Friday, January 30, 2015

How can science help us answer the question “what should we do about climate change?”

by Ben Brown-Steiner

“...there are known-knowns; there are things we know we know. We also know there are known-unknowns; that is to say we know there are some things we do not know. But there are also unknown-unknowns—the ones we don't know we don't know.” - Donald Rumsfeld

When we approach a scientific topic, be it climate science, cancer research, crop yields, or nuclear physics, we typically expect two distinct things. First, we expect the scientists in the field to have answers to our questions. What’s the weather going to be tomorrow? How do I stop my tomato plants from dying? Can we use nuclear energy to power our city? Second, we expect individual scientists to expand the general body of scientific knowledge by attempting to understand the unknown. We fund research hoping to shine a light in the many dark corners of the natural world that are not yet understood. Can we create better weather forecasts? Can we improve on current methods of tomato gardening/farming? Can we discover a safer way to use nuclear energy?

These two expectations, to know and to find, are examples of known-knowns and known-unknowns. We expect scientists to have a body of knowledge (known-knowns) as well as be capable of pushing the boundaries to expand this body of knowledge (known-unknowns). We look to science textbooks for answers, and fund research grants with clear expectations of improving our world. The unknown-unknowns are pesky and troublesome. Since we don’t know what they are, it’s difficult to make a plan to discover them. Often, we rely on luck to uncover these unknown-unknowns (I’m going to talk about unknown-unknowns in a future post).

However, science (in general) and scientists (in particular) are often approached with another question that the tools and methods of science are not designed to address: what should we do? What should we do to address climate change? What should we do to reverse the trend in cancer rates? What should we do about nuclear energy, tar sands, wind energy, habitat loss, ocean acidification, and infant mortality?

I want to point out here that I am talking about pure science. Applied science and engineering have real-world, practical components with budget, political, or regulatory limitations and expectations that direct "what should be done." But even a pure scientist, who is solely concerned with scientific discovery, does not always wear a pure science hat. Every scientist is also a citizen, with a personal belief or value system that is almost always partially independent of their scientific training.

This “should” question is the question of our time, and science, on its own, is utterly unable to address it. The following conceptual diagram, which traces back to Plato (I’ve changed Plato’s ‘values’ to beliefs) helps us understand why:

This figure makes a clear distinction between what is true and what we believe. By and large, most people strive to merge the two spheres, but often fail. Beliefs are not always true. For instance, nearly 90% of Americans who drive a car believe that they are better than average drivers [1]. By definition, this cannot be true; the average American is a better driver than 50% of Americans and a worse driver than 50% of Americans.

There may also be truths that we don’t believe. For instance, many of us are afraid of sharks and believe them to be really dangerous. However, more people are killed by horses, cows, bees, and deer than sharks in a given year [2]. This true fact, however, is very unlikely to alter many people's beliefs about the dangerous nature of sharks.

Where does science fit into this? Science, which is concerned exclusively with the observable world, is able to speak only to the truth circle. In addition, science is certain of some things and uncertain about others. We are certain (in the center of the truth circle) that CO2 is made up of an atom of carbon and two atoms of oxygen. We are highly certain (towards the center of the truth circle) that increased concentrations of CO2 in the Earth’s atmosphere increase the global temperature. We are uncertain (towards the boundary of the truth circle) exactly how increased CO2 concentrations will influence the atmosphere/ocean/ice/land/biological system.

And, according to Plato and the above diagram, knowledge, wisdom, and decisions for action exist at the overlap between truth and belief. For instance, the question “how much CO2 should we emit?” depends both on the truth (via science) and our beliefs about how much is acceptable (via a multitude of sources). Or the question “should I eat meat?” depends on the current scientific understanding of the state of meat production in the modern world and each person’s individual belief in what is right [3].

This is an important distinction because people frequently advocate a course of action and cite science, as if that is all that was needed to make a decision. What people often leave out when advocating a course of action is a statement about their particular belief system. There are numerous examples: climate change, renewable energy, hydrofracking, meat production, vaccines, abortion, and so on.

Much of the bitter debate, anger, and confusion related to these issues comes from this basic misunderstanding. When someone says, “we should do [this action] because of [this scientific conclusion]!”, what they are really saying is “we should do [this action] because of [this scientific conclusion] and because I believe in [this value system]!” A scientific conclusion cannot on its own address questions of “should.”

Future attractions:
I’ve written a lot about issues surrounding actual climate science, so for the following posts I will review many of the “known-knowns” of climate science. After that I will address some of the known-unknowns, and then I’ll discuss some of the potential unknown-unknowns.

[3]: Personally, I love meat, but I strive to make sure it is locally sourced and humanely treated. That’s not a scientific evaluation but a belief/value that I hold.

Friday, January 9, 2015

Finding a Signal in the Noise

by Ben Brown-Steiner

(Note: This post follows up on ideas presented in my previous post, and I highly recommend you read that post before this one).

Take a look at the following two graphs.

Screen Shot 2014-11-12 at 5.38.42 PM.png Screen Shot 2014-11-12 at 5.39.00 PM.png

They both cover the same years (1986 - 2007), and I’ve removed the vertical axis labels because that would (for the moment) ruin all the fun.

Before I give hints to what these two plots represent, can you venture any guesses? Is there a signal in either of these plots or is it just noise?

For a first pass I’d say they both are generally increasing, but not consistently. They both wiggle, although the one on the right wiggles more dramatically (higher variability). The left one seems to plateau and then drop off after 2004, while the right shows a large jump around 1998 and seems to plateau after that.

Now for a hint: both of these plots represent something which we suspect has changed or is changing over time, and we have some expectation that we’d be able to detect these changes by studying these graphs. Can you guess where these changes happened (either one year or a range of years)?

A second hint: in one of these graphs, a distinct change happened in 1998. In the other graph the changes have been gradual over time.

Alright. The left graph is the number of home runs hit by Barry Bonds each year throughout his career. It’s generally accepted that Bonds started taking steroids in 1998. The right graph is the average annual temperature anomaly (meaning the mean temperature from 1951 - 1980 has been removed) over the US, and it’s generally believed that the climate has been warming over these years.

And, almost maliciously, the graph of Bond’s home runs doesn’t show a clear jump after 1998 (when he started taking steroids) while the temperature plot does. While we could speculate that the US temperature spikes as a result of Bond’s steroid use, it’s better to look at the 1998 jump in temperatures as a result of the 1997/1998 El NiƱo event (which I’ll write about in a future post) and the plateau afterwards as some form of variability (see my previous post).

What can we say about the influence of steroids on Barry Bond’s home runs? We can confidently look at year-to-year changes and try to explain what we see because we would expect an athlete to improve every year, reach a peak, and then either decline or retire. We expect any changes to his body (i.e. steroids) to be reflected in the amount of home runs he makes in a year. We see that before he started taking steroids, his home run total was in a slight decline. We also see that after he started taking steroids, his home runs spiked. However, after 2001 his home runs dropped again. Perhaps this is because he stopped taking steroids, or maybe he was just getting old (I’m not really a baseball fan so don’t know much about Bond’s career).

[As a side note: steroids actually make an excellent climate change analogy. See this video from AtmosNews.]

What can we say about the temperature records and their fluctuations? Since this time period is over 20 years, and we aren’t really talking about climate until we’re looking at at least 20 years (see my previous post), we can’t really say much. The year-to-year fluctuations are so large that it’s hard to draw any strong conclusions. To get a better idea of the climate, let’s look at the full US temperature record (1880 - 2011):

Screen Shot 2014-11-12 at 5.58.42 PM.png

We can see more clearly now an increasing trend starting in the 1960s, but there’s still a lot of wiggles (or noise). One common method for reducing the noise level (also called smoothing) is to take a moving average. In the following figure, every yearly datapoint is the moving five-year average (we average the two previous years, the current year, and the two future years together) from the same data as the previous graph:

Screen Shot 2014-11-12 at 5.59.57 PM.png

Without the annual noise it’s easier to see a trend, especially after 1960. This particular dataset stops at 2009, and I want to note that the following three years were all warmer than 2009 [1]. This method has allowed us to reduce the “noise” which enables us to detect the “signal” better. We can also see the “warming hiatus” during the last 10 years, but once again, 10 years isn’t long enough to really be climate yet. It’s still weather. I’ll write a post about the warming hiatus in the near future.

There’s so much more we can explore with climate signals and weather noise (and I will address more of these in future posts). But for now, let’s leave it here.

The data for the plots was obtained from these sites: