What Determines the Amplitude of ENSO Events?

ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2013, VOL. 6, NO. 2, 90−96

What Determines the Amplitude of ENSO Events? WANG Yu-Xing1, YANG Hai-Jun1, and Tore FUREVIK2 1

Department of Atmospheric and Oceanic Sciences & Laboratory for Climate and Ocean-Atmosphere Studies (LaCOAS), School of Physics, Peking University, Beijing 100871, China 2 Geophysical Institute and Bjerknes Centre for Climate Research, University in Bergen, Bergen, Norway Received 12 June 2012; revised 27 July 2012; accepted 15 August 2012; published 16 March 2013

Abstract In this paper, the dynamic effect of oceanic upwelling on the intensity of El Niño-Southern Oscillation (ENSO) is studied using a simple coupled model (Zebiak-Cane Model). The term balance analysis in the temperature variability equation shows that the anomalous upwelling of the mean vertical temperature gradient and the mean advection of the anomalous meridional temperature gradient are the two of most important factors that determine the intensity of ENSO events, in which the “vertical oceanic heat flux” in the eastern equatorial Pacific (EEP) is the primary influencing factor. The lag correlation between “vertical heat flux (VHF)” and ENSO intensity shows that the highest correlation occurs when the former leads the latter by one to two weeks. The VHF is positively correlated with the background thermocline strength in the EEP, and an increase of both could result in strong ENSO variability. Comparison of the forced and coupled experiments suggests that the coupled process can affect both the intensity and frequency of ENSO. Keywords: ENSO amplitude, vertical heat flux, thermocline strength Citation: Wang, Y.-X., H.-J. Yang, and T. Furevik, 2013: What determines the amplitude of ENSO events? Atmos. Oceanic Sci. Lett., 6, 90–96.

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Introduction

El Niño-Southern Oscillation (ENSO) is the most prominent interannual oscillation in the tropical Pacific and can significantly affect global climate. The predictability of ENSO has been studied extensively. As ENSO is a self-sustaining fluctuation or a stochastic forced oscillation, the onset of ENSO is difficult to predict and is still a topic of debate (Chen, 2004; McPhaden, 2004). Some studies show that the predictability is largely limited by the growth of initial errors (Goswami and Shukla, 1991; Chen and Zebiak, 1995; Xue et al., 1997), while other studies emphasize the importance of atmospheric noise (McPhaden, 1999; Fedorov et al., 2003), such as westerly wind bursts. Studies regarding features of ENSO have made great progress in the past 30 years. The properties of ENSO can be altered by global warming, the Pacific Decadal Oscillation, the seasonal cycle, and the Madden-Julian Oscillation (McPhaden, 1999). Trenberth and Hoar (1996) have suggested an increase in the occurrence of El Niño events and a decrease in La Niña events since the late 1970s. The Corresponding author: WANG Yu-Xing, [email protected]

examination of past observations shows that the variability of ENSO has increased by as much as 50% during the past 50 years (Zhang et al., 2008). Eisenman et al. (2005) have suggested that modulated westerly wind bursts (WWBs), which are different from stochastic WWBs, could result in stronger ENSO. Coupled climate models are widely used to study the factors affecting the amplitude of ENSO events. Meehl et al. (2001) showed that strong ENSO is usually associated with low background vertical diffusivity as well as strong equatorial zonal wind stress. An et al. (2008) show that a change in the vertical temperature gradient in the tropical Pacific can influence the amplitude of ENSO. Yang and Zhang (2008) examine the magnitude of ENSO in the global warming scenario, and emphasize the effect of the destabilizing factor, the so-called “virtual vertical heat flux” (VHF), within the thermocline in the eastern equatorial Pacific (EEP). In this study, using a simple coupled model, the Zebiak-Cane model (Z-C model), we explicitly confirm the dynamic connection between the so-called VHF and the intensity of ENSO. The temperature variability equation is derived and used to check the connection between each term and the intensity of ENSO. The VHF is found to be the most important factor driving changes in the intensity of ENSO, which is consistent with results from the fully coupled climate model (Yang and Zhang, 2008).

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Model and experiments

The Z-C model used in this work was used to successfully predict the 1982/83 El Niño event (Zebiak and Cane, 1987). Monthly surface wind datasets from National Centers Environmental Predition (NCEP) reanalysis are used here and cover the period from January 1960 to October 2009. The observational wind stress anomaly field is used to initiate the model. First, we tested the performance of the Z-C model. Figure 1a shows the observed and the predicted 1982/83 and 1997/98 El Niño events. The wind stress anomaly data of March, April, and May of 1982 and 1997 were utilized to simulate the sea-surface temperature anomaly (SSTA) patterns in the Niño 3 region (5°S–5°N, 90°W–150°W) in these years. The simulated SSTA was then compared with the observed SSTA in the same region. The correlation coefficients between the simulated and observed SSTA were as high as 0.91 for the 1982/83 event and 0.95 for 1997/98 event, showing that Z-C model could be reliably used in this study.

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Figure 1 (a) Forecast of the 1982/83 (blue) and 1997/98 (red) El Niño events in the Z-C model (solid lines), compared with observed (dashed lines) sea surface temperature anomaly (SSTA) averaged over the Niño 3 region, units:°C; (b) Observed SSTA (black), modeled SSTA in the forced experiment (red), and modeled SSTA in the coupled experiment (blue), units: °C; (c) Power spectrum (S2, units: °C2) of SSTA in the forced run (red), coupled run (blue), and observed (black), as well as their χ2-test at the 95% confidence level (dashed lines).

Two types of experiments were performed in our study: forced and coupled experiments. In the forced experiment, the wind stress anomaly field was input throughout the entire process of integration from 1964 to 2009, with the coupling process being turned off. In the coupled experiment, 12 ensemble runs were carried out with only three months of wind stress anomaly initially being input into the model. In the coupled experiment, the average of the ensemble runs was analyzed. For example, the wind stress anomaly data from March to May 1964 were input in the

first ensemble run while the data from June to August 1964 were used in the second ensemble run, and so on. All ensemble experiments were integrated until December 2009 using a coupling process. The data we analyzed were the average of all the ensembles from December 1966 to 2009 with a time interval of 10 days. In the coupled experiment, we used model years to represent time, with 1964 defined as the first year of the model. In Z-C model, the atmospheric resolution is 2° latitude × 5.625° longitude with only one level. The circulation in

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the atmosphere part could be driven by SSTA and moisture convergence. The ocean part is a 2.5 layer reduced gravity ocean model, whose resolution of the surface mixed layer is similar to the atmosphere and that of the thermocline is 0.5° latitude × 2° longitude. The ocean is driven by the surface wind stress anomaly from the atmosphere part. For the temperature variability equation, let us start with the temperature anomaly equation: ∂T ′ ∂T ′ ∂(T + T ′) ∂T ′ ∂ (T + T ′) = −u − u′ −v − v′ ∂t ∂x ∂x ∂y ∂y ∂T ′ − M ( w + w′) − [ M ( w + w′) ∂z ∂(T + T ′) − M ( w)] − αT ′ . (1) ∂z Here, ∂T ′ ∂t is the SSTA tendency; ( −uTx′ , −vTy′ ) shows the zonal and meridional advections resulting from a change of temperature gradient, and ( −u ′(Tx + Tx′ ) ,

−v′(Ty + Ty′ ) ) shows advection terms that result from a change in the surface current. M(x) is a special function: if x > 0, M(x) = x; however, if x < 0, M(x) = 0; so − [ M ( w + w′) − M ( w) ] (Tz + Tz′) and − M ( w + w′)Tz′ are the vertical advection terms due to anomalous upwelling and anomalous vertical temperature gradient, respectively. −α T ′ is the damping term. The temperature variability Eq. (2) can be obtained when both sides of Eq. (1) are multiplied by T ′ . Equation (2) shows that the temperature amplitude tendency ( ∂T ′2 ∂t ) is due to zonal advection ( −2u ′T ′(Tx + Tx′ ) , −2uT ′Tx′ ), meridional advection ( −2v′T ′(Ty + Ty′ ) ,

−2vT ′Ty′ ), vertical advection ( −2 w′T ′(Tz + Tz′) , −2wT ′Tz′ )

and the damping term (−2α T ′2 ). The importance of the different terms in the forced and coupled experiments is analyzed in our study, with all terms being output every 10 days. ∂T ′2 ∂(T + T ′) ∂T ′ ~ −2u ′T ′ − 2uT ′ ∂t ∂x ∂x ∂(T + T ′) ∂T ′ −2v′T ′ − 2vT ′ ∂y ∂y

−2w′T ′

3 3.1

∂ (T + T ′) ∂T ′ − 2wT ′ − 2α T ′2 . ∂z ∂z

(2)

Mechanisms affecting ENSO intensity Forced experiment

As expected, in the forced experiments, the simulated SSTA is in good agreement with the observed SSTAs in the Niño 3 region (5°S–5°N, 90°W–150°W, Fig. 1b). The two are in phase, and the correlation coefficient is 0.74. The strong El Niño events (1972/73, 1982/83, and 1997/98) could be forced exactly as observed, though the amplitudes in the forced run are not as large as those in

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the observations. Additionally, the simulated La Niña events are not as robust as the El Niño events. The power spectrum of the SSTA (Fig. 1c) shows a period of three to five years in both the forced run and the observations. This suggests that the Z-C model is capable of reproducing the actual ENSO frequency. In this work, we focus on strong El Niño events. To identify the contribution of different processes to the amplitude of ENSO events, the temperature variability equation is analyzed. Figure 2a shows that the zonal, meridional, and vertical temperature advections contribute to the increase in the amplitude of temperature, whereas the damping is the only term that maintains the steadiness of the system. Of the terms favoring the increase in amplitude of temperature, the vertical temperature advection is the most important, and the meridional term plays a minor role. The vertical temperature advection term can be further decomposed into two sub-terms: the mean upwelling of the anomalous vertical temperature gradient −wT ′Tz′ (the “remote term” as defined in Yang and Zhang (2008)) and the anomalous upwelling of mean vertical temperature gradient − w′T ′(Tz + Tz′) (the “local term”). In the vertical temperature advection term, the “local term” depicts the anomalous upwelling of the mean temperature gradient ( − w′T ′Tz ), the magnitude of which is proportional to the mean vertical temperature gradient ( Tz ) and vertical heat flux (−w′ T ′ ). It represents local oceanic processes and plays a dominant role in temperature variation. The “remote term” represents the mean upwelling of the anomalous temperature gradient ( − wT ′Tz′ ). To some extent, the “remote term” represents the effect of climate change on the variability of ocean temperatures. For strong ENSO events, the local term is much larger than the remote term, and the variability of the local term is strictly consistent with the evolution of temperature variability (Fig. 2b). In the Z-C model, the climatological Tz is fixed at a positive value, and the change in the temperature gradient, Tz′ , is much smaller than the climatology ( Tz >> Tz′ ). Therefore, the background vertical temperature gradient is positive ( Tz >0), which is to be expected because warmer water resides in the upper layers of the ocean. Hence, the upward vertical heat flux, −w′ T ′ (>0) (designated as the virtual vertical heat flux, VHF, to differentiate this term from the heat flux at the air-sea interface), tends to increase the temperature variability, and the large VHF is associated with strong El Niño events. This process can be further understood as follows: considering a positive vertical temperature gradient ( Tz + Tz′ > 0 ) in the ocean, the upper layers are warmer. Thus, in the case of anomalous upwelling (w′ > 0), colder water deviates upwards. As a result, the temperature of water decreases in the upper layers ( T ′ < 0), and the local term contributes to the increase in the temperature amplitude ( − w′T ′(Tz + Tz′) > 0 ). In contrast, the anomalous down-welling (w′ < 0) brings warmer water to lower levels in the ocean, leading

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Figure 2 The time series of temperature variability (black, units: ×5 °C2), the zonal (green), meridional (red), and vertical (blue) advections, and the damping term (dashed red, units: ×10–5 °C2 s–1) averaged over the Niño 3 region in the (a) forced experiment and (d) coupled experiment; Time series of the vertical remote term (red), local term (blue, units: ×10–5 °C2 s–1), vertical heat flux (cyan, ×10–4 °C m s–1), and background vertical temperature gradient (green, ×10–1 °C m–1) in the (b) forced experiment and (e) coupled experiment; Time series of the meridional local term (red), remote term (blue, units: ×10–5 °C2 s–1), meridional heat transport (cyan, ×10–2 °C m s–1), and meridional temperature gradient anomaly (green, ×10–3 °C m–1) in the (c) forced experiment and (f) coupled experiment.

to a positive temperature anomaly ( T ′ > 0). Thus, the local term also intensifies the strength of the ENSO events ( −w′T ′(Tz + Tz′) > 0 ). Therefore, the vertical heat flux (−w′ T ′ ) and background vertical temperature gradient ( Tz + Tz′ ) are positively correlated, and the local term causes an increase in the magnitude of ENSO (Fig. 2b). It should be noted that the causality between the advection terms and the temperature variability need to be examined. Figure 3a shows the lag correlation between the temperature variability and temperature advection terms. The largest correlation between the vertical temperature advection ( − w′T ′(Tz + Tz′) ) and the temperature variability occurs when they are in phase. However, the correlation between the VHF (−w′ T ′ ) and the temperature variability is the highest (0.95) when the former leads the latter by 10–20 days (this passes the Student t-test at 99% confidence level), suggesting that the VHF could be causing the change in the temperature variability. In other words, VHF is a factor in determining the magnitude of ENSO events. Clearly, the combination of the VHF and the background temperature gradient could affect the strength of the ENSO events. However, to what extent they deter-

mine the amplitude of ENSO events requires further investigation. A series of experiments were carried out to test the sensitivity of the amplitude of ENSO events to the VHF and the vertical temperature gradient. Figure 4a shows the time series of temperature anomalies in the Niño 3 region for different strengths of the VHF. At first, the VHF is initially unchanged and then becomes artificially magnified by factors of 2, 4, and 6. The amplitude of the temperature variability correlates with the VHF positively and almost linearly. However, the period and phase of ENSO does not change accordingly. The sensitivity region was also checked (figure not shown). Our analysis shows that temperature variability is most sensitive to changes in VHF in the Niño 3 region (150°W– 90°W). For the sake of comparison, we also examined the sensitivity of the intensity of ENSO events to the mean vertical temperature gradient, as its intensity is amplified by factors of 2, 6, and 8 (Fig. 4b). As expected, strong ENSO events correspond to strong gradients in background temperature. The periods and phases in these ensemble runs are also unchanged, similar to the VHF ensemble experiments. Meridional temperature advection is also an important factor that affects the temperature variability, but less so

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20% towards the temperature amplitude (Fig. 2c), which is quite different compared with vertical advection. In a strong El Niño event the anomalous meridional temperature gradient ( Ty′ ) is always negative (Fig. 2c). However, the meridional heat transport ( −vT ′ ), whose magnitude is much larger than the VHF, is positively correlated with the anomalous meridional temperature gradient ( Ty′ ), leading to an increase in the strength of the ENSO events. The lag correlation analysis between the meridional “remote term” and the temperature variability shows that the highest correlation (0.73) occurs when the meridional heat flux leads by 50 days (Fig. 3a). Therefore, meridional heat flux as well as the meridional “remote term” could affect the amplitude of ENSO events, following the VHF and the vertical “local term”. 3.2

Figure 3 Lag correlation between the temperature variability and the vertical local term (solid blue), vertical heat flux (dashed blue), meridional remote term (solid red), meridional heat transport (dashed red) in the (a) forced experiment and (b) coupled experiment. Minus time (units: day) is for the situation in which other terms lead temperature variability.

than vertical advection. With meridional temperature advection, the “remote term” ( −vT ′Ty′ ) contributes 80%, and the “local term” ( −v′T ′(Ty + Ty′ ) ) contributes only

Coupled experiment

In the coupled experiments, 12 ensemble runs were performed and each started with different initial conditions. Here, the ensemble mean was analyzed. The temperature anomaly (Fig. 1b) and temperature variability equation (Fig. 2d) were re-checked using the outputs from the coupled ensemble mean. There is a very regular period of slightly more than five years in the ENSO variability (Fig. 1c) and slightly smaller amplitude when compared to that of the forced run (Fig. 1b). As with the forced run, the vertical and meridional temperature advection terms determine the ENSO amplitude (Fig. 2e). The “local term” ( − w′T ′(Tz + Tz′) ) in the vertical advection term tends to enhance ENSO, while the “remote term” sometimes plays the opposite role in these coupled runs. The VHF (−w′ T ′ ) is positively correlated to the background vertical temperature gradient, leading to a positive local effect (Fig. 2e). Lag correlation analysis

Figure 4 Temperature anomaly (square root of temperature variability, units: °C) averaged in Niño 3 region with vertical heat flux changes in the (a) forced experiment and (c) coupled experiment; and with background vertical temperature gradient changes in the (b) forced experiment and (d) coupled experiment.

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(Fig. 3b) shows that when the VHF and the local term lead the temperature amplitude by 15–30 days, the correlation reaches its maximum, which is generally consistent with the forced experiment. For the contribution of the meridional temperature advection, the conclusion is also similar to that of the forced run (Fig. 2f); the “remote term” ( −vT ′Ty′ ) tends to enhance the amplitude of ENSO events, in which the meridional heat transport, −vT ′ , is positively correlated with Ty′ . The meridional heat transport could partially contribute to the amplitude of ENSO, with the maximum correlation occurring when heat transport leads by 60 days. However, the effect of meridional heat transport is relatively weaker than that of the vertical heat flux on the strength of ENSO events. Similar experiments were performed in coupled runs to test the sensitivity of the strength of ENSO events to the magnitude of the VHF (Fig. 4c) and the background vertical temperature gradient (Fig. 4d). As the coupled oceanic system may shift to an unstable state when the strength of the VHF or the thermocline is amplified several times, we chose to reduce or slightly increase the VHF and the vertical temperature gradient and then select the optimal ensemble runs. As the forced run reveals, the temperature amplitude is also well correlated with the intensity of the heat flux. For example, when the VHF is reduced by 40%, the mean temperature amplitude can be weakened by 50%–60%, suggesting that the variability of the latter is nonlinearly related to the change of heat flux in the coupled experiment. This relationship is different from that in the forced run, which suggests that the coupled process can be more effective in increasing ENSO variability. Another discrepancy from the forced run is that the periods of ENSO variability vary significantly when varying the VHF in the coupled system. Therefore, coupling not only enhances the nonlinear process in the ocean but also affects the frequency of ENSO events. Large amplitudes of ENSO events also correspond to strong vertical temperature gradients. Figure 4d shows the time series of Niño 3 temperature variability in the coupled experiment, with different colored curves indicating different temperature gradients. Obviously the strength of the ENSO events weakens when the background temperature gradient is reduced to 80%, 60%, and 40%. However, the temperature variability changes nonlinearly according to variations in the background thermocline strength, and the periods of the ENSO events significantly change as well. The above test verifies that coupling could lead to nonlinear effects on oceanic processes.

4

Conclusions and discussions

As a work complementary to Yang and Zhang (2008), we examine the factors determining the intensity of ENSO events using the simple coupled Z-C model, which has the capability to reproduce and predict El Niño events because of its simple physical processes. Two types of experiments were performed. The temperature variability equation and its terms are carefully investigated. The lo-

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cal term in the vertical temperature advection and the remote term in the meridional temperature advection greatly affect ENSO variability; the VHF is the most important factor in determining the intensity of ENSO events. The VHF has been checked quantitatively in our study. According to the so-called K-theory, which links the vertical heat flux to the mean vertical temperature gradient ( − w′T ′ ∝ K ( ∂T ∂z ) , where K is a constant), the VHF within the EEP thermocline is proportional to the background thermocline strength in the EEP. Their interaction causes the vertical local term to contribute to the strength of ENSO events. In the forced run, the changes in VHF or thermocline strength only affect the amplitude of ENSO events. However, when the coupling process is present, the frequency of ENSO changes. Further studies on the role of air-sea coupling in the EEP on ENSO properties are still required. This study emphasizes the importance of VHF and the vertical “local term” in determining the intensity of ENSO events. Because the Z-C model is a tropical Pacific model, physical processes between tropical-extratropical are largely simplified. Hence, the analysis of the contributions of meridional advection to the intensity of ENSO events depends on parameterization. The vertical process in the ocean as well as the air-sea interaction is also simplified in this idealized model, so the quantitative analysis between the VHF and background thermocline strength is incomplete. In addition, the change in the frequency of ENSO events in response to VHF and the strength of the thermocline requires additional investigation. In our next study, a more complex model will be employed to investigate the role of advection terms in both the intensity and frequency of ENSO events and the relationship between VHF and the vertical temperature gradient. Acknowledgements. This work is jointly supported by the National Natural Science Foundation of China (Grant Nos. 40976007 and 41176002), the Special Fund for Meteorological Scientific Research in the Public Interest of China Meteorological Administration (Grant No. GYHY201006022), and the Norwegian Research Council through the East Asian DecCen Project (Grant No. 193690/S30). It contributes to the Centre for Climate Dynamics at the Bjerknes Centre. All of the experiments were performed on the supercomputer at Peking University.

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