Working Group I: The Scientific Basis

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This synoptic downscaling approach relates “weather classes” to local and regional climate variations. The weather classes may be defined synoptically or fitted specifically for downscaling purposes by constructing indices of airflow (Conway et al., 1996). The frequency distributions of local or regional climate are then derived by weighting the local climate states with the relative frequencies of the weather classes. Climate change is then estimated by determining the change of the frequency of weather classes. However, typing procedures contain a potentially critical weakness in assuming that the characteristics of the weather classes do not change.

In many cases, the local and regional climate states are derived by sampling the observational record. For example, Wanner et al. (1997) and Widmann and Schär (1997) used changing global to continental scale synoptic structures to understand and reconstruct Alpine climate variations. The technique was applied similarly for New Zealand (Kidson and Watterson, 1995) and to a study of changing air pollution mechanisms (Jones and Davies, 2000).

An extreme form of weather typing is the analogue method (Zorita et al., 1995). A similar concept, although mathematically more demanding, is Classification And Tree Analysis (CART) which uses a randomised design for picking regional distributions (Hughes et al., 1993; Lettenmaier, 1995). Both analogue and CART approaches returnapproximately the right level of variance and correct spatial correlation structures.

Weather typing is also used in statistical-dynamical downscaling (SDD), a hybrid approach with dynamical elements (Frey-Buness et al., 1995 and see references in Appendix 10.4). GCM results of a multi-year climate period are disaggregated into non-overlapping multi-day episodes of quasi-stationary large-scale flow patterns. Similar episodes are then grouped in classes of different weather types, and, members of these classes are simulated with an RCM. The RCM results are statistically evaluated, and the frequency of occurrence of the respective classes determines their statistical weight. An advantage of the SDD technique over other empirical downscaling techniques is that it specifies a complete three-dimensional climate state. The advantage over continuous RCM simulations is the reduction in computing time, as demonstrated in Figure 10.17.

10.6.3 Issues in Statistical Downscaling Temporal variance

Transfer function approaches and some weather typing methods suffer from an under prediction of temporal variability, as this is related only in part to the large-scale climate variations (see Katz and Parlange, 1996). Two approaches have been used to restore the level of variability: inflation and randomisation. In the inflation approach the variation is increased by the multiplication of a suitable factor (Karl et al., 1990). A more sophisticated version is “expanded downscaling”, a variant of Canonical Correlation Analysis that ensures the right level of variability (Bürger, 1996; Huth, 1999; Dehn et al., 2000). In the randomisation approachs, the unrepresented variability is added as noise, possibly conditioned on synoptic state (Buma and Dehn, 1998; Dehn and Buma 1999; Hewitson, 1999; von Storch, 1999b).

Often weather generators have difficulty in representing low frequency variance, and conditioning the generator parameters on the large-scale state may alleviate this problem (see Katz and Parlange, 1996; Wilby, 1998; Charles et al., 1999a). For example, Katz and Parlange (1993, 1996) modelled daily time-series of precipitation as a chain-dependent process, conditioned on a discrete circulation index. The results demonstrated that the mean and standard deviation of intensity and the probability of precipitation varied significantly with the circulation, and reproduced the precipitation variance statistics of the observations better than the unconditioned model. The method describes the mean precipitation as a linear function of the circulation state, and the standard deviation as a non-linear function (Figure 10.18).

Figure 10.17: Similarity of time mean precipitation distributions obtained in a continuous RCM simulation and through statistical-dynamical downscaling (SDD) for different levels of disaggregation. Black line: mean absolute error (mm/day), grey line: spatial correlation coefficient. Horizontal axis: computational load of SDD. N is the number of days simulated in SDD, Ñ the number of days simulated with the continuous RCM simulation.

Figure 10.18: Hypothetical changes in mean and standard deviation of January total precipitation at Chico, California, as a function of changing probability that January mean sea level pressure is above normal.

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