processes that allows realistic and fast climate simula -tions that often involve large ensembles for the purpose of reducing uncertainty and estimation of the forced and internal variability of the system. The forced signal is typically estimated by an ensemble mean of many simulations, but ensembles of state-of-the-art models are often too small to reduce the remaining internal variability. The ensemble size needed to estimate the mean accurately depends on the signal-to-noise ratio for the variable and region under consideration. For example, the ensemble size to estimate midlatitude 500-hPa height accurately is about 20, which is larger than most ensembles used in seasonal hindcast data-sets or climate projections performed by individual centers. Intermediate complexity models can also be used efficiently to investigate the sensitivity of simu-lated climate to changes in parameters in the physical parameterizations. Another application is related to climate change. For example, Forest et al. (2002) and Sokolov at al. (2009) use the MIT Integrated Global System Model (MIT IGSM) to investigate topics such as climate sensitivity, aerosol forcing, ocean heat uptake rate, and probabilistic projections of climate change. There are many intermediate complexity system models of intermediate complexity (EMICs). A number of them are participating in the IPCC Fifth Assessment Report and can be found at http://climate .uvic.ca/EMICAR5 (one of which is based on a previous version of the model introduced here). This website also provides information about experiments that are performed with these models that range from en-sembles of 1,000-year-long historical simulations to the assessment of different C
