Abstract
We present a fast model for stratospheric ozone chemistry based on a
neural network approach. The model is intended to replace the detailed
chemistry schemes of chemistry and transport models (CTMs), general
circulation models (GCMs) or Earth system models (ESMs), which are
computationally very expensive. The neural network (NN) model estimates
the rate of change of ozone in 24 hours at a grid point and is trained
on data of the detailed full chemistry model of the ATLAS chemistry and
transport model (CTM). The benefit of this surrogate models is a much
lower computation time (minutes instead of days) while the same level of
accuracy is achieved. This represents a necessary step from
understanding the chemistry and building sophisticated CTMs towards the
usage of this knowledge in climate models, which is only feasible if
much lower computation times can be achieved. Modelling of the Earth
system is a complex task and models usually contain a large number of
sub-modules and parameterizations. This applies for example to the
atmosphere, hydrosphere, solid earth and the ice sheets. Atmospheric
chemistry is complex and usually involves dozens of species and hundreds
of reactions with a wide range of concentrations and lifetimes. This
project concentrates on the estimation of the rate of change of ozone in
the extrapolar stratosphere. The dynamics from the polar regions and
from other layers of the atmosphere regarding the ozone change are not
treated within this work. The ATLAS model is a Lagrangian CTM for
stratospheric chemistry. It solves a coupled differential equation
system using a stiff solver and a variable time-step. The stratospheric
chemistry scheme of ATLAS has 46 active species, 171 reactions and
heterogeneous chemistry on polar stratospheric clouds. It is not using
the concept of chemical families. The application of the ATLAS CTM has
high requirements on computational power. This is the reason why the
coupling of full chemical models to climate models is generally not
feasible with respect to the computation time of a global climate model.
However, the incorporation of detailed chemistry is often desirable, in
order to account for various feed-backs between chemistry, atmosphere
and ocean. These complex chemical models motivate the formulation of
faster but still accurate surrogate models, that are tailored to the
coupling into earth climate models. This project builds on the SWIFT
project, which has a polar and an extrapolar surrogate model for the
stratospheric ozone chemistry. We investigate an alternative approach to
the polynomial approach used by extrapolar SWIFT by exploiting the
improved approximation capability of NN with respect to nonlinear
contexts.