Oceanic space is profoundly fascinating; informal writing demands scientific integrity.

If everyone continues tagging ten bloggers, we’ll run up hard against the ocean blogosphere’s carrying capacity PDQ. So I’ll try to ease our transition from an R- to K-type life history strategy—from a salp chain in heat to a motherly dogfish, if you will—by tagging fewer than ten. A smaller number of offspring, but hopefully of high quality and fit to survive. So here they are, along with the Blogger’s Prayer…May the cool people read this, Lord, and save us all from the trolls. Amen.

• Miriam, Dr. M, and Kevin Z. of Deep Sea News.
• Mark at Blogfish.
• John at Sky Truth. Don’t think he reads this blog, but man, what a badass.

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.

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.

“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.”

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.”

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

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.

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…

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…