Monthly Archives: Jun 2012

Modelling Geoengineering

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Later in my career as a climate modeller, I expect to spend a lot of time studying geoengineering. Given the near-total absence of policy responses to prevent climate change, I think it’s very likely that governments will soon start thinking seriously about ways to artificially cool the planet. Who will they come to for advice? The climate modellers.

Some scientists are pre-emptively recognizing this need for knowledge, and beginning to run simulations of geoengineering. In fact, there’s an entire model intercomparison project dedicated to this area of study. There’s only a small handful of publications so far, but the results are incredibly interesting. Here I summarize two recent papers that model solar radiation management: the practice of offsetting global warming by partially blocking sunlight, whether by seeding clouds, adding sulfate aerosols to the stratosphere, or placing giant mirrors in space. As an added bonus, both of these papers are open access.

A group of scientists from Europe ran the same experiment on four of the world’s most complex climate models. The simulation involved instantaneously quadrupling CO2 from preindustrial levels, but offsetting it with a reduction in the solar constant, such that the net forcing was close to zero.

The global mean temperature remained at preindustrial levels. “Great,” you might think, “we’re home free!” However, climate is far more than just one globally averaged metric. Even though the average temperature stayed the same, there were still regional changes, with cooling in the tropics and warming at both poles (particularly in their respective winters):

There were regional changes in precipitation, too, but they didn’t all cancel out like with temperature. Global mean precipitation decreased, due to cloud feedbacks which are influenced by sunlight but not greenhouse gases. There were significant changes in the monsoons of south Asia, but the models disagreed as to exactly what those changes would be.

This intercomparison showed that even with geoengineering, we’re still going to get a different climate. We won’t have to worry about some of the big-ticket items like sea level rise, but droughts and forest dieback will remain a major threat. Countries will still struggle to feed their people, and species will still face extinction.

On the other side of the Atlantic, Damon Matthews and Ken Caldeira took a different approach. (By the way, what is it about Damon Matthews? All the awesome papers out of Canada seem to have his name on them.) Using the UVic ESCM, they performed a more realistic experiment in which emissions varied with time. They offset emissions from the A2 scenario with a gradually decreasing solar constant. They found that the climate responds quickly to geoengineering, and their temperature and precipitation results were very similar to the European paper.

They also examined some interesting feedbacks in the carbon cycle. Carbon sinks (ecosystems which absorb CO2, like oceans and forests) respond to climate change in two different ways. First, they respond directly to increases in atmospheric CO2 – i.e., the fertilization effect. These feedbacks (lumped together in a term we call beta) are negative, because they tend to increase carbon uptake. Second, they respond to the CO2-induced warming, with processes like forest dieback and increased respiration. These feedbacks (a term called gamma) are positive, because they decrease uptake. Currently we have both beta and gamma, and they’re partially cancelling each other out. However, with geoengineering, the heat-induced gamma goes away, and beta is entirely unmasked. As a result, carbon sinks became more effective in this experiment, and sucked extra CO2 out of the atmosphere.

The really interesting part of the Matthews and Caldeira paper was when they stopped the geoengineering. This scenario is rather plausible – wars, recessions, or public disapproval could force the world to abandon the project. So, in the experiment, they brought the solar constant back to current levels overnight.

The results were pretty ugly. Global climate rapidly shifted back to the conditions it would have experienced without geoengineering. In other words, all the warming that we cancelled out came back at once. Global average temperature changed at a rate of up to 4°C per decade, or 20 times faster than at present. Given that biological, physical, and social systems worldwide are struggling to keep up with today’s warming, this rate of change would be devastating. To make things worse, gamma came back in full force, and carbon sinks spit out the extra CO2 they had soaked up. Atmospheric concentrations went up further, leading to more warming.

Essentially, if governments want to do geoengineering properly, they have to make a pact to do so forever, no matter what the side effects are or what else happens in the world. Given how much legislation is overturned every time a country has a change in government, such a promise would be almost impossible to uphold. Matthews and Caldeira consider this reality, and come to a sobering conclusion:

In the case of inconsistent or erratic deployment (either because of shifting public opinions or unilateral action by individual nations), there would be the potential for large and rapid temperature oscillations between cold and warm climate states.

Yikes. If that doesn’t scare you, what does?

A New Kind of Science

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Cross-posted from NextGen Journal

Ask most people to picture a scientist at work, and they’ll probably imagine someone in a lab coat and safety goggles, surrounded by test tubes and Bunsen burners. If they’re fans of The Big Bang Theory, maybe they’ll picture complicated equations being scribbled on whiteboards. Others might think of the Large Hadron Collider, or people wading through a swamp taking water samples.

All of these images are pretty accurate – real scientists, in one field or another, do these things as part of their job. But a large and growing approach to science, which is present in nearly every field, replaces the lab bench or swamp with a computer. Mathematical modelling, which essentially means programming the complicated equations from the whiteboard into a computer and solving them many times, is the science of today.

Computer models are used for all sorts of research questions. Epidemiologists build models of an avian flu outbreak, to see how the virus might spread through the population. Paleontologists build biomechanical models of different dinosaurs, to figure out how fast they could run or how high they could stretch their necks. I’m a research student in climate science, where we build models of the entire planet, to study the possible effects of global warming.

All of these models simulate systems which aren’t available in the real world. Avian flu hasn’t taken hold yet, and no sane scientist would deliberately start an outbreak just so they could study it! Dinosaurs are extinct, and playing around with their fossilized bones to see how they might move would be heavy and expensive. Finally, there’s only one Earth, and it’s currently in use. So models don’t replace lab and field work – rather, they add to it. Mathematical models let us perform controlled experiments that would otherwise be impossible.

If you’re interested in scientific modelling, spend your college years learning a lot of math, particularly calculus, differential equations, and numerical methods. The actual application of the modelling, like paleontology or climatology, is less important for now – you can pick that up later, or read about it on your own time. It might seem counter-intuitive to neglect the very system you’re planning to spend your life studying, but it’s far easier this way. A few weeks ago I was writing some computer code for our lab’s climate model, and I needed to calculate a double integral of baroclinic velocity in the Atlantic Ocean. I didn’t know what baroclinic velocity was, but it only took a few minutes to dig up a paper that defined it. My work would have been a lot harder if, instead, I hadn’t known what a double integral was.

It’s also important to become comfortable with computer programming. You might think it’s just the domain of software developers at Google or Apple, but it’s also the main tool of scientists all over the world. Two or three courses in computer science, where you’ll learn a multi-purpose language like C or Java, are all you need. Any other languages you need in the future will take you days, rather than months, to master. If you own a Mac or run Linux on a PC, spend a few hours learning some basic UNIX commands – it’ll save you a lot of time down the road. (Also, if the science plan falls through, computer science is one of the only majors which will almost definitely get you a high-paying job straight out of college.)

Computer models might seem mysterious, or even untrustworthy, when the news anchor mentions them in passing. In fact, they’re no less scientific than the equations that Sheldon Cooper scrawls on his whiteboard. They’re just packaged together in a different form.

Ten Things I Learned in the Climate Lab

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  1. Scientists do not blindly trust their own models of global warming. In fact, nobody is more aware of a model’s specific weaknesses than the developers themselves. Most of our time is spent comparing model output to observations, searching for discrepancies, and hunting down bugs.
  2. If 1.5 C global warming above preindustrial temperatures really does represent the threshold for “dangerous climate change” (rather than 2 C, as some have argued), then we’re in trouble. Stabilizing global temperatures at this level isn’t just climatically difficult, it’s also mathematically difficult. Given current global temperatures, and their current rate of change, it’s nearly impossible to smoothly extend the curve to stabilize at 1.5 C without overshooting.
  3. Sometimes computers do weird things. Some bugs appear for the most illogical reasons (last week, the act of declaring a variable altered every single metric of the model output). Other bugs show up once, then disappear before you can track down the source, and you’re never able to reproduce them. It’s not uncommon to fix a problem without ever understanding why the problem occurred in the first place.
  4. For anyone working with climate model output, one of the best tools to have in your arsenal is the combination of IDL and NetCDF. Hardly an hour of work goes by when I don’t use one or both of these programming tools in some way.
  5. Developing model code for the first time is a lot like moving to a new city. At first you wander around aimlessly, clutching your map and hesitantly asking for directions. Then you begin to recognize street names and orient yourself around landmarks. Eventually you’re considered a resident of the city, as your little house is there on the map with your name on it. You feel inordinately proud of the fact that you managed to build that house without burning the entire city down in the process.
  6. The RCP 8.5 scenario is really, really scary. Looking at the output from that experiment is enough to give me a stomachache. Let’s just not let that scenario happen, okay?
  7. It’s entirely possible to get up in the morning and just decide to be enthusiastic about your work. You don’t have to pretend, or lie to yourself – all you do is consciously choose to revel in the interesting discoveries, and to view your setbacks as challenges rather than chores. It works really well, and everything is easier and more fun as a result.
  8. Climate models are fabulous experimental subjects. If you run the UVic model twice with the same code, data, options, and initial state, you get exactly the same results. (I’m not sure if this holds for more complex GCMs which include elements of random weather variation.) For this reason, if you change one factor, you can be sure that the model is reacting only to that factor. Control runs are completely free of external influences, and deconstructing confounding variables is only a matter of CPU time. Most experimental scientists don’t have this element of perfection in their subjects – it makes me feel very lucky.
  9. The permafrost is in big trouble, and scientists are remarkably calm about it.
  10. Tasks that seem impossible at first glance are often second nature by the end of the day. No bug lasts forever, and no problem goes unsolved if you exert enough effort.