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The global circulation models (GCMs) are useful tools for simulating climate change, projecting future temperature changes, and therefore, supporting the preparation of national climate adaptation plans. However, different GCMs are not always in agreement with each other over various regions. The reason is that GCMs’ configurations, module characteristics, and dynamic forcings vary from one to another. Model ensemble techniques are extensively used to post-process the outputs from GCMs and improve the variability of model outputs. Root-mean-square error (RMSE), correlation coefficient (CC, or R) and uncertainty are commonly used statistics for evaluating the performances of GCMs. However, the simultaneous achievements of all satisfactory statistics cannot be guaranteed in using many model ensemble techniques. In this paper, we propose a multi-model ensemble framework, using a state-of-art evolutionary multi-objective optimization algorithm (termed MOSPD), to evaluate different characteristics of ensemble candidates and to provide comprehensive trade-off information for different model ensemble solutions. A case study of optimizing the surface air temperature (SAT) ensemble solutions over different geographical regions of China is carried out. The data covers from the period of 1900 to 2100, and the projections of SAT are analyzed with regard to three different statistical indices (i.e., RMSE, CC, and uncertainty). Among the derived ensemble solutions, the trade-off information is further analyzed with a robust Pareto front with respect to different statistics. The comparison results over historical period (1900–2005) show that the optimized solutions are superior over that obtained simple model average, as well as any single GCM output. The improvements of statistics are varying for different climatic regions over China. Future projection (2006–2100) with the proposed ensemble method identifies that the largest (smallest) temperature changes will happen in the South Central China (the Inner Mongolia), the North Eastern China (the South Central China), and the North Western China (the South Central China), under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios, respectively.

The goal of the paper is to present and examine the method of classification of daily circulation patterns that allows (i) a fair comparison of groupings among different datasets (typically representing the observed climate and that simulated by a general circulation model, GCM) and (ii) huge datasets, common in GCM studies based on daily values, to be classified. The circulation classification method is shown to be a useful tool in GCM validation and analysis of climate change response, particularly in comparisons of (i) shapes of the mean type patterns, (ii) the frequency and persistence of the types, (iii) the probability of transitions from one type to another, and (iv) conditional surface temperature distributions. It is also shown that a simultaneous examination of multiple classifications is beneficial in eliminating subjectivity of any single classification and allowing a detailed inspection of differences between climates. The classification method is a modification of the T-mode principal component analysis (PCA). The T-mode refers to the input data matrix where gridpoint values are arranged in rows and daily patterns in columns. The classification procedure is applied to observed daily 500 hPa geopotential height patterns and those simulated by the control and ECHAM3 GCM runs.