Published in

American Geophysical Union, Journal of Geophysical Research, D5(106), p. 5005-5019, 2001

DOI: 10.1029/2000jd900694

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Estimating relationships between air mass origin and chemical composition

Journal article published in 2001 by John Methven, Mathew Evans ORCID, Peter Simmonds, Gerard Spain
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

Observations of a chemical at a point in the atmosphere typically show sudden transitions between episodes of high and low concentration. Often these are associated with a rapid change in the origin of air arriving at the site. Lagrangian chemical models riding along trajectories can reproduce such transitions, but small timing errors from trajectory phase errors dramatically reduce the correlation between modeled concentrations and observations. Here the origin averaging technique is introduced to obtain maps of average concentration as a function of air mass origin for the East Atlantic Summer Experiment 1996 (EASE96, a ground-based chemistry campaign). These maps are used to construct origin averaged time series which enable comparison between a chemistry model and observations with phase errors factored out. The amount of the observed signal explained by trajectory changes can be quantified, as can the systematic model errors as a function of air mass origin. The Cambridge Tropospheric Trajectory model of Chemistry and Transport (CiTTyCAT) can account for over 70% of the observed ozone signal variance during EASE96 when phase errors are side-stepped by origin averaging. The dramatic increase in correlation (from 23% without averaging) cannot be achieved by time averaging. The success of the model is attributed to the strong relationship between changes in ozone along trajectories and their origin and its ability to simulate those changes. The model performs less well for longer-lived chemical constituents because the initial conditions 5 days before arrival are insufficiently well known.