Links between tropical Pacific SST and the regional climate of ...

Links between tropical Pacific SST and the regional climate of Bangladesh: Role of the western tropical and central extratropical Pacific

Benjamin A. Cash1, Xavier Rodó2, James L. Kinter III1

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Center for Ocean-Land-Atmosphere Studies Institute of Global Environment and Society 4041 Powder Mill Rd, Suite 302 Calverton, MD, 20705

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Climate Research Laboratory University of Barcelona Torre D, c/Baldiri i Reixach, 4-6 08028 Barcelona, Catalunya, Spain

August 2007

Corresponding author: Benjamin A. Cash [email protected]

Abstract Recent studies arising from both statistical analysis and dynamical disease models indicate there is a link between incidence of cholera, a paradigmatic water-borne bacterial illness endemic to Bangladesh, and the El Niño – Southern Oscillation. We use a regionally coupled, or pacemaker, configuration of the Center for Ocean-LandAtmosphere Studies atmospheric general circulation model to investigate links between sea surface temperature (SST) in the western tropical and central extratropical Pacific and the regional climate of Bangladesh. We find that forcing the model with observed SST anomalies in this region produces only weak SST anomalies outside the forcing region, in contrast to the results of forcing in the eastern tropical Pacific. Despite the weak response outside of the forcing region, reduced precipitation tends to follow winter El Niño events in the model, in contrast to the observations. In the absence of warm SST anomalies in the eastern Pacific, cold anomalies in the western Pacific produce a La Niña like response in the model, suppressing convection over the western Pacific and leading to a shift in large-scale convergence into the eastern Indian Ocean. This shift in convergence modifies the summer monsoon circulation over India and Bangladesh. Easterly wind anomalies over land to the west of Bangladesh lead to increased divergence in the zonal wind field and hence decreased moisture convergence and rainfall.

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1. Introduction Cholera is a bacterial, water-borne disease (WBD), endemic to the Bangladesh region. Infection arises from ingesting water contaminated with the bacteria vibrio cholerae, and thus occurs primarily in regions with limited or impaired access to clean water. Left untreated, mortality rates as high as 50% are common. However, mortality in even severe cases can be limited to less than 1% (Cook 1996) with proper treatment. The ability to forecast cholera risk would thus be of great benefit to society, allowing public health workers to prepare for a potential epidemic and for treatment to be administered as quickly and efficiently as possible. Observations of cholera incidence in Bangladesh and elsewhere suggest that forecasting of cholera risk based on environmental factors may be possible (e.g. Colwell 1996; Pascual et al. 2000; Pascual et al. 2002; Rodó et al. 2002; Koelle and Pascual 2004; Koelle et al. 2005). In Bangladesh, cholera incidence typically peaks during Northern Hemisphere (NH) spring, prior to the arrival of the monsoon rains. Cases decrease during the rainy season and then reach a second, larger maximum during the fall after the rains retreat. In a companion paper (Cash, Rodó and Kinter 2007a; hereafter CRK07a), the authors explored links between the interannual variability of cholera and climate. This analysis consists in part of calculating the lagged rank-correlation between September cholera incidence and sea surface temperature (SST) anomalies in the preceding winter (see Fig. 1). The authors found large regions of statistically significant correlation that closely resemble the pattern of SST anomalies associated with a warm El Niño –

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Southern Oscillation (ENSO) event. These results are consistent with those of previous studies (e.g. Koelle et al. 2005; Pascual et al. 2007), which found significant correlations between cholera variability, independent of internal disease dynamics, and the NINO3.4 index. In addition to confirming the statistical relationship between cholera incidence and SST, CRK07a explored the physical mechanism by which ENSO can influence the regional climate of Bangladesh and potentially cholera. Despite the extensive literature concerning the influence of ENSO on the monsoon (e.g., Walker 1923, 1924; Rasmusson and Carpenter 1983; Goswami 1998; Ju and Slingo 1995; Krishnamurthy and Goswami 2000), relatively little attention has been given to the impact of ENSO on Bangladesh. CRK07a established that, in both observations and in a pacemaker-type model, warm SST anomalies in the central and eastern tropical Pacific give rise to enhanced precipitation over Bangladesh in the following summer. This enhancement in precipitation in turn may lead to greater flooding, breakdown in sanitation, and enhanced possibility for cholera infection (A. Dobson, personal communication). An intriguing result from CRK07a is that the strongest correlations between cholera and SST are not the positive correlations with SST in the central and eastern tropical Pacific, but rather the negative correlations with SST in the western Pacific “horseshoe” region (Fig. 1). This is a region that is known to be associated with the eastern tropical Pacific through the ‘atmospheric bridge’ (e.g. Klein et al. 1999), and CRK07a as well as other studies have found that anomalies in this region similar to those in observations can be reproduced through forcing a model with observed eastern tropical Pacific SST. However, it is not clear from those studies whether or not this region plays a

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significant role in influencing the climate of Bangladesh, and hence may play an important role in influencing cholera variability. It may be that cold SST in this region alters the monsoon circulation in such a fashion as to enhance the possibility for a cholera outbreak. Alternatively, the high correlations in the observations between cholera and the western Pacific may simply be a consequence of the well-known anticorrelation between the eastern and western Pacific during ENSO events. In the current work we examine this question by exploring in detail the influence of the western tropical and extratropical Pacific on the climate of the Bangladesh region, with the goal of diagnosing the potential influence of this region on cholera incidence in Bangladesh. A detailed analysis of links between ENSO and Bangladesh climate in the observations appears in another companion paper (Cash et al. 2007b). Our primary tool in this study is a regionally coupled model, similar to the one used in CRK07a. The model and methodology are described in section 2. Results from the analysis are described in section 3, and a summary of our results and conclusions are presented in section 4.

2. Data and Methodology The model and methodology used in this study in are described in CRK07a and readers are referred to that work for a more detailed description. We use a regionally coupled, or pacemaker, model, in which SST is prescribed in a limited portion of the ocean domain. Outside of the prescribed region temperature and surface fluxes are determined by an ocean model. The atmospheric model consists of the Center for OceanLand-Atmosphere Studies (COLA) v3.1 atmospheric general circulation model (AGCM) coupled to a 50 m slab-ocean mixed-layer model. The atmospheric model has 28 vertical

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levels and is run at T62 horizontal resolution. We prescribe SST in the ‘horseshoe’shaped region in the western tropical and central extratropical Pacific (hereafter referred to as the Westpac region; see Fig 2), which is chosen to coincide with the area of significant negative correlations. For points in the prescribed region we use the observed record of monthly-mean SST from the HadISST1 (Rayner et al. 2003) data set. The transition between the prescribed and mixed-layer domains is handled through a ‘blending region’, in which the total SST is calculated from the weighted average of the prescribed and predicted SST (see Fig. 2a for weighting values). To prevent the model SST from drifting away from climatology, an implied ocean heat-flux, or q-flux, is also prescribed at all mixed-layer points (including those within the blending region). This qflux field is calculated from a separate 20-year integration using prescribed climatological SST in the pacemaker region and a 60 Wm-2K-1 relaxation towards climatology in the mixed-layer region. The annual cycle of monthly-mean restoring tendencies from the relaxation term is calculated for each gridpoint and introduced as an additional term in the pacemaker mixed-layer temperature tendency equation, along with a much weaker (10 Wm-2K-1) relaxation to climatology. The model remains relatively close to the observed SST (Fig. 2b), although the root-mean-square (rms) error is clearly time-dependent. A number of the peaks in rms error appear to correspond to large El Niño and La Niña events, a point that we will explore in detail in section 3. Unless otherwise noted, all model results presented are mean values from an eight-member ensemble, with each ensemble member starting from slightly different initial conditions and covering the period 1950-2002.

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3. Results When we compare the model composite ensemble mean warm-cold ENSO SST anomalies (Fig. 3a) to the observed anomalies (Fig. 3b), we find that there is relatively little resemblance. Composite anomalies are calculated by averaging over all ensemble mean El Niño and La Niña events separately, and finding the difference between the resulting patterns. We consider here only those events from 1976 onward, corresponding to the period in which the El Tor strain of cholera dominates in the environment (see Table 1 for the list of events). The characteristic warm anomalies in the central and eastern tropical Pacific are entirely absent, as is the warming in the Indian Ocean. Instead, there is a clear spread of the cold anomalies eastward from the pacemaker region, particularly near 20°N. This indicates that in this model, warming in the central and eastern tropical Pacific cannot be considered a response to changes in the Westpac region. This is consistent with the results of Fig. 2b, which indicates the largest rms errors occur during ENSO events. In contrast, CRK07a found that, when forced with the observed record of SST in the central and eastern tropical Pacific, this same model reproduces most of the observed global ENSO signal. As the model reproduces the observed SST anomalies only where they are prescribed, the simulations presented here are significantly further removed from the behavior of the climate system than those presented in previous pacemaker studies (e.g. Lau and Nath 2003; CRK07a). However, they also allow for the possibility of studying the influence of the Westpac region in the absence of anomalies in the central and eastern tropical Pacific. To the extent that the model response in the Bangladesh region is linear

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with respect to SST anomalies in the Pacific, we can determine how much of the response over Bangladesh in CRK07a is due to the Westpac anomalies. Following cold SST anomalies in the Westpac region, June precipitation (Fig. 4a) is decreased to the west of Bangladesh and to a lesser extent over Bangladesh itself. This anomaly is present in a weaker form during July (Fig. 4b) and prominent again in August (Fig. 4c), at which time eastern Bangladesh has a weak positive anomaly. In each month this anomaly is accompanied by weaker positive anomalies over the Bay of Bengal. The influence of the Westpac region on Bangladesh is weaker than that of the central and eastern tropical Pacific documented in CRK07a, and opposite in sign (compare to CRK07a Fig. 5). This is somewhat surprising, given that the SST anomalies in the Westpac region in CRK07a are very similar to those shown here. Examination of individual ensemble members (Fig. 5) shows that the large negative anomaly west of Bangladesh is found in most members, albeit with variation in location and intensity, indicating this is a relatively robust response of the model. The changes in precipitation (Fig. 4) tend to correspond to changes in the divergence of the vertically integrated moisture transport (VIMT), defined as the vertical average of -!"(u*q, v*q) from 1000 hPa to 500 hPa (Fig. 6), where u, v, and q are the zonal wind, meridional wind, and specific humidity, respectively. There are clear regions of moisture divergence in June (Fig. 6a) and August (Fig. 6c) corresponding to the regions of negative precipitation anomalies (compare to Figs. 4a and 4c, respectively). Similarly, the positive rainfall anomalies in July (Fig. 4b) across the Bay of Bengal and India correspond to positive VIMT anomalies (Fig. 6b). The negative anomaly west of

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Bangladesh in July (Fig. 4b) is not well represented in the VIMT (Fig. 6b) for reasons that are unclear. Exploring the origins of the VIMT anomalies, we find that they can be traced most directly to changes in the divergence of the wind field (Fig. 7), and do not correspond well to changes in the humidity field (not shown). In June (Fig. 7a), we find anomalous easterlies covering much of the Indian Ocean region. West of Bangladesh, there is an acceleration in the zonal winds, leading to a region of divergence. In July (Fig. 7b) and August (Fig. 7c), there is a well-developed cyclonic circulation over the Bay of Bengal, with maximum wind speeds to the southwest. The corresponding pattern of acceleration and deceleration leads to divergence west of Bangladesh and convergence over the Bay of Bengal and contributes to the precipitation anomalies seen in Fig. 4. The circulation anomalies described above are part of a larger alteration in circulation affecting the Indian Ocean and Indonesia (Fig. 8). The easterly anomalies over India and Bangladesh in June (Fig. 7a) are due to a region of lowered geopotential heights stretching from northern India and Bangladesh across Indonesia and into the southern hemisphere (Fig. 8a). In July (Fig. 8b) and August (Fig. 8c), these lowered heights push northward, accompanied by an anticyclonic anomaly off the southeast coast of India. This shift in circulation leads to the westerlies over India in July and the intensification of the winds southeast of Bangladesh noted in Fig. 7. The negative height anomalies in the lower troposphere in the Indian Ocean region during the summer months appear to be a continuation of a pattern established during the winter months (Fig. 9). We find negative anomalies over the entire Indian Ocean region, as well as over the tropical Atlantic, as early as December (Fig. 9a). These

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anomalies deepen throughout January (Fig. 9b) and February (Fig. 9c) and spread west from the Atlantic into the eastern tropical Pacific. The signal in the extratropics also increases in strength in the latter part of the winter. Combining these results with those of CRK07a, we arrive at a more complete picture of the influence of Pacific SST on precipitation over Bangladesh. When the central and eastern tropical Pacific warms and the western Pacific cools, large-scale organized convection and associated convergence zones shift to the eastern Pacific (Fig. 10a). This leads to anomalous subsidence and positive height anomalies over Indonesia and the Indian Ocean region. These anomalies in turn weaken the climatological monsoon circulation, leading to increase convergence over Bangladesh and enhanced rainfall. The same changes in circulation act to decrease rainfall over India, resulting in the observed out-of-phase relationship between precipitation in India and Bangladesh. In the model configuration presented here there are no significant anomalies in the central and eastern tropical Pacific. Thus, during an El Niño, year cold anomalies in the western tropical Pacific lead to a suppression of convection and convergence in that region, but the convection does not shift to the eastern Pacific. Instead, convergence is slightly enhanced over Indonesia and the eastern Indian Ocean (Fig. 10b), in a pattern more reminiscent of a La Niña year. This leads to enhanced convergence and lowered heights across Indonesia and the Indian Ocean, which in turn interacts with the general monsoon circulation to produce enhanced easterlies and divergence over Bangladesh, suppressing precipitation.

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4. Summary and Conclusions In this work we have focused on the influence of the western tropical and central extratropical Pacific on the regional climate of Bangladesh in a pacemaker model, extending the results of CRK07a. We find that forcing the model with observed SST anomalies in the Westpac region produces only relatively weak SST anomalies outside the forcing region, with relatively little resemblance to the observations. This is in contrast to the results of CRK07a, which found that forcing with observed SST anomalies in the central and eastern tropical Pacific reproduced much of the observed global ENSO signal. Following El Niño events (cold anomalies in the Westpac region), summer monsoon rains in the model are suppressed over Bangladesh and central India and are slightly enhanced over the Bay of Bengal. These changes in precipitation result from changes in the overall monsoon circulation, with enhanced easterlies over Bangladesh leading to anomalous divergence in the vertical moisture transport field. In contrast, enhanced westerlies over southern India and the northern Indian Ocean lead to greater moisture convergence and enhanced rainfall over these regions. The cyclonic circulation pattern associated with these anomalies encompasses parts of Eurasia and Indonesia as well as the Indian Ocean, and is a continuation of a pattern established during the winter months. The circulation changes following El Niño winters in the current work are nearopposites of those found in CRK07a. The fact that the SST anomalies in the western Pacific are of similar extent and magnitude in both models serves to highlight the importance of the central and eastern tropical Pacific in determining the overall response

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of the model. In both models, cold SST anomalies in the western Pacific act to suppress convection locally. In the absence of warm anomalies in the central and eastern tropical Pacific, convergence in the Westpac-forced model shifts only slightly, over Indonesia and into the eastern Indian Ocean. When the cold Westpac anomalies occur simultaneously with warming in the central and eastern tropical Pacific, however, as is the case in CRK07a and the observations, the region of maximum convection follows the warm SST anomalies into the eastern tropical Pacific. As stated in the Introduction, our primary purpose in investigating the influence of the Westpac region on the regional climate of Bangladesh is to understand how, if at all, this region influences the interannual variability of cholera. Given that both the tropics-wide response and, more specifically, the monsoon rainfall response over Bangladesh to Westpac SST anomalies are determined by the presence or absence of SST anomalies in the central and eastern tropical Pacific, it seems unlikely that Westpac SST anomalies are playing a dominant role in determining the response of cholera to climate. While the influence of the Westpac region on Bangladesh and India is not negligible, and may contribute to the weakening of the monsoon rains over central India following an El Niño event, it is clearly dominated by the influence of the central and eastern tropical Pacific. Hence, the model strongly suggests that the pattern of correlations in Fig. 1, at least in the Pacific basin, is primarily a response to the forcing in the central and eastern tropical Pacific and does not indicate a separate and significant contribution from the Westpac region.

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5. References

Cash, B. A., X. Rodó and J. L. Kinter III, 2007: Links between tropical Pacific SST and the regional climate of Bangladesh: Role of the eastern and central tropical Pacific. COLA Technical Report, 236, 32pp, Center for OceanLand-Atmosphere Studies, Calverton, Maryland. Cash, B. A., J. L. Kinter III and X. Rodó, 2007b: Links between tropical Pacific SST and the regional climate of Bangladesh: Observations. In preparation. Colwell, R. R., 1996: Global climate and infectious disease: The cholera paradigm. Science, 274, 2025-2031. Cook, G. C., 1996: Management of cholera: the vital role of rehydration. Cholera and the Ecology of Vibrio Cholerae, B. S. Drasar and B. D. Forrest, Eds., Chapman and Hall, 54-94. Goswami, B. N., 1998: Interannual variations of the Indian summer monsoon in a GCM: External conditions versus internal feedbacks. J. Climate, 11, 501-522. Ju, J. and J. Slingo, 1995: The Asian summer monsoon and ENSO. Quart. J. Roy. Meteor. Soc., 121, 1133-1168. Klein, S. A., B. J. Soden, and N.-C. Lau, 1999: Remote sea surface variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12, 917-932. Koelle, K., X. Rodó, M. Pascual, Md. Yunus and G. Mostafa, 2005: Refractory periods and climate forcing in cholera dynamics. Nature, 436, 696-700. Koelle, K. and M. Pascual, 2004: Disentangling extrinsic from intrinsic factors in disease dynamics: a nonlinear time series approach with an application to cholera. The American Naturalist, 163(6), 901-913. Krishnamurthy, V. and B. N. Goswami, 2000: Indian Monsoon-ENSO relationship on interdecadal timescale. J. Climate, 13, 579-595. Lau, N.-C. and M. J. Nath 2003: Atmosphere-Ocean variations in the Indo-Pacific sector during ENSO Episodes. J. Climate, 16, 3-20. Pascual, M., X. Rodó, S. P. Ellner, R. Colwell, and M. J. Bouma, 2000: Cholera dynamics and El Niño-Southern Oscillation. Science, 8, 1766-1769. Pascual, M., M. Bouma and A. P. Dobson, 2002: Cholera and climate: revisiting the quantitative evidence. Microbes and Infection, 4, 237-245.

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Pascual, M., L. F. Chaves, B. Cash, X. Rodó and Md. Yunus, 2007: Predictability of endemic cholera: the role of climate variability and disease dynamics. Climate Research, submitted. Rasmusson, E.M., and T.H. Carpenter, 1983: The relationship between eastern equatorial Pacific sea surface temperatures and rainfall over India and Sri Lanka. Mon. Wea. Rev., 111, 517-528. Rayner, N. A. and Co-authors, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, doi:10.1029/2002JD002670. Rodó, X., M. Pascual, G. Fuchs and A. S. G. Faruque, 2002: ENSO and cholera: A nonstationary link related to climate change? PNAS, 99, 12901-12906. Walker, G. T., 1923: Correlation in seasonal variations of weather VIII: A preliminary study of world weather. Memoirs of the Indian Meteorological Department, 24, 75-131. Walker, G.T., 1924: Correlation in seasonal variations of weather IX: A further study of world weather. Memoirs of the Indian Meteorological Department, 24, 275-332.

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Table 1: El Niño and La Niña years used in this study. Events are based on DJF values of the Niño 3.4 region index and are taken from the Climate Prediction Center listing at http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml. Years listed below are for January and February; event definitions include December values from the previous year.

El Niño

La Niña

1977

1976

1978

1984

1983

1985

1987

1989

1988

1996

1992

2000

1995

2001

1998

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Figure 1: Lagged rank-correlation between September El Tor cholera cases and preceding (a) December, (b) January and (c) February SST. Contours denotes regions significant at the 90% level. Shading and interval is 0.1. Reproduced from Cash et al. (2007a).

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Figure 2: (a) Prescribed and blending regions used in pacemaker experiments. Shading denotes weighting of prescribed, observed SST from HadISST dataset. Weighting is set to 1 in tropical central and eastern Pacific and Polar region. (b) RMS error in °C in SST field.

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Figure 3: (a) Composite DJF model ensemble mean warm-cold ENSO SST anomalies and (b) composite DJF observed warm-cold ENSO SST anomalies. Warm and cold events are based on CPC DJF Niño3.4 index. Shading interval is 0.2°C.

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Figure 4: Model ensemble mean precipitation difference for warm-cold ENSO events for (a) June, (b) July, and (c) August. Warm and cold events are based on CPC DJF Niño3.4 index. Shading interval is 0.5 mm day-1.

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Figure 5: JJA mean precipitation difference for warm-cold ENSO events for ensemble members a-h. Warm and cold events are based on CPC DJF Niño3.4 index. Shading interval is 1 mm day-1. 19

Figure 6: Model ensemble mean vertically integrated moisture transport convergence (1000 hPa to 500 hPa) difference for warm-cold ENSO events for (a) June, (b) July, and (c) August. Warm and cold events are based on CPC DJF Niño3.4 index. Shading interval is 10-9 kgm-1s-1.

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Figure 7: Model ensemble mean vertically averaged (1000 hPa to 500 hPa) streamline difference for warm-cold ENSO events for (a) June, (b) July, and (c) August. Warm and cold events are based on CPC DJF Niño3.4 index. Color denotes wind magnitude and shading interval is 0.1 ms-1.

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Figure 8: Model ensemble mean vertically averaged (1000 hPa to 500 hPa) geopotential height difference for warm-cold ENSO events for (a) June, (b) July, and (c) August. Warm and cold events are based on CPC DJF Niño3.4 index. Shading interval is 1 m, streamlines are reproduced from Figure 7 for comparison.

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Figure 9: Model ensemble mean vertically averaged (1000 hPa to 500 hPa) geopotential height difference for warm-cold ENSO events for (a) December, (b) January, and (c) February. Warm and cold events are based on CPC DJF Niño3.4 index. Shading interval is 2 m.

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Figure 10: Model ensemble mean vertically averaged (1000 hPa to 500 hPa) convergence difference for warm-cold ENSO events for (a) Eastern Pacific-forced model (Cash et al. 2007a), (b) Westpac-forced model. Warm and cold events are based on CPC DJF Niño3.4 index. Shading interval is 10-6 s-1. Streamlines denote vertically averaged winds.

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