Recently we have developed a new, publicly available, large eddy simulation code for the simulation of atmospheric turbulence (Pressel et al., 2015). The code is called PyCLES and is available on Github. Here we discuss aspects of its design that position it well for answering fundamental questions regarding clouds in the climate system.
Perhaps the most widely discussed uncertainties in predictions of climate sensitivity are related to boundary layer clouds and their climate feedbacks (e.g., Bony and Dufresne, 2005; Vial et al., 2013), both because of the size of the uncertainties and how defiant they have been to reduction. Clouds are problematic because they exert strong control on the large-scale climate, mostly through their radiative effects, yet the energetic turbulent dynamics of clouds themselves occur on scales down to meters or centimeters. These scales are much smaller than those directly resolved in general circulation models (GCM). This makes them critically important, but at the same time incredibly difficult to parameterize in GCMs.
Developing a general theory for clouds that can serve as a basis for parameterization in large-scale models is difficult for many reasons, not the least of which is the fact that observing the instantaneous, three-dimensional turbulent structure of clouds, say from aircraft, is essentially impossible. This, along with ever increasing high-performance computing resources, has led to reliance on computational methods to directly simulate important parts of the unobservable turbulent dynamics of clouds. In essence, we rely on supercomputers to solve equations, namely those dictated by Newton’s laws of motion and fairly well understood thermodynamic principles, that generate three-dimensional cloud fields. These simulated cloud fields provide a three-dimensional picture of cloud dynamics to aid in the development of GCM parameterization.
One of the most widely used approaches to simulating clouds is called Large Eddy Simulation (LES). Because resolving all scales of turbulent motion in clouds, even for limited areas, remains beyond the capability of modern computing, LES seeks only to directly represent the large features (on scales of meters) of the three-dimensional turbulent motions in clouds, and then parameterize the rest. Despite what their name suggests, large eddy simulations are high-resolution simulations in the hierarchy of computational models for the atmosphere, which resolve much smaller eddies than climate models. However, since they do not fully resolve all length scales relevant to clouds, LES provide a simplified view of reality, and not a panacea. LES results are sensitive to fine details of the equations used to represent the dynamics and thermodynamics of clouds and the way the equations are solved numerically (Ghosal, 1996; Chow and Moin, 2003). With PyCLES (Python Cloud Large Eddy Simulation), we have attempted to address many of these potential sensitivities and have developed a modern LES code ready to tackle many of the challanges posed by clouds in the climate system. Below, we will describe the specific features that make PyCLES an important tool for studying cloud-climate interactions. We begin by discussing the novel software design of PyCLES and then turn to a discussion of unique aspects of its dynamical equations and numerical implementation.