Richard

Interestingly, the tail-end of the essay that preceded Roger’s in the GEWEX Newsletter cited a 1951 article by Hurst. Now I understand that water management was simply one example of “irreducible uncertainty,” but it’s my understanding that the methods pioneered by Hurst for sizing dams & resevoirs are implicitly based on uncertainty (or non-stationarity), and that hydrologists have been using them successfully ever since.

Demetris Koutsoyannis has certainly stirred the pot lately by suggesting that the statistical methods routinely used by hydrologists could also be applied to the climate meta-system, perhaps more successfully than those ubiquitous GCMs.

I also believe that Mandelbrot honored Hurst as a sort of proto-fractalist/chaoticist with his term “The Hurst Exponent.”

I’ve been wanting to dig deeper into this topic but haven’t had time yet. If Roger or anyone else can point me to some good primers, I’d be much appreciative.

1) a focus on observations through monitoring and flexibility and quickness to deal with problems as they arise in nature,

2) assessments of vulnerability given less specific projections

The problem, in this case, was that too many people know we don’t know some fundamentals – how plants and their offspring adapt to differences, their interactions with pests, microclimates that plants perceive compared to macroclimates that are modelled. So the key things are to stay agile in our responses, and to manage as much as possible so that no matter what scenario is predicted we will still do OK. This is either hedging your bets or designing resilient systems or whatever.

In other words you don’t need to know to manage.. we don’t depend on downscaled economic models and make financial decisions every day. And a corollary is that you can make worse decision from pretending you know (from models) than to admit you don’t.

I sit in meetings with biologists or climate scientists who think that due to climate change, decades of decision science need to be thrown out .. but no one has explained to me why they feel that these sciences are no longer relevant.

I never used the phrase “viable predictions” — my article is about “skillful predictions”.

You write: “It may be that at a given time, we don’t have the knowledge to choose among them, but you also seem to be saying that we will never have such ability when you use words like irreducible.”

Often, on time scales of decisions, uncertainties and ignorance are indeed irreducible.

You can see my views on prediction more generally here:

Pielke Jr., R. A., D. Sarewitz and R. Byerly Jr., 2000: Decision Making and the Future of Nature: Understanding and Using Predictions. Chapter 18 in Sarewitz, D., R. A. Pielke Jr., and R. Byerly Jr., (eds.), Prediction: Science Decision Making and the Future of Nature. Island press: Washington, DC.
http://sciencepolicy.colorado.edu/admin/publication_files/resource-73-2000.06.pdf

However for you to say that our ignorance is irreducible is certainly no more scientific. It _predicts_ that we won’t gain enough accumulated knowledge in the future to make viable prejections, when I would assert that it is quite feasible that we will gain adequate knowledge to do so.

I also don’t see how you can defend this statement: “However, when there are competing predictions of the future, we then have no empirical basis on which to choose among them.” It may be that at a given time, we don’t have the knowledge to choose among them, but you also seem to be saying that we will never have such ability when you use words like irreducible.

Taken as a whole, you seem to be saying that we can never know what the future will hold, so why bother trying. Is that correct?

Your confidence is not based on demonstrated skill, it is based on something more like faith — faith in science, in models, etc.. We can have plenty of confidence in faith-based predictions. My article is about the prospects for demonstrations of skill in forecasts of nonstationary processes, which is a term of art meaning proven experience. However, when there are competing predictions of the future, we then have no empirical basis on which to choose among them, hence the need for robust decision making.

As the idea that we have to know the future in order to adapt is not an assumption universally shared. See:

Dessai, S., M. Hulme, R. Lempert, and R. Pielke, Jr. 2009. Do We Need Better Predictions to Adapt to a Changing Climate? Eos, Vol 90, No. 13, pp. 111-112.
http://sciencepolicy.colorado.edu/admin/publication_files/resource-2720-2009.08.pdf

Thanks

And it undermines adaptation much more than mitigation, since we can only attempt to plan for adaptation if we have some idea of what we need to adapt to. Mitigation doesn’t really require much knowledge on specific impacts, just that there will be enough to justify mitigation.