Posts in Research
A key challenge in climate science is to distinguish temperature changes in response to external forcing (e.g., global warming in response to anthropogenic greenhouse gasses) from temperature changes due to atmosphere-ocean internal variability. Extended integrations of forced and unforced climate models are often used for this purpose. In Wills et al. (2018), we demonstrated a novel method called low-frequency component analysis (LFCA), which separates modes of internal variability from global warming based on differences in time scales and spatial patterns, without relying on climate models.
In a previous post, I described the concept of emergent constraints, which allow us to narrow uncertainties in climate change projections through empirical relationships that relate a model’s climate response to observable metrics. The credibility of an emergent constraint relies upon the strength of the statistical relationship, a clear understanding of the mechanisms underlying the relationship, and the accuracy of observations. A number of emergent constraints have already been identified, with different weaknesses and strengths. This post aims to summarize some of them.
Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized in models, such as clouds, turbulence, and ecosystems. But breakthroughs in the accuracy of climate projections are finally within reach. New tools from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Scientific, computational, and mathematical challenges need to be confronted to realize such an ESM, for example, developing parameterizations suitable for automated learning, and learning algorithms suitable for ESMs. While these challenges are substantial, building an ESM that learns automatically from diverse data sources is achievable now. Such an ESM offers the key opportunity for dramatic improvements in the accuracy of climate projections.
Various attempts have been made to narrow the likely range of the equilibrium climate sensitivity (ECS) through exploitation of “emergent constraints.” They generally use correlations between the response of climate models to increasing greenhouse gas (GHG) concentrations and a quantity in principle observable in the present climate (e.g., an amplitude of natural fluctuations) to constrain ECS given measurements of the present-day observable. However, recent studies have arrived at different conclusions about likely ECS ranges. The different conclusions arise at least in part because the studies have systematically underestimated statistical uncertainties.
The weakening of the Walker circulation in the tropical Pacific is a robust response to global warming in climate models. This can have a global impact on climate, because the convection in the ascending branch of the Walker circulation triggers planetary scale waves that radiate to higher latitudes. In a recent article in the Journal of Atmospheric Sciences (Wills et al. 2017), we study the physical mechanisms responsible for the Walker circulation weakening in an idealized model. Here, we discuss how this work applies to the real-world climate system and how Walker circulation changes are related to tropical Pacific sea-surface temperature changes.
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 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.
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.