We all seem to agree that our state-of-the-art models aren't satisfactory representations of
climate on Earth--at least not to the degree required to make decisions with them. We also agree
that people are concerned with climate change and eager to incorporate information about future
changes in their decision making, and we're conscious of the need to relate our research agenda and
findings to real-world demands. Finally, there's consensus that we cannot look at climate
forecasts--in particular, probabilistic forecasts--the same way we view weather predictions, and
none of us would sell climate-model output, either at face value or after statistical analysis, as
a reliable representation of the complete range of possible futures.
Beyond this common ground, we fall on different points of the spectrum between James's pragmatic
approach, where he proposes giving decision makers information as our "best guess" about future
outcomes nonetheless, and Lenny's highly skeptical position--namely, there's no hope in
approximating the real world in any useful sense. (Interestingly, Lenny turns the issue on its head
and proposes we work at characterizing what we
cannot say rather than what we can.) Gavin and I are somewhere in-between. Gavin still
finds qualitative value in a reasoned interpretation of model output, while I claim further that
there's still value in quantifying uncertainty if the results aren't distributed for public
consumption.
The reader who doesn't dabble in climate modeling or statistics is probably asking herself,
"What am I to make of all this?" To which I would say, "That's exactly what I want you to
think!"
Let me explain: If I can say anything for sure, it's that I don't want anyone to take a
precooked climate projection--whether a single model or a multi-model ensemble, probabilistic or
not--and run with it. Any decision will be best served by looking at the available observational
and modeled information and listening to the opinion of climate modelers and climatologists. The
experts will be able to form an integrated evaluation based on changes already observed, the
processes known to influence the regional climate of interest, and projections from those models
that have demonstrated accuracy in describing that region's climate--all to a degree consistent
with the kind of projection required. (For example, if we're interested in changes in large average
quantities, we may be willing to set the bar lower for our models than if we're interested in
changes in extremes. If we're looking at a flat, large region in the middle of a continent, we may
have better luck than if we're looking at a coastal region with complex topography.)
After careful synthesis of what's available to assess specific regional climate change, we may
go as far as presenting a probability distribution based on this information--if we think the
statistical assumptions are supported by the data. Why not? But in all of this, there's no
substitute for clear, two-way communication between suppliers and users of the information--both to
guide and qualify.
Meanwhile, in the convenient isolation of our research centers, I hope we pursue the
obvious--better models and ways to represent the data we gather from them in a statistical
framework--while also designing experiments with our models that serve the purposes Lenny suggests.
Rather than pushing exclusively for ever-more complex models with ever-higher resolutions, we
should think of ways to explore model errors, dependencies, and sensitivities.
I'd even propose a totally selfless design that takes the point of view of a scientist 20 year
from now who, endowed with 20 years of observational records, looks back and says, "I wish those
2008 simulations had tried to do this and that; I could assess them now and use the validation to
learn what that modeled process is really worth." By doing so, we may get closer to a full
characterization of the uncertainties that we know exist.
As for the unknown unknowns. . . There's no way around those. But isn't that an inescapable
characteristic of our ever-evolving scientific enterprise--not to mention most significant
real-life decisions?