Abstract

Abstract. There is presently renewed interest in diurnal variations of stratospheric and mesospheric ozone for the purpose of supporting homogenization of records of various ozone measurements that are limited by the technique employed to being made at certain times of day. We have made such measurements for 19 years using a passive microwave remote sensing technique at the Mauna Loa Observatory (MLO) in Hawaii, which is a primary station in the Network for Detection of Atmospheric Composition Change (NDACC). We have recently reprocessed these data with hourly time resolution to study diurnal variations. We inspected differences between pairs of the ozone spectra (e.g., day and night) from which the ozone profiles are derived to determine the extent to which they may be contaminated by diurnally varying systematic instrumental or measurement effects. These are small, and we have reduced them further by selecting data that meet certain criteria that we established. We have calculated differences between profiles measured at different times: morning–night, afternoon–night, and morning–afternoon and have intercompared these with like profiles derived from the Aura Microwave Limb Sounder (Aura-MLS), the Upper Atmosphere Research Satellite Microwave Limb Sounder (UARS-MLS), the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES), and Solar Backscatter Ultraviolet version 2 (SBUV/2) measurements. Differences between averages of coincident profiles are typically < 1.5% of typical nighttime values over most of the covered altitude range with some exceptions. We calculated averages of ozone values for each hour from the Mauna Loa microwave data, and normalized these to the average for the first hour after midnight for comparison with corresponding values calculated with the Goddard Earth Observing System Chemistry Climate Model (GEOSCCM). We found that the measurements and model output mostly agree to better than 1.5% of the midnight value, with one noteworthy exception: The measured morning–night values are significantly (2–3 %) higher than the modeled ones from 3.2 to 1.8 hPa (~39–43 km), and there is evidence that the measured values are increasing compared to the modeled values before sunrise in this region.

Highlights

  • Chapman (1930) first described a theory that, it included only five reactions between various oxygen species and did not consider dynamics, predicted several of the major features of the stratospheric ozone layer

  • We have calculated profile differences in ozone expressed in normalized units as described in Sect. 2.1 between two times of day at or over Mauna Loa Observatory (MLO) from measurements made by microwave ozone profiling radiometer (MWR), Aura-MLS, UARS-MLS, Submillimeter-Wave Limb-Emission Sounder (SMILES), and two pairings of Solar Backscatter Ultraviolet version 2 (SBUV/2) instruments

  • We have developed measurements of the diurnal variations of stratospheric ozone over Mauna Loa, Hawaii from the original data produced by the Network for Detection of Atmospheric Composition Change (NDACC) microwave ozone profiling instrument (MWR) located there

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Summary

Introduction

Chapman (1930) first described a theory that, it included only five reactions between various oxygen species and did not consider dynamics, predicted several of the major features of the stratospheric ozone layer. The orbit of the International Space Station carrying the Superconducting Submillimeter-Wave Limb-Emission Sounder (hereafter SMILES) cycled through 24 h every 60 days, enabling the diurnal observations reported by Sakazaki et al (2013) In such cases, seasonal ozone variations must be accounted for when comparing a measurement at one time of day with that from another, as the latter may have been taken several tens of days later or earlier, depending on the rate of precession of the satellite orbit. The hourly data are not included in the submission, but are available from the corresponding author upon request

A note on units
Systematic error tests using spectral differences
Summary of comparisons
Uncertainty in MWR measurements
Findings
Conclusions

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