【摘要】 基于ENVISAT-1/GOMOS(Global Ozone Monitoring by Occultation of Stars)和TIMED/SABER(Sounding of the Atmosphere using Broadband Emission Radiometry)卫星的臭氧廓线数据结合模式HAMMONIA(HamburgModeloftheNeutralandIonizedAtmosphere)对热带低纬度地区(20°S-20°N)中间层(60～110 km高度)夜间(20:00 LT-24:00 LT)臭氧的分布进行统计,分析其与27天太阳周期的相关性.观测与模式均表明中间层夜间臭氧在95 km达到峰值,并在中间层上层存在半年振荡.通过与相同时间段内的太阳辐射强迫Lyman-α数据对比发现,中间层上层臭氧与太阳强迫作用可能是反相关,中间层下层臭氧与太阳强迫作用可能呈正相关.虽然观测结果与模型结果在臭氧随月份的时间和空间分布上有一定的相同特征,但在臭氧峰值的数值上发现有较大的差异,其中臭氧敏感性的振幅被模型低估.更多还原

【Abstract】 The satellite ozone data of ENVISAT-1/GOMOS(Global Ozone Monitoring by Occultation of Stars) and TIMED/SABER(Sounding of the Atmosphere using Broadband Emission Radiometry) are analyzed to provide a statistical analysis of the distribution of ozone in the tropical mesosphere(60～110 km) at night(20:00 LT-24:00 LT) and explore its correlation with the 27-day solar cycle with HAMMONIA(Hamburg model of the neutral and ionized atmosphere). Both observations and model indicate that the nighttime ozone in the mesosphere peaks at 95 km and there is a semiannual oscillation in the upper mesosphere; Comparison with Lyman-α solar radiative forcing data over the same period shows that upper mesospheric(above 80 km) ozone may be inversely correlated with solar forcing, and lower mesospheric ozone may be positively correlated with solar forcing.. In order to better explore the correlation with solar activity, the ozone data were processed by filtering, and it was found that the inverse correlation between ozone at 95 km and Lyman-α was more significant. This correlation is more pronounced when long-term and short-term fluctuations are removed, especially in the months before and after the period of maximum amplitude of the 27-day solar radiative forcing cycle(around January and July 2004). Although the observations and the model results share some common features in the temporal and spatial distribution of ozone variations with months, large differences are found in the values of the peaks where the amplitude of ozone sensitivity is greatly underestimated by the model.更多还原