A data-driven probabilistic network approach to assess model similarity in CMIP ensembles

Abstract

The different phases of the Coupled Model Intercomparison Project (CMIP) provide ensembles of past, present, and future climate simulations crucial for climate change impact and adaptation activities. These ensembles are produced using multiple Global Climate Models (GCMs) from different modeling centres with some shared building blocks and inter-dependencies. Applications typically follow the ‘model democracy’ approach which might have significant implications in the resulting products (e.g. large bias and low spread). Thus, quantifying model similarity within ensembles is crucial for interpreting model agreement and multi-model uncertainty in climate change studies. The classical methods used for assessing GCM similarity can be classified into two groups. The a priori approach relies on expert knowledge about the components of these models, while the a posteriori approach seeks similarity in the GCMs’ output variables and is thus data-driven. In this study we apply Probabilistic Network Models (PNMs), a well established machine learning technique, as a new a posteriori method to measure inter-model similarities. The proposed methodology is applied to surface temperature fields of the historical experiments from the CMIP5 multi-model ensemble and different reanalysis gridded datasets. PNMs are capable to learn the complex spatial dependency structures present in climate data, including teleconnections operating on multiple spatial scales, characteristic of the underlying GCM. A distance metric building on the resulting PNMs is applied to characterize GCM model dependencies. The results of this approach are in line with those obtained with more traditional methods, but have further explanatory potential building on probabilistic model querying.