  Clouds play a key role in regulating climate change but are difficult to
simulate within Earth system models (ESMs). Improving the representation of
clouds is one of the key tasks towards more robust climate change projections.
This study introduces a new machine-learning based framework relying on
satellite observations to improve understanding of the representation of clouds
and their relevant processes in climate models. The proposed method is capable
of assigning distributions of established cloud types to coarse data. It
facilitates a more objective evaluation of clouds in ESMs and improves the
consistency of cloud process analysis. The method is built on satellite data
from the MODIS instrument labelled by deep neural networks with cloud types
defined by the World Meteorological Organization (WMO), using cloud type labels
from CloudSat as ground truth. The method is applicable to datasets with
information about physical cloud variables comparable to MODIS satellite data
and at sufficiently high temporal resolution. We apply the method to
alternative satellite data from the Cloud\_cci project (ESA Climate Change
Initiative), coarse-grained to typical resolutions of climate models. The
resulting cloud type distributions are physically consistent and the horizontal
resolutions typical of ESMs are sufficient to apply our method. We recommend
outputting crucial variables required by our method for future ESM data
evaluation. This will enable the use of labelled satellite data for a more
systematic evaluation of clouds in climate models.
