STATISTICAL TESTS FOR COMPARISON OF DAILY VARIABILITY IN OBSERVED ANDSIMULATED CLIMATES

Tests for differences in daily variability based on the jackknife are presented. These tests properly account for the effect of autocorrelat ion in the data and are reasonably robust against departures from norm ality. Three measures for the daily variability are considered: proces s, within-month, and innovation variance. The jackknife statistic comp ares the logarithm of these measures. The standard errors of this loga rithm are obtained by recomputing the variance estimates for all subsa mples wherein one month is omitted from the complete sample. A simple extension of the jackknife procedure is given to obtain a powerful mul tivariate test in situations that the differences in variance have the same sign across the region considered or over the year. As an illust ration the tests are applied to near-surface temperatures over Europe simulated by the coupled ECHAM/LSG model. It is shown that the control run of the model significantly overestimates the process variance in winter and spring and the within-month variance in all seasons. Signif icantly differences are also found for the innovation variances of the daily temperatures, but the sign of the differences varies over the y ear. In a perturbed run with enhanced atmospheric greenhouse gas conce ntrations the daily temperature variability over Europe significantly decreases in winter and spring compared with the control run.