Rising uncertainties associated with climate change compel forest management planning to include forest ecosystem simulations. The output of such models is often of high spatio-temporal complexity and difficult to interpret for the user. This contribution describes a novel visualization method called four-dimensional (4-D) statistical surfaces, which aims at improving the visual detection of change in time series. The method visualizes attribute values as surfaces, which are interpolated and animated over time; the interactive attribute surfaces are combined with color-coding and contour lines to support absolute and relative height judgment as well as faster perception and better location of change. A design study and prototypical implementation of the visualization method is described in this contribution. Time-series simulation results of LANDIS-II, a commonly used modeling tool in forest ecology, as well as a temporal vegetation index dataset (NDVI) are visualized using 4-D statistical surfaces. Usability challenges are addressed based on explorative interviews with a small group of users. The method is not limited to ecological model output; it can be used to create three-dimensional (3-D) temporal animations of arbitrary time-series datasets where parameters are supplied in regular raster format.