This is a guest post by Dave Rutledge, Chair for the Division of Engineering and Applied Science at Caltech, which has 12 departments with 75 faculty members and 500 graduate students.

Dave is fascinated by the possibility that the key to understanding the future of world coal production may be in the history of the mining areas in the northern Appalachians and the north of England. Dave is also interested in the question of how California will make the transition from fossil fuels to renewable fuels for electricity production.

At The Oil Drum, there has been much discussion of the modeling of future oil production and the reliability of reserve data. It is also understood that burning fossil hydrocarbon fuels increases the CO2 concentration in the atmosphere, and that this is likely to affect our climate. What about coal? Can we figure out how much coal is likely to be produced, and how quickly the coal reserves will be exhausted? How reliable are coal reserve numbers? What can our models for coal and hydrocarbon production tell us about atmospheric CO2 concentrations? About climate? It turns out that we can give answers to all of these questions, using the same Hubbert linearizations and normal curve fits that we use for oil.

The importance of these approaches to estimating future production is emphasized by this astonishing statement in the pre-publication version of the National Academy of Sciences Report on coal, released yesterday:

Present estimates of coal reserves are based upon methods that have not been reviewed or revised since their inception in 1974, and much of the input data were compiled in the early 1970s. Recent programs to assess reserves in limited areas using updated methods indicate that only a small fraction of previously estimated reserves are actually minable reserves.

Part I- Texas Myths

Like Cindy Crawford, I have done quite a bit of modeling in my career. However, mine has been in front of a computer. There are various types of models. They can be empirical, such that you curve fit data without having a clear explanation of the underlying mechanisms. Or they can be theoretical, in which the system is modeled according to the governing scientific principles and mathematical equations.

However, one thing is critical to keep in mind. If you are going to use the model for forecasting, the model must be tested. Testing the model is called “validation”, or sometimes “back-casting.” This involves feeding the model real data, and observing how well the predictions match up with the observations. If the predictions match up on a consistent basis, and any large variations are explainable, you have the makings of a predictive model. If you have not validated your model, or if you have attempted to validate it and found that the predictions were inconsistent, the model should be used with caution (if at all). In this essay I have done some back-casts on the Hubbert Linearization (HL) model and attempted to use it to make predictions using historical data.

Kangerdlugssuaq Glacier

I've been trying this week to get a grip on the science of the Greenland Ice Sheet. It's a complex and poorly understood business, but there seems to be enough meat here that I think it should be on The Oil Drum agenda.

We might do worse than start with with a report from the BBC. They covered a talk at the American Geophysical Union meeting in San Francisco this last week. (I didn't get to go, alas).

Kangerdlugssuaq Glacier on the east coast of Greenland has been clocked using GPS equipment and satellites to be flowing at a rate of 14km per year. It is also losing mass extremely fast, with its front end retreating 5km back up its fjord this year alone. The glacier "drains" about 4% of the ice sheet, dumping tens of cubic km of fresh water in the North Atlantic.

"We've seen a 5km retreat of the terminus, we've see an almost 300% acceleration in the flow speed and we've seen about a 100m thinning of the glacier - all occurring in the last one or so years," said Dr Gordon Hamilton, of the Climate Change Institute at the University of Maine.

I think most readers of TOD are persuaded that corn ethanol is a boondoggle right now. Robert Rapier made the case well again on Wednesday. Also, a little while back, Kyle argued pretty persuasively that cellulosic ethanol will not be much better in the near term.

Fair enough. I buy all that.

But, I think it's very important to ask: do biofuels suck for deep fundamental unchangeable reasons? Or for contingent reasons that might be amenable to change over time with technological innovation? And I'm starting to think the answer might not be so obvious.

Flows of carbon in biomass products entering the global economy. Source: FAO.