African monsoon synoptic variability: Validation of the Météo-France GCM

Pergamon PII: S1464-1909(00)00232-X

Phys. Chem.Earth(E), Vol. 26, No. 2, pp. 149-153, 2001 0 2000 Elsevier Science Ltd All rights reserved 1464- 1909/00/$ - see front matter

African Monsoon Synoptic Variability: Validation of the M&o-France

GCM

J. P. C&on, J. F. Gu6r6my and A. Sarr M&to-France, Ecole Nationale de la MCtCorologie, Unite Formation et RecherchelUnitC 42 avenue G. Coriolis, 31057 Toulouse CCdex 1, France. E-mail: [email protected].

de MCtCorologie Tropicale,

Received 17 May 1999; revised 30 June 2000; accepted 3 July 2000

Abstract The aim of this study is to describe the African monsoon synoptic variability and investigate the ability of the Memo-France GCM to reproduce that variability. We used the ECMWF Reanalysis as a reference dataset. The main results about the characterization of the synoptic variability is a “Northern and Continental” mode vs a “Southern and Oceanic” mode. The fast one is predominant during the “dry” years while the second one is predominant during the “wet” years. The model failed to capture the pattern of these modes and their interannual variability. Interestingly, the times series of these modes present a Low Frequency Modulation (LFM) both in the Reanalysis and in the model. Looking at this LFM, the improvement of the behavior of the model seems to be related to improvements in the new land/surface scheme. 0 2000 Elsevier Science Ltd. All rights reserved.

the synoptic variability as seen in a reference dataset (here ECMWF reanalysis) with the help of two complementary objective methods (a space-time spectral analysis - hereafter called STSA - and a complex empirical orthogonal functions analysis - hereafter called CEOFA). Then, we will be able to move on toward the validation process itself (i.e., GCM simulation against ECMWF reanalysis). In order to address the link with the interannual variability of rainfall over Sahel, the characterization of the variability of the synoptic activity and its interannuaI variability is particularly relevant for the GCM in so far as easterly waves are the smallest dynamical phenomena (which modulate rain bringing systems and might be modulated by lower frequency oscillations) which might be simulated by the model. After a discussion of the data and methods used in this study, the results obtained with the help of STSA and CEOFA will be presented looking first at the reference dataset then comparing the simulation to the former results and, finally, we will conclude.

1 Introduction

2 Data and methods

The aim of the West African Monsoon Project (WAMP) is to increase our understanding of the processes involved in the evolution of the West African Monsoon with special emphasis on interannual variability of rainfall, scale interactions and tropical land-based convection (http://www.met.reading.ac.uk). More specifically, in the frame of the WorkPackage 1 (WPl), we are interested in the validation of the synoptic variability of the West African Monsoon (particularly Easterly Waves) simulated by the new version of the M&o-France GCM and the relationship between the synoptic activity over West Africa and the variability of rainfall. A previous study (using an older version of the GCM so-called “Emeraude” and ECMWF analyses - C&on and Gueremy, 1999 - CG99 hereafter) has shown that the GCM had some large discrepancies in the space location of the wave activity and that the lowfrequency modulation of the synoptic activity, as revealed in the ECMWF analyses, was poorly represented in the model. Nevertheless, the main feature of the interannual variability described as a mode seesaw between “dry” and” wet” years was quite well simulated. So, the goal of this study is to determine the ability of the new GCM in an AMIP simulation (79-88) to better :reproduce the space-time variability of the AEWs and its mterannual characteristics. First, we will try to characterize Correspondence to: C&on Jean-Pierre, ENM/UFR, avenue G. Coriolis, F3 1057, Toulouse cedex, FRANCE Email: [email protected]

2.1 Data The dam used in this study are, on one hand the ECMWF reanalyses, widely described and used in many reports or papers (e.g. ECMWF Web site or Proceeding of the First International Conference on Reanalyses, 1997), and, on the other hand, simulated data provided by the CNRM GCM integrated over the Atmospheric Model Intercomparison Project (AMIP) period, namely 1979-1988. The simulation was performed using observed sea surface temperature and observed ice pack extension on a monthly basis in the AMIP context (Gates, 1992). The version of the GCM (so-called “Arpbge”) used for the AMIP experiment, is a spectral model with a T42 triangular truncation in the horizontal and an L30 discretization for the vertical, including 21 levels in the troposphere. This model includes a classical set of physical parameterizations such as radiative processes, convection using a mass flux scheme and surface processes (Mahfouf, 1993). All the data were sampled 6 hours in time on a regular grid with a 2”5 mesh resolution (consistant with the T42 resolution) over the May-October period covering the monsoon period. The space domain was quite large and focused on West Africa (40”W-40°E and YS-35%) for the STSA but was reduced (40”W-30”E and O”N-25”N) for the CEOFA. We described the large scale environment using monthly average of the wind at 700 hPa, the meridional moisture flux in the low level (925 hPa) and the Equivalent Potential

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Temperature both in the low level and mid-troposphere (at 925 and 700 hPa). In order to characterize the synoptic activity we used variables related to the dynamics of AEWs and others more related to the rainfall through AEWs and convection. Namely, we used the Relative Vorticity at 850 hPa, the Divergence at 850 hPa, the Moisture Convergence at 925 hPa, the Vertical Gradient of Equivalent Potential Temperature (700 - 925), the Potential Vorticity at 850 hPa and obviously the Rainfall. 2.2 Methods We performed first an extensive study on the year 85 in so far as this year was already well documented about AEWs (Reed et al., 1988; CG 99). Then, looking at these results, we have choosen a limited set of variables for the other years. Consequently, we will present in the following results from the relative vorticity at 850 hPa and from the moisture convergence at 925 hPa. The applied methods try to start from a global view then going into more details. Thus, we used first the STSA (Hayashi, 1982; CG99) on the raw datasets for the global view on spectral characteristics and variances. Second, we concentrate on the synoptic phenomena to derive more detailed aspects of their manifestation. We choose a 2.5-6 day band for the synoptic activity, according with the results from the STSA, and we use a 4th order Butterworth filter (Murakami, 1979) applied twice (in ascending and descending order) for filtering our variables at each grid point. Then, we performed a CEOFA on filtered datasets (CG99) to catch the main features of the synoptic variability through a more precise view of the variability decomposed into the most dominant modes (spatial and time representation). Finally, mainly because the first modes in the Reanalysis were not well separated both in space and time, we added a rotation of the CEOF using a varimax criterion (Blomfield and Davis, 1994). Of course, in order to address the interannual variability, we performed a year by year analysis.

Synoptic

Variability

3 Results 3.1 Space-Time Spectral Analysis (STSA) ,The Space-Time Spectra for the relative vorticity and for travelling waves only are shown in figure n”1. In the ECMWF reanalysis (fig la), the spectrum shows a conspicuous westward motion for the travelling waves. Indeed, there is a clear lack of variance in the eastward travelling spectral domain. Then, focusing on the spectral domain of African Easterly Waves (namely 3 to 6 days for Westward travelling waves), one can see large values of variance located slightly more than 4 days in period and around 2700 km in zonal wavelength, well corresponding to AEWs characteristics. Interestingly, one can remark also some variance in the range of 6 up to 9 days in period and 4000 km in wavelength that seems to well describe characteristics of the 6-9 days component highlighted by Diedhiou and al (1999). Finally, on can see again large values of variance in the low-frequency band (around 36 days) for large zonal wavelength. Looking at the spectrum from the model (fig lb), the first difference appears with the total variance which is less in the model. In spite of this, the model give more variance in the spectral band of AEWs. As one can see on figure lb, the local maxima are wider over the range of AEWs period. So, consequently, the variance will be greater compared to that of the reanalysis and the periods of AEWs in the model will be greater because of the presence of local variance maxima between 4 and 6 days which don’t exist in the reanalysis. About the standing waves (not shown) in the ECMWF reanalysis, it exists a signal for all the variables which seems to be clearly related to the orography. In the model this signal also exists but is not well located probably in relationship with the truncation of the model (T42) which is less than the Reanalysis (T106), even if the reanalyse were regridded. Finally, these results allowed us first to define a time spectral window in order to study in more details a filtered signal and second to design the space domain used for the CEOFA using the spatial location of the variance in the AEWs spectral window (not shown).

Figure 1 : Traveling waves spectra of Relative Vorticity at 850 Wa (Mav \- to October roeriod - Year 1985) from STSA a) ECMWF Reanalysis b) Arpege GCM I

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3.2 Complex Empirical Orthogonal Function Analysis As discussed in CG99 (see section 2), the CEOFA domain was focused over the domain of AEW activity, domain deduced from the STSA results. For the relative vorticity at 850 hPa., the decomposition of the variance of the filtered signal gave a partition in two main modes of variability which explained from 10 up to 30% of variance (each) depending of the rank, the dataset and the year. These two modes depict reasonably the variance given by the STSA method in the space-time spectral band of AEWs. Consequently, one can capture the main features of the synoptic variability and its interannual variability using these two modes. So using the EChJWF Reanalysis to characterize the synoptic variability one can summarize the results in a schematic way (see fig 2). One mode is, roughly speaking, rather north and continental. Even if the mode can extend over the Atlantic, the maxima of variance are always located over the continent above 15”North (generally around 17”N), West of the Greenwich meridian. This mode is quite stable for the 10 years used

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and will be named “N” mode in the following. Then, the second mode presents features which are rather South and Oceanic for 6 years (80 to 84 and 88). For these years, the maxima of variance of this mode are latitudinally located around 10”/12”N and West of the coast in longitude. For the 4 remaining years, the variance of this mode is mainly located in the North and over the continent even if one can have some low values of variance in the South over the continent. Anyway and quite improperly, we will name this mode “S” mode hereafter. These results are very consistent with those highlighted in a previous study using “Emeraude” and ECMWF analysis (CG99) even if some differencies appear due on one hand to the differences between analysis and reanalysis and on the other hand to the rotation method which was not used in CG99. About the description of the interannual variability of the synoptic variability, as already mentioned, one can capture the main feature using the two previous modes. First, looking at the total variance in the filtered band, there is no discrimination between “dry” and “wet” years.This discrimination seems to be more clear when we look at the behaviour of the two modes (see fig 3).

Figure 2 : Schematic space representation of the time variance of the modes for the relative vorticitv at 850 hPa. Contour interval is 2 x 10.” s-‘. “S” mode

Figure 4 : periods of the “N” and “S” modes The arrows indicate the S “oceanic” mode.

Figure 3 : Interannual variability of the variance of the modes The arrows indicate the S “oceanic” mode. Variance of relatlve vortlcity ECMWF Fleanalysls

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Perlode of relatlve vortlclty ECMWF Reanalysls

4.2.

Indeed, the driest years over Sahelian region (e.g. figure 2 from Lare and Nicholson 1994) exhibiting a “S oceanic” mode (1981 to 1984) are clearly characterised by a large difference of variance between the “N” mode (being large) and the “S oceanic” (being weak), which appears to be the more robust result concerning the innterannual variability, while the wettest (or least dry) years

exhibiting a “S oceanic” mode (1980 and 1998) are rather characterised by an opposite difference of variance between the two considered modes. Interestingly, the periods of the “S oceanic” modes are shorter compared to the “N” modes or to the “S continental” modes (see fig 4). Consequently, the phase speed of the “S oceanic” mode should be faster because of

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the larger wavelength and the shorter period. This is quite consistent with results shown in previous studies which indicate that the more intense is the convection the faster is the easterly wave and with the interpretation of the two modes as presented in CG99. How does the model work? Unfortunately not very well! The modes given by the model (not shown) are, generally speaking, too zonal and continental. The maxima of variance are always too constricted near the AEJ axis even if there is some improvements compared to results given by the older version of the model. Indeed, the added variance of the two first modes has a better location and its patterns is more widespread compared to Emeraude results. In a consistent way with STSA, the variances of the modes are greater and the periods of the modes are too large compared to the Reanalysis characteristics. Finally, the interannual variability of the synoptic variability is not well captured by the model, particularly the “N”/“S” mode seesaw for the year 80 and 88. However, there is a tendancy to have the largest difference of variance between the ‘TV mode (being large) and the “S oceanic” (being weak) during the driest years. For the Moisture Convergence at 925 hPa (not shown), the decomposition of the variance of the filtered signal gave results quite similar to those from the relative vorticity. Namely, the main decomposition captured by CEOFA is a North-Continental versus South-Oceanic mode decomposition even if the pattern of the modes is less stable than for the vorticity.

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Then, the conclusion about the interannual variability of the modes is quite the same; the wettest years correspond to a “Southern and Oceanic” predominant mode while the driest years correspond to a “Northern and ContinentaY predominant mode. The conclusion about the model behaviour are again not very good for the space location of the variance. Finally as already presented in CG99, the time series of the modulus of the CPC’s exhibit a low frequency modulation (LFM) of the synoptic signal (roughly 3 modulation over the May/October period). This LFM exists for all the variables and seems to be well phased at least for the moisture convergence and the vorticity (see fig 5). This later remark seems to indicate that the AEWs modulate in a significant way the large scale moisture convergence and more particularly for the “S oceanic” mode for which the phasing is very clear and robust. Looking at the GCM results, the LFM also exists and seems to not too badly reproduce the reanalysis characteristics. Comparing these results with the older version, Arpbge improves the LFM representation, notably giving a shorter periods for the LFM. This shorter period is very consistent with known properties of the new land/surface scheme, more particularly with the time response of the evaporation, and with results coming from the Shiva Project (Rupa Kumar et al., 1999) or from sensitivity experiment over West African regions (C&on and Gueremy, 1994).

Figure 5 : Time series of theModulus of the rotated CPCs of the “S oceanic” mode for the relative vorticity (left) and the moisture convergence (right) for the year 1984

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4 Conclusion

About the characterization of the synoptic activity, the Relative Vorticity at 850 hPa and the Moisture Convergence at 925 hPa seem to be interesting parameters to capture its main features. The main result about the characterization of the synoptic variability is a “Northern and Continental” mode vs a “Southern and Oceanic” mode. This latter mode has shorter periods than the “Northern and Continental” mode in a consistent way with known properties of AEWs and convection interaction. Finally, the synoptic activity is strongly modulated by a Low Frequency signal. About the validation exercise, the periods in the model are generally too large and variance patterns too zonal and continental even if “Arpege” is definitely better (or less worse) than “Emeraude”. Particularly, the latitudinal location of the northern variance maxima are shifted southward in relationship with the AEJ southward shift in the model. Finally, some discrepancies exist in the standing waves probably linked to the resolution (T42) which does not represent so well the orography. Considering the interannual variability of the synoptic variability, the variance is mainly located in the North and over the Continent in “dry” years while “Southern and Oceanic” variances tend to be as large or larger than their northern conterpart during “wet” years. Unfortunately, this behaviour is badly reproduced by the model. It has to be stressed at this point that those results have to be confirmed using other interannual simulations or longer interannual simulations. Finally, it exists a low frequency modulation of the synoptic activity; this modulation has also been observed in the rainfall by the authors and it should be phased with the LFM of the “S oceanic” modes. Interestingly, Arpbge gives a better representation of this modulation compared to Emeraude, particularly shorter periods of the LFM, this improvement being undoubtly related to improvement in the land surface scheme ISBA. Consequently, this suggests that, at least in the model, land surface processes are probably involved in the LFM.

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As prospects, we would like to perform an extensive study of the Low Frequency Modulation of the synoptic activity notably in relationship with the land surface processes. Of course, the improvement of the convection scheme should lead to a better representation of the AEWs, particularly, but not only, regarding the “S mode” and the interannual variability; this is one of the great challenge of WAMP.

References : Bloomfield, P., and Davis, J. M., 1994: Orthogonal Rotation of Complex Principal Components. Inf. J. Climatol., 14,759-775. CCron, J. P., and GuBrCmy, J. F., 1999: Validation of the space-time variability of African easterly waves simulated by the CNRM GCM. J. Climate, 12,2531-2X355. Ceron, J. P., and GuCrCmy, J. F., 1994: Numerical sensitivity studies on the West African monsoon: Initial conditions and land/surface processes. Proceedings of the international conference on monsoon variability and prediction, WCRP-84, WMO-TD N”619, 2,47-471). Diedhiou, A., Janicot, S., Viltard, A. and De Felice, P., 1998: Evidence of two regimes of easterly waves over West Africa and the tropical Atlantic. G. R. L, 15, 2805-2808. Hayashi, Y., 1982: Space time spectral analysis and its applications to atmospheric waves. J. Meteor. Sot. Japan, 60,156-171. Lare, A. R. and Nicholson, S. E., 1994: Contrasting conditions of surface water balance in wet years and dry years as a possible land surface-atmosphere feedback mechanism in the West African Sahel. J. Climate, 7,653-668. Mahfouf, J.F., Manzi, A.O., Noilhan, J., Giordani, H. and DCquC, M., 1995. The land surface scheme ISBA within the M&Co-France climate model ARPEGE. Part I. Implementation and premiminary results. .I. Climate, 8,2039-2057. Murakami, M., 1979: Large-scale aspect of deep convective activity over the GATE area. Mon. Wea. Rev., 107,994-1013. Reed, R. J., A. Hollingsworth, W. A. Heckley and F. Delsol, 1988a: An evaluation of the performance of the ECMWF operational forecasting system in analysing and forecasting tropical easterly waves disturbances over Africa and tropical Atlantic. Mon. Wea. Rev., 116, 824-865. Rupa Kumar, K., GuCrCmy, J.-F. and C&on, J.-P., 1999. Sensitivity of the intraseasonal variability of the indian summer monsoon to landatmosphere feedbacks induced by soil hydrology. Submitted to GRL