Gravity wave mixing effects on the OH*-layer

Journal Pre-proofs Gravity Wave Mixing Effects on the OH*-layer E. Becker, M. Grygalashvyly, G.R. Sonnemann PII: DOI: Reference:

S0273-1177(19)30721-5 https://doi.org/10.1016/j.asr.2019.09.043 JASR 14468

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Advances in Space Research

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11 July 2019 23 September 2019

Please cite this article as: Becker, E., Grygalashvyly, M., Sonnemann, G.R., Gravity Wave Mixing Effects on the OH*-layer, Advances in Space Research (2019), doi: https://doi.org/10.1016/j.asr.2019.09.043

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Gravity Wave Mixing Effects on the OH*-layer Becker, E., M. Grygalashvyly, and G. R. Sonnemann Leibniz-Institute of Atmospheric Physics at the University Rostock in Kühlungsborn, Schloss-Str.6, D-18225 Ostseebad Kühlungsborn, Germany

Abstract: Based on an advanced numerical model for excited hydroxyl (OH*) we simulate the effects of gravity waves (GWs) on the OH*-layer in the upper mesosphere. The OH* model takes into account 1) production by the reaction of atomic hydrogen (H) with ozone (O3), 2) deactivation by atomic oxygen (O), molecular oxygen (O2), and molecular nitrogen (N2), 3) spontaneous emission, and 4) loss due to chemical reaction with O. This OH* model is part of a chemistry-transport model (CTM) which is driven by the high-resolution dynamics from the KMCM (Kühlungsborn Mechanistic general Circulation Model) which simulates midfrequency GWs and their effects on the mean flow in the MLT explicitly. We find that the maximum number density and the height of the OH*-layer peak are strongly determined by the distribution of atomic oxygen and by the temperature. As a results, there are two ways how GWs influence the OH*-layer: 1) through the instantaneous modulation by O and T on short time scales (a few hours), and 2) through vertical mixing of O (days to weeks). The instantaneous variations of the OH*-layer peak altitude due to GWs amount to 5-10 km. Such variations would introduce significant biases in the GW parameters derived from airglow when assuming a constant pressure level of the emission height. Performing a sensitivity experiment we find that on average, the vertical mixing by GWs moves the OH*-layer down by ~2-7 km and increases its number density by more than 50%. This effect is strongest at middle and high latitudes during winter where secondary GWs generated in the stratopause region account for large GW amplitudes.

1

1. Introduction Ground-based measurements of the emission from excited hydroxyl in the mesopause region are a standard method to infer information about temperatures and minor chemical constituents (ozone, atomic hydrogen, and atomic oxygen). Such data can be used to monitor a variety of dynamical processes: 1) seasonal variations (e.g. She and Lowe, 1998; Espy et al., 2007; Reid et al., 2017), 2) large-scale variability patterns such as sudden stratospheric warmings (Shepherd et al., 2010; Damiani et al., 2010; Gao et al., 2011) or the quasi-biennial oscillation (Gao et al., 2010), 3) traveling planetary waves (Takahashi et al., 1999; LopezGonzalez et al., 2009; Reisin et al., 2014) and tides (e.g., Lopez-Gonzalez et al., 2005, Xu et al., 2010), 4) variations induced by the solar cycle (Espy and Stegman, 2002; Pertsev and Perminov, 2008; Offermann et al., 2010; Holmen et al., 2014; Kalicinsky et al. 2016), and 5) even trends (Espy and Stegman, 2002; Bittner et al., 2002; Beig et al, 2003). A review of previous studies that us emission from excited hydroxyl to investigate dynamical processes was given by Shepherd et al. (2012). Note that ground-based measurements of airglow infer the temperature by using the relative intensities of two lines in one of the vibrationalrotational bands of OH*; temperature variations are inferred by making the usual assumption that the emission emanates from a constant height of ~87-88 km. As pointed by von Savigny (2015), the emission height is subject of significant latitudinal and seasonal variations. In addition to dynamical processes, also minor chemical constituents and chemical heat are in the focus of airglow observations. While the first measurements of atomic oxygen in the MLT were based on rocket soundings (Good, 1976), this minor constituent can also be retrieved from satellite-based observations of the emission from the OH* Meinel bands (Russell et al., 2005; Smith et al., 2010; Mlynczak et al., 2013a,b; Zhu and Kaufmann, 2018). Smith et al. (2009) used airglow measurements to derive ozone; and Thomas (1990), Takahashi et al. (1996), and Mlynczak et al. (2014) retrieved atomic hydrogen in the MLT, which is hardly possible by any other method. Further applications of OH* airglow 2

measurements in the mesopause region include estimates of exothermic chemical heat (Mlynczak et al., 2013b) and characteristic chemical times (Belikovich et al., 2018; Kulikov et al., 2018, 2019). In this study we focus on the role of gravity waves (GWs) in the variability of the OH* layer. GWs are the most important dynamical variations that contribute to the large-scale momentum and energy budgets MLT (e.g., Fritts and Alexander, 2003).

GW-induced

variations of the OH* layer have intensively been studied on the basis of observations (e.g., Taylor et al., 1991, 1997, 1998; Wachter et al. 2015) and modeling (e.g., Walterscheid and Schubert, 1987, 1989; Hickey et al., 1992; Makhlouf et al., 1995, 1998; Liu and Swenson, 2003; Huang and Hickey, 2007). Modeling of the OH* layer in these former studies was subject to either one-dimensional of two-dimensional models, the assumption of linear steadystate chemistry, or linear description of GWs. Huang and Hickey (2007) noted that in order to correctly assess the effects of GWs on the OH* layer, a numerical model should be threedimensional and time-dependent, and that it should explicitly describe a broad spectrum of GWs and include nonlinear chemistry. The model concept used in this study satisfies these criteria. We will investigate two kinds of GW impact on the OH* layer: 1) local and instantaneous effects, and 2) the averaged (mean) effect of GWs that is due to mixing. The mechanism of GW mixing can be described as follows: The explicit consideration of GWs in the transport equation of a tracer that is subject to some subgrid-scale (SGS) turbulent and/or molecular diffusivity gives rise to a down-gradient mean tracer flux that occurs in addition to 1) the transport by the residual circulation and 2) the flux related directly to the SGS diffusivity. Grygalashvyly el al. (2011, 2012) analyzed this mechanism using a chemistry-transport model (CTM) in combination with a mechanistic, GW-resolving General Circulation Model (GCM). They found, for example, that water vapor is efficiently mixed upward while atomic oxygen is generally mixed downward by resolved GWs. More recently, Becker and Vadas (2018) found that in addition to primary GWs (i.e., GWs having 3

tropospheric sources), secondary GWs that are generated in the winter stratopause region by the body force mechanism (e.g., Vadas and Becker, 2018; Vadas et al., 2018, see also references therein) are quite important for the residual circulation in the MLT, particularly at middle and high latitudes during wintertime where secondary GWs presumably account for the largest GW amplitudes in the middle atmosphere. These secondary GWs presumably play a role also in the mixing of minor constituents and will therefore affect the OH* layer. Note that down-gradient mixing due to GWs occurs regardless of the direction of the residual circulation. This mixing is automatically included in a CTM that describes GWs explicitly. In the next section we describe our combination of models and numerical experiments. Results are presented and discussed in Sec. 3, and a summary is given in Sec. 4.

2. Models and numerical experiment

2.1. Dynamical and chemical model components The dynamical part of our combination of models is a new version of the Kühlungsborn Mechanistic general Circulation Model (KMCM) (Becker and Vadas, 2018; Vadas and Becker, 2018). The KMCM is a spectral model with a terrain-following vertical coordinate and a staggered vertical grid (Simmons and Burridge, 1981). We use a triangular spectral truncation at total horizontal wavenumber 240 and 190 vertical levels (T240L190). This corresponds to a ~55 km horizontal grid-point resolution and a vertical level spacing of ~ 0.6 km from the lower troposphere to ~100 km. The level spacing is coarser in the lower thermosphere, and the highest model layer is placed at ~0.00002 hPa (z≈135 km). Model data are stored with a snapshot interval of 1350 s. The KMCM includes explicit computations of the moisture cycle and radiative transfer, as well as a closed surface energy budget (Becker et al., 2015; Becker, 2017). There is no parameterization of GWs in the KMCM since these are simulated explicitly. This is 4

made possible by combining the high resolution with an advanced parameterization of horizontal and vertical diffusion that induces GW drag and energy deposition in a selfconsistent fashion (Becker, 2009). Primary GWs (GWs having tropospheric sources) are generated dynamically within the extratropical storm tracks mainly by fronts and nonlinear dynamics of Rossby waves (Plougonven and Zhang, 2014), as well as by orography. Even though small-scale orographic GWs are not resolved by the KMCM, the medium-scale orographic GWs from the most prominent hotspots relevant for the middle atmosphere are reasonably well simulated. In addition, the KMCM simulates secondary GWs that are generated by the body-force mechanism in the stratopause region during wintertime (Becker and Vadas, 2018; Vadas and Becker, 2018, 2019). The dataset that is used in this paper is an extended simulation with the KMCM as used in Becker and Vadas (2018) and Vadas and Becker (2018). The dynamical fields from the KMCM are then used to drive our Chemistry Transport Model (CTM) for the MLT. This model includes chemical reactions, radiation, transport, and SGS vertical diffusion. Our present simulations were performed on a grid having 576 longitudinal and 286 latitudinal grid points, as well as 118 vertical levels in log-pressure coordinates from the ground to approximately 150 km, using a scale height of 7 km. This distribution of grid points in the CTM takes full advantage of the scales resolved by the KMCM. To drive the CTM, the precalculated dynamical fields are interpolated to the grid of the CTM. The chemistry module consists of 19 constituents. The reaction scheme includes 49 chemical reactions and 14 photo-dissociation processes. The reaction rates are taken from Burkholder et al. (2015) and are calculated on-line; thus, they are sensitive to temperature variations. The chemistry is based on the family concept (Shimazaki, 1985), considering the odd hydrogen (H, OH, HO2, H2O2), the odd oxygen (O, O(1D), O3), and the odd nitrogen (NO, NO2, N(4S), N(2D)) families. Chemical processes are computed with the implicit Euler scheme, including a quadratic loss term. The long-lived and short-lived constituents are 5

solved separately, where the short-lived constituents are included in the chemical families, which are solved as fully implicit subsystems. The CTM uses fixed absorber concentrations for the calculation of the photo-dissociation rates (Röth, 1992). The dissociation rates in the model are taken from a pre-calculated library and depend on height and zenith angle (Kremp et al., 1999). The advection is calculated according to Walcek (2000). The vertical diffusion includes both turbulent and molecular diffusivity according to Colegrove et al. (1965) and Morton and Mayers (1994). For the turbulent diffusion coefficient we employ a fixed vertical profile for all horizontal gridpoints that is based on the results of Lübken (1997). Further information about the CTM, including the treatment of upper and lower boundary conditions, can be found in Grygalashvyly et al., (2011, 2012) and in Sonnemann et al. (2015).

2.2. Model of excited hydroxyl In order to derive the excited hydroxyl number density for a given vibrational level we assume photochemical equilibrium. Thus, we can calculate this number density as the ratio of the production term Pv to the loss rate Lv:

OH   Pv

.

Lv

(1)

In the production term and the loss rate we include contributions from chemical reactions and deactivation due to quenching and spontaneous emission. The following expression includes all processes that are taken into account in the production term:

Pv  k1 ( )O3 H   k 2 ( )HO2 O   

9

9

 p OH O  q(  1)OH N  

v ' v 1

9

 Q  OH O     A  OH ,

 ' 1

'

'

2

' 1

'

'

v 'v

v'

1

2

(2)

(v   v )

Here, the ki are the reaction rates, v is the vibrational number, p, q, Q are the rates for quenching by atomic oxygen, molecular nitrogen and molecular oxygen, respectively, and Av’v are the Einstein-coefficients for spontaneous emission. The first term on the rhs of (2) 6

describes the main source of excited hydroxyl, namely the reaction of atomic hydrogen with ozone. The corresponding reaction rate is k1 (v)  f v  k1 , where f v is the nascent distribution according to Adler-Golden (1997). This reaction populates the five highest vibrational levels, from level v=5 (1%) to level v=9 (47%). The second term of (2) is the reaction of hydroperoxy with atomic oxygen. Some authors found that this reaction is not important (Llewellyn et al., 1978; McDade and Llewellyn, 1987; Xu et al., 2012), while other publications state that this reaction is indeed important at high latitudes for vibrational numbers lower than 6 (Takahashi and Batista, 1981; Turnbull and Lowe, 1983; Sivjee and Hamway, 1987, Lopez-Moreno et al., 1987). Given this controversy, we include the reaction of hydroperoxy with atomic oxygen as a source of excited hydroxyl in our model. The dependence of the reaction rate on the vibrational number is given by k 2 (v)  ev  k 2 , where ev is the nascent distribution. This reaction generates OH* at the three lowest vibrational

levels (~48% of the reaction product), as well as OH in its ground state (~52%) (Kaye, 1988; Makhlouf et. al., 1995). The 3rd, 4th and 5th terms on the rhs of Eq. (2) represent transitions from the highest vibrational levels due to quenching by atomic oxygen, molecular nitrogen, and molecular oxygen, respectively. For the quenching by molecular nitrogen we apply the “cascade” scheme with quenching coefficients q(v) taken from Makhlouf et al. (1995). Molecular and atomic oxygen are the most important quenchers; we utilize a multi-quantum scheme in these cases, with quenching rates Qv`v for molecular oxygen according to Adler-Golden (1997) and quenching rates pv`v for atomic oxygen taken from Caridade et al. (2013). The last term on the rhs of Eq. (2) represents multi-quantum transitions due to spontaneous emissions with Einstein-coefficients Av`v (Xu et al., 2012). The loss rate in Eq. (1) is  1

 1

 1

 ''  0

 ''  0

 ''  0

Lv  k 3 ( )O    p '' O   q ( )N 2    Q '' O2    A '' , (v   v) . 7

(3)

In addition to loss processes that correspond to quenching and spontaneous emission (see also Eq. (2)), the loss rate includes the reaction of excited hydroxyl with atomic oxygen:

OH * O  H  O2 . The corresponding reaction rate, k3(v), depends on the vibrational number according to Varandas et al. (2004). Our hydroxyl model is identical with the model of Grygalashvyly et al. (2014), except that multi-quantum quenching by atomic oxygen with quenching coefficients introduced by Caridade et al. (2013) is included in the current updated version.

2.3. Numerical experiments As a first step we calculate annual distributions of minor chemical constituents and OH* using dynamical fields for the year 2009 from the Canadian Middle Atmosphere Model (CMAM) (de Grandpre et al., 2000; Fomichev et al., 2002; Scinocca et al., 2008). In this case, we use a low resolution version of the CTM with 32 latitudinal and 64 longitudinal grid points. As a second step, we calculate several 10-day time series where the CTM is driven by the dynamical fields pre-calculated with the KMCM, using 286 latitudinal and 576 longitudinal grid points. For this purpose we interpolate the initial conditions from step 1 to the high-resolution horizontal grid. These high-resolution integrations of the CTM are started 10 days before the solstices and the equinoxes. We need this complex procedure because of the computational costs required for the high-resolution simulations; a full annual cycle at this resolution is currently not feasible with the CTM (though a 2-year simulation with the KMCM is available). We perform two high-resolution simulations with the CTM for each initial condition: GW-perturbation simulations that take all scales resolved by the KMCM into account (spectral truncation at total horizontal wavenumber 240), and control simulations where the same KMCM data are spectrally filtered such that only horizontal wavelengths down to 1000 km (spectral truncation at total horizontal wavenumber 40) are retained to drive 8

the CTM. Note that the large scale-scale dynamical fields are identical in both cases. Hence, the control simulations correspond to a conventional model setup where the effects of GWs are only accounted for in the large-scale fields and the residual circulation (except for the effects that secondary GWs have on the residual circulation). We interpret the differences in the minor chemical constituents and corresponding OH* concentrations between the GWperturbation and control runs as the CTM response to GWs having horizontal wavelengths between ~165 and ~1000 km. In the following section we present and discuss our results for the OH* layer.

3. Results and discussion Figure 1 shows zonal-mean and temporally averaged (from June 13 to 22) temperature, zonal wind, atomic oxygen, and nightly averaged OH*(v=6) for the GWperturbation case. The KMCM simulates the main dynamical features of the MLT that result from the mean-flow effects of GWs (Becker and Vadas, 2018): a cold summer mesopause, a warm winter stratopause, and a reversal from westward to eastward flow with height in summer. These features are linked to a strong summer-to-winter-pole residual circulation that is driven by the resolved GW drag (not shown). The wind reversal and the cold summer mesopause appear to be too high in altitude. This is a result of using a log-pressure vertical coordinate with a constant scale height of 7 km. Indeed, the temperature minimum over the summer pole is located at ~0.001 hPa (or a geometric height of ~89 km) in the KMCM (Becker and Vadas, 2018), which compares well to observations (Lübken, 1999). Here, the log-pressure height deviates by more than 5 km from the geometric height. Also note that the differences between the log-pressure height and the geometric height are much smaller at low latitudes and in the winter hemisphere. According to Fig. 1c, atomic oxygen decreases with latitude towards the poles; it has typical values 1011-1012 cm-3 at 95-105 km and is maximal at low latitudes. Such a 9

distribution of atomic oxygen was also found in satellite-based and rocket-borne measurements (Lopez-Gonzalez et al., 1992; Smith et al., 2010; Mlynczak et al., 2013a; Zhu and Kaufmann, 2018; Strelnikov et al., 2019). The nightly averaged excited hydroxyl layer is depicted in Fig. 1d for OH* with vibrational number v=6.

This layer shows typical

concentrations of ~200-2000 cm-3 at altitudes of ~80-90 km. Our model reproduces the wellknown increase in altitude of the layer from the winter hemisphere towards the tropics (Marsh et al., 2006; Liu et al., 2008; Xu et al., 2010). This increase in altitude of the OH* layer is associated with a decrease in OH* number density (Yee et al., 1997; Liu and Shepherd, 2006; Mulligan et al., 2009). A theoretical explanation for this behavior was given by Grygalashvyly et al. (2014, 2015), and Sonnemann et al. (2015). Note that in the equatorial region the behavior is more complex, and is not subject of our current study. The fact that the explicit consideration of GWs in the CTM has significant effects on the simulated minor constituents is illustrated in Figs. 2 and 3, which show snapshots of the temperature, vertical wind, atomic oxygen, and excited hydroxyl (v=6) on June 21 in longitude-altitude plots at 54°N for the GW-perturbation and the control run (as defined in the previous section). Figure 2 shows the characteristic properties of eastward propagating GWs. In particular, the phases become more flat around 80-90 km as is expected from the Doppler shifting due to the reversal of the mean zonal wind. The stronger vertical shear of the GWs then triggers the turbulence parameterization in the KMCM such as to induce damping by enhanced horizontal and vertical diffusion (Becker, 2009). As a result, the GW amplitudes in the vertical wind are weaker at 100 than at 90 km (log-pressure height). Since the resolved GWs are associated with strong wind and temperature variations, they induce significant variations in the minor constituents simulated with the CTM. Results from the control run are shown in Fig. 3. As a result of spectrally filtering the KMCM output, mesoscale GW variations are no longer visible, and the variations in all fields are much weaker than in Fig. 2. This simulation corresponds to the usual case when a CTM is 10

driven by the dynamics from a conventional GCM that describes the large-scale flow explicitly, while the GW drag, the energy deposition, and the turbulent diffusion due to GWs are parameterized. In this case, the short-term variations are due to mainly tides, traveling planetary waves, and synoptic-scale inertia GWs. It is well known that atomic oxygen has a strong influence on the OH* layer formation (e.g., Grygalashvyly, 2014; 2015; and references therein). A redistribution of atomic oxygen due to GW mixing was simulated and explained in Grygalashvyly et al. (2011, 2012). We therefore expect that GW mixing also affects the morphology of the OH* layer. Let us first consider the distribution of atomic oxygen in the control case for summer solstice (June 21, Fig. 4a) and fall equinox (September 21, Fig, 4b). The residual circulation below the mesopause is upward in summer and downward in winter; therefore, atomic oxygen increases from the (northern) summer to the (southern) winter hemisphere, and this difference between the two hemispheres widely disappears during equinox. Atomic oxygen also possesses strong vertical gradients in the mesopause region. Therefore, it is mixed downward due to subgridscale (SGS) diffusivity. This SGS diffusivity is prescribed in the CTM and identical in the control and GW-perturbation run. Nevertheless, the differences in the GW-perturbation run from the control run (Fig. 4 c and d), that is, the CTM response to the explicit consideration of GWs, show a clear downward mixing of atomic oxygen, with positive absolute deviations beneath the atomic oxygen layer. Similar results were also found in Grygalashvyly et al. (2012). Figure 5 shows the zonally averaged concentration of OH*v=6 for summer solstice (a) and fall equinox (b) in the control run, as well as the corresponding model response to GWs (c and d). In analogy to the response of atomic oxygen, we observe an increase of OH* beneath the OH* layer and a decrease at higher altitudes. In order to describe the changes in the OH* layer caused by GWs, it is necessary to introduce some parameters to characterize this layer. Here we use the altitude of the peak concentration (AP) and the concentration at the 11

peak (CP) of the OH* layer. Note that both AP and CP are much larger during the nighttime (corresponding to longitudes from 60°W to 60°E at 0 UT) than those during the daytime. In the following we discuss the behavior of the nighttime averaged AP and CP. Figure 6 depicts nightly averaged AP for the control run (solid lines) and the GWperturbation run (dashed lines) for a) spring equinox, b) summer solstice, c) fall equinox, and d) winter solstice. The differences between the dashed and solid lines illustrate the downward shift of the OH* layer altitude due to GWs. The differences are largest at middle to high latitudes in the winter hemisphere, as well as at high latitudes during equinox in both hemispheres, and they amount to ~5-7 km. The AP is shifted due to GWs by ~1-3 km at low latitudes. The fact that the OH* layer at middle and high latitudes occurs at its lowest altitudes during wintertime has been known from satellite observations (e.g., Winick et al., 2009) and modelling (e.g., Grygalashvyly et al., 2014; Sonnemann et al., 2015). It can be explained by down-welling due to the residual circulation during wintertime and the corresponding downward transport of atomic oxygen (Marsh et al, 2006; Liu et al., 2008). Our model results show that the downward shift of AP during wintertime is strongly amplified due to the explicit consideration of GWs in the CTM (compare the seasonal variations of the dashed lines in Fig. 6 to that of the solid lines). These differences between our GW-perturbation and control simulations are attributed to the effect of vertical mixing by GWs, which acts in addition to the mixing effect from SGS diffusivity. Furthermore, both simulations reproduce the well-known increase of OH* layer altitude from the winter pole to the summer pole (Marsh et al, 2006; Liu et al., 2008). Again, the feature is stronger when GWs are explicitly accounted for in the CTM. Figure 7 shows the nightly averaged CP for the control runs (solid lines) and for the GW-perturbation runs (dashed lines) for a) spring equinox, b) summer solstice, c) fall equinox, and d) winter solstice. Comparing Fig. 7 to Fig. 6 confirms that there is an anticorrelation between the height of the OH* layer and the volume emission rate (OH* 12

concentration). When the AP is lower (as during wintertime at middle and higher latitudes), the CP is larger. This is most prominent when we consider the results for solstice (panel b and d in Fig. 6 and 7). Such an anti-correlation was also found in many previous studies (e.g., Yee et al., 1997; Liu and Shepherd, 2006; Mulligan et al., 2009; Shepherd et al., 2010). The anticorrelation can be explained by an approximate analytical solution of Eq. (1) (Grygalashvyly et al., 2014; Grygalashvyly, 2015). Particularly the differences between the dashed and solid curves in Figs. 6 and 7 illustrate this anti-correlation; in our control simulation the downward mixing of atomic oxygen gives rise to a lower AP and a stronger CP when compared to the conventional model result. Both effects are strongest during winter solstice at middle and high latitudes. In summary, the summer-winter asymmetry and corresponding annual cycle of OH* emission in our control simulation, with weaker emission at higher altitudes during summer and stronger emission at lower altitudes during winter, is to a significant extent attributable to the downward mixing of atomic oxygen by GWs. The fact that there is strong mixing by GWs in the upper mesosphere and lower thermosphere (mesopause region) during wintertime is unexpected from conventional GCMs where GWs are parameterized. The reason is that parameterized GWs are usually launched in the troposphere. As a result, there are mainly westward propagating GWs (orographic and non-orographic) from the lower stratosphere on; and these waves break well below the winter mesopause such that no significant mixing induced by GWs (including the turbulent mixing) is possible in the mesopause region (e.g., Lindzen, 1981; Becker, 2012). This is illustrated in Fig. 8 which shows the annual variation of the GW kinetic energy from the long-term control simulation analyzed in Becker (2017). In that paper we used the KMCM with a coarse resolution and parameterized orographic and non-orographic GWs using methods of McFarlane (1987) and Becker and McLandress (2009). Figure 8 confirms the annual variations at middle and high latitudes that are expected from the considerations of Lindzen 13

(1981). In particular, GW amplitudes are very weak in the winter mesopause region. In contrast, the simulated GW kinetic energy in the KMCM with resolved GWs (Fig. 9) shows an absolute maximum of GW amplitudes in the winter mesopause region. As demonstrated in several recent studies, this feature results from secondary GWs that are generated due to localized (and intermittent) body forces in the stratopause region which is turn result from the dissipation of primary GWs having tropospheric sources (Becker and Vadas, 2018; Vadas and Becker, 2018, 2019; Vadas et al., 2018). As discussed in these studies, there is also strong observational evidence for maximum GW amplitudes in the winter mesopause region (e.g., Chen et al., 2016, Hoffman et al., 2010). Hence, the simulated strong GW-mixing effect on the OH*-layer in the winter mesopause region largely results from secondary GWs. Furthermore, this mixing effect is stronger in the southern than in the northern winter hemisphere due to a stronger polar night jet and stronger body forces from primary GWs, leading to stronger secondary GWs in the southern winter hemisphere.

4. Summary and conclusions We performed numerical experiments using a CTM that is driven by pre-calculated dynamical field using a GCM with resolved GWs. We demonstrated that the instantaneous variations of the OH*-layer caused by GWs are quite significant. For example, the hourly variations of the OH* peak altitude amount to several km. However, there are also significant effects on average: GWs lead to a downward mixing of O that occurs in addition to the mixing by turbulent and molecular diffusivity; this leads to a downward shift of the OH*layer by ~2-7 km and a corresponding increase of its peak number density by more than ~50%. These effects are strongest at middle and high latitudes. According to our model results, GW mixing yields the predominant contribution to the well-known summer-winter asymmetry of the OH*-layer at middle and higher latitudes, with the OH*-layer having the

14

lowest altitudes and largest emission rates (concentrations) during winter. Moreover, the differences between winter and summer are more pronounced in the southern hemisphere. The mixing due to GWs was investigated in previous studies (Grygalashvyly et al., 2011, 2012). Here, we argue that secondary GWs in the winter mesopause region give rise to the strongest GW amplitudes in the extratropical middle atmosphere (Becker and Vadas, 2018). Hence, the vertical mixing due to secondary GWs and its effect on the OH*-layer is expected to be strongest during wintertime, which is in accordance with our results. Moreover, since the secondary GWs are stronger in southern than in northern winter (Fig. 8), also the summer-winter asymmetry of the OH*-layer is more pronounced in the southern winter hemisphere (Figs. 6 and 7). The secondary GW are well simulated in our GW-resolving circulation model (Becker and Vadas, 2018; Vadas and Becker, 2018, 2019). We conclude that the vertical mixing induced by these waves is essential to understand the winter-summer asymmetry of OH* layer at middle and high latitudes. In the future, the mixing effect of secondary GWs needs to be investigated with regard to other minor constituents that are important for the energy budget of the middle atmosphere (e.g., Randall et al., 2015).

Acknowledgements. MG was funded by the Leibniz Society through the SAW project MATMELT.

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Figures

Figure 1. Latitude-height section of zonal-mean field, averaged over the 10-day GWperturbation simulation starting 10 days prior to summer solstice. a) Temperature, b) zonal wind, and c) atomic oxygen number density. Panel d) shows the corresponding nightly-mean OH*v=6 concentration (also averaged over 10 days prior to summer solstice).

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Figure 2. Longitude-height variation at 54° N of a) temperature, b) vertical wind, c) atomic oxygen number density, and d) OH*v=6 concentration from the GW-perturbation simulation starting 10 days prior to summer solstice. The snapshot shows results on June 21, 12 UT.

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Figure 3. Same as Fig. 2, but for the control simulation

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Figure 4. Zonally averaged number density of atomic oxygen at summer solstice (a) and fall equinox (b) from the control simulations; the respective absolute deviations of the atomic oxygen number density due to GWs (GW-perturbation minus control simulations) for c) summer solstice and d) fall equinox.

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Figure 5. Same as Fig. 4, but the number density of OH*v=6.

35

Figure 6. Nightly averaged altitude of the OH*v=6 layer peak for the control simulations (solid lines) and the GW-perturbation simulations (dashed lines) for a) spring equinox, b) summer solstice, c) fall equinox, and d) winter solstice.

36

Figure 7. Same as Fig. 5, but for the nightly averaged number density at the OH*v=6 layer peak.

37

Fig. 8: Annual variation of the zonal-mean GW kinetic energy from middle to high latitudes from the long-term model simulation of Becker (2017), using the KMCM with conventional resolution (spectral truncation at total horizontal wavenumber 32 and 80 levels up to ~200 km) and parameterized GWs. Here, the GW kinetic energy is deduced from the GW schemes for orographic and non-orographic GWs. Also note that we use pressure as vertical coordinate. The x-axis starts at June 20 (day 170) of the first year and ends 380 day later on July 10 (day 550). For the sake of simplicity, the length of a year in the KMCM is set to 360 days.

38

Fig. 9: Same as Fig. 8 but for the annual variation of the GW kinetic energy using the KMCM with resolved GWs. We extended the simulation of Becker and Vadas (2018) to more than one year. Here we show the kinetic energy of the non-vortical horizontal wind components due to total horizontal wavenumbers larger than 30 (horizontal wavelengths shorter than 1350 km).

39