219 Self Organizing Map Based Evaluation of Present Day GCM Cloud Simulations

Wednesday, 11 July 2018
Regency A/B/C (Hyatt Regency Vancouver)
Alex J Schuddeboom, University of Canterbury, Christchurch, New Zealand; and A. J. McDonald, O. Morgenstern, M. Harvey, and S. Parsons

Global cloud clusters are derived by applying the self organizing map technique to the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud top pressure - cloud optical thickness joint histograms. These cloud clusters are then used to classify CFMIP Observation Simulator Package output from the HadGEM3 (GA7) atmosphere-only climate model. Discrepancies in the GA7 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 MODIS (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 which 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. These clusters were then tied to the Cloudsat data which allowed for a more comprehensive analysis of the structure of cloud associated with the clusters. When combined with Calipso data this also allowed us to investigate the role of phase in these model biases.
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