Upper atmosphere response to stratosphere sudden warming: Local time and height dependence simulated by GAIA model

GEOPHYSICAL RESEARCH LETTERS, VOL. 4, 635 64, doi:1.12/grl.5146, 213 Upper atmosphere response to stratosphere sudden warming: Local time and height dependence simulated by GAIA model Huixin Liu, 1,2 Hidekatsu Jin, 3 Yasunobu Miyoshi, 1,2 Hitoshi Fujiwara, 4 and Hiroyuki Shinagawa 3 Received 17 December 212; revised 6 January 213; accepted 7 January 213; published 4 February 213. [1] The whole atmosphere model GAIA is employed to shed light on atmospheric response to the 29 major stratosphere sudden warming (SSW) from the ground to exobase. Distinct features are revealed about SSW impacts on thermospheric temperature and density above 1 km altitude. (1) The effect is primarily quasi-semidiurnal in tropical regions, with warming in the noon and pre-midnight sectors and cooling in the dawn and dusk sectors. (2) This pattern exists at all altitudes above 1 km, with its phase being almost constant above 2 km, but propagates downward in the lower thermosphere between 1 and 2 km. (3) The northern polar region experiences warming in a narrow layer between 1 and 1 km, while the southern polar region experiences cooling throughout 1 4 km altitudes. (4) The global net thermal effect on the atmosphere above 1 km is a cooling of approximately 12 K. These characteristics provide us with an urgently needed global context to better connect and understand the increasing upper atmosphere observations during SSW events. Citation: Liu,H.,H.Jin, Y. Miyoshi, H. Fujiwara, and H. Shinagawa (213), Upper atmosphere response to stratosphere sudden warming: Local time and height dependence simulated by GAIA model, Geophys. Res. Lett., 4, 635 64, doi:1.12/grl.5146. 1. Introduction [2] The stratosphere sudden warming (SSW) is a dramatic meteorological event in the winter polar stratosphere. Its formation mechanism and cooling impact on the mesosphere has been well demonstrated by Matsuno [1971]. Extension of SSW impacts to the lower thermosphere was predicted by Liu and Roble [22] using the Thermosphere-Ionosphere-Mesosphere Electrodynamic General Circulation Model (TIME-GCM), showing a warming effect near 12 km. SSW impacts on the upper atmosphere have been recently revealed in various observations. Using Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) satellite observations, Funke et al. [21] found warming effect in the polar lower 1 Department of Earth and Planetary Science, Kyushu University, Fukuoka, Japan. 2 International Center for Space Weather Research and Education, Kyushu University, Fukuoka, Japan. 3 National Institute of Information and Communications Technology, Tokyo, Japan. 4 Faculty of Science and Technology, Seikei University, Tokyo, Japan. Corresponding author: H. Liu, Earth and Planetary Science Division, Kyushu University, Fukuoka, Japan. (huixin@serc.kyushu-u.ac.jp) 213. American Geophysical Union. All Rights Reserved. 94-8276/13/1.12/grl.5146 635 thermosphere during the record-breaking major SSW event in January 29, hence supporting the prediction of Liu and Roble [22] in the northern polar region. At middle latitude, Goncharenko and Zhang [28] reported ion cooling above 15 km, but warming around 12 km during the SSW28 event. Since the ion temperature closely follows neutral temperature [Schunk and Nagy, 2], their results indicate similar SSW impact on the thermosphere. However, this pattern is in opposite sense to the prediction of Liu and Roble [22, see Figure 2 therein] at midlatitudes. At even higher altitude, Liu et al. [211] reported decrease of thermospheric density simultaneously observed by CHAMP and GRACE satellites at 325 and 475 km altitude in the dawn and dusk sectors. Therefore, one asks: What is the global context to consistently connect these observations at different altitude and latitude? [3] Furthermore, the SSW impact on the ionosphere has been demonstrated to strongly depend on local time (), being roughly semidiurnal as observed in various ionospheric parameters like the total electron content, the Sq current, etc. [e.g., Goncharenko et al., 21; Yamazaki et al., 212]. Given the close coupling between the thermosphere and ionosphere via either ion drag or atmospheric waves, it is easy to postulate that SSW impacts on the thermosphere may well be local time dependent. So far, no thermosphere observation covering all local times has been reported during SSW events. The prediction of SSW effects on the lower thermosphere by Liu and Roble [22] was given as all local time averaged. This lack of local time information greatly limits our understanding of upper atmosphere observations during SSWs. [4] Therefore, the present study seeks to obtain a global picture of the SSW impact on the upper atmosphere, with a focus on the local time and height dependence of thermosphere response in terms of temperature. For this purpose, we employ the fully coupled atmosphere-ionosphere model, GAIA. 2. GAIA Model [5] The GAIA model is a self-consistent fully coupled model of the Earth s lower atmosphere, thermosphere, and ionosphere. It covers the altitude range from the ground to exobase (~5 km at solar minimum). Details about the model are described in Jin et al. [212]. By interactively coupling the physical processes in the lower and upper atmosphere, GAIA has been proved to be highly capable in modeling prominent features in the thermosphere and ionosphere, e.g., the equatorial mass density anomaly, the equatorial wind jet, and the wave-4 structures [Stolle and Liu, 213, and references therein]. The model s

capability in characterizing gravity and tidal waves has also been demonstrated in various studies [e.g., Miyoshi and Fujiwara, 23]. [6] The GAIA model is employed to simulate the upper atmosphere response to the major SSW in January 29. A nudging technique is used to converge the model results below km altitude to observations represented by the Japanese 25 year Reanalysis Project data [Jin et al., 212]. During the simulation period of 1 November 28 to 31 March 29, the F1.7 index varied only about 1.5 % around 69.3. For investigating SSW effects, a fixed crosspolar cap potential of kv and a quiet particle precipitation condition were held throughout the simulation period to exclude any geomagnetic activity effect. Simulation results on the stratosphere and ionosphere are presented in Jin et al. [212]. Those results show good agreement to satellite observations in both the stratosphere and ionosphere, hence demonstrating the model s capability in capturing key processes during the SSW29. This current paper reports the corresponding results on the thermosphere from the same simulation. Global features of SSW impacts are presented in terms of temperature and density. 3. Results 3.1. Comparison with Thermospheric Density Observations [7] As a validation of GAIA results on the thermosphere, we first compare with available observations. Figure 1 depicts thermospheric densities observed by CHAMP satellite near 17 and 5 at ~325 km altitude and that simulated by GAIA model. Longitudinally averaged data are used throughout the present study. The CHAMP density is normalized to Kp = 1 using NRLMSIS- model to minimize geomagnetic activity effects [Liu et al., 211]. Good agreements between the observations and simulations exist in large-scale features, with prominent density drop during the SSW (pink line) at low and middle latitudes in both sectors. The obvious difference in absolute density values is due to difficulties in determining a true baseline for both the instrument and model. However, it is the temporal variation that is more pertinent to the physical processes in dramatically changing events like SSW. [8] To examine the temporal variation more closely, the average density between S and N is extracted and line plotted in the third and fourth rows of Figure 1. Despite differences in absolute values, the same general trend emerges in both CHAMP and GAIA density. That is, the density starts dropping from day of year (DOY) 18/19, reaches a minimum near DOY 25/26, and gradually recovers afterward. Overlaid on this slow-varying trend, CHAMP density shows some rapid fluctuations during DOY 25 37. These might be remnant effects of imperfect removal of geomagnetic activity during the Kp normalization using NRLMSIS-. As Kp briefly increased from 1 to 3 on DOY 26 and to 4 on DOY 35, density peaks are discernible around these days. Possible geomagnetic contribution to CHAMP observations was also pointed out by Fuller-Rowell et al. [211]. Thus, the CHAMP density actually consists of a short time-scale (~1 2 days) perturbation driven by geomagnetic activity and a long time-scale (~ days) perturbation driven by SSW. The GAIA density instead represents exclusively the SSW-driven component. 3.2. Local Time Dependence [9] The polar-orbiting CHAMP satellite could sample only two local times 12 h apart during the SSW 29, hence giving an incomplete picture. To complement this, we utilize the GAIA simulation. The good model-observation agreement on thermospheric density presented above, along with those in the ionosphere and stratosphere [Jin et al., 212] warrants GAIA as a suitable alternative for investigating global features of SSW impacts. [1] Figures 2a and 2b display the longitudinally averaged thermospheric mass density and temperature at 325 km altitude in tropical regions averaged between S and N. White line indicates SSW peak on DOY 23. A significant phase shift occurs around DOY 18 19, with temperature and density maxima shifting to earlier. To examine more closely perturbations during the SSW, we take the average density and temperature during DOY 1 1 as references and calculate the deviation from them. Figures 2c and 2d show perturbations averaged during DOY 25. Both density and temperature response are seen to strongly depend on, exhibiting a quasi-semidiurnal pattern with increase (warming) in the noon and pre-midnight sectors, but decrease (cooling) in the dawn and dusk sectors. The CHAMP observations near 5 and 17 fall both into cooling sectors, thus could observe only density decrease but not increase. Another feature to note here is that the warming magnitude (15 K near 21 ) is significantly smaller than the cooling magnitude ( 4 K near 17 ). This indicates the drop of zonal mean during SSW, which is presented in section 3.4. 3.3. Height Dependence [11] To see how persistent this quasi-semidiurnal feature is in altitude, we examine its height variation. Since perturbation patterns in density and temperature are nearly identical, only the temperature is presented in the following. Figures 3a and 3b display temperature perturbations in tropical and polar regions during DOY 25. In tropics (Figure 3a), the quasi-semidiurnal feature exists at all altitudes above 1 km, with interchanging warming and cooling sectors. Its phase remains constant above 2 km altitude, but propagates downward between 1 and 2 km. In northern polar regions (Figure 3b), warming of 2 K occurs at all s in a narrow layer between 1 and 1 km. Above 1 km, weak temperature perturbation occurs whose sign varies with. Below 1 km, little dependence is seen as generally known. [12] Next, we examine temperature perturbations in the meridional plane. Figures 3c and 3d present perturbations in the warming and cooling sectors near 11 and 17, respectively (warming and cooling refer to regions above 2 km in the tropics). At 11 in northern polar regions (Figure 3c), the well-known SSW feature appears below 1 km, with stratosphere warming of ~35 K and mesosphere cooling of ~ K. In the lower thermosphere (1 15 km), warming of 1 K occurs north of 4 N while strong cooling of K occurs near the equator. Above 15 km, warming occurs at most latitudes except for regions south of S. The warming in northern polar thermosphere with peaks of ~15 K around 12 km is consistent with MIPAS satellite observations [Funke et al., 21] and TIME-GCM predictions [Liu and Roble, 22]. The GAIA model further reveals 636

CHAMP 17 1.8 2.6 3.4 4.2 CHAMP 5.5 1.2 1.9 2.6 24 2 Tn (K) 22 GAIA 17 6.2 6.9 7.6 8.3 GAIA 5 21 4.1 4.8 5.5 6.2 24 2 Tn (K) 22 ρ (1 12 kg m 3 ) 4 3.5 3 CHAMP 17 1.8 1.6 1.4 1.2 1 CHAMP 5 21 2.5.8 ρ (1 12 kg m 3 ) 8 7.5 7 GAIA 17 5.2 5 4.8 4.6 GAIA 5 6.5 4.4 15 2 25 35 4 15 2 25 35 4 DoY 29 DoY 29 12 Figure 1. Geographic latitude versus day of year (DOY) distribution of the thermospheric mass density (in unit of 1 kg m 3 )at~325 km near 17 and 5 observed by CHAMP and simulated by GAIA during DOY 15 4 29 (first and second rows); the pink line is the stratospheric temperature at 1 hpa averaged over 7 N 9 Ν. Temporal variation of tropical density averaged within S Ν. A slow-varying trend with density decreasing before DOY 26 27 and recovering afterward emerges in both observations and model results. that this warming continues to extend to upper thermosphere around N. [13] At 17 (Figure 3d), the temperature perturbation below 1 km resembles that at 11, reflecting the non- dependent characteristic below 1 km. Above 1 km, however, the perturbation structure significantly differs from that at 11. In the lower thermosphere (1 15 km), warming occurs at most latitudes with peaks near the equator and northern middle latitudes. Above 15 km, cooling occurs south of 45 N, while slight warming of a few kelvin occurs around N. The altitude dependence at northern middle latitudes shown in Figures 3c and 3d is consistent with ion temperature observations near 43 N [Goncharenko and Zhang, 28], which show cooling above 15 km and warming around 12 km. 3.4. Global Net Effect [14] The above results reveal that SSW impacts on thermospheric temperature significantly depend on, altitude, and latitude. To estimate the global net effect, we examine the zonal mean temperature perturbation (average over all s). Figure 4a shows that the zonal mean above 1 km is a slight warming in northern polar regions but cooling at other latitudes. Strongest cooling of 4 K occurs in southern polar regions. This zonal mean temperature drop during SSW has caused the asymmetric feature in Figures 2c and 2d, where the warming amplitude (15 K near 21 ) is much smaller than the cooling amplitude ( 4 K near 17 ). When further averaged over all latitudes, the global net effect above 1 km is estimated to be cooling of approximately 12 K (see Figure 4b), with a cooling rate of about.9 K/d during DOY 19 34. The high cooling rate during the 2 weeks period apparently cannot be explained by long-term seasonal variations. Furthermore, this upper atmosphere cooling is accompanied by the stratosphere warming, mesosphere cooling, and warming between about 8 and 1 km (see Figure 4c). 4. Discussions and Conclusions [15] The GAIA model is used to simulate the atmosphereionosphere system response to the SSW 29 event. The model results agree well with reported observations carried out at various time and locations. On top of these point-to-point agreements, the model reveals several distinct global features as discussed below. [16] First, SSW impacts on the tropical upper atmosphere are primarily quasi-semidiurnal, with warming in the noon 637

4 a. ρ @325 km 4.2 6.7 9.2 4 b. 65 75 85 Tn @325 km DoY 29 2 DoY 29 2 1 1 4 8 12 16 2 24 4 8 12 16 2 24 Δρ (1 12 kg m 3 ).5.5 c. Δρ 5 17 ΔTn (K) 2 2 d. ΔTn 5 17 1 4 8 12 16 2 24 4 4 8 12 16 2 24 Figure 2. (a and b) Local time versus DOY distribution of GAIA thermospheric density (in unit of 1 12 kg m 3 )and temperature (in unit of kelvin) averaged over S N. White line indicates SSW peak on DOY23. (c and d) Density and temperature deviations from pre-ssw level (DOY 1 1), averaged during DOY 25. A quasi-semidiurnal pattern is seen, with an increase in noon and pre-midnight sectors and a decrease in dawn and dusk sectors. Height(km) 4 35 2 15 a. ΔTn [ ] 2 1 1 4 35 2 15 c. ΔTn @ 11 2 1 1 1 2 1 2 5 5 4 8 12 16 2 24 4 9 9 4 4 35 b. ΔTn [ 9 ] 2 4 35 d. ΔTn @ 17 2 Height(km) 2 15 1 1 2 15 1 1 1 2 1 2 5 5 4 8 12 16 2 24 4 9 9 4 Figure 3. Temperature perturbations (ΔTn (K)) averaged during DOY 25. (a and b) Height versus distribution in tropical ( S Ν) and northern polar regions ( N 9 Ν). (c and d) Height versus geographic latitude distribution at 11 and 17. In tropics, ΔTn exhibits strong dependence above 1 km, with downward phase propagation between 1 and 2 km. 638

Latitude ΔTn (K) Height(km) 9 45 45 9 5 5 1 15 4 2 1 15 15 a. ΔTn Zonal Mean [1 4]km K b. 5 1 15 2 25 35 4 ΔTn Global Mean [1 4] km 5 1 15 2 25 35 4 2 1 1 2 c. ΔTn Global Mean K 5 1 15 2 25 35 4 DoY, 29 Figure 4. (a) Latitude versus DOY distribution of the zonal mean ΔTn (average over all s and 1 4 km altitudes). (b) DOY variation of global mean ΔTn (average over all s, 1 4 km altitudes, and latitudes). (c) Altitude versus DOY distribution of the global mean ΔTn (average over all s and latitudes). The SSW s global net effect on the upper atmosphere is a cooling of approximately 12 K. and pre-midnight sectors and cooling in the dawn and dusk sectors. This contrasts greatly to the lower atmosphere (below 1 km) where little dependence is seen (Figure 3a). This difference can be understood as the following. Although enhancement of semidiurnal and terdiurnal tides occurs at all altitudes during SSW, their magnitude is less than 2 K below 1 km but over K above it [Jin et al., 212]. Thus, the tidal signature (hence dependence) below 1 km is easily dominated by the zonal mean temperature changes and hard to be discerned in the total perturbation. The constant phase of the perturbation above 2 km altitude along with the downward propagation between 1 and 2 km seen in Figure 3a demonstrates the typical feature of an upward-propagating semidiurnal tides [Forbes, 1982]. [17] Second, due to the downward phase propagation, a rapid switch of SSW effects occurs near 15 km altitude above the equator (see Figures 3c and 3d). The switch direction depends on local time. It is from cooling below 15 km to warming above 15 km in sectors around 11 and 23, but in the opposite direction near 5 and 17. It would be interesting to compare these predictions with observations when available. [18] Third, GAIA reveals a global net cooling of approximately 12 K above 1 km, with a cooling rate of about.9 K/d during the SSW. At the moment, we do not have clear explanation for the rapid cooling during this period. However, one thing is clear that the seasonal variation cannot explain this. It is known that zonal mean temperature perturbations are largely caused by changes in the global circulation which are affected by various atmospheric waves [Matsuno, 1971; Liu and Roble, 22; Pancheva et al., 27]. Detailed analysis of the neutral wind, molecular diffusion, and ion drag in the GAIA simulation will be carried out to explore the underlying processes. In contrast to our result, a slight warming at 325 km altitude is reported by Fuller-Rowell et al. [211] using the Whole Atmosphere Model (WAM). This difference might be partly due to the fact that GAIA is an atmosphere-ionosphere coupled model, while WAM has no ionosphere included. However, closer examination is needed to clarify this. 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