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Supplemental Online Materials for“The equatorial Pacific cold tongue bias in CESM1 and its influence on ENSO forecasts”

Xian Wu,a Yuko M. Okumura,b Pedro N. DiNezio,c Stephen G. Yeager,a and Clara Deser aabClimate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado

Institute for Geophysics, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas
cDepartment of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado

Journal of Climate
Submitted June 21, 2021
Revised December 12, 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.edu

1Figure S1. The timeseries of monthly thermocline depth in the (a) western equatorial Pacific (3°S–3°N, 120°E–180°) and (b) eastern equatorial Pacific (3°S–3°N, 150°W–80°W) in the FOSI (darkred) and 7 ocean reanalysis datasets [ORAS4 (purple), SODA (dark blue), EN4l09 (blue), EN4g10(light blue), ECCOV4R4 (yellow), GOADS (orange), ICOADS_Kobe (red)]. (c,d) as in (a,b) but
for the anomalous values after removing the monthly climatology.

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Figure. S2 (top) As in Fig. 1e but only the SST and surface wind biases statistically significant atthe 99.5% confidence level. The thermocline depth anomalies (contours) that are not statisticallysignificant at the 99.5% confidence level are masked with black stippling. The effect of different
ensemble sizes on the estimate of climatological bias in the three forecast ensembles are evaluatedusing bootstrap analysis. To test whether a climatological bias is significantly different from zero,a bootstrapped distribution of 5,000 climatological bias is calculated based on any 10 members
randomly selected (with replacement) from the full forecast ensemble. A positive bias is significantat 99.5% confidence level if its bootstrapped distribution having fewer than 25 values that arebelow zero (p<25/5,000), vice versa for a negative bias. (bottom) The climatological SST bias (°C)
averaged over the equatorial Pacific (3°S-3°N; 140°E-80°W) for the ensemble forecasts initializedin November (red), March (green), and June (blue). The thick curves represent the ensemble mean

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based on all available members, and the shading indicates the range (minimum to maximum) ofensemble mean based on 5,000 bootstrapped samples of 10 members. There are 40, 20, and 10
members in the November, March, and June forecasts, respectively.

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Figure S3. As in Fig. 5, but for composites of 1-yr La Niña, 2-yr El Niño, and 2-yr La Niña events.
The statistical significance of these anomalies is shown in Fig. S4.

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Figure S4. As in Figs. 5 and S3 but only the SST and surface wind anomalies statisticallysignificant at the 95% confidence levels are shown for all four types of events. The thermocline
depth anomalies (contours) that are not statistically significant at the 95% confidence level aremasked with black stippling.

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Figure S5. As in Fig. 6, but for composites of 1-yr La Niña, 2-yr El Niño, and 2-yr La Niña events.
The statistical significance of these anomalies is shown in Fig. S6.

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Figure S6. As in Figs. 6 and S5 but only the SST and surface wind anomalies statisticallysignificant at the 95% confidence levels are shown for all four types of events.

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Figure S7. As in Fig.7, but for composites of 1-yr La Niña, 2-yr El Niño, and 2-yr La Niña events.
The statistical significance of these anomalies is shown in Fig. S8.

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Figure S8. As in Figs 7 and S7 but only the SST and surface wind anomalies statisticallysignificant at the 95% confidence levels are shown for all four types of events. The SLP and Z200
anomalies (contours) that are not statistically significant at the 95% confidence level are masked
with black stippling.

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Figure S9. As in Figs 7 and S7 but SLP, surface wind, Z200, and ocean precipitation anomalies
are based on the JAR55, and land precipitation anomalies are based on the CRU.

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