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The Tropical Pacific-Indian Ocean Associated Mode Simulated by LICOM2.0


doi: 10.1007/s00376-017-6176-5

  • Oceanic general circulation models have become an important tool for the study of marine status and change. This paper reports a numerical simulation carried out using LICOM2.0 and the forcing field from CORE. When compared with SODA reanalysis data and ERSST.v3b data, the patterns and variability of the tropical Pacific-Indian Ocean associated mode (PIOAM) are reproduced very well in this experiment. This indicates that, when the tropical central-western Indian Ocean and central-eastern Pacific are abnormally warmer/colder, the tropical eastern Indian Ocean and western Pacific are correspondingly colder/warmer. This further confirms that the tropical PIOAM is an important mode that is not only significant in the SST anomaly field, but also more obviously in the subsurface ocean temperature anomaly field. The surface associated mode index (SAMI) and the thermocline (i.e., subsurface) associated mode index (TAMI) calculated using the model output data are both consistent with the values of these indices derived from observation and reanalysis data. However, the model SAMI and TAMI are more closely and synchronously related to each other.
    摘要: 大洋环流模式已成为研究海洋状态及其变化的重要工具. 本文利用中科院大气所的LICOM2.0模式和来自CORE的强迫场进行了一个数值模拟, 并重点分析了热带太平洋-印度洋联合模(以下简称联合模)特征在模式中的表现. 与SODA再分析资料和ERSST. V3b等观测资料进行对比发现, 数值试验很好地再现了联合模的形态和变率. 即, 当热带中西印度洋和中东太平洋异常偏暖/冷时, 热带东印度和西太平洋相应地偏冷/暖. 这进一步证实了联合模是热带太平洋—印度洋非常重要的一个模态, 不仅在表层海温异常场上显著, 而且在次表层海温异常场上表现更为明显. 研究还发现, 模式海温资料计算得到的表层联合模指数和温跃层(次表层)联合模指数与从观测资料中计算得到的都较为一致. 然而, 模式中这两个指数相关性更好而且更加同步.
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Manuscript received: 05 January 2017
Manuscript revised: 07 June 2017
Manuscript accepted: 16 June 2017
通讯作者: 陈斌, bchen63@163.com
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The Tropical Pacific-Indian Ocean Associated Mode Simulated by LICOM2.0

  • 1. Institute of Meteorology & Oceanography, National University of Defense Technology, Nanjing 211101, China
  • 2. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Abstract: Oceanic general circulation models have become an important tool for the study of marine status and change. This paper reports a numerical simulation carried out using LICOM2.0 and the forcing field from CORE. When compared with SODA reanalysis data and ERSST.v3b data, the patterns and variability of the tropical Pacific-Indian Ocean associated mode (PIOAM) are reproduced very well in this experiment. This indicates that, when the tropical central-western Indian Ocean and central-eastern Pacific are abnormally warmer/colder, the tropical eastern Indian Ocean and western Pacific are correspondingly colder/warmer. This further confirms that the tropical PIOAM is an important mode that is not only significant in the SST anomaly field, but also more obviously in the subsurface ocean temperature anomaly field. The surface associated mode index (SAMI) and the thermocline (i.e., subsurface) associated mode index (TAMI) calculated using the model output data are both consistent with the values of these indices derived from observation and reanalysis data. However, the model SAMI and TAMI are more closely and synchronously related to each other.

摘要: 大洋环流模式已成为研究海洋状态及其变化的重要工具. 本文利用中科院大气所的LICOM2.0模式和来自CORE的强迫场进行了一个数值模拟, 并重点分析了热带太平洋-印度洋联合模(以下简称联合模)特征在模式中的表现. 与SODA再分析资料和ERSST. V3b等观测资料进行对比发现, 数值试验很好地再现了联合模的形态和变率. 即, 当热带中西印度洋和中东太平洋异常偏暖/冷时, 热带东印度和西太平洋相应地偏冷/暖. 这进一步证实了联合模是热带太平洋—印度洋非常重要的一个模态, 不仅在表层海温异常场上显著, 而且在次表层海温异常场上表现更为明显. 研究还发现, 模式海温资料计算得到的表层联合模指数和温跃层(次表层)联合模指数与从观测资料中计算得到的都较为一致. 然而, 模式中这两个指数相关性更好而且更加同步.

1. Introduction
  • El Niño-Southern Oscillation (ENSO) is an important interannual climate system. Since (Bjerknes, 1969) noted that ENSO is a result of air-sea interaction, it has been the subject of much detailed research that has revealed its physical mechanisms and global-scale climate impacts. However, (Saji et al., 1999) found that a similar interannual zonal oscillation of SST anomalies (SSTAs) also exists in the equatorial Indian Ocean and named it the Indian Ocean dipole (IOD). Since then, many papers have discussed its mechanisms and impacts. Originally, the IOD was thought to be the result of ocean-atmosphere interaction in the Indian Ocean itself (Saji et al., 1999; Webster et al., 1999). Gradually, however, research has focused on the relationship between the IOD and ENSO. For example, the asymmetric SSTA between the eastern and western Indian Ocean in 1997/98 was probably triggered by the strong El Niño of that year (Yu and Rienecker, 1999; Ueda and Matsumoto, 2000), as ENSO can affect the sea surface wind field through the anti-Walker circulation over the equator (Yu and Rienecker, 1999). Based on statistical analysis, (Li and Mu, 2001) reported a close relationship between the SSTA dipole mode in the equatorial Indian Ocean and ENSO (which can also be treated as a dipole) in the Pacific. By analyzing the SSTA features of the Indian Ocean in the warm and cold phases of the ENSO cycle, (Yan et al., 2001) showed that a significant dipole oscillation phenomenon develops in the Indian Ocean during ENSO events. In addition, SST variations in the Indian Ocean commonly play an important role in the development of El Niño events (Annamalai et al., 2005; Izumo et al., 2010; Yuan et al., 2011, 2013). Thus, the IOD can also influence ENSO (Annamalai et al., 2005; Yuan, 2005; Yuan et al., 2011). Taken together, the above studies indicate that there are strong interactions between the Indian and Pacific oceans, and hence we should consider the SSTA field of the tropical Pacific and Indian oceans as a whole.

    Based on this idea, the tropical Pacific-Indian Ocean SSTA was analyzed by (Ju et al., 2004) using EOF decomposition. The first mode shows that the SSTA in the equatorial central-western Indian Ocean and central-eastern Pacific is opposite to that in the equatorial western Pacific and eastern Indian Ocean. As this mode contributes over 50% of the total variance, it is referred to as the tropical Pacific-Indian Ocean temperature anomaly mode or the Pacific-Indian Ocean associated mode (PIOAM), and this has been further researched in other studies (e.g., Wu et al., 2005; Yang and Li, 2005). This mode is meaningful because it better reflects the SSTA differences between the east and west in the tropical basin, and has an important influence on the South Asian high and even on the Asian climate (Yang and Li, 2005; Yang et al., 2006). It is also closely associated with the evolution and propagation of the subsurface ocean temperature anomaly (SOTA) (Wu and Li, 2009).

    The above studies were based mainly on SST data. However, the ocean temperature anomaly in the subsurface zone, especially in the thermocline, is much stronger than that at the surface (Qian et al., 2004; Chao et al., 2005). The temperature anomalies usually appear first in the thermocline and then propagate along this layer. For example, the SOTA in the equatorial western Pacific and its eastward propagation are an important driver of El Niño (Li and Mu, 1999). In fact, the variation in the SOTA in the western Pacific warm pool is closely associated with the entire ENSO cycle, and they interact with each other (Li and Mu, 2000). Moreover, the IOD is also more prominent in the subsurface zone than at the surface, and it appears as a dipole in a real physical sense (Chao et al., 2005). Thus, (Li et al., 2013) further studied the tropical Pacific-Indian Ocean temperature anomaly mode in the thermocline, analyzing the monthly thermocline temperature anomaly (TOTA) over the period 1958-2007 and the weekly sea surface height (SSH) anomaly between 1992 and 2011 in the tropical Pacific-Indian Ocean using the EOF method. Both of the first two modes show coupled variations between the tropical Indian Ocean and the Pacific. That is, when the subsurface temperature in the tropical central-western Indian Ocean and central-eastern Pacific is abnormally warmer/colder, the subsurface temperature in the tropical eastern Indian Ocean and western Pacific is abnormally colder/warmer. This is seen as a major tripole pattern and is referred to as the tropical Pacific-Indian Ocean thermocline temperature anomaly associated mode. This mode shows a good correlation with both ENSO and the IOD, and its evolution is closely related to the propagation of the TOTA. This mode also has a high positive correlation with that defined by the SSTA, but can better represent the spatial distribution and temporal variation of the associated features between temperature anomalies in the tropical Indian Ocean and Pacific. Considering the spatial patterns of SST and SSH in the first modes of multi-variable EOF of SST, SSH and surface wind stress both resemble a tripole, and (Lian et al., 2014) named the dominant mode in the tropical Pacific-Indian Ocean the Indo-Pacific tripole. For simplicity, these modes are referred to collectively as the PIOAM in this paper.

    As observational data are sparse in the ocean, especially in the subsurface zone, extended simulations produced by ocean general circulation models (OGCMs) are needed for further exploration of the characteristics and dynamics of the PIOAM in the tropical Pacific-Indian Ocean. Using an OGCM developed by (Jin et al., 1999) and (Li, 2005), (Wu and Li, 2009) investigated the three-dimensional thermal and dynamic structures of the associated mode and its mechanism of evolution. Their subsequent numerical experiments indicated that the Indonesian Throughflow plays an important role in the formation of the PIOAM, especially in the subsurface (Wu et al., 2010). However, the spatial resolution of their OGCM was relatively low and so could not identify the narrow channels in the Indonesian Sea. Thus, a relatively high-resolution OGCM is required to further improve the simulation of the PIOAM (Wu et al., 2010). In the present study, a state-of-the-art eddy-resolving OGCM, LICOM2.0, is used to examine the properties of the surface and subsurface PIOAM, with an emphasis on the evolution of the subsurface PIOAM and its relationship to the surface PIOAM.

2. Model and data
  • LICOM2.0 is the newest version of the fourth-generation OGCM developed by the State Key Laboratory of Numerical Modeling for Atmospheric Science and Geophysical Fluid Dynamics at the Institute of Atmospheric Physics, Chinese Academy of Sciences. It is also the oceanic component of FGOALS2.0, which participated in CMIP5. The model was forced by the normal forcing data derived from CORE (Griffies et al., 2009), as detailed in Table 1. More information on LICOM2.0 can be found in (Liu et al., 2012).

    With respect to the standard edition of LICOM2.0 (Liu et al., 2012), for this study we made some improvements to the model according to (Yu et al., 2012), as follows: (1) The horizontal resolution was increased to an eddy-resolving 1/10° and the number of vertical layers to 55. In the upper 300 m, there were 36 uneven layers with a mean thickness of less than 10 m, and the depth of the first layer was 5 m. (2) To exclude the Arctic Ocean, the model domain was set to 66°N-79°S. (3) Biharmonic viscosity and diffusivity schemes were adopted in the horizontal direction in the momentum and thermohaline equations, respectively, while the parameterization of mesoscale eddies was turned off in the thermohaline equations. (4) The methods of barotropic and baroclinic decomposition were improved.

    In our simulation, LICOM2.0 was spun-up for 500 years from zero velocity and initialized from the observed temperature and salinity obtained from WOA05, repeating the daily-corrected Normal Year Forcing data from (Large and Yeager, 2004) as the forcing condition. Then, according to the parameterization schemes of Liu et al. (2014a, 2014b), the model was forced using the forcing data in Table 1 and integrated for 60 years. The last 50 years of output, from January 1958 to December 2007, were used for the analysis and discussion below.

    To validate the model results, we used two datasets——one observational (ERSST.v3b) and one reanalysis (SODA v2.2.4). The ERSST.v3b data were obtained from the National Oceanic and Atmospheric Administration with a horizontal resolution of 2°× 2° and were constructed using ICOADS SST data and improved statistical methods (Smith et al., 2008). The SODA v2.2.4 ocean reanalysis data (Carton and Giese, 2008) were provided by the University of Maryland with a horizontal resolution of 0.5°× 0.5° and 40 levels in the vertical direction.

3. Model verification
  • Firstly, the error associated with simulated variables, such as SST, thermocline depth and heat content, was analyzed to examine the results of the numerical simulation. Here, the "error" is the difference between the model data and the ERSST or SODA data, as these two datasets are relatively realistic in their representation of ocean conditions (Smith et al., 2008; Carton and Giese, 2008).

  • Figures 1a and b show the January climatological SST simulated by LICOM2.0 and that obtained from ERSST, respectively. The results show that they are essentially consistent in their spatial pattern, although the warm pool SST in the simulation is significantly higher than that in the observations, as is the SST at the East African coast. On the other hand, although the spatial pattern of the SST standard deviation in the simulation (Fig. 1c) is similar to that in the ERSST data (Fig. 1d), the values of the former are generally larger than those of the latter. One possible cause of this is that the relatively high resolution of the model may contain some dynamic and thermodynamic processes that are undetectable in the observational data. Another possibility is that the model systematically overestimates the SST variability in the tropical Pacific-Indian Ocean.

    Figure 1.  The (a, b) January climatological SST and (c, d) SST standard deviation (units: °C) in (a, c) LICOM2.0 and (b, d) ERSST in the tropical Pacific-Indian Ocean.

    Figure 2.  Distribution of the SST trend [units: °C (50 yr)-1] in (a) LICOM and (b) ERSST in the tropical Pacific-Indian Ocean, and the time series of (c) the ño3 index and (d) the IOBMI calculated using the original LICOM2.0 (solid line) and ERSST (dotted line) data.

    We also examined the SST trend in the LICOM2.0 simulation, as shown in Fig. 2a. Compared with the result based on the ERSST data (Fig. 2b), the simulated SST trend is obviously higher in the central and eastern Pacific but markedly lower in the warm pool region and Indian Ocean. Thus, the ño3 region (5°S-5°N, 150°-90°W) and tropical Indian Ocean basin region (20°S-20°N, 40°-110°E) were selected to further compare the SST trend in the simulation and observational data. Figure 2c shows the time series of the original ño3 index in the LICOM2.0 simulation and the ERSST data, respectively. In LICOM2.0, the index shows a warming trend [up to 0.92°C (50 yr)-1], whereas in ERSST this warming trend is relatively weaker [only 0.51°C (50 yr)-1]. However, the warming trend of the Indian Ocean basin mode index (IOBMI) in the simulation is not as obvious as it is in the observational data [0.27°C (50 yr)-1 versus 0.55 °C (50 yr)-1. These results again indicate that LICOM2.0 overestimates the warming trend in the central and eastern Pacific but underestimates the warming trend in the tropical Indian Ocean. These deficiencies may stem from systematic biases in the model. Thus, we removed all the trends in the model and observational data hereinafter.

    Figure 3.  Time series of (a) the ño3.4 index and (b) the DMI calculated using the detrended LICOM2.0 (solid line) and ERSST (dotted line) data.

    Figure 3 shows the ño3.4 index and Dipole Mode Index (DMI) calculated separately using the simulated SST and that in the ERSST observations. The correlation coefficient between the LICOM2.0 ño3.4 index and the ERSST ño3.4 index reaches 0.94 (exceeding the 99% significance level; Fig. 3a), indicating that LICOM2.0 can simulate the ENSO variability very well. Meanwhile, the correlation coefficient between the two DMI time series is about 0.71 (exceeding the 99% significance level; Fig. 3b), showing that the model also performs well in simulating the main SST variability in the Indian Ocean, but not as well as in Pacific. This may be because the processes of air-sea interaction are more complex in the Indian Ocean than in the Pacific, which is hard for a single OGCM to simulate. Regardless, considering the performance of LICOM2.0 in simulating ENSO and the IOD, we had no reason to doubt that the surface PIOAM could be satisfactorily reproduced in this model.

  • The thermocline depth was calculated using the vertical gradient method by separately applying the LICOM2.0 and SODA data. The results showed that the climatology of the thermocline depth in LICOM2.0 (Fig. 4a) is consistent with that of SODA (Fig. 4b) in most regions, but is much deeper than the latter in the equatorial northwestern Pacific and in the ITCZ domain (5°-15°N, 150°-110°W), leading to the thermocline ridge along 10°N in the northeastern Pacific being poorly defined in the model (Fig. 4a). By comparing the amplitude (standard deviation) of thermocline depth in these two datasets, we found that the spatial pattern in the model (Fig. 4c) is generally similar to that in the reanalysis data (Fig. 4d). The major differences are that the oscillation of the thermocline in the equatorial eastern Pacific and eastern Indian Ocean are larger in LICOM2.0 than they are in SODA, whereas the reverse is true for the tropical northeastern Pacific, equatorial western Pacific, and western Indian Ocean (Figs. 4c and d). This may indicate that the oceanic long waves in LICOM2.0 are stronger than those observed in the eastern part of the ocean basin, but weaker in the western part.

    The simulated upper ocean heat content is also consistent with the observations (figures not shown). These results indicate that LICOM2.0 is also capable of accurately simulating the tropical Pacific-Indian Ocean variability in the subsurface zone. Thus, we were confident that the model could be used to examine the PIOAM both at the sea surface and in the thermocline, and in considering their relationship with each other. These points will be discussed in the next section.

    Figure 4.  (a, b) January climatological thermocline depth (units: m) in (a) LICOM2.0 and (b) SODA, and (c, d) thermocline depth standard deviation (units: m) in (c) LICOM2.0 and (d) ERSST, in the tropical Pacific-Indian Ocean.

4. Characteristics of the PIOAM in LICOM2.0
  • We began by analyzing the SSTA in LICOM2.0 using the EOF method and comparing it with the same in the ERSST data. The spatial pattern of EOF1 presents as a tripole mode; that is, the SSTAs are positive across most of the tropical Indian Ocean and central eastern Pacific, whereas they are negative across the equatorial southeastern Indian Ocean and in the tropical western Pacific (Fig. 5a), which is consistent with the pattern seen in the ERSST data (Fig. 5b). The first mode in the model and in the ERSST data explains 51.4% and 49.8% of the variance of the SSTA, respectively, indicating that this SSTA mode is typical. The correlation coefficients between the corresponding PC1 in LICOM2.0 and in the ERSST data reaches 0.94 (exceeding the 99% significance level). Thus, the major mode of SSTA variation in the tropical Pacific-Indian Ocean, the surface PIOAM, is well represented in the LICOM2.0 simulation.

    Figure 5.  EOF-1 of tropical Pacific-Indian Ocean SSTA derived from (a) LICOM2.0 and (b) ERSST; (c) temporal variation of the corresponding PC1 in LICOM2.0 (solid line) and ERSST (dotted line).

    Figure 6.  Time series of the SAMI (a) and the composed SAMI (b) in LICOM and ERSST, respectively.

    Following the definition proposed by (Yang and Li, 2005), the SSTA associated mode index (SAMI) was calculated from the LICOM2.0 and from the ERSST data. Their time series can be seen in Fig. 6a. Generally, the SAMI in the model is consistent with that in ERSST (with correlation coefficients above 0.8 and exceeding the 99% significance level), although it is obviously higher or lower than observed in several years. Positive SAMI years were selected when the SAMI was larger than +1.0 standard deviation in three consecutive months, and vice versa. The composite analysis from these data (Fig. 6b) shows that positive SAMI in the model tends to develop rapidly in late spring and summer, peaks in autumn, and decays in the following spring. In the year prior to positive SAMI years, weak negative SAMI often occurs. However, the negative PIOAM in the model seems to grow slowly from the previous autumn, peaks in the early part of the concurrent autumn, and can then last through to the following spring. Notably, the evolution of positive SAMI in the model is very close to that in the observational data (Fig 6b), whereas negative SAMI in the model develops 6-9 months earlier and decays 2-3 months later than observed. Moreover, the amplitude of negative SAMI in the model is larger than that in the observational data. In short, the simulated negative PIOAM is more durable and stronger than observed. This may indicate that there are some problems related to the ability of LICOM2.0 to simulate the negative PIOAM and the amplitude asymmetry of PIOAM.

    The next step was to investigate the performance of LICOM2.0 in reproducing the subsurface PIOAM. In section 3, we showed that there are systematic "errors" in the climatology and standard deviation of the thermocline depth in LICOM2.0, indicating the temperature variation in the simulated thermocline does not accurately represent the upper ocean thermal anomaly seen in the observations. However, the climatology and standard deviation of the upper ocean heat content anomaly (HCA) are close to those in the reanalysis data; consequently, we used the HCA of the upper 300 m (HCA300) to examine the subsurface ocean variability in LICOM2.0. The EOF1 of HCA300 in LICOM2.0 explains 32.6% of the variance. In this mode, the HCA300 in both the Pacific and Indian oceans shows a significant dipole pattern, while the HCA300 in the eastern Indian Ocean has the same sign as that of the western Pacific (Fig. 7a). Compared to the surface PIOAM (Fig. 5a), the pattern exhibits clearer physical significance and more associated features between the Pacific and Indian oceans. Thus, it forms an HCA300 associated mode in the tropical Pacific-Indian Ocean that is similar to the TOTA mode referred to by (Li et al., 2013). The PC1 of the simulated HCA300 (Fig. 7b) shows stronger amplitude asymmetry than that of the simulated SSTA (Fig. 5c). These results indicate that the subsurface PIOAM is also significant in LICOM2.0. However, there are still some differences between the HCA300 associated mode in LICOM2.0 and that in the SODA data; e.g., the variability in the model is often stronger than that seen in the reanalysis data, especially in the ño3 region and eastern Indian Ocean. This may indicate a need to further improve the representation of the dynamic and thermodynamic processes in these regions.

    Figure 7.  The (a) EOF-1 of HCA300 in the tropical Pacific-Indian Ocean derived from LICOM2.0 and (b) the temporal variation of the corresponding PC1.

    Figure 8.  (a) Time series of the TAMI in LICOM2.0. (b) Wavelet power spectrum of TAMI, the yellow shading denotes where the value exceed 95% confidence level.(c) Global wavelet spectrum of TAMI, the dotted line represent the 95% confidence level.

    The subsurface associated mode index, referred to as TAMI by (Li et al., 2013), was calculated using the HCA300 from LICOM2.0. From the simulated TAMI series (Fig. 8a), it is apparent that the amplitude of positive SAMI is clearly larger than that of negative SAMI, indicating the subsurface PIOAM also has a significant feature of amplitude asymmetry. Wavelet analysis shows that the TAMI mainly has 2-4-yr interannual variability and a 10-14-yr interdecadal periodicity (Figs. 8b and c). Composite analysis shows that the subsurface PIOAM usually develops in summer, peaks in late autumn, and ends in early spring, and the amplitude of its warm phase is larger than that of the cold phase (figure not shown). These characteristics are similar to the findings of (Li et al., 2013), again indicating that the PIOAM is a robust mode in the subsurface of the Pacific-Indian Ocean and that LICOM2.0 can accurately describe the co-variations of temperature in the tropical Pacific and Indian oceans.

    Figure 9 shows that the maximum correlation coefficient between TAMI and SAMI in LICOM2.0 is 0.75 (exceeding the 99% significance level), at a lag of 0 months. This indicates that there is close relationship between the temperature variations at the surface and in the subsurface zone of the Pacific-Indian Ocean. The zero lag in the maximum correlation between TAMI and SAMI may imply that the exchange of momentum and heat between the surface and subsurface of the Pacific-Indian Ocean occurs rapidly, and the progress of both is likely to be forced by the wind anomaly.

    Figure 9.  Time series of (a) TAMI, and Hovmöller diagrams of HCA300 (color scale bar; units: °C) propagating along (b) the equatorial zone (2.5°S-2.5°N) in the Pacific-Indian Ocean, (c) the zonal belt off the equator in the Northern Hemisphere (10°-15°N), and (d) the zonal belt off the equator in the Southern Hemisphere (10°-15°S). Positive/negative values denote an anomalously warm/cold upper ocean.

    Using ocean reanalysis data, (Li et al., 2013) revealed that the propagation of the SOTA results in the development and transition of the subsurface PIOAM. It can also affect the surface, and leads to the evolution of the surface PIOAM.

    Figure 10.  Time-lag correlation between TAMI and SAMI in LICOM2.0.

    We further explored whether LICOM2.0 can reproduce these processes and relationships. The evolution of TAMI (Fig. 10a), combined with the longitude-time profile of HCA300 along key latitude belts (Fig. 10b-d), shows that the upper ocean heat content in the equatorial western Pacific and eastern Indian Ocean is significantly warmer than normal before the PIOAM develops into its warm phase. Then, the warming signals propagate along the equator (Fig. 10b) and westwards along the zonal belt of 10°-15°S in the southern Indian Ocean (Fig. 10d). Thus, the equatorial eastern Pacific and western Indian Ocean become regions of warming, thereby contributing to the development of a positive PIOAM. After the positive PIOAM reaches its peak, the warming signals in the eastern Pacific begin to propagate westwards along 10°-15°N (Fig. 10c). Meanwhile, the equatorial western Pacific and eastern Indian Ocean have been gradually occupied by anomalously cold water, due to upwelling Kelvin and Rossby waves, respectively. The anomalous cold signals in the equatorial western Pacific then propagate eastwards along the equator, while some of the warming signals propagate eastwards along the equator in the Indian Ocean (Fig. 10b), leading to decay of the PIOAM. When the warming signals return to the western Pacific and eastern Indian oceans, the PIOAM transitions to a negative phase. In the development of a negative PIOAM, these processes are opposite to those during a positive PIOAM. Clearly, the evolution of the PIOAM is closely related to the propagation of the ocean HCA (i.e., the SOTA) in LICOM2.0, with a specific passage of the SOTA signals in the Pacific and in the Indian Ocean. These results support the findings of (Li et al., 2013) and further confirm the important role of the SOTA in the evolution of the PIOAM. Meanwhile, there is a suggestion that LICOM2.0 can describe the subsurface dynamic and thermodynamic processes well, such as oceanic long waves.

5. Discussion and conclusions
  • Following improvements including the vertical resolution, horizontal range, parameterization schemes, and barotropic and baroclinic split, LICOM2.0 was integrated with forcing data from CORE. The outputted monthly ocean temperature data for the period 1958-2007 were analyzed and compared with those of the SODA and ERSST datasets. The results showed that the climatology and variability of temperature in the tropical Pacific-Indian Ocean are reproduced well by LICOM2.0, as are the basic features and evolution of the PIOAM.

    EOF analysis of the surface and subsurface temperature anomalies showed that the co-variations of the Indian and Pacific oceans (i.e., the associated modes) are captured by LICOM2.0. The surface and subsurface PIOTM indexes (SAMI and TAMI, respectively) calculated from the model data are consistent with those based on the SODA data, and the high correlation between these two associated modes is also reproduced by LICOM2.0. This indicated that the model can reliably simulate the upper ocean dynamic and thermodynamic processes in the tropical Pacific-Indian Ocean.

    The zonal propagations of the HCA300 in LICOM2.0 showed that the evolution of the PIOAM is closely related to the propagations of HCA300 along the equator and in the off-equatorial zonal belt. In the Pacific, the anomaly signals propagate mainly eastwards along the equator and westwards along 10°-15°N, but the westward propagation is not present, or is very weak, in the South Pacific. However, in the Indian Ocean the signals propagate mainly westwards along 10°-15°S and then partially return to the east along the equator, while the westward propagation in the North Indian Ocean is rather weak. These features are similar to the propagation signals of the SOTA based on SODA data, as reported by (Li et al., 2013).

    In summary, the results obtained from the LICOM2.0 model further confirm that the PIOAM is the principle pattern of the tropical Pacific-Indian Ocean temperature anomaly in both the surface and subsurface zones. As seen in the observational data, the evolution of the PIOAM is related to the development and propagation of the SOTA, and there are also close relationships between the variations of the surface and subsurface PIOAM.

    Based on the SODA ocean reanalysis data and NCEP atmospheric circulation reanalysis data, the physical mechanisms of the PIOAM were initially inferred by (Li, 2015). The general framework is that the anomalous atmospheric circulation (often the Walker circulation) incites the surface wind stress and sea level anomalies, exciting anomalous currents, oceanic long waves, and upwelling. This can further lead to surface and subsurface ocean temperature anomalies through a series of thermodynamic-dynamic processes. These anomalous heat conditions of the ocean then change the atmospheric circulation and SST through evaporation, precipitation, sensible heat fluxes, and latent heat fluxes, which can eventually lead to the system returning to normal or even reversing. In these processes, the coupled Walker circulation above the Pacific and Indian oceans plays an important bridging role, while evaporation, precipitation, shortwave radiation and the reflection of oceanic long waves at the ocean boundary play a role in regulation and feedback.

    However, this framework is based only on data analysis and deductions, combined with an OGCM simulation, which limits its capacity to reveal the mechanisms involved in the evolution of the PIOAM in more depth. Thus, an ocean-atmosphere coupled model is needed to explore the key factors and physical processes associated with the development and evolution of the PIOAM, supported by a series of sensitivity experiments; e.g., by turning the Indonesian Throughflow on or off or by modulating the parameters of air-sea coupling.

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