Modelling under uncertainly: Monte Carlo methods for temporally varying parameters

Uncertainty enters the modelling world through a variety of routes. Paramet er values are nut always known with adequate precision, and they may vary i n time and/or space due to processes, which are not included in the model. One of the more popular methods by which the effect of uncertainty is incor porated into numerical models is the use of randomised methods based on Mon te Carlo techniques in which parameters are randomly sampled from underlyin g probability distributions. However, in the case of temporally varying par ameters, some care is required in the use of Monte Carlo methods and in the interpretation of model results. This short note addresses the problem of applying Monte Carlo techniques in the case of temporally varying parameter s. It is shown that the correct approach is to resample the parameters at a time interval equal to double the decorrelation time of the parameter. Thi s is illustrated with reference to a simple model of algal growth. (C) 2001 Elsevier Science B.V. All rights reserved.