Climatic features of atmospheric stability in the Mediterranean region ( ): spatial modes, inter-monthly and inter-annual variability

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1 METEOROOGICA APPICATIONS Meteorol. Appl. 14: (7) Published online 17 October 7 in Wiley InterScience ( Climatic features of atmospheric stability in the Mediterranean region ( 6): spatial modes, intermonthly and interannual variability Christos J. olis* aboratory of Meteorology, Department of Physics, University of Ioannina, 41 Ioannina, Greece ABSTRACT: The predominant spatial modes of atmospheric stability over the Mediterranean region are revealed by applying factor analysis and cluster analysis on the daily grid point values of Kindex for the period 6. At first, Smode factor analysis is applied on the Kindex time series in order to reduce the dimensionality of the data set. The kmeans cluster analysis is then applied to the factor scores time series, and the days are classified into 12 clusters with distinct and characteristic spatial distributions of the Kindex. The 12 spatial modes revealed are associated with the prevailing atmospheric circulation types and/or the thermal and humidity characteristics of the lower and the middle troposphere. The intermonthly variations of their frequency reveal the seasonal factors that contribute to their predominance. The seasonality of the 12 clusters shows that three of them prevail mainly in winter, three of them prevail mainly in summer, and the other six clusters present a relatively even seasonal distribution. The interannual variations of their frequency exhibit considerable longterm changes, providing also some possible indications of the oncoming climatic change. The continuous decrease of the typical high instability pattern of late spring and early summer and the recent increase of one of the typical high instability winter patterns provide some useful information about climate variability in the Mediterranean. Copyright 7 Royal Meteorological Society KEY WORDS Kindex; mediterranean sea; cluster analysis; factor analysis Received 14 May 7; Revised 26 July 7; Accepted 17 August 7 1. Introduction Atmospheric stability is one of the most significant factors that modulate weather and climate characteristics in a specific area of the Earth s surface. igh atmospheric stability conditions generally favour the predominance of sunny and dry weather, while low atmospheric stability conditions are usually responsible for cloudy and rainy weather. The appearance of violent atmospheric phenomena (for example hailfalls and tornadoes) caused by intense convection (see, e.g. Johns and Doswell, 1992) is generally associated with atmospheric instability. The vertical profiles of air temperature and humidity determine the degree of static stability or instability. The quantification of this degree has been achieved by the definition of the static stability indices. The static stability indices constitute a measure of atmospheric stability and they have been presented, tested and compared by many researchers (see, e.g. Jacovides and Yonetani, 199; untrieser et al., 1997). Some of the widely used static stability indices are the following (1) the Kindex estimates static stability as a combination of 8 hpa thermal lapse rate, 8 hpa moisture and a measure of thickness of moist layer (Reap and Foster, 1979) * Correspondence to: Christos J. olis, aboratory of Meteorology, Department of Physics, University of Ioannina, 41 Ioannina, Greece. chlolis@cc.uoi.gr (2) the umidity Index assesses the degree of saturation in 8, 7 and hpa levels (itynska et al., 1976) (3) the Potential WetBulb Index assesses the potential instability of 8 hpa cloud layer (Pickup, 1982) (4) the Yonetani Index is a combination of 9 8 hpa and 8 hpa lapse rates and a measure of the mean relative humidity of the 9 8 hpa layer (Jacovides and Yonetani, 199) () and, finally, the Showalter Index assesses the potential instability of an hypothetical 8 hpa cloud layer (Showalter, 193). In the Mediterranean area, atmospheric stability is affected by the thermal and humidity characteristics of the Earth s surface and the origin of the air masses dominating over the region. During the cold period of the year, the sea surface is averagely warmer than the air above it, favouring upward sensible and latent heat fluxes and contributing to low atmospheric stability in the lowest tropospheric layers (see, e.g. Metaxas, ; olis et al., 4). Furthermore, the air masses being transferred over the Mediterranean are usually of a polar origin, further contributing to atmospheric instability, especially over the relatively warm seasurface. The depressions formed over the main cyclogenesis areas of the Mediterranean (see,e.g.maheraset al., 1; Trigo et al., 2) favour static instability, which is responsible for high precipitation amounts and frequent thunderstorm events. On the other hand, during the warm period of the year, the Copyright 7 Royal Meteorological Society

2 362 C. J. OIS sea surface is averagely cooler than the air above it, favouring downward sensible heat fluxes and enhancing atmospheric stability in the lowest tropospheric layers. The air masses dominating over the region are mainly of a subtropical origin, due to the strong action of the subtropical (Azores) anticyclone during this season (Davis et al., 1997; Xoplaki et al., 3). These warm and dry air masses lying over the cool Mediterranean waters make the lowest tropospheric layers very stable. This is not valid for the continental areas north of the northern Mediterranean coasts, where heating of the land along with the frequent presence of cold air mass in the middle troposphere result in considerably lower atmospheric stability conditions. atent instability over these continental areas frequently allows the generation of severe thunderstorm events, especially during the afternoon when the convection associated with land heating is highest (see, e.g. Camuffo et al., ). Many researchers have introduced specific classification methodologies and schemes for daily synoptic patterns in the Mediterranean region (see, e.g. Maheras et al., ; Alpert et al., 4). These studies are mainly based on geopotential height or sealevel pressure patterns. In the present study, a classification scheme involving atmospheric stability is constructed, in order to provide useful climatological information about the vertical structure of the atmosphere. The main advantage of such a scheme is the fact that atmospheric stability is a parameter referring to an atmospheric layer defined by two isobaric surfaces and not to an individual isobaric surface. The classification is based on the characteristics of the atmosphere at both the upper and the lower isobaric surfaces and a specific atmospheric stability pattern corresponds to a specific pair of circulation patterns in the two isobaric surfaces. The Kindex is selected among the stability indices, as it can be properly estimated by using grid point data and it may be a good indicator of the general spatial characteristics of atmospheric stability in the lower troposphere over an extended area, from a climatological point of view, without taking into account the smallscale topographical singularities that might be interesting mainly for meteorological purposes. An objective methodology including factor analysis (FA) and cluster analysis (CA) is applied to the Kindex data set, in order to reveal the predominant spatial modes of atmospheric stability over the Mediterranean region. The seasonal atmospheric circulation factors that contribute to their predominance and the longterm changes of their frequency are examined by studying the intermonthly and interannual variations of their frequency. 2. Data and methods The data used consists of daily ( UTC) grid point values of: (1) 8, 7 and hpa air temperature, (2) 8 and 7 hpa specific humidity, and, (3) and hpa geopotential height at 189 grid points that cover the Mediterranean region ( W E and N N) (Figure 1), for the period 6, obtained from NCEP/NCAR Reanalysis data set (Kalnay et al., 1996; Kistler et al., 1). Dew point temperature is extracted from specific humidity and air temperature, for 8 and 7 hpa levels. Finally, the Kindex is calculated from air temperatures and dew point temperatures according to the known formula (see, e.g. Jacovides and Yonetani, 199): K = (T 8 T ) T Dew8 (T T Dew ) 7 where T and T Dew are the drybulb and dew point temperatures and the subscripts indicate the pressure level in hpa. K estimates static stability as a combination of 8 hpa thermal lapse rate, 8 hpa moisture and a measure of thickness of moist layer. When K is higher than C over a specific region a thunderstorm event is highly possible (see, e.g. Andersson et al., 1989; Peppler and amb, 1989). The expected weather conditions associated with different values of K are presented in Table I. It is noted that since the calculation of K includes the dew point depression at 7 hpa, dry air at this level leads to a relatively low value of K. owever, thunderstorms may still occur, provided that moisture below this level is high and it is combined with unstable air and lifting. This may be characterized as one of the drawbacks of the Kindex. The statistical methods used are FA and CA, briefly described in the following paragraphs. FA, which essentially is a modification of the standard principal component approach (the two methods are Figure 1. The Mediterranean region.

3 ATMOSPERIC STABIITY IN TE MEDITERRANEAN 363 Table I. The expected weather conditions associated with different values of Kindex. K values ( C) Expected weather < No significant activity 2 Possible steady precipitation; isolated thunderstorms 26 Probable steady precipitation; widely scattered thunderstorms 31 3 Steady precipitation; scattered thunderstorms >3 Steady precipitation; numerous thunderstorms practically equivalent), describes a set of p correlated variables X 1, X 2,..., X p in terms of a smaller number of new uncorrelated indices, elucidating the relationship between the p initial variables (Jolliffe, 1986; Manly, 1986). Each of the p initial variables can be expressed as a linear function of m (m <p) factors, i.e. X i = a i1 F 1 a i2 F 2 a im F m (i = 1,...,p) where F 1, F 2,..., F m are the factors and a i1, a i2,..., a im are the factor loadings, which, in fact, express the correlation between the p initial variables X i (i = 1,..., p) andthem new variables F j (j = 1,..., m), the factors. The values of the factors are called factor scores and they are usually presented standardized. The number m of the retained factors is decided by using various rules (Overland and Preisendorfer, 1982; Jolliffe, 1986) and considering the physical interpretation of the results (see, e.g. Bartzokas and Metaxas, ). A widely used process is the rotation of the axes, which by maximizing some factor loadings and minimizing some others succeeds in a better discrimination among the variables and thus in a better interpretation of the results (Richman, 1986). It has been found that rotation overcomes certain characteristics of the unrotated solutions (dependence on the domain, subdomain stability and sampling errors) that obscure the isolation of the individual modes of variation (Richman, 1986). CA is a clustering technique and classifies n observations of p variables into objectively defined groups, the clusters, such that each cluster is as homogeneous as possible with respect to the clustering variables. At first, a measure of similarity is selected and a decision is made on the type of clustering technique to be used (e.g. a hierarchical or a nonhierarchical one). The type of clustering method for the selected technique is then chosen and, finally, a decision regarding the number of clusters is made. The decision about the number of clusters is made by taking into account the various rules (see, e.g. Yarnal, 1992) but, mainly, by testing various numbers and examining the physical hypostasis of the results, as the reliability of the various statistical rules depends on the singularities of the specific data set (Sharma, 199). In the case of the present work, the squared Euclidean distance is used as a measure of similarity, while the kmeans technique of the nonhierarchical clustering approach is selected. The above technique allows the rearrangement of the observations once they have been classified into a group by optimizing the final classification (Davis and Walker, 1992; Kalkstein et al., 1996). In the present study, Smode FA with varimax rotation (Richman, 1986) is applied on the daily time series of Kindex in order to reduce the dimensionality of the data set. Kmeans CA is then applied on the factor scores time series revealed by FA, in order to group the days presenting a similar spatial distribution of Kindex. For the days classified in each one of the clusters, the mean patterns of Kindex are constructed. In this way, the predominant spatial modes of static stability are revealed. The above procedure, i.e. the application of a data reduction technique prior to CA can be considered as the appropriate one and it has been followed by many researchers in climatological and environmental studies (see, e.g. McGregor and Bamzelis, 199; Kassomenos et al., 3). 3. Results and discussion The first step of the selected statistical methodology, i.e. the application of FA on the daily values of Kindex, leads to 16 factors accounting for 79% of the total variance. FA is applied in the present study only as a data reduction tool and the results (factor loadings and factor scores) are not presented. The second step, i.e. the application of CA on the 16 factor scores time series, leads to 12 clusters. The number of days classified in each of the 12 clusters and the corresponding percentages are shown in Table II. For each of the 12 clusters, the mean patterns of Kindex, the corresponding and hpa mean geopotential height patterns and the intermonthly and interannual variations of cluster frequencies are constructed and presented in Figures Table II. The number of days classified in each one of the 12 clusters and the corresponding percentages. Cluster Number of days Percentage (%) Total 21.

4 364 C. J. OIS CUSTER 1 CUSTER 1 JU Figure 2. Cluster 1 The mean patterns of: Kindex ( C) ( and indicate high and low value centres), hpa geopotential height (gpm) and hpa geopotential height (gpm). The intermonthly variation of cluster frequency (%) and the interannual variation of the yearly number of cases, smoothed (bold line) by using year moving averages with binomial coefficient weights Cluster 1 Cluster 1 is characterized by high K values over central Europe and the Middle East and low K values over North Africa (Figure 2). According to the and hpa patterns (Figure 2, ), a westerly upper air flow prevails over southern Europe and the Mediterranean, forming a ridge over Tunisia and a trough over the eastern Mediterranean. The upper air circulation is mirrored to a surface anticyclone over North Africa being responsible for the low K values there and a surface low over Cyprus and the Middle East connected to the corresponding high K values over the same area. Such patterns appear to be more frequent in winter (Figure 2). The interannual variability of their frequency shows two maxima, the first at about 196 and the second in the middle 199s, and a decrease during recent years (Figure 2) Cluster 2 The pattern of cluster 2 is characterized by low stability over the central and the northeastern Mediterranean region, especially over Asia Minor (Figure 3). This pattern is associated with an upper air trough affecting the whole Mediterranean region and an extended surface low pressure system over south Italy, the southern Balkans and south Asia Minor (Figure 3, ). The K minimum over the western Iberian Peninsula is connected to the anticyclonic circulation prevailing over the same area. The intermonthly variation of cluster 2 shows

5 ATMOSPERIC STABIITY IN TE MEDITERRANEAN 36 CUSTER 2 CUSTER 2 JU Figure 3. As in Figure 2, but for cluster 2. that it prevails during the whole year except summer, but especially during the transitional months of November and March (Figure 3). The interannual variation shows two main maxima, the first one during the early 19s and the second one during the early 199s, and two main minima, the first one in the late 196s and the second one about (Figure 3). The correlation coefficient between cluster 2 time series and the yearly North Atlantic Oscillation (NAO) index (see, e.g. Jones et al., 1997) is found to be.33, statistically significant at the 9% confidence level, indicating that the corresponding pattern may be weakly connected to a positive phase of the NAO. A positive phase of the NAO is associated with the enhancement of the Azores subtropical anticyclone and the subsequent northward displacement and intensification of the jet stream (Wanner et al., 1). In the present case of cluster 2, the anticyclone west of France does not allow the westerlies to prevail over southern Europe, while a low pressure system is formed in the central and the eastern Mediterranean, where cold air masses are transferred from northern Europe. The prevention of the westerlies from blowing over the Mediterranean may be associated with a positive phase of the NAO and this is generally in agreement with the pattern of cluster 2, as (1) the enhancement of the Azores anticyclone is connected to the high static stability over the Iberian Peninsula and (2) the instability maximum over Asia Minor may be connected to the fact that, when a positive phase of NAO prevails negative air temperature anomalies dominate in the middle troposphere over Asia Minor and the Middle East, contributing to low static stability in those regions. The pattern of cluster 2 may

6 366 C. J. OIS also be connected to the Western Mediterranean Oscillation (WeMO), which has been found to be responsible for precipitation variability in the eastern Iberian Peninsula (MartínVide and opezbustins, 6) Cluster 3 The cluster 3 pattern presents two main maxima; the first one over NW Africa and the south Iberian Peninsula and the second one over central and eastern Europe and Asia Minor. Negative K values prevail west of France (Figure 4). The geopotential height patterns (Figure 4, ) show that there is anticyclonic activity in west Europe and cyclonic activity in the central and the eastern Mediterranean and the Middle East. At the hpa level, the anticyclone west of France is responsible for the northerly flow over central Europe transferring cold air masses from northern Europe towards the Mediterranean. The intermonthly variability seems to be generally low (Figure 4), while the interannual variability shows that pattern of cluster 3 was more frequent during the 19s and the early 196s and about 199 (Figure 4) Cluster 4 Cluster 4 can be characterized as a summer one, as it generally prevails fromapril to September and especially during the highsummer months of July and August (Figure ). The spatial distribution of K (Figure ) shows a minimum over the sea and two main maxima, the first one over NW Africa and the second one over CUSTER 3 7 CUSTER 3 6 JU Figure 4. As in Figure 2, but for cluster 3.

7 ATMOSPERIC STABIITY IN TE MEDITERRANEAN CUSTER 4 6 CUSTER 4 JU Figure. As in Figure 2, but for cluster 4. Asia Minor. The and hpa geopotential height patterns (Figure, ) show an upper air trough over the Balkans and a northerly flow in the lower troposphere over the central and the eastern Mediterranean. The K minimum over the sea may be attributed to: (1) the high static stability during the warm period of the year, associated with the cooling of the lowest atmospheric layers because of their contact with the cool seasurface (relatively to the air above it); this surface cooling leads to downward sensible heat fluxes that make the lowest atmospheric layers very stable, and (2) the cold and dry air masses in the lowest atmospheric layers, which are transferred from central Europe over the central and the eastern Mediterranean Sea via the northerly flow. The two instability maxima are mainly of a thermal origin, as both areas are characterized by intense land heating during summer. This land heating along with the presence of cold air masses in the middle troposphere, especially over Asia Minor where this is frequent in summer (Spanos et al., 3), is responsible for the low stability and the corresponding high K values (above C). The very high values of K (above 2 C) over Asia Minor can be attributed to the fact that the upper air masses are cold due to the presence of the hpa trough, while the surface air masses are humid due to their passage over the Black Sea. 3.. Cluster The pattern of cluster is characterized by high K values over almost the whole Mediterranean region, especially over the land (Figure 6). Very high values

8 368 C. J. OIS 2 CUSTER 1 CUSTER 8 6 JU Figure 6. As in Figure 2, but for cluster. (above C) are shown over a broad area north of the Mediterranean Sea and over the Atlas mountains. This pattern dominates during late spring and early summer, especially in May and June (Figure 6). It is known that during late spring and early summer the intense land heating along with the latent instability associated with the high humidity of the lower troposphere and the persistence of relatively cold upper air masses may sometimes lead to violent atmospheric phenomena (e.g. severe thunderstorms). The geopotential height patterns (Figure 6, ) show that height gradient is low in both levels. At hpa, a weak westerly flow prevails, while the hpa pattern is generally characterized by high geopotential heights over the sea and low heights over the land. The interannual variation of cluster (Figure 6) shows a continuous decrease since the middle 197s. Specifically, the frequency of cluster decreases from about 8 cases per year in the middle 197s to about cases per year in 6. This decrease implies a continuous change in the temperature and/or humidity characteristics of the middle or the lower troposphere during late spring and early summer, possibly associated with the recent climate change Cluster 6 Cluster 6 can be characterized as a summer one, as it prevails mainly from June to September, especially during the highsummer months of July and August (Figure 7). As it has been already mentioned, this is also the case for cluster 4. The difference between clusters 4 and 6 refers to the position of K minimum. Specifically, in cluster 4, the minimum values are shown along the Mediterranean Sea axis and mainly over the

9 ATMOSPERIC STABIITY IN TE MEDITERRANEAN 369 CUSTER 6 8 CUSTER JU Figure 7. As in Figure 2, but for cluster 6. central and the eastern Mediterranean Sea (Figure ), while in cluster 6, the minimum is restricted in the southeastern Mediterranean and it is deeper (Figure 7). This is mainly due to the fact that, in the case of cluster 4, the northerly flow in the lower troposphere (Figure ) prevails over the central and the east Mediterranean, while in cluster 6, the northerly flow prevails over the eastern Mediterranean only (Figure 7). The cluster 6 pattern is associated with the blowing of the etesian winds (see, e.g. Metaxas and Bartzokas, 1994; Ziv et al., 4), which contribute to very low humidity values in the lower troposphere, as they become katabatic during their passage over the mountains of Asia Minor. The interannual march of cluster 6 (Figure 7) shows that its frequency is generally lower during the second half of the study period Cluster 7 The pattern of cluster 7 shows high K values over the western Mediterranean and the Middle East and low values over the eastern Balkans, the Aegean Sea and the Black Sea (Figure 8). This spatial distribution is associated with cyclonic activity over the western Mediterranean and anticyclonic activity over the eastern Balkans and the Black Sea (Figure 8, ). The seasonal distribution of cluster 7 presents maxima in October and March (Figure 8). The spring maximum is in agreement with the results of Kostopoulou and Jones (7) regarding the frequency of an anticyclonic pattern similar to the hpa pattern of Figure 8. The interannual march of cluster 7 frequency (Figure 8) does not present any considerable longterm changes during the period under study.

10 37 C. J. OIS CUSTER 7 CUSTER 7 JU Figure 8. As in Figure 2, but for cluster Cluster 8 Cluster 8 is characterized by high K values over the Iberian Peninsula and Asia Minor and low values over central Europe (Figure 9). According to Figure 9 and, a blocking anticyclone, located over central Europe, is responsible for the high stability over this area. The upper air trough over the eastern Mediterranean and the associated low surface centred over Cyprus are responsible for the high instability over Asia Minor. The instability maximum of the Iberian Peninsula and NW Africa may be attributed to the corresponding low gradient of geopotential height and the high humidity in the lower troposphere. The seasonal distribution of cluster 8 shows a maximum in October (Figure 9), which is in agreement with the seasonal distribution of blocking frequency in the European sector (D Andrea et al., ; Trigo et al., 4). The interannual variation presents remarkable fluctuations during the last years (Figure 9) Cluster 9 Cluster 9 can be characterized as one of the typical winter clusters, as it prevails during the period November March (Figure ). Its pattern shows an instability maximum over the Aegean Sea, the Black Sea and Asia Minor (Figure ). According to the geopotential height patterns (Figure, ), there is an upper air trough affecting the central and the eastern Mediterranean and a surface depression centred over the southern Balkans. The instability maximum over Asia Minor is

11 ATMOSPERIC STABIITY IN TE MEDITERRANEAN 371 CUSTER 8 CUSTER 8 JU Figure 9. As in Figure 2, but for cluster 8. due to the warm and humid air masses in the lower troposphere being transferred from the warm seasurface via the southerly flow and the cold air masses in the middle troposphere associated with the upper air trough. In Figure, it is shown that there is a remarkable increase in the frequency of cluster 9 since the early 199s. 3.. Cluster In cluster, the instability centre is located over the western Mediterranean and the Atlas Mountains and the stability centre is located over the eastern Mediterranean and the southern Balkans (Figure 11). The main circulation characteristic of this cluster is an anticyclone centred over the northern Balkans (Figure 11). This anticyclone and the associated upper air ridge (Figure 11) are responsible for the high stability over the Balkans and the eastern Mediterranean. On the other hand, the upper air trough over the west Mediterranean and the land heating in NW Africa provoke the instability maximum over the Atlas Mountains. Such conditions favour the formation of the socalled Saharan depressions (see, e.g. Prezerakos et al., 199). The seasonal distribution shows that the pattern of cluster prevails during the whole year except summer, while the highest frequency occurs in October (Figure 11). According to the interannual variability (Figure 11), cluster is more frequent during the second half of the period under study Cluster 11 Cluster 11 is one of the typical winter clusters, as its intermonthly variation shows that it prevails from November

12 372 C. J. OIS CUSTER 9 CUSTER 9 2 JU Figure. As in Figure 2, but for cluster 9. to March (Figure 12). As already noted, this is also the case for clusters 1 and 9. According to the spatial distribution of K, an extended area of high positive values comprises central Europe and the western Mediterranean, while negative values are shown over the eastern Mediterranean and the Middle East (Figure 12). The cold upper air masses obviously cover almost the whole area under study except the eastern Mediterranean and the Middle East where warm upper air masses prevail. The geopotential height patterns are characterized by an upper air trough over the western Mediterranean and a surface depression over the Tyrrhenian Sea (Figure 12, ). Such conditions are associated with a southwesterly flow over Greece provoking high precipitation amounts on the windward slopes of the Pindus Mountains (Metaxas and Kallos, 198). The anticyclone that is centred east of the Black Sea transfers dry air masses from the Middle East towards the eastern Mediterranean, enhancing the static stability over this area. The main characteristic of the interannual march of cluster 11 (Figure 12) is the double maximum of the 197s Cluster 12 The pattern of cluster 12 is characterized by a high Kindex values zone along the northern Mediterranean coasts and a low Kindex values area north of the Black Sea (Figure 13). The high stability over the area north of the Black Sea is attributed to the presence of an anticyclone over the Ukraine, while the high values zone shows the presence of cyclonic activity along the northern Mediterranean coasts (Figure 13, ). The intermonthly variation of the frequency (Figure 13) shows

13 ATMOSPERIC STABIITY IN TE MEDITERRANEAN 373 CUSTER CUSTER JU Figure 11. As in Figure 2, but for cluster. maxima during the transitional seasons of spring and autumn, while the interannual variation (Figure 13) indicates a general decrease in the predominance of cluster 12 since the middle 198s Other characteristics of the 12 clusters The distribution of the cluster frequencies for each one of the 12 months is presented in Figure 14. In winter months, all clusters appear to be relatively frequent except clusters 4, and 6. In April, May and June, cluster is the most frequent, as atmospheric instability over the continental areas is highest during late spring and early summer. In July and August, clusters 4 and 6 are also very frequent, as both patterns correspond to typical summer conditions. From September to November the frequencies of the typical summer clusters 4 and 6 gradually decrease, while the frequencies of the cold period clusters gradually increase. The maximum and mean values of the duration of the 12 clusters are presented in Table III. It is shown that clusters and 6 last longer (on average longer than two days) than the other clusters. This seems logical, as cluster, which dominates in late spring and early summer, is connected to the persistence of cold upper air masses over the continental areas, while cluster 6, which dominates in summer, corresponds to one of the most persistent summer circulation types, associated with the blowing of the etesian winds. The cluster frequencies for the day that follows the appearance of each one of the 12 clusters are presented in Figure. It can be seen, that clusters and 6 are characterized by the highest persistence (higher than %). This is in agreement with the fact that they present

14 374 C. J. OIS CUSTER 11 CUSTER 11 JU Figure 12. As in Figure 2, but for cluster 11. Table III. The maximum and mean values of the duration of the 12 clusters. Cluster Maximum duration (number of days) Mean duration (number of days) the highest mean durations (Table III). As it concerns the succession between the clusters, it is generally shown that: cluster 4 is frequently followed by cluster 6 (both correspond to typical summer conditions), cluster 6 is followed by clusters 4 and, cluster 7 is followed by clusters 11 and 12, cluster 8 is followed by cluster, cluster 9 is followed by cluster 1, and, finally, cluster is primarily followed by clusters 7 and Conclusions In the present work, the main patterns of atmospheric static stability in the Mediterranean region are revealed and their intermonthly and interannual variabilities are examined. According to the results, the following main conclusions can be derived.

15 ATMOSPERIC STABIITY IN TE MEDITERRANEAN 37 CUSTER 12 7 CUSTER 12 6 JU Figure 13. As in Figure 2, but for cluster 12. The 12 patterns revealed correspond to 12 distinct circulation structures, as it is evident from the mean geopotential height patterns constructed for the middle and the lower troposphere. The surface cyclonicity or anticyclonicity seems to be a significant factor controlling the static stability in the lower troposphere. Some of the 12 patterns are also associated with the thermal and humidity characteristics of the Earth s surface (sea or land). In winter, the upward sensible and latent heat fluxes over the sea surface and the surface air cooling over the land control the static stability in the lowest tropospheric layers. The opposite is valid in summer, when the surface air cooling over the sea and the surface air warming over the land affect the static stability significantly. The seasonal distributions of the frequency of the 12 patterns depend on the seasonal variability of (1) the specific circulation types prevailing over the Mediterranean region and (2) the thermal and humidity characteristics of the Earth s surface. According to the seasonality of the above factors, three clusters prevail mainly in winter, three clusters prevail mainly in summer and, the remaining six clusters present a relatively even seasonal distribution, but they are more frequent in the transitional seasons. The interannual variations of the frequency of the 12 patterns reveal significant information about the interannual variability of: (1) some specific atmospheric circulation types, and, (2) the temperature and humidity characteristics of the lower and the middle troposphere. Some of them may be considered as indications of oncoming climate change.

16 376 C. J. OIS 4 UARY 4 RUARY C 4 I E JUY UST Figure 14. The frequency distribution (%) of the 12 clusters for each one of the 12 months. The xaxis represents the 12 clusters.

17 ATMOSPERIC STABIITY IN TE MEDITERRANEAN TEMBER 4 OBER EMBER 4 EMBER Figure 14. (Continued). 6 CUSTER 1 6 CUSTER CUSTER 3 6 CUSTER Figure. Succession of the clusters: the frequency distribution (%) for the day that follows the appearance of each one of them. The xaxis represents the 12 clusters.

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