Year-to-year variations of the El Niño-Southern Oscillation (ENSO) produces robust, large-scale changes in the distribution of global precipitation (Ropelewski and Halpert 1987, 1989) and surface temperature (Halpert and Ropelewski 1992) in addition to altering both the frequency and location of tropical cyclones (Gray et al. 1994; Nicholls 1992; Lander 1994). These teleconnected effects are due to ENSO-forced global circulation changes (Horel and Wallace 1981; Arkin 1982). Thus, successful seasonal forecasts of ENSO variability are crucial for useful predictions of these precipitation, temperature, and tropical cyclone variations.

In recent years, efforts to understand the ENSO phenomena have moved into the real-time forecasting arena of ENSO itself. Predictions associated with this activity are published quarterly within the Experimental Long-lead Forecast Bulletin (hereafter referred to as ELLFB; Barnston 1996). Methodologies presently being used to forecasting ENSO variability can be broadly subdivided into two groups including: (I) statistical models [non-linear analogue model (NLAM) - Drosdowsky 1994; linear inverse model (LIM) - Penland and Magorian 1993 and Zhang et al. 1993; single spectrum analysis-maximum entropy method (SSA-MEM) - Keppenne and Ghil 1992 and Jiang et al. 1995; constructed analogue forecasts (CAF) - Van den Dool 1994; canonical correlation analysis (CCA) - Barnston and Ropelewski 1992)] and, (II) dynamical models [(hybrid coupled model (HCM) - Barnett et al. 1993; Cane and Zebiak model (CZ) - Zebiak and Cane 1987; LDEO2 - Chen et al. 1995; coupled model project 9 and 10 (CMP9 and CMP10) - Ji et al. 1994; Center for Ocean-Land-Atmosphere (COLA) - Kirtman et al. 1996; Bureau of Meteorology Research Centre (BMRC) - Kleeman 1994; Oxford - Balmaseda et al. 1994)]. Many of these models (LIM, HCM, CZ, LDEO2, CMP9, CMP10, COLA, BMRC) predict the spatial aspects of the SST field, however, it is common to compare the predictive capabilities of all of these models by examining regionally averaged SST anomalies.

The real-time ENSO forecasting ability of a subset of these models - two statistical (CCA and CAF) and three dynamical (CZ, HCM and CMP9) was assessed by Barnston et al. (1994). Two-season lead time predictions (as for example, a forecast of December through February conditions issued two seasons in advance on the first of June) were examined. In this analysis, forecast ability was measured by computing linear correlation coefficients of predicted SST variations versus those observed. All of the models achieved correlations of around 0.65; about 40 percent of the variance of observed eastern and central equatorial Pacific SST anomalies during the years 1982-93. These results were suggested to show ``skill" in that they were able to exceed the forecast ability of a simple one month persistence forecast which could explain only about five percent of the variance in observations. (A forecast of ``persistence" is the use of the current one month anomalous conditions as a predictor itself of future anomaly values. For example, if the SSTs were 0.73° C above average, persistence would forecast also a +0.73° C anomaly to occur for the following months.) Note that validation tests of the three dynamical models, because of their relatively recent origin, entailed four years of hindcast tests in lieu of independent data. (A ``hindcast" specifically refers to prediction of some past event based upon initial conditions assessed prior to the event in question. Both statistical and dynamical models utilize hindcast testing.) However, successful hindcasts, even with dynamical models, do not insure equally successful future predictions. As noted by Barnston et al. (1994), ``there is no substitute for real-time forecasting."

Nonetheless, two issues complicate the assessment of real-time forecast ``skill" in these statistical and dynamical ENSO models. The first concern is the lack of success by these models during the last few years. For example, during late 1994 and early 1995, there was a re-emergence of El Niño conditions including SST anomalies ranging from +1.0 to 2.0° C above average developing throughout the eastern and central equatorial Pacific. Southern Oscillation Index (SOI) values averaging 1.5 standard deviations below average, convective activity well-above normal near the dateline and equator and weakened trade winds throughout much of the equatorial Pacific also occurred, consistent with a mature El Niño event (Halpert et al. 1996). The June 1994 issue of ELLFB (Barnston 1994) noted that nearly all models - statistical and dynamical - suggested that no El Niño was imminent, even though the models were run only a half year before the 1994-95 El Niño appeared and reached its mature stage. The statistical models including the LIM, SSA-MEM, CAF and CCA schemes, all predicted neutral ENSO conditions for December 1994 through February 1995. The dynamical models including the CMP9 and BMRC schemes, called for weak ENSO cold phase conditions (or La Niña) and the CZ model suggested near neutral conditions. Only the HCM correctly forecast the warming which was later observed in the central tropical Pacific (140° to the dateline). However, in June 1995 (Barnston 1995), even the HCM was unable to forecast the moderate La Niña event of late 1995 and early 1996 (Halpert et al. 1996) and also incorrectly predicted a very strong El Niño event to occur in late (boreal) Spring and Summer 1996 - neutral conditions actually prevailed. Whereas, the reasons for these wide scale forecast failures are beyond the scope of this paper, suffice it to say that the forecast ``success" reported by Barnston et al. (1994) may have been premature.

The second ENSO forecasting assessment issue regards the method for determining skill in the seasonal forecasts. Traditionally, ``skill", as defined by Barnston et al. (1994) and in most of the statistical and dynamical modeling studies referenced previously, is the ability to show significantly greater forecasting success when compared to persistence of initial conditions. Success has usually been achieved either by maximizing the linear correlation coefficient or by minimizing the root mean square error (RMSE). However, we suggest that persistence is an inappropriately limited test of for determining the threshold of skill in forecasting the ENSO phenomena. For example, in January 1988 the Niño-3 (5° N to 5° S and 90° to 150° W) SST anomaly value was +0.75° C, which though moderately warm was nevertheless much reduced from the +2.03° C value that occurred during September 1987, at the height of the 1986-87 El Niño event. The use of the January 1988 anomaly value as the benchmark persistence forecast to improve upon would subsequently provide large errors as conditions moved quickly to a strong La Niña within only a few months. Any modeling scheme which predicted a cold ENSO event or even a return to neutral conditions would show success above persistence and thus ``skill", at least for this particular case. However, in determining whether skillful forecasts were made by a particular model, the inclusion of month-to-month trends of the initial conditions would have made for a more stringent test. In this case, persistence plus trend would have suggested that a return to La Niña or at least neutral ENSO conditions was to occur. Of course the addition of trend of initial conditions will not always lead to correct forecasts as borne out by the lack of a La Niña following the 1991-1992 and 1993 El Niño events. The point is that adding trend to persistence generally provides improved forecasts over persistence alone.

In addition to trend of initial values, ENSO conditions are also known to decay preferentially depending on the season (Wright 1985; Wright et al. 1988). Persistence of SST anomalies typically produces the smallest errors when forecasting the boreal late fall and winter conditions and the largest errors for late spring and summer values. This tendency is due to the as yet unexplained strong phase locking between ENSO and the annual cycle. Rasmusson et al. (1990) showed that the ENSO sequel has two dominant time scales. One is a biennial mode with a period very close to 24 months. The other has a period of four to five years. More importantly, the biennial cycle is tightly locked to the annual cycle. Therefore, consideration for the calendar date with respect to the climatology of composite ENSO events can enhance the forecast ability of ENSO prediction schemes. For instance, if January initial conditions are warm, it is likely that the conditions in the following January will be cooler.

To provide a more stringent test for skill in seasonal ENSO forecasting, 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. Thus, the ENSO-CLIPER, which is based upon 43 years of surface data, is in this sense an optimal ``no-skill" forecasting procedure, in that when other models' performance compares unfavorably with ENSO-CLIPER they are said to have ``no-skill". Note that optimal combinations of climatology, persistence and trend to provide ``no-skill'' forecasts is already in common usage for both tropical cyclone motion (CLIPER - Neumann 1972) and intensity (SHIFOR - Jarvinen and Neumann 1979) forecasting. CLIPER-type models have proved invaluable for providing benchmarks in testing tropical cyclone track and intensity forecasting algorithms (Neumann and Pelissier 1981; DeMaria et al. 1990; DeMaria and Kaplan 1994; Gross and Lawrence 1996). All statistical and dynamical tropical cyclone models are compared for their relative improvement with respect to CLIPER and SHIFOR, rather than simple persistence (see DeMaria et al. 1990). The use of these two ``no-skill" models has provided an invaluable tool in validating new tropical cyclone prediction schemes, in both real-time and hindcasts.

This paper will detail the development of an ENSO-CLIPER scheme and its suggested ENSO forecast ability including the SOI and the various SST indices. The next section describes the developmental data sets utilized for both the predictors and predictands. Section 3 details the methodology utilized in the creation of the ENSO-CLIPER model and section 4 presents the results of hindcasts on dependent data and estimates of future forecast ability. Section 5 compares the performance of the ENSO-CLIPER with other ENSO prediction schemes. Section 6 provides an example of an independent forecast of ENSO conditions for 1996-1998. Following a summary and discussion section, Appendix A presents all of the independent ENSO-CLIPER forecasts.