Monthly averaged ozone and nitrous oxide from the Improved Limb Atmospheric Spectrometer (ILAS) in the Northern and Southern Hemisphere polar regions

Abstract

Northern and southern hemispheric averaged ozone (O3) and nitrous oxide (N2O) measured by the Improved Limb Atmospheric Spectrometer (ILAS) were used to examine photochemical and dynamical changes in high‐latitude O3 distributions. Using correlations of O3 versus N2O, the ILAS data are organized monthly in both hemispheres by partitioning these data into equal bins of altitude or potential temperature. The resulting families of curves help to differentiate O3 changes due to photochemistry from those due to transport. Our study extends the work of Proffitt et al. [2003] for the Northern Hemisphere to the Southern Hemisphere. Further, our study confirms and extends their results for the Northern Hemisphere by applying their analysis to a significantly greater altitude range. As in the Northern Hemisphere, the families of curves for the altitude, and potential temperature bins in the Southern Hemisphere are separated and generally do not cross. In both hemispheres a better separation is found for the potential temperature binning. In the Southern Hemisphere November and December data, preserved photochemical O3 loss is evident in the lower stratosphere. Further, summer ozone loss is evident in the Southern Hemisphere from January to March. In the Arctic, ongoing photochemical O3 loss is evident in the Northern Hemisphere spring data. While at higher altitudes the correlation between N2O and O3 is generally positive (increasing N2O with increasing O3), at lower levels the correlation is negative. This change of correlation from positive to negative can be interpreted in terms of photochemical and dynamical processes. Strong descent causes a steepening of the positively correlated curves, while the curves change their slope from positive to negative if photochemical destruction of O3 is present and descent is weak. The level of slope change is also photochemically influenced and therefore changes with season. Data sets such as the one derived here may be useful for testing atmospheric models and for identifying future changes in stratospheric ozone.