temporal and spatial variability of phytoplankton...

527

Journal of Oceanography, Vol. 56, pp. 527 to 538, 2000

Keywords:⋅ Japan Sea,⋅ pigment concentra-tion,

⋅ critical depth,⋅ mixed layer depth,⋅ seasonal variation.

* Corresponding author. E-mail: [email protected]

* Present address: Faculty of Geo-Environmental Science, Rissho Uni-versity, 1700 Magechi, Kumagaya-City, Saitama 360-0194, Japan.† Present address: Faculty of Fisheries, Nagasaki University, 1-14Bunkyo, Nagasaki 852-8521, Japan.

Copyright © The Oceanographic Society of Japan.

Temporal and Spatial Variability of PhytoplanktonPigment Concentrations in the Japan Sea Derivedfrom CZCS Images

SANG-WOO KIM1*, SEI-ICHI SAITOH1, JOJI ISHIZAKA2†, YUTAKA ISODA1 and MOTOAKI KISHINO3

1Graduate School of Fisheries Sciences, Hokkaido University, 3-1-1, Minato-cho, Hakodate 041-8611, Japan2National Institute for Resources and Environment, Tsukuba 305-8569, Japan3The Institute of Physical and Chemical Research (RIKEN), Hirosawa 2-1, Wako-shi, Saitama 351-0198, Japan

(Received 13 April 1998; in revised form 3 April 2000; accepted 3 April 2000)

Temporal and spatial variability of phytoplankton pigment concentrations in theJapan Sea are described, using monthly mean composite images of the Coastal ZoneColor Scanner (CZCS). In order to describe the seasonal changes of pigment concen-tration from the results of the empirical orthogonal function (EOF) analysis, we se-lected four areas in the south Japan Sea. The pigment concentrations in these areasshow remarkable seasonal variations. Two annual blooms appear in spring and fall.The spring bloom starts in the Japan Sea in February and March, when critical depth(CRD) becomes equal to mixed layer depth (MLD). The spring bloom in the southernareas (April) occurs one month in advance of that in the northern areas (May). Thisindicates that the pigment concentrations in the southern areas may increase rapidlyin comparison with the northern areas since the water temperature increases fasterin spring in the southern than in the northern areas. The fall bloom appears first inthe southwest region, then in the southeast and northeast regions, finally appearingin the northwest region. Fall bloom appears in November and December when MLDbecomes equal to CRD. The fall bloom can be explained by deepening of MLD in theJapan Sea. The pigment concentrations in winter are higher than those in summer.The low pigment concentrations dominate in summer.

equal to the destruction of phytoplankton was the criticaldepth (hereinafter referred to as the CRD). Only whenthe depth of mixing is shallower than the CRD do posi-tive productions occur in the water column and thepopulations sustain net growth. Thus, the vertical extentof the mixed layer depth (MLD) in relation to the sur-rounding light field is of crucial importance.

Satellite ocean color remote sensing has improvedour capability to define the variability of pigment con-centrations over wide areas. The Nimbus-7 Coastal ZoneColor Scanner (CZCS), which operated from November1978 through June 1986, provided synoptic sea surfaceocean color distribution at a spatial scale of 1 km andmeasurements of the spectral radiance back-scattered outof the ocean successfully provided estimates of the near-surface pigment concentrations (e.g., Gordon et al., 1983).Yoder et al. (1993) and Banse and English (1994) havealready analyzed the seasonality of pigment concentra-tions in the global ocean using CZCS imagery. Obata et

1. IntroductionPhytoplankton dynamics in the temperate zone are

generally dominated by the spring and fall bloom(Parsons et al., 1984); a period of rapid population growththat often begins after the water column becomes ther-mally stratified (spring bloom) or when the stratificationis destroyed (fall bloom) in the surface layer. Sverdrup(1953) proposed a useful concept for understanding someaspects of the initiation of a spring bloom. He stated thatthe depth to which phytoplankton could be mixed and atwhich the total photosynthesis for the water column was

528 S.-W. Kim et al.

al. (1996) examined the global verification of CRD theoryfor phytoplankton bloom with CZCS data. They reportedthat the predictions of spring bloom from CRD theorywere consistent with the timing of the spring bloom inthe western North Pacific.

Although there have been many studies conductedall over the world, relatively fewer studies have been doneon the variability of CZCS pigment concentration in thewestern North Pacific Ocean (Ishizaka et al., 1992;Fukushima and Ishizaka, 1993). In the Japan Sea,Fukushima and Toratani (1997) pointed out the problemsin applying the CZCS data to the sea around the JapaneseIslands during spring, when CZCS data are affected by ayellow sand dust aerosol (“KOSA”) from the Chinesedesert. Nagata and Ogawa (1997) analyzed Secchi-diskdata and indicated that the spring bloom occurred in Aprilwhen the CRD was deeper than the MLD and fall bloomoccurred in November parallel with the onset of suffi-cient nutrients with deepening of the MLD in the south-ern Japan Sea. However, fewer studies have been con-ducted on the temporal and spatial variability of pigmentconcentrations in the Japan Sea.

The objective of this paper is to explore the seasonalvariability of CZCS pigment concentration distributionsin the Japan Sea. We use empirical orthogonal function(EOF) analysis on the CZCS images in the Japan Sea.Based on the EOF analysis, the influence of water tem-perature, solar radiance, and MLD on the developmentof the spring and fall blooms will be discussed with ref-erence to selected areas of the Japan Sea. Furthermore,we shall discuss the spring and fall blooms in the JapanSea in terms of CRD theory.

2. Data and Methods

2.1 CZCS data setsThe pigment concentration data set utilized in the

present study consists of the results from the global CZCSprocessing project at NASA Goddard Space Flight Center(GSFC). These data products are monthly global com-posite images of pigment concentrations (chlorophyll a+ phaeophytin), each with 18 km × 18 km spatial resolu-tion from 1978 to 1986 (Feldman et al., 1989). Map pro-jection is a linear latitude-longitude projection with a glo-bal dimension of 1024 × 2048. The CZCS images for theEOF analysis are available for the Japan Sea from 34°Nto 43°N in latitude (54 lines) and from 127°E to 140.5°Ein longitude (75 columns), as shown in Fig. 1. The origi-nal composite images of CZCS data are arranged accord-ing to digital number (DN) from 0 to 255 and include theland pixels and lack of data. The pixel values of CZCSdata sets are also composed of logarithm values. The pig-ment concentrations (Chl) at a given pixel values ex-pressed as a DN is calculated as

Chl = 10DN × 0.012–1.4. (1)

The EOF analysis method requires a complete dataset with no spatial gaps. The data series of CZCS imagescontains pixels without pigment concentration data dueto the presence of clouds. The lack of data was relativelysevere in February and September. We filled absence ofdata with a linear interpolated value of the temporal av-erage using the images before and after each month. It isalso at this step that land pixels were discarded. For thetwelve interpolated images, we calculated mean and stand-ard deviation of pigment concentrations.

2.2 EOF analysisEOF analysis provides a compact description of spa-

tial and temporal variability of data series in terms of

Fig. 1. Study area in the Japan Sea between 34°N and 43°N inlatitude (54 lines) and 127°E and 140.5°E in longitude (75columns). The line of polar frontal zone is located alongabout 40°N.

Phytoplankton Pigment Concentrations in the Japan Sea 529

orthogonal functions, or statistical modes. ConventionalEOF analysis can be used to detect standing variationsonly. This EOF analysis is a useful tool to analyze satel-lite imagery such as CZCS data because it explores spa-tial and temporal variability simultaneously, reducing theoriginal information to a few time-varying spatial pat-terns (Lagerloef and Bernstein, 1988; Eslinger et al., 1989;Fuentes-Yaco et al., 1997).

In general, EOF analysis (e.g., Lagerloef andBernstein, 1988) is transformed by decomposition of tem-poral variance difference, that is, temporal-EOF (T-EOF)dominates spatial structure of temporal variance, whilespatial-EOF analysis (Lagerloef and Bernstein, 1988;Isoda et al., 1992) is transformed by decomposition ofspatial variance difference. That is, spatial-EOF (S-EOF)analysis heavily dominates temporal variance of spatialstructure. In order to represent spatial variability of pig-ment concentrations, we applied the S-EOF analysis tothe CZCS data that are more densely sampled in spacethan in time.

We performed the S-EOF analysis procedure on thepigment concentrations in the order shown in the flow

chart (Fig. 2). To analyze for the spatial variance of theCZCS data, a data matrix Dmn with the spatial means re-moved from dmn is computed as:

D dM

dmn mn mnm

M

= − ( )=

∑12

1

where m is the number of spatially distributed data (totalgrid number M = 2448, which excludes the land and Pa-cific ocean data), n is the number of data over time. Wecalculated a 60-months data set through operating fivetimes on twelve months of data, assuming that there wasthe same seasonal change in five years (n is 12 months ×5 years = 60 months) to promote the statistical signifi-cance of seasonal change. For these data sets we applythe method suggested by Isoda et al. (1992) to this study.This reduces the size of the variance-covariance matrixto 2448 × 60. Moreover, S-EOF analysis using these datais performed by exchange of spatial (m) and temporal (n).

2.3 Solar radiation and MLD data setsSolar radiance data used in this study come from the

long-term monthly mean solar radiance during 1960~1990with the spatial resolution on a 1° × 1° grid, which iscalculated for each month by Hirose et al. (1996). Thearea covered by this data is from 23°N to 53°N in lati-tude and from 117°E to 143°E in longitude. We employedmonthly mean solar radiance as the Japan Sea subset fromthis database.

In order to derive monthly MLD, the monthly meantemperature data of the World Ocean Atlas 1994 CD-ROMseries (referred to herein as WOA94) 1° × 1° grid areused. WOA94 data included the temperature and salinitydata as the monthly mean data during 1900–1992. ThisWOA94 data comprises the global distribution maps ofthe data held in these files for 93 years (1900–1992), pro-duced by Levitus and Gelfeld (1992). We employedmonthly averaged temperature as the Japan Sea subsetfrom the WOA94 database. In the Japan Sea the varia-tions of density mainly depend on the temperature ratherthan the salinity variations (Kim and Isoda, 1998). Wethen defined the mixed layer depth as the depth at whichthe temperature was “sea surface temperature minus0.5°C”, after Obata et al. (1996).

2.4 CRD data setsCRD proposed by Sverdrup (1953) is defined by Eq.

(3) as follows

D

e k

I

Icr

kDe

ccr1

13

−= ( )−

Fig. 2. Flow chart for the spatial-EOF (S-EOF) analysis ofpigment concentrations derived from CZCS images.


where Dcr is the CRD, e is exponent, k is the extinctioncoefficient, Ic is the compensation light intensity, Ie isphotosynthetically active radiance at the surface, and theoverbar represents an average over a time interval.Parsons et al. (1984) reduced Sverdrup’s CRD as follows

Dk

I

Icro

c

= ( )14

where Io is the incident solar radiance just below the seasurface. For the extinction coefficient k, we use the an-nual mean of k calculated by Obata et al. (1996) based onthe optical water types drawn by Jerlov (1976). In theJapan Sea, the optical water types of k are type II (k =0.074) and III (k = 0.127). Obata et al. (1996) made theglobal CRD based on Sverdrup’s CRD and consideredthe light saturation of photosynthesis at midday. Whenan hourly Ie exceeded 21 W m–2, the CRD is calculatedby assuming that the production of phytoplankton in thewater column was constant within the depth at which theenergy is exponentially attenuated from the surface to 21W m–2. The energy (Ic) at the compensation depth wasconstant (1.7 W m–2). In this study we calculated CRDusing parameters of both Sverdrup (1953) and Parsons etal. (1984). We could estimate minimum CRD (Ic = 1.4W m–2) and maximum CRD (Ic = 6.3 W m–2), respec-tively, as reported by Parsons et al. (1984).

3. Results

3.1 Distribution of phytoplankton pigment concentrationsThe monthly mean pigment concentrations in the

western region (about 0.7 µg l–1) in the Japan Sea are notmuch different from those in the eastern region (about0.6 µg l–1) (Fig. 3). The temporal variance (standard de-viation) also shows the characteristics of the spatial vari-ability, where the western region is greater than the east-ern region.

Patterns of pigment concentrations during wintermonths (January–March) reveal a broad distribution ofpigment concentrations higher than 1.0 µg l–1 extendingoffshore from the northwestern coastal region (Fig. 4). Inthe northwestern region the pigment concentrations higherthan 1.0 µg l–1 with a crescent-shape linking Sogcho toChungjin appeared during January–February, but it ap-pears in the vicinity of 40°N along the polar front moredistinctly in March than in February.

High pigment concentrations over the Japan Sea ap-pear during April–May. The pigment concentrationshigher than 1.0 µg l–1 appear in the southern region of thepolar frontal zone in April, appear in the northern regionin May and then appear only in the vicinity of Vladivostokin June. The highest pigment concentrations (above 3.0

µg l–1) appear in the eastern coast of the Korean Penin-sula in April. Pigment concentrations lower than 0.2µg l–1 appear in the southeastern region in June.

During summer months (July–September), the pig-ment concentrations are low over the Japan Sea exceptfor the southwestern region. In September, in particular,isolated distributions of pigment concentrations higherthan 0.5 µg l–1 appear off the eastern coast of the KoreanPeninsula and pigment concentrations lower than 0.1µg l–1 appear in the center of the northern Japan Sea.

High pigment concentrations occur again in the Ja-pan Sea during the fall months (October–December). Abroad distribution of pigment concentrations higher than1.0 µg l–1 appears in the southwestern region in October,in the northeastern region in November, and then in thenorthwestern region in December.

3.2 CZCS-EOFsIn S-EOF analysis the space and time axes are re-

versed relative to the EOF decomposition of temporal

Fig. 3. Interpolated mean and standard deviation images for12 months. The gray tone goes from gray, for low concen-trations, to black, for high concentrations. The gray tonebar indicates that the mean image ranged from 2.5 µg l–1 tobelow 0.1 µg l–1 and standard deviation image ranged from0.2 µg l–1 to below 0.05 µg l–1.


Fig. 4. Monthly mean CZCS images. These data products are monthly global composite images of pigment concentrations withineach 18 km × 18 km pixel resolution from 1978 to 1986 (Feldman et al., 1989). The color bar goes from violet, for lowconcentrations, to red, for high concentrations.


variance. That is, the amplitude functions become spatialpatterns, and the eigenvectors become dimensionless timeseries describing the time histories of the modes. In thisstudy the first five modes of the pigment concentrationsanomalies, which are associated with about 69% of thetotal variance, were significant. Here we consider onlythree modes of the first-third to be significant since they

explain substantially more variance than the other modes.The first, second, and third modes of the S-EOF analysisexplain 20.3%, 15.3%, and 13.5% of the variance, respec-tively (Table 1). The first three modes explain about 50%of the all variance.

The first S-EOF mode of spatial variance (amplitudefunction) shows a primarily zonal pattern of north and

Fig. 5. Monthly change in amplitude function ((a), (c), and (e)) and temporal variation ((b), (d), and (f)) of eigenvectors for thefirst three S-EOF modes of spatial variance. The positive (negative) values were hatched by a white (gray) tone.


south regions along the polar front (Fig. 5(a)). The tem-poral evolution (eigenvector) denotes the polar front re-gion, which has a large seasonal variability, being posi-tive in fall–winter and negative in summer–fall (Fig. 5(b)).The most pronounced seasonal cycle is recorded withmaximum values in February and October.

The second S-EOF mode of spatial variance shows aroughly north and south pattern on the boundary alongthe polar front (Fig. 5(c)). In particular, large positivevalues (>0.025) appear off the Korean Peninsula. Nega-tive values appear over the northern end of the Japan Seaand a narrow-band shape offshore of Honshu. Large nega-tive values (<–0.025) appear off Vladivostok, and theseare shown hatched in a dark gray tone. The temporal evo-lution shows a positive during winter-spring, late sum-mer and early fall, and a negative in summer and late fall(Fig. 5(d)).

The third S-EOF of the spatial variances shows anorthwest pattern in the other areas, except for the north-west area (Fig. 5(e)). The temporal evolution shows nega-tive values in early winter and in early fall. The positivevalues appear in spring and late fall (Fig. 5(f)).

3.3 Regional differences in seasonal changes of pigmentconcentrationsSeasonal changes of pigment concentration can be

divided into four areas because each area from the re-sults of S-EOF analysis explores spatial variability simul-taneously. These areas are the northwestern part (KN),southwestern part (KS), northeastern part (JN), and south-eastern part (JS), as shown in Fig. 6. We selected repre-sentative areas of 270 km × 180 km near the center ofeach area as indicated by boxes. The sea surface tempera-ture (SST), MLD, solar radiance, and CRD data are alsosampled from the same areas.

The pigment concentrations show remarkable sea-sonal variations (Fig. 7(a)). The pigment concentrationsin all areas are high in spring and fall, and low in sum-mer. In the southern areas (KS and JS) the increase ofpigment concentrations occurs rapidly from March toApril, while the increase in the northern areas (KN andJN) occurs slowly as the month advances from Februaryto May. The spring bloom in KS and JS (in April) occursone month earlier than in KN and JN (in May). The pig-ment concentrations in the other areas, except for JN,decrease rapidly from May to June. The low pigment con-

centrations appear in August and September. However,KS retains relatively high pigment concentrations in Julyto September. The fall blooms occur from October toDecember. For JS and JN, which are located in the east-ern Japan Sea, the pigment concentrations increase rap-idly from September to October and the peaks appear inNovember. In KN the pigment concentration increasesslowly from September and a peak appears in December.In KS the pigment concentration starts increasing fromAugust and two peaks appear in October and December.The pigment concentration in the winter season is higherthan that in summer.

The SST shows a seasonal increase in four areas asthe month advances from March to August (Fig. 7(b)).Although the amplitudes of SST are different in each area,the seasonal variations of SST are similar. An annual av-erage of SST in the southern areas (KS and JS) shows thehigher values (about 4°C) than that in the northern areas(KN and JN). The SST increases from March due to seasurface heating and reaches a maximum value of 25.5°Cin August. After August, the SST decreases gradually andreaches a minimum value of 2.5°C in February–March.

The MLD in each area gradually shallows duringMarch to May (Fig. 7(c)). During May–June, the MLDdoes not vary over whole areas. The MLD deepens fromSeptember to February. Though our MLD definition dif-fers somewhat from the MLD defined as SST minus 1.0°Cby Kim and Isoda (1998), similar characteristics are foundthat the maximum MLD in the Japan Sea developed inJanuary and February.

The solar radiance shows a seasonal change in fourareas and the difference of amplitude is insignificant (Fig.7(d)). From January to May, the solar radiance graduallyincreases. The solar radiance reaches a maximum in May,ranging from 170 to 200 W m–2 through August. Afterthat, solar radiance decreases to a minimum (50~70W m–2) in December.

Mode number 1 2 3 4 5

EOF(%) 20.3 15.3 13.5 10.6 9.6

Fig. 6. Selected four regions (KN; northwestern part, KS; south-western part, JN; northeastern part, JS; southeastern part)of the Japan Sea. The areas of these regions were 270 km ×180 km.

Table 1. Percentage variance accounted for by the fiveeigenvectors of spatial-EOF from CZCS images.


Fig. 7. Seasonal changes of mean pigment concentration (a), sea surface temperature (b), MLD (c), and solar radiance (d) for thefour regions (as shown in Fig. 6) from January to December.

Fig. 8. Seasonal changes of MLD, minimum CRD, maximum CRD, and Sverdrup CRD for the four regions (as shown in Fig. 6)from January to December.


3.4 Comparison with CRD theoryThe compensation light intensities for phytoplankton

pigment are generally found to be in the approximaterange of Ic = 1.4~6.3 W m–2 (Parsons et al., 1984). Themaximum CRD based on the compensation light inten-sity of 6.3 W m–2 during spring shows that the CRD be-comes equal to MLD in KN in March and in JN in earlyMarch. The CRD becomes equal to MLD in KS in earlyApril and in JS in late March. The CRD during fall be-comes equal to MLD in late October or early November.On the other hand, the minimum CRD, using the com-pensation light intensity of 1.4 W m–2, shows that the CRDis always deeper than MLD, and conditions are theneuphotic, defined as the time when CRD becomes deeperthan MLD (Obata et al., 1996). Sverdrup (1953) suggestedthat the pigment concentrations start to increase whenCRD becomes equal to MLD. However, the minimumCRD and maximum CRD for the pigment concentrationsin the Japan Sea, as shown in Fig. 8, are not consistentwith the timing of the spring bloom suggested by Sverdrup(1953) and Obata et al. (1996). Here we should look foran optimized value of compensation light intensities from

Fig. 9. CRD, MLD, and pigment concentration. The CRD is calculated with a parameter of compensation light intensity of 3.8 W m–2.

1.4 to 6.3 W m–2 to adjust CRD for Sverdrup’s theoryusing Eq. (4). The adjustment of CRD is made so that thespring bloom begins after the CRD becomes equal to ordeeper than MLD and the peak of fall bloom appears whenMLD becomes equal to or deeper than CRD. As a result,we obtain the optimized value of compensation lightintensities of 3.8 W m–2 to fit CRD, as shown in Fig. 9.

The spring bloom for three areas (KN, JN, JS) isconsistent with the timing when CRD becomes equal toor deeper than MLD, as shown in Fig. 9. In particular,the spring bloom in KN begins in mid-February becausethe MLD deepens in February. The spring bloom in KS isnot consistent with the timing in the other areas. Thespring bloom in KS begins before CRD becomes equal toor deeper than MLD (Fig. 9(c)).

The fall bloom for three areas (KN, JN, JS) is con-sistent with the timing when MLD becomes deeper thanCRD (Obata et al., 1996). The fall bloom appears inNovember and December, except for KS. The peaks offall bloom in KS appear in early October when MLD isshallower than CRD and appear again in December whenMLD becomes deeper than CRD (Fig. 9(c)).


4. Discussion

4.1 Temporal and spatial variability of the pigment con-centrationsAs a result of the S-EOF analysis, the first three

modes accounted for 50% of the total variance in thepresent study. Abbott and Barksdale (1991) reported thatthe first and second modes accounted for 40% of the to-tal variance off central California, using CZCS data. Thelow variance implies that there is considerable unresolvedmesoscale variability in the CZCS data.

The first three modes are consistent with the coolingand heating seasons of the sea surface (e.g., Hirose et al.,1996). The first mode of the spatial pattern shows a sea-sonal variability dominated in the northern areas alongthe polar front from November to March and in the south-ern areas from July to October with deepening of MLDcaused by surface cooling convection and the seasonaldecrease of solar radiance (Fig. 7).

The second mode of spatial pattern dominates in thesouthern areas from winter to spring by deepening of CRDwith the spring increase of solar radiance and shallowingof MLD, and dominates the early fall with deepening ofMLD (Figs. 7, 8 and 9). This mode also dominates in thenorthern areas in late spring and fall with deepening ofMLD caused by the surface cooling convection and theseasonal decrease of solar radiance.

The spatial pattern of the third S-EOF dominates lo-cal blooms of spring and late fall in the northwest area,and dominates fall and winter in other areas, except forthe northwest area.

From winter to spring, the distributions of the highpigment concentration areas appeared at first in KN inFebruary–March, then appeared in KS and JS (the pig-ment concentration in KS was higher than that in JS) inApril, and finally appeared in KN and JN (the pigmentconcentration in KN was higher than that in JN) in May(Fig. 7(a)). From fall to winter, the distributions of thehigh pigment concentration areas appeared first in KS inOctober, then appeared in JS and JN (the pigment con-centration in JN was higher than that in JS), in Novemberand finally appeared in KN in December. These spatialoccurrences of blooms are summarized schematically inFig. 10.

The spring blooms in KS and JS appear in April (Fig.7(a)) when the MLD becomes shallower than CRD (Fig.8), which is same result as reported by Nagata and Ogawa(1997). Wroblewski (1989) and Obata et al. (1996) re-ported a similar relationship between the MLD and thespring bloom using a numerical model of the North At-lantic and global monthly data sets of a surface MLD andCZCS imagery. According to their models, when MLDbecomes shallower than CRD through the period fromMarch to May, the condition for the spring bloom is sat-

Fig. 10. Areas with higher pigment concentration than 1.0µg l–1 appeared in winter–spring and in fall–winter in thefour areas (KN, KS, JN, and JS). From winter to spring, thespatial occurrences of the high pigment concentration ap-peared first in KN in February–March, then in KS and JS inApril, and finally in KN and JN in May. From fall to win-ter, the spatial occurrences of the high pigment concentra-tion appeared first in KS in October, then in JS and JN inNovember, and finally in KN in December. The arrows in-dicate the sequence of areas with high pigment concentra-tions.

isfied for photosynthesis. The pigment concentration inKN was high even in February and March, although thepigment concentration in JN was low in February. Thepeaks of pigment concentration in KN and JN appear laterthan that in KS and JS. And also, for the four selectedareas in the southern and northern regions from March toApril, the difference of the average concentration (0.95µg l–1) of KS and JS was 3.8 times as high as the differ-ence of the average concentration (0.25 µg l–1) of KNand JN. This indicates that the pigment concentrations inKS and JS in comparison with the KN and JN may in-crease rapidly because the water temperature in the south-ern areas increases faster than in the northern areas withthe shallow MLD in spring.


4.2 Ecological and Physiological CRD parameterObata et al. (1996) suggested that the CRD might

become shallower than the Sverdrup value, if zooplanktongrazing increases the destruction rate of phytoplanktonor if the iron and other factors decrease the growth rateof phytoplankton. Although the parameters of Sverdrup’sCRD concept included grazing losses to macro-zooplankton and phytoplankton metabolic losses in hisdestruction term, the impact of zooplankton grazing onphytoplankton loss rate is often ignored. Zooplanktongrazing losses can be as important as destruction in con-trolling the spring bloom of phytoplankton. Here we de-fine an “Ecological CRD”, which takes account ofzooplankton grazing. We also define a “PhysiologicalCRD” at which total photosynthesis of the phytoplanktonin the water column equals destruction of thephytoplankton. When CRD changes from PhysiologicalCRD to Ecological CRD due to the zooplankton grazing,the destruction of phytoplankton overwhelmsphytoplankton photosynthesis. By contrast, when CRDchanges from Ecological CRD to Physiological CRD, thephytoplankton photosynthesis becomes more significantthan the destruction of phytoplankton.

The compensation light intensity is one of the CRDparameters, as shown in Eq. (4). As a result of the adjust-ment of the compensation light intensity, we have foundthat the Ecological CRD corresponds to the compensa-tion light intensity of 3.8 W m–2. If the compensation lightintensity is higher than 3.8 W m–2, the CRD becomes shal-low. On that occasion, the spring bloom may begin laterthan reported in this study and the fall bloom may beginearlier than in this study. If the compensation light inten-sity is less than 3.8 W m–2, the CRD becomes deep and itapproaches the state of Physiological CRD. In particular,the CRD, with a compensation light intensity of less than1.7 W m–2 (Sverdrup’s estimate), almost always becomesdeeper than MLD in each area (Fig. 8). The spring bloomin KS begins before the CRD becomes equal to or deeperthan MLD compared with other regions. An et al. (1994)presented the characteristics and fluctuations of structuresand spatial distributions of warm eddies in the southwest-ern part of the Japan Sea using the Fisheries Researchand Development Agency of Korea data from 1967 to1986. An et al. (1994) reported that the warm eddiesshould be divided into two groups; one group is a shal-low warm eddy with strong baroclinic characteristics andthe other is a deep one with strong barotropic character-istics. It is thus difficult to explain local distribution us-ing the long-term average data of eight years in KS re-gion, where a warm eddy exists as suggested by An et al.(1994). Furthermore, a local phenomenon in KS regionshould be examined in terms of the seasonal change fromcontinuous data such as SeaWiFs, which is now operat-ing as ocean color sensor on SEASTAR.

The Ecological CRD is well consistent with the threeareas, except for KS during fall bloom. There are twopeaks of the fall bloom that appear in KS. The fall bloomin this area cannot be explained using CRD adjusted bythe compensation light intensity of 3.8 W m–2. If the fallbloom in this area is to be explained in terms of CRDtheory, the compensation light intensity may have a largervalue than 3.8 W m–2 and the CRD may become shal-lower. Nagata and Ogawa (1997) suggested that an earlyfall bloom appeared in September and October in theTsushima warm current region with the nutrient-rich anddeep MLD. Imai et al. (1990) reported that the nutrient-rich water is supplied with the development of MLD dueto the northwest monsoon in October in the Tsushimawarm current region. Thus, the early fall bloom in KSindicates that the nutrient-rich water within the MLD maybe immediately consumed by the phytoplankton due tothe MLD being shallower than the CRD in October(Nagata and Ogawa, 1997).

5. SummaryThe temporal and spatial variability of phytoplankton

pigment concentrations in the Japan Sea have been de-scribed using monthly mean composite images of theCZCS. We used S-EOF analysis to explore the temporaland spatial variability in distributions of phytoplanktonpigment concentrations in the Japan Sea. The first S-EOFmode showed that the seasonal variation dominated inthe northern areas from fall to winter and in the southernareas from summer to fall with deepening of MLD causedby the surface cooling convection and the seasonal de-crease of solar radiance. The second S-EOF mode domi-nated in the southern areas with seasonal succession fromthe winter to spring, concomitant with a deepening ofCRD with the spring increase of solar radiance andshallowing of MLD and dominated the early fall withdeepening of MLD. This mode also dominated in thenorthern areas in late spring and fall with deepening ofMLD caused by the surface cooling convection. The thirdS-EOF mode dominated local blooms of spring seasonand late fall in the northwest area.

In order to describe the seasonal changes of pigmentconcentration from the results of the above S-EOF analy-sis, we selected four areas of the south and north regionsin the Japan Sea. The pigment concentrations among fourareas showed remarkable seasonal variations. The twoblooms appeared in spring and fall. Spring bloom in theJapan Sea began in February and March when CRD be-came equal to MLD. The spring blooms appeared in Apriland May when CRD became deeper than MLD. The springbloom in the southern areas (in April) occurred one monthearlier than in the northern areas (in May). The fall bloomappeared in November and December when MLD be-comes equal to CRD. The fall bloom can be explained by


deepening of MLD in the Japan Sea. The fall bloom ap-peared first in the southwest region, then in the southeastand northeast regions, and finally in the northwest region.The pigment concentration in winter was higher than thatin summer. The low pigment concentrations dominatedin summer.

AcknowledgementsWe thank Dr. D. L. Eslinger of University of Alaska

Fairbanks for his helpful comments on an earlier version.Thanks are Dr. A. Obata of Meteorological Research In-stitute for providing the CRD data sets, Japan Oceano-graphic Data Center for providing the WOA94 CD-ROMdata series and Mr. N. Hirose of Kyushu University forproviding the solar radiance data sets. Thanks are ex-tended to Dr. M. Shimizu and Mr. M. Shonai of HokkaidoUniversity for their kind help with the EOF analysis. Wealso thank Dr. K. Yoneta and Mr. H. Onishi of HokkaidoUniversity and Dr. K. D. Cho of Pukyong National Uni-versity for their encouragements during this study. Thepresent work was supported in part by the Basic ResearchProgram for the Global Ocean Observing System (GOOS)under the Ministry of Education, Science, Sports andCulture, Japan. A part of this study was also supported byNational Space Development Agency of Japan (NASDA)for enhancing the activities of Japanese researchers inNASA SeaWiFS and SIMBIOS Science Programs.

ReferencesAbbott, M. R. and B. Barksdale (1991): Phytoplankton pigment

patterns and wind forcing off central California. J. Geophys.Res., 96, 14649–14667.

An, H. S., K. S. Shim and H. R. Shin (1994): On the warmeddies in the southwestern part of the East Sea (the JapanSea). J. Oceanol. Soc. Korea, 29, 152–163.

Banse, K. and D. C. English (1994): Seasonality of coastal zonecolor scanner phytoplankton pigment in the offshore oceans.J. Geophys. Res., 99, 7323–7345.

Eslinger, D. L., J. J. O’Brien and R. L. Iverson (1989): Empiri-cal orthogonal function analysis of cloud-containing coastalzone color scanner images of northeastern North Americancoastal waters. J. Geophys. Res., 94, 10884–10890.

Feldman, G. C., N. Kuring, C. Ng, W. Esaias, C. McClain, J.Elrod, N. Maynard, D. Enderes, R. Evans, J. Brown, S.Walsh, M. Carle and G. Podesta (1989): Ocean color. Avail-ability of the global data set. Eos Trans, AGU, 70, 634–635, 640–641.

Fuentes-Yaco, H., A. F. Vezina, P. Larouche, Y. Gratton and M.Gosselin (1997): Phytoplankton pigment in the Gulf of St.Lawrence, Canada, as determined bye coastal zone colorscanner—part II: multivariate analysis—. Cont. Shelf Res.,17, 1441–1459.

Fukushima, H. and J. Ishizaka (1993): Special features and ap-plications of CZCS data in Asian waters. p. 213–236. In

Ocean Color: Theory and Applications in a Decade of CZCSExperience, ed. by V. Barale and P. M. Schlittenhandt,Kluwer Acad., Norwell. Mass.

Fukushima, H. and M. Toratani (1997): Asian dust aerosol:Optical effect on satellite ocean color signal and a schemeof its correction. J. Geophys. Res., 102(14), 17119–17130.

Gordon, H. R., J. W. Brown, O. B. Brown, R. H. Evans and W.W. Broenkow (1983): Phytoplankton pigment concentrationin the Middle Atlantic Bight: Comparison between shipdeterminations and coastal zone color scanner estimates.Appl. Opt., 22, 20–36.

Hirose, N., C. H. Kim and J. H. Yoon (1996): Heat budget inthe Japan Sea. J. Oceanogr., 52, 553–574.

Imai, M., S. Ebara, H. Okimura and K. Kadono (1990): On thenutrients in the Tsushima warm current water. Umi to Sora,66, 93–111 (in Japanese with English abstract).

Ishizaka, J., H. Fukushima, M. Kishino, T. Saino and M.Takahashi (1992): Phytoplankton pigment distributions inregional upwelling around the Izu Peninsula detected bycoastal zone color scanner on May 1982. J. Oceanogr., 48,305–327.

Isoda, Y., S. Saitoh and M. Mihara (1992): Seasonal variationsof SST patterns in the Japan Sea. Umi to Sora, 68, 15–26(in Japanese with English abstract).

Jerlov, N. G. (1976): Marine optics. Elsevier Oceanogr. Ser.,14, 231 pp., Elsevier, New York.

Kim, S. W. and Y. Isoda (1998): Interannual variations of thesurface mixed layer in the Tsushima Current Region. Umito Sora, 74, 11–22 (in Japanese with English abstract).

Lagerloef, G. S. E. and R. L. Bernstein (1988): Empiricalorthogonal function analysis of advanced very high resolu-tion radiometer surface temperature patterns in Santa BabaraChannel. J. Geophys. Res., 93, 6863–6873.

Levitus, S. and R. Gelfeld (1992): NODC inventory of physi-cal oceanographic profiles. Key to oceanographic recordsdocumentation, No. 18, NODC, Washington, D.C.

Nagata, H. and Y. Ogawa (1997): Seasonal variability of trans-parency and its relationship to the Critical Depth in the seaadjacent to Japan. Umi no Kenkyu, 6, 351–360 (in Japanesewith English abstract).

Obata, A., J. Ishizaka and M. Endoh (1996): Global verifica-tion of critical depth theory for phytoplankton bloom withclimatological in situ temperature and satellite ocean colordata. J. Geophys. Res., 101, 20,657–20,667.

Parsons, T. R., M. Takahashi and B. Hargrave (1984): Biologi-cal Oceanographic Processes. 3rd ed., Pergamon Press, 330pp.

Sverdrup, H. U. (1953): On conditions for the vernal bloomingof phytoplankton. J. Cons. Int. Explor. Mer., 18, 287–295.

Wroblewski, J. S. (1989): A model of the spring bloom in theNorth Atlantic and its impact on ocean optics. Limnol.Oceanogr., 34, 1,563–1,571.

Yoder, J. A., C. R. McClain, G. C. Feldman and W. E. Esaias(1993): Annual cycles of phytoplankton chlorophyll con-centrations in the global ocean: A satellite view. GlobalBiogeochem. Cycles, 7, 181–193.

temporal and spatial variability of phytoplankton...

Documents