The Sixth Assessment Report of the Intergovernmental Panel on Climate Change concluded that climate warming is unequivocal and human influence on the climate system is evident. Increases in greenhouse gasses have significantly contributed to atmospheric and oceanic warming, sea-ice loss, and sea-level rise, and in the twenty-first century, these effects will become more severe. Improving our understanding of the climate system and reducing current uncertainties in projections of future change are priorities. Understanding the likely consequences of climate change requires better representation of ecosystems in land models coupled within Earth system models. Numerous land model simulations are needed to quantify the sensitivity of model predictions to parameters, but running such simulations over every grid cell is computationally prohibitive. Thus, we applied cluster analysis to identify a subset of representative grid cells for which parameter sensitivity simulations can be performed. We implemented cluster analysis using a custom-developed parallel K-means clustering code on a parallel server at multiple levels of division, where K is the number of groupings/clusters. Land characteristics represent axes in a multi-dimensional data space into which every grid cell is registered. We clustered grid cells from a historical land-model simulation to identify a smaller number of representative grid cells to use for parameter perturbation experiments designed to understand the sensitivity of vegetation productivity to model parameters. Since the experiments relate to vegetation, the characteristics used in the cluster analysis will include primary climate drivers, vegetation properties, and carbon fluxes. Maps of ecoregions (land areas that are relatively homogeneous) with identified representative grid cells will be produced using Python in a Jupyter Notebook. Through these interconnected parts, investigators should be able to understand the role of human choices in energy production.