Considerations for parameter optimization and sensitivity in climate models

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An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “Neelin et al. (2010) made use of a surrogate modeling based parameter optimization and sensitivity analysis method to identify precipitation bias and sensitivity of an atmospheric General Circulation Model.”
A mathematical theory of climate sensitivity or, How to deal with both anthropogenic forcing and natural variability? (2015, background): “A comparison with numerical results on parameter dependence for a somewhat simplified GCM (Neelin et al., 2010) goes beyond the scope of this brief overview, and will be given elsewhere.”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, background): “…or by minimizing deviations between simulated and observed longer-term aggregates of climate statistics, such as global-mean TOA radiative fluxes accumulated over seasons or years [e.g., Jackson et al., 2008; Järvinen et al., 2010; Neelin et al., 2010; Solonen et al., 2012; Tett et al., 2013].”
Tuning the climate of a global model (2012, background): “Alternatively, one can use a relatively small number of model runs to build a statistical model, or emulator, of the error as a function of parameter space to obtain parameter sets that minimize model error [Neelin et al., 2010].”