La disensión funcional del lenguaje

and RCM simulations. The largest differences lie in a 0.2^◦C higher increase signal over the western Sahel and the central Sahara.

Precipitation Precipitation, as illustrated in figures 6.14, is lower in the MM5 simula-tions for Domain 1 than in the ECHAM simulasimula-tions. Nevertheless an improvement in the

(a) MM5 (81 km resolution) (b) ECHAM4 (2.5^◦resolution) Figure 6.14: Mean annual precipitation [mm] (1991-2000)

spatial representation of rainfall can be observed. One example is found in the mountain range along the border of Cameroon and Nigeria, where the highest peak reaches 4380 m. Here, the maximum in observed rainfall is also found. This rainfall maximum is not represented within the global simulation at coarse resolution, but appears in the regional simulations of D1.

A comparison of the three MM5 model domains (figure6.14, left: D1 and figure6.15: D2 and D3) demonstrates the improvement in rainfall amounts that accompany an increase in model resolution. The cause might be that the convective parameterization scheme works better at the finer resolution. It is also likely that the higher resolution in topography contributes to improved results, such as the increase in precipitation amounts in the moun-tainous region of central Benin. Nevertheless the precipitation change signal within the simulations is similar (figure 6.16). There is a precipitation increase over most of the con-tinental part of West Africa, with a maximum along the mountain range along the border between Nigeria and Cameroon. The strongest precipitation reduction signal in both sim-ulations can be found in the Gulf of Guinea region and along the southern coastline, as well as in the region of Senegal, Mauritania and West Sahara. A difference occurs along the southern West African coastline; where there is a precipitation decrease simulated in the ECHAM4 run, but an increase with MM5.

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(a) MM5 (27 km resolution) (b) MM5 (9 km resolution) Figure 6.15: Mean annual precipitation [mm], MM5 (RCM)

(a) MM5 (b) ECHAM4

Figure 6.16: Mean annual precipitation change [%] (2030-2039 vs. 1991-2000)

CHAPTER 6. VALIDATION 86

6.3.2 Comparison to Observed Climate

Because precipitation in both GCM and RCM simulations is strongly dependent on the sub-gridscale parameterization scheme used, it is also necessary to compare the downscaled precipitation to observed data. However single years of a climate simulation can not be com-pared to single years of observation. Within this study, a comparison of the 10-year model time slice to a time slice of the same length of observed climate is not feasible, especially in West Africa, given the regions’ strong decadal variability. Acknowledging that averaging over a longer time span removes part of the low frequency variability, the comparison was performed with respect to long term climate mean conditions. Therefore all observed time series of 30 years that were available for Ghana and Burkina Faso were spatially interpo-lated to the model grid of D3¹ and compared to the MM5 (downscaled ECHAM4) climate run for 1991-2000. Even though the informative value is smaller because of the short time slice, the comparison is justified to achieve an approximation of model performance.

Liu and Moncrieff (2004) demonstrated that the position of the Inter Tropical Dis-continuity (ITD) is dependent on the convective parameterization scheme used in a GCM and therefore it can be expected that the position of the ITD is modified within the RCM simulation. For this reason, the same argumentation as for rainfall is addressed for the ITD position. Subsequently the position of the ITD is also compared between RCM simulations and long term means later in this chapter.

Precipitation The spatial representation of annual rainfall deviations (modelled - ob-served) (figure6.17), demonstrates a strong underestimation of rainfall along the coast, (up to 80%) similar to what was found for the MM5 reanalysis runs, but only a small overes-timation of rainfall in the Sahel (10-30%), which is most likely introduced to the regional climate simulations through the wet bias of ECHAM4, described in section 6.1.

The spatially and monthly averaged values of precipitation for the long term mean of observed climate and the MM5 run in figure 6.18 indicate reasonable accuracy in the representation of the annual cycle of precipitation, and in the spatial means of precipitation sums.

Inter Tropical Discontinuity (ITD) One very important characteristic that influences the climate in West Africa is the annual movement of the ITD. The meridional wind direction was chosen as a measure for the position of the ITD in the MM5 simulations, because along the ITD (as it is a convergence zone) the meridional wind component changes its algebraic sign. Figure 6.19 shows the months January, June and September to provide insight into the mean displacement of the ITD for the ECHAM4-MM5 simulated years 1991-2000 and the long term mean position for the respective months (Leroux, 2001).

The southernmost position of the ITD within the year (December/January) is captured well. The movement of the ITD to the North appears to happen too rapidly, showing a larger deviation of the simulated from the climate mean position from April to June. In the simulations, it already reaches its northernmost position in June, whereas for the long term

1Spatially interpolated station data: the data consists of 95 stations’ time series with a length of 30 years. The spatial interpolation was done with a combination of inverse distance weighting (IDW) and multiple linear regression (MLR), weighting IDW with 60%. For the MLR, latitude, longitude, height, slope, curvature and aspect were considered

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Figure 6.17: Simulated mean annual precipitation (1991-2000) versus observed long term mean [mm], D3

Figure 6.18: Spatially averaged, simulated mean monthly precipitation (1991-2000) versus observed long term mean [mm], D3 land area

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(a) January (b) June

(c) September

Figure 6.19: Mean position of the ITD (D1), shaded: modelled 1991-2000, dashed line:

observed long term mean (Leroux,2001)

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