"Spagetti-western
properties of least-squares estimates of spectrally red signals: (good)
can be approximated by a few modes, (bad)
have less variance than the true signal, and (ugly)
redder than the true signal.
"These
properties can be used for making analyses of sparse climate data cheaper
and less ambiguous in their setup.
"Since
the effect of these properties is stronger for poor data, and the data quality
generally improves with time, use of least-squares
analyses at face value, as if they were the truth, poses a threat of misinterpretation.
"A
possible way out (however expensive): use of ensembles drawn from the
posterior distributions rather than a single ensemble
mean.