Abstract
Abstract. Observational studies of stratospheric ozone often involve data from multiple instruments that measure the ozone at different times of day. There has been an increased awareness of the potential impact of the diurnal cycle when interpreting measurements of stratospheric ozone at altitudes in the mid- to upper stratosphere. To address this issue, we present a climatological representation of diurnal variations in ozone with a 30 min temporal resolution as a function of latitude, pressure and month, based on output from the Goddard Earth Observing System (GEOS) general circulation model coupled to the NASA Global Modeling Initiative (GMI) chemistry package (known as the GEOS-GMI chemistry model). This climatology can be applied to a wide range of ozone data analyses, including data intercomparisons, data merging and the analysis of data from a single platform in a non-sun-synchronous orbit. We evaluate the diurnal climatology by comparing mean differences between ozone measurements made at different local solar times to the differences predicted by the diurnal model. The ozone diurnal cycle is a complicated function of latitude, pressure and season, with variations of less than 5 % in the tropics and subtropics, increasing to more than 15 % near the polar day terminator in the upper stratosphere. These results compare well with previous modeling simulations and are supported by similar size variations in satellite observations. We present several example applications of the climatology in currently relevant data studies. We also compare this diurnal climatology to the diurnal signal from a previous iteration of the free-running GEOS Chemistry Climate Model (GEOSCCM) and to the ensemble runs of GEOS-GMI to test the sensitivity of the model diurnal cycle to changes in model formulation and simulated time period.
Highlights
Stratospheric ozone has been an environmental concern since the suggestion 45 years ago that anthropogenic chemicals released into the atmosphere could destroy ozone (Stolarski and Cicerone, 1974; Molina and Rowland, 1974)
We present a climatology of diurnal variability as derived from the NASA Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) general circulation model coupled to the NASA Global Modeling Initiative (GMI) chemistry package (GEOS-GMI; e.g., Oman et al, 2013; Orbe et al, 2017)
The paper is divided into the following sections: in Sect. 2 we describe the model and the data used in this study; in Sect. 3 we present GDOC and compare its variability to that observed by the Submillimeter-Wave Limb-Emission Sounder (SMILES) and the Upper Atmosphere Research Satellite (UARS) and Aura Microwave Limb Sounder (MLS) satellite instruments, as well as to that from previously published observational and model-based studies; in Sect. 4 we explore several example uses of GDOC in data analysis; and, in Sect. 5 we summarize our results, evaluate the robustness of the diurnal signal in multiple model runs and detail how to access GDOC
Summary
Stratospheric ozone has been an environmental concern since the suggestion 45 years ago that anthropogenic chemicals (collectively known as ozone depleting substances; ODSs) released into the atmosphere could destroy ozone (Stolarski and Cicerone, 1974; Molina and Rowland, 1974). Our understanding of ozone chemistry and dynamics has vastly evolved, and high quality satellite and groundbased observations of ozone have been key to that evolution. These observations were used to quantify ozone loss during the 1980s and early 1990s and are being used to quantify the turn around and expected increase in ozone after the ban of many ODSs. the slow decline in these chemicals, resulting from their long atmospheric lifetimes and the staged reduction of their use through the Montreal Protocol and subsequent amendments, means that the ozone recovery rate will be much slower than the loss rate. Measurements from more than one source are required to provide adequate spatial and temporal coverage to evaluate the full range of effects of ODSs on ozone, such that data must be consistent across multiple observation platforms
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