  Accounting for the annual climatic variability is a well-known issue for
simulation-based studies of environmental models. It often requires intensive
sampling (e.g., averaging the simulation outputs over many climatic series),
which hinders many sequential processes, in particular optimization algorithms.
We propose here an approach based on a subset selection of a large basis of
climatic series, using an ad-hoc similarity function and clustering. A
non-parametric reconstruction technique is introduced to estimate accurately
the distribution of the output of interest using only the subset sampling. The
proposed strategy is non-intrusive and generic (i.e. transposable to most
models with climatic data inputs), and can be combined to most "off-the-shelf"
optimization solvers. We apply our approach to sunflower phenotype optimization
using the crop model SUNFLO. The underlying optimization problem is formulated
as multi-objective to account for risk-aversion. Our approach achieves good
performances even for limited computational budgets, outperforming
significantly more "naive" strategies.
