Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification
This work addresses uncertainty in climate modeling for researchers and policymakers, but it is incremental as it builds on existing inversion methods with specific adaptations for stratospheric aerosols.
The paper tackles the problem of estimating stratospheric aerosol sources, such as from volcanic eruptions, which are uncertain due to noise and variability, by presenting a Bayesian framework that accounts for these factors using earth system model simulations, and it demonstrates the approach's ability to estimate sources and quantify uncertainty with synthesized data.
Stratospheric aerosols play an important role in the earth system and can affect the climate on timescales of months to years. However, estimating the characteristics of partially observed aerosol injections, such as those from volcanic eruptions, is fraught with uncertainties. This article presents a framework for stratospheric aerosol source inversion which accounts for background aerosol noise and earth system internal variability via a Bayesian approximation error approach. We leverage specially designed earth system model simulations using the Energy Exascale Earth System Model (E3SM). A comprehensive framework for data generation, data processing, dimension reduction, operator learning, and Bayesian inversion is presented where each component of the framework is designed to address particular challenges in stratospheric modeling on the global scale. We present numerical results using synthesized observational data to rigorously assess the ability of our approach to estimate aerosol sources and associate uncertainty with those estimates.