Accurate simulation of mean climate and variability is crucial for numerical climate models. Traditional methods assess variability using two-dimensional standard deviation fields, like sea surface temperature (SST) and precipitation, to identify key regions. However, this approach can overlook large-scale patterns, such as ocean modes of variability, used in traditional climatology and oceanography to define climate variability. We propose a method incorporating large-scale climate patterns to evaluate and compare decadal variability in four coupled models (EC-Earth, GISS, iCESM, and CCSM-Toronto). Shannon’s Entropy compares the models’ sensitivity to different scenarios: pre-industrial period, mid-Holocene with default vegetation, and mid-Holocene with prescribed Green Sahara conditions. Results show contrasting model responses, with little consensus on the effects of Green Sahara vegetation and orbital forcing. Three models (EC-Earth, iCESM, and CCSM-Toronto) show reduced precipitation variability under Green Sahara conditions, but with differing SST responses. The GISS model shows minimal effects on variability. Additionally, reducing dust in the Green Sahara scenario significantly impacted EC-Earth’s model, increasing precipitation while decreasing SST variability. These findings highlight the diverse representations of climate variability across models and offer a new methodology for comprehensive model analysis.