# ENSO Features, Dynamics, and Teleconnections to East Asian Climate as Simulated in CAMS-CSM

• Corresponding author: Hong-Li REN, renhl@cma.gov.cn
• Funds:

Supported by the National Key Research and Development Program of China (2018YFC1506002, 2017YFC1502302, and 2016YFA0602104), Key Research Program of Xinjiang Meteorological Bureau (ZD201802), China Meteorological Administration Special Public Welfare Research Fund (GYHY201506013), and National Natural Science Foundation of China (41205058, 41375062, 41405080, 41505065, 41606019, and 41605116)

• doi: 10.1007/s13351-019-8101-6
• This study evaluates the performance of CAMS-CSM (the climate system model of the Chinese Academy of Meteorological Sciences) in simulating the features, dynamics, and teleconnections to East Asian climate of the El Niño–Southern Oscillation (ENSO). In general, fundamental features of ENSO, such as its dominant patterns and phase-locking features, are reproduced well. The two types of El Niño are also represented, in terms of their spatial distributions and mutual independency. However, the skewed feature is missed in the model and the simulation of ENSO is extremely strong, which is found—based on Bjerknes index assessment—to be caused by underestimation of the shortwave damping effect. Besides, the modeled ENSO exhibits a regular oscillation with a period shorter than observed. By utilizing the Wyrtki index, it is suggested that this periodicity bias results from an overly quick phase transition induced by feedback from the thermocline and zonal advection. In addition to internal dynamics of ENSO, its external precursors—such as the North Pacific Oscillation with its accompanying seasonal footprinting mechanism, and the Indian Ocean Dipole with its 1-yr lead correlation with ENSO—are reproduced well by the model. Furthermore, with respect to the impacts of ENSO on the East Asian summer monsoon, although the anomalous Philippine anticyclone is reproduced in the post-El Niño summer, it exhibits an eastward shift compared with observation; and as a consequence, the observed flooding of the Yangtze River basin is poorly represented, with unrealistic air–sea interaction over the South China Sea being the likely physical origin of this bias. The response of wintertime lower-tropospheric circulation to ENSO is simulated well, in spite of an underestimation of temperature anomalies in central China. This study highlights the dynamic processes that are key for the simulation of ENSO, which could shed some light on improving this model in the future.
• Fig. 1.  Climatological annual mean (a, b) SST (color shaded; °C) overlapped with surface wind (vectors; m s–1) and (c, d) precipitation (mm day–1) from (a, c) observation and (b, d) CAMS-CSM.

Fig. 2.  The mean (a) thermocline depth (indicated by the 20°C isotherm) and (b) equatorial (5°S–5°N average) zonal wind stress along 120°E–80°W in observation (black curve) and the CAMS-CSM simulation (red curve).

Fig. 3.  Spatial patterns of the (a, b) first and (c, d) second EOF modes of SST anomalies in the tropical Pacific in (a, c) observation and (b, d) the CAMS-CSM simulation.

Fig. 4.  Scatterplots between the normalized modified Niño3 and Niño4 indices in boreal winter (December–February) during (a, b) El Niño events and (c, d) La Niña events from (a, c) observation and (b, d) the CAMS-CSM simulation. Note that the red (blue) dots denote the CT (WP) El Niño and La Niña. The correlation coefficients are also given. The definition of the modified Niño3 index is the seasonal mean SST anomalies averaged over 5°S–5°N, 170°–110°W, while that of Niño4 is over 5°S–5°N, 140°E–170°W.

Fig. 5.  Evolution of Niño3.4 anomalies during (a, b) El Niño and (c, d) La Niña events from (a, c) the CAMS-CSM simulation and (b, d) observation. The composites are indicated by the thick red curves. Here, the events with an absolute peak value exceeding 0.5°C are selected.

Fig. 6.  Standard deviation of SST anomalies (°C) averaged within the (a) Niño3.4 region (5°S–5°N, 170°–120°W), (b) modified Niño3 region (5°S–5°N, 170°–110°W), and (c) modified Niño4 region (5°S–5°N, 140°E–170°W), in each calendar month, in observation (blue bars) and the CAMS-CSM simulation (yellow bars).

Fig. 7.  Spectra of Niño3.4 anomalies in observation (black curve) and the CAMS-CSM simulation (red curve).

Fig. 8.  Scatterplots of (a, g) $[{\tau _x}] = {\mu ^*}_a < T >$, (b, h) $< {T_{\rm sub}} > = {a_h} < h >$, (c, i) $< w > = - {\beta _w}[{\tau _x}]$, (d, j) $< Q > = - {\alpha _s} < T >$, (e, k) $< h > - [h] = {\beta _h}[{\tau _x}]$, and (f, l) $< u > - {\beta _{uh}}[h] = {\beta _u}[\tau ]$, in (a–f) observation and (g–l) the CAMS-CSM simulation. The linear fitting lines are indicated by the red straight lines. Please refer to Jin et al. (2006) for more details about these balance equations.

Fig. 9.  The BJ-index (BJ) and its five contributing terms (MA: mean advective damping, ZA: zonal advective feedback, TH: thermocline feedback, EK: Ekman feedback, and TD: thermal damping) in observation (blue bars) and the CAMS-CSM simulation (red bars). The calculation of the BJ-index is given in Eqs. (1) and (2).

Fig. 10.  Regression patterns of the anomalous precipitation (color shaded; mm day–1 °C–1) and surface wind (vectors; m s–1 °C–1) against Niño3.4 anomalies in (a) observation and (b) the CAMS-CSM simulation.

Fig. 11.  Regression patterns of anomalous thermocline depth (m Pa–1) against the surface wind stress anomalies along the equator (5°S–5°N, 140°–80°W) in (a) observation and (b) the CAMS-CSM simulation.

Fig. 12.  Regression of anomalous shortwave radiation (SW), longwave radiation (LW), sensible heat flux (SH), and latent heat flux (LH) against the SST anomaly in the Niño3.4 region in observation (blue bars) and the CAMS-CSM simulation (red bars).

Fig. 13.  Regression patterns of anomalous shortwave radiation flux (W m–2 °C–1) against the Niño3.4 index in (a) observation and (b) the CAMS-CSM simulation.

Fig. 14.  Seasonal composites of negative-minus-positive SLP index cases for SST (color shaded; °C) and 10-m winds (arrows; m s–1) in observation (left-hand panels) and the CAMS-CSM simulation (right-hand panels).

Fig. 15.  Scatterplots between the normalized Niño3.4 index averaged during November (0) to January (1) and the normalized SLP index averaged during November (–1) to March (0) from (a) observation and (b) the CAMS-CSM simulation. The correlation coefficients are given in parentheses on top of each panel.

Fig. 16.  Lag-correlation between the IOD [averaged from September to November of Year (0)] and the Niño3.4 index (three-month running average applied) in (a) observation and (b) the CAMS-CSM simulation. The dashed lines indicate the 95% confidence level. The vertical dashed lines denote the September–October–November in Year (0) for a co-occurring IOD.

Fig. 17.  Composite patterns of the (a, b) anomalous precipitation (color shaded; mm day–1) overlapped with horizontal wind at 850 hPa (vectors; m s–1) and (c) SST (°C) during boreal summer in the decaying year of strong El Niño events from (a, c) observation (1982/83, 1997/98, and 2015/16) and (b, d) the CAMS-CSM simulation. A modeled strong El Niño event is defined as occurring when the value of the November–December–January mean Niño3.4 index exceeds 1.5 times its standard deviation.

Fig. 18.  Scatterplots of (a, b) local precipitation against the local SST anomaly and (c, d) the local SST anomaly against local shortwave radiation, over the South China Sea (8°–22°N, 110°–120°E), during boreal summer in (a, c) observation and (b, d) the CAMS-CSM simulation. Linear fitting lines are indicated by the straight lines. Correlation coefficients are given in parentheses on top of each panel.

Fig. 19.  The 11-yr moving correlations between wintertime Niño3.4 anomalies and the EAWM indices designed by (a) Li and Yang (2010) and (b) Shi (1996) in observation (black curves) and the CAMS-CSM simulation (red curves). The horizontal dashed line denotes the 95% confidence level.

Fig. 20.  Differences in wintertime anomalous surface air temperature (color shaded; °C) and horizontal wind at 850 hPa (vectors; m s–1) between El Niño years and La Niña years in (a) observation and (b) the CAMS-CSM simulation.

###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## ENSO Features, Dynamics, and Teleconnections to East Asian Climate as Simulated in CAMS-CSM

###### Corresponding author: Hong-Li REN, renhl@cma.gov.cn
• 1. Laboratory for Climate Studies & China Meteorological Administration–Nanjing University Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing 100081
• 2. Xinjiang Climate Center, Urumqi 830002
• 3. Department of Atmospheric Science, School of Environmental Studies, China University of Geoscience, Wuhan 430074
Funds: Supported by the National Key Research and Development Program of China (2018YFC1506002, 2017YFC1502302, and 2016YFA0602104), Key Research Program of Xinjiang Meteorological Bureau (ZD201802), China Meteorological Administration Special Public Welfare Research Fund (GYHY201506013), and National Natural Science Foundation of China (41205058, 41375062, 41405080, 41505065, 41606019, and 41605116)

Abstract: This study evaluates the performance of CAMS-CSM (the climate system model of the Chinese Academy of Meteorological Sciences) in simulating the features, dynamics, and teleconnections to East Asian climate of the El Niño–Southern Oscillation (ENSO). In general, fundamental features of ENSO, such as its dominant patterns and phase-locking features, are reproduced well. The two types of El Niño are also represented, in terms of their spatial distributions and mutual independency. However, the skewed feature is missed in the model and the simulation of ENSO is extremely strong, which is found—based on Bjerknes index assessment—to be caused by underestimation of the shortwave damping effect. Besides, the modeled ENSO exhibits a regular oscillation with a period shorter than observed. By utilizing the Wyrtki index, it is suggested that this periodicity bias results from an overly quick phase transition induced by feedback from the thermocline and zonal advection. In addition to internal dynamics of ENSO, its external precursors—such as the North Pacific Oscillation with its accompanying seasonal footprinting mechanism, and the Indian Ocean Dipole with its 1-yr lead correlation with ENSO—are reproduced well by the model. Furthermore, with respect to the impacts of ENSO on the East Asian summer monsoon, although the anomalous Philippine anticyclone is reproduced in the post-El Niño summer, it exhibits an eastward shift compared with observation; and as a consequence, the observed flooding of the Yangtze River basin is poorly represented, with unrealistic air–sea interaction over the South China Sea being the likely physical origin of this bias. The response of wintertime lower-tropospheric circulation to ENSO is simulated well, in spite of an underestimation of temperature anomalies in central China. This study highlights the dynamic processes that are key for the simulation of ENSO, which could shed some light on improving this model in the future.

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