The squall line of 21-22 August 1992, documented during the HAPEX-Sahel campaign, is simulated using the regional atmospheric model (MAR). The simulated results are compared to observational data. The aim of this work is both to test the capacity of this model to reproduce tropical disturbances in West Africa and to use this model as a meteorological one. It allows simulating high moisture content in the lower layers. The MAR simulates well updrafts whereas downward currents are neglected. This result may be due to convective scheme used to parameterize the convection in the model. The forecast of stability indexes used to define violent storms shows that the model is able to reproduce the squall line. Despite some differences with the observational data, the model shows its ability to reproduce major characteristics of the mesoscale convective disturbances.
The climate in West Africa is characterized by high variability over a wide range of scale. The most striking example is the transition from a wet period during 1950- 1968 to a period of rainfall deficit from the early 1970s [1,2]. The drought caused starvations and destabilized the local economy based on agricultural resources. At this effect, numerous studies were undertaken to both establish the cause and to identify the main mechanisms of the climate variability in that region. For instance, some studies [3,4] demonstrated that the main cause of the interannual variability of the rainfall was related to the number of convective systems. These disturbances represent extreme weather events in the Sahel region where they account for more than 90% of the rainfall [5]. For a better study of these phenomena, a tool seems necessary to specifically address the major processes and integrate information from a large scale. Regional climate models seem well suited for this experiment.
Some studies of the extreme weather events using regional climate models were successfully conducted. For instance, the simulation of the African wave disturbances during 8-15 August 1988 [6] showed that the dynamics and the physics of the model used seems to be more important than the lateral boundary conditions. The extreme events of temperature, storm and thunderstorms in Belgium, and the precipitations over the Lake Victoria in the Central Africa were also investigated [7] with the regional atmospheric model (hereafter MAR). The frontal systems, the development and the deepening of the depressions were reproduced successfully during the events of winter 1990 in Europe. The refinement of the horizontal resolution of the model improved the representation of low pressure systems characterized by rapid intensification. The model also helped to simulate the temporal evolution of the estimated amount of the rainfall in Belgium at different horizontal resolutions of about 10 km, 25 km and 50 km. In the case of the Lake Victoria in Central Africa, the MAR allowed to qualitatively study the patterns of the convective parameterization. It represented well the diurnal cycle of the cloudy disturbance and the rainfall over the lake. It also highlighted the role of land and lake breezes and that of the katabatic and anabatic winds. In the West Africa case, several works were undertaken with this regional climate model. The study of Ramel et al. [8] showed that MAR reasonably well reproduced the monsoon jump which is associated with the shift of the Saharan heat low between the Sahelian and the Saharan areas. Vanvyve et al. [9] studied the internal variability of the model illustrated by the difference between two 1-year simulations and their initial conditions. This analysis showed a concomitant increase between the signal due to internal variability and the averaging period or spatial scale. This occurred more rapidly for variations in precipitation, which appeared essentially random, than for dynamical variables, which showed some organisation on larger scales. Ramel [10] also used MAR to simulate the main features of the West African rainfall regime. He showed that the simulated rainfall was in agreement with observation for time scales larger than 3 - 5 days. Despite the good results obtained with the model, the simulated moisture was more important in the lower layers than the observations. Moufouma-Okia [11] explained this difference by the errors of the large-scale lateral forcing due to the input ERA-40 reanalyses.
In this work, the squall line during 21-22 August 1992 documented during the HAPEX1-Sahel experiments [12] is simulated with MAR. This model is implemented in the laboratory of atmospheric physics and fluids mechanic at the University of Cocody (Côte d’Ivoire) through a Belgian cooperation. The study aims to represent extreme events which are squall lines for the tropical region of West Africa. The originality of this work is to represent sub-grid phenomena with a climate model. That will allow to analyze the behaviour of the model and to determine its limits and abilities to reproduce such atmospheric disturbances. The analysis of the behaviour of the model at the meteorological scale will constitute a preliminary step to use this climatic model as a meteorological one. To that effect, the duration of the model integration which does not exceed 4 days allows taking into account the deep convection in the climate simulations.
In the first part of the simulation analysis, the characteristics of the selected squall line are studied. We describe this disturbance through the cloud coverage, the barotropic and baroclinic instabilities of the African easterly jet and both the vertical profiles and the currents in the squall line. In the second part of the investigation, a study of the impact on the environment is undertaken. Studies of a set of stability indexes allow determining the probability and the intensity of the development of the convection and evaluating the associated weather phenomena at the synoptic scale.
This manuscript is outlined as follows: An overview of the model description and the data sets are presented in the second section. The early simulation of the squall line is described and discussed in the third section. A conclusion is provided in the last section.
2. Materials and Method2.1. Overview of the Description of the Regional Atmospheric Model
MAR [13] is a hydrostatic model in which the vertical coordinate σ is the normalized pressure used to better represent the topography. MAR is forced by the European Centre for Medium-range Weather Forecast (EC MWF) reanalyses. Various schemes are used to perform prognostics variables which are given by the Equation (1).
This equation gives the unresolved processes that are the hydrological cycle, the turbulent transfers, the convection, the radiative and the dissipation processes. In Equation (1), y represents the prognostic variables and u, v and q are respectively the water vapour, the liquid water and the ice. The advection terms are calculated from the primitive equations discretized by the numerical “leap frog” and the semi-lagrangian schemes. The dynamic relaxation scheme [14,15] pulls the prognostic variables towards the large scale fields. At the top of the model, the damping layer [16,17] is used to minimize the reflections. The hydrological cycle of MAR [18] is based on the Kessler scheme [19-21] whereas the Bechtold scheme [22] is used to parameterized the sub-grid convection. This mass flow scheme is a one-dimensional cloud model proposed by Kain and Fritsch [23], in which the convection is triggered by local instability and the precipitant cloud is represented by vertical currents. The coupling between the convection scheme and the microphysics of the model allows to take into account the detrainment of cloudy water and the prognostic ice in the simulation of the precipitations at the grid scale. The closing of the convection scheme is based on the available convective potential energy (hereafter CAPE).
For our experiments, the model is forced with ERA-40 reanalyses of the ECMWF on the lateral edges with a regular recall each six hours. It is initialized on 19 August 1992 at 00:00 UT considered as a spin-up. The calculations are integrated over 4-day period until 23 August 1992 on a 40 km horizontal scale.
2.2. Observational Data
The Meteosat-4 images are used to study the squall line over West Africa. This dataset is available in 3-hour time intervals at full spatial resolution (10 × 10 km) in the infrared channel. The input data used to initialize the model are from the ERA-40 fields of the ECMWF reanalyses [24]. Synoptic sounding data from the station of Niamey (2.16˚E; 13.47˚N) come from the database stored at the University of Wyoming [25]. The bad sounding data during 21 august at 00:00 UT and 22 August constrained us to use only those obtained during 21 August 1992 at 12:00 UT.
3. Results3.1. Location of the Squall Line
Satellite images and simulated cloud coverage: The characteristics of the squall lines (SL) over West Africa were studied for the first time by Houze [26] in the Gulf of Guinea during the GATE2 experiments and later by Roux [27] in the north of Côte d’Ivoire during COPT813. Using satellite images, Desbois et al. [28] studied the displacements of a SL between Tchad and Senegal and Laing et al. [29] estimated the rainfall contribution of a convective cluster in the Sahel. SLs are the more violent phenomena which caused 90% of the Sahel rainfall [4,5]. The passage of a SL is marked by an abrupt jump of the pressure followed by a rapid change in the direction of the wind. Finally, a decrease of the temperature, an intensification of the wind and strong precipitations are observed [30]. On a satellite image, SLs are easily spotted because 1) they have a sharper border line on their western edge, and 2) they move westward in West Africa [31]. Following these both criteria, the SL of 21 August 1992 is studied using the Meteosat-4 images over the West African region. This date is used according to the available synoptic sounding data.
The left panels on Figure 1 present the 3-hourly evolution of the cloud coverage from 12:00 UT to 21:00 UT in the Meteosat-4 images. A SL is observed in the north-western region of Nigeria at 6˚E - 13˚N. The size of the atmospheric disturbance increased when moving westward in the successive images. At 21:00 UT, the SL covered some regions of Benin, Burkina Faso, Mali, Niger and Nigeria. The right panels on Figure 1 illustrate the simulated water concentration from the non-precipitating cloudy water and cloudy ice at 9000 m level. Two convective clusters participated by merging to the initiation and the development phases of the SL observed in the north-western region of Nigeria at 6˚E - 13˚N (see left panel). The first cell is simulated in the northern area of the Jos plateau around 7˚E - 9˚E; 10˚N - 12˚N, and the second one around 8˚E - 10˚E; 17˚N - 18˚N over the Aïr mountainous region. The simulated SL seemed to be shifted eastward when comparing with the satellite image. That is explained by the 30-minutes lag between the model outputs and the satellite images. This time step, used for the deep convection adjustment in the Bechtold scheme, is necessary to remove the CAPE from the mesh grid. It corresponds also to the displacement of the disturbance between two grids.
The 650 hPa Horizontal wind and pressure fields: Figure 2 illustrates the 650 hPa wind vectors and pressure fields (shaded area) at 12:00 UT during 21 August 1992. The simulated cyclonic area, at 5˚E - 15˚E; 10˚N - 16˚N in the Northern region of Nigeria, is coincident with the position of the SL at this date (see Figure 1). Strong westward flow which corresponds to the African easterly jet (AEJ) is noted towards 16˚N. A disturbance embedded in the flow is localized ahead the cyclonic area between 5˚W - 0˚E; 16˚N - 20˚N. This phenomenon, called easterly wave, is a disturbance of the wind field at 650 hPa. It plays an important role on Africa and on tropical Atlantic climate. It promotes and/or organizes
the convection that is a favourable environment for the birth and the development of the SLs and sometimes for the tropical Atlantic cyclones [32,33]. The simulated pressure field showed the talweg of the easterly wave which coincided with the wind disturbance. Note that the talweg is a depression in which the isobars form a “V” and the pressure decreases when moving to the concavity. The SL position, which is also coincident with the minimum pressure was previously analysed by Reed et al. [34] and by Barnes and Sieckman [35]. These authors argued that rapid SLs formed and reached their mature phase ahead of the talweg whereas slow SLs are observed at the rear of it.
3.2. Barotropic and Baroclinic Instability in the African Easterly Jet
Figure 3 shows the altitude-latitude diagram of the simulated potential vorticity (PV) and zonal wind along 7.5˚S - 25˚N, averaged between 5˚W - 10˚W. Three vortices representted by positive PV are noted. The first one extended from the surface up to 6000 m at 15˚N of latitude. It is located in the vicinity of the AEJ core that coincides with the negative PV [36]. A less intense second vortex is localized around 13˚N from 1000 m to 7000 m. The third one is as intense as the first vortex and is noted at 10˚N from 4000 m to 8000 m. Reversal signs of PV, marked by a northward succession of negative and positive values of PV, are simulated on the AEJ area between 10˚N to 20˚N [37]. Such result is a condition for barotropic and baroclinic instabilities [38].
ReferencesS. E. Nicholson, B. Some and B. Koné, “An Analysis of Recent Rainfall Conditions in West Africa, Including the Rainy Seasons of the 1997 El Ni?o and the 1998 La Ni?a Years,” Journal of Climate, Vol. 13, No. 14, 2000, pp. 2628-2640.
doi:10.1175/1520-0442(2000)013<2628:AAORRC>2.0.CO;2L. Le Barbé, T. Lebel and D. Tapsoba, “Rainfall Variability in West Africa during the Years 1950-1990,” Journal of Climate, Vol. 15, 2002, pp. 187-202.
doi:10.1175/1520-0442(2002)015<0187:RVIWAD>2.0.CO;2L. Le Barbe and T. Lebel, “Rainfall Climatology of the HAPEX-Sahel Region during the Years 1950-1990,” Journal of Hydrology, Vol. 188-189, 1997, pp. 43-73.
doi:10.1016/S0022-1694(96)03154-XN. D’Amato and T. Lebel, “On the Characteristics of the Rainfall Events in the Sahel with a View to the Analysis of Climatic Variability,” International Journal of Cli- matology, Vol. 18, No. 9, 1998, pp. 955-974.
doi:10.1002/(SICI)1097-0088(199807)18:9<955::AID-JOC236>3.0.CO;2-6V. Mathon and H. Laurent, “Life Cycle of the Sahelian Mesoscale Convective Cloud Systems,” Quarterly Journal of the Royal Meteorological Society, Vol. 127, No. 572, 2001, pp. 377-406. doi:10.1002/qj.49712757208L. M. Druyan, M. Fulakeza and W. Thiaw “Regional Model Simulations of African Wave Disturbances,” Journal of Geophysical Research, Vol, 105, No. D6, 2000, pp. 7235-7255. doi:10.1029/2000JD900017O. Brasseur, “Mesoscale Modeling of Extreme Meteorological Events of Belgium,” Ph.D. Thesis, Université Catholique de Louvain, Louvain, 1999.R. Ramel, H. Gallee and C. Messager, “On the Northward Shift of the West African Monsoon,” Climate Dynamics, Vol. 26, 2006, pp. 429-440.
doi:10.1007/s00382-005-0093-5E. Vanvyve, N. Hall, C. Messager, S. Leroux and J.-P. Van Ypersele, “Internal Variability in a Regional Climate Model over West Africa,” Climate Dynamics, Vol. 30, No. 2-3, 2008, pp. 191-202. doi:10.1007/s00382-007-0281-6R. Ramel, “Etude des Processus de Surface sur le Climat en Afrique de l’Ouest,” Ph.D. Thesis, Université Joseph Fourier de Grenoble, Grenoble, 2005.W. Moufouma-Okia, “Modelisation Climatique de l’Afri- que de l’Ouest avec le Modèle Regional MAR,” Ph.D. Thesis, Institut National Polytechnique de Grenoble, Grenoble, 2003.J. P. Goutorbe, T. Lebel, A. Tinga, P. Bessemoulin, J. Brouwer, H. Dolman, T. Engman, J. H. C. Gash, M. Hoepffner, P. Kabat, Y. H. Kerr, B. Monteny, S. Prince, F. Sad, J. Sellers and J. Wallace, “HAPEX-Sahel: A Large- Scale Study of Land Atmosphere Interactions in the Semi-Arid Tropics,” Annales Geophysicae, Vol. 12, No. 1, 1994, pp. 53-64. doi:10.1007/s00585-994-0053-0H. Gallee, and G. Schayes, “Developpement of a Three Dimensional Meso-Y Primitive Equations Model. Kata- batic Wind Simulation the Area of Terra Nova Bay, Antartica,” Monthly Weather Review, Vol. 122, 1994, pp. 671-685.
doi:10.1175/1520-0493(1994)122<0671:DOATDM>2.0.CO;2H. C. Davies, “A Lateral Boundary Formulation for Multi-Level Prediction Models,” Quarterly Journal of the Royal Meteorological Society, Vol. 102, No. 432, 1976, pp. 405-418.D. R. Durran, “Numerical Methods for Wave Equations in Geophysical Fluid Dynamics,” Springer-Verlag, Berlin, 1999, p. 465.J. B. Klemp and D. K. Lilly, “Numerical Simulation of Hydrostatic Mountain Waves,” Journal of Atmospheric Sciences, Vol. 35, No. 1, 1978, pp. 78-107.
doi:10.1175/1520-0469(1978)035<0078:NSOHMW>2.0.CO;2D. R. Durran and J. B. Klemp, “A Compressible Model for the Simulation of Moist Mountain Waves,” Monthly Weather Review, Vol. 111, No. 12, 1983, pp. 2341-2361.
doi:10.1175/1520-0493(1983)111<2341:ACMFTS>2.0.CO;2V. Ntezimana, “Simulation d’un Front C?tier avec un Modèle Atmospherique Regional Comprenant une Repré- sentation Explicite de la Microphysique des Nuages,” Ph.D. Thesis, Universit Catholique de Louvain, Louvain, 1993.E. Kessler, “On the Distribution and Continuity of Water Substance in Atmospheric Circulation,” American Meteo- rological Society, Vol. 10, No. 32, 1969, p. 84.E. Kessler, “On Accumulation of Water Substance in a Column of Rising Air,” Atmosphere-Ocean, Vol. 20, No. 1, 1982, pp. 62-68. doi:10.1080/07055900.1982.9649129E. Kessler, “On the Continuity and Distribution of Water Substance in Atmospheric Circulation,” Atmospheric Research, Vol. 38, No. 1-4, 1995, pp. 109-145.
doi:10.1016/0169-8095(94)00090-ZP. Bechtold, E. Bazile, F. Guichard, P. Mascart and E. Richard, “A Mass Flux Convection Scheme for Regional and Global Models,” Quarterly Journal of the Royal Meteorological Society, Vol. 127, No. 575, 2001, pp. 869-886. doi:10.1002/qj.49712757309J. S. Kain and J. M. Fritsch, “A One-Dimensional En- training/Detraining Plume Model and Its Application in Convective Parameterizations,” Journal of Atmospheric Sciences, Vol. 47, No. 23, 1990, pp. 2784-2802.
doi:10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2ECMWF ERA-40, “Reanalyses Data,” 2011.
www.ecmwf.intUniversity of Wyoming, “Sounding Database,” 2011.
http://weather.uwyo.edu/upperair/sounding.html.R. A. Houze, “Structure and Dynamics of a Tropical Squall Line System Observed during GATE,” Monthly Weather Review, Vol. 105, 1977, pp. 1540-1567.
doi:10.1175/1520-0493(1977)105<1540:SADOAT>2.0.CO;2F. Roux, “The West African Squall Line Observed on 23 June 1981 during COPT 81: Kinematics and Thermo- dynamics of the Convective Region,” Journal of Atmospheric Sciences, Vol. 45, 1988, pp. 406-426.
doi:10.1175/1520-0469(1988)045<0406:TWASLO>2.0.CO;2M. Desbois, T. Kayiranga, B. Gnamien, S. Guessous and L. Picon, “Characterization of Some Elements of the Sahelian Climate and their interannual Variations for July 1983, 1984 and 1985 from the Analysis of METEOSAT ISCCP Data,” Journal of Climate, Vol. 1, 1988, pp. 867-904.
doi:10.1175/1520-0442(1988)001<0867:COSEOT>2.0.CO;2A. G. Laing, J. M. Fritsch and A. J. Negri, “Contribution to Mesoscale Convective Complexes to Rainfall in Sahelian Africa: Estimates from Geostationary Infrared and Passive Microwave Data,” Journal of Applied Meteo- rology, Vol. 38, No. 7, 1999, pp. 957-964.
doi:10.1175/1520-0450(1999)038<0957:COMCCT>2.0.CO;2Y. Lemaitre, “Etude Thermodynamique et Dynamique des Lignes de Grains Tropicales Observées à Korhogo Pendant l’Expérience COPT 79,” Ph.D. Thesis, Uni- versity Paris 7, Paris, 1981, p. 198.D. P. Rowell and J. R. Milford, “On the Generation of African Squall Lines,” Journal of Climate, Vol. 16, 1993, pp. 1181-1193.
doi:10.1175/1520-0442(1993)006<1181:OTGOAS>2.0.CO;2Y. L. Chen and Y. Ogura, “Modulation of Convective Activity by Large Scale Flows Pattern Observed during GATE,” Journal of Atmospheric Sciences, Vol. 39, No. 6, 1982, pp. 1260-1279.
doi:10.1175/1520-0469(1982)039<1260:MOCABL>2.0.CO;2K. Y. Kouadio, L. A. T. Machado and J. Servain, “Tro- pical Atlantic Hurricanes, Easterly Waves and West African Mesoscale Convective Systems,” Advances in Meteorology, Vol. 2010, 2010, Article ID: 284503.
doi:10.1155/2010/284503R. J. Reed, D. C. Norquist and E. Recker, “The Structure and Properties of African Wave Disturbances as Ob- served during Phase III of GATE,” Monthly Weather Review, Vol. 116, No. 3, 1977, pp. 824-860.
doi:10.1175/1520-0493(1988)116<0824:AEOTPO>2.0.CO;2G. M. Barnes and K. Sieckman, “The Environment of Fast and Slow Moving Tropical Mesoscale Convective Cloud Lines,” Monthly Weather Review, Vol. 112, 1984, pp. 1782-1794.
doi:10.1175/1520-0493(1984)112<1782:TEOFAS>2.0.CO;2J. L. Redelsperger, A. Diongue, A. Diedhiou, J.-P. Ceron, M. Diop, J.-F. Gueremy and J.-P. Lafore, “Multi-Scale Description of a Sahelian Synoptic Weather System Representative of the West African Monsoon,” Quarterly Journal of the Royal Meteorological Society, Vol. 128, No. 582, 2002, pp. 1229-1257.
doi:10.1256/003590002320373274J. Charney and M. E. Stern, “On the Stability of Internal Baroclinic Jets in a Rotating Atmosphere,” Journal of Atmospheric Sciences, Vol. 19, No. 2, 1962, pp. 113-126.
doi:10.1175/1520-0469(1962)019<0159:OTSOIB>2.0.CO;2R. W. Burpee, “The Origin and Structure of Easterly Waves in the Lower Troposphere of North Africa,” Journal of Atmospheric Sciences, Vol. 29, No. 1, 1972, pp. 77-90.
doi:10.1175/1520-0469(1972)029<0077:TOASOE>2.0.CO;2R. A. Jr. Houze, “Mesoscale Convective Systems,” Review of Geophysics, Vol. 42, No. 4, 2004, Article ID: RG4003.J.-P. Lafore and M. W. Moncrieff, “A Numerical Inves- tigation of the Organization and Interaction of the Convective and Stratiforme Regions of the Tropical Squall Lines,” Journal of Atmospheric Sciences, Vol. 46, No. 4, 1989, pp. 521-544.C. A. Doswell, “The Distinction between Large-Scale and Mesoscale Contribution to Severe Convection: A Case Study Example,” Weather Forecast, Vol. 2, No. 1, 1987, pp. 3-16.
doi:10.1175/1520-0434(1987)002<0003:TDBLSA>2.0.CO;2A. Diongue, J.-P. Lafore, J.-L. Redelsperger and R. Roca, “Numerical Study of a Sahelian Synoptic Weather Sys- tem: Initiation and Mature Stages of Convection and Its Interactions with the Large-Scale Dynamics,” Quarterly Journal of the Royal Meteorological Society, Vol. 128, No. 584, 2002, pp. 1899-1927.
doi:10.1256/003590002320603467J. G. Galway, “The Lifted Index as a Predictor of Latent Instability,” Bulletin of the American Meteorological Society, Vol. 37, 1956, pp. 528-529.J. J. George, “Weather Forecasting for Aeronautics,” Academic Press, London, 1960, p. 673.