1. Introduction
The Indian Ocean basin (IOB) mode is characterized by basin-scale anomalous warming or cooling over the tropical Indian Ocean (Dommenget and Latif 2002; Li et al. 2003; Yang et al. 2007; Xie et al. 2009), which has climatic repercussions on the surrounding regions and is important for the variability and predictability of the Asian summer monsoon (Klein et al. 1999). An IOB event usually follows an El Niño event, peaks in late boreal winter and early spring, and persists until August (Nigam and Shen 1993; Klein et al. 1999; Lau and Nath 2003). The IOB appears as the leading mode in an empirical orthogonal function (EOF) analysis of interannual sea surface temperature anomalies (SSTA) in the tropical Indian Ocean. The second mode is the Indian Ocean dipole (IOD) with its positive phase characterized by anomalous warming in the western and cooling in the eastern equatorial Indian Ocean (Saji et al. 1999).
Existing studies have emphasized the influence of the Pacific El Niño–Southern Oscillation (ENSO) on the IOB event through the atmospheric bridge, in the background of the pantropical climate cross-basin interactions (Cai et al. 2019; Wang 2019). Here, the atmospheric bridge refers to the atmosphere Walker circulation variations (Klein et al. 1999). The prevailing view is that ENSO forced IOD, followed by IOB about one season after the mature phase of ENSO, through the Walker circulation variability (Chowdary and Gnanaseelan 2007; Schott et al. 2009; Du et al. 2009; Guo et al. 2018). The response of the Walker circulation to El Niño SSTA leads to downwelling winds and low-level high pressure in the western Pacific/Indian Ocean region, which increases the solar radiation flux in the eastern Indian Ocean and decreases the latent heat flux in the west (e.g., Venzke et al. 2000). Consequently, there is a basinwide warming of the Indian Ocean in response to the El Niño event, which is referred to herein as the IOB (Du et al. 2009). The forcing of the basinwide warming in the equatorial Indian Ocean through the atmospheric bridge by ENSO events has been demonstrated in numerical experiments (Klein et al. 1999; Alexander et al. 2002; Huang and Kinter 2002).
In addition, the propagation of ocean Rossby waves in the southern Indian Ocean suggested the importance of ocean dynamics in the wintertime warming of the western Indian Ocean during concurrent IOD and ENSO years, which persists until the next summer (Masumoto and Meyers 1998; Xie et al. 2002; Chowdary and Gnanaseelan 2007; Schott et al. 2009). In other words, the internal oceanic dynamics in the equatorial Indian Ocean play a major role in the heat content recharge of the tropical Indian Ocean upper thermocline during concurrent IOD and ENSO years (Yuan and Liu 2009; McPhaden and Nagura 2014; Wang and Yuan 2015). However, some studies suggest that the IOB pattern does not appear in the following season of the IOD in the ENSO-independent IOD years based on reanalysis data (Tokinaga and Tanimoto 2004).
Climate simulations present that the Indian Ocean variability acts to damp ENSO via the IOB-forced atmospheric bridge (Santoso et al. 2012), and IOB still exists when ENSO variability is suppressed (Behera et al. 2006; Kajtar et al. 2017; Ding et al. 2022). So far, the effects of the oceanic heat recharge on the IOB formation during independent IOD events have not been investigated. The IOB can exert significant climatic influence beyond the lifetime of an ENSO event (Yang et al. 2007; Xie et al. 2009). A recent study suggests that the IOB could induce a central-Pacific-type El Niño event 1 year later based on observations and Coupled Model Intercomparison Project version 5 (CMIP5) simulations (Zhao et al. 2022). Some studies have shown that an IOB event can transform into an IOD event, but not vice versa (Guo et al. 2018). Therefore, the study of the IOB dynamics is of importance for climate variability and predictability with global repercussions.
The strongest positive IOD event on record took place in 2019 but was evidently not a passive response to an El Niño event, because of a near-normal state concurrent in the Pacific Ocean (Doi et al. 2020; Du et al. 2020; Wang et al. 2020; Zhang et al. 2021). This strong IOD peaked in November 2019, which was followed by a basin warming event (a.k.a. an IOB event) persisting from late 2019 through the summer of 2020. This IOB event was evidently not induced by an El Niño event but instead could be induced by the positive IOD event prior to its onset. A recent study suggested that the development of 2020 IOB was induced by wind–evaporation–SST (WES) feedback and subsequent “C-shaped” wind anomalies (cross-equatorial northeasterly/northwesterly anomalies) during the extreme 2019 IOD (Zhang and Du 2021). However, the roles of the Indian Ocean circulation in the coupled development have not been investigated so far. In this study, a global fully coupled climate prediction system that has been carefully calibrated to provide forecasts at 12-month leads is used to explore the dynamics of the IOB development in 2020. Sensitivity experiments were designed to examine the roles of the Indian, Pacific, and Indo-Pacific Ocean temperature initializations on the 2020 IOB development to isolate the forcing of the tropical Indian Ocean subsurface temperature anomalies during the 2019 IOD.
This paper is organized as follows. Section 2 describes the experiment design and the analysis methods. The model validation and the dynamics of the IOD forcing on the IOB development are presented in section 3. Conclusions are given in section 4.
2. Model and data
a. Model description
The National Marine Environmental Forecasting Center–Community Earth System Model (NMEFC-CESM) version 1.0 is used in this paper. The NMEFC-CESM is an operational real-time seasonal climate prediction system running in the NMEFC of China. The NMEFC-CESM version 1.0 system is developed based on the CESM of the National Center for Atmospheric Research (NCAR), which is a global coupled model consisting of atmosphere, land, sea ice, land ice, and ocean models and a coupler. The details of the model configuration were described by Wang et al. (2023) and Zhang et al. (2023). The ocean model, called the Parallel Ocean Program, version 2 (POP2; Danabasoglu et al. 2012), has a resolution of 1° longitude × (1/3)° latitude in the tropics and 1° longitude × 1° latitude in the extratropical region. This model has 60 layers in the vertical direction, allowing reasonable equatorial and off-equatorial wave propagation and reflection in tropical oceans. Notably, the CESM can simulate the IOD, IOB, and ENSO events fairly well compared with the Global Ocean Data Assimilation System (GODAS) analysis data, which has been described in detail in previous publications (Yao et al. 2016; Zhang et al. 2019; Song et al. 2022).
b. Experimental design
To assess the influence of the Indian Ocean on triggering the IOB event, the temperature anomalies in the upper 400 m of the Indian Ocean for November 2019 were subjected to multiplication by five distinct coefficients (0.98, 0.99, 1.00, 1.01, and 1.02). Subsequently, these anomalies were added to the climatological temperature, which had been assimilated exclusively within the Indian Ocean, while the temperatures in other regions were set to their respective climatological values. Similarly, another five forecast experiments are performed in the Pacific Ocean (PORef_2019/11), with the Pacific Ocean temperature in the upper 400 m assimilated. The ocean temperature in other regions was nudged to the climatology. This experiment is to investigate the role of the Pacific Ocean in inducing the IOB event while excluding the influence of the Indian Ocean. The details of the experimental design with different assimilation schemes are listed in Table 1. Here, observation and climatology indicate the GODAS data and model climatology, respectively.
Names and descriptions of all the sensitivity experiments conducted in this study. IO, PO, and AO indicate the Indian, Pacific, and Atlantic Oceans, respectively; Obs. is observations and Clim. is climatology.
Furthermore, sensitivity experiments are designed to quantify the influence of the IOD on the IOB with perturbed Indian Ocean temperature anomalies. The GODAS temperature data are nudged into the Indian Ocean with amplified and weakened amplitudes, while the model climatology is nudged elsewhere. Three sets of tropical Indian Ocean sensitivity experiments are designed to evaluate the effects of the subsurface, surface, and thermocline temperature initializations. Efforts to do so could elucidate the roles of local air–sea coupling processes and thermocline processes. The first set assimilates the subsurface temperature in the upper 400 m by amplifying and weakening the amplitude of the subsurface temperature amplitudes (Subt-IO_2019/11; Table 1), and the second assimilates the sea surface temperature (SST) only with the temperatures below 10 m nudged to the climatology in the Indian Ocean (SST-IO_2019/11). Consequently, the Indian Ocean basinwide warming can be primarily influenced by the local air–sea interactions linked to the variations in the Walker cell, which specifically operate within the Indo-Pacific Ocean region. The thermocline process is essentially shut down since the subsurface temperature is nudged to the climatology. The third set assimilates GODAS temperature data between 35- and 400-m depths in the Indian Ocean, while elsewhere nudged to the climatology. With this treatment, only the thermocline process is at work, so that we can compare the relative roles of oceanic and atmospheric bridge processes. The original GODAS subsurface temperature anomalies in the upper 400 m in November 2019 were multiplied by five different coefficients of 1.5, 1.6, 1.7, 1.8, and 1.9 and were added onto the climatological temperature before being assimilated into the Indian Ocean. These experiments with amplified temperature anomalies are called the “strong experiments.” In contrast, the assimilation of the subsurface temperature anomalies in November 2019 in the Indian Ocean multiplied by the coefficients of 0.1, 0.2, 0.3, 0.4, and 0.5 are called the “weak experiments.”
To evaluate the influence of perturbed temperature conditions in the Pacific Ocean on the tropical Indian Ocean, the amplified or weakened temperature anomalies and the climatology were assimilated into the Pacific Ocean in November 2019, with the subsurface temperature in the Indian Ocean nudged to the GODAS data (PO_Real-IO_2019/11). The model temperature is nudged to the model climatology outside the Indian Ocean and the Pacific Ocean. The forecast results with amplified and weakened temperature anomalies are compared to evaluate the effects of the initial Pacific Ocean conditions on the Indo-Pacific Ocean circulation and climate predictions.
The subsurface temperature anomalies from 5 to 400 m of the GODAS dataset in November 2019 are assimilated into the model, based on which the subsequent 12-month forecasts are produced. The subsurface temperature anomalies and SST anomalies (SSTA) of the GODAS data during the 2019 IOD event are calculated relative to the GODAS climatology during 1982–2010. A model climatology is obtained from the model forecasts starting from December each year over 1982–2010, based on which the monthly anomalies are calculated. Each monthly climatology is averaged over 29 years. Unless explicitly specified, all climatology mentioned in this paper pertains to model-derived climatology.
The 2019 IOD peaked in November, with the wave processes occurring in the developing phase of the IOD in summer. To reduce the shock effects and ensure a balance between the atmosphere and the ocean, we begin the assimilation 6 months before the forecast and then make the 12-month forecasts from the first day of December. The model was initialized in November 2019, since we focus on the decay process of the 2019 IOD in this study.
c. Data
The temperature and sea surface height (SSH) data used in this study are from the Global Ocean Data Assimilation System (GODAS) dataset provided by the National Centers for Environmental Prediction (NCEP) (https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html). The dipole mode index (DMI) data can be downloaded at the following website: https://stateoftheocean.osmc.noaa.gov/sur/ind/dmi.php. The IOB index is calculated using NCEP OISST data averaged over the 20°S–20°N, 40°–110°E region (Zheng et al. 2011; Guo et al. 2018). Delayed-time (DT) monthly sea level data derived from AVISO are obtained from the Copernicus Marine Environment Monitoring Service (CMEMS) (ftp.sltac.cls.r/Core/). The monthly data cover the period from 1993 to 2020. The wind curl data in the Indian Ocean are derived from the NCEP reanalysis wind data, which can be downloaded as follows: http://apdrc.soest.hawaii.edu/las/v6/dataset?catitem=17010.
3. Results
a. Model evaluation
The IOD event developed into a mature phase from September to November with strong cooling in the east and warming in the west over the tropical Indian Ocean. Strong upwelling anomalies in November 2019 characterized by negative SSH anomalies (SSHA) and SSTA from the global forecast experiments (GLRef_2019/11) were compared with the GODAS data associated with strong easterly wind anomalies in the eastern Indian Ocean (Figs. 1a,b). The 2019 anomalous cooling in the eastern Indian Ocean was a super strong positive IOD event according to the dipole mode index (DMI), followed by a strong 2020 IOB event based on the IOB index (Fig. 1d). The forecasted SSTA in January 2020 were also reproduced fairly well in comparison with the GODAS data, further suggesting that the IOB event could be forecasted successfully. A recent study demonstrates the model’s ability to successfully forecast the 2020/21 La Niña event by initializing it with global subsurface temperature data or Indian Ocean subsurface temperature data from November 2019, using the GODAS dataset (Wang et al. 2023; Zhang et al. 2023). The results underline the importance of the historically strongest IOD event, which occurred in 2019, on the development of the 2020/21 La Niña event. The forecast experiments (GLRef_2019/11) assimilated the GODAS temperature in the upper 400 m in November 2019 at the global scale. The ensemble forecast of the seasonal SSTA evolution in the Indian Ocean in this experiment agreed well with the GODAS reanalysis data from winter to fall 2020, suggesting that the forecasted 2020 IOB event is realistic and the NMEFC-CESM can be used to investigate the roles of different ocean–atmosphere coupled processes (Fig. 2, left and middle columns). Here, the ensemble forecast is obtained by skillfully averaging the predictions of five forecast members while applying corresponding coefficients to each, resulting in a robust and reliable combined forecast. Therefore, the fully coupled model can be used for analyses to study ocean–atmosphere coupled processes in the Indian Ocean.
Model hindcasted (a) SSHAs (shading; cm), wind stress anomalies (vectors; Pa), and (b) SSTAs in the Indian Ocean in November 2019 compared to the SSHAs and SSTAs from the GODAS dataset (contours); (c) model-forecast SSTAs in January 2020 compared to the GODAS dataset (contours); (d) DMI (black) results and the IOB mode index (red) from OISST data during 2019/20. The dashed lines indicate the standard deviations of the IOB mode index (red) and DMI (black).
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
(left) GODAS and ensemble model-predicted SSTAs in the Indian Ocean by the (center) GLRef_2019 and (right) IORef_2019/11 experiments in boreal winter (December 2019–February 2020), spring (March–May 2020), summer (June–August 2020), and fall (September–November 2020). Stippling indicates that the ensemble average anomalies are statistically significantly different from zero above the 95% confidence level, as determined by the Student’s t test.
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
To isolate the influence of the Pacific Ocean on the IOB forecast, Indian Ocean forecast experiments (IORef_2019/11) were performed with the upper 400 m temperature data in November 2019 assimilated into the model Indian Ocean and with the climatology initialization applied elsewhere. The ensemble-forecast SSTA of the IORef_2019/11 experiments could reasonably reproduce the amplitude of the GODAS reanalysis SSTA evolution well in the southern and equatorial Indian Ocean from winter 2019/20 to fall 2020 especially in the Bay of Bengal and Arabian Sea, compared with the GLRef_2019 experiments (Fig. 2, right column). The dots on the plot indicate that the ensemble average anomalies are statistically significantly different from zero above the 95% confidence level, as determined by the Student’s t test. A negative-IOD-like pattern occurred in the summer and fall of 2020 in the IORef_2019/11 experiments, probably due to the biennial tendency of the IOD event (Saji and Yamagata 2003). These phenomena are not shown in the GODAS data and GLRef_2019 experiments, suggesting that the upper 400-m temperature initialization in the regions outside the Indian Ocean may also play a role in the forecast SSTA.
The ensemble subsurface temperature anomalies in the equatorial Indian Ocean show consistency with the GODAS data, except for the smaller temperature anomalies in the forecast experiments (Fig. 3). Consistently, thermocline depression characterized as warming in the whole equatorial Indian Ocean in winter and spring was captured in the western and eastern Indian Ocean in both sets of the forecast experiments of GLRef_2019 and IORef_2019. The eastward propagation of subsurface warming anomalies suggests that the basinwide warming originates from anomalous warming at depths of 5–300 m in the equatorial western Indian Ocean during the previous winter. The NMEFC-CESM can simulate the ocean processes in the Indian Ocean. The IOD event peaked in the fall of 2019 and decayed quickly in the winter. The forecasted subsurface dipole-like temperature anomalies decayed quickly in winter, although they were weaker than those in the GODAS data (Fig. 3), probably due to model errors like the more diffused thermocline in the model than in nature. Here, we will focus on the impact of the IOD on the basin-scale warming in this study.
(left) Ensemble model-predicted subsurface temperature anomalies in the equatorial Indian Ocean from GODAS data compared to the results of the (center) GLRef_2019 and (right) IORef_2019/11 experiments in boreal winter (December 2019–February 2020), spring (March–May 2020), summer (June–August 2020), and fall (September–November 2020). The ensemble temperature anomalies are averaged between 2°S and 2°N along the equator from two batches of forecast experiments. Stippling indicates that the ensemble average anomalies are statistically significantly different from zero above the 95% confidence level, as determined by the Student’s t test.
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
b. Roles of atmospheric and oceanic processes
1) Role of the atmospheric bridge process
The atmospheric bridge process forces the SSTA in the tropical Indian Ocean through the Walker circulation variations (Lau and Nath 1996; Klein et al. 1999; Reason et al. 2000; Alexander et al. 2002). To evaluate the influence of atmospheric bridge from the remote Pacific Ocean on the IOB forecast, forecast experiments with the Pacific Ocean initialized in November 2019 in the upper 400 m were performed (PORef_2019/11), with the temperature in all the regions outside the Pacific Ocean nudged to the climatology. None of the forecast experiments could predict the 2020 IOB event successfully, suggesting that the atmospheric bridge process induced by the Pacific Ocean SSTA was not able to generate a significant IOB event in 2020 (Fig. 4a, dotted curve). Given that the Pacific Ocean was in a weak warming state in November 2019, the experiments suggest that the 2020 IOB event was not a passive response of the Indian Ocean to the Pacific forcing through the atmospheric bridge. The 2020 IOB was suggested to be induced by the IOD event, as evidenced by the results of the Indian Ocean-only experiments (IORef_2019/11), showing a strong IOB event generated in the winter to spring of 2020 (Fig. 4a, solid curve).
(a) Forecasted IOB for ensemble-averaged results from the Pacific Ocean-only (dashed; PORef_2019/11) and Indian Ocean-only (solid; IORef_2019/11) assimilation experiments. Forecasted IOB index from the (b) PO_Real-IO_19/11 experiments with amplified and weakened temperature anomalies in the Pacific Ocean while with the Indian Ocean temperatures initialized in November 2019, (c) SST assimilation SST-IO_2019/11 experiments with the atmospheric bridge at work, and (d) thermocline assimilation Therm-IO_2019/11 experiments emphasizing the oceanic memory. The purple and green curves indicate the ensemble-averaged IOB index of runs with amplified and weakened temperature anomalies from experiments PO_Real-IO_19/11 in (b), SST-IO_2019/11 in (c), and Therm-IO_2019/11 in (d). The dashed black lines indicate the standard deviation of the IOB mode index based on the observations.
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
To further examine the effects of the Pacific forcing, a set of forecast experiments was performed with the upper 400-m temperature in the Indian Ocean initialized in November 2019 while the temperature anomalies in the Pacific Ocean amplified or weakened in amplitudes (PO_Real-IO_2019/11). The comparisons allowed us to evaluate the effects of the temperature anomalies in the Pacific Ocean on the IOB evolution, with the upper ocean temperature in the Indian Ocean in the IOD state. All the experiments, with amplified or weakened temperature anomalies, have generated an IOB event in 2020 (Fig. 4b, purple and green), which underscores the importance of the tropical Indian Ocean upper ocean initialization in the forecasting of the IOB. Without the IOD forcing, the Pacific forcing could not generate the IOB event above the statistical significance (cf. Fig. 4a). The experiments with amplified temperature anomalies in the Pacific have shown a more persistent IOB lasting until the end of 2020 than the experiments with weakened temperature anomalies. The comparisons suggest that the Pacific forcing plays an important role in the persistence of the IOB event (Fig. 4b), consistent with the earlier results of ENSO-forced IOB dynamics (Chowdary and Gnanaseelan 2007; Hong et al. 2010).
Utilizing the Student’s t-test method, we computed the probability of significant mean differences for each month for different sensitivity experiments. For the PO_Real-IO_2019/11, the strong and weak experiments have no significant different means, suggesting the dominant forcing of the Indian Ocean. For the strong experiments between Therm-IO_2019/11 and SST-IO_2019/11, the Student’s t-test results consistently revealed that there is a statistically significant difference in the means above 95% confidence level as determined by the Student’s t test throughout all 12 months from December 2019 to November 2020. These results also suggest the significant difference between local air–sea interaction and thermocline dynamics. However, there are no significant different means for the weak experiments between Therm-IO_2019/11 and SST-IO_2019/11 for all 12 months. This suggests that the processes of local air–sea interaction and thermocline dynamics are dependent on the amplitudes of temperature anomalies during IOD events. As the amplitudes of temperature anomalies decrease, the Indo-Pacific Ocean tends to approach a state closer to the climatology. For the IORef_2019/11 and PORef_2019/11, the results of the Student’s t test indicate significant differences in means from November 2019 to February 2020. These findings strongly suggest that the Indian Ocean forcing played a pivotal role in triggering the IOB event during the winter of 2020, through its association with local air–sea interactions and thermocline dynamics.
Figure 5 shows a comparison of the temperature anomalies in the equatorial vertical section between the Pacific-Ocean-only experiment (PORef_2019/11) and the perturbed-Pacific-Ocean experiment (PO_Real-IO_2019/11). A weak warming state occurred in the Indian Ocean in the winter of 2019 and no IOD-like temperature anomalies were forecasted in the Indian Ocean, suggesting that the IOD event in 2019 was not induced by the Pacific forcing (Fig. 5a). In other words, the IOD event in 2019 is an independent physical mode in the Indian Ocean. Moreover, no IOB event was generated in the earlier spring of 2020 by the Pacific forcing (Fig. 5b). If the subsurface temperature conditions were initialized in the Indian Ocean in November 2019, the IOD-like temperature anomalies and the ensuing IOB temperature anomalies should be generated in the Indian Ocean notwithstanding the amplitudes of the Pacific forcing (Figs. 5e,f), suggesting the dominant IOD forcing on the ensuing IOB event.
Predicted subsurface temperature anomalies in the equatorial vertical section of the Indian Ocean in the (left) Pacific Ocean-only and (right) PO_Real-IO_2019/11 experiments with amplified (shading) or weakened (contours) subsurface temperatures initialized in November 2019 in the Indian Ocean. The ensemble temperature anomalies are averaged between 2°S and 2°N along the equator from the results of the sensitivity experiments. Stippling indicates that the ensemble average anomalies from strong experiments are statistically significantly different from zero above the 95% confidence level, as determined by the Student’s t test. Here, statistical analysis of weak experiments is not shown.
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
2) Role of the Indian Ocean initial conditions
The Indian Ocean reference experiments (IORef_2019/11) are able to forecast the 2020 IOB event successfully (Fig. 3), suggesting that the initialization of the Indian Ocean subsurface temperature in November 2019 is very important to the prediction of the 2020 IOB event. To evaluate the influence of the positive IOD amplitudes in 2019 on the 2020 IOB, three sets of Indian Ocean sensitivity experiments were performed with temperature in the upper 400 m (Subt-IO_2019/11), at the surface (SST-IO_2019/11), and between 35- and 400-m depths (Therm-IO_2019/11) in November 2019 in the Indian Ocean initialized, while with climatological conditions applied elsewhere. The configuration of these sensitivity experiments is listed in Table 1.
Ensemble subsurface temperature anomalies in the vertical section of the equatorial Indian Ocean were derived from the experiments with amplified (color shading) and weakened (contour) temperature anomalies in the three sets of experiments (Fig. 6). Stronger temperature anomalies were forecasted in the Subt-IO_2019/11 experiment than in the other two sets of experiments. Warm temperature anomalies are associated with a significantly depressed thermocline in the equatorial Indian Ocean, providing favorable conditions for the development of the 2020 IOB event. A clear propagation of the temperature anomalies can be identified in the upper 100 m (Fig. 6 color shading, left column). If the subsurface temperature anomalies were weakened in the Indian Ocean in November 2019, the warming anomalies became much weaker in the winter of 2019 and the spring of 2020 in the equatorial Indian Ocean, suggesting that the basinwide warming anomalies in the equatorial Indian Ocean were associated with the thermocline variability during the IOD (Fig. 6, left column contours). In other words, stronger subsurface temperature anomalies during the IOD lead to stronger IOB events. Therefore, the initialization of the Indian Ocean subsurface cooling in 2019 is essential for the successful prediction of the 2020 IOB event.
Predicted subsurface temperature anomalies in the vertical section in the equatorial Indian Ocean in the (left) Subt-IO_2019/11, (center) SST-IO_2019/11, and (right) Therm-IO_2019/11 experiments with the strong (shading) or weak (contours) subsurface temperatures initialized in November 2019 in the Indian Ocean. The ensemble temperature anomalies are averaged between 2°S and 2°N along the equator from the results of the sensitivity experiments. Stippling indicates that the ensemble average anomalies from strong experiments are statistically significantly different from zero above the 95% confidence level, as determined by the Student’s t test. Here, statistical analysis of weak experiments is not shown.
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
In contrast, the SST-IO_2019/11 experiments, in which only the SSTA in the Indian Ocean in November 2019 were assimilated in the prediction system, showed weak warming, or even cooling in the equatorial Indian Ocean in the following season. The assimilation of SSTA data suggested that the local air–sea interaction within the basin was initialized efficiently, whereas the ocean circulation controlled by the thermocline depth variations was not initialized. The results of this set of experiments suggest that the local air–sea interaction within the basin alone cannot lead to the generation of the 2020 IOB event as evidenced by the results of the experiments of SST-IO_2019/11 with amplified and weakened temperature anomalies (Fig. 4c). It is noticed that the forecasted IOB indices are greater than the standard deviation in the first 2 months in the experiments with amplified SSTA, which is due to the wind fields associated with the 2019 IOD. Without the dynamics of the ocean thermocline anomalies, the local air–sea interaction within the basin quickly lost the memory of the IOD, resulting in the quick decrease of the IOB index. The Therm-IO_2019/11 experiments, in which the temperature anomalies between 35 and 400 m in the Indian Ocean in November 2019 were initialized in the prediction system, showed basinwide warming in the equatorial Indian Ocean in the following season (Fig. 6, right column), as in the Subt-IO_2019 experiments. The forecasted IOB indices from the Subt-IO_2019/11 experiments with assimilated temperatures in the upper 400 m were stronger than the forecasts of the Therm-IO_2019/11 experiments, suggesting the importance of the IOD wind anomalies in the initial development of the IOB event. The forecasted IOB indices underline the dominating role of the Indian Ocean subsurface temperature in fall on the IOB generation in the following winter to spring season (Fig. 4d).
The atmospheric circulation in the SST-IO_2019/11 and Therm-IO_2019/11 experiments demonstrates that thermocline processes induced stronger westerly wind anomalies over the equatorial Indian Ocean than the local air–sea interaction within the basin alone (Fig. 7). A stronger sinking branch at ∼60°E developed in the Therm-IO_2019/11 experiments with amplified temperature anomalies until May 2020 (Figs. 7e,f). In comparison, an ascending branch developed in the western Indian Ocean in the weak nudging experiments (Figs. 7g,h), suggesting that the IOD thermocline variations have strong impact on atmospheric circulation. Although the experiments with amplified SSTA have forecasted weak westerly winds in the longitudinal range of 120°–160°E (Fig. 7a), these winds were over the western equatorial Pacific and were less effective in forcing the IOB event. The atmospheric circulation pattern, with a sinking branch at ∼160°E and an ascending branch at ∼170°W, is the culprit of the failed forecast of the 2020/21 La Niña events based on the initialization of the atmospheric bridge (Wang et al. 2023).
Ensemble equatorial atmospheric vertical velocities (Pa s−1; shaded) and horizontal velocities (m s−1; vectors) averaged between 1°S and 1°N in the (left) SST-IO_2019/11 experiments compared to the (right) Therm-IO_2019/11 experiments in DJF (December 2019–February 2020) and MAM (March–May 2020). The (a),(b),(e),(f) strong and (c),(d),(g),(h) weak temperature initializations in November 2019 in the Indian Ocean were averaged from the two batches of forecast experiments.
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
The forecasted zonal wind stress anomalies (contours) and SSTA (color shading) in the Indian Ocean from the IORef_2019/11 experiments are in good agreement with the GODAS reanalysis data (Figs. 8a,b). The experiments with amplified temperature anomalies can forecast stronger equatorial zonal wind stress anomalies and SSTA in the Indian Ocean, which can be attributed to stronger nudging initialization in the Indian Ocean in November 2019 than those in the weak nudging experiments in the Subt-IO_2019/11 experiments (Figs. 8c,d). The positive SSTA in the equatorial Indian Ocean and the associated westerly wind anomalies forced downwelling Kelvin waves quickly propagating across the basin, depressing the thermocline and forcing basinwide warming in the equatorial Indian Ocean; this was favorable for IOB event development. Similar results were reproduced in the Therm-IO_2019/22 experiments (not shown). The experiments with amplified temperature anomalies in the SST-IO_2019/11 experiments forecasted weak westerly anomalies in the equatorial Indian Ocean, while those with weakened temperature anomalies forecasted even weaker westerlies. These results are due to the atmospheric Walker cell variations (Fig. 7). A comparison between the Subt-IO_2019/11 and SST-IO_2019/11 experiments suggested that the amplitudes of the westerly anomalies in the equatorial Indian Ocean are amplified by subsurface processes in the Indian Ocean through the wind stress–thermocline–SST feedbacks (Saji et al. 1999; Guo et al. 2018). The SSHA showed a consistent pattern with the SSTA and zonal wind stress anomalies, showing eastward propagations of downwelling Kelvin waves from the western boundary in the Indian Ocean (Fig. 9). In other words, ocean wave dynamics associated with thermocline processes are suggested to force the 2020 IOB.
Forecasted zonal wind stress anomalies (contours; dyn cm−2) and SSTAs (shading) of the (a) IORef_2019/11 experiments compared to (b) the GODAS data, as well as averaged zonal wind stress anomalies and SSTAs in Subt-IO_2019/11 (c) amplified experiments compared to (d) the weakened experiments, and in SST-IO_2019/11 experiments with (e) amplified and (f) weakened temperature anomalies from November 2019 to November 2020. The ensemble SSTAs and zonal wind stress anomalies are averaged between 2°S and 2°N along the equator from sensitivity experiments and the GODAS dataset.
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
Forecasted equatorial SSHA (cm) of the (a) IORef_2019/11 experiments compared to (b) the GODAS data, and equatorial SSHA in Subt-IO_2019/11 (c) amplified experiments compared to (d) the weakened experiments and in SST-IO_2019/11 experiments with (e) amplified and (f) weakened temperature anomalies from November 2019 to November 2020. The ensemble SSHA are averaged between 2°S and 2°N along the equator from the sensitivity experiments and GODAS dataset.
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
Following the study of Tokinaga and Tanimoto (2004), the ocean–atmosphere heat fluxes are examined to quantify the role of the atmospheric forcing. The total net heat fluxes (Fig. 10) are calculated together with the temperature anomaly (Fig. 11) in the mixed layer in the entire Indian Ocean (30°–110°E, 20°S–20°N) in all the sensitivity experiments. The positive (negative) values of surface net heat fluxes mean downward (upward) anomaly. The positive SSTA in the entire Indian Ocean persist from boreal winter to spring despite a cooling effect of surface net heat fluxes (Figs. 10 and 11a, solid), which can be found in all the sensitivity experiments with subsurface temperature in the Indian Ocean assimilated in November 2019 (Figs. 10 and 11a,b,d). These results demonstrate that the warming in the Indian Ocean is not determined by the atmospheric forcing. However, the total net heat fluxes show consistent changes with temperature anomaly in the mixed layer when only the SST in the Indian Ocean is assimilated or only the subsurface temperature in the Pacific Ocean is assimilated [Fig. 10a (dashed) and Figs. 10c and 11a,c]. In those sensitivity experiments, the temperature anomaly in the mixed layer is determined by the atmospheric forcing. This suggests that local air–sea interaction within the basin dominates in those sensitivity experiments without thermocline dynamics involved. Further, ocean dynamics plays a significant role in the persistence of warm SSTA.
The anomaly of the total net heat flux averaged over the entire Indian Ocean (30°–110°E, 20°S–20°N) from (a) the Pacific Ocean-only (dashed; PORef_2019/11) and Indian Ocean-only (solid; IORef_2019/11) assimilation experiments, (b) the PO_Real-IO_19/11 experiments with amplified and weakened temperature anomalies in the Pacific Ocean while with the Indian Ocean temperatures initialized in November 2019, (c) SST assimilation SST-IO_2019/11 experiments with the atmospheric bridge at work, and (d) thermocline assimilation Therm-IO_2019/11 experiments emphasizing the oceanic memory. The purple and green curves indicate the ensemble-averaged results of runs with amplified and weakened temperature anomalies from experiments PO_Real-IO_19/11 in (b), SST-IO_2019/11 in (c), and Therm-IO_2019/11 in (d).
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
As in Fig. 10, but for the mixed layer temperature anomalies averaged over the entire Indian Ocean (30°–110°E, 20°S–20°N).
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
The seasonal upwelling usually occurs in summer and fall along the Java coast driven by the southeast monsoon and is eventually terminated by the reversal of winds associated with the onset of the northwest monsoon and the arrival of the equatorial Kelvin wave from the equatorial Indian Ocean. The upwelling exhibits strong interannual variability associated with the strong IOD events (Saji et al. 1999; Webster et al. 1999; Murtugudde et al. 2000; Chen et al. 2016). During boreal winter to early spring, the upwelling is primarily forced by the strong easterlies in the western-to-central equatorial basin. At the interannual time scale, there are two downwelling Kelvin waves from the reflection of equatorial and off-equatorial Rossby waves, respectively, to terminate the positive IOD event: one weakens the upwelling anomalies, which usually occur at the end of IOD year, whereas the other terminates the upwelling anomalies and further terminates the positive IOD events, which usually occur in the next spring (Yuan and Liu 2009; Wang and Yuan 2015).
To investigate the source of the downwelling Kelvin waves, the ensemble SSHAs from five IORef_2019/11 experiments are shown in Fig. 12. In the experiments with the subsurface temperatures in the Indian Ocean initialized in November 2019, easterly wind anomalies associated with Bjerknes feedbacks forced strong upwelling equatorial Kelvin waves and downwelling equatorial Rossby waves in the eastern Indian Ocean, and these waves propagated eastward and westward, respectively (Fig. 12). To separate the equatorial and off-equatorial downwelling Rossby waves in the southern Indian Ocean, the wind stress curl was calculated as shown in Fig. 13 (contour). Equatorial Rossby waves are very long, low-frequency waves found near the equator (usually between 5°S and 5°N along the equator). Westerly and easterly equatorial wind stress anomalies forced upwelling and downwelling equatorial Rossby waves, respectively (Fedorov and Brown 2009). Downwelling Kelvin waves form from the reflection of downwelling equatorial Rossby waves at the western boundary during an IOD, only to weaken or terminate the negative SSTA in the eastern basin. In addition, off-equatorial Rossby waves are produced by wind stress curl outside the equatorial band (roughly ±5° of latitude from the equator) and be reflected from the downwelling Kelvin waves at the eastern boundary within the critical latitude. These off-equatorial downwelling Rossby waves are reflected into downwelling Kelvin waves later, due to the slow propagating speed of the off-equatorial Rossby waves, which work to terminate the already weakened SSTA in the east. Positive wind curl anomalies persisted until February 2020 east of Madagascar (Fig. 13), which forced off-equatorial downwelling Rossby waves that reflect into downwelling equatorial Kelvin waves at the western boundary. The SSHA were weaker in December 2019 than those in November 2019 in Fig. 12, suggesting the arrival of the first pulse of the downwelling Kelvin waves. In January 2020, the negative SSHA disappeared in the eastern Indian Ocean, suggesting the arrival of the other downwelling Kelvin wave pulse, since it only takes 1 month or so for the first baroclinic mode Kelvin waves to transverse the equatorial Indian Ocean. The development of an IOB event was then initiated in the spring of 2020. In Fig. 8a, the easterlies persisted until December 2019. The equatorial basin-scale warming started in January 2020 in Fig. 9, which is consistent with the arrival of the downwelling Kelvin waves. Therefore, downwelling Kelvin waves can be generated from westerly wind forcing and the western boundary reflections from the equatorial and off-equatorial downwelling Rossby wave. In other words, the upwelling anomalies in the eastern Indian Ocean associated with the 2019 IOD were weakened and further terminated by persistent and strong downwelling Kelvin waves from westerly wind forcings and the western boundary reflections.
Ensembled SSHAs from the IORef_2019/11 forecast experiments with subsurface temperatures initialized during November 2019–October 2020. The bell-shaped SSHAs schematics indicate Kelvin wave (green ellipses). The curved arrows in black indicate the reflections of equatorial Rossby waves at the western boundary.
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
Wind stress curl anomalies generated from NCEP–NCAR (contours) together with the SSHAs in the Indian Ocean (shading) from December 2019 to November 2020. The contour interval for the wind stress curl is 10−7 N m−3.
Citation: Journal of Climate 37, 1; 10.1175/JCLI-D-22-0741.1
After a positive IOD event was terminated, an IOB event was triggered in the spring of 2020, lasting until the boreal summer of 2020. The positive SSHAs persist from the western tropical Indian Ocean to the western coast until June 2020. After March 2020, negative wind curl anomalies dominated and off-equatorial upwelling Rossby waves are generated, and these upwelling Rossby waves did not participate in the generation of the downwelling Kelvin waves but rather the upwelling Kelvin waves. The positive SSHA were weakened and further terminated in the western Indian Ocean due to the arrival of the upwelling off-equatorial Rossby waves forced by the negative wind curl. The IOB event was terminated eventually. These reflected and forced Kelvin waves constitute our proposed delayed negative feedback mechanism for the positive and negative IOD-decay periods (Yuan and Liu 2009; Wang and Yuan 2015). Analogous to the processes during ENSO events, western boundary reflection changed thermocline depth and hence SSTA off the Sumatra–Java coast in an opposite sense and further led to a decay and possible phase turnaround of the IOD. The reflection of the downwelling Rossby wave into the equatorial Kelvin wave elevated the SSH, and hence the heat content, in the equatorial band (McPhaden and Nagura 2014). This negative feedback from the western boundary reflection persists through the following upwelling season of the IOD in the eastern Indian Ocean, when thermocline feedbacks on SSTA are most effective, and plays the role of recharging the heat content of the equatorial Indian Ocean, leading to the outburst of an IOB event.
From the above results, all the experiments from Subt-IO_2019/11 and Therm-IO_2019/11 with amplified temperature anomalies in the Indian Ocean predicted the 2020 IOB event successfully in advance. In other words, the oceanic dynamics forced by the 2019 IOD led to the successful forecasting of the 2020 IOB event. All the surface assimilation experiments in the Indian Ocean failed to predict the 2020 IOB event. The comparisons underlined the dominance of the oceanic dynamics associated with the negative feedback from the downwelling equatorial Kelvin waves reflected from the equatorial downwelling Rossby waves. We found that the effects of the oceanic processes and atmospheric bridge dynamics are strongly intertwined and not linearly mutually superposed because of the strong nonlinearity associated with ocean–atmosphere coupling. Therefore, the effects of oceanic processes cannot be isolated completely independently of the atmospheric bridge effects. However, the role of the thermocline initialization associated with the propagation and reflection of equatorial Kelvin and Rossby waves is found to be deterministic in generating the 2020 IOB event.
4. Conclusions
In this work, experiments were performed using the NMEFC-CESM seasonal climate prediction system to investigate the influence of the 2019 IOD on the prediction of the 2020 IOB event. The roles of surface and subsurface ocean processes in connecting the IOD and ensuing IOB event were evaluated. The 2019 IOD event was found to be responsible for inducing the onset of the 2020 IOB event through the oceanic dynamics associated with the propagation and reflection of equatorial waves in the Indian Ocean. In the experiments in which the subsurface temperatures in the Indian Ocean were initialized in November 2019 (Subt-IO_2019/11), easterly wind anomalies forced strong upwelling equatorial Kelvin waves and downwelling Rossby waves in the eastern Indian Ocean, which propagated eastward and westward, respectively. The warming in the west has large meridional scales due to the propagation of the off-equatorial downwelling Rossby waves forced by the wind curl associated with the equatorial easterlies. The cooling is confined near the east boundary along the Sumatra and Java coasts, due to the anticyclonic wind curl that forces downwelling Rossby waves in the off-equatorial areas. The downwelling Rossby waves were reflected at the western boundary into downwelling equatorial Kelvin waves to propagate eastward, producing warm subsurface temperature anomalies in the eastern equatorial Indian Ocean after November 2019. These subsurface anomalies gradually rose to the sea surface through the upwelling advection and were amplified by the coupled ocean–atmosphere processes (Fig. 6). Associated with the thermocline wave dynamics, westerly wind anomalies were generated in the equatorial Indian Ocean, which strengthened the downwelling Kelvin waves in the equatorial Indian Ocean (Figs. 7 and 8). The first baroclinic-mode Kelvin waves took less than a month to transverse the equatorial Indian Ocean basin, terminating the upwelling near the eastern boundary quickly, resulting in the IOB formation. This process was found to be the key precursor of the 2020 IOB development, which took place in the Indian Ocean independently of the Pacific Ocean ENSO states.
To quantify the roles of atmospheric and oceanic processes, several sensitivity experiments were performed to initialize the SSTA and subsurface temperature anomalies, respectively, in November 2019. Inevitably, the climatological subsurface temperature nudging not only shut down the oceanic anomalous connections among the three oceans but also restrained the interaction through the atmospheric bridge. Note that the impact of subsurface temperatures on the SSTA was suppressed in the Therm-IO_2019/11 experiments. However, the relative contributions of the oceanic processes and the atmospheric bridge could still be substantiated separately, even though it was impossible to isolate the effects of either process completely since the Earth system is a coupled system.
The results of the sensitivity experiments suggest that subsurface temperature variabilities dominated the development of the 2020 IOB event. Oceanic processes associated with equatorial wave dynamics were responsible for inducing the 2020 IOB in the Indian Ocean. The atmospheric bridge mechanism associated with the Indian Ocean SSTA in November 2019, without the assistance of subsurface ocean conditions, could not lead to a successful prediction of the 2020 IOB event following the IOD. In other words, the 2020 IOB was not developed from the surface processes of the 2019 IOD, and neither was it a passive response to El Niño in the Pacific Ocean, since the Pacific SSTA were close to the climatological state throughout the IOB development. These results of the sensitivity experiments underlined the importance of thermocline ocean wave dynamics in the onset of the 2020 IOB event. Our results derived from a global coupled seasonal prediction system have highlighted the importance of subsurface and thermocline temperature initializations in the Indian Ocean in IOB predictions, which represents new dynamics of IOB development. Due to the mostly ENSO-concurrent IOD events in history, the dynamics of IOB induced by IOD has not been investigated in the existing studies. The seasonal prediction system is potentially useful in IOB prediction if only more attention is paid in the subsurface initialization in the tropical Indian Ocean.
Studies have revealed that the IOB pattern typically does not appear in the season following an independent IOD event. This phenomenon may be attributed to factors such as the proximity of the Asian landmass north of the equator, which could lead to the termination of IOD events (Tokinaga and Tanimoto 2004). Furthermore, an IOB event can transition into an IOD event, the reverse transformation is less common, as suggested by Guo et al. (2018). The dynamics of local air–sea interaction and thermocline behavior are closely dependent on the amplitudes of temperature anomalies during IOD events as shown in sensitivity experiments. In 1994, a weak independent IOD event occurred, and the upwelling anomalies were directly terminated by the first Kelvin wave pulse from the western boundary reflection. Therefore, there was no subsequent IOB event after the 1994 IOD because off-equatorial downwelling Rossby waves are also weak during such events, as reported by Yuan and Liu (2009). Therefore, the IOD event in 1994 failed to trigger a following IOB event without the help of the recharge process associated with the propagation of off-equatorial downwelling Rossby waves.
In contrast, an IOB event did occur in 1998, coinciding with an IOD event and an El Niño event in 1997. The thermocline dynamics may have played a role in triggering the IOB event in 1998, in addition to the influence of ENSO forcing. However, the relative contribution of ENSO forcing and thermocline ocean wave dynamics requires further study, which is beyond the scope of this research.
The strongest IOD event in 2019 was able to induce a stronger La Niña event over a 1-yr time lag (Wang et al. 2023; Zhang et al. 2023; Zhao et al. 2023), while a strong IOB event was induced by the IOD in the following summer, both of which are associated with the thermocline processes of the Indo-Pacific Ocean. This study highlighted the importance of the Indian Ocean subsurface processes in long-lead predictions of climate variations, which are important for global climate variations and prediction.
Acknowledgments.
We gratefully acknowledge the comments from the anonymous reviewers and the editor who helped to improve this manuscript. This work was financially supported by Laoshan Laboratory (LSKJ202202704, LSKJ202201904), the Natural Science Foundation of China (NSFC) (92258301), and the National Key R&D Program of China (2019YFA0606702, 2020YFA0608800). It was also supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB42000000) and the NSFC (41776011). D. Y. was supported by the “Taishan Scholar Program” of the Shandong Province and by the “Kunpeng Outstanding Scholar Program” of the FIO/MNR of China. This work was also supported by the Oceanographic Data Center, IOCAS.
Data availability statement.
The GODAS data provided by the National Centers for Environmental Prediction (NCEP) can be downloaded from the following website: https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html. The DMI data were calculated using Reynolds Optimum Interpolation V2 Sea Surface Temperature (OIv2SST) analysis, made available by National Oceanic and Atmospheric Administration Earth System Research Laboratories (NOAA/ESRL) (https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html). The index data can be downloaded at the following website: https://stateoftheocean.osmc.noaa.gov/sur/pac/nino34.php. The IOB index was calculated using the NCEP SST data. The model data and codes used for the analyses in this paper are available from the first author upon request. The monthly sea level data were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS) (ftp.sltac.cls.r/Core/). The NCEP wind data can be downloaded at the following website: http://apdrc.soest.hawaii.edu/las/v6/dataset?catitem=17010.
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