@article{fdi:010053983,
  title = {{A}n ensemble generation method for seasonal forecasting with an ocean-atmosphere coupled model},
  author = {{V}ialard, {J}. and {V}itart, {F}. and {B}almaseda, {M}. {A}. and {S}tockdale, {T}. {N}. and {A}nderson, {D}. {L}. {T}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}easonal forecasts are subject to various types of errors: amplification of errors in oceanic initial condi-tions, errors due to the unpredictable nature of the synoptic atmospheric variability, and coupled modelerror. {E}nsemble forecasting is usually used in an attempt to sample some or all of these various sources of error. {H}ow to build an ensemble forecasting system in the seasonal range remains a largely unexplored area.
{I}n this paper, various ensemble generation methodologies for the {E}uropean {C}entre for {M}edium-{R}ange {W}eather {F}orecasts ({ECMWF}) seasonal forecasting system are compared. {A} series of experiments using wind perturbations (applied when generating the oceanic initial conditions), sea surface temperature ({SST}) perturbations to those initial conditions, and random perturbation to the atmosphere during the forecast, individually and collectively, is presented and compared with the more usual lagged-average approach. {SST} perturbations are important during the first 2 months of the forecast to ensure a spread at least equal to the uncertainty level on the {SST} measure. {F}rom month 3 onward, all methods give a similar spread. {T}his spread is significantly smaller than the rms error of the forecasts. {T}here is also no clear link between the spread ofthe ensemble and the ensemble mean forecast error. {T}hese two facts suggest that factors not presently sampled in the ensemble, such as model error, act to limit the forecast skill. {M}ethods that allow sampling of model error, such as multimodel ensembles, should be beneficial to seasonal forecasting.},
  keywords = {},
  booktitle = {},
  journal = {{M}onthly {W}eather {R}eview},
  volume = {133},
  numero = {2},
  pages = {441--453},
  ISSN = {0027-0644},
  year = {2005},
  URL = {https://www.documentation.ird.fr/hor/fdi:010053983},
}