Climate Models and Policy

August 31st, 2004

Posted by: Roger Pielke, Jr.

The primary justification for public investments in climate models is that these tools will help to inform decision making related to climate. Of course, for many scientists, climate models are worth creating and studying regardless of their possible utility. But I think it is safe to say that the resources devoted to climate models would be much less if they were only of intrinsic merit.

In this light an article by Andy Revkin in today’s Science Times of The New York Times raises some difficult questions for the climate modeling community. The article carries with it the headline, “Computers Add Sophistication, but Don’t Resolve Climate Debate.” The article observes, “…advances in research on climate change do not guarantee that a consensus will soon be reached on what to do about it. Computer models of climate, particularly, have become a lightning rod in the climate debate, and are likely to remain so for years to come.”

In fact, ASU’s Dan Sarewitz makes the provocative case in a recent paper that advances in science in fact may make environmental controversies worse. It is an article of faith among many that more climate science, and in particular, predictive (or projective, or scenario generation, etc.) results from computer models, will facilitate action on climate change. But what if this assumption is wrong?


One difficult question that might be asked is how we might evaluate the policy utility of climate models. [For some thoughts on this see this book.] By contrast the evaluation of climate models scientific progress according to scientific standards is fairly straightforward. A recent article in the UCAR Quarterly on the new version of the NCAR climate model describes some of these criteria:

Resolution: “The high-res CCSM3 features four times the number of data points as CCSM2 for its land and atmosphere components.”

Speed: “Benchmark tests using CCSM’s atmosphere and ocean components showed Lightning to be 30% to 40% faster per processor than Blue Sky, the larger IBM cluster used since 2001 for much of NCAR’s climate modeling.”

Expandability: “Much of the improvement in CCSM3 is in the model’s foundation for follow-up work, such as in biogeochemistry and land-atmosphere interactions.”

But the article provides some reasons for thinking that the science of modeling can never be completed. Consider the following statements:

“The quest for resolution continues, especially in the realm of clouds and convection. Cloud particles form on scales of microns (0.00004 inches), while cloud formation is now simulated in global models on scales closer to 100 km (60 mi). “So there are 11 orders of magnitude separating us from the fundamental phenomena. What we’re trying to do is start bridging that gap,” says [NCAR scientist William] Collins.”

“Of course, each improvement in a component model makes it more challenging to produce full interactivity in the overall model. That task promises to keep Collins and his colleagues busy for model generations to come. “We’re building a railroad from the east to west coast,” he says, “and we haven’t yet driven the golden spike.””

“Preliminary results indicate that the new version yields greater surface warming than the last version when carbon dioxide is increased to twice its present-day value. Several scenarios for emissions suggest that the atmospheric concentration of CO2 could double by 2100. Researchers have yet to pin down exactly what is making the CCSM3 more sensitive to CO2 …”

[Comment: Today’s climate models are so sophisticated that virtual worlds created in the models can be studied by climate modelers in as much detail as other climate scientists study the real earth.]

And according to NCAR’s Collins, “The model development never ends.”

From the perspective of policy makers, never ending model development may not seem particularly attractive. This places climate modelers in a difficult position. If on the one hand, they make the case that models are currently good enough for the needs of policy makers, then they undercut their best justification for significant funding. But on the other hand, if they say that models are not good enough for the needs of policy makers, then they undercut justifications for action on climate change.

I am on who thinks that climate models are very important to both science and policy, just not in the way that has been conventionally assumed. For more on this see our book on Prediction.

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