Model evaluation and intercomparison for marine warm low cloud fractions with neural network ensembles

Low cloud fractions (LCFs) and meteorological factors (MFs) over an oceanic region containing multiple cloud regimes are examined for three data sets: one Energy Exascale Earth System Model (E3SM) simulation with the default 72-layer vertical grid (E3SM72), another one with 8-times vertical resolution via the Framework for Improvement by Vertical Enhancement (E3SMx8), and one with MFs from ERA5 reanalysis and LCFs from the CERES SSF product (ERA5-SSF). Neural networks (NNs) are trained to capture the relationship between MFs and LCF and to select the best-performing MF subsets for predicting LCF. NN ensembles are used to confirm the performance of selected MF subsets, to serve as proxy models for each data set to predict LCFs for MFs from all data sets, and to classify MFs into those in shared and uniquely occupied MF subspaces. Overall, E3SM72 and E3SMx8 have large fractions of MFs in shared MF subspace, but less so near the Californian and Peruvian stratocumulus decks. E3SMx8 and ERA5 have small fractions of MFs in shared MF subspace but greater than E3SM72 and ERA5, especially in the Southeast Pacific. The differences in LCFs between three pairs of data sets are decomposed into those associated with the differences in the LCF-MF relationship and those involving different MFs. Given the same MFs, LCFs produced by E3SMx8 are greater than those produced by E3SM72, but are still different from those generated by ERA5-SSF. In general, the shift in MFs dominates the difference in the LCFs.