On Tue, Sep 15, 2009 at 9:00 AM, wjp <wjp@swcp.com> wrote:
> Dave:
>
> I think we've talked about this before.
> What he means, I think, is that the codes produce less variance (e.g.,
> less sensitivity to initial conditions) on "long" time scales than for
> shorter ones. That is, the code is more stable.
>
> This is slightly, at least, contrary to my experience. My experience would
> be with shocked radiation hydrodynamics. And we would generally expect
> better
> results for shorter time scales, (e.g., courant constraints).
>
> I wonder if longer time scales are being time averaged in some sense.
>
Not time averages, spatial averages. It's called GLOBAL warming for a
reason. Just as when you heat a pot of water you can predict the average
temperature of the whole pot at any one time easily but the local
temperature variation is much more chaotic. The other thing that makes
longer-term climate change easier to predict during the industrial age is
the prime driver of long-term change is well-mixed anthropogenic greenhouse
gasses. CO2 increases temperature on an approximately a logarithmic basis
and CH4 increases temperature on an approximately square root basis. This
makes for smoother changes that are easier to predict in the longer term
than in the shorter term. Shorter term temperature variability such as the
El Nino Southern Oscillation is much, much more chaotic but as the name
suggests it's cyclical and does not have any noticeable long term change.
Solar variability is an order of magnitude smaller than CO2-based
variability and even on decadal time scales is swamped by ENSO.
> Larger mesh size often produces "better" results because of spatial
> averaging,
> although by missing much detail. But generally we don't think of longer
> time
> scales as temporal averaging.
>
Again we now want the smaller meshes so that we can predict the local
variability such as how quickly will the southwest U.S. run out of water.
(Current simulations look really, really bad.) We've gone beyond proving
anthropogenic global warming, it's a fact. But now we need to predict the
local effects so that we can best adapt to it and also determine what level
of CO2 is intolerable and worth spending economic resources to mitigate it.
This requires more processor power and greater precision.
Rich Blinne
Member ASA
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Received on Tue Sep 15 14:02:04 2009
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