Regional Regime‐Based Evaluation of Present‐Day General Circulation Model Cloud Simulations Using Self‐Organizing Maps

Global clusters are derived by applying the self‐organizing map technique to the Moderate Resolution Imaging Spectroradiometer cloud top pressure‐cloud optical thickness joint histograms. These cloud clusters are then used to classify Cloud Feedback Model Intercomparison Project Observation Simulator Package output from the HadGEM3 (Global Atmosphere version 7) atmosphere‐only climate model. Discrepancies in the Global Atmosphere version 7 representation of particular clusters can be established by examining the two sets of cluster's occurrence rate and radiative effect. The overall differences in the occurrence rates show major discrepancies in several of the clusters, resulting in a shift from five dominant clusters in Moderate Resolution Imaging Spectroradiometer (above 10% occurrence rate) to two dominant clusters in the model. A comparison of the geographic distributions of occurrence rate shows that the differences are strongly regional and unique to each cluster. While comparisons of the global mean longwave and shortwave cloud radiative effect (CRE) show strong agreement, examination of the CRE of individual cloud types reveals larger errors that highlight the role of compensating errors in masking model deficiencies. CRE data for each of the clusters is further partitioned into regions. This establishes that the bias associated with a cluster is highly variable globally, with no clusters showing consistent biases across all regions. Therefore, regional level phenomena likely play an important role in the creation of these errors. Clusters are generated by applying a self‐organizing map to MODIS joint histograms of cloudRadiative differences between the model and observations are attributed to specific clustersRegional cloud radiative effect errors are obscured by compensating errors between clusters