Thank You, Stephen Schneider

Sad news yesterday. Stephen Schneider, a leading climatologist from Stanford University, passed away at age 65, apparently of a heart attack. He was on an airplane, flying from Sweden to London on his way back from a scientific meeting. I didn’t know Schneider personally, and I never had the opportunity to take a class from him while I was at Stanford. But I did get to hear him speak on a couple of occasions, and I can honestly say the first one of these was a small revelation for me.

The winter of my freshman year, I took “Introduction to Earth Systems”, the eponymous introductory course for my major. Each lecture was by a different professor, covering his or her area of expertise. Schneider gave one of the talks on global warming. I was familiar with the basics of climate change from middle school science class and reading the newspaper. But, because of where I was coming from personally, I was predisposed to view it as cut and dried, right and wrong politics. To see not just the policy, but the science of climate change, in moral terms—one more reason why I was right and They were wrong.

Schneider’s lecture allowed me no such luxury. Andy Revkin was right to call him “caustically honest”—within five seconds of taking the podium, he was informing us of the immense uncertainty as to the extent and consequences of manmade climate change. Not just the uncertainty, but the impossibility of certainty when forecasting the climate system 50 or 100 years in the future. “We can’t know what will happen,” he said, “because it hasn’t happened yet.” I was expecting certain science followed by a call to arms. I did not receive it, at least not in the form I was expecting.

For the next 50 minutes, he took us on a wide-ranging tour of the science and politics of global warming, from black-body radiation to why certain skeptics were willfully ignorant, paid hacks, or both. That caustic honesty was present throughout. It was electric. I was unsettled to be thrown into a topic on a level that was both more technical and less certain than any previous experience. Looking back, though, his perspective worked its way into my head. It’s something like the perspective I tried to convey in this post: that dealing with uncertainty about frighteningly important questions is unnerving, to say the least. But ultimately, gauging that uncertainty while trusting in the facts you do know is profoundly liberating. For his part in leading me to that realization, I am truly indebted to Professor Schneider.

Closing his lecture, as the students were already ruffling their papers and bagging their notebooks, he left us with his three commandments of communicating science, which I have yet to forget. “Know thy audience,” he said. “Know thy self. And know thy stuff.”

You will be missed, Dr. Schneider.

2010/07/21

Filed under: Uncategorized — Tags: , , , , — Sam @ 12:56 am

The Lighter Side of Black

Well, it looks like BP has maybe-possibly-if-nothing-else-goes-wrong-at-least-for-now managed to stanch the flow of oil into the Gulf of Mexico, using a tighter-fitting containment cap. And I just stumbled across this video, which I, somehow, had missed before now. BP deals with a boardroom coffee spill…

Heh.

See, it’s funny because in the video, the oil has only been leaking for forty-seven days, but by now it’s been leaking for eighty-seven…

Ah, Christ.

2010/07/15

Filed under: Uncategorized — Tags: , , — Sam @ 11:10 pm

Grain, Extent, and 8-bit Cities

A friend sent me a link to this cool website a couple of days ago: a guy named Brett Camper has coded it up to show zoomable maps of several major cities, pixellated à la old video game world maps. It’s visually neat, and it also illustrates how observed patterns change with changing scale of observation. What exactly does “scale of observation” mean? The “scale of observation” is not just one value—it comes in several parts. These parts go by several different names; one of the common frustrations with trying to use scale as an organizing concept is that the terminology is often vague and varied. For the purposes of this blog post, I’ll use the terms “grain” and “extent.”

“Grain” refers to the size of the most fundamental sampling unit: a pixel in a satellite image, a net tow on an oceanographic cruise, or a single quadrat in an ecological field study. Values for those examples might be 1 square km of the earth’s surface, 1000 cubic meters of seawater filtered, and a single square meter of a prairie soil, respectively. Any variation within the grain will be averaged out: you can’t tell where the squashed creatures in your net’s cod end came from with any more specificity than that they came from somewhere in the net’s 1000-odd meter track. (There is also the issue of how far apart the sampled units are. In the satellite image, they are contigous, while the net tows might be tens of kilometers apart, and the quadrats separated by tens of meters.)

“Extent” is the total area or distance over which measurements are collected. A satellite image might span a couple thousand kilometers from one side to the other. A long cruise could likewise cover thousands of kilometers, while the prairie grass study would probably only span a few kilometers, at most. Thinking about the grain and extent of your sampling, you can start to imagine the types of patterns and phenomena you would be capable of detecting. To illustrate, look at this clip from 8-Bit Cities, centered above Seattle’s University District—specifically, my office in the UW’s Fishery Science Building.

At this zoom level, you can’t actually see the Fishery Science Building, but if you click the “+” button on the map four times, it will show up. Being zoomed in, the real-world, geographical size of each tile is now smaller that it was zoomed out, and the geographical distance from one side of the map to the other is smaller as well. Both the grain and extent have decreased, and as a result, finer-scale patterns are visible: surface streets and buildings, for example. If you let your eyes go blurry and just look at the patterns as you zoom in and out, you will notice that they look quite different.

You may also notice that as you zoom in, the number of tiles doesn’t actually change. There are always about 26 of them across the map. Though the grain and extent have both changed, their ratio has not. This ratio is known as the “scope,” and it is also an interesting one to ponder. Scope is the extent divided by the grain, and characterizes how densely information is packed inside the boundaries of your measurements. As an example, look at the image below, from Google Maps, showing the same area as the 8-bit map, but with a much smaller grain, and, therefore, a much larger scope (clicking the “+” button four times here will also zoom you in to the same area as above):


View Larger Map

Looks pretty different, huh? We are fairly used to looking at maps, so the loss of relevant information with the 8-bit map is obvious. But for some other space, or measurement, or phenomenon—say, the variance of fish density in space and time in some region—and it is not immediately obvious what the appropriate sampling scales should be to measure the things we’re interested in.

This is just scratching the surface of all the ways you can look at grain, extent, and scope. You can consider them in space, as I did here, or in time, as I tend to do in my thesis work, or in both space and time, or in terms of spectral resolution, or taxonomy…the list goes on. As Camper puts it on 8-Bit Cities:

Maps offer us visual architectures of the world, encouraging us to think about and interact with space in particularly constrained ways. Take some time to think about your surroundings a little differently. Set out on a quest. Be an adventurer.

And as Chuang Chou put it circa 300 B.C., in a truly awesome quote I found at the top of this paper [pdf]:

“This being so,” asked the Earl of the River, “may I take heaven and earth as the standard for what is large, and the tip of a downy hair as the standard for what is small?”

“No,” said the Overlord of the North Sea. “Things are limitless in their capacities, incessant in their occurrences, inconstant in their portions, uncertain in their beginning and ending. For this reason, great knowledge observes things at a relative distance; hence it does not belittle what is small or make much of what is big, knowing that their capacities are limitless.”

2010/07/10

Filed under: Uncategorized — Tags: , , , , , — Sam @ 12:57 am

Our Hold on the Planet

We asked for rain. It didn’t flash and roar.

It didn’t lose its temper at our demand

And blow a gale. It didn’t misunderstand

And give us more than our spokesman bargained for;

And just because we owned to a wish for rain,

Send us a flood and bid us be damned and drown.

It gently threw us a glittering shower down.

And when we had taken that into the roots of grain,

It threw us another and then another still,

Till the spongy soil again was natal wet.

We may doubt the just proportion of good to ill.

There is much in nature against us. But we forget;

Take nature altogether since time began,

Including human nature, in peace and war,

And it must be a little more in favor of man,

Say a fraction of one percent at the very least,

Or our number living wouldn’t be steadily more,

Our hold on the planet wouldn’t have so increased.
-Robert Frost

My freshman year, in our required Introduction to the Humanities course, one of our assignments was to memorize a poem—any poem. This one was mine, and it’s been a favorite since then. It steps so cautiously around “the just proportion of good to ill.”

2010/07/03

Filed under: Uncategorized — Tags: , — Sam @ 12:28 am

Methods of sampling and analysis and our concepts of ocean dynamics

This post was chosen as an Editor's Selection for ResearchBlogging.orgI read a paper today (actually, more like an essay) by Peter Wangersky, a longtime chemical oceanographer. Titled “Methods of sampling and analysis and our concepts of ocean dynamics,” it is essentially a personable ramble through six decades of marine science, reflecting on the technical capabilities and sampling methods over time and the way those capabilities and methods influenced the assumptions that were made and the questions that were posed—in essence, the working mental picture we have of the ocean. Things have indeed changed a lot since he began during World War II. The paper is full of quotable nuggets:

Adding machines did exist, and some could even be made to multiply, after a fashion. They were strictly mechanical, however, and the wear and tear on various joints, machine and human, coupled with the high frequency of random inputs, discouraged us from the use of any but the most necessary statistical tools. Perhaps this is why most analytical chemists of this vintage believe that if one does the analyses right, there’s no need for statistics. This is a self-reinforcing fallacy; if you don’t do the statistics, you never discover your limitations or the limitations of your methods and the universe you are sampling.

He also talks about the analysis of water samples for salinity, in the days before CTDs could measure it in-situ with electrical conductivity:

A group of female technicians, the salinity girls, ran the Mohr-Knudsen silver nitrate titrations to a chromate endpoint for eight hours a day. Needless to say, there was a considerable turnover in this group. There was always a shortage of salinity girls, and the sample bottles kept coming in from the ships and stacking up in the hallways.

Those were different times…

The most interesting aspect of the paper is his discussion of the shift in emphasis starting to occur from ship-based sampling to automated sampling with collections of stationary instruments. My thesis research is using a stationary echosounder, so I’ve done a fair amount of thinking about the differences between the traditional ship-based approach and the more recent ocean observatory approach. I appreciated hearing about this shift from the perspective of someone who has seen the entire evolution of modern oceanography firsthand. He doesn’t discuss it in the same vocabulary I might, but he comes to essentially similar conclusions.

Wangersky’s point is that our perception of the ocean and its dynamics is hugely dependent on the tools we have available to study it. It will be a very interesting time in the coming years, as more types of sensors and instruments are deployed long-term at more locations around the world. Just by virtue of observing the ocean at a new spatio-temporal scale, we’re likely to find stuff going on that we missed before. In the old days, oceanographers generally assumed that the ocean was in a steady state. When hydrographic samples were few and far between, and you had to bring them back to shore for the “salinity girls” to analyze before you knew what they meant, this assumption was kind of necessary. These assumptions were also kind of wrong, which is the point that Stommel made in his famous 1963 paper (remember Stommel?).

As instruments, methods, and computing power have increased, these types of oversimplifications have retreated, to the point where we now embrace variability and dynamic changes, and try to understand them in and of themselves, rather than as unwanted noise on top of some supposed equilibrium or steady state that isn’t really there. Every advance in instrumentation and every expansion of the scope of our observations has yielded a new perspective on the oceans, adding on to an understanding that is slowly and gradually becoming more complete.

Peter J. Wangersky (2005). Methods of sampling and analysis and our concepts of ocean dynamics Scientia Marina, 69 (S1), 75-84 : 10.3989/scimar.2005.69s175

2010/06/12

Filed under: Research Blogging — Tags: , — Sam @ 3:33 am

Does Peer Review Need Fixing?

I read this by Michael Brooks in New Scientist a couple days ago, and it got me thinking. The piece essentially questions the effectiveness of the peer review system if it can let through (bunk) studies saying that homeopathic remedies can cure cancer, or that the universe is in fact filled with luminous aether. A couple of the commenters take mild issue with his point. I especially liked this part of Ben Goldacre’s response:

Almost all studies are less than ideal, to a varying degree, because all studies must make methodological concessions to what is practical or affordable. It is the job of academics – and indeed others who are interested – to read each study, and critically appraise its merits and shortcomings.

It’s often good that poor studies are published if they contain any useful information – with everyone spotting that they are poor – and it’s also good that criticisms of them are made in the public domain where all can learn from them. It is even better if the critical comments from peer reviewers are available as well.

The simple fact that something has been published in an academic journal does not mean that the findings are correct (it may have been a fluke, for example) or that the conclusions the researchers ascribe to their own findings are valid (they may have measured something with an inaccurate method, that is prone to systematic error in one direction, or they might be guilty of wishful thinking). If something is published in an academic journal, it means it was an interesting piece of work whose outcome should be available to those who wish to read about it.

One suggestion that I’ve heard on a number of occasions, and that has been put into action at PLoS, is to let the peer review happen after publication, by allowing other scientists to comment and respond on the online paper. It struck me after reading Goldacre’s comment that this is already more or less how we do it. The bulk of the actual reviewing happens after a paper is published, as the other researchers in the field read it, judge it, and discuss it. Most of a study’s street cred (or lack thereof), at least within a scientific community, comes from the collective judgment of its merits by that community.

In general, this works pretty well, because reputation and trust are so powerful within a given field. Someone doing shoddy research will find their stock of both declining rapidly, even if they get it published. The problem, of course, is that if you aren’t part of that research scene it can be really hard to know who has the trust of their colleagues and who doesn’t. John Q. Public, watching some scientist on the news, doesn’t have the specialized knowledge to assess the scientist’s claims. Even worse, he has no way of picking up on the scientist’s reputation through the TV screen. Without either of those things—the ability to critically assess the research, or a good reason to trust the scientist—John Q. will do one of two things. He will either accept the scientist’s story based solely on lab coat and university credentials, or reject it because it seems to go against common sense or threatens his sense of identity somehow. (“Me? From a damn monkey?”)

I’m not sure how to transfer this reputation/trust factor out of a specialized community into a public forum. Making the post-publication peer-review more transparent, a la PLoS, is one approach, though I’m still not sure it deals with this translation of trust. That seems like a basic problem that is hard to get around.

2010/06/07

Filed under: Uncategorized — Tags: , , , , , , — Sam @ 4:51 am

The Joy of Fortran

Well, maybe “joy” is a strong word. But over the past couple of days, I’ve been programming in Fortran a bit for the first time in about two years. Fortran, for the uninitiated, is the oldest programming language still in widespread use today. It gets a lot of grief (much of it deserved, some not) for being a kind of blockheaded dinosaur language, written, so its critics would have you believe, for the sole use of crew-cut IBM engineers wearing pocket protectors and skinny blue ties. I do a lot of programming for my research, but most of it is using Python/SciPy and R. Both are much easier to write in than Fortran, and they are both wonderful for 99% of what I need to do.

But I hit a snag this week: I need to compute empirical semivariograms for a large dataset. This is a statistic that requires comparing every point in the dataset to every other point. SciPy and R do vector-based math very efficiently, but pairwise comparisons do not lend themselves to vector-based math. The number of calculations increases as the number of datapoints squared, and trying to do this kind of brute-force calculation in pure Python or R is just too damn slow.

Fortran to the rescue. It may be a blockheaded dinosaur, it may have been invented to calculate the trajectories of missiles launched at the Russkies, but it isn’t slow. I wrote the relevant section of the program in Fortran, and then, using a tool called f2py, packaged it as a Python module. The result: an empirical semivariogram, in about 1/70 th the time. This process is kind of a hassle—not least because it required struggling with f2py for two days to get it working properly. I wouldn’t want to write large programs in Fortran. But I can see myself using it to clear certain computational bottlenecks now and then.

It’s kind of cool to still be coding in the direct descendent of the first real programming language. There are many scientists out there still using Fortran, especially in number-crunchy fields like weather and climate modeling, both because of its speed, and because it has been the lingua franca of these models for decades. And there is a kind of stark beauty in Fortran code. I had a little moment last winter at the Museum of Science in Boston, where I found, in a corner of the permanent “computers” exhibit, a copy of the program that guided the Apollo lander down to the Moon. It was a stack of Fortran punch-cards in a glass case.

You’ve got to know your roots…

2010/06/04

Filed under: Uncategorized — Tags: , , , , , , — Sam @ 3:36 am

A Moment of Levity

Sometime early tomorrow, BP will try to stop their month-old geyser of red, sludgy death in the Gulf of Mexico with a “top kill,” injecting a mixture of concrete and mud into the well from the surface. If it doesn’t work, the only option left for stopping the flow is the “junk shot,” injecting a bunch of balls, rubber, and rope into the well head in the hope of clogging it. If neither of these work, I understand we’re SOL until a relief well can finish drilling.

The Boston Globe’s Big Picture has a series of photos of the blowout’s ongoing aftermath. They are truly heart-breaking, and bile-raising. But I couldn’t help but laugh when I read the following comment on The Oil Drum blog:

An analysis of the plans provided by British Petroleum has demonstrated a weakness in the well site. But the approach will not be easy. You are required to maneuver straight down this trench and skim the surface to this point. The target area is only two meters wide. It’s a small thermal exhaust port, right below the main port. The shaft leads directly to the well head. A precise hit will start a chain reaction which should cap the well. Only a precise hit will set off a chain reaction. The shaft is ray-shielded, so you’ll have to use proton torpedoes.

That’s impossible! Even for a computer.

It’s not impossible. I used to bullseye womp rats in my T-16 back home, they’re not much bigger than two meters.

Then man your ships. And may the Force be with you.

May the force be with us…

2010/05/26

Filed under: Uncategorized — Tags: , , , — Sam @ 1:59 am

Powers of Ten

More scale candy: an awesome video, made by the designers Ray and Charles Eames in 1968. Starting at our familiar human scale, it zooms way out and then way in, crossing 40 orders of magnitude:

It’s amazing how the patterns change so immensely from one scale to another. Zooming from the entire Earth down to the man’s hand, we go from a round, bluish ball on a dark background, to streaks of clouds, to land and lake, to the pattern of streets, to a white square on a green background. There will be a stretch with uninteresting emptiness, and then a burst of patterns, followed by another homogenous void.

I went camping this past weekend in Umtanum Creek Canyon, near Ellensburg, WA. Before going, I checked it out on Google Earth and a USGS quadrangle, because I hadn’t been there before. When I got out of my car and started hiking, I started thinking about how different the ground-level experience of a landscape is from the map-level experience. On the ground, bushes and trees are the dominant features, but hills and drainage systems are most prominent on the topographic map. An interesting challenge in cross-scale thinking.

2010/05/20

Filed under: Uncategorized — Sam @ 1:48 am

Varieties of Oceanographic Experience

This post was chosen as an Editor's Selection for ResearchBlogging.org

I’ve been thinking a lot lately about issues of scale in ecology, both because I’m taking a fascinating seminar on the topic this quarter, and because my particular research is conducive to thinking about them. “Scale” came to the fore as a topic of interest starting in the late 70′s, and is tied up with other concepts that were on the rise at that time, like chaos theory, fractal geometry, and nonlinear dynamics. An example of one of these ties is Benoit Mandelbrot’s famous question, “how long is the coast of Britain?” The answer, as he shows, depends on how long your ruler is. This turns out to be a recurring conundrum: the pattern detected by an ecological study depends very strongly on the scale of the measurements.

In 1963, fifteen years before most other people started thinking about this stuff, a short paper was published in Science that, in just four pages, managed to lay out how and why we need to consider the scale of our measurements when designing an experiment to measure oceanographic phenomena. It was written by Henry Stommel, which is not at all surprising if you know who he was. For those who don’t, Stommel was one of the original badass physical oceanographers. The kind of guy who could sit down with a pen and paper and demonstrate why the upper ocean circulates the way it is observed to. Or correctly derive the circulation of the deep ocean before it was observed. No big deal. But thanks to his 1963 paper, titled “Varieties of Oceanographic Experience,” he is cited far and wide to this day as one of the first scientists to explicitly consider the importance of scale to experimental design.

To illustrate why scale is important, take one of Stommel’s examples: say we want to measure changes in the height of the sea surface from month to month. At first glance, it would appear that only 12 measurements are necessary: go out once a month for a year, and you’re set. At second glance, however, this is ridiculous on several levels. The ocean has tides, for one. Measuring sea level once a month would give you a near-random sample of different parts of the tidal cycle, and you wouldn’t be able to detect any long-term trends. There are also shorter- and longer-term fluctuations: regular wind-driven waves, and sea-level deviations due to large oceanic eddies. To resolve sea-level changes from month to month, you will actually need to measure it every hour or so. Do it less frequently, and you will get results that are inconclusive or just plain wrong.

Choosing the right scale of measurement for your question of interest is very important. It is also not trivial, especially when the measurement is not as straightforward as the water level on a ruler nailed to the dock. Even today some people are prone to testing hypotheses using data collected at a scale inappropriate to the question. If Stommel had stopped with this message, the paper might still have found a fair number of readers. But the coup de grace was the graphic he came up with to make the message explicit:

Figure 1 from Stommel (1963).

Figure 1 from Stommel (1963).

It’s a three-dimensional surface, with time along the x-axis and distance along the y. Both are shown on a logarithmic scale, so that each tick mark is a factor of 10 larger than the one before it. Various phenomena that make the sea level go up and down are located on this surface based on their typical size and duration. The height of the surface at each point represents how much the sea level goes up or down—that is, how much energy or variability is concentrated at that space and time scale.

Gravity waves (aka wind waves, the normal ones that crash on the beach and make you seasick on boats) are typically several meters long and perturb the sea surface for a few seconds. They can therefore be placed in the lower left corner. Tides happen every 12-13 hours and affect the entire ocean basin, so they are located near the middle of the time axis, stretching from about a kilometer up to 10,000 km. And every ten thousand years or so, we hit an ice age that lowers the surface of the ocean everywhere about 100 meters, allowing humans to do things like cross the Bering Strait into North America. This figure is an elegant summary of all the different processes that perturb the sea surface, and of the spatio-temporal scales at which they all take place.

This kind of diagram (now known as a Stommel diagram) has found its way over the years into all kinds of different contexts. One direct descendent near and dear to my own heart is the one drawn up by Haury et al. in 1978. It is along similar lines, but shows variability in zooplankton abundance, not sea level. The possibilities for these diagrams are nearly endless, and not limited to the ocean, or even the natural sciences. Any system that has stuff going on over short and long distances and time spans can be clarified by sketching up a Stommel diagram. Drawing a picture like this can help make it clear how to approach your research question, and will hopefully help you avoid screwing it up by choosing the wrong scale of measurements. The hope, as Stommel put it in the last line of the paper, is to “look forward to a time when theory and observation will at last advance together in a more intimately related way.” Amen.

Stommel, H. (1963). Varieties of Oceanographic Experience: The ocean can be investigated as a hydrodynamical phenomenon as well as explored geographically Science, 139 (3555), 572-576 DOI: 10.1126/science.139.3555.572

2010/05/16

Filed under: Research Blogging — Tags: , , — Sam @ 9:50 pm
Older Posts »

Powered by WordPress