Estuarine, Coastal and Shelf Science...coastal region into different coastal environments (inner estuaries, outer estuaries, whole estuarine systems, mangroves, salt marshes, coral

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Estuarine, Coastal and Shelf Science xxx (2008) 1–10

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Estuarine, Coastal and Shelf Science

journal homepage: www.elsevier .com/locate/ecss

Time series of carbonate system variables off Otaru coast in Hokkaido, Japan

Ai Sakamoto a,*, Yutaka W. Watanabe a, Masato Osawa b, Kazuo Kido b, Shinichiro Noriki a

a Graduate School of Environmental Earth Science, Hokkaido University, Sapporo, Hokkaido, Japanb Department of Marine Geoscience, Geological Survey of Hokkaido, Otaru 047-0008, Japan

a r t i c l e i n f o

Article history:Received 3 July 2007Accepted 18 April 2008Available online xxx

Keywords:CO2 (carbon dioxide)coastal regionsensitivity analysisinterannual variations of fCO2sea (fCO2 in thesea) and DIC (dissolved inorganic carbon)Japan, Hokkaido, Otaru coast

* Corresponding author.E-mail address: [email protected] (A. Sakamoto

0272-7714/$ – see front matter � 2008 Elsevier Ltd.doi:10.1016/j.ecss.2008.04.013

Please cite this article in press as: Sakamoto,Shelf Sci. (2008), doi:10.1016/j.ecss.2008.04.

a b s t r a c t

We report several biogeochemical parameters (dissolved inorganic carbon (DIC), total alkalinity (TA),dissolved oxygen (DO), phosphate (PO4), nitrateþ nitrite (NO3þNO2), silicate (Si(OH)4)) in a region offOtaru coast in Hokkaido, Japan on a ‘‘weekly’’ basis during the period of April 2002–May 2003. To betterunderstand the long-term temporal variations of the main factors affecting CO2 flux in this coastal regionand its role as a sink/source of atmospheric CO2, we constructed an algorithm of DIC and TA using otherhydrographic properties. We estimated the CO2 flux across the air–sea interface by using the classicalbulk method. During 1998–2003 in our study region, the estimated fCO2sea ranged about 185–335 matm.The maximum of fCO2sea in the summer was primarily due to the change of water temperature. Theminimum of fCO2sea in the early spring can be explained not only by the change of water temperature butalso the change of nutrients and chlorophyll-a. To clarify the factors affecting fCO2sea (water temperature,salinity, and biological activity), we carried out a sensitivity analysis of these effects on the variation offCO2sea. In spring, the biological effect had the largest effect for the minimum of fCO2sea (40%). In summer,the water temperature effect had the largest effect for the maximum of fCO2sea (25%). In fall, the watertemperature effect had the largest effect for the minimum of fCO2sea (53%). In winter, the biological effecthad the largest effect for the minimum of fCO2sea (35%).We found that our study region was a sink region of CO2 throughout a year (�0.78 mol/m2/yr). Fur-thermore, we estimated that the increase of fCO2sea was about 0.56 matm/yr under equilibrium with theatmospheric CO2 content for the period 1998–2003, with the temporal changes in the variables (T, S, PO4)on fCO2sea, thus as the maximum trend of each variable on fCO2sea was 0.22 matm/yr, and the trend ofresidual fCO2 including gas exchange was 0.34 matm/yr. This result suggests that interaction amongvariables would affect gas exchange between air and sea effects on fCO2sea. We conclude that this studyregion as a representative coastal region of marginal seas of the North Pacific is special because it wasmeasured, but there is no particular significance in comparison to any other area.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Carbon dioxide (CO2) is a metabolic gas, which plays an im-portant role in the climate regulation. The concentration of CO2 inthe atmosphere has been increasing 35.4% in comparison with theaverage value before the industrial revolution (280 ppm, IPCC,2001) according to the World Data Center for Greenhouse Gases(WDCGG), being affected by human activities such as fossil–fuelcombustion, deforestation and cement production. The role of theocean as a sink for anthropogenic CO2 has generated considerableinterest (e.g., Tans et al., 1990; McNeil et al., 2003; Sabine et al.,2004a,b; Wakita et al., 2005; Mikaloff Fletcher et al., 2006). Manystudies have focused on quantifying the influx and efflux of CO2 to/from the surface of oceanic waters. Recent model calculations have

).

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A., et al., Time series of carbon013

estimated that during the 1990s, the ocean took up w1.7� 0.5 GtC/yr of anthropogenic CO2 (IPCC, 2001). Especially, the high lat-itudinal areas of deep water formation in the North Atlantic and theSouthern Ocean have a large uptake rate of CO2 from the atmo-sphere (e.g., Tans et al., 1990; Takahashi et al., 2002). On the otherhand, the coastal regions have a high biological productivity, forwhich the average primary production rate differs by a factor of 2between open oceanic and coastal regions (Wollast, 1998; Borges,2005). Although the entire surface area of the coastal region coversonly about 10% of the global ocean, the coastal primary productionaccounts for about 20% of the total oceanic organic matter pro-duction, 80% of the total oceanic organic matter burial, 90% of thetotal oceanic sedimentary mineralization, and 30% of the totaloceanic production and 50% of the accumulation of particulate in-organic carbon (Gattuso et al., 1998; Wollast, 1998; Borges, 2005).We can expect that coastal regions are significant sinks for atmo-spheric CO2. However, since the quantification of the CO2 budget inthe coastal region has been limited due to the complexity of factors

ate system variables off Otaru coast in Hokkaido, Japan, Estuar. Coast.

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A. Sakamoto et al. / Estuarine, Coastal and Shelf Science xxx (2008) 1–102

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such as the hydrography, circulation of water masses, mixed layerdynamics, wind stresses and biological processes (e.g., Borges andFrankignoulle, 2001; Alvarez et al., 2002; Gago et al., 2003; Borgeset al., 2005; Schiettecatte et al., 2006), the coastal region has beenalmost neglected in the budget of global carbon, even though theinfluence on global budgets of carbon and nutrients is dispropor-tionately large in comparison with its surface area (Smith andHollibaugh, 1993; Gattuso et al., 1998; Wollast, 1998; Liu et al.,2000; Chen et al., 2003; Borges, 2005). Borges (2005) divided thecoastal region into different coastal environments (inner estuaries,outer estuaries, whole estuarine systems, mangroves, salt marshes,coral reefs, upwelling systems, and open continental shelves) todetail factors affecting the air–sea CO2 fluxes in the major coastalecosystems. These coastal regions have considerably differentvalues of fCO2sea (50–9425 matm; Borges, 2005) due to differencesin biogeochemical cycling. Borges (2005) examined whether estu-aries and salt marsh are included in the coastal region or not. Ifthese regions are included in the coastal region, the coastal regionbehaves as a source for atmospheric CO2 (0.38 mol C/m2/yr) and theuptake of atmospheric CO2 from the global ocean decreases by 12%.On the other hand, if these regions are not included in the coastalregion, the coastal region behaves as a sink for atmospheric CO2

(�1.17 mol C/m2/yr) and the uptake of atmospheric CO2 by theglobal ocean increases by 24%. Therefore, it is necessary to integrateinformation on the behavior of CO2 and an efficiency for absorbingthe atmospheric CO2 in the diverse coastal region.

In this study, to investigate the temporal variations in the air–sea CO2 flux in the coastal region and the role of coastal region asa sink/source of atmospheric CO2, we used the routine hydro-graphic data with DIC and TA in Otaru coast, Hokkaido Island, Japanfrom 1998 to 2003, which belongs to open continental shelf asdefined by Borges (2005) (Fig. 1). With regard to DIC and TA, as wehave these data sets for 1 year (April 2002–May 2003) only, wereconstructed these time series using the routine data from 1998 to2003. Then we examined the impact of our study region on the

Fig. 1. Location of the sampling station of multiple chemical parameters of CO2 in Otar

Please cite this article in press as: Sakamoto, A., et al., Time series of carbonShelf Sci. (2008), doi:10.1016/j.ecss.2008.04.013

uptake of atmospheric CO2 as a representative area of the westernmarginal seas of the North Pacific and the Sea of Japan, usinga short-term interval data sets (<1 week) of CO2 related parametersfor the period 1998–2003 and bulk method for evaluating CO2

system in our study region. Thus, we could be able to analyze theresult of CO2 flux with high temporal resolution. The Sea of Japanhas specific characteristics that it is isolated from the Pacific exceptthe four straits shallower than 130 m (i.e., it is a semi-enclosed sea),with no strong ocean currents (maximum flow rate of 0.8 m/s)(Japan Meteorological Agency, 2007). Especially, in the northernpart of the Sea of Japan, as the nutrient concentration in the seasurface is supplied from the deep water by winter vertical mixing,an extensive phytoplankton bloom occurs in the springtime.Therefore we could expect that our study region in the northernpart of the Sea of Japan shows significant changes in each effect(temperature, biological, salinity by evaporation and precipitation,and gas exchange between air and sea effects) on fCO2 in the seadue to the shallow depth and the active biological production, andthe effects on the coastal carbonate system. Therefore, we couldmore exactly evaluate each effect affecting the coastal carbonatesystem in our study region.

2. Sampling and methods

2.1. Sampling site and hydrological features

We sampled on a weekly basis several biogeochemical variablesduring the period from April 2002 to May 2003 in the inner shelf of4 km from the Otaru coast in Hokkaido Island, Japan (43�2.74N,141�3.55E) (Fig. 1). The water depth was 24 m at this site. IshikariBay including this sampling site is characterized by a coastal cur-rent, the Tsushima current that flows generally northwest along thecoastline (Yoshida, 1985). There is a large river, the Ishikari Riverthat flows into the bay north of this site. However, the Ishikari Riverflows northward along the shoreline (Yoshida, 1985) and thus we

u Bay, Hokkaido. The division of outer and inner gulf is the isoline of 30 m depth.


A. Sakamoto et al. / Estuarine, Coastal and Shelf Science xxx (2008) 1–10 3

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can expect that our sampling site is not influenced by the IshikariRiver (Fig. 1). The maximum tidal amplitude at this site was ap-proximately 0.4 m for the period 1998–2003 (Japan MeteorologicalAgency, accessible at http://www.data.kishou.go.jp/kaiyou/db/tide/genbo/genbo.php). Considering the difference between the waterdepth of 24 m and the tidal difference, we can neglect the influenceof the tide. Surface temperature typically ranges from 2 �C in winterto 26 �C in summer. In general, the seawater column is isothermalin winter while it shows a greater tendency to stratification insummer. However, at this site the wind strongly affects this oceancondition in the summertime due to the shallow depth. Accord-ingly, we assumed that this site was under uniform conditionswithout a density gradient and the instantaneous air–sea gas ex-change was in equilibrium state.

2.2. Methods

2.2.1. Sampling methodsWe used a boat to collect seawater samples at 6 depths from

surface to bottom (0, 5, 10, 15, 20, 22 m, by using two bath pumpswith the pump-up rate of about 140 ml/s and the hose length of 25 m(NP-90, NAKASA CO., LTD) and a bucket) with conductivity-tem-perature-depth (CTD) profiles. We also analyzed routine data (water

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Fig. 2. The time series of routine data of Otaru coastal water from January 1998 to May 2003Si(OH)4 (mmol/kg), (f) DO (mmol/kg), (g) Chl. a (mg/l), (h) AOU (mmol/kg), respectively. The srespectively.


temperature (T), salinity (S), nutrients (NO3þNO2, PO4, Si(OH)4), DO,chlorophyll-a (Chl. a)) from January 1998 to May 2003 (Fig. 2).

2.2.2. CTD measurementsWe used the AREX Electronic make (AST1000-PK (ALEC ELEC-

TRONICS Co., Ltd), down speed rate: about 0.5 m/s, depth interval:1 m) to measure the temperature and salinity. We calibrated thesalinity data by simultaneous bottle data.

2.2.3. Measurement of DIC and TA, calculated fCO2 and air–sea CO2

fluxesWe pumped up seawater samples except 0 m using two pumps

in series, and sampled seawater for 0 m using a bucket. We carriedout the collection, measurement and standardized DIC according tothe method of Tsunogai et al. (1993). We took samples for DICmeasurement in 120 ml glass bottles, immediately treated withsaturated solution of HgCl2 after sampling, and stored them underrefrigeration in the dark. In the laboratory, we took an accuratevolume of sample (about 32 ml) and acidified it with phosphoricacid (1.5 N) and stripped the samples with purified air. We led thecarrier gas together with the CO2 gas through a cell containinga solution of ethanolamine and an indicator (thymolphthalein so-lution). We electrochemically backtitrated the solution to its original

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http://www.data.kishou.go.jp/kaiyou/db/tide/genbo/genbo.php

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Fig. 3. Plots of (a) the predicted DIC (preDIC, mmol/kg) and the observed DIC (obsDIC,mmol/kg), (c) the predicted TA (preTA, mmol/kg) and the observed TA (obsTA, mmol/kg)and (e) the fCO2sea (prefCO2sea, matm) predicted by using the preDIC and preTA and thefCO2sea (obsfCO2sea, matm) computed by using the obsDIC and obsTA with inset whichrevealed the slope and intercept of regression line and the statistical parameters, andthe time series of the observed (symbol: B) and the predicted (symbol: �) (b) DIC, (d)TA and (f) fCO2sea for April 2002–May 2003. The unit of S.D. and intercept in each insetis mmol/kg for DIC, mmol/kg for TA, and matm for fCO2sea.


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color. We determined the sum of all inorganic carbonate species thatis known as DIC, in seawater by the modified coulometric method ofJohnson et al. (1985). The precision of DIC obtained from the du-plicate determinations was �0.08% (n¼ 162). We made a seawaterbased reference material and used it as a working standardaccording to the method of Dickson et al. (2003). We determined theabsolute value of the working standard by sodium carbonate (AsahiGlass Co., Ltd., purity 99.97%) as a primary standard at least every 6months according to a DOE method (DOE, 1994).

We also took samples for TA by the same method as DIC. Wedetermined the TA in seawater by the method of Ono et al. (1998),which improved the single point titration method of Culbersonet al. (1970). We measured TA in seawater using a glass electrodestandardized against Tris buffer, 2-aminopyridine buffer (DOE,1994) and 0.03 M phthalic acid buffer of a seawater base to correctthe drift of the glass electrode and interpolate the acidified samplesby 0.009 M HCl evaluated around 3.38 pH with three buffer de-terminations. We calibrated the pH electrode for the TA measure-ments at three points calibration by using pH of phthalic acidbuffer, Tris buffer, and 2-aminopyridine buffer. Phthalic acid buffer(w3.4) was determined by an US certified reference material dis-tributed by A. G. Dickson of Scripps Institution of Oceanography(SIO-CRM). The precision of TA obtained from the duplicate de-terminations was �0.04% (n¼ 117).

We calculated the fCO2 using Dickson (1993) (K1 and K2)(modified the dissociation constant of Mehrbach et al., 1973 andWeiss, 1974) (K0) constants with in situ temperature and pressure,T, S, DIC, TA, Si(OH)4, and PO4. We calculated the net flux of CO2

from the equation

FCO2¼ ka

�fCO2sea

� fCO2air

�(1)

where FCO2is the CO2 flux across air–sea interface, the positive and

negative numbers represent the efflux and influx of CO2 from/to thesurface of oceanic waters, respectively, k is the gas exchange co-efficient (Wanninkhof et al., 1999), a is the CO2 solubility in sea-water (Weiss, 1974), and fCO2air and fCO2sea are the CO2 fugacities(matm) for air and sea, respectively. We converted from xCO2 (ppm,in dry air) to fCO2 (matm) (Fig. 3(c)) according to DOE (1994). Ac-cordingly, this flux depends on the wind velocity, the CO2 partialpressure gradient between the surface water and atmosphere, andthe CO2 solubility in seawater. To estimate FCO2

in this study, weused the daily mean values of wind velocity (MeteorologicalAgency Otaru observation), and the monthly mean values of at-mospheric temperature, atmospheric pressure, and xCO2 for 2002–2003, which were sampling period, with the maximal peak in Apriland the minimal peak in August (DxCO2: 13 ppm) in Ryori in Iwate,Japan as the nearest region to our study region because there are nodata of the direct Otaru midair of these data (Japan MeteorologicalAgency, 2007). In the case of xCO2, we used the monthly values for2002–2003 because the equations of the parameters (DIC, TA) usedto estimate fCO2sea was reconstructed by using data obtained in2002–2003. Thus we could expect the atmosphere CO2 bias in-cluded in our reconstructed equations for DIC and TA.

2.2.4. OxygenWe collected oxygen samples in certified 100 ml glass bottles

from the pumps for 5–22 m and sampled seawater for 0 m usinga bucket, and added MnCl2 and KI–NaOH mixed solution to theoxygen samples. We stored the oxygen samples in the dark andanalyzed them within 24 h after sampling. After acidification, wemeasured the concentration of DO for duplicate samples accordingto the JGOFS protocols including sampling, measurement andstandardization methods (Knap et al., 1996). We visually titratedthe content of DO using a piston burette (Metrohm Co. Ltd.). Theprecision of DO obtained from the duplicate determinations was�0.37% (n¼ 129).


2.2.5. Nutrients (NO3þNO2, PO4, Si(OH)4)We directly collected nutrient samples from the pumps into

500 ml polystyrene bottles, afterward filtered them through0.45 mm membrane filter (Gelman Sciences Inc.), and preservedthem in a freezer until analysis. After defrosting the samples, wemeasured in triplicate the contents of nutrients for each using theAACS Z (Auto Analyzer Compact SystemZ, Branþ Luebbe Co.Ltd.). These precision were �1.18, 0.46, 0.46% for 1, 2, 5 mmol/l ofNO3þNO2, �5.05, 3.29, 1.04% for 0.1, 0.2, 0.5 mmol/l of PO4, and�1.72, 0.80, 0.33% for 1, 2, 5 mmol/l of Si(OH)4, respectively (n¼ 15for each parameter).

2.2.6. Phytoplankton pigmentsWe directly collected Chl. a samples from the pumps into 3 l

opaque polystyrene bottles. Afterward, we filtered the water sam-ples through Whatman GF/F filters, wrapped the filter in aluminumand preserved them at �20 �C. Then we extracted the filters in 90%acetone in the dark at 4 �C for 24 h, and then measured the


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Fig. 4. The time series of reconstructed (a) DIC and (b) TA in Otaru coastal water fromJanuary 1998 to May 2003, and (c) fCO2sea reconstructed by using the results of (a) and(b). The symbols A, :, ,, �, >, and 6, represent data from 0, 5, 10, 15, 20 and 22 mdepth, respectively, and þ in (c) represent fCO2air in Ryori in Iwate, Japan as the nearestregion to our study region.


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absorbance. We determined the concentration of Chl. a usinga spectrophotometer (U-2001, Hitachi). We calibrated the spec-trometer with pure Chl. a (Sigma Chemical Co.). The precision of Chl.a was �7.6% (n¼ 250).

3. Results and discussion

3.1. Temporal variations of physical, chemical and biologicalparameters in this coastal region for 1998–2003

We showed temporal variations of physical, chemical and bi-ological parameters (T, S, nutrients, DO, Chl. a) from January 1998 toMay 2003 in Fig. 2. T, NO3þNO2, PO4, Si(OH)4, DO and Chl.a showed seasonal patterns, which ranged about 2 �C (February) to26 �C (August) for T (Fig. 2 (a)), <0.1 (April–October; exhaustedperiods) to 5 mmol/kg (December–February) for NO3þNO2

(Fig. 2(c)), 0 (same periods with NO3þNO2) to 0.4 mmol/kg(December–February) for PO4 (Fig. 2(d)),<3.5 (April–September withweek peak) to 15 mmol/kg for Si(OH)4 (November–February; how-ever, interannual variability was high) (Fig. 2(e)), 220 (September)to 350 mmol/kg (March–April) (Fig. 2(f)) for DO and <1.0 (periodsexcept peaks) to 10 mg/l for Chl. a (February–April; however,interannual variability was high) (Fig. 2(g)). S showed no seasonalpattern and the sporadic events affected by meteorological situationssuch as a typhoon and a heavy fall of snow. We think that the extentof S (33–34&) of our study region is determined by the inflow ofwater mass with the different salinity value to the gulf, which ischanged by the weather condition. We analyzed the coastal car-bonate system and evaluated each effect affecting this system in thisstudy region, based on these data.

3.2. Estimate of FCO2in this coastal region

3.2.1. Reconstructions of DIC and TA for the period 1998–2003 byusing the routine data

We aimed to understand the role of the coastal region as a sink/source of atmospheric CO2. The sink/source capacity depends onthe difference of CO2 fugacity between the seawater and the at-mosphere. In this study, we estimated the CO2 fugacity in seawaterusing the observed values of DIC (Fig. 3(b), observational range:1920–2040 mmol/kg) and TA (Fig. 3(d), observational range: 2210–2300 mmol/kg). As we have these data sets for 1 year (April 2002–May 2003) only, it is insufficient to clarify the interannual variationof CO2 fugacity in the seawater and therefore we reconstructed thetime series of DIC and TA using the routine data from 1998 to 2003(Fig. 3, Fig. 4).

3.2.1.1. Reconstruction of DIC. We expressed the content of DIC inthis region by using multiple linear regression analysis (e.g., Breweret al., 1995) based on the relationship between DIC and T, S, AOUand phosphate (PO4) based on hydrographic parameters obtainedfrom April 2002 to May 2003. In this study, we presumed that thecolumn was uniform due to the shallow depth. Then we used alldepth except 22 m, because we considered that this depth wasinfluenced by the bottom sediment and/or the bottom currents.

DIC ¼ f ðT ; S;AOU; PO4Þ ¼ aT þ bSþ cAOUþ dPO4 þ e (2)

where ‘a–e’ are constants.In general, the F-test is used to validate the usefulness of each

parameter in the multiple linear regression. In this study, the pa-rameter with F value of more than 2.4 has a significant meaning(e.g., Wilks, 1995), indicating that it is useful to obtain an empiricalequation in the multiple linear regression. By using a stepwiselinear fitting regression for Eq. (2) with F-test, the third term ofright-hand side in Eq. (2), AOU was only found to become negligible


(F¼ 1.4�10�2) due to F< 2.4 and it can therefore be deleted. Weobtained the algorithm for DIC as follows

DIC ¼ �3:18T þ 50:80Sþ 79:23PO4 þ 301:43�n ¼ 219; r2

¼ 0:82;RMSE ¼ 15:63; p < 0:0001�

ð3Þ

where ‘n’, ‘r2’, ‘RMSE’, and ‘p’ are the number of samples, thecoefficient of determination, the root mean standard error of re-gression and the probability at a 95% confidence level, respectively.As we had only data of DIC during April 2002–May 2003, we as-sumed that the past data of DIC were revealed by using our algo-rithm Eq. (3) for DIC. We showed the result of correlation betweenthe predicted and the observed DIC in Fig. 3(a) and (b). From theresult of regression line, we concluded that this correlation wasstatistically significant. We reconstructed the time series of DIC byusing Eq. (3) with the data sets of T, S, and PO4 during the periodfrom January 1998 to May 2003, including a period with no DICdata (Figs. 3(a) and (b), 4(a)).

3.2.1.2. Reconstruction of TA. In general, in the pelagic ocean, the TAis revealed as a function of the salinity which is conservative (e.g.,Millero et al., 1998). In this study region, since biological effectssuch as the calcification were low due to the predominance of di-atoms (rarely dinoflagellates), based on data of Geological Survey ofHokkaido, we also reconstructed the time series of TA by using therelational expression to S, which the difference between CTD andbottle data was less than 0.05, as follows



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TA ¼ f ðSÞ ¼ 63:19Sþ 122:41�n ¼ 132; r2 ¼ 0:83;RMSE�

¼ 10:70;p < 0:0001 ð4Þ

As we had only data of TA during April 2002–May 2003, we as-sumed that the past data of TA were revealed by using our algo-rithm Eq. (4) for TA. We showed the result of correlation betweenthe predicted and the observed TA in Fig. 3(c) and (d). From theresult of regression line, we concluded that this correlation wasstatistically significant. We reconstructed the time series of TA byusing Eq. (4) with the discrete bottle data of S from January 1998 toMay 2003, including the period with no TA data (Fig. 4(b)).

3.2.2. FCO2analysis

3.2.2.1. FCO2analysis by bulk method. We estimated the FCO2

be-tween the seawater and atmosphere by using Eq. (1), with k, fCO2air

and fCO2sea estimated from DIC and TA (Fig. 4(c)). We estimated theerror on the computed fCO2sea by propagating the estimated errorson DIC and TA with a Monte-Carlo simulation. The estimated erroris 16 matm, which is smaller than the value of seasonal variation offCO2sea in our study region. From the results of coefficient of de-termination (r2) and slope of regression line, we concluded that thecorrelation of predicted� observed fCO2sea was statistically signif-icant (Fig. 3(e) and (f)). We determined the estimated error of FCO2

to be �0.58 mmol/m2/day by summing all errors of parameters inEq. (1). The fCO2sea was always lower than the fCO2air during 1998–2003 (Fig. 4(c)). Especially, in spring, the coastal capacity of sink foratmospheric CO2 was largest probably due to biological effect. ThefCO2sea tends to gradually increase in August due to the increase oftemperature. Averaged value of FCO2

between the seawater andatmosphere ranged from�3.36 mmol/m2/day in January–March to�0.85 mmol/m2/day in August–September for the period 1998–2003, indicating that our study region was a sink for atmosphericCO2 (Fig. 5). In this study, we also estimated the FCO2

between theseawater and atmosphere by using ‘time step’ method (Tsunogaiand Tanaka, 1980) replacing O2 with CO2, in order to compare theresult derived from the bulk method. However, we did not use ‘timestep’ method for the FCO2

analysis in order to have considerableuncertain in estimated FCO2

between the seawater and atmosphere(not shown data).

3.2.2.2. Comparison of FCO2with the closest ocean. Using the result

of FCO2in the bulk method, FCO2

was estimated to be�0.78� 0.10 mol/m2/yr in this study region. For the comparison ofFCO2

in this study region with other ocean region, we used the FCO2

result of east Hokkaido (40–44�N, 147.5–152.5�E) (Takahashi et al.,2002) and East China Sea (Wang et al., 2000), which were�1.71 mol/m2/yr and�0.10 to�0.23 mol/m2/yr, respectively, as theclosest grid point to our study region. Our result could suggest thatour study region is special because it was measured but there is noparticular significant in comparison to any other region. Based onthe substratum figure in the Ishikari Bay including our study site,mud extends from the Ishikari River to the north, and we cannotfind the sedimentation of the organic material in the whole bay.

-151998 1999 2000 2001 2002 2003

-10

-5

0

5

flu

x (m

mo

l/m

2/d

ay)

bulk

Fig. 5. The time series of FCO2by bulk method from January 1998 to May 2003. The

error is propagation error, which is �0.58 mmol/m2/day.


There is also no sedimentation of organic matter in our site.Therefore, we propose the mechanism in oceanic carbonate systemin our study region that the organic material repeat sedimentationand resuspension, and are carried to the north pelagic ocean,without a release of CO2 from the sea to the air in our site in order tobe lower fCO2sea than fCO2air throughout a year. Accordingly, weconcluded that this study region was always sink for atmosphericCO2 throughout a year but no significant one.

3.3. Fluctuation factor of fCO2 in the coastal region

3.3.1. Seasonal variability of fCO2sea

The fCO2sea in our study region had a seasonal periodicity(Fig. 4(c)). We here defined spring, summer, fall, and winter in ourstudy region, as the periods of February–April, May–July, August–October, and November–January, respectively. The fCO2sea in-creased from spring to fall due to the effect derived from theincrease of T (Fig. 2(a)), and it decreased from fall to spring due tothe cooling effect and biological effect derived from the increase ofDO, and Chl. a, and decrease of NO3þNO2, PO4, and T (Fig. 2(a), (c),(d), (f) and (g)). The fCO2sea ranged about 185–335 matm. This valuein our study was lower than those of other coastal regions (about211–658 matm, e.g., Boehme et al., 1998) and the variation wassmall. Our error of fCO2sea estimated from DIC and TA is 16 matm,which was smaller than the value of seasonal variation of fCO2sea inour study region.

3.3.2. Control factors of fCO2sea

In general, the fluctuation of fCO2sea appears as a net sum ofvarious processes. In our study region, the seasonal changes of Tand fCO2sea ranged from w2 �C to 26 �C (DT¼ 24 �C) and 185–335 matm (DfCO2sea¼ 150 matm), respectively. If the minimumvalue of fCO2sea (185 matm) resulted from the maximum CO2 solu-bility due to the minimum temperature, the temperature variationof 24 �C would induce thermodynamic changes of fCO2sea untilabout 365 matm based on the temperature dependence (4%/�C forfCO2sea) (Takahashi et al., 1993). However, the observed fCO2sea

changes (DfCO2sea¼ 150 matm) were less than those (the predictedDfCO2sea¼ 180 matm) associated with the thermodynamic pro-cesses. These results indicated that the seasonal variation of fCO2sea

was partially compensated by other processes. Therefore, we clar-ified how other processes contributed to the fluctuation of fCO2sea.To estimate these contribution ratios on the seasonal variation offCO2sea, we used the sensitivity analysis of each process to thevariation of fCO2sea (Boehme et al., 1998; Sabine and Key, 1998).Appling the observed T, S, DIC, TA, PO4, and Si(OH)4 to the empiricalequation of Dickson (1993), we calculated the time series of fCO2sea

in our study region. Thus we assumed that the temporal changes offCO2sea in our study region were caused by the sum of the followingfour primary processes controlling the distributions of the six pa-rameters (T, S, DIC, TA, PO4, and Si(OH)4): the heat exchange (H), thewater mass exchange (W), the production/remineralization oforganic matter (B), the residue (R) (air–sea gas exchangeþ a in-cluding errors of other parameters, where a is arbitrary). We hereused the temporal changes of T and S as indices of the heat ex-change and the water mass exchange. Although we could considerother processes (e.g., carbonate production/decomposition and themixing of ambient water such as freshwater input from a river), weassumed that they were negligible in our study region due to thepredominance of diatoms and limited influence of Ishikari River.Applying T, S, DIC, TA, PO4 and Si(OH)4 to each of these processesvarying DfCO2sea (H, W, and B) during the sampling periods, weestimated the amount of contribution of each process (DfCO2sea(X),X represents any one of these processes (H, W, B, and R)) to theperturbation of net fCO2sea (DfCO2sea). We revealed DfCO2sea(X) asfollows



ARTICLE IN PRESS

Df CO2seaðXÞ ¼ f CO2seaðX; tþDtÞ � f CO2seaðX;tÞ (5)

where ‘Dt’ is the sampling time interval, ‘fCO2sea(X, t)’ is fCO2sea(X) inan arbitrary day (t), ‘fCO2sea(X, t þ Dt)’ is fCO2sea(X) on the next sam-pling day of t, when pass by Dt (day) from t. In fCO2sea(X, t þ Dt), weused only suitable parameter(s) on the sampling day of tþDt whileholding the values of the other parameters at the sampling day of t.We defined R as the residue derived by subtracting the DfCO2sea(X)

of three processes (H, W, and B) from the DfCO2sea at each samplinginterval. However, like the time step analysis, as there was highlyuncertain in the residue, we could not deal R as the air–sea gasexchange. Therefore, in this study, we discussed the seasonal var-iation of fCO2sea by above three processes (H, W, and B). Using thesensitivity analysis, we also estimated the ratio of the contribution(CR) of each process to the DfCO2sea (Fig. 6, Table 1). Then we rep-resented these ratios as DfCO2sea(X)/

PjDfCO2sea(X)j100 with the unit

of %. The positive and negative values of this ratio indicate thecontribution of the increase and decrease of fCO2sea for each pro-cess, respectively. We used the DIC (nDIC) and TA (nTA) normalizedaccording to Friis et al. (2003).

3.3.2.1. Heat exchange: water temperature effect. H influences thevariations of fCO2sea according to the variations of T. We esti-

Df CO2seaðBÞ ¼ fðtþDtÞ�TðtÞ; SðtÞ; corDICðtþDtÞ; corTAðtþDtÞ; PO4ðtÞ; SiðOHÞ4ðtÞ

�� fðtÞ

�TðtÞ; SðtÞ; corDICðtÞ; corTAðtÞ; PO4ðtÞ; SiðOHÞ4ðtÞ

�(6)

mated the variable effects of H on DfCO2sea based on only thevariations of T during the sampling periods with other parame-ters (S, DIC, TA, PO4 and Si(OH)4) being not changed. Therefore,the difference between the t and tþDt values of fCO2sea(H)

reflected only the changes of T.This effect had the largest positive contribution on the DfCO2sea

in June or July for the period 1998–2003, when the water tem-perature increased and accordingly the gas solubility decreased.The positive values of CR of H were 14% in spring and 25% insummer. Thereafter, the negative values of CR of H were 53% in falland 25% in winter.

3.3.2.2. Water exchange: salinity effect. The addition/removal ofwater due to the precipitation/evaporation influence S. We esti-mated the variable effects of W on DfCO2sea based on only thevariations of S during the sampling periods with other parameters(T, DIC, TA, PO4 and Si(OH)4) being not changed. Therefore, the

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

spring (Feb-Apr) summer (May-Jul)

Fig. 6. The relative significance of each seasonal effect using the sensitivity analysis (con(February–April), summer (May–July), fall (August–October) and winter (November–JanuarfCO2sea: heat exchange (temperature effect), production/decomposition of organic matte(including air–sea gas exchange effect), respectively. The standard deviation (S.D.) of H is �deviation (S.D.) of B is �30.0% in spring, �37.6% in summer, �22.4% in fall, and �39.9% in winfall, and �8.4% in winter.


difference between the t and tþDt values of fCO2sea(W) reflectedonly the changes of S. In this case, the positive and negative valuesof this ratio were caused by the removal and addition of water dueto the evaporation and precipitation, respectively. Since we usedthe nDIC and nTA, it is here not necessary to consider the dilution/concentration of DIC and TA.

The positive value of CR of W was 19% in summer and thenegative values of CR of W were 16% in spring, 12% in fall and 11% inwinter.

3.3.2.3. Production/remineralization of organic matter: biologicaleffect. We estimated the variable effects of B on DfCO2sea based ononly the variations of DIC and TA during the sampling periods withother parameters (T, S, PO4 and Si(OH)4) being not changed. Ingeneral, we cannot estimate the change of B based on only theobserved DIC and TA data due to the other processes changing DICand TA, such as the gas exchange and the behavior of water masseson DIC, and the proton flux associated with the uptake of nitrate onTA (Brewer et al., 1975; Kanamori and Ikegami, 1982; Sabine et al.,1995). Thus, we estimated DfCO2sea(B) as followswhere ‘corDIC’ is the value of DIC to which is added the PO4 cor-rected by using the stoichiometric ratio of C/P (Redfield et al., 1963)

to the observed DIC, that is

corDIC ¼ obsDICþ C=P � DPO4 (7)

where

DPO4 ¼ obsPO4 � prePO4 (8)

where the subscripts ‘obs’ and ‘pre’ of PO4 mean observed andpreformed PO4, respectively. We define preformed PO4 as con-stant and bioproduction-unutilized nutrient (PO4). We assumethat prePO4(t) was equal to prePO4(t þ Dt). ‘corTA’ is the potentialalkalinity (PA) which is the total alkalinity corrected for thechange of NO3 produced by respiration of organic matter duringthe time interval. Therefore, the difference between the t andtþDt values of fCO2sea(B) reflected only the changes of corDICand corTA. In this case, the positive and negative values of this

fall (Aug-Oct) winter (Nov-Jan)

R

W

B

H

tribution ratio %) in the Otaru coastal region. We divided four seasons into springy). The initial letters H, B, W, and R represent the four primary processes controllingr (biological effect), water exchange (precipitation/evaporation effect), and residual24.3% in spring, �42.2% in summer, �20.9% in fall, and �29.9% in winter. The standardter. The standard deviation (S.D.) of W is �14.3% in spring, �15.1% in summer, �24.3% in


Table 1The relative significance of each seasonal effect using the sensitivity analysis (con-tribution ratio % average). The initial letters H, B, W, and R represent the four primaryprocesses controlling fCO2sea: heat exchange (temperature effect), production/de-composition of organic matter (biological effect), water exchange (precipitation/evaporation effect), and residual (including air–sea gas exchange effect), re-spectively. The numbers represent contribution ratios. The positive and negativevalues represent the increasing and decreasing fCO2sea, respectively

Average (%) H B W R

Spring 14.4 �39.6 �15.5 30.6Summer 25.4 18.3 18.6 �37.7Fall �52.7 20.9 �12.4 14.0Winter �25.2 �35.2 �11.2 28.4


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ratio indicate the dominances of the respiration and photosyn-thesis, respectively.

The positive values of CR of B were 18% in summer and 21% in falldue to the dominance of the respiration compared to photosyn-thesis and the negative values of CR of B were 40% in spring and 35%in winter due to the dominance of the photosynthesis compared torespiration.

3.3.2.4. Comparison of primary control factors on the seasonal vari-ation of fCO2sea with different coastal environments. In our studyregion, each effect remarkably appeared in the seasonal variationof fCO2sea due to the shallow depth. This result is similar toanother survey which observed in a similar depth and site en-vironment on the eastern coast of USA (off New Jersey) (Boehmeet al., 1998). They set seven observational stations from the in-ner to the outer part of the bay. Their H was one of the primaryprocesses resulting in the seasonal variation of fCO2sea. Sub-sequently, B was also one of the primary processes resulting inthe seasonal variation of fCO2sea, and its effect declined from theinner to the outer part of the bay. They indicated that themixing of eutrophic river and/or upwelling water significantlyinfluenced the variability of fCO2sea at the three stations whichclose to the greatest potential source of freshwater input; theMullica River. However, R did not have a strong effect on thevariability of fCO2sea in their stations.

Furthermore, we compared the seasonal variation of fCO2sea inour study region with the fCO2sea of continental shelves similar toour study region. For example, in the case of European continentalshelves with upwelling and non-upwelling continental shelves, theaverage variation of fCO2sea showed about 200–490 matm (Borgeset al., 2006). Our variation of fCO2sea (185–335 matm, DfCO2-

sea¼ 150 matm) was small compared to the above range, and ourresult of fCO2sea was overall lower than the value of fCO2air. Weconclude that our study region is special because it was measured,but there is no particular significance in comparison to any otherregion. The value lies in the fact that it is normal, and thus an in-dicator of general processes at work.

3.4. Interannual variations of fCO2sea and DIC in the coastal region

3.4.1. Increasing trend of the rate of fCO2sea and DICWe estimated the interannual variations of fCO2sea and DIC in

our study region from 1998 to 2003. We found that fCO2sea and DICincreased at a rate of 0.56� 0.005 matm/yr (R¼ 0.87) and0.99� 0.004 mmol/kg/yr (R¼ 0.86) (Fig. 4(c) and (a)), respectively,for the period 1998–2003, using a equation of the Fourier sineexpansion (fCO2sea¼�ayþ bþ csin{2p(y� d)/e} (y is the calendaryear, a–e are constants)). To understand the processes increasedfCO2sea and DIC, we assessed in detail what the factors control theincrease of fCO2sea and DIC, as follows.


3.4.2. Factors controlling the increase of fCO2sea and DICTo understand the factors controlling the increase of fCO2sea and

DIC, we assessed the temporal changes on fCO2sea and DIC for thevariables (T, S, PO4).

For sensitivity of temperature on fCO2sea and DIC, we thoughtthe 2 cases for temperature change because we found decrease ofair temperature during 1998–2003 such as a short-term range, butincrease of air temperature during 1980–2006. As a result, weconcluded that the former was an episodic trend, as follows.

Firstly, during 1998–2003, the monthly mean water tempera-ture of this period trended to decrease (�0.57 �C/100 yr). As weconsider the relation between water temperature and fCO2sea (4%/�C), with the variation range of fCO2sea in our study region (185–335 matm), the decreasing rate of fCO2sea would be from �0.08 to�0.04 matm/yr. This rate corresponds with the rate of �0.16 to�0.09 mmol/kg/yr for DIC, when we fixed other parameters (T, S, TA,PO4, Si(OH)4) being constant with time, by using the mean valuesfor the observed period 2002–2003.

Secondly, during 1980–2006 such as a long-term range, the airtemperature has increased to 2.61 �C/100 yr, while we un-fortunately do not have water temperature during this period.However, there is the correlation between water and air tem-peratures due to the shallow depth between 1998 and 2003(Twater¼ 0.62� Tairþ 6.23; r2¼ 0.82, where the subscripts ‘water’and ‘air’ of T mean water and air temperatures, respectively).Using this relation, we estimated that the water temperaturewould have the increasing trend by 1.61 �C/100 yr. As a result, theincreasing rate of fCO2sea would be 0.12–0.22 matm/yr. This ratecorresponds with the rate of 0.21–0.40 mmol/kg/yr for DIC, whenwe fixed other parameters (T, S, TA, PO4, Si(OH)4) being constantwith time, by using the mean values for the observed periodduring 2002–2003.

For sensitivity of salinity on fCO2sea and DIC, we estimated thechange of the CO2 solubility with the salinity change, by using thechange of 1&-Sal because the salinity of our study region had noseasonality and almost ranged 33–34&. As a result, we found thatthe increase of salinity by 1& raised the fCO2sea by 6.1�10�2 matmand the DIC by 1.1�10�2 mmol/kg, when we fixed other parameters(T, TA, PO4, Si(OH)4) being constant with time, by using the meanvalues for the observed period during 2002–2003.

For sensitivity of biological activity on fCO2sea and DIC, we ex-amined the trends of AOU, nutrients, and Chl. a during 1998–2003.As a result, we found that the AOU had the increasing trend of0.49� 0.003 mmol/kg/yr (R¼ 0.67). However, we could not find thetrends for nutrients and Chl. a during 1998–2003 because theseinterannual variations were high. If we assumed that the change ofAOU revealed the change of biological activity and the chemicalcomposition of organic matter consisted of Redfield ratio, we foundthat the DIC had the increasing trend of 0.38 mmol/kg/yr, andfCO2sea had the increasing trend of 0.20 matm/yr, when we fixedother parameters (T, S, TA, PO4, Si(OH)4) being constant with time,by using the mean values for the observed period during 2002–2003.

As we considered the maximum trend of each variable, thefCO2sea and DIC had 0.48 matm/yr (in detail, 0.22þ 0.06þ 0.20)and 0.79 mmol/kg/yr (in detail, 0.40þ 0.01þ0.38) for 1980–2006as long-term range, while had 0.22 matm/yr (in detail, �0.04þ0.06þ 0.20) and 0.30 mmol/kg/yr (in detail, �0.09þ 0.01þ0.38)for 1998–2003 as our study period with routine data (T, S, nu-trients). On the other hand, as the trends of observed fCO2sea andDIC were 0.56� 0.005 matm/yr and 0.99� 0.004 mmol/kg/yr, re-spectively, during 1998–2003, the trends of residual fCO2sea andDIC including gas exchange were 0.34 matm/yr and 0.69 mmol/kg/yr, respectively. This result suggests that the interaction amongvariables would affect gas exchange between air and sea effectson fCO2sea.



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4. Conclusion

We conducted the quantification of FCO2between the seawater

and atmosphere in our study region by using DIC and TA datareconstructed by the time series of multiple chemical parameters ofCO2 in Otaru coast, Hokkaido Island, Japan during 1998–2003. Thecalculated fCO2sea ranged from 185 to 335 matm between January1998 and May 2003. FCO2

was �0.78 mol/m2/yr, indicating that ourstudy region acted as a sink of atmospheric CO2. Then it had theincreasing trend of 0.56 matm/yr for fCO2sea, which would includethe interaction among variables.

Furthermore, we also conducted a sensitivity analysis of eachprocess (H, W, and B) to the seasonal variation of fCO2sea withweekly data in order to determine the role of each process on thelarge temporal variation of the coastal environment. Each processhad the respective features as to the seasonal unit with the largestnegative CR (53%) in fall for H, the largest positive CR (19%) insummer for W, and the largest negative CR (40%) in spring for B,although there was the variation of contribution rate of each pro-cess to the variation of fCO2sea in the short span. Thus, we couldsuggest that the estimation method developed in this study ac-count for almost 100% of the variability in the observed CO2 signal,and so that as this signal evolve, it should be possible to decode thereason for the observed changes and separate out each effect.

In the future, it will be necessary to continue obtaining hightemporal resolution and long time series data of marine carbonatesystem with other hydrographic parameters and collecting them inthe coastal regions more intensively in order to evaluate integratedFCO2

in the whole coastal regions.

Acknowledgement

We were grateful to Mr. Harunobu Dateyama who belongs to thefishery cooperative society in Otaru city for the significant assis-tance, in seawater sampling. We used the wind data and the cli-matological data as follows and would like to express our gratitudeto it: the data provided by Japan Meteorological Agency, from theirWeb site at http://www.jma.go.jp/jma/indexe.html. We also ap-preciate the support of Mr. Harunobu Dateyama and Japan Mete-orological Agency in this study.

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