How Much Skill Was There in Forecasting the Very Strong 1997–98 El Niño?

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The very strong 1997–98 El Niño was the first major event in which numerous forecasting groups participated in its real-time prediction. A previously developed simple statistical tool—the El Niño–Southern Oscillation Climatology and Persistence (ENSO–CLIPER) model—is utilized as a baseline for determination of skill in forecasting this event. Twelve statistical and dynamical models were available in real time for evaluation. Some of the models were able to outperform ENSO–CLIPER in predicting either the onset or the decay of the 1997–98 El Niño, but none were successful at both for a medium-range two season (6–8 months) lead time. There were no models, including ENSO–CLIPER, able to anticipate even one-half of the actual amplitude of the El Niño's peak at medium-range (6–11 months) lead. In addition, none of the models showed skill (i.e., lower root-mean-square error than ENSO–CLIPER) at the zero season (0–2 months) through the two season (6–8 months) lead times. No dynamical model and only two of the statistical models [the canonical correlation analysis (CCA) and the constructed analog (ANALOG)] outperformed ENSO–CLIPER by more than 5% of the root-mean-square error at the three season (9–11 months) and four season (12–14 months) lead time. El Niño impacts were correctly anticipated by national meteorological centers one half-year in advance, because of the tendency for El Niño events to persist into and peak during the boreal winter. Despite this, the zero to two season (0–8 month) forecasts of the El Niño event itself were no better than ENSO–CLIPER and were, in that sense, not skillful—a conclusion that remains unclear to the general meteorological and oceanographic communities.

*NOAA/AOML/Hurricane Research Division, Miami, Florida.

+NOAA/Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado.

Corresponding author address: Dr. Chris Landsea, NOAA/AOML/Hurricane Research Division, 4301 Rickenbacker Causeway, Miami, FL 33149. E-mail: landsea@aoml.noaa.gov

The very strong 1997–98 El Niño was the first major event in which numerous forecasting groups participated in its real-time prediction. A previously developed simple statistical tool—the El Niño–Southern Oscillation Climatology and Persistence (ENSO–CLIPER) model—is utilized as a baseline for determination of skill in forecasting this event. Twelve statistical and dynamical models were available in real time for evaluation. Some of the models were able to outperform ENSO–CLIPER in predicting either the onset or the decay of the 1997–98 El Niño, but none were successful at both for a medium-range two season (6–8 months) lead time. There were no models, including ENSO–CLIPER, able to anticipate even one-half of the actual amplitude of the El Niño's peak at medium-range (6–11 months) lead. In addition, none of the models showed skill (i.e., lower root-mean-square error than ENSO–CLIPER) at the zero season (0–2 months) through the two season (6–8 months) lead times. No dynamical model and only two of the statistical models [the canonical correlation analysis (CCA) and the constructed analog (ANALOG)] outperformed ENSO–CLIPER by more than 5% of the root-mean-square error at the three season (9–11 months) and four season (12–14 months) lead time. El Niño impacts were correctly anticipated by national meteorological centers one half-year in advance, because of the tendency for El Niño events to persist into and peak during the boreal winter. Despite this, the zero to two season (0–8 month) forecasts of the El Niño event itself were no better than ENSO–CLIPER and were, in that sense, not skillful—a conclusion that remains unclear to the general meteorological and oceanographic communities.

*NOAA/AOML/Hurricane Research Division, Miami, Florida.

+NOAA/Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado.

Corresponding author address: Dr. Chris Landsea, NOAA/AOML/Hurricane Research Division, 4301 Rickenbacker Causeway, Miami, FL 33149. E-mail: landsea@aoml.noaa.gov
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