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PyCLES: A New (Open Source) Atmospheric LES Code

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.

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Understanding the width of the ITCZ

The intertropical convergence zone (ITCZ) is narrow, but why? Was the ITCZ narrower or wider in past climates? How will the width of the ITCZ respond to global warming? These questions challenge our understanding of climate dynamics, and have implications for the impact of climate change in the tropics.

Figure 1. The observed ITCZ: Average precipitation rate (1998-2014) over oceans from the Tropical Rainfall Measuring Mission (colours). The red contour maps where the vertical velocity in the mid-troposphere is zero, i.e. the boundary separating regions of ascending and descending air in the tropics. Vertical velocity data are from the ERA-Interim reanalysis.

As described in the previous blogpost, the ITCZ is a band of intense rainfall that circles the Earth (Fig. 1), moving north and south across the equator over the course of a year following the seasonal cycle of solar insolation. Averaged over a year, the centre of the ITCZ lies just north of the equator. Considerable research has focused on why the ITCZ sits at 6° north on average, and how the ITCZ position varies with climate. What has received comparatively little attention is the width of the ITCZ. Despite being of fundamental importance for controlling tropical climate and sea-surface temperatures (Pierrehumbert 1995), it is not clear what controls the ITCZ width nor how it should respond to changes in climate. Studies with climate models have noted that the ITCZ width depends on interactions between radiation and clouds (Voigt & Shaw 2015) and how the model represents sub-grid scale convection (Kang et al. 2009), but a physical understanding of why the ITCZ width is affected by these processes is lacking. Here we present results from Byrne & Schneider (2016) in which we combine basic theory and idealised climate-model simulations to investigate the physical processes determining the width of the ITCZ and its sensitivity to climate change.

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Why does the ITCZ shift and how?

Annual-mean precipitation (colors) and surface winds (arrows). The precipitation data are from TRMM-TMPA for the years 1998– 2012, and the wind data are based on the ECMWF interim reanalysis for the same years. From Schneider et al. (2014).

Most rain on Earth falls in the tropical rain belt known as the Intertropical Convergence Zone (ITCZ), which on average lies 6° north of the equator. Over the past 15 years, it has become clear that the ITCZ position can shift drastically in response to remote changes, for example, in Arctic ice cover. But current climate models have difficulties simulating the ITCZ accurately, often exhibiting two ITCZs north and south of the equator when in reality there is only one. What controls the sensitivity of the ITCZ to remote forcings? And how do the model biases in the ITCZ arise?

Paleoclimate studies (e.g., Peterson et al. 2000, Haug et al. 2001) and a series of modeling studies starting with Vellinga and Wood (2002), Chiang and Bitz (2005) and Broccoli et al. (2006) have revealed one important driver of ITCZ shifts: differential heating or cooling of the hemispheres shifts the ITCZ toward the differentially warming hemisphere. So when the northern hemisphere warms, for example, because northern ice cover and with it the polar albedo are reduced, the ITCZ shifts northward. This can be rationalized as follows: When the atmosphere receives additional energy in the northern hemisphere, it attempts to rectify this imbalance by transporting energy across the equator from the north to the south. Most atmospheric energy transport near the equator is accomplished by the Hadley circulation, the mean tropical overturning circulation. The ITCZ lies at the foot of the ascending branch of the Hadley circulation, and the circulation transports energy in the direction of its upper branch, because energy (or, more precisely, moist static energy) usually increases with height in the atmosphere. Southward energy transport across the equator then requires an ITCZ north of the equator, so the upper branch of the Hadley circulation can cross the equator going from the north to the south. Read more “Why does the ITCZ shift and how?” »