weigh the costs of moving to it. With the experience, successes, and lessons learned of the past decade, the climate modeling community is positioned to accelerate infrastructure adoption. Cross-laboratory intercomparisons are now routinely conducted and, more importantly, are the way forward. End users require climate model information to be robust and reliable. Common infrastructure improves the ability to enforce scientific methodology (e.g., controlled experimentation, reproducibility, and model verification) across institutions and is one of the primary building blocks of that robustness and reliability.

So far, no one software framework has become a universal standard, because modeling centers that initially invested in one framework have had insufficient incentive to switch to another. Nevertheless, we believe that two critical strategic needs—that the U. S. climate community needs to more effectively collaborate, and that it needs to nimbly adapt to a wave of disruptive new computing technology—position the community for a further unifying step. The vector to parallel disruption led to widespread adoption of framework technologies at the scale of individual institutions. The climate modeling community can now conceive of a framework that could be subscribed to by all major U.S. climate modeling groups, supports a hierarchy of models with component-wise interchangeability, and also supports development of a highperformance implementation that enables climate models of unprecedented resolution and complexity to be efficiently adapted to new architectural platforms. This idea is explored below.

Finding 10.5: Shared software infrastructures present an appealing option for how to face the large uncertainty about the evolution of hardware and programming models over the next two decades.


Very complex models have emergent behavior whose understanding requires being able to reproduce phenomena in simpler models. Chapter 3 makes a strong case for hierarchies of models adapted for different climate problems. From the computational perspective, some model types can be classified by a rough pace of execution needed (i.e., model simulated time per computer clock time) to make efficient scientific progress:

•  process study models and weather models (single component or few components; dominated by “fast” physics; 1 year/day),

•  comprehensive physical climate models (ocean-atmosphere, land and sea ice,

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