Highlights from Week 1 of UCSD Course on Climate Change

So week 1 of the online course I’m taking from UCSD, Climate Change in Four Dimensions, has just concluded.  What’s wonderful is that 12,000 people around the world are taking this course, evenly split between men and women and between English and non-English speakers.  Online courses in some crucial ways are no substitute for physical-presence classroom courses — but this does give one the feeling of not being isolated in the face of climate change.

What stood out from the first week?  We had lectures by Dr. Richard Somerville, who is a climate scientist at UCSD engaged in climate modeling.  These were about the basic science of climate change, with history, foundations, detection, attributions, climate models and sensitivity.  Since I’d taken a full course on the science of global warming from David Archer of the University of Chicago last fall (another online course), and since I know enough of the basic science to be able to teach it in my courses, there was not much that was new for me, science-wise.  However I deepened my knowledge of some of the history of climate change science, from John Tyndall to Charles Keeling, and I learned more about the IPCC, to which august body Dr. Somerville has contributed.  One scientific aspect that did stand out was related to a theoretical question I’ve been pondering for a while, which is this:

We know that weather exhibits chaotic behavior, as Edward Lorenz discovered (Butterfly effect).  But climate is a space-and-time average over weather, so we expect climate to be smoother and more predictable, and in fact that is true from all the data so far.  However some scientists are concerned about tipping points in the climate system — points beyond which there is a major shift in the behavior of the climate, maybe even irreversible.  These concerns seem to be motivated by the existence of positive feedback loops such as those in the Arctic.  So it seems that in some ways at least climate is exhibiting an exaggerated sensitivity to some effects, but in a different way than weather.  Dr. Somerville said something about the difference between weather and climate that brought me some clarity — he said that weather is an initial value problem, while climate is a boundary value problem.  This made sense!  I want to investigate this further, particularly in the context of the other online course I’m taking with the Santa Fe institute’s Melanie Mitchell on Complexity.

I learned a lot in Lesson 2, which was an overview of climate models and how far they’d come.  Current climate models have a resolution (grid size) of around a 100 km, much improved from earlier, although smaller-scale effects like hurricanes slip through the mesh.  I learned (as David Archer had also mentioned in his course) how the behavior of clouds is the major source of uncertainty in climate modeling, since clouds can have both a warming effect (greenhouse effect of water vapor) and a cooling effect (increased albedo).  It seems that satellite data indicate that the effect is a net cooling, but way more work needs to be done, and in fact Richard Somerville speculates that perhaps some aspects of climate might need to be considered locally rather than globally.

One really fascinating story is that of Lewis Fry Richardson, who, in the first half of the twentieth century, first tried to predict weather using physics equations and available data.  He also anticipated by several decades the concept of massively parallel computing, used by climate modelers today.  He did the early part of his work of building the first ever model for weather prediction while (as a pacifist) driving ambulances in WWI.  Somebody needs to write his biography.

An important concept is climate sensitivity, which is “the equilibrium change in global average surface atmospheric temperature in response to doubling of CO2 in the atmosphere.”  It turns out that GCMs (Global Climate Models) predict sensitivities between 2.0 and 4.5 C, which is quite a range.

Dr. Somerville speculates that perhaps climate sensitivity is not very useful as a global parameter.  In some cases such as cloud feedback, global feedback may be small but local feedbacks not so.  Apparently aerosol effects are largely local (I’m assuming he’s talking about increased reflectivity due to more aerosols rather than the ozone hole) and perhaps there is an important local component to the cloud feedback too.

I was impressed to learn that despite room for improvement, GCMs are pretty effective, both when used for confirming past climate events, and going forward.  So for instance the predictions of models since the 1990s have closely matched what the actual climate has been doing, although reality is at the higher end of predictions, and in some instances such as sea ice melt, the models are underestimating what’s actually happening.

One of the most interesting aspects of week 1 was for participants to calculate their carbon footprint.  I suggest to anyone who has never done this to try it out.  You need your electricity and heating bills, and it really doesn’t take very long.  It is truly revealing.  My footprint was about 16.7 metric tons a year, lower than the US average of 20.4, but not by much.  The aim for a sustainable world is 2 metric tons a year.  A long way to go!  It was helpful because it confirmed that my house heating and electricity were major sources, even though I have modest needs — this is pointing to leaky energy and a need for insulation, and moving away from oil.  I’m thinking about a heat pump, since my house is not ideal for solar panels.

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