Technical Note: SWIFT – a fast semi-empirical model for polar stratospheric ozone loss
An extremely fast model to estimate the degree of stratospheric ozone depletion during polar winters is described. It is based on a set of coupled differential equations that simulate the seasonal evolution of vortex-averaged hydrogen chloride (HCl), nitric acid (HNO<sub>3</sub>), chlori...
Main Authors: | , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2014-07-01
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Series: | Atmospheric Chemistry and Physics |
Online Access: | http://www.atmos-chem-phys.net/14/6545/2014/acp-14-6545-2014.pdf |
Summary: | An extremely fast model to estimate the degree of stratospheric ozone
depletion during polar winters is described. It is based on a set of coupled
differential equations that simulate the seasonal evolution of
vortex-averaged hydrogen chloride (HCl), nitric acid (HNO<sub>3</sub>),
chlorine nitrate (ClONO<sub>2</sub>), active forms of chlorine (ClO<sub>x</sub> =
Cl + ClO + 2 ClOOCl) and ozone (O<sub>3</sub>) on
isentropic levels within the polar vortices. Terms in these equations account
for the chemical and physical processes driving the time rate of change of
these species. Eight empirical fit coefficients associated with these terms
are derived by iteratively fitting the equations to vortex-averaged
satellite-based measurements of HCl, HNO<sub>3</sub> and ClONO<sub>2</sub>
and observationally derived ozone loss rates. The system of differential
equations is not stiff and can be solved with a time step of one day,
allowing many years to be processed per second on a standard PC. The inputs
required are the daily fractions of the vortex area covered by polar
stratospheric clouds and the fractions of the vortex area exposed to
sunlight. The resultant model, SWIFT (Semi-empirical Weighted Iterative Fit
Technique), provides a fast yet accurate method to simulate ozone loss rates
in polar regions. SWIFT's capabilities are demonstrated by comparing measured
and modeled total ozone loss outside of the training period. |
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ISSN: | 1680-7316 1680-7324 |