Thursday, 25 May 2000: 11:00 AM
Christopher W. Landsea, NOAA/AOML/HRD, Miami, FL; and J. A. Knaff
To provide a more stringent test for skill in seasonal El Nino-
Southern Oscillation (ENSO) phenomena, a multiple regression technique
has been fashioned that takes best advantage of CLImatology, PERsistence
and trend of initial conditions - the ENSO-CLIPER. This new model is
presented as a replacement of the use of pure persistence for determining
the skill threshold for ENSO forecasting. We then redefine "skill" in
ENSO prediction as "the ability to show significant improvements over the
forecast capability of ENSO-CLIPER", rather than just persistence.
Multiple least squares regression using the method of leaps and bounds is
employed to test a total of fourteen possible predictors for the
selection of the best predictors, based upon 1950-1992 developmental
data. A range of zero to four predictors were chosen in developing
twelve separate regression models, developed separately for each initial
calendar month. The predictands to be forecast include the Southern
Oscillation Index (SOI) and the Nino 1+2, Nino 3, Nino 4 and Nino 3.4
SST indices for the equatorial eastern and central Pacific at lead times
ranging from zero seasons (0-2 months) through seven seasons (18-20
months). Though hindcast ability is strongly seasonally dependent,
substantial improvement is achieved over simple persistence wherein
largest gains occur for two to seven season (6 to 21 months) lead times.
Comparisons of ENSO-CLIPER versus the suite of statistical and
dynamical ENSO prediction models available are performed for the
very strong 1997-98 El Nino event and the strong and long-lasting 1998-2000
La Nina. Tests for skill are done for the onset of the El Nino, its peak
magnitude and the termination of the event in early summer 1998. Similarly,
an analysis is performed of the onset, peak magnitude and (likely)
completion in early 2000 of the La Nina event. Additionally, we analyze the
entire period of 1993-2000 when out-of-sample independent hindcasts were
available from ENSO-CLIPER. Testing reveals that some ENSO models
(both statistical and numerical) do not have any true skill for
real-time operational forecasting of the ENSO phenomena as they simply
latch on to trends in the environmental fields and were not able to
outperform ENSO-CLIPER.
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