A simple framework for modelling the photochemical response to solar spectral irradiance variability in the stratosphere

Abstract

The stratosphere is thought to play a central role in the atmospheric response to solar irradiance variability. Recent observations suggest that the spectral solar irradiance (SSI) variability involves significant time-dependent spectral variations, with variable degrees of correlation between wavelengths, and new reconstructions are being developed. In this paper, we propose a simplified modelling framework to characterise the effect of short term SSI variability on stratospheric ozone. We focus on the pure photochemical effect, for it is the best constrained one. The photochemical effect is characterised using an ensemble simulation approach with multiple linear regression analysis. A photochemical column model is used with interactive photolysis for this purpose. Regression models and their coefficients provide a characterisation of the stratospheric ozone response to SSI variability and will allow future inter-comparisons between different SSI reconstructions. As a first step in this study, and to allow comparison with past studies, we take the representation of SSI variability from the Lean (1997) solar minimum and maximum spectra. First, solar maximum-minimum response is analysed for all chemical families and partitioning ratios, and is compared with past studies. The ozone response peaks at 0.18 ppmv (approximately 3%) at 37 km altitude. Second, ensemble simulations are regressed following two linear models. In the simplest case, an adjusted coefficient of determination <span style="border-top: 1px solid #000; color: #000;">R</span>² larger than 0.97 is found throughout the stratosphere using two predictors, namely the previous day's ozone perturbation and the current day's solar irradiance perturbation. A better accuracy (<span style="border-top: 1px solid #000; color: #000;">R</span>² larger than 0.9992) is achieved with an additional predictor, the previous day's solar irradiance perturbation. The regression models also provide simple parameterisations of the ozone perturbation due to SSI variability. Their skills as proxy models are evaluated independently against the photochemistry column model. The bias and RMS error of the best regression model are found smaller than 1% and 15% of the ozone response, respectively. Sensitivities to initial conditions and to magnitude of the SSI variability are also discussed.