It works like this. Say you want to test the effectiveness of a new screening method. You recruit a large group of women and you split them into two groups. One group gets the screening regularly. The other, the control group, doesn't get the screening. Then you follow them over time and track how many women in both groups died of cancer. That's a pretty basic scientific method. It's also something that prompts big questions about the treatment of women in the control group.

The people conducting the study say women in the control group were told they could seek out screening on their own. Critics argue that point (and the way the study worked) wasn't clearly explained, and that those alterante options weren't as available to the women as researchers imply. The majority of the women participating in the studies are poor and have very little formal education.

There are some important differences between this and the infamous Tuskegee syphilis experiment. In that case, researchers identified men with syphilis and neither told them about their disease nor offered them treatment — just monitored the deadly disease's progress. Here, there's clearly an attempt (however poorly executed) at being open with the women about what the study is and what is being done. And nobody is intentionally trying to prevent sick women from being treated. But the study definitely exists in an uncomfortable space and could reasonably be called unethical. Is it ever okay to not screen people for a disease that are pretty sure some of them have? If not, how do we figure out whether potentially life-saving screening methods are actually useful? How do you do statistics ethically when people are the numbers? I don't have good answers for these questions.

Here's what we do know. There are 76,000 women enrolled in the National Cancer Institute study, and another 31,000 in The Gates Foundation study. So far, they've been tracked for 12 years and at least 79 of the women in the control groups have died of cervical cancer.

Read Bob Ortega's full story at The Arizona Republic

Last year, I wrote a piece for BoingBoing about destructive storm systems and why it's so difficult to say, in concise sound-bite form, what relationship that destruction has to climate change. In that case, we were talking about tornadoes. But over the last couple of days, lots of people have been having roughly the same conversations about Hurricane Sandy. When the clouds have passed and everybody is done sleeping in airports, people are going to want answers. Was this an unavoidable act of nature? Or was this something caused directly by changes to Earth's climate that have happened because we burn fossil fuels which increase the concentration of carbon dioxide in the atmosphere?

Again, there's not an easy answer. And, again, part of the problem here is that we're expecting science to operate on the scale of American media news cycles, which doesn't really work. We want to talk about this while the storm is raging or, barring that, at least immediately afterwards. But scientists aren't really going to have anything particularly deep to say about this specific storm for months, if not years. During that time, data will be analyzed and compared, and other events will happen, and that's really the stuff that we need in order to say much of anything other than, "We don't know for certain." In some ways, expecting anything else means forcing scientists to speculate and extrapolate in ways they aren't usually comfortable with and that aren't a terribly great way to understand the big picture.

But there's also something new, that I kind of didn't really think about when I was writing that post on the tornadoes. The answer to these questions also really depends on the motivations behind why you asked, and what it is that you really want to know.

First off, you should know that this kind of extreme (and extremely weird) storm system happening in fall or winter is a trend that some scientists had already been predicting. Those predictions stem from the steep reduction in quantities of sea ice in the North Atlantic and what we know (and think we know) about how that change affects climate patterns and storm formation as a whole.

Remember the times that we've talked about how climate change can, seemingly paradoxically, lead to heavier snowfall in winter? This is connected to that. Here's how Kate Spinner with The Herald Tribune explained it:

A big bubble of high pressure, with sinking air that moves clockwise, is interrupting the typical steering patterns in the atmosphere. That high pressure creates a blockage, backing up the jet stream so that it bends south, eventually looping north again, instead of flowing toward the east as usual.

The blocking pattern, centered just south of Greenland, will significantly slow the eastward-moving cold front once it reaches the coast. And it will steer Sandy into the U.S. rather than allowing it to turn east.

Blocking events are the force behind a lot of crazy weather anomalies, not just hurricanes. And there's evidence suggesting that, as the ice in the Arctic melts, the frequency and/or intensity of the blocking events may be increasing. The Climate Crocks blog did a nice interview about this a few months ago with Jennifer Francis, who studies marine and coastal sciences at Rutgers.

There's more on this from Francis, and other scientists, at Andy Revkin's DotEarth blog.

Another thing worth taking into account: Weather is a lot more complicated than you think it is. If it rains today — or if it doesn't rain — there are lots of different, interacting factors that influenced that outcome. A good way to think about it is like a plane crash. It is very, very rare for a plane crash to be caused by a single mistake. Instead, when you're reading the final report, you find that lots of things have to go wrong all at the same time. Even then, you still might not get an accident if the mix of mistakes that happen don't interact with each other in such a way as to make them all worse than the sum of their parts.

Plane crashes are complicated. And so is weather. That matters, because it means that Hurricane Sandy could be both a completely natural occurrence and a product of climate change. Simultaneously. Some of the factors that caused this storm might be nature-made. Others might be man-made. And teasing apart which factors were responsible for which aspect of the storm's damage is incredibly hard.

Greg Laden, an anthropologist who does some very good blogging on climate science, had a lot to say on this topic — particularly, the fact that even though we can't say "Hurricane Sandy was caused solely by climate change", we can say that climate change is probably affecting several factors that probably influence the development, growth, and movement of hurricanes.

It is often said that storms are going to happen anyway, but global warming ramps up the probability, which is akin to saying that there is always going to be variation in temperature or some other weather related factor but global warming raises the baseline. That’s true. But the corollary to that is NOT that you can’t link climate change to a given storm. All storms are weather, all weather is the immediate manifestation of climate, climate change is about climate. Before we started talking about global warming, storms were caused by … things. Climate things. Did we ever say, back in the 1950s when a hurricane hit Florida, “Oh, ya, that was some hurricane, but the thing is, you can’t really attribute a given hurricane to the Intertropical Convergence Zone’s relationship to warm Mid Atlantic currents. The former is a weather event and the latter is a climate system.” Why did we not ever say that? Because it would have been irrelevant, even dumb.

The truth is, we experience more Atlantic severe storms because of global warming, though we are still working out the details of which features of which kinds of storms are affected most. Beyond this, it may well also be possible that something I hinted at above is true: We may be experiencing kinds of storms today that were very rare in recent centuries, because of global warming.

Adam Frank at NPR also wrote a good post on this subject. In it, he explains another issue that muddies the waters. When we say that weather is complicated and that a storm is caused by the interaction of lots of different factors, what we are really saying is that weather is a system. Just like climate is a system. Currently, there are some systems that science understands better than others. Hurricanes are, unfortunately, pretty far down on the list.

There is a hierarchy of weather events which scientists feel they understand well enough for establishing climate change links. Global temperature rises and extreme heat rank high on that list, but Hurricanes rank low. As the IPCC special report on extreme events put it "There is low confidence in any observed long-term (i.e., 40 years or more) increases in tropical cyclone activity (i.e., intensity, frequency, duration), after accounting for past changes in observing capabilities."

The reasons for "low confidence" are manifold. Some part of the caution comes from the complexity of the problem, and some part comes from the lack of good data before the satellite era (about 1970). Thus, many climate scientists will not want to go out on a limb for hurricanes. They just don't have the tools to make strong inferences.

This is not to say progress isn't being made. One thing that does seem clear is that warmer oceans (a la global warming) mean more evaporation, and that likely leads to storms with more and more dangerous rainfall of the kind we saw with Hurricane Irene last year. In addition, a paper published just last month, used records of storm surges going back to 1923 as a measure of hurricane activity. A strong correlation between warm years and strong hurricanes was seen. Thus if you warm the planet, you can expect more dangerous storms.

Basically, we know that the effects of climate change probably has an impact on factors that cause massively destructive storms — even if we don't know exactly how much of an impact; even if we can't really use that information to exactly predict what's going to happen with massive storms in the future; and even if we can't tell you whether Sandy, specifically, was caused by climate change.

So, really, the answer to the question, "What is the relationship between Hurricane Sandy and climate change", depends primarily on why you're asking the question.

If you're just kind of curious and/or looking for something to blame, we don't have great answers on that yet. I'm sorry. Nobody is really going to be able to tell you one way or the other.

But if you're using that question as a proxy to really ask, "Is climate change real and do I have to care about it?", well, good news! We have enough information to answer your question. And the answer is, emphatically, yes.

Read More:
Besides the links I included in the story, I want to point you towards a couple more Hurricane Sandy-related things:
• NOAA's Storm Central has all the maps, satellite images, and projections of Sandy that a concerned citizen (or giant nerd) could want
• The director of the National Center for Disaster Preparedness would like you to know that we are seriously, seriously NOT prepared for big disasters
• Atlantic City is totally flooded
• Marketplace Tech Report has a really fascinating piece on the future of weather forecasting
• If you're in Sandy's path and aren't really clear what to do with your pets, read this
• The NASA Satellite video will haunt your nightmares
• Meanwhile, the news that the satellites we rely on for forecasts of hurricanes are aging rapidly (and there aren't great plans to replace them) will create your nightmares
• Use this handy slider to compare Hurricane Irene and Hurricane Sandy

]]> http://boingboing.net/2012/10/29/did-climate-change-cause-hurri.html/feed 36 http://boingboing.net/2012/08/16/cow-week-welsh-cattle-hate-do.html http://boingboing.net/2012/08/16/cow-week-welsh-cattle-hate-do.html#comments Thu, 16 Aug 2012 13:01:11 +0000 Maggie Koerth-Baker http://boingboing.net/?p=176732

Editorial note — Cow Week is a tongue-in-cheek look at risk analysis and why we fear the things we fear. It is inspired by the Discovery Channel's Shark Week, the popularity of which is largely driven by the public's fascination with and fear of sharks. Turns out, cows kill more people every year than sharks do. Each day, I will post about a cow-related death, and add to it some information about the bigger picture.

In 2009 and again in 2011, Welsh cattle joined forces to surround and kill women who were out walking their dogs on the outskirts of Cardiff. Apparently, cows really do not like it when you bring a dog around them. So, FYI on that. This story is from a survivor of the 2009 attack:

"I was slightly ahead when I saw the cows, they looked up and seemed curious and started to move towards us both," she said.

"They were coming in a semi-circular formation so I was heading towards the end so I could get away from them."

The next time she looked around Ms Hinchey appeared to be surrounded by the cows, she said.

One of things that made me post this particular story was the disconnect between the idealized image of a field full of docile cattle, happily grazing on grass ... and the truly creepy and threatening image presented in the quote above. I mean, it's like something from a Stephen King novel. Of course, I also don't have a lot of experience with cows in my personal, daily life. So my idealized image isn't based so much on what I think cows are actually like, but what I want them to be like. That's what really makes this image creepy for me. The cows are behaving ways that I don't imagine cows should behave.

People who spend their careers thinking critically about risk say disconnects like this can play a role in determining what we fear. Craig Cormick is the manager of public awareness and community engagement for the Australian Department of Innovation, Industry, Science and Research. Part of his job is understanding what technologies the public finds really risky and why. Last year, he spoke at the University of Michigan's Risk Science Center. The discussion touched on the way people in Western countries often assign more risk to food issues—and obsess about the possible risks of food more—than they do with other areas of their lives.

... we’ve never lived at a time and society when people are so far divorced from agricultural production, most people never get to see a farm, they have no idea how livestock is produced, no idea how food is produced and have a perception that it should all be natural, and it should be great and that would – ideally that would be marvelous but reality is that’s not how our food is produced. large agricultural production is the only way to feed the numbers of people we have and so there’s a romantic idealized view of what is good natural food as opposed to food that’s not and so when people perceive that you are tinkering with the food yes they have outrage and they have rage about this and when you have rage and fear together it’s a very-very dominant cocktail of emotions it’s very hard to turn around, very hard to turn around.

READ MORE
Read more about the two cow-related deaths near Cardiff.
Read a transcript of Craig Cormick's discussion
Watch the webcast of the discussion

READ ALL OF COW WEEK
• Cow Kills Irish Pensioner
• Bull Kills Man, Follows Him Until Certain He Is Dead
• Angry cows vs. angry mothers

Paul Douglas is a Minneapolis/St.Paul meteorologist. Meteorologists don't study the same things as climate scientists—remember, weather and climate are different things—but Douglas is a meteorologist who has taken the time to look at research published by climate scientists and listen to their expertise. Combined with the patterns he's seen in weather, that information has led Douglas to accept that climate change is real, and that it's something we need to be addressing.

Paul Douglas is also a conservative. In a recent guest blog post on Climate Progress, he explains why climate isn't (or, anyway, shouldn't be) a matter of political identity. We'll get back to that, but first I want to call attention to a really great analogy that Douglas uses to explain weather, climate, and the relationship between the two.

You can’t point to any one weather extreme and say “that’s climate change”. But a warmer atmosphere loads the dice, increasing the potential for historic spikes in temperature and more frequent and bizarre weather extremes. You can’t prove that any one of Barry Bond’s 762 home runs was sparked by (alleged) steroid use. But it did increase his “base state,” raising the overall odds of hitting a home run.

Mr. Douglas, I'm going to be stealing that analogy. (Don't worry, I credit!)

A few weeks ago, I linked you to the introduction from my new book, Before the Lights Go Out, where I argue that there are reasons for people to care about energy, even if they don't believe in climate change—and that we need to use those points of overlap to start making energy changes that everyone can agree on, even if we all don't agree on why we're changing.

But there's another, related, idea, which Paul Douglas' essay gets right to the heart of. Just like there's more than one reason to care about energy, there's also more than one way to care about climate. Concern for the environment—and for the impact changes to the environment could have on us—is not a concept that can only be expressed in the terms of lefty environmentalism.

You and I can think about the environment in very different ways. We can have very different identities, and disagree on lots of cultural and political issues. All of those things can be true—and, yet, we can still come to the same, basic conclusions about climate, risk, and what must be done. Here's Douglas' perspective:

I’m a Christian, and I can’t understand how people who profess to love and follow God roll their eyes when the subject of climate change comes up. Actions have consequences. Were we really put here to plunder the Earth, no questions asked? Isn’t that the definition of greed? In the Bible, Luke 16:2 says, “Man has been appointed as a steward for the management of God’s property, and ultimately he will give account for his stewardship.” Future generations will hold us responsible for today’s decisions.

This concept—Creation Care—is something that I've summed up as, "Your heavenly father wants you clean up after yourself." It's not a message that is going to make sense to everybody. But it's an important message, nonetheless, because it has the potential to reach people who might not otherwise see a place for themselves at this table.

Too often, both liberals and conservatives approach climate change as something that is tangled up in a lot of lifestyle, political, and cultural choices it has nothing to do with. Those assumptions lead the right to feel like they can't accept the reality of climate change without rejecting every other part of their identities and belief systems. Those same assumptions lead the left to spend way too much time preaching to choir—while being confused about why people outside the congregation aren't responding to their message.

That's why essays like Douglas' are so important. We look at the world in different ways. We come by our values for different reasons. But even though we might take different paths, we can come to some of the same places. Let's respect that. And let's have those conversations. Climate change is about facts, not ideologies. It's about risks that affect everyone. We need to do a better job of discussing climate change in a way that makes this clear. And that means reaching out to people with language and perspectives that they can identify with.

Read Paul Douglas' full post on Climate Progress.

Read more about energy, climate, and what we can do to make the message of climate science more universal in my book, Before the Lights Go Out.

Here are two myths you need to let go of:

The solution to high gas prices is more oil.

Climate change is something that happens to polar bears and people from Kiribati.

The truth is that fossil fuels are extremely useful and valuable. And, by their very nature, the supplies are limited. Likewise, climate change isn't just something that's going happen—it's already taking place, and you can see the effects in your own backyard.

Too often, I think, we talk about the risks of fossil fuel dependence and climate change in ways that make them seem abstract to the very people who use the most fossil fuels and create the most greenhouse gases. That's a problem. There are lots of reasons to care about energy. But I think that fossil fuel limits and climate change are the most pressing reasons. And I think it's incredibly important to discuss those very real risks in a way that actually feels very real.

This isn't about morality, or lifestyle choices, or maintaining populations of cute, fuzzy animals. (Or, rather, it's not just about those things.) Instead, we have to consider what will happen to us and how much money we will have to spend if we choose to do nothing to change the way we make and use energy.

Over at Scientific American, you can read an excerpt from my upcoming book, Before the Lights Go Out. In it, you'll read about the energy risks hanging over the Kansas City metro area—a place that, in many ways, resembles the places and lifestyles shared by a majority of Americans. You've probably never been to Merriam, Kansas. But you can look at Merriam and see what could happen in your hometown.

Merriam isn't a small town. There's nothing really recognizable as a small town central business district. Instead, Merriam's stores and offices are mostly concentrated along two major thoroughfares—Shawnee Mission Parkway and Johnson Drive. These wide, multilane roads are dotted with clusters of shopping centers and big box stores, like necklaces strung with fat pearls. The municipal building and the police station are a couple of nondescript offices that sit off the frontage of Shawnee Mission Parkway, on a ridge overlooking the Interstate. Nothing about that says, "Classic Americana."

Yet Merriam isn't a suburb, either—or an urban city. It's too dense to be the first and not dense enough to be the latter. Merriam has a mixture of house styles. Drive down one street, and you'll see a 1930s bungalow standing shoulder to shoulder with a spare little 1950s Cape Cod. Next to that, there's a 1980s split-level with windows on the front and the back but none on the sides. More than three generations of the American Dream are living here.

It's ironic that Merriam doesn't really fit any of the classic American paradigms, because, quite frankly, most of us have already left those paradigms behind. We talk about this country as if it's full of neatly defined small towns, big cities, and tidy suburbs. In reality, the places where we live are lot mushier than that. Merriam isn't the exception. Merriam is the rule.

Read the rest of this excerpt from Before the Lights Go Out at Scientific American.

Eric Kostelich is one of the researchers who is trying to change that, by approaching the problem of brain cancer  from a new angle. Kostelich is a mathematician. In particular, he's interested in how we can use math to better predict the behavior of complex and chaotic systems. Right now, this mostly means that he studies the weather. In fact, he's part of a team that developed a new algorithm for weather prediction, called the Local Ensemble Transform Kalman Filter. But Kostelich thinks that the LETKF could have applications outside the nightly news.

In a recent study, published December 21 in Biology Direct, he joined forces with cancer researchers, to see whether the statistical methods that make chaotic weather patterns more predictable could do the same thing for chaotic behavior in cancer cells. The results are promising. A couple of weeks ago, I spoke to Kostelich to find out more about the history of forecasting uncertainty, how algorithms like LETKF work, and what we might learn if we apply these systems to cancer. 

 Maggie Koerth-Baker: When you set out to apply the methods used to forecast the weather to cancer, why did you choose brain cancer? 

Eric Kostelich: Partly it's because I had a family member with a brain tumor. The scientist in me got interested because these really are terrible tumors. Getting to know a number of clinicians, it seemed to me that new ideas, anything that could help people live a little better would be welcome.

From a math standpoint these tumors are interesting in that they don't metastisize on the central nervous system. For instance, melanoma is a process that starts in a mole on surface of skin but spreads to the liver and other vital organs. You have to look at it throughout the entire body, and that's a daunting task. But a brain tumor tends to stay in the brain. That keeps it more mathematically attractive for our initial studies. And the brain also has some interesting geometry, with folds and so on, and with different functions in different places. The liver and kidneys are relatively heterogenous, but where a tumor is in the brain really affects what symptoms you see. My partner in this thought it would be really useful to be able to say, for an individual patient, based on studies of patients with similar tumors, there's a 60% chance that the tumor is more likely to grow in one direction, rather than another. They might be able to give a dose of radiation therapy in that one direction.

If you could help someone live a couple extra months, that would be a big advance for this type of cancer. Patients usually only live 12-14 months. Ted Kennedy suffered from this and his experience was typical. He died about 14 months from diagnosis. We've made a lot of strides in treating other kinds of cancer, but our approach to brain cancer hasn't changed much.

MKB: Why are brain cancers so difficult to treat?

EK: The brain isn't easily accessible. Between the brain and the outside, you've got the skull and accessing it involves drilling a hole. Plus, it's your brain. With a breast cancer you can remove a breast and a margin of tissue and still live. You can live without an arm or a breast. But you can't just remove a big chunk of the brain without crippling the patient. One of the objectives of treatment is to not make the patient's neurological condition any worse than tumor already has made it.

MKB: In this study, you used an algorithm called the Local Ensemble Transform Kalman Filter. What does all that mean? What does this algorithm do when applied to weather?

EK: Meteorologists never make just one prediction. They make many. In any of these models, you always have a grid of points on which you're trying to approximate behavior. To run that model on the computer you put in models for all the grid points, but you can't actually measure them all. What I mean is, you can measure the temperature and barimetric pressure and so forth at one point on the ground and plug it in. But the model also has another grid point above that one, high up in the air, and more points on up into the atmosphere. You can't measure the data at all of those. Another example, if you look at high impact weather like a major hurricane, they get started over remote tropical ocean where it's hard to get any measurements at all. Satellites are a big help, but there's always uncertainty. And because there's uncertainty, you get the idea of an ensemble forecast.

Ensemble forecasts go back to Edward Lorentz. [He's a pioneer in chaos theory—MKB] You run a number of forecasts with slightly different realizations of the numbers at all the different grid points. Forecast uncertainties depend not only on time, but also on space. You might have quite a bit of uncertainty about where a hurricane will end up in five days. But quite a bit of certainty about Phoenix being sunny for five days in June. If you look at many forecasts, you can get a handle of both where your uncertainties are and what magnitude they are. 

MKB: What makes it different from other algorithms used for making predictions about uncertain systems?

EK: You're trying to get mathematical combinations of forecasts that best fit observed conditions. Weather models are updated every six hours. Your uncertainties tend to grow with time. You can forecast tomorrow, but a week from now is more iffy. So the models have to be updated on a regular basis. By doing things locally you get better computational efficiency. We thought, "Well, if we can do this for the weather ..."

Our system outperforms the Weather Service's system by quite a bit. The Brazilian government is actually using our approach for their next gen weather prediction systems. What we try to exploit in our approach is, in a mathematical sense, the uncertainties in a forecast. Uncertainties tend to lie in certain directions. All these mathematical models are partial differential equations in dynamical systems. In math, we have a concept we call a phase space. That's "phase" as in phases of the moon. It's a math abstraction and it's where we think of all the math action taking place. In the case of the weather, it appears that the uncertainties in weather models lie primarily in certain directions of the abstract phase space. Our approach takes advantage of that in a clever way. Because we know where the uncertainties are more likely to be, we can pay more attention to those places.

MKB: The ideas here—combining new observations with prior forecasts, and paying the most attention to where you know the most uncertainty is likely to be—these are things that can seem like a bit of an obvious thing to laypeople. How new are these ideas really? Is there something that makes this more special and surprising than it seems on the surface?

EK: The notion of data assimilation, combining observations and prior forecasts, has been an integral part of weather forecasting for several decades now. Computers were used to do this on a regular basis, starting around the mid 1960s. By that point they were powerful enough that you could build a realistic enough grid to say something about the weather. You and I take weather forecasting for granted. But the reality is that this is one of the great triumphs of modern science.

Think about Hurricane Katrina. It was known three days in advance that this hurricane would come close to New Orleans. Thirty years ago, we couldn't have been able to tell you that. Today we can evacuate a few hundred miles of coastline instead of telling the entire Gulf region, "This is coming and we don't know where." That's a huge advance.

There's some really interesting history on this. The term forecast goes back to Robert Fitzroy, he was the captain of the Beagle, the ship that Darwin traveled on to the Americas in the 1830s and 40s. He was very intersted in questions of weather and storms because he'd lost several crewmen in a gale. The ship basically tipped over. He saved the ship, but lost several lives. When he was back in England he was appointed meterological statist. He was appointed to keep all the records for the crown. So, by the late 1850s, the telegraph had come in and Fitzroy had people at all the ports telegraph in the weather information to him. He looked at patterns of temperature and pressure rising and falling and was able to combine those patterns with new observations and say, "There's a storm coming in. Stay at port." This was the beginning of forecasting. Shipwrecks fell by half in a few years. 

MKB: So where do you make the connection between all of this, and something like brain cancer? 

EK: No forecast is perfect. In Fitzroy's day, if the storm didn't materialize, then there were complaints. People lost money by staying in port and Fitzroy actually got into political trouble. They stopped doing forecasting for a while until a fisherman's lobby brought him and his methods back.

Now fast forward 150 years. Say you have a brain tumor. What you'd like to know is, "What is going to happen to me?" But doctors are really very much where Fitzroy was 150 years ago before he invented forecasting. It's like, stick your finger up in the air and make a guess. Nowadays, for weather, we have mathematical models that, combined with satellites, can predict hurricanes before they materialize. We can tell several days in advance where the storm will go. Doctors can't do anything near that with cancer. All they can do is look at a scan and some bloodwork and say we'll see you in a couple months. But we're on the verge of being able to gather lots more information about what's going on in the human body. 

What we'd like to do with this oncoming data deluge, what we're trying to do, is devise math tools that will help clinicians make sense of all the new data and associate probabilties with that data. Then they can tell people something more useful. Not perfect. But useful.

MKB: I think most lay people operate under the assumption that weather prediction like this isn't very accurate, beyond a day or so ahead of time. You're wanting to do cancer prediction over 60-day cycles. Why would that kind of time frame be reliably accurate enough to matter?  

EK: Weather service looks hard and long at that question. One way in which you can assess the goodness or lack thereof of the forecast is to say, "I'm going to predict the weather two, three, four days out. Then you go out and measure on those days and compare the reality to the forecast. The bigger the difference, the worse the forecast. By that measure, forecasts today made 3-4 days out are as accurate as a 36-hour forecast was 30 years ago. So the weather forecasts are more accurate than people give them credit for. It's just that when you blow it that's what people remember.

A famous case was Veterans' Day a few years ago in Washington DC.  The Weather Service forecasted a dusting of snow, but there ended up being something like 14 inches. Basically, the snow was 100 miles off from where they thought it would be. A 100-mile error, 24 hours out, that isn't so bad really in a global perspective, but the local impact is very great. People remember that. On the flip side, though, in 1900 a hurricane hit Galveston, Texas. The Cubans had telegraphed DC to tell us that there was a storm heading West. But we blew the Cubans off and there were no warnings for Galveston. 8000 people drowned. We still have bad hurricanes today, but we don't have 8000 drowning because they don't know it's coming. In that respect, we're doing pretty good. But it's still not perfect.

Our basic approach here, in thinking about cancer in general and brain cancer specifically, is could we adapt the accuracy of a 3-4 day weather forecast for a month or two or three. So that the models show what is likely to happen to an individual patient's tumor. It's a fair question about whether we can move that timeline up. But it works because of differences in what you're forecasting. For weather, the uncertainties tend to double every couple of days. As far as we understand cancer, the uncertainties you have in the state of a tumor, they don't double every two days. They double over a month or two. It's a different mathematical beast than the weather. A couple of months for cancer is like a couple of days for the weather. Now, they can vary quite a bit in how aggressive they are. There are cases in the literature where in some patients the tumors double in size in a couple weeks. But more typically the doubling times are on the order of a month or two.

On the other hand, forecasting cancer can be harder than forecasting the weather. In the case of the weather, air is a fluid, and there's a couple hundred years of physics that go into understanding how fluids move in laboratory conditions. If you're going to write a weather model there's no doubt what equations you start with. In the case of cancer, we don't know how glioblastoma cells really work very well. It's a much greater challenge to write that mathematical model because our understanding is much less complete. We're trying to take into account that whatever model we write down is likely to be off. Possibly by quite a bit. But the question is, "Can data assimiliation system make a clinically useful forecast?" It doesn't have to be perfect to be useful.

MKB: The brain cancers you're looking aren't really treatable. Like you say, most people die from them within 14 months. What's the benefit, then, of having a more accurate prediction of how they will spread? If you still can't treat the cancer, what does it help to know how it will behave? 

EK:  My understanding, and from personal experience with a family member, is that you're right, this isn't going to cure cancer in general or glioblastoma specifically. But one of the real goals of treatment is to help patients live as well as possible for as long as possible. The age of highest incident for the type of brain cancer we studied is between 40 and 65. If this result allows you to live two months longer than you otherwise would maybe that makes the difference between seeing your daughter get married or not. We can't prevent the inevitable, but we might help them live better or longer. If we can develop good enough mathematical models and be able to tell patients that going through another round of chemo isn't likely to help, then they can decide to spend that time with family instead of in the hospital. That's beneficial in it's own way.

Eric Kostelich's research is available to read, for free, online. 

Right now, I'm reading a book about why catastrophic technological failures happen and what, if anything, we can actually do about them. It's called Normal Accidents by Charles Perrow, a Yale sociologist.

I've not finished this book yet, but I've gotten far enough into it that I think I get Perrow's basic thesis. (People with more Perrow-reading experience, feel free to correct me, here.) Essentially, it's this: When there is inherent risk in using a technology, we try to build systems that take into account obvious, single-point failures and prevent them. The more single-point failures we try to prevent through system design, however, the more complex the systems become. Eventually, you have a system where the interactions between different fail-safes can, ironically, cause bigger failures that are harder to predict, and harder to spot as they're happening. Because of this, we have to make our decisions about technology from the position that we can never, truly, make technology risk-free.

I couldn't help think of Charles Perrow this morning, while reading Popular Mechanics' gripping account of what really happened on Air France 447, the jetliner that plunged into the Atlantic Ocean in the summer of 2009.

As writer Jeff Wise works his way through the transcript of the doomed plane's cockpit voice recorder, what we see, on the surface, looks like human error. Dumb pilots. But there's more going on than that. That's one of the other things I'm picking up from Perrow. What we call human error is often a mixture of simple mistakes, and the confusion inherent in working with complex systems.

Let me excerpt a couple of key parts of the Popular Mechanics piece. You really need to read the full thing, though. Be prepared to feel tense. This story will get your heart rate up, even though (and possibly because) you know the conclusion.

We now understand that, indeed, AF447 passed into clouds associated with a large system of thunderstorms, its speed sensors became iced over, and the autopilot disengaged. In the ensuing confusion, the pilots lost control of the airplane because they reacted incorrectly to the loss of instrumentation and then seemed unable to comprehend the nature of the problems they had caused. Neither weather nor malfunction doomed AF447, nor a complex chain of error, but a simple but persistent mistake on the part of one of the pilots.

Human judgments, of course, are never made in a vacuum. Pilots are part of a complex system that can either increase or reduce the probability that they will make a mistake. After this accident, the million-dollar question is whether training, instrumentation, and cockpit procedures can be modified all around the world so that no one will ever make this mistake again—or whether the inclusion of the human element will always entail the possibility of a catastrophic outcome. After all, the men who crashed AF447 were three highly trained pilots flying for one of the most prestigious fleets in the world. If they could fly a perfectly good plane into the ocean, then what airline could plausibly say, "Our pilots would never do that"?

One of the pilots seems to have kept the nose of the plane up throughout the growing disaster, making this choice over and over, even though it was the worst possible thing he could have done. At the same time, everyone in the cockpit seems to have completely ignored an alarm system that was, explicitly, telling them that the plane was stalling.

Why would they do that? As Wise points out, this is the kind of mistake highly trained pilots shouldn't make. But they did it. And they seem to have done it because of what they knew, and thought they knew, about the plane's complex safety systems. Take that stall alarm, for instance. Turns out, there's a surprisingly logical reason why someone might ignore that alarm.

Still, the pilots continue to ignore it, and the reason may be that they believe it is impossible for them to stall the airplane. It's not an entirely unreasonable idea: The Airbus is a fly-by-wire plane; the control inputs are not fed directly to the control surfaces, but to a computer, which then in turn commands actuators that move the ailerons, rudder, elevator, and flaps. The vast majority of the time, the computer operates within what's known as normal law, which means that the computer will not enact any control movements that would cause the plane to leave its flight envelope. "You can't stall the airplane in normal law," says Godfrey Camilleri, a flight instructor who teaches Airbus 330 systems to US Airways pilots.

But once the computer lost its airspeed data, it disconnected the autopilot and switched from normal law to "alternate law," a regime with far fewer restrictions on what a pilot can do. "Once you're in alternate law, you can stall the airplane," Camilleri says. It's quite possible that Bonin had never flown an airplane in alternate law, or understood its lack of restrictions. According to Camilleri, not one of US Airway's 17 Airbus 330s has ever been in alternate law. Therefore, Bonin may have assumed that the stall warning was spurious because he didn't realize that the plane could remove its own restrictions against stalling and, indeed, had done so.

That, I think, is where Charles Perrow and Air France 447 cross paths. It follows closely with a concept that Perrow calls "incomprehensibility." Basically, the people involved in an accident like this often can't figure out fast enough what is happening. That's because, in high-stress situations, the brain reverts to well-trod models that help you understand your world. You think about the stuff you've practiced 1000 times. You think about what you've been told will happen, if x happens.

But what happens if what's actually going on doesn't mesh with your training? Then the brain finds ways to make it mesh. Those rational explanations might make a whole lot of sense to you, in the moment. But they will lead you to make mistakes that exacerbate an already growing problem.

This is not comforting stuff.

Perrow doesn't tell us that we can figure out how to design a system that never becomes incomprehensible. There is no happy ending. We can design better systems, systems that take the way the brain works into account. We can make systems safer, to a point. But we cannot make a safe system. There is no such thing as a plane that will never crash. There is no such thing as a pilot who will always know the right thing to do.

Instead, Perrow's book is more about how we make decisions regarding risky technologies. Which high-risk technologies are we comfortable using and in what contexts? How do we decide whether the benefit outweighs the risk?

We must have these conversations. We cannot have these conversations if we're clinging to the position that anything less than 100% safety is unacceptable. We cannot have these conversations if we're clinging to the position that good governance and good engineering can create a risk-free world, where accidents only happen to idiots.

I used to believe both those myths. I want to believe them still. Increasingly, I can't. Looking at technological safety in terms of absolutes is child's view of the world. What Perrow is really saying is that it's time for us to grow up.

Some German researchers are trying to make that process a little easier, using a computer model and a whole lot of probability power. They published a paper about this method recently, using their system to estimate an 80% likelihood that the 2010 Russian heatwave was the result of climate change. Wired's Brandon Keim explains how the system works:

The new method, described by Rahmstorf and Potsdam geophysicist Dim Coumou in an Oct. 25 Proceedings of the National Academy of Sciences study, relies on a computational approach called Monte Carlo modeling. Named for that city’s famous casinos, it’s a tool for investigating tricky, probabilistic processes involving both defined and random influences: Make a model, run it enough times, and trends emerge.

“If you roll dice only once, it doesn’t tell you anything about probabilities,” said Rahmstorf. “Roll them 100,000 times, and afterwards I can say, on average, how many times I’ll roll a six.”

Rahmstorf and Comou’s “dice” were a simulation made from a century of average July temperatures in Moscow. These provided a baseline temperature trend. Parameters for random variability came from the extent to which each individual July was warmer or cooler than usual.

After running the simulation 100,000 times, “we could see how many times we got an extreme temperature like the one in 2010,” said Rahmstorf. After that, the researchers ran a simulation that didn’t include the warming trend, then compared the results.

“For every five new records observed in the last few years, one would happen without climate change. An additional four happen with climate change,” said Rahmstorf. “There’s an 80 percent probability” that climate change produced the Russian heat wave.