missed the post in which it showed up. But clearly, given his August 24th and 29th comments, his prediction of 5.1 million square km doesn't surface publicly until after the morning of the 29th.

JAXA's observed ice cover on August 23rd was 5.60 million km^2 (last observation he'd have been able to look at in commenting on the 24th). 24th was 5.55. August 31st was 5.33 (already 'busting' all of his 'June' forecasts). The Sea Ice Outlook was released September 1st, so in the last week of August, apparently, after the June forecasts were busted, Goddard made a revised forecast. (See point of 'how wrong could you be' above; I'll make it its own note later this week. The answer is, for JAXA, not very if you get to predict the seasonal minimum day from August 23rd.)

It is also with the post of September 7th that I (finally) can be positive that Goddard means to verify minimum day's ice extent as computed by JAXA:
"My June forecast of 5.5 million km² (JAXA) is currently off by 7%."
-- you can't make that statement if you mean monthly average. Who knows what's going to happen the rest of the month?

So at last, I'll return to greenman3610 and Watts' comments on Goddard's prediction(s) made at WUWT. One part of it being that fundamentally, greenman3610 is not focused on predictions as such. It it, instead the months of Goddard talking of sea ice being in recovery. That belief in recovery driving his predictions of ice extent. But, fundamentally we're looking at at least 6 months of 'sea ice is recovering' posts from Goddard, with numbers or references that compute to numbers from 5.5 million km^2 to over 6 for the extent based on that belief. Then, in the last 2 days of August, entering a forecast of 5.1 million km^2, which is less than 2009's 5.25. It's a recovery, there's just less ice? Don't follow that reasoning.

As to predictions as such, only the June predictions (between 5.78 and 6.03 June 6th, 'just below' 5.78 June 14th, 5.5 June 23rd; in the first two he was referencing years, I filled in the values for minimum day from JAXA for those years) seem to have been made in notes of their own at WUWT. It's correct to refer to those as his WUWT forecasts lacking any sighting of a post with the 5.1 there before September, and his clear comment on the 24th of August of 5.5 (still) being his prediction with none others (no 'mulligan' as he called it) existing. Further, after going through all the posts I could find on the topic, it's clear (see above) that he meant to forecast the minimum day's ice cover as computed by JAXA. That figure, this year, was 4.81 million km^2. So his last (and, turned out to be, best, and the one he consistently referred to as his forecast from June 23rd to at least August 24th) June forecast was off by almost 0.7 million km^2.

On the other hand, Watts points, in November, to only the final prediction from Goddard, which had to have been made in the last two days of August, that was 5.1 million km^2 for September monthly average computed by NSIDC. (You can tell by noting the horizontal line in the verification figure from SEARCH that Watts shows is at 4.9 million km^2, vs. JAXA's minimum day of 4.81, or NSIDC's minimum day of 4.60, or JAXA's monthly average of 5.10.) The 5.1 is not so far off from 4.9. At least SEARCH is more or less clear (not clear enough, I think, that'll be a different email) that this is what they're looking for. Not clear at all to me that either Watts or Goddard realize the different quantities being forecast or verified with. Pointing to only one of multiple forecasts violates one of the forecast verification principles I mentioned above.

Was Goddard's forecast pretty good? Pretty bad? Off by 0.2, 0.3, 0.7, 1.1 million km^2? Who knows. We can get all those results and more by varying what we take to be his forecast and how what we choose to verify against. That's why it's such a central point that you say just what you're predicting (guessing, estimating, ...) and how it's to be validated.

Ok, so all good fun in seeing why we want to do forecast verification in the direction that I like rather than waiting until afterwards to figure out what number will be compared against what other number. There was one other point of contention between Watts and greenman3610 -- the business of Goddard talking of recovery or not. From August 9th, for example, we see Goddard (he, or Watts, italicized it, so I'll follow suit) saying:
Can we find another year with similar ice distribution as 2010? I can see Russian ice in my Windows. Note in the graph below that 2010 is very similar to 2006. 2006 had the highest minimum (and smallest maximum) in the DMI record. Like 2010, the ice was compressed and thick in 2006. Conclusion : Should we expect a nice recovery this summer due to the thicker ice? You bet ya..

-- The DMI record is even shorter than JAXA, starting in 2005, vs. 2002. Given that we want 30 years for climate purposes, either is too short for much use, except to cherry pick for 'highest in the record'. Kind of like being the tallest person in my house. Sure, I am. But there aren't many of us here. The satellite period as a whole only begins in October 1978, so even taking the whole period is pretty short.

Anyhow, there's kerfuffle between greenman3610 and Watts regarded whether there was a 'guarantee' of recovery in Goddard's comment. You decide (go read the whole note, of course, if you're going to).

I'm hard pressed, to return to science, to see 2010 extent being below 2009 and all years 2006 and before that we have data for, as 'recovery'. And that takes us back to what constitutes a forecast and how you'd verify it.

* ok, going way back for those who remembered that asterisk. Allan Murphy was one of the major figures in meteorological forecast verification. One of the people I discussed verification with fairly often had learned a fair part of what he knew from Murphy. For the major 'small world' effect, I have a couple of textbooks that Murphy used himself. (One is 'Strength of Materials', so I guess that he started out in engineering, as I had.) Anyhow, if you were to read all his papers on verification, you'd be quite knowledgeable indeed.

Note:
I try to tell people when I write about their work. But I could not find any email contact for Goddard at his blog. I sent a note (available on request) via Watts' contact page 9:45PM Eastern time 24 Nov asking a couple questions and notifying him of this post's scheduled Monday appearance, and to greenman3610 about the same time. Watts couldn't answer my questions, but did forward (he said evening of the 24th) my note to Goddard. (No surprise that he couldn't -- they were about Goddard's actions and knowledge.) Watts, in his response to me mentioned a prediction by Bastardi at WUWT. It illustrates another bunch of violations of the principles I mention above, so gives another chance to discuss how to do forecast verification. That'll appear later this week, as well as a consideration of how wrong could you be if you wait until the last week of August to make your prediction of the seasonal minimum.

Verifying forecasts 1

2010-11-24T16:42:00.000-05:00

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 ...

The two more interesting parts of the story are that scientists can't always do what they want, the way they want to do it, and that sometimes you're better off to make your predictions from farther ahead. This is known, or rather its opposite, in El Nino prediction as the 'loss of predictability'. You can do a better job of predicting El Ninos from spring than from summer (I may have misremembered exact timing, but its about a 4 month difference).

The practicality part is that our May estimate was actually from an analyzed state from December of the preceding year. We were really predicting 9 months ahead, not the apparent 4. The analysis system had only gotten that far. Our August estimate was from end of April conditions, not August. By next year's sea ice outlook period, we'll have the option of starting with current initial conditions.

I also found it interesting that the two estimates had opposite error -- December's condition leading to too much ice extent, April's leading to too little. That something for us to follow up. We're correcting a very large bias in the model, on the other hand, the magnitude or the nature of the bias changes seasonally. ? Have to do some experiments to figure that out.

That change is also what points me to thinking that we will want to explore whether we have a 'loss of predictability' situation going on in the Arctic. At the seasonal maximum extent, we have 13-14 million square km of ice ranging from 10 cm to a few meters. At seasonal minimum, 9 million square km of that has melted away, so for them, we know they've go exactly 0 cm thick ice. The remaining 4.6 (this year) we only know that it ranges from 10 cm to a few meters, still. Maybe starting from the previous ice minimum will be the best way to go -- most accurate initial conditions? Then we hope that the coupled model is unbiased, so does the right thing over the next year? Again, some experiments to run.

I'll digress slightly again (you're shocked!):

It is very important to be specific about what you're predicting (seasonal minimum extent? area? September average extent? area?) and how it's to be measured (NSIDC? JAXA? ...). Our August prediction of 4.60 is excellent, if you let us choose after the fact to say that we meant minimum extent, rather than monthly average. On the other hand, our model prediction from May is excellent if you let us say that it is September's monthly average, rather than minimum day (which we did say then) as measured by JAXA (which we did not). JAXA's September average was 5.10 million km^2, vs. our May model estimate of 5.13. http://www.ijis.iarc.uaf.edu/en/home/seaice_extent.htm

For both monthly average (5.10 vs. 4.90) and day's minimum (4.81 vs. 4.60), JAXA is about 0.2 larger than NSIDC. That's one reason you have to specify your verification criterion. As to why they differ, take a look at the area around, say, 75 N, 90 W -- the Canadian Archipelago. There are a ton of islands up there, which means a ton of coastline. That means it's hard to decide which grid cells to call 'land' and which are 'ocean'. I tried the experiment myself before, and you can change the area of the Arctic ocean by about 1 million square km depending on how you decide what to call land. Further, the Canadian Archipelago is generally a pretty icy place. If your definition of 'land' puts more water there, you'll also report more ice. Easily 0.2 million km^2. This is another part of why I'm particular about specifying which verification data you're using. This is an issue for everybody who creates a grid of data. And, unfortunately, I've never seen any one best solution either for the land masking or for transferring data from original satellite observation onto that grid. (If you know the one best way, let me know. Minus a bunch of points to you if I've already tried it :-(, but plus a bunch if it's new and I can sell my colleagues on using it.) Of course then you also should be asking just how much area of water our model has in the arctic ocean. ... maybe we did better, or worse, then we think?

Once you get serious about verifying predictions, there's a good amount of work involved. It's only if the predictions are pretty poor that it's easy to assess them.

Thanks Teachers!

2010-11-16T22:31:00.000-05:00

Quoting one of my sisters' pages:

Tonight, a teacher somewhere in your community is preparing lessons to teach your children while you are watching television. In the minute it takes you to read this, teachers all over the world are sacrificing their own time and, more often than not, investing their own money for your child's literacy, prosperity, and future. Re-post if you are teacher, love a teacher, or appreciate a teacher!!!

As for most things, the most media coverage is of the bad performers of a profession. But I grew up seeing my grandmother (another teacher) doing exactly as described, and see my sisters doing so when I visit them. And some of my teachers, my kids' teachers, and so on were/are obviously doing likewise.

It's American Education week, so I'll invite folks to contribute their stories of favorite teachers this week.

Vererans Day

2010-11-11T17:44:00.001-05:00

Thank you to all veterans!

Happy Veterans day to all, my son included.

Sorry about the late word.

Sea Ice Predictions vs Reality

2010-11-08T05:51:00.000-05:00

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.
But first, some other predictive types.

One was the poll I put up for readers here (which I also entered my two). Two said 5-5.25 milion km^2, so were a bit high. 3 had 4.75-5.0, which was the correct bin. 3 more took 4.50 to 4.75, a bit low. 4 each took 4-4.5, and under 4 million, so were fairly to very low. This is a better showing than last year, when everybody (except William Connolley, who didn't enter the poll but did make a bet with me) was too low, some by quite a lot.

I also mentioned some simple predictors -- climatology and simple trends. They did very poorly:

Climatology 1979-2000: 7.03 million km^2
Climatology 1979-2008: 6.67 million km^2
Linear Trend 1979-2009: 5.37 million km^2

Anyone saying Arctic ice has recovered is clearly far wrong. It hasn't even recovered enough to be as much as the declining linear trend would predict.

Now for mine (ours -- I was working with Xingren Wu):

Wu and Grumbine modeling: 5.13 million km^2
Grumbine and Wu statistical ensemble: 4.78 million km^2
Grumbine and Wu best fit statistical: 4.59 million km^2

The best fit statistical one did an excellent job of predicting the September minimum, which was 4.60. The problem being, it wasn't trying to do that. This is another reason you have to be specific about just what you're predicting. As for last year, the best fit statistical was too low. 2 is a rather small sample, but being wrong in the same direction does point to something for us to keep in mind when working on next year's prediction. Finding some way of getting a predictor that's too high as often, and by as much, as it's too low.

The ensemble statistical predictor did pretty well -- off by 0.12 million square km. I wouldn't want to mow that large an area! But compared to the natural variability of about 0.5, it's a pretty good result. So I think the ensemble approach did us some good at representing reality a little better.

The model (actually an ensemble of model runs) was only off by 0.23 million square km. Again, pretty good compared to the natural variability. More about how it got there in a moment.

If you averaged our two predictions, you get 4.95 million square km, and off by 0.05, extremely close. While the sea ice outlook did that, I won' take credit for that accuracy (same as I wouldn't have taken any blame for errors). Again, we submitted two separate predictions precisely because we did not consider averaging the two to be meaningful.

With the model, we learned something useful. Namely, the prediction entered was not exactly what came out of the model. Our adjustment method is what produced the good prediction. The problem was that the model, we knew, was biased in favor of having too much ice extent and area, and to making the ice too thick. A model which is consistently biased can actually be very useful, once you figure out how to correct for its biases. A well-known weather model in the 1970s and 1980s was useful in this way. Its rain predictions were always a factor of 2 wrong. Once you divided (or multiplied, I forget which), the model's prediction by 2, you had very good guidance.

Our hope was that we could figure out a way of using the model to get a better estimate than the model itself gave. Then, if it worked, that our method would shed light on how we could improve the model itself. There's a fair chance that we have exactly that. Our method was to use the area of ice that was thicker than 60 cm (or so), rather than ice thicker than zero (using the model straight). That such a correction method worked tells us that we might be much better off to restart the model with all ice, at least in the Arctic, being 60 cm thinner. If everything else is correct in the model system, this restart might cure all problems in the ice model. (We're probably not going to be that lucky, but it's a direction of hope!) Then we'd have accurate predictions without a need for bias correction.

It is these sorts of things -- finding good ideas on how to restart the model, looking too see how much variability is natural, see what kinds of statistical methods are useful, ... -- that I find useful in the outlook process. Something to help focus our thinking. We could do such things anyhow, but it's sometimes also an interesting plus to work on the same problem as other people. If nothing else, you have something to talk about in the hallway.

Young scientists

2010-11-05T00:35:00.000-04:00

In Knight anoles, you got to see part of the reason I made one of my goals for this blog to be inclusive of middle school students. They can be quite interesting to listen to about science, and can learn quite a lot of it themselves. Biased as I am in being a father and uncle, I still believe that kids other than mine can match, or at least approach :-) mine.

So I'll mention that if you're a teacher, parent, or a student yourself, and your kid/you write up a science essay, you're welcome to submit it here for consideration. I'll look for the essay to teach me something about the science, and to show the love of learning about your topic that Kristen showed for hers. As you might guess from my usual topics being climate and ice, but this note of Kristen's being lizards, you're not limited to my professional areas.

There will be details to work out, maybe later we'd want to establish it independently of this blog. But think of it as something in the vein of Journal of Young Scientists. A chance to share your love of your topic with others. You can send to me at bobg at radix dot net. We'll play things by ear. I've created the tag 'young scientists' and retro-applied it to Kristen's (first! :-) note.

Knight anoles and science writing

2010-11-04T05:56:00.002-04:00

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.

    This lizard is a very recognizable species. It is the largest anole in the world, so if any researcher was walking around in its territory, he/she would definitely notice. The knight anole is known as a “crown giant” because if its crested head. This also explains why it is called the “knight anole”-- because of the “helmet” on its head! This head is usually 7 inches (18 centimeters) long. That is a large percentage of its body! The whole body of a knight anole is 13-19.375 inches (33-49.2 centimeters) long. Its maximum snout-vent length is 188 millimeters of it massive head. (Wilson, 1997) Its eye is like any other anole’s eye, with a black and round pupil. The anole’s eye has a black spot around it, much like one on a domesticated dog. In its mouth, a knight anole houses an unusually bright orange tongue and extremely sharp teeth. I think that the tongue is used to scare off predators because of its bright color. Male anoles have an enormous pink throat fan below the lower jaw. Females do not have a throat fan. Going down from the head is a nape that has a small crest on it, which looks like a wrinkle on the knight anole’s skin. The body has yellow and/or white highlights on it between the body scales that look like plates of armor. The short legs make it relatively slow. Its feet have special pads that can cling to some surfaces, including trunks of trees.
They are mostly lime green with black, orange, and sometimes white speckles on the back. The speckles resemble sprinkled pepper on a lime. Yellow streaks are also seen on their sides. Their scales look a bit wrinkly, making any knight anole look like an old warrior. The skin can change color. Some people mistake the knight anole for a chameleon because of this, which makes me a bit annoyed. I think that many scientists seem to argue about the true maximum length of this lizard because they all know that there is always something bigger and better out in the environment.
    Knight anoles live in Florida and Cuba. They were introduced to Florida accidentally, probably by ship or airplane. Animals that come from one country to another are called invasive species. These lizards live all over Cuba where there is a tropical climate and shady trees. I like to imagine the knight anoles in their homeland, climbing up large and leafy trees to find shade so they can extend their dewlap in pleasure. In Florida, this reptile is becoming an established species because of its ability to adapt to new environments easily. The knight anole’s ability to adapt makes it able to colonize in natural areas as well as urban ones (Wilson, 1997). So far, the knight anole has occupied four of Florida’s counties, including Broward and Dade and somewhat into the Keys. It is also slowly fanning out into other southern states such as Georgia and Alabama. The knight anole has been found commonly in the shade trees along streets in urban areas such as Miami. Sadly, the knight anoles don’t usually survive Florida winters because the sudden drop of temperature is too much for their bodies. It is just like when you take a hot pot and put it into iced water; it shatters. Anoles live for 15 to 16 years, which is pretty long for a lizard (Green anoles live for at most five years). Many people like to keep these lizards as pets because of their astonishing appearance. Some people get mad at their anoles for biting them or getting too big and toss them back out into the wild without knowing what might happen to them. If somebody buys a knight anole as a baby, the lizard might not know how to act in the wild once released. In these cases, the knight anoles are usually considered an easy meal for snakes because of their lack of survival skills. Fewer natural predators means that the knight anole population is larger in housing developments.
    Their diet consists of insects and other smaller anoles, such as the brown anole, green anole, bark anole, or any other small knight anoles that dare come in a mightier one’s way. The knight anole also eats any insect (that is not poisonous) that gets in its way, such as butterflies, beetles, and ants. When a knight anole spots a juicy beetle, it waits in silence and turns its color to match brown bark or green leaves/grass so the prey doesn’t notice. The lizard creeps closer very slowly and snatches the beetle up in no time at all! It munches on the prey with its razor-like teeth. Knight anoles don’t usually fight over food because there are plenty pesky bugs in Cuba and Florida. In captivity, they are known for eating pinky mice.
    Knight anoles are fierce warriors, just like their name, when they meet a predator or a rival male. When the lizard sees its only predator, a snake, it turns sideways, extends the dewlap, raises the back crest, and gapes menacingly at it. This isn’t all a bluff. If the snake comes any closer, the anole will hiss or bite. If the snake moves closer to the anole, the lizard runs away. Sometimes, a snake might get the anole’s tail within its grasp. The lizard’s tail then falls off. Then a new tail will grow back, though it is replaced with cartilage, not bone, so the new tail isn’t as tough as the old one.
Fighting with other males is much different than fighting predators in the knight anole kingdom. Lizards might fight over territory or a mate, but they always fight the same way. Both males extend the dewlap and retract it many times while bobbing their heads stiffly up and down. I think that this behavior looks like the lizard is doing push-ups to scare away others. This is all about bluffing to show who is mightier. Sometimes, the fights turn into battles where the lizards nip at each other until one goes away. The winner gets to keep the territory, or the mate. Sometimes, people call the knight anole “the new Godzilla” because of its fearsome look when defending territory or mates.
    The breeding season for knight anoles is in the summer, or the spring if the temperature is right. Sometimes, the knight anole mistakes males for females when breeding because the reptiles can’t tell the difference between the two. This comical event doesn’t lead to anything serious, just a possible fight for territory. When two knight anoles breed, the female produces up to four clutches of one to two eggs. The female lays them in a depression in the ground and then covers the leathery eggs and leaves them alone. After five to seven weeks, little one to two inch lizards emerge from their eggs to greet the world. The hatchlings are a bright green with white bars on their sides and are already fully independent. Many predators such as birds, other lizards, and small mammals eat these newborn lizards because they haven’t learned much about survival yet. The hatchlings that survive the early days of their life change drastically as they grow from 2 inches to the massive 19-inch length of an adult knight anole.
    The knight anole is a truly magnificent creature that awes me. Its size, name, and abilities make it very unique. I think that it is the most fearsome and ancient looking reptile in the anole kingdom. I also think that there are many more things to learn about this lizard, such as how long the largest one is and how they came to America. What still makes me gawk at this lizard is that fact that it has come from a completely different country and has made its home in Florida along with all of the other amazing reptiles here. I wonder if the knight anole will crossbreed with another lizard species to make another extraordinary new type of lizard. The possibilities are endless and there is still much more research to do on this lime green beauty.

*****

Back to your host:
The above was written by my niece. To my eyes, this is wonderful science writing and I'd like to see more of it, in more venues. Certainly I encourage such lively, passionate writing. If a teacher is reading, take this for a model, not the vapid soulless passive 'it has been observed that lizards are green' writing that is inflicted on scientists by most journals.

Election Day

2010-11-02T05:51:00.002-04:00

US readers:
Vote today, Tuesday November 2nd.
If you're not registered, get registered.
If you do neither, but are eligible, you don't get to complain about the results.

Hiatus to extend

2010-10-23T13:36:00.000-04:00

Oh well. I'd been blogging less (none) because I've been using that time to work out an idea for publication. That is now finishing up ok. But I went out for a run yesterday, to get away from my desk, get the blood flowing, and hope for some ideas to flow on how to handle the 'last' nagging issue on the paper (graphical, not scientific). Instead, I took a pretty impressive fall and broke my wrist. Since I'm left-handed, it had to be my left wrist. This will crimp my blogging for a few weeks, as I can only type one-handed, which is about 20% the speed of my normal typing. (and error rate is up about 5x as well). Grr.

Still, I can hit the 'approve' button for comments, and there are several good topics which are getting comment.

And, by way of a chance for me to listen, this seems like a good time to open the floor for suggestions. One commenter a while back mentioned a blog-usable LaTeX processor. I'll look in to that. Other ideas for the presentation? Content you'd like to see?

It's been a recurring thought from readers that I really need a better way of organizing, or at least displaying the organization, the content here. I agree with this. The idea I've had regarding how to do it is either to test it out on a wiki, or to use the wiki as the main place. I've set up one for experimentation. If you're interested in the idea, please send me an email (bobg at radix dot net) and I'll add you to the list of editors (and tell you where it is). That address gets a lot of spam, and I'm liable to be intermittent, so a follow-up message a couple days later won't be a bad idea.

Does Lake Superior Remember the Last Ice Age?

2010-09-28T05:46:00.095-04:00

I'm more than a little surprised by this post by Steven Goddard. His answer to my title question is yes. That he's wrong isn't very interesting. We all make mistakes, and particularly so when speaking outside areas that we've studied. The two main physical processes which show his error are interesting in their own right, and I'll take this chance to discuss them -- they are rivers (which say 200 years should be noticeable), and what happens to fresh water at 4 C (which says the memory is 1 year [oops, 6 months]).

First, I'll take a look at a less interesting error that minimal self-checking would have pointed to a difficulty. But that introduces a useful tool -- the 'sanity check'. Namely, he suggests that the reason Lake Superior is still cold is because it's so large that it is still adjusting to the end of the last ice age. That's about 10,000 years ago. Ok, suppose this line of reasoning is true. While Superior is large, is it tiny compared to the oceans. If Superior takes 10,000+ years to adjust, something 10 times bigger should take 100,000+ years to adjust. The ocean is about 100,000 times larger (in volume) than Lake Superior.
Goddard's line of reasoning, then, suggests that the ocean's time to react to climate change is over 1,000,000,000 years. This is not a rigorous argument, of course, it's what we call a 'sanity check'. If your argument is true in one area, what happens when you apply it to a different area? Does it still make sense? If the ocean's response time to climate change were a billion years, the present ocean would still not know that we moved towards ice age conditions about 35 million years ago -- it should be warm, as most of climate over this period has been warm.

The sanity check alerts us that there may well be something wrong with the line of reasoning. We still need to look at what kinds of things could be at play to cause the answers to be so unreasonable. Perhaps it's true after all, there being some extra thing going on to make Lake Superior wildly different than the oceans. One process is river inflow, something which affects both the oceans and Great Lakes. Namely, every year, water flows in to Lake Superior, and to the oceans. Water also flows out of Superior (though not the oceans, at least not to speak of). In other words, the water that's in Lake Superior today wasn't there at some time in the past.

What we can do is divide the volume of the oceans, or Superior, by the volume of water flowing in each year. That gives us what is called a 'residence time' -- the period that, if the body mixed up thoroughly every year, it would take to replace all the water that was there when you started. The residence time for Lake Superior is 191 years (same wikipedia link as above), round it to 200. For the ocean, I compute it at 40,000 years by this consideration and some remembered figures. In both cases, then, figures quite a lot faster than was given by Goddard, or inferred by applying his reasoning to the oceans.

Let's look specifically to Superior, now. Does anything happen to mix it up? Well, there are winds, of course. The winds kick up waves, which stir up the upper parts of the lake. If it were true (it isn't) that the river inflow all stayed at the top of the lake, and all flowed out from the tops of the lake, then maybe the deeper waters would remain unaware of more recent climate. (This possibility is why you don't stop with the sanity check, and why you don't stop with the rivers. Keep pushing.)

4 C is the figure to remember. What happens is that fresh water is densest at 4 C (39 F). So let us think about Lake Superior as it heads for winter. At the end of the summer, the water is 'warm'. Meaning just that it's over 4 C, though often not by a whole lot. You can see current conditions at the Great Lakes Environmental Research Lab. As I'm writing, most of the lake is above 50 F (10 C).

As we get in towards winter, the lake will cool. 9 C water is denser than 10 C water, but this warm water (for Superior!) is all near the surface. So a bit of mixing will occur in that near surface layer . Cool some more, to the point that the water is only slightly above 4 C. Not a difficult thing, as northern Minnesota and southern Canada are far colder than that in winter. Mixing occurs deeper, since the water is almost as dense as it can get, but, still, only the upper portion of the lake. Now continue cooling, to the point of the surface water being 4 C. This is the densest you can make fresh water (ocean water is different). The entire water column then overturns all the way to bottom. This thoroughly mixes up the lake, and does so every year that northern Minnesota gets cold.

update: As the lake warms up from its winter cold, the surface warming from near 0 C towards its balmy summer 10 C, the same process happens on reaching 4 C. So every fall and every spring, for as long as winders are cold, the lake overturns. (original) The longest 'memory' Lake Superior, or any cold winter lake, can have is 6 months -- since the last overturning event. Lake Superior certainly does not 'remember' the last ice age. It doesn't even remember the cold winters of the 1970s.

Lake Superior is cold because fresh water is densest at 4 C, and humans think that's a cold temperature for a lake.

The field of science that studies lakes is limnology, so this is a useful term to include in your searches for further information. A nifty process related to this 4 C business is called 'thermal bar'.

Science Cafe

2010-09-27T14:46:00.000-04:00

This Thursday (September 30th) I'll be talking about ice, and, better, yet, answering questions about ice at the Annapolis Cafe Scientifique. The time will be 6 PM instead of the usual 6:30. Same location as usual -- Cafe 49 West. Local folks are invited, and non-local are welcome to pose questions here.

Kitchen experiments

2010-09-24T06:23:00.001-04:00

Some simple, if possibly messy, fun. Ingredients: Water and corn starch, baking powder and vinegar.

The baking powder and vinegar mix to release carbon dioxide gas. If you put it inside something with a tight cap that can blow off, you've got a 'rocket'. Just be sure to aim it away. I don't really remember well, but I think equal vinegar and baking powder is the right recipe. But it's something to experiment with.

Corn starch and water is a chance to explore the mechanical properties of matter. (read: mess around while claiming to be doing science) Ordinary fluids, like air or water, react straightforwardly to pushing on them. If you push, they move out of the way. Push harder, they move out of the way faster. Corn starch and water (again, I think it's equal amounts, but experiment) are a different kind of thing. Set a marble on top of the mixture and it will sink through. Throw the marble at it, and it will bounce. !? Experiment. It makes a difference how fast you push. Lots of room for experimentation.

Anyone else have comparably simple experiments?

Unity of science and reaching decisons

2010-09-20T06:23:00.001-04:00

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:

Scientists are real people

2010-09-10T05:58:00.002-04:00

If you are accustomed to the media representation of scientists, my subject line is something of a shock. What, do I mean scientists aren't all junior Dr. Spock's off Star Trek?! Contrary to everything you've ever seen on TV and films?! Well, yes. We're human, no less than anyone else on the planet, and unlike fictional Vulcans.

That's relevant to the post, not so much for content, but at the reason that comments and posts have somewhat gotten away from me. There were many good comments in the What is a good experiment? thread, and I haven't commented there myself. (I'll encourage you to go have a look.). And there have been good comments to later notes that I, again, haven't commented on (see the list of most recent comments that's, currently, buried way to the bottom of the page). Not that my comments are required, or any such thing. But, since I like conversation, it pains me to not be engaging the way I'd like to be. (Don't worry, even if I'm not commenting, I am definitely reading. I read far faster than I compose.)

For the subject at hand, the answers are entirely mundane -- 'real person' -- sorts of reasons. I've been doing other things. I'm a parent with 3 kids. And they've been doing things over the last month. Good things for them, and me (at least to spectate). But they do tend to mean that I'm focusing some of my time, energy, and attention in places other than the blog. Some (many) scientists are parents (and grandparents). Many of us are very concerned about parenting well. Or least are seriously interested in our kids. Even with my youngest being 20, I still think there's room, and need, for a parent. And they're great kids, so who wouldn't want to be involved?! Or at least sitting in the back of the audience cheering.

I'm also a spouse. My spouse and I have been doing things together in evenings and weekends which are very good and which we enjoy together -- visiting friends, having friends over, going places, and so forth. Good, 'real person', things, but while I'm doing those, I'm not blogging.

And I have a day job different from the sorts of things that I write on the blog. There's a small degree of transference. I can point out to you that my May predictions of September's ice extent are looking to have bracketed the likely result pretty well. The high (model-based) figure was 5.13 and the low (statistically-based) figure was 4.78. We passed below 5 in the last few days. Probably won't be as low for the monthly average as 4.78. But the spread between the two forecasts was fairly small, and succeeding in bracketing reality with that narrow range is ... not bad. I'll have more to say once we get to the end of the month and see what really happens. The day job has been showing up interesting things, which turns around to mean more time at the office, and less time taken from my lunch hour to write on the blog.

The end of the month provides a chance for me to meet up with folks who are local. I'll be speaking at the Annapolis Science Cafe, on Thursday, the 30th of September. More about that to come. It'll be about ice (you're shocked, I know).

Last night I earned 'Beastmaster' status. My wife has two dogs. (Her dogs -- she's had them longer than she's known me. We've only been married a little over 4 years; newlyweds.) Both are small dogs, of, as Dave Barry said, of the 'pillow' family. The older one, Tater, is pushing 12 and has his hair growing over his eyes -- to the point that he often can't see what is around him, like walls. One reason that hair grows so long is that he has traditionally (I'm told) reacted violently whenever anyone approached with scissors to trim off the overhang. Last night I sat him down, solo, and trimmed his bangs. No sedation or armies to hold him down. He's doing better now. Here's a picture of him during 'snowmageddon' last February (the snow is about 30 cm, 1 foot, next to him; double that farther away from the door). He'd just had his hair trimmed (after sedation, at the veterinarian's). He had far less vision last night before I started trimming.

In between all that, I've been nudging an idea towards being able to submit it for serious publication. It's difficult doing that from home. I'm used to publishable ideas being things I work on at work. This one, however, is not related to what I do at work beyond the fact that it involves the earth. Not really close enough to persuade the folks who sign my paycheck that I should be devoting work time to it. Once I've sent it off for a round of preliminary review by friends who have some good general science knowledge (to see if I've made a generally well-formed argument), I'll be thinking more bloggy things. Not least being various things to talk about here regarding doing science and some offshoots of interest. The climate cycles 1 post is one such already. There are more to come. Not least, while that first climate cycles post talked about seasonal variations, we also should take a look at daily variations. Same as we (middle and high-latitude residents) expect summer to be warmer than winter, we (all) expect daytime to be warmer than night time. That expectation makes it climate. Figuring out by just how much becomes science.

And there are the usual 'real life' sorts of things -- paying bills, getting my car fixed, trying to take care of an injured shoulder, blah, blah, and very blah. Scientists are real people, with all the same issues as anybody else. Irritates me that so many seem to think we're Vulcans. Plus, of course, that we stand in closets in between times of saying something or other annoying and irrelevant to the human issues at hand. We all have the usual problems, responsibilities and joys of being 'real people'. Some of that affects the blog. All of it is just the usual, for scientists, same as for anybody else. I'll be getting back to more regular writing here in the near future, as this part of my regular life becomes more active.

Constructing an analysis 1: Drop in a bucket

2010-09-01T05:46:00.167-04:00

'Analysis' is what we call an attempt to represent the state of the atmosphere/ocean/sea ice/... given a set of observations. One such analysis is the global surface air temperature analysis. That, then, spawns efforts to find a global mean temperature, or global mean temperature trends, and so forth. Several of the recently-added blogs aim to study that, in one way or another. That particular one is not my interest in two different ways.

One is, I'm an oceanographer, so I'm more interested in a sea surface temperature (sst) analysis. The other is, most of the interest in the surface air temperature analysis seems to come from its role as a detector of climate change. On the scale of things, I consider this the second weakest climate change indicator. The only thing weaker, in my view, is the so-called 'Hockey Stick'. But enough raw opinion.

Regardless of what it is you're trying to analyze, and what your reason for doing so is, there are quite a few ways of setting about doing so objectively. The fact that there are many makes this the first of something like eight notes I'll be writing up on the idea. There turn out to be many different ways of making an analysis, each objective, each with strengths, each with weaknesses.

The simplest one, if not as simple as you might think, is the 'drop in a bucket' method.
First, a bit of language. We typically divide the earth's surface in to a bunch of boxes/cells. Also typically, they're some number (or fraction) of degree latitude by so many degrees (or fraction) of longitude. Depending on which sst analysis I'm looking at, a cell can be anything from 5 degrees on a side to 1/100th of a degree on a side.

The basic idea for drop in a bucket is very simple -- if you have temperature observations in a cell, you use them to find the temperature of (analyze) that cell. If you have no temperatures in a cell, then you have no analysis for that cell. So 0 observations in a cell is very easy -- you report no analysis. 1 observation is also very easy, your analysis temperature is the temperature from that one observation.

But what about having more than 1 observation in a cell? The very simplest thing to do is just average all of them. We just blindly treat all observations as being equally good. Hmm. That sounds a bit problematic. Some observing methods are better than others, after all. The quality of the observations is described by the standard error, which is the standard deviation between the true value and the observed value -- computed after you have many such observation to truth comparisons.

For typical drifting buoys and satellite methods, this is about 0.5 degrees. For ships, let's say 1 degree. This being science, of course I mean degrees C. One could pursue this to substantial complexity, as it's probably the case that every type of buoy has a somewhat different standard error, different ship observing methods have different standard error, and the different satellites and satellite methods have still other standard errors. Life is probably no simpler for surface air temperature observations; and for rain it's even harder.

For the sake of illustration, let's consider a cell with a buoy that observed a temperature of 25 C, and a ship in the same area that observe 26 C. In blind averaging, we'd treat them as equal, and give our analysis as 25.5 C. But ... the buoy is a better observer than the ship. Shouldn't our analysis be closer to the buoy? Maybe we should just throw out the worse observer?

Probably not. The observations have a distribution of likelihood (which is not the vertical axis! beware!) around the value they report. For each observation, the most likely value is what is reported. But those standard errors give us a curve of likelihood. The ship is in orange, the buoy in blue, and a third thing in black:

The ship (orange) has the large standard error, so it's a very wide curve. It's odd, but true, that the ship observation is more in agreement with a claim of 23 than the buoy observation, even though the buoy observation is colder. The thing is, the buoy observation is less uncertain.

The third curve is where we get to the creative part. What it is, is that I've multiplied the likelihood curves for the buoy and the ship. The result is a joint likelihood. The peak of the curve is our point of maximum likelihood. It's a temperature of 25.2 C. You can, in principle, do this sort of thing graphically regardless of how many observations you have. It gets tedious and ugly, of course. That's why we invented mathematics. In this case, calculus. (See bottom for the gory math details).

We also see that our resulting estimate, the maximum likelihood curve, is narrower than the two original ones. The more observations we have, the better our resultant estimate -- even better than the original observations. This is the same sort of thing we saw result in How can annual average temperatures be so precise?

Our estimate based on considering the quality of the different observing platforms is 25.2, rather than the 25.5 of treating them as equally good. When we're looking to deal with climate change and detecting small differences over time, it's obviously important to pay attention to this sort of change. If you change from one to the other, there's a 0.3 C change -- not because of climate, but because you changed your methods for filling cells. (Not a mistake I think anyone has made, but a heads up if you are getting started.)

The general term for this sort of thing (treating some observations as better than others) is 'weighting'. We give more weight to some sources than others. I'll give the exact method at bottom for the mathy folks. Different methods that we'll be getting to will do their weighting in different ways.

Now let's go back to thinking about the general approach. We select boxes of some size, and then if there's an observation in the box, then we say that we know nothing about what's going on in the box. There are about 5000 drifting buoy observations per day. If the cells are 5 degrees on a side, and the buoys are distributed randomly, there are about 3 observations per grid cell and we are doing pretty well. On the other hand, if our cells are 1/100 degree on a side, then probably only one cell in 80,000 has an observation. How did we go from knowing most of the globe pretty well (3 observations, obs, per cell on average) to knowing almost nothing? On the other hand, for every cell you say you know something, you definitely have at least one observation in support, and you haven't made any assumptions (at least not past selecting the cell size).

But suppose you are running a numerical weather prediction (NWP) model. You can't accept an 'I don't know' for starting your prediction. Consequently the people involved in NWP were early people to develop more advanced methods. That's next.

Methods (so far):
1a) Drop in bucket, blind averaging
1b) Drop in bucket, maximum likelihood averaging

Gory math details:

To find the maximum likelihood estimate for temperatures given N observations, multiply together your N curves, each of the form exp( - (T-T_i)^2/s_i^2), and find the T for which this is a maximum. T_i is the ith observation, s_i is its standard error.

The maximum likelihood value is sum(T_i / s_i^2) / sum(1 / s_i^2).

I'll leave it for those interested to compute the standard error of the maximum likelihood estimate itself.

Sea ice on the blogs

2010-08-31T05:34:00.006-04:00

Always a bit of a question whether I should comment elsewhere, or save my writing for here. I've usually resolved that question in favor of making comments even though that does crimp my writing time for here. Most recently, or at least most recently at length, I was visiting the Stoat's burrow. The topic at hand is the Antarctic sea ice that I was writing about back in March WUWT trumpets result supporting climate modeling.

William has a follow up post today. I'll probably be commenting there, or maybe taking up a point here soon.

No comment there from me, but also a recent post/show about Arctic sea ice cover Arctic Sea Ice is Just Fine. Of course it isn't, and the author makes a nice presentation illustrating that it isn't.

Were the 70s cold?

2010-08-25T05:47:00.001-04:00

I was surprised to see that the 1970s weren't particularly cold. My surprise is partly because where I lived (Chicago area) we were busy setting all-time records for cold, and that was true for much of the US and across to the UK.

The other part of the surprise is that it's common to hear people (see them write) something on the lines of "Of course we're seeing a warming since the 70s; it was cold in the 70s!" Surely someone along the way did their homework and checked out what the global temperatures were?

Fortunately, if we're looking at science, we don't have to assume that other people did their work, or did it correctly. The alternate word for it is, skepticism. Real skeptics don't make those assumptions, they do the work themselves. The fact that it's work also explains why there are a lot of fake skeptics -- it's much easier to pick the answer you like and reject everything else.

So let's apply some real skepticism and ask what was really going on with temperatures in the 1970s.
I'm working from the NCDC global mean temperatures. I'll take the average of each decade they give, and plot that:

That's rather disappointing. The 1970s were the 4th warmest (of 12) decades in that record. Actually a little worse, as you'll notice I don't have the decade starting in 2000 computed. (I downloaded the file in 2009, it's gotten warmer since then). 4th warmest of 13 decades, behind only 1940s, 1980s, 1990s, and 2000s. But -- be a proper skeptic -- compute for yourself the average for January 2000 through December 2009. I got +0.35 for the 1990s.

My disappointment is twofold. First off is that the decade that I remember as being so very cold, wasn't. In my area it was, and even for a good distance away. But the entire US is only about 2% of the globe. The global average could easily be quite different, and turns out that it was. The 1970s were a relatively warm decade.

The second part of my disappointment is that those people calling themselves skeptics are clearly not skeptics. They never computed decadal temperatures, and never looked to see whether the 1970s were particularly cold. Not only were they not cold, but they were warmer than the two decades before them. Real skeptics would not claim that they were cold in talking about climate change.

Teacher preparing for new year

2010-08-24T05:23:00.000-04:00

My wish for us this year: let's take care of each other so that we can take care of our students. I picture our jobs as a great big, wonderful tree house full of knowledge. Our students have to leave their life's baggage on the ground and as they climb up they realize the sky isn't all that far off. May we all remember the simple joy that comes from a fresh box of crayons and a friend to sit with at lunch. BAM!

Liz Martinet, teacher

Bad Astronomy: The Wonders of the Universe

2010-08-20T05:40:00.101-04:00

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.
The Moon is Shrinking
Bad Universe Premier August 29, 2010
Low mass black hole?
Planetary triangle 6 August 2010
Saturn's rings and a tiny moon
Sunset from space
Possible Naked eye Comet (8 June 2010)
Hubble at 20 -- still amazing!
The Red Lagoon (Nebula)
Amateur Astronomy and the newest new moon ever
The amazing Mimas (no, Saturn's moon, not some circus performer!)
90% of the distant universe
How big is a Billion?
Star at birth
Saturnian moon dance
The Whirlpool Galaxy revisited
Otherworldly eclipse
Cassini craft 10 years (9) since Jupiter
Norway spiral
Fermi and the shape of space
What was before the Big Bang? Nobel Laureate answer
Winter solstice 2009
Top 10 astronomy pictures of 2009
More planets found
Apollo 12 footsteps photographed
Adler Planetarium giga-galaxy image (The Adler Planetarium in Chicago is one of my favorite places to visit. I have this on my list for next time I'm in Chicago.)
Scale of the Solar System (Something I've previously tried my hand at illustrating. It's truly hard to convey, and the author did well.)
Dark Matter
Water on Moon, 2009
Beautiful Hubble
Lunar Landing revisited
Lunar Eavesdropping
When Worlds Collide
To be or not to be
Apollo landing site images
Optical delusions
Summer Solstice, 2009
Death From the Skies: Magnetars
Moon Occulting Antares, 2009
The Amazing Sun
Differential elemental ablation of micrometeoroids (If that seems intimidating, rest assured that it isn't, really, and maybe have a look at my own Science Jabberwocky
100 hours of Astronomy, 2009
Galileoscope
(My own plans to distribute a few were bitten by a supply chain bug. Still, one of these days, I'll be engaging my several nearby schools in something similar.)
Nerdity on parade (I'd probably lose, but I could actually enter a nerdity contest with Phil.)
Galileo and the Moon
2009 Perihelion

Open Lab 2010 nominations

2010-08-12T05:28:00.004-04:00

I have the logo on the right for nominating blog articles to the best of collection -- the Open Laboratory 2010. I'll suggest that you look back at the articles that you think are particularly good, here and elsewhere, and follow that link from the decorative logo, or this link to make your nominations. I was reminded of this because Bora Zivkovic, who does the 'Blog Around the Clock' (a title he perhaps is living out), is also the lead on that activity.

Note that, because of Bora's publication schedule, articles are eligible from December 2009 through November 2010.

New locations for two from blogroll

2010-08-11T19:27:00.000-04:00

Two blogs from the blogroll have moved -- A Blog Around the Clock and All My Faults are Stress-Related.

Are there other updates? Blogs I should be adding to the roll? Note that 'should' requires that the blog be substantially about science -- learning and doing it -- rather than ... well, quite a few other things that are otherwise. I grant more leeway for blogs that link over here, figuring that reciprocity is good citizenship.

Designing good experiments

2010-08-09T05:26:00.004-04:00

This is another time I don't really have great answers, but a question over at the question place has me thinking about it. Namely, what makes for a good experiment? As far as the science goes, I'm comfortable about knowing the answer. Or at least knowing enough of an answer.

But for the purposes of you readers -- what makes for a good experiment that you could do yourself? How big or small could it be? How long should it take to run? How much expense is ok? Is following a circuit diagram to assemble test equipment something you're comfortable with? Carpentry? And so on.

For the original question -- a tabletop demonstration of the greenhouse effect -- I might actually have an answer of sorts. I went running shortly after first reading the question. That's often a good time for ideas to come to me, and a few did. But at the moment, they'd take a pretty big table (like, say, 10 feet), you'd have to get hold of a dry ice supply (for the CO2), and you'd have to assemble a fairly simple circuit. A several Watt laser would also be a plus, but I'm going to try to make sure that the experiment will work without it.

Question place

2010-08-09T05:15:00.000-04:00

Time to hang out the shingle for questions. As always, response time will be variable, including that I may not be able to answer. If so, maybe a reader can. And maybe we can have some discussion about the question and how we might go about finding the answer.

Climate -- cycles 1

2010-08-05T13:00:00.000-04:00

One of the things we expect about the weather is that it will change. Following the dictum, as I do, that climate is what you expect and weather is what you get, change is part of climate. But what changes do we expect? One sort of change we -- those of us living in middle or high latitudes (above, say, 30 N, or 30 S) -- expect is that winter will be colder than summer. Namely, we expect an annual cycle to temperature.

As part of a different project, I've computed the size of the annual cycle in the 2 meter air temperature. (When meteorologists talk about 'surface air temperature', it really means the air 2 meters above the ground). The scale is degrees Celsius for the amplitude -- the difference between the average temperature and the warmest, or between average and coldest. If you want the range between the warmest part of the annual cycle and coldest, double this number. If you want the amplitude in Fahrenheit, double it (well, multiply by 1.8).

This is a beautiful scientific picture. Why, may not be immediately obvious, and there is more to the story than the annual cycle.

One thing is, remember that I like time series analysis, and some of the concepts from there, I'm using here. In saying 'annual cycle', I actually mean a perfect sine wave that goes from peak to peak in exactly 1 year. While the seasons do repeat on a 1 year basis, they don't do it precisely on a perfect sine wave. In the sense of music, there are overtones. Not just 1 cycle per year, but also 2 cycles and 3 cycles. Those figures are below and I'll get to them in more detail later.

I confess that the beauty of the picture is not the color scheme or labelling, or pretty much any graphic arts aspect. The beauty is in the science. On seeing the picture, several ideas leap to mind:

Annual cycle is higher over land than water
Annual cycle is higher on eastern sides of continents than western
Annual cycle is higher at high latitudes than low latitudes

All seem pretty reasonable at the casual eyeball level. Putting them together, we'd expect the greatest annual cycle to be on the eastern side of a continent at high latitudes. Siberia fits that bill well, and we do see the greatest annual cycle in eastern Siberia -- about 65 N, 130 E, 27 C! (warmest to coldest of 54 C, 97 F!). So that's a good start.

But here's the fun: let's try to get rigorous about it. Is it always the case that the cycle is larger farther to the pole (still being on land, and just as far east)? Well, no. Antarctica is certainly farther to the pole than 65 degrees -- it goes all the way to 90, and poleward of 78 it is all land -- there's no western edge to the continent. Yet its annual cycle is not as large as Siberia's. More interesting, the largest annual cycles in the Antarctic are seen at the edge of the Ross Sce Shelf and the Filchner-Ronne ice shelf (Weddell Sea). Edge of the continent rather than interior. We also see that in southeastern Mongolia, the annual cycle is larger than in the part of Siberia to its immediate north, and for a good distance.

Hmm. An idea of mine is that when you have a fairly decent general rule, if something breaks the rule, you can learn something interesting by studying it. Exceptions are more interesting in science than rule-following.

Let's now take a look at the 2 cycles per year (semi-annual cycle) figure:

A quick look at the color bar tells us that the magnitudes are substantially smaller -- maximum is only 10 C instead of 27 C. It's also quite striking, though, that the Arctic and Antarctic are the places with the largest amplitude for this cycle. Also northernmost Siberia and Alaska, Hudson Bay, southern Baffin Bay and ... central India (!?).

Aside from India, the places with high amplitude semi-annual cycles a) are places that experience at least 1 day of no sun at all (namely, they're above the polar circles, 66.5 N or S) or b) have sea ice cover. But some places that have sea ice cover (the Sea of Okhotsk, for instance) don't have a large annual cycle. So probably we will wind up at a more complex rule.

The size of the semi-annual cycle in Antarctica is so great that it has a 'coreless winter' (van Loon's excellent term). At the same time as the annual cycle is heading for its coldest temperatures, the semi-annual is heading for its warmest. And with 8-10 C amplitude, that offsets a lot of the coldness you'd otherwise see. Instead of steadily cooling off through the winter, the way, say, Siberia does (notice that Siberia doesn't have much of a semi-annual cycle), Antarctica gets cold soon, but then temperatures hold relatively steady until spring.

And we still don't know what's up with India. (Actually, I do, but that's because I plotted some other figures.) Some further research to be done.

At 3 cycles per year (ter-annual), the sizes are even smaller -- now reaching only up to 2.7 C. Almost all the places with large ter-annual cycles are sea ice places, particularly the Ross Sea, Baffin Bay, north of Eastern Siberia and Western North America, and the Sea of Okhotsk.

Excluding the sea ice places, we see northern India and the Tibetan plateau as regions with large amplitudes. What leads to that?

Since these are expectable features of temperatures -- getting warmer and colder on a regular schedule, by observable amounts -- they're climate. (Yes, I used at least 30 years of data!). If we want to understand climate, these are features to understand. If we think we have a good model for climate, they're figures to compare against. And ideally, you'll develop a theory that can predict all these observations.

No grand final answers. Mostly just observations. But they're observations that strongly invite you (me, anybody interested in science) to try to answer. The annual cycle portion of this started being studied over 90 years ago -- C. E. P. Brooks, Continentality and temperature, Quarterly Journal of the Royal Meteorological Society, 43, 159-174, 1917. (He examined more factors than just the three I mentioned.)

I do have the numbers, and will be happy to make them available once we figure out how. They're on an irregular grid (the spacing in longitude is regular, but the edges of the boxes in latitude are different distances apart). I prefer working with this because it is how the data were originally developed. But I realize that most people don't like that sort of data, and it is certainly harder to work with. I could also construct a best approximation data set that is on a regular grid. It would be 2 degrees spacing in latitude and longitude. What's best, though? Spreadsheets with latitude and longitude in a column and row respectively? Simply listing off latitude-longitude-amplitude (for each cycle) in a plain text file?

My analysis used as its input the NCEP/NCAR reanalysis from 1962 to 2007. The reason for that odd set of years is my other project, which starts when the International Earth Rotation Service starts providing daily observations.

An aside: One of the thing people in my fields develop is a fair knowledge of geography. Given the number of place names I mentioned above, you see why. It basically amounts to another sort of vocabulary. If you want to study the earth, it helps to be able to name the place you're looking at.

The small world of science and internationality

2010-07-30T04:26:00.003-04:00

Science is an international activity; it's also a rather small world. I've mentioned both of those points before, I expect, but was a little surprised to be reminded of just how small a world it is. I was in Russia for work recently, specifically St. Petersburg. That's the story behind Dostoevsky being my summer reading. At the meeting, of course, I met a number of Russian scientists in my area. One of them being Dmitry Kiktev, deputy director of the Hydrometeorological Center.

Come ahead a little, and Michael Tobis (whom I know from some years of internet contact) posts a climate/weather news bit at In it for the Gold, regarding heat and fires in Russia. The scientist quoted is ... Dmitry Kiktev. In St. Petersburg, we experienced temperatures 20-25 F (10-12 C) above normal the whole week I was there (normal being 72-75 F, we had 95+). The article is talking about Moscow, but to the same end -- extraordinary temperatures being observed.

A different thing which I'll get to is the Climate Doctrine of the Russian Federation, which I received a copy of when we visited the Main Geophysical Observatory in St. Petersburg. One virtue it has (at least the English version; I don't speak or read Russian) is that it's short -- 22 pages of 5x8" (12x20 cm) text. Not so much a story as points for discussion. Different stories about the visit.

Scientific spectating

2010-07-29T04:37:00.001-04:00

The peculiar subject line is to introduce a new series of posts I'll be making -- scientific spectating. My idea is that there is too much science in the universe for us (any of us) to be expert about all of it. On the other hand, same as there are too many sports to be expert at doing them all, we can all learn to be good spectators. And being an informed spectator is its own kind of rewarding activity.

It can be helpful to keep Science Jabberwocky in mind. Individual terms can be pretty mystifying, but it can be obvious that certain ones are important -- CCR5 means nothing to me directly, but I know that it has something or other to do with plague and partial resistance that some Europeans have towards AIDS. In a similar vein, you can know that Shaquille O'Neal is a center, without knowing exactly what a basketball center does. On the other hand, you will find it easier to follow basketball if you know that he is a center. Knowing that, you can watch what he does, and what other centers do. After some time of that, you can appreciate watching the game much more.

It was in this vein that I appreciated some papers and comments in the late 1990s and early 2000s, regarding the expansion of the universe. The expansion of the universe (the 'toves') had been expected to be slowing ('slithy'). After all, gravity was pulling everything together. But then there were some observations presented which said that the toves were not slithy after all (that the expansion of the universe was not slowing). It turned out that the expansion of the universe looked to be accelerating (mimsy). In terms of doing the science myself, it may as well have been Jabberwocky. But I could spectate -- clearly there was a conflict between the expected slithy-ness and the newly-observed mimsy-ness.

As a spectator, I knew to start looking for papers defending the slithy-ness of the toves, or attacking the claimed observations of the mimsy-ness, or both. That, or even newer papers supporting the recently new claims of the mimsy-ness of the toves (er, accelerating expansion of the universe). And I saw just that. As it worked out, the papers supporting the mimsy-ness of the toves were stronger, and held the field. I was able to watch and appreciate that much. In the same vein, I can appreciate watching a college basketball game -- seeing one team take up a zone defense, and the other break the zone by feeding the ball to their excellent outside shooter, or fail in their attempt to do so. As a spectator, I know that the offensive team has to do something to counter the zone defense, and look for it.

It is this that the series will attempt to do -- help educate readers in how to be good spectators of science. A related point being, most of the best spectators of sports are people who love playing the game themselves (whatever the game is). You may not be professional level, any more than I am at basketball (or any other sport!). But it can be more fun to spectate when you play the game sometimes yourself. To that end, see my 'project folder' links, and keep asking questions.

Since I like a conversational approach to blogging, I'll invite comments, questions, suggestions at this point as to how you'd like to see this series go, whether you think it can be useful (and how), and so forth.

Revisiting a sea ice prediction

2010-07-28T13:12:00.000-04:00

Regular readers will recall that in late March to early April, there was a fair amount of excitement in some parts of the web about the Arctic sea ice cover having reached almost to climatology. That was rather exciting given that for much of the last several years the Arctic extent had been from moderately to extremely below climatology. I wrote up my take in Arctic Sea Ice Updates. Some of the excitable sources on the web were talking about sea ice recovering and the like. My comment back then (April 7, 2010) was:

So my guess for where we are in the Arctic: The ice formed by late season freezing and conveyor belt is thin. There has not been time for it to freeze thickly, nor for it to get mechanically piled up to be thick. The expansive winds that lead to the increase in extent also mean driving the ice towards warmer water. If the current pattern of blowing the ice out towards the edge were to be sustained, it points to a temporary high value for extent, and then a rapid drop in extent as the ice melts, or as winds reverse and compact the ice pack.

It's now almost 4 months later. What happened to the ice pack? Did it continue to hang near climatology? Go above climatology? Or did it sink rather rapidly back below climatology, as I'd suggested it would? The NSIDC report for July 6th notes that June saw the fastest recorded decline in June Arctic sea ice extent, and the lowest June Arctic sea ice extent.

I am John Abraham

2010-07-21T20:01:00.000-04:00

Those of you of a certain age, or a certain other age, will remember the scene from the movie Spartacus where everyone steps forward and declares himself or herself to be Spartacus. So it goes now. Fortunately, it is only threats against jobs and not, yet, lives which are at hand.

Still, someone's job is indeed being threatened, and the 'transgression' involved is to address the scientific content, or lack thereof, in the ~~whiner's~~ threatener's presentations. That would be no matter for concern if the threatener were some nonentity. But it is a person who has testified to the US Senate regarding the science of climate change. That makes it rather a serious issue -- this is not a marginal person whining from the distant reaches of the auditorium. This is a person with the ear of US Senators.

The person being threatened is John Abraham. He's a scientist at a small university in Minnesota who took the time to address the scientific claims of the person who styles himself as Lord Monckton, and, among other things, who recently was invited to address the US Senate on climate change. Abraham's response is at his University of St. Thomas web page. I encourage you to view/listen to the presentation Abraham made. And, of course, to examine yourself the original comments of Monckton's. And then to hit the scientific literature yourself to see who represented the science most accurately.

I'll include a raft more links below the fold, as many comments are already out there.

The thing which has me writing is the fact that this is such an absurd response from Monckton -- if he were at all interested in the science. That places this in to the 'weeding sources' category. If you're interested in the science, you, first, try to get it right yourself. Then, if someone else points to places where you might have gotten your science wrong (and, in fact, spectacularly wrong), your response is to correct your errors. You don't have to like it. Scientists are human, after all, and nobody likes to have it shown that they're wrong. Still, you do it. What you don't do is try to get fired the person who showed that you were wrong. But Monckton indeed responds to correction by trying to get his corrector fired.

So I encourage you to send your support to Abraham, by facebook group, to email his university, or the like (see, for instance, Hot Topic's petition to sign). We need more people who are willing to address the scientific content of public statements about climate. And they need to be reasonably confident that they're not going to lose their jobs for trying to speak honestly about the science.

Facebook Group -- Prawngate*

Stoat

Deltoid

Hot Topic

Desmog blog (has full text of Monckton's latest)

Eli Rabett

Hot Topic - 2

Only In it For the Gold

Deltoid 2

Scruffy Dan (Dan also started the facebook group) (see this post for the originals about 'prawn' and 'prawngate')

Tenny Naumer at Climate Change Psychology reproduces George Monbiot's Guardian article

Skeptical Science

Skeptical Science 2

* I should explain the 'prawngate' if you haven't seen it before. The thing is, one of Monckton's early comments was to say that Abraham looked rather like a prawn. I don't see the resemblance myself. More to the point, Monckton is sensitive about his own appearance, which results from a disease he has. You'd think that he'd be particularly sensible about not making fun of somebody's appearance.

Catching up on comments

2010-07-20T21:15:00.001-04:00

I'm on my way back from break and catching up in general, not just on comments. It looks like several species of bizarreness chose to break loose while I was taking my break, and it'll be a while to catch up to those too.

Several comments in on summer reading. My current summer reading is The Karamazov Brothers, by Fyodor Dostoevsky. It's the Wordsworth Classics edition, which I picked up for about $4.50 in St. Petersburg, Russia. Yes, there's a story involved, and I'll get to that in a later note. There might even be a picture or two.

Two more comments on When will the ice be gone?. Ecologists are a good group to try to get ideas from (per Hank's comment about how to get an idea of what levels of ice loss are particularly meaningful). And I see that Belette and I have an area of interesting agreement, [update, that's disagreement] which suggests a later post to be made. Our point of broader agreement is that neither of us takes my estimate in that post terribly seriously. My aim being more to illustrate a way to approach the question. The exact answer ... not so important.

Crandles has some thoughts about Sea ice estimations. One quick response I'll make is that a reason for some of the variation, or lack thereof, is that the curves are for 14 day averages. Consequently, you won't see much happening at shorter time scales.

Summer break

2010-06-14T21:27:00.000-04:00

It'll be very quiet here for the next few weeks. Even though I'm not in school, summer break is not a bad idea.

Not sure what I'll be doing for my break reading. Thoughts about good summer reading, for me or others, welcome.

Supporting some good work

2010-06-04T05:18:00.002-04:00

Following April's mention of world autism awareness day in April, I'll mention an effort to improve a school for special needs students. It is for a school near my sister (see more of her comments about autistic kids at that awareness day note) that includes autistic children:

Create safe, sensory Environments for special needs school children

When will Arctic ice be gone?

2010-06-03T04:40:00.029-04:00

The short answer, before I give you all the qualifiers needed to make sense of it, is 2035, give or take 7 years.

The curve above will take some explaining. But first some other important clarifications:

Often, people don't distinguish between types of ice. So you'll hear them talk about 'ice is growing', when what they mean is the center of the Greenland ice cap, or Antarctic sea ice. My comment is specific to Arctic sea ice.

Another trap people fall in to is not paying attention to what sort of statement about ice is being made. There are two parts to this. I'm referring to sea ice extent, not area (well, at 0 extent you also have 0 area, but it's still something to keep in mind). Also, I'm referring to the monthly average for September. If some day showed zero ice cover before my 2035, give or take, that doesn't disprove the prediction. It takes a solid calendar month, September, of no ice to support or refute the prediction.

Then there's the fact that it's a probabilistic prediction. 2035 is the mid-point. By my estimation method, there's about a 50% chance (54%) that 2035 or some year before that will show zero ice extent for September. It's only 6% that we'd see zero ice in 2029 (or before) . And rises to 96% that we'll see zero ice (for the month) in 2042 or before. The 'or before' is important.

How I got to those predictions turns on the probability thing I mentioned in this year's sea ice estimation note, of it sometimes being easier to work with the probability of something not happening, than trying to figure out directly the chances of it happening.

The sea ice estimation note shows a best fit curve through the data and projects it in to the future. The fit has some fuzziness to it, meaning that it doesn't pass perfectly through the data points. We don't really expect it to either -- there is weather variability. The size of this mis-fit, or, conversely, weather variability, is about 0.45 million km^2.

If we pretend that the variability follows the Normal distribution, then we can use that and our predictions to estimate the probability that we'll see 0 ice in any given year. That doesn't turn out to be useful. For instance, suppose there was a 1% chance of seeing no ice this year, and 4% of seeing no ice next year. What's your chance of seeing no ice in either year? I can compute it, but it's more tedious. It get rapidly more tedious with each year you want to consider. At 30-60 years ... ouch!

But we can consider quite easily the chance of there not being ice this year and there not being ice next year, and the year after ... out however far you want to go. What makes this easy is the and in the statement. If you're dealing with combining probabilities, and they combine by 'and', to find the chance of every single one happening, you just multiply the individual probabilities together. For the example from the previous paragraph, 1% chance of seeing no ice this year means 99% chance of seeing ice this year. 3% chance of no ice, means 97% chance of ice. The probability, then of seeing ice in all (2) years is 0.99 * 0.97, for 0.96 (96%). We can then turn around and see that the chance of some year not having ice is 100% - 96%, for 4%.

You might be noticing that 4% = 1% + 3%, and thinking you can just add the percentages. Consider tossing two coins. There's a 50% chance of getting heads on the first one, and 50% chance of getting heads on the second. (conversely, 50% chance of not getting heads). If you added, you would conclude that there's a 100% chance of not getting 2 heads. (50% chance of not getting a heads on the first coin and 50% chance of not getting heads on the second). The correct thing is to multiply (0.5*0.5), which says there's a 25% chance of getting no heads. Try tossing some pairs of coins and see which method is more correct.

In the top figure, I performed this kind of calculation for sea ice for every year out to 2070. As you see, the probability of not having any year with 0 ice for all of September is very high out to 2025. After that, we start accumulating some chances.

Now I can't say that I entirely believe this predictor. It is based only on my best fit logistic curve, rather than the ensemble. And this method knows nothing about sea ice thickness, only extent. But it makes a start on helping me (at least) think about what kinds of things are plausible. Given this, I'd need some good evidence behind a prediction of no ice for the month of September in, say, 2013. That seems extraordinarily unlikely. Conversely, 2060 looks extremely late (about a million to 1 against).

Sea Ice Estimations

2010-06-01T12:32:00.000-04:00

It's time to start making our estimates of sea ice for September. I'm submitting two this year to the Sea Ice Outlook, one based on a coupled air-sea-ice model, and one based on a more mature version of my statistical method from last year. You can join the fun by submitting a guess in the poll I'll put at the bottom of the blog. Remember, what we're trying to predict is the September monthly average extent. This is not the minimum daily extent, nor is it the area of ice. Keep your eye out for these details when comparing what different people say.

I'll start by summarizing predictors:

Climatology 1979-2000: 7.03 million km^2
Climatology 1979-2008: 6.67 million km^2
Linear Trend 1979-2009: 5.37 million km^2
Wu and Grumbine modeling: 5.13 million km^2
Grumbine and Wu statistical ensemble: 4.78 million km^2
Grumbine and Wu best fit statistical: 4.59 million km^2

In doing this year's estimates, I worked with Xingren Wu. As usual, talking out ideas with somebody made them better. That is part of why you see two statistical predictions.

The basis of the statistical prediction starts with my eyeball reaction to this figure (this particular copy is from Julienne Stroeve by way of the Weather Underground). It is comparing IPCC model estimates of ice against observations:

My version for predictive purposes looks like this:

(vertical axis is September average extent in million km^2; the red/orange are observations, blue is the best fit curve.)

So what's going on?
To make a curve fit, as usual, you first consider what shape curve you would like (straight line, or something more involved), and then look for the best version of that curve. The curve of the models doesn't look like a straight line to me. Nor does the observed ice. One way of deciding that is that the straight line that my eye puts through the observations for the first half of the data record doesn't sit on the straight line my eye puts on the second half. We prefer to be quantitative, of course, and running the numbers for the first 16 years and the last 15 years gives slopes of -0.045 and -0.152, respectively. To be rigorous, we would then want to perform statistical significance tests. Still, those look quite different.

The curve that I chose is the logistic curve. Except it's upside down, so I had a little work to do of a sort that we often do in math and science.

The logistic curve is most common in areas like population growth. At least in mathematical idealizations of population growth. Early on, when the population is small, the growth is proportional to the population. This makes for exponential growth, for instance 2% per year. But, as time goes on, the population starts to get large enough that resource limits take over. Those limits slow down the growth. You then slowly approach a limiting population.

Considering the opposite of the thing of interest is often useful in math and science. In computing probabilities, it is often easier to determine directly how likely it is for something not to happen, than for it to happen. But, since the probabilities have to total to 1, if you know one side (happen versus not-happen), you can then easily get the other. So go with what is easiest to work with first. We'll come back to this point in thinking about when we might see an ice-free arctic (September average).

In my case here, the ice cover is not growing -- it's shrinking. So I can't do a logistic curve of ice cover growth. But the opposite of ice cover is open water. What I can do instead of considering ice cover is to consider the area of open water. This fits nicely the general idea of a logistic curve. In the early part of the growth (of open water), there is a feedback that more open water leads to more open water (due to increased heat absorption by the ocean, thinner ice, ...). The 'population' of open water is limited, though. You can only have as much open water as there was ice cover to begin with.

One thing which has bothered me about, say, linear extrapolation, or quadratic, etc., is that if you go far enough into the future, they say that ice cover will be negative. With the logistic curve growth of open water area, this will not happen. As you see above, the ice extent goes to zero and stays there. But it never becomes negative.

I then ran my program to search for the best logistic curve -- one that has the least squared errors. That gives my plot above. But, in talking with Xingren, we realized that quite a lot of logistic curves were very, very close to as good as the best one. We could ignore those others (the 'ensemble' of curves). But, given the uncertainties in the data, this didn't make much sense to us. What we did was to take all the logistic curves that were pretty good, and average their predictions of what 2010 would see. That's the ensemble prediction. For the Sea Ice Outlook, it is our preferred prediction (preferred over the best single logistic curve).

Notes to come:
Making predictions with an imperfect model
When might we see an ice free (September average) Arctic?

Reminder: I don't speak for my employer, whoever that is.

More Grumbine Science

Starting a bestiary of oscillations and cycles

National Center for Science Education now also defending science on climate change

High School Student Science Opportunity in the Arctic

Parts per million

Happy New Year

Is it science?

Veteran's Day

AMSR-E failure and fallout

Climate change science history

Happy Huskies

Question Place

Off net

Peer review and Wagner Resignation over Spencer and Braswell

Too-early consideration of sea ice estimates

Earthquake followup

Now twittering @rgrumbine

My first earthquake

Is climate a random walk?

Endogenous Retroviruses and a Different Writing Style

Is it really normal?

How to find climate normals?

Giant Mutant Imperial Moths

Odds and Ends -- July 2011

Best Frenemies

How not to compute trends

Reconsidering forecasts and wagers

Sea Ice Wagers

Making your own sea ice estimates

Adding to the blogroll

2011 Sea Ice Outlooks

How large a conspiracy?

Happy Anniversary ...

Hiatus to end shortly

Hiatus

Setting Goals in Running

Starting to work with data

Messy Science

Says who?

Internationality of Science

Where is north?

What is a day?

Running in the rain

Science Fairs

The Invention of Air

The Way Things Break

Question Place

Rabett on History of Radiation

Whiteboard on the end of global warming

Wrestling with data

Help save data

Penguins

Was Easterbrook talking science?

Back and becoming active

Kids are scientists

Off-blog and Happy New Year

Evolving Thoughts

How to make sanity checks

Verifying forecasts 2

Verifying forecasts 1

Thanks Teachers!

Vererans Day

Sea Ice Predictions vs Reality

Young scientists

Knight anoles and science writing

Election Day

Hiatus to extend

Does Lake Superior Remember the Last Ice Age?

Science Cafe

Kitchen experiments

Unity of science and reaching decisons

Scientists are real people

Constructing an analysis 1: Drop in a bucket

Sea ice on the blogs

Blogroll news

Were the 70s cold?

Teacher preparing for new year

Bad Astronomy: The Wonders of the Universe

Open Lab 2010 nominations

New locations for two from blogroll