Evaluation of the inter-annual variability of stratospheric chemical composition in chemistry-climate models using ground-based multi species time series

Abstract

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Clear-sky ultraviolet radiation modelling using output from the Chemistry Climate Model Initiative

“…Another factor could explain the differences observed at Mauna Loa and Palmer station. For short timescales of about 10 years, a part of the TOZ variability observed at groundbased stations is due to the stratospheric circulation above the station (Poulain et al, 2016). In the refC2 runs, models produce their own wind and temperature fields.…”

“…After the implementation of the Montreal Protocol, emissions of chlorine and bromine-containing ozone-depleting substances (ODSs) have started to decrease and the stratospheric ozone layer is showing signs of recovery (Morgen-stern et al, 2008;Solomon et al, 2016). Nonetheless, greenhouse gas (GHG) emissions are generally still increasing and are expected to affect future ozone levels (Fleming et al, 2011;Revell et al, 2012). Global circulation model simulations project that the Brewer-Dobson circulation will accelerate over the next century (Butchart, 2014), which would lead to a decrease in ozone levels in the tropics and an enhancement at higher latitudes (Hegglin and Shepherd, 2009).…”

Response to referee 2

“…For short time scales of about 10 years, a part of the TOZ variability observed at ground-based stations is due to the wind variability above the station (Poulain et al, 2016). In the refC2 runs, models produce their own wind and temperature fields.In a separate simulation which we do not analyze here, the refC1SD simulation, the model is forced by boundary conditions obtained from reanalyses (Eyring et al, 2013).…”

Sensitivity of the tropical stratospheric ozone response to the solar rotational cycle in observations and chemistry–climate model simulations

“…Adding this synthetic residual time series to the original fitted model allows creating a new synthetic time series (so-called bootstrap sample) to which the linear regression is applied to derive a synthetic slope value. For each value of L, this procedure is repeated 10 000 times in order to construct a distribution of synthetic slopes (Poulain et al, 2016). Finally, we estimate, from this distribution, the likelihood (p value) for the slope to be greater than -or equal to -0 (i.e., null hypothesis).…”