Abstract
Abstract. We present an empirical model for nitric oxide (NO) in the mesosphere (≈60–90 km) derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartoghraphY) limb scan data. This work complements and extends the NOEM (Nitric Oxide Empirical Model; Marsh et al., 2004) and SANOMA (SMR Acquired Nitric Oxide Model Atmosphere; Kiviranta et al., 2018) empirical models in the lower thermosphere. The regression ansatz builds on the heritage of studies by Hendrickx et al. (2017) and the superposed epoch analysis by Sinnhuber et al. (2016) which estimate NO production from particle precipitation. Our model relates the daily (longitudinally) averaged NO number densities from SCIAMACHY (Bender et al., 2017b, a) as a function of geomagnetic latitude to the solar Lyman-α and the geomagnetic AE (auroral electrojet) indices. We use a non-linear regression model, incorporating a finite and seasonally varying lifetime for the geomagnetically induced NO. We estimate the parameters by finding the maximum posterior probability and calculate the parameter uncertainties using Markov chain Monte Carlo sampling. In addition to providing an estimate of the NO content in the mesosphere, the regression coefficients indicate regions where certain processes dominate.
Highlights
It has been recognized in the past decades that the mesosphere and stratosphere are coupled in various ways (Baldwin and Dunkerton, 2001)
We omit the harmonic parts in the model, and the non-linear model is given by Eq (3): xNO(φ, z, t) = a(φ, z)+b(φ, z)·Lyα(t)+c(φ, z)·auroral electrojet index (AE)(t) . (3). This approach shifts all seasonal variations to the AE index and attributes them to particle-induced effects, we found that the residual traces of particle-unrelated seasonal effects were minor compared to the overall improvement of the fit
Thereafter we show the results from the non-linear model and continue to use that for further analysis of the coefficients
Summary
It has been recognized in the past decades that the mesosphere and stratosphere are coupled in various ways (Baldwin and Dunkerton, 2001). Climate models have been evolving to extend to increasingly higher levels in the atmosphere to improve the accuracy of medium- and long-term predictions. Nowadays it is not unusual that these models include the mesosphere (40–90 km) or the lower thermosphere (90–120 km) (Matthes et al, 2017). It is important to understand the processes in the mesosphere and lower thermosphere and to find the important drivers of chemistry and dynamics in that region. The atmosphere above the stratosphere ( 40 km) is coupled to solar and geomagnetic activity, known as space weather (Sinnhuber et al, 2012). Electrons and protons from the solar wind and the radiation belts with sufficient kinetic energy enter the atmosphere in that region. Since as charged particles they move along the magnetic field, this precipitation occurs primarily at high geomagnetic latitudes
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