  The Reseau de Transport d'Electricit\'e (RTE) is the French main electricity
network operational manager and dedicates large number of resources and efforts
towards understanding climate time series data. We discuss here the problem and
the methodology of grouping and selecting representatives of possible climate
scenarios among a large number of climate simulations provided by RTE. The data
used is composed of temperature times series for 200 different possible
scenarios on a grid of geographical locations in France. These should be
clustered in order to detect common patterns regarding temperatures curves and
help to choose representative scenarios for network simulations, which in turn
can be used for energy optimisation. We first show that the choice of the
distance used for the clustering has a strong impact on the meaning of the
results: depending on the type of distance used, either spatial or temporal
patterns prevail. Then we discuss the difficulty of fine-tuning the distance
choice (combined with a dimension reduction procedure) and we propose a
methodology based on a carefully designed index.
