The determination of relationships between climate variables and the identification of the most significant associations between them in various geographic regions is an important aspect of climate model evaluation. The EDEN visual analytics toolkit has been developed to aid such analysis by facilitating the assessment of multiple variables with respect to the amount of variability that can be attributed to specific other variables. EDEN harnesses the parallel coordinates visualization technique and is augmented with graphical indicators of key descriptive statistics. A case study is presented in which the focus is on the Harvard Forest site (42.5378N Lat, 72.1715W Lon) and the Community Land Model Version 4 (CLM4) is evaluated. It is shown that model variables such as land water runoff are more sensitive to a particular set of environmental variables than a suite of other inputs in the 88 variable analysis conducted. The approach presented here allows climate scientists to focus on the most important variables in the model evaluations.