More Grumbine Science

Technological progress

2009-12-03T21:31:00.000-05:00

A couple of videos that caught my eyes. First is one on an upside of technological progress -- cars today are enormously safer in a crash than cars 50 years ago. This video shows the collision, and the driver crash test dummies, between a 1959 and 2009 car. The 2009 car undoubtedly weighed far less than the 1959. Superior engineering is the key -- a point that Consumer Reports routinely winds up making in their vehicle reviews.
Crash test video.

Digressing a second: It occurs to me that Consumer Reports is probably the popularly available magazine that does the most consistent job of displaying a scientific approach. The typical review article shows what they were testing, how they tested it, adds information about how significant the test differences are like, and so on.

The second video is one on a topic that weather and climate folks are probably more than a little tired of. Namely, the accusation that we didn't realize that there's such a thing as an urban heat island effect. I've never taken up the search seriously, but a few years ago, an urban heat island reference was in the Bulletin of the American Meteorological Society's '50 years ago' column. So, well-known (the referenced article was clearly not the discovery of the effect, just another illustration) by the early 1950s.

The video is from Peter Sinclair's Climate Crock of the week. In it, he carries out a good practice for science -- suppose an argument is correct, then look for observations that will confirm or reject that argument. The argument is that the urban heat island is producing the observed warming trend. Ok, says Peter, if that's the case, we should see that the trend is the strongest (most positive) in urban areas. Now, it isn't hard to figure out where the urban areas are. Nor is it hard to map out what the trends are for different areas of the globe. Compare the two.

In truth, as he illustrates, the warmings are highest in areas that have very few people -- Siberia, the Arctic, and Hudson Bay being leading zones. His figure is for the 2008 anomalies -- after the 'decade of cooling' (what cooling?), rather than the 30 year trends ending that date. If anything, the trend map is worse for the urban heat island fans, as it shows large trends across northern Canada as well.

Nothing obvious connecting the two videos. But the thing is, I have a lot of confidence in engineers to solve engineering problems. One such problem is car safety. Others would be things like more efficient cars, new and better ways of producing energy, and so on. In the 1950s and 60s, it was an article of faith in the car industry that customers did not care about safety. And that if they were forced to engineer safety, they'd go out of business (it would be too expensive). Instead, we have vastly safer cars today, and tens of thousands of people are still alive because of it. The engineers were more than up to the challenge. On the other hand, if the engineers aren't allowed to work on a solution, they won't find it.

I'm not taking up geoengineering in this; that's a topic for a lengthier post of its own. I'm just minded that there are quite a few climate-related technology issues that exist regarding efficiency of old technologies, or new technologies to develop, that we're being told would drive companies out of business, cost jobs, and other alarmist statements -- as there were in the 50s and 60s regarding automobile safety. Yet the engineers found ways of improving safety even as we drove more, drove lighter cars, and so on. And the companies didn't go out of business, indeed make quite a lot of money.

Fake ice

2009-12-02T04:17:00.001-05:00

The ice is real ice, but the satellites are being fooled. Actually, not even that. The satellites are correctly reporting what they're seeing, but the humans have been making an assumption that no longer holds true due to change in the Arctic. A friend pointed to an interview (audio only) of David Barber, on Quirks and Quarks on CBC. Early on, he mentioned how the satellites were being fooled. That isn't exactly what's up, but was the right answer for the circumstance. I'll take a bit more time to discuss the details of what is happening on this part of the story.

Our standard method for observing sea ice from space is to use satellites to measure the microwave energy emitted from the earth's surface. Sea water is a very bad emitter, so has a very low brightness temperature. Sea ice is a pretty good emitter, so has a much warmer brightness temperature. You go through a couple of elaborations on this, and out pops the fraction of the surface that is sea ice instead of sea water.

You can also be a little more demanding than that. Particularly in the Arctic, this makes sense. The elaboration comes from the fact that not all ice emits microwaves equally well. Salty ice is a better emitter than fresher ice. In the summer time, when ice floes do some melting (but not enough to get rid of the whole floe for the ones we're interested in), it is the saltier parts of the floe that melt away first. This is the same thing happening when you salt the sidewalk -- the salt lowers the melting point, and the salty parts melt first. When you get to this time of year, and the melting stops, what is left is a relatively fresh ice floe. It is also called 'multiyear' ice, since it's now in its second winter. With a more detailed analysis of the microwaves, you can try to distinguish between the first year ice (saltier and a better emitter) from the multiyear ice (less salty, but still a better emitter than sea water) from the sea water (very poor emitter).

Analogy for the remote sensing: The satellite is listening to the surface. Sea ice is much louder than sea water, so you can start by just checking how loud things are. Louder = more sea ice. You can then listen a little more carefully. Multiyear ice is a little quieter than first year ice, and a little different pitch. So there's a choir of sea ice, and the multiyear ice is, say, the sopranos singing a little quiter than the basses (first year ice), but both are much louder than the baritones (sea water). If you've listened to a choir, a band, or just a room of people talking (and deciding how many people were in it, and how many of them were men vs. women), you've done the same sort of discrimination that we're doing with the satellite observations.

Now for the spot where we were fooled.

Up until basically this year, it was a standard assumption that multiyear ice was thick ice. It made sense. Ice that was thick enough to survive the summer can't be terribly thin (we figured). And in the next winter it has a chance to pile up even more thickness either by freezing more ice on, or by getting piled up as the circulation jammed ice floes on top of each other. This had been confirmed many times in field expeditions starting long before the satellites were flying.

What Barber found instead was that the ice breaker was cruising along at 13 knots (25 kph) through what the satellite said was multiyear ice. He was amazed. That is about the speed the breaker would go through open ocean! What he realized had happened, after going up to the bridge, was that the satellite was seeing just a thin scum of the fresh multiyear ice, that was on top of also a thin layer of first year ice. There was only just enough multiyear ice for the satellite to see that. It is no longer the case that we can assume that the sound of multiyear ice means thick ice. There's still ice, so methods for deciding total ice coverage are ok. But we can't use the multiyear ice signature to mean 'thick' any more. Probably (my opinion) this has become a progressively less accurate thing to do over the last few years, and it's only now that the difference has become as drastic as David found.

This is only a small part of the interview, and the whole thing is well worth listening to.

Data set reproducibility

2009-11-30T05:45:00.096-05:00

Data are messy, and all data have problems. There's no two ways about that. Any time you set about working seriously with data (as opposed to knocking off some fairly trivial blog comment), you have to sit down to wrestle with that fact. I've been reminded of that from several different directions recently. Most recent is Steve Easterbrook's note on Open Climate Science. I will cannibalize some of my comment from there, and add things more for the local audience.

One of the concerns in Steve's note is 'openness'. It's an important concern and related to what I'll take up here, but I'll actually shift emphasis a little. Namely, suppose you are a scientist trying to do good work with the data you have. I'll use data for sea ice concentration analysis for illustration because I do so at work, and am very familiar with its pitfalls.

There are very well-known methods for turning a certain type of observation (passive microwaves) in to a sea ice concentration. So we're done, right? All you have to do is specify what method you used? Er, no. And thence comes the difficulties, issues, and concerns about reproducing results. The important thing here, and my shift of emphasis, is that it's about scientists trying to reproduce their own results (or me trying to reproduce my own). That's an important point in its own right -- how much confidence can you have if you can't reproduce your own results, using your own data, and your own scripts+program, on your own computer? Clearly a good starting point for doing reliable, reproducible, science.

This turns out, irrespective of any arguments about the honesty of scientists, to be a point of some challenge even as software engineering. Some of this was prompted by a discussion I had some months ago at a data-oriented meeting -- where someone was asserting that once the data were archived, future researchers could 'certainly' reproduce the results you got today. I was not, shall we say, impressed.

We'll start by assuming that the original data have been archived. (Which I've done for my work, at least for the most recent period.) Ah, but did you also archive the data decoder? Turns out that even though the data were archived exactly as originally used, the decoder itself is allowed to give slightly different results when acting on the same data (or maybe there was a bug in the old decoder that was fixed in the newer one?). So, even with the same data, merely bringing it out of the archive format in to some format you can work with can introduce some changes. Now, do you archive all the data decoding programs along with the data? Use the modern decoders? (but when is modern? If this year's decoder gives different answers than 5 years ago, or than 5 years from now, what should the answer be today?)

Having decoded the data to something usable, now we run our program that translates the data we have in to something that is meaningful. In my case, this means translating 'brightness temperatures' (themselves the result of processing the actual satellite observations in to something that is meaningful to people like me) in to sea ice concentrations. The methods are 'published'. Well, some of the method is published. The thing is, the basic algorithm (rules) for translation are published -- it's the NASA Team algorithm from 1995 through 23 August 2004, then my variation from then to August 2006, and then my variation on top of NASA Team2 from there to the present. One issue being, my variation is too minor to be worth its own peer-reviewed scientific literature (though I confess I've been reconsidering that statement lately, as more trivial-to-me papers are being published). So, where, exactly is the description of the methods? Er. In the programs themselves.

That's not a problem in itself. I have, I think, saved all versions of my programs. Related, though, is that the algorithms don't really stand on their own. There is also the matter -- seldom publishable in the peer-reviewed literature, but vital to being able to reproduce the results -- of what quality control criteria were used, and what, exactly, the weather filtering was. To elaborate: As I said, data are messy and ugly. One of the problems is that the satellite can report impossible values, or at least values that can't possibly correspond to an observation of the sea ice pack. Maybe it's a correct observation, but of deep space instead of the surface of the earth. Maybe the brightness temperatures are correct, but the latitude and longitude of the observation are impossible (latitude greater than 90 N, for instance). And maybe just there was a corruption in the data recorder and garbage came through. In any case, I have some filters to reject observations on these sorts of grounds before bothering the sea ice concentration algorithm with them. And then there's the matter of filtering out things that might be sea ice cover (at least the algorithm thinks so) but which are probably just a strong storm that's got high rain rates, or is kicking up high waves ('weather').

But, of course, these quality control criteria, and weather filter, have changed over the years. Again, not publishable results on their own, but something you need in order to reproduce my results. And, again, the documentation, ultimately, is the program itself. Since I've saved (I think) all the versions of the relevant program(s), you might figure we're home free -- fully reproducible results.

Or, at least the results would be exactly reproducible if you also had several other things.

One of them is, the programs rely on some auxiliary data sets. For instance, the final results like to know where the land is in the world. So I have land masks. If you want my exact results, you need the exact set of land masks I used that day. Again, I've saved those files. Or at least I think I have. As many a person has discovered the hard way at home -- sometimes your system back up won't restore. Or what you actually saved wasn't what you meant to save.

It's worse than that, though. You probably can't run my programs on your computer. At least not exactly my programs. I wrote them in some high level language (Fortran/C/C++) and then a compiler translated my writing in to something my computer (at that time) can use. The exact computer that I was running on that day. Further, there are mathematical libraries that my program uses (things to translate what, exactly, it means to compute the sine of an angle, or a cosine), that were used the day the program originally ran.

What you can do is compile my program's source code (probably, I'm pretty aggressive about my programs being able to be compiled anywhere) on your computer. But ... that uses your compiler, and compilers don't have to give exactly the same results. And it's on your cpu, which doesn't have to be exactly the same as mine. And it uses your computer's math libraries, which also don't have to be exactly the same as mine.

So all is lost? Not really. The thing is, these sorts of differences are small, and you can analyze your results with some care (and effort) to decide that the differences are indeed because compilers, processors, libraries, or operating systems, etc., don't always give exactly the same answers. I've done this exercise before myself, as I was getting different answers from another group. I eventually tracked down a true difference -- something beyond just the processors (etc.) doing things slightly differently (but different in a legal way). The other group was doing some rounding in a way that I thought was incorrect, and which gave answers that differed from mine in the least significant bit about 3/4ths of the time.

With that understood, we could get exactly the same answers in all bits, all of the time, in spite of the different processors and such. But, it was a lot of work to get to that point. And this is a relatively simple system (in terms of the data and programs).

So are you lost on more complex systems, like general circulation models? Again, no. The thing is, if your goal is science -- understanding some part of nature -- you understand as well that computers aren't all identical and have done some looking in to how those differences affect your results. The catchphrase here is "It's just butterflies". Namely, the line goes that because weather is chaotic, a butterfly flapping its wings in Brazil today can lead to a tornado in Kansas five days from now. What the catchphrase is referring to is that small differences (can't even call them errors, just different legal ways of interpreting or carrying out commands) in the computer can lead to observable changes down the road. They don't change the main results -- if you're looking at climate, the onset of a tornado at 12:45 PM, April 3 1974 is not meaningfully different from 3:47 on the same day (though it certainly is if you live in the area!) -- but they do change some of the details.

What do we do, then? At the moment, and I invite the software and hardware engineers out there to provide some education and corrections, what you need for exact reproducibility is to archive all the data, all the decoders, all the program source, all the compilers, all the system libraries, and all the hardware, exactly. The full hardware archive is either impossible or close enough as makes no difference. The system software archives (compilers and system libraries) are at least extraordinarily difficult and lie outside the hands of scientists (there's a reason they're called system tools). The scientists' data and programs, not as easy as you might think, but doable. Probably.

As you (I) then turn around and try to work with some archived processing system, then, when (not if) you get different results than the reference result, your first candidate for the difference is 'different operating system/system libraries/compilers/...', not dishonesty. That means we have work to do, unfortunately. I hope the software engineers have some good ideas/references/tools for making it easier. I can say, though, from firsthand experience working with things I wrote 5-15 years ago, that there is just an astonishingly large number of ways you can get different answers -- even when you wrote everything yourself. If you go on a fault-finding expedition, you'll find fault. If you try to understand the science, though, even those 5-15 years worth of changes don't hide the science.

There's nothing special to data about this; models have the same issues. Nor is there anything special to weather and climate. The data and models used to construct a bridge, car, power plant, etc., also have these same issues. If you've got some good answers to how to manage the issues, do contribute. I'll add, though, that Steve (in his reply to my comments, a separate post, and a professional article) has found that climate models, from a software engineering perspective, appear to be higher quality than most commercial software.

Last call for submissions

2009-11-29T04:27:00.004-05:00

The deadline for submissions to the Openlab 2009 is midnight EST, 1 December. This is aimed at being a collection of the best blogging from 1 December 2008 through 30 November 2009. Use this submission form to submit your favorites (from here and elsewhere). The current summary of submitted articles is at Blog Around the Clock. Two of mine, Science Jabberwocky, and Results on Deciding Trends, are already submitted. If there's something else you like as well or better, time to submit them. If those are your favorites from here, no need to do anything.

Though it would probably help my odds if you didn't submit others' articles, I see there are several quite good articles from other blogs that haven't been submitted. I'll be doing some of that submission myself, and I encourage you to do so as well. I'd just like to make coturnix's (the editor) job as hard as reasonably possible :-) -- give him a lot of excellent articles to consider.

Science Anniversaries

2009-11-28T09:51:00.002-05:00

150 and 400 years ago, two major events in the history of science occurred.

400 years ago, the telescope was invented and started to be used for astronomy. For $100-$150 you can now get a telescope far superior to what Galileo used to carry out a major revolution in our understanding of the universe. More in a moment.

150 years ago yesterday (November 27th), Charles Darwin's On the Origin of Species by Means of Natural Selection was published. Different major revolution in our understanding of the universe. You can read this for yourself. I don't actually recommend reading it unless you are really interested in history of science, and like Victorian-era writing. (If you like my style, you're a couple steps in that direction. My wife noted that I write something like Trollope, a prolific Victorian whom she likes.) We've learned an awful lot in the 150 years since then, and many things that were mysteries to Darwin, such as how inheritance occurs, are well-known to us now. Instead I'll suggest you read the evolution sections of modern biology texts. Two such texts recommended by my biologist friends are Futuyma's, and Campbell and Reece.

For the telescope side, since this is a time when a lot of people buy telescopes, a few words about how to shop for one. The first thing to do is ignore magnification levels. In looking through the atmosphere, you can only magnify so much before what you're seeing is the turbulence in the atmosphere instead of anything astronomical. A fairly good rule of thumb is 50x for each inch of the main lens/mirror, or 2x for each millimeter. Many telescopes advertise much higher than this, but the extra is somewhere between useless and actively harmful. Your children will not have much fun exploring atmospheric turbulence when they expected to be seeing the moons of Jupiter or the rings of Saturn (and even small telescopes can show these!)

The diameter of the main lens or mirror is more important 'score' than magnification -- this controls how much light the telescope catches. Bigger = catch more light. Galileo's telescopes were about 40 mm diameter (1.6 inches). The typical small telescope you can buy today is at least 40 mm, typically 60 mm lens. It isn't hard to find telescopes with a main mirror 150 or 200 mm (6 or 8 inches), though they'll be more expensive. (If you're especially gung-ho, it isn't hard to find 20 inch, 0.5 meter, telescopes!) Note that the size we're talking about is the mirror, not the length of the tube. Length of tube doesn't help you collect light. For lens telescopes (and eyepieces) you want to test that the images don't have rainbow halos. If they do, it's bad optics, not that the universe really does have rainbow halos around everything.

Last major element in telescope hunting is a stable mounting. If the image keeps shaking for some time (tens of seconds) after you make a minor adjustment, or a slight breeze passes, it's hard to pay attention to the astronomy when the universe seems to be swimming about before your eyes.

So, for not terribly much (this time of year you can often do better than the figures I quoted), you and your child can discover the universe and carry out a revolution in your own understanding. For myself, I prefer the discovery/revolution approach, over the 'hunt down other peoples' lists of objects'. For one thing, if you're in an urban area, many of those listed objects will be invisible to you.

On the other hand, many people really enjoy chasing the object lists, and astronomy clubs and societies often have guides and guidance on which ones to look for, and how to do it. Great accessory to the telescope itself is an astronomy club. A place to start your looking for local astronomy clubs is the Astronomy League, which also has a number of lists of things to go looking for. Internationally, you can try this, but their links seem often to be broken.

PhD Thesis Defended

2009-11-24T21:17:00.001-05:00

I've been the (name of employer)-side mentor for a student working on her PhD. Yesterday, she successfully defended her PhD thesis. Not sure she wants to be named, so I won't for now. But it's a lot of work to get to where she is, and I'll congratulate her. She knows who she is :-) Good job!

Update:
Jamese Sims successfully defended her thesis at Howard University on Monday. She'll be writing up something about her experiences for the blog one of these days.

Where is the surface?

2009-11-16T21:48:00.000-05:00

I just commented on my facebook status that I'm at a meeting about sea surface temperature. That part was safe. Rest of the comment was to observe that I'm now back to wondering whether the sea has a surface, where it is if it does, and if it does, whether it has a temperature. That prompted a friend to comment 'Great ... this is going to bug me now.' So for him, here's a longer version.

This sort of question is very common to science. Of course my musing for facebook is overstated. But there is usually a real question about what exactly it is you've observed when you take an observation. When you have very different observing methods, they may well observe things that are different from each other. There are, let's say 4, different ways of observing the sea surface's temperature. For a diagram, see the wikipedia article on sea surface temperature

The standard method, and reference for others, is calibrated buoys that carry a thermometer at a known depth, typically 1 meter. A major drawback to this method (all methods of observing have drawbacks!) is that you need a buoy. They're not cheap, and it would take several million of them to give us a high resolution data set for global sea surface temperature (acronymed SST).

The longest-used satellite method for obtaining SST is to observe the earth in infrared wavelengths. Infrared doesn't propagate well through water, so the satellite sees a 'skin' temperature averages over 1 wavelength -- about 10 millionths of a meter (10 microns). A drawback to this method is that it can't see through clouds. Clouds also emit infrared, so the satellite tells you the temperature of the clouds, rather than the sea surface. Weather forecasters use this to improve their forecasts. But it means there's a lot of the earth these satellites can't see on any given day.

A recent addition to our satellite observing of sea surface temperature is to look at microwave wavelengths. This usually can see through clouds as the wavelength is carefully chosen to be one that cloud drops don't emit or absorb much. This gives us a temperature at about 1 mm depth. The drawback for these is that the satellite averages over a large area -- 25-50 km diameter, as opposed to the 1-4 km for the infrared satellites.

The fourth class is the 'everything else' group: ship 'bucket' temperatures, hull contact temperatures, water intake temperatures, temperature sensors on chains (often from buoys) extending well below the surface (or 1 m depth), or the ARGO floats -- which observe temperatures from 2000 m depth up to close to the surface. All of these observe temperatures at depths greater, sometimes much greater, than 1 meter below the surface.

If the 10 micron, 1 millimeter, 1 meter, 5-20 meter temperatures reported by the different methods were the same (within observing error), then it'd be a concern for specialists alone. The reality, however, is that the different temperatures can be substantially different. Most of the time, over most of the globe, they're close. But once you have calm winds (less than 5 m/s, 10 mph, roughly) and strong sunlight, you can accumulate skin (that 10 micron temperature the infrared satellites see) heating enough to warm temperatures. If the winds are very calm, under 2 m/s, it can be by a few degrees -- but only in the skin. The 1 millimeter ('subskin') warms, but not by as much. 1 meter down the temperature may change only a little. And at 5-20 meters, almost entirely unchanged. So ... if you want the 'sea surface temperature', which of the 4 do you want? And is that 5, or 20 meters for the 'unchanged'? How close to entirely unchanged is close enough?

Hence my question: Where is the sea surface?

As mentioned by someone else -- under hurricane conditions, are you even sure that there is a sea surface?

Veteran's Day

2009-11-11T12:17:00.000-05:00

Veteran's Day or, in some other parts of the world, Remembrance Day today.

My thanks to those who have served. My daughter and son-in-law are among you.

Racing again

2009-11-09T09:41:00.000-05:00

Saturday I got to meet a barn owl and a red shouldered hawk. Both were amazingly calm for all the runners who were milling around. The owl was looking around at all the dogs, deciding whether they were snack sized or not. (Concluded 'not', though I think a couple of dogs caused some serious calculation.) Fun to watch an owl look around. They were out as part of the parks and planning commission entertainment for the Jug Bay 10k (and 5k, and 3k walk).

I and a fellow club member were out for the 10k, with our plan being to run 1 minute, walk 1 minute. This being a much flatter course than last week's cross-country, I was able to follow the plan pretty well. Passed the mile 5 marker in 49:45, vs. last week's 8k (a touch shorter) in 54:25. My rule of thumb for the cross country course worked out pretty well -- about 10% slower than a flat course. Finished the 10k in 61:23, which also satisfied my check list for the conservative goal at my February 10 miler. Needed 72 minutes to be in line with the 2:00 goal; this time also meets the more aggressive notion of a 1:45 10 mile, having needed 63 minutes for that.

Bad news, good news being that my calf/achilles acted up again. Bad news being that it did so. The good news being that I've now got a better line on what, exactly, is the problem child. The major muscle in the calf area is the gastrocnemius. That is the one I had been focusing on when doing my stretching and Alfredson exercises. Day after the race with the calf complaining as I started to walk, I stretched (as my doctor had advised) the calf -- the gastroc. Didn't feel any response, no complaint, no difficulty. So finally I stretched the other muscle down there -- the soleus. That is where the problem is (now). I may well have just rehabilitated the gastroc earlier. Either way, the soleus is what needs the work now. It's a little harder to stretch, and a little harder to do the Alfredson exercise for. Not a lot, but enough that I'd been slack about doing it.

To go back to the more typical theme of this blog, I'll observe that probably a fair number of the people running with me were scientists. In particular, in earth sciences (lumping geology, oceanography, meteorology, glaciology, paleontology, ...) it seems very much the norm that scientists are physically active in one way or another. Running is not the only sport. We also have tennis players, swimmers, basketball players, bikers, .... Team sports are harder to manage later in life, so most people are doing individual sports, even where we like team sports. But, whatever it is, we get out and do something. And this is true whether the person does field work (which would require a degree of physical fitness, just to carry out the job) or sits in an office (as I and my coworkers do). It might be that scientists in other fields don't do as much sport as earth science types do. I don't know of any research on it. But we folks interested in the earth also seem to like to run/walk/bike/swim/... around it.

To turn back for a minute to the running .... In terms of final race times, this 10k was very slow for me. When walking, I averaged 16-17 minutes/mile (10-11 minutes per km), which is normal for my walking. In running, I was around 7 minutes/mile (4.5 minutes per km). If I were in good shape, which is the goal, I'd have run the whole 10k at about that pace. For my current training level, with the current goal (that 10 miler, 16.1 km) run/walk is the way to get to the finish of a workout or race in best health. Best health then means I can get out for the next workout, and the ones after that. It's getting out consistently that is the key for training. Given the achilles/calf issues, the next workout is tonight -- swimming. Rest the calf and work the lungs. The lungs (cardiovascular system) have a long way to go as well.

Plus, one of these years I'll be doing a sprint triathlon. My plan being: don't drown in the swim, don't fall off the bike, and then pass a lot of people in the 5k.

Experimental reading

2009-11-05T08:17:00.000-05:00

I've been reading more general audience science books lately, which is part of the reason for relative quiet here. But it makes for ideas later.

The first book in the experimental line is geared for middle school to junior high students. It's perfectly useful for older folks as well. And it will probably be a good idea to have one at hand for some of the experiments if performed by the younger. 101 Incredible Experiments for the Weekend Scientist by Rob Beattie. The experiments cover a range of things, from making slime to making a cloud. Some weather and climate examples, but not especially aimed to that. The experiment descriptions do also contain a 'how it works' section, which I take to be very important.

The second is geared to an older audience, college age, but I think most experiments and observations can be done by middle school to jr. high students. They just might want someone else to do the translation to more familiar language. That's Clouds in a Glass of Beer: Simple Experiments in Atmospheric Physics by Craig F. Bohren. This is the book (chapter 10) that prompted my Tuesday note. Its 22 chapters include much more by way of explanation of the science behind what you're observing in doing the experiments, and how this ties in to the atmosphere.

In both cases, the authors mention some things that the experimenter can use for proceeding to further experiments. They usually aren't laying it out exactly this way, so keep your eye open for comments like 'best results are for doing X' (using a small tube, for instance, to see surface tension). That's a sign that you can get different results if you use a larger tube, and it can be informative to see just how much the result depends on the size of the tube.

How CO2 matters

2009-11-03T04:42:00.137-05:00

It turns out that the argument that there isn't a lot of CO2 (true, compared to total mass of the atmosphere) and therefore it can't matter much for climate (false) has been around longer than I had thought. I was just reading Craig Bohren's book Clouds in a Glass of Beer: Simple Experiments in Atmospheric Physics and he's got reference to it (chapter 10, on the Greenhouse Effect), The collection of experiments was published originally in 1987, and had evolved over some period before that. So at least 22 years that the argument has been around.

From page 82 in my Dover edition:There seems to be little dispute that carbon dioxide concentrations in the atmosphere have been increasing because of increased burning of carbonaceous fuels such as coal and oil. At present, for every one million molecules in the atmosphere, about 340 of them ar carbon dioxide (this is written 340 ppm, parts per million). To those who snort that 340 ppm of anything must surely be of no consequence, I recommend 340 ppm of arsenic in their coffee. I don't second the recommendation as the lethal dose is somewhere around 1 ppm. Craig was being sarcastic, and blunt, two common words for describing him. The 340 ppm was about the Mauna Loa station's reading for 1981, and the last year that would round to that (nearest 10 ppm rounding) is 1984, so it's probably 3-6 years before book publication that Craig was writing. It's now past 385 ppm.

For climate purposes, we'll consider two different things. First is, how can a rare thing (CO2) be important to the system? Second is, is CO2 really all that rare?

As is obvious from the arsenic example, rare things can be important in some systems. What we need to explore is the how. For CO2, its importance comes from the fact that it is a greenhouse gas. Most of the atmosphere, in fact the overwhelming majority of the atmosphere, has no great absorption for the energy emitted by the earth. The three major gases are nitrogen (N2), oxygen (as O2), and Argon (Ar), which comprise well over 99% of the atmosphere, and none of which absorb energy emitted by the earth. All greenhouse gases are trace gases, water vapor (H2O) included.

We'll get to a less-simplified notion of the greenhouse effect, but let's start with the oversimplified version. In that version,
0) The sun throws energy at the earth
1) the earth emits energy towards space.
2) a greenhouse gas molecule captures a bit of that energy
3) it then spits it out in a random direction
3a) if it's towards space, no change from what was going to happen anyhow
3b) if it's towards the surface, the surface catches more energy than it would have otherwise
4) Because of 3b, the surface gets hotter.

This is correct as far as it goes, but it doesn't go very far. An important thing missing is that step 3 almost never happens alone or immediately. Related is that this picture only tells you about the temperature of the ground and the greenhouse gases -- not of the 99+% of the atmosphere that is not greenhouse gas. The important missing part is between 2 and 3 -- A) most of the time, the greenhouse gas molecule that just absorbed some energy emitted by the earth will collide with a non-greenhouse gas molecule and pass the energy on to the other molecule. The converse thing can also happen -- a greenhouse molecule get clobbered by a non-greenhouse molecule and then emits some energy (to space or the ground). Greenhouse gases play an important role in setting the temperature of the non-greenhouse gases in the atmosphere, not just the surface. And they do this in spite of being only a small fraction of the atmosphere.

Need to emphasize that, I think. The image is out there that H2O is 4% (40,000 ppm) of the atmosphere. That's only true in exceedingly warm air very near a water supply (ocean or lake). Averaged through the entire atmosphere, it's more like 2000-4000 ppm. I'll invite you to construct your own estimates, show the rationale and calculations as to the correct figure. In any case, while water vapor is the most common greenhouse gas (in number) it is only 5-10 times CO2, on average, not 100 times. Conversely, this leaves CO2 as 10-20% of greenhouse gas molecules.

Anyhow, we've got our answer to the first question -- these rare molecules (greenhouse gas molecules) are important because they set the temperature of the ground, and help set the temperature of the atmosphere itself (whether greenhouse gas molecules or otherwise). Even though they're 'rare', they matter.

But, to the second part -- are they really 'rare'? As a fraction of all molecules in the atmosphere, yes. There are, however, other ways of deciding rarity. I'll start with some farther afield. 385 ppm means that in a city of 1 million, you could find 385 people who were that unusual. Refining it a little, in a group of 2600, you'd expect to find someone that unusual. In a sports stadium with 52,000, there would be 20 people that unusual. A key being, given our understanding from the first question, that the 20 are not hard to find -- they're the ones being exceptionally obnoxious, starting the fights in the stands, etc. -- you know that they're there because they bump in to you, or you see the fight start, or they're the 20 who start 'the wave' in the stands, and so on.

That suggests a different way of looking at 'rare'. They're rare if they have no observable effect. The one person in the stands who is reading a book, you don't know they're there unless you're extremely close by. We already know that this isn't the case for greenhouse gases -- they do have effects and we do observe them. But let's pretend we are a photon (energy packet) emitted by the earth towards space. We could consider greenhouse gases rare if we could expect to get out to space without ever encountering one of them.

I'll put up my math for folks to check. If you don't like the math, you can skip ahead a little. But I think it's important to show that there's nothing up my sleeve here. Over each square meter of the surface (at sea level) of the earth, there are about 10,000 kg of air. CO2 is approximately evenly distributed throughout the atmosphere, so the current (2009) 385 ppm CO2 means that there are about 4 kg of CO2 over each square meter. That's a fairly noticeable number to us as large bodies, but not necessarily to a photon. So I'll continue. Update: per carrot's comment, I had oopsed here. Even though I pay attention to the fact that CO2 molecules don't weigh the same as average air does (44 vs. 29) in the next section, I failed to do so here. That makes it 4*44/29 kg of CO2, for 6 kg CO2 over each square meter. Corrected figure used for rest.

In chemistry, we learned about moles of things. 1 mole is a standard count for molecules (1 Avagadro's number of molecules; the chemists and others who know the size of this number already know how this story turns out). 1 mole of CO2 has a mass of 44 grams. So the 6 kg of CO2 represent about 136 moles of CO2. Again that seems large, but now think about yourself as a photon emitted by the earth. Your 'size' is about 10 millionths of a meter (10 microns); that's your wavelength. As you go speeding through the atmosphere, a CO2 molecule has to be sitting in that window only 10 microns wide before you're likely to notice each other. I'll take a disk with 10 micron diameter to represent the zone a CO2 molecule has to be sitting in for you to be concerned. The relevant area is not 1 square meter, but about 80 trillionths of a square meter. So the relevant number of moles is this times the 136 moles over a full square meter, for about 11e-9 (11 billionths) of a mole. [Throughout, I'm using more precise numbers than I'm quoting here. Some of the math won't seem to line up because of the rounding.]

If you don't know Avagadro's number, 11 billionths of a mole looks like it's awfully small. That 11 billionths of a mole gives us the number of CO2 molecules (it's worse for H2O, meaning larger -- more molecules to escape) that we have to hope don't notice us as we try to race off to space. Photons can't dodge -- they have to move in straight lines at the speed of light through whatever medium they find themselves in. Our only hope for the race to space is that the CO2 doesn't grab us.

The thing is, Avagadro's number is gargantuan. It is about 6e22. Try that again -- it is 6 billion (approximately number of humans on the planet) times 10 trillion (approximate gross domestic product of the US in dollars). In other words, if every person in the world had as much money, themselves, as the entire US economy exchanged last year, they would have 1 Avagadro's number of dollars.
Update: Copied the number wrong. Avagadro's number is 10 times bigger than that, 6e23. Rest of note corrected for this.

For you as a photon trying to reach space, it means that there are about 6400 trillion CO2 molecules that have a chance to grab you (6.4 quadrillion). This is not a small number! The only reasons that any photons do reach space from the surface is that a) molecules are extremely selective about what colors they will absorb (your wavelength has to be exactly right) b) even if you have the right wavelength, molecules are typically extremely lazy and still probably won't grab you.

For our two questions, we see the answers now as
1) These very rare gases matter because the cause the entire greenhouse effect, and contribute to setting the temperature of the atmosphere (not just the ground).
2) Given 6400 trillion CO2 molecules that sit in the path of a photon trying to escape from the surface, it also isn't very reasonable to call them 'rare'.

I've tagged this note 'project folder'. That's my flag for posts that include things that lend themselves better to projects, things for people to check me on, or things that you can take further. In this note, the particular challenge is to come up with a way to compute the average atmospheric content of H20. But you're also invited to recheck my math on how many CO2 molecules sit in the path of a photon from the earth's surface trying to escape to space. An extension would be to look at the numbers for wavelengths of 4 microns and 15 microns (wavelengths CO2 is particularly likely to absorb -- it doesn't absorb at 10; I took 10 because it's the peak for what the earth emits, and it's between 4 and 15).

Racing

2009-11-02T09:15:00.000-05:00

Yesterday I ran a good, challenging, and distinctly muddy 8k cross-country race. It's one of my favorite races. My training, which I've been sparing you, has not been going well, so injury rehabilitation notes are at the bottom. September was good. The last Saturday I walked/ran the 5k according to plan, and finished in 28:25. Not a great time compared to if I were in reasonable training, but a lot better than a few months earlier. The following Monday, I forgot about a certain gravel path being irritating to my calves, and the fact that I've been on the edge or over it for calf problems (maybe it's Achilles), and went running on the path. That did seriously annoy the calf/Achilles, which the race hadn't, and October was mostly given over to non-running (and, unfortunately, non-exercising).

I had, of course, signed up for the cross-country race right before nailing the calf. On the other hand, even when I was in very good shape (for me), I walked some of the cross-country course. In years of moderate training, the walk fraction goes up. This year, it was going to be even more walking, I decided. It was. I flew on the downhills (though I can't run for very long at the moment, when I do, I can carry a good pace), took it easy on the uphills (walking more slowly than I would if the cardio system were in condition), and mixed on the flats. Not a lot of flat to this course.

The plan worked out pretty much. I did not injure the calf/Achilles, and I did finish the race. I feel invigorated for getting out and doing more exercise, and, in one of those ironies of peoples' psychologies, am more willing to do 'just' swimming/biking/rowing/.... Final time of 54:37 (my watch -- I didn't start at the front of the pack!) Which I mention more for establishing it so that when I talk next year of my improvement, you'll know the base. It would not be unreasonable to improve by 10 minutes in the next year.

The real work in October was doing Achilles rehabilitation exercises. Specifically the Alfredson exercises (heel drop). Once an injury shows up, it's important to figure out what caused the problem. I've been having recurring calf/Achilles issues through the year. My first time in 13 years of running of having a running injury, especially one that persisted. I've never been able to identify the source of the problem. The second side is, particularly if you don't know what caused the problem, rest (hence my light October) and rehabilitate the injured system. The rehab includes stretching, a lot, and strengthening by way of the Alfredson exercise. This is going pretty well (knock wood).

The plan for now is to keep my running to only 2 days/week, and 2-3 days of other aerobics. Continue the Alfredson exercises for the Achilles, shoulder exercises for the previously torn rotator cuffs (while I've had very few running injuries, I've had several non-running injuries like the torn rotator cuffs), and some strength work. On the running side, 2 days a week is enough to preserve running-specific conditioning and even advance it some if you, as I am, start from a low enough point. The other aerobic activities will keep improving my aerobic condition, so that by the time the Achilles is fully rehabilitated, I'll have some lung power to support the running.

It's helpful to have a longer term goal in keeping getting out for exercise. Mine is the RRCA 10 mile championship (DC and MD clubs) race in February. The cutoff time to count for your team (my club being small, every person who can beat the cutoff is important -- many years we don't show up with enough runners to count as a team) is 2 hours. So that's my conservative goal. And I try to avoid thinking about others at this point. I should be able to beat that goal time with run/walk of 1:1 (one minute run to one minute walk). The proportion at the race yesterday was unknowable. (I'm certainly not looking at my watch while running cross country!) But the time suggests that for me being equally well-prepared (i.e., not very well at all) and on an equally difficult course (the 10 miler is much easier, though difficult for a road race), I would manage about 1:55 on the 10 miler. That's sufficient for my goal. And, given that I expect to be in much better shape 3 months from now, and I know it's a much easier course, room for much more aggressive times. 1:45 is a nice round number. (Ok, I don't avoid thinking of more optimistic times very well. But it's a good idea.)

If you've thought of using run/walk strategies, I'll suggest my run/walk calculator. I've been surprised how fast a run/walk can turn out to be. Oddly, if I'd run less at the 5k, I'd probably have finished faster. At that point, I could run 7 minute/mile pace, but only for about 2 minutes. For the 3 minutes I was running at a time in the race, I could only hold 8 minute/mile pace. As you can see at the calculator those, plus my 16 minute/mile walking pace, equate to a 5k about 1 minute faster if I ran less.

A challenge to the computer folks

2009-10-28T11:46:00.000-04:00

Something I'd like to be able to do is to track the citation history backwards from a given paper. But I want a couple of things that it looks like typical bibliographic sources don't do. As matters of computer or library science, I don't think they're terribly difficult. I've seen things done which strike me as much more complex.

Let's start with some paper, call it paper A. It cites, say, 15 papers (papers B, second generation). Each of those cites another, say 15, which at least temporarily means a list of 225 papers (C, third generation). Easy to get the list of papers cited by paper A (the 15 papers B1..B15), but significant manual effort, it seems, to get the collected list of papers C1..C225. One thing I would like, however, and which seems completely unsupported, is that I'd like a count of how many times each paper shows up in this tree. Some of the papers in the second generation probably cite others in the second generation. And it's near certainty that many of the third generation papers are cited by several of the second, and probably a good number of third generation cite each other. This is pretty much just a simple social network kind of analysis -- some papers have lots of friends, and some not so much. I'd like to see which papers are highly connected, and which aren't, working within the group established by papers cited by a paper of my interest (actually won't be one of my own in practice) and lines of reference descent from there.

The second sort of thing I'd like to see is for the chart to be continued through enough generations that sources like Newton's Principia start appearing on the list. I'm curious how many generations, in terms of citation history, modern work is removed from some of the landmark sources. Unfortunately, it seems that the bibliographic databases I have access to die out in the mid 1980s, which is a long time from when I want to be getting to.

Doing science, with sea ice

2009-10-26T04:11:00.096-04:00

Every so often, I commit an act of science. Like most acts of science, you almost certainly never heard about it. Like many, however, life was eventually improved for some people somewhere. I'm rather pleased about that side of it.

What was at hand was, on one hand (it does help to have many hands if you're in science), a fairly straightforward piece of engineering. On the other hand, a bit of science. Remember that I think both engineering and science are good things, if different. Engineering is mainly aimed at 'apply what is known to achieve benefit for someone', while science is aimed at 'try to understand more about the universe'.

Back in 1993, I was at the National Meteorological Center (NMC), the part of the National Weather Service (in US -- NOAA) that develops the new weather forecast models or tries to make the old ones better. My area was sea ice. Now, one thing we sea ice, polar oceanography, polar meteorology people were entirely confident about was that sea ice mattered, a lot. For, well, everything, or at least enough. If we didn't think it mattered, we'd hardly be spending our time studying it. People outside our little community, including folks working on numerical weather prediction, didn't think sea ice mattered for much. And, if it did matter, surely it was only something that mattered for long time modeling -- climate scale forecasting. Surely the ice was already well enough represented to be good enough for weather prediction purposes.

Partisan as I was, and am, in favor of sea ice, I must confess that there were (and are) good reasons to believe that for short range forecasting, you didn't need very accurate representation of sea ice. It doesn't cover much of the surface area of the earth. And, while it might be very reflective, at the times that there is the most ice that is most reflective, there isn't much sun for the ice to reflect. I could have simply sat back in a wrangle with the weather folks, endlessly asserting that sea ice was important, and how much energy sea ice reflected was still important, and weather is chaotic so it had to matter, vs. endless repetitions of their counter-arguments. Perhaps you've seen that sort of thing happen a time or two on a blog or two.

Instead, time to do some science. Run the experiment and see what happens. This has the downsides that it requires my time, and I have to run the risk of the experiment showing that I was wrong -- that modest changes to how much of the sun's energy sea ice reflects really did not affect weather.

But that, seriously, is what makes it a good experiment to run. I wasn't certain, nor was anyone else, exactly how it would turn out. If I turned out to be wrong, then there's a contribution to our understanding of the universe -- indeed weather for a few days really doesn't care a lot about exactly how reflective sea ice is. It was previously assumed and expected that this was the case. But here, finally, would be evidence that the common assumption was correct. If I turned out to be right, and those small changes did matter to weather forecasts, then the contribution is that not only climate (which everyone agreed was sensitive to such things) but weather as well cared. Me being right or wrong is not where the science sat (much as I would prefer to be right, of course). Whether sea ice reflectivity (albedo) mattered for even short time scales is where the science was.

So I ran the experiments. That was a plural because you need more than a single forecast to decide whether a change is for the better. Weather is chaotic, which means, among other things, that things just happen. You could be a little better in the forecast for no reason of skill, but just because the chaos ('the butterflies' we often call it) kicked things over better. I ran experiments -- 5 day forecasts starting on different days. I chose 40 days, from March through June (10 days per month). The 10 days per month, for months spanning the seasonal transition from spring to summer (northern hemisphere) or fall to winter (southern hemisphere) were a large enough collection that the butterflies couldn't swamp the results, and we'd be able to see how much sun-dependence there was.

To represent the reflectivity of ice, I used work done elsewhere (Ross and Walsh, 1987 -- I was working in 1993) as my estimated improved version. That was to replace a representation that dated back to 1964. As usual, we hope that the additional observations that 20 more years of science had would make for a better representation. But, you still have to test it.

As it turned out, the new reflectivity representation really did improve the weather forecasts. See A sea ice albedo experiment with the NMC Medium Range Forecast Model for the gory details.

So the short term good news was both that my prediction was shown to be right, and, not quite as short term, I got to publish an article making an addition to what we understood about how weather works. As it worked out, it was longer before the last leg of good news happened. But it did -- my suggestion for change was incorporated in the NMC's official medium range forecast model.

There was a post script to the story. After the change went in to the operational weather model, I heard from marine forecasters. They had noticed that storms heading up towards the Arctic, particularly those heading past Iceland, were being forecast better by the model. This made their jobs easier. Not that they ever entirely trust the model, but it makes their job easier, and they can do it better, if they are working with the last 30 km of correction, rather than something like 300 km range. (I make up the numbers, the important reality being that the better the model is in providing the first guess, the more accurate the forecaster refinements are.) I looked back to my experimental runs and realized that most of the skill improvement was from the occasional (one or two out of 10) forecast that was very much better at predicting a storm's path.

Never have written that up, though it's been something I've relayed to other people in the 15 years since then.

In the note here, I've focused on the engineering side. The science details are in the paper. But there are a few good illustrations about how science works:

There was no drama
A modest improvement was made
Interesting things were learned afterwards

The lack of drama is perhaps the most different from what most people think science is like. The reality is that several thousand papers were published that year (1994) in meteorology/oceanography/glaciology. Of those, maybe a few dozen were really 'dramatic'. Call it 1%. Most of science, the 99%, is an incremental business -- verifying that things really do work out the way that we expect (least exciting), or that models/representations/hypotheses/... are more limited than we hoped (a little more exciting -- how do we handle what happens beyond those limits?!), or that reality is opposite what we expected (interesting!). For me, the result was what I expected. For the weather prediction folks, it was against what they expected; so it was more interesting for them than me.

I'll note this article is the outgrowth of a comment, and my reply, over at A Few Things Ill-Considered (neither having much to do with the main post, the initial comment is at #6, my reply at #8).

Antarctic Snow and Ice

2009-10-21T04:29:00.115-04:00

The Antarctic has long been a favorite area of mine, going back to graduate school days. This particular note, however, is prompted by a question over in the question place -- regarding Antarctic mass balance and snow.

The question at hand turns on just what is going on with Antarctic mass balance. The apparent 'conflict' is between a study showing a recent decline in snow melt, and other studies that Antarctic ice mass is decreasing. This is a particularly simple conflict to resolve, so I'll note that it really is taken as a serious conflict (per the questioner's link) over at WUWT (haven't we heard that name recently?)

The simple reality that the authors of the snowmelt paper are perfectly aware of, but WUWT ignored, is that there is more than one way for the Antarctic to lose mass. I grant that melting the snow is the most obvious one. But, when you're dealing with a continent as incredibly dry as the Antarctic is (the driest, and probably largest, desert in the world), you have to pay attention to more subtle processes. One of them is not at all subtle -- huge icebergs break off of the Antarctic from time to time. In these cases, you're talking about chunks of ice several hundred meters (call it 1000 feet for simplicity if you're non-metric) thick, and 50-100 km (30-60 miles) on a side. Chunks large enough to be the size of entire US states and some countries. (I have an ancient listing of some iceberg sizes and country, state, lake sizes for your comparisons -- additions welcome.) There's also the very subtle process of evaporation straight from the surface of the ice sheet (sublimation) into the atmosphere. And there's the not subtle but easy to forget about fact that Antarctica has ice shelves -- ice floating on the ocean that's fed by the continental (sitting on land) ice sheet -- and the bottoms of those ice shelves can and do melt.

Finally, there is the rather bizarre fact that ice is not a solid. Once you build up to having an ice sheet, the pressure of the ice above a point near the ground is so enormous that the ice flows. Ok, it's a really, really, thick fluid (think very cold molasses). But it flows. This means that the ice sheet move mass out to the edges -- out to the ice shelves where there can be snow melt, ice evaporation, or ice shelf melting, or massive icebergs can break off.

So, just on a fairly cursory consideration -- there's more than one way to skin a cat, or, rather, there's more than one way for an ice sheet to lose mass -- we already know there's a problem with the WUWT article. In the science, no real conflict. More below the fold.
The scientific papers involved are ...

First -- a hearty thank you to Jesus for providing the links! As you can see from my link policy I appreciate substantive links being provided. That's really the only way I can be sure that I know what science you mean, and only way for you to show what good science (or bad, alas) it is that you've found. And not only me, since I'm only one reader of the blog, but all my readers (all '6'* of you). We can all go straight to where the good, substantial, material is, and learn something!

The first paper shows that Antarctica has been losing mass -- Increasing rates of ice mass from the Greenland and Antarctic ice sheets revealed by GRACE (also available from thingsbreak -- I hope he's gotten appropriate permissions.) -- and that the rate of mass loss has been increasing in recent years. It's not just a simple linear decline. Rather, the mass loss is not only getting more negative (losing more mass year by year), but the rate it's going more negative is getting even bigger (the increase in mass loss from year to year is getting bigger too).

The second paper shows that in the last couple of years, snowmelt -- only one of the several ways that the Antarctic can lose mass -- has been lower than usual, with the most recent year being the lowest snowmelt year of the last 30. An updated Antarctic melt record through 2009 and its linkages to high-latitude and tropical climate variability, also at thingsbreak.

So what do we have? Well, in all seriousness, it's a couple of interesting papers on the science (yay!) and not a whole lot of conflict today. But we may take a sign of something to keep reading the scientific literature for. We have on one hand, observations that the total mass lost by the Antarctic ice sheet is going up (over the 6 years that this data source is available). On the other hand, we have observations that the summer 2008-2009 was a low point for surface melting (of the 30 years this has data for). But we know that's just one of the many ways the ice sheet can lose mass.

What we keep reading the science for are:
1) Do either data analysis continue to get support from later observations? When we're looking at relatively new approaches, which both are, one of the things we have to keep in mind is that the method might be wrong somewhere. Both look plausible to my non-expert (in these methods) self. But the real story will be told over the next couple of years as people seriously expert in these methods start doing their own work, and the original authors keep after the issue. Keep your eyes peeled for more.
2) Only one of the mass sinks for the Antarctic has been examined directly. Look for (some articles may exist already) or keep your eye out for new articles to come on those other mass loss mechanisms. It might be that when we add up the individual mass loss mechanisms we don't match what people observe from GRACE. Such a thing happened in the early 1990s regarding the sinks for CO2 -- the observable amount taken up by the ocean was much too small. That told us something else (land uptake) was going on. (In this case, maybe we discover that GRACE isn't accurate about the total mass loss. Or maybe it's that snowmelt isn't accurately inferred, or iceberg loss, or .... If we've got many things involved, and we do, then any of them could be the cause of a discrepancy.)

Either way, the serious resolution of a conflict, if there is one, will take place in the scientific literature. At the moment though, there's no conflict. Just some interesting science that suggests we have more to be looking for (as the GRACE and the snowmelt methods get more data) and other interesting science to look for, or keep our eyes out for.

* I realize, and appreciate, that I have more than 6 readers. I'm minded, though, of a local radio person I listen to, who talks of his '13' listeners. Probably more like 130,000. (I just wish I were understating as thoroughly as him!). The thing being, I do realize that this is not one of the higher-traffic blogs around, or even around and on topics somewhat like mine. I therefore appreciate those of who who read, and who contribute substantive comments.

Sound and Fury at WUWT

2009-10-19T17:12:00.001-04:00

From the question place, where a reader noted a high traffic item at Watt's Up With That and asked for a science response. Where to begin? First, I guess I'll note that most of the post is bluster and personal attack. Once you cross out those parts, it's a much shorter article.

Second, as always, go back to the original source. In this case, it is a Mann et al. 2008 paper Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia, with supplementary material.

Then, consider exactly what the claims (in this case, at WUWT) are, and just what evidence is produced for it.

The fundamental claim at WUWT is that the entire reconstruction is upside down. (We're treated to pictures of other things that are upside down.) Right off, we know WUWT is wrong.

There are three major features of temperature over the past 1000 or so years -- the 'Medieval Warm Period', the 'Little Ice Age', and the warming of the past century. We've known about the first two since at least the 1970s -- Hubert H. Lamb's Climate: Past, Present, and Future. The Mann and others reconstruction shows a Medieval Warm Period and a Little Ice age.

If WUWT were correct about Mann et al. having the curve upside down, then they (WUWT) must be insisting that it was a Medieval Cold Period and Little Warm Period -- which we've known for decades it wasn't, irrespective of anything that Mann or coworkers have done. WUWT is simply wrong from the get go.

There is then a lot of sound and fury regarding 'Tiljander'. This turns out to mean a paper by Tiljander and others in 2003, cited in the supplementary material of Mann and others. WUWT cites CA citing personal communication claiming that Mann et al. used this data set upside down. This looks more like a game of 'whispers' or 'telephone' than a serious scientific claim. If Tiljander (then CA, then WUWT) had serious evidence of error by Mann et al., the scientific literature is the place for it. Or, at the very least, Tiljander et al. could place a short note on their own blog/web site/university press release/.... Neither CA nor WUWT seem to cite any such thing, so I will draw the inference that this is because it doesn't exist.

Still, there might be a question as to whether the data were used correctly. A more significant question is whether the data and its usage materially affect the reconstruction. If, for instance, the only reason for showing a warming in the 20th century is Mann and others' use of this data set, we might be more concerned about whether there's been such a warming. Or at least it becomes a much more important question whether they did use the data set properly. So let's go back to the original source and see what usage was made, how, and what effects it has.

Page 2 of the supplementary material, under Sensitivity Analysis (NH Temperatures)Potential data quality problems. First, we'll note that the authors do indeed consider the possibility of data quality problems. I'll quote that paragraph here (all typos mine, see the original):
In addition to checking whether or not potential problems specific to tree-ring data have any significant impact on our reconstructions in earlier centuries (see Fig. S7), we also examined whether or not potential problems noted for several records (see Dataset S1 for details) might compromise the reconstructions. These records include the four Tiljander et al. (12) series used (see Fig S() for which the original authors note that human effects over the past few centuries unrelated to climate might impact records (the original paper states "Natural variability in the sediment record was disrupted by increased human impact in the catchment area at A.D. 1720." and later, "In the case of Lake Korttajarvi it is a demanding task to calibrate the physical varve data we have collected against meteorological data, because human impacts have distorted the natural signal to varying extents."). These issues are particularly significant because there are few proxy records, particularly in the temperature-screned dataset (see Fig. S9) available back through the 9th century. The Tiljander et al. series constitute 4 of the 15 available Northern Hemisphere records before that point.
They also note 3 other data sets with problems.

So, do the authors proceed blindly, pretending that all data are good (and equally good, at that)? No, there's the reconstructed figure in S7, using all data, and another using all data except for the tree rings. And then in figure S8, they show what happens after removing the 7 problematic data sets (The Tiljander 4 plus 3 from elsewhere). Same kinds of curves either way -- still a Medieval Warm Period, a Little Ice Age, and a warm recent century. As Mann and others note that before the 9th century there are few data if one withdraws Tiljander, I'm ignoring that part of the reconstructions.

From just reading the paper, we don't know whether the Tiljander data were used correctly. We do, however, know that the answers are quite similar whether they're used or not (S7 vs. S8). If there's an error, in other words, it's an error with little effect.

Contrast that with WUWT's initial claim of the whole reconstruction being upside down -- thereby turning the Medieval Warm Period into an ice age, and the Little Ice Age into a Warm Period. They give no evidence at all that this is the case. Irrespective of whether the Tiljander data were used wrongly, WUWT is wrong in their main claim.

Question Place

2009-10-18T04:15:00.000-04:00

About time again for one of these. Questions, suggestions, ideas, and so forth.

Barb Didrichsen's Blog

2009-10-17T11:22:00.002-04:00

I've been remiss about mentioning a friend, Barb Didrichsen, who has been blogging for a while. Her latest note is A Conversation on Climate Change. It was also a contribution to Blog Action Day, where an attempt was made to have climate be the topic of the day on as many blogs as possible.

As you'll see in reading her article, Barb is not a scientist. She's an interested and interesting person and a writer. Take a look.

Holding place

2009-10-13T22:12:00.003-04:00

Not much going on by way of things that I'm writing up. Or at least not that I'm finishing for blog purposes. The better news is that I'm doing more reading, of various sorts. That includes some fun -- Terry Pratchett's _Unseen Academicals_, _The Callendar Effect_ by James Rodger Fleming (biography of Guy S. Callendar, arguably the inventor of the CO2-induced climate change theory) and _Species: A History of the Idea_ by John S. Wilkins (which I've mentioned here before; I should be meeting up with John at the National Museum of Natural History on the 24th). And some also fun, but also more serious, in some respects, reading, including some books on amateur scientist and amateur engineer experiments, and my too-large backlog of Science and Nature (and EOS, Bulletin of the American Meteorological Society, Association for Computing Machinery, ...).

Reading, learning what other people are doing or have found out about the universe, is almost always the start of my own creative activities. At the very worst it is like when I volunteered at the mile 21 water stop for a marathon. At that point, my longest race was 10 miles (16.1 km), and my longest run was a half marathon (13.1 miles, 21.1 km). After seeing the people coming past me, who were still perfectly able to chat, thank us volunteers, ask where to toss their empties, and such, there was just no excuse left for me about finishing my own marathon. Some, I wouldn't have bet a quatloo could run 2 miles from looking at them, much less be cheerfully passing mile 21. They proved to me that if you do the training, you can do the race.

For writing, if I see some very poor stuff, or some stuff that is not all that 'brilliant' (more than one paper has been published on things that I never bothered to write up), then it's a bit of a kick to get up and start my own writing. If it's great stuff, then it's energizing -- look at all that great stuff people are doing out there. Time for me to add a good thought or two. Win-win.

Also to be coming, and in keeping with my aim for educational content, is that I'll be visiting a school at the end of this month. Still working out some details on the what and how for my visit. I want, always, for my visits to classrooms to add to what the teacher was trying to do, and support learning by the students. Plus, obviously, there are some messages of my own I want to get across at the same time -- science is interesting, the universe around us is interesting, and it is understandable, and the students can indeed do some of that understanding and figuring out. Related to that, I'll probably be putting up a note or two. Looks like it's time for something about clouds and hurricanes.

Along with doing my reading, I'll be writing up some thoughts about some of the books. A pair I'll definitely be mentioning shortly are Danica McKellar's Kiss My Math and Math Doesn't Suck. If you are, or know someone who is, in what I take to be the target audience -- teenage girls who are struggling with math, and/or are having boy-induced problems about math -- go ahead and get the books.

Plus the usual odds and ends. Clearly there's a lot more to say about evaluating forecasts, and I'll be doing so. And much more to the world of sea ice, and climate. And ... well, the universe is a very interesting place. Suggestions always welcome too. Several of the notes I've liked most have been from reader suggestions.

In the mean time, for new content I'll suggest again to my adult readers my wife's blog Vickie's Prostitution Blog. Current is a 2 (maybe more, part 1 is up now) part look at How much money do prostitutes make. I give away little indeed in observing that the answer is very, very little. That contrasts starkly with the impression you might have from media, or some economists' write ups (one of which Vickie addresses more directly).

Sea Ice Finals 2009

2009-10-10T10:43:00.021-04:00

The final September figures are in from the NSIDC. In terms of the Connolley-Grumbine bet, William lost. So 50 quatloos will find their way to me. Or, given how close it was (5.38 was our dividing line, and 5.36 was the observation), we could go double or nothing on next year's ice.

I'll also mention a few other predictions, or methods:

Method	Prediction	Error
Climatology-15a	7.23	1.87
Climatology-30	6.63	1.17
Climatology-15b	6.16	0.8
Connolley Line	5.84	0.48
Grumbine Curve	4.92	0.44
Persistence	4.68	0.68

Climatology 15a is the average of the first 15 years. 15b is the average for the last 15 years (not counting 2009!). Climatology 30 is the average of the first 30 years of the satellite record.

Persistence is to say that this year's ice will be the same as last year's. For atmospheric temperatures, persistence is a pretty good forecaster for the first two days (at least in the sense that it is closer to what you see than climatology). For sea ice, it is not so good, beating climatology only 17 of 30 years. It's interesting, however, that its losses are strongly clumped. In the 14 years from 1990-2003, persistence won 2 and lost 12 versus climatology-30. In the remaining 16 years, it went 15-1.

Update: Per William's request, I'll add the ARCUS estimates (as given in the full report) for June's report. I believe the values all were rounded to the nearest 0.1 million km^2, so for consistency will list mine at 4.9 here.

Method	Prediction	Error
Canadian Ice Service	5.0	0.36
Hori, Naoki, Imaoka	5.0	0.36
Nguyen, Kwok, Menemenlis	4.9	0.46
Lindsay, Zhang, Stern, Rigor	4.9	0.46
Kaleschke and Halfmann	4.9	0.46
Grumbine	4.9	0.46
Fowler, Drobot, Maslanik	4.9	0.46
Stern	4.7	0.66
Arbetter, Helfrich, Clemente-Colon	4.7	0.66
Pokrovsky	4.6	0.76
Stroeve, Meier, Serreze, Scambos	4.6	0.76
Kauker, Gerdes, Karcher, Kaminski, Giering, Vossbeck	4.3, 4.6	1.06, 0.76
Zhang	4.2	1.16

To judge from the graphic that accompanied, however, the bar chart was done with figures that had more precision, as Kaleschke and Halfmann's 4.9 is clearly higher than Fowler and company's.

Saving lives

2009-10-08T19:24:00.008-04:00

A while back, I mentioned the fact I'm still walking around and able to write to you is due to modern science.

I've not going to write often about it (this being only the second post in over a year), but, the truth remains that a lot of us are still walking around because of modern science. The single biggest contributor to that is vaccination. There's a very nice video here, from a pediatrician, Joseph Albeitz (h/t Phil Plait) that outlines some of the magnitude of good that vaccination has done:

In looking at vaccination, as he discusses, you're looking at saving hundreds of millions of lives. The list of things that could contend for saving more is awfully short.

One thing he mentions in passing, which I'll spend a little more time on, is herd immunity. There's a feeling out there, a false sense of security, that as long as 'everyone else' is vaccinated, it doesn't matter if your kids are. If your kids were the only unvaccinated kids, that might be true. But, in reality, you're not the only person who might think that way. Your kids interact with many other children. Once the number of unvaccinated children is high enough (depending on the disease it's in the range 10-30%), the disease can establish itself and spread. The 'herd' is immune only if enough people are immune. Once enough fail to vaccinate, you're a breeding ground for the disease. Worse, you're a breeding ground to infect people who did get vaccinated -- vaccines aren't 100% effective in all people. If enough of you are carriers, then the disease can spread to other kids and kill them.

I know that measles is commonly considered a trivial disease. But that 'trivial' disease kills over a million per year (listen to the video). I'm thinking that not killing off a million children each year would be a good thing. Similarly for the numbers of polio victims -- with vaccination, the number who would get it goes to zero. Both of these diseases are like smallpox in an important way -- they only spread between people. If we reached a point where nobody had the disease, as was the case for smallpox, then nobody would ever again need to be vaccinated against it. It would be gone. As the Dr. mentions, they're about 99% of the way there for polio. Measles have farther to go. Both, amazingly, can be eradicated.

Digressing a second, but not really, is my genealogy. One of my direct ancestors died from smallpox. Lived long enough to have kids, obviously. But died 20-30 years early because of the smallpox. As many a person who looks in to genealogy has observed, you see a lot of very short lives when you look back then (1700s - mid 1800s), many of them children who were never even named. Much of the reason for that change is vaccination. That ancestor (Zeboeth Brittain, how's that for a name?) died before the vaccine was discovered -- 1790, vs. 1796 for Edward Jenner's discovery. But I think he'd be amazed at the idea that the disease that killed him could be erased from the face of the planet -- and it now has been. Measles and polio can be as well.

Assessing predictions

2009-09-30T02:00:00.001-04:00

It's a little premature to make a detailed assessment of the predictions for September's average extent as the final numbers aren't in. They will be soon, but my focus is actually over on the question of how to go about doing the comparisons. Earlier, I talked about testing ideas, but there, the concern was more one of how to find something that you could meaningfully test. Here, with the September's average extent, we already have a well-defined, meaningful thing to look at.

Our concern now is to decide how to compare the observed September average extent with the climatological extent, and a prediction. While mine wasn't the best guess in the June summary at ARCUS, it was mine, so I know what the author had in mind.

Let's say that the true number will be 5.25 million km^2. My prediction was 4.92. The useless approach is to look at the two figures, see that they're different, and declare that my prediction was worthless. Now it might be, but you don't know that from just the fact that the prediction and the observation were different. Another part of my prediction was to note that the standard deviation to the prediction was 0.47 million km^2. That is a measure of the 'weather' involved in sea ice extents -- the September average extent has that much variation just because weather happens. Consequently, even if I were absolutely correct -- about the mean (most likely value) and the standard deviation, I'd expect my prediction to be 'wrong' most of the time. 'Wrong' in that useless sense that the observation differed by some observable amount from my prediction. The more useful approach is to allow for the fact that the predicted value really represents a distribution of possibilities -- while 4.92 is the most likely value from my prediction, 5.25 is still quite possible.

We also like to have a 'null forecaster' to compare with. The 'null forecaster' is a particularly simple forecaster, one with no brains to speak of, and very little memory. You always want your prediction to do better than the null forecaster. Otherwise, people could do as well or better with far less effort than you're putting in. The first 'null forecaster' we reach to is climatology -- predict that things will be the way they 'usually' are. Lately, for sea ice, we've been seeing figures which are wildly different from any earlier observations, so we have to do more to decide what we mean by 'climatology' for sea ice. I noticed that the 50's, 60's, and 70's up to the start of the satellite era had as much or somewhat more ice than the early part of the satellite era (see Chapman and Walsh's data set at the NSIDC). My 'climatological' value for the purpose of making my prediction was 7.38 million km^2, the average of about the first 15 years of the satellite era. A 30 year average including the last 15 years of the pre-satellite era would be about that or a little higher. Again, that figure is part of a distribution, since even before the recent trend, there were years with more or less (than climatology) ice covers.

It may be a surprise, but we also should consider the natural variability in looking at the observed value for the month. Since we're really looking towards climate, we have in mind that if the weather this summer were warmer, there'd be less September ice. And if it were colder, or different wind patterns, there would have been more ice this September. Again, the spread is the 0.47 (at least that's my estimate for the figure).

I'll make the assumption (because otherwise we don't know what to do) that the ranges form a nice bell curve, also known as 'normal distribution', also known as 'Gaussian distribution'. We can then plot each distribution -- from the observed, the prediction, and what climatology might say. They're in the figure:

This is one that makes a lot of sense immediately from the graphic. The Observed and Prediction curves overlap each other substantially, while the curves for Observed and Climatology are so far from each other that there's only the tiniest overlap (near 6.4). That tiny overlap occurs for an area where the curves are extremely low -- meaning that neither the observation nor the climatology is likely to produce a value near 6.4, and it gets worse if (as happened) what you saw was 5.25.

The comparison of predictions gets harder if the predictions have different standard deviations. I could, for instance, have decided that although the natural variability was .47, I was not confident about my prediction method, so taken twice as large a variability (for my prediction -- the natural variability for the observation and for the climatology is what it is and not subject to change by me). Obviously, that prediction would be worse than the one I made. Or at least it would be given the observed amount. If we'd really seen 4.25 instead of 5.25, I would have been better off with a less narrow prediction -- the curve would be flatter, but lower. I'll leave that more complicated situation for a later note.

For now, though, we can look at the people who said that the sea ice pack had 'recovered' (which would mean 'got back to climatology') and see that they were horribly wrong. Far more so than any of the serious predictions in the sea ice outlook (June report, I confess I haven't read all of the later reports). The 'sea ice has recovered' folks are as wrong as a prediction of 3.1 million km^2 would have been. Lowest June prediction by far was a 3.2, but the authors noted that it was an 'aggressive' prediction -- they'd skewed everything towards making the model come up with a low number. Their 'moderate' prediction was for a little over 4.7. Shift my yellow triangle curve 0.2 to the left and you have what theirs looks like -- still pretty close.

To go back to my prediction, it was far better than the null forecaster (climatology), so not 'worthless'. Or at least not by that measure. If the variability were small, however, then the curves would have narrow spikes. If the variability were 0.047, ten times smaller than it is, the curves would be near zero once you were more than a couple tenths away from the prediction. Then the distribution for my prediction would show almost no overlap with the observation and its distribution. That would be, if not worthless (at least it was closer than climatology), at least hard to consider having done much good.

Sea Ice Bet Status

2009-09-17T21:00:00.008-04:00

It looks like the Arctic sea ice extenthas bottomed out, as of the 12th or so. I'm confident that a good storm system could give us a new minimum -- both by slamming up the loose ice in the western Arctic (reducing extent by pushing the ice pack together) and by mixing up warmer water from the ocean (reducing extent by melting the ice). But, as a rule, this sort of thing is rare. A storm would have to hit the right area in the next few days. Otherwise the atmosphere will be cold enough to simply keep freezing new ice.

So, starting to be time to assess our various guesses. William Connolley and I made our 50 quatloo wager on over (his side) or under 5.38 million km^2 for the September average. The minimum, if we have indeed seen the minimum, is about 5.1 million. That looks favorable for my side of the bet. Though it would mean I definitely missed the September average, as I said that would be 4.92. More about that in a moment. The figure below suggests that since the extent dropped below 5.38 right about the start of September, I should be safe. Usually (see the climatological curve) the pack doesn't gain much area in September. But William could still win if we have an unusual last two weeks and the ice pack gains a lot of extent.

I've added a few lines to the NSIDC graphic of 12 September. One is the vertical line, to highlight when it is we dropped blow the climatological minimum. We've been below normal since early August. That in itself suggests a climate change. We're now about 3 standard deviations below the climatological minimum, which again, in such a short record, suggests a climate change. The significance of the extra large amount of ocean being exposed to the atmosphere, for an extra long time, is that it lets more ocean absorb more heat from the sun. Though this year looks to be a higher extent than 2007 and 2008, it's still below any year except 2007 and 2008. If we didn't know about those two years, we'd be surprised by this year being so low -- the 2005 September average extent (record before 2007) was 5.57 million km^2 -- far higher than this year is liable to average.

Still early to decide whether I owe William, or vice versa. Both of us will win our bets with Alastair. Looking down to the poll that I invited you to answer back in June, I'll say that the people who called for 7.5 million (the previous climatology) and 6.0 million km^2 are wrong. Also the 1 who went for 3, the 2 who went for 3.5, and the 4 who went for 4 million km^2 for the month's average. The 12 who went for 4.5 (which means anything in the range 4.25 to 4.75) should be pulling for a really massive storm to hit the western Arctic and obliterate huge amounts of ice extent. The main candidates are the 3 who went for 5, and the 1 who went for 5.5 (ranges of 4.75 to 5.25, and 5.25 to 5.75, respectively).

Something else this brings up (or at least this plus some comments I saw at a different site) is "How do you judge the quality of predictions?" I'll be coming back to this, using the Sea Ice Outlook estimates for my illustrations.

Title decoding

2009-09-16T05:22:00.004-04:00

Title of a recent paper in Science: Motile Cilia of Human Airway Epithelia are Chemosensory (Shah and others, vol 325, pp. 1131-1134, 2009.)

Time to apply the Science Jabberwocky approach, as I'm unfamiliar with many of those terms:

Mimsy borogoves of Human Airway Bandersnatches are Frumious.
(motile) (cilia) of Human Airway (epithelia) are (chemosensory)

4 terms we need to get definition of (those of we who don't already know them, that is).

Cilia, whatever they are, can apparently be motile or non-motile. By the writing, that doesn't seem to be the new observation. But that they can be frumious, er, chemosensory, is apparently news.

The abstract itself tells us what the cilia are -- microscopic projections that extend from eukaryotic cells. (If we know what a eukaryotic cell is, we're set. Otherwise, we have to do a little more research, and discover that eukaryotic cells are those with separate parts to them, including a nucleus -- that covers all animals, plants, and fungi).

We also have to go look up 'epithelial'. We're ahead of the game if we know that epi- tends to have something to do with 'on the surface'. Epithelial cells are those that are on the surface of our body cavities -- lungs, digestive system, etc..

Chemosensory ... well, sensory is nicely obvious. Chemo- as a prefix means that the cells are sensing chemicals.

So with a little decoding work, and perhaps using google search for definitions (enter define:epithelial as your search and you'll get links to the definition of epithelial), we arrive at our understanding of the title. -- There are cells lining the surface of our airways that have little extensions. The authors show that the extensions are sensitive to chemicals.

In reading the paper itself, we find that it is particular kinds of chemicals that these cilia are sensitive to -- 'bitter'. When they detect such compounds in the air, they start getting active and try to flush out the bad stuff they've detected.

The conclusion is not especially a surprise to me. I've long been confident that my airways were sensitive to certain chemicals (though I didn't know which). Walking past a perfume counter has always been a problem for me, as my lungs shut down or at least try to. Folks have said that it's just my imagination, and all that is happening that I'm smelling the perfume and causing the rest. That doesn't work well as a hypothesis because I have an exceptionally bad sense of smell. Usually the way I know the perfume is present is because I start having more difficulty breathing. The paper also corresponds to a different experience of mine. Namely, I don't have such reactions to flowers, even flowers in large masses as we get in spring with the honeysuckle, or lilac bush. The cilia are reactive to bitter compounds (known from the paper) and probably (a point that's very testable) perfumes have more such compounds than flowers do.

Per my usual, I've written the corresponding author about this post. Also, if the sample donation process is quick, easy, painless, harmless, I'm willing to donate a sample of my highly-reactive (I think) epithelial cells for their further research.

Good science, wrong answer

2009-09-15T04:14:00.003-04:00

Sometimes it happens that somebody does good science, but has arrived at a wrong answer. Since most of us think that the answer has to be right (and I'll agree that it's better when it is), this will take some explaining. Let's go back to what science is about -- trying to understand the universe in ways that can be shared. Good science, then, is something that leads to us understanding more about the universe.

For my illustration, I'll go back to something now less controversial than climate. In the 1980s, paleontologists David Raup and J. J. Sepkoski advanced the idea that mass extinctions, such as the one that clobbered the dinosaurs, were periodic. Approximately every 26 million years, for the last about 250 million years, they observed a spike in the extinction rate. Not all were as large as the one that got the dinosaurs.

It so happened that they were at the University of Chicago, in the Department of Geophysical Sciences, and so was I. Further, I was working with time series for my master's thesis. So I asked them about working with their data and seeing what I would find with my very different approach. They were gracious and spent some time explaining what I was looking at, knowing that I didn't think they were right. By my approach, indeed, their idea did not stand. My approach, however, was not a strong one, being susceptible to some important errors. So I never published about it. Still, along the way, I learned more both about time series, and about paleontological data. So that's a plus making the periodic extinction idea 'good science' -- I, at least, learned more about the universe, even if not enough to make original contribution.

One mark of good science is that it prompts further research. Raup and Sepkoski, in their original paper, had made a reasonable case. 'reasonable' being that it could not be shot down by any simple means. 'simple' meaning that the answer was already in the scientific literature. So to knock down the case, itself a normal process in science, the critics had to do some research to show how weaknesses or errors in one or more of the following lead to the erroneous conclusion:
* The statistical methods
* The geological time scale
* The paleontological data (extinction figures, and their dating)

The idea was not sensitive to the geological time scale used, so that fell away fairly quickly. The statistical methods did develop a longer-lasting discussion -- new ones devleoped, flaws in the new and the old methods described (and then discussion about whether the claimed flaws were real).

Most interesting to me, and I think where the greatest good for the science was, was going back to the data. In saying that the extinctions were periodic, one carried the image of something crashing into the earth (like the meteor that did in the dinosaurs) and killing off huge numbers of species (and genera, and families) very quickly. One of the data problems, then, was getting accurate dates for the time of extinction. Often the data could only say that the things went extinct sometime within a several million year window. That's a problem, as then your view of whether it was periodic could depend on whether you put the date of extinction at one end of the geological period or another. So people went to work on getting better dates for when the species went extinct.

Also, I noted above that the original idea applied to the last 250 million years. The reason was, when they started that was as far back as you could go with reasonable data. So work also went in to trying to push back the period of reasonable data.

I don't know what the field ultimately concluded about the idea. I do know that the work to advance or refute the idea resulted in more data about when species went extinct, and better dates for when they did. Further, those newer and better data are themselves useful for learning more about the universe -- there's more to be gained than just answering the original question about whether mass extinctions were periodic.

So, not only did the original publication result in more being learned about the universe, but it was in a way that enables even more learning to happen. That makes it good science. The original idea might have been wrong, but it definitely was good science.

I've focused on the side of scientific merit here. There was a lot of, well, unprofessional, response as well. You can read about both parts in The Nemesis Affair: A Story of the Death of Dinosaurs and the Ways of Science by David Raup. Part of it was because the idea that any mass extinction had to do with things crashing in to the earth was still new, and still widely not accepted. Then this idea comes up and says that not only does it happen (bad enough) but it had happened many times, and happens regularly.