Take home points
"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.