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The image below is an example of the crucial role of the hindcasts in seasonal forecasting. It shows the predicted time evolution of monthly-average temperature in a given region. The , between May and December: the blue line represents the average conditions observed over a period in the past (the reference period; in this case, 1993-2014), for each month between May and December; the red line is the equivalent model climate average over the same reference period. The difference between the two clearly indicates a significant cold bias, increasing with the time into the forecast. An ensemble of forecasts for a particular year is shown as green lines. When compared to the observed 'normal', all green ensemble predictions are for colder-than-normal conditions - not necessarily surprising when remembering that the model is systematically colder than the real world. However, when comparison is made with the model's 'normal' (the red line), all forecast ensemble members indicate warmer-than-normal conditions. Clearly, as the forecast is an output of the model, the latter comparison is the more appropriate.
Additionally to their unavoidable As well as playing an essential role in correcting the correction of systematic errors, hindcasts are also used to assess the skill of the different seasonal forecasting systems. In that way, the real-time forecasts are expected to behave with the same skill that the system has shown for past dates, i.e. the skill shown by the hindcastsforecast systems (by comparing each of the forecasts for the years in the reference period with the respective observed conditions). Information on forecast skill is important to avoid overconfident decision making.
Note that for reasons related with the availability of computing resources, the hindcasts usually have fewer ensemble members per start date than the real-time forecasts, e.g. ECMWF SEAS5 5 has 51 members for the real-time forecasts, and just 25 members for the hindcasts.
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