  Baryonic effects are amongst the most severe systematics to the tomographic
analysis of weak lensing data which is the principal probe in many future
generations of cosmological surveys like LSST, Euclid etc.. Modeling or
parameterizing these effects is essential in order to extract valuable
constraints on cosmological parameters. In a recent paper, Eifler et al. (2015)
suggested a reduction technique for baryonic effects by conducting a principal
component analysis (PCA) and removing the largest baryonic eigenmodes from the
data. In this article, we conducted the investigation further and addressed two
critical aspects. Firstly, we performed the analysis by separating the
simulations into training and test sets, computing a minimal set of principle
components from the training set and examining the fits on the test set. We
found that using only four parameters, corresponding to the four largest
eigenmodes of the training set, the test sets can be fitted thoroughly with an
RMS $\sim 0.0011$. Secondly, we explored the significance of outliers, the most
exotic/extreme baryonic scenarios, in this method. We found that excluding the
outliers from the training set results in a relatively bad fit and degraded the
RMS by nearly a factor of 3. Therefore, for a direct employment of this method
to the tomographic analysis of the weak lensing data, the principle components
should be derived from a training set that comprises adequately exotic but
reasonable models such that the reality is included inside the parameter domain
sampled by the training set. The baryonic effects can be parameterized as the
coefficients of these principle components and should be marginalized over the
cosmological parameter space.
