Processes controlling interannual variations in wintertime (Northeast Monsoon) primary productivity in the central Arabian Sea

Deep-Sea Research II 49 (2002) 2319–2343

Processes controlling interannual variations in wintertime (Northeast Monsoon) primary productivity in the central Arabian Sea J.D. Wiggerta,*, R.G. Murtuguddea, C.R. McClainb a

Interdisciplinary Center, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742-2465, USA b NASA Goddard Space Flight Center, Code 970.2, Greenbelt, MD 20771, USA Received 28 February 2001; received in revised form 28 September 2001; accepted 1 December 2001

Abstract Three years of ocean color observations obtained by SeaWiFS reveal significant interannual variation in surface chlorophyll a (Chl a) concentrations in the central Arabian Sea during the Northeast (winter) Monsoon (NEM). Consistent with previous findings in the literature, no obvious relation to sea-surface temperature is apparent. A strong relationship with interannual variability in thermocline depth has been established using an interannually forced ocean general circulation model (OGCM). This relationship consists of reduced Chl a concentration associated with a deeper thermocline. Deeper winter convection is generally associated with higher nutrient concentrations and therefore higher phytoplankton biomass. Both in situ observations from the US JGOFS Arabian Sea Expedition and net wintertime nutrient entrainment estimated with the OGCM simulation indicate that mixed-layer concentrations are always sufficiently high to be non-limiting for phytoplankton growth. This raises two questions. What process(es) check phytoplankton growth? What leads to the observed relationship between deeper thermocline and reduced chl a concentration? A prominent feature of the NEM is a large-amplitude diurnal cycle of the mixed layer that is evident in moored temperature time-series. We hypothesize that the night-time penetration of this diurnal mixing, which is defined by the interannually varying thermocline depth, determines the magnitude of phytoplankton biomass that will be retained in the euphotic zone for the following photoperiod. This daily dilution acts to check the accumulation of phytoplankton biomass and prevents a full phytoplankton bloom. A simple 1-D model has been developed to quantify this process. An excellent correspondence exists between model-predicted mixed-layer Chl a concentration as it varies with thermocline depth and the similarly represented SeaWiFS observations. r 2002 Published by Elsevier Science Ltd.

1. Introduction The seasonally reversing winds of the monsoon system drive the Arabian Sea’s dynamic physical environment. The boreal winter component of this *Corresponding author. Tel.: +1-301-405-4971; fax: +1301-405-8468. E-mail address: [email protected] (J.D. Wiggert).

annual cycle is referred to as the Northeast (winter) Monsoon (NEM) and typically occurs from early November through mid-February. Within the lower troposphere, this period is characterized by a cool, dry northeasterly flow that emanates from the atmospheric high-pressure region situated behind the Tibetan Plateau and propagates across the Arabian Sea (Fig. 1). Typical attributes of the offshore flow over this

0967-0645/02/$ - see front matter r 2002 Published by Elsevier Science Ltd. PII: S 0 9 6 7 - 0 6 4 5 ( 0 2 ) 0 0 0 3 9 - 5

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Fig. 1. AVHRR-observed SST and NCEP wind vectors illustrating typical NE Monsoon conditions for the Arabian Sea. The solid orange line represents the standard southern line cruise track for the US JGOFS Arabian Sea Expedition. The bowtie-shaped portion is specific to the SeaSoar cruises. The central mooring (’) is located at the center of the bowtie. The dashed rectangular region denotes the focus region that is consistently applied to the remotely sensed and OGCMpredicted oceanic properties we present.

3-month period are moderate wind stresses (j t, jE0.07 N/m2), air temperatures around 11C lower than sea-surface temperatures and a relative humidity (RH) of B70% (Weller et al., 1998). This low RH drives evaporation that, within a 3-month period, accounts for E60% of the region’s annual net E–P (i.e. evaporation–precipitation) of 1000 mm/yr (Tomczak and Godfrey, 1994) and elevates surface salinity (Weller et al., 2002). The momentum, heat and buoyancy fluxes resulting from these atmospheric conditions promote oceanic convective mixing that leads to a cooler and saltier surface layer as the thermocline erodes and the mixed layer deepens over the winter. Below this surface layer, a sharp, permanent thermocline and nutricline have been consistently observed between 100 and 150 m. Within and below the nutricline, nitrate concentrations exceed 15 mM (Ryther and Menzel, 1965; Morrison et al., 1998). This substantial nutrient pool is readily entrained into the surface layer during the NEM by the

convective mixing just described (Madhupratap et al., 1996; Wiggert et al., 2000). Within this physical and chemical context, phytoplankton biomass and primary productivity historically exhibit significant interannual variation in the central Arabian Sea. In situ incubations from two separate NEM cruises during the US JGOFS Arabian Sea Expedition found areal primary production to be 1.64 gC/m2/d (February 1995) and 1.06 gC/m2/d (November 1995) (Barber et al., 2001). The magnitude of these observations from two successive NEMs came as a surprise (Barber, pers. comm.) since they were up to five times greater than reported previously (Kabanova, 1968; Banse, 1987; Bauer et al., 1991). However, values that are reasonably consistent with those determined during the US JGOFS cruises also have been reported (Ryther et al., 1966; Madhupratap et al., 1996). Notable interannual variability was prominently featured in ocean-color distributions from the first 2 years of Coastal Zone Color Scanner (CZCS) observations, which showed a two- to three-fold difference in chlorophyll a (Chl a) concentration between the winters of 1978/1979 and 1979/1980 (Banse and McClain, 1986). A review of all available CZCS observations shows close to a five-fold range (0.15–0.72 mg Chl a/m3) in monthly mean pigment concentrations during the NEM within the central Arabian Sea (Banse and English, 2000). Literature values of near-surface Chl a concentration from in situ observations within this region and time period also exhibit significant interannual variability, ranging from 0.1 to 1.2 mg Chl a/m3 (Banse, 1987; Bauer et al., 1991; Madhupratap et al., 1996; Gundersen et al., 1998). However, caution is warranted in interpreting these data, since prior to 1980 picophytoplankton were not included in biomass measurements (Banse and McClain, 1986; Bauer et al., 1991). Hypotheses attempting to account for this interannual variability have been introduced in the literature. Proceeding from the assumption that wintertime (NEM) primary productivity is nutrient-limited in this region, clear relationships between surface Chl a (from CZCS), sea-surface temperature (SST) and surface wind speeds (i.e., enhanced mixing leads to cooler temperatures and

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higher Chl a) were not satisfactorily established (Banse and McClain, 1986). A subsequent reexamination of these data, stimulated by significant improvements in the CZCS pigment algorithms, did not substantially revise the previous ambiguities that arose when attempts were made to identify a physical control that generated the observed interannual variation in pigment concentration. However, it was suggested that interannual differences in phytoplankton-grazerpredator balance could enhance nutrient recycling within the euphotic zone and lead to higher Chl a concentrations (Banse and English, 1993). In situ observations from the recent US JGOFS Arabian Sea Expedition provided several insights into the physical and biogeochemical processes that occur during the NEM. Moored time-series recorded a prominent diurnal cycle in the mixed layer that shoaled to 10–15 m during the day and penetrated down to the seasonal thermocline (B95 m) at night (Wiggert et al., 2000). This homogenized the surface layer’s physical and biogeochemical properties, nominally on a daily basis (Gardner et al., 1999). As already noted, the cumulative effect of this mixing was for the surface waters to become cooler, saltier, and nitrate-enriched. In conjunction with this standard winter scenario, mixed-layer Chl a concentrations increased (Wiggert et al., 2000). An additional point of interest was provided by nutrient-uptake experiments performed on cruises that took place during successive (January 1995, December 1995) NEMs (McCarthy et al., 1999). Results from these two sets of experiments indicated that primary production was principally supported by the uptake of ammonium (mean f-ratios were 0.15 and 0.13, respectively). Furthermore, as nitrate was entrained during the NEM, its residence time increased from 1 to 3 months, which demonstrated that while rates of primary productivity increased two-fold, nitrate slowly accumulated in the surface layer and was available for the subsequent spring bloom (McCarthy et al., 1999). These observations indicate that despite relatively high rates of primary productivity, some physical and/or biological control limits the full utilization of available nutrients. Recent studies suggest that this control consists of a combination

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of zooplankton grazing and the mixed layer’s diurnal cycling. During the NEM, microzooplankton grazing accounted for 86% of phytoplankton growth (Landry et al., 1998). Furthermore, estimated rates of fecal pellet production by mesozooplankton were significantly higher than the observed rates of export production (Roman et al., 2000). These studies suggest significant grazing control and recycling of biogenic material within the mixed layer, which is consistent with the f -ratios determined during the nutrient-uptake experiments already noted. The diurnal behavior of the mixed layer during the NEM has been the focus of two recent modeling studies. Both show that mixing down to the permanent thermocline on a daily basis repeatedly homogenizes all biogeochemical constituents well into the subeuphotic zone, which checks the accumulation of phytoplankton biomass in the euphotic zone (Wiggert et al., 2000; McCreary et al., 2001). Furthermore, Wiggert et al. (2000) demonstrated that a significant reduction in surface Chl a concentrations occurred when a downward shift of 15–30 m in thermocline position was imposed on the initial hydrographic structure. This last result suggests an alternative physical mechanism capable of generating the interannual variation in Chl a concentration and primary productivity that has been observed in the central Arabian Sea during the NEM. This mechanism has a unique juxtaposition of physical processes, with disparate time-scales, consisting of the superposition of a diurnally cycling mixed layer on the depth of the permanent thermocline, which varies interannually. In this report, we present surface Chl a distributions from the first three NEM periods observed by the Sea-viewing Wide Field of view Sensor (SeaWiFS). These reveal notable interannual variability in the NEM surface Chl a concentrations of the central Arabian Sea, to which we apply the thermocline-shift hypothesis just described. To this end, we examine several remote sensing data sets and the results of an interannually forced ocean general circulation model (OGCM) in order to determine whether interannual variations in the ocean’s physical properties coincide with those observed in the SeaWiFS

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imagery. In addition, we use climatological data and in situ observations from the US JGOFS Arabian Sea Expedition to explore the interplay between nutrient concentration, photosynthetically available radiation (PAR), diurnal mixing and thermocline depth and their relative importance in determining mixed-layer Chl a concentration. The physical and bio-optical characterization revealed by these in situ observations defines the constraints of a simple 1-D model constructed to quantify the impact of diurnal mixing on surface Chl a concentration.

by SeaWiFS to date. We also have examined, but do not present, aerosol index (AI) distributions provided by the total ozone mapping spectrometer (TOMS), since significant concentrations of atmospheric aerosols could contaminate the SeaWiFS observations (Andre! and Morel, 1989; Gordon, 1997). However, given the prevailing atmospheric conditions over the Arabian Sea typical of the NEM, it was not surprising that the TOMS/AI distributions showed no indication that aerosols degraded the ocean color data and contributed to its interannual variability. 2.2. Interannual variability: OGCM description

2. Methods 2.1. Interannual variability: remote sensing data sets Throughout this analysis, we focus on variability of oceanic properties over a region of the central Arabian Sea delimited by 15–181N and 60–651E (Fig. 1). This region is consistent with one of several previously defined regions within published studies of seasonal and interannual pigment variability in the Arabian Sea (e.g., Banse and McClain, 1986; Banse and English, 1993) and is depicted on all relevant figures. Retaining this region as a focal point in the present study allows us to compare our findings directly with the analyses that appear in these previous reports as well as the listing of mean monthly CZCS pigment concentrations presented in Banse and English (2000). The SeaWiFS imagery presented here consists of level 3, 8-day ocean color (v3) obtained from the Goddard DAAC (http://daac.gsfc.nasa.gov/). We also present monthly sea-surface temperature anomaly (SSTA) (Reynolds and Smith, 1994) and Ekman upwelling velocity ðwe Þ: The latter data were derived from NCEP re-analysis monthly surface (10 m) wind vectors (Kalnay et al., 1996), using a simple velocity to stress conversion (Apel, 1987). The SSTA and NCEP wind vector data sets were obtained from the IRI/LDEO climate data library (http://iri.ldeo.columbia.edu/). They provide a means of assessing whether interannual variability in physical processes was significant over the three NEM periods observed

To gain insight into the likelihood and extent of interannual variations in thermocline depth during the NEM, we examined the results of an interannually forced OGCM. This model is a reduced gravity, primitive equation, sigma coordinate scheme that has been extensively used in a number of recent climate studies of the tropical oceans, and the Indian Ocean in particular (Murtugudde and Busalacchi, 1999; Murtugudde et al., 2000). The model grid consists of 1/21 (longitudinal) by 1/31 (latitudinal) in the horizontal and 20 layers in the vertical, with the surface layer being a variabledepth mixed layer. Some of the model’s principal features include: (1) coupling to an advective atmospheric mixed layer (Seager et al., 1995) and (2) a hybrid vertical mixing scheme (Chen et al., 1994). The time period of the simulation whose results we present is 1958–2000. Interannual forcing is supplied by surface momentum flux, which is based on daily winds from the NCEP re-analysis (Kalnay et al., 1996; Kistler et al., 2001). The NCEP wind fields also are included in the determination of air–sea exchanges of latent and sensible heat, causing these boundary conditions to vary interannually as well. All other boundary conditions (i.e., long-wave heat flux, solar radiation and freshwater fluxes) have been based on climatological data. Mean temperature and nitrate profiles were extracted from the NODC 1998 World Ocean Atlas (Antonov et al., 1998; Conkright et al., 1998). In conjunction with the hydrographic profiles obtained during the US JGOFS Arabian

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Sea Expedition, these led to the choice of the 211C isotherm as a proxy for the permanent thermocline and nitracline in the central Arabian Sea. A series of spatial distributions of 211C isotherm depth ðZ21 Þ were extracted from the OGCM simulation, and a time-series of the mean value within the region defined above was created for each NEM period of interest. We also extracted mixed-layer deepening from the end of the Fall Intermonsoon through the NEM. This deepening, in conjunction with a T-NO3 relation based on the NODC seasonal (fall) climatology, provides an estimate of total NO3 entrainment into the mixed layer due to wintertime (NEM) convection. These derived quantities allow for an assessment of how the combination of hydrographic structure and nutrient availability relates to variations in surface Chl a concentration. 2.3. Diurnal variability: in situ observations Some observations from the 1994/1995 US JGOFS Arabian Sea Expedition are presented to illustrate the temperature structure of the water column and the amplitude and persistence of the mixed layer’s diurnal cycle. Data obtained during these surveys also provide initial profiles and surface boundary conditions for the 1-D model used to explore how diurnal cycling of the mixed layer affects phytoplankton growth and areal accumulation. These data were obtained from two observational platforms. The first was a moored array that recorded time-series of the upper ocean’s physical and bio-optical properties. Heat, momentum and buoyancy fluxes across the air–sea interface also were measured by the meteorological instrumentation deployed on the mooring’s surface buoy (Dickey et al., 1998; Weller et al., 1998). The second was the SeaSoar, a towed, undulating instrument platform that provided quasi-synoptic spatial observations of a full suite of physical and biogeochemical parameters over the upper 300 m (Brink et al., 1998). The SeaSoar data used here were obtained on 4 December 1994 and are from the bowtie-shaped portion of the cruise track (Fig. 1). The moored array was located at the center of the bowtie (15.51N, 61.51E).

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From the moored observations, we present temperature time-series from several depths to demonstrate variability within the mixed layer and the upper thermocline. The full complement of moored temperature records, which included 27 thermistors deployed through the upper 100 m, were used to create time-series of mixed-layer depth ðZML Þ: Two criteria (DT from surface temperature of 0.11C and 1.01C) were used to define the depth of the mixed layer. The 0.11C criterion illustrates variations in stratification sufficient to impact phytoplankton growth by suppressing vertical mixing within the euphotic zone. The 1.01C criterion emphasizes mixing that penetrates the permanent thermocline, thereby illustrating episodes of nitrate entrainment. It is worth noting that salinity certainly must play an important role in the hydrodynamic structure since the ongoing evaporation results in a surface layer that is 0.25–0.5 psu greater than the waters of the permanent thermocline (Wiggert et al., 2000; Weller et al., 2002). However, there were more thermistors than salinometers, which made it easier to distinguish between two mixed-layer definitions. Time-series of downwelling shortwave radiation ðQSW ðtÞÞ from the mooring’s surface buoy were used to estimate downwelling irradiance just below the sea-surface IO ðtÞ; which is subsequently used to determine the subsurface light field (see Section 2.4). The SeaSoar data used here consist of measurements of PAR and stimulated fluorescence. These fluorescence measurements were transformed to Chl a concentrations with a calibration curve derived from shipboard, underway Chl a measurements (D. Phinney, pers. comm.) and the near-surface SeaSoar fluorometer voltages. The resulting spatial observations of Chl a were the basis for the initial profile required by the primary productivity model, described below. 2.4. Diurnal variability: the primary productivity model The propagation of QSW ðtÞ across the air–sea interface to estimate surface PAR ðIO ðtÞÞ and its subsequent attenuation as downwelling irradiance to generate the subsurface PAR field ðIðz; tÞÞ were accomplished with previously applied methods

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(Wiggert et al., 2000). The primary productivity model we apply (Eqs. (1)–(4), listed below) is also based on that presented in the previous report, except nutrients are now assumed to be nonlimiting. The assumption that nutrients do not limit phytoplankton growth rates during the NEM is based on evidence presented in Section 3.3. One consequence of this assumption is that the phytoplankton growth rate (p1 ðz; tÞ; Eq. (2)) is determined solely by Iðz; tÞ: Phytoplankton concentration ðNa ðz; tÞÞ dNa ¼ ðp1  r1  g1 ÞNa : dt

ð3Þ

Carbon to chlorophyll ratio ðYðz; tÞÞ Y¼




achl Ifm þ bY pmax

pmax

achl

Ym bY

Phytoplankton losses (death and cell lysis) Zooplankton grazing rate Maximum phytoplankton growth rate Chlorophyll specific absorption Maximum quantum yield Minimum C:Chl ratio Empirical constant

d1

0.01

d1

0.28

d1

1.30

m2/g Chl a

Value

11.1

molC/Ein molC/g Chl a —

0.06 2.00 15.00

2.5. Diurnal variability: determination of biooptical characteristic depths (ZEU and ZCR )

I : I þ ðpmax Y=achl fm Þ

Y2m

g1

Units

ð2Þ

Light limitation ðrn ðz; tÞÞ

"

r1

Descriptiona

a More detailed information on these parameters appears in Wiggert et al. (2000).

Phytoplankton growth rate ðp1 ðz; tÞÞ

rn ¼

Symbol

fm

ð1Þ

p1 ¼ pmax drn :

Table 1 Constant terms used in the primary productivity model

2 #1=2 :

ð4Þ

The values applied for the constant terms in Eqs. (1)–(4) ðr1 ; g1 ; pmax ; achl ; fm ; Ym ; by Þ are listed in Table 1 and are consistent with those used in the interdisciplinary model described in Wiggert et al. (2000). The constant rates applied in Eq. (1) (r1 and g1 ) represent losses due to cell lysis and zooplankton grazing, respectively. The value for g1 was based on in situ wintertime observations from the US JGOFS process cruises (Caron and Dennett, 1999). Eqs. (3) and (4) are taken from the bio-optical production model developed by Kiefer (1993). Terms in Eqs. (1)–(4) that vary in depth and time are indicated in the descriptive header for each equation. The one additional term that varies with depth and time is Iðz; tÞ: The reader should note that the spatio-temporal variability of these five terms (I; Na ; p1 ; rn ; and Y) has not been explicitly included in the above set of equations.

Two bio-optical characteristic depths have been determined, which, in combination with the timeseries of mixed-layer depth, provide insight into whether the physical environment will support or inhibit the accumulation of phytoplankton biomass. These are the euphotic-zone depth (ZEU ; i.e., 1% light level) and the critical depth ðZCR Þ: The SeaSoar PAR measurements were used to calculate ZEU ; which is commonly interpreted as the lower boundary for net primary production and is also referred to as the compensation depth. A representative 24-h time-series of QSW ; used to generate PAR (Iðz; tÞ; Section 2.4), and a Chl a initial profile based on the SeaSoar measurements (Section 3.5), were used in the primary productivity model to calculate ZCR : Briefly, this consists of generating a 24-h time-series of phytoplankton growth rate profiles ðp1 ðz; tÞÞ with the primary productivity model and averaging these over time to form a 24-h mean profile ðp1 ðzÞÞ: The mean phytoplankton growth rate within the mixed layer can then be obtained by averaging this profile down to ZML : Performing a similar procedure on the phytoplankton loss terms (phytoplankton respiration ðr1 Þ and zooplankton grazing ðg1 Þ; Table 1) and subtracting them from the growth term provides us with a mean rate of

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phytoplankton accumulation (or loss) within the mixed layer. This procedure is represented mathematically by Eq. (5).
 Z ZML
 Z 24 1 1 p1 ðz; tÞ dt dz ZML 24 0 0
 Z ZML
 Z 24 1 1 ðg1 þ r1 Þ dt dz ¼ a1 :  ZML 24 0 0 ð5Þ The functional definition of ZCR is the depth at which the accumulation rate ða1 Þ is identically zero (Mann and Lazier, 1991). The position of ZML relative to ZCR provides an indicator of whether hydrographic conditions will promote or inhibit the accumulation of phytoplankton biomass within the mixed layer (Sverdrup, 1953). In other words, if ZML oZCR then a1 > 0 and phytoplankton biomass will accumulate in the mixed layer, whereas if ZML > ZCR then a1 o0 and a net loss of phytoplankton biomass within the mixed layer will occur. 2.6. Diurnal variability: physical processes imposed on the vertical distribution of phytoplankton Previously, the primary productivity model described in Section 2.4, including the nutrient equations that were eliminated, was applied as the biological component of a coupled model that included a turbulence closure scheme (Wiggert et al., 2000). In that case, mixed-layer dynamics were simulated by forcing the model with the seasurface boundary conditions recorded by the meteorological array on the mooring’s surface buoy (Weller et al., 1998). In the present application, we have further simplified the model configuration by removing the turbulence closure scheme and imposing a diurnal mixed layer whose characteristics (i.e., phase and amplitude) have been based on the moored observations presented in the results (Section 3.4). We have also applied an algorithm that emulates irradiance fluctuations caused by the vertical cycling of cells in the (diurnal) mixed layer due to wind stirring, based on the arguments presented in Denman and Gargett (1983). Such vertical cycling within the mixed layer has been observed using neutrally

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buoyant floats (D’Asaro et al., 1996). Below the daytime mixed layer (i.e. within the diurnal thermocline), a static light profile was implemented such that irradiance at depth is solely a function of attenuation by seawater and phytoplankton. After sundown, the phytoplankton profile (Na ðzÞ; Eq. (1)) was redistributed such that concentrations became uniform down to the seasonal thermocline ðZTH Þ; which was treated as an impenetrable barrier.

3. Results 3.1. Interannual variability: remote sensing observations Distributions of surface Chl a are shown in Fig. 2. These are 8-day composite images taken at monthly intervals for the first three NE Monsoons observed by SeaWiFS (97/98, 98/99, 99/00). The complete sequence of concentrations within the focus region for the entire NEM period (November to mid-February) is listed in Table 2. The SeaWiFS imagery reveals a consistent seasonal behavior that starts with a notable increase in Chl a between mid-November and mid-December, which is followed with no appreciable change over the following month. The seasonal evolution is highlighted by the month-to-month percent change in Chl a concentration within the focus region (Table 2). The mean percent increase between the November and December images ranges from 60.5% to 112.9%, while between December and January, the mean percent change in Chl a concentration over all 3 years is 715%. The subsequent images from mid-February have been included to illustrate the winter to spring transition, revealing Chl a concentrations that are consistently higher than during the previous months. The mean concentration within the focus region increased by at least 32% from the previous month, with more than a doubling in surface Chl a indicated for the 98/99 NEM (Table 2). The mean mid-February concentration for these 3 years ranges from 0.82 to 1.37 mg Chl a/m3. The transition from the NEM to the Spring Intermonsoon is marked by a reduction in surface

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Fig. 2. Three-year intercomparison of Chl a distributions observed by SeaWiFS during the NE Monsoon. These are 8-day composite images from the level 3, global area coverage data (v3) and were obtained from the Goddard DAAC (http://daac.gsfc.nasa.gov/). For a given NEM period, one image is shown from each month affected by the NE Monsoon (November–February). These images reveal the typical seasonal progression as well as significant interannual variation. The color bar has been designed to provide information on variability in pigment concentrations up to 4 mg Chl a/m3, which emphasizes the observed interannual variation. The white box highlights the focus region (15–181N and 60–651E).

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Fig. 2 (continued).

wind stress and outgoing latent heat flux. This shuts down the convective mixing and allows the mixed layer to shoal, triggering the spring

phytoplankton bloom that manifests as the observed increase in phytoplankton biomass revealed in the mid-February imagery (Fig. 2).

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2328 Table 2 SeaWiFS 8-day imagery 8-Day period NEM 97/98 1–8 Nov 9–16 Nov 17–24 Nov 25 Nov–2 Dec 3–10 Dec 11–18 Dec 19–26 Dec 27–31 Dec 1–8 Jan 9–16 Jan 17–24 Jan 25 Jan–1 Feb 2–9 Feb 10–17 Feb Mean over entire NEM

Chl a 98/99

(mg/m3) Y2/Y1a Y3/Y1a 99/00

0.37 0.36 0.37 0.39

0.44 0.48 0.54 0.57

0.53 0.46 0.73 0.73

1.20 1.34 1.45 1.46

1.45 1.28 1.95 1.86

0.48 0.46 0.48 0.59 0.52 0.51 0.47 0.69 0.68 0.82 0.51

0.65 0.88 0.93 0.96 0.73 0.70 0.75 0.66 0.91 1.37 0.75

0.75 0.72 0.70 0.68 0.84 0.68 0.64 0.66 0.98 0.84 0.71

1.35 1.90 1.92 1.61 1.40 1.38 1.60 0.96 1.33 1.67 1.47

1.54 1.56 1.44 1.15 1.61 1.35 1.37 0.95 1.44 1.03 1.43

Intercomparison periods Nov–Dec 60.5 112.9 Dec–Jan 15.1 13.3 Jan–Feb 69.7 111.2 4.04 1.90 Entrained nitrate (mM)

67.9 0.0 31.7 2.72

Mean Reynolds SSTA (1C) Nov 0.57 0.58 Dec 0.63 0.58 Jan 0.59 0.59 Feb 0.54 0.50 Mean we (m/d) Nov 0.08 Dec 0.10 Jan 0.11 Feb 0.05 a

0.03 0.09 0.07 0.07

0.24 0.50 0.43 0.10

0.14 0.14 0.06 0.05

The ratios Y2/Y1 and Y3/Y1 consist of the two latter Chl a concentration time-series normalized by the 97/98 time-series. This emphasizes and, provides a quantification of, the reduced concentrations observed during the 97/98 NEM. The Chl a concentrations (mg Chl a/m3) listed here are the spatial mean within 15–18 N, 60–65 E. The mean concentration for the entire 3.5 month period has been calculated. The percent change in observed Chl a concentration between months is shown for the three intercomparison periods. The estimate for entrained nitrate concentration by the end of the NEM period, mean Reynolds SSTA and mean Ekman velocity ðwe Þ are also included.

In addition to this wintertime progression in Chl a concentration, the Chl a distributions reveal significant interannual variation between the three NEMs shown here. Until mid-February, Chl a concentrations during the 97/98 NEM were markedly lower in comparison to those of the following 2 years. Mean concentrations within the focus region for each 8-day composite from the three NEM periods presented here have been listed (Table 2). The ratio of the 98/99 NEM and 99/00 NEM Chl a concentrations to those of the 97/98 NEM also are listed (Y2/Y1 and Y3/Y1). These emphasize that, over the latter 2 years, the mean Chl a concentration within this region was generally 35–95% higher than that observed during the 97/98 NEM. Similar to previous analyses in the literature, monthly SSTA was checked to ascertain whether cooler ocean temperatures (implying enhanced nutrient entrainment) were directly associated with the higher Chl a concentrations. The December distributions of SSTA indicate that all 3 years were slightly warmer than the climatological mean state and no obvious relation between temperature and Chl a concentration was apparent (Figs. 2 and 3a–c). Indeed, SSTA within the focus region during December 1997 and December 1998 are markedly similar, though the 97/98 and 98/99 NEMs exhibit the largest interannual variation in Chl a concentration. In December 1999, SSTA are slightly cooler, yet there is little difference in the Chl a concentrations when compared to those from the 98/99 NEM (Figs. 2, 3b and c and Table 2). Interestingly, the December 1999 SSTA distribution indicates a coastal upwelling feature along the southern part of the Arabian Peninsula, which is not seen during the previous two NEMs. This is consistent with the ocean-color distributions, which reveal elevated Chl a concentrations along this coast that persist from December through February. 3.2. Interannual variability: OGCM-predicted thermocline depth The mid-December distributions of Z21 provide an indication of the region’s hydrographic structure (Fig. 3d–f). As an approximation for the

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depth of the main thermocline and nitracline, this isotherm directly relates to the primary controls on phytoplankton photosynthesis: nutrient availability and the potential range of mixed layer irradiance. The Z21 distributions for the 98/99 and 99/00 NEMs are relatively consistent. Within the focus region, the range of depths for these 2 years was 95–115 m, while during the 97/98 NEM, the range of depths was significantly deeper (115–140 m). The interannual variation in isotherm depth predicted by the OGCM prompted an examination of Ekman pumping velocity ðwe Þ as a possible source mechanism for this variability. However, as was observed in the SSTA distributions, the monthly distributions of we from December reveal little interannual variation (Figs. 3g–i). Over these three winter periods, we within the focus region is always downwelling, with a magnitude of 0.08–0.16 m/d. In addition, the spatial distribution over the northern Arabian Sea is remarkably consistent. Only the we distribution from November 1998 deviates noticeably from the corresponding distributions from the two surrounding years (figure not shown). In this case, the we distribution indicates upwelling instead of downwelling within the focus region, as positive velocities, normally confined to the eastern and southern portions of the depicted area, extend significantly to the north and west. However, the net difference in mean we between November 1998 and the other 2 years is B0.1 m/d within the focus region (Table 2). Over the entire month, this suggests an upward displacement of only 3 m relative to the 97/98 and 99/ 00 NEMs and cannot account for the difference in Z21 that is apparent between the 97/98 NEM and the two subsequent NEMs (Figs. 3d–f). Superimposed time-series of mean Z21 within the focus region emphasize this difference (Fig. 4). This figure also depicts the mean behavior of Z21 over the last 35 years of the model run, as well as the range of one standard deviation (the gray shaded region). Except for a 3-week period during November 1999, the Z21 time-series for both the 98/99 and 99/00 NEMs are within the 7s envelope, with the 98/99 time-series closely tracking the mean. In contrast to this, the Z21 timeseries for the 97/98 NEM is always outside the

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envelope and generally at least two s below the mean. 3.3. Surface-layer nutrients Nitrate concentrations in the upper 75–100 m consistently reach 2–5 mM in the two sets of NEMperiod observations made during the US JGOFS Arabian Sea expedition (Fig. 5a and b). These data were obtained during hydrographic surveys along the B1000 km-long portion of the SeaSoar track that proceeds perpendicularly offshore (Fig. 1). Note that although the profiles from cruise TN054 (Fig. 5a) are later chronologically, they were made earlier in the NEM period than those from TN043 (Fig. 5b). It is apparent in both sets of nutrient profiles that waters with concentrations exceeding 10 mM reside just below the mixed layer. Intercomparing these two sets of profiles illustrates the ongoing mixed-layer deepening and nutrient entrainment that occur over the course of the NEM. Uptake experiments performed during this time period revealed that this entrainment of nitrate significantly exceeded the rate of nitrate uptake by phytoplankton. Indeed, these uptake experiments showed that primary productivity was supported primarily by regenerated nitrogen (i.e., ammonium-based), which furthers the disparity between the entrainment and uptake rates of nitrate and contributes to nitrate turnover time within the mixed layer increasing from 1 to 3 months over the NEM (McCarthy et al., 1999). While the presence of ammonium will reduce nitrate uptake, the overall utilization of nitrogen will be unaffected. The ultimate source of the ammonium is nitrate entrained by the convective mixing. The accumulation of nitrate over the NEM and the high concentration of nitrogenous nutrients both indicate that these nutrients were underutilized, indicating that phytoplankton growth is unlikely to have been N-limited. Low iron concentrations have been shown to inhibit phytoplankton growth, leading to the establishment of high nutrient-low chlorophyll (HNLC) regions (Landry et al., 1997). However, in the Arabian Sea, the range of iron concentrations during the NEM was observed to be 0.48–2.38 nM, which is at least four times greater than what is

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J.D. Wiggert et al. / Deep-Sea Research II 49 (2002) 2319–2343 Z21 Time-Series [15-18 N, 60-65E]

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Mean (66:00) NEM97/98 NEM98/99 NEM99/00

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Dec

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95

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Fig. 4. Time-series of Z21 for the three NEM periods observed by SeaWiFS. The 35-year mean time-series is shown (black line). The shaded region illustrates the range of 7s around the mean. The axis on the right-hand side illustrates the distance of the 211C isotherm from the critical depth ðZCR Þ; described in Section 2.4 of the text.

presently considered to be a limiting value (Measures and Vink, 1999). Silicon limitation also could play a role (Dugdale and Wilkerson, 1998). Morrison et al. (1998) note that NO3/SiO4 ratios were typically B2 and that significantly higher values for this ratio, which would indicate potentially limiting concentrations of reactive silicate, were rarely observed. In addition, nearsurface values of this ratio were o1 (B. Jones, unpublished data). In summary, the US JGOFS observations show that mixed-layer nitrate was underutilized and that this was due to neither Fe-limitation nor Si-limitation. Although the US JGOFS cruises encompassed two successive NEMs, it is possible that the observed nutrient conditions were abnormal. In order to explore whether wintertime entrainment of nitrate into the surface layer is likely to exhibit

significant interannual variability, the Z21 timeseries extracted from the OGCM solution were employed. In conjunction with a T-NO3 relation derived from the NODC seasonal climatology, these were used to estimate the concentration of surface-layer nitrate at the end of the NEM (in the absence of uptake). Results of this calculation show that, over the 35-year duration of the simulation, the estimated wintertime concentration of surface nitrate falls below 2 mM only once (Fig. 5c). In that instance, the estimated concentration was 1.9 mM. This low nutrient concentration happens to coincide with the 98/99 NEM, which is not the year of reduced Chl a seen in the SeaWiFS imagery (Fig. 2). The estimated surfacelayer concentration of nitrate generally falls between 2 and 4.5 mM. While this range does represent significant interannual variation, none of

3 ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Fig. 3. Reynolds sea-surface temperature anomaly (SSTA, a–c), OGCM-predicted 211C isotherm depth (d–f) and Ekman pumping velocity (g–i). The December distribution of each of these oceanic properties is shown for the 3 years represented in the ocean-color imagery (Fig. 2). The Reynolds SSTA were obtained from the IRI website (http://iri.ldeo.columbia.edu/). The depth of the 211C isotherm was extracted from the output of an interannually forced OGCM simulation. The Ekman velocities were determined from wind-stress-curl values that were derived from the NCEP monthly wind vectors. The dashed line box highlights the focus region (15–181N and 60–651E) in each distribution.

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(b)

TN054 10-26 Dec 1995 data to 840km offshore

TN043 17-31 Jan 1995 data to 840km offshore

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Surface Layer Nitrate Concentration (µM)

Fig. 5. Nitrate profiles obtained during the US JGOFS hydrographic surveys. The profiles in (a) are from mid-December while the profiles in (b) are from mid-January. They are shown here in seasonal order to illustrate the seasonal evolution of the nitrate concentrations although it should be noted that this is the inverse of their chronological order. The histogram in (c) shows the predicted 35-year distribution of surface-layer nitrate concentrations. These are estimates of net wintertime entrainment and were obtained using the OGCM in conjunction with a temperature-nitrate relation. This relation (NO3 ¼ 82:6222:948  T; R2 ¼ 0:94) was developed with data extracted from the seasonal (fall) climatology in the NODC World Ocean Atlas (http://www.nodc.noaa.gov). The spatial domain of the extracted data was consistent with the focus region (15–181N and 60–651E).

these entrained nitrate estimates indicate that surface-layer nutrient concentrations would be likely to limit phytoplankton productivity. This combination of in situ observations and net entrainment estimates lead to the conclusion that the interannual variability apparent in the oceancolor imagery does not result from variations in mixed-layer nutrient concentration. This conclu-

sion serves as our justification for eliminating nutrient dependence in the primary productivity model (Section 2.4). 3.4. Diurnal mixing: moored observations For a 24-day period starting on 17 January 1995, four moored temperature records from the

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upper 200 m have been superimposed (Fig. 6a). The 4.5-m record has an exaggerated vertical scale to emphasize near-surface diurnal variability, which is dominated by insolation. These data show that homogeneous temperatures over the upper 100 m of the water column are generally re-established at night, following daytime stratification. The 200-m record illustrates the magnitude of the temperature gradient just below the surface boundary layer in the permanent thermocline (B71C over 100 m). The 125-m record captures a number of cooling episodes that typically occur just after sunset. A closer examination of the time-series reveals that these abrupt temperature reductions were short-lived, typically lasting no more than 2 h and in some cases o30 min. The two mixed-layer depth time-series, based on the 0.11C and 1.01C temperature criteria, are shown in Fig. 6b as the black and gray curves, respectively. The 0.11C mixed layer consistently returns to B100 m at night after shoaling to between 10 and 20 m during the day. The 1.01C mixed layer typically ranges from 84 to 110 m, though on a number of occasions it penetrates to at least 130 m. The 1.01C mixed-layer time-series is also shown with the plot of stacked temperatures. This highlights the direct correspondence between the cooling episodes noted in the 125-m temperature record and the shoaling (to B85 m) of the 1.01C mixed layer. These are interpreted as instances where the salinity inversion between the surface layer and thermocline waters overcomes the significant temperature gradient within the upper thermocline. As noted above, these mixing episodes tend to occur just after sundown, in conjunction with the deepening of the 0.11C mixed layer. This lack of temporal persistence and the tendency to coincide with sundown both indicate that these episodes represent manifestations of local, vertical mixing as opposed to horizontal advection of spatially heterogeneous hydrodynamic structure. The euphotic-zone depth ðZEU Þ and the critical depth ðZCR Þ are also shown with these mixed-layer time-series. The depth of the 1.01C mixed layer (B110 m) relative to ZEU and ZCR (estimated to be 61 and 84 m, respectively) indicates that an accumulation of phytoplankton biomass would be inhibited by

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these hydrographic conditions (see Section 2.5). However, the 0.11C mixed layer shoals to within 20 m of the surface every morning, which indicates that sufficient stratification develops to ensure that phytoplankton cells above ZEU ; when the stratification initiates, would remain within the euphotic zone over the entire photoperiod. The deepening of the 0.11C mixed layer to B100 m every evening homogenizes the phytoplankton to a depth below ZCR ; which results in a significant dilution of the near-surface concentration of phytoplankton. These characteristics of the 0.11C mixed layer were the basis for the idealized representation of mixing incorporated within the 1-D model that was developed to quantify this process of diurnal growth and dilution and its impact on near-surface phytoplankton concentration. 3.5. Diurnal mixing: 1-D model results Before presenting results from the 1-D mixing model (first introduced in Section 2.6), additional details regarding its construction are included in light of the in situ observations just presented. Idealized forms of daily PAR(t) and ZML ðtÞ; as they have been represented in the 1-D model, are shown (Fig. 7a). These forms (1) illustrate the observed coincidence between sunrise and the (0.11C) mixed layer’s shoaling; (2) depict the lag between sunset and the deepening of this mixed layer and; (3) reinforce, conceptually, the relative position of the mixed layer, euphotic zone boundary, and critical depth. The upper and lower bounds of the mixed layer (ZDML and ZTH ) are also shown. These are the depths to which the mixed layer shoals during the day and the top of the permanent thermocline, respectively. The time scale, tmix (Fig. 7a), defines the amount of time required by a phytoplankton cell to cycle around the daytime depth of the actively stirred mixed layer. For the typical values applied here (jtj ¼ 0:07 Pa, rw ¼ 1025 kg/m3 and ZML =12 m), this resulted in B15 cycles per photoperiod. Irradiance fluctuations associated with such mixed-layer stirring were included to account for the subtle effects such fluctuations can have on near surface primary productivity (e.g. Holloway and Denman, 1989). This was accomplished by

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simulating the irradiance intensities that would accompany the noted vertical cycling for all depths down to ZML ; where the model’s vertical resolution was 1 m. This effectively introduced a Lagrangian ensemble of phytoplankton into the

mixed layer, similar to that employed by Woods and Onken (1982). However, in the present application, depth variation is achieved by modulating the perceived light field, not through explicit vertical displacement. Mixed-layer Chl a

Time-series of temperature at 4.5, 100, 125 and 200m 25.4 (a)

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Fig. 7. (a) The idealized diurnal variation of the mixed layer and PAR as they are applied in the simple 1-D modeling scheme is illustrated. Several Ekman-layer characteristic parameters that are discussed in the text have been defined. (b) The Chl a initial condition (IC) used to calculate the bio-optical characteristics (ZEU and ZCR ) and in the 1-D model is shown, along with the set of SeaSoar profiles from which it was determined.

at the times of interest (noon and dusk) was determined by averaging all depth levels through ZML : Phytoplankton growth below ZML was modeled using the more typical ‘continuum’ approach. The mixed layer’s lower boundary ðZTH Þ was generally deeper than the turbulent Ekman-layer thickness ðLEK Þ but could easily be

reached because of the additional mixing impetus provided by the surface buoyancy flux that resulted from the ongoing evaporation and nighttime cooling. An hour after sundown, the phytoplankton profile was homogenized down to ZTH to simulate the biomass dilution associated with the night-time deepening of the diurnal mixed layer.

3 ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Fig. 6. The temperature time-series in (a) were obtained by thermistors deployed on the central mooring, the position of which is depicted in Fig. 1. Observations from four depths (4.5, 100, 125 and 200 m) were chosen to illustrate various characteristics of temporal variability and thermal structure. The 4.5 m time-series is dominated by QSW and demonstrates the timing of the photoperiod. The 125 m time-series is shown in white to help distinguish it from the 100 m time-series. The mixed-layer time-series in (b) were calculated from the full complement of temperature time-series logged by the moored array. Two mixed-layer criteria were applied. These consist of differences from the surface temperature of 0.11C and 1.01C. The two criteria were chosen in order to highlight stratification that provides hydrographic structure for phytoplankton (0.11C) and episodes of nutrient entrainment (1.01C). The depth of the euphotic zone ðZEU Þ and the critical depth ðZCR Þ are shown with the mixed-layer time-series to illustrate their positions relative to the diurnally cycling mixed layer and the top of the permanent thermocline. Details of their determination and their relevance to areal primary productivity are provided in the text (Section 2.4). The 1.01C mixed layer has been reproduced on the stacked temperature plot to emphasize episodes of deep mixing indicated by the 125-m temperature time-series.

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Finally, while convective penetration into the permanent thermocline is clearly evident in the temperature and mixed-layer time-series, detrainment of phytoplankton into the permanent thermocline is assumed negligible. We justify this assumption by noting that the duration of these mixing episodes is an order of magnitude smaller than the characteristic time-scale for water column overturn ðtmix Þ: With nutrients assumed to be replete, this simple model provides insight into how the concentration of surface Chl a will adjust to the applied range of ZTH : This range (80–110 m) was chosen to cover the interannual range in 211C isotherm ðZ21 Þ predicted by the OGCM (Fig. 4), where SeaSoar temperature measurements (not shown) have been used to ascertain a nominal distance of 25 m between ZTH and Z21 : SeaSoar observations were also used to form the profile of Chl a used to initialize the 1-D simulations (Fig. 7b). Implicit with the application of this model’s framework is the assumption that for the latter half of the NEM, the biological system operates near a steady state (i.e. phytoplankton concentration is relatively constant) which has been uniquely defined by local, 1-D processes that dominate the physical system. The supposition that physical processes for this time period may be considered local is supported by the temperature and mixed-layer time-series (Fig. 6). A recent analysis of momentum, heat and salt budgets provides further corroboration (Weller et al., 2002). The supposition that the biological system is near steady state is based on the SeaWiFS observations from the last 2 months of the NEM, which reveal little change in surface Chl a concentration from midDecember to mid-January for all three winter periods (Fig. 2, Table 2). Based on these observations, the 1-D scheme is utilized with an eye toward providing insight into how the biological equilibrium point will adjust in response to the applied range of ZTH : After running the model out 20 days, the dilution of surface phytoplankton that occurs when the water column is homogenized down to ZTH ranges from 37% to 40%. This represents a significant reduction in the magnitude of phytoplankton biomass that is retained within the

(daytime) mixed layer and upper euphotic zone. This is in addition to the standard grazing and respiration losses, and demonstrates how the convective mixing inhibits the development of a full-fledged phytoplankton bloom by preventing the biomass accumulation necessary for explosive growth. The cumulative impact on surface Chl a concentration, over one winter, by a given thermocline position has been estimated by carrying the growth/dilution simulations out for a 20day period (Fig. 8). The noon concentration has been extracted for each day to form these timeseries. The curve corresponding to the case with ZTH set to 95 m, shows an essentially constant value of noon Chl a over the entire period (net increase is 0.5%). This value of ZTH is consistent with conditions observed during the Arabian Sea expedition, so this case acts as a baseline for results obtained with other values of ZTH : As the thermocline is deepened, the predicted surface concentration of Chl a decreases from the baseline value of 0.46 mg Chl a/m3 to a concentration of 0.28 mg Chl a/m3 for ZTH set to 110 m. With ZTH set to 80 m, the predicted surface concentration reaches 0.78 mg Chl a/m3. The increases in Chl a concentration are dramatic while the decreases are relatively subtle. This demonstrates the compounding effect on phytoplankton biomass that takes place as the permanent mixing barrier rises above the critical depth. Surface Chl a concentrations observed by SeaWiFS (8-day averages) and CZCS (monthly averages from Banse and English, 2000) have been matched up with the appropriate OGCM-predicted values for Z21 (Fig. 9, the caption contains further details on the ocean color data). A linear fit applied to the SeaWiFS observations (dashed line, R2 ¼ 0:63) indicates that Chl a concentrations range from 0.8 mg/m3 down to 0.4 mg/m3 for a range in Z21 consistent with that applied in the 1-D model. This range of concentrations is in good agreement with those of the 1-D model, which are represented by the two solid lines that surround the curve fit and bracket much of the scatter in the SeaWiFS observations. The lower of these two curves coincides with the concentration distribution shown in Fig. 8 for day 20. The higher curve is from the same 1-D result except that these

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Sensitivity of surface chlorophyll a to thermocline depth (ZTH) 0.8

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are the concentrations of Chl a at the end of the photoperiod instead of at noon. The CZCS data show a similar trend of decreasing concentration with increasing Z21 : However, except for a handful of elevated concentrations, these data occupy a separate distribution space and their trend is clearly not as striking as that found in the SeaWiFS observations.

4. Discussion Three years of SeaWiFS Chl a observations reveal significant interannual variation in pigment concentration in the central Arabian Sea during the NE (winter) Monsoon (NEM). Similar to previous investigations intent on determining the

cause of CZCS-observed interannual ocean color variability (Banse and McClain, 1986; Banse and English, 1993), SST (in the present research, the SST anomaly) was examined for a correlation with the pigment concentrations. An inverse relation between SSTA (or SST) and Chl a (i.e., lower temperatures coinciding with higher concentrations of Chl a) would indicate that variations in nutrient availability were the source of the noted variability in surface Chl a. Such a relation has never been demonstrated for phytoplankton blooms occurring during the height of the NEM, either in the previous investigations or in the current results. One conclusion of a recent study of NEM nutrient dynamics using a 1-D coupled model (Wiggert et al., 2000) was that interannual

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Variation in surface chlorophyll a with thermocline depth 1.2

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o

Depth of 21 C isotherm (Z21) Fig. 9. Distribution of observed Chl a concentration versus OGCM-predicted 211C isotherm depth ðZ21 Þ for SeaWiFS (.) and CZCS (m) observations. Linear fit to the SeaWiFS observations (dashed line; Slope=0.0195, R2 ¼ 0:63) provides a comparison to the curve predicted by the 1-D model (solid line). For the model result (solid lines), surface Chl a concentration is taken as the mean over the upper 10 m. The lower curve shows concentrations at noon while the upper curve shows concentrations at the end of the photoperiod (1800 h). ZTH used in 1-D model is transformed to Z21 based on the SeaSoar-estimated difference of 25 m between the top of the thermocline and the 211C isotherm. The CZCS data are monthly averaged values that were published in Banse and English (2000).

variations in thermocline depth could lead to variations in surface pigment concentration consistent with those first reported by Banse and McClain (1986). In order to explore this conclusion further, an interannually forced OGCM simulation was analyzed to determine whether a relation between Chl a and model-predicted thermocline displacement was evident. The 211C isotherm ðZ21 Þ was chosen as a suitable indicator of thermocline displacement. For the 3 years of SeaWiFS ocean color presented here, a striking correspondence was found between significantly lower values of Chl a during the 97/98 NEM (Fig. 2) and the associated time-series of Z21 ; which was generally 2s below the 35-year mean (Fig. 4). The distinct inverse relation between ocean color and OGCM-predicted thermocline

depth is consistent with the 1-D model results, which indicate that higher Chl a should coincide with shallower convective mixing. Such an inverse relation runs counter to the standard paradigm of deeper mixing enhancing nutrient concentration that in turn leads to increased primary production. The latter scenario was the basis for the supposition made in earlier studies that cooler SSTs would be associated with higher pigment concentrations. For such a relation to hold, phytoplankton growth during the NEM would need to be nutrient-limited. The hydrographic surveys and nutrient-uptake experiments performed during the US JGOFS cruises do not support this nutrient limitation assumption (Morrison et al., 1998; McCarthy et al., 1999). Indeed, they reveal significant quantities of mixed-layer

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nitrate, which are not fully utilized after being entrained by the NEM’s active convective mixing. Additional in situ observations indicate that the incomplete utilization of mixed-layer nitrate is not related to phytoplankton growth being inhibited by low concentrations of either Fe or Si. The source of the entrained nitrate is the largemagnitude (>15 mM) supply that is typically present within 100 m of the surface (Figs. 5a and b). The wintertime convection, mixed-layer nutrient-enrichment scenario is rather typical of the world’s oceans. What is atypical of this period in the central Arabian Sea is that significant primary productivity is simultaneously supported and mixedlayer Chl a is observed to increase while nutrients accumulate (McCarthy et al., 1999; Wiggert et al., 2000; Barber et al., 2001). The OGCM simulation was also used to create a 35-year time-series of total wintertime nutrient entrainment. These entrainment estimates did show significant interannual variability but never indicated that nutrient concentrations would be low enough to inhibit phytoplankton growth rates (Fig. 5c). Eliminating nutrient limitation as the restraint on phytoplankton growth suggests that light limitation may play an important role. When taken with the values determined for euphotic-zone depth ðZEU Þ and critical depth ðZCR Þ; the mixed-layer depth timeseries provides a means of assessing the degree to which light limitation controls the magnitude of areal primary production (Fig. 6). The paradigm presented by Sverdrup (1953) states that when the mixed layer does not exceed the critical depth ðZCR Þ; a phytoplankton bloom can occur. As stated in Section 2.5, ZCR is the depth at which vertically integrated daily primary productivity is balanced by the similarly integrated phytoplankton loss terms. The 0.11C mixed-layer time-series (Fig. 6b) clearly illustrates that stratification sufficient to retain phytoplankton significantly above ZCR is consistently present during the photoperiod, suggesting that light limitation does not restrain areal production. Yet it is also apparent that stratification breaks down at night, leading to a vertical homogenization of the water column’s constituents down to the permanent thermocline ðZTH Þ; which was 10–15 m deeper than ZCR : This diurnal dilution of the

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phytoplankton biomass that accumulates within the euphotic zone over the photoperiod exemplifies one of the refinements to Sverdrup’s critical depth paradigm introduced by Platt et al. (1991). They stipulate that it would be more precise to interpret ZML oZCR as a necessary but not sufficient condition for a phytoplankton bloom to occur. They suggest a further, more rigorous, bloom criterion which specifies that the characteristic growth time-scale (1=a1 ; Eq. (5)) be considerably shorter than the interval between ‘significant’ mixing events (i.e., mixing that penetrates below ZCR ). In the present context of a persistent, deepcycling diurnal mixed layer, the mean areal phytoplankton growth rate would need to exceed 4=d to satisfy this more rigorous criterion! These conceptual arguments provide a framework for interpreting the interannual variability in SeaWiFS-observed, surface Chl a as they relate to OGCM-predicted depth variability in Z21 : In order to illustrate better, and quantify, how surface Chl a would be impacted by the combination of a diurnally cycling mixed layer and interannual variations in thermocline depth, a simple 1-D model was constructed. The percent reduction in surface phytoplankton concentration was calculated when the dilution step was implemented in the model. This resulted in a dilution of B39% that was rather consistent over the full range of applied ZTH : The magnitude of these dilutions may be somewhat inflated by the idealized nature of this model. However, overnight reductions in particle concentration of 27% and 55%, associated with significant diurnal mixed layer cycling, have been observed in beam transmissometer measurements obtained during the North Atlantic Bloom Experiment (NABE) * Equatorial Pacific Ocean (Eqand non-El Nino Pac) JGOFS cruises, respectively (Gardner et al., 1993, 1995). Sensitivity to the range of applied ZTH (80–110 m) is more dramatically illustrated by the time-series of noon Chl a (Fig. 8). These predict close to a three-fold range in surface Chl a, solely due to the prescribed value for ZTH : In addition, as ZTH decreases, the final concentration increases non-linearly, which illustrates the compounding effect that occurs as biomass accumulates and blooms develop. Finally, the

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1-D model’s predicted concentrations from noon and at the end of the photoperiod (prior to dilution) are shown with the remotely sensed Chl a, which have been plotted with the appropriate values of OGCM-predicted Z21 (Fig. 9). Both the CZCS and SeaWiFS data indicate that a deeper thermocline leads to reduced concentrations of surface Chl a. Furthermore, the two curves from the 1-D model bracket much of the scatter in the SeaWiFS data as well as the linear fit to these data.

5. Conclusion The results we present demonstrate that the depth of the seasonal thermocline ultimately controls the magnitude of mixed-layer Chl a and areal primary production during the NEM in the central Arabian Sea. This is accomplished by regulating the night-time penetration depth of convective mixing, which, in turn, determines the magnitude of phytoplankton biomass that is retained within the euphotic zone for the subsequent photoperiod. Thus, the paradigm that applies during the NEM is that primary productivity is never nutrient-limited and a deeper thermocline will result in lower values of mixedlayer Chl a, due to the deeper penetration of the diurnal mixed layer. Recently, observations of Chl a and primary production obtained during the Indian JGOFS program have been used to illustrate interannual variability between the 94/ 95 NEM and the 96/97 NEM at a time-series site (211N, 641E) northeast of our focus region (Kumar et al., 2001). These data clearly show lower SST coinciding with higher surface nutrients and Chl a and would appear to be a realization of the paradigm that eluded the previous studies (Banse and McClain, 1986; Banse and English, 1993). However, since the Indian JGOFS observations date from 10 February onward for both years, we would argue that they were made during the transition from the NEM to the Spring Intermonsoon and that the physical conditions during this time frame were not consistent with those presented in this report. In support of this assertion, we note the NEM and Spring Intermonsoon period definitions

(1 November–15 February and 15 February–1 June, respectively) developed by Weller et al. (2002), were derived from mooring-based meteorological time-series data that included the 94/95 NEM. Furthermore, in the wintertime evolution of pigment concentration we have presented (Fig. 2, Table 2), Chl a in mid-February is clearly higher than during the previous 2 months, which we have attributed to the onset of the spring phytoplankton bloom (see Section 3.1). It is possible that the interannual variability in Chl a and primary production reported by Kumar et al. (2001) is related to interannual variations in total NEM nitrate entrainment and/or the degree to which vertical cycling during the height of the NEM inhibits nutrient uptake within the surface layer. One could speculate that a deeper thermocline that more effectively suppresses primary productivity, and entrains more nutrients, during the NEM should be followed by a higher magnitude spring bloom than would occur after a NEM characterized by a shallower thermocline, higher primary productivity and less net nutrient entrainment. Indeed, such a reversal in pigment concentration dominance with the development of seasonal stratification has been reported for the interannual variability in surface Chl a measured by CZCS (Banse and English, 1993). However, interannual variations in the timing and character of the mixed layer’s shoaling also must come into play, making generalizations for this NEM-Spring Intermonsoon transition period problematic. The processes that determine the extent of interannual variability in thermocline depth remain unclear. We have examined Ekman upwelling velocities during the NEM and found that local forcing may have been a contributing factor but it could not completely account for the magnitude of these variations in thermocline depth. This indicates that remote forcing may play a role in determining thermocline depth in the central Arabian Sea and merits future inquiry. It is interesting to note that the NEM with significantly lower Chl a in the Arabian Sea coincides with the appearance of uncharacteristically high Chl a along the entire eastern half of the Indian Ocean’s equatorial region in late 1997 (Murtugudde et al., 1999). The elevated thermocline that caused these

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anomalous values of Chl a in the east was part of an extreme manifestation of the Indian Ocean’s dipole mode (Saji et al., 1999; Murtugudde et al., 2000), which impacts the equatorial region as an elevated thermocline and cooler SSTs in the east and a deeper thermocline and warmer SSTs in the west. The temporal coincidence of the dipole mode and the low Chl a anomaly observed in the SeaWiFS imagery during the 97/98 NEM in the Arabian Sea suggest that the dipole’s influence may extend well into the northern portions of the Indian Ocean basin. A coupled bio-physical version of the OGCM used to identify the interannual variability in thermocline position is presently being employed to investigate physical/biological interactions such as this in the Indian Ocean.

Acknowledgements This research was supported by the NASA Oceanography Program (NAG 58595). RGM also received support from NASA TRMM (RTOP 621-15-32). CRM was funded by the NASA Ocean Biogeochemistry Program. Jim Beauchamp (ESSIC), Steve Gaurin (HPML) and Christine Hsu (NASA/GSFC) provided assistance with the data presented. We would also like to express our appreciation to Ken Brink (SeaSoar), Lou Codispoti (hydrographic observations) and Bob Weller (moored time-series) for making their measurements available. This manuscript has benefited from the suggestions of, and discussions with, Tony Busalacchi, Jim Christian, Raleigh Hood and Sergio Signorini. It has also greatly benefited from the insightful comments of John Kindle, Sharon Smith and an anonymous reviewer. We would also like to express our gratitude to Sharon Smith for her much appreciated support. This is US JGOFS contribution number 701.

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