One of the recognized weaknesses of land surface models when used in weather and climate models is the current assumption of constant soil thickness. This is because there is a lack of global estimates of bedrock depth. Using a 30-arc-s global dataset for the thickness of relatively porous, unconsolidated sediments over bedrock, researchers, including a Department of Energy scientist at Pacific Northwest National Laboratory, included spatial variation in soil thickness in version 4.5 of the Community Land Model (CLM4.5). They determined the number of soil layers for each grid cell from the average soil depth for each grid cell. They found the greatest changes in the simulation with variable soil thickness are to baseflow, with the annual minimum generally occurring earlier. They saw smaller changes in latent heat flux and surface runoff, primarily as a result of an increase in the annual cycle amplitude. They determined these changes are related to soil moisture changes that are most substantial in locations with shallow bedrock. Total water storage (TWS) anomalies are not strongly affected over most river basins since most basins contain mostly deep soils, but TWS anomalies are substantially different for a river basin with more mountainous terrain. Additionally, they found the annual cycle in soil temperature is partially affected by including realistic soil thicknesses resulting from changes in the vertical profile of heat capacity and thermal conductivity. However, the largest changes to soil temperature are introduced by the soil moisture changes in the variable soil thickness simulation. This implementation of variable soil thickness represents a step forward in land surface model development.