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Interannual variability and predictability of African easterly waves in a GCM

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Abstract

The interannual variability of African Easterly Waves (AEWs) is assessed with the help of spatio-temporal spectral analysis (STSA) and complex empirical orthogonal functions methods applied to the results of ten-member multiyear ensemble simulations. Two sets of experiments were conducted with the Météo-France ARPEGE-Climat GCM, one with interactive soil moisture (control), and the other with soil moisture relaxed towards climatological monthly means calculated from the control. Composites of Soudano–Sahelian AEWs were constructed and associated physical processes and dynamics were studied in the frame of the waves. It is shown that the model is able to simulate realistically some interannual variability in the AEWs, and that this dynamical aspect of the West African climate is potentially predictable (i.e. signal can be extracted from boundary conditions relatively to internal error of the GCM), especially along the moist Guinean coast. Compared with ECMWF 15-year reanalysis (ERA15), the maximum activity of AEWs is located too far to the South and is somewhat too zonal, but the main characteristics of the waves are well represented. The major impact of soil moisture relaxation in the GCM experiments is to reduce the seasonal potential predictability of AEWs over land by enhancing their internal variability.

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Acknowledgements

We thank Jean-Pierre Céron and Jean-François Guérémy for their statistical analysis software package and help. This package containing a description of the methods and validation examples is available at the PROMISE web page (http://ugamp.nerc.ac.uk/promise). Discussions with Jean-François Guérémy were very useful. Robin Clark brought some useful corrections to the English language in the draft version of the paper. Alain Braun provided the ERA15 fields used in this study and Michel Déqué was often requested for his statistical knowledge. The two anonymous reviewers must be mentioned here for their useful comments and suggestions. This study has been supported by a grant from the European Commission Fifth Framework Programme (PROMISE contract EVK2-CT-1999-00022) and by the French Programme National d’Etude de la Dynamique du Climat (PNEDC). The figures were drawn with GrADS software (http://grads.iges.org/grads/).

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  1. CNRM/GMGEC/UDC, Météo-France, 42, avenue G. Coriolis, 31057, Toulouse Cedex 01, France

    Fabrice Chauvin, Jean-François Royer & Hervé Douville

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  1. Fabrice Chauvin
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Correspondence to Fabrice Chauvin.

Appendix: classification of the rotated CEOF modes

Appendix: classification of the rotated CEOF modes

When performing the rotated CEOF analysis over each of the simulations (150 runs) or reanalysis year (15 years), the order in which the modes are selected may vary. Indeed, according to the prominent feature of the AEWs during the year studied, one mode may dominate the variability while another year will favor another mode. Thus, averaging the modes to produce what we have called the MMPs has no sense if we do not previously perform a classification of the modes before the aggregation. To perform this classification, we iterate a process which will be described now.

To calculate MMP(i, k), where i represents the rank of the iteration and k the number of the mode, we use MMP(i−1, k) and the five first eigenvectors EV(k) of the CEOF, for each run. For a given run, spatial correlation is calculated between each of the eigenvectors and MMP(i−1, k) and the greatest one indicates the rank of the eigenvector selected for the kth mode. Thus, for the ith iteration, MMP(i, k) is the mean of all the EV(k) presenting the maximum correlation with MMP(i−1,k).

Before calculating the correlation, eigenvectors are modified in such a way that values under a given threshold are replaced by the corresponding values of the MMP(i−1, k) at the same grid point. The use of a threshold suppresses the effect of cumulated low but non zero differences between the MMP and the eigenvector in regions where its values are low. Thus, correlation takes into account non negligible values of EV(k). The process is iterated until it converges, i.e. selection of the ranks is stable when iteration is increased.

To initialize the process, MMP(1, k) is simply the average of the EV(k) for each mode k. We assume that the distortion introduced in the first average is not so large that it may bias the final result. If the initial state of the global eigenvectors was too scattered, the mean would no longer represent the ensemble pattern and it may cause some troubles in the initialization of the iterative process. It is not the case in this study, despite the coexistence of different modes in the initial MMP. Modes are sufficiently stable, from one run to another, for this process to work.

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Chauvin, F., Royer, JF. & Douville, H. Interannual variability and predictability of African easterly waves in a GCM. Clim Dyn 24, 523–544 (2005). https://doi.org/10.1007/s00382-004-0507-9

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