Seasonal and Interannual Variability of Carbon Cycle in South China ...

Journal of Oceanography, Vol. 65, pp. 703 to 720, 2009

Seasonal and Interannual Variability of Carbon Cycle in South China Sea: A Three-Dimensional PhysicalBiogeochemical Modeling Study F EI C HAI1*, GUIMEI LIU1,2, HUIJIE XUE1, LEI SHI1, YI CHAO3, C HUN-MAO TSENG4, WEN-CHEN CHOU5 and KON -KEE LIU6 1

University of Maine, School of Marine Sciences, 5706 Aubert Hall, Orono, ME 04469, U.S.A. National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China 3 California Institute of Technology, Jet Propulsion Laboratory, 4800 Oak Grove Rd., Pasadena, CA 91109, U.S.A. 4 Institute of Oceanography, National Taiwan University, P.O. Box 23-13, Taipei 106, Taiwan 5 Institute of Marine Environmental Chemistry and Ecology, National Taiwan Ocean University, 2 Pei-Ning Road, Keelung, Taiwan 6 Institute of Hydrological and Oceanic Sciences, National Central University, Jhongli 32001, Taiwan 2

(Received 1 October 2008; in revised form 18 July 2009; accepted 20 July 2009)

The South China Sea (SCS) exhibits strong variations on seasonal to interannual time scale, and the changing Southeast Asian Monsoon has direct impacts on the nutrients and phytoplankton dynamics, as well as the carbon cycle. A Pacific basin-wide physical-biogeochemical model has been developed and used to investigate the physical variations, ecosystem responses, and carbon cycle consequences. The Pacific basinwide circulation model, based on the Regional Ocean Model Systems (ROMS) with a 50-km spatial resolution, is driven with daily air-sea fluxes derived from the National Centers for Environmental Prediction (NCEP) reanalysis between 1990 and 2004. The biogeochemical processes are simulated with the Carbon, Si(OH)4, Nitrogen Ecosystem (CoSINE) model consisting of multiple nutrients and plankton functional groups and detailed carbon cycle dynamics. The ROMS-CoSINE model is capable of reproducing many observed features and their variability over the same period at the SouthEast Asian Time-series Study (SEATS) station in the SCS. The integrated airsea CO2 flux over the entire SCS reveals a strong seasonal cycle, serving as a source of CO2 to the atmosphere in spring, summer and autumn, but acting as a sink of CO2 for the atmosphere in winter. The annual mean sea-to-air CO2 flux averaged over the entire SCS is +0.33 moles CO 2 m–2year–1, which indicates that the SCS is a weak source of CO2 to the atmosphere. Temperature has a stronger influence on the seasonal variation of pCO2 than biological activity, and is thus the dominant factor controlling the oceanic pCO2 in the SCS. The water temperature, seasonal upwelling and Kuroshio intrusion determine the pCO2 differences at coast of Vietnam and the northwestern region of the Luzon Island. The inverse relationship between the interannual variability of Chl-a in summer near the coast of Vietnam and NINO 3 SST (Sea Surface Temperature) index in January implies that the carbon cycle and primary productivity in the SCS is teleconnected to the Pacific-East Asian large-scale climatic variability.

Keywords: ⋅ Carbon cycle, ⋅ South China Sea, ⋅ physicalbiogeochemical modeling, ⋅ seasonal and interannual variability.

1. Introduction The ocean has been recognized as the largest of the carbon reservoirs on annual to millennial time-scales and ocean carbon cycle exhibits significant spatial and temporal variability (Takahashi et al., 2002). Globally, the

* Corresponding author. E-mail: [email protected] Copyright©The Oceanographic Society of Japan/TERRAPUB/Springer

703

China

20 N

Taiwan Taiwan Strait Luzon Strait

Gulf of Tonkin

Pacific SEATS

15 N

Luzon

25 N

Mindoro Strait

Vietnam

10 N Gulf of Thailand

Sunda Shelf

5N

Karimata Strait

105 E

110 E

Sulu Sea

Borneo 115 E

120 E

125 E

Fig. 1. Map of the South China Sea. Star shows the location of SouthEast Asia Time-series Station (SEATS).

oceanic uptake of anthropogenic CO2 is estimated as ~2 Pg C year–1 (Takahashi et al., 2002; Sabine et al., 2004; Fletcher et al., 2006). As those global ocean estimations have not accounted fully for the carbon fluxes and dynamics on the continental margins, where sediment-water interactions are significant, the terrestrial inputs and human-induced perturbations are consdierable; the importance of the continental margins in the global carbon budget has been pointed out repeatedly (Thomas et al., 2004; Chen, 2004; Bates, 2006; Jahnke, 2009). Based on the sparse and limited observations, Cai et al. (2006) showed that the continental shelves can be either a large sink or a source for atmospheric CO 2, and they suggested that those shelves could generally be a sink for atmospheric CO 2 at mid- to high-latitudes and a source of CO2 at low latitude. However, previous fieldwork and syntheses have only focused on a few locations and the role of the vast majority of the shelves in the carbon budget remains unclear. As it is one of the biggest marginal seas in the western Pacific Ocean, understanding carbon cycle in the South China Sea (hereafter referred to as the SCS), therefore, helps us to quantify the role of marginal seas in the global carbon cycle. Isolated from the western Pacific Ocean by a chain of islands, the SCS (0°–25°N, 100°–125°E) constitutes vast continental shelf area (Shelves of Tonkin, Gulf of Thailand and Sunda shelf) and an abyssal basin more than 5000 m deep (named as the SCS basin) (Fig. 1). The water is exchanged between the SCS and the Pacific Ocean via the channels between islands, but mainly through the

704

F. Chai et al.

Taiwan Strait, the Luzon Strait, the Mindoro Strait, and the Karimata Strait (Xue et al., 2004; Fang et al., 2005; Liu et al., 2009). Kuroshio, the western boundary current, penetrates the SCS with a net annual mean flow of 2.0 Sv (Sv =106 m3s–1) through the Luzon Strait (Xue et al., 2004). All the other straits have water depths of less than 100 m, and the heat and salt exchanges are limited. Outflow is primarily through the Taiwan Strait to the north all year-round. Flows vary seasonally through the Mindoro Strait in the east and the wide Karimata Strait in the south with small net annual mean fluxes (Xue et al., 2004). The physical and chemical properties remain essentially constant below 2200 m in the SCS (Gong et al., 1992; Metzger and Hurlburt, 1996; Chen et al., 2001). Situated in between the Tibetan Plateau and the West Pacific Warm Pool (WPWP), the complicated dynamics of the airflow in the SCS region is controlled by the combination of the seasonally reversing East Asian Monsoon and the complex geometry (Wong et al., 2007). The East Asian Monsoon systems prevail over the SCS with wellpronounced seasonality: the southwesterly monsoon starts in June and lasts until August in the summer, whereas the northeasterly monsoon starts in October and predominates over the winter and early spring (Chu and Fan, 2001). The transition periods are in April–May for the southwesterly monsoon and September for the northeasterly monsoon. The alternating monsoons in winter and summer lead to the transformation of the upper circulation in the SCS (Wu et al., 1998; Xue et al., 2004; Su, 2004). The basin-wide circulation pattern displays a large cyclonic gyre persists throughout the year in the northern half of the SCS, while the circulation in the southern half is predominantly cyclonic in winter and anti-cyclonic in summer (Takano et al., 1998; Wu et al., 1998; Chai et al., 2001; Su, 2004; Wang et al., 2005). Besides the seasonal cycle, the SCS circulation also exhibits interannual variation related to ENSO (El Niño/the Southern Oscillation) events (Chao et al., 1996; Wu et al., 1998; Kuo et al., 2004; Straub et al., 2006; Fang et al., 2006). Any assessment of long-term trends in oceanic pCO2 is complicated by the large seasonal variability of the total inorganic carbon due to physical processes (such as vertical and horizontal mixing, upwelling) and biological production (Bates et al., 2002; Dore et al., 2003; Brix et al., 2004; Takahashi et al., 2006). Long-term observations at several ocean time-series stations show upward trends of dissolved inorganic carbon (DIC) and pCO2 responding directly to the increase of anthropogenic CO2 in the atmosphere (Bates, 2001; Gruber et al., 2002; Takahashi et al., 2006). SEATS, see Fig. 1 for the location, was initiated in September 1998 by the National Center for Ocean Research (NCOR), Taiwan, to understand both the long-term and the short-term variations in the biogeochemical processes induced by either periodic

or episodic physical forcing in the SCS (Wong et al., 2007). Based on the observed SEATS data, the monsoondriven primary production, phytoplankton blooms, carbon isotopic composition of suspended and sinking particulate organic matter have been investigated (Liu et al., 2002, 2007a, 2007b; Chen et al., 2006). The fugacity of CO2 fluctuated between 340 and 400 µatm, following the temporal changes in temperature closely and increasing over time at a rate of ~4 µatm yr–1 (Chou et al., 2005; Tseng et al., 2007). Several other fieldworks on air-sea carbon exchange have been conducted at sub-regions in the SCS: the Pearl River estuary and the northern SCS shelf is a source of CO2 to the atmosphere; the Kuroshio intrusion region in the eastern SCS is a source of CO2 in the summer; the Taiwan Strait is a weak source in summer and a sink in the winter (Zhang et al., 2000; Rehder and Suess, 2001; Cai et al., 2004; Dai et al., 2004; Zhai et al., 2005a, b). Wong et al. (2007) reviewed the biogeochemical studies in the SCS and documented the fact that the SCS is actually a source of CO2 to the atmosphere in spring, summer and fall, but serving as a sink of atmospheric CO2 in winter. The winter invasion of CO2 is large enough to balance the evasion of CO2 in the other three seasons so that there is a small net annual exchange of CO2 between this marginal sea and the overlying atmosphere. Field observation alone is not enough for a comprehensive investigation on the spatial-temporal variability of carbon fluxes, which is also limited by the methods used to extrapolate into un-sampled areas and time. Physical-biogeochemical models, linking the physics and ecosystem components with the carbon cycle, are useful tools to fill the gaps in space and time between the observations and model results for estimating the carbon fluxes. Therefore, physical-biological models, once their performance has been evaluated with available observations, would provide alternative means of investigating factors in regulating air-sea CO2 flux. A basin-wide physical model is necessary for simulating seasonal and interannual variations of physical and biogeochemical property exchanges between the SCS and the western Pacific Ocean. In this context, a 3D physical-biogeochemical model has been used to assess the role of physical and biological controls of carbon fluxes. Analyses of the model results for the SCS are presented with focus on model-data comparison at the SEATS timeseries station in the northern SCS (18°N, 116°E). The overall goal is to elucidate quantitatively the seasonal to interannual variability of the air-sea CO2 flux and the factors regulating carbon fluxes in the SCS. The organization of this paper is as follows. A brief outline of the physical-biogeochemical model (ROMSCoSINE) and the data (SEATS) are introduced in Section

2. The Results section (Section 3) focuses on model and data comparison. The Discussion section (Section 4) addresses the modeled seasonal to interannual variability of carbon fluxes. The Summary section (Section 5) is presented at the end. 2. Model and Data 2.1 Coupled physical-biogeochemical model The physical model for this study is based on the Regional Ocean Model System (ROMS), which represents an evolution in the family of terrain-following verticalcoordinate models. It solves the primitive equations with hydrostatic and Boussinesq approximations. ROMS contains innovative algorithms for advection, pressure-gradient force, free-surface variations, and K-profile vertical-mixing parameterizations for surface and bottom boundary layers (Large et al., 1994; Shchepetkin and McWilliams, 1998, 2003). Wang and Chao (2004) have configured the ROMS circulation model for the Pacific Ocean (45°S to 65°N, 99°E to 70°W) at 50-km resolution, with realistic geometry and topography. There are 20 vertical levels. For this biogeochemical modeling study, we followed the approach of Wang and Chao (2004) for setting up the circulation model. Near the two closed northern and southern walls, a sponge layer with a region of 5° from the walls is applied for temperature, salinity, and nutrients. The treatment of the sponge layer consists of a decay term κ(T*–T) in the temperature equation [κ(S*–S) for salinity equation, κ(N*–N) for nutrient and carbon equations], which restores the modeled variables to the observed temperature T* (salinity S*, nutrients N*) field at the two closed walls. The value of κ varies smoothly from 1/30 day–1 at the walls to zero at 5 degrees away from them. The biogeochemical model is based on the Carbon, Si(OH)4, Nitrogen Ecosystem (CoSINE) model developed by Chai et al. (2002) and Dugdale et al. (2002) (Fig. 2). The CoSINE model includes silicate, nitrate, and ammonium, two phytoplankton groups, two grazers, and two detrital pools. Below the euphotic zone, sinking particulate organic matter is converted to inorganic nutrients by a regeneration process similar to the one used by Chai et al. (1996), in which organic matter decays to ammonium and then is nitrified to NO3. The CoSINE model has been well used in studies of decadal variability in primary production of the North Pacific (Chai et al., 2003), nutrient and pCO2 distributions in the equatorial Pacific (Jiang and Chai, 2005), responses of diatom productivity and biogenic silica export to iron enrichment in the equatorial Pacific (Chai et al., 2007), and low Si– high-nitrate low-chlorophyll condition and the sizefractionated new and regenerated production rates of the equatorial upwelling zone (Dugdale et al., 2007). Re-

Seasonal and Interannual Variability of Carbon Cycle in South China Sea

705

Fig. 2. The inter-compartmental flow chart of the ecosystem and linkage to physical processes in the euphotic zone. The flow of nitrogen is indicated by black line; the flow of silicon is indicated by red line.

cently, the ROMS-CoSINE model output has been used for an inter-model comparison study of air-sea CO2 flux for the Pacific Ocean (McKinley et al., 2006). Liu and Chai (2009a) used the ROMS-CoSINE model output to analyze the seasonal and interannaul variability of the circulation and its impact on biological productivity in the Japan Sea. Also using the same model output, Liu and Chai (2009b) analyzed nutrient transport and its impact on biological productivity on seasonal and interannual time scales in SCS. Liu and Chai (2009b) also investigated the factors controlling the new production (uptake of nitrate) and export of carbon in SCS, and derived the SCS basin-wide averaged f-ratio of 0.33 and eratio of 0.24. Since the sea surface pCO2 is calculated from the modeled surface temperature, salinity, silicate, phosphate, nitrate, total CO2 (TCO2) and total alkalinity, we need to include total alkalinity in our calculation. The total alkalinity has been incorporated according to the OCMIP-2 approach (Ocean Carbon-Cycle Model Intercomparison Project, Phase 2: http://www.ipsl.jussieu.fr/OCMIP/ phase2). Since the current CoSINE model does not include the calcifying plankton groups or the dissolution process of calcium carbonate, we linked the time rate of change for total alkalinity with the time rate of change for nitrate. By doing so, we used the biological removal of nitrate in the euphotic zone and the remineralization process at depth as a proxy for the calcification process near the surface and the dissolution of calcium carbonate at depth. 706

F. Chai et al.

Initialized with climatological temperature, salinity, and nutrients from the World Ocean Atlas (WOA) 2001 (Ocean Climate Laboratory National Oceanographic Data Center, 2002), the Pacific ROMS-CoSINE model has been forced with the climatological air-sea fluxes calculated using the bulk formula for several decades in order to reach quasi-equilibrium. Total CO2 and total alkalinity were initialized by interpolation of the GLobal Ocean Data Analysis Project (GLODAP) data set (http:// cdiac.esd.ornl.gov/oceans/glodap/Glodap_home.htm), adjusted for the values for 1990. The ROMS-CoSINE model is then integrated for the period of 1990–2004 forced with daily air-sea fluxes of momentum, heat, and freshwater derived from the NCEP/NCAR reanalysis (Kalnay et al., 1996). The surface wind stress is calculated from the 10 m wind based on Large and Pond’s (1982) bulk formula. The heat flux is derived from the short- and long-wave radiations, the sensible and latent heat fluxes that are calculated using the bulk formula with prescribed air temperature and relative humidity. The fresh-water flux is derived from the prescribed precipitation and the evaporation converted from the latent heat release. River discharges are not included in the model configuration. The monthly averaged results are used in this analysis, focusing on the period of 1990–2004. 2.2 SEATS observations The SEATS station was established and maintained by NCOR with support from the National Science Council of Taiwan. The first observations were carried out on

Fig. 3. Comparing modeled Chl-a (a, e), nitrate (b, f), silicate (c, g), and Total CO 2 (d, h) with observational data at SEATS in January and July 2000.

board the R/V Ocean Researcher III bimonthly from September 1999 to July 2000, then about four times per year afterwards onboard R/V Ocean Researcher III or Ocean Researcher I (Wong et al., 2007). The sampling program at SEATS approximately followed the scheme developed at HOTS (Karl and Lukas, 1996). Water samples were collected from the surface down to 2500 m at 25 depths. In the top 200 m of the water column, samples were collected at 10 depths. Aside from the measurements of temperature, salinity, fluorescence and PAR, discrete water samples were collected for determination of dissolved oxygen, phosphate, nitrate, silicate, chlorophyll a, total dissolved inorganic carbon (TCO2) and alkalinity. More detailed on observational methods, instruments, and data analyses have been presented in publications by Tseng et al. (2007), Wong et al. (2007), and Chou et al. (2007). 3. Results 3.1 Vertical profiles of Chl-a, nitrate, silicate, and TCO2 First, we evaluate the model performance by directly comparing the model results with the available SEATS observations. Figure 3 compares ROMS-CoSINE model simulated Chl-a, nitrate, silicate, and TCO2 profiles with SEATS field data collected in January and July 2000. The modeled chlorophyll concentration is derived from the phytoplankton biomass concentration (mmol N m–3), converted to mg m–3 using a nominal gram chlorophyll to molar nitrogen ratio of 1.67, corresponding to a chlorophyll to carbon mass ratio of 1/50 and a C/N molar ratio

of 7.3. In the SCS, the Chl-a concentration tends to be low in both January and July (Figs. 3(a) and (e)). In January 2000, the concentration of Chl-a is in the low range 0.1– 0.7 mg m–3 in the water column (Fig. 3(a)). It is less than 0.2 mg m–3 at the surface, increases with the depth to about 60 m to reach a sub-surface maximum value of 0.7 mg m–3. From 60 m to 100 m water depth, the modeled Chl-a decreases sharply to 0.1 mg m–3. In July 2000, the concentration of Chl-a is in the low range 0.1–0.4 mg m–3 in the water column (Fig. 3(e)). Similarly as in January 2000, it is less than 0.2 mg m–3 at surface, increases with the depth and reaches its sub-surface maximum value of 0.4 mg m–3 at 60 m. The model reproduces the observed Chl-a profile reasonably well in January, but the modeled subsurface maximum value at 60 m depth in July is less than in the data. The discrepancy between the model and the data could result from a number of factors, including model error or inconsistent sampling resolutions between the model and the observations. The modeled results are monthly averaged while the observation data are obtained at a specific time in January and July 2000. The present model uses a constant conversion factor between the phytoplankton biomass and chlorophyll, which should be a variable that depends upon the light and nutrient conditions (Geider et al., 1998; Liu et al., 2007a). In the modeling paper by Liu et al. (2007a), they incorporated the photo-adaptation into an ecosystem model with one phytoplankton functional group, and showed that it is necessary to use a variable chlorophyll-


707

(a)

(c)

(b)

(d)

Fig. 4. ROMS-CoSINE model of Nitrate (a), silicate (b), TCO2 (c), and Total alkalinity (d) vs. SEATS data from September 1999 to July 2000.

to-nitrogen ratio in order to reproduce the subsurface chlorophyll maximum. By incorporating phytoplankton photoadaptation into our current ecosystem model, we expect to improve the modeled vertical profile of chlorophyll and compare with the observations better. As the first order comparison, the model reproduces the vertical profile of chlorophyll sufficiently well, especially during the winter, which indicates that the model captures the large scale and monthly mean conditions of the key physical and biological processes in the SCS. The nitrate/silicate concentration at SEATS in January and July 2000 ranges from 0 near the surface, to 40/ 150 µM at 3000 m, showing an increase with depth (Figs. 3(b), (c), (f), (g)). The model captures the vertical distribution of nitrate and silicate concentration very well for January and July 2000 at SEATS. The vertical distribution of TCO2 increases with depth both in winter and summer in the SCS (Figs. 3(d) and (h)), ranging from 1900 µmol kg –1 at the surface to 2150 µ mol kg–1 at 300 m water depth. For the further validation, Fig. 4 shows the linear regressions of nitrate, silicate, TCO2 and total alkalinity between the ROMS-CoSINE model result and the SEATS bimonthly observations from September 1999 to July 2000 (September and November 1999, January, March, May, and July 2000). There are 201 vertical profiles of nitrate, silicate, TCO2 and total alkalinity, which sampled from the surface down to 2500 m. We only compared the ob708

F. Chai et al.

Fig. 5. Comparing modeled Sea-to-Air CO2 flux (solid line) with observational data at SEATS data (dash line with square) during 1999–2003.

servations and the model results for the top 300 m. The linear regression slopes for the nitrate and silicate concentrations are 0.9 and 1.1, respectively, indicating the model is able to reproduce the absolute values for both nutrients. Although high R-square values are obtained for all four variables, 0.93 for the nitrate and 0.90 for silicate, 0.93 for TCO2, and 0.92 for the total alkalinity, the linear regression slopes for TCO2 and total alkalinity are

(a)

(b)

(c)

(d)

Fig. 6. The modeled surface climotological distributions of pCO2 (a: February; b: May; c: August; d: November). The unit is µatm.

significantly smaller than 1. One likely reason could be the lack of calcium carbonate dissolution in the model. Since the effects of carbonate dissolution on TCO2 and total alkalinity are at a ratio of 1:2, we expect that the slope of total alkalinity is smaller than that of TCO 2. Apparently, the results are consistent with this expectation. Moreover, the error may result from using the Redfield ratio stoichimometry of C/N = 7.3 to convert nitrogen to carbon in the model. In the SCS, the observed C/N ratios range from 5.5 to 11.4 (Liu et al., 2007b). 3.2 Sea-to-air CO2 flux The CO2 gas transfer across the sea surface and the direction of the net transfer of CO2 is controlled by the difference between the partial pressure of carbon dioxide (pCO2) in the surface water and that in the air. Based on the air-sea pCO2 difference, the modeled sea-to-air CO2 flux is compared with the 4-year SEATS data (Fig. 5). Sea-to-air CO2 flux is in a range of –4 to 2 mol C m–2yr–1 with pronounced seasonal variations. Positive sea-to-air CO2 flux occurs from March to October with a maximum of 1.0 mol C m–2yr –1 in July, and thus the SEATS location is a source that releases CO2 into the air during this

period of the year. From November to February, the SEATS site serves as a sink of atmospheric CO2. The maximum downward flux of CO2 occurs in December with a value of about –2.0 mol C m–2yr–1. Similar seasonal variations were observed in the Taiwan Strait and Kuroshio, the northern and eastern bounds of the SCS (Zhang et al., 2000; Rehder and Suess, 2001; Wong et al., 2007). The basic variability of sea-to-air CO2 flux at SEATS has been captured by the model both in the magnitude and phase of seasonal variation, although the model shows multiple small peaks in the seasonal variation of sea-to-air CO2 flux. Semi-annual variations of the Sea Surface Temperature (SST) in the northern SCS (Xie et al., 2003) are responsible for these small peaks in the seasonal cycle of sea-to-air CO2 flux, which controls the sea surface pCO2 calculation. The relatively low temporal resolution of observational sampling cannot capture any sub-seasonal variability. Another discrepancy between the model results and the observations is that the CO2 flux is slightly overestimated in the winter of 1999 and slightly underestimated in the summer of 2001, which could be resulted from the time mismatch between the monthly averaged model outputs and the short term variability in


709

(a) SST

(b) pCO2

pCO2 Chl-a

Fig. 7. Domain averaged climatologic seasonal variation of pCO2 (solid) and SST (dash) (a), pCO 2 (solid) and Chl-a (dash) (b) in 1990–2004.

the data, since the data are instantaneous fluxes estimated from the dissolved carbonate species. Beside the seasonal cycle, there are mesoscale eddy activities in the northern SCS (Wu and Chiang, 2007), which influence the hydrodynamics and carbon fluxes at SEATS. The current physical-biological model has a 50-km resolution, which could not resolve these mesoscale eddies in the model. In turn, this would lead to model-data mismatch from time to time. 4. Discussion 4.1 Seasonal variation of pCO 2 In this section, monthly averaged value, from January 1990 to December 2004, is processed to obtain the climatological seasonal variation. The monthly spatial distribution of pCO2, and the seasonal variations of pCO2, SST and Chl-a at the surface are analyzed. The modeled climatological pCO2 shows strong seasonal variations (Fig. 6). In February, from south to north, pCO2 decreases with the increasing latitude—from 410 µatm at the low latitude to 310 µ atm in the north (Fig. 6(a)). The value of >400 µatm is located in the southern section of the SCS, while it is less than 350 µatm north of 18°N. Except a small region near the west coast of Borneo, the pCO2 is lower than 370 µatm, indicating the CO2 gas transport is from the air to the sea in most regions of SCS in February. In May, as shown in Fig. 6(b), the pCO2 reaches the highest value comparing to the other three months in the SCS. The higher value (~460 µatm) appears in the southern section of the SCS, and the lower value, less than 400 µatm, locates in the northern section of the SCS. Since the lowest pCO2 in May is above 390 µatm, which is still higher than that of the atmosphere, the CO2 flux is from the sea to the air for the entire region of the SCS. In August (Fig. 6(c)), the pCO2 decreases, lower than that in May, but still higher than that in February. Unlike the spatial pattern both in February and in May, it varies largely from region to region: low off the east coast of Vietnam and the west coast of Luzon Island 710

F. Chai et al.

with less than 385 µatm, whereas values are greater than 390 µatm in the rest of the coastal regions. Such low pCO2 values near the coastal regions are caused by a combination of cold upwelling water and enhanced biological productivity. Therefore, the exchange of CO2 between the sea and the air varies from region to region in August. In November (Fig. 6(d)), the spatial distribution of pCO2 is similar to the patterns in February (Fig. 6(a)), higher at the low latitude and lower in the north. A small region with the value above 380 µatm is located near the west coast of Borneo, while it is less than 370 µatm in the middle and northern section of the SCS. Except the regions off the west coast of Borneo, the CO2 flux is from the air to the sea in the middle and northern region of the SCS in November. Overall, the climatological monthly spatial pCO2 distribution in the SCS varies geographically, especially during spring and summer. The regional physical and biologeochemical mechanisms of controlling pCO2 in the SCS will be explored further in Subsection 4.4. Next, we integrated the surface variables (pCO2, SST, and chlorophyll) over the entire SCS region to examine the seasonal variation (Fig. 7). The SCS domain-averaged monthly pCO2 varies between 350 and 420 µatm with the highest value in May. The second small peak of pCO2 occurs in September with a value of 395 µatm. The lowest pCO2 is in December and January. The averaged atmospheric pCO2 for the same period of 1990 to 2004 is about 373 µatm, hence the SCS is a weak source of CO2 to the atmosphere in the spring, summer and fall, but a sink for the atmosphere CO2 in the winter, which agrees with the conclusions of Wong et al. (2007). The SCS domain-averaged monthly SST in the SCS is in the range of 25–30°C (Fig. 7(a)). Like the seasonal variation trend of pCO2, the SST reaches the highest value in May with 30°C and another relatively smaller peak in September with a value of 29°C. The lowest SST value is in December and January. The seasonal cycle of pCO2 is closely linked with temperature variation, high in sum-

Fig. 8. pCO2 sensitivity experiments to SCS domain averaged sea surface temperature (SST), TCO 2, and Total Alkalinity (Talk). In the “Standard” experiments, surface pCO2 is calculated with monthly averaged SST, TCO2, and Talk. For the experiment of fixed SST, the annual mean SST is used, but monthly averaged TCO2 and Talk are used. For the experiment of fixed TCO2, the annual mean TCO2 is used, but monthly averaged SST and Talk are used. For the experiment of fixed Talk, the annual mean Talk is used, but monthly averaged SST and TCO2 are used. In the fixed SST experiment, the pCO 2 differs the most from the Standard experiment, which indicates the pCO2 is very sensitive to the changes of SST.

mer and low in winter. Both pCO2 and SST show a double peak in the seasonal cycle, one in May and a secondary one in September. The SST exhibits a cooling period in August, which corresponds to the pCO2 decrease. The seasonal cycle of Chl-a in the SCS varies from 0.02 to 0.2 mg m–3 (Fig. 7(b)). It also has double peaks: one is in January with concentration of 0.2 mg m–3 and another one is in August with concentration of 0.1 mg m–3. The winter is the most productive season of the year in the SCS, which is in agreement with the findings of Liu et al. (2002). The seasonal variation of Chl-a is consistent in both magnitude and phase with the previous studies of Chen (2005) and Tseng et al. (2005). An inverse relationship exists between the pCO2 and Chl-a concentration. That is, with higher biological productivity, more CO2 is removed from the surface water, which decreases pCO2. The in-phase relationship between pCO2 and SST and out-of-phase between pCO2 and Chl-a suggest that pCO2 is regulated by both SST and the biological productivity in the SCS. The strong mixing in winter entrains nutrients and DIC from deep water to the upper layer. High nutrients stimulate the photosynthesis, and the enhanced biological production consequently removes part of the carbon from the euphotic zone. The net result depends upon the amount of DIC brought to the surface, the in-

creased CO2 solubility due to low temperature in winter, and the carbon consumption due to the enhanced productivity. The combined effect results in the SCS as a sink for the atmosphere CO2 in winter. Although the strong mixing entrains DIC to the surface layer in winter, pCO2 still has the lowest value, which indicates the biological utilization has a stronger influence to draw down the carbon than the vertical mixing effect that brings higher DIC concentration water from the depth. In the summer, the water stratification prevents high nutrients and high DIC water from below (except at the upwelling regions). The photosynthesis is limited by the lack of nutrient supply, and thus less CO2 is removed by biological productivity in the summer. Meanwhile, warm water in the summer results in high surface pCO2 value in the SCS. Therefore, the SCS releases CO2 to the atmosphere and acts as a source to the atmospheric CO2. Since SST and the biological utilization regulate the sea-to-air carbon fluxes in the SCS, it’s necessary to separate effects of these two factors in controlling pCO2 value. The pCO2, hence the exchange of CO2 with the atmosphere, is determined primarily by temperature, salinity, TCO2 and total alkalinity concentration. The water temperature is regulated by solar radiation, air-sea heat exchanges, and mixed-layer depth. TCO2 and total alkalinity concentration, beside the physical processes, are also controlled by biological productivity and respiration. Generally, the effects of biological draw down and temperature effects on surface pCO2 vary greatly from region to region. Therefore, the oceanic uptake and release of CO2 are governed by a balance between the changes in temperature, net biological utilization of CO2, and the upwelling flux of subsurface waters with higher CO2 concentration (Feely et al., 2001). In order to isolate each of these effects, a series of sensitivity experiments was conducted by setting one factor as a constant with annual mean value for basin-wide averaged SST, salinity, TCO2, and total alkalinity, respectively. The standard experiment is based on the seasonal cycle of monthly averaged SST, salinity, TCO2, and total alkalinity. The seasonal cycle of pCO2 ranges from 355 to 420 µatm in the standard experiment, low in the winter and high in the summer. When fixing the SST, seasonal variation of pCO2 reduces significantly (Fig. 8). The seasonal amplitude of pCO2 is between 380 and 405 µatm, higher from December to April, and lower from May to November than the values in the standard experiment. Since the annual mean SST is higher in the winter and lower in the summer than the standard seasonal values of SST (Fig. 7(a)), the seasonal cycle of pCO2 responds to the SST changes accordingly. The substantial reduction in the seasonal amplitude of pCO2 implies that the SST strongly regulates the pCO2 variation. When setting the TCO2 and total alkalinity to their annual mean values, the seasonal


711

Fig. 9. For the period of 1990–2004, monthly averaged surface anomaly of TCO2 (a) and pCO2 (b), and linearly de-trended anomaly of TCO 2 (c) and pCO 2 (d) at SEATS.

variation of pCO2 shows a similar trend as the standard result (Fig. 8). These two experiments indicate that TCO2 and total alkalinity have less influence on pCO 2 . The amplitudes of pCO 2 annual variation (∆pCO 2 = ∆pCO2max – ∆pCO2min) are 64 µatm for the standard experiment, and 26, 83, and 56 µatm in the experiments with annual mean of SST, TCO2, and total alkalinity, respectively. The amplitude of ∆pCO2 decreased by 38 µatm when the SST was set to its annual mean value. Only by a small amount of 8 µatm when the total alkalinity was set to a constant, whereas it increased by 19 µatm when TCO2 was set to its annual mean value (Fig. 8). The experiment with constant salinity had only a minor effect, which is not shown here. Using the same method given by Takahashi et al. (2002), the relative importance of the temperature effect on pCO2 to the biological carbon utilization can be expressed as a ratio T/B = ∆pCO2(T=standard)/∆pCO2(T=mean) = 64/26 = 2.46, suggesting that the influence of SST is greater than that of biological activity on the carbon cycle in the SCS. Therefore, the temperature in SCS is the primary driver controlling the pCO2 change, while the biological utilization is only a secondary factor. This is quite different comparing to the eutrophicated coastal waters, such as the southern bight of the North Sea, where biological activity has a stronger influence on the seasonal pCO2 cycle than the temperature (Gypens et al., 2004). 712

F. Chai et al.

Comparing with other regions of the world oceans calculated by Takahashi et al. (2002), the T/B ratio is 2.7 at the BATS in the North Atlantic (32°50′ N, 64°10′ W), 0.02 in the Ross Sea (76.5°S, 169°E–177°W), and 0.9 at the weather Station P in the North Pacific (50°N, 145°W), respectively. The SCS result is similar to the BATS, where the latitudes are similar. The seasonal T/B effects on pCO2 are in the range –15 to 30 µatm as estimated by Takahashi et al. (2002) with a spatial pattern of low in the south and high in the north of the SCS. Although T/B at the northwest coast of Borneo Island is –15 µatm, the entire region averaged results show that the effect of temperature exceeds the biological effect in SCS, which is consistent with our model results. 4.2 Anthropogenic TCO2 and pCO2 increase in the SCS After removing the seasonal variation, the long-term time-series of sea surface TCO2 and pCO2 at SEATS show pronounced interannual variations and clearly upward trends over time from 1990 to 2004 (Fig. 9). The year-toyear variation is in the range of –12 to 12 mmol m–3 for TCO2 (Fig. 9(a)). The time series of the pCO2 anomaly ranges from –25 to 30 µatm (Fig. 9(b)). The linear regression slopes for the surface TCO2 and pCO2 are 0.65 µmoles kg–1yr–1 and 2.0 µatm yr–1, respectively. Since the atmospheric pCO 2 was increasing at rate of 1.5 µatm yr–1 in the Central Equatorial and subtropical North Pacific Ocean as a result of anthropogenic emissions

Table 1. Comparing long-term oceanic CO2 changes at surface layer with other four time-series sites observed: (1) ESTOC (European Station for Time-series in the Ocean Canary Islands), located near Gran Canaria in the NE Atlantic Ocean; (2) ALOHA (A Long-term Oligotrophic Habitat Assessment), located near Hawaii (22°45′ N, 158°W) in the North Pacific Ocean; (3) BATS (Bermuda Atlantic Time-series Study), located near Bermuda (32°10′ N, 64°30′ W) in the NW Atlantic Ocean; (4) Hydro S (Hydrostation S - 32°50′ N, 64°10′ W) and BATS combined, located near Bermuda in the NW Atlantic Ocean. Site ESTOC 29°N, 15°W ALOHA (HOT) 22°45′ N, 158°W BATS 32°N, 64°W Hydro S/BATS 32°N, 64°W SEATS 18°N, 116°E

Measurement period

pCO2

Reference

TCO2 (µmoles kg – 1 yr– 1 )

(µatm yr– 1 )

10/95–12/00

0.4 a) ± 1.6

0.71 ± 5.1

González-Dávila, 2003

10/88–12/02

1.2 b ) ± 0.1

2.5 ± 0.1

Keeling et al., 2004

10/88–12/98

1.60 c) ± 5.6

1.40 ± 5.1

Bates, 2001

05/83–09/01

0.64 a) ± 0.05

1.5 ± 0.1

Gruber et al., 2002

09/99–09/03

0.65

2.0

this paper

a)

DIC data was normalized to constant salinity of 35. DIC data was not seasonally detrended (Gruber et al., 2002; GonzálezDávila et al., 2003). b) DIC data was normalized to constant salinity of 35, close to the average salinity observed at the ALOHA site. DIC data was also seasonally detrended (Dore et al., 2003; Keeling et al., 2004). c) DIC data was normalized to constant salinity of 36.6, close to the average salinity observed at the BATS site. DIC data was not seasonally detrended (Bates, 2001).

(Dore et al., 2003; Takahashi et al., 2003), the SCS waters as a whole follow at a consistent rate of the atmospheric pCO2 increase. According to the observed data at SEATS from 1999 to 2003, Tseng et al. (2007) estimated TCO2 (normalized to constant salinity of 33.5) and pCO2 in the mixed layer increased at rates of 1.5 ± 1.4 µmoles kg–1yr–1 and 4.2 ± 3.2 µatm yr–1, respectively. The modeled increasing rates of surface TCO2 and pCO2 are in the range of their variability, although they increase at slower rates than those of the observed. This discrepancy could be resulted from the fact that the observational estimate is derived within the mixed layer and the modeled results are for the surface value. Also, the estimated rates of increase have very large standard deviations. For the other four long-term time-series stations (namely: ALOHA, BATS, Hydrostation S, ESTOC), the upward trends of salinity-normalized, dissolved inorganic carbon (nDIC) and seawater pCO2 were summarized in Table 1. The modeled TCO2 increasing rate in the SCS (0.65 µ moles kg –1 yr –1 ) is close to the rate of 0.64 µmoles kg–1yr–1 at Hydrostation S and BATS combined (Gruber et al., 2002), and the modeled pCO2 increasing rate is between those at BATS and HOTS. After removing the best-fit linear regression trend from the time series of anomalous sea surface TCO2 and pCO2, the remaining signals are rather complex (Figs. 9(c) and (d)). The TCO2 anomaly has the lowest value of –12

mmol m–3 in the strong El Niño year of 1998, but the highest value of 14 mmol m–3, in the winter of 1991, which is a moderate El Niño year. Such interannual changes of TCO2 and pCO2 are due to the changes of circulation in the SCS and exchanges of water masses with the surrounding western Pacific. Further discussion on the causes of interannual variability is presented in the following sections. 4.3 Interannual variation of air-sea CO2 flux in the SCS In addition to the single point assessment, the seato-air CO2 flux was averaged over the entire SCS (1.5°– 24.5°N, 104°–121.5°E) and evaluated from 1990 to 2004 (Fig. 10). The annual minimum of sea-to-air CO2 flux in December varies between –0.35 and –1.25 mol C m–2yr–1, with the highest value of –0.35 mol C m–2yr –1 in 1997 and the lowest value of –1.25 mol C m–2yr –1 in 1995 and 1999. The cause of strong interannual variation of CO2 flux is most likely related to changes in wind forcing, thermal structure, and biological activity in the upper ocean. The weakest CO2 uptake occurred in December 1997 (Fig. 10) coinciding with the strong positive SST anomaly and negative sea surface chlorophyll anomaly observed at the SEATS station in December, 1997 during the strongest El Niño event in recent history (Tseng et al., 2009). Both anomalies disfavored drawdown of CO2 in surface water. It is likely that such conditions prevailed


713

Sea-to-air CO2 Flux SST

CO2 Flux (mol C m-2 yr-1)

SST (°C)

(a)

in the major portion of the SCS resulting in the basin wide weakening of CO 2 drawdown in winter. The annual mean sea-to-air CO2 flux (dash line in Fig. 10) of each year varies from 0.2 to 0.5 mol C m–2yr–1 from 1990 to 2004, indicating that the SCS is a weak source of CO 2 to the atmosphere. The 15-year mean CO2 evasion to the air predicted by the ROMS-CoSINE model is about 0.33 mol C m–2yr–1 averaged over the entire SCS, which is equivalent to a regional total of 8.25 Tg of carbon per year from the ocean to the atmosphere (1 Tg C = 1 × 1012 g C). Based on the observed pCO2 at SEATS, the annual mean sea-to-air CO 2 flux is estimated of –0.02 ± 1.06 mol C m–2yr–1 during 1999–2003 by Tseng et al. (2007). Chou et al. (2005) estimated the sea-to-air CO2 flux of –0.11 ± 0.08 mol C m–2yr –1 and –0.23 ± 0.18 mol C m–2yr –1 during 2002 and 2003, respectively. For the period of 1990–2004, the modeled annual mean seato-air CO 2 flux at the SEATS location is –0.04 mol C m–2yr–1, which is in good agreement with those observations. Both modeled and observed pCO2 show that the SEATS location is a very weak sink for the atmospheric CO 2. Zhai et al. (2005b) estimated the sea-to-air CO2 flux of 0.3 mol C m–2yr –1 on the shelf and slope in the northern SCS, which indicates the northern coastal region serves as a CO 2 source to the atmosphere. This opposite direction of sea-to-air CO 2 flux between the SEATS and the northern SCS is likely resulted from the spatial and temporal difference between those studies (Tseng et al., 2007). The SEATS has lower primary productivity in the oligtrophic water, while higher productivity occurs on the shelf and slope in the northern SCS. The biological pump should play an important role for these regional pCO2 differences. Therefore, the mechanisms for creating these regional differences in the SCS need to be investigated. 714

F. Chai et al.

pCO2

Chl-a (mg m-3)

Fig. 10. Domain averaged Sea-to-Air CO2 Flux (mol m–2year–1) (solid line) and its annual mean value (dash line) in the SCS from 1990 to 2004.

pCO2 (µatm)

(b)

Chl-a

Fig. 11. The latitudinal variations of the annual mean of the zonally averaged SST and sea-to-air CO2 flux (a), pCO 2 and Chl-a (b) at the surface.

4.4 SST, pCO2, air-sea CO2 flux, and Chl-a in different regions in the SCS The SCS covers more than 24 degrees in latitude, which has very pronounced north-south gradients in physical and biogeochemical properties. Averaged within the longitude from 104 to 121.5°E, the modeled annual mean SST, pCO 2 , sea-to-air CO 2 flux, and Chl-a are shown in Fig. 11. In general, the zonally averaged annual mean SST is above 25.5°C in the SCS, decreasing with the latitude from the south to the north (Fig. 11(a)). The highest SST is above 29.5°C near the equator. The averaged sea-toair CO2 flux is within ±1.0 mol C m–2yr –1 (Fig. 11(a)). Following the latitudinal trend of the SST, the sea-to-air CO2 flux decreases with the latitude north of 8°N, but it increases with the latitude between 2–8°N (Fig. 11(a)). Such changes indicate the combined temperature and biological effects in regulating the surface pCO2, with the temperature as the primary driver. Using the criteria of sink/source to the atmospheric CO2 (e.g., positive or negative for sea-to-air CO2 flux), the sink/source characteristics in the SCS are separated by the latitude of 18°N, serving as a source in the region south of 18°N, and as a sink north of 18°N (Fig. 11(a)). The zonally averaged annual mean pCO2 in the SCS varies from 350 to 415 µatm (Fig. 11(b)). The pCO2 decreases with the SST decreasing, and this trend follows closely the latitudinal trend of the SST (Fig. 11), which again demonstrates the dominant role of SST in control-

Chl-a (mg m −3)

ling the spatial variation of pCO2. The Chl-a concentration is low in most areas, with the highest value of 0.16 mg m–3 near 10°N (Fig. 11(b)). Generally, it decreases to the north except that there are three pronounced peaks of Chl-a, one occurs between 8 and 12°N with a peak value of 0.16 mg m –3, and two others occur between 18°N and 22°N with peak values of 0.06 mg m–3 and 0.08 mg m–3, respectively. These zonal bands correspond to the Vietnam coastal upwelling and the Kuroshio intrusion near the Luzon Strait. Liu et al. (2002) reported the enhanced production by wind-driven upwelling along the Vietnam coast and by Kuroshio intrusion-induced upwelling in the SCS, which possibly drive the unique physical and biological processes in these regions, and hence lead to the spatial variations of CO2 flux. To examine these regional differences of SST, upwelling, and Kuroshio intrusion in controlling the carbon cycle in the SCS, the SST, pCO2, sea-to-air CO2 flux, and Chl-a from the Vietnam coastal region (106–110°E, 8–12°N) and the northwestern coast off Luzon Island (118–120°E, 18–22°N) were analyzed and compared with the entire SCS averaged values (Fig. 12). The annual mean SST in these regions shows a weak increasing trend from 1990 to 2004 (Fig. 12(a)), which indicates decadal or longer temporal variation. The entire SCS domain aver-

Nino3 SST index (°C)

Fig. 12. Annual Mean SST (a), pCO2 (b), sea-to-air CO2 flux (c), and Chl-a (d) for different regions in the SCS (Black: total region average; Blue: Vietnam upwelling region; Red: Luzon Strait).

Fig. 13. Nino3 SST index in January (Blue line, left axis) and Chl-a off the Vietnam coast of the same year in July but shifted 6 months ahead (red line, right axis, mg m–3).

aged SST is above 27.5°C with the highest value of 28.5°C in 1998 during the strong ENSO event. The annual mean SST is lower by 1°C in the Vietnam coastal region than the domain averaged SST, and lower by another 0.5°C off the northwest coast of Luzon Island (Fig. 12(a)). The annual mean pCO2 from these regions increases


715

consistently with time (Fig. 12(b)), which is due to the anthropogenic atmospheric CO 2 increases during the model simulation period. The pCO2 in the Vietnam coastal region is close to the entire SCS domain averaged value in the range of 375~402 µatm. In contrast, the pCO2 from the coastal region off Luzon Island is lower by 20–30 µatm than the SCS domain averaged value. Following the SST anomalies in 1998–1999, pCO2 also shows a small anomalous increase during the El Niño year, when the warmer temperature was responsible for the pCO 2 increase. The annual mean sea-to-air CO2 flux varies between 0.1 and 0.6 mol C m –2yr–1 in the SCS (Fig. 12(c)), indicating that the SCS as a whole is a weak CO2 source to the atmosphere. The sea-to-air CO2 flux near the Vietnam coastal region varies from 0.3 to 0.8 mol C m–2yr –1 and acts as a stronger CO2 source comparing to the SCS and the northwest off Luzon Island (Fig. 12(c)). The seato-air CO 2 flux near the northwest off Luzon Island varies between –0.8 and 0 mol C m –2yr–1, indicating as a CO2 sink (Fig. 12(c)). In 1998, more CO2 emanates to the atmosphere corresponding to the SST increase in the SCS and its sub-regions, although the CO 2 exchange seems to reach equilibrium between the atmosphere and the ocean near the Kuroshio intrusion region (Fig. 12(c)). The teleconnection of the strong El Niño event in 1998 has enhanced the sea-to-air CO2 flux for the entire SCS and its sub-regions. Unlike the long-term trend of increasing surface pCO2 (Fig. 12(b)), there is no long-term trend in the sea-to-air CO2 flux, which is due to the equilibrium between the oceanic pCO 2 and the atmospheric pCO2. The whole SCS domain averaged annual mean Chla concentration is very low, about 0.10 mg m–3. Higher Chl-a concentration is found in the Vietnam coastal region, ranging from 0.15 to 0.26 mg m–3, more than a factor of two above the whole SCS domain averaged value (Fig. 12(d)). Higher biological productivity in the Vietnam coastal region tends to draw down CO2 concentration (Fig. 12(d)). But the relatively lower temperature (Fig. 12(a)) results in decreasing CO 2 flux to the atmosphere. However, the persistent upwelling near the Vietnam coastal region would also bring high CO 2 concentration water from depth to the surface, which increases CO2 concentration at surface. The combined effects result in higher sea-to-air CO2 flux in the Vietnam coastal region. The upwelling near the Vietnam coast not only stimulates the biological activities by pumping up more nutrients, but also degasses CO2 to the atmosphere. Therefore, the sea-to-air CO2 flux in the Vietnam coastal region is regulated jointly by the biological activities, seawater solubility and upwelling. At the Luzon Strait, the biological productivity is low and it tends to remove less CO 2 (Fig. 12(d)). On the 716

F. Chai et al.

other hand, the upwelling off northwest Luzon Island caused by subsurface convergence of the northward jet off the west coast of Luzon Island (Shaw et al., 1996) is relatively weak, which results in lower TCO2 (Fig. 12(b)). The temperature in this region is 0.5°C lower than temperature near the Vietnam upwelling region (Fig. 12(a)), which increases CO2 gas solubility. It appears that the high CO2 solubility associated with lower temperature and the less biological removal of CO2 outplays the upwelling effect (i.e., bringing higher TCO2 water to the surface) in controlling the sea-to-air CO2 flux near the Kuroshio intrusion region, Fig. 12(c). In 1998, the Chl-a concentration reaches the lowest for the entire SCS and in northwest coast off Luzon Island. But low Chl-a concentration was not found in 1998 near the Vietnam coastal region. This is likely due to averaging over the annual cycle, which obscures seasonal upwelling impacts. The results raise the question: what are the other factors control the large interannual variation of Chl-a concentration in the Vietnam coastal region (Fig. 12(d))? To address this issue, we explore the possible connection between the primary productivity along the Vietnam coast and the ENSO event in the Pacific. A typical El Niño event lasts for about a year, and occurs irregularly about two to seven years. The Nino3 index (Fig. 13) measures the deviation from the normal SST in the Eastern Pacific (5°S–5°N, 150°W–90°W). To show clearly the relationship between Nino3 SST index and Chl-a near the Vietnam coast, we shifted Chl-a concentration 6 months ahead of Nino3 SST index in the same year (Fig. 13). The inverse relationship is well pronounced for the Vietnam coastal region: high Chl-a concentration with low Nino3 index and low Chl-a concentration with high Nino3 index. For example, during the 1998 El Niño (one of the strongest El Niño events) the Nino3 SST index was high whereas the low Chl-a concentration occurred in July at the Vietnam coast. This is primarily due to the decrease of along-shore wind during the summer, which results in weaker upwelling along the Vietnam coast (Xie et al., 2003). Thus, the primary productivity in the Vietnam coast upwelling region is inversely connected with the ENSO events. El Niño leads to weaker upwelling and lower productivity along the Vietnam coast in the SCS. 5. Summary and Conclusions We have constructed a three-dimensional physicalbiogeochemical model (ROMS-CoSINE) to evaluate seasonal and interannual variation of air-sea CO2 flux in the SCS and associated controlling factors. The model is capable of reproducing the observed Chl-a, nutrients, TCO2, Total alkalinity, and air-sea CO2 flux at SEATS station in the northern SCS. The ROMS-CoSINE simulation shows strong seasonal variations as well as interannual varia-

tions of pCO2 and air-sea CO2 flux. The modeled results suggest that the SCS is a CO2 source to the atmosphere in summer and a sink in winter. The annual mean air-sea CO2 flux shows that SCS is a weak CO 2 source to the air, 0.33 mol C m–2yr –1 (8.25 Tg C yr–1). We have conducted sensitivity analysis to identify the factors (SST, TCO2, Total alkalinity) controlling the seasonal cycle of surface water pCO2. The variability of air-sea CO2 flux is controlled by SST and upwelling, the role of the biological activities in regulating surface pCO2 is secondary in the SCS. From 1990 to 2004, TCO2 and pCO2 increase at a rate of 0.67 µmol kg–1yr–1 and 2.02 µatm yr–1, respectively. Although the simulated variation of air-sea CO2 flux tends to be controlled by SST in the SCS, it also shows regional differences. Our model results indicate that the region south of 18°N acts as a CO2 source to the air, whereas the region north of 18°N is a sink. Physical and biological processes in two upwelling regions, the Vietnam coast and near Luzon Strait, are examined and compared. In the Vietnam upwelling region, the high surface pCO2 is determined primarily by higher concentration of TCO2; the effect of low temperature and high biological productivity are secondary. Near Luzon Strait, the lower temperature results in lower pCO2, which produces negative sea-to-air CO2 flux. The model results also show significant correlations of pCO2 and air-sea CO2 flux in the SCS with the ENSO events, attributed to a strong influence of ENSO on upwelling dynamics along the Vietnam coast. The primary productivity in the Vietnam coast upwelling region in July is inversely related to the Nino3 SST index in January, providing evidence that biological productivity in the SCS responds to the Pacific large-scale climatic variability. The ROMS-CoSINE model simulations clearly illustrate the role of physical and biological factors in regulating the air-sea CO2 flux in the SCS. This modeling investigation of carbon cycle in response to seasonal and interannual climate variability, as well as the anthropogenic effects, serves as an example on how future climate change may influence physical and biogeochemical processes in marginal seas, such as the SCS. Remote sensing observations only provide the measurements of ocean surface properties, such as SST and chlorophyll, but it is difficult to reveal any information below the surface. Time series measurements not only provide long-term temporal variation but also offer information on the dynamics below the surface, for example, sub-surface chlorophyll maximum zone and remineralization processes at the depth. This study demonstrates the need for monitoring important variables in a few key locations, and using these observations for constructing and evaluating physical and biogeochemical models. By combining both observations

and model simulations, we can advance our understanding on global carbon cycle and climate change. Acknowledgements This research was supported by a NASA grant (NNG04GM64G) and NSFC (90711006 and 90211021) to F. Chai. NSFC (40376039 and 40531006) also supported G. Liu for her work in analyzing some of the model results. This research was carried out, in part, by the Jet Propulsion Laboratory (JPL), California Institute of Technology, under contract with the National Aeronautics and Space Administration (NASA). The observations of the SEATS program were carried out with support from the National Science Council (Taipei, Taiwan). We are grateful to three reviewers for their comments and suggestions that improved the manuscript greatly. Finally, we thank Drs. Toshiro Saino and James Christian, the co-editors for this special section on the carbon cycle in the North Pacific. Reference Bates, N. R. (2001): Interannual variability of oceanic CO2 and biogeochemical properties in the Western North Atlantic subtropical gyre. Deep-Sea Res. II, 48, 1507–1528, doi:10.1016/S0967-0645(00)00151-X. Bates, N. R. (2006): Air-sea CO2 fluxes and the continental shelf pump of carbon in the Chukchi Sea adjacent to the Arctic Ocean. J. Geophys. Res., 111, C10013, doi:10.1029/ 2005JC003083. Bates, N. R., A. C. Pequignet, R. J. Johnson and N. Gruber (2002): A short-term sink for atmospheric CO2 in subtropical mode water of the North Atlantic ocean. Nature, 420(6915), 489–493. Brix, H., N. Gruber and C. D. Keeling (2004): Interannual variability in the upper ocean carbon cycle at Station ALOHA, Hawaii. Global Biogeochem. Cycles, 18, GB4019, doi:10.1029/2004GB002245. Cai, W. J., M. H. Dai, Y. C. Wang, W. D. Zhai, T. Huang, S. T. Chen, F. Zhang, Z. Z. Chen and Z. H. Wang (2004): The biogeochemistry of inorganic carbon and nutrients in the Pearl River estuary and the adjacent Northern South China Sea. Cont. Shelf Res., 24, 1301–1319. Cai, W. J., M. Dai and Y. Wang (2006): Air-sea exchange of carbon dioxide in ocean margins: A province-based synthesis. Geophys. Res. Lett., 33, L12603, doi:10.1029/ 2006GL026219. Chai, F., R. T. Barber and S. T. Lindley (1996): Origin and maintenance of high nutrient condition in the Equatorial Pacific. Deep-Sea Res. II, 42(4–6), 1031–1064. Chai, F., H. Xue and M. Shi (2001): General circulation and its seasonal variation in the Northern and Central South China Sea. p. 39–56. In Oceanography in China (13)—South China Sea Circulation Modeling and Observations, ed. by H. Xue, F. Chai and J. Xu, China Ocean Press, Beijing. Chai, F., R. C. Dugdale, T.-H. Peng, F. P. Wilkerson and R. T. Barber (2002): One dimensional ecosystem model of the Equatorial Pacific upwelling system, Part I: Model devel-


717

opment and silicon and nitrogen cycle. Deep-Sea Res. II, 49(13–14), 2713–2745. Chai, F., M. Jiang, R. T. Barber, R. C. Dugdale and Y. Chao (2003): Interdecadal variation of the Transition Zone Chlorophyll Front, a physical-biological model simulation between 1960 and 1990. J. Oceanogr., 59, 461–475. Chai, F., M. S. Jiang, Y. Chao, R. C. Dugdale, F. Chavez and R. T. Barber (2007): Modeling responses of diatom productivity and biogenic silica export to iron enrichment in the Equatorial Pacific Ocean. Global Biogeochem. Cycles, 21, GB3S90, doi:10.1029/2006GB002804. Chao, S. Y., P. T. Shaw and S. Y. Wu (1996): El Niño modulatin of the South China Sea circulation. Prog. Oceanogr., 38, 51–93. Chen, C. C., F. K. Shiah, S. W. Chung and K. K. Liu (2006): Winter phytoplankton blooms in the shallow mixed layer of the South China Sea enhanced by upwelling. J. Mar. Sys., 59(1–2), 97–110. Chen, C. T. A. (2004): Exchanges of carbon in the coastal seas. p. 341–350. In The Global Carbon Cycle: Integrating Humans, Climate, and the Natural World, ed. by C. B. Field and M. R. Raupach, Island Press, Washington, D.C. Chen, C. T. A., S.-L. Wang, B.-J. Wang and S.-C. Pai (2001): Nutrients budgets for the South China Sea basin. Mar. Chem., 75, 281–300. Chen, Y. L. L. (2005): Spatial and seasonal variation of nitratebased new production and primary production in the South China Sea. Deep-Sea Res. I, 52, 319–340. Chou, W. C., D. D. D. Sheu, C. T. A. Chen, S. L. Wang and C. M. Tseng (2005): Seasonal variability of carbon chemistry at the SEATS Time-Series Site, northern South China Sea between 2002 and 2003. Terr. Atmos. Oceanic Sci. (TAO), 16(2), 445–465. Chou, W. C., D. D. Sheu, B. S. Lee, C. M. Tseng, C. T. A. Chen, S. L. Wang and G. T. F. Wong (2007): Depth distributions of alkalinity, TCO2 and δ 13C TCO2 at SEATS time-series site in the northern South China Sea. Deep-Sea Res. II, 54(14– 15), 1469–1485. Chu, P. C. and C. W. Fan (2001): A low salinity cool-core cyclonic eddy detected northwest of Luzon during the South China Sea Monsoon Experiment (SCSMEX) in July 1998. J. Oceanogr., 57, 549–563. Dai, M. H., W. D. Zhai, Z. M. Lu, P. H. Cai, W. J. Cai and H. S. Hong (2004): Regional studies of carbon in China—progress and perspectives. Adv. Earth Sci., 19(1), 120–130 (in Chinese). Dore, J. E., R. Lukas, D. W. Sadler and D. M. Karl (2003): Climate-driven changes to the atmospheric CO2 sink in the subtropical North Pacific Ocean. Nature, 424, 754–757. Dugdale, R. C., R. T. Barber, F. Chai, T. H. Peng and F. P. Wilkerson (2002): One dimensional ecosystem model of the Equatorial Pacific upwelling system, Part II: Sensitivity analysis and comparison with JGOFS EqPac data. DeepSea Res. II, 49(13–14), 2746–2762. Dugdale, R. C., F. P. Wilkerson, F. Chai and R. Feely (2007): Size-fractionated nitrogen uptake measurements in the Equatorial Pacific and confirmation of the low Si–high-nitrate low-chlorophyll condition. Global Biogeochem. Cycles, 21, GB2005, doi:10.1029/2006GB002722. 718

F. Chai et al.

Fang, G., D. Susanto, I. Soesilo, Q. Zheng, F. Qiao and Z. Wei (2005): A note on the South China Sea shallow interocean circulation. Adv. Atmos. Sci., 22(6), 946–954. Fang, G., H. Chen, Z. Wei, Y. Wang, X. Wang and C. Li (2006): Trends and interannual variability of the South China Sea surface winds, surface height, and surface temperature in the recent decade. J. Geophys. Res., 111, C11S16, doi:10.1029/2005JC003276. Feely, R. A., C. L. Sabine, T. Takahashi and R. Wanninkhof (2001): Uptake and storage of carbon dioxide in the ocean: The global CO2 survey. Oceanography, 14(4), 18–32. Fletcher, M., N. Gruber, A. R. Jacobson, S. C. Doney, S. Dutkiewicz, M. Gerber, M. Follows, F. Joos, K. Lindsay, D. Menemenlis, A. Mouchet, S. A. Muller and J. L. Sarmiento (2006): Inverse estimates of anthropogenic CO2 uptake, transport, and storage by the ocean. Global Biogeochem. Cycles, 20, GB2002, doi:10.1029/ 2005GB002530. Geider, R. J., H. L. MacIntyre and T. M. Kana (1998): A dynamic regulatory model of phytoplankton acclimation to light, nutrients and temperature. Limnol. Oceanogr., 43, 679–694. Gong, G. C., K. K. Liu, C. T. Liu and S. C. Pai (1992): The chemical hydrography of the South China Sea west of Luzon and a comparison with the West Philippine Sea. Terr. Atmos. Oceanic Sci., 3, 587–602. González-Dávila, M., J. M. Santana-Casiano, M. Rueda, O. Llinás and E. González-Dávila (2003): Seasonal and interannual variability of sea-surface carbon dioxide species at the European Station for Time Series in the Ocean at the Canary Islands (ESTOC) between 1996 and 2000. Global Biogeochem. Cycles, 17(3), 1076, doi:10.1029/ 2002GB001993. Gruber, N., C. D. Keeling and N. R. Bates (2002): Interannual variability in the North Atlantic Ocean carbon sink. Science, 298, 2374–2378. Gypens, N., C. Lancelot and A. V. Borges (2004): Carbon dynamics and CO2 air-sea exchanges in the eutrophied coastal waters of the southern bight of the North Sea: a modeling study. Biogeosciences Discussions, 1, 561–589. Jahnke, R. A. (2009): Global synthesis. In Carbon and Nutrient Fluxes in Continental Margins: A Global Synthesis, ed. by K.-K. Liu, L. Atkinson, R. Quiñones and L. TalaueMcManus, Springer, Berlin (in press). Jiang, M. S. and F. Chai (2005): Physical and biological controls on the asymmetry of surface nutrients and pCO 2 in the central and eastern Equatorial Pacific. J. Geophys. Res., 110, C06007, doi:10.1029/2004JC002715. Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetma, R. Reynolds, R. Jenne and D. Joseph (1996): The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteor. Soc., 77, 437–471. Karl, D. M. and R. Lukas (1996): The Hawaii Ocean Timeseries (HOT) program: Background, rationale and field implementation. Deep-Sea Res. II, 43, 129–156. Keeling, C. D., H. Brix and N. Gruber (2004): Seasonal and long-term dynamics of the upper ocean carbon cycle at Sta-

tion ALOHA near Hawaii. Global Biogeochem. Cycles, 18, GB4006, doi:10.1029/2004GB002227. Kuo, N. J., Q. A. Zheng and C. R. Ho (2004): Response of Vietnam coastal upwelling to the 1997–1998 ENSO event observed by multisensor data. Rem. Sens. Environ., 89, 106– 115. Large, W. and S. Pond (1982): Sensible and latent heat flux measurements over the ocean. J. Phys. Oceanogr., 12, 464– 482. Large, W. G., J. C. McWilliams and S. C. Doney (1994): Oceanic vertical mixing: a review and a model with a non-local K-profile boundary layer parameterization. Rev. Geophys., 32, 363–403. Liu, G. and F. Chai (2009a): Seasonal and interannual variation of physical and biological processes during 1994–2001 in the Sea of Japan/East Sea: a three-dimensional physicalbiogeochemical modeling study. J. Mar. Sys., doi:10.1016/ j.jmarsys.2009.02011. Liu, G. and F. Chai (2009b): Seasonal and interannual variability of primary and export production in the South China Sea: A three-dimensional physical-biogeochemical model study. ICES J. Mar. Sci., 66, 420–431. Liu, K.-K., S.-Y. Chao, P.-T. Shaw, G.-C. Gong, C.-C. Chen and T.-Y. Tang (2002): Monsoon-forced chlorophyll distribution and primary production in the South China Sea: observations and a numerical study. Deep-Sea Res. I, 49, 1387– 1412. Liu, K.-K., Y.-J. Chen, C.-M. Tseng, I.-I. Lin, H.-B. Liu and A. Snidvongs (2007a): The significance of phytoplankton photo-adaptation and benthic-pelagic coupling to primary production in the South China Sea: Observations and numerical investigations. Deep-Sea Res. II, 54(14–15), 1546– 1574. Liu, K.-K., S.-J. Kao, H.-C. Hu, W.-C. Chou, G.-W. Hung and C.-M. Tseng (2007b): Carbon isotopic composition of suspended and sinking particulate organic matter in the northern South China Sea—From production to deposition. DeepSea Res. II, 54(14–15), 1504–1527. Liu, K.-K., C.-M. Tseng, C.-R. Wu and I.-I. Lin (2009): South China Sea. In Carbon and Nutrient Fluxes in Continental Margins: A Global Synthesis, ed. by K. K. Liu, L. Atkinson, R. Quiñones and L. Talaue-McManus, Springer, Berlin (in press). McKinley, G. A., T. Takahashi, E. Buitenhuis, F. Chai, J. R. Christian, S. C. Doney, M.-S. Jiang, K. Lindsay, J. K. Moore, C. Le Quéré, I. Lima, R. Murtugudde, L. Shi and P. Wetzel (2006): North Pacific carbon cycle response to climate variability on seasonal to decadal timescales. J. Geophys. Res., 111, C07S06, doi:10.1029/2005JC003173. Metzger, E. J. and H. E. Hurlburt (1996): Coupled dynamics of the South China Sea, the Sulu Sea, and the Pacific Ocean. J. Geophys. Res., 101(C5), 12331–12352. Ocean Climate Laboratory National Oceanographic Data Center (2002): World Ocean Atlas 2001. http://www.nodc.noaa.gov Rehder, G. and E. Suess (2001): Methane and pCO2 in Kuroshio and the South China Sea during maximum summer surface temperatures. Mar. Chem., 75(1), 89–109. Sabine, C. L., R. A. Feely, N. Gruber, R. M. Key, K. Lee, J. L. Bullister, R. Wanninkhof, C. S. Wong, D. W. R. Wallace, B.

Tilbrook, F. J. Millero, T.-H. Peng, A. Kozyr, T. Ono and A. F. Rios (2004): The oceanic sink for anthropogenic CO2. Science, 305(5682), 367–371. Shaw, P.-T., S.-Y. Chao, K.-K. Liu, S.-C. Pai and C.-T. Liu (1996): Winter upwelling off Luzon in the northeastern South China Sea. J. Geophys. Res., 101(C7), 16435–16448. Shchepetkin, A. and J. C. McWilliams (1998): Quasi-monotone advection schemes based on explicit locally adaptive dissipation. Month. Wea. Rev., 126, 1541–1580. Shchepetkin, A. F. and J. C. McWilliams (2003): A method for computing horizontal pressure-gradient force in an ocean model with a non-aligned vertical coordinate. J. Geophys. Res., 108, 35.1–35.34. Straub, K. H., G. N. Kiladis and P. E. Ciesielski (2006): The role of equatorial waves in the onset of the South China Sea summer monsoon and the demise of El Niño during 1998. Dyn. Atmos. Oceans, 42, 216–238. Su, J. L. (2004): Overview of the South China Sea circulation and its influence on the coastal physical oceanography outside the Pearl River Estuary. Cont. Shelf Res., 24, 1745– 1760. Takahashi, T., S. C. Sutherland, C. Sweeney, A. Poisson, N. Metzl, B. Tillbrook, N. Bates, R. Wanninkhof, R. A. Feely, C. Sabine, J. Olafsson and Y. Nojiri (2002): Global air-sea CO 2 flux based on climatological surface ocean pCO 2, and seasonal biological and temperature effects. Deep-Sea Res. II, 49(9–10), 1601–1623. Takahashi, T., S. C. Sutherland, R. A. Feely and C. E. Cosca (2003): Decadal variation of the surface water pCO 2 in the western and central Equatorial Pacific. Science, 302(5646), 852–856, DOI:10.1126/science.1088570. Takahashi, T., S. C. Sutherland, R. A. Feely and R. Wanninkhof (2006): Decadal change of the surface water pCO2 in the North Pacific: A synthesis of 35 years of observations. J. Geophys. Res., 111, C07S05, doi:10.1029/2005JC003074. Takano, K., A. Harashima and T. Namba (1998): A numerical simulation of the circulation in the South China Sea—preliminary results. Acta Oceanogr. Taiwanica, 37(2), 165–185. Thomas, H., Y. Bozec, K. Elkalay and H. De Baar (2004): Enhanced open ocean storage of CO2 from shelf sea pumping. Science, 304, 1005–1008. Tseng, C.-M., G. T. F. Wong, I.-I. Lin, W.-C. Chou and K.-K. Liu (2005): A unique seasonal pattern in phytoplankton biomass in low-latitude waters in the South China Sea. Geophys. Res. Lett., 32, L08608, doi:10.1029/ 2004GL022111. Tseng, C.-M., G. T. F. Wong, W.-C. Chou, B.-S. Lee, D.-D. Sheu and K.-K. Liu (2007): Temporal variations in the carbonate system in the upper layer at the SEATS station. DeepSea Res. II, 54(14–15), 1448–1468. Tseng, C.-M., G.-C. Gong, L.-W. Wang, K.-K. Liu and Y. Yang (2009): Anomalous biogeochemical conditions in the northern South China Sea during the El-Niño events between 1997 and 2003. Geophys. Res. Lett., 36, L14611, doi:10.1029/2009GL038252. Wang, S. L., C. T. A. Chen, G. H. Hong and C. S. Chung (2005): Carbon-dioxide and related parameters in the East-ChinaSea. Cont. Shelf Res., 20, 525–544. Wang, X. and Y. Chao (2004): Simulated sea surface salinity


719

variability in the tropical Pacific. Geophys. Res. Lett., 31, L02302, doi:10.1029/2003GL018146. Wong, G. T. F., T.-L. Ku, M. Mulholland, C.-M. Tseng and D.-P. Wang (2007): The SouthEast Asian Time-series Study (SEATS) and the biogeochemistry of the South China Sea— An overview. Deep-Sea Res. II, 54(14–15), 1434–1447. Wu, C. R. and T. L. Chiang (2007): Mesoscale eddies in the northern South China Sea. Deep-Sea Res. II, 54, 1575–1588. Wu, C. R., P. T. Shaw and S. Y. Chao (1998): Seasonal and interannual variations in the velocity field of the South China Sea. J. Oceanogr., 54, 361–372. Xie, S. P., Q. Xie, D. X. Wang and W. T. Liu (2003): Summer upwelling in the South China Sea and its role in regional climate variations. J. Geophys. Res., 108(C8), 3261, doi:2003JC001867. Xue, H., F. Chai, N. R. Pettigrew, D. Xu, M. Shi and J. Xu (2004): Kuroshio intrusion and the circulation in the South China Sea. J. Geophys. Res., 109, C02017, doi:10.1029/ 2002JC001724.

720

F. Chai et al.

Zhai, W. D., M. H. Dai, W. J. Cai, Y. C. Wang and Z. H. Wang (2005a): High partial pressure of CO2 and its maintaining mechanism in a subtropical estuary: the Pearl River estuary, China. Mar. Chem., 93, 21–32. Zhai, W. D., M. H. Dai, W. J. Cai, Y. C. Wang and H. S. Hong (2005b): The partial pressure of carbon dioxide and air-sea fluxes in the northern South China Sea in spring, summer and autumn. Mar. Chem., 96, 87–97. Zhang, Y. H., Z. Q. Huang, W. Q. Wang et al. (2000): A study carbon dioxide in Taiwan Strait. J. Oceanogr. in Taiwan Strait, 19(2), 163–169 (in Chinese).

*This paper was first presented at the Topic Session on “Decadal changes in carbon biogeochemistry in the North Pacific” convened at the PICES Sixteenth Annual Meeting in Victoria, Canada, October 2007.