Abstract

Abstract. The Extrapolar SWIFT model is a fast ozone chemistry scheme for interactive calculation of the extrapolar stratospheric ozone layer in coupled general circulation models (GCMs). In contrast to the widely used prescribed ozone, the SWIFT ozone layer interacts with the model dynamics and can respond to atmospheric variability or climatological trends. The Extrapolar SWIFT model employs a repro-modelling approach, in which algebraic functions are used to approximate the numerical output of a full stratospheric chemistry and transport model (ATLAS). The full model solves a coupled chemical differential equation system with 55 initial and boundary conditions (mixing ratio of various chemical species and atmospheric parameters). Hence the rate of change of ozone over 24 h is a function of 55 variables. Using covariances between these variables, we can find linear combinations in order to reduce the parameter space to the following nine basic variables: latitude, pressure altitude, temperature, overhead ozone column and the mixing ratio of ozone and of the ozone-depleting families (Cly, Bry, NOy and HOy). We will show that these nine variables are sufficient to characterize the rate of change of ozone. An automated procedure fits a polynomial function of fourth degree to the rate of change of ozone obtained from several simulations with the ATLAS model. One polynomial function is determined per month, which yields the rate of change of ozone over 24 h. A key aspect for the robustness of the Extrapolar SWIFT model is to include a wide range of stratospheric variability in the numerical output of the ATLAS model, also covering atmospheric states that will occur in a future climate (e.g. temperature and meridional circulation changes or reduction of stratospheric chlorine loading). For validation purposes, the Extrapolar SWIFT model has been integrated into the ATLAS model, replacing the full stratospheric chemistry scheme. Simulations with SWIFT in ATLAS have proven that the systematic error is small and does not accumulate during the course of a simulation. In the context of a 10-year simulation, the ozone layer simulated by SWIFT shows a stable annual cycle, with inter-annual variations comparable to the ATLAS model. The application of Extrapolar SWIFT requires the evaluation of polynomial functions with 30–100 terms. Computers can currently calculate such polynomial functions at thousands of model grid points in seconds. SWIFT provides the desired numerical efficiency and computes the ozone layer 104 times faster than the chemistry scheme in the ATLAS CTM.

Highlights

  • Modern climate models include an increasing number of climate processes and run with ever higher model resolutions

  • To estimate the error of Extrapolar SWIFT, we examine the difference of ATLAS Ox minus SWIFT Ox divided by the Ox volume mixing ratio (VMR)

  • The version of Extrapolar SWIFT coupled to the ATLAS CTM is implemented in MATLAB because the ATLAS model was written in MATLAB

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Summary

Introduction

Modern climate models include an increasing number of climate processes and run with ever higher model resolutions. Its goal is to provide sufficient accuracy and efficiency to enable ensemble simulations with atmosphere–ocean coupled GCMs, while maintaining the physical and chemical properties of the processes that govern ozone chemistry in the stratosphere so that the SWIFT approach is valid for a wide range of climatic conditions, including future climate scenarios. By approximating the output of the full system, we ensure that all physical and chemical properties of the full chemical model are maintained in the repro-model In this application the rate of change of ozone in the lower and middle stratosphere is parameterized by one polynomial function per month. The development of Extrapolar SWIFT and the results of the simulations are discussed in Kreyling (2016)

Setting up the repro-model
Approximation algorithm
Latitude and altitude boundaries of the repro-model
Training data
Domain of definition of polynomial functions
Handling outliers
Comparison of the rate of change of ozone
Error estimation
SWIFT coupled to the ATLAS CTM
Computational cost of Extrapolar SWIFT
Findings
Conclusions
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