This chapter describes Global Climate Models (GCMs), limitations and uncertainties associated with the formulation of GCMs due to the effect of aerosols which are differently parameterized in GCMs, initial and boundary conditions for each GCM, parameters and model structure of GCMs, randomness, future greenhouse gas emissions, and scenarios leading to significant variability across model simulations of future climate. This chapter discussed the necessity of performance indicators for evaluating GCMs and explained mathematical description of these indicators. It also emphasized on normalization approach, weight computing techniques such as entropy and rating, ranking approaches, namely, compromise programming, cooperative game theory, TOPSIS, weighted average, PROMETHEE, and fuzzy TOPSIS. Spearman rank correlation which measures consistency in ranking pattern and group decision-making that aggregates individual rankings obtained by different techniques to form a single group preference is also part of this chapter. Ensembling methodology of GCMs is also discussed. Reader is expected to understand various uncertainties associated, role of decision-making techniques for ranking of GCMs by studying this chapter.
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