Options for Improving Climate Modeling to Assist Water Utility ...

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