Interannual and Interdecadal Variability of Thailand Summer Monsoon
Nkrintra Singhrattna1,2, Balaji Rajagopalan+1,3, K. Krishna Kumar3,4 and 
Martyn Clark3

1 Department of Civil, Environmental and Architectural Engineering, University of Colorado at Boulder, Boulder, USA
2 Thailand Public Works Department, Bangkok, Thailand
3 Co-operative Institute for Research in Environmental Sciences, University of Colorado, Boulder, USA
4 Indian Institute of Tropical Meteorology, Pune, India

Journal of Climate
Submitted December, 2003
Revised October, 2004
Abstract
Summer monsoon rains are a critical factor in Thailand’s water resources and agricultural planning and management. In fact, they have a significant impact on the country’s economic health. Consequently, understanding the variability of the summer monsoon rains over Thailand is important for instituting effective mitigating strategies against extreme rainfall fluctuations. To this end, we systematically investigated the relationships between summer monsoon precipitation from the central and northern regions of Thailand and large-scale climate features. We find that Pacific Sea Surface Temperatures (SSTs), in particular, the El Niño/Southern Oscillation (ENSO), have a negative relationship with the summer monsoon rainfall over Thailand in recent decades. However, the relationship between summer rainfall and ENSO was weak prior to 1980. We hypothesize that the ENSO teleconnection depends on the SST configuration in the tropical Pacific Ocean in that, an Eastern Pacific-based El Niño pattern, such as the case in most of the post 1980 El Niño events, tends to place the descending limb of the Walker circulation over the Thailand/Indonesian region, thereby, significantly reducing convection and consequently, rainfall over Thailand. This shift in the Walker circulation during the recent decades, we believe, is instrumental for the non-stationarity in ENSO-monsoon relationships in Thailand. The El Niños of 1997 and 2002 corroborate this hypothesis. This has implications to monsoon rainfall forecasting and consequently, to resources planning and management. 

1. Introduction and Background
The El Niño-Southern Oscillation (ENSO) has been widely known to modulate the Asian summer monsoons on interannual and interdecadal time scales.  Some examples include relationships between ENSO and the Indian monsoon (Krishna Kumar et al. 1999, Krishnamurthy and Goswami 2000); and between ENSO and South China monsoon (Xu and Chan 2001, Lau and Wu 2001, Wang et al. 2001). ENSO also affects the onset of the monsoons – e.g., Indonesian rainfall (Hamada et al. 2002). In general, most previous studies suggest a reduction in the strength of monsoons in these regions with El Niño and vice-versa (Rasmusson and Carpenter 1983, Rupakumar and Pant 1997), caused largely due to shifts in the Walker circulation. Furthermore, this dynamical relationship has been extensively used for issuing seasonal forecast of monsoons, in particular the Indian summer monsoon (Shukla and Paolino 1983, Krishna Kumar et al. 1995). Land surface processes such as variability in continental heating associated with snow cover and vegetation structure can also play a significant role in modulating the land-ocean gradients and consequently, impacting the strength of the monsoons and also the ENSO teleconnections (Blanford 1884, Bamzai and Shukla 1999, Chase et al. 1996, 2000, Krishna Kumar et al. 1999).
Interestingly, in recent decades there has been a weakening of the relationship between ENSO and Indian summer monsoon rainfall (Krishna Kumar et al. 1999) including East Asian summer monsoon (Chen et al. 2002). On the other hand, Wang et al. (2001) find a steady relationship between ENSO and Western North Pacific summer monsoon. Such non-stationarity in the large-scale relationship may indicate a wider re-adjustment of tropical atmospheric circulation with potential impacts on other monsoons in the region. Monsoon variability over Thailand has not been studied as widely as its counterparts in the Indian subcontinent and China. There have been some efforts to understand the relationship between rainfall over Thailand, Singapore, Indonesia with Indian rainfall based on 20 years of data before 1980 (1961-1980) by Kripalani and Kulkarni, (1997, 1998, 2001). In general, these investigations depicted a weak relationship between the summer rainfall over Thailand and Indian rainfall. 
Past research, as mentioned above, has focused on several regions in Asia, but there are hardly any studies investigating the variability of Thailand hydroclimate. This research work systematically investigates the temporal variability of Thailand hydroclimatology - in particular, the summer monsoon. The paper is organized as follows: data sets and Thailand climatology are first described, trends in precipitation, temperature and streamflow are presented next, understanding the temporal variability and proposal of potential physical mechanism concludes the paper.

2. Data 
2.1 	Hydroclimate Data
The central region of Thailand is the most important area for Thailand’s economy – this region produces huge quantities of agricultural products for both domestic consumption and export. The Chao Phraya River passing through this central region is a key factor for the agricultural production and other economic activities. Therefore, hydroclimate data from this region have been selected for this study. Long records of precipitation, temperature and streamflow were obtained from the Global Energy and Water Cycle Experiment (GEWEX), Asian Monsoon Experiment (GAME) project website http://hydro.iis.u-tokyo.ac.jp/GAME-T/GAIN-T/routine/rid-river/longterm.html, Thailand Meteorological Department and the Royal Irrigation Department. The GAME program, part of GEWEX, has done a good job of collecting and archiving data from South East Asian countries. Although, data is available from these sources, it is sparse and in general, it has been difficult obtaining long hydroclimate data from Thailand.   
  Daily precipitation and temperature data have been obtained from six locations in the central Thailand region for the 1951-2001 period (shown as points in Figure 1). The stations are: Nakhon Sawan (Nk), located in the northernmost province of central region and the origin of Chao Phraya River; Suphan Buri (Sp), in the central region; Lop Buri (Lp), located near the northern boundary of the central region; Kanchana Buri (Kn), located near the western boundary of the central region; Bangkok (Bkk), the capital of Thailand, located near the mouth of the Chao Phraya River; Don Muang (Dm), a city in Bangkok and location of the biggest international airport in Thailand. Daily streamflow data at three stations along the Chao Phraya River were also obtained. The stations are: Nakhon Sawan (Nk), drainage area about 110,569 square kilometers, (1956 – 2000); Chai Nat (Cn), a province adjacent to Nakhon Sawan, drainage area about 120,693 square kilometers, (1948 -2000) and Ang-Thong (At), a province near Bangkok, (1976 –1996). 
To check for consistency, annual values of precipitation, streamflow and temperature, at the stations were correlated among themselves – i.e., for each variable, the data from a station is correlated with data from all other stations. The data at the stations are generally well correlated (average correlation ranges from 0.5 ~ 0.9), with streamflows and temperatures exhibiting stronger correlations than precipitation. This is to be expected, as the time series of rainfall is more variable than the temperature and streamflow time series. Overall, these inter-station correlations suggest that the hydroclimate is fairly homogeneous. Similar correlations were found for the monsoon season. Consequently, we averaged the data over selected representative stations that exhibited good inter-station correlations for precipitation, temperature and streamflow to develop “index” time series. For rainfall, we selected, Nk, Sp and Dm stations; for temperature, we used all the five stations and for flows, station Cn was used. Almost all the stations are from West Central Thailand (see Figure 1.) which is the core region of the Chao Phraya river basin – the “life blood” of Thailand’s socio-economic well being. Hence, these average “index” time series are referred as Thailand precipitation, streamflow and temperature, respectively.  
The streamflow data that we obtained are at reservoir locations and as such, appear to be highly regulated. In light of this we restrict the analysis of streamflow data to long-term trends only.

2.2 	Large-Scale Climate Data
	Large-scale ocean and atmospheric circulation variables such as, sea level
Pressures (SLP), velocity potential ere obtained from NCEP/NCAR Re-analysis
(Kalnay et al., 1996). The sea surface temperature (SST) data are from Kaplan et al.
(1998) obtained from a ‘‘reduced space’’ optimal smoother algorithm applied to 131
years (1865 – 1995) of global SST monthly anomalies obtained from the U.K. Hadley

Centre archives (Bottomley et al., 1990). These data and ENSO indices spanning the
period of 1948 – current are available from  (http://www.cdc.noaa.gov).

3.  Climate Diagnostics
In this section climatology and long-term trends in the hydrometeorological variables - precipitation, surface air temperature and streamflow are first analyzed. Investigations into the links between large-scale ocean-atmospheric circulation features and interannual and decadal variability in the summer monsoon rainfall are then presented. Correlation and composite analyses are used for the diagnostics.
First, we check the temporal consistency of the developed precipitation and temperature time series. Thailand precipitation during the monsoon season (August-October) and pre-monsoon spring seasonal (March-May) temperatures were correlated with gridded CMAP global rainfall data (Xie and Arkin 1997) and NCEP/NCAR Re-analysis (Kalnay 1996) surface temperature data, respectively shown in Figures 2(a) and (b). It can be seen that a strong positive correlation exists over Thailand region, in particular, over west central part of the country, from where the rainfall and temperature data are obtained. This indicates that the hydroclimate data obtained above can be considered to be fairly representative of the country and certainly the important region of west central Thailand.

3.1 Hydroclimatology
	Climatological annual cycle of Thailand temperature, rainfall and streamflow are shown in Figures 3. It can be seen that the “hottest” season in Thailand is the Northern Hemisphere spring season (Figure 3(a)), March-May (MAM), which is also the pre-monsoon season. Precipitation peaks during August-October (ASO) (Figure 3(b)). In addition, May shows a secondary maximum that occurs consistently over all the stations. This secondary peak is associated with the Northward movement of the Inter Tropical Convergence Zone (ITCZ). The Southwest monsoon that starts during May in the Indian Ocean brings warm moist airstreams passing from the Indian Ocean toward Thailand in May, causing thunderstorms during this period. The ITCZ covers southern part of Thailand in May as well; however, it then moves northward to Southern China around June to early July. In August the ITCZ moves southward and lies over Northern and Northeastern Thailand, and then over central regions in September and southern parts in October, thus, causing the primary peak in seasonal rainfall (i.e. monsoon season) to be August-September-October. Henceforth, we refer to monsoon rainfall as the total rainfall during ASO period and the pre-monsoon temperature as the average temperature during MAM period. For streamflow (Fig. 3(c)), it is apparent that the annual peak occurs during September-November (SON), lagging a month from rainy season. Large flows are frequently found in October.
The pre-monsoon air temperatures over the land play a key role in setting up the land-ocean gradient, which is important to the strength of the monsoon rainfall. Enhanced land-ocean gradient leads to a stronger monsoon and vice versa. These links can be corroborated in Figure 4.  Figure 4(a) shows the scatter plot of surface temperatures over Thailand during pre-monsoon season with the monsoon seasonal rainfall. A linear relationship (statistically significant at 95% confidence) can be seen, indicating that higher temperatures during spring tend to favor a stronger monsoon. Mean surface temperatures over the entire pre-monsoon period (i.e., March – July) also exhibit similar linear relationship with the monsoon rainfall (figure not shown). Pre-monsoon land temperatures are generally thought to be associated with developing the land-ocean contrast, a key ingredient for the summer monsoon (e.g., Webster, 1996, Krishna Kumar et al., 1999; Liu and Yanai, 2001).  However, we feel that the land temperatures are one of the main drivers of the climatological monsoon than its interannual variability.
Figure 4(b) shows the scatter plot of rainfall and temperature during the monsoon season. As expected, an inverse relationship is apparent (though not statistically significant) - i.e. enhanced seasonal rainfall tends to cool the land and the atmosphere, thereby, decreasing the temperatures and vise versa. One reason for this weak inverse relationship is that the monsoon seasonal temperature exhibits an increasing trend, especially in the post-1970 period (figure not shown) and corroborated in IPCC (2001). This increasing trend counters the inverse relationship and consequently, a weak correlation. These figures confirm the physical consistency between the hydroclimate variables – thus, enhancing the confidence in the data sets.

3.2 	Time Series Trends
In order to investigate the temporal variability and the long-term trends of the hydroclimate variables, linear trends were computed. Figure 5(a) and (b) shows the standardized anomaly time series of spring temperatures and monsoon rainfall. Interestingly, both these series exhibit a strong decreasing (negative) trend over the 50-year period. They are consistent in that, decreasing spring temperatures lead to decreasing monsoon rainfall. Linear regression fits to the data show a temperature trend of -0.4 degree Celsius and –180 mm trend in precipitation over 50 years. These trends are found to be statistically significant at 90% confidence level when tested using the standard t-test (Helsel and Hirsch 1995). Also note that the El Nino and La Nina years are consistent with low and high rainfall years (Figure 5(b)), respectively, in the recent decades. Time series of monsoon seasonal temperature and seasonal streamflow are shown in Figure 5(c) and (d), respectively. An increasing trend in monsoon seasonal temperatures can be seen. This is consistent in that, decreasing rainfall trends (Fig. 5(b)) tend to result in reduced cooling of the land and atmosphere, thereby, increasing the temperatures (Fig. 5(c)).  Decreasing seasonal streamflows (Fig. 5(d)) are also consistent with the negative trend in rainfall. While general trends are apparent in these figures, decadal trends can also be seen. To elucidate this, local polynomial fits (Loader, 1999) are shown as solid lines in Figure 5. Local polynomial method is a nonparametric approach that has the ability to capture any arbitrary relationship (i.e., linear or nonlinear) exhibited by the data without apriori assumptions as to the form of the variability, as in the case of linear regression. IPCC (2001) reports a warming trend over Thailand in the spring temperatures during 1976-1999. In fact, a warming trend can be seen from 1980 until present in Figure 5(a); however, the overall trend from 1950 is a decreasing one. IPCC (2001) also shows a decreasing trend in precipitation over Thailand consistent with Figure 5(b). In summary, the trends in temperature and precipitation seen here are consistent with those reported in IPCC (2001) — the reversal in precipitation trends in the late 1970’s should be kept in mind for the discussion in the following sections. Significant interannual and interdecadal variability is also seen in these variables.

3.3 	Relationship to ENSO
While the long-term trends in temperature and precipitation may be linked to global trends, it is important to understand the interannual and interdecadal variations. As Thailand is close to the equatorial Indo-Pacific basins, the first step is to investigate the relationships with ENSO. To this end, 21-year moving window correlations between Thailand monsoon rainfall and Southern Oscillation Index (SOI), a sea level pressure based ENSO index, are plotted in Figure 6 (solid line). Significant correlations are seen after 1980. Prior to 1980 there is hardly any relationship. 
To further understand this relationship, the monsoon rainfall is correlated with large-scale ocean-atmospheric circulation variables for the pre- and post-1980 periods. Figure 7(a) and (b) show the correlation between monsoon rainfall and SSTs and SLPs for the two periods. The pre-1980 period shows very weak correlations in the tropical Pacific region – in fact, a small region of positive correlation in the eastern tropical Pacific can be seen. But in the post-1980 period, a negative correlation is seen in the central to eastern Pacific and positive correlations are evident in the western Pacific – reminiscent of the ENSO pattern. Similar observations can be found with SLPs (Fig. 7 (b)) – in fact, the pattern resembling the southern oscillation is much stronger in SLPs. To examine relationships with the Walker circulation, correlations between monsoon rainfall and velocity potential at 200 hPa (a surrogate for Walker circulation) were computed for the pre- and post-1980 periods (Fig. 7(c)). It can be seen that the correlations are significant in the post-1980 (Fig. 7(c)) period resembling the SLP correlations (Fig. 7(b)) and are dynamically consistent over Thailand – i.e. enhanced subsidence and reduced rainfall (negative correlation over Thailand). Interestingly, the pattern seen in Figure 7(c) for the post-1980 period  is very similar to the correlation between Indian monsoon and velocity potential at 200 hPa during pre-1980 period reported by Krishna Kumar et al. (1999) (see their Figure 2).
	To further confirm the results in Figure 7, we checked for significance of the Aug-Oct mean SST difference between wet and dry years in the two periods. Data in the top and bottom 15th percentile as being wet (high) and dry (low) rainfall years (see Table 1). Figure 8 shows the monsoon season composite mean SST difference between high and low rainfall years for the two periods. Regions with significant differences in mean SST at 90% confidence levels on a traditional t-test (Helsel and Hirsch, 1995) are shaded and the mean SST differences are shown as contours. It can be seen that during the post-1980 period high (low) years tend to go with significant cooling (warming) in the tropical Pacific region well to the East of dateline – this is a clear ENSO pattern. In the pre-1980 period, this pattern is largely absent – supporting the correlation plots in Figure 7.  These results are suggestive and should be viewed with caution because of the small sample size.
Spectral analysis of rainfall shows a significant band around 2.5 years and a trend (Fig. 9(a)). A moving window spectral coherence of the monsoon rainfall (Fig. 9(b)) and SOI index shows significant power in the 2.5 ~ 3-year band in recent decades. These results are based on the multi-taper method (MTM) of spectral analysis (Thomson 1982; Mann and Lees, 1996).  The MTM is a nonparametric spectral analysis method which has been shown to perform much better than traditional spectral methods. The MTM codes can be obtained from http://holocene.evsc.virginia.edu/Mann/tools/tools.html. The spectral results further corroborate the recent ENSO connection to Thailand rainfall.
4. The Physical Mechanism
The interesting question is why does ENSO modulate Thailand rainfall in recent decades? Is there a change in the ENSO pattern in recent decades? If so, does this have regional impacts?  What could be the physical mechanism?
To answer these, composite of ASO mean SST difference between El Niño years in the pre-1980 (1957, 1965, 1972) and post-1980 (1982, 1987, 1997, 2002) period were made and shown in Figure 10. Notice significantly warmer SST anomalies (shaded regions in the figure) to the East of date line in the tropical Pacific in the El Niño events of the recent period relative to the earlier period. To examine if this has an impact on the atmospheric circulation (i.e. the Walker cell), the same was done on the velocity potential fields for the pre- and post-1980 El Niño events (Figure 11). Strengthening of the velocity potential (i.e. positive anomalies) during recent El Nino events relative to earlier period, over a large region extending from the Indian subcontinent to Australia can be seen. Furthermore, there is a South Eastward shift (shaded region in the figure) of the descending limb of the Walker Cell over to Thailand in the recent decades, consistent with the shifts in the SST patterns. The pattern seen in Figure 11 is similar to the South Eastward shift in Walker circulation anomalies identified by Krishna Kumar et al. (1999) (see their Figure 2). The ENSO related SST shifts and the associated shifts in Walker circulation and correlations between Thailand rainfall and Walker circulation all appear to be consistent. Clearly, these shifts will have implications to the Indian monsoon and Thailand monsoon in terms of their relationship to ENSO. This is clearly seen in Figure 6 (dotted line), which shows the moving window correlation between Indian and Thailand

monsoons with SOI. Around 1980, the Indian monsoon starts to lose its correlation with ENSO while the Thailand monsoon picks up correlation. 
The hypothesis proposed by Krishna Kumar et al. 1999 in the context of the Indian monsoon seems to be responsible for the shifting correlations as seen in Figure 6. The hypothesis is as follows: the Eastern Pacific centered ENSOs tend to constrain the descending branch of the Walker circulation within the Pacific domain, thereby, reducing convection in the tropical Western Pacific regions and consequently, impacting the rainfall in these regions (including the Thailand monsoon). Under this situation, the Indian subcontinent is not directly influenced by ENSO, and land surface processes (e.g., snow cover) are the dominant driving force for monsoonal variability. On the other hand, if the ENSOs are dateline centered then the descending branch of the Walker circulation has a wider reach over the Indian subcontinent, which decreases convection, and the Indian monsoon gets impacted much more than the Thailand monsoon.
Interestingly, this hypothesis is corroborated during the El Niños of 1997 and 2002. Figure 12(a) shows the composite seasonal (June-October) SSTs and precipitation for 1997 and Figure 12(b) for 2002. It can be seen that in 1997, the year of the strongest El Niño in recorded history, the SST pattern shows a strong warming in the Eastern Pacific, and the rainfall over Thailand shows a deficit while over the Indian subcontinent is close to normal.  In 2002 (Fig. 12(b)), on the other hand, the El Niño was weaker in comparison but with stronger warming around the dateline region, has produced a normal to above normal rainfall over Thailand region and a substantial deficit over the Indian region. Climate model experiments have to be performed to further understand this hypothesis.
Given the climatologically colder SSTs in the eastern Pacific Ocean, it is likely that strong SST anomalies in the eastern Pacific are necessary to force an eastward shift in tropical convection and in the Walker circulation. Such strong SST anomalies did in fact occur during the 1982/1983 and 1997/1998 El Niño events. The relative importance of the longitudinal position of SST anomalies and the strength of SST anomalies is difficult to ascertain in observational studies, but this knowledge is critical for improved seasonal forecasts of monsoon precipitation. Further research is warranted on this topic.

5. Summary
Thailand hydroclimatology exhibits a strong trend and inter-decadal variability. The variability in recent decades (post-1980) seems to be strongly linked with ENSO. Interestingly, during the same period Indian monsoon shows a weakening in its relationship with ENSO. These findings seem to argue for a tropical wide ENSO-related circulation shifts with more Eastern Pacific-centered ENSOs in recent decades.
We are cautious that the atmospheric circulation changes in the NCEP-NCAR Re-analysis data for pre and post 1970 period presented here (i.e. when satellite data was ingested in the analysis) may not agree with observations (see e.g., Trenberth et al., 2001, Wu and Xie 2003, Kinter et al., 2004).  However, our results are corroborated with independent information, e.g., in Figure 6 we show moving window correlations between the Thailand monsoon rainfall and SOI, and this clearly shows significant correlations in the post 1980 period. Furthermore, the significant results are in the post-1980 period during which the Re-analysis data is much more reliable.

The dominant mechanism to impact rainfall over Thailand seems to be via the Walker circulation. Eastern Pacific centered ENSOs tend to largely contain the descending branch of the Walker circulation within the Pacific domain, while the descending branch from dateline centered ENSOs tend to extend strongly over the Indian subcontinent. Two recent years, 1997 and 2002 clearly corroborate this hypothesis.  The tantalizing question of what causes the shifts in ENSO related SST patterns in the equatorial Pacific remains. Perhaps, it could be a result of enhanced midlatitude warming as Krishna Kumar et al. (1999) suggest, or it could be natural variability of the tropical Pacific system (Wang et al. 2001). This needs further investigations using model experiments and data.

Acknowledgments
The authors would like to thank Somkiat Apipattanavis and Tom Chase for useful
discussions. The first author is grateful to Thailand Government for the
graduate student fellowship and to Thailand Public Works Department for their
encouragement and support. Travel support from IUGG to the first author to
present preliminary results from this work at the IUGG General Assembly 2003 in
Sapporo, Japan is very much appreciated. Funding for the third author via CIRES
Visiting Fellowship is also gratefully acknowledged. Finally, the authors are very much thankful to two anonymous reviewers whose comments greatly improved the manuscript.

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Figure Captions

Figure 1: Map of Thailand and surroundings with the location of hydroclimate data stations shown as points. 

Figure 2: Correlation between (a) Thailand summer rainfall and CMAP rainfall during monsoon season (ASO) and (b) Thailand temperature and Re-Analysis Surface Temperatures during pre-monsoon spring season (MAM). 95% significant levels are ±0.21

Figure 3: Annual cycle of Thailand (a) Temperature (b) Rainfall and (c) Streamflow

Figure 4: Scatter plot of (a) pre-monsoon MAM mean temperature and ASO rainfall (b) ASO rainfall and ASO temperature. Linear regression fits are shown as solid line

Figure 5: Standardized anomaly time series plot of (a) MAM temperature and (b) ASO rainfall (c) ASO temperature and (d) SON streamflow. Dashed lines are linear regression fits to the data  and solid lines represent the local polynomial fits. El Nino and La Nina years are shown as starts and dots, respectively, in (b).

Figure 6: 21-year moving window correlation between Thailand monsoon (ASO) rainfall and (ASO) SOI index (solied line); between Indian summer monsoon (JJAS) and (JJAS) SOI index(dotted line). The dashed line at 0.45 is the  95% significant level.

Figure 7: Correlation between ASO rainfall and (a) ASO SST (b) ASO SLP and (c) Velocity Potential at 200 hPa, for the pre and post 1980 periods. 95% significant levels are ±0.45

Figure 8:  Composite maps of mean ASO SST anomaly difference between High rainfall and Low rainfall years during (a) pre-1980 and (b) post-1980. Shaded regions are significant at 90% confidence level on a standard t-test.

Figure 9: (a) spectrum of Thailand monsoon rainfall (dashed line is the 95% significance level) and (b) 21-year moving window spectral coherence between Thailand monsoon rainfall and ASO SOI. Shaded regions are significant at 95% confidence level.

Figure 10: Composite map of mean ASO SST anomaly difference between El Nino years in post-1980 and pre-1980 period. Shaded regions are significant at 90% confidence level on a standard t-test. 

Figure 11: Same as Figure 10 but of mean ASO velocity potential at 200 hPa.

Figure 12: Composite maps of SST and June-October rainfall anomalies for the (a) 1997 and (b) 2002 El Nino events.

Table 1: List of three highest and lowest rainfall years during pre- and post-1980
	Pre-1980	Post-1980	
High Years	1952, 1957, 1962	1983, 1988, 1995	
Low Years	1968, 1977, 1979	1984, 1987, 1994	

Figure 1

(a)

(b)

Figure 2

Figure 3

Figure 4

Figure 5 

Figure 6

Figure 7

							

Figure 8

Figure 9

Figure 10

	
Figure 11

SST

Rainfall

(a)
Figure 12 

SST

Rainfall

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Figure 12