Flood Risk and Vulnerability Analysis Project - Atlantic Climate ...

Development of Projected Intensity-Duration-Frequency Curves
for Corner Brook and Goulds/Petty Harbour, Newfoundland May 16, 2012
scope is relatively local and their boundary conditions are set by the results of a
GCM run, use of an RCM is sometimes referred to as ―dynamical downscaling‖.
RCMs also run at a finer temporal resolution than GCMs and some RCMs can
produce output at 1-hour time steps. These data have been used directly to
characterize short-term precipitation, but cannot directly address time steps shorter
than the time step of the RCM. Because RCMs are computationally expensive to
run and their spatial extent is limited, there are fewer runs available. The results of
an RCM run will reflect any biases in the GCM run on which it is based.
Applying scale factors to climate model output. Scale factors relate short-term
precipitation to longer-term precipitation, e.g. monthly average to total precipitation.
Applying statistical models to climate model output. Statistical models can be
developed to represent a functional relationship between short-term precipitation
and a predictor variable, usually at a larger temporal and spatial scale (e.g. monthly
total precipitation at a GCM or RCM resolution). The most common statistical model
is a linear model developed using regression. Linear models of the parameters of
extreme value distributions (e.g. Gumbel, Weibull) have also been used.
Conditional weather generators applied to climate model output. Weather
generators are stochastic models that generate time series of weather conditions.
These models may be parametric (they rely on classical statistical distributions
described by parameters) or they can be non-parametric (they rely on re-sampling
analogs of weather from the historical data) or a combination of the two. When
applied to investigating climate change impacts, weather generators are usually
conditioned on large-scale weather variables using statistical models as described
above or using non-parametric techniques like the K-nearest neighbor technique.
In the approaches described above, climate model output may be ―raw‖ output at the
native grid scale of the climate model (either a GCM or a RCM) or downscaled output.
Despite the remarkable improvements in the scientific state of knowledge about the
processes that drive weather, and ultimately climate, there is considerable uncertainty
about the sensitivity of climate to increasing greenhouse gas concentrations. Because
of that uncertainty, and because of practical constraints, each of the methods described
above have shortcomings. It is fair to say that all of the methods described above rely
to some degree on an assumption that the relationships between large-scale and smallscale
processes, both in time and space, will continue unchanged into the future. This
is true of all approaches that use downscaling, scale factors and statistical models,
including weather generators. Climate models simulate climate sensitivity, but even
these models contain statistical sub-models and are limited in spatial and temporal
resolution. In short, there is no perfect method to project climate impacts, there will be
AMEC Environment & Infrastructure 18