JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, D01110, doi: /2003jd003796, 2004

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi: /2003jd003796, 2004 Atmospheric response to the observed increase of solar UV radiation from solar minimum to solar maximum simulated by the University of Illinois at Urbana-Champaign climate-chemistry model E. V. Rozanov, 1 M. E. Schlesinger, 2 T. A. Egorova, 1 B. Li, 2 N. Andronova, 2 and V. A. Zubov 3 Received 22 May 2003; revised 19 August 2003; accepted 30 September 2003; published 15 January [1] The University of Illinois at Urbana-Champaign general circulation model with interactive photochemistry has been applied to estimate the changes in ozone, temperature and dynamics caused by the observed enhancement of the solar ultraviolet radiation during the 11-year solar activity cycle. Two 15-yearlong runs with spectral solar UV fluxes for the minimum and maximum solar activity cases have been performed. It was obtained that due to the imposed changes in spectral solar UV fluxes the annual-mean ozone mixing ratio increases 3% over the southern middle latitudes in the upper stratosphere and 2% in the northern lower stratosphere. The model also shows a statistically significant warming of 1.2 K in the stratosphere and an acceleration of the polar-night jets in both hemispheres. The most pronounced changes were found in November and March over the Northern Hemisphere and in September October over the Southern Hemisphere. The magnitude and seasonal behavior of the simulated changes resemble the most robust features of the solar signal obtained from observational data analysis; however, they do not exactly coincide. The simulated zonal wind and temperature response during late fall to early spring contains the observed downward and poleward propagation of the solar signal, however its structure and phase are different from those observed. The response of the surface air temperature in December consists of warming over northern Europe, USA, and eastern Russia, and cooling over Greenland, Alaska, and central Asia. This pattern resembles the changes of the surface winter temperature after a major volcanic eruption. Model results for September October show an intensification of ozone loss by up to 10% and expansion of the ozone hole toward South America. INDEX TERMS: 0341 Atmospheric Composition and Structure: Middle atmosphere constituent transport and chemistry (3334); 0340 Atmospheric Composition and Structure: Middle atmosphere composition and chemistry; 1650 Global Change: Solar variability; 3319 Meteorology and Atmospheric Dynamics: General circulation; 3334 Meteorology and Atmospheric Dynamics: Middle atmosphere dynamics (0341, 0342); KEYWORDS: solar influence, dynamics, stratosphere, ozone, surface temperature, climate Citation: Rozanov, E. V., M. E. Schlesinger, T. A. Egorova, B. Li, N. Andronova, and V. A. Zubov (2004), Atmospheric response to the observed increase of solar UV radiation from solar minimum to solar maximum simulated by the University of Illinois at Urbana- Champaign climate-chemistry model, J. Geophys. Res., 109,, doi: /2003jd Introduction [2] The possible relationship between solar variability and the Earth s weather/climate system for different timescales has been intensively studied using numerical models and statistical analyses of observational data. Some evidence of such relations can be found in the extensive reviews by McCormac and Seliga [1978] and Pittock 1 PMOD/WRC and IAC ETHZ, Davos Dorf, Switzerland. 2 Climate Research Group, Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. 3 Main Geophysical Observatory, St. Petersburg, Russia. Copyright 2004 by the American Geophysical Union /04/2003JD [1978, 1983], together with discussions of their soundness. During the last few decades the availability of data on the variation of solar irradiance, measurements in the middle atmosphere obtained with radiosondes, ozonesondes and satellites, and substantial progress in numerical modeling of solar and atmospheric processes has enabled a more detailed evaluation of solar-terrestrial connections. [3] The most significant evidence of the solar-atmosphere relationship for the decadal timescale was reported by Labitzke and van Loon [1988]. They found a very high correlation of the 11-year solar cycle of solar activity with the temperature record at 30 hpa over the North Pole when the observed data were stratified according to the phase of Quasibiennial Oscillation (QBO). For the westerly phase of the QBO the correlation coefficient is positive and was estimated as high as For the easterly phase of the QBO 1of16

2 the correlation is negative and slightly less pronounced. Later Labitzke and van Loon [1989] reported a high correlation of sea level pressure, surface-air temperature, 700 hpa geopotential height and the frequency of lows in the Northern Hemisphere with the 11-year solar cycle. A similar correlation for total ozone for different phases of the QBO was found by Varotsos [1989]. A high correlation (around 0.6) was found by van Loon and Labitzke [1990] between the 10.7 cm solar flux and the mean height of the 100 hpa surface in the Northern Hemisphere for data not stratified by the phase of the QBO. The sensitivity of the temperature, geopotential and ozone fields in the stratosphere to the 11-year solar activity cycle has been recently updated and summarized by Labitzke [2001], Kodera and Kuroda [2002], and Hood [2003], thereby providing a solid basis for theoretical studies. [4] Although potential candidates have been extensively discussed during the last two decades, the physical mechanisms that can explain the discovered correlations have not been identified and validated against observational data. Reid [2000] pointed out that the candidates are variations in solar irradiance and variations in the solar wind and energetic particles. However, the 0.1% variation in the total solar irradiance (TSI) detected from satellite measurements [Fröhlich, 2003] being uniformly distributed among all wavelengths cannot significantly affect the stratosphere [Rozanov et al., 2002a]. The mechanism involving Galactic Cosmic Rays and their influence on cloud cover, first reported by Svensmark and Friis-Christiansen [1997], has not been confirmed yet by the data and theoretical analysis. Another possible explanation of the above mentioned correlations is the influence of the solar UV (SUV) flux on ozone in the middle atmosphere, and in turn on the heating rate, temperature structure and dynamics of the entire atmosphere via perturbation of planetary-wave propagation. This mechanism has been put forward during the last decade by several groups [Hood et al., 1993; Rind and Balachandran, 1995; Kodera, 1995; Kodera and Kuroda, 2002] and is mostly consistent with the work of Hines [1974]. This mechanism is based on the assertion that for solar maximum conditions a small additional heating in the tropical stratosphere during late northern autumn is able to stimulate an atmospheric state with a strong Polar Night Jet (PNJ) and corresponding weather pattern for the entire winter. [5] Using a UGAMP GCM extending to 10 hpa in a perpetual January mode, Haigh [1996, 1999] demonstrated the existence of a coupling between UV solar flux variations, ozone changes, and the state of the atmosphere. It was shown that for solar maximum the tropical Hadley cell is weakened and broadened and the sub-tropical jets are shifted poleward, which causes sub-tropical warming. The calculated effects were found to be similar to the observed, but their magnitudes were much smaller than in the observations. Support for such a mechanism has been furthered by the analysis of Shindell et al. [1999]. Using the GISS GCM they found that ozone and temperature changes in their simulations led to geopotential height changes in the troposphere that resemble the solar signal obtained from the data analysis. Although the magnitude of the signal was rather small compared to the observations, their results allowed the conclusion that the ozone changes caused by the variation of SUV could lead to corresponding temperature changes and the subsequent alternation of the zonal winds, meridional transport and planetary-wave pattern which drives the temperature response in the troposphere. [6] The obtained underestimation of the magnitude of the solar signal could be connected with the fact that all the above mentioned simulations, as well as similar GCM experiments performed in the framework of the GRIPS project [see Matthes et al., 2003], have been carried out with ozone changes obtained from the experiments with 2-D models, not with a climate-chemistry model that interactively includes all ozone-related processes. However, it is well known [see, for example, Huang and Brasseur, 1993; Fleming et al., 1995; Hood, 2003] that 2-D models underestimate the magnitude of the ozone changes, especially in the tropical stratosphere. Taking into account the extremely high sensitivity of the ozone perturbation to the solar effects, this simplified 2-D representation of the ozone increase could lead to smaller-than-observed ozone changes and underestimation of the resulting signal in the entire atmosphere. The first attempts to address this issue have been briefly presented by Rozanov et al. [2000], Nakamoto et al. [2000] and Tourpali et al. [2003]. To elucidate this problem we applied the UIUC Climate-Chemistry Model (CCM) described in detail by Rozanov et al. [2001]. Using this model we simulated the response of ozone, temperature, geopotential height and zonal wind to the imposed increase of solar UV flux. The main goals of this study are to estimate how close the simulated changes are to the data obtained from the observational analysis and to the results of other models, and to define desirable future experiments. [7] A brief description of the model and experimental setup is presented in section 2. The changes of the simulated quantities are described and compared with available observational data in section 3. Discussion of the results and conclusions are presented in section Model Description and Experimental Set-Up [8] The University of Illinois at Urbana-Champaign (UIUC) 24-layer stratosphere/troposphere Climate-Chemistry Model (CCM), described and validated by Yang et al. [2000] and Rozanov et al. [2001], has been used to estimate the changes of temperature, dynamics and photochemistry due to the observed increase of solar UV radiation from solar minimum to solar maximum. The model s horizontal resolution is 4 latitude by 5 longitude. In the vertical direction the model spans the atmosphere from the Earth s surface to 1 hpa. The vertical representation is based on the sigma coordinate system. If the pressure at the surface is 1000 hpa, the pressures at the upper boundaries of the model layers are equal to 980, 950, 900, 850, 800, 750, 650, 550, 450, 350, 250, 200, 150, 100, 63.1, 39.81, 25.12, 15.85, 10, 6.3, 3.98, 2.51, 1.59, and 1 hpa, respectively. [9] The chemical-transport part of the model simulates the time-dependent three-dimensional distributions of 42 chemical species, which are determined by 199 gas-phase, 16 heterogeneous and 45 photolysis reactions. The chemical solver is based on a purely implicit iterative Newton- Raphson scheme [Rozanov et al., 1999]. The reaction coefficients are taken mostly from DeMore et al. [1997] and Sander et al. [2000]. Photolysis rates are calculated using the look-up-table approach [Rozanov et al., 1999]. 2of16

3 For the advective transport of the chemical species we applied the Hybrid scheme described by Zubov et al. [1999]. The model time steps are 3 min for the dynamical core, and one hour for the treatment of the physical, photochemical and transport processes. The model has full representation of the physical processes in the atmosphere and at the surface. To maintain the links between the chemical and physical processes, the GCM part of the model supplies to the transport-chemical routine hourly averaged wind components, temperature, surface pressure, and tropospheric humidity. The chemical-transport routine returns the mixing ratios of the optically active gases in the stratosphere and troposphere, ozone in particular, which are used to calculate the radiative fluxes. A 1-hour time step is used for the gas transport and chemistry calculations. [10] We have performed two 15-year equilibrium model simulations with prescribed sea-surface temperature: (1) a control run with the observed spectrum of solar radiation averaged over ; and (2) an experiment with the observed increase in solar UV radiation from solar minimum to solar maximum added to the average spectrum of solar radiation. In these simulations, the increased solar UV radiation, described by Matthes et al. [2003], influences both the solar heating rates calculated by the CCM s radiation code [Yang et al., 2000] and the photolysis rates calculated by the CCM s photochemical code [Rozanov et al., 1999]. [11] Here we present the results of the comparison between these two runs. In the paper we analyze only 12 years of the simulation. Three years of the two 15-yearlong simulations were excluded from the analysis because for the solar-maximum run they generated abnormally high-frequency stratospheric warmings during boreal winter which smear out the ensemble-mean solar signal. [12] To validate the simulated results we have calculated the observed solar signal in temperature, zonal wind and geopotential height using the composite analysis applied by Kodera and Kuroda [2002]. The data used are the NMC/ Climate Prediction Center (CPC) model analysis [Randel, 1992] for the period After removing the linear trends over the period October 1978 May 1999, we select the solar maximum group (1980, 1981, 1983, 1989, 1990, and 1998) and solar minimum group (1985, 1986, 1987, 1994, 1995, 1996, and 1997) based on the total solar irradiance variations over this period published by Fröhlich [2003]. First we calculated the annual mean TSI for each year between 1979 and 1999, then we calculated the mean TSI for this period and divided all years into solar maximum and minimum groups. Note that several post-volcanic years (1982, 1991, and 1992) were not included in our selection because we intend to isolate the solar signal from the volcanic one. Also, we did not sort the data by the phase of the QBO because, at present, the model does not simulate the westerly phase of the QBO. However, as van Loon and Labitzke [1999] pointed out, the unsorted data give a solar signal similar to that for the easterly phase of the QBO. In our analyses we focus on three quantities: temperature, zonal velocity, and geopotential height. We obtain the annual-mean composite solar maximum and minimum by averaging the corresponding data in each group. To follow the signal through the year we also compute the composite monthly values for each group. In the next section we show the differences between these solar maximum and minimum groups. Student s t-tests are performed to estimate whether the solar signal is statistically significant. 3. Results [13] In this section we present and analyze changes in the annual zonal-mean ozone, temperature and zonal wind (section 3.1); changes in the seasonal march of the zonalmean zonal wind and temperature (section 3.2); and changes in the geographical distributions in geopotential height, temperature, total ozone and surface air temperature (section 3.3) Annual Zonal-Mean Quantities Ozone [14] Because the changes in ozone and the direct radiative heating due to the increase in solar UV flux are assumed to be the primary mechanisms that drive the changes of the atmospheric state, we start with an analysis of the ozone and temperature changes. It is well known [Brasseur and Solomon, 1986] that the imposed SUV increase intensifies the photolysis rate of molecular oxygen and, therefore, the ozone production. However, an increase in the production of HO X through intensification of H 2 O photolysis could lead to some ozone destruction. From the numerical experiments performed with relatively simple 1-D [Rozanov et al., 2002a] and 2-D models [Huang and Brasseur, 1993] it can be concluded that the ozone increase dominates in the stratosphere, while in the lower mesosphere ozone depletion prevails. Therefore from these theoretical considerations involving only the chemical processes we can expect that the ozone response should be maximal in the layer 5-10 hpa layer and then steadily decrease with altitude. [15] The simulated annually averaged response of the zonal-mean ozone to the SUV enhancement is presented in Figure 1a in comparison with the observational data analysis in Figure 1b. Our results show a statistically significant increase in ozone mixing ratio throughout almost the entire stratosphere, except in the high-latitudes, as a result of the increased photodissociation of molecular oxygen. The latitudinal distribution of the ozone response simulated by the CCM is characterized by two pronounced maxima and a decrease in ozone in the high-latitude lower stratosphere. One maximum appears in the southern upper stratosphere and the other in the northern lower stratosphere - the latter is better seen in Figure 1c and Figure 1d, where the simulated data are presented for March April May (MAM) and September October November (SON). These features are explained by the intensification of the PNJ shown later. [16] The ozone response obtained from the analysis of satellite data [Hood, 2003] has a different structure in the tropical stratosphere. Comparison with the simulated ozone changes reveals dramatic differences in the tropics where the observational data analysis shows significant decrease of the ozone mixing ratio from 20 to 5 hpa that contradicts the model results and theoretical expectations mentioned earlier. Substantial disagreement is also evident for the vertical profile of the solar signal. In the model the ozone has the most pronounced response around 3 hpa while the observed signal increases with altitude to reach its maximum value at the stratopause. Chandra and McPeters [1994] pointed out, 3of16

4 Figure 1. Latitude-height cross-section of (a) simulated and (b) observed solar-maximum-minus-solarminimum changes in annual zonal-mean ozone mixing ratio (%). Simulated changes for MAM and SON seasons are illustrated in (c, d). The isolines are 2, 1, 0, 0.5, 1, 1.5, 2, 2.5, 3, 5%. The annual-mean observational data are from Hood [2003]. The light (heavy) shading in panel (a) shows the regions where the changes are judged statistically significant at or better than the 20% (5%) level. The shading in panel (b) shows the regions where the changes exceed the 2s level. however, that the accuracy of the detection of the ozone response in this particular area is very low because of the influence of the aerosol clouds formed in the middle stratosphere after the El-Chichon [1982] and Pinatubo [1991] volcanic eruptions which coincided with the maxima of the two solar activity periods, ( and ). [17] The CCM results are in better agreement with the observational data analysis in the middle latitudes of both hemispheres. The model reproduces well the Southern Hemisphere ozone response maximum in the upper stratosphere, the ozone increase in the northern middle latitudes and the ozone decrease poleward of 65 N. [18] The simulated seasonal-mean ozone changes are similar to the annual-mean results except for the MAM and SON seasons (Figures 1c and 1d). During northern spring (MAM) the magnitude of the ozone changes increases with altitude in the Southern Hemisphere and reaches 5% at the stratopause. This behavior of the solar signal in the Southern Hemisphere resembles that obtained from the observations. During the northern fall (SON) the model generates a second maximum in the northern lower stratosphere which also is quite close to the observational data. The shift of the expected tropical signal toward the Southern Hemisphere implies that the meridional circulation has changed. [19] The simulated total ozone response is presented in Figure 2 and is compared with two sets of the satellite data [World Meteorological Organization (WMO), 2003]. The comparison shows that the model matches the observed total ozone changes from 50 S to 60 N within the uncertainty of the observational analysis and variability of the 4of16

5 Figure 2. Latitudinal distribution of the simulated and observed solar-maximum-minus-solar-minimum changes in annual zonal-mean total ozone (D. U.). The changes of the total ozone are from the CCM (solid line) and from two observational datasets: SBUV-SBUV/2 (dotted line with open squares) and merged satellite data (dotted line with open triangles) [WMO, 2003]. The shaded area illustrates the variability of the simulated total ozone response among the ensemble members. simulated results. The total-ozone response simulated by the CCM is negative over high latitudes reflecting an intensification of the PNJ in both hemispheres. It should be noted that the CCM also reproduces the local total-ozone response maximum around 30 N which is visible in the merged satellite data set. Unfortunately, the high-latitude totalozone depletion simulated by the CCM cannot be verified because of the lack of observational data in this area Temperature [20] The ozone changes and the resulting enhanced direct radiational heating increase the temperature in the stratosphere. The annual-mean latitude-pressure cross-sections of the zonal-mean observed and simulated temperature changes are presented in Figure 3. The solar-maximumminus-solar-minimum temperature changes simulated by the CCM are significant almost everywhere in the stratosphere, except in the high-latitude lower stratosphere where the model variability is highest. The simulated temperature response in the tropical area reaches its maximum magnitude of about 1.0 K at the stratopause and gradually decreases further down, reaching 0.4 K in the lower stratosphere. The temperature response simulated by the CCM has a pronounced dipole structure in the vertical in the high latitudes of both hemispheres, which is due to the intensification of the PNJ. The simulated warming exceeds 1 K in the upper stratosphere and the corresponding cooling of about 0.8 K takes place in the lower stratosphere. Intensification of the downward transport in the middle latitudes of the Northern Hemisphere is revealed by the local maximum in the temperature increase in the lower stratosphere around 30 N. [21] Comparison with the temperature response obtained from the observations and reanalysis data is rather complicated because there is not good agreement between the different data sets. We have put together in Figure 3 the results published in several papers. The temperature signal simulated by the CCM is close to the analysis of SSU/ MSU4 data [WMO, 1999] in the tropical middle stratosphere between 2 and 70 hpa. However, the simulated response is higher than the observed in the upper and lower stratosphere. The simulated temperature response in the upper stratosphere, which is the most robust feature of the observed temperature signal, is about half as large as of the response shown by the different analyses of the NMC/CPC data. Another area of disagreement emerges in the lower stratosphere. [22] All of the NMC/CPC data analyses [Hood, 2003; Kodera and Kuroda, 2002] reveal significant warming in the lower stratosphere, but the model response shows only a small warming in this area. At 50 hpa the model shows a maximal response around 30 north and south, resembling the two warming spots depicted in Figure 3e. It should be noted, however, that the location of the observed warming differs among the different analyses. The Kodera and Kuroda [2002] data display a pronounced warming around 50 hpa while the analysis performed by Labitzke [2001] and Hood [2003] shows the warming in the hpa layer. On the basis of the observational analysis performed in the present work we detect two spots of warming around 80 hpa over the southern and northern tropics. In the stratosphere between 10 and 3 hpa the simulated and observed temperature responses are quite different. Some of the observed data show strong cooling in the tropical middle stratosphere around 10 hpa or slightly higher, which is not seen in the model results and disagrees with theoretical expectations [e.g., Huang and Brasseur, 1993] but it agrees with the ozone minimum in this region seen in the observations presented in Figure 1b. In the high latitude some of the data analyses show cooling in the lower stratosphere typical for the accelerated PNJ and consistent with the model results, but most of the temperature changes are not statistically significant Zonal Wind [23] The annual-mean response of the zonal-mean zonal wind to the increase in SUV is presented in Figure 4. In accord with the temperature response, the simulated changes of zonal wind consist of an acceleration of the stratospheric jets. This acceleration is robust in the northern upper stratosphere and only marginally significant in the middle and lower stratosphere. The annual-mean increase in the subtropical upper stratospheric wind is about 0.5 m/s and up to 2.5 m/s in the high latitudes. The analysis of the NMC/CPC data performed by Kodera and Kuroda [2002] does show some increase of the intensity of the jets in the Northern Hemisphere, but it is well below the noise. Our analysis of the NMC/CPC data shows some increase in the high-latitude wind, which is not statistically significant in the Northern Hemisphere and only marginally significant in the Southern Hemisphere. It should be noted that our analysis also showed a significant reduction of the northern upper stratospheric winds of up to 2.5 m/s, which is opposite to the Kodera and Kuroda [2002] data. This emphasizes again the rather large uncertainty of the observational data analysis. Also, we note that in their analysis of the data, Kodera and Kuroda [2002] did not exclude the years with the major volcanic eruptions as we did, which can contribute to the reported differences because it has been shown by Rozanov et al. [2002b] that the zonal wind at 5of16

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7 the stratopause after powerful volcanic eruption can be substantially (up to 6 m/s) accelerated Seasonal March of Zonal-Mean Quantities Zonal Wind [24] The simulated and observed seasonal march of the zonal-mean zonal wind changes due to the SUV enhancement are presented in Figure 5. The observational data have been calculated using the composite analysis of the NMC/CPC data. The simulated changes of the zonal wind have a similar behavior throughout the entire stratosphere. During the cold period in the Northern Hemisphere the solar signal appears first in September around 30 N reflecting the intensification of the upper-stratospheric subtropical jet (USSJ) caused by an increase in the latitudinal temperature gradient. The magnitude of the USSJ intensification is about 1 m/s and it is statistically significant in the upper stratosphere. In October the acceleration of the zonal wind starts to propagate northward without substantial changes in its magnitude. Then in November the signal propagates to the high latitudes and the PNJ substantially (up to 5 8 m/s) increases. In December the simulated changes of zonal wind are small in the upper stratosphere. However, the zonal wind perturbation propagates downward causing a large but marginally significant increase of the PNJ at 30 hpa levels in December and January. In February the zonal wind changes are very small. A second peak of the solar signal in the simulated zonal wind appears in March simultaneously at all levels considered, with a maximum effect around 10 hpa. The decrease of the solar signal during January and February is accompanied by a small and not statistically significant easterly wind anomaly over the northern middle latitudes. [25] The simulated seasonal behavior of the zonal-wind changes over the Southern Hemisphere look similar to those of the Northern Hemisphere, however their timing is slightly different. A small acceleration of the PNJ occurs in May and June. Then the signal penetrates toward high latitudes and reaches its most pronounced increase in September in the upper stratosphere and in October in the middle stratosphere. A decrease of the signal occurs in November and a second peak appears in December. [26] The main features of the solar signal in the zonal wind resemble the changes of the zonal wind after the Pinatubo eruption simulated by the same CCM [Rozanov et al., 2002b]. In that study the zonal-wind changes over the Northern Hemisphere also have a double-peak structure with two maxima in November and April. It appears that in our CCM the zonal wind is sensitive to the changes in the equator-pole temperature gradient during the months when the radiative processes do not completely define the state of the atmosphere, that is, during fall and spring. At this time of year a small change in the temperature in the tropics can alter the distribution of the zonal wind. During winter the radiative cooling in our model appears to be sufficiently strong to prevent any substantial change in the zonal-wind state in both hemispheres. Here we should mention that our CCM has the cold pole problem [Rozanov et al., 1999] which may contribute to this seasonal dependence of the zonal-wind response. [27] From the presented results we may conclude that the magnitude of the simulated response is in rather good qualitative agreement with the observational data analysis presented in Figure 5, however they do not exactly coincide in phase. In the northern upper stratosphere the observed zonal-wind changes also appear during fall, but this happens in November. The increase in the PNJ in the observational data reaches its maximum magnitude in January while the simulated response maximizes in November. The recess period also lasts for two months (February and March) when an easterly anomaly of the zonal wind dominates, while the model results do not show substantial changes over high latitudes. The simulated easterly anomalies are visible, but they do not propagate further northward. In the middle stratosphere the observed signal propagates downward causing some (about 2 m/s) PNJ increase at 30 hpa in February that is consistent with the model results. The secondary acceleration of the zonal wind appears in the observational data in April May while in the model it occurs 1 month earlier. Over the Southern Hemisphere the simulated zonal-wind changes resemble the observational data quite well. The model matches well the location and timing of the observed PNJ increase from June to December, including the period of relatively low signal in November Temperature [28] The simulated and observed seasonal march of the zonal-mean temperature response to the SUV enhancement is presented in Figure 6. The simulated changes consist of a warming of about 0.5 K at 10 and 30 hpa, except in the high latitudes during the cold time of the year and the tropical/middle-latitude stratosphere from December to February. At 70 hpa the warming appears only in the tropics. [29] Over the northern high latitudes the structure of temperature signal is more complicated. From the simulation results presented in Figures 6a, 6b, and 6c we can conclude that during the period when the intensification of the PNJ dominates (November) the model shows substantial cooling inside the polar vortex throughout the middle stratosphere (at 10, 30 and 70 hpa) and intense warming in the upper stratosphere (not shown), which forms the wellknown dipole structure of the temperature changes (also see Figure 3). The warming in the upper stratosphere can be explained by an intensification of the PNJ, which is accompanied by the more intense downward motions in the upper stratosphere and subsequent compressional heating. Therefore the stratosphere warms up at 1 hpa (not Figure 3. Latitude-height cross-section of simulated and observed solar-maximum-minus-solar-minimum changes in annually averaged zonal-mean temperature (K). The observational data have been taken from WMO [1999], Labitzke [2001], Kodera and Kuroda [2002], Hood [2003] and this work. The light (heavy) shading in Figures 3a and 3e shows the regions where the changes are judged statistically significant at or better than the 20% (5%) level. The shading in Figures 3b and 3c shows the regions where the changes exceed 2s level. The shading in Figure 3d shows the regions where the changes exceed the 1s level. 7of16

8 shown). It also cools down at 10, 30 and 70 hpa, which reflects cooling of the lower stratosphere due to radiative processes and the decrease of the horizontal heat transport due to more strong and isolated vortex. This cycle repeats in our CCM simulation again in March and April. [30] Similar features can be seen from the observational data analysis depicted in Figures 6d, 6e, and 6f. The first phase starts in December and produces a strong cooling of the entire stratosphere (at 10, 30 and 70 hpa levels) followed by a substantial warming in February. The second cycle starts in March at 10 hpa and a month later at 30 and 70 hpa, which resembles the simulated temperature response with a 1 month time lag. Similar but not-sowell pronounced cycles can be seen over the southern high latitudes. In the model the first cycle starts in May and is followed by a period when the changes are relatively small. The second cycle starts in August and the cooling reaches its maximum in September October. In the observational data these cycles appear 1 2 month later than in the model. [31] In the middle and low latitudes the magnitude of the simulated temperature signal decreases slightly with altitude, while in the observational data the situation is quite the opposite. A maximum warming of about 1.5 K appears in the lower stratosphere (at 70 hpa) and is mostly statistically significant. The observed temperature changes are rather small during January and February when the temperature anomalies are positive over northern high latitudes at 10 hpa. This implies that deceleration of the PNJ during this period of time intensifies the meridional circulation which in turn pumps additional heat to the higher latitudes. The same but less pronounced feature is also present in the simulated results in December and January Geographical Distribution of the Changes Northern Hemisphere Geopotential Height [32] The geographical distributions of the changes in geopotential height at 30 hpa over the Northern Hemisphere due to the SUV enhancement are presented in Figure 7. The warming in the tropical stratosphere presented earlier provides the condition for the propagation of the solar signal northward. The increase of the stratospheric equator-to-pole temperature contrast induces an intensification of the polar vortex during the boreal cold season, which is most pronounced in November and March. In November the geopotential height of 30 hpa surface substantially (up to 250 m) decreases over Greenland and Northern Canada which reflects the deeper and more stable polar vortex. The intensification of the PNJ and the subsequent changes in the downward transport leads to a warming and an increase in the geopotential heights in the 8of16 Figure 4. Latitude-height cross-section of (a) simulated and (b, c) observed solar-maximum-minus-solar-minimum changes in annually averaged zonal-mean zonal wind (m/s). The observational data have been taken from Kodera [2002] and the present work. The light (heavy) shading in Figures 3a and 3c shows the regions where the changes are judged statistically significant at or better than the 20% (5%) level.

9 Figure 5. Latitude-month cross-section of (a, b, c) simulated and (d, e, f ) observed solar-maximumminus-solar-minimum changes in zonal-mean zonal wind (m/s) at 2 hpa, 10 hpa and 30 hpa. The observational data have been taken from the present work. The light (heavy) shading shows the regions where the changes are judged statistically significant at or better than the 20% (5%) level. 9of16

10 Figure 6. Latitude-month cross-section of (a, b, c) simulated and (d, e, f ) observed solar-maximumminus-solar-minimum changes in zonal-mean temperature (K) at 10 hpa, 30 hpa and 70 hpa. The observational data have been taken from the present work. The light (heavy) shading shows the regions where the changes are judged statistically significant at or better than the 20% (5%) level. 10 of 16

11 Figure 7. Geographical distribution of the solar-maximum-minus-solar-minimum changes in geopotential height (m) at 30 hpa over the Northern Hemisphere simulated by the CCM. The light (heavy) shading shows the regions where the changes are judged statistically significant at or better than the 20% (5%) level. middle latitudes over Central Europe, Siberia and Northern China. These statistically significant ridges with a magnitude of about m appear over Asia, the Pacific Ocean, Southern Europe and the North Atlantic. Similar but less pronounced features also took place in December and January. [33] Comparison of the simulated results for January with the observational data analysis presented by Matthes et al. [2003] shows rather good agreement over Europe and Asia. However, the model does not reproduce the substantial (120 m) intensification of the Aleutian High and underestimates the decrease of geopotential height inside the polar vortex. The simulated response in geopotential height for DJF over Europe and inside the polar vortex are in reasonable agreement with the analysis of the observational data published by Balachandran et al. [1999]. However, the simulated increase in geopotential height over Asia is almost absent in that observational data analysis Northern Hemisphere Temperature and Total Ozone Changes [34] The simulated geographical distribution of the changes in temperature and total ozone over the Northern Hemisphere are presented in Figure 8 for November, January and March. In accord with the simulated changes in geopotential height the model produces a substantial (up to 5 K) and statistically significant cooling inside the polar vortex during November and March, and a warming over the middle latitudes with a maximum magnitude over Asia and the Pacific Ocean. [35] The total ozone changes reflect the changes of the meridional circulation in the lower stratosphere. The intensification of the PNJ leads to greater isolation of the polar vortex, a cooler environment therein and, as a result, a total 11 of 16

12 Figure 8. Geographical distribution of the solar-maximum-minus-solar-minimum changes in temperature (K) at 30 hpa and total ozone (%) over the Northern Hemisphere simulated by the CCM. The light (heavy) shading shows the regions where the changes are judged statistically significant at or better than the 20% (5%) level. 12 of 16

13 effects in September and October over the South American sector (50W 90W) of the polar vortex, with the magnitude of the geopotential height decrease of about 150 m. During August and November the response of the vortex is smaller and statistically insignificant. As in the Northern Hemisphere, the deepening of the polar vortex is accompanied by an intensification of the downward transport over middle latitudes and warming in the middle stratosphere. The most pronounced ridges appear over the Indian Ocean in September and Australia in August, with a magnitude of about m. In November this feature appears in the Pacific sector. These processes lead to a substantial enlargement of the ozone hole shown in Figure 10. As the result of the intensification of the polar vortex and the resulting colder environment, the depletion in September and October for the experiment is almost 8 10% larger than for the control run. The maximum effects occur in the South American sector (50W 90W) of Antarctica. Figure 9. Geographical distribution of the solar-maximum-minus-solar-minimum changes in surface air temperature (K) over the Northern Hemisphere in December simulated by the CCM. The light (heavy) shading shows the regions where the changes are judged statistically significant at or better than the 20% (5%) level. ozone deficit inside the polar vortex up to 10% in November and March. Intensification of the downward ozone transport leads to an increase of the total ozone over middle latitudes up to 10% over Asia in November. In March the increase in total ozone over the middle latitudes is about 2 4% and has no pronounced extremes Northern Hemisphere Surface Air Temperature [36] The changes in surface air temperature (T s ) over the Northern Hemisphere are presented in Figure 9. The simulated T s changes in December consist of a statistically significant warming of up to 2K over northern Europe, Russia and the U.S., and a cooling over Alaska, Greenland and Asia. This pattern is similar to that observed and simulated following powerful volcanic eruptions, with a concomitant warming in the lower tropical stratosphere [e.g., Robock, 2000; Rozanov et al., 2002b]. This is not surprising because the initial perturbation in both cases consists of the warming of the middle stratosphere in the tropics which increases the equator-pole temperature gradient and intensifies the polar vortex. This results in a redistribution of the surface air temperature over the continents Southern Hemisphere Geopotential Height [37] The geographical distributions of the changes in geopotential height at 30 hpa and total ozone from August to November over the Southern Hemisphere are presented in Figures 10 and 11. During early spring, an acceleration of the zonal wind leads to an intensification of the polar vortex and subsequent cooling (not shown) within the polar vortex. The simulated changes are not symmetric in longitude, with the model producing more significant 4. Conclusions [38] We have explored one of the many possible physical mechanisms that can be responsible for a solar-climate relationship. We simulated the response of the Earth s atmosphere to the enhancement of the solar UV radiation from the minimum to the maximum of the solar activity cycle using the UIUC troposphere-stratosphere GCM with interactive chemistry. Analysis of the simulated ozone, temperature, geopotential height and zonal-wind responses to the imposed increase of solar UV flux and their comparison with observations and some other model results provides some useful insights for understanding the above mechanism. [39] The model simulation successfully reproduces the theoretically expected increases in ozone and temperature that are statistically significant features compared to the model s level of variability when examined as part of a 12-year equilibrium/time-slice study. These results are also in reasonable agreement with the results of other models [Huang and Brasseurv, 1993; Rozanov et al., 2002a; Matthes et al., 2003]. The warming in the stratosphere results in some intensification of the polar-night jets which is also seen in the observational data analysis [Kodera and Kuroda, 2002]. The intensification of the polar-night jets alters the meridional circulation in the model. This leads to the appearance of maxima in the increases in ozone and temperature in the southern upper and northern lower stratosphere. It also decreases the temperature in the lower stratosphere at high latitudes, which is consistent with the total ozone decrease there. This feature does not appear in the results obtained from 2-D and 3-D non-interactive models, which emphasizes the importance of climatechemistry interactions. [40] The model simulates a poleward and downward propagation with time of the solar signal in the zonalmean zonal wind. This resembles the observed pattern, but with some time lag. The perturbation of the polar-night jet during northern winter penetrates downward and alters the surface air temperature. In December these changes consist of a statistically significant warming of up to 2K over northern Europe, Russia and the U.S., and a cooling over 13 of 16

14 Figure 10. Geographical distribution of the solar-maximim-minus-solar-minimum changes in geopotential height (m) at 30 hpa over the Southern Hemisphere simulated by the CCM. The light (heavy) shading shows the regions where the changes are judged statistically significant at or better than the 20% (5%) level. Alaska, Greenland and Asia. The pattern of these changes is similar to the positive phase of the observed Arctic Oscillation. [41] Comparison of the simulated results with observations does reveal some significant disagreement. For example, in the middle stratosphere the observational data analysis performed by Hood [2003] shows a statistically significant decrease of the ozone mixing ratio which is in contrast to the model results and theoretical expectations for the solar-signal influence. The simulated warming in the tropical upper stratosphere is about half that obtained from the observational data analyses, and the observed second maximum of the warming in the tropical lower stratosphere (see Figure 3) is almost absent in the simulated results. [42] Such disagreement could be explained by some flaws in both the data analyses and model simulation. First the CCM, like virtually all such contemporary models, does not simulate the QBO. Therefore if the solar influence is realized through the alteration of the QBO, as has been suggested by Salby and Callaghan [2000], we are at present incapable of simulating the solar signal properly, especially in the tropical stratosphere. We are, of course, working to improve the CCM such that it does simulate the QBO. Also, we believe that moving the model s top higher (up to 100 km), together with the use of an appropriate gravitywave drag parameterization should improve the propagation paths of gravity and planetary waves and their interactions with the mean flow. This should result in a better representation of the influence of the solar signal on the atmospheric dynamics. At the moment we are working on a mesospheric version of our model (with top at 100 km) and we intend to repeat the experiment described here. This will show how robust the simulated response is in the upper stratosphere which is close to the top of the present model. Preliminary 14 of 16

15 Figure 11. Geographical distribution of the solar-maximum-minus-solar-minimum changes in total ozone (%) over the Southern Hemisphere simulated by the CCM. The light (heavy) shading shows the regions where the changes are judged statistically significant at or better than the 20% (5%) level. comparison with some other models (with top at the 0.01 hpa) presented by Matthes et al. [2003] showed that the temperature response in the upper stratosphere is not very sensitive to the position of the model top, but we cannot draw the same conclusion for other dynamical and chemical quantities. [43] Another possible problem is with the experimental set up. For this study we performed two equilibrium model runs for average and average plus maximum-minusminimum solar activity. However, the solar signal is transient by nature. Thus it may be more appropriate to perform a transient simulation covering several 11-year cycles. The results of such a simulation, and its analysis using the statistical tools for fingerprint detection within the observational data, could well be in better agreement with observations. [44] It should be noted that the solar signal obtained from the observations also has a rather high level of uncertainty because the analyzed time series are rather short and the previous two peaks of solar activity coincided with powerful volcanic eruptions. To address this issue the analysis of the model response to solar variability on the shorter timescale (27-day solar rotation cycle) may be helpful, because substantially more cycles have been covered by satellite observations. Such an experiment, similar to that reported by Williams et al. [2001] but performed with the same model, could help to elucidate why the model-simulated temperature and ozone response in the tropical stratosphere do not agree with the available observational data. [45] Acknowledgments. This material is based upon work supported by the National Science Foundation and the Carbon Dioxide Research Program, Environmental Sciences Division of the U.S. Department of Energy under Award No. ATM , and by the National Science Foundation under Award No. ATM Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science 15 of 16

16 Foundation or Department of Energy. The work of E.V.R. and T.A.E. is supported by the Swiss Federal Institute of Technology, Zürich. We thank L. Hood, K. Labitzke, K. Kodera and W. Randel for providing their data and recent results. Also we thank B. Soukhorev for his help in acquiring the data. References Balachandran, N. K., D. Rind, P. Lonergan, and D. T. Shindell (1999), Effects of solar cycle variability on the lower stratosphere and the troposphere, J. Geophys. Res., 104, 27,321 27,340. Brasseur, G., and S. Solomon (1986), Aeronomy of the Middle Atmosphere, 452 pp., D. Reidel, Norwell, Mass. Chandra, S., and R. D. McPeters (1994), The solar cycle variation of ozone in the stratosphere inferred from Nimbus 7 and NOAA 11 satellites, J. Geophys. Res., 99, 20,665 20,671. DeMore, W. B., et al. (1997), Chemical kinetics and photochemical data for use in stratospheric modeling, Evaluation 12, NASA Jet Propulsion Lab., Pasadena, Calif. Fleming, E. L., S. Chandra, C. Jackman, D. 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Rozanov (2000), Description and performance of the UIUC 24-layer stratosphere/troposphere general circulation model, J. Geophys. Res., 105, 17,925 17,954. Zubov, V. A., E. V. Rozanov, and M. E. Schlesinger (1999), Hybrid scheme for 3-dimentional advective transport, Monthly Wea. Rev., 127, N. Andronova, B. Li, and M. E. Schlesinger, Climate Research Group, Department of Atmospheric Sciences, University of Illinois at Urbana- Champaign, 105 S. Gregory Street, Urbana, IL 61801, USA. T. A. Egorova and E. V. Rozanov, PMOD/WRC and IAC ETHZ, Dorfstrasse 33, Davos Dorf CH-7260, Switzerland. (e.rozanov@ pmodwrc.ch) V. A. Zubov, Main Geophysical Observatory, 7 Karbyshev Street, Saint Petersburg , Russia. 16 of 16