This model type is characterized by an element of randomness. The randomness could be the forcing functions, particularly the climatic forcing functions, or it could be the model parameters. The randomness is in both cases caused by a limitation in our knowledge. We can for instance not know the temperature the fifth of May next year at a given location, but we know how the normal distribution of the temperature has been for instance the last hundred years and can use the normal distribution to represent the temperature on this date. Similarly, many of the parameters in our models are dependent on random forcing functions or on factors that we hardly can include in our model without making it too complex. A normal distribution of these parameters is known and by use of Monte Carlo simulations based on this knowledge, it is possible to consider the randomness. By running the model several times, it becomes possible to obtain the uncertainty of the model results. A stochastic model may be a biogeochemical/bioenergetic model, a spatial model, a structural dynamic model, an IBM, or a population dynamic model. There are no differences among these model types on how a model can be made a stochastic model.
This model type has the following advantages:
• able to consider the randomness of forcing functions or processes;
• the uncertainty of the model results is easily obtained by running the model many times.
This model type has the following disadvantages:
• it is necessary to know the distribution of the random model elements;
• has a high complexity and requires much computer time.
It is recommended to apply stochastic models whenever the randomness of forcing functions or processes is significant.
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