  Large bias exists in shortwave cloud radiative effect (SWCRE) of general
circulation models (GCMs), attributed mainly to the combined effect of cloud
fraction and water contents, whose representations in models remain
challenging. Here we show an effective machine-learning approach to dissect the
individual bias of relevant cloud parameters determining SWCRE. A surrogate
model for calculating SWCRE was developed based on random forest using
observations and FGOALS-f3-L simulation data of cloud fraction (CFR),
cloud-solar concurrence ratio (CSC), cloud liquid and ice water paths (LWP and
IWP), TOA upward clear-sky solar flux (SUC), and solar zenith angle. The model,
which achieves high determination coefficient > 0.96 in the validation phase,
was then used to quantify SWCRE bias associated with these parameters following
the partial radiation perturbation method. The global-mean SWCRE bias (in W
m-2) is contributed by CFR (+5.11), LWP (-6.58), IWP (-1.67), and CSC (+4.38),
while SUC plays a minor role; the large CSC contribution highlights the
importance of cloud diurnal variation. Regionally, the relative importance
varies according to climate regimes. In Tropics, overestimated LWP and IWP
exist over lands, while oceans exhibit underestimated CFR and CSC. In contrast,
the extratropical lands and oceans have, respectively, too-small CSC and the
'too few, too bright' low-level clouds. We thus suggest that machine learning,
in addition for developing GCM physical parameterizations, can also be utilized
for diagnosing and understanding complex cloud-climate interactions.
