Ten Things I Learned in the Climate Lab

  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.

18 thoughts on “Ten Things I Learned in the Climate Lab

  1. Well, science selects for calmness, probably consequent to the attention span requirements. For the species, it may be that this trait will prove to be contra-survival.

  2. I have learnt that wordpress can be exceedingly annoying

    Miss Kate

    I hope they are right about the 8.5 scenario being impossible.

    Is the permafrost already doomed? If so do not worry about it, save your worries for that which can be changed.

    The value of your current efforts is significant. Already you are ahead of some graduates, but then some graduates achieve nothing.

    All the best

  3. Another excellent post, Kate, thank you. However, I have a question and some observations regarding #2…

    If it is someone other than James Hansen, can you supply a reference or references for studies indicating 1.5 Celsius is the safe limit for post-Industrial temperature rise?

    I was thinking of James Hansen and his colleagues at GISS. -Kate

    On the subject of overshoot, not being a biologist, I found the writings of Garrett Hardin, Paul Ehrlich, and William Ophuls incredibly enlightening (as I feel the best way to deal with “scepticism” is to present climate change as a limits to growth phenomenon#). Taken together, these biologists warn us that, in the absence of predation and disease, populations will grow until access to food becomes a problem. When food supply begins to become a problem, population growth naturally slows as the ecological carrying capacity (ECC) is approached. However, in experiments where predators and/or food were artificially removed, overshoot and collapse were observed. Having no predators – but delayed by technological intervention – this is the point at which humanity now finds itself. Now that we know what is going on, the extent to which we experience overshoot, collapse, and reduction in ECC is now up to us to determine. For those like me that are new to this subject, an excellent summary of the subject may be found at: http://www.greatchange.org/ophuls,ecological_scarcity.html

    # Burning fossil fuels has only become a problem because the rate at which we are doing it vastly exceeds the capacity of the planet to recycle the waste products.

    • Thanks for the clarification, Kate. I thought it must be Hansen et al (but am more accustomed to hearing safe CO2 limits not safe Temperature limits quoted).

  4. Kate, fabulous post again from you and one that may creep into Learning from Dogs! Assuming I haven’t hung myself over the week-end! ;-)

    (To no-one in particular) I try so hard to convince myself that this is truly not the ‘end of the world’ and that change will come about in the nick of time – but, then again, I have periods of very great uncertainty. Is this just me or do others know these feelings?

    • Natural Resources Canada has some recent work on it.

      Try these:

      Natural Resources Canada (NRCAN), 2008. From Impacts to Adaption: Canada in a Changing Climate 2007. Ottawa, Ontario, Canada.

      Government of Canada.Stratos. 2009. Climate Change Impacts on Mining Operations and Infrastructure: A scoping study for the Centre for Excellence in Mining Innovation. Ottawa: Reinecke, S., Hedley, C., Douglas, A., & van Aanhout, M.

      Stratos Inc. 2011. Climate Change and Acid Rock Drainage – Risks for the Canadian Mining Sector. Unpublished report prepared for MEND, Natural Resources Canada.

  5. “(last week, the act of declaring a variable altered every single metric of the model output)”

    Most likely you have a out-of-bounds memory block read or write that has more or less impact depending on how the memory assigned to particular variables is layed out, and that is a function of what is declared, and even in what order it is declared. It is a defect that can be difficult to track down because the problematic behavior can be observed a long way from the code causing the problem. Tools like Rational Purify used to be good at finding these kinds of problems.

    In other words, somewhere in the code, there as a variable accessing memory that was coded to contain some variable meaning other than the variable accessing it.

  6. I’ve worked on these where a non-debug build reproduced the problem and a debug build did not. That can be a major pain.

  7. I’m a little late to this party.

    “The permafrost is in big trouble, and scientists are remarkably calm about it.”

    I assume the point of this comment is to say that Kate is feeling less calm than most other scientists and wonders why they are calm about a major issue, rather than Kate reporting that this issue is overhyped and most scientists who actually get it are not as concerned about it as activists/others. Is that right? I’d also like to hear more on this point.

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