An Open Letter to the Future

To the citizens of the world in the year 5000:

It’s 2012, and nobody is thinking about you.

These days, Long Term Thinking means planning for 2050, and even that is unusual. Thoughts of Future Generations don’t go beyond grandchildren. If my government knew I was thinking about people three thousand years in the future, they would probably call me a “radical”.

However, three thousand years isn’t such a long time. The ancient Greeks flourished about three thousand years ago now, and we think about them all the time. Not just historians, but people in all walks of life – scientists, policymakers, teachers, and lawyers all acknowledge the contributions of this ancient civilization to today’s culture. Our society is, in many ways, modelled after the Greeks.

I was walking outside today, at the tail end of the warmest winter anyone can remember in central Canada, and thought to myself: What if the ancient Greeks had caused global climate change back in their day? What if they had not only caused it, but understood what was happening, and had actively chosen to ignore it? The effects would still be apparent today. Global temperature might have stabilized, but the biosphere would still be struggling to adapt, and the seas would still be gradually rising. What would we think of the ancient Greeks if they had bestowed this legacy upon us? Would we still look upon their civilization so favourably?

The Golden Rule is usually applied to individuals living in the same time and place, but I think we should extend it across continents and through millennia so it applies to all of human civilization. Before we make a major societal decision, like where to get our energy, we should ask ourselves: If the ancient Greeks had gone down this path, would we care?

The future is a very long time. Thinking about the future is like contemplating the size of the universe: it’s disturbing, and too abstract to fully comprehend. Time and space are analogues in this manner. 2050 is like Mars, and the year 5000 is more like Andromeda.

I can handle Andromeda. And I can handle the concept of 5000 A.D., so I think about it when I’m outside walking. My first thoughts are those of scientific curiosity. Tell me, people in 5000 – how bad did the climate get? What happened to the amphibians and the boreal forest? Did the methane hydrates give way, and if so, at what point? How much did the oceans rise?

Soon scientific curiosity gives way to societal questions. Were we smart enough to leave some coal in the ground, or did we burn it all? Did we open our doors to environmental refugees, or did we shut the borders tight and guard the food supply? How long did it take for Western civilization to collapse? What did you do then? What is life like now?

And then the inevitable guilt sets in, as I imagine what you must think of us, of this horrible thoughtless period of history that I am a part of. But with the guilt comes a desperate plea for you to understand that not everyone ignored the problem. A few of us dedicated our lives to combating denial and apathy, in a sort of Climate Change Resistance. I was one of them; I am one of them. With the guilt comes a burning desire to say that I tried.

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A Vast Machine

I read Paul Edward’s A Vast Machine this summer while working with Steve Easterbrook. It was highly relevant to my research, but I would recommend it to anyone interested in climate change or mathematical modelling. Think The Discovery of Global Warming, but more specialized.

Much of the public seems to perceive observational data as superior to scientific models. The U.S. government has even attempted to mandate that research institutions focus on data above models, as if it is somehow more trustworthy. This is not the case. Data can have just as many problems as models, and when the two disagree, either could be wrong. For example, in a high school physics lab, I once calculated the acceleration due to gravity to be about 30 m/s2. There was nothing wrong with Newton’s Laws of Motion – our instrumentation was just faulty.

Additionally, data and models are inextricably linked. In meteorology, GCMs produce forecasts from observational data, but that same data from surface stations was fed through a series of algorithms – a model for interpolation – to make it cover an entire region. “Without models, there are no data,” Edwards proclaims, and he makes a convincing case.

The majority of the book discussed the history of climate modelling, from the 1800s until today. There was Arrhenius, followed by Angstrom who seemed to discredit the entire greenhouse theory, which was not revived until Callendar came along in the 1930s with a better spectroscope. There was the question of the ice ages, and the mistaken perception that forcing from CO2 and forcing from orbital changes (the Milankovitch model) were mutually exclusive.

For decades, those who studied the atmosphere were split into three groups, with three different strategies. Forecasters needed speed in their predictions, so they used intuition and historical analogues rather than numerical methods. Theoretical meteorologists wanted to understand weather using physics, but numerical methods for solving differential equations didn’t exist yet, so nothing was actually calculated. Empiricists thought the system was too complex for any kind of theory, so they just described climate using statistics, and didn’t worry about large-scale explanations.

The three groups began to merge as the computer age dawned and large amounts of calculations became feasible. Punch-cards came first, speeding up numerical forecasting considerably, but not enough to make it practical. ENIAC, the first model on a digital computer, allowed simulations to run as fast as real time (today the model can run on a phone, and 24 hours are simulated in less than a second).

Before long, theoretical meteorologists “inherited” the field of climatology. Large research institutions, such as NCAR, formed in an attempt to pool computing resources. With incredibly simplistic models and primitive computers (2-3 KB storage), the physicists were able to generate simulations that looked somewhat like the real world: Hadley cells, trade winds, and so on.

There were three main fronts for progress in atmospheric modelling: better numerical methods, which decreased errors from approximation; higher resolution models with more gridpoints; and higher complexity, including more physical processes. As well as forecast GCMs, which are initialized with observations and run at maximum resolution for about a week of simulated time, scientists developed climate GCMs. These didn’t use any observational data at all; instead, the “spin-up” process fed known forcings into a static Earth, started the planet spinning, and waited until it settled down into a complex climate and circulation that looked a lot like the real world. There was still tension between empiricism and theory in models, as some factors were parameterized rather than being included in the spin-up.

The Cold War, despite what it did to international relations, brought tremendous benefits to atmospheric science. Much of our understanding of the atmosphere and the observation infrastructure traces back to this period, when governments were monitoring nuclear fallout, spying on enemy countries with satellites, and considering small-scale geoengineering as warfare.

I appreciated how up-to-date this book was, as it discussed AR4, the MSU “satellites show cooling!” controversy, Watt’s Up With That, and the Republican anti-science movement. In particular, Edwards emphasized the distinction between skepticism for scientific purposes and skepticism for political purposes. “Does this mean we should pay no attention to alternative explanations or stop checking the data?” he writes. “As a matter of science, no…As a matter of policy, yes.”

Another passage beautifully sums up the entire narrative: “Like data about the climate’s past, model predictions of its future shimmer. Climate knowledge is probabilistic. You will never get a single definitive picture, either of exactly how much the climate has already changed or of how much it will change in the future. What you will get, instead, is a range. What the range tells you is that “no change at all” is simply not in the cards, and that something closer to the high end of the range – a climate catastrophe – looks all the more likely as time goes on.”