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Water Utility Climate Alliance White Paper Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change 3. Historic climate simulation. The downscaling technique is provided data from a GCM historic climate simulation. The differences between this and the baseline climate simulation provide a measure of the errors introduced by the inherent inconsistency of climate conditions between downscaling techniques and GCMs, the skill of the GCM at representing large-scale climate conditions, and the possibility that slowly varying ocean conditions in the GCM and observations are out of phase. 4. Future climate projection. The downscaling technique is provided data from a GCM future climate projection. The differences between this and the historic climate simulation is a measure of the climate change signal, i.e., how much change is projected due to increased GHG concentrations. 8 3.2.6 What types of climate model downscaling techniques have been developed, and what are their strengths and weaknesses? Climate model downscaling techniques involve three types of models: statistical downscaling models, RCMs (a form of dynamical downscaling), and time-slice general circulation models (also a form of dynamical downscaling). Statistical downscaling models Statistical downscaling models use empirical mathematical relationships between output from GCM simulations of historic climate and local climate observations (Figure 3.3). Alternatively, or additionally, these relationships can be developed from observations that have been aggregated to the spatial scales represented in the GCMs. For example, estimations of temperature at a particular location may be correlated with upper-level pressure patterns and wind fields. The GCM’s projections of these pressure and wind patterns would then be used to derive the temperature projections at this location. The mathematical relationships are presumed unchanged when given data from the GCM future climate projections. Statistical downscaling techniques tie local climate conditions to the large-scale conditions, but provide no feedback from these local conditions to the large-scale conditions. Therefore, these techniques are best applied in regions in which local processes have little influence on the largescale circulation. A region where climate change is dominated by changes in the frequency and location of large-scale weather systems would be a good candidate for statistical downscaling. A region where feedback between soil moisture and convective rainfall is important may not be a good candidate. 8. The robustness to changing climate conditions can be compared to a climate change signal to determine regions where lack of robustness may overwhelm the climate change signal. Page 3-33
Water Utility Climate Alliance White Paper Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change Figure 3.3. Statistical downscaling using classification of weather types based on atmospheric pressure patterns. This figure illustrates some general properties of statistical downscaling such as the derivation of relationships between large-scale and local weather variables in observations, and the use of these relationships in downscaling GCM projections. Source: Canadian Institute for Climate Studies, 2006. Three assumptions are implicit in all statistical downscaling techniques. 1. The large-scale climate variables are being modeled realistically by the GCM 2. The empirical mathematical relationships are unchanged when the climate changes 3. The global climate variables are capable of representing the entire climate change signal. Results show that no single statistical downscaling method is uniformly better than all others in every application. However, it is necessary to be mindful of the limitations of some statistical downscaling techniques. Change factors only change the means and extremes of the GCM output. The range, variability, and spatial pattern generated from change factors remain Page 3-34
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