Seasonal variability of SeaWiFS chlorophyll a in the Malacca Straits in relation to Asian monsoon

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Continental Shelf Research 26 (2006) 168 178 www.elsevier.com/locate/csr Seasonal variability of SeaWiFS chlorophyll a in the Malacca Straits in relation to Asian monsoon Chun Knee Tan a,, Joji Ishizaka b, Satsuki Matsumura c, Fatimah Md. Yusoff d, Mohd. Ibrahim Hj. Mohamed e a Graduate School of Science and Technology, Nagasaki University, 1-14 Bunkyo, 852-8521 Nagasaki, Japan b Faculty of Fisheries, Nagasaki University, 1-14 Bunkyo, 852-8521 Nagasaki, Japan c Department of Marine Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand d Department of Biology, Faculty of Science, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia e Department of Environmental Management, Faculty of Environmental Studies, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia Received 28 February 2005; received in revised form 30 August 2005; accepted 23 September 2005 Available online 7 December 2005 Abstract The influences of Asian monsoon on chlorophyll a (chl a) in Malacca Straits (MS) are investigated using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data (November 1997 January 2003). Results show that chl a in MS experienced seasonal variability. At northern MS, SeaWiFS chl a increased as much as twice during northeast monsoon (NEM) compared to southwest monsoon (SWM). At the southern MS, SeaWiFS OC4 algorithm was found over-estimating chl a at the areas of high normalized water leaving radiance at 555 nm value (nlw 555). Strong relationship between the nlw 555 and total suspended sediment concentration suggested that high turbidity could contribute to the over-estimation of SeaWiFS chl a. Monsoon wind strongly influenced the spatial distribution and seasonal variation of the chl a at northern MS. During NEM, bloom of phytoplankton started from eastern MS, and it was advected towards Andaman Sea by northeasterlies. NEM wind created positive wind stress curl along 4.5 6.51N in the Straits, which caused positive Ekman pumping resulting upwelling. Consequently, upwelling helped replenish the surface water nutrients, thus, maintaining the phytoplankton bloom over the continental shelf. Low chl a was observed during SWM, and this was attributed to negative wind stress curl and downwelling. The unique topology of MS, where the mountain ranges at Malay Peninsular and Sumatra Island acted as the monsoon windshield, enable both upwelling and downwelling at northern MS during different monsoons. r 2005 Elsevier Ltd. All rights reserved. Keywords: Southeast Asia; Malacca straits; SeaWiFS; Chlorophylls; Seasonal variations; Monsoons; Upwelling Corresponding author. Tel./fax: +81 95 819 2804. E-mail addresses: gs04127@hotmail.com (C.K. Tan), ishizaka@net.nagasaki-u.ac.jp (J. Ishizaka), smatsu@sc.chula.ac.th (S. Matsumura), fatimamy@fsas.upm.edu.my (F.M. Yusoff), mibrahim@env.upm.edu.my (M.I.H. Mohamed). 1. Introduction Seasonal to interannual changes in phytoplankton biomass and productivity are very important components of the total variability associated with ocean biological and biogeochemical processes 0278-4343/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.csr.2005.09.008

C.K. Tan et al. / Continental Shelf Research 26 (2006) 168 178 169 (Yoder and Kennelly, 2003). For several decades, chlorophyll a (chl a), which is the major pigment involved in phytoplankton photosynthesis, and its concentration has been used as a surrogate of phytoplankton biomass and photosynthetic potential. With Sea-Viewing Wide Field-of-view Sensor (SeaWiFS), data on optical properties of the global oceans is collected daily from satellite, from which surface chl a can be estimated (McClain et al., 1998). The Asian monsoon system is characterized by two monsoon regimes, namely, the southwest monsoon (SWM) from late May to September, and the northeast monsoon (NEM) from November to March (Malaysian Meteorological Service, 2005). Primary production in the Southeast Asian regions, for instance South China Sea and Indian Ocean, depict seasonal variability, due to influence of the Asian monsoon. Dey and Singh (2003) found that chl a concentration in southern Bay of Bengal is higher during the NEM compared to the intermonsoon period. In the areas of South China Sea, phytoplankton was found bloom at Vietnam coast during SWM, and bloom near Luzon Straits during NEM (Tang et al., 1998, 1999,2004; Liu et al., 2002). In southeastern Java, increase of chl a concentration is found due to upwelling event during SWM (Hendiarti et al., 2004). Malacca Straits (MS) is one of the world s busiest sea-lanes that connect the Indian Ocean to the South China Sea and Pacific Ocean. It has a tropical climate and is strongly influenced by two monsoons. The topography in MS varies between the northern and southern areas (Fig. 1). Depth of the water is deeper at the northern than at the southern parts. Sudden decrease of depth around One Fathom Bank creates a physical barrier for the water exchange between the north and south (Liong, 1974). Chua et al. (2000) reveal that the primary productivity in MS is constant throughout the year. Higher phytoplankton is found in the south where water is shallower, and where there is both vertical mixing and high nutrient input from rivers from Sumatra. The average surface chl a is 0.54 mg m 3 at northern MS without any distinct seasonal variation (Setiapermana et al., 1984). However, previous investigation of the phytoplankton variability in MS was based solely on data collected from oceanographical cruises, which provided rather non-synoptic and coarse resolution sampling of 94 E 96 E 98 E 100 E 102 E 104 E 8 N 6 N 2000 Andaman Sea -2000-1000 1000-200 Phuket B -50 58 59 A 60 Malacca Straits -50-25 Langkawi -25 Penang 61-25 Legend Elevation (m) 251-1,000 1,001-3,035 SEAFDEC St. -50 South China Sea 8 N 6 N 4 N 62-50 63 Malay Peninsular 4 N 2 N -2000-5000 -200 Sumatra One Fathim Bank Malacca -25-50 2 N Indian Ocean -5000 50-200 -25 94 E 96 E 98 E 100 E 102 E 104 E Fig. 1. Bathymetry and topology of Malacca Straits and its surrounding areas. Shaded areas on land show the mountain ranges in both Sumatra and Malay Peninsular. SEAFDEC cruise stations 58 63 are marked. Dark dotted lines show the boundary of Malacca Straits. Gray dotted line at the middle of the Straits shows the division of the northern and southern areas.

170 ARTICLE IN PRESS C.K. Tan et al. / Continental Shelf Research 26 (2006) 168 178 overall spatial and temporal pattern. Consequently, the seasonal phytoplankton variability could be poorly quantified. We hypothesize that chl a in MS experienced seasonal variability which is influenced by the monsoon similar to other Asian monsoon regions. By using the satellite ocean color time series data, we were able to investigate the phytoplankton variability in a more synoptic and systematic way. This paper provides evidences of seasonal chl a variation in MS by using the SeaWiFS ocean color images. Factors that contributed to the chl a seasonal variability were also examined. 2. Materials and methods This study was focused on the MS areas as shown in Fig. 1. The partition line of northern and southern MS was located slightly to the north of One Fathom Bank that is close to the isobath of 50 m (Dotted line in Fig. 1). Daily SeaWiFS level 2 and monthly level 3 ocean color images (version 4) were downloaded from NASA GES Distributed Active Archive Center (DAAC). These ocean color images were further processed and analyzed by using SeaWiFS Data Analysis System (SeaDAS). NCEP Reanalysis monthly mean wind speed and topography data were acquired from Live Access Server at the National Virtual Ocean Data System (NVODS) (data available at http://ferret.pmel.noaa. gov/nvods/servlets/dataset), while monthly wind stress and curl data were downloaded from the Fleet Numerical Meteorology and Oceanography Center (FNMOC) datasets at Pacific Fisheries Environmental Laboratory (PFEL) Live Access Server (data available at http://las.pfeg.noaa.gov/las/). Rainfall data from January 1991 to December 2001 was acquired from Malaysian Meteorological Service (MMS). Oceanographic data were provided by the Southeast Asian Fisheries Development Center (SEAFDEC) and Malacca Straits Research and Development Centre (MASDEC). From SEAFDEC oceanographic cruise no. 68, we selected only the data collected during 8 9 February 2003 that located within Andaman Sea and Malacca Straits for this analysis. MASDEC had conducted five cruises along Malaysian waters in MS during monsoon and inter-monsoon seasons for the period from 1998 to 2002. The MASDEC oceanographic data used in this study consisted of cruise 1 (23 November 2 December 1998) (NEM), cruise 4 (29 July 7 August 2000) (SWM) and cruise 5 (18 29 October 2002) (Inter-monsoon). In situ surface chl a from MASDEC cruises were input into ArcView 8.1 GIS for spatial analysis. Daily SeaWiFS level 2 images during each cruise were composited into single image using SeaDAS in order to reduce the effect of cloud cover. Chl a and normalized water leaving radiance at 555 nm (nlw 555) values from these composite images were extracted for the comparison with in situ data. 3. Results 3.1. Seasonal SeaWiFS chl a variation Analysis of the 5-year composite monthly mean chl a images showed that there was a seasonal variability of chl a in MS. In the northern part of MS, surface chl a increased from November to March (Fig. 2). The high chl a area emanated from the area around Penang to Langkawi Island in November, and extended to the west and northwest in January when the northeast monsoon was fully developed. The high chl a area started decaying since March. During the SWM, chl a in northern MS remained low throughout the season. The chl a increased as much as twice during the NEM compared to SWM. Monthly mean chl a at northern MS (Fig. 3A) showed highest concentration during January (1.38 mg m 3 ) and lowest during August (0.57 mg m 3 ). The peak of chl a was observed during NEM. At the coastal and southern MS, SeaWiFS chl a showed more than 2.0 mg m 3 throughout the year (Fig. 2). Higher chl a was found at the southeastern entrance of MS from June to September during the SWM. Besides, very high chl a was observed at the middle of the Straits at Sumatra Island from November to December. Monthly mean chl a at southern MS (Fig. 3A) recorded highest concentration during November (5.39 mg m 3 ) and lowest during March (3.70 mg m 3 ). The peak of chl a in southern MS occurred earlier than in northern MS. Maximum chl a at southern MS was about four times higher than in northern MS. However, the accuracy of SeaWiFS chl a estimation at these shallower areas need to be further examined due to turbid water. 3.2. Comparison of MASDEC in situ and SeaWiFS satellite data During MASDEC cruise 1 (NEM), most of the stations, which located closed to the coastal area

C.K. Tan et al. / Continental Shelf Research 26 (2006) 168 178 171 Fig. 2. Monthly composite of SeaWiFS chl a (mg m 3 ), 1998 2002. showed chl a more than 1.00 mg m 3 (Fig. 4A). Surface chl a was higher at northern MS than southern MS. Mean surface chl a during cruise 1 was 1.23 mg m 3. Maximum chl a of 2.81 mg m 3 was sampled at station 2, whereas minimum chl a was 0.22 mg m 3 at station 3 (Fig. 4A). During cruise 4 (SWM), higher surface chl a was observed at the middle of the Straits (Fig. 4B). Mean surface chl a during cruise 4 was 0.72 mg m 3. Maximum chl a of 3.06 mg m 3 was sampled at station 18, whereas minimum chl a was 0.16 mg m 3 at stations 9 and 13 (Fig. 4B). During cruise 4, surface chl a at northern and southern MS was much lower than cruise 1. Increasing SeaWiFS chl a trend towards southern MS was observed in both cruise 1 and 4 (Fig. 5).

172 ARTICLE IN PRESS C.K. Tan et al. / Continental Shelf Research 26 (2006) 168 178 However, no increasing tendency was observed in MASDEC in situ chl a towards the south. The in situ chl a value was higher for certain coastal Rainfall EP North WS North Chl a 1.5 (A) 1 0.5 0 4 (B) 3 2 4 2 0-2 -4 400 200 (C) (D) 0 4 5 6 7 8 9 10 11 12 1 2 3 Month 6 5 4 3 2 Fig. 3. Monthly mean SeaWiFS chl a (mg m 3 ) (A) and NCEP reanalysis monthly mean wind speed (m s 1 ) (B) averaged over northern (K) and southern (J) MS. The scale for northern and southern MS is located on the left and right, respectively. Monthly Ekman pumping rate (m day 1 ) (C) is calculated using the FNMOC wind stress curl data for two locations at 61N 991E (Point A, D) and 71N 981E (Point B, ) as shown in Fig. 1. Monthly rainfall (mm) (D) at Langkawi (K) and Malacca (J) (Locations shown in Fig. 1) averaged over the period from 1991 until 2001. 1.5 1 South WS South Chl a stations, for instance stations 14 and 18. During cruise 1 (NEM), a few stations at the northern MS showed values close to the in situ chl a. Stations 2, 9, 10 and 13 at the north were under-estimated by the SeaWiFS chl a, whereas most of the stations at the middle of the Straits showed over-estimation of SeaWiFS chl a (Fig. 5A). During cruise 4 (SWM), good correlation between the MASDEC and SeaWiFS chl a was also found at the northern MS (from stations 1 13, and stations 15 17) (Fig. 5B). Whereas, stations to the southern MS and station 14, showed over-estimation of the surface chl a values. The increasing trend of nlw 555 values for both cruise 1 and 4 was very similar to the SeaWiFS chl a (Fig. 5). The analysis results showed that SeaWiFS chl a tended to be over-estimated at the stations with high nlw 555. This observation was evident in cruise 4, where all the over-estimated stations were accompanied with nlw 555 values more than 1.00 mw cm 2 mm 1 sr 1. During cruise 4, SeaWiFS chl a value at station 14 was over-estimated almost six times from the in situ chl a, where the nlw 555 value 5.25 mw cm 2 mm 1 sr 1 was the highest during these two cruises. Monthly nlw 555 distributions in MS did not show much variation like the chl a distribution. The distribution pattern is always similar: extremely high value (nlw 555 more than 1.00 mw cm 2 mm 1 sr 1 ) at the coastal and southern of MS, and values less than 0.50 mw cm 2 mm 1 sr 1 at the offshore areas at northern MS (Fig. 6B). Correlation analysis Fig. 4. In situ chl a (mg m 3 ) of MASDEC oceanographic cruises. Cruise 1 (A) was conducted during NEM (23 November 2 December 1998) and cruise 4 (B) was conducted during SWM (29 July 7 August 2000).

C.K. Tan et al. / Continental Shelf Research 26 (2006) 168 178 173 nlw 5.0 4.0 3.0 2.0 1.0 nlw555 MASDEC SeaWiFS 5.0 4.0 3.0 2.0 1.0 Chlorophyll a 0.0 1 2 3 7 8 9 10 13 15 16 17 18 21 0.0 (A) Station nlw 6.0 5.0 4.0 3.0 2.0 1.0 nlw555 MASDEC SeaWiFS 7.0 6.0 5.0 4.0 3.0 2.0 1.0 Chlorophyll a 0.0 1 2 7 8 9 10 12 13 14 15 16 17 18 20 21 23 24 0.0 (B) Station Fig. 5. MASDEC in situ chl a (mg m 3 )(m), composite SeaWiFS chl a (mg m 3 )(&), and composite normalized water leaving radiance at 555 nm (mw cm 2 mm 1 sr 1 ) (bar) during MASDEC oceanographic cruise 1 (A) and cruise 4 (B). Fig. 6. Monthly SeaWiFS chl a (mg m 3 ) (A) and normalized water leaving radiance at 555 nm (mw cm 2 mm 1 sr 1 ) (B) image on January 2002. Cloud cover and noise is colored black. between total suspended sediment (SS) and SeaWiFS showed that the concentration of in situ SS is positively related to the nlw 555 values with R 2 ¼ 0:71 (Fig. 7). Most of the high SS concentrations were recorded at the southern and coastal stations, which was very similar to the nlw 555 distribution pattern from SeaWiFS satellite.

174 ARTICLE IN PRESS C.K. Tan et al. / Continental Shelf Research 26 (2006) 168 178 3.3. Monthly wind and rainfall variation Wind speed at the northern MS showed two peaks during monsoon seasons (Fig. 3B). Maximum wind speed 3.86 m s 1 was observed in January during NEM. Besides, another peak of wind speed 3.43 m s 1 was noted in July during SWM. It was generally lower during inter-monsoon (less than 3.00 m s 1 ). Wind speed in the southern MS was about half of that in northern MS (Fig. 3B). There were two wind speed peaks at southern MS in July and December. Maximum wind speed 1.74 m s 1 was observed in December, whereas lowest wind nlw555 3 2.5 2 1.5 1 0.5 y = 0.4782x - 0.5478 R 2 = 0.7093 0 0 1 2 3 4 5 6 SS Fig. 7. Correlation of MASDEC in situ suspended sediment (mg l 1 ) (SS) and composite SeaWiFS nlw 555 values (mw cm 2 mm 1 sr 1 ) (nlw 555) of MASDEC oceanographic cruise 5 (18 29 October 2002). speed 1.25 m s 1 in September. At northern MS, the peak of wind speed in NEM coincided in time with the chl a peak; however, chl a did not increase during the peak of wind speed in SWM. The wind speed variation at southern MS showed a different trend compared to chl a variation. Wind pattern in MS (Fig. 8) showed observation similar to the monthly wind speed variation mentioned above, higher wind speed at the northern and lower wind speed in the south. During NEM, higher wind speed was observed at the areas above latitude 61N in the Straits (Fig. 8A). Strong NEM wind at the South China Sea and Gulf of Thailand was getting weaker when it blows across Malay Peninsular. Strong positive wind stress curl (more than 0.10 10 6 Pa m 1 ) occurred at latitudes 4.51N 6.51N at northern MS, whereas negative wind stress curl 0.10 10 6 Pa m 1 was observed at southern Phuket (Fig. 8A). Positive wind stress curl is associated with upwelling, while negative wind stress curl is associated with downwelling. During SWM, stronger monsoon wind was noted at the northern opening to Andaman Sea (Fig. 8B). Most of the area at northern MS showed negative wind stress curl, particularly very strong negative wind stress curl (lower than 0.20 10 6 Pa m 1 ) was observed at the northern Sumatra. Briefly, the wind stress curl pattern at northern MS differs much between the two monsoons. Monthly rainfall variation at Malay Peninsular illustrated some similarities (Fig. 3D). Both rainfall stations at Langkawi and Malacca showed increasing trend from NEM, and reached the peak during inter-monsoon or early NEM, and lowest rainfall during NEM. Rainfall at Langkawi showed more Fig. 8. FNMOC surface wind stress (N m 2 ) and curl (Pa m 1 10 6 ) during January 2002 (A) and July 2002 (B). The wind stress (vector) is superimposed on the curl of wind stress (raster).

C.K. Tan et al. / Continental Shelf Research 26 (2006) 168 178 175 variation than at Malacca. At Langkawi, highest monthly mean rainfall (389 mm) was recorded in October, and lowest (24.6 mm) in Janaury. The trend of monthly rainfall at Langkawi was much different from those of both chl a and wind speed monthly variation. At Malacca, maximum rainfall 247 mm was recorded in November, and minimum rainfall 86.8 mm in February. The increasing trend of rainfall at Malacca was similar to the increasing trend of SeaWiFS chl a at southern MS. Besides, the peak of rainfall at Malacca was coincided with the SeaWiFS chl a peak at southern MS. 4. Discussions 4.1. Discrepancy of SeaWiFS chl a estimation The coastal water in Malacca Straits is very turbid due to discharge of rivers and the resuspension of bottom sediment from tides (Chua et al., 1997). In areas around the river mouth and the coastal region, water transparency is less than 10.0 m (Chua et al., 1997). SeaWiFS chl a OC4 algorithm is known to be broken down in area loaded with high SS or colored dissolved organic matter (CDOM) (IOCCG, 2000). Studies in Korean waters by Ahn et al. (2001) found that the presence of SS in coastal areas lead to overestimation of chl a concentration greater than 20 500%. The 555 nm band on SeaWiFS sensor is found to be particularly good at detecting the reflectance of light from suspended sediment (Acker, 2004). Our study showed that the nlw 555 values were closely related to the SS in MS. As the result, the overestimation of SeaWiFS chl a at coastal and southern water in MS could be due to high SS. After rainfall, more sediment will be flushed into the sea. Thus, the effect of SS on the chl a estimation is expected to be more severe during rainy season. Consequently, higher chl a at southern MS during rainy season was likely been over-estimated. Beside the high SS, CDOM could be another factor that contributed to the inaccuracy of chl a estimation. There are many rivers and mangrove forests along the coastal areas, especially at the southern MS. The effect of CDOM could be significant for the coastal water close to these CDOM sources. There was some under-estimation of chl a from SeaWiFS when compared to the MASDEC in situ data. We suspected that this discrepancy was caused by the temporal differences between the sampling day and the satellite data available. In contrast to the southern MS, the comparatively high SeaWiFS chl a in the northern MS during NEM is likely not an artifact but a phytoplankton bloom. Monthly SeaWiFS chl a image on January 2002 showed high chl a area, extending from the Penang to Langkawi coast toward northern Sumatra Island (Fig. 6A). However, nlw 555 values in the tongue of high chl a area in northern MS were lower than in coastal area (nlw 555 less than 0.60 mw cm 2 mm 1 sr 1 )(Fig. 6B). Slight increase in the nlw 555 nm in this area could probably be attributed to the increase of turbidity by phytoplankton cells. The MASDEC cruise 1 (NEM) in situ chl a at northern MS appeared to be higher than cruise 4 (SWM) (Fig. 4). Northern MS was experiencing the dry season during NEM (Fig. 3D). Low rainfall is expected to reduce the chl a as there is less nutrient input from the river. Besides, less sediment and CDOM will be discharged into the sea. We further conceived that the increase of chl a at northern MS was contributed by the bloom of phytoplankton and the effects of SS and CDOM were negligible. Apart from river discharge, there was another mechanism that helped supplying the nutrient for the phytoplankton bloom during NEM. 4.2. Ekman pumping The offshore surface water of northern MS is basically oligotrophy (Yusoff et al., 2001). Hence, any physical mechanism that is capable of supplying nutrient to the surface water would be important for the NEM bloom. When the NEM wind started blowing across the northern MS from the northeast direction, the surface water seems to be advected offshore towards the Andaman Sea. Wind pattern analysis showed that the northwest open area in MS (latitude more than 5.51N) received stronger influence of the monsoon wind than other areas inside the Straits (Fig. 8A). Monsoon wind is relatively weak in the southeastern part of the study area due to the topological influence of the high mountain range in both Malay Peninsular and Sumatra (Namba and Ibrahim, 2002). Positive wind stress curl at latitudes from 4.51N to 6.51N was attributed to the wind pattern in that area (Fig. 8A). Consequently, the positive wind stress curl had induced positive Ekman pumping (EP) rate that caused upwelling at northern MS (Fig. 3C). Corroborating evidence of upwelling event was observed during SEAFDEC cruise No. 68 which

176 ARTICLE IN PRESS C.K. Tan et al. / Continental Shelf Research 26 (2006) 168 178 included measurements at a number of stations in MS (see Fig. 1). High saline water (33.5 p.s.u.) was upwelled from 50 m at station 58 to 25 m at station 59 (Fig. 9). It is expected that upwelling had brought nutrients from the deep water to the surface water, then initiated the phytoplankton bloom. This upwelling event was apparently distinct from the previous findings near One Fathom Bank located in the middle part of MS (Wyrtki, 1961; Uktolseya, 1988; Chua et al., 1997). The effect of river discharge was observed at stations 62 and 63 (Fig. 9), where the surface salinity was much lower compared to the stations at north. The EP analysis results suggested that the upwelling phenomena were confined only to the narrow positive wind curl area in northern MS. At point A (Fig. 1), maximum EP rate was 3.53 m day 1 in December (Fig. 3C). However, point B, which is located outside the positive wind curl areas, did not show significant pumping rate during NEM. The intensification of EP during NEM agreed well with the development of the chl a bloom (more than 1 mg m 3 ) that started in the east of northern MS at latitudes from 4.51N to 6.51N (from Penang to Langkawi Island) and extended toward Andaman Sea (Fig. 2). The possible mechanism of the NEM bloom in northern MS can be explained as follows: At the beginning of the bloom (November), chl a increased along the coastal areas. When the NEM fully developed in December, strong upwelling by EP occurred along 4.5 6.51N in the Straits. This upwelling phenomenon was important in supplying nutrients to phytoplankton for maintaining its bloom throughout the northern MS, and along the continental slope at northern Sumatra from January Depth [m] 40 80 120 58 59 60 61 62 63 33 34 34.5 33.5 33.5 0 100 200 300 400 Distance [km] 34 31.5 32 33.5 32.5 33 30.5 35 Fig. 9. Bathymetry and vertical salinity profiles (p.s.u) for the SEAFDEC Cruise No. 68 (8 9 Feb 2003) showing the transect from northwest to southeast along northern MS. Locations of the sampling stations (58 63) are shown: see also Fig. 1. Gray color shows sea floor. to March. When the NEM wind was weakening during inter-monsoon, the bloom was simultaneously reduced. During SWM, when the strong southwesterlies reached eastern Indian Ocean, the high mountain ranges at Sumatra block the strong wind from MS. As the result, the wind speed in MS was much lower as compared to the open ocean near Andaman Sea (Fig. 8B). Strong negative wind stress curl occurred due to the large differences of wind speed at northern tip of Sumatra (Fig. 8B). The negative wind stress curl caused negative EP rate (Fig. 3C) and induced downwelling event in the study area between May and September. As there was no further supply of nutrient to the surface water, the chl a concentration remained low throughout SWM. 4.3. Comparison with other regions The seasonal chl a variation in MS suggests that it is similar to other Asian Monsoon regions, where the chl a variation is related to the monsoon wind. Most of these areas show the chl a peak during monsoon either in NEM or SWM. Tang et al. (2004) reported that the phytoplankton bloom at eastern Vietnam from June and decay in October. The chl a reaches a peak of 0.9 mg m 3 in August during SWM (Liu et al., 2002). In northwestern of Luzon, the bloom starts in October, and reveals a strong peak (0.5 mg m 3 ) in December (Liu et al., 2002). The Luzon phytoplankton bloom started much earlier compared to the phytoplankton bloom at northern MS could probably attribute to the higher latitude of Luzon, where the influence of NEM begins earlier. Monsoon induces upwelling event is one of the major factors that contribute to the phytoplankton bloom in Southeast Asian monsoon regions. Although the main force of the upwelling was the monsoon wind, the mechanism of upwelling could be different due to differences in local conditions. In southern Java, the coastal upwelling is caused by the southeasterly alongshore winds that generate an Ekman offshore transport of coastal water (Susanto et al., 2001). In the coast of Vietnam, Xie et al. (2003) revealed that in addition to the classical coastal upwelling mechanism, EP associates with a wind jet offshore is important for the ocean upwelling off the coast. A strong wind jet occurs when the southwesterly wind impinge on Annam Cordillera (mountain range on the east coast of

C.K. Tan et al. / Continental Shelf Research 26 (2006) 168 178 177 Indochina) resulting in positive wind stress curl and intense open ocean EP (upwelling) (Xie et al., 2003). The mechanism of the EP-induced upwelling off Vietnam was very similar to northern MS. However, compared to the single mountain range in Vietnam coast, both the mountain ranges in Peninsular Malaysia and Sumatra Island act as the double shield of monsoon wind on the left and right of MS. This unique topography in MS has produced both upwelling and downwelling events over the same area in northern MS during different monsoon. These monsoon induced upwelling are important phenomena that support and enrich the local fishery resources of the region. 5. Conclusion Seasonal SeaWiFS chl a variation in MS is different between the northern and southern areas. This study demonstrates that the seasonal phytoplankton variability in northern MS is strongly influenced by the Asian monsoon wind and the land topography. The coastal and southern waters in MS have complex optical properties and that could mislead the standard chl a estimation. Development of local algorithm will help increase the accuracy of the chl a prediction. This is the first study that found phytoplankton bloom at northern MS during NEM. The discovery of phytoplankton bloom at northern MS has altered our understanding of the spatial and temporal extent of the primary productivity in MS, which has been previously considered seasonally invariant. The surface enrichment and phytoplankton bloom throughout northern MS could play an important role in sustaining the rich fishery resources in this area. Understanding these mechanisms will aid to forecast the possible biological response and could further assist in the marine resource management practices. Acknowledgments We want to thank the SeaWiFS Project (Code 970.2) and the Distributed Active Archive Center (Code 902) at the Goddard Space Flight Center, Greenbelt, MD 20771, for the production and distribution of SeaWiFS data, NOAA for NCEP Reanalysis wind data and FNMOC wind stress and curl data. Besides, we would also like to express our gratitude to SEAFDEC and MASDEC for providing the oceanography data, and MMS for the rainfall data. We are grateful to N.H. Saji (IPRC, Honolulu), L.C. Quah (Brunei Meteorological Service), H. Marcial, M.V. Grageda and T. Gonzales for providing helpful comments and suggestions for this work. References Acker, J., 2004. Sedimentia. WWW Page, http://daac.gsfc.nasa. gov/oceancolor/sedimentia.shtml. Ahn, Y.H., Moon, J.E., Gallegos, S., 2001. Development of suspended particulate matter algorithms for ocean color remote sensing. Korean Journal of Remote Sensing 17 (4), 285 295. Chua, T.E., Ross, S.A., Yu, H.M. (Eds.), 1997. Malacca Straits environmental profile, MPP-EAS Technical Report 10. GEF/ UNDP/IMO Regional Programme for the Prevention and Management of Marine Pollution in the East Asian Seas, Quezon City, Philippines, p. 259. Chua, T.E., Gorre, I.R.L., Ross, S.A., Bernad, S.R., Gervacio, B., Ebarvia, M.C., 2000. The Malacca Straits. Marine Pollution Bulletin 41 (1 6), 160 178. Dey, S., Singh, R.P., 2003. Comparison of chlorophyll distributions in the northeastern Arabian Sea and southern Bay of Bengal using IRS-P4 Ocean Color Monitor Data. Remote Sensing of Environment 85, 424 428. Hendiarti, N., Siegel, H., Ohde, T., 2004. Investigation of different coastal processes in Indonesian waters using SeaWiFS data. Deep-Sea Research II 51, 85 97. IOCCG, 2000. Remote sensing of ocean colour in coastal, and other optically complex, waters. In: Sathyendranath, S. (Ed.), Reports of the International Ocean Colour Coordinating Group, No. 3. IOCCG, Dartmouth, Canada, p. 140. Liong, P.C., 1974. Hydrography of the Strait of Malacca. Malaysian Agriculture Journal 49 (3), 381 391. Liu, K.K., Chao, S.Y., Shaw, P.T., Gong, G.C., Chen, C.C., Tang, Y.T., 2002. Monsoon-forced chlorophyll distribution and primary production in the South China Sea: observations and a numerical study. Deep-Sea Research I 49, 1387 1412. Malaysian Meteorological Service, 2005. Monsoon: changing winds. WWW Page, http://www.kjc.gov.my/english/education/weather/monsoon01.html. McClain, C.R., Cleave, M.L., Feldman, G.C., Gregg, W.W., Hooker, S.B., Kuring, N., 1998. Science quality SeaWiFS data for global biosphere research. Sea Technology 39, 10 16. Namba, T., Ibrahim, H.M., 2002. Mixed layer depth in the Straits of Malacca. In: Yusoff, F.M., Shariff, M., Ibrahim, H.M., Tan, S.G., Tai, S.Y. (Eds.), Tropical Marine Environment: Charting Strategies for the Millennium. Malacca Straits Research and Development Centre (MASDEC), University Putra Malaysia, Serdang, Malaysia, pp. 269 279. Setiapermana, D., Triyati, E., Nontji, A., 1984. Pengamatan klorofil dan seston di perairan Selat Melaka, 1978 1980. In: Moosa, M.K., Praseno, D.P., Kastoro, W. (Eds.), Evaluasi kondisi perairan Selat Melaka, 1978 1980. Lembaga Oseanologi Nasional, Jakarta, Indonesia, pp. 63 66 (in Indonesian).

178 ARTICLE IN PRESS C.K. Tan et al. / Continental Shelf Research 26 (2006) 168 178 Susanto, R.D., Gordon, A.L., Zheng, Q., 2001. Upwelling along the coast of Java and Sumatra and its relation to ENSO. Geophysical Research Letters 28, 1599 1602. Tang, D.L., Ni, I.H., Mu ller-karger, F.E., Liu, Z.J., 1998. Analysis of annual and spatial patterns of CZCS-derived pigment concentrations on the continental shelf of China. Continental Shelf Research 18, 1493 1515. Tang, D.L., Ni, I.H., Kester, D.R., Mu ller-karger, F.E., 1999. Remote sensing observation of winter phytoplankton blooms southwest of the Luzon Strait in the South China Sea. Marine Ecology Progress Series 191, 43 51. Tang, D.L., Kawamura, H., Dien, T.V., Lee, M.A., 2004. Offshore phytoplankton biomass increase and its oceanographic causes in the South China Sea. Marine Ecology Progress Series 268, 31 41. Uktolseya, H., 1988. Physical and biological characteristics of the Straits of Malacca in the framework of coastal management. In: Coastal zone management in the Straits of Malacca, Proceeding of a Symposium on the Environmental Research and Coastal Zone Management in the Straits of Malacca. School for Resource and Environmental Studies, Dalhousie University, Halifax, Nova Scotia, pp. 118 131. Wyrtki, K., 1961. Scientific results of marine investigation of the South China Sea and the Gulf of Thailand 1959 1961. In: Physical oceanography of Southeast Asian waters, Naga Report II, Vol. 2. Scripps Institute of Oceanography, University of California, La Jolla, p. 195. Xie, S.P., Xie, Q., Wang, D.X., Liu, W.T., 2003. Summer upwelling in the South China Sea and its role in regional climate variations. Journal of Geophysical Research 108 (C8), 3261. Yoder, J.A., Kennelly, M.A., 2003. Seasonal and ENSO variability in global ocean phytoplankton chlorophyll derived from 4 years of SeaWiFS measurements. Global Biogeochemical Cycles 17 (4), 1112. Yusoff, F.M., Ichikawa, T., Matias, H.M., Azhar, O., 2001. Total carbon, dissolved silica and chlorophyll a in the Straits of Malacca. In: Sidik, B.J., Arshad, A., Tan, S.G., Daud, S.K., Jambari, H.A., Sugiyama, S. (Eds.), Aquatic Resource and Environmental Studies of the Straits of Malacca: Current Research and Reviews. Malacca Straits Reseach and Development Centre (MAS- DEC), University Putra Malaysia, Serdang, Malaysia, pp. 51 64.