Analysing Major Sudden Stratospheric Warmings in the coupled middle atmosphere ocean model MAECHAM5/MPI-OM

Transcription

1 Analysing Major Sudden Stratospheric Warmings in the coupled middle atmosphere ocean model MAECHAM5/MPI-OM Master Thesis of Severin Bancalá Kiel, September 21 Faculty of Mathematics and Natural Sciences Christian-Albrechts-Universität zu Kiel Maritime Meteorology First supervisor: Prof. Dr. Kirstin Krüger, IFM-GEOMAR Kiel Second supervisor: Dr. Noel Keenlyside, IFM-GEOMAR Kiel

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3 Abstract In this study, the stratospheric winter circulation in the coupled middle atmosphere ocean model MAECHAM5/MPI-OM, is analysed. Due to the dynamical and thermodynamical interaction with the ocean, the simulated atmospheric circulation is affected by the internal variability of the ocean. Differences of the stratospheric winter circulation between MAECHAM5/MPI-OM and former MAECHAM5 simulations may be attributed to the interactive ocean. This work is divided into three parts: first the climatology of the model is examined, then major Sudden Stratospheric Warmings (SSWs), and at last the relationship between these warmings and tropospheric blockings. To examine how the model reproduces the stratospheric winter circulation, the climatology of the zonal mean zonal wind and of planetary waves of zonal wavenumber 1 to 3, are carefully analysed and compared with those obtained from ERA-4 observations. While the zonal mean zonal wind is in good agreement with observations, amplitudes and phases of zonal wavenumber 2 are not well represented. Major SSWs are analysed because of the strong impact that such phenomena can have throughout the atmosphere, influencing the weather at the surface for several weeks after the onset of the warming. To identify major SSWs, a new algorithm based on the 1 hpa zonal mean zonal wind at 6 N, is developed. This is done because a comparison between a recent study by Charlton and Polvani (27) and the Freie Universität Berlin climatology of mid-winter major SSWs, reveals a significant disagreement. The new algorithm is applied to two databases: one obtained from the model simulation and one from the ERA-4 assimilation. ERA-4 data are used for validation of MAECHAM5/MPI-OM. Comparison of the obtained frequencies of major SSWs shows that in the model a slightly higher number of events occurs. While in ERA-4 the average frequency is of.6 events per year, in the model it is of.7 events per year. The seasonal distributions show also that the highest number of major SSWs occurs in January and in February respectively for ERA-4 and model data, which is improved for MAECHAM5/MPI-OM compared to former MAECHAM5 simulations. In this work, unlike previous model studies, the state of the polar vortex is also examined during the pre-warming phase of major SSWs, by analysing the planetary wave activity, to determine the behaviour of waves with different zonal wavenumbers. Only planetary waves of zonal wavenumber 1 and 2 appear to have a key role in the development of major SSWs, with wavenumber-1 events being more frequent than wavenumber-2 events and a ratio of 57:13 similar as observed. Because of the influence of tropospheric blockings on major SSWs via alteration of planetary waves, a correlation analysis is performed to determine if the model represents this relationship well. It appears that Pacific blockings are correlated with wavenumber-2 major SSWs although a larger number of wavenumber-2 events would be necessary to make such assertion. No significant correlation is instead obtained for wavenumber-1 major warmings. i

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5 Contents Abstract Contents i iii 1 Introduction 1 2 The Middle Atmosphere Structure of the atmosphere Circulation in middle atmosphere Atmospheric waves Sudden stratospheric warmings Classification Dynamical mechanisms Frequency/occurrence Major SSWs identification criterion Tropospheric blockings Data and Methods Model data: MAECHAM5/MPI-OM The atmospheric model: MAECHAM The ocean model: MPI-OM MPI-OM coupled to MAECHAM Observational data The ERA-4 dataset The FUB dataset The TOVS dataset Data analysis/methods New major SSW criterion New final warming criterion New wavenumber-1/wavenumber-2 warming criterion Blocking index Model Climatology Zonal mean temperature Zonal mean zonal wind iii

6 iv CONTENTS 4.3 Interannual variability North Pole temperature Zonal mean zonal wind Planetary wave climatology Amplitudes and phases Heat fluxes Major Sudden Stratospheric Warmings Analysis Intercomparison between the criteria Time series of major SSWs Seasonal distribution of major SSWs Preconditioning of major SSWs Final warming distribution Atmospheric Blockings Analysis Blockings distribution in MAECHAM5/MPI-OM Correlation between blockings and major SSWs Conclusion 59 A Planetary Wave Analysis 61 A.1 Fourier analysis A.2 Eddy heat flux B Figures 63 References 83 Declaration 89

7 Chapter 1 Introduction The winter circulation in the stratosphere is governed by the polar vortex. This is a large-scale cyclone characterized by very low temperatures (up to -9 C) and surrounded by a band of strong westerlies. However, this typical winter circulation may be altered, with the replacement of the polar vortex by a high pressure system. An easterly flow is then established and contemporaneously the stratosphere exhibits a sudden and strong temperature increase (up to 3-5 C over a few days). Such an event is called major Sudden Stratospheric Warming (SSW) and can eventually propagate downward affecting the circulation in the troposphere for several weeks after the onset of the warming (Kodera et al., 199). Since this phenomenon was discovered in 1952, several studies have tried to figure out the origins and the effects of such an event. The upward propagation of planetary waves from the troposphere into the stratosphere, where they may influence the circulation, is recognized to be the essential dynamical mechanism responsible for the development of major SSWs. Because these waves can be forced by the orography, it is observed that major warmings occur quasi-exclusively in the Northern Hemisphere. In fact, since regular observations began in 1957, only one event has occured in the Southern Hemisphere (Krüger et al., 25). The average frequency for the Northern Hemisphere is of roughly one major SSW every two years, although even two cases per season have been observed in some winters, for example in 1998/99 and 21/22 (Naujokat et al., 22; Manney et al., 25). With the introduction of middle atmosphere general circulation models, in which processes of the higher stratosphere and lower mesosphere are simulated, the interest has focused on how these models represent major SSWs. In a recent study by Charlton et al. (27) six different model simulations were examined. The results showed that although the models are able to simulate major SSWs, they have difficulty in correctly representing the seasonal distribution of the warmings. In this study, a middle atmosphere circulation model (MAECHAM5) is used coupled with an ocean model (MPI-OM). The aim of this work is then to determine if the inclusion of the ocean leads to a better representation of the stratospheric winter circulation thus of major SSWs. Information about the circulation of the middle atmosphere, the planetary waves and the major SSWs are given in chapter 2. The model and the observational data used in this study, and the new algorithms used for the data analysis are described in chapter 3. In chapter 4, the climatology of the model is shown and compared with observations. The analysis of major SSWs is performed in chapter 5, while the results of the atmospheric blockings analysis are presented in chapter 6. Finally the conclusions of this study are summarized (chapter 7). 1

8 2 Introduction

9 Chapter 2 The Middle Atmosphere In this chapter the structure of the atmopshere is described in the first section. The circulation of the middle atmosphere, which is governed by atmospheric waves, is then described in section 2. A distinction between the different atmospheric waves, focusing on planetary waves, is then done in section 3. At last, in section 4, information about a typical phenomenon of the Arctic winter stratospheric circulation, major Sudden Stratospheric Warming, are given. 2.1 Structure of the atmosphere The atmosphere is conventionally divided into layers based on the vertical structure of the temperature field. The five main layers, the troposphere, the stratosphere, the mesosphere, the thermosphere, and the exosphere, are separated by the tropopause, the stratopause, the mesopause, and the thermopause, respectively. These boundary regions are characterised by constant temperature with increasing altitude while in the other layers temperature either increases or decreases (Fig. 2.1). The maximum temperatures are observed in the troposphere, the stratopause and the thermosphere where the incoming solar radiation is absorbed at different wavelengths. While the troposphere is mostly warmed by convection from the surface and less by absorption of IR radiation (.3 µm < λ < 5 µm) by water vapor and carbon dioxide, the temperature maximum in the stratopause is a direct result of absorption of medium wavelength UV (.1 µm to.35 µm) by ozone. The maximum in the thermosphere is instead associated with the absorption of very short wavelength UV ( λ <.1 µm) by oxygen-containing molecules (Andrews et al., 1987). Two temperature minima of about -55 C and -15 C are instead observed respectively at around 1 and.1 hpa and identify the tropopause and the mesopause. While the minimum of the tropopause is primarily due to infrared emission by water vapour and clouds, the minimum of the mesopause is associated with the large decrease in ozone concentration at that level, which greatly reduces the absorption of solar radiation. The part of the atmosphere between the tropopause (1 hpa) and approximately.1 hpa, including the stratosphere and the mesosphere, is denoted as middle atmosphere (Andrews et al., 1987), and is the focus of this study. 3

10 4 The Middle Atmosphere Figure 2.1: Zonal mean vertical profile of temperature ( C) during June at 45 N (Science of doom, 21). 2.2 Circulation in middle atmosphere In the middle atmosphere, the circulation is radiatively driven and depends on the magnitude and distribution of the net diabatic heating rate. Within the troposphere the net heating is determined by the transfer of heat from the surface via convection, by the release of latent heat and by the thermal emission of radiation to space. In the stratosphere and the mesosphere the net heating depends almost exclusively on the imbalance between local absorption of solar ultraviolet radiation and infrared radiative loss (Andrews et al., 1987). In this region, ozone is the dominant absorber while carbon dioxide is the dominant emitter. The other atmospheric gases play a minor role in the absorption and emission of radiation. The distribution of the radiative sources and sinks due to these gases controls the large-scale seasonally varying mean temperature and zonal wind fields of the middle atmosphere. Although the globally averaged temperature field at each altitude in the stratosphere and mesosphere is approximately in radiative equilibrium, eddy motions (waves) induce substantial local departures from equilibrium, especially in the winter stratosphere and near the mesopause in both winter and summer. The overall latitudinally dependent temperature distribution in the middle atmosphere arises from the balance between the net radiative heating and the dynamical heating which is due to heat transport and local temperature changes achieved by eddy motions. The net radiative heating has a strong seasonal dependence, with maximum heating at the summer pole and maximum cooling at the winter pole. The meridional circulation that balances this differential heating is called the diabatic circulation and is principally driven by eddy forcing and not directly by radiative heating (Andrews et al., 1987). At the solstices this circulation consists of rising motion near the summer pole, a meridional drift into the winter hemisphere, and sinking

11 2.3 Atmospheric waves 5 near the winter pole. As the air is moving toward the winter pole, the effect of the Coriolis force is the generation of mean zonal westerlies and easterlies respectively in the winter and the summer hemisphere. At the equinoxes the circulation is weaker because of a different radiative drive with the maximum heating at the equator and a cooling at the poles. In these cases the air rises in the equatorial region and then drifts towards both poles, resulting in weaker mean zonal westerlies in both hemispheres. These zonal winds are regulated by the temperature field. The relation between the horizontal temperature gradient and the vertical shear of the geostrophic wind in a zonal mean flow is described by the thermal wind equation: f ū z = R T ah φ (2.1) where ū is the zonal mean geostrophic wind, z the height, f the Coriolis parameter, R the gas constant referred to unit mass of air, T the zonal mean temperature, a earth radius, H the scale height, and φ the latitude (Andrews et al., 1987). The equation states that if the temperature varies with latitude, then the zonal geostrophic flow will vary with height. The westerlies increase therefore with height because they are associated with the poleward decrease of the temperature. Not all features of the climatological temperature distribution are in qualitative accord with the distribution of radiative sources and sinks. It is the case, for example, of the observed reversed temperature gradient above 6 km where temperature increases uniformly from the summer pole to the winter pole and not vice versa as expected from the radiative equilibrium distribution. The net effect is that the summer polar mesopause is much colder than radiative equilibrium, while the winter polar temperatures are above radiative equilibrium throughout the entire middle atmosphere. Therefore, dynamical processes, such as eddy forcing, play a role in establishing the observed temperature distributions. 2.3 Atmospheric waves The eddy motions in the middle atmosphere occur on scales ranging from planetary-scale waves to microscale turbulent patches. Although turbulent mixing plays a role in the momentum budget of the middle atmosphere, much of the momentum and heat transport is due to wave motions. These motions result from the competition between inertia and restoring forces acting on fluid parcels displaced from their equilibrium latitudes and/or elevations. The restoring force is supplied either by gravity, which is responsible for the so called internal gravity waves, or by the poleward gradient of planetary vorticity that is responsible for Rossby waves also called planetary waves (Andrews et al., 1987). A third type of waves exists, the inertio-gravity waves which result from a combination of gravity and Coriolis force. Both the gravity modes and the Rossby modes can be classified depending on their horizontal structure, their vertical structure, and their sources of excitation. According to the fact that some waves can propagate in all directions, while others may be trapped in some directions, it is possible to make a distinction between extratropical or global modes versus equatorially trapped modes, and vertically trapped modes versus vertically propagating modes. From the excitation mechanism that originates the waves, which could be continually maintained or not, it is possible to distinguish between forced modes and free normal modes. Waves can also be separated into stationary waves ( t = ), whose amplitude is constant and the phase speed is zero, and transient (travelling) waves ( t ), whose phase speed differs

12 6 The Middle Atmosphere from zero. Stationary planetary waves do not effect the mean zonal flow whereas the transient ones accelerate or decelerate the flow. In the middle atmosphere the vertically propagating modes forced in the troposphere are of primary importance. Forcing mechanisms could be an eastward blowing wind over the surface orography, the land-sea temperature contrast or heat sources. The vertical structure of forced Rossby modes is critically dependent on their horizontal scale and on the mean zonal wind distribution. Synoptic-scale Rossby waves are strongly trapped in the troposphere and decay rapidly with height in the middle atmosphere. Planetary-scale Rossby waves can instead propagate vertically and are associated with disturbances of the geopential height and of the temperature fields. Once the waves are in the stratosphere, they interact with the stratospheric flow for which two types of flow are distinguishable: a zonal mean flow and a zonally varying part. Because waves can be considered as periodic functions, it is possible, by means of the Fourier analysis, to expand the time series f(x) of the geopotential height or of the temperature field into zonal Fourier harmonics up to some zonal wavenumber n representing the limit of the resolution data. f(x) = a 2 + (a n cos(nx) + b n sin(nx)). (2.2) n=1 While the term a 2 corresponds to the zonal mean flow, the terms included in the sum represent the zonally varying part of the flow that is associated with stationary waves. The time-dependent departures from the climatological mean flow are instead known as transient waves. The combination of stationary and transient waves, give the net waves. The Fourier coefficients a n and b n are also used to compute the amplitude of the wave, that shows how much a wave is zonally and meridionally developed, and the phase of the wave, that displays where the maximum amplitude is located (see A.1 in Appendix). Planetary waves carry momentum and heat fluxes which are released when the waves break. This occurs either through wave amplitude increase with decreasing density (Polvani and Saravanan, 2) or through interaction with the so called "critical layers" which are those regions where the mean zonal wind matches the zonal phase speed of the waves. But critical layers not necessary Figure 2.2: Propagation of planetary waves in January, shown is a 4-year mean. The length of the arrows depends on the intensity of planetary waves while the direction indicate to where the waves propagate (Andrews et al., 1987).

13 2.4 Sudden stratospheric warmings 7 have to absorb the waves, they could also reflect them. The usual propagation of the waves is from the troposphere into the stratosphere where they are then deflected equatorward (Fig. 2.2) and, due to the interaction with the pre-existing mean flow, they dissipate releasing energy. The upward propagation is possible only in the winter hemisphere and only for the ultralong stationary-wave components (n=1,2,3) when the mean winds are westerlies throughout the middle atmosphere and the zonal phase speed of the waves is comparable with the magnitude of the mean zonal wind (Charney and Drazin, 1961). If the wind turns westward (i.e. easterlies) or in case of strong westerlies, the propagation of the waves and the associated energy transfer is stoped. Therefore it is evident that in the summer hemisphere, where the westerlies vanish in the stratosphere, the planetary waves are not able to propagate beyond the lower stratosphere. To describe their propagation the Elliasen-Palm flux F vectors are used. Two components are important, the meridional component relative to the momentum fluxes and the vertical component relative to the eddy heat fluxes. In this study, the latter component is derived as carried out by Pawson and Kubitz (1996) who computed the heat flux using amplitudes and phases obtained from the Fourier analysis of the waves (for further details see A.2). Convergence or divergence of the Elliasen-Palm flux vectors indicate respectively a deceleration or an acceleration of the mean zonal westerly flow. As said before, the equatorward propagating planetary waves can be reflect and thus forced to propagate poleward where they release their energy. This could strongly effect the stratospheric polar winter circulation and explains the interannual variability of the polar stratosphere. 2.4 Sudden stratospheric warmings The middle and upper polar troposphere and the polar stratosphere are characterised by the presence of the polar vortex (Fig. 2.3). This is a large-scale cyclone whose strength is determined Figure 2.3: Schematic representation of the polar vortex. Shown is the geopotential height at the 1 hpa pressure level ( Marquardt, 1993). by the meridional temperature gradient. The vortex is thus most powerful during the polar night

14 8 The Middle Atmosphere when, due to the lack of solar radiation, the polar stratosphere can significantly cool, while it disappears during the summer. A band of strong westerlies surrounds the vortex and is called the polar night jet. While the Southern Hemisphere (SH) polar stratosphere has weak interannual variability, the Arctic polar stratosphere is highly variable because of the enhanced Rossby wave activity, resulting principally from the land-sea distribution and the orography effect, that disturbs the vortex (Krüger et al., 25). During winter the typical zonal mean climatological temperature field in the stratosphere decreases towards the winter pole (see chapter 4, Fig. 4.1a and Fig. 4.1d). However, it happens that this configuration is altered, with strong temperature increases (up to 3-5 C over a few days) in the winter polar stratosphere which can eventually leads to a reversal of the zonal mean zonal wind (Andrews et al., 1987). Such changes of the winter circulation occur generally in the Northern Hemisphere (NH) and are responsible for the high interannual variability observed in the Arctic polar stratosphere. In Fig. 2.4 the high interannual variability of the North Pole temperature can be seen. These disturbances of the stratospheric circulation can be restricted to the upper stratosphere Figure 2.4: Daily evolution between November and May of the 1 hpa temperature ( C) at the North Pole for the years 1988/89 to 1998/99. Data are from the the Freie Universität Berlin meteorology department (Von Zahn et al., 1998). or can penetrate into the middle stratosphere. In the first case, the circulation of the underlying layers would not be affected (a minor warming) whereas in the second case (a major warming), the winter circulation would be disrupted Classification These warmings of the stratosphere do not always develop in the same way therefore it is possible to classified them according to their characteristics (Labitzke, 1977; Andrews et al., 1987). Maior sudden stratospheric warming An event is defined as a major sudden stratospheric warming (SSW) when a wind reversal

15 2.4 Sudden stratospheric warmings 9 occurs at 1 hpa and 6 N additionally with the reversal of the temperature gradient between 6 and 9 N (World Meteorological Organization (WMO) definition, Andrews et al., 1987). The temperature gradient reversal is due to a rapid increase of the temperature in the stratosphere which, as indicated by the thermal wind equation, must be associated with a rapid wind deceleration. Major SSWs disturb therefore the polar vortex which is either replaced by an anticyclone or forced to move south of 6-65 N. The vortex is then splitted into two weaker lows or entirely displaced thus influencing the circulation of the entire NH. Minor warming If no reversal of the westerlies is observed but only a rapid temperature increase, at least of 25 C within one week in the upper stratosphere in any area of winter time hemisphere, the event is defined to be a minor warming (Labitzke, 1977). Such warmings, unlike major SSWs, do not substantially influence the polar vortex which remains more or less undisturbed, thus their effect on the circulation is restricted to a smaller region. Canadian midwinter warming Also a third type of warmings exists. Labitzke (1977) has defined Canadian midwinter warmings as those events that result from an anomalous strengthening in early winter (November, December) of the Aleutian anticyclone over Canada. This generally leads to a reversal of the temperature gradient poleward of 6 N and occasionally to the wind reversal, but nevertheless they do not lead to a breakdown of the cyclonic polar vortex. These warmings are more pronounced in the lower and middle stratosphere. Final warming The last type of events that should be mentioned are final warmings which are those events that are not followed by a restoring of the winter circulation (cold cyclonic polar vortex and westerly winds) but by the transition to the summer circulation characterised by a warm anticyclone centred over the pole and easterly winds (Labitzke, 1977). Final warmings occur between March and May and are generally caused by the return of the solar radiation into the polar region. Sometimes the transition to the summer circulation can be associated with a major SSW. In this case the warming is called major final warming and observations show that these events may occur even in February Dynamical mechanisms The essential dynamical mechanism responsible for SSWs involves the upward propagation of planetary-wave disturbances from the troposphere into the stratosphere where they dissipate by interacting with the pre-existing mean stratospheric flow (section 2.3). Generally, these waves propagate equatorward where they break down releasing their energy. However, it regularly happens that during the meridional propagation, critical layers reflect the waves forcing them to propagate poleward (Matsuno, 1971). In this case the Elliasen-Palm flux vectors and therefore the energy flux, converge towards the polar night jet causing a deceleration of the mean zonal wind. If the wind reduction is strong enough, an easterly flow is established and contemporaneously a strong temperature increase occurs in the polar stratosphere. This warming can then propagate downward (Fig. 2.5), affecting the circulation up in the troposphere (Kodera et al., 199).

16 1 The Middle Atmosphere Figure 2.5: Daily evolution of the zonal mean zonal wind (m/s) at 6 N (top) and temperature gradient (K) between 6 and 9 N (bottom) for winter 23/24. The red areas indicate easterlies and a positive temperature gradient (courtesy of K. Krüger). The growth of the upward propagating planetary waves may be due to an independently generated forcing mechanism in the troposphere or to some process that disturbs the stratosphere and troposphere together. Typical tropospheric forcing mechanisms are the orography or the blocking events which induce long-lasting, quasi-stationary distortions of the tropospheric flow (i.e. large amplitude planetary waves). SSWs could also occur as a consequence of the internal instability of the zonal mean flow (baroclinic and barotropic instability). Before a SSW the stratosphere can also be in some suitable state with the polar vortex being tighter than usual and eventually displaced off the pole, so that the polar night jet would be further poleward than its climatological position (Andrews et al., 1987). This preconditioned state of the vortex could be detected by analysing the evolution of the waves before a SSW. Labitzke (1977), mainly on the basis of the analysis of the zonal wavenumber 1 and 2 for the 12 NH winters 1964/ /76, pointed out, according to the SSW type, a different evolution of the waves before and after the warming. For major SSWs, during the pre-warming phase, the polar vortex is well developed and located over the pole, with a strong negative meridional temperature gradient. The temperature wavenumber 1 in the middle stratosphere (1 hpa) amplifies and reaches a well-marked peak. Simultaneously the geopotential height wavenumber 1 increases and reaches a pronounced peak together with a pronounced minimum of wavenumber 2 (Labitzke, 1977). The ratio between wavenumber 1 and wavenumber 2 is about 1:1 at 3 hpa. An increased geopotential height wavenumber 1 together with a reduced wavenumber 2 are also observed in the upper troposphere. Some major SSWs may present a pulse of wavenumber 2 instead of wavenumber 1 during the preconditioning. Although this occurs very rarely, like for example in February 1963, January 1985, and February 1989 (see Naujokat and Labitzke, 1993). During the breakdown phase, the amplitude of the geopotential height wavenumber 1 rapidly decreases with usually a simultaneous strengthening of wavenumber 2 which indicates the splitting of the polar vortex into two lobes. This last condition is not necessary, sometimes the breakdown occurs only with a displacement off the Pole. During this event, the temperature over the pole strongly increases leading to the reversal of the meridional gradient and of the mean zonal wind. This typical major SSW development, for which

17 2.4 Sudden stratospheric warmings 11 the wavenumber 1 pulse and the associated heat flux in the pre-warming phase are dominant (Labitzke, 1981), is called wavenumber-1 warming. The major SSW events characterised instead by an increased wavenumber 2 pulse in the preconditioning are called wavenumber-2 warmings (Krüger et al., 25). Such an event is shown in Fig. 2.6, for February 1989, where the increasing heat flux connected with the amplification of the zonal wavenumber 2, one to two weeks before the onset of the major warming (2 February 1989), is clearly evident. Figure 2.6: Time series of T (K), ū (m/s), of the amplitude (gpm) and phases ( E) of the geopotential height wave for zonal wavenumber 1 and 2 and of the heat flux for zonal wavenumber 1, 2 and 3 (K m/s) at 6 N for the indicated pressure levels from 1 December 1988 to 31 March 1989 (FUB and SSU/TOVS data). The vertical line indicates the major SSW (Krüger et al., 25). For minor warmings the waves behave similar as for major warmings but with one difference regarding the amplitude of the geopotential height waves during the pre-warming phase. In this case the ratio between wavenumber 1 and wavenumber 2 is about 2:1 or wavenumber 2 may even be larger than wavenumber 1 (Labitzke, 1977). Then the large wavenumber 2 amplitude in the pre-warming phase is not due to the splitting of the polar vortex but to its elongation towards lower latitudes. Canadian warmings are instead characterised by a large amplitude of wavenumber 1 in the geopotential height field (due to the intense development of the Aleutian anticyclone) coinciding with the reversal of the temperature gradient. In contrast to major SSWs, the amplitude of the

18 12 The Middle Atmosphere wavenumber 1 temperature field is larger at 3 hpa than at 1 hpa indicating that Canadian warmings affect the upper stratosphere less (Labitzke, 1977). At last for intense final warmings the waves behave similar as in the major SSW events. Several factors may influence the state of the stratospheric circulation and so the frequency of SSWs. The quasi-biennial oscillation (QBO) which is a quasi-periodic oscillation of the equatorial zonal wind between easterlies and westerlies in the tropical stratosphere affects the propagation of Rossby waves in the extratropics (Labitzke, 1987). When it is in its easterly phase, the planetary waves are focussd more poleward, intensifying their interaction with the zonal mean flow and thus leading to a more warmer and disturbed Arctic stratosphere during early winter. Differently in the westerly phase of the QBO, the polar vortex is colder and more stable in December and January. The latter part of the winters is instead influenced by the solar cycle with a clear difference between periods of high and low solar activity. During high solar activity, winters in the westerly phase of the QBO tend to be more disturbed and are often connected with major SSWs (Labitzke et al., 26). Other natural factors influencing the stratospheric circulation are the El Niño-Southern Oscillation, the North Atlantic Oscillation, and volcanic eruptions (Labitzke, 1999) Frequency/occurrence The occurrence of SSWs differs for the two hemispheres. While minor SSWs are common in both hemispheres, major SSWs occur generally only in the NH. The quasi-absence of major SSWs in the SH, with only one single event observed in September 22 since regular observations began in 1957 (Krüger et al., 25), is due to the weak winter planetary wave activity resulting from the different orography distribution and from the reduced land-sea temperature contrasts. Only in the Antarctic spring, the wave activity increases leading thereby to final warmings during October and November. For the NH, the frequency of major SSWs is of roughly one event every two years, although even two major SSWs per season have been observed in some winters, for example in 1998/99 and 21/22 (Naujokat et al., 22; Manney et al., 25). All the winters between 1957 and 22 have been daily monitored and analysed by the Freie Universität Berlin (FUB) meteorology department. The occurrence of SSWs, obtained from this analysis, is displayed in Fig The seasonal distribution of major SSWs obtained from this table is shown in Fig. 2.8a. Note that the distribution includes one more major SSW, namely the one occuring in February 1995 which is not reported in the table. Major mid-winter SSWs per WMO definition occur only in December, January, and February with frequency of respectively.7,.13, and.22. It is therefore evident that most events occur in February and that the average frequency is of.42 events per winter Major SSWs identification criterion The FUB analysis is a subjective analysis based on daily inspecting the synoptic charts, Charlton and Polvani (27) developed therefore an algorithm for an objective identification and classification of major SSWs. According to the synoptic structure in the middle stratosphere, major warmings are identified and classified between vortex displacement and vortex split. To identify major SSWs, the WMO definition (Andrews et al., 1987) has to be fulfilled. This states that a

19 2.4 Sudden stratospheric warmings 13 Figure 2.7: RJ: monthly mean of the sunspot number in January; FW: indicates the timing of the final warmings. CW stands for Canadian warmings, * indicates the occurrence of a major warming, C stands for a cold monthly mean. (Labitzke and Collaborators, 22). major midwinter warming occurs when the zonal mean zonal wind at 6 N and 1 hpa becomes easterly during winter. Charlton and Polvani (27) prolonged the NH winter period from November to March (NDJFM), which is in contrast to the WMO (or FUB) definition, concentrated on major midwinter warmings (December to February). The first day on which the zonal mean zonal wind becomes easterly is defined as the central date of the warming. The WMO definition additionally requires that the 1 hpa zonal mean zonal temperature gradient between 6 and 9 N is positive. Charlton and Polvani (27) did not included this criterion in their algorithm because it slightly influences the number of major SSWs identified (only one event in the European Centre for Medium-Range Weather Forecasts Re-Analysis dataset (ERA-4) does not meet this criterion). Once a major warming is identified, no day within 2 days of the central date can be defined as a major SSW. This time interval is chosen because it corresponds approximately to two radiative time scales at 1 hpa. in this way, the algorithm can not count the same major SSW twice, as the wind fluctuates between easterly and westerly after the central date. At last, Charlton and Polvani (27) assumed final warmings to be cases where the zonal mean zonal wind becomes easterly but do not return to westerly for at last 1 consecutive days before 3 April. The algorithm was then applied to the re-analysis data of the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) and to the ERA-4 dataset. Results for the

20 14 The Middle Atmosphere ERA-4 dataset are shown in Fig. 2.8b. Major SSWs occur between November and March with Distribution of Major Midwinter SSW 1957/58 21/2 (FUB).25.2 frequency / ev yr Oct Nov Dec Jan Feb Mar Apr May (a) (b) Figure 2.8: Distribution by month of major SSWs obtained from the subjective analysis of the FUB data (a) and with the algorithm of Charlton and Polvani (27) (b). the highest number of events in January. The frequencies of occurrence for November, December, January, February and March are.2,.11,.22,.17 and.11 respectively. This corresponds to an average frequency of.64 events per year thus approximately six events per decade. It is therefore evident that this distribution does not fit with the one computed with the FUB data. The most evident differences are the lack of major SSWs in November and March in the FUB dataset, due to the fact that events in November are Canadian warmings while those in March are generally major final warmings, and the different frequencies detected in midwinter when the algorithm of Charlton and Polvani (27) identifies most warmings in January whereas the FUB data distribution presents most events in February. The different midwinter frequency may be also due to the use of different independent datasets as well, which can vary potentially on a daily basis (Manney et al., 25). In this study, a modified version of the algorithm of Charlton and Polvani (27) is used to analyse major SSWs in a control simulation of the coupled atmosphere ocean circulation model MAECHAM5/MPI-OM (chapter 5). Seasonal distribution of major SSWs and of final warmings are computed both for the model data and for ERA-4 data. Additionally the planetary wave activity is examined to find out how the model simulates these waves. In particular the analysis is calculated for the pre-warming phase to determine which planetary wavenumber plays the major role for the development of the major SSWs in the model. Do most major warming events have an enhanced planetary wavenumber 1 as observed or does wavenumber 2 strongly affect the development of the major SSWs in MAECHAM5/MPI-OM? Tropospheric blockings In subsection has been said that blocking events are one typical tropospheric forcing mechanism for SSWs. Recent studies tried therefore to connect tropospheric blockings with the occurence of major SSWs (Taguchi, 28; Martius et al., 29; Castanheira and Barriopedro, 21;

21 2.4 Sudden stratospheric warmings 15 Woollings et al., 21). In Fig. 2.9, the typical synoptical structure of a tropospheric blocking event is shown: the strong Figure 2.9: An example of a blocking pattern over the Euro-Atlantic sector, showing 5 hpa geopotential height (gpm) at 12Z 15 February (Shutts, 1986). high pressure system persists for longer than the typical synoptic time scale causing an alteration of the zonal mean flow, which is interrupted by a strong persistent meridional flow. Such synoptical structures have therefore a significant impact on the weather of mid-latitude regions and because they may generate large amplitude upward propagating planetary waves, they can influence the stratospheric circulation. Taguchi (28) showed no statistically significant correlation between tropospheric blocking events and major SSWs, implying that not all major SSWs are forced by blocking events. Martius et al. (29) and Woollings et al. (21) showed instead a clear forcing of tropospheric blocking events on major SSWs although some differences in the geographical location of the blockings and in the role of the planetary wavenumber 1 and 2 were observed. Castanheira and Barriopedro (21) argued that Euro-Atlantic blockings interfere constructively with the climatological stationary wavenumber 1, thus enhancing its amplitude, whereas Pacific blockings interfere destructively with the wavenumber 1 but constructively with the climatological stationary wavenumber 2. These enhanced amplitudes also influence the strength of the polar vortex with the wavenumber 1 and the wavenumber 2 causing respectively a deceleration and an acceleration of the vortex. Note that all of these new studies are based on observational data, therefore they suffer from the short time series and the small amount of major SSW events. The last part of this study (chapter 6) concerns therefore how the model is able to reproduce the relationship between major SSWs and tropospheric blockings in more detail, given the advantage of the longer timeseries and a larger set of major SSW events.

22 16 The Middle Atmosphere

23 Chapter 3 Data and Methods In this chapter the MAECHAM5/MPI-OM and the observational datasets used for comparison and validation of the model are firstly described. New algorithms are developed for the identification of major SSWs, final warmings, and the preconditioning of major warmings by planetary waves, which are applied to the stratospheric data. Finally, the blocking index by Tibaldi and Molteni (199), which is used to detect blocking situations of the tropospheric flow, is introduced. 3.1 Model data: MAECHAM5/MPI-OM Here, the coupled atmosphere ocean circulation model MAECHAM5/MPI-OM developed at the Max Planck Insitute (MPI) for Meteorology in Hamburg is shortly described. First the atmosphere model is presented then the oceanic one The atmospheric model: MAECHAM5 MAECHAM5 is the middle atmosphere version of the ECHAM5 model which has been developed from the operational weather prediction model of the European Centre for Medium Range Weather Forecasts (first part of its name: EC) and a comprehensive parameterisation package developed at Hamburg (last part of its name: HAM). The model is based on the primitive hydrodynamic and thermodynamic equations. The hydrostatic equation, the advection equation, and the continuity equation are numerically resolved in time and space to obtain values of vorticity, divergence, temperature, specific humidity and surface pressure (Roeckner and Coauthors, 23). Processes occuring at a too small-scale or which are too complex to be physically represented in the model, are parameterized by mean of a simplified process. Examples of parameterized processes include the atmospheric radiative transfer, the convective clouds, the cloud microphysics, the descent rate of raindrops, the effects of subgrid-scale topography and the gravity waves. To describe the dynamical fields, the variables are horizontally represented on a Gaussian quadrature grid for which the latidude points are approximately equally-spaced. The variables are then represented in the horizontal by truncated series of spherical harmonics. For the triangular truncations, the typical truncations wavenumbers 21, 31, 42, 63, 85, 16, and 159 are used, therefore the model resolution is denoted as T21, T31, T42, T63, T85, T16, and T159 (Roeckner and Coau- 17

24 18 Data and Methods thors, 23). For the vertical resolution a hybrid coordinate system is used. This is made of a combination of pressure depending coordinates (p-coordinates) and orography dependig coordinates (σcoordinates). The atmosphere is thus devided into layers which are defined by the pressure of the interfaces between them. These pressures are given by p k+1/2 = A k+1/2 + B k+1/2 p s, (3.1) for k=,1,2... NLEV. In the expression, A and B are constants whose values determine the effect of the orography on a given layers while p s is the surface pressure. If A= the usual σ-coordinates system is obtained whereas if B= we get a p-coordinates system (Roeckner and Coauthors, 23). By using this hybrid coordinate system, the effects of orography are considered. The coordinates follow the terrain in the lowest layers while in the highest ones they are parallel to the isobars (see Fig. 3.1). In the standard ECHAM5 configuration, the model has 19 or 31 layers with the top level Figure 3.1: Schematic representation of the vertical resolution of a hybrid σ-p-coordinates system with 19 vertical layers (Roeckner and Coauthors, 1992). at 1 hpa while in the middle atmosphere version the layers can be increased up to 9 with top level at.1 hpa, or approximately 8 km altitude (Manzini et al. 26, Giorgetta et al. 22). In the latter case, the model belongs to the "High Top" general circulation models (GCM) category which simulate the dynamics in the troposphere, the stratosphere and the lower mesosphere. This study uses the horizontal resolution T63 of the model corresponding to a gaussian grid size of ( grid points). The vertical resolution L47 is given by 47 levels, 26 of which are within the troposphere, from surface to 11 hpa, 9 are between 11 hpa and 1 hpa while the last 12 are between 1 and.1 hpa (Giorgetta et al., September 27). Models with a too low vertical resolution are not able to simulate the QBO (Giorgetta et al., 22). To get a realistic QBO the vertical resolution must be sufficient to adequately reproduce vertically propagating tropical waves and the associated momentum flux required for the QBO generation. It was observed that the stratospheric vertical resolution should be better than 1 km (Giorgetta et al., 26). Also tropical deep convection and the relative convective heating should

25 3.1 Model data: MAECHAM5/MPI-OM 19 be well represented in order to obtain a realistic spectrum of atmospheric waves. The model includes gravity waves parametrizations and thus the relative momentum fluxes. In particular the effects of orography on the atmospheric flow are considered in the parameterization developed by Lott and Miller (1997) and Lott (1999) while the Hines parameterization (1997) is used to represent the gravity waves of tropospheric origin. The Lott and Miller parameterization considers the effects of orographic variations on subgrid scale, in particular the two main mechanisms of interaction between the orography and the atmospheric flow are taken into account: the momentum transfer from the earth to the atmosphere accomplished by the orographic waves and the drag exerted by the subgrid scale mountains when the air is blocked at low levels. The Hines parameterization assumes that the various forcing mechanisms (convective activity, shear instabilities, frontal systems, transient flow over orography), which are mostly located in the troposphere, give rise to an interacting and upward propagating broad band and continuous gravity wave spectrum. Further model details are given in Roeckner and Coauthors (23) and Manzini et al. (26) The ocean model: MPI-OM The MPI Ocean Model, MPI-OM, is an ocean general circulation model (OGCM) based on the primitive equations for an hydrostatic Boussinesq fluid. It is a free surface model capable of simulating the oceanic circulation from small scales (oceanic eddies) to gyre scales, in response to atmospheric forcing fields. Because of the coarse horizontal and vertical resolution of OGCMs, it is necessary to use subgrid-scale parameterizations. The MPI-OM has for this reason formulations for bottom boundary layer slope convection, horizontal and vertical viscosity, vertical and isopycnal diffusivity, eddy-induced mixing, and convection in the ocean (Wetzel et al., 21). The model grid presents an horizontal discretization on a staggered Arakawa C-grid (Arakawa and Lamb, 1977), while the vertical discretization is on z levels (geopotential levels) (Wolff et al., 1997). The standard horizontal ocean grid used for climate studies has a spatial resolution of (GR1.5) (Jungclaus et al., 26). The vertical resolution L4 is characterised by 4 levels with level thickness increasing with depth. Eight layers are within the upper 9 m and twelve in the following 5 m. A bipolar orthogonal spherical coordinate system is used (Fig. 3.2). If the poles are diametrically opposed then the coordinate system is reduced to a rotated spherical grid. Otherwise, orthogonal meridians and parallels are constructed according to the choice of zonal and meridional resolution and are used to define the spatial grid. In the current setup the model s North Pole is located over Greenland and the South Pole over Antarctica. This approach allows a better resolution in the deep water formation regions near Greenland and in the Weddel Sea with a horizontal grid spacing between 1 km in the Arctic and 17 km in the Tropics (Jungclaus et al., 26). To force the MPI- OM, two datasets are used for the set up: the OMIP atmospheric forcing from the German Ocean Model Intercomparison Project (OMIP) climatology and the NCEP/NCAR atmospheric forcing from the re-analysis of the National Centers for Environmental Prediction and the National Center for Atmospheric Research. These forcings have daily temporal resolution and atmospheric synoptic scale variability. The first one provides the heat, freshwater, and momentum fluxes at the

26 2 Data and Methods Figure 3.2: Standard MPI-OM orthogonal curvilinear grid with poles located over Greenland and Antarctica (white regions) (Wetzel et al., 21). air-sea interface while the second one provides 2 m air and dew-point temperatures, precipitation, cloud cover, downward shortwave radiation, 1 m wind speed and surface wind stress (Wetzel et al., 21). The ocean model includes also a sea ice model (subroutine OCICE) which simulates the dynamics of sea ice circulation, the thermodynamics of sea ice growth and melt and the thermohaline coupling to the ocean model (Wolff et al., 1997). The sea ice dynamics depend on the wind stress, on the ocean current stress, and on the sea ice rheology which determines the way in which ice flows, cracks, ridges, rafts, and deforms (Hibler, 1979). The sea ice thermodynamics concern the determination of the local growth or melt rate at the base of the sea ice and the local melt rate at the surface. During sea ice growth and melt, salt and fresh water are exchanged, for this reason the ocean model should consider the effect of this thermohaline coupling. In particular it is assumed that sea ice has a constant salinity of 5 psu independent of its age MPI-OM coupled to MAECHAM5 Atmosphere and ocean are coupled by means of the Ocean-Atmosphere-Sea Ice-Soil (OASIS) coupler. The ocean model receives from the atmospheric model the total heat flux over water, the downward short wave heat flux, the conductive heat flux and the residual heat flux of sea ice, the wind speed, the wind stress over water and over ice, and the solid and liquid precipitation including river runoff, and passes, to the atmospheric model, the sea surface temperature (SST), the sea ice cover (SIC), the sea ice thickness and the snow thickness on sea ice (Jungclaus et al., 26, Wetzel et al., 21). SST and SIC are the most important ocean variables for determining oceanatmosphere fluxes and, together with the freshwater fluxes, are sensitive performance indicators of any given coupled-model realisation. Note that the ocean model does not require the use of flux adjustment. For this study, a 16-yr control simulation was carried out, initialized with constant preindustrial greenhouse gas concentrations from 186 and with a monthly zonal-mean climatological ozone distribution based entirely on ozone observations over the period (Fortuin and Kelder, 1998). A control experiment of the CMIP3 (Coupled Model Intercomparison Project 3) was also used to initialize the ocean model. As for the first 6 years an initial drift of the global mean surface temperature is present, only the last 1 years are analysed. This drift is due to the

27 3.2 Observational data 21 spin-up period of the ocean model to reach an equilibrium state under the applied forcing. 3.2 Observational data Here the observational datasets used for comparison and validation of the MAECHAM5/MPI-OM results are shortly described The ERA-4 dataset The 4-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-4) dataset comprises 45 years of global analyses of the state of the atmosphere, land and surface conditions over the period September 1957 to August 22 (Uppala and Coauthors, 25). The name ERA- 4 is due to the fact that at the begining the idea was that only 4 years of observations should be reanalysed. The dataset was obtained by a combination of satellite and conventional observations. Before the beginning of the satellite period in the early 197s, wind, temperature, and humidity data were obtained from radiosondes, ship measurements and from ground stations. The introduction of additional satellite measurements allowed a more observational-driven stratospheric analysis during the second half of the ERA-4 period. It was thus possible to compute the ozone content, the wind speeds and the total content of water vapor in the air column by using satellite measurements. Ocean buoys were also used to receive data for the SH were less ground station measurements were available. The data assimilation process is the same for the whole re-analysis period but due to the huge amount of data, 5 simultaneous streams, covering the periods September 1957-April 1973, May 1973-May 1985, June 1985-June 1986, July 1986-January 1989, and February 1989-August 22, have been computed on different computers causing discontinuity in data series at the transition points between the runs (Uppala and Coauthors, 25). The observational data from the different sources are then combined and used to solve the primitive equations in a GCM whose output constitutes the re-analysis field. The re-analysis data are avalaible every six hours ( UTC, 6 UTC, 12 UTC, and 18 UTC) and are obtained applying the three dimensional variational assimilation technique which uses the observed data in the time intervall [t n 3h, t n + 3h] to produce data for time t n. The ERA-4 dataset has resolution T159L6. The T159 model s reduced Gaussian grid is symmetric about the equator, with a north-south separation which is close to uniform in latitude, with a spacing of about There are 8 points aligned along the Greenwich Meridian from equator to pole. The number of points in the east-west varies with latitude, with uniform grid spacing along a particular line of latitude. The spacing is in the tropics which corresponds to circa 125 km at the equator. For the vertical resolution, an hybrid coordinate system is used with 6 layers included between the surface and the lower mesosphere where the last layer is located at.1 hpa or approximately 64 km altitude (Fig. 3.3). The dataset presents spurious oscillations in the temperature field of the stratosphere. This can be seen, for instance, in the standard deviation of monthly zonal-mean temperature, at polar latitudes, for the last 2 years of the re-analysis data. More detailed information concernig the ERA-4 assimilation are given in Uppala and Coauthors (25).

28 22 Data and Methods Figure 3.3: Schematic representation of the vertical resolution of a hybrid σ-p-coordinates system with 6 vertical layers for the ERA-4 data (Uppala and Coauthors, 25) The FUB dataset The Freie Univesität Berlin (FUB) dataset consists of daily analysis charts based on wind, geopotential height, and temperature observations from radiosondes, with rocket observations being included when available. Data were subjectively analysed at 4 levels in the stratosphere: 1, 5, 3 and 1 hpa, and were available for the time period July 1957 to July 21 with geopotential height and temperature time series starting in 1957 and 1964 respectively (Labitzke and Collaborators, 22). While at the begining the analysis were hampered by poor data coverage over large areas, since 1984 the radiosonde analysis in data-sparse areas have been supplemented with data derived from satellite analysis which are invaluable over the polar region and the oceans. Prior to 1973, the daily Berlin analysis charts were read out onto regular grid points manually. Therefore, all data prior to this year are available in a 1 x 1 horizontal resolution, and it covers the northern hemisphere northward of 1 N only. At 8 N, values are only sampled in a 2 longitudinal resolution. Later, the interpolation onto grid points was done by a computer, allowing for a horizontal regular resolution of 5 5. In addition, the analysis was extended to the equator. All charts were analysed for UTC with priority being given to observations at that time; these were supplemented by 12 UTC observations, which were used to guide the analysis when the UTC observations were missing or for the time evolution in rapidly growing disturbances. Monthly means analysis are also available and were obtained by averaging the daily data. More detailed information are given in Labitzke and Collaborators (22) The TOVS dataset The TOVS dataset consist of a global analyses of the stratosphere produced by the U.K Meteorological Office (UKMO) using data from the TIROS (Television Infrared Observation Satellite) Operational Vertical Sounder (TOVS) instruments on board the National Oceanic and Atmospheric Administration (NOAA) series of operational satellites (Bailey et al., 1993). The TOVS system comprises 3 radiometers: the Stratospheric Sounding Unit (SSU), the Microwave Sounding Unit

29 3.3 Data analysis/methods 23 (MSU), and the High Resolution Infrared Sounder (HIRS), which scan the subsatellite track to retrieve radiance data. These are then used to derive deep layer-mean thicknesses which are added to an independently analyzed field of geopotential height at 1 hpa to give geopotential heights at standard pressure levels in the stratosphere up to 1 hpa. The primary products are global synoptic fields of measured radiances and geopotential heights. The radiances are stored on a 5 5 grid for x layers, while the geopotential heights are stored on the same grid on the 85, 5, 3, 2, 1, 5, 2, 1, 5, 2 and 1 hpa pressure levels. It is also possible to calculate brightness temperatures from the radiances and temperatures, geostrophic winds, and potential vorticity from the geopotential height fields. The analyses were produced daily from December 1978 until June More detailed information are given in Bailey et al. (1993). 3.3 Data analysis/methods Here the new algorithms applied in this study to identify and characterize major SSWs are presented. Also the blocking index, used to detect blocking situations of the tropospheric flow in the mid and high latitudes, is described New major SSW criterion A new criterion to identify major SSWs in ERA-4 and MAECHAM5/MPI-OM data, is developed. This necessity arises from the fact that using Charlton and Polvani s (27) criterion, some problems emerge during the detection of major SSWs. In particular, for the years 1969/7, the algorithm identifies two events per season while in reality it is one long lasting event (see the FUB table Fig. 2.7). A careful analysis of the 1 hpa zonal mean zonal wind at 6 N for this case is shown in Fig The vertical lines are introduced to explain how the old and the new critera detect major SSWs. The red line indicates the central date (i.e. the first day of easterlies) which is the same for both criteria. The criterion of Charlton and Polvani (27) searches again for major SSWs after 2 days from the central date (magenta line), therefore a second event should be counted at the end of January, which was not monitored based on the FUB data (see table Fig. 2.7). The new criterion assumes the 2 days masking (cyan line) to start from the first day with westerly wind after the central date (blue line) and not from the central date (red line). Additionally, if for the first day after this 2 days mask the winds are easterly, the algorithm searches for the next day with westerly wind exceeding 1.5 m/s (black line) and starts from that day to search for other major SSWs. This threshold value of 1.5 m/s is introduced because sometimes the wind oscillates around the line like for example in 1979/8 when westerlies were observed on March 23, weak easterlies on the 24 and again westerlies on the 25. The value should thus avoid counting this insignificant wind oscillation as a major SSW. Applying the new criterion to MAECHAM5/MPI- OM data, it occurs that too much late major SSWs were counted in comparison to those detected with a subjective analyse. For this reason, the criterion is further modified by introducing a second condition to identify final warmings.

30 24 Data and Methods (m/s) (m/s) / Dec Jan Feb Mar Apr May / Jan Feb Mar Apr May Jun Figure 3.4: Zonal mean zonal wind (m/s) at 1 hpa, 6 N for the years 1969/7 and 1979/8 characterised by long lasting major SSWs (ERA-4 data). The evolution is shown only for the period close to the major SSW. The red line indicates the central date, the blue line indicates the first day of of westerly wind after the central date, the magenta line indicates the first day after the 2 days mask of Charlton and Polvani s criterion (27), the cyan line indicates the first day after the 2 days mask of the new criterion, the black line indicates the first day with westerly winds exceeding 1.5 m/s after the 2 days mask of the new criterion, the green line indicates the final warming. The first arrow marks the 2 days mask of the Charlton and Polvani s criterion (27) while the second arrow referes to the new criterion whose mask can comprehend more than 2 days New final warming criterion An additional condition to detect final warmings is introduced because, within the model, small amplitude oscillations (< 5 m/s) often occur after a major SSW, whereby strong westerly flow is not yet fully established (i.e. no polar vortex). Fig 3.5 shows one of these typical cases in March where weak westerly wind prevailed for more than 1 days. Charlton and Polvani s criterion (m/s) year 55 Figure 3.5: Zonal mean zonal wind (m/s) at 1 hpa, 6 N for one year of MAECHAM5/MPI-OM. The green and red line indicate the day of the final warming obtained respectively with the new criterion and with Charlton and Polvani s one (27). Note that after the warming of the middle of March, the wind oscillates for long time between westerly and easterly with intensity of less than 5 m/s (dashed line). (27) would assume that the polar vortex re-establishes in April and count the March warming as a major SSW. The new criterion instead assumes that the vortex is reformed only if during these 1

31 3.3 Data analysis/methods 25 days the westerly wind reaches at least for one day the intensity of 5 m/s. Therefore, for example, the warming occuring in the middle of March in Fig. 3.5 is considered as a final warming and insofar is not counted as a major SSW. Results obtained applying this new algorithm and the one of Charlton and Polvani (27) to MAECHAM5/MPI-OM and ERA-4 data, are shown in the next chapter New wavenumber-1/wavenumber-2 warming criterion Examples of wavenumber-1 and wavenumber-2 major warmings are shown in Fig. 3.6 and Fig.3.7 where time series of the zonal mean zonal wind at 5 and 1 hpa at 6 N, of the geopotential height wave amplitude and of the heat fluxes at 1 hpa and 6 N, are presented. These quantities are Figure 3.6: Time series of the zonal mean zonal wind ū (m/s), of the amplitude (gpm) of Z 1,2,3,, and of the heat flux (K m/s) of Z 1,2,3, at 6 N for the indicated pressure levels from 1 October to 31 May of one wavenumber-1 warming in MAECHAM5/MPI-OM. The red line indicates the central date of the warming. computed as explained in section A.1 and A.2 in the Appendix. The increased zonal wavenumber 1 and 2 amplitudes and the enhanced heat flux connected to the zonal wavenumber 1 and 2, one to two weeks before the central date, are clearly evident. From the figures is evident that, one to two weeks prior to the central date, the amplitude of the zonal wavenumber 1 or the zonal wavenumber 2 geopotential height wave increases together with an enhancement of the zonal wavenumber 1 or the zonal wavenumber 2 heat flux. The 1 hpa zonal mean zonal wind at 6 N of each winter characterized by a major SSW is used to compute, for each day, the difference between the wind of that day and 5 days after. The

32 26 Data and Methods Figure 3.7: Time series of the zonal mean zonal wind ū (m/s), of the amplitude (gpm) of Z 1,2,3,, and of the heat flux (K m/s) of Z 1,2,3, at 6 N for the indicated pressure levels from 1 October to 31 May of one wavenumber-2 warming in MAECHAM5/MPI-OM. The red line indicates the central date of the warming. day of the strongest wind reduction, D, within 14 days immediately prior to the central date, is then sought. The behaviour of the planetary waves and of the associated heat fluxes before this day is then analysed to determine which zonal wavenumber mostly influences the developement of the major SSW. In particular, an event is denoted as a wavenumber-2 major warming when, within the 1-day window period (D-7 and D+3), the following conditions are fullfilled for at least one day: the geopotential height Z amplitude of the zonal wavenumber 2 at 5 and 1 hpa is greater than that of the zonal wavenumber 1, the heat flux associated with the zonal wavenumber 2 at 5 and 1 hpa exceeds the value of the zonal wavenumber 1 by 15 K m/s. The 15 K m/s value has been choosen following a subjective analysis which has shown that, without this 15 K m/s value, the algorithm identifies as wavenumber-2 warming several cases for which the heat flux of the zonal wavenumber 2 is only slightly larger than that of the zonal wavenumber 1. In this way, instead, only significant cases are considered. To determine if the zonal wavenumber 3 may be responsible for the developement of a major SSW, the algorithm also searches if the the geopotential height Z amplitude and the heat flux of the zonal wavenumber 3 at 5 and 1 hpa is greater than those of the zonal wavenumber 1 and the zonal wavenumber 2. If

33 3.3 Data analysis/methods 27 the above mentioned conditions are not fullfilled, the major SSW is classified as a wavenumber-1 warming Blocking index To find out if MAECHAM5/MPI-OM is able to simulate blocking events of the tropospheric circulation in the mid and high latitudes (chapter 6), the blocking index for the NH, as defined by Tibaldi and Molteni (199), is calculated. For each longitude of the model grid, two gradients of the 5 hpa geopotential height field are computed: the southern geopotential height gradient GHGS and the northern geopotential height gradient GHGN: GHGS = Z(φ ) Z(φ s ) (φ φ s ), GHGN = Z(φ n) Z(φ ), (3.2) (φ n φ ) where φ s = 4 N +, φ = 6 N +, φ n = 8 N +, and = -4, or 4. The first gradient considers the geopotential height Z difference between approximately 6 N and the mid latitudes while the second considers the geopotential height difference between the high latitudes and circa 6 N. The latitudes between which the gradients are computed vary according to the value of. For example, the southern geopotential height gradient is computed between φ = 56 N and φ s = 36 N, φ = 6 N and φ s = 4 N, and φ = 64 N and φ s = 44 N. A given longitude is then defined as blocked when, at a given instant in time, the following conditions are satisfied for at least one value of : GHGS >, GHGN < -1 m/deg lat. The blocking index therefore indicates whether the zonal flow of a given longitude is blocked or not. The latitude points in MAECHAM5/MPI-OM are not equally spaced therefore the is adapted to be generally 1 to 1.5 higher than ±4. A 5-day running mean is also applied to the 5 hpa geopotential height field before calculating GHGS and GHGN to isolate blocking episodes of sufficient duration as suggested on the NCEP webpage (NOAA, Climate Prediction Center, 26).

34 28 Data and Methods

35 Chapter 4 Model Climatology In this chapter the climatology of MAECHAM5/MPI-OM and the comparison with avalaible observations is presented. In particular, the zonal mean temperature and the zonal mean zonal wind are analysed in more detail as they are used for the major warming identification. Due to the crucial role played by planetary-waves in the development of stratospheric warmings, the climatology of the waves including the heat flux is compared with observations. 4.1 Zonal mean temperature The zonal mean temperature is shown for January and July in Fig. 4.1 as function of latitude and pressure for MAECHAM5/MPI-OM and ERA-4. During January there is generally a good agreement between the model (Fig. 4.1a) and the re-analysis (Fig. 4.1c). The most evident deviations in the model are the smaller area of -7 C close to the North Pole in the lower stratosphere (between 1 and 3 hpa), the lack of temperature higher than 1 C in the southern polar stratopause (1 hpa), and the fact that in the lower Arctic mesosphere (between 1 and.1 hpa) temperatures higher than -1 C are simulated. The difference between MAECHAM5/MPI-OM and ERA-4 are more evident during July. The northern polar stratopause is colder in the model (Fig. 4.1b) with values around C compared to the 1 C of ERA-4 data (Fig. 4.1d). In the SH instead, a warmer lower stratosphere and lower mesosphere is simulated. Temperatures of -8 C between 1 and 3 hpa and up to C between 1 and.1 hpa are distinguishable near the South Pole while values of respectively -9 C and of -2 C are seen in the re-analysis. The temperature minimum of -9 C is now located in the higher mesosphere of the NH and is less pronounced compared to the -1 C of the SPARC climatology (not shown here). The tropical tropopause is also colder with values up to -8 C compared to the -7 C of the ERA-4 data. It is evident that MAECHAM5/MPI-OM present some temperature bias in the higher stratosphere and lower mesosphere of the high latitudes. A negative bias of the order 1 C is found near the Antarctic and the Arctic stratopause respectively during January and July, while, at the same time, the lower Arctic and the Antarctic mesosphere present a positive bias, of the order of 1-2 C. This problem occurs in the most middle atmosphere climate models (MACMs) even if it is most common to have a negative bias (the cold-pole problem ) over the winter pole and not over the summer pole (Pawson et al., 2). Similar to results of other MACMs, which simulate 29

36 3 Model Climatology a too warm or too cold tropical tropopause region, MAECHAM5/MPI-OM has also a too warm tropical tropopause in July. pressure [hpa] MAECHAM5MPI OM: January S 6 S 3 S EQ 3 N 6 N 9 N latitude 2 pressure [hpa] MAECHAM5MPI OM: July S 6 S 3 S EQ 3 N 6 N 9 N latitude (a) (b).1 ERA4: January.1 ERA4: July pressure [hpa] S 6 S 3 S EQ 3 N 6 N 9 N latitude 2 pressure [hpa] S 6 S 3 S EQ 3 N 6 N 9 N latitude (c) (d) Figure 4.1: Zonal mean temperature climatology [ C] of January and July in a latitude-pressure section for the 1 years of MAECHAM5/MPI-OM data and the 45 years of the ERA-4 re-analysis. Countour interval is 1 C. 4.2 Zonal mean zonal wind Latitude-height sections of the zonal mean zonal wind are displayed in Fig. 4.2 for January and July and show an important part of the dynamical structure of the atmosphere which is related, through the thermal wind balance, to the thermal structure. In the model, during January (Fig. 4.2a), the subtropical jets with maxima of 3 and 4 m/s are simulated at 2 hpa around 45 S and 3 N. They are located at the poleward limit of the equatorial tropical air above the transition zone between tropical and mid-latitude air where the mid-troposphere temperature gradients are relatively strong. The comparison to ERA-4 (Fig.

37 3 4.2 Zonal mean zonal wind MAECHAM5MPI OM: January MAECHAM5MPI OM: July pressure [hpa] S 6 S 3 S EQ 3 N 6 N 9 N latitude 3 2 pressure [hpa] S 6 S 3 S EQ 3 N 6 N 9 N latitude (a) (b).1 ERA4: January.1 ERA4: July pressure [hpa] S 6 S 3 S EQ 3 N 6 N 9 N latitude (c) pressure [hpa] S 6 S 3 S EQ 3 N 6 N 9 N latitude (d) Figure 4.2: Zonal mean zonal wind climatology of January and July in a latitude-pressure section for MAECHAM5/MPI-OM and ERA-4. Contour interval is 1 m/s. 4.2c) shows that the jets in the troposphere are well simulated. The polar-night jet (westerly winds, positive values) and the summer easterlies (negative values) are detectable in the stratosphere and in the mesosphere. Both wind regimes increase in strength with height with maximum intensities of -7 m/s at.5 hpa in the SH and of 4 m/s at.6 and.7 hpa in the NH. The tilt of these flows is visible: equatorwards for the polar-night jet and polewards for the summer easterlies. By comparison with the re-analysis it is evident that the polar-night jet presents a stronger tilt in particular in the mesosphere where it extends until the equator. Additionally, even if the westerlies present the same intensity as in ERA-4, the area of the jet s core is smaller probably linked with the positive temperature bias in the lower Arctic mesosphere. The model also overestimates the intensity of the summer easterlies by 1 m/s. In July (Fig. 4.2b) the subtropical jet in the SH shifts equatorward to 3 S while in the NH it shifts poleward to 45 N. The first also increases in strength (4 m/s) while the latter weakenes (2

38 32 Model Climatology m/s) according to the changed temperature gradients. The polar-night jet and the summer easterly regime again increase in strength with height but in this case the maximum of the polar-night jet (7 m/s) lies mostly in the higher stratosphere at 2 hpa, while the core of the summer easterlies is instead located at.2 hpa with wind speeds up to 6 m/s. The wind regimes are tilted as in January. The comparison with the re-analysis (Fig. 4.2d) shows that in this case the intensity of the polarnight jet is underestimated and a different location of the jet s core. MAECHAM5/MPI-OM has maximum wind speed of 7 m/s in the higher stratosphere at around 6 S while in ERA-4 values up to 1 m/s are reached in the lower mesosphere and are shifted a bit equatorward. Concerning the summer easterlies, the maximum (6 m/s) in the re-analysis is situated above.1 hpa while in the model it is simulated below this level. The MAECHAM5/MPI-OM and ERA-4 climatology were also compared with the SPARC climatology (not shown here) which extends up to.5 hpa to assess the realism of the top model levels of ERA-4 and to find out if the model well simulates the lower mesosphere dynamics. The comparison of ERA-4 and SPARC shows that during January, in the former climatology, the area of 4 m/s in the NH extends down to 3 hpa while in the SPARC climatology westerlies of 4 m/s are present only down to.2 hpa. Similarly in the SH where 1 m/s stronger summer easterlies are evident in ERA-4 data between.3 and.1 hpa. In July the westerlies are more intense in the ERA-4 climatology: 2 m/s stronger winds are evident in the upper layers close to the model top, as well as a 1 m/s stonger polar-night jet. Differently for the summer easterlies which present the same intensity in both climatology. Thus it is evident that ERA-4 has some problems in the upper layers due to contamination from the model top boundary. From the comparison between MAECHAM5/MPI-OM and SPARC climatology it is seen that in January the polar-night jet is well simulated while the second easterlies maximum of 6 m/s visible in the SH between.1 and.1 hpa in the SPARC climatology is not reproduced by the MAECHAM5/MPI-OM model. In July the summer easterlies are instead well simulated while the polar-night jet in the SH remains too far poleward and is 2 m/s weaker compared to the 9 m/s of the SPARC data. Pawson et al. (2) linked the weaker polar-night jet to the parameterized travelling waves, which benefit the simulation of the NH stratosphere but provide too much forcing in the SH winter causing too weak midwinter westerlies. Concerning the position of the jet s core, Rind et al. (1988) linked this problem to the absence of nonorographic gravity waves with nonzero phase speeds but in MAECHAM5/MPI-OM the problem persists even if these waves are parametrized. The zonal mean zonal wind is investigated further at the 1 hpa pressure level (Fig. 4.3). In both figures a westerly flow (positive values) is found mostly northward of 2 N before the onset of the summer circulation (about the middle of April). Unlike the re-analysis, MAECHAM5/MPI- OM has westerlies until the equator during November and December. This area of climatological westerly is associated with the semiannual oscillation (SAO) in the zonal wind in the equatorial stratosphere and lower mesosphere which results to be excessively deep and strong and can propagate from the top of the model down to 2 hpa (Giorgetta et al., 26). Easterlies are instead climatologically observed at low latitudes throughout the year and in the whole hemisphere after the onset of the summer circulation. In Fig. 4.3a can be seen that at the begining of October, in the high latitudes, the wind speeds reach 1 m/s while in the re-analysis (Fig. 4.3b) they reach values of 15 m/s. The same wind speeds occur in the model only 1 days later. This time shift is man-

39 4.2 Zonal mean zonal wind 33 9 N MAECHAM5MPI OM 9 N ERA4 51 latitude 6 N 3 N (a) latitude 6 N 3 N (b) Figure 4.3: Seasonal evolution of the NH winter zonal mean zonal wind climatology (m/s) at 1 hpa in a time-latitude cross section. Daily values are shown between October and May for MAECHAM5/MPI-OM (a) and ERA-4 (b) with contour interval of 5 m/s. tained in the model through all winters (not shown here). In fact, looking at MAECHAM5/MPI- OM, the maximum wind speeds (values up to 25 m/s) occur between 6 N and 7 N from the end of December until the begining of February while in the re-analysis the highest values (up to 35 m/s) are reached between 55 N and 7 N from the middle of December until the middle of January. During February and March the wind becomes weaker until transition to summer circulation (change to easterly winds) occurs around the middle of April for both re-analysis and model. Therefore the polar night jet, in MAECHAM5/MPI-OM, is weaker, shifted a few degree northward and its maximum strength is reached about 2 weeks later than in the re-analysis during NH winter. This time shift is not much longer present, in fact the change to the summer circulation occurs at the same time in the model and in the re-analysis. Regarding the smoothing of the model climatology, it might be related with the different length of the datasets used. The seasonal evolution of the zonal mean zonal wind was computed by Charlton et al. (27) for 6 GCMs including the MAECHAM model. In that case the model performed a 29 years simulation which presents a seasonality shifted toward late winter with the strongest westerlies (3 m/s) occuring between late January and February and the change to the summer circulation at the end of April (not shown here). From the comparison between MAECHAM and MAECHAM5/MPI-OM simulations it is evident that the coupled run has a better seasonality while the other simulates better the intensity of the polar night jet. Fig. 4.4 displays again the zonal mean zonal wind at 1 hpa as function of latitude but this time is shown the winter mean (NDJFM) together with the standard deviation values (shadings). The two curves are close together with the model s spread being within and close to one standard deviation from the re-analysis curve respectively between and 45 N and 45 N and 9 N. For both data negative values occur near the equator while positive one are found north of the subtropics. Near the North Pole the wind weakens to almost m/s. The maximum wind speeds (27 m/s in ERA-4 and 2 m/s in MAECHAM5/MPI-OM) occur, as seen before in Fig. 4.3, in the high

40 34 Model Climatology Figure 4.4: Climatological winter mean (NDJFM) of the zonal mean zonal wind (m/s) at 1 hpa as function of latitude for MAECHAM5/MPI-OM (red line) and ERA-4 (blue line). Shadings show ±one standard deviation from the winter mean. latitudes. The northward shift of the polar-night jet in the model is also evident from the fact that the peaks of the two curves do not exactly concide. Both datasets present large standard deviation (14 m/s for ERA-4 and 12 m/s for MAECHAM5/MPI-OM) in the high latitudes associated with high winter variability of the zonal flow (see section 4.3.2). An evident difference is present in the low latitudes where ERA-4 has standard deviations up to 17 m/s while the model has values of only 3 m/s. These small standard deviations in MAECHAM5/MPI-OM are due to the fact that the model does not reproduce the QBO but overall it is possible to affirm that the zonal flow is well reproduced. 4.3 Interannual variability North Pole temperature Daily values of the North Pole temperature at 1 hpa of MAECHAM5/MPI-OM data are compared with those of the FUB dataset with the FUB data refering to the winters 1965/66 until 21/22 for the months November to April. At the begining of October, in MAECHAM5/MPI-OM (Fig. 4.5a), the temperature is slightly variable and falls between -58 C and -48 C, decreasing towards the end of the month with more variable values ranging from -7 C to -4 C. Temperature fluctuations still increase in November (between -78 C and -36 C) towards February. January and February are characterised by the highest variability with values falling respectively in the interval -83/-14 C and -79/-4 C. The variability decreases during spring: temperatures between -74 C and -13 C, -6 C and -2 C and, -46 C and -29 C occur respectively in March, April and May. The North Pole temperature of the FUB data (Fig. 4.5b) is slightly different. November, with values between -82 C and -46 C, is not so variable. More variability occurs during December, January, and February when temperature falls respectively in the intervals -85/-17 C, -86/-6 C, and -83/-5 C. A slight reduction of variability is observed during February but it is in March and

41 4.3 Interannual variability 35 MAECHAM5MPI OM: North Pole temperature [ o C] (a) (b) Figure 4.5: Daily temperature ( C) at 1 hpa at the North Pole between October and May in MAECHAM5/MPI-OM (a) and from November to April in the FUB data (b). April, with temperatures respectively between -76 C and -15 C and between -59 C and -25 C, that it reduces further. The comparison between MAECHAM5/MPI-OM and the FUB re-analysis evidences that the model presents higher variability during November while it has less variability during December and January. During mid and late winter the simulated strong warmings of the middle stratosphere, with values up to -4 C in February, are in good agreement with the re-analysis which display temperatures up to -5 C Zonal mean zonal wind Fig. 4.6 displays daily values of the 1 hpa zonal mean zonal wind at 6 N between October and May for MAECHAM5/MPI-OM and ERA-4. Each line is indicative of the zonal mean zonal wind s evolution during one year while the red line represents the mean value over all the 1 years of the model or the 45 years of the re-analysis data. The displayed easterly winds are related with major SSWs or with the onset of the summer circulation. In the first case, after the SSW, the western flow is restored while in the latter the reversal of the circulation extends throughout the summer. In MAECHAM5/MPI-OM (Fig. 4.6a) at the begining of the time series, the zonal mean zonal wind presents only positive values generally below 2 m/s. The high variability of the jet appears during winter when the wind values are in the range of -38 m/s to 58 m/s. During spring, the wind intensity reduces. After the middle of May an easterly zonal mean zonal wind with values between -5 m/s and -1 m/s is simulated. Looking at the mean, wind intensity increases from October until January when the maximum value of 25 m/s is reached. Then, after the middle of February, it constantly decreases until the middle of April when the change to summer circulation takes place. In ERA-4 (Fig. 4.6b) the wind is stronger, in particular during midwinter values up to 7 m/s are assimilated. This affects the mean which has values close to 4 m/s during December and January. On the other side, during sudden stratospheric warmings, easterlies are weaker

42 36 Model Climatology 12 MAECHAM5MPI OM: 6 o N 12 ERA4: 6 o N wind [m/s] wind [m/s] (a) 4 (b) Figure 4.6: Daily zonal mean zonal wind (m/s) at 1 hpa, 6 N between October and June for MAECHAM5/MPI-OM (a) and ERA-4 (b). The red lines indicate climatological daily means. compared to MAECHAM5/MPI-OM (values up to -3 m/s). As pointed out before (section 4.2), the transition to the summer circulation occurs at the same time as in the model but stronger winds are assimilated for ERA-4 during the easterly phase (values between -5 m/s and -17 m/s). From recent publications it is know that only one major SSWs occured in the SH since regular observations became avalaible in 1957 (Krüger et al., 25). For this reason the zonal mean zonal wind is also analysed at 6 S (see Fig. B.1 in Appendix). No major SSWs is detected but MAECHAM5/MPI-OM simulates one disturbed midwinter, connected with a strong minor warming. Fig. 4.7 presents the monthly mean standard deviation of the 1 hpa zonal mean zonal wind as function of time and latitude. In MAECHAM5/MPI-OM (Fig. 4.7a) highest standard deviations are found in both hemispheres principally during the respective winters as a consequence of the variability of the polar-night jet in winter season. In the NH the maximum value of 14 m/s occurs in February between 65 N and 75 N while in the SH a maximum of 12 m/s is present during September and October around 6 S. During summer, reduced standard deviations are evident in both hemispheres due to the less variable summer circulation in the stratosphere. In ERA-4 (Fig. 4.7b) the maximum values are higher. Standard deviations up to 16 m/s occur both in the NH between 6 N and 7 N during January and February than in the SH between 55 S and 65 S in September and October. Values of the same intensity occur also along the equator throughout the year and are representative of the high variability associated with the QBO. The smallest standard deviations are instead present, as in the model, during summer. The most evident difference between the re-analysis and MAECHAM5/MPI-OM is the lack of the QBO which is propably due to the low vertical resolution of the model. Other differences are that MAECHAM5/MPI-OM slightly underestimates the variability of the polar-night jets and that the highest values in the NH occur during February while in the re-analysis the jet is highly variable from December to March.

43 4.4 Planetary wave climatology 37 latitude 9 N 6 N 3 N EQ 3 S MAECHAM5MPI OM latitude 9 N 6 N 3 N EQ 3 S ERA S S Jan FebMar Apr MayJun Jul AugSep Oct NovDec Jan (a) S S Jan FebMar Apr MayJun Jul AugSep Oct NovDec Jan (b) Figure 4.7: Standard deviations of monthly mean zonal mean zonal wind at 1 hpa in a time-latitude section for MAECHAM5/MPI-OM (a) and ERA-4 (b). Contour interval is 2 m/s. 4.4 Planetary wave climatology Amplitudes and phases In this section the wave climatology for MAECHAM5/MPI-OM is presented and compared with publications by Pawson and Kubitz (1996) and Scaife et al. (2). The first study produced a climatology of the stationary and transient planetary waves for the NH using daily temperature T and geopotential height Z data of the 3-years time series of FUB data. Scaife et al. (2) instead used data from TIROS Operational Vertical Sounder Satellite (TOVS). While daily data of temperature and geopotential height are used to compute amplitudes and phases of net waves, monthly means data are used to calculate stationary waves. The differece between net waves and stationary waves gives the transient waves (section 2.3). In the observational climatology the amplitude and phase of the geopotential height and temperature wavenumbers n are denoted as Z n, δn Z, T n, and δn T. The associated heat fluxes are instead denoted with H n. Only wave quantities which are shown by Pawson and Kubitz (1996) and Scaife et al. (2) are avalaible, and are therefore discussed. Further plots, for all parts of planetary waves, are shown in the Appendix (Fig. B.2 - B.6). In Fig. 4.8 the DJF mean amplitude of stationary waves is presented in a latitude-pressure section for the first two zonal wavenumbers. For zonal wavenumber 1, the comparison of the observed amplitudes (Fig. 4.8b) (Scaife et al., 2) with those of MAECHAM5/MPI-OM data (Fig. 4.8a) shows that the model simulates the amplitude increase with height and the position and the intensity of the amplitude maximum (8 gpm) in the upper stratosphere. For zonal wavenumber 2 the model (Fig. 4.8c) does not properly reproduce the intensity and the location of the amplitude maximum, which is 2 gpm weaker than in the observational climatology (Fig. 4.8d) and located in the upper stratosphere instead of the middle stratosphere (1 hpa). The maximum is also slightly shifted latitudinally being close to 6 N while in the observational climatology it is between 6 N and 7 N. A further analysis of the stationary zonal wavenumber 1 is shown in Fig. 4.9, where the latitudinal structure of the wave computed with MAECHAM5/MPI-OM data (left column) is compared with observations by Pawson and Kubitz (1996) (right column). Shown are DJF mean values of

44 38 Model Climatology 1 wave 1 pressure [hpa] N 6N 3N (a) (b) 1 wave 2 18 pressure [hpa] N 6 6N 3 3N (c) (d) Figure 4.8: Latitude-height sections of geopotential height amplitudes (gpm) of stationary waves. DJF mean values are shown between and 9 N for zonal wavenumber 1 (first row) and wavenumber 2 (second row). MAECHAM5/MPI-OM data are shown in the left column while the observational climatology (Scaife et al., 2) is shown in the right column. amplitudes and phases of geopotential height Z and temperature T. In MAECHAM5/MPI-OM the amplitude of the geopotential height (Fig. 4.9a) maximizes at 7 N for all given pressure levels while in the FUB analysis (Fig. 4.9b) it maximizes between 6 N and 7 N. For both datasets, due to the decreasing density the wave amplitude increases with height with values of 25, 35, and 6 gpm at 5, 3, and 1 hpa respectively. The phase presents a similar behaviour in both observations (Fig. 4.9d) and the model climatology (Fig. 4.9c) at 3 and 5 hpa with a phase increase from the subtropics to the mid latitudes and a decrease in the high latitudes. Differences are found at 1 hpa in the subtropics where the phase is located between 24 E (eastern Pacific) at 2 N and 12 E (western Pacific) at 3 N in the model and at about 16 E (east-central Pacific) in the observational climatology. This is maybe an effect of the SAO, which is excessively deep and strong (section 4.2). The westward phase slope with decreasing pressure indicates the baroclinic character of these waves.

45 4.4 Planetary wave climatology 39 (gpm) (deg E) Amplitude of Z wave 1 1hPa 3hPa 5hPa 3N 6N 9N (a) Phase of Z wave 1 1hPa 3hPa 5hPa 3N 6N 9N (c) Amplitude of T wave 1 1hPa 3hPa 5hPa (b) (d) (K) 6 (deg E) N 6N 9N (e) Phase of T wave 1 1hPa 3hPa 5hPa 3N 6N 9N (g) (f) (h) Figure 4.9: Latitudinal structure of stationary zonal wavenumber 1 between 2 N and 9 N in MAECHAM5/MPI-OM (left column) and in the observational climatology (right column) (Pawson and Kubitz, 1996). Shown are amplitudes of the geopotential height (gpm) and temperature (K) and their phases (deg E). DJF mean values are given at 5, 3, and 1 hpa and are represented in the model with the green, red, and blue lines and in the observational climatology, with the solid, dashed, and dottet lines respectively. In MAECHAM5/MPI-OM the amplitude of the temperature wave (Fig. 4.9e) maximizes slightly equatorward with decreasing pressure: at 5 hpa the maximum of 7.5 K is located near

46 4 Model Climatology 7 N while at 1 hpa it is located at 65 N with a value of more than 1 K. In the observations instead (Fig. 4.9f), the maximum amplitude of the temperature wave of 8.5 K at 5 hpa and of 1 K at both 3 and 1 hpa, are found near 6 N for all three pressure levels. For all shown pressure levels, the phase of the temperature wave decreases from 2 N to 3-35 N then increases towards higher latitudes in the model (Fig. 4.9g). As for the geopotential height wave, a westward slope of the phase with decreasing pressure is visible. In the observational climatology (Fig. 4.9h) the phase decreases between 2 N and 3 N then increases towards higher latitudes only for the 3 and 5 hpa pressure level. At 1 hpa instead, a constant phase increase from the low latitudes until the polar cap is visible, which is not reflected in the model. For the geopotential height, the seasonal cycle of the mean amplitude and phase for zonal wavenumbers 1 and 2 of all part of the planetary waves (net, stationary and transient) are also computed with MAECHAM5/MPI-OM data (Fig. 4.1). For both wavenumbers, the amplitudes (gpm) Mean Amplitude of Z wave 1 net stationary transient (deg E) Mean Phase of Z wave 1 net stationary (gpm) Oct Nov Dec Jan Feb Mar Apr May (a) Mean Amplitude of Z wave 2 Oct Nov Dec Jan Feb Mar Apr May (c) net stationary transient (deg E) Oct Nov Dec Jan Feb Mar Apr May (b) Mean Phase of Z wave 2 net stationary Oct Nov Dec Jan Feb Mar Apr May (d) Figure 4.1: Time evolution for wavenumber 1 and 2 of net (blue), stationary (red), and transient (green) waves between October and May in MAECHAM5/MPI-OM. Shown are monthly averages at 3 hpa 6 N of amplitude and phase of geopotential height. The phase is shown only for net and stationary waves. (Fig. 4.1a and 4.1c) show a steady increase in autumn and early winter and a similar decrease in late winter. Stationary waves have larger amplitude than transient waves and their sum gives the net waves. The maximum amplitudes of stationary waves are reached during February with values of 31 and 15 gpm respectively for the zonal wavenumber 1 and the zonal wavenumber 2. For transient waves the largest amplitudes occur instead in January with values of 9 and 7 gpm respectively for the first and the second zonal wavenumber. For the net waves, which are mainly depending on the stationary waves, the amplitudes maximize therefore in February with values of 39 and 22 gpm respectively for the zonal wavenumber 1 and the zonal wavenumber 2. These large amplitudes during winter coincide with the period of maximum interannual variability

47 4.4 Planetary wave climatology 41 of the northern winter stratosphere reflecting the fact that the wave activity is the most important dynamical source of variability of the extratropical large scale flow. The phase (Fig. 4.1b) of net and stationary waves are very similar for zonal wavenumber 1 and varies between 2 E and 26 E (Eastern North Pacific-North America): The seasonal evolution shows that the waves lie further westward until January and only in late winter and spring (up to April), they are located further eastward. During the transition to easterlies in April (Fig. 4.6a and 4.3a), a further westward shift is simulated. The amplitude and phase values of 3 gpm and 2 E simulated for stationary wave number 1 at 3 hpa during midwinter are comparable with Pawson and Kubitz (1996), who found that the observed DJF mean amplitude and phase are respectively 342 gpm (Fig 4.9b) and 213 E (Fig 4.9d). For zonal wavenumber 2, the phase (Fig. 4.1d) of the stationary wave is more variable than that of the net wave with values between 35 E and 9 E (west-central Russia) for the first one and between 6 E and 85 E (central Russia) for the latter. Both type of waves present the same seasonal march although that of the stationary waves is more variable. Initially they are located further westward until early winter, then they are shifted eastward until February, when maximum amplitudes are simulated, the waves lie more westward again. During winter, the stationary waves present phases between 5 E and 9 E, which is highly different from the phase of 1 E (central Europe) determined by Pawson and Kubitz (1996) for the DJF mean of FUB data. Also the zonal wavenumber 2 amplitude does not coincide: circa 14 gpm for the DJF mean in the model compared to the 226 gpm determined by Pawson and Kubitz (1996) (no figure avalaible). The wrong representation of zonal wavenumber 2 in the model is also evident by comparison with the amplitudes and phases computed by Scaife et al. (2) for the net waves (Fig. 4.11). While the model is able to simulate the ratio between the amplitudes of the zonal wavenumber 1 (a) (b) Figure 4.11: Time series of the number of occurrences of net wave-1 and wave-2 amplitudes in five-day periods within 5 gpm ranges and of the respective phases within 1 ranges at 3 hpa, 6 N. Values are shown for the period July trough June using TOVS data (Scaife et al., 2).

48 42 Model Climatology and the zonal wavenumber 2, with the first being about twice as large as the latter during winter, some discrepancies arise for the phase. Fig. 4.11b shows that the phase is characterised by a large spread with some clustering between 18 E and 25 E (Central Pacific- North America) for zonal wavenumber 1 and near (Europe) for zonal wavenumber 2. In particular for zonal wavenumber 1, there is a westward shift of the clustering from October until February when it is located respectively at 18 E and 225 E. The zonal wavenumber 1 phases simulated in the model are therefore comparable with those computed by Scaife et al. (2) for the observational data. The observed zonal wavenumber 2 presents instead a clustering close to with no evident shifts between October and May. Again there is evidence, that the model does not well represent the phase of the zonal wavenumber Heat fluxes The amplitude and phase values are used to compute the heat flux of the planetary waves. Here the climatological heat fluxes are presented in two different ways: as a latitudinal distribution and as function of time and latitude. In Fig the latitudinal distribution of the heat fluxes (DJF mean) for stationary zonal wavenumber 1 at 5, 3, and 1 hpa is shown for MAECHAM5/MPI-OM and FUB data. In the (Km/s) Heat Fluxes wave 1 1hPa 3hPa 5hPa 3N 6N 9N (a) (b) Figure 4.12: Latitudinal structure of the heat flux (K m/s) of stationary zonal wavenumber 1 between 2 N and 9 N in MAECHAM5/MPI-OM (left) and in the observational climatology (Pawson and Kubitz, 1996) (right). DJF mean values are given at 5, 3, and 1 hpa and are represented in the model with the green, red, and blue line and in the observational climatology with the solid, dashed, and dottet line respectively. observational climatology (Fig. 4.12b) the heat fluxes maximize at 6 N, with values respectively of 12 and 2 K m/s at 5 and 3 hpa, and at 7 N for the 1 hpa pressure level where a value of 53 K m/s is found. The heat fluxes maximize therefore poleward with decreasing pressure. For the model data (Fig. 4.12a) the same maximum values are simulated but the heat fluxes of the three pressure levels maximize all close to 65 N. The strong increase of the heat fluxes with decreasing pressure is thus common for both datasets. In Fig the latitudinal distribution of the heat fluxes (DJF mean) for net, stationary and transient waves at 3 hpa between 2 N and 9 N is shown. The positive values in the extratropics indicate that all type of waves transport energy poleward. In particular stationary wavenumber 1 plays the major role carring about 2 to 4 times of the energy of transient waves, 21 versus 1 K m/s for observations (Fig. 4.13b) and 21 versus 6 K m/s, for the model (Fig. 4.13a). For wavenumber 2 instead, stationary and transient waves transport 13 and 1 K m/s, and 7 and 6 K

49 4.4 Planetary wave climatology 43 (Km/s) (Km/s) Heat Fluxes 3hPa wave 1 net stat trans 3N 6N 9N (a) Heat Fluxes 3hPa wave 2 3N 6N 9N (c) net stat trans (b) (d) Figure 4.13: Latitudinal distribution of heat fluxes (DJF mean) for net (blue line), stationary (red line) and transient waves (green line) for zonal wavenumber 1 and 2 at 3 hpa between 2 N and 9 N. MAECHAM5/MPI-OM data are shown in the left column while in the right one is presented the FUB climatology (Pawson and Kubitz, 1996) for which dottet, dashed, and solid lines indicate net, stationary and transient waves. m/s respectively for observations (Fig. 4.13d) and model (Fig. 4.13c). In both datasets, the fluxes maximize between 6 N and 7 N for zonal wavenumber 1 and at 6 N for zonal wavenumber 2. Comparison of the data shows that the model underestimates the intensity of the transient wave heat flux for both wavenumbers. For stationary waves instead, the wavenumber 1 heat flux is well simulated in contrast to the wavenumber 2 heat flux. The final effect is that the model underestimates the heat flux of the net waves. This can also be seen in Fig. 4.14a, where the net heat flux of all wavenumbers at 1 hpa is shown in a time-latitude section. However, note that the model simulates the position of the heat fluxes maxima well. The discrepancies of the heat flux intensity of zonal wavenumber 2 can be attributed to the incorrect model simulation of the geopotential height and temperature phases for the zonal wavenumber 2 (see Fig. B.3 in Appendix). In particular, the geopotential height phase does not present a westward phase slope with decreasing pressure and the simulated values differ from the FUB climatology. Pawson and Kubitz (1996) found that the 3 hpa geopotential height mean phase for the zonal wavenumber 2 lies at 4 E, 1 E, and 12 E respectively at 4 N, 6 N, and 8 N (no figure avalaible) while in the model the DJF mean phase lies at 8 E, 7 E, and 75 E (Fig. B.3, second row, second column). Differences occur also for the temperature wave: while in the FUB climatology the mean phase of zonal wavenumber 2 lies at 149 E, 16 E, and 2 E (no figure avalaible), in MAECHAM5/MPI- OM it lies at 12 E, 13 E, and 1 E for respectively 4 N, 6 N, and 8 N (Fig. B.3, second row, fourth column).

50 44 Model Climatology The climatological eddy heat flux at 1 hpa from November through April is shown in Fig There is a good overall agreement between model and observations with the strongest heat fluxes during midwinter, between 5 N and 7 N. In particular the maxima exceeds 2 K m/s in the model (Fig. 4.14a) at the end of January and 25 K m/s in the re-analysis (Fig. 4.14b). The only difference is the intensity of the net heat flux with the model underestimating the maxima with up to 5 K m/s. 9 N MAECHAM5MPI OM latitude 6 N 3 N Nov Dec Jan Feb Mar Apr (a) (b) Figure 4.14: Climatological eddy net heat flux (K m/s) of all wavenumbers at 1 hpa as function of time and latitude for MAECHAM5/MPI-OM and ERA-4 (from Schimanke et al. (21)). Values are shown from November through April between and 9 N.

51 Chapter 5 Major Sudden Stratospheric Warmings Analysis In this chapter the analysis of the major sudden stratospheric warmings is presented. First, the climatology of major SSWs obtained with the new criterion (section 3.3.1), is shown for ERA- 4 and MAECHAM5/MPI-OM data. To investigate the preconditioning of the major warming events, planetary waves are then analysed to distinguish between zonal wavenumber 1 and zonal wavenumber 2 major warmings. Finally, the distribution of final warmings is presented. 5.1 Intercomparison between the criteria In this study, the new algorithm and the one of Charlton and Polvani (27) are applied over a longer time interval including the months between October and May because the polar vortex is disturbed even in those months. The number of major SSWs (no major final warmings) detected with the two criteria in both datasets is reported in table 5.1 and 5.2. While for ERA-4 data Table 5.1: Major SSWs distribution in ERA-4. Oct Nov Dec Jan Feb Mar Apr May Charlton & P New criterion Table 5.2: Major SSWs distribution in MAECHAM5/MPI-OM. Oct Nov Dec Jan Feb Mar Apr May Charlton & P New criterion (table 5.1), almost the same number of events is identified, for MAECHAM5/MPI-OM data (table 5.2), the algorithm of Charlton and Polvani (27) overestimates, in comparison with the new algorithm, the late winter major SSWs. The differences in ERA-4 are due to the four years for 45

52 46 Major Sudden Stratospheric Warmings Analysis which the final warming takes place on different dates. For these cases, the time evolution of the zonal mean zonal wind at 1 hpa, 6 N is shown in Fig For 1972/73 and 1987/88 the Figure 5.1: Zonal mean zonal wind (m/s) at 1 hpa, 6 N for the years 1972/73, 1983/84, 1987/88, and 1988/89. Final warmings are occurig at different days according to which criterion is used (green line-new criterion, red line-charlton and Polvani criterion (27)). criterion of Charlton and Polvani (27) identifies two major SSWs for each year, in January and April 1973, and in December 1987 and March 1988 respectively. Final warmings are also found to occur in May and April respectively. The new criterion instead recognizes only the first event as a major SSW while the second one is counted as a final warming. For both 1983/84 and 1988/89 Charlton and Polvani s method (27) detects one major warming during February and the final warming in April while the new criterion considers the event in February as a final warming. Comparison for these four cases with the FUB table (Fig. 2.7) shows that the results of the new algorithm agree well with the FUB results. The new criterion therefore better individuates the final warmings. Below, the results of applying the new algorithm are presented. 5.2 Time series of major SSWs In Fig. 5.2, the annual distributions of major SSWs obtained with the new criterion for ERA- 4 and MAECHAM5/MPI-OM data, are shown. For the re-analysis data (Fig. 5.2a), 27 events

53 5.2 Time series of major SSWs 47 3 SSWs / year /58 61/62 65/66 69/7 73/74 77/78 81/82 85/86 89/9 93/94 97/98 1/2 year 3 (a) SSWs / year model year (b) Figure 5.2: Distribution of major SSWs by year in ERA-4 (a) and in MAECHAM5/MPI-OM (b). are identified with generally one major SSWs per year within the 45-year period. Two events per year occured only in 1965/66, 1968/69, 197/71, 1998/99, and 21/2. The lack of major SSWs during the 199 s is also evident. The zonal mean zonal wind at 1 hpa, 6 N of the years characterised by two events is displayed in Fig For the year 1965/1966 and 1968/1969 the first warming in December 1965 and November 1968 are classified in the FUB table (Fig. 2.7) as a Canadian warming therefore only one major SSW is counted in February 1966 while the event of March 1969 is not considered as a warming. For the year 197/71 the warming in March 1971 is considered in the FUB data as a final and thus not considered, therefore only one warming is counted in this year. From the FUB table, it is evident that two major warmings in the same winter are observed only in 1998/99 (December 1998, February 1999) and in 21/22 (December 21, February 22). A detailed description of these events is given in Naujokat et al. (22). For MAECHAM5/MPI-OM data (Fig. 5.2b), a total amount of 7 major SSWs is detected within a 1-year period, with nine years presenting two events per year. The frequency of years with two events per season (.9) is therefore comparable with that of the ERA-4 dataset (.9). In addition, no long quiescent period of without major warmings occurs, unlike that observed in ERA-4 data during the 199 s even if up to four consecutive years without major SSW are simulated between year 2 and year 3. Pawson and Naujokat (1999) analysed the northern winters of the middle 199 s. Comparisons with observations over more than three decades indicate that these years were characterized by an atipically cold stratosphere, with the largest anomalies occuring in the late winter and spring. This stratospheric cooling is addressed in several studies (Fels et al., 198; Miller et al., 1992; Mc- Cormack and Hood, 1994; Ramaswamy et al., 1996) and attributed to the increase in well-mixed radiatively active trace gases or decrease in stratospheric ozone, occurred during the last century.

54 48 Major Sudden Stratospheric Warmings Analysis (m/s) (m/s) (m/s) (m/s) (m/s) / / / / / Figure 5.3: Zonal mean zonal wind (m/s) at 1 hpa, 6 N for the years with two major SSWs. Red lines indicate major warmings while green lines indicate the final warming. In the MAECHAM5/MPI-OM control simulation, there is no greenhouse gas concentrations forcing, thus a trace gases increase as that of the last decades is not simulated. This may then explain the lack of quiescent periods like that observed in the 199 s. 5.3 Seasonal distribution of major SSWs Fig. 5.4 shows, the monthly distribution of major SSWs for ERA-4 and for MAECHAM5/MPI- OM data. The abscissa represents the relative frequency of major SSWs obtained dividing the number of events in one month by the total number of observation years. Thus, if one month has a frequency of.11, it means that for a period of 1 years, 11 major SSWs are expected to occur during that month. The whole bar is relative to all the counted events during one month while the

55 5.3 Seasonal distribution of major SSWs 49 red and the green part indicate if it is the first or the second major SSW during one season Distribution of SSW MAECHAM5MPI OM first SSW second SSW.25.2 Distribution of SSW ERA4 first SSW second SSW frequency / ev yr frequency / ev yr Oct Nov Dec Jan Feb Mar Apr May Oct Nov Dec Jan Feb Mar Apr May (a) (b) Figure 5.4: Distribution of major SSWs by month in MAECHAM5/MPI-OM (a) and in ERA-4 (b). In ERA-4 data (Fig. 5.4b), the major SSWs occur between November and April with most events happening in January and February. The frequencies of occurrence for November, December, January, February, March and April are.2,.11,.22,.13,.9 and.2 respectively, thus resulting an average frequency of.6 events per year or 6 events per decade. Of the warming events occuring in February-March, half are due to a second major SSW. The results therefore suggest a 5% chance of a second warming event to occur in late boreal winter. In MAECHAM5/MPI-OM data (Fig. 5.4a), the major SSWs occur between October and April. The frequencies of occurrence for October, November, December, January, February, March and April are.1,.5,.16,.15,.21,.11 and.1 respectively. The average frequency is.7 events per annum, thus the model has a higher frequency of occurrence of major SSWs. Note that since the late 199 s, there was an increase of the number of events, with seven major SSWs between 1998/99 and 23/4 (Manney et al., 25), therefore the frequency of observed events could be higher. Second warming events are detected in February, March and April, even if the percentage of these cases is reduced in comparison to ERA-4. Note that the case in October is identified at the end of that month and that the October-December events may be classified as Canadian warmings since those events can be occasionally associated with a wind reversal. The main difference between the two distributions is the period with the highest occurrence of major SSWs. For ERA-4 data, most events are identified in January while in MAECHAM5/MPI- OM they are detected in February. Another notable difference is the higher number of major SSWs detected in December in the model data. These differences in the seasonal distributions are also due to the different intensity of the stratospheric zonal mean zonal wind in the model and in the reanalysis, and to the different planetary wave activity. In the model, the 1 hpa zonal mean zonal wind reaches its maximum intensity in January and is weaker in December and February (Fig. 4.3a, 4.6a). Major SSWs are therefore more likely in the latter two months because, due to the weaker westerlies, planetary waves can easier propagate upward and influence the stratospheric circulation. This is particularly evident in February for which the large number of major SSWs

56 5 Major Sudden Stratospheric Warmings Analysis is associated with the enhanced wave activity of wavenumber 1 (Fig. 4.1a). The December warmings can also be related to the increased planetary wave activity and the enhanced heat flux (Fig. 4.14a) as well. For ERA-4 data instead, the maximum wind speed occur at the end of December (Fig. 4.3b, 4.6b) thus planetary waves are favoured to propagate upward only in the following months. The highest number of major SSWs in January is in fact associated with the enhanced planetary wave activity (Fig. 4.11a) and the heat flux maximum (Fig. 4.14b) of this month. 5.4 Preconditioning of major SSWs The wave activity analysed here focuses on the behaviour of planetary wavenumber 1, 2, and 3 during the pre-warming phase of major SSWs. The main goal is to find out whether the model can reproduce the wavenumber-1 to wavenumber-2 warming ratio. For ERA-4 the distribution of wavenumber-2 events is obtained from literature (Naujokat and Labitzke, 1993; Krüger et al., 25) with three events which occured respectively in February 1963, January 1985, and February All other events detected with the algorithm used to count major SSWs are classified as wavenumber-1 warming. Some problems arise for the February events: the algorithm detects the warming of 1963 at the end of January and not in February, also the event of 1989 is identified as a final warming (Fig. 5.1), and is consequently not counted. In contrast, within MAECHAM5/MPI-OM data, the distinction between wavenumber-1 and wavenumber-2 warming is done automatically by the algorithm which has identified thirteen wavenumber-2 warmings. Time series, for these thirteen cases, of the variables used for the classification are shown in the Appendix (Fig. B.9 - B.21). The distributions of wavenumber-1 and wavenumber-2 major SSW are shown in Fig Here.25.2 Distribution of SSW MAECHAM5MPI OM wave 1 SSW wave 2 SSW.25.2 Distribution of SSW ERA4 wave 1 SSW wave 2 SSW frequency / ev yr frequency / ev yr Oct Nov Dec Jan Feb Mar Apr May Oct Nov Dec Jan Feb Mar Apr May (a) (b) Figure 5.5: Distribution of wavenumber-1 and wavenumber-2 major SSWs by month in MAECHAM5/MPI-OM (a) and in ERA-4 (b). the blue and magenta part of the bars indicate the frequency of wavenumber-1 and wavenumber-2 warming. In ERA-4 data (Fig. 5.5b), the wavenumber-2 warmings occur only in January with a

57 5.5 Final warming distribution 51 frequency of occurrence of.4, while all other events are wavenumber-1 warming. Note again that the wavenumber-2 warming of February 1989 is not counted because it is identified as a major final warming. Further analysis shows also that the observed wavenumber-2 events are all first warmings (see Fig. B.7b in the Appendix). No wavenumber-3 warming is found althought it seems that sometimes the zonal wave 3 can play a role in the developement of major warmings. See for example the events shown in Fig. B.9 and Fig. B.14 in the Appendix for which, within 1 days prior to the central date, the geopotential height wave amplitude at 1 hpa and the heat fluxes at 1 and 1 hpa have a maxima for the zonal wavenumber 3 and a minimum for the other two wavenumbers. In MAECHAM5/MPI-OM data (Fig. 5.5a) the wavenumber-2 warmings occur in January, February, and March with frequency of.2,.5, and.6 respectively. Therefore more than half of the major SSWs occuring in March is characterised by an enhanced activity of wavenumber-2 in the pre-warming phase. The distinction between first and second warmings (see Fig. B.7d in the Appendix) once again shows that most wavenumber-2 events are first warmings, with only 2 cases of second warmings in March (frequency of.2). 5.5 Final warming distribution The change to summer circulation in the stratosphere happens generally in spring, therefore it is expected that most final warmings would occur during this season. In Fig. 5.6 the distributions of final warmings, including major final warmings, are shown. For both datasets, the distributions Distribution of Final Warming MAECHAM5MPI OM Distribution of Final Warming ERA4 frequency / ev yr frequency / ev yr Oct Nov Dec Jan Feb Mar Apr May Oct Nov Dec Jan Feb Mar Apr May (a) (b) Figure 5.6: Distribution of final warmings in MAECHAM5/MPI-OM (a) and in ERA-4 (b). look similar with changes to the summer circulation occuring between February and May. The frequencies of occurrence in February, March, April and May are respectively.5,.29,.53, and.13 for ERA-4 data (Fig. 5.6b), and.1,.3,.57 and.12 for MAECHAM5/MPI-OM data (Fig. 5.6a). Most final warmings take place in April while those rare events occuring in February correspond to major final warmings. The exact number of final warmings occuring in each single month can be seen in table 5.3.

58 52 Major Sudden Stratospheric Warmings Analysis These distributions show once again that the model simulates well the transition to the summer Table 5.3: Final Warming Distribution. Oct Nov Dec Jan Feb Mar Apr May ERA MAECHAM5/MPI-OM circulation as was also visible for the transition to easterlies in chapter 4. The distributions of final warmings obtained with Charlton and Polvani s criterion (27) are shown in the Appendix. For ERA-4 data (Fig. B.8b), the algorithm detects the final warmings in March, April and May, with frequency of occurrence of.27,.58, and.15 respectively. For MAECHAM5/MPI-OM data (Fig. B.8a), the final warmings are detected again in these three months with frequencies respectively of.17,.62, and.21. The most evident difference between the distributions obtained with the two criteria is therefore the detection, with the new criterion, of major final warmings in February.

59 Chapter 6 Atmospheric Blockings Analysis In this chapter the results of the analysis of the blocking episodes are shown. The frequency of occurrence of blocked zonal flow is computed for each longitude and compared with the climatology of the NCEP/NCAR re-analysis. Thereafter, the seasonal and annual distributions of blocking events are presented. Owing to the ostensible similarity between the seasonal distribution of major SSWs and blocking events, a correlation analysis is carried out to find if there are correlations between blockig events and different type of major SSWs. 6.1 Blockings distribution in MAECHAM5/MPI-OM The spatial and temporal occurrence of blocking events is displayed in Fig 6.1. Here, for each (a) (b) Figure 6.1: Frequency of DJF blocked days as function of longitude for MAECHAM5/MPI-OM (a) and for the NCEP/NCAR re-analysis (b) (NOAA, Climate Prediction Center, 25). The red and blue line in the model plot refer respectively to the filtered and unfiltered data while the red line in the observations plot refers to unfiltered data. Shadings correspond to the Euro-Atlantic (3 W-3 E) and to the Pacific sector (15 E-115 W). longitude, the DJF frequency of blocked days (i.e. days with a blocked zonal flow) computed using the blocking index, is shown for MAECHAM5/MPI-OM data and NCEP/NCAR re-analysis. For the model (Fig. 6.1a) two curves are displayed, one relative to data filtered with a 5 day running 53

60 54 Atmospheric Blockings Analysis mean (red) and one to the unfiltered data (blue). The difference between them is the reduction of the number of blocked days for the filtered data. If one point presents a frequency value of.1, it means that between December and February (9), 9 days with a blocked zonal flow are expected for that longitude. By comparison with the NCEP/NCAR re-analysis (Fig. 6.1b) one can note that even if the model reproduces the two well know frequency maxima in the Euro-Atlantic (3 W- 3 E) and in the Pacific sector (15 E-115 W) (see chapter 1), it underestimates the number of blocked days, with values of unfiltered data being about half those of the re-analysis. Also note that the Pacific maximum, which peaks at 15 E in the observations, is shifted eastward to 14 W in the model. The third maximum around 6 E in both data is associated with the relatively frequent occurrence of Euro-Asian blockings. In order to show how MAECHAM5/MPI-OM represents blockings, a characteristic year is displayed in Fig The Hovmöller diagram of the 5 hpa geopotential height gradient measured Blocking Strength GHGS (m/deg lat) Oct Nov Dec Jan Feb Mar Apr May Jun 6W 6E 12E 18 12W Figure 6.2: Hovmöller plot of the blocking strength GHGS (m/deg lat) for year 47 of MAECHAM5/MPI-OM. The red line corresponds to the central date of the major SSW, while the vertical black lines delimit the Euro-Atlantic sector (3 W-3 E) and the Pacific sector (15 E-115 W). from the blocking ridge equatorward (denoted GHGS in the blocking index definition, section 3.3.4) is shown, with colored areas depicting regions where the zonal flow is blocked and the color scheme denoting the strength of the blocked flow. The larger those areas are, the greater the time and spatial extent of the blocking events. It is again visible that most blockings mainly occur in the two distinct sectors mentioned above. To compute the seasonal and annual distributions, episodes characterised by a blocking strength GHGS greater than 1 m/deg lat are taken into account. Only these events are considered, as it is expected that only strong blockings can affect the stratospheric circulation. Fig. 6.3 shows the annual distribution of the blocking episodes occuring between November and March in the entire NH, in the Euro-Atlantic sector, and in the Pacific sector. High levels of variability are observed in the number of blocking events. In Fig. 6.3(a) years with no events are alternated to years with up to three events. An 6-year period without blockings is also simulated

61 6.1 Blockings distribution in MAECHAM5/MPI-OM 55 (a) (b) (c) Figure 6.3: Distribution of strong blocking events by winter between November and March in MAECHAM5/MPI-OM for the entire NH (a), the Euro-Atlantic sector (3 W-3 E) (b), and the Pacific sector (15 E-115 W) (c). between year 2 and year 3 even if it does not coincide exactly with the quiescent period of without major SSWs mentioned in section 5.2. The distinction in the two sectors shows that less strong blocking episodes occur in the Euro-Atlantic region (Fig. 6.3b) in comparison to the Pacific one (Fig. 6.3c), which was not detected when taking all blocked days (i.e. weaker ones) into account (Fig. 6.1). The seasonal distribution is shown in Fig The distribution of the NH peaks in January with a frequency of.22 (Fig. 6.4a). This means that in each decade, at least two strong blocking events should occur in that month. The other winter months present frequencies of.13 in December,.2 in February, and.15 in March while a more evident reduction in the number of blocking episodes is found in spring and autumn when the frequencies are equal to or below.9. The other two plots (Fig. 6.4b and 6.4c) reveal that the winter peak is a consequence of the blocking frequency in the Pacific sector. For this region (Fig. 6.4c), the distribution presents an