Assessing the relationship between spectral solar irradiance and stratospheric ozone using Bayesian inference

TL;DR

This paper develops a Bayesian method to infer spectral solar irradiance changes from ozone measurements, revealing current limitations and potential for improved understanding of solar-atmosphere interactions.

Contribution

It introduces a Bayesian formalism that links ozone responses to SSI variations, accounting for uncertainties and enabling better inference of solar cycle SSI changes.

Findings

Ozone variations can be approximated by independent wavelength bands between 176 and 310 nm.

Ozone varies approximately linearly with SSI changes.

Current data cannot distinguish between different SSI datasets due to uncertainties.

Abstract

We investigate the relationship between spectral solar irradiance (SSI) and ozone in the tropical upper stratosphere. We find that solar cycle (SC) changes in ozone can be well approximated by considering the ozone response to SSI changes in a small number individual wavelength bands between 176 and 310 nm, operating independently of each other. Additionally, we find that the ozone varies approximately linearly with changes in the SSI. Using these facts, we present a Bayesian formalism for inferring SC SSI changes and uncertainties from measured SC ozone profiles. Bayesian inference is a powerful, mathematically self-consistent method of considering both the uncertainties of the data and additional external information to provide the best estimate of parameters being estimated. Using this method, we show that, given measurement uncertainties in both ozone and SSI datasets, it is not currently possible to distinguish between observed or modelled SSI datasets using available estimates of ozone change profiles, although this might be possible by the inclusion of other external constraints. Our methodology has the potential, using wider datasets, to provide better understanding of both variations in SSI and the atmospheric response.