The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

Answering questions about empirical downscaling methodologies
<strong>The</strong> increasing demand for regional scale climate change projections, and the increasing
availability of empirical downscaling products, raise important questions about the role and
influence of methodology choice on the derived projections. <strong>The</strong>re are a wide range of applied
downscaling methods in the literature, and which seek to accommodate the components of
deterministic and stochastic variance in the local climate response to large scale forcing. In
general the methods fall into one of two approaches; using some form of scale transfer
function to capture the deterministic component and then adding the stochastic element, or
alternatively using a technique such as a weather generator conditioned in some form to
accommodate the deterministic large scale forcing.
We compare two such methodologies which have matured and been used to provide
regional scenarios for a broad range of end user applications. <strong>The</strong> two approaches are used to
project climate change for four locations that present a challenge due to location and complex
local features. <strong>The</strong> four locations span Africa and include tropical and higher latitudes, coastal
and inland regions, and complex topography.
Does nonlinearity bring an improvement in statistical downscaling of daily temperature?
Speaker: Radan Huth
Radan Huth
Institute of Atmospheric Physics, Prague, Czech Republic
huth@ufa.cas.cz
Stanislava Kliegrova
Czech Hydrometeorological Institute, regional office, Hradec Kralove, Czech Republic
Ladislav Metelka
Czech Hydrometeorological Institute, regional office, Hradec Kralove, Czech Republic
<strong>The</strong> aim of the contribution is to evaluate the potential gain in the performance of
statistical downscaling due to introduction of nonlinearity into the transfer functions. This issue
has not been resolved so far. <strong>The</strong> nonlinearity is introduced in two different ways: (i) by using
artificial neural networks (ANNs), namely the multilayer perceptron architecture, and (ii) by
stratification of the dataset by a classification of circulation patterns, the linear method being
built in each class separately. <strong>The</strong> study concerns daily maximum and minimum temperatures
in winter at eight European stations differing in their geographical and climatological settings,
spread from Ireland to Russia and from northern Finland to Spain. <strong>The</strong> predictors are the 500
hPa heights and 850 hPa temperature defined in a network covering whole Europe and a large
part of the neighbouring Atlantic Ocean. <strong>The</strong> performance of the methods is quantified in terms
of correlations between the downscaled and observed values. Other measures of
correspondence, such as mean absolute error or root mean square error, lead to identical
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