What are you measuring?

Real Climate’s post on Ed Lorenz brings to mind the idea that all of this climate modeling may have a measurement focus problem.

Another way of saying it is that for the climate problem, the weather (or the individual trajectory) is the noise. If you are trying to find the common signal that is a signature of a particular forcing then averaging over a number of simulations with different weather works rather well. (There is a long standing quote in science – “one person’s noise is another person’s signal” which is certainly apropos here. Climate modellers don’t average over ensemble members because they think that weather isn’t important, they do it because it gives robust estimates of the signal they are usually looking for.)

This is a common management problem. Climate is not well defined and that makes it difficult to measure. When something is difficult to measure, it is difficult to track or model or predict. This may be why there is such contention.

One often used measure of climate is global average temperatures. Even this is problematic. How is such an average derived and what is the quality of its sources? Meteorologists have put a lot of effort into instrumentation and methodology for measuring atmospheric temperature yet even that basic measure is subject to all sorts of conditions, caveats, and adjustments.

Other measures used for climate change include atmospheric composition (e.g. carbon dioxide), glacial ice, ocean surface height, sunspots, ocean temperature patterns, average ocean temperatures, and cloud coverage.

The lack of precision in even knowing what to measure much less the measurement itself is an indicator that conclusions based on that measure need to be carefully qualified. Much of the global climate change debate has really been about this point and not about the topic of climate change itself.

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