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 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:
Posted by Congressman Adam Schiff on Friday, April 21, 2017
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.