Supplemental Material
Journal of Climate
Two-Year Dynamical Predictions of ENSO Event Duration during 1954–2015
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Supplemental Online Materials for“Two-year Dynamical Predictions of ENSO Event Duration during 1954-2015”

Xian Wu and Yuko M. Okumura
Institute for Geophysics, Jackson School of Geosciences
The University of Texas at Austin, Austin, TX

Clara Deser
Climate and Global Dynamics Division,
National Center for Atmospheric Research, Boulder, CO

Pedro N. DiNezio
Department of Atmospheric and Oceanic Sciences,
University of Colorado Boulder, Boulder, CO

Journal of Climate
Submitted August 04, 2020
Revised December 31 2020
Revised February 7, 2021

Corresponding author address: Dr. Xian Wu, Climate and Global Dynamics Division, National
Center for Atmospheric Research, 1850 Table Mesa Drive, Boulder, Colorado, 80305
Email: xianwu@ucar.edu2Figure S1. As in Fig. 1, but for all other 1-yr El Niño (1963, 1965, 1982, 1991, 1994, 1997, 2002,2006, and 2009), 2-yr El Niño (1957, 1968, 1976, and 2014), 1-yr La Niña (1964, 1995, and 2005),and 2-yr La Niña (1954, 1970, 1973, 1983, 2007, 2010, and 2016) events. The Nov-1 (Mar0–Jun+1)
forecasts are not available for the 2-yr La Nina event that develops in 1954 (2016).

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Figure S2. Average (top) correlation coefficient and (bottom) root-mean-square-difference (°C)
of (left) ensemble mean and (right) ensemble spread between the 40-member ensemble of the Novinitialized forecasts and 10,000 bootstrapped ensembles of 1 to 40 members of the Nov-initialized
forecasts. The x-axis denotes the size of bootstrapped ensembles, and the y-axis denotes theforecast lead time (month) from the initialization month November.

9Figure S3. As in Fig. 5, but only SST and surface wind anomalies statistically significant at the95% confidence levels are shown. The insignificant thermocline depth anomalies are masked bystipples.

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Figure S4. Longitude-time sections of SST (°C; shading), thermocline depth (contours at intervalsof 8 m starting at ±4 m; zero contours thickened and negative contours dashed), and surface wind(m s-1; vectors) anomalies in the equatorial Pacific (3°S–3°N) composited for 1-yr (top) El Niñoand (bottom) La Niña events in (first column) observations and (second–fourth columns) ensemble
forecasts initialized in (second column) Jun0, (third column) Nov0, and (fourth column) Mar+1. The
thermocline depth anomalies are smoothed with a 1-2-1 filter in the time direction and a 9-point
running-mean filter in the longitudinal direction in both observations and forecasts.

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Figure S5. Longitude-time sections of climatological SST (°C; shading) in the equatorial Pacific(3°S–3°N) in observations and ensemble forecasts initialized in Jun0, Nov0, and Mar+1. Note thatthe climatology in the forecasts is a function of lead time.

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Figure S6. As in Fig. 6, but only SST and surface anomalies statistically significant at the 95%
confidence levels are shown.

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Figure S7. As in Fig. 8, but only SST and surface wind anomalies statistically significant at the95% confidence levels are shown. The insignificant thermocline depth anomalies are masked bystipples.

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Figure S8. As in Fig. 10, but only SST correlations statistically significant at 95% confidence
levels are shown. The insignificant SLP correlations are masked by stipples.

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