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Natash Lawson
Summary Sentence: A seminar by Dr. Laura Fierce, School of Earth and Atmospheric Sciences
Full Summary: No summary paragraph submitted.
Laura FierceParticle-Based Methods for Improving the Representation of Aerosol Effects on Climate
Aerosol effects on clouds and radiation have remained a large source of uncertainty in quantifying anthropogenic changes to Earth’s energy balance, despite increases in model complexity.
Climate‐relevant aerosol properties depend on characteristics of individual particles, but particle‐level characteristics are not easily represented in atmospheric models. Instead, global and regional aerosol schemes approximate the representation of particle size and composition, leading to errors in predictions of climate-relevant aerosol properties that have not been well quantified.
In my talk, I will describe two different particle-based methods for advancing aerosol representations in large-scale models.
In the first part of my talk, I will discuss a multi-scale framework for combining particle-resolved model simulations with detailed laboratory measurements to quantify error in, and improve, reduced representations of aerosol absorption in large-scale models. I will show that neglecting diversity in composition and morphology yields large errors in modeled light absorption and introduce a method for improving the representation of aerosol absorption in large-scale models.
In the second part of my talk, I will introduce a new sparse-particle model based on the quadrature method of moments, which is designed for use in large-scale atmospheric models. I will demonstrate that cloud condensation nuclei activity of particle-resolved populations, which are comprised of 10,000 to 1,000,000 Monte Carlo particles, are accurately represented by an optimized set of only ten sparse particles.
This study is a first step toward a new aerosol simulation scheme that will track multivariate aerosol distributions with sufficient computational efficiency for inclusion in large-scale atmospheric models.
