Modern climate models are indispensable yet computationally expensive research tools. The computational cost of climate simulations continues to increase at a fast rate due to the perpetual desire to include higher levels of details. Current high-resolution simulations usually take multiple days, if not weeks or months, to finish even on the fastest computer systems. This is partly because the simulations need to be sufficiently long so that robust statistics can be derived to separate the signal they seek from the noise that is inherent for the highly complex climate system. In this paper, Department of Energy scientists from Pacific Northwest National Laboratory developed a new method that speeds up the turnaround of numerical experimentation several hundred times faster than previous simulations. Such a dramatic improvement in efficiency will help extend the scope and depth of detail in research investigations within a typical project lifetime.

The researchers compared two examples using the Community Atmosphere Model, version 5. In the first example, they characterized sensitivities of the simulated clouds to time-step length. Results showed that 3-day ensembles of 20 to 50 members were sufficient to reproduce the main signals revealed by traditional 5-year simulations. They applied a nudging technique to an additional set of simulations to help understand the contribution of the physics–dynamics interaction to the detected time-step sensitivity. In the second example, they simultaneously perturbed multiple empirical parameters related to cloud microphysics and aerosol life cycle to find out which parameters have the largest impact on the simulated global mean top-of-atmosphere radiation balance. It turns out that 12-member ensembles of 10-day simulations are able to reveal the same sensitivities as seen in 4-year simulations performed in a previous study. In both cases, the ensemble method reduces the total computational time by a factor of about 15, and the turnaround time by a factor of several hundred. The efficiency of the method makes it particularly useful for the development of high-resolution, costly, and complex climate models.