A Conceptual M odel for the Identification of Active Red Sea Trough Synoptic 1

Transcription

1 A Conceptual M odel for the Identification of Active Red Sea Trough Synoptic 1 Events over the southeastern M editerranean Simon O. Krichak 1, Joseph S. Breitgand 1, Steven B. Feldstein Department of Geophysics and Planetary Sciences, Raymond and Beverly Sackler 11 Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel 12 2 Department of Meteorology, The Pennsylvania State University, University Park, 13 USA Contributing author: 17 Simon O. Krichak ( shimon@cyclone.tau.ac.il) 18 Submitted to Journal of Applied Meteorology and Climatology 21 October 15, Revised 07 February,

2 Abstract 1 A phenomenon characterized by a tongue of low-pressure extending northward from 2 the southern Red Sea (Red Sea Trough, RST) toward the eastern Mediterranean (EM) 3 is analyzed. Generally, the RST is associated with hot and dry weather, resulting 4 from east-southeasterly flows in the lower troposphere. In some cases, the RST is 5 found to be accompanied by an upper tropospheric trough extending from the north 6 over the eastern Mediterranean. Such conditions are associated with unstable 7 stratification, favoring the development of mesoscale convective systems. This kind 8 9 represents a serious threat to human society populated in the northeastern Africa 10 southeastern Mediterranean region being in some cases associated with devastating 11 floods. 12 In this study, a conceptual model of the ARST phenomenon is discussed, and then an 13 algorithm for the identification of ARST events is presented. The identification 14 algorithm has been applied to a multi-year NCEP/NCAR Reanalysis data archive for 15 both Red Sea Trough (RST) and ARST events. Based on the results of a composite 16 analysis of several different atmospheric flow parameters associated with ARST 17 events, the key features associated with ARST events are identified. The results from 18 the analysis of the composite patterns support the suggestion that high amounts of 19 moisture transported from tropical Africa in the form of an atmospheric river to the 20 Red Sea-eastern Mediterranean play a key role in determining the intensity of the 21 ARST events

3 1 Introduction 1 Among the various synoptic processes that take place over northeastern Africa and the 2 southeastern Mediterranean region (MR) which contribute significantly to 3 precipitation, an important place is occupied by a phenomenon characterized by a 4 tongue of low-pressure extending northward from the southern Red Sea toward the 5 eastern Mediterranean (EM) (Ashbel, 1938; El-Fandy, 1948), i.e. the Red Sea Trough 6 (RST) system. The RST attains its largest amplitude in the lower troposphere, and is 7 strongly influenced by the topography of the surrounding region (El-Fandy, 1950; 8 Krichak et al., 1997a,b). The RST is regarded as an extension of the African 9 Monsoon trough, a large-scale subtropical-tropical low-pressure thermal synoptic 10 system (El-Fandi, 1948). Interannual variation in the intensity and location of the 11 African Monsoon Trough may be linked to those of the Inter-Tropical Convergence 12 Zone, which is located between 5 N 10 N in the winter and 20 N 25 N in the 13 summer (at 0 E). An algorithm for automatic identification of the RST events, 14 based on grid-point data on the 1000 hpa (H-1000) isobaric surface, has been 15 suggested and implemented by Tsvieli and Zangvil (2005). 16 Generally, the RST over the southeastern MR is associated with hot and dry weather, 17 resulting from east-southeasterly flows in the lower troposphere. Such conditions 18 correspond to a (Kahana et al. 2002). In some cases, the RST is 19 found to be accompanied by an upper tropospheric trough extending from the north 20 over the EM. Such conditions are associated with unstable stratification, favoring the 21 development of mesoscale convective systems. This kind of RST has been defined as 22 Dayan and Sharon 1981; Goldreich 2003; Saaroni et al., 1998; 23 Kahana et al., 2002; Kahana et al. 2004; Ziv et al., 2005; Tsvieli and Zangvil 2005). 24

4 Although ARST events usually lead to reasonably intense precipitation over the 1 region, in some quite seldom situations, they are associated with heavy, torrential 2 rains and devastating floods. The ARST is a major cause of flash floods in the arid 3 Southern Levant (Ashbel, 1938; El-Fandi, 1948; Dayan and Sharon, 1981; Kahana et 4 al., 2004). Another synoptic-scale process, referred to as a Syrian low, is 5 characterized by a Mediterranean mid-latitude cyclone that deepens while 6 approaching Syria (Kahana et al. 2004). The upper and lower level troughs that are 7 found to be associated with Syrian lows are located somewhat to the east of those 8 associated with ARSTs. As with ARSTs, Syrian lows often coincide with intense 9 rains and floods in the Southern Levant region. However, the frequency of Syrian 10 lows is significantly lower than that of ARSTs (Kahana et al., 2004). The current 11 analysis is limited to the investigation of the ARSTs. 12 The ARSTs and associated extreme rainfall are often not well predicted, apparently 13 due to several factors such as a lack in our understanding of the physical process, the 14 limited amount of observational data for the area, as well as the small horizontal scale 15 (5-10 km) of individual mesoscale convective systems (Cotton 1990). 16 Kahana et al. (2004) have suggested that a synoptic approach be used for the 17 identification of the ARST-type synoptic events that cause intense floods in the Negev 18 Desert. Their methodology is based on the evaluation of several atmospheric 19 parameters, such as the meridional wind/zonal wind ratio, the relative vorticity and 20 wind speed on the 500-hPa surface over the Negev Desert, the hPa 21 temperature gradient over the northern Red Sea, the 500-hPa geopotential height over 22 upper Egypt, and the sea level pressure gradient between 25 N and 30 N. As the 23 focus of Kahana et al. (2004) was on the identification of the synoptic conditions that 24 cause major floods over the Negev Desert during , they used a relatively 25

5 coarse resolution data set. As a result, their study was limited in the extent to which it 1 could enhance our understanding of the physical mechanisms that cause the ARST 2 formation. 3 Our methodology for identifying ARSTs takes a broader physical perspective since it 4 takes into account processes that occur both in the tropics and midlatitudes, and 5 includes the impact of moist processes. The approach is based on consideration of the 6 ARST-associated extreme precipitation events as being similar to those over the 7 eastern Pacific and west coast of North America caused by the intrusions of moist 8 tropical air masses. The precipitation events have been attributed to the effect of 9 atmospheric rivers (AR), i.e. narrow (<1000 km wide) and relatively long (>2000 km) 10 bands of enhanced poleward water vapor flux (Zhu and Newell 1998; Ralph et al , 2005, 2006; Bao et al. 2006; Neiman et al. 2008), characterized by high values 12 of the vertically integrated water vapor content (i.e., precipitable water, PRWT). A 13 critical PRWT value of 20 mm (kg of water per square meter) has been suggested for 14 the identification of AR events (Zhu and Newell 1998). Also, in the Mediterranean 15 region, the intrusion of moist tropical air masses has been detected. (Alpert and Shai- 16 el 1994; Turato et al. 2004; Pinto et al. 2009; Krichak et al. 2004). The AR is typically 17 viewed in the framework of warm conveyor belt systems (Browning 1990; Carlson warm sector where there is a strong meridional energy transport. Its development is 20 typically characterized by extremely high values of convective available potential 21 energy (CAPE) (e.g. Emanuel 1993). 22 In this study, a conceptual model of the ARST phenomenon is proposed, and then an 23 algorithm for the identification of ARST events is presented. The model uses physical 24 variables that represent air masses originating over the tropics (PRWT and CAPE) in 25

6 addition to those associated with midlatitude dynamical processes appears. As 1 discussed above, this approach allows for a broader physical perspective than those 2 used previously, since it takes into consideration both the tropical and midlatitude 3 processes. The identification algorithm has been applied to a multi-year data archive 4 for both RST and ARST events. Based on the results of a composite analysis of 5 several different atmospheric flow parameters associated with ARST events, the key 6 features of ARST events are identified Data used 8 The primary aim of the analysis is to propose an approach for the investigation of the 9 physical mechanisms responsible for ARST development in the current climate and 10 that expected in future climate conditions based on the climate change projections 11 available from multiyear simulations with coupled atmosphere-ocean global climate 12 models typically characterized by quite coarse ( km) space resolution. 13 To perform this research, we use six-hourly gridded data from the multiyear dataset of 14 the National Centers for Environmental Prediction National Center for Atmospheric 15 Research (NCEP-NCAR) Reanalysis Project (NNRP) (Kalnay et al., 1996) for The NNRP data are available for the entire globe with 2.5º x 2.5º spatial and 6-17 hour temporal resolution. The spatial resolution of the data archive appears to be 18 sufficient for the aim of this investigation. 19 The NNRP archive is constructed through a consistent assimilation and forecast 20 model procedure, which allows for the incorporation of all available observations. 21 The data assimilation system includes the 28-level global spectral model with T62 22 horizontal resolution. The precipitation data are obtained from the model runs. It may 23 be noted here that although the application of gridded reanalysis based data for 24 climate evaluations over the euro-mediterranean region has became a widely accepted 25

7 strategy, there are significant limitations to the data because of important changes in 1 the global observation system during the 20 th century, as well as global warming 2 associated regional trends in temperature, water vapor content, kinetic energy, etc.. 3 Nevertheless, for the short time scale RST and ARST events, these limitations are for 4 the most part not relevant. These data have been used to CAPE according to the 5 algorithm of Emanuel (1993) Conceptual model of ARST and algorithm for its identification 7 We consider the ARST as a synoptic-scale, lower tropospheric RST centered over the 8 the Red Sea EM accompanied by an AR system forming over the same region, 9 arising as a consequence of the establishment of a mid-troposphere cut-off low or 10 intense trough over the north Africa-southeastern Mediterranean region. In such 11 situations, taking place during the cool (typically October-April) Mediterranean 12 season, the ARST development may be seen as arising from the interaction between a 13 lower tropospheric northward inflow of warm, humid air of tropical origin and an 14 mid-tropospheric southward inflow of cold and dry air from midlatitudes. The low 15 level flow from the tropics is characterized by mesoscale features associated with 16 unstable stratification and a high concentration of air moisture, all embedded within 17 the larger synoptic-scale pattern. Such synoptic conditions are typically characterized 18 by high amounts of energy available for convection (i.e. high convective available 19 potential energy, CAPE) which may be directly related to the high lapse rate values in 20 the lower troposphere; indicating greater potential for severe weather. 21 In accordance with this model of the phenomenon, we suggest an algorithm for the 22 identification of ARST events. This algorithm is based on the analysis of three- 23 dimensional atmospheric grid-point data over the northeast Africa - Red Sea region. 24 For the lower troposphere case, i.e., RST events, the algorithm is quite similar to that 25

8 of Tsivieli and Zangvil (2005). According to the new algorithm for ARSTs, the 1 development takes place over the Red Sea-EM when a RST is accompanied by two 2 additional flow features. These are (1) high amounts of CAPE due to the southward 3 mid-troposphere flow over the EM and (2) tropical moisture transport in the form of 4 an atmospheric river type system transporting warm air from equatorial Africa toward 5 the EM in narrow elongated bands with enhanced amounts of air moisture. The 6 suggested procedure for ARST identification is based on the simultaneous evaluation 7 with the gridded data archive of the following variables; 1000-hPa geopotential height 8 (H1000), 500-hPa geopotential height (H500), CAPE, and PRWT over the EM and 9 northeast Africa. The algorithm is applied for each 6 hr time level of the NNRP data 10 during The occurrence of ARST events is defined to have taken place 11 when 12 (1) the CAPE or PRWT exceed particular threshold values over a target area that 13 covers the EM (28 32 N, E) (see Fig. 1a), 14 (2) a northward oriented 1000-hPa trough (H1000) extends from north-eastern Africa 15 to the EM within a target area of ( N, E) (see Fig. 1b) and 16 (3) a mid-troposphere 500-hPa trough (H500) that is detected over one of the 17 following two target areas (25 30 N, E or N, E) (see Fig. 18 1b); 19 The third condition corresponds to the southward penetrating mid-tropospheric 20 trough and/or a cut-off low over the EM. The size and location of the H1000 target 21 area is similar to that used by Tsvieli and Zangvil (2005), which allows for a 22 comparison with their results on RST formation. The following area-averaged critical 23 values of CAPE and PRWT are adopted based on results of a synoptic analysis 24

9 focusing mainly on the data for the autumn-early winter season CAPE_crit = 130 (J 1 kg 1 ), PRWT_crit = 21mm. 2 It must be indicated here that these critical values as well as the coordinates of the 3 chosen target area must be seen only as tentative, in order to allow for the evaluation 4 of our conceptual model. It is clear that the implementation of our approach for the 5 purpose of weather prediction would require a more sophisticated strategy for the 6 determination of the critical values Results 8 Evaluation of the efficiency of the new algorithm is complicated by a lack of reliable 9 synoptic analyses of ARST events during most of the analysis period. The dates 10 identified with the algorithm are given in Table 1. According to data by D. Edry 11 (personal communication) for the time period beginning in 1980, the algorithm allows 12 for the identification of about 90% of his list of ARST events in Israel. Thus, a small 13 fraction of ARST events are not detected, most likely due to the already noted 14 simplistic choice of the critical parameter values for the procedure. Nevertheless, the 15 number of ARST cases is sufficient for an analysis of the phenomenon. 16 An analysis of the results has been performed. It is found that the maximum 17 frequency of RST events (days month 1 ) over northeastern Africa is October (2.6), 18 November (4.0), December (5.3) and January (4.9). These frequencies are somewhat 19 different from those of Tsvieli and Zangvil (2005), who obtained values of 10.2, 7.9, , and 3.4, respectively. The differences in the results may be attributed to the sizes 21 and locations of the target areas, as well as (including the effect of long-term trends) 22 to the time period (40 years versus 11 years in Tsvieli and Zangvil (2005) of the 23 analysis. 24

10 To evaluate the processes that drive the development of ARST events, composite 1 calculations were performed using all 48 ARST events for of the multi- 2 year dataset. The H1000 and H500 patterns (Figs. 2a, b) are characterized by 3 northward- and southward-oriented troughs, respectively. The composite PRWT 4 pattern for the 44 ARST events (Fig. 2c) indicates a broad zone of high PRWT values 5 that extend from tropical Africa along the Red Sea coast to the EM. A narrow tongue 6 with high PRWT values appears to be among the main factors that determine the 7 intensity of the precipitation over the Red Sea basin during the ARST events. The 8 composite pattern for air temperature lapse rate (Fig. 2d) demonstrates the existence 9 of a zone with high instability in the lower troposphere over the northern part of the 10 Arabian Peninsula and the eastern Mediterranean Middle East forming as a 11 consequence the cold air masses transport in the mid-troposphere. 12 To distinguish between strong and weak ARTS events, the ARST dates have been 13 identified, and then sorted into two groups comprised of the 24 highest and lowest 14 area averaged PRWT values over the Fig.1b target area. The H1000, H500, PRWT, 15 and CAPE patterns for the two cases are shown in Figs.3a-d, and 4a-d, respectively. 16 The events with high PRWT values are associated with a well-developed lower 17 tropospheric trough, i.e., a RST (Fig. 3a), a mid-tropospheric cut-off low system over 18 the east-central MR (Fig. 3b), strong northward air moisture transport from tropical 19 Africa into the eastern Mediterranean (Fig. 3c), and a narrow zone with high CAPE 20 values along the Red Sea (Fig. 3d). This contrasts the low PRWT ARST cases where 21 the southward-oriented upper air trough is much weaker (Fig. 4a-d). These differences 22 between the weak and strong ARST events reveal the importance of the southward 23 middle and upper tropospheric flow from midlatitudes for the intense ARST events. 24 The fact of large CAPE values (Figs. 3d, 4d) associated with the ARST events 25

11 supports the suggestion (also Krichak and Alpert 1998) that high amounts of air 1 moisture play a key role in determining the intensity of the ARST events over 2 northeastern Africa and the eastern MR Discussion 4 The ARST phenomenon represents a real threat to human society populated in the 5 northeastern Africa southeastern Mediterranean region. ARST events are often 6 associated with devastating floods. Among such cases is, for example, that of 7 November which was accompanied by heavy floods in Egypt, Israel, Italy, 8 France and other countries of the region (e.g. Buzzi and Tartaglione, 1995; Lionetti ; Krichak and Alpert, 1998). In that event more than 500 people lost their lives 10 and large areas were inundated (Obasi (1997). Also, the case characterized by 11 November 25, 2009 floods in Jeddah on the western coast of the Red Sea in Saudi appears to be a 14 consequence of an ARST event. 15 Since the studies of Ashbel (1938) and El-Fandi (1948), the RST has been a well 16 known feature associated with a high risk of torrential rains over northeastern Africa. 17 The results from the present analysis support the suggestion that, similar to ARs, 18 narrow elongated bands with enhanced amounts of air moisture transported from 19 tropical Africa to the Red Sea-EM play a key role in determining the intensity of the 20 ARST events over the MR. The physically-based methodology presented here has 21 been developed to allow for the investigation of the physical mechanisms responsible 22 for the formation of ARST events, with the additional aim using this methodology to 23 evaluate the role of ARSTs in the climate of the future based on the climate change 24 projections available from multiyear simulations with coupled atmosphere-ocean 25

12 global climate models. For this reason, we have used the coarse resolution of the 1 NNRP data set. It appears reasonable to apply a similar methodology in weather 2 forecasting based on the results from numerical weather prediction models, It must be 3 pointed out, however, that additional evaluation of the methodology based on 4 significantly higher resolution data (than that available in the NNRP) will be 5 necessary for its possible implementation in weather prediction. The future realization 6 of this work using the data from the recently released NASA Modern-Era 7 Retrospective analysis for Research and Applications (MERRA) (Rienecker et al ) appears to be promising. 9 Acknowledgments 10 The financial support for this work has been provided by the United States-Israel 11 Binational Science Foundation (BSF) under research grant No and the 12 United States National Science Foundation (NSF) under grants ATM and 13 AGS Discussions supported through the European Cooperation in Science 14 and Technology (COST) Earth System Science and Environmental Management 15 (ESSEM) Action ES0905 "Basic concepts for convection parameterization in weather 16 forecast and climate models" and European Science Foundation (ESF) Program 17 "Mediterranean climate Variability and predictability" (MedCLIVAR) as well as a 18 partial support from a research grant for from the Water Authority of the 19 Ministry of Infrastructures of Israel are also acknowledged. Finally, we acknowledge 20 the use of the NCEP Reanalysis data provided by the NOAA/OAR/ESRL PSD, 21 Boulder, Colorado, USA from their website at The 22 CAPE values have been computed using a NCL script by C. Bruyere 23 ( The helpful comments by 24 two reviewers are greatly appreciated. 25

13 2 3 REFERENCES Alpert, P., and Y. Shay-El,1994: The moisture source for the winter cyclones in the 7 Eastern Mediterranean. Israel Meteorological Research Paper 5: Ashbel, D., 1938: Great floods in Sinai Peninsula, Palestine, Syria and the Syrian 10 desert, and the influence of the Red sea on their formation. Quarterly Journal of the 11 Royal Meteorological Society 64: Bao, J.-W., S. A. Michelson, P.J. Neiman, F. M. Ralph, and J. M. Wilczak, 2006: 14 Interpretation of enhanced integrated water vapor bands associated with extratropical 15 cyclones: Their formation and connection to tropical moisture. Mon. Wea. Rev., 134, , doi: /mwr Browning, K.A., 1990: Organization of clouds and precipitation in extratropical 19 cyclones. In: Newton CW, Holopainen EO (eds) Extratropical Cyclones. The Erik 20 Palmén Memorial Volume. American Meteorological Society, Boston, MA, pp Buzzi, A., and N. Tartaglione 1995: Meteorological modeling aspects of the Piedmont flood, MAP Newslett., 3, Carlson, T.N., 1991: Mid-latitude weather systems. Harper Collins, London, p Cotton, W.R., Storms, Geophysical Science Series Vol. 1 ASTeR Press, Fort 29 Collins, 158 pp Dayan, U., and D. Sharon, 1981: Meteorological parameters for discrimination 32 between widespread and spotty storms in the Negev, Israel Journal of Earth Science 33 29: El-Fandy, M.G., 1948: The effect of Sudan monsoon low on the development of 36 thundery conditions in Egypt, Palestine and Syria. Quarterly Journal of the Royal 37 Meteorological Society 74: El-Fandy, M.G., 1950: Effects of topography and other factors on the movement of 40 lows in the Middle East and Sudan. Bulletin of theamerican Meteorological Society 41 10: Emanuel, K.A., 1993: Atmospheric Convection", Oxford University Press Eckhardt, S., A. Stohl, H. Wernli., P. James, C. Forster, and N. Spichtinger, 2004: A year climatology of warm conveyor belts. J Clim 17 (17):

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16 Table and Figure captions 3 Table 1. ARST cases identified based on the six hourly NNRP data for Figure 1 Map of study region for the objective identification of RST situations (a) 7 target areas for ARST identification according to locations of the troughs in H and H500 patterns; (b) target areas for ARST identification according to mean PRWT 9 and CAPE values. 10 Figure 2 Composite patterns for all (44) cases with ARST (a) H-1000 (hpa, contour 11 interval 10 hpa), (b) H-500 (hpa, contour interval 20 hpa), (c) precipitable water (mm, 12 kg of water per square meter) PWRT and (d) hpa lapse rate (in º km 1 ) for 13 the ARST cases identified. 14 Figure 3 Same (except for (d) as in Figs. 2 but for composite patterns for 24 cases 15 with highest PRWT (a) H-1000 (b) H-500 (c) PRWT, (d) CAPE [(J kg 1 ) (contour 16 interval 200)]. 17 Figure 4 Same as in Figs. 3 but for 24 cases with lowest PRWT

17 1 2 Tables 3 Table 1. ARST cases identified based on six-hourly NNRP data for NN Year Month Day Hour CAPE PRWT

18

19 1 2 Figures

20 1 2 Figure 1 Map of study region for the objective identification of RST situations (a) 3 target areas for ARST identification according to locations of the troughs in H and H500 patterns; (b) target areas for ARST identification according to mean PRWT 5 and CAPE values

21 1 2

22 1 2 Figure 2 Composite patterns for all (44) cases with ARST (a) H-1000 (hpa, contour 3 interval 10 hpa), (b) H-500 (hpa, contour interval 20 hpa), (b) precipitable water 4 (mm, kg of water per square meter) PWRT and (d) hpa lapse rate (in º km 1 ) 5 for the ARST cases identified. 6

23

24 1 2 3 Figure 3 Same (except for (d) as in Figs. 2 but for composite patterns for 24 cases 4 with highest PRWT (a) H-1000 (b) H-500 (c) PRWT, (d) CAPE [(J kg 1 ) (contour 5 interval 200)]. 6

25

26 1 2 3 Figure 4 Same as in Figs. 3 but for 24 cases with lowest PRWT