The December 2011 issue of the Journal of Agricultural, Biological, and Environmental Statistics is on the topic “Computer models and spatial statistics for environmental science.” This is a topic of great interest as the study of complex environmental phenomena increasingly relies on deterministic computer models. These models, for example regional climate models or rainfall-runoff simulators, are mathematical models that describe the evolution in time of a physical process. Usually, they consist of complex differential or partial differential equations that are not solvable in closed form. Hence, these are typically solved using numerical techniques, yielding deterministic predictions of a process. In this special issue, researchers tackle several important statistical problems that arise in the analysis of computer model output, for example calibrating model output with observed data, comparing and combing output from several computer models and physical observations, and building statistical emulators for computer models to predict the outcome of the models for new sets of input conditions. An important contribution of statisticians in the analysis of deterministic models is to quantify uncertainty in inferences and predictions in rigorous fashion. Uncertainty quantification is of great interest, especially as information from complex computer models and messy observational data is used for decision making. There are several types of uncertainty, including (1) parametric uncertainty in the model’s inputs or tuning parameters and (2) structural uncertainty in the mathematical equations that define the model. In “First-Order Emulator Inference for Parameters in Nonlinear Mechanistic Models”, Mevin B. Hooten, William B. Leeds, Jerome Fiechter, and Christopher K. Wikle provide a computationally-efficient method for quantifying parametric uncertainty. They approximate the complicated computer model with a more tractable statistical model, and use