Initial Condition Ensembles
Initial condition ensembles involve the same model, with the same atmospheric physics, run from a variety of different start dates. Because the climate system is chaotic, tiny changes in things such as temperatures, winds, and humidity in one place can lead to very different paths for the system as a whole. We can work around this by setting off several runs started with slightly different starting conditions, and then look at the evolution of the group as a whole. This is similar to what they do in weather forecasting.
Initial condition ensembles allow us to investigate the internal variability of the climate system. Such ensembles, driven with present day forcing conditions, can be used to validate a climate model by comparing the model output with actual observed data. This process is called a hindcast: it’s like a forecast, only you know the outcome.
We know what happened in the historic period we simulate because we have observed data, but it’s still a challenge for the model to do a good job of simulating it. In a ‘good’ model we expect initial condition ensembles to represent weather events with a similar frequency of occurrence as that in the observed records. Only ‘good’ models will be used for predicting the future. This does not necessarily mean they are actually good for that job too, but it does at least rule out models that are ‘bad’ at simulating the past and so won’t produce reliable future projections.
Initial condition ensembles also let us investigate how sensitive certain processes in the climate system are to changing initial conditions.
Every single model run by participants will be unique.