Abstract
Abstract. This paper explores the feasibility of an experimentation strategy for investigating sensitivities in fast components of atmospheric general circulation models. The basic idea is to replace the traditional serial-in-time long-term climate integrations by representative ensembles of shorter simulations. The key advantage of the proposed method lies in its efficiency: since fewer days of simulation are needed, the computational cost is less, and because individual realizations are independent and can be integrated simultaneously, the new dimension of parallelism can dramatically reduce the turnaround time in benchmark tests, sensitivities studies, and model tuning exercises. The strategy is not appropriate for exploring sensitivity of all model features, but it is very effective in many situations. Two examples are presented using the Community Atmosphere Model, version 5. In the first example, the method is used to characterize sensitivities of the simulated clouds to time-step length. Results show that 3-day ensembles of 20 to 50 members are sufficient to reproduce the main signals revealed by traditional 5-year simulations. A nudging technique is applied to an additional set of simulations to help understand the contribution of physics–dynamics interaction to the detected time-step sensitivity. In the second example, multiple empirical parameters related to cloud microphysics and aerosol life cycle are perturbed simultaneously in order 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.
Highlights
Climate, by definition, is the statistical characterization of the state of the earth’s atmosphere, land, and ocean on time scales longer than a few months (e.g., IPCC, 2013)
Kooperman et al (2012) showed that anthropogenic aerosol indirect effects could be estimated from substantially shorter simulations if temperature and horizontal winds in the atmospheric general circulation models (AGCMs) are relaxed towards prescribed conditions to reduce variability in those fields, while allowing the model to calculate the responses to aerosol emissions in cloud, water, and aerosol fields
The climate model used here is CAM5.1 (Neale et al, 2010), with a finite volume dynamical core that uses the numerical schemes of Lin and Rood (1996) and Lin (2004) to represent the hydrostatic adiabatic fluid dynamics and large-scale tracer transport
Summary
By definition, is the statistical characterization of the state of the earth’s atmosphere, land, and ocean on time scales longer than a few months (e.g., IPCC, 2013). State-of-the-art AGCMs are computationally expensive to integrate when resolution is high, or when a large number of simulations are needed Recent examples of such studies include those of Wehner et al (2013), Zhao et al (2013), Yang et al (2012, 2013), and Qian et al (2014), to name a few. Significant gain in computational efficiency can be expected for two reasons: firstly, unlike a serial-in-time multi-year simulation, the ensemble of realizations can be integrated simultaneously. This introduces an additional dimension of parallelism to better exploit modern supercomputer systems that consist of order 105–106 cores, leading to substantial reduction of the turnaround time in sensitivity experiments.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
