l . . . .
. . .
CSIRO-9 climate 1
Climate for the observed (thick solid line) and model simulated (thick dashed line) annual rainfall cycle over the summer grids as well as for the observed (thin solid line) and model simulated (thin dashed line) annual rainfall cycle over the prominent summer grids.
DJFMAMJJASOND
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Results and discussion
Results from the observed (thick solid lines), global run (thin solid lines) and tropical run (thin dashed lines) monthly rainfall totals and anomalies over the summer and prominent summer grids during the years 1996 and 1997 are graphically displayed in Figs. 3a and b.
Similar to the climate (Fig. 2) rainfall totals over the summer and prominent summer grids are predominantly been overestimated by the model simulations. Unlike r and ERa, the ER-score only provides information concerning an improvement or weakening in terms of model simulated rainfall totals, and is probably not the best measure for variability about the mean. To compensate for biases caused by the overestimation, it is recommended to rather compare model simulated and observed rainfall anomalies (Figs. 3a and b, bottom). A quantitative measure of the contribution of extra- tropical SST anomalies to model simulated rainfall anomalies is given by the ERa-score (Eq. (2)) results listed in Table 1. The ERas-scores shown in Table 1 denote ERa-scores calculated for the summer months October to January only, since the bulk of the rainfall over the summer grids occurs during this period.
A qualitative comparison of the two model simulations, for both regions, reveals an improvement during the November to March tropical run simulated rainfall totals (shaded intervals in Figs. 3a and b, top). An objective measure of this is given by the larger ER-score (Eq. (1)) results obtained by the tropical run, which are listed in Table 1. The ER-scores are positive for the summer grids, but negative for the prominent summer grids and suggests, for both regions, an improvement by the tropical run. Correlation coefficients (r) between observed and model simulated rainfall over the summer grids are positive and above the 99% significance levels (Table 1), and reveal a stronger relationship in the global run. Correlations are also higher for the summer grids. The ERa- and ERas-scores, however, show a significant improvement in the global run simulated rainfall. Both these global run scores (with a single exception) provide positive values meaning that the model performed better than climate, while the corresponding scores for the tropical run are negative. The result suggests that the global run simulation generally succeeded in capturing anomalous summer rainfall during the two years correctly, while the tropical run failed to do so. The global run also performs better over the summer grids than prominent summer grids. The improvement over both the summer and prominent summer grids can only be attributed to a fundamental remote extra- tropical SST influence, while stronger tropical contributions are evident for the prominent summer
JFMAMJJASONDJFMAMJJASOND
I I I I r I I I I I ,I I I I I I1 I1 I I
180 - - 180
160 - - 160
140 120 100 80 60 40 20
140 120 100 80 60 40 20
JFMAMJJASONDJFMAMJJASOND
1996 1997
Figure 3b: Same as figure 3a but for the prominent summer grids.
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A comparison of the ERa- and ERas- scores for either the summer and prominent summer grids reveals the existence of the seasonal dependency between the rainfall and extra-tropical SST forcing. During the austral winter, the model satisfactorily simulates the observed strengthening in the mid-latitude westerlies (not shown). Under conditions of relatively stronger zonal advection (compared to the austral summer), the modifying effect of extra-tropical SST’s on the overlying MABL is less profound (Engelbrecht and Rautenbach, 1999) and ocean-atmosphere interactions are suppressed. This results in weaker annual calculated ERa scores than those calculated for the period October to March only.
TABLE 1
OBSERVED VS. MODEL SIMULATED RAINFALL CORRELATION COEFFICIENTS (r), ER-SCORES (ER), ER ANOMALY-SCORES (ERa) AND ER ANOMALY-SCORES FOR THE
The contribution of extra-tropical SST anomalies to the 1996/97 model simulated rainfall over South Africa has been simulated using the CSIRO-9 Mark II (T63) AGCM. According to model grid locations and prevalent rainfall patterns, two South African rainfall regions namely the summer grids and prominent summer grids have been identified.
The results presented here indicate that substantial differences exist between the model responses obtained by the inclusion or exclusion of extra-tropical SST anomalies respectively. Over the summer and prominent summer grids, the inclusion of the effect of extra-tropical SST-anomalies increases the model’s ability to simulate anomalous rainfall. Exclusion of this effect causes an almost complete failure in the model’s ability of simulating the sign of anomalous rainfall correctly.
The including of extra-tropical SST anomalies in the model runs clearly results in the capturing of
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definite signals sent from the extra-tropical oceans, resulting in a significant improvement in the simulation of rainfall anomalies over the summer and prominent summer grids.
Finally, there exists a marked seasonal dependency in the relative importance of the contribution of extra-tropical SST anomalies on the simulated rainfall. During the November to March the relatively weaker westerlies allows extra-tropical SST anomalies to modify the MABL overlying the extra- tropical oceans. This modification is communicated to the summer rainfall regions through systems like tropical-temperate troughs, resulting in a significant improvement in simulated rainfall anomalies over the summer rainfall regions. During the months April to August strong zonal advection suppress meridional feedback processes between the extra-tropical oceans and tropical atmosphere. The model capture this process satisfactorily, resulting in extra-tropical SST anomalies that are of less importance during this period, or even slightly contaminating the model simulations.
Acknowledgements
The authors thank the CSIRO in Australia for supplying the AGCM used in this study, as well as the Water Research Commission for funding the research. Observed data have been provided by the SAWB. Many thanks to Mr Mike Haslam of the SAWB for providing additional computer space, and to Mrs Helen Kenyon for her contribution in editing the text.
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