Spatio-Temporal Variability and Predictability of Relative Humidity Over West African Monsoon Region
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| Figure 9: Retreat south cluster composite plots showing surface temperature (top) and 925mb winds (bottom) for low years (left), high years (middle) and the low – high years (right)
Composite analysis was performed to investigate the large-scale climate features responsible for relative humidity extremes. For this, we selected years with ‘high’ and ‘low’ relative humidity, ± outside of one standard deviation respectivelyaway from the mean, for a given period and maps of climate variables averaged over these years are produced. We show representative composite maps for the south cluster for onset (Figure 8) and retreat periods (Figure 9). Composite maps of surface temperature for low years for onset period (Figure 8) shows a cooler land and warmer ocean, indicative of a weaker land-ocean temperature gradient - the winds show an anomalously southerly flow consistent with the temperature pattern. During high years the patterns are reversed, although the warming over land is a bit stronger than during low years, also the wind pattern is weaker than that of the low years and. The asymmetry in the relationship during low and high years indicates nonlinearity in the relationship and the difference maps in the same figure show this. These patterns are similar during retreat (Figure 9) and also for other clusters (figures not shown). Predictability As mentioned in the motivation, relative humidity during onset and withdrawal periods are important for the retreat and onset of meningitis season, respectively - thus, the ability to predict the relative humidity during these periods is of immense specific interest. To this end, predictors and forecasting models are developed understand the predictability and the potential long-lead skill. To identify predictors, the cluster time indices are correlated with large-scale climate variables from preceding periods. We selected three lead times to issue forecasts for the onset – i.e., the first of Mar, Apr and May, giving a lead time of 75, 45 and 15 day lead times, respectively. Figure 10 shows the correlation between the onset index of the southern cluster with Jan climate variables. It can be noticed that the correlation patterns with surface temperature, sea level pressure and winds are similar to the correlation patterns seen during the concurrent period (Figure 6) – indicating that the large scale patterns are persistent and thus, lending potential predictability. The boxes indicate regions of high absolute correlation. Correlations with climate variables on May 1 (Figure 11) also show similar patterns. Regions with high absolute correlation, approximately 0.4 or above in these maps are identified and the corresponding climate variables averaged over these regions provide potential predictors. Figures 12 and 13 show the correlation plots for June and August used to select retreat predictors. The red boxes in Figures 10 – 13 show the regions used to generate these predictors. The list of predictors identified for the different lead times and for onset and withdrawal are shown in Tables 1 and 2.    
(a) (b) (c) (d) Figure 10: Onset South Cluster correlation plots with January (a) Surface Temperature, (b) MSLP, (c) 200mb Zonal Winds, (d) 600mb Meridional Winds   
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