Working Group I: The Scientific Basis

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10.7 Intercomparison of Methods

Few formal comparative studies of different regionalisation techniques have been carried out. To date, published work has mostly focused on the comparison between RCMs and statistical downscaling techniques. Early applications of RCMs for climate change simulations (Giorgi and Mearns, 1991; Giorgi et al., 1994) compared the models against observations or against the driving GCMs, but not against statistical/empirical techniques.

Kidson and Thompson (1998) compared the RAMS (Regional Atmospheric Modelling System) dynamical model and a statistical regression-based technique. Both approaches were applied to downscale reanalysis data (ECMWF) over New Zealand to a grid resolution of 50 km. The statistical downscaling used a screening regression technique to predict local minimum and maximum temperature and daily precipitation, at both monthly and daily time-scales. The regression technique limits each regression equation to five predictors (selected from Empirical Orthogonal Functions (EOFs) of atmospheric fields). Both monthly and daily results indicated little difference in skill between the two techniques, and Kidson and Thompson (1998) suggested that, subject to the assumption of statistical relationships remaining viable under a future climate, the computational requirements do not favour the use of the dynamical model. They also noted, however, that the dynamical model performed better with the convective components of precipitation.

Bates et al. (1998) compared a south-western Australia simulation using the DARLAM (CSIRO Division of Atmospheric Research Limited Area Model) model with a down-scaled DARLAM simulation where the downscaling model had been fitted independently to observational data. The downscaling reproduced observed precipitation probabilities and wet and dry spell frequencies while the DARLAM simulation under-estimated the frequency of dry spells and over estimated the probability of precipitation and the frequency of wet spells. In a climate change follow-on experiment, again using both methods, Charles et al. (1999b) found a small decrease in probability of precipitation under future climate conditions.

Murphy (1999) evaluated the UK Meteorological Office Unified Model (UM) RCM over Europe against a statistical downscaling model based on regression. Monthly mean surface temperature and precipitation anomalies were down-scaled using predictor sets chosen from a range of candidate variables similar to those used by Kidson and Thompson (1998) (EOFs of atmospheric fields). The results showed similar levels of skill for the dynamical and statistical methods, in line with the Kidson and Thompson (1998) study. The statistical method was nominally better for summertime estimates of temperature, while the dynamical model gave better estimates of wintertime precipitation. Again, the conclusion was drawn that the sophistication of the dynamical model shows little advantage over statistical techniques, at least for present day climates.

Murphy (2000) continued the comparative study by deriving climate change projections for 2080 to 2100 from a simulation with the HadCM2 AOGCM. The dynamical and statistical downscaling techniques were the same regional and statistical models as used by Murphy (1999). The statistical and dynamical techniques produced significantly different predictions of climate change, despite exhibiting similar skill when validated against present day observations. The study identifies two main sources of divergence between the dynamical and statistical techniques: firstly, differences between the strength of the observed and simulated predictor/predictand relationships, and secondly, omission from the regression equations of variables which represent climate change feedbacks, but are weak predictors of natural variability. In particular, the exclusion of specific humidity led to differences between the dynamical and statistical simulations of precipitation change. This point would seem to confirm the humidity issue raised in Section 10.6.3 (Hewitson and Crane 1996, Crane and Hewitson, 1998, Charles et al., 1999b; Hewitson 1999).

Mearns et al. (1999) compared RCM simulations and statistical downscaling using a regional model and a semi-empirical technique based on stochastic procedures conditioned on weather types which were classified from circulation fields (700hPa geopotential heights). While Mearns et al. suggest that the semi-empirical approach incorporates more physical meaning into the relationships than a pure statistical approach does, this approach does impose the assumption that the circulation patterns are robust into a future climate in addition to the normal assumption that the cross-scale relationships are stationary in time. For both techniques, the driving fields were from the CSIRO AOGCM (Watterson et al., 1995). The variables of interest were maximum and minimum daily temperature and precipitation over central-northern USA (Nebraska). As with the preceding studies, the validation under present climate conditions indicated similar skill levels for the dynamical and statistical approaches, with some advantage by the statistical technique.

In line with the Murphy (2000) study, larger differences were also noted by Mearns et al. (1999) when climate change projections were produced. Notably for temperature, the statistical technique produced an amplified seasonal cycle compared to both the RCM and CSIRO data, although similar changes in daily temperature variances were found in both the RCM and the statistical technique (with the statistical approach producing mostly decreases). The spatial patterns of change showed greater variability in the RCM compared with the statistical technique. Mearns et al. (1999) suggested that some of the differences found in the results were due to the climate change simulation exceeding the range of data used to develop the statistical model, while the decreases in variance were likely to be a true reflection of changes in the circulation controls. The precipitation results from Mearns et al. (1999) are different from earlier studies with the same RCM (e.g., Giorgi et al., 1998) that produced few statistically significant changes.

Extending the comparison beyond simple methodological performance, Wilby et al. (2000) compared hydrological responses using data from dynamically and statistically down-scaled climate model output for the Animas River basin in Colorado, USA. While not a climate change projection, the use of output from an RCM and a statistical downscaling approach to drive a distributed hydrological model exemplify the objective of the downscaling. The results indicate that both the statistical and dynamical methods had greater skill (in terms of modelling hydrology) than the coarse resolution reanalysis output used to drive the downscaling. The statistical method had the advantage of requiring very few parameters, an attribute making the procedure attractive for many hydrological applications. The dynmical model output, once elevation-corrected, provided better water balance estimates than raw or elevation-corrected reanalysis output.

Overall, the above comparative studies indicate that for present climate both techniques have similar skill. Since statistical models are based on observed relationships between predictands and predictors, this result may represent a further validation of the performance of RCMs. Under future climate conditions more differences are found between the techniques, and the question arises as to which is “more correct”. While the dynamical model should clearly provide a better physical basis for change, it is still unclear whether different regional models generate similar downscaled changes. With regard to statistical/empirical techniques, it would seem that careful attention must be given to the choice of predictors, and that methodologies which internally select predictors based on explanatory power under present climates may exclude predictors important for determining change under future climate modes.

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