Hydroxyl layer - AGU Publications

PUBLICATIONS Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE 10.1002/2014JD022094 Key Points: • We study seasonal, latitudinal, and long-term behavior of the OH* layer • We find seasonal/latitudinal variability in height and concentration of OH* • We find trends in OH* layer altitudes and corresponding temperatures

Correspondence to: M. Grygalashvyly, [email protected]

Citation: Grygalashvyly, M., G. R. Sonnemann, F.-J. Lübken, P. Hartogh, and U. Berger (2014), Hydroxyl layer: Mean state and trends at midlatitudes, J. Geophys. Res. Atmos., 119, 12,391–12,419, doi:10.1002/ 2014JD022094. Received 30 MAY 2014 Accepted 10 OCT 2014 Accepted article online 14 OCT 2014 Published online 11 NOV 2014

Hydroxyl layer: Mean state and trends at midlatitudes M. Grygalashvyly1, G. R. Sonnemann1, F.-J. Lübken1, P. Hartogh2, and U. Berger1 1 2

Leibniz-Institute of Atmospheric Physics at the University Rostock in Kühlungsborn, Kühlungsborn, Germany, Max-Planck-Institute for Solar System Research, Katlenburg-Lindau, Germany

Abstract

Based on an advanced model of excited hydroxyl relaxation we calculate trends of number densities and altitudes of the OH*-layer during the period 1961–2009. The OH*-model takes into account all major chemical processes such as the production by H + O3, deactivation by O, O2, and N2, spontaneous emission, and removal by chemical reactions. The OH*-model is coupled with a chemistry-transport model (CTM). The dynamical part (Leibniz Institute Model of the Atmosphere, LIMA) adapts ECMWF/ERA-40 data in the troposphere-stratosphere. The change of greenhouse gases (GHGs) such as CH4, CO2, O3, and N2O is parameterized in LIMA/CTM. The downward shift of the OH*-layer in geometrical altitudes occurs entirely due to shrinking (mainly in the mesosphere) as a result of cooling by increasing CO2 concentrations. In order to identify the direct chemical effect of GHG changes on OH*-trends under variable solar cycle conditions, we consider three cases: (a) variable GHG and Lyman-α fluxes, (b) variable GHG and constant Lyman-α fluxes, and (c) constant GHG and Lyman-α. At midlatitudes, shrinking of the middle atmosphere descends the OH*-layer by ~ 300 m/decade in all seasons. The direct chemical impact of GHG emission lifts up the OH*-layer by ~15–25 m/decade depending on season. Trends of the thermal and dynamical state within the layer lead to a trend of OH* height by ~ ±100 m/decade, depending on latitude and season. Trends in layer altitudes lead to differences between temperature trends within the layer, at constant pressure, and at constant altitude, respectively, of typically 0.5 to 1 K/decade.

1. Introduction The airglow measurements in the Meinel band of excited hydroxyl are used to infer temperatures at mesopause height [Offermann and Gerndt, 1990; She and Lowe, 1998; Bittner et al., 2002] and information about dynamical processes, such as gravity waves, planetary waves, and tides [Tarasick and Shepherd, 1992; Taylor et al., 1991, 1995a, 1995b, 1997; Makhlouf et al., 1995, 1998; Bittner et al., 2000; Offermann et al., 2009]. Moreover, the observation of emission from OH* Meinel bands is a tool used to determine atomic oxygen, ozone, and atomic hydrogen which are very difficult to measure by other methods [Thomas, 1990; Takahashi et al., 1996; Russell et al., 2005; Smith et al., 2009, 2010], and, in perspective, can be used to retrieve the chemical heating rate from the most significant exothermic reaction in the mesopause [Mlynczak and Solomon, 1993; Sonnemann et al., 1997]. A number of investigations are focused on temperature trends obtained from measurements of the relative intensities of two lines in one of the vibrational-rotational bands [e.g., Lowe, 1999, 2002; Espy and Stegman, 2002; Offermann et al., 2003, 2006, 2010]. An overview of temperature trends including those derived by airglow measurements can be found in Beig et al. [2003]. The investigations mentioned above are based on a corner-stone assumption regarding the altitude of the excited hydroxyl layer. As a result of measurements by von Zahn et al. [1987] and Baker and Stair [1988], it is commonly implied that hydroxyl emissions come from an altitude of ~87 km, with a thickness of ~8 km. The altitude is assumed to stay constant throughout the entire period of observation. Concerning the OH*-layer and its long-term behavior a number of questions arise. How does the altitude of OH* change? How does the intensity (which is directly proportional to the number density) of OH* change? Which trend in temperature corresponds to OH* trend? What is the main reason for the OH* trend? How does the trend in OH* depend on the Lyman-alpha variation? How much of the trend is dependent on chemistry, dynamics, and the Lyman-alpha variation, respectively? How does the OH* trend depend on the vibrational number? What is the relative long-term behavior for OH* with different vibrational numbers? What is the dependence between the height and intensity of the emission (number density) of the layer? In this paper we intend to answer some of these questions.

GRYGALASHVYLY ET AL.

©2014. American Geophysical Union. All Rights Reserved.

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The same questions can be asked about the variation of height, number density, and corresponding temperature in the framework of seasonal and latitudinal variability. Some hopes are related to the Global Ozone Monitoring by Occultation of Stars (GOMOS) onboard the Environmental Satellite (EnviSat) and new technique to retrieve OH* emission [Bellisario et al., 2014]. Several attempts to study these questions have been carried out recently. Marsh et al. [2006] study seasonal and latitudinal variability of OH*-layer based on the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) on board the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite and utilizing the ROSE model. They found significant variations of height and magnitude of the maximum as a function of latitude. An annual variability was inferred at all latitudes. A semi-annual variability, associated with tides, was inferred at low latitudes. A similar study was performed based on Wind Imaging Interferometer (WINDII) satellite data and the Thermosphere-Ionosphere-Mesosphere Electrodynamics General Circulation Model (TIME-GCM) by Liu et al. [2008]. The authors confirm the existence of an annual as well as a semi-annual variation of emission and infer its dependence on altitude, latitude, and local time. The authors state that the annual and semi-annual variations are determined by general circulation and by tides, respectively. Some information on seasonal and latitudinal variation of OH* emission can be found in the papers of Xu et al. [2010, 2012]. In addition to the annual and semi-annual cycles mentioned above, Xu et al. [2010] found strong correlation between airglow emission and temperature at 85–90 km based on the TIMED/SABER measurements. Previously, the questions of correlation between airglow temperature and emission rate were highlighted in works of Cho and Shepherd [2006], Espy et al. [2007], and Shepherd et al. [2010]. The relation between the OH*-layer altitude and number density is studied in a limited number of papers [Yee et al., 1997; Liu and Shepherd, 2006; Mulligan et al., 2009; Shepherd et al., 2010]. Liu and Shepherd [2006], based on WINDII measurements for latitude band 40°S–40°N and the period from November 1991 to August 1997, derived an empirical formula for the dependence of the height of the layer on integrated intensity (that is proportional to number density), day of year, and local time. Then, they subdivide the latitudinal band by five bins and derive coefficients for each one. Later on, the coefficients for the given empirical formula were derived for 78°N ± 5°N [Mulligan et al., 2009]. Finally, the dependence of the emission peak on vibrational numbers was explored in a number of works. The measurements of Lopez-Moreno et al. [1987] and Baker and Stair [1988] suggest a height difference of 0.5 km per vibrational number. Adler-Golden [1997] find a height difference of 0.7 km per vibrational number by modeling. Recently, von Savigny et al. [2012], using the Scanning Imaging Absorption spectrometer for Atmospheric ChartographY (SCIAMACHY) onboard the EnviSat and by extended model found the difference between peaks with highest and lowest vibrational levels to be ~4 km and the step between two nearest levels to be ~0.5 km. Unfortunately, no systematic information, neither by observations nor by modeling, exists on latitudinal and seasonal variation of OH*-layer altitude and number density. A number of problems relate to satellite observations of OH*. Satellite measurements often do not capture high latitudes. The large step in tangent height in the case of limb observation introduces uncertainty regarding the measured height. Satellites measurements of the OH*-layer parameters began just recently and do not allow yet to draw conclusions about OH*-layer trends. While ground-based measurements are limited by local latitude and integrated volume emission, the retrieval of latitudinal variation and emission peak are straitened. A purely theoretical attempt to answer questions related to latitudinal, seasonal and long-term variation of OH*-layer also encounters difficulties, due to the complexity and nonlinearity of processes involved in the OH*-layer [Sonnemann and Fichtelmann, 1987; Fichtelmann and Sonnemann, 1992; Sonnemann and Feigin, 1999a, 1999b]. For example, Scheer and Reisin [2012] found in their airglow measurements in Argentina in summer clear indications of a subharmonic oscillation of the OH* layer. However, from brief considerations we might conclude that anthropogenic changes have an impact on the OH*-layer. The main source of the excited hydroxyl in the mesopause is the reaction H þ O3 →OHv¼19 þ O2 , which is responsible for the population of the first 9 vibrationally excited levels [Bates and Nicolet, 1950]. The secondary source is the reaction HO2 + O → OH * + O2 [Nagy et al., 1976]. Excited hydroxyl is deactivated by collisions with O2, N2, and O, and due to spontaneous emission. There is no unique opinion, is it significant or not, regarding chemical removal of OH* by means of the reaction OH * + O → H + O2, and its reaction rates dependence on the vibrational numbers. All of these processes are modulated by anthropogenic changes and natural variability.



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The growth of methane concentration leads to an increase of water vapor and odd-hydrogen in the middle atmosphere and, consequently, moves the isosurface (the surface at which the number density of the given constituent is constant) of atomic hydrogen upward. Since the growth of odd-hydrogen reduces oddoxygen, it consequently moves the isosurface of ozone upward, too. Thus, the direct chemical effect of the growth of GHG on the OH*-layer consists of the shift of the layer upward. This effect was already shown in the OH-layer based on a simplified model [Grygalashvyly et al., 2009]. Another channel of impact is the shrinking of the middle atmosphere (SMA) [Lübken et al., 2009]. Unlike the direct chemical effect of the growth of GHG, the SMA moves the isosurfaces of ozone as well as atomic hydrogen downward in geometrical altitude. The natural variation of the Lyman-alpha flux has an influence on ozone and atomic hydrogen [Sonnemann and Grygalashvyly, 2005]. There is a stronger photodissociation of water vapor and molecular oxygen in the mesopause under the conditions of enhanced Lyman-alpha flux, and consequently a downward shift in the constant isosurfaces of O3 and H. For the correct treatment of OH*-layer trends, the loss term should be considered as well as the production term. The spontaneous emission does depend neither on anthropogenic nor on natural variability. Deactivation by O2, N2, and O is not the subject of the direct photochemical effect, because the given species are long-lived chemical constituents in the vicinity of OH*layer. The main changes in the loss term occur because SMA moves the isosurfaces all of these constituents downward. The effect of Lyman-alpha variability in frame of long-term changes can be considered as minor because it only modulates atomic oxygen isosurface due to a higher dissociation of molecular oxygen at a stronger Lyman-alpha flux. In this paper we also present results regarding the solar cycle although it has no strong impact on our results in view of trends because we consider time periods much longer than a solar cycle. This simplified picture, which considers the main processes of the production and losses, is already complex because different competition processes are engaged that are dependent on each other. These processes are too complicated to provide simple answers to the questions about OH*-layer trends. A purely theoretical discussion, as seen in chapter 3, is only possible for highly idealized cases. The complete answers to the questions stated above can be obtained by theoretical modeling. In the second chapter we describe a model used for investigation. In the third chapter we derive analytical solution for height and the number density of OH*-layer peak. In the fourth chapter we present the latitudinal and seasonal variability of height and the number density of OH*-layer. The trends of height of OH*, an attempt to separate different impacts, and questions related to the corresponding temperature are discussed in the fifth chapter. Concluding remarks and a summary are given in the last chapter. The questions related to OH* number densities will be highlighted in the next complimentary paper.

2. Model 2.1. Dynamical and Chemical Parts of the Model The dynamical part of the Leibniz-Institute Middle Atmosphere model (LIMA) is a triangular grid model with horizontal resolution of ~110 km and 118 pressure levels from the ground up to ~150 km. The model is nudged by ECMWF data below ~35 km. It utilizes the daily Lyman-alpha flux. The data of CO2 from Mauna Loa ground based measurements (http://www.esrl.noaa.gov/gmd/ccgg/trends) and O3 from the Solar Backscatter in the Ultraviolet (SBUV) satellite measurements (http://acdb-ext.gsfc.nasa.gov/Data_services/merged) are assimilated in the radiative block. This model has been widely described in numerous papers [Berger, 2008; Lübken et al., 2009; Lübken and Berger, 2011; Berger and Lübken, 2011]. The dynamical fields calculated in LIMA are used in the Chemistry Transport Model (CTM). This model consists of a chemical, radiation, and transport code. Our present simulations were performed on a grid with 64 longitudinal and 72 latitudinal grid points, as well as 118 vertical levels from the ground to approximately 150 km. The chemistry module consists of 19 constituents. The reaction scheme includes 49 chemical reactions and 14 photodissociation processes. The reaction rates used in the model are taken from Sander et al. [2006]. The temperature-dependent reaction rates are calculated online and are therefore sensitive to small fluctuations in temperature. The chemistry is based on a family concept [Shimazaki, 1985], considering odd hydrogen (H, OH, HO2, H2O2), odd oxygen (O, O(1D), O3), and odd nitrogen (NO, NO2, N(4S), N(2D)) families. The chemical processes in the atmosphere are calculated using the implicit Euler method with a quadratic loss term. The long-lived constituents are calculated separately, and the short-lived constituents are included in the chemical families, which are solved as fully implicit subsystems.



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The diurnal variation of the constituents in CTM results from the change of the zenith angle of the Sun and is different for different molecules, depending on the corresponding photodissociation rate. We used the same dissociation rates as Sonnemann et al. [1998]. The code was primarily developed by Fichtelmann and Sonnemann [1989] for use in mesospheric and thermospheric modeling and considers the EUV and X-ray spectrum of solar radiation. The lower mesospheric rates were revised by Kremp et al. [1999] by implementing a code developed by Röth [1992] for stratospheric modeling. The annual variation due to the eccentricity of the Earth’s orbit is approximated by a sine function with an amplitude of 3.5% of the calculated rates, and thus the absolute variation between summer and winter is 7%. The radiation code is running offline, i.e., it uses fixed absorber concentrations for the calculation of the photodissociation rates. The dissociation rates in the model are taken from a pre-calculated library. They depend on height and zenith angle. The variation of Lyman-alpha flux is parameterized for H2O, O2, CO2, and CH4 with semi-empirical parameterization of Chabrillat and Kockarts [1997, 1998], which is based on the reduction factor method [Kockarts, 1994] and absorption cross-section measurements [Lewis et al., 1983]. The parameterization of Lyman-alpha impact is included online in the CTM. We utilize Lyman-alpha cross-section coefficients for water vapor (1.59°1017 cm2), methane (1.85°1017 cm2), and carbon dioxide (8.14°1020 cm2), as derived by Kley [1984], Nicolet [1985], and Nakata et al. [1965], respectively. We apply Lyman-alpha flux values according to Woods et al. [2000] (data available at: ftp://laspftp.colorado.edu/pub/SEE_Data/composite_lya/composite_lya.dat). The transport code includes both advection by the resolved flow and vertical diffusion. A transport scheme that is almost free of numerical diffusion is applied [Walcek and Aleksic, 1998; Walcek, 2000]. The explicit vertical diffusion includes both turbulent and molecular diffusivity according to Colegrove et al. [1965] and Morton and Mayers [1994]. The molecular diffusion coefficient depends on the type of molecule, pressure, and temperature. For the turbulent diffusion coefficient we employ the summer-winter average found by Lübken [1997], i.e., a single vertical profile for all constituents. Green-house gases (CH4, CO2, and N2O) at the lower border are parameterized by the data measurements taken at Mauna Loa and published by the NOAA Earth System Research Laboratory [Keeling et al., 1976; Thoning et al., 1989; Tans and Keeling, 2011, http://www.esrl.noaa.gov/gmd/aggi/]. For carbon dioxide, we used the data from observations over the entire period of modeling. For methane, we utilized the data from May 1983 to the present; before this date, we parameterized the trend using the linear growth of 14 ppbv/year [Blake and Rowland, 1988; Dlugokencky et al., 1998; Simpson et al., 2002]. Nitrous oxide was parameterized linearly from 286 ppbv (1961) to the present time with a growth rate of 0.75 ppbv/year [Khalil et al., 2002; IPCC, 2007; Artuso et al., 2010]. The trend of ozone in the stratopause region does not influence the mesospheric chemistry directly but only by the dynamics. Thus, the long-term changes in the region of the first ozone layer are parameterized in LIMA [Lübken et al., 2013]. Further information about the CTM can be found in the papers referred to above and in more contemporary works [Grygalashvyly et al., 2009; Hartogh et al., 2010; Sonnemann et al., 2012]. 2.2. Model of Excited Hydroxyl Relaxation In order to derive the expression for the calculation of the excited hydroxyl number density at each vibrational level, we assume that it is in photochemical equilibrium, because the timescale of OH* relaxation is much smaller than the dynamical timescales and photochemical lifetimes of minor chemical constituents involved in OH* formation and destruction. Thus, we can calculate it as the ratio of production term Pv to the loss term Lv: ½OHν ¼

Pv : Lv

(1)

In the production and loss terms we summarize contributions from the chemical reactions, contributions due to deactivation by quenching and due to spontaneous emission. The following expression includes all the processes that were taken into account in the production term: Pv ¼ k 1 ðνÞ½O3 ½H þ k 2 ðνÞ½HO2 ½O þ p½OHνþ1 ½O þ qðν þ 1Þ½OHν þ1 ½N2 þ 9 9 X X ′ Qν’ν ½OHν’ ½O2 þ Aν’ν ½OHν’ ; v >v þ ν’¼νþ1

(2)

ν’¼νþ1

where ki are the reaction rates, v is the vibrational number, p, q, and Q are the rates for quenching by atomic oxygen, molecular nitrogen, and molecular oxygen, respectively, and A are the Einstein-coefficients for



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spontaneous emission. The first term of (2) describes the main source of vibrationally excited hydroxyl, which is the reaction of atomic hydrogen with ozone. The reaction rates depend on vibrational numbers k1(v) = fv k1, where fv is the nascent distribution [Adler-Golden, 1997]. This distribution populates five highest vibrational levels, from level v = 5 (1%) to level v = 9 (47%). The second term of (2) is the secondary source of vibrationally excited hydroxyl, which is the reaction of hydroperoxy radicals with atomic oxygen. Several sources mark that the contribution of the reaction of hydroperoxy with atomic oxygen is not necessary or very small [Xu et al., 2012; McDade and Llewellyn, 1987; Llewellyn et al., 1978], while other research states that this reaction is important for vibrational numbers lower than 6 and at high latitudes [Takahashi and Batista, 1981; Turnbull and Lowe, 1983; Sivjee and Hamwey, 1987; Lopez-Moreno et al., 1987]. We find these last arguments to be substantial, and thus we are considering this reaction to be a minor source of excited hydroxyl. The dependence of the reaction rate on the vibrational number is given by k2(v) = ev k2, where ev is the nascent distribution. The three lowest vibrational levels (48% of the reaction yield) and the not excited level (52% of the reaction yield) are populated by this distribution [Kaye, 1988; Makhlouf et al., 1995]. The third, fourth, and fifth terms represent transitions from the highest vibrational levels to the lower ones due to quenching by atomic oxygen, molecular nitrogen, and molecular oxygen, respectively. For the quenching by atomic oxygen, we utilize a so-called “cascade” scheme [McDade and Llewellyn, 1987; AdlerGolden, 1997; Xu et al., 2012] at which the excited molecule relaxes to the one vibrationally excited level below, with relaxation coefficients p(v) of Varandas [2004] for 210 K. For the quenching by molecular nitrogen we apply “cascade” scheme with quenching coefficients q(v) from Makhlouf et al. [1995]. The quenching by molecular oxygen is assumed to be multi-quantum [Adler-Golden, 1997; Xu et al., 2012] for which we utilize state-to-state quenching rates Qv`v from Adler-Golden [1997]. The last term represents multi-quantum transitions due to spontaneous emissions with Einstein-coefficients Av`v according to Xu et al. [2012]. The loss term Lv ¼ k 3 ðνÞ½O þ pðv Þ½O þ qðνÞ½N2 þ

ν1 X

Qνν’’ ½O2 þ

ν’’¼0

ν1 X

Aνν’’ ;

ðv ″ < v Þ;

(3)

ν’’¼0

among the processes previously mentioned, takes in to account the reaction of vibrationally excited hydroxyl with atomic oxygen OH * + O → H + O2 with the reaction rate k3(v), which depends on vibrational numbers according to Varandas [2004] calculated for 210 K. The newly developed advanced model of OH* relaxation differs from former models and concentrates all modern knowledge about excited hydroxyl relaxation. 2.3. Comparison to Other Models A large number of OH* relaxation models, applied to retrieving measurement results or for theoretical calculations, have been developed since the first attempts to infer the parameters of OH*-layer [Llewellyn et al., 1978; Lopez-Moreno et al., 1987; McDade and Llewellyn, 1987, 1993; McDade, 1991; Makhlouf et al., 1995]. Therefore, we are only discussing the differences between the most recent ones and our model. Our model mimics, in great part, the Adler-Golden [1997] model, which was developed to test the semiempirical parameterization of multi-quantum (state-to-state) quenching by O2. It is widely utilized today [e.g., Xu et al., 2012]. In spite of the similarities, such as the nascent distribution for the main source of OH*, the “cascade” scheme and corresponding coefficients for quenching by N2, and multi-quantum parameterization for quenching by O2, there are crucial differences such as: utilization of a secondary source of OH* for the population of the three first vibrationally excited levels [Makhlouf et al., 1995], vibrationally dependent “cascade” relaxation by O [Varandas, 2004], vibrationally dependent chemical removing by atomic oxygen [Varandas, 2004], revised according with HITRAN-2008 [Rothman et al., 2009] Einstein-coefficients following by Xu et al. [2012], and updated reaction rates according with Sander et al. [2006]. Unlike the simplified model of OH* relaxation by Marsh et al. [2006], we separate chemical removing and quenching by O, and utilize revised Einstein-coefficients and reaction rates. Moreover, it is hard to compare our model with the model of Marsh et al. [2006] because they only calculate volume emission from two vibrational bands (v = 8, 9) and in this case it is not possible to parameterize the emission with assumption of “cascade”, “sudden death”, or multi-quantum state-to-state relaxation. Although the TIME-GCM model is well described [Roble et al., 1987; Roble and Ridley, 1994; Roble, 2000], it is hard to infer several significant details of the model of excited hydroxyl relaxation, applied to calculate the



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latitudinal-seasonal structure of volume emission in the work of Liu et al. [2008]. Therefore, we refrain from a more detailed comparison. Xu et al. [2012] give more comprehensive description of their model. The similarities between Xu et al. [2012] model and our model stem from the fact that the Adler-Golden model is the basis for both. In contrast to Xu et al. [2012] we take into account the secondary source of OH* (HO2 + O → OH * + O2) according to Krassovsky [1963], Schiff [1962], Nicolet [1970], Breig [1970], Nagy et al. [1976], Takahashi and Batista [1981], Turnbull and Lowe [1983], and Sivjee and Hamwey [1987]. In fact, such approach does not contradict to the opinion of Llewellyn et al. [1978] and McDade and Llewellyn [1987], who consider the contribution of this reaction is small. Following Kaye [1988] and Makhlouf et al. [1995] we include it as a source for the population of vibrational levels v ≤ 3. In contrast to Xu et al. [2012], we, according to Varandas [2004], separate chemical removal by O and quenching by O, and apply vibrationally dependent rates for both processes. Finally, it should be noted that we do not apply the α and β empirical parameters derived from the comparison of TIMES/SABER measurements and modeling [Xu et al., 2012] for quenching by O2 and reaction O + OH*, respectively, due to a lack of generality (such parameters are valid only in frame of given measurements and given model). The model of OH* relaxation applied by von Savigny et al. [2012] to study the dependence of the OH* altitude on the vibrational number is based on research by McDade [1991] utilizing (as we do) multi-quantum quenching by O2 and single-quantum quenching by N2 [Adler-Golden, 1997], and it differs from our model by the absence of a secondary source (HO2 + O → OH * + O2), chemical removal (OH * + O → H + O2), and the absence of dependence of quenching by O on vibrational numbers. Additionally, as distinct from us, the authors use the old spontaneous emission coefficients of Murphy [1971]. Thus, our results discussed below should be considered to be the results achieved in the frame of current knowledge about OH* relaxation, keeping in mind the difference to other models.

3. Theoretical Aspects To better understand the OH* layer’s long-term and annual variations, we need a theory which describes, at least qualitatively, the simplified case and shows the directions of changes and functional dependences. The production of OH* is mainly determined by the reaction H þ O3 →OHv¼19 þ O2 , and thus the production term POH* ≈ k 1 ½H ½O3 ;

(4)

10

exp(460/T) [Sander et al., 2006], and square brackets as traditionally where the reaction rate k1 = 1.4 10 denote number density of given constituents. From the point of view of ozone balance, according to Smith et al. [2008] we can equate production and loss of the ozone during the night k 4 ½O ½O2 ½M ≈ k 1 ½H ½O3 þ k 5 ½O ½O3 ;

(5)

where M is the number density of air, k4 = 6 10 34(300/T)2.4 and k5 = 8 10 12 exp(2060/T) are the reaction rates for O + O2 + M → O3 + M and O + O3 → 2O2, respectively. Note, we may only write equation (5) assuming, according to Smith et al. [2008], that ozone is approximately in the photochemical equilibrium (d[O3] /dt = 0). The reaction of ozone with atomic oxygen amounts to less than 10% of the total ozone loss in the region of the OH* peak [Smith et al., 2008], because the reaction rate k5 is several orders of magnitude smaller than that of k4 below ~95 km. Consequently, we can skip the second term in rhs of (5). From (4) and (5) POH* ≈ k 4 ½O ½O2 ½M:

(6)

The main loss process is the quenching by molecular oxygen [Adler-Golden, 1997; Knutsen et al., 1996]. Thus, it follows from (1), (3), and (6), writing the reaction rate k4 explicitly, ½OH*≈


61034 ð300=T Þ2:4 ½O ½O2 ½M : Q½O2


(7)

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Table 1. Numerical Experiments Leibniz Institute Model of the Atmosphere (LIMA) (Run 7 [Lübken et al., 2013]) Run A

ECMWF + Ly-α + O3 + CO2

Run B


Run C


Chemistry-Transport Model (CTM) Ly-α + greenhouse gas (GHG) 11

GHG + Ly-α const. (3.42°10 p. · cm

2

·s

1

)

Ly-α + GHG const. (1961)

Taking into account the ideal gas law ([M] = p/κT, where κ is the Boltzmann constant) and reducing [O2], it follows from (7) ½OH* ≈

6 1034 3002:4 pT 3:4 ½O ¼ B pT 3:4 ½O; κQ

(8)

where B is a constant which contains all constants from (8). Differentiating (8) with respect to p and equating to zero we get ∂½OH* ∂½O ∂T ¼ B T 3:4 ½O þ p T 3:4 3:4T 4:4 ½O ¼ 0: ∂p ∂p ∂p

(9)

From (9) the approximated expression for pressure altitude of OH* peak is p≈

½O 3:4T

1

½O ∂T ∂p

∂½O ∂p

¼

1 3:4 ∂lnT ∂p

∂ln½O ∂p

1 ¼ ∂ð3:4lnTln½OÞ ¼ ∂p

∂ ∂p

1 3:4 : ln T½O

(10)

Substituting (10) in (8), we derive an expression for maximum of number density at the peak of the layer ½OH* ≈

BT 3:4 ½O 3:4 : ln T½O

∂ ∂p

(11)

The expressions (8), (10), and (11) will be applied below for an analysis of our results.

4. Seasonal and Latitudinal Variation of OH* Layer We perform the discussion of seasonal and latitudinal variation based on calculations for the year 2009. We analyze the data averaged over the nighttime period 0–3 LT. We are building up the discussion based on OH*v=6 as it is widely used in measurements. In this chapter we analyze conventional run (run A, see Table 1 and discussion below), which includes real-date dynamics (run 7 from [Lübken et al., 2013]), the variation of Lyman-alpha, and the parameterized release of GHG. 4.1. Height and Number Density Figure 1 shows a sliding average over 15 days of OH*v=6 number densities at equatorial (1.25°N), middle (51.25°N, and high (68.75°N) latitudes. At equatorial latitudes (Figure 1a) the peak height varies over the course of year between ~87 and ~90 km and the number density at the peak between ~200 and ~500 cm3. From Figure 1a one can infer an annual variation, with maxima in winter and minimum in summer, and a semi-annual variation with two maxima in March–April and September–October. Both variations were observed by SABER and WINDII, simulated by ROSE and TIME-GCM, and discussed earlier by Marsh et al. [2006] and Liu et al. [2008]. In the works mentioned the annual variability of the general mean circulation and corresponding variability of atomic oxygen transport was found to be the main reason for the annual cycle of the OH*-layer. The semi-annual variability of OH* was connected with the semi-annual behavior of tides near the equator. We should mention the alternative point of view which connects the semi-annual variations at low latitudes with the seasonal variability of gravity waves [Garcia and Solomon, 1985]. In the present paper we do not study the reasons for semi-annual variation in equatorial latitudes, and may not distinguish between the tides, gravity waves, or potential photochemical reasons (the equinoxes near the equator are consistent with the maxima of diurnally averaged solar flux, thus with a higher dissociation of molecular oxygen and water vapor, leading to higher ozone and atomic hydrogen formation, respectively). At 51.25°N (Figure 1b) the altitude of the OH*v=6 peak varies between ~87 and ~89 km with max/min number density 900 cm3 and 450 cm3. The annual variation is characterized by a number of sporadic maxima and



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not too great a variation in height. Such behavior is likely determined by meridional or vertical transport of atomic oxygen. An implicit confirmation that such behavior can be determined by meridional transport is recent work by Peters et al. [2014], who inferred the strong influence of meridional transport by planetary waves and polar vortex displacement on water vapor at high and midlatitudes in the mesosphere. At high latitudes (68.75°N), Figure 1c, such oscillatory behavior with periodicity approximately 1 month during the fall-winter-spring seasons is more apparent. One reason for this is the planetary wave transport of chemically long-lived constituents such as water vapor and atomic oxygen. Altitude and number density vary over the course of the year by ~82–88 km and ~500–1300 cm3, respectively, i.e., much larger compared to middle and low latitudes. Note that the strong increase in altitude but decrease in concentration in May–July at high latitudes is determined by polar day conditions, when ozone is reduced, and thus the production of OH* is reduced, * Figure 1. Seasonal variation of the OHv=6 number density in night time at too. The photochemistry of OH* at (a) equatorial (1.25°N), (b) middle (51.25°N), and (c) high (68.75°N) latitudes. daytime and nighttime are different. The black cross shows measurements by von Zahn et al. [1987]. This leads to impossibility to apply the ozone balance equation (5) for daytime conditions. The consideration of photo-dissociation would entail an additional term in (5), and consequently a more intricate theory, which is out of scope of our paper. Evidently, the strongest variation of height and number density occurs at high latitude. The signatures of major (end of January) and minor (beginning of December) Sudden Stratospheric Warming (SSW) are manifested by short but strong period of increasing altitude (~5 km) and decreasing number density. The response of the OH*-layer on SSW is in good agreement with the expected correlation between OH* peak altitude and meridional wind [Dyrland et al., 2010] and anticorrelation between altitude of OH*-layer and emission rate (number density) [Yee et al., 1997; Liu and Shepherd, 2006; Mulligan et al., 2009]. In view of expression (8), the anticorrelation between height and number density (intensity) of peak is not surprising, because one can see that [OH*] is directly proportional to pressure and, consequently, inversely proportional to height. Figure 2 illustrates the latitude-height variation of OH*-layer for winter (December-January-February) (a), spring (March-April-May) (b), and summer (June-July-August) (c) in the northern hemisphere. We can reveal several significant conclusions from this figure. The lowest altitude and highest number density occur at high latitudes during winter with values ~77 km and ~1600 cm3, respectively. The altitude is highest, reaching ~90 km at high latitudes in summer season. The variation of altitude and number density at equatorial latitudes over the course of the year is weaker than at high and midlatitudes (see also Figure 1). The strongest variations occur in the southern hemisphere. Winick et al. [2009] report an unusually low layer altitude (down to ~75 km) and strong emissions (more than 50% brighter) at high latitudes during the winter of 2004 and 2006, in general agreement with our model. Our calculations indicate that this is a rather common behavior at high latitudes in winter. Vice versa, an opposite behavior is caused by strong planetary wave



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activity during winter. A decline in altitude of the OH*-layer and an increase in the number density at winter high latitudes are caused by general mean circulation and corresponding downward vertical wind, which enhance the fluxes of atomic oxygen [Marsh et al., 2006; Liu et al., 2008; Xu et al., 2010], which can be explained by expressions (8) and (11). From (11) the values of [OH*] peak are directly proportional to the atomic oxygen number density; on the other hand, the altitude should decline because of formula (8). To gain a better understanding of latitudinal-seasonal variability, we consider the data (height and number density) at the peak of the OH*v=6 layer. Figure 3 presents the time-latitude variation of height (a) and number density (b) of layer peak. Note that the periods May–August at northern high latitudes and November–February at southern high latitudes belong to the sunlight conditions when the photochemistry of OH* differs from that of nighttime. Again, we can identify annual variation at high-to-midlatitudes (with high altitude and a low number * Figure 2. Latitude-height cross section of the nighttime mean OHv=6 density in summer season and vice averaged over (a) winter, (b) spring, and (c) summer of 2009. versa in winter), and both, annual and semi-annual at mid-to-equatorial latitudes. Such behavior forms positive gradients in altitude and negative gradients of number density from ~30° to the pole in the period from spring to the fall equinox and vice versa in the period between the fall and spring equinox. As was noted by Marsh et al. [2006] such variations at middle and high latitudes are determined by the vertical component of the general mean circulation, which transports atomic oxygen and water vapor. The amplitude of semi-annual variation at the equator is weaker compared to that shown by Marsh et al. [2006], Liu et al. [2008], and Xu et al. [2010] for several reasons. As shown in Marsh et al. [2006] and Liu et al. [2008] the semi-annual variation near the equator is a phenomenon that depends on the local time of night; it is stronger before midnight and almost absent after midnight [Marsh et al., 2006; Figure 4], but we base the discussion on the data averaged over 0–3 LT. As shown in the study by Xu et al. [2010], the semi-annual variation at the equator depends on longitude, but our discussion is based on the data averaged over 0°E–45°E. Liu et al. [2008, Figure 5] showed that the equatorial seasonal behavior of the OH*-layer depends on altitude and may have annual and semi-annual components at the same local time, but in Figure 3 we show the values at the peak of OH*-layer. Thus, in the present analysis the semi-annual variation at low latitudes (Figures 1a and 3) is reduced compared to that found by Marsh et al. [2006] and Liu et al. [2008]. In Figure 3 we note, as before, the signatures of major and minor SSWs at high northern latitudes in the end of January and beginning of December, respectively. The question of the impact of SSW on the OH*-layer has been studied in detail just recently in limited number of papers and requires further consideration in future research. Most detailed information about the variability of the hydroxyl airglow layer, its intensity, atomic oxygen density, and temperature in the mesopause/lower thermosphere region at high Northern latitudes during SSW one can find in work of Shepherd et al. [2010]. We briefly note that in the time of SSW, the altitude GRYGALASHVYLY ET AL.


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of the layer is rising by ~5–7 km, and the number density is reduced by more than 50%, that is in agreement with results of Shepherd et al. [2010; Figures 1b and 7]. The reasons for such a reduction, as was shown in the paper Dyrland et al. [2010], are the poleward meridional and upward vertical fluxes, typical for SSW events. An important feature that can be inferred from a comparison of Figures 3a and 3b is the anticorrelation between height and number density of the layer [Yee et al., 1997; Liu and Shepherd, 2006; Mulligan et al., 2009; Shepherd et al., 2010]. Evidently, from Figure 3, the relationship of height and number density is not uniform and depends on latitude. The questions of height-number density dependence will be highlighted in more detail in a succeeding paper. 4.2. The Thickness

Figure 3. Sliding average (15 days) of the nighttime mean season-latitude * variation of the geometrical altitude (a) and number density (b) of OHv=6 peak.

Figure 4. Time-latitude variation of the thickness (Δh9–1 = hv=9 hv=1) of OH*-layer.



It is well-known that OH*-layers with higher vibrational numbers have peaks higher than those for smaller vibrational numbers [e.g., McDade, 1991, and references therein]. We define the thickness of the OH*-layer, following other authors, as the altitude difference between the highest (v = 9) and lowest (v = 1) vibrationally excited levels (Δh9–1 = hv=9 hv=1). The question arising in this context is whether the thickness is constant or whether it has seasonal and latitudinal distributions. Figure 4 shows 15 days sliding averaged season-latitude distribution of thickness (h(OH*v=9)-h (OH*v=1)). Note, as discussed above, that the May–August at high northern latitudes and November–February at high southern latitudes are the polar day areas. A very large area of the OH*-layer has a thickness of ~3–4 km, which is in agreement with former results. The layer is thicker at the low and equatorial latitudes where it reaches ~7–8 km. The thickness has an absolute minimum (~1–2 km) at midlatitudes in the summer season. It is interesting to note that the

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thickness anticorrelates with number density (compare Figures 4 and 3b). Thus, the thickness of the OH*-layer, as well as a step between vibrationally excited levels, is not constant in the frame of seasonallatitudinal variability and may change from ~2 to ~8 km (corresponding step ~0.25 to 1 km). The question of the thickness and shape of the layer will be considered in more detail a later paper.

90

85

80

Annual Mean Annual Min Annual Max +/- Standard Deviation

75

70 81S 61S

41S

21S

1S1N

21N

41N

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61N 81N

4.3. Statistical Data

Latitude

We subdivide the latitudes into 18 bins from 81.25°S to 81.25°N with step 10° (at the Annual Mean equator the bins 1.25°S and 1.25°N are taken Annual Min in to account for the sake of symmetry), and Annual Max search for the absolute minimum (Min), +/- SD absolute maximum (Max), averaged over given period (Ave), and the standard 1000 deviations (SD) for those bins. As we discuss OH* during nighttime conditions, the polar day areas are omitted; in the analyses we skip the data for May–August at 61.25°N, 71.25°N, November–February at 61.25°S, 100 71.25°S, and between equinoxes at 81.25°N, 81S 61S 41S 21S 1S1N 21N 41N 61N 81N 81.25°S. At this point and onward in the text, Latitude for simplicity, we will write latitudes * Figure 5. The variation of height (a) and number density (b) of OHv=6 rounding up to the whole numbers. Table 2 peak in frame of annual cycle as a function of latitude. and corresponding Figure 5 show the variability in height (Figure 5a) and number density (Figure 5b) for 1 year (2009). The absolute minimum height occurs at 81°S with a value of 72 km. The absolute maximum height occurs at 1°N, and it equals 94 km. The strongest variability in the OH*-layer altitude takes place at high latitudes (≥61°). The largest range (Xmax–Xmin) occurs at 81°S and the smallest at 31°S with values of 17.4 and 6.8 km, respectively. The largest standard deviation in OH* altitude reaches 3.9 km at 81°S, and the smallest one 1.1 km at 41°N–51°N. The strong variability at high latitudes is determined by the influence of planetary wave activity on the distributions of long-lived constituents (O and H2O) and by variations of vertical flux. The number density reaches its absolute maximum (~8°103 cm3) at high southern latitudes and minimum (~2°102 cm3) at equatorial latitudes. The greatest variability in number density occurs at high latitudes (range ~3–7°103 cm3 and SD ~0.5–1°103 cm3). The minimum of variability takes place at equatorial latitude (range ~600–800 cm3 and SD ~120–140 cm3). The variability of number density at high and midlatitudes of the southern hemisphere is stronger than at the northern hemisphere because of the stronger variability of atomic oxygen. Figure 6 and Table 3 show the annual geometric height variability of the OH*v=6 peak at selected latitudes. The absolute minimum (72 km) occurs in July at 81°S and maximum (94 km) in November at 1°N. From mid to high (~50°–80°) latitudes the variation in the average height has a clear annual structure, with the minimum occurring in winter and the maximum in summer. Relative to the background of annual variation one can see at high northern latitudes (~60°N–80°N) growth of height (as well as growth of range and standard deviation) in January and December, determined by major and minor SSWs, respectively. From middle to equatorial latitudes (~30°N–30°S) there is a semi-annual variation of height with maxima in March–April and September–October. An analysis of the values in Table 3, and partially in Figure 6, shows that the greatest variability (range between minima and maxima is ~7–10 km, and SD ~1.8–2.7 km) occurs at high northern latitudes (~60°N–80°N) in October–March, and at high southern latitudes (~60°S–80°S) in April–September. Both



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*

Table 2. Latitudinal Variation of Height and Number Density of OHv=6 Peak on an Annual Scale in Geometrical Coordinates 3

Height [km]

81°N 71°N 61°N 51°N 41°N 31°N 21°N 11°N 1°N 1°S 11°S 21°S 31°S 41°S 51°S 61°S 71°S 81°S

Number Density [cm

]

Min

Ave

Max

SD

Min

Ave

Max

SD

78 79.9 80 84.8 84.2 84.1 84.2 84.6 85 85.8 84.4 84.6 85 83.6 81 78.8 76.2 72

83.8 85.4 86.8 88.2 87.9 87.2 87.7 88.5 89.1 89.1 88.7 88.3 88.1 87.7 87.3 85.4 83.5 80.4

93.6 92.9 91.2 92.2 92 92.1 92 93 94 93.6 93.9 92.6 91.8 90.9 91 89.8 90 89.4

2.7 2.4 1.7 1.1 1.1 1.4 1.7 1.7 1.4 1.4 1.5 1.4 1.5 1.3 1.9 2.8 3.7 3.9

329 208 332 282 238 212 184 163 178 183 214 240 227 221 278 424 670 585

1288 1205 995 756 742 592 475 431 400 398 423 457 503 666 874 1276 1878 2541

3286 3689 2046 1639 1584 1398 1630 1374 980 847 1053 930 1823 3015 2788 3779 8199 7022

591 472 311 261 258 225 203 196 140 127 119 116 212 364 447 656 1044 1081

the seasonal-latitudinal areas are polar night areas, and are subject to planetary waves’ impact. Two maxima of range and standard deviation, absolute in January (range ~13 km, SD ~3.8 km) and secondary in December (range ~13 km, SD ~3 km), are notable relative to the background of strong variability at high winter latitudes, and correspond to enhanced amplitude at the time of major and minor SSWs, at the end of January and the beginning of December, respectively. This confirms that strong variability of OH* height at winter polar latitudes is the result of planetary wave impacts and their interactions with zonal mean zonal flow. The minimum variability (range is ~1.9–3.8 km, and SD ~0.5–0.9 km) was found at northern midlatitudes (~40°N–60°N) in April–September, and in the mid southern latitudes (~40°S–60°S) in October–March—both the seasonal-latitudinal areas are placed at the edge of the polar day area. Figure 7 and Table 4 show the number density variability of OH*v=6 peak at some selected latitudes. The maximal values have their place in polar night areas and correspond to the altitude minimums. The absolute maximum amount is 8199 cm3 (71°S, August), and the secondary is 7022 cm3 (81°S, July). Generally, the averaged number density and achieved maxima at middle and high latitudes of the southern hemisphere are higher than those in the northern hemisphere (compare Figures 7a–7d, and values in Table 4). At high latitudes there is an annual cycle with the maximum in the winter and the minimum in the summer. Midlatitudes are characterized by clear semi-annual variation with the minima of number density around equinoxes. The behavior at equatorial latitudes is more complicated, but it is superimposed on an annual cycle with minima in the summer period and the maximum occurring between December and January. The strongest variability occurs at ~70°–80° in the polar night period. The minimum–maximum range and the standard deviation reach at the southern pole are 5–7°103 cm3 and 1.3–1.5°103 cm3, respectively. At the northern pole the range and SD amount are ~2–3°103 cm3 and ~400–500 cm3, respectively. The variability (range and SD) at middle and high latitudes in the southern hemisphere is stronger than the variability in the northern hemisphere. The minimum of variability throughout the entire year takes place at low and equatorial latitudes (~30°S–30°N). It shifts over the equator from summer in the northern hemisphere to summer in the southern hemisphere. The minima of range and SD, depending on the month, are ~200–400 cm3 and ~50–100 cm3, respectively. The equatorial minimum of variability in summer is smaller in northern hemisphere than in the southern hemisphere. Regarding the annual-scale behavior, we conclude that the model reproduces OH*-layer measurements [Marsh et al., 2006; Liu et al., 2008] like, for example, annual variation at high latitudes, semi-annual variation at middle and equatorial latitudes, a decrease in altitude along with a growth in number density at high winter latitudes [Winick et al., 2009], and an anticorrelation of height and number density of OH*-layer [Yee et al., 1997; Liu and Shepherd, 2006; Mulligan et al., 2009; Shepherd et al., 2010]. Additionally, we infer the number of



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*

Figure 6. The annual variation of the geometric height variability of the OHv=6 peak at some selected latitudes.

facts that require experimental confirmation: (1) a greater thickness of the layer at equatorial latitudes, (2) the growth of height of the peak (and joint decline of number density) during the SSWs, (3) strong summer-winter variations in height and number density at high latitudes, (4) a negative gradient of altitude (and a positive gradient of number density) from the equator to the pole in the winter hemisphere, (5) an inversion of the gradient over equator during the summer period, (6) a greater variability (range and SD) of height and number density at high latitudes around the winter season, (7) the minima in variability of height at the polar day edge and number density at equatorial latitudes, and (8) stronger variability (excluding time of SSWs) in the southern hemisphere than in the northern for corresponding seasons and latitudes. Keeping in mind that the OH* emission in the Meinel band is extensively used for temperature measurements (assuming constant height ~87–88 km) and often compared with temperatures from other methods (e.g., Lidar) at fixed geometrical or pressure altitudes, the deviations between temperatures at a GRYGALASHVYLY ET AL.


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*

Table 3. Seasonal Variation, Extrema, and Standard Deviations of Height [km] of OHv=6 Peak in Geometrical Coordinates at Different Latitudes

81°N

71°N

61°N

51°N

41°N

31°N

21°N

11°N

1°N

1°S

11°S

21°S

31°S

41°S

51°S

Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD

Jan

Feb

Mar

92.2 85.5 78.9 3.9 92.9 85.7 80 3.8 91.1 86 80 2.7 90.6 87.6 84.8 1.4 89.6 87.6 84.2 1.3 89.9 87.5 85.9 1.1 90.2 88.2 86.1 1.1 90.9 88.5 85 1.4 91.8 88.7 85 1.6 91 89 86.5 1.3 90.4 87.7 84.4 1.6 89.9 87.5 84.8 1.3 90 86.9 85.4 1.1 88.9 86.9 85.1 0.9 90.1 88.5 85.4 1.3

85.4 81.7 78 1.9 85.5 82.7 80.4 1.4 88.1 85.2 82.2 1.3 90.1 87.2 85.5 1 90.2 87.9 86 1.1 90 87.6 85.6 1.2 91.4 88.7 85.8 1.4 91 88.7 85.8 1.4 91 88.3 86.2 1.4 90.8 88.2 86.4 1.1 92.2 88.6 86 1.5 92.2 88.4 85.6 1.4 90.6 88.1 85.9 1.1 89.5 87.4 86.1 1 91 88.8 87 0.9

84.6 81.5 78.5 1.5 87.4 84.3 79.9 2 87.2 85.9 84.4 0.8 89.1 87.3 86 0.9 90.1 86.5 88.6 1.1 90.2 87.6 86 1.2 91.8 89.2 86.8 1.2 92.1 89.7 87 1.5 91.6 88.7 86.2 1.3 91.4 89.2 87.1 1.2 92 89.1 86.2 1.4 92.6 89.5 86.6 1.5 91.1 89.4 87.8 1.1 90.1 88.1 86.4 1 90.5 87.6 86.2 0.8


Apr

89 87.4 84.9 1.2 88.8 87.5 85.4 0.9 89.5 87.6 86 0.8 89.9 87.6 85.8 1 89.9 86.9 85.1 1.1 90.5 87.6 85.2 1.5 93 88.7 86 1.8 92.5 89.6 87 1.3 93.2 89.3 86.4 1.5 92 88.7 86.4 1.3 90.5 88.8 87.6 0.9 91.4 88.4 86.5 1.2 90.8 89.2 87.2 1.1 90.4 88.1 85.8 1

May

90 88.2 87 0.7 89.1 87.6 86.4 0.7 88.1 86.4 84.1 0.8 86.9 85.7 84.4 0.6 89.5 86.9 85.1 1.2 91.4 88.6 85.8 1.3 91.6 88.9 86.6 1.2 91.2 88.7 87.2 1 90 88.5 87 0.8 90.9 88 85.8 1.4 89.8 87.6 85.8 1 88.5 85.8 83.4 1.3

Jun

90.5 88.9 88 0.6 89.8 87.6 86.2 0.8 89.8 86.1 84.2 1.3 89.6 86.2 84.5 1.2 89.8 86.7 85.4 1 91.6 88.8 86.2 1.3 91.8 88.8 85.9 1.3 91 88.9 86.5 1.1 90.6 88.9 86.8 1 91.8 88.6 86.2 1.4 89.5 86.8 84 1.1 88.4 84.7 81.4 1.8

Jul

89.8 88.8 87.9 0.5 88.2 87 86 0.5 88 85.9 85 0.7 88.6 86.2 84.2 1.1 90.1 87.2 84.6 1.3 92.6 89.2 86 1.5 92.4 89 85.8 1.6 91.2 89 86.5 1.4 90.6 88.6 86.5 0.9 91.1 89 87 1.2 90.9 88 83.6 1.7 89 85.7 81 1.9

Aug

90 88.6 87.5 0.6 89.2 87.2 85.8 0.9 89.8 86.6 84.9 0.9 89.8 86.9 84.4 1.2 92.2 88.8 85.8 1.6 91.5 89.6 87.6 1.1 92.9 89.6 87.2 1.3 92.2 88.9 87.2 1.2 90.5 88.9 87.2 1 91.5 89 86.8 1.1 90.8 87.4 84.9 1.5 88.8 85.4 82.8 1.3


Sep

Oct

Nov

Dec

88.8 87.3 85.1 0.9 90.5 88.2 86.8 0.8 89.8 88.5 86.6 0.7 91.8 88.5 87 1 91 88.4 86.5 1.2 90.9 88 85.6 1.4 91.2 89.2 86.1 1.3 93.1 90.3 86.9 1.4 93.6 90.3 87 1.4 93.9 89.9 87.5 1.5 91.9 88.8 86.8 1.2 91.5 88.4 86 1.1 90.6 87.9 86.4 1.1 90.1 87.8 85.1 1.2

87 84.8 82 1.4 89.4 86.2 83.2 1.5 91.2 87.5 84 1.6 92.2 88.7 85.6 1.4 92 88.9 87 1.2 91 88.3 85.8 1.3 91 89 86.8 1.1 92.5 89.7 87.5 1.2 91.9 89.6 88.1 1 93.1 89.6 87.4 1.2 92.5 89.9 87.5 1.3 91.5 88.6 86.2 1.3 89.6 87.9 85 1.1 89.2 87.6 86.1 0.8 89.2 87.8 86.5 0.7

89.8 83.7 80.1 2.4 87.6 83.8 80.6 1.8 90.8 86.8 83.4 1.8 90.9 88.6 85.6 1.2 90.2 88.4 85.6 1.1 92.1 87.9 84.9 1.7 92 88.6 86.1 1.3 92.4 88.7 86.1 1.3 94 88.6 86.8 1.4 92.9 88.4 86.2 1.5 90.8 87.6 85.4 1.5 89.8 86.6 85 1 89.5 87.2 86 0.9 89.5 88 86.5 0.7 90.5 88.7 87.5 0.7

93.6 85.6 79.8 3 88.4 85.9 81.9 1.6 90.2 87.1 83.6 1.4 90.6 87.9 84.9 1.5 89.5 87.6 85.4 1 89.9 87.2 85.2 1.2 89.8 88 85 1.4 90.8 89 87.4 0.9 92 89.4 87.5 1.1 91.8 89.4 87.6 0.9 89.5 87.5 85.5 1 88.5 86.2 84.6 0.8 87.2 86.1 85 0.6 88.1 87.2 85.5 0.7 90.6 89.1 87.1 0.9

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Table 3. (continued)

Jan 61°S

71°S

81°S

Max Ave Min SD Max Ave Min SD Max Ave Min SD

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

89.4 88.2 86.5 0.8 89 87 84.9 1.2

89.5 86.6 83 1.5 87.9 84.3 80.2 2 87 81.8 78.6 1.8

87.6 84 80.4 1.7 85.9 81.9 77.8 2.1 82.9 79.9 78 1.3

85.6 81.8 79.2 1.6 82.9 79.2 76.2 1.4 82 78.5 74 1.6

89.4 83.1 78.8 2.7 84.9 79.2 76.2 1.8 82.5 78 72 2.3

86.5 83.8 81 1.4 85.9 81.5 76.5 2.2 83.8 78.7 76 2.1

89.8 87.4 85.1 1.1 88.8 86.6 83 1.5 89.4 86.2 82.5 1.887

89.6 88 86.5 0.7 90 88 86.2 0.9

Nov

Dec

constant height and at the peak of the layer require special attention. Figure 8a shows this deviation between temperature at the peak of OH*v=6 and temperature at constant geometrical height 87 km (TOH* T87km). Sometimes the measurements assumed so-called approximate altitudes (or pseudo altitudes) that correspond to a constant pressure [e.g., Marsh et al., 2006]. Thus, Figure 8b illustrates the deviation between temperature at the OH*v=6 peak and the constant pressure height that corresponds to ~87.9 km (for more about the transformation from pressure on the geometrical coordinates see Lübken et al. [2013]). Typical differences for geometric as well as for pressure coordinates range from 5 to 5 K at middle and high latitudes, and from 5 to 10 K at low and equatorial latitudes. In the course of the year from middle to high latitudes, the temperature at the peak of the OH* layer can differ by ±15 K from the temperature at constant altitudes or pressures. During the SSWs the temperature corresponding to the OH* layer can be up to 5 K lower than the temperature at 87 km (Figure 8a). At the equator, the deviation of the constant pressure height (Figure 8b) varies by ~15 K, and it shows semi-annual variability with two maxima in February–March and October–November, and deep minima in June–August. Because of such strong deviations between the temperatures at constant altitudes (pressure) and temperatures which correspond to the maximum of the OH* peak, great care must be taken in case of airglow temperature measurements, and the assumption of constant height (pressure) should be avoided by application of additional measurements which help infer real altitude or by theoretical modelling. The questions of correction method will be highlighted in the future research. A strong alteration in OH* height and the corresponding deviation of temperature were found during the year. As the airglow measurements are utilized for long-term observations of temperature, we come to the question of whether the height and corresponding deviation of temperature are variable on the long-term scale due to anthropogenic and natural variability. This and related problems are highlighted in the next chapter.

5. Trends of OH* Layer Height and Temperature at Midlatitudes In a complex system such as the mesopause, dynamics and photochemistry are strongly coupled and characterized by numerous feedbacks. In order to not only obtain trends but to understand the coupling processes between dynamics and chemistry on annual and long-term timescales, to uncover feedbacks between dynamics and chemistry and study their role for long-term changes, and to gain an insight into the mechanisms which determine resulting trends, we should estimate the contributions to the trend due to dynamics, chemistry, and radiative processes. The idea about negative feedback between dynamics and chemistry in context of anthropogenic changes was expressed by Sonnemann et al. [2012]. To prove it we have to eliminate the isolated influences which occur due to dynamics and due to direct effect on chemistry. The 11 year solar cycle is the strongest solar variation in nature. The solar maximum, in 1969, was weaker than the last one (2002). Studies of the long-term changes often apply a correction for the solar flux influence [e.g., Offermann et al., 2010]. The Lyman-alpha flux has a strong effect on water vapor dissociation at mesopause altitude (~80% of dissociation at 70–100 km). Moreover, it amounts to ~90% of CO2 dissociation GRYGALASHVYLY ET AL.


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*

Figure 7. The annual variation of the number density variability of OHv=6 peak at some selected latitudes.

at 70–95 km, and ~20% of O2 dissociation at 70–90 km. Hence, we evolve and briefly study the direct photochemical effect of Lyman-alpha 11 year solar cycle on the OH*-layer. GHGs directly influence the chemical composition in the MLT-region by changing the distributions of minor chemical constituents, and indirectly, by modulating dynamics. The impact of a modified composition on dynamics is considered explicitly, because the dynamical model itself uses assimilation of ozone and carbon dioxide and implicitly, due to assimilation of ECMWF data. This simplifies the interpretation of results because variations in the dynamical fields result only from the assimilated data and not from possible positive feedback between chemistry and dynamics. In order to estimate the impacts of different contributions, we performed 3 numerical experiments (XA, XB, and XC). Note that in all numerical experiments the dynamics is the same and already contains the influence of increasing GHGs due to the assimilation of ECMWF data, ozone and carbon dioxide (run 7 from Lübken et al. [2013]). The settings for all three runs are different only in



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Table 4. Seasonal Variation, Extrema, and Standard Deviations of Number Density [cm

81°N

71°N

61°N

51°N

41°N

31°N

21°N

11°N

1°N

1°S

11°S

21°S

31°S

41°S

51°S

Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD Max Ave Min SD

Jan

Feb

Mar

2185 965 329 537 2551 1092 208 528 1907 1084 332 392 1386 996 454 256 1496 905 389 264 1398 891 587 236 1383 749 357 230 1131 650 363 191 980 564 319 164 800 533 255 128 902 517 348 137 765 475 324 90 773 504 326 104 1134 851 670 136 1271 749 455 171

2862 1737 785 548 2413 1425 695 381 2011 994 481 294 1584 755 393 297 993 682 450 134 973 702 482 123 1630 774 452 269 1374 744 333 305 795 410 214 134 629 383 244 100 729 438 267 108 597 422 278 85 602 399 265 90 818 462 308 141 953 640 425 134

3286 2229 1211 535 3689 1655 871 556 1733 1322 756 317 1634 908 523 228 1256 587 238 215 899 544 236 172 873 518 308 136 681 448 290 107 513 359 193 91 466 331 183 84 643 353 251 85 679 430 297 95 597 398 276 81 398 329 252 42 527 378 278 56


Apr

2222 1168 687 427 1262 929 609 152 1002 646 283 179 702 492 303 104 511 361 274 64 535 356 237 74 616 354 222 90 528 374 268 68 531 370 270 62 503 375 234 72 647 438 245 100 757 489 283 126 690 428 221 125 1183 531 279 200

May

891 687 495 105 882 684 532 93 761 537 364 105 483 373 270 58 588 312 195 84 505 327 202 77 614 350 214 87 823 440 277 131 745 484 303 107 701 495 273 108 942 509 300 155 2149 1019 547 477

Jun

1337 686 472 185 1473 955 608 190 1052 728 517 153 720 460 325 80 450 357 250 54 492 342 206 76 547 353 230 76 620 434 295 77 670 442 272 88 808 390 227 119 1103 578 277 177 2788 1161 361 620

10.1002/2014JD022094

*

] of OHv=6 Peak at Different Latitudes Jul

1268 818 466 200 1584 1102 599 233 1108 718 340 193 488 326 220 73 378 271 163 56 527 293 198 65 448 292 192 60 1053 405 223 166 862 514 300 142 992 489 289 143 2120 762 335 444 2633 1217 375 610

Aug

1030 528 337 139 1070 713 505 144 507 370 278 45 360 273 184 42 415 263 185 47 455 282 178 79 605 316 187 98 839 393 214 131 930 485 273 153 1493 469 247 252 1171 546 223 218 1568 936 381 322


Sep

Oct

Nov

Dec

1840 1065 523 361 1415 751 402 259 1373 590 282 227 844 520 305 153 558 330 212 82 549 326 205 80 500 363 251 71 727 425 268 123 666 408 266 111 570 396 266 84 727 438 299 104 716 488 305 119 1085 701 461 160 2332 1048 576 372

2100 1112 436 430 2439 1041 534 380 1168 817 372 196 1619 726 298 326 1123 590 329 190 1042 556 312 135 876 517 273 146 738 395 231 123 686 423 246 112 681 454 268 114 686 449 303 110 746 408 240 103 809 442 281 113 864 580 410 123 1737 722 419 249

1724 961 342 344 2114 1094 489 420 1584 903 526 239 1217 795 442 216 1175 779 512 170 1177 598 322 196 817 485 302 126 978 459 284 130 849 470 264 153 769 473 259 140 577 400 283 80 752 385 253 108 847 501 237 152 1025 746 498 150 1148 813 539 135

1633 822 329 416 2324 1106 370 507 2046 1141 601 292 1639 967 556 271 1396 881 427 245 1059 765 450 158 970 561 354 122 1038 576 353 148 861 533 289 143 847 510 279 146 836 472 300 119 818 553 362 101 1823 961 598 302 3015 1468 775 452 2695 1249 589 420

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Table 4. (continued)

Jan 61°S

71°S

81°S

Max Ave Min SD Max Ave Min SD Max Ave Min SD

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

997 642 424 147 2162 1298 781 335

1302 831 537 227 3644 1482 827 610 4399 2246 1033 697

2838 1647 706 591 3953 2077 950 825 5703 2934 874 927

2798 1781 854 447 4344 2672 1209 857 4129 2787 1495 719

3779 1754 465 918 5863 2721 739 1120 7022 3086 1282 1326

3232 1375 734 553 8199 2198 761 1544 5737 2861 689 1092

2582 1360 718 447 4803 1490 725 736 2491 1372 585 501

1443 820 510 251 1448 1089 670 202

Nov

Dec

the CTM. The first case (run XA) considers the increase of GHGs in the atmosphere and variation of Lymanalpha radiation in the CTM. In a second run (XB) we use the same conditions as run A but with constant Lyman-alpha according to the minimum value (3.42°1011 phot.cm2 s1). In a third run (XC), we hold the GHGs constant according to year 1961, but with variable Lyman-alpha flux. The information about calculation is summarized in Table 1. The difference between trends of the layer maximum in geometrical and pressure coordinates infers trend due to SMA. The difference XLyman = XA XB allows us to exclude variations of SMA, dynamics, temperature (LIMA), and GHG (CTM) in order to study the direct photochemical effect of 11 year solar cycle. The difference XCTM = XA XC infers the direct effect of growth of GHGs in CTM. The total effect can be represented by XA = XSMA + XCTM + XLyman + XLIMA, where XLIMA represents the term due to long-term changes in dynamics and temperature. Considering long-term behavior on pressure coordinates (XSMA = 0), we can extract the trend due to changes of dynamics and temperature in LIMA: XLIMA = XA XCTM XLyman = XA (XA XC) (XA XB) = XC + XB XA.

Figure 8. Sliding average (15 days) of absolute deviations of temperature * at OHv=6 peak and (a) at constant geometrical altitude 87 km and (b) at constant pressure altitude corresponding ~87.9 km.



We calculate and discuss trends of OH* with vibrational number six because the rotational temperature of the OH*(6–2) vibrational transition is often used for long-term measurements [e.g., Innis et al., 2001; Reisin and Scheer, 2002; Sigernes et al., 2003]. The height and temperature at the peak of OH*v=6 are averaged for the period between midnight and 3:00, because ground based airglow measurements are concentrated in the nighttime. Because summer, winter, and spring seasons are quite different we analyze seasonally averaged data separately. In order to calculate linear trends, we utilize least

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square method, and we estimate uncertainties, as well as error bars, t test values, etc. according to Bevington and Robinson [2003]. We assume Gaussian distribution of our samples. Further, we assume that our samples of nightly mean values are quasi-independent. For discussion of chemical, physical, and radiative processes the pressure coordinates are more natural, because on pressure isosurfaces these processes are free of the SMA effect. However, some measurements are performed in geometrical altitudes. Therefore we show both geometrical and pressure altitudes, where it is necessary (the transformation see in paper of Lübken et al. [2013]). In the next part of the chapter we present results of the numerical experiments restricting our discussion to northern midlatitudes because of limited size of the paper. 5.1. Trends of OH* Layer Height at Midlatitudes Figures 9a and 9b show long-term variations and corresponding linear trends of OH*v=6 height at 51.25°N in Figure 9. (a, b) Long-term variations (dashed lines) and corresponding pressure coordinates and geometrical * linear trends (solid lines) of OHv=6 height in pressure and geometrical coordinates for winter, spring, and coordinates for winter (blue line), spring (green line), and summer (red line) summer. Here and beyond, the at 51.25°N. pressure coordinates are represented by pressure height (or “pseudo altitude”) z * = H ln(p/p0), where H = 7 km is the scale height, p is the pressure, and p0 = 1013 hPa is the pressure at the surface. The error bars represent uncertainties of seasonally mean values due to model grid resolution [Bevington and Robinson, 2003]. For calculations of trend uncertainties and t test we assume that seasonally mean values are quasi-independent. The values for trends, uncertainties, total changes through the entire period, year-to-year variability, etc. are collected in Table 5. For pressure coordinates (Figure 9a and Table 5) a positive trend in OH* peak height occurs in summer and winter, with nearly zero negative trend in spring. The strongest rate of growth is in the summer. The year-to-year variability (the difference between two consecutive years) on pressure coordinates is strongest in winter and weakest in summer. The year-to-year changes in altitude are strongest in the winter because winter seasons fall under the influence of SSWs, which differ from winter to winter. Note that these values are so-called “pseudo-altitudes” z* (see above), thus they are a little bit higher than the commonly assumed geometrical altitudes of OH* layer. The OH* layer on pressure altitude at midlatitudes through the period of 1961–2009 years more often have the highest altitude in the winter season and the lowest in spring. The long-term changes on pressure levels at midlatitudes in summer through the entire period (~0.5 km) are comparable with maximum of year-to-year variability (~0.7 km) and are larger than the standard deviation (~0.33 km). The shift of the layer at midlatitudes in winter on pressure coordinates over 49 years (~57 m) amounts to ~3% of maximal year-to-year variability and ~11% of SD (0.5 km). It is negligible relative to the year-to-year variability and SD in spring. Note, that for pressure altitudes, trends are free of the SMA effect; thus, the long-term changes occur because of modulation by dynamics, modifying of chemistry and solar flux variability. Below we make an attempt to consider different effects in deriving trends due to dynamics, chemistry, and solar flux variability. GRYGALASHVYLY ET AL.


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*

Table 5. Trends and Variations of OHv=6 Height in Pressure and Geometrical Coordinates for Winter, Spring, and Summer at 51.25°N

Pressure altitude

Geometrical altitude

Win Spr Sum Win Spr Sum

Trend [m/yr]

Uncer ± ε, [m/yr]

t test

Prob [%]

Total Change ~ [m]

Y.-to-Year Var. [km]

Min Alt. [km] (year)

Max Alt. [km] (year)

Ave Alt. [km]

SD [km]

1.2 0.2 10.3 28.3 27.9 20.6

5.4 3.5 3.1 6.0 3.7 3.9

0.2 0.1 3.4 4.7 7.6 5.4

16 4 99 99 99 99

57 9 506 1386 1366 1008

1.8 1 0.7 2.5 1.9 1

91.2 (81/82) 91 (1992) 91.6 (1962) 86.4 (88/89) 87.2 (2001) 87.5 (1994)

93.6 (97/98) 92.3 (1995) 93.3 (2009) 89 (69/70) 89.6 (1975) 89.5 (1975)

92.5 91.6 92.4 87.6 88.1 88.5

0.53 0.34 0.33 0.71 0.54 0.48

In geometrical coordinates (Figure 9b) a negative trend in the OH* maximum height occurs in all seasons. The strongest total change, as well as trend, at midlatitudes occurs in the winter season. The year-to-year variability (the difference between two consecutive years) in geometrical coordinates is strongest in winter and weakest in summer. Generally in geometrical coordinates OH* peaks at midlatitudes are highest in spring and lowest in winter. The total changes in the geometrical altitude of the OH* layer (including the effect of SMA) over the years 1961–2009 are larger than standard deviations and year-to-year variability for all seasons. A comparison of trends in geometrical and pressure coordinates points to the fact that the main reason for the negative height trend in geometrical coordinates is shrinking of the middle atmosphere due to cooling by CO2 and O3 [Lübken et al., 2009, 2013]. The difference in pressure and geometrical altitudes mirrors the changes because of SMA. Thus, the trends due to SMA are ~ 29.5 m/year, ~ 27.7 m/year, and ~ 30.9 m/year in winter, spring, and summer, respectively. The total shifts through the entire period due to SMA are ~ 1443 m, ~ 1357 m, and ~ 1514 m in winter, spring, and summer, respectively. Such a strong altitude change may result in the differences between the temperature at the layer peak, temperatures at constant altitude, and at constant pressure. This is discussed in chapter 5.2. In order to assess other impacts (growth of GHG in CTM, trends of the dynamics and temperature in LIMA, trends of Lyman-alpha), we analyze differences between runs A, B, and C on pressure coordinates. Figure 10 illustrates long-term variations and corresponding linear trends of OH*v=6 height on pressure coordinates due to the direct chemical effect of the growth of GHG in CTM (run A –run C) at 51.25°N for winter (blue line), spring (green line), and summer (red line). The values for trends due to different impacts and their relations to the trends due to SMA are collected in Table 6. The direct chemical effect of GHG growth is positive for all seasons. The strongest absolute trend, as well as relative to the SMA trend, occurs in the spring season. The direct chemical effect of GHG on OH* peak altitude is opposite to changes in altitude because of SMA and partially compensates it. The growth of methane leads to an increase in water vapor and odd-hydrogen in the middle atmosphere. Thus, it moves the isosurfaces of water vapor products, and particularly atomic hydrogen, upward. The growth of oddhydrogen reduces the odd-oxygen concentration, particularly that of ozone in the mesopause region. The effect comes from lower altitudes, and, consequently, it shifts the isosurfaces of ozone upward. This results in an upward shift of peak of the [O3]·[H] product. Hence, the direct chemical effect of GHG growth on the OH*Figure 10. Long-term variations (dashed lines) and corresponding linlayer leads to an upward shift of the layer. * ear trends (solid lines) of OHv=6 height due to growth of greenhouse The direction of the effect is in agreement gas (GHG) in chemistry-transport model (CTM) for winter (blue line), with results of Grygalashvyly et al. [2009], spring (green line), and summer seasons (red line) at 51.25°.



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*

Table 6. Trends and Changes of OHv=6 Height due to Different Effects for Winter, Spring, and Summer Seasons at 51.25°

Due to SMA

Due to GHG

Due to Dyn. and T

Due to Ly-α

Win Spr Sum Win Spr Sum Win Spr Sum Win Spr Sum

Trend [m/yr]

Uncer. ±ε [m/yr]

t test

Prob. [%]

Total Change ~[m]

Relative to SMA, [%]

29.5 27.7 30.9 1.6 2.4 1.7 0.3 1.9 8.5 0.2 0.8 0.2

8.1 5.1 5.0 0.6 0.6 0.7 5.2 2.7 3.1 1.2 2.1 1.0

– – – 9.8 12.2 6.9 0.05 0.7 2.8 0.19 0.39 0.23

– – – 99 99 99 3 51 99 15 30 18

1443 1357 1514 74.5 119.6 81.8 12 91 415 10 38 9

100 100 100 5.5 8.8 5.4 0.86 6.7 27.4 0.69 2.8 0.62

achieved by modeling for OH-layer with a simplified 3D-model. The authors used constant year-to-year dynamics and increase of GHGs at lower border in the chemical part of their model (this is analogous to our direct photochemical effect of growth of GHG) and derived a positive trend of 0.5–1 km for the period 1880–2003 at 67.5°N in summer. The values are slightly different from those presented in this paper because they analyzed a larger period, higher latitudes, applied a simpler model, and the chemistry of OH and OH* are slightly different, but the direction of the effect and order of values are similar. Figure 11 displays long-term variations and corresponding linear trends of OH*v=6 due to the variability of dynamics and temperature in LIMA (run B + run C run A) at 51.25°N in pressure (height) coordinates for winter (blue line), spring (green line), and summer (red line). The trend is negative in winter and spring, and positive in summer. Note, the trends due to dynamics and temperature in winter and spring are smaller than the uncertainties, thus, they should not be considered as important. The trend due to the dynamics and temperature in winter and spring can be ignored compared to SMA trends. The strongest relative as well as absolute effect at midlatitudes occurs in the summer season. It is opposite to the changes in altitude because of SMA and partially compensates it. Note that we calculate this difference in pressure coordinates, and thus it is free of the SMA effect and determined only by dynamics and temperature. Year-to-year variation due to the dynamics and temperature reaches ~1 km, and it is strongest in winter. The changes over 49 years are comparable with year-to-year variability (because of dynamics and temperature) for summer season, and they are smaller in winter and spring. Dynamics and temperatures influence the OH* layer via the production term. The production term of OH* depends on temperature due to temperaturedependent reaction rates. On the other hand, production term depends on ozone and atomic hydrogen. During the night, the only reaction for ozone production is the reaction of atomic oxygen with molecular oxygen. Atomic oxygen in the mesopause region is a photochemically long-lived constituent, and it has a strong vertical gradient. Thus, its distribution, as well as its long term behavior, is determined by the vertical component of general Figure 11. Long-term variations (dashed lines) and corresponding linear * mean circulation, by tides [e.g., Smith trends (solid lines) of OHv=6 height due to the variability of dynamics et al., 2010], by gravity waves and temperature for winter (blue line), spring (green line), and summer [Grygalashvyly et al., 2011, 2012], and season (red line) at 51.25°.



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by eddy diffusion [e.g., Angelats i Coll and Forbes, 1998]. Long-term changes in tides and GWs may modify atomic oxygen and, consequently, the excited hydroxyl layer. For example, due to longterm changes of GWs in MLT by the effective diffusivity [Hoffmann et al., 2011; Grygalashvyly et al., 2012]. Thus, we can suppose the long-term effect of GWs, mediated by atomic oxygen, on the excited hydroxyl layer. In the frame of the present research there is no ability to separate different dynamical impacts on long-term behavior of OH*. Figure 12 shows long-term variations and corresponding linear trends of OH*v=6 due to Lyman-alpha variability (run A run B) at 51.25°N in pressure (height) coordinates for winter (blue line), spring (green line), and summer (red line). Note, the trends are smaller than the uncertainties of trends. Thus, the trend due to Lyman-alpha can be ignored. The largest absolute as well as relative to SMA impact occurs in spring. The small negative impact, as it was expected (section 1), occurs because the two last solar maxima are stronger than those in the 60th–70th, and can be ignored compared to other trends. Under the conditions of enhanced Lyman-alpha flux there is a stronger photodissociation of water vapor and molecular oxygen in the mesopause, which results in a downward shift of constant isosurfaces of O3 and H, and, consequently, in downward shift of peak of the product [O3] · [H]. Note that in reality a large part of Lymanalpha solar variability affects the chemistry by modulation of dynamics and temperature. Thus, the part of impact discussed here represents the direct photochemical effect of Lyman-alpha variability on the photochemical system of MLT and should be considered as a lower limit of the total effect. The amplitude of OH* height variation with 11 year solar cycle amounts to 200–700 m depending on season and latitude. The strongest influence of the Lyman-alpha variation on the height of OH* layer occurs in spring season. Figure 12. Long-term variations (dashed lines) and corresponding linear * trends (solid lines) of OHv=6 height due to Lyman-alpha variability for winter (blue line), spring (green line), and summer season (red line) at 51.25°.

Figure 13 illustrates the sensitivity of OH* height dependence on Lyman-alpha flux. The height is inversely proportional to the Lyman-alpha with the rate of changes Δh/ΔLy-α [km/ (1011 phot.cm2 s1)] ~ 0.13 in winter (correlation coefficient 0.93), ~ 0.26 in spring (correlation coefficient 0.96), and ~ 0.11 in summer (correlation coefficient 0.95). The amplitudes of pressure (height) between solar min and solar max amount to ~ 350 – 200 m in winter, ~ 700 – 500 m in spring, and ~ 250 – 150 m in summer. As one can see, the total changes of the OH* layer at midlatitudes, due to the direct photochemical effect of Lymanalpha variability over the entire period of 49 years (38 m – 9 m), are essentially smaller than the amplitude of height variation (~200–700 m), as well as the SMA effect (0.62%–2.8% of SMA). Therefore the direct photochemical effect of Lymanalpha on the height of the OH* layer is of minor importance for our trend studies and can be neglected, but it allows to Figure 13. Lyman-alpha-height dependence at 51.25°N for winter estimate the effect on trends for measurements which are taken over a too (blue), spring (green), and summer (red).



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short time period. Nevertheless, it can be larger at other latitudes, for example, at the equator. Summarizing we state that strongest negative trends of OH* peak altitudes at middle latitudes are determined by the SMA-effect in all seasons. The changes due to dynamics, temperature, and the direct effect of the growth of GHG are of secondary importance and partially compensate the impact of SMA. The direct photochemical effect of Lyman-alpha variability on the long-term changes of OH* altitude is not important and can be neglected, particularly at midlatitudes. In a view of such great total (due to SMA, dynamics, temperature, and direct effect of the growth of GHG) long-term changes in OH* layer height, we can expect a change in temperature which corresponds to the layer and its differences in temperature at constant height and at constant pressure.

Figure 14. Nightly and seasonally averaged temperature trends in northern hemisphere at constant pressure p = 0.00185 hPa (black line), constant height * h = 87 km (cyan line), and at OHv=6 peak (magenta line) in summer (a), spring (b), and winter (c), and corresponding measurements by Lowe [2002] (black square), Semenov et al. [2002] (black rhomb), Espy and Stegman [2002] (black stars), Sigernes et al. [2003] (black triangle), and Offermann et al. [2010] (black circle).

5.2. Trends of OH* Temperature at Midlatitudes

The fact that the absolute temperatures at constant height, constant pressure, and at OH* layer peak are distinguished (Figure 8) push us to question how the trends are distinguished. We analyze temperatures averaged over the period of 0–3 LT at night and over the seasons (analogously to the OH* parameters).

Figure 14 illustrates the temperature trends at constant pressure (0.00185 hPa, z* ≈ 92 km), constant height (87 km), and at the peak of OH*v=6 for summer (a), spring (b), and winter (c) in the northern hemisphere. Strongest negative trends occur in winter at middle and high latitudes. The result obtained for the summer season in agreement with those of Lübken et al. [2013; Figures 8a and 9] which shows a small positive temperature trend in summer at high latitudes between ~85 and 95 km. For all seasons, the best agreement is found between the temperatures trends at constant pressure and at the OH* layer peak (black and magenta lines, respectively), with typical differences are ~0.01–0.02 K/year. Except at middle latitudes (~45°N–65°N) in summer and spring temperature trend at 87 km is weaker if positive and stronger if negative. As one can see trends are different for winter, spring, and summer, and thus separation by the seasons is essential here and for future research. Trends in the northern hemisphere vary between 0.23 K/year and +0.175 K/year. For comparison we add several trends measured recently by means of airglow observations. There are trends from observations which are outside the scale shown in Figure 14. We discuss them below. The best agreement between measurements and modeling occurs in summer. Lowe [2002] using hydroxyl rotational band (3–1) finds trend ~0.06 K/year at 43°N for period 1989–2001 years (Figure 14a). Based on combined data from several hydroxyl rotational temperature bands, at different stations between 42.8°N and 62°N, and for different periods since 1960 to1998, Semenov et al. [2002] found a trend of ~0.03 K/year for summer season (Figure 14a) and ~ 0.9 K/year for winter (which is beyond of Figure 14 range and is discussed below). GRYGALASHVYLY ET AL.


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The trend at Wuppertal (51°N) for summer season [Offermann et al., 2010; Figure 9] is ~ 0.07 K/year (Figure 14a). It was derived for period of 1987–2008 based on OH*(3,1) measurements. The same measurements show the trends ~ 0.39 K/year and ~ 0.26 K/year for winter and spring, respectively, which are outside the limits of Figure 14. Espy and Stegman [2002, Figure 2] infer trends of ~0.04 K/year, ~ 0.04 K/year, and ~0.18 K/year for summer, spring, and winter, respectively, at 59.5°N. As one can see the trends for summer are in perfect agreement with our modeling. The same we can state for the result of Espy and Stegman [2002] for spring. The most appreciable deviation between measured trends with our model, as well as between the measured trends, exists in winter. For example, observation of them at middle latitudes is scattered between ~ 0.9 K/year [Semenov et al., 2002] and ~0.18 K/year [Espy and Stegman, 2002]. Such a scattering is determined by several factors. As it was noted by Offermann et al. [2010, page 7] “The analysis window must be substantially longer than one solar cycle. In each published analysis, it should be mentioned which time window has been used.” From this point of view our analyses satisfy this criteria (it takes place Figure 15. The differences of temperature trends (a) at constant height over 49 years), but the analysis of Lowe * (h = 87 km) and at OHv=6 peak, and (b) at constant pressure * [2002] and Espy and Stegman [2002] does (p = 0.00185 hPa) and at the OHv=6 peak at the northern hemisphere in not (the time windows 1989–2001 and winter (blue line), spring (green line), and summer (red line). 1991–2001, respectively). Offermann et al. [2004, 2010] use very similar measurements to arrive at different trends. Offermann’s numbers for different periods (1980–2002 and 1987–2009, respectively) at the same latitude (51°N, 7°E) reveal quite different trends (0.06 K/year and 0.23 K/year, respectively). In fact, if we calculate the temperature trend at OH* layer peak for annual mean data analogically to the Figure 8 of Offermann et al. [2010] for the same period (1987–2008), we derive a value of 0.13 K/year, that is in good agreement with trends in the mentioned paper (0.23 K/year). We should mark some additional reasons for the discrepancies in already (and in future) published trends. As the hydroxyl layer has strong diurnal and tidal variation, the analysis is sensitive to the period of averaging over the night. The trends have strong latitudinal gradients and change of sign in latitudinal direction (Figure 14). An analysis based on mixing of measurements at different latitudes for different periods [e.g., Semenov et al., 2002] needs to take this into account. In Figure 15, we represent latitudinal distributions of the following differences: (a) between temperature trends at peak of OH*v=6 and temperature trends at 87 km; and (b) between temperature trends at peak of OH*v=6 and constant pressure (p = 0.00185 hPa). The essence is to perform corrections to derive realistic temperature trends at 87 km or at constant pressure. For example, in order to derive temperature trend at 87 km, 51°N, in winter, we should subtract ~0.07 K/year from temperature trend by airglow measurements. The largest difference between the temperature at 87 km and OH*layer is ~5–10 K and occurs in summer. The



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temperature at 87 km is approximately 1–5 K higher than at OH* layer peak in winter. The best agreement of the temperatures at the OH* layer and at 87 km occurs in spring. The differences between the temperatures at OH* layer peak and constant pressure are as such: the winter temperature at 0.00185 hPa is higher, the summer temperature is smaller than at OH* layer, and the best agreement is found in spring. The differences at constant pressure are smaller than those at 87 km and do not exceed 2 K. Figure 15a makes it clear that temperature trends at 87 km, except the middle latitudes in summer and spring, are always smaller than measured by airglow observations. The difference commonly amounts to ~0.04–0.08 K/year. The strongest discrepancy occurs in winter when it may reach 0.16 K/year. This is not surprising because the strongest linear trend of OH* altitude in geometrical coordinates takes place in the winter season (Figure 9b). Taking into account that trends vary between 0.23 K/year and 0.17 K/year (Figure 14), we conclude that the deviation between temperature trends at 87 km and at OH* layer peak is valuable relative to the trends. The differences in temperature trends at a peak of OH*v=6 with temperature trends at a constant pressure (p = 0.00185 hPa) are smaller than those with trends at 87 km, and vary between 0.04 K/year and 0.07 K/year (Figure 15b). For example, the trend at constant pressure 0.00185 hPa at 51°N in winter is just ~0.015 K/year weaker than those at OH*v=6 peak. However, the discrepancy of trends at constant pressure and the OH* peak (from 0.04 K/year to 0.07 K/year), relative to the trend at constant pressure variability depending on season and latitude (between 0.15 K/year and 0.17 K/year, Figure 14) is essential. Hence, as it was expected, the long-term changes in the altitude of the OH*-layer have a strong impact on the deviations of temperature at the OH* peak, from temperature at a constant pressure and from temperature at a constant (87 km) altitude.

6. Summary and Conclusions We performed a number of numerical experiments and studied seasonal, latitudinal, and long-term behavior of the OH* layer. We find strong seasonal and latitudinal variability in height and number density of the layer peaks. There are semi-annual and annual variations at the equator. As mentioned in former works, the reason for the annual cycle is the general mean circulation and corresponding fluxes of atomic oxygen. The reason for the semi-annual variation at low and equatorial latitudes is most likely a combination of semi-annual variation of tides and gravity waves, and photochemical reasons. At high and midlatitudes there is an annual cycle of the height and number density at the OH* peak, where the strongest variation occurs at high latitudes. The variability in the southern hemisphere is greater than in the northern hemisphere. The lowest altitudes (highest number density) of the layer were found at high latitudes in winter. The absolute minimum height of 72 km was found at 81°S in July, and the absolute maximum (94 km) at 1°N in November. The variation of OH* altitude within a given month is largest at ~60°N–80°N in October– March, and at ~60°S–80°S in April–September (i.e., during polar night) and is on the order of ~7–10 km. The variation is smallest (~1.9–3.8 km) close to the lower latitude edge of the midnight Sun area (~40°N–60°N in April–September, and ~40°S–60°S in October–March). Due to seasonal and latitude variations, temperatures at the peak height of OH* deviate from temperatures at a constant geometrical altitude (87 km) and constant pressure (0.00257 hPa, corresponding to appr. 87.9 km) by up ~ ±15 K. There is an anticorrelation of height and number density when considering variations with season and latitude. This anticorrelation, as well as some other effects, is theoretically explained (expression (8)) in frame of analytical solution for a simplified case. The thickness of the layer (h(OH*v=9)–h(OH*v=1)) varies with season and latitude and is maximum at the equator (~7–8 km) and minimum at midlatitudes during summer (~1–2 km). During a SSW the altitude of the layer increases by ~5–7 km and number density decreases by ~50%. The main driver for long-term changes in the OH* layer is shrinking of the middle atmosphere (SMA). Trends of the OH* layer altitude due to the SMA effect at midlatitudes amount to ~ 29.5 m/year, ~ 27.7 m/year, and ~ 30.9 m/year, in winter, spring, and summer, respectively. Trends in altitude due to the direct effect of the GHG increase are positive, and thus opposite to the SMA effect, and amount to ~1.6 m/year, ~2.4 m m/year, and 1.7 m/year, in winter, spring, and summer, respectively. Trends of dynamics and temperatures within the layer lead to additional trends of OH* height which are negative in spring (~ 1.9 m/year), positive in summer (~8.5 m/year), and negligible in winter (~ 0.3 m/year). The total effect of all impacts amounts to (in geometrical altitudes) ~ 28.3 m/year, ~27.9 m/year, and 20.6 m/year, in winter, spring, and summer, respectively. In pressure heights, the total effect is ~1.2 m/year in winter, ~ 0.2 m/year in spring, and ~10.3 m/year in summer. GRYGALASHVYLY ET AL.


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Because of changes in the OH* layer altitude there are differences between temperature trends at constant height (87 km) and at the peak of the OH*v=6 layer. Depending on latitude, this difference varies between 0.02 K/year and 0.16 K/year in winter, 0 K/year and 0.07 K/year in spring, and 0.02 K/year and 0.17 K/year in summer. Differences between temperature trends at the OH*v=6 peak and at constant pressure (see above) are smaller and amount to 0.02–0.05 K/year, 0.04–0.03 K/year, and 0.03–0.07 K/year in winter, spring, and summer, respectively. We summarize, that differences between temperature trends at a constant altitude (87 km), at a constant pressure, and at the peak of the OH* layer can be of similar magnitude as the temperature trend itself and must therefore be taken into account when comparing, for example, trends from airglow measurements with models. Analytical solution for height (pressure) and number density at peak of the layer was derived for a simplified case. According to this solution the number density at the OH* peak is directly proportional to the atomic oxygen number density and inversely proportional to a power of temperature, and it depends not only on absolute values of T and [O], but also on their vertical gradients. Acknowledgments The data used in this study are supported by Leibniz-Institute of Atmospheric Physics (Kuehlungsborn, Germany) and inquiries about calculated OH* distributions used for this paper can be addressed to Grygalashvyly ([email protected]). The authors are thankful to D. H. W. Peters for help with statistical problems and three anonymous referees for their useful comments and improvements of the paper.


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