In a recent article in JAMES (Pressel et al., 2017), we explore how numerical error and subgrid-scale modeling in LES interact to determine the quality of LES of stratocumulus clouds and show that a technique called implicit large eddy simulation provides particularly high fidelity LES. Here we offer a bit of background and a discussion of that work. If you are not familiar with stratocumulus clouds you can see a high resolution LES of stratocumulus here. Read more “Challenges and Solutions in LES of Stratocumulus Clouds”»
How much does a cloud weigh? That was the question Karen LaMonte asked us in an email a year and a half ago. LaMonte—an artist who lives in Prague and is known for monumental sculptures in ceramic, bronze, and glass—wanted to create a marble cloud sculpture of similar weight as a real cloud. What resulted is LaMonte’s sculpture Cumulus, which is an accurate visualization, in marble, of a numerically simulated tropical cumulus cloud. Cumulus is currently on display during the Biennale in Venice.
Last Friday, on the eve of Earth day, Congressman Adam Schiff hosted a town hall on climate change at Caltech. Schiff and Francesca Hopkins from UC Riverside, Alex Hall from UCLA, and I gave introductory remarks and answered questions from the audience. Here is a summary, and here the recording of the official live feed (from a phone, hence with apologies for the poor audio quality):
Starting my town hall in Pasadena at Caltech to discuss the perils of climate change and the Trump Administration's counterproductive — and destructive efforts — to reverse the gains we are making. Watch here:
How low clouds respond to warming remains the greatest source of uncertainty in climate projections. Climate models projecting that much less sunlight will be reflected by low clouds when the climate warms indicate that CO2 concentrations can only reach 470 ppm before the 2℃ warming threshold of the Paris agreement is crossed—a CO2 concentration that will probably be reached in the 2030s. By contrast, models projecting a weak decrease or increase in low-cloud reflection indicate that CO2 concentrations may reach almost 600 ppm before the Paris threshold is crossed. In a new paper, we outline how new computational and observational tools enable us to reduce these vast uncertainties.
Large-eddy simulation (LES) of clouds can help resolve one of the most important and challenging question in climate dynamics, namely, how subtropical low clouds respond to global warming. However, earlier LES studies have generally prescribed large-scale conditions (e.g., surface temperatures) in a way that does not guarantee energy balance. We have developed an energetically consistent framework for driving LES, in which the LES domain is coupled to a simple slab ocean. In this framework, the cloud responses to global warming can be very different than in the traditional frameworks that prescribe surface temperatures.
We are organizing a summer school for graduate students and early career scientists on Fundamental Aspects of turbulent flows in climate dynamics, to take place from July 31 through August 25, 2017 at the Ecole de Physique in Les Houches, in the French Alps. The program will feature international renowned principal lecturers and visitors.
Steadily increasing carbon dioxide concentrations in the atmosphere are warming the Earth. Today (2006-2014) it is 0.8°C warmer than in the preindustrial period in the middle of the 19th century. Climate models try to project how this global warming will continue, but they differ in their response to increasing concentrations of greenhouse gases. Emergent constraints attempt to use information about the current climate to constrain the evolution of climate in the future.
Through their reflection of sunlight and absorption/re-emission of thermal radiation, clouds regulate Earth’s energy balance. But it remains uncertain, in particular, how the fraction of sunlight reflected by clouds will change as greenhouse gas concentrations rise. Projections differ widely among climate models, and differences in the solar reflection by low clouds over tropical oceans account for much of the spread in climate projections across current models. We investigate to what extent this uncertainty can be reduced through the use of observations from space.
A convenient yardstick to measure how sensitive the climate system is to increases in the concentration of greenhouse gases is the equilibrium climate sensitivity (ECS)—the surface warming eventually reached after a sustained doubling of carbon dioxide concentrations. ECS ranges from 2.1 to 4.7 K across current climate models (IPCC AR5).More than half of the ECS variance across models can be traced to differences in the reflection of sunlight by tropical low clouds (TLCs) (Bony and Dufresne 2005; Vial et al. 2013). Neither the sign nor the strength of this TLC feedback are well constrained. Yet constraining the TLC feedback is essential for narrowing the wide range of ECS projected by current models.
The hydrological cycle will change substantially in response to global warming. For the most part, wet regions will get wetter and dry regions will get drier as the amount of water the atmosphere can carry increases with warming. But regional patterns of precipitation minus evaporation are influenced by planetary-scale stationary waves, which are subject to substantial shifts and changes in strength as the planet warms. These stationary-wave changes lead to large regional changes in the hydrological cycle and modify the sensitivity of the hydrological cycle to global warming.
One of the most substantial climate changes in response to global warming is the increase in atmospheric water vapor content. Because of the increase in moisture content, existing wind patterns carry more moisture and strengthen the atmospheric branch of the hydrological cycle: storms bring more rainfall, wet regions get wetter, and dry regions get drier (Held and Soden 2006, O’Gorman and Schneider 2009).
Changes in the winds lead to further changes in the hydrological cycle with global warming. For example, there is an expansion of the subtropical dry zones associated with the poleward expansion of the Hadley circulation with global warming (Lu et al. 2009). Even bigger changes can result from shifts or changes in strength of tropical and subtropical convergence zones. These circulation changes lead to regional departures from the “wet gets wetter, dry gets drier” idea (Chou and Neelin 2004, Seager et al. 2010).
Wills et al. (2016) present an analysis of how circulation changes influence the global pattern of change in net precipitation (precipitation minus evaporation, P – E). The focus is on the east-west (or zonal) variations of P – E, and how they change with global warming. Here, we overview some of the findings from this paper.
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.