Chapter 18Solar Variability and Trend Effectsin Mesospheric Ice Layers

Franz-Josef Lübken, Uwe Berger, Johannes Kiliani, Gerd Baumgarten,and Jens Fiedler

Abstract In this paper we summarize results from the SOLEIL project (SOLarvariability and trend Effects in Ice Layers) which was part of the CAWSES pri-ority program in Germany. We present results from LIMA/ICE which is a globalcirculation model concentrating on ice clouds (NLC, noctilucent clouds) in the sum-mer mesopause region. LIMA/ICE adapts to ECMWF data in the lower atmospherewhich produces significant short term and year-to-year variability. The mean icecloud parameters derived from LIMA/ICE generally agree with observations. Theformation, transport, and sublimation of ice particles causes a significant redistribu-tion of water vapor (‘freeze drying’). Model results are now available for all yearssince 1961 for various scenarios, e.g., with and without greenhouse gas increase etc.Temperatures and water vapor are affected by solar activity. In general it is warmerduring solar maximum, but there is a small height region around the mesopausewhere it is colder. This complicates the prediction of solar cycle effects on icelayers. The magnitude of the solar cycle effect is ∼1–3 K which is similar to theyear-to-year variability. Therefore, only a moderate solar cycle signal is observedin temperatures and in ice layers. Temperature trends at NLC altitudes are partlycaused by stratospheric trends (‘shrinking effect’). Trends are generally negative,but are positive in the mesopause region. Again, this complicates a simple predictionof temperature trends on ice layers and requires a complex model like LIMA/ICE.Trends in CO2 and stratospheric O3 enhance mesospheric temperature trends buthave comparatively small effects in the ice regime. Comparison of contemporaryand historic observations of NLC altitudes leads to negligible temperature trends atNLC altitudes (∼83 km). For the time period of satellite measurements (1979–2009)LIMA/ICE predicts trends in ice cloud brightness and occurrence rates, consistentwith observations. Temperature trends are not uniform in time but are stronger untilthe mid 1990s, and weaker thereafter. This change is presumably related to strato-spheric ozone recovery. The accidental coincidence of lowest temperatures and solarcycle minimum in the mid 1990s led to large NLC activity. It is important to con-sider the time period and the height range when studying temperature and ice cloud

F.-J. Lübken (�) · U. Berger · J. Kiliani · G. Baumgarten · J. FiedlerLeibniz-Institute of Atmospheric Physics, Schlossstr. 6, 18225 Kühlungsborn, Germanye-mail: luebken@iap-kborn.de

F.-J. Lübken (ed.), Climate and Weather of the Sun-Earth System (CAWSES),Springer Atmospheric Sciences,DOI 10.1007/978-94-007-4348-9_18, © Springer Science+Business Media Dordrecht 2013

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trends. In the mesosphere temperature trends can be as large as −(3–5) K/decade(in agreement with observations) or rather small, depending on the time period andheight range.

18.1 Introduction

The mesosphere/lower thermosphere (MLT) is very suitable for trend studies sincean increase of greenhouse gases in general affects this region much more stronglycompared to the troposphere. Furthermore, some parameters are very sensitive tochanges in the background atmosphere, for example ice layers. Ice layers form inthe very cold summer mesopause at middle and polar latitudes and are known asnoctilucent clouds (NLC) or PMC (polar mesospheric clouds). Ice layers are impor-tant for trend studies in the MLT region because they constitute the longest recordof quantitative observations in the middle atmosphere starting in the 1890s [Jesse,1896]. They are very sensitive to temperatures and to water vapor. As will be shownin this paper, NLC parameters show significant long term variations, but also someincredible persistences which provide a stringent constraint for any model on trendsin the MLT region. Whether or not ice layers show trends is disputed in the liter-ature [von Zahn, 2003; Thomas et al., 2003]. In this paper we consider variationswith solar cycle and on longer time scales (‘trends’). We summarize results fromthe SOLEIL project (SOLar variability and trend Effects in Ice Layers) which con-centrates on simulations of ice layers and compares with observations of NLC andPMC.

18.2 The LIMA/ICE Model

The LIMA model (Leibniz-Institute Middle Atmosphere model) is a general circula-tion model of the middle atmosphere which especially aims to represent the thermalstructure around mesopause altitudes [Berger, 2008]. LIMA is a fully non-linear,global, and three-dimensional Eulerian grid-point model which extends from theground to the lower thermosphere (0–150 km) taking into account major processesof radiation, chemistry, and transport. LIMA applies a triangular horizontal gridstructure with 41804 grid points in every horizontal layer (�x ∼ �y ∼ 110 km) andadapts to tropospheric and lower stratospheric data from ECMWF.1 This introducesshort term and year-to-year variability which influences, amongst other parameters,ice formation. LIMA has recently been upgraded to include additional radiation pa-rameterizations which improve simulations of solar irradiance variations, e.g., dur-ing the 11-y solar cycle. The old and new versions of LIMA are called LIMA-I andLIMA-II, respectively. One of the important applications of LIMA is modeling ofNLC formation. To this extent a dust/ice particle module is added to LIMA which

1European Center for Medium-range Weather Forecasts.

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Fig. 18.1 Flow chart of LIMA and LIMA/ICE. See Table 18.1 for more details

is called LIMA/ICE. This module consists of a multiple particle system containing40 million meteoric dust particles which act as potential condensation nuclei in thecold summer mesopause region at high latitudes. Several microphysical processesare present in LIMA/ICE, such as nucleation, growth, sublimation, sedimentation,and diffusion [see Rapp and Thomas, 2006, for a recent update on microphysics ofice particles]. The background water vapor interacts with ice particles which is re-distributed vertically and horizontally (‘freeze drying’). It is important to note thatwe initialize LIMA/ICE with a fixed latitude/longitude/height distribution of H2Oat the beginning of each summer season at 20 May (15 November) in the northern(southern) hemisphere (NH/SH). This fixed water vapor distribution is used as aninitial condition for all years (1961–2010), identical in both hemispheres. When theice season starts the water vapor is exposed to photo-dissociation by varying Lyα

radiation, 3-d transport, turbulent diffusion, and redistribution by freeze drying untilthe season ends at 20 August (15 February) in the NH (SH). As will be discussedlater, these processes induce solar cycle variations and long-term trends in watervapor in the upper mesosphere in LIMA/ICE. In Fig. 18.1 a sketch of the interplaybetween LIMA and LIMA/ICE is presented.

LIMA and LIMA/ICE were run for different scenarios summarized in Table 18.1.In order to account for realistic atmospheric conditions LIMA adapts several obser-vational data sets, such as a) tropospheric and stratospheric temperatures and winds(ECMWF data) from 0 to 40 km since 1961 (every 6 h; global coverage); b) dailyLyman-alpha fluxes2; c) monthly carbon dioxide concentrations3 from 0–130 km;and d) stratospheric ozone which is quantified by monthly, latitudinal, longitudi-nal, and altitude-dependent profiles available from SBUV/TOMS4 satellite data for1979–2008.5 The uppermost level of SBUV ozone data corresponds to a pressureof 0.5 hPa (∼50 km). Above this level LIMA interpolates and fits the SBUV ozonefields within one scale height to the standard ozone climatology used in LIMA.

2ftp://laspftp.colorado.edu/pub/SEE-DATA/composite-lya.In this paper we express Lyα radiation in units of 1011 photons/(cm2·s).

3http://www.esrl.noaa.gov/gmd/ccgg/trends.4Solar Backscatter in the Ultraviolet, Total Ozone Mapping System.5http://acdb-ext.gsfc.nasa.gov/Data_services/merged/.

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Table 18.1 Runs of LIMA and LIMA/ICE

LIMAa LIMA/ICE

global, 0–150 km, �z = 1.1 km, �x = 110 km,ECMWF (0–35 km), dynamics, radiation,chemistry, transport, H2O fixed

37.5°–90°N/S, z = 78–94 km, �z = 0.1 km,u, v, w, T, H2O from LIMA, 40 milliondust/ice particles, cloud microphysics,H2O: transport, photolysis, freeze drying

1 ±10 % CO2, ±10 % H2O, ±10 % O3b Lyα at 2005 solar conditions

2 Lyα at solar min and maxb Lyα at solar min and max

3 1961–2009 dynamics, fixed Lyα , fixed GHG (I) H2O varies with Lyα

4 1961–2009 dynamics, fixed Lyα , fixed GHG ”

5 1961–2010 dynamics, T(Lyα), GHG fixed H2O and temperatures vary with Lyα

6 1961–2009 dynamics, T(Lyα), CO2⇑ ”

7 1979–2009 dynamics, T(Lyα), CO2 and O3⇑ ”

aRun no. 3 was performed with LIMA-I, whereas all other runs were performed with LIMA-IIbRelative to 2005

For the period 1964–1978 we adapt annual total ozone data from ground-basedmeasurements published in the WMO report 2011. The years 1961–1963 have beenextrapolated from 1964.

Three different simulations have been performed with LIMA to assess the sen-sitivity of mesospheric temperature trends and their effect on ice layers: a) keep-ing CO2 and O3 constant from year to year (run no. 5), b) taking into accountlong term changes of CO2 in the entire atmosphere and keeping O3 constant fromyear to year (run no. 6), and c) same, but taking into account O3 changes up toapproximately 55 km from SBUV in years 1979–2008 and WMO 2011 in years1961–1978 (run no. 7). We note that all runs except run no. 7 use the same an-nual standard ozone climatology constant from year to year. This global data set forJanuary-December is a composite of a modeled ozone climatology provided by theLIMA chemistry-transport model (mesosphere/thermosphere) and an ozone clima-tology based on ozonesonde and satellite measurements (stratosphere) by Fortuinand Kelder [1998]. Furthermore, we note that global long term changes of CO2 andO3 in the troposphere and lower stratosphere are implicitly taken into account in allruns by the adaption of ECMWF data.

Certainly the most important background parameter controlling the morphologyof ice layers is temperature. In Fig. 18.2 LIMA temperatures in the northern hemi-sphere ice domain are shown for run no. 7. Ice particles can grow and exist if thedegree of saturation, S, is larger than unity. S increases exponentially with decreas-ing temperatures and linearly with enhanced water vapor concentration. For typicalwater vapor concentrations of 2–4 ppmv at NLC altitudes S is larger then unityfor temperatures smaller than approximately 148 K. As can be seen from Fig. 18.2NLC/PMC may exist poleward of ∼50°N. As will be shown later, natural vari-ability can extend this latitude range more southward. Note that ice particles maybe present but may be too small to be detectable by remote sounding instruments.

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Fig. 18.2 Mean Julytemperatures averaged for theyears 1997–2007, namely forthe last solar cycle period(run no. 7). The black isolinehighlights the approximateregion where ice particlesmay exist. White linesindicate the mesopausealtitude

Fig. 18.3 Left: Trajectories of 100 ice particles (out of 40 million) for a period of 5 days. Right:Snapshot of the ice cloud in the northern hemisphere on 24 July 2009, 09:12 UT

Therefore, the total altitude/latitude range of super-saturation is difficult to specifyexperimentally. The isothermal lines at NLC heights are nearly constant in altitudefrom ∼55°N to the pole. This suggests that also the lower edge of ice clouds shouldbe nearly constant with latitude. Indeed, a review of lidar observations at variousstations confirmed that NLC heights vary only very little with latitude [see Fig. 3in Lübken et al., 2008]. This nicely supports the background temperature fields inLIMA and imposes a prominent constraint on any model of the polar MLT. The con-stant NLC altitude concerns mean layer characteristics. In contrast, NLC altitudesfrom LIMA/ICE generally decrease with increasing brightness, again in agreementwith lidar observations at ALOMAR [Baumgarten and Fiedler, 2008].

In Fig. 18.3 the trajectories of 100 (out of 40 million) ice particles fromLIMA/ICE are shown for a period of 5 days. Particles move with the dominant winddirection which is nearly westward below the mesopause. Ice particles have a typicallifetime of a few days and only a small fraction of them grows large enough (radiuslarger than ∼20 nm) to be visible from the ground or from satellites. In the right

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Fig. 18.4 Temporal evolution of ice particles observed at t = 0 at ALOMAR (69°N). Mean cloudaltitudes (blue), backscatter signal (relative to the maximum signal at t = 0, green), particle radii(black), and ambient temperatures (red) are shown, averaged over 50 clouds which all show maxi-mum scatter at ALOMAR. Vertical lines indicate the variability in these 50 clouds

panel of Fig. 18.3 we show the brightness distribution of an NLC from LIMA/ICEat a certain point in time. More precisely, peak backscatter coefficients βmax in unitsof 10−10/(m·sr) at λ = 532 nm are shown.6 This plot demonstrates that the ice cloudis heterogeneous in time and space and may occasionally extend as far southwardas ∼50°N. This morphology generally agrees with space borne and ground basedmeasurements [Russell et al., 2009; Lübken et al., 2008].

LIMA/ICE allows to study in detail the development of ice clouds. We selected50 ice clouds from LIMA which are detectable by our lidar at ALOMAR (69°N)and studied their genesis (Fig. 18.4). Mean values and variabilities for ice particlealtitudes, radii, and ambient temperatures are shown. They agree in general withobservations [see for example von Cossart et al., 1999; von Savigny et al., 2005].Furthermore, the sum of the backscatter signal from all 50 ice clouds is shown,normalized to the maximum value (which is observed at ALOMAR). Ice particlesinitially stay in the cold region near the mesopause for an extended time periodand grow very slowly. About a day before observation, particle growth and sedi-mentation accelerates. The main growth phase occurs within only a few hours priorto observation. This implies that the ‘local’ background conditions at ALOMARdetermine the main features of the ice clouds as observed by lidars. After leavingALOMAR, the ice clouds sediment quickly to warmer regions and sublimate withina few hours.

We have studied the variability of ice cloud parameters within a summer sea-son. In Fig. 18.5 the cumulative occurrence frequency distribution as a functionof backscatter coefficients βmax is shown taking into account all ice clouds withβmax > 4.5 from year 2000 at 69°N (total number of clouds: 21258). This means,for each β the relative occurrence frequency of clouds being brighter than β isshown. The distribution shows an exponential decrease with increasing β which

6In this paper all backscatter coefficients β are given in units of 10−10/(m·sr).

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Fig. 18.5 Cumulativeoccurrence frequencydistribution of backscattercoefficients in the summerseason 2000 at 69°N (runno. 5). The occurrence isnormalized to 1 at thesmallest βmax-value(βmax = 4.5). All βmax-valuesare given in units of10−10/(m·sr). Fitting anexponential functionfc(βmax) = A · e−m·βmax givesA = 1.662 and m = 0.1411(red line)

Fig. 18.6 Water vapor concentration from LIMA (run no. 5) in ppmv (see color bar) in the sum-mer season 2009 at ALOMAR (69°N, 15°E). The freeze drying effect caused by ice particle for-mation, transport, and sublimation reduces the H2O concentration in the upper atmosphere andleads to an enhancement around 82 km. Sporadic mixing events lead to temporary redistributions.Note the non-uniform color scale

points to randomly distributed processes forming ice [Thomas, 1995]. The distribu-tion is in nice agreement with the so called ‘g-distributions’ deduced from satelliteobservations of PMC [DeLand et al., 2003]. The similarity between observed andLIMA/ICE distributions suggests that the mechanisms controlling the variability ofice clouds are replicated in LIMA. In practical terms this result implies that occur-rence rates from instruments with different β-thresholds can easily be compared(within limits).

Water vapor varies in LIMA/ICE due to photo-dissociation by Lyα and due toredistribution of H2O by ice particle formation, sedimentation, and sublimation(see Sect. 10.2). This effect is called ‘freeze drying’ and depends on temperatures,winds etc. As can be seen from Fig. 18.6 water vapor is redistributed, i.e., water

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Fig. 18.7 Tides in NLC and temperatures at ALOMAR (69°N, 16°E). Occurrence rates from theRMR-Lidar at ALOMAR are shown for βmax > 4 using the entire data set 1997–2010 [blue dots,update from Fiedler et al., 2011]. Temperature deviations from LIMA are shown for the samelocation (run no. 5, red curve) together with occurrence rates from LIMA/ICE (blue squares)

vapor concentration decreases above ∼84–85 km and can be more than doubled(compared to normal conditions) in the sublimation zone at ∼82 km. Indeed, satel-lite observations have shown strong indications for such an enhancement of watervapor at ∼82–84 km [Summers et al., 2001]. Significant freeze drying sets in withsome delay after the first occurrence of ice layers, and terminates when ice forma-tion disappears (beginning of August in Fig. 18.6). Ice particles can also redistributewater vapor horizontally, but mainly along latitudes since ice clouds mainly move inthe zonal direction. As can be seen from Fig. 18.6 vertical winds may occasionallyblow water vapor from the sublimation zone back up into the dry region (‘episodicmixing’) which causes a temporary humidification. The freeze drying effect has po-tential implications since some photochemical reactions in the MLT region involveH2O. Furthermore, ice particles can react directly with other substances, for exam-ple with metal atoms [Plane et al., 2004; Lübken and Höffner, 2004]. Therefore,ice layers play a more general role for the photochemistry in the MLT region whichshould be taken into account in general circulation models.

We have also studied tides in LIMA/ICE and compared our results with observa-tions at ALOMAR. In Fig. 18.7 we show the diurnal variations of LIMA tempera-tures at 83 km and ice cloud occurrence frequencies averaged for June and July ofall years from 1997–2010, as in Fiedler et al. [2011]. We also show occurrence fre-quencies for clouds with βmax > 4 as measured by the RMR lidar. The temperaturedeviations from LIMA show a nice anti-correlation with the occurrence frequencyobserved at ALOMAR (and modeled by LIMA/ICE) which suggests that tidal tem-perature variations are the main reason for tides in ice clouds.

LIMA/ICE modeling is now available for all years since 1961, both for the north-ern (NH) and southern hemisphere (SH). All model runs apply the nudging tech-nique and the ice particle routine LIMA/ICE introduced above. A collection of lati-tudinal/longitudinal distributions of ice layers from 1961 to 2009 is presented in theliterature for run no. 3 [Lübken et al., 2009] and run no. 5 [Lübken and Berger,2011]. In the meantime more runs with varying Lyα and increasing greenhousegases have been performed with LIMA-II (see Table 18.1). A total of 20 trillion

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Fig. 18.8 NLC altitudesversus temperatures at 83 km(from run no. 6) showing avery high correlation(r = 0.96). A straight line fitignoring the spurious years1975/1976 (squares) gives aslope of 0.212 km/K

single data points are available for each run, namely 40 million particles × 24 hours× 90 days × 50 years × 3 positions × 2 parameters (radii) × 2 hemispheres∼20 × 1012.

18.3 Correlations and Sensitivity Studies

We have studied in detail various correlations between ice clouds and backgroundparameters [see, e.g., Lübken et al., 2009, where run no. 3 was used]. We haverepeated these studies with LIMA-II including solar cycle variations with and with-out CO2 and O3 trends. The correlation coefficients and slopes vary somewhat fordifferent runs, but some important results are unchanged. For example, in all runswe find a very high correlation between NLC altitudes and temperatures at 83 km(see Fig. 18.8). The correlation coefficient is close to one and is highly signifi-cant. This result allows to use historical altitude measurements of NLC heightsto constrain temperature trends (see Sect. 18.5). Strong positive correlations arealso found for occurrence rates versus brightness. Weaker positive correlation ex-ists for occurrence and brightness versus water vapor concentration. More examplesfor (anti-)correlations are presented in the literature stated above. We will discussthe dependence of background and ice cloud parameters on Lyα in more detail inSect. 18.4.

We have performed sensitivity studies for NLC altitude, occurrence frequency,and brightness by varying water vapor (by ±25 %), temperatures (by ±1 K), and so-lar activity (max, min) relative to a mean state. More details are presented in Lübkenet al. [2009]. Roughly speaking, NLC altitudes vary mostly with temperatures andare rather insensitive to H2O change. Occurrence rates and brightness vary with allthree parameters (H2O, T, Lyα) and none of the influences is small compared tothe others. This implies that a given variation of occurrence or brightness cannotunambiguously be related to a change of one of the three background conditions.

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Fig. 18.9 Solar cycle effect on temperatures for run no. 2 as a function of pressure (left panel) andgeometric altitude (right panel). The mean temperature difference (in Kelvin) of solar maximumminus minimum for July is shown

18.4 Solar Cycle Variations

Solar Cycle Effect on Temperatures and H2O In Fig. 18.9 the effect of Lyα-radiation on temperatures in July is shown (run no. 2). In pressure coordinates tem-peratures are generally higher during solar maximum compared to minimum byapproximately 1–3 K in the MLT region. At mesopause altitudes the difference in-creases with latitude. The temperature increase is mainly due to increased heatingcaused by photo-dissociation of molecular oxygen by Lyα . To a smaller extent vari-able solar activity also alters the strength of solar absorption from the near infraredto the UV. Heating generally increases with altitude up to the thermosphere. Themaximum heating around 93–95 km in Fig. 18.9 (left panel) is due to very low tem-peratures and consequently large number densities leading to a local enhancementof optical thickness. The effect of solar radiation on the thermal structure in thepolar summer MLT region is more structured in geometric altitudes (right panel ofFig. 18.9). Even negative values occur at ∼90–95 km at high latitudes (i.e., colderduring solar maximum). This is explained by an expansion of the atmosphere belowthese altitudes which leads to an increase of geopotential altitudes (inverse to theeffect of increased greenhouse gases discussed later).

We have compared the solar cycle effects on temperatures in LIMA with resultsfrom general circulation and climate models such as HAMMONIA7 and WACCM8

which include fully coupled chemistry [Schmidt et al., 2010; Marsh et al., 2007;Tsutsui et al., 2009]. In general we find good agreement. We note that LIMA ignoresany feedback of solar irradiance on chemistry. However, fully coupled chemistry-climate models indicate that e.g. ozone changes due to solar variation are on theorder of few percent only which has only little effect on the thermal structure. At

7HAMburg MOdel of the Neutral and Ionized Atmosphere.8Whole-Atmosphere Community Climate Model.

18 Mesospheric Ice Layers: Solar Effects and Trends 327

Fig. 18.10 Water vaporconcentration at 83 km inppmv (color coded, seelegend) as a function oftemperatures at 83 km andLyα for run no. 5. Years 1975and 1976 are marked forreasons explained in the text

lower latitudes (44°N) and altitudes (<75 km) solar cycle temperature variationsfrom LIMA agree with lidar observations [Keckhut et al., 2005].

In summary, stronger solar activity increases MLT temperatures by a few de-grees in the ice domain and cools the atmosphere just above the mesopause. Themagnitude of this effect is in the same order as natural year-to-year variability. It istherefore not surprising that the correlation between temperatures at 83 km and Lyα

is poor (r = 0.34) because natural variability counteracts the influence of Lyα .Solar radiation destroys water vapor by photo-dissociation. Furthermore, H2O

is modified by freeze-drying which in turn is temperature dependent. These effectsare taken into account in LIMA/ICE (note that the initial water vapor is fixed inLIMA/ICE). More precisely, enhanced photo-dissociation during solar maximumleads to less water vapor available for ice formation. Furthermore, higher temper-atures reduce freeze-drying which leaves less water vapor from ice sublimation at∼83 km. In total, one would expect an anti-correlation between H2O and Lyα for al-titudes around 83 km. Indeed, there is a tendency for high (low) water vapor concen-trations to appear at low (high) Lyα values (see Fig. 18.10). However, temperaturesand Lyα only partly control the amount of water vapor at 83 km. Other mechanismssuch as natural variability, non-linear interaction in ice formation (causing freezedrying), and vertical/horizontal winds are of comparable importance. These effectsdestroy a clear anti-correlation of H2O and Lyα . For example, at low Lyα values(3.5–4) a large range of H2O values can appear (see Fig. 18.10).

Lyα Effects on Ice Layers Since in LIMA/ICE NLC brightness and occurrencerates are closely correlated with water vapor, a variation of these ice cloud parame-ters with solar cycle and temperatures looks similar to Fig. 18.10. This means thatwe cannot expect a simple anti-correlation between ice cloud parameters and Lyα .We have studied the effects of water vapor and temperatures (both modified by Lyα)on ice cloud parameters in more detail. The effect of Lyα on H2O alone causessome correlation between occurrence rates and Lyα (r = −0.44, run no. 4) whichincreases if the Lyα effect on temperatures is added (r = −0.59, run no. 5). Re-sults are similar for occurrence rates: the H2O effect alone causes some correlation

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Fig. 18.11 NLC occurrencerates observed at ALOMAR(69°N) for brightnessthresholds of βmax > 13(green solid line) andβmax > 4 (blue line). The Lyα

activity is shown forcomparison (red line, rightaxis). Results for LIMA/ICErun no. 5 are shown for strongNLC (green dashed line)

(r = −0.33, run no. 4) which increases if solar effects on temperatures are included(r = −0.51, run no. 5). Including CO2 increase does not significantly modify thesevalues, as expected (r = −0.55 for brightness, and r = −0.48 for occurrence, re-spectively). Since NLC altitudes zNLC barely depend on water vapor, the Lyα effecton H2O has basically no effect on zNLC . Only if the temperature effect is included,we find some correlation (r = +0.42).

In summary, solar activity only partially influences NLC/PMC brightness andoccurrence rates. This result is confirmed by observations. In Fig. 18.11 we showlidar measurements of NLC at ALOMAR (69°N). A total of 4224 hours of obser-vations with 1023 hours of NLC contribute to this plot [see Fiedler et al., 2011,for a recent update on NLC from ALOMAR]. Occurrence rates are shown for twodifferent thresholds of NLC brightness: βmax > 13 (‘strong’) and βmax > 4 (‘longterm’). The latter represents the instrumental sensitivity when measurements startedin 1997. Fiedler et al. [2011] studied in detail the potential influence of sampling onNLC brightness, occurrence rates, and altitudes for various β-thresholds. An impor-tant conclusion is that the main characteristics of ice clouds at ALOMAR are mostlikely not severely hampered by sampling issues. As can be seen from Fig. 18.11there is a tendency for occurrence rates of strong NLC (βmax > 13) to be lowerduring solar maximum, i.e., in the years 2000 to 2003, as expected. However, oc-currence rates were also very low in 2007 when solar activity was close to its recordminimum. Occurrence rates with long term threshold (βmax > 4) also show no clearanti-correlation with Lyα . For example, occurrence rates decreased(!) from 2004 to2007/2008 during the declining phase of solar cycle 23. On the other hand, we ob-served almost maximum occurrence rates in 2010 when solar activity was still low,which is in line with expectations based on the simple scenario outlined above. Thecomparison between LIMA/ICE and ALOMAR in Fig. 18.11 generally shows niceagreement with some deviations, in particular in the late 2000s. We have not yetidentified a specific reason for these differences and speculate that perhaps watervapor abundance was different in these years compared to LIMA/ICE (note that we

18 Mesospheric Ice Layers: Solar Effects and Trends 329

assume the same May profile of H2O in all years varying throughout the season dueto Lyα and transport).

NLC observations at mid latitudes in Kühlungsborn (54°N) show a similar non-coherent picture: in 2009 we observed record high NLC brightness and occurrencerates, whereas one year earlier (i.e., still during solar minimum) the activity was verylow. Visible observations of NLC and satellite measurements of PMC also showlittle correlations with solar cycle. Kirkwood et al. [2008] analyzed 43 years of NLCdata from ground based observers from the UK and from Denmark. They foundcorrelation coefficients of typically r = 0.45–0.55 depending on which selection ofNLC was analyzed (strong/faint, part of the season only etc.). We note that mostof the year-to-year variation, namely 70–80 %, cannot(!) be explained by solar fluxvariability.

The longest record of PMC observations (28 years) comes from SBUV instru-ments on various satellites. The data set has been intensively analyzed for trendsand solar cycle variations [see DeLand et al., 2007; Shettle et al., 2009, for somerecent results]. A solar cycle modulation and an increase of PMC albedo and occur-rence rates was identified. However, the correlation coefficients are on the order ofr = 0.5 only, i.e., again the majority of the variation (75 %) cannot be explained bysolar influence.

As will be shown in Sect. 18.5 the long term temperature trend at NLC altitudesled to minimum temperatures in the mid 1990s which accidentally coincides witha period of solar minimum. This combination of low temperatures and low Lyα ledto strong and frequent ice clouds detected in basically all long term records of NLCand PMC. This accidental coincidence over-emphasizes the solar signal in shorttime series of ice cloud observations. For example, an analysis of PMC data fromHALOE from 1993 to 2005 shows comparatively large correlation between solarcycle and extinction with values up to r ∼ −0.8 [see Fig. 4 in Hervig and Siskind,2006]. But the first years of this data set is from the mid 1990s where the accidentalcoincidence mentioned above creates many and strong PMC. We conclude that tooshort time series may lead to misinterpretations of the importance of solar influenceon mesospheric ice layers.

18.5 Trends

Water Vapor Trends Any temperature trend in LIMA induces trends in H2Oin LIMA/ICE due to freeze drying. More precisely the collection of water vapor byfreezing ice particles is more effective for lower temperatures. Therefore, the releaseof water vapor around 83 km during sublimation is larger for lower temperatures.The inverse is true in the region of water vapor collection, namely above the subli-mation regime. In total, a negative temperature trend causes a negative water vaportrend at ∼84–92 km, and a positive trend at ∼81–84 km. We note that the freezedrying efficiency depends on the degree of saturation (i.e. on temperature) whichintroduces a NH/SH asymmetry in water vapor trends and subsequently in ice layer

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Fig. 18.12 Temperatures at83 km (black line) and at42 km (red line) at 69°N fromrun no. 6, i.e., including solarcycle effects and CO2increase. Note the differentscales. The blue linesrepresent straight line fits.The Lyα activity is shown forcomparison (green line, rightaxis)

trends [see Fig. 10 in Lübken and Berger, 2011]. From observations little is knownabout water vapor trends in the polar summer MLT region [von Zahn et al., 2004].Some comparison of water vapor profiles from LIMA with measurements has beenpresented in the literature [Lübken et al., 2009; Hartogh et al., 2010]. In general,LIMA water vapor profiles agree with observations. In the future we intend to in-corporate water vapor trends in LIMA/ICE from observations (if available), or fromother models [e.g., Grygalashvyly et al., 2009].

Temperature Trends and Influence of the Stratosphere In Fig. 18.12 weshow seasonal (19 June–29 July) mean temperatures at 83 km for the ALOMARstation at 69°N from run no. 6, i.e., including solar cycle effects and CO2 in-crease. Temperatures decrease in the period ∼1961–1997 at a rate of approximately−0.80 K/decade, and increase thereafter. This implies that temperature trends in themesosphere are not uniform in the last 50 years, but may even change sign. It istherefore important to specify the time period for which a trend is determined. Thisbehavior is also seen in run 5 (constant CO2 and O3) and run 7 (trends in CO2 andO3) but with different magnitude. This suggests that a major source for the trend(and its reversal in the mid 1990s) is located in the stratosphere which is implicitlytaken into account in LIMA in all runs. This speculation is further supported bycomparing temperatures at 83 km with temperatures in the stratosphere (42 km, seeFig. 18.12). The similarity of both curves indicates that trends in the stratosphere im-pact mesospheric temperatures and ice layers. We note that ECMWF temperaturesin 1975 and 1976 are considerably higher compared to the general trend which isdue to a bias in satellite temperatures [Gleisner et al., 2005]. These spurious tem-peratures appear both in the stratosphere and mesosphere, which highlights the cou-pling between stratosphere and mesosphere. We have shown in detail that shrinkingof the stratosphere is an important mechanism causing mesospheric cooling andice layer enhancement [Lübken et al., 2009; Lübken and Berger, 2011]. Our tem-perature trends in the lower stratosphere at high latitudes agree with other studiesbased on radio sonde measurements (see Fig. 1 in Lübken and Berger, 2011, whichis based on Randel et al., 2009). This agreement is perhaps not surprising sinceLIMA adapts ECMWF data at these altitudes. Also, model results of mesospherictemperature trends at mid-latitudes for summer have been recently compared with

18 Mesospheric Ice Layers: Solar Effects and Trends 331

Fig. 18.13 Temperature trends (in K/decade) in the summer mesopause region for July consider-ing increase of CO2 and O3 (run no. 7). Trends are determined in the period 1979–2009. Left: as afunction of pressure, right: as a function of geometric altitude

lidar observations of temperatures at 44°N, phase height measurements at 51°N, andtemperature trends derived from SSU9 satellite data (channel 47X). In general thereis excellent agreement between trends from LIMA and observations [Berger andLübken, 2011]. We realize that little is known about the cause of long term trendsin the stratosphere at polar latitudes in summer. Furthermore, for the mesosphere nodirect multi-decadal measurements of temperatures by satellites are available. Pre-sumably, greenhouse gas increase and ozone trends play an important role in thiscontext. The latter is particularly intriguing since the curves in Fig. 18.12 show achange of trends in the mid 1990s, perhaps related to stratospheric ozone recoverywhere a change of trends also occurs in the mid 1990s [Jones et al., 2009]. Ourmodel results suggest that the differences of mesospheric temperature trends at analtitude of 83 km in the periods 1961–1997 and 1997–2009 originate from longterm ozone changes in the upper stratosphere. A potential influence of stratosphericozone on mesospheric ice layers was recently suggested also for the southern hemi-sphere [Smith et al., 2010].

Temperature trends were determined from all runs listed in Table 18.1. Here wediscuss results from run 7 only which includes CO2 increase and stratospheric O3change. In Fig. 18.13 we show temperature trends in K/decade for the time period1979–2009 as a function of pressure and altitude. This period was chosen becauseozone trends measured by SBUV satellites are available since 1979 [WMO, 2011].Furthermore, PMC measured by SBUV are also available in this period. As can beseen from this figure, temperature trends are positive in the summer mesopauseregion which is mainly caused by infrared radiative effects of CO2 [see Fig. 3in Akmaev et al., 2006]. If plotted as a function of geometric altitude, tempera-ture trends at polar latitudes are positive in a narrow region around the mesopauseonly (more precisely from ∼86 to 92 km) with maximum values of approximately

9Stratospheric Sounding Units.

332 F.-J. Lübken et al.

Fig. 18.14 Similar to Fig. 18.13 but for sub-periods, namely 1979–1997 (left) and 1997–2009(right panel)

Fig. 18.15 Temperature trends from LIMA at 69°N for 3 scenarios regarding trends in meso-spheric GHG: CO2 + O3 fixed (run no. 5, black lines), CO2 increase (run no. 6, blue lines), andCO2 plus O3 change (run no. 7, red lines). Similarly to Fig. 18.14 trends are shown for two dif-ferent time periods, namely 1979–1997 (solid lines) and 1997–2009 (dashed lines). Temperaturetrends are shown as a function of pressure (left) and geometric altitude (right). Note that trendscoincide below 40 km because of adaption to ECMWF

+2–2.5 K/decade. This is in the same order of magnitude as other model studies[Garcia et al., 2007]. It is important to notice that both negative and positive tem-perature trends occur in the ice particle regime, depending on altitude.

We noted earlier that temperature trends are not uniform (see Fig. 18.12). In or-der to highlight this effect further we show in Fig. 18.14 temperature trends fromrun 7 for sub-periods, namely 1979–1997 and 1997–2009. This facilitates compari-son with lidar measurements and SBUV observations of PMC. Trends are largestaround the mesopause in the 1979–1997 period with maximum values of up to+4–5 K/dec. Thereafter, trends are rather small and distributed more uniformly. Itis tempting to associate this change of trends with the stratospheric ozone recovery.

In Fig. 18.15 we show temperature trends from LIMA at 69°N for 3 scenariosregarding trends of mesospheric greenhouse gases (see figure legend). Tempera-ture trends generally increase if GHG increase is included, but the differences are

18 Mesospheric Ice Layers: Solar Effects and Trends 333

Fig. 18.16 NLC altitudesfrom LIMA at 69°N forruns 5 (blue), 6 (red), and7 (black). For comparison,mean NLC altitudes fromALOMAR are shown forβmax > 1 (dots). A typicalvariability throughout theseason of ±1 km is indicatedby dotted lines. The Lyα

activity is shown forcomparison (green line, rightaxis)

comparatively small in the ice layer domain. We note that for the 1979–1997 pe-riod trends are fairly large (up to −(3–5) K/dec) in the mesosphere, and are muchless (and even positive) for the later period. We compared temperature trends fromLIMA with lidar measurements at a mid latitude lidar station, namely ObservatoireHaute Provence (44°N) [see Fig. 10 in Keckhut et al., 2011]. For the time periodanalyzed in that paper, namely from 1979–1997, we find similarly large trends andgood agreement in the altitude dependence of the trends.

Ice Layer Trends The altitudes of NLC from LIMA/ICE show only very littlevariation over the last 50 years and agree nicely with ALOMAR (Fig. 18.16). Thisis basically true for all long term runs, i.e., whether or not solar cycle and CO2/O3trends are included. We have shown in Fig. 18.8 that there is a very strong corre-lation between NLC altitudes and temperatures at 83 km. For run no. 6 we arriveat a slope of 0.212 km/K corresponding to a temperature change of 4.7 K/km. Theobserved altitude change from the late 1890s to today is only ∼350–670 m [seeLübken et al., 2009, and references therein]. Using the altitude/temperature corre-lation from above, this corresponds to a temperature change of only 1.6–3.2 K innearly 120 years. This is an extremely small temperature trend and imposes a strin-gent constraint on all modeling of trends in the middle atmosphere. We note thatthe slopes given in Lübken et al. [2009] were slightly different (0.263 km/K) sinceLIMA-I was used for that study. However, this has no influence on the main conclu-sion outlined above. The cooling trend as inferred from NLC altitudes is somewhatsmaller compared to the trends shown in Fig. 18.13 which suggests that trends weresmaller in the earlier part of the 20th century.

We have determined albedo values from LIMA/ICE applying Mie scattering cal-culations with conditions very similar to SBUV, i.e. using the same wavelength(λ = 252 nm) and applying scattering angles which vary for each instrument. Albe-dos were calculated in three different latitudes bands as given in DeLand et al.[2007], namely 56–64°N, 64–74°N, and 74–82°N. Seasonal mean values were de-termined considering only those PMC which have albedos larger than thresholds of5.5, 6.5, and 7.5 × 10−6/sr for the three latitude bands given above. This is again

334 F.-J. Lübken et al.

Fig. 18.17 Comparison of LIMA/ICE albedos from run no. 7 with satellite observations fromSBUV. Albedos of polar mesospheric clouds (PMC) from the LIMA model (solid lines) for threelatitude bands: 56–64°N (red), 64–74°N (green), and 74–82°N (blue), similar to the bins chosenfor SBUV observations (symbols) presented by DeLand et al., 2007. The LIMA curves representmultiple regression fits consisting of straight lines plus a solar cycle function. These fits were de-termined using LIMA values calculated with similar parameters as in SBUV observations, namelya wavelength of 252 nm and varying scattering angles, depending on instrument and time of ob-servation. Zonal and seasonal mean values are determined applying albedo thresholds of 5.5, 6.5,and 7.5 × 10−6, again similar to constraints imposed by the SBUV instruments

similar to the data analysis applied for SBUV where the sensitivity of detecting PMCdecreases with increasing latitude. Note that we have determined albedos from βmaxassuming that the maximum backscatter at visible wavelengths contributes most tothe backscatter at UV wavelengths. As can be seen from Fig. 18.17 SBUV albedosand their variation with latitude are nicely reproduced by LIMA.

18.6 Discussion and Conclusion

We present results from the MLT region based on the LIMA model which adapts toreal conditions (ECMWF) in the troposphere/lower stratosphere. A Lagrangian iceparticle model (LIMA/ICE) is included which allows to study the morphology of iceclouds in the summer mesopause region. In general there is good agreement betweenLIMA/ICE and the characteristics of ice clouds observed from the ground (e.g., bylidars) and from satellites. This concerns, for example, cloud altitude, brightness,occurrence frequencies, variation with latitude and season, interhemispheric dif-ference, cumulative frequency distribution etc. The formation, sedimentation, andsublimation of ice particles causes a horizontal and vertical redistribution of H2O(‘freeze drying’) which significantly impacts the distribution of H2O and thereby af-fects photochemistry. We note that this effect should be taken into account in GCMmodels. LIMA/ICE results are now available for all years since 1961 and for vari-ous scenarios: with and without Lyα effect on temperatures, with and without trendsof CO2 and O3, etc. (see Table 18.1). We have not yet considered trends in watervapor from the lower atmosphere since too little is known about such trends in the

18 Mesospheric Ice Layers: Solar Effects and Trends 335

summer MLT region. We note that H2O trends are observed in LIMA/ICE causedby temperature-dependent freeze drying.

We have studied interhemispheric differences in detail in Lübken and Berger[2007, 2011]. Generally speaking, ice layers in the southern hemisphere are weakerand less frequent compared to the northern hemisphere. Apart from the southernMLT region being slightly warmer, freeze drying is less effective, which contributessignificantly to the interhemispheric differences of ice layer morphology.

Solar radiation leaves a major impact on H2O (through photo-dissociation) andon temperatures. In general it is warmer during solar maximum, but there is a smallheight region around the mesopause where it is colder during solar maximum. Thiscomplicates the forecast of solar cycle effects on ice layers, since the first stageof ice particle growth (around the mesopause) is supported during solar maximum(because it’s colder), whereas the later stage of growth is hampered. Therefore, acomplex model like LIMA/ICE is needed to take these counteracting effects intoaccount. The magnitude of the solar cycle effect is ∼1–3 K which is on the sameorder of magnitude as natural year-to-year variability. Consequently, there is only amoderate solar cycle signal in temperatures and in ice layers. This is in agreementwith long term observations of ice clouds where only a small fraction of variabilitycan be accounted for by solar cycle influence.

We have studied long term effects in MLT temperatures and ice layers. Temper-ature trends at NLC altitudes are partly caused by stratospheric trends (‘shrinkingeffect’) without any direct effects of GHG effects in the mesosphere. Trends are gen-erally negative, as expected, but are very small or even positive in the mesopauseregion. Considering the ice particle regime from roughly 80 to 95 km, trends arenegative in the lower part and positive in the upper part. This complicates a pre-diction of the effect of temperature trends on ice layers. Again, a complex modellike LIMA/ICE is needed. Adding CO2 and O3 trends enhances temperature trendsin the mesosphere but has comparatively small effects in the ice regime. We ap-ply the strong correlation between ice layer altitudes and temperatures at 83 km toobservations made nearly 120 years ago. This gives negligible temperature trendsat this altitude over this time period. For the time period of satellite measurements(1979–2009) LIMA/ICE predicts trends in ice cloud brightness and occurrence ratesconsistent with satellite observations.

It turns out that temperature trends are not uniform in time but are stronger in theperiod from 1961 to the mid 1990s, and weaker (even positive) thereafter. In a re-cent study Berger and Lübken [2011] showed that in the summer period 1979–1997at mid-latitudes strong cooling of up to ∼3–4 K/decade occurs in the middle meso-sphere, in the period 1961–1979 the middle atmosphere cools significantly less, andfor the period 1997–2009 they find a warming of ∼1 K/decade. For the first time,modeled temperature trends confirm the extraordinarily large temperature trendsobserved at mid-latitudes during the period 1979–1997 derived from lidar measure-ments, satellite data, and phase height measurements. The differences in temperaturetrends in the mesosphere originate from the evolution of stratospheric ozone in thepast 50 years, e.g. the observed reversal of both stratospheric and mesospheric tem-perature trends in the mid 1990s is caused by the recovery of stratospheric ozone

336 F.-J. Lübken et al.

[see Figs. 2.2 and 4.8 in WMO, 2011]. We note that lowest temperatures acciden-tally coincided with solar minimum in the mid 1990s which gave strong appearanceof NLC/PMC. This accidental coincidence over-emphasizes the importance of solaractivity on ice layers and implies that too short time series may lead to misinterpre-tations of the importance of solar influence on mesospheric ice layers.

Our results from the SOLEIL project show that it is important to consider the timeperiod and the height range when studying temperature and ice clouds trends in theMLT. For example, temperature trends can be negative or positive in the ice particleregime, depending on altitude. Temperature trends can be very large (in agreementwith some observations) or small, depending on the time period considered. Weintend to continue our studies on trends in the MLT region including potential watervapor trends and more observations. We will also investigate potential implicationsfor the entire atmosphere taking into account various coupling mechanisms.

Acknowledgements We appreciate the continuing financial support from the DFG for theSOLEIL project. The European Centre for Medium-Range Weather Forecasts (ECMWF) is grate-fully acknowledged for providing ERA-40 and operational analysis data. Several students havespent a considerable time at the ALOMAR observatory for making measurements. FJL thanks MrsRosenthal for her technical support in chairing the CAWSES priority program.

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