Lead time, initialization and valid period
How seasonal forecast lead months are counted, why lead 1 is the initialization month, and how to line up different runs on the same valid period.
Three dates on every map
A seasonal forecast has an initialization, the nominal start month; a valid period, the month or three-month window the map is about; and a lead, describing how far ahead that period lies. On this site lead 1 is the initialization month itself, following the C3S convention. A September run therefore covers September at lead 1 through February at lead 6 in this workspace. Lead month 1 is an index, not one full month of advance warning; release follows initialization.
Comparing runs on the same valid month
The interesting comparison is rarely lead against lead but valid period against valid period. November as seen from the August run is lead 4; November from the September run is lead 3. This site always aligns panels on the valid period, so when you compare monthly runs you are looking at the same target month from successive initializations, with the lead shown on each panel.
Available months are intersected
When panels use different initializations, only the valid periods that every panel can show are offered. Two runs one month apart share five monthly periods; a three-month window has fewer, because each window needs three forecast months inside the six-month horizon. If an expected period is missing, at least one selected panel lacks that product in the imported catalogue; this can reflect the forecast horizon or incomplete local coverage.
Lagged ensembles and nominal dates
Some centres, notably the UK Met Office, do not start all members on one day but run a few every day and assemble the most recent ones. C3S assigns these a nominal initialization month so they line up with the rest. The site uses that nominal month and validates it on import, so a “September” GloSea6 forecast really contains the members C3S associates with September.
Lead is only one influence on skill
Forecast skill often declines with lead, but not uniformly or monotonically. It also depends on the target season, variable, region and climate state. A long-lead forecast can retain a strong predictable signal; a short-lead forecast need not have a large anomaly. Check verification for the relevant start month and target period. See comparing seasonal models and runs for how to use successive runs.
Sources and method
These references explain the underlying science and provider conventions. Descriptions of this site’s calculations, colours and coverage were checked against its processing code and imported data.