0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 (a) z Omz 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 (b) z Omz 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 (c) z Omz 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 (d) z Omz FIG. 5: The marginalized 1 σ and 2 σ errors of Om ( z ) reconstructed in the Wetterich model. (a) uses the combination of Csta SNIa+BaoR, (b) uses the combination of Csta SNIa+BaoZ, (c) uses the combination of Sdss2 SNIa+BaoR, and (d) uses the combination of Sdss2 SNIa+BaoZ. SNIa data sets, respectively. As indicated in Fig. 1 and Fig. 2, the green lines are just for SNIa data alone, the dashed black lines are for SN+BaoR, the cyan lines are results for SNIa+Bao2, the magenta lines indicate SNIa+BaoZ, the blue lines are for SNIa+Bao4, and the solid red lines are for the combination of SNIa+Bao4+WMAP5. It is interesting to see that different from the CPL ansatz, this parametrization allows the compatibility of different SNIa, Bao and CMB data sets, even for comparing the combinations of Csta+BaoR, Csta+BaoZ and Csta+Bao4+WMAP5. The Wetterich ansatz is found able to fit different data sets both at low and high redshifts. In Fig. 5, we present the behaviors of the Om ( z ) reconstructed by comparing the influence of different SNIa and BAO data sets. Interestingly, the evolution of Om ( z ) is not affected much by different SNIa and BAO data sets. The 12