Climate Dynamics, doi: 10.1007/s00382-010-0947-3
ECMWF seasonal forecast System 3 and its prediction of sea surface temperature
Timothy N. Stockdale, David L.T. Anderson, Magdalena A. Balmaseda, Francisco J.
Doblas-Reyes, Laura Ferranti, Kristian Mogensen, Timothy N. Palmer, Franco Molteni
and Frederic Vitart
ECMWF, Shinfield Park
RG2 9AX, Reading, UK
The latest operational version of the ECMWF seasonal forecasting system is
described. It shows noticeably improved skill for sea surface temperature (SST)
prediction compared with previous versions, particularly with respect to El Nino
related variability. Substantial skill is shown for lead times up to 1 year,
although at this range the spread in the ensemble forecast implies a loss of
predictability large enough to account for most of the forecast error variance,
suggesting only moderate scope for improving long range El Nino forecasts. At
shorter ranges, particularly 3-6 months, skill is still substantially below the
model-estimated predictability limit.
SST forecast skill is higher for more recent periods than earlier ones. Analysis
shows that although various factors can affect scores in particular periods, the
improvement from 1994 onwards seems to be robust, and is most plausibly due to
improvements in the observing system made at that time.
The improvement in forecast skill is most evident for three-month forecasts starting
in February, where predictions of NINO3.4 SST from 1994 to present have been almost
without fault. It is argued that in situations where the impact of model error is
small, the value of improved observational data can be seen most clearly.
Significant skill is also shown in the equatorial Indian Ocean, although predictive
skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can
be especially high in the Southern Ocean.