Abstract
Abstract. The well-established "Match" approach to quantifying chemical destruction of ozone in the polar lower stratosphere is applied to ozone observations from the Microwave Limb Sounder (MLS) on NASA's Aura spacecraft. Quantification of ozone loss requires distinguishing transport- and chemically induced changes in ozone abundance. This is accomplished in the Match approach by examining cases where trajectories indicate that the same air mass has been observed on multiple occasions. The method was pioneered using ozonesonde observations, for which hundreds of matched ozone observations per winter are typically available. The dense coverage of the MLS measurements, particularly at polar latitudes, allows matches to be made to thousands of observations each day. This study is enabled by recently developed MLS Lagrangian trajectory diagnostic (LTD) support products. Sensitivity studies indicate that the largest influence on the ozone loss estimates are the value of potential vorticity (PV) used to define the edge of the polar vortex (within which matched observations must lie) and the degree to which the PV of an air mass is allowed to vary between matched observations. Applying Match calculations to MLS observations of nitrous oxide, a long-lived tracer whose expected rate of change is negligible on the weekly to monthly timescales considered here, enables quantification of the impact of transport errors on the Match-based ozone loss estimates. Our loss estimates are generally in agreement with previous estimates for selected Arctic winters, though indicating smaller losses than many other studies. Arctic ozone losses are greatest during the 2010/11 winter, as seen in prior studies, with 2.0 ppmv (parts per million by volume) loss estimated at 450 K potential temperature (~ 18 km altitude). As expected, Antarctic winter ozone losses are consistently greater than those for the Arctic, with less interannual variability (e.g., ranging between 2.3 and 3.0 ppmv at 450 K). This study exemplifies the insights into atmospheric processes that can be obtained by applying the Match methodology to a densely sampled observation record such as that from Aura MLS.
Highlights
The chemical, microphysical, dynamical and radiative processes that give rise to the Antarctic ozone hole each year (Farman et al, 1985; Solomon, 1999; World Meteorological Organization, 2014) occur in the Arctic, though with less severity and large interannual variability (e.g., Manney et al, 2003; Santee et al, 2003; Kuttippurath et al, 2010; Manney et al, 2011)
Matches are defined as cases where an Microwave Limb Sounder (MLS) profile measured during a 20 min Lagrangian trajectory diagnostic (LTD) output timestep lies within 100 km of the location of a trajectory launched from an earlier MLS observation
Matched observations are only included in ozone loss estimates if all of the following conditions are met: 1. both the origin and destination observations are within the polar vortex; 2. the range of sPV variability along the central trajectory is less than 25 % of the mean sPV value; 3. the flanking and central trajectories remain within 100 km of each other in the time between the origin and destination observations
Summary
The chemical, microphysical, dynamical and radiative processes that give rise to the Antarctic ozone hole each year (Farman et al, 1985; Solomon, 1999; World Meteorological Organization, 2014) occur in the Arctic, though with less severity and large interannual variability (e.g., Manney et al, 2003; Santee et al, 2003; Kuttippurath et al, 2010; Manney et al, 2011). Many approaches to ozone loss quantification compute the difference between observed ozone and a model estimate of “passive ozone”, the latter being a chemically inert tracer subject to the same dynamical processes as “real” ozone This method was first developed by Manney et al (1995b, a) to quantify ozone loss during the 1992/93 Arctic winter, using a trajectory-based passive ozone estimate, and has been subsequently applied in similar form to other seasons (e.g., Manney et al, 1996a, b, 1997, 2003; Schoeberl et al, 2002). The Appendix gives more information on the MLS Lagrangian trajectory diagnostics
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