The parameter optimization problem in state-of-the-art climate models and network analysis for systematic data mining in model intercomparison projects.

The focus of this work is on two major problems facing the scientific community when using increasingly complicated climate model outputs to investigate the past and future evolution of our climate. On one hand, it is important to assess the reliability of such models and how their response to increased greenhouse gas concentrations may depend on the parameters and parameterizations chosen; on the other, it is fundamental to improve our ability to validate and compare model results in a robust, compact, and meaningful way. Understanding how sensitive climate models are to changes in their parameters is of fundamental importance when addressing the problem of modeled climate sensitivity. Here a quadratic metamodel that uses a polynomial approximation to describe the parameter dependency is presented together with its application to the Community Atmospheric Model, CAM, in its two latest versions. Furthermore, the application of complex network analysis to climate fields is briefly summarized and a novel methodology that allows for robust model intercomparisons is presented together with a set of metrics to quantify the topological properties of model outputs. The application of the network analysis to outputs from the Coupled Model Intercomparison Project - Phase 5 (CMIP5) completes the notes.