Re: Open Source Climate Modeling was Re: [asa] Crop Yields FaceNon-LinearEffects Due to Climate Change

From: Bill Powers <>
Date: Tue Sep 15 2009 - 16:24:39 EDT


Just a short reply. The problem is how are the codes obtaining long
term temporal results? All the codes operate on the presumption of
infinitesimal time steps. At each step presumably energy and the like
is conserved (not true of all codes). In looking at longer temporal
scales are we taking larger time steps or simply taking the same time
steps, but looking further down the road.

It does not seem that it is the longer time scale that is the salient
feature, but the kinds of variables that are being examined on longer
time scales. Examining average temperature will likely be fairly
reliable at all time scales.

Well, I've got to go. I hope my question is clear.


On Tue, 15 Sep 2009,
Rich Blinne wrote:

> On Tue, Sep 15, 2009 at 9:00 AM, wjp <> 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

To unsubscribe, send a message to with
"unsubscribe asa" (no quotes) as the body of the message.
Received on Tue Sep 15 16:25:42 2009

This archive was generated by hypermail 2.1.8 : Tue Sep 15 2009 - 16:25:43 EDT