Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification

TL;DR

This paper introduces a Bayesian framework for estimating stratospheric aerosol sources from observational data, accounting for noise and variability, and demonstrates its effectiveness using Earth system model simulations.

Contribution

It develops a comprehensive, scalable inversion framework that incorporates noise, variability, and uncertainty quantification for stratospheric aerosol source estimation.

Findings

Successfully estimates aerosol sources from synthetic data

Quantifies uncertainty in aerosol source estimates

Addresses challenges in global-scale stratospheric modeling

Abstract

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.