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Seasonal forecasts and the Copernicus Climate Change Service (C3S)

Introduction to seasonal forecasting

The production of seasonal forecasts, also known as seasonal climate forecasts, have experienced 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 provide estimates of statistics of weather in the seasonal and monthly time scales, and they lie in a place somewhere between conventional weather forecasts and climate predictions.

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We consider here as seasonal forecasts data both the higher-frequency (sub-daily and daily) data outputs from numerical earth system weather or climate models and the derived monthly and seasonal products, up to a few months ahead of their initialization date.

Seasonal forecasting within the C3S

The C3S seasonal forecasting products are based on data from several state-of-the-art seasonal prediction systems. Multi-system combinations, as well as predictions from the individual component 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 and Centro Euro-Mediterraneo sui Cambiamenti Climatici will be included in the C3S multi-system.

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Currently, there are graphical products available in the C3S web site and public access to the data is provided via the ECMWF WebAPI. From 2018 onwards, C3S seasonal forecast data products will be made available via the C3S Climate Data Store (CDS).

Seasonal forecasts are not weather forecasts: the role of the hindcasts

Due to its long leadtimes, a few months since the start date of the forecast, some systematic errors appear that can completely kill the signals that are expected to be predicted. To avoid that, seasonal forecasting systems work with a reference climate to determine how predicted values differ from what is normal for a given region and time of year. The same forecast system is run for past dates, so that kind of model climate can be estimated, and the forecasts be bias-corrected with respect to that model climate. Those forecast runs for earlier years are known as 'hindcasts' or 're-forecasts' and they are a key concept in seasonal forecasting, in such a way that a forecast itself it's not useful without relating it with the relevant hindcasts.

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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.

Seasonal forecasting systems' versions and updates

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.

NOTE: In the current data service based on web-API access to the MARS archive, where the data is stored using the GRIB format, those version changes are tracked using the MARS keyword "SYSTEM" included in every GRIB field.blahblahblah (system keyword, etc)

How the seasonal forecasting systems build their ensembles? And how data are produced?

"Burst" vs. "lagged" mode

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.

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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

Fixed vs. on-the-fly hindcasts

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:

• Fixed hindcasts: Some systems are designed so their expected lifetime will be around 4-5 years. Thus, once the system has been designed and tested, its development gets frozen and exactly that version of the model is used to run all the hindcast members for the whole climate period for that model. In that way, the hindcasts are produced well in advance the real-time forecasts and they constitute a fixed dataset during the lifetime of that seasonal forecasting system
• On-the-fly hindcasts: Some other systems run the necessary set of hindcasts every time they produce a new real-time forecast. They are produce just slightly in advance (a few weeks) the real-time forecast and using exactly the same version of the forecasting system. In this way changes in the system can be introduced more frequently and the computing of the hindcasts can be spanned over a longer period, then balancing the load of the computing resources.

Production schedules in the seasonal forecasting systems contributing to C3S

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

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^(a) Despite they are produced in a lagged mode, the data from MeteoFrance system5 is produced and archived 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 C3STable with production per system and per forecast/hindcast type

Description of the c3s-seasonal dataset in the MARS archive

Data tree

In the following sections, a brief description of the contents will be presented. Usually in addition to the explanations about what can be found in every menu, you will find some notes about the MARS keywords involved, i.e., about those elements that are required to define the code that it will be used to retrieve the requested data.

Currently, just the original and post-processed data from all the individual contributors is made available using this data service. Products based on the multi-system combination are just currently available as graphical products

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"Multi-model seasonal forecast" (STREAM=MMSF)

The original data in daily and subdaily frequencies for all the individual members from each one of the forecasting systems. Both forecast and hindcasts are included here

"Multi-model seasonal forecast atmospheric monthly means" (STREAM=MSMM

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Monthly aggregated values (average, minimum, maximum and standard deviation) for the individual members. Both forecast and hindcas are included here.
Additionaly it is included here the ensemble mean of the real-time forecasts and the mean of the hindcasts over the complete climate period.

"Multi-model seasonal forecast monthly anomalies" (STREAM=MMSA)

Monthly anomalies with respect to every model's climate (hindcast mean) for the individual members and the ensemble mean

Record of system changes in the contributions to C3S

In the following table, the values of the MARS keyword SYSTEM for the operational C3S seasonal contributors are shown. The systems updated/changed at every row are highlighted in blue colour.

MMSA

*** Note: check EUROSIP documentation (it is said that MMSA is not calculated for "lagged" ensembles)

(*) Due to some peculiarities of GRIB formatting and MetOffice forecasting system design, the value of the MARS keyword SYSTEM is changed on a yearly basis, so it has no direct relation with the model version of their systemTable 1: Values of the MARS keyword SYSTEM for the operational C3S seasonal contributors

High frequency data (stream MMSF)

TYPE=fc (hidden)

YEAR (note, hindcasts formally equivalent to forecasts)

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Leaf contents: number, step (freq= 6h, 24h, 12h), [level], parameter

Monthly means and other monthly statistics (stream MSMM)

Derived probability products

TYPE  (em, ensemble mean; hcmean: hindcast climate mean, one for every real-time forecast)

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*** Ask Manuel, why time is not "hidden" in the final leaf (as it is for MMSF) 

Derived forecast products

TYPE (fcmean/max/min/stdev: "Forecast mean,...")

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*** Ask Manuel, why time is not "hidden" in the final leaf (as it is for MMSF) 

Real-time forecasts monthly anomalies (stream MMSA)

*** Note: check EUROSIP documentation (it is said that MMSA is not calculated for "lagged" ensembles)

Derived probability products

TYPE  (em, ensemble mean)

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*** Ask Manuel, why time is not "hidden" in the final leaf (as it is for MMSF) 

Derived forecast products

TYPE (fcmean "Forecast mean")

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