The production of seasonal forecasts, also known as seasonal climate forecasts, has undergone a huge transformation in the last few decades: from a purely academic and research exercise in the early '90s to the current situation where several meteorological forecast services, throughout the world, conduct routine operational seasonal forecasting activities. Such activities are devoted to providing estimates of statistics of weather on monthly and seasonal time scales, which places them somewhere between conventional weather forecasts and climate predictions.
In that sense, even though seasonal forecasts share some methods and tools with weather forecasting, they are part of a different paradigm which requires treating them in a different way. Instead of trying to answer to the question "how is the weather going to look like on a particular location in an specific day?", seasonal forecasts will tell us how likely it is that the coming season will be wetter, drier, warmer or colder than 'usual' for that time of year. This kind of long term predictions are feasible due to the behaviour of some of the Earth system components which evolve more slowly than the atmosphere (e.g. the ocean, the cryosphere) and in a predictable fashion, so their influence on the atmosphere can add a noticeable signal.
The C3S seasonal forecast products are based on data from several state-of-the-art seasonal prediction systems. Multi-system combinations, as well as predictions from the individual participating systems, are available. The centres currently providing forecasts to C3S are ECMWF, The Met Office and Météo-France; in the coming months data produced by Deutscher Wetterdienst (DWD) and Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) will be included in the C3S multi-system.
Each model simulates the Earth system processes that influence weather patterns in slightly different ways, makes slightly different approximations, leading to different kinds of model error. These errors typically increase with the increase of integration time, so that the accumulated model errors become significant in comparison to the signal that the model is meant to predict. Some such errors are shared by the different models but others are not, so combining the output from a number of models enables a more realistic representation of the uncertainties due to model error. In most cases, such combined forecasts are, on average, more skilful than forecasts from the best of the individual models.
Currently, the C3S seasonal service offers graphical forecast products, available on the C3S web site, and public access to the forecast data, via the C3S Climate Data Store (CDS).
Seasonal forecasts are started from an observed state of (all components of) the climate system, which is then evolved in time over a period of a few months. Errors present at the start of the forecast (due to the imprecise measurement of the initial conditions and the approximations assumed in the formulation of the models) persist or, more often, grow through the model integration, reaching magnitudes comparable to that of the predictable signals. Some such errors are random; the effect of these on the outcome is quantified through the use of ensembles. Some errors, however, are systematic; if these systematic errors were determined, corrections could be applied to the forecasts to extract the useful information. This is achieved by comparing retrospective forecasts (hindcasts) with observations. The same forecast system is run for several starting points in the past in the same way as a forecast would be run (with only knowledge of the starting point), for the same length of time as an equivalent forecast. The resulting data set constitutes a 'climate' of the model, which can then be compared with the observed climate of the real world. The systematic differences between the model and the real world - usually referred to as biases - are thus quantified and used as the basis for corrections which can be applied to future, real-time forecasts. Given the relative magnitude of such biases, some basic corrections are essential to convert the data into forecast information - therefore a forecast by itself is not useful without relating it to the relevant hindcasts.
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 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 role in correcting 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 hindcasts.
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.
Every forecasting system that contributes to C3S will have a different lifetime, so different versions of the systems are expected to be changed, upgraded from their original institutions. For the real-time forecasts just one version of each one of the contributors will be made available to C3S as a real-time forecasts. For instance, in November 2017 ECMWF has changed its operational seasonal forecasting system from system 4 to SEAS5, but both systems will be kept routinely running in parallel at ECMWF for a while. Despite that fact, the only version of ECMWF seasonal forecasts available at C3S from November 2017 onwards will be SEAS5.
In the last few decades, in the earth system modelling it has been an established technique the use of "ensemble" runs to take into account errors due to both the uncertainty in the initial conditions and model deficiencies. This means that the forecasting systems produced a set of "slightly" different runs of the same forecast which form the members of the ensemble, in a way that the outcome of the forecasting system is not a single model output but a set of different results which allow to produce a forecast in terms of a probability distribution as opposed to a single deterministic forecast.
Different techniques are commonly used to build the different members of an ensemble forecast, but one of the most common ways to create a set of slightly different members that mainly maps the uncertainty in the initial conditions, is the use of a "lagged" approach in the start dates.
Among all the systems that contribute to the C3S seasonal forecasts, some of them have opted to use a "burst" mode, while some others lag the start dates of the members of their ensembles. For more details, refer to the table below in the "Production schedules" subsection
Due to several reasons, from computer load balance to flexibility in the introduction of changes in the systems, the different seasonal forecast contributors to C3S use different schedules to produce their hindcast sets:
In the following table, it is shown the information about the ensemble sizes, start dates and production schedule for the seasonal forecasting systems contributing to C3S.
| SYSTEM | FORECASTS | HINDCASTS | |||
|---|---|---|---|---|---|
| ENSEMBLE SIZE and START DATES | PRODUCTION | ENSEMBLE SIZE and START DATES | PRODUCTION | ||
| ECMWF | System 4 | 51 members start on the 1st | real-time | 15 members start on the 1st | fixed dataset |
| SEAS5 | 51 members start on the 1st | real-time | 25 members start on the 1st | fixed dataset | |
| Météo-France | System 5 (a) | 51 members 26 start around the 22th | real-time | 15 members | fixed dataset |
| System 6 | 51 members 1 starts on the 1st | real-time | 25 members 1 starts on the 1st | fixed dataset | |
| MetOffice | GloSea5 (b) | 2 members every day (c) | real-time | 7 members on the 1st | on-the-fly produced around 4-6 weeks in advanced |
| CMCC | SPSv3 (d) | 50 members start on the 1st | real-time | 40 members start on the 1st | fixed dataset |
| DWD | GCFS1 | 50 members start on the 1st | real-time | 30 members start on the 1st | fixed dataset |
(a) Despite they are produced in a lagged mode, the data from MeteoFrance system5 is produced and provided at CDS as if all the members were initialized on the 1st
(b) The production schedule of the MetOffice forecasting system doesn't prescribe how to build an ensemble for an specific nominal start date. The following choices are currently in use for the data archived in C3S:
FORECASTS: 50 members starting on or before the 1st of the month (NOTE: The original daily/subdaily data from all the daily members, and not just those 50, is processed and made available at C3S)
HINDCASTS: 28 members (7 starting on the 1st of the month and 7 on each of the 9th,17th and 25th of the previous month)
(c) Due to the flexibility of MetOffice forecasting system, incidences on a given data are not usually recovered re-running the missed forecast but incrementing the number of members in one of the following days.
Example: An incidence happened affecting the 22nd of August/2017 forecast so no members are available for that date. Instead, there are 4 members available starting on the 23rd of August
(d) CMCC seasonal forecast system routinely produces 80 real-time forecast members but just 50 of them are made publicly available as their contribution to C3S