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.
A number of observational studies points to a weakening of solar reflection by TLCs under warming (Clement et al. 2009; Dessler 2010, 2013; Zhou et al. 2013), suggesting a positive TLC feedback. Other studies indicate that models with strongly positive low-cloud feedback are more consistent with observations than models with weakly positive or negative feedback (Qu et al. 2014, 2015b, Myers and Norris 2016). This is in line with other model–observation comparisons that also point to higher ECS (Fasullo and Trenberth 2012; Sherwood et al. 2014; Tian 2015). By contrast, studies focusing on Earth’s energy budget generally point to a lower ECS (Otto et al. 2013), albeit with large uncertainties that still allow a high ECS. In Brient and Schneider (2016), we show how space-based observations can be used to robustly constrain the TLC feedback and constrain ECS.
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.
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.
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.
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?”