Mean-state acceleration of cloud-resolving models and large eddy simulations

PUBLICATIONS Journal of Advances in Modeling Earth Systems RESEARCH ARTICLE 10.1002/2015MS000488 Key Points: We introduce an acceleration technique for CRMs, LES, and superparameterized GCMs We achieve substantial speedup (2–16X) with little impact on cloud condensate for a range of cases We present applications to superparameterization and LES cloud-climate feedback studies Correspondence to: C. R. Jones, crj6@uw.edu Citation: Jones, C. R., C. S. Bretherton, and M. S. Pritchard (2015), Mean-state acceleration of cloud-resolving models and large eddy simulations, J. Adv. Model. Earth Syst., 7, 1643–1660, doi:10.1002/2015MS000488. Mean-state acceleration of cloud-resolving models and large eddy simulations C. R. Jones 1 , C. S. Bretherton 1 , and M. S. Pritchard 2 Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA, 2 Department of Earth System Science, University of California, Irvine, California, USA Abstract Large eddy simulations and cloud-resolving models (CRMs) are routinely used to simulate boundary layer and deep convective cloud processes, aid in the development of moist physical parameter- ization for global models, study cloud-climate feedbacks and cloud-aerosol interaction, and as the heart of superparameterized climate models. These models are computationally demanding, placing practical con- straints on their use in these applications, especially for long, climate-relevant simulations. In many situa- tions, the horizontal-mean atmospheric structure evolves slowly compared to the turnover time of the most energetic turbulent eddies. We develop a simple scheme to reduce this time scale separation to accelerate the evolution of the mean state. Using this approach we are able to accelerate the model evolution by a fac- tor of 2–16 or more in idealized stratocumulus, shallow and deep cumulus convection without substantial loss of accuracy in simulating mean cloud statistics and their sensitivity to climate change perturbations. As a culminating test, we apply this technique to accelerate the embedded CRMs in the Superparameterized Community Atmosphere Model by a factor of 2, thereby showing that the method is robust and stable to realistic perturbations across spatial and temporal scales typical in a GCM. Received 28 MAY 2015 Accepted 9 OCT 2015 Accepted article online 15 OCT 2015 Published online 29 OCT 2015 1. Introduction Accurately simulating moist convective processes is critical to representing atmospheric motions from local to global scales. The difﬁculty of realistically representing cloud and convective processes is exacerbated by the wide range of spatial and temporal scales that come into play [Stevens and Lenschow, 2001], with increased spatial resolution going hand-in-hand with increased computational cost. Turbulence-resolving large eddy simulations (LESs) with grid spacings of 100 m or less are the highest reso- lution models typically used for probing the interplay among turbulence, clouds, and large-scale conditions. LES studies are generally limited to localized or short duration simulations due to the computational cost of high spatial resolution and the associated short time steps forced by Courant-Friedrichs-Lewy (CFL) stability considerations. C 2015. The Authors. V This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modiﬁcations or adaptations are made. JONES ET AL. Cloud-resolving models (CRMs), similar to LES but with horizontal grid spacings of up to a few kilometers that do not fully resolve boundary layer eddies but do resolve the larger updrafts in cumulonimbus cloud systems, are skillful at simulating deep convection. They are typically used in regional domains up to 1000 km on a side, but pioneering global CRM simulations have been performed [Tomita et al., 2005; Miyamoto et al., 2013]. Superparameterization [Grabowski, 2001; Khairoutdinov and Randall, 2001], in which the cloud physics and moist convection parameterizations in a general circulation model (GCM) are replaced by an embedded 2-D CRM, is an approach to circumventing the limitations of cloud parameteriza- tions in global models that has shown to be skillful in representing cloud and convection-related structures such as the Madden-Julian Oscillation [Khairoutdinov and Randall, 2001]. A practical challenge common to LES, CRMs, and superparameterized GCMs is the high computational cost imposed by the range of spatial and temporal scales being modeled. At the heart of this is the fact that resolving motion across scales is limited by the smallest spatial and shortest time scales. In cases exhibiting some degree of scale separation, one approach to reducing the computational cost is to artiﬁcially modify the governing equations of motion to reduce the separation between fast and slow time scales without adversely affecting the ﬁdelity of the simulation. CRM AND LES MEAN-STATE ACCELERATION