Shuai Hu1,2, Tianjun Zhou1,2*, Bo Wu1

1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric

Physics, Chinese Academy of Sciences, Beijing 100029, China

2 University of the Chinese Academy of Sciences, Beijing 100049, China

Improved ENSO prediction skill resulting from reduced climate drift in

IAP-DecPreS: a comparison of full-field and anomaly initialization

1. Motivation

2. Model, experiment and methods

✓When initiated from observational conditions [the full-field initialization (FI)] , coupled climate models used inseasonal predictions generally experience climate drifts. How climate drift affects ENSO prediction is importantbut not clearly understood.

✓The anomaly initialization (AI) assimilates the observed anomalies added to the model climatology, which isdesigned to reduce the model climate drift.

✓A comparison of the fundamental processes casing the difference in the predictive skill for the ENSO betweenthe AI and FI can aid understanding the influence from model drift.

◆ Climate prediction system

◆ Seasonal hindcast experiment

◆ Analytical methods

3. Systematic biases, ENSO simulation and Model drift

4. Predictive skill for ENSO

5. Factors causing the differences in predictive skill

6. Summary

Email: hus@lasg.iap.ac.cn

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SST Precipitation

OBS

Hindcast-F

Hindcast-A

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Hindcast-A

Hindcast-A Hindcast-F

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Hindcast-A Hindcast-F(a) 1997 (b) 1997

(c) 2015 (d) 2015

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✓ The IAP near-term climate prediction system DecPreSwas constructed based on a state-of-the-art coupledglobal climate model (CGCM) FGOALS-s2, developed byLASG, IAP. The initialization scheme is ensemble optimalinterpolation-incremental analysis update (EnOI-IAU)

(Wu et al., 2018)

✓The hindcast runs are initiated from February, May, August and November respectively for each year inthe period of 1979-2015. Each hindcast have 9 ensemble members.✓Initiated from the initial states derived from the full-field or anomaly initialization runs, systematichindcast runs were conducted. They are referred to as full-field (Hindcast-F) or anomaly hindcasts(Hindcast-A)

✓ Model drift is defined as the change in the predictive climatological state deviated from the modelintrinsic climatological state with the lead time during a prediction, which is quantified as follows:

𝑿𝒅 𝒊, 𝒋 = 𝑿𝒑 𝒊, 𝒋 − 𝑿𝒎(𝒋)

where 𝑋𝑝(𝑖, 𝑗) is the climatological state of prediction initiated from date 𝑖 (four times for this study) for

target time j, estimated as the average of the raw output data by using all hindcast experiments. 𝑋𝑚(𝑗) isthe corresponding model intrinsic climatological state, which can be estimated based on historicalsimulations. 𝑋𝑑(𝑖, 𝑗) represents the model drift, which is the difference between 𝑋𝑝(𝑖, 𝑗) and 𝑋𝑚(𝑗).

✓The mixed–layer heat budget (MLHB)

The mixed layer ocean temperature equation is written as :𝝏𝑻′

𝝏𝒕= 𝑫𝑶

′ +𝑸′ + 𝑹𝒆𝒔

𝐷𝑂′ : the ocean temperature transport effect due to 3-D advections;

𝑄′: the net surface heat flux converted to temperature units, 𝑸′ =𝑸𝑳𝑯′ +𝑸𝑺𝑯

′ +𝑸𝑳𝑾′ +𝑸𝑺𝑾

′

𝒉⋅𝝆⋅𝑪𝒑; 𝑹𝒆𝒔: the residual term.

⚫ The biases of annual mean SST and precipitation (Fig.1), and of the annual SST cycle (Fig. 2).

⚫ The model systematic errors of the FGOALS-s2 are consistent with the multi-model mean of the CMIP5

models , suggesting that the results based on FGOALS-s2 have a certain degree of representativeness.

⚫ The hindcasts based on anomaly initialization (Hindcast-A) have higher ENSO prediction skill

compared to those based on full-field initialization (Hindcast-F) (Fig. 5 and 6).

⚫ To investigate the impact of drift on the prediction skill, the 1997/1998 and 2015/2016 El Niño cases are analyzed (Fig. 7).

This study investigates the impact of model drift on ENSO prediction by usingseasonal hindcasts conducted with two distinct initialization approaches, anomalyand full-field initializations, in a climate prediction system termed IAP-DecPreS.Analysis shows that the ENSO predictive skill is dynamically influenced by modeldrift, whose impact can be partly overcome by using anomaly initializations. Themajor difference between the two hindcast types emerges during the period of Mayto September, during which the ENSO intensity growth rate in Hindcast-A is strongerthan that in Hindcast-F associated with the greater warming tendency over thecentral eastern Pacific for 1997/98 and 2015/16 cases.

Oceanic mixed–layer heat budget analyses are performed in this area. Theresults show that the underestimation of ENSO intensity in Hindcast-F is primarilycontributed by the terms of thermal advection via zonal mean currents (−ത𝑢𝜕𝑇′/𝜕𝑥)and surface shortwave radiation (𝑄′

𝑆𝑊), as both are modulated by model drifts.

The model drift in the hindcast based on the full field initialization influences ENSOprediction through both dynamic and thermodynamic processes. On the one hand,the drift enhances the mean zonal oceanic transport through the response of low–level winds, leading to stronger mean zonal advection feedback. On the other hand,the drift shifts the ENSO-driven precipitation anomalies eastward, which enhancesthe cloud–radiation–SST negative feedback. Neither processes are favorable toENSO development.

Fig. 1 Left panel: Spatial distributions of annual mean SST (Units: °C) over the

tropical Pacific during the period of 1979–2005 for the (a) observation, (b)

FGOALS-s2 historical simulations and (c) FGOALS-s2 biases. Right panel: as in

the left panel, but for the annual mean precipitation (Units: mm/day).

Fig. 2 Annual SST cycle (shaded, Units: °C) along the equatorial Pacific (5°S–5°N average) during the period of 1979–2005 for the (a) observation and (b)

FGOALS-s2 historical simulations. The contours in (b) represent the seasonal

cycle biases, at an interval of 0.5. Negative values are dashed. Units: °C.

Fig. 3 Regression pattern of SSTAs (shaded, units: °C per standard deviation of

Niño3.4 SSTA) with respect to the standardized Niño3.4 index for the (a) observation

and (b) FGOALS-s2 historical simulations. (c, d) as in (a, b), but for anomalous

precipitation (contour, contour interval is 0.5, negative values are dashed, Units:

mm/day) and anomalous shortwave radiation (shaded, units: W/m2). (e) Seasonal

cycles of standard deviations and (f) power spectrum of the observed (black) and

simulated (red) Niño3.4 index; the dashed lines in (f) are the upper 95% confidence

bound for Markov.

Fig. 4 Left panel: Spatial distributions of SST drift (shaded, Units: °C) and

1000–hPa wind drift (vector, Units: m/s) for the target seasons of (a) JJA, (b)

SON and (c) DJF when initialized from May during the period 1979–2016 for

Hindcast-A. Right panel: (a–c) as in (d–f), but for Hindcast-F.

Fig. 5. Left panel: Spatial distribution of temporal

correlation predictive skill scores (shaded, units: 1) of

the boreal winter SSTAs at (a) 1–month, (b) 4–month, (c)

7–month and (d) 10–month lead times for Hindcast-A.

Right panel: (e–f) as in (a–d), but for Hindcast-F. The

dots denote values passing the 1% significance level.

⚫ Although biases are present in the mean state of

the tropical Pacific, FGOALS-s2 has a reasonable

ENSO simulation performance (Fig. 3).

⚫ Due to the existence of systematic errors, the

hindcast based on the FI have larger El Niño-like

climate drift (Fig. 4).

Fig. 6. Time series of Niño3.4 index values predicted

by Hindcast-A (red line) and Hindcast-F (blue line) at

(a) 1–month, (b) 4–month, (c) 7–month and (d) 10–

month lead times. The observed Niño3.4 index values

are shown as the black lines in (a–d). The 3–month

running mean is applied to the Niño 3.4 index before

plotting.

Fig. 7. Temporal evolutions of the observed and

predictive Niño34 index for El Niño events of (a)

2015/2016, (b) 1997/1998 and (c) 1982/1983. The

curves are the Niño3.4 index for the observations

(black), Hindcast-A (red) and Hindcast-F (blue),

respectively. Thin dashed and thick solid lines

represent individual ensemble members and

ensemble mean.

Fig. 8. Left panel: Spatial distribution

of initial SSTAs conditions (shaded,

units: °C) in the April 2015 for the

2015/2016 El Niño event derived from

the (a) observation, (c) anomaly

initialization run and (e) full-field

initialization run. The numbers at the

top right of subfigures c and e present

the pattern correlation coefficient

(PCC) and root–mean–square error

(RMSE) with the observation for the

area within the dashed boxes (20°S–20°N,140°E–90°W). Right panel: (b, d,

f) as in (a, c, e), but for initial SSTAs

conditions in the April 1997 for the

1997/1998 El Niño event.

Fig. 9. (a) Mixed–layer heat budget analysis for the areas of the CEP (dashed

boxes in Fig. 8) during the period of May to September in 1997 simulated by

Hindcast-A (red) and Hindcast-F (blue) (Units: °C/month). Tendency, SUM

represent the observed time tendency of area–averaged SSTAs, sum of the

thermal advection by zonal currents (–UTx), meridional currents (–VTy),

vertical currents (–WTz) and net surface flux (HF). (b) as in (a), but for 2015.

(c) the contributions of decomposition terms of –UTx and HF for 1997. (d) as

in (c), but for 2015. The red (blue) bar represents ensemble means, and the

vertical lines are the spread of the ensemble members, Units: °C/month.

Figure 10. Left panel: The climatological subsurface temperature (contour, units:

°C), vertical advection (vector, the vertical velocity has been multiplied by 5×104,

units: m/s), and the subsurface temperature anomalies (shaded, units: °C) averaged

meridionally in 5°S–5°N in Hindcast-A during the period of May to September in

(a) 1997 and (c) 2015. The thick black line is the 20°C isotherm. Right panel: as in

left panel, but for Hindcast-F.

Figure 11. Left panel: Spatial distributions of anomalous precipitation

(shaded, Units: mm/day) and SSTAs (contour interval is 0.5, negative

values are dashed, Units: °C) during the period May to September for

1997 from (a) observation predicted by Hindcast-A during the period

May to September for (a) 1997 and (b) 2015. Right panel: (b, d) as in (a,

c), but for Hindcast-F.

⚫ The major difference between the two hindcast types emerges during the period of May to September, the developing phase of

El Niño. During this period, a discrepancy in the time tendency of the tropical Pacific SSTAs occurs over the central eastern

Pacific (CEP; 5°S–5°N,170°W–90°W) between Hindcast-A and Hindcast-F, with the former being larger than the latter (Fig. 8).

⚫ The ocean mixed–layer heat budget analyses in CEP (box in Fig. 8), as shown in Figure 9. The results indicate that the

Underestimated warming in Hindcast-F is primarily contributed by the terms of horizontal zonal advection and net radiation flux.

⚫ The El Niño-like SST drift in Hindcast-F (Fig. 4) enhances the eastward climatological equatorial zonal currents in the

central-eastern Pacific (Fig. 10), which cause stronger mean zonal advective negative feedback and thus weaker ENSO

amplitude.

⚫ the background mean SST in the tropical eastern Pacific in Hindcast-F is warmer than those in Hindcast-A, which

shifts the ENSO-driven precipitation anomalies eastward (Fig. 11).