Factors controlling the flux of organic carbon to the bathypelagic zone of the ocean

Transcription

1 GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 16, NO. 4, 1087, doi: /2001gb001722, 2002 Factors controlling the flux of organic carbon to the bathypelagic zone of the ocean Roger Francois, Susumu Honjo, Richard Krishfield, and Steve Manganini Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA Received 24 September 2001; revised 19 April 2002; accepted 8 May 2002; published 14 November [1] Particle fluxes measured with time series sediment traps deployed below 2000 m at 68 sites in the world ocean are combined with satellite-derived estimates of export production from the overlying water to assess the factors affecting the transfer of particulate organic matter from surface to deep water. Multiple linear regression is used to derive an algorithm suggesting that the transfer efficiency of organic carbon, defined as the settling flux of organic carbon normalized to export production, increases with the flux of carbonate and decreases with water depth and seasonality. The algorithm predicts >80% of the organic carbon transfer efficiency variability in diverse oceanic regions. The influence of the carbonate flux suggests that the ballasting effect of this biogenic mineral may be an important factor promoting export of organic carbon to the deep sea by increasing the density of settling particles. However, the lack of a similar effect for biogenic opal suggests that factors other than particle density also play a role. The adverse effect of increasing seasonality on the transfer efficiency of carbon to the deep sea is tentatively attributed to greater biodegradability of organic matter exported during bloom events. In high latitude opal-dominated regions with high f-ratios and seasonality, while a higher fraction of net production is exported, a higher fraction of the exported organic matter is remineralized before reaching bathypelagic depths. On the other hand, in warm, low latitude, carbonate-dominated regions with low f-ratios and seasonality, a higher fraction of the exported organic matter sinks to the deep sea. INDEX TERMS: 1050 Geochemistry: Marine geochemistry (4835, 4850); 1615 Global Change: Biogeochemical processes (4805); 4842 Oceanography: Biological and Chemical: Modeling; KEYWORDS: biological pump, carbon flux, sediment trap, remineralization profile Citation: Francois, R., S. Honjo, R. Krishfield, and S. Manganini, Factors controlling the flux of organic carbon to the bathypelagic zone of the ocean, Global Biogeochem. Cycles, 16(4), 1087, doi: /2001gb001722, Introduction [2] The growing database obtained with deep-sea sediment traps tethered to the seafloor [Honjo, 1996; Lampitt and Antia, 1997] is providing an increasingly precise understanding of the factors that control particle flux to the ocean interior. Organic carbon is one of the constituents of settling particles that has attracted much interest, because of its role in sequestering atmospheric CO 2 on century to millennial timescales and providing the energy that fuels benthic ecosystem and diagenetic reactions in deep-sea sediments. Understanding the factors that control the flux of carbon to the deep sea has been one of the primary motivations for establishing the international JGOFS program. [3] Although particulate organic matter has a specific gravity close to that of seawater, time series sediment traps have clearly established that this material is sinking rapidly to the deep sea. This has been attributed to aggregation into larger particles [Honjo et al., 1982, 1982b; Honjo and Copyright 2002 by the American Geophysical Union /02/2001GB Manganini, 1993] and to ballast effect or protection against biodegradation resulting from the close association between settling organic detritus and biogenic or lithogenic minerals [Ittekkot, 1993; Armstrong et al., 2002]. In this work, we use data from 68 sites distributed ocean-wide (Figure 1) to further investigate the factors that affect the transport of organic carbon to the deep sea. The database (Table 1) encompasses a wide range of mineral composition and flux, from the more lithogenic samples of the Bay of Bengal [Schäfer et al., 1996] and Nordic Seas [Honjo, 1990; von Bodungen et al., 1995], to the opal-rich samples from the Antarctic Polar Frontal Zone [Honjo et al., 2000] and the northwest Pacific [Wong et al., 1994; Takahashi et al., 2000], to carbonate-dominated settling material from low latitude regions [Honjo et al., 1995]. 2. Trends in the Transfer Efficiency of Organic Carbon to the Deep Sea [4] We define transfer efficiency of organic carbon to the deep sea, (F Corg /EP) as the ratio of the flux of organic 34-1

2 34-2 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX Figure 1. Sediment trap sites (see Table 1 for references). carbon measured with sediment traps deployed at depth >2000 m (F Corg ) to the flux of organic carbon exported from the euphotic zone (i.e., export production; EP), estimated at the same site from a satellite-derived ocean productivity model [Behrenfeld and Falkowski, 1997] and the f-ratio derived from a pelagic food web model [Laws et al., 2000], averaged over a km grid. CZCSderived productivity algorithms are known to underestimate productivity in the southern ocean [Moore and Abbot, 2000; Schlitzer, 2002], and in this ocean, export production and f-ratios directly measured during the AESOPS program [Nelson et al., 2002] were applied (Tables 1 and 2). [5] We have chosen the 2000 m depth cut-off because moored deep-sea sediment traps have been shown to intercept the vertical flux accurately in the bathypelagic zone of the water column, but are observed to be prone to undertrapping when deployed at depths shallower than 1500 m [Yu et al., 2001; Scholten et al., 2001]. The 250-km grid size for the integration of EP is based on consideration of the statistical funnel through which particles sink before reaching the sediment trap [Deuser et al., 1990; Siegel and Deuser, 1997]. With an eddy kinetic energy of 10 cm 2 s 2, and a mean particle sinking rate of 200 m d 1,the spatial averaging scale at the surface (L x ) for a trap deployed at 4000 m depth is 60 km [Siegel et al., 1990]. Assuming a Gaussian collection probability density, the radius around the trap site from pwhich ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 90% of the collected particles would originate is 21nð0:1ÞL x ¼ 125 km [Siegel and Deuser, 1997]. It should be noted, however, that on a timescale ranging from one to a few years, sediment traps do not collect particles originating uniformly throughout the statistical funnel, but instead sample particles that have followed a limited number of trajectories within the funnel [Siegel and Deuser, 1997]. Therefore, if there is a large variability in EP within the area of potential sampling, the mean EP calculated over the entire km grid may be significantly different from the actual EP that initiated the settling flux captured by the sediment trap. [6] Ocean-wide, there is an increasing trend between F Corg /EP and the flux of minerals (carbonate, opal, and lithogenic particles) calculated by subtracting the flux of organic matter from the total flux (Figure 2). Although there is a wide scatter, the data tend to cluster systematically according to the nature of prevalent mineral phase. Particles with higher carbonate content are generally much more efficient, for an equivalent mineral flux, at transferring carbon to the deep sea (i.e., F Corg /EP is higher) than particles dominated by opal. Note, however, that samples originating from the Nordic Seas do not display this general trend. Nonetheless, Figure 2 generally supports the view that the flux of minerals may be an important factor in establishing the transfer efficiency of organic carbon to the deep sea. In addition, it indicates that the nature of the minerals is equally important, and denser biogenic carbonates appear more efficient at transferring organic matter to the deep sea than less dense biogenic silica. The effect of the lithogenic particles is less clear-cut, largely because they rarely dominate the composition of particles sinking in the open ocean. 3. Effect of Mineral Ballast on the Transfer Efficiency of Organic Carbon to Deep Sea [7] Our goal is to find the simplest empirical relationship that would predict the flux of organic carbon at depth >2000 m from satellite-derived EP estimates [Laws et al., 2000]. Figure 2 suggests that, within the range of F minerals measured over most of the ocean, F Corg /EP may be broadly proportional to the flux of minerals, but the proportionality factor would depend on the relative proportion of biogenic silica, calcium carbonate, and lithogenic material. A depth-

3 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX 34-3 Table 1. Sampling Sites, Fluxes and Composition of Material Collected with Sediment Traps, Satellite-Derived Estimates of Export Production, f-ratio, SST, and Seasonality, Transfer Efficiency of Organic Carbon to the Deep Sea Measured and Estimated from Equation (5) Station Latitude Longitude Trap Depth, m FCorg, gcm 2 yr 1 EP a, gcm 2 yr 1 f-ratio a DPP b SST, C Fballast, gm 2 yr 1 ball CaCO 3 ball Opal ball Lithog Measured F Corg /EP T eff Predicted c b d Reference Nordic Seas Fram Strait 78.9 N 01.4 E Honjo [1990] Greenland-Parflux 74.6 N 6.7 W Honjo [1990] Greenland-von B N 7.7 W von Bodungen et al. [1995] Jan Mayen 70.0 N 0.0 W Honjo [1990] Norwegian-von B N 0.0 W von Bodungen et al. [1995] Lofoten 69.5 N 10.0 E Honjo [1990] Aegir Ridge 65.5 N 0.1 W Honjo [1990] Atlantic NABE N 20.9 W Honjo and Manganini [1993] NABE48 e Honjo and Manganini [1993] NABE N 21.0 W Honjo and Manganini [1993] NABE34 e Honjo and Manganini [1993] OFP 32.1 N 64.3 W Conte et al. [2001] Guinea Basin 2N 01.8 N 11.1 W Fischer and Wefer [1996] Guinea Basin 2S 02.2 S 09.9 W Fischer and Wefer [1996] Southern Ocean AESOPS S W f 0.38 f g 1.03 Honjo et al. [2000] h Bay of Bengal Central 13.2 N 84.4 E Schäfer et al. [1996] South 04.5 N 87.3 E Schäfer et al. [1996] Arabian Sea AS N 58.8 E Honjo et al. [1999] AS N 59.6 E Honjo et al. [1999] WAST 16.3 N 60.3 E Haake et al. [1993] EAST 15.6 N 68.6 E Haake et al. [1993] AS N 61.5 E N 61.5 E CAST 14.5 N 64.6 E Haake et al. [1993] AS N 65.0 E Honjo et al. [1999] 10.0 N 65.0 E Equatorial Pacific Kempe-12N 12.0 N E Kempe and Knaack [1996] MANOP-S 11.1 N W Dymond and Lyle [1994] EqPac-9N 09.0 N W Honjo et al. [1995] CP N E Kawahata et al. [2000] Kempe-5N 05.0 N E Kempe and Knaack [1996] EqPac-5N 05.0 N W Honjo et al. [1995] EqPac-2N 02.0 N W Honjo et al. [1995] MANOP-C 01.0 N W Dymond and Lyle [1994]

4 Table 1. (continued) Station Latitude Longitude Trap Depth, m F Corg, gcm 2 yr 1 EP a, gcm 2 yr 1 f-ratio a DPP b SST, C F ballast, gm 2 yr 1 ball CaCO3 ball Opal ball Lithog Measured FCorg/EP Teff Predicted c b d Reference EqPac N W Honjo et al. [1995] 20.8 i 0.05 I i 0.05 I CP N E Kawahata et al. [2000] EqPac-2S 02.0 S W Honjo et al. [1995] EqPac-5S 05.0 S W Honjo et al. [1995] EqPac-12S 12.0 S W Honjo et al. [1995] 12.5 i 0.11 I Panama Basin j 05.4 N 81.9 W Honjo et al. [1982a, 1982b] W. Caroline Basin 04.1 N E Kawahata et al. [2000] North Pacific GD 51.4 N E Wong et al. [1994] P 50.0 N W Wong et al. [1999] 50N 50.0 N E Honda [2001] PAPA-c 49.6 N W Honjo and Wong [1987] SA 49.0 N W Takahashi et al. [2000] GA 45.0 N E Wong et al. [1994] GB 45.1 N W Wong et al. [1994] KNOT 44.0 N E Honda [2001] MW 42.2 N W Dymond and Lyle [1994] MW e Gyre 41.6 N W Dymond and Lyle [1994] 40N 40.0 N E Honda [2001] CP N E Kawahata et al. [1998] CP N E Kawahata et al. [1998] CP N E Kawahata et al. [1998] PARFLUX-1 k 15.5 N W Honjo et al. [1982a, 1982b] Bering Sea Bering-P 58.0 N E Honjo [1996] Bering-T 53.5 N W Takahashi et al. [2000] a Export production and f-ratio estimated from algorithm proposed by Laws et al. [2000]. b In gc m 2 (3-month)-1 from satellite data [Behrenfeld and Falkowski, 1997]. c Using equation (5). d b = 1n(FCorg /EP)/1n(Z/100) [Martin et al., 1987]. e Taking into account trapping efficiency derived from 230 Th and 231 Pa mass balance [Yu et al., 2001]. f Export production and f-ratio measured directly from 234 Th deficit, 14 C, and 15 N incubation [Nelson et al., 2002]. g Using equation (6). h Using equation (7). Export production and f-ratio measured directly during trap deployment [Barber et al., 1996; McCarthy et al., 1996]. Deployed for less than one full year (112 days). k 3-month deployment. i j 34-4 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX

5 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX 34-5 Table 2. Transfer Efficiency to the Seafloor in the Pacific Sector of the Southern Ocean F Corg a, gc m 2 yr 1 EP b, gc m 2 yr 1 f-ratio c SST, C F carbonate d, gm 2 yr 1 Region Latitude Longitude Depth, m F Corg /EP e T eff f-ratio f T eff SST Northern ACC 55 S 59 S 170 W Polar Frontal 59 S 61.5 S 170 W Region Southern ACC 61.5 S 65.5 S 170 W Ross Gyre 65.5 S 68 S 170 W a Average from pore water models [Sayles et al., 2001; Nelson et al., 2002]. b Average from 15 N incubation, nitrate drawdown, and 234 Th deficit [Nelson et al., 2002]. c Ratio of 14 C-based production rate to export production [Nelson et al., 2002]. d From sediment trap deployed at 1000 m at station AESOPS-2, -3, -4, and -5 [Honjo et al., 2000]. e Using equations reported in Figure 6. dependent term must also be added to the algorithm to reflect the gradual remineralization of organic carbon below 2000 m. The effect of depth is relatively small below 2000 m, as >90% of the organic matter is typically remineralized at shallower depths, and the exact nature of the equation used to describe this dependency is not critical. In order to keep the algorithm simple, we arbitrarily chose an inverse function F Corg =EP ¼ k1 ball CaCO 3 þ k2 ball Opal þ k3 ball Lithog þ k 4 Z 1 Fminerals where Z is the depth of deployment of the sediment traps (>2000 m); ball CaCO 3, ball Opal, and ball Lithog are the proportion of carbonate, biogenic silica, and lithogenic material in the mineral fraction at each station; k 1, k 2, k 3, ð1þ and k 4 are constants, and F minerals is the flux of minerals (g m 2 yr 1 ). The latter was calculated by subtracting the flux of organic matter (1.87 F Corg ; based on the chemical formula for marine organic matter C 106 H 175 O 42 N 16 P proposed by Anderson [1995]) from the total dryweight flux. Carbonate and biogenic silica were measured with an analytical precision of 3 and 5%, respectively [Honjo et al., 1999, 2000]. Biogenic silica was calculated by multiplying Si concentration by 2.4 [Mortlock and Froelich, 1989], and lithogenic fractions were calculated by difference. [8] At each station, we have independent estimates of F Corg (from sediment traps deployed at depth >2000 m), EP [Behrenfeld and Falkowski, 1997; Laws et al., 2000], and measurements of carbonate, biogenic silica, and lithogenic material in the mineral ballast (Table 1). We thus have a total of 64 equations (four sites were removed from the Figure 2. Transfer efficiency (F Corg /EP) versus the flux of minerals (i.e., total mass flux 1.87 F Corg ). The symbols identify sinking particles whose mineral constituents are above or below different arbitrary thresholds. All data are annual averages reported in Table 1.

6 34-6 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX Figure 3. Linear regressions between F Corg /EP obtained by dividing organic carbon flux measured with sediment traps deployed at depth >2000 m by estimates of export production (annual averages) derived from satellite data and T eff calculated with equation (2). regression; see subsequently) that can be fitted by multiple linear regressions to obtain the best estimate for the four constants k 1, k 2, k 3, and k 4 from which a model-derived transfer efficiency (T eff ) can be calculated as T eff ¼ 3: ball CaCO 3 0: ball Opal 1: ball Lithog Fballast þ 52:1Z 1 0:005 ð2þ [9] A reasonable fit is obtained (r 2 = 0.67) when comparing T eff calculated with equation (2) and F Corg /EP obtained by dividing sediment trap F Corg by satellite-derived EP (Figure 3). The relatively high and positive value for k 1 ( ) confirms the importance of carbonate for transferring organic carbon to the deep sea, possibly as a result of its high density and ballasting capacity. However, the coefficients for biogenic silica ( ) and lithogenic materials ( ) are negative, which cannot be explained by ballasting or protection from degradation by either of these minerals. In fact, the influence of opal and clay appears to be negligible, as indicated by the linear correlation (r 2 = 0.65) between F Corg /EP and the flux of carbonate alone (Figure 4). The lack of influence of clay flux is most likely because lithogenic fluxes are generally small in the deep sea. The ballast effect of clay [Ittekkot, 1993] may be restricted to nearshore regions and its quantitative evaluation would require additional data from margins with high clay fluxes. On the other hand, the lack of ballasting effect associated with opal cannot be explained in the same manner, since our database encompasses stations with very high opal fluxes. Equation (2) suggests that another factor that correlates with opal flux has an adverse effect on the transfer efficiency of organic carbon, which overwhelms the relatively weak ballasting capacity of this mineral. [10] In the Bay of Bengal, the West Caroline Basin, and the northern Bering Sea, F Corg /EP is much higher than T eff estimated from equation (2), which suggests significant addition to the traps deployed at these sites of organic matter originating from the Ganges/Brahmaputra river system [Ittekkot, 1993], the nearby Indonesian islands [Milliman and Meade, 1983; Kawahata et al., 1997], and the Bering Shelf, respectively. In contrast, in some of the samples collected in the Nordic Seas, F Corg /EP is much lower than T eff predicted by equation (2) (Figure 3). This is consistent with the observation in Figure 2, where the Nordic Seas samples had low F Corg /EP, despite their relatively high carbonate content. [11] This interpretation of equation (2) and the following discussion rely on the validity of the satellite-derived EP estimates. The coherent regressions that we obtain between satellite-derived export production and independent sediment trap flux measurements are encouraging, but do not rule out the possibility of systematic errors in the satellitederived EP estimates. Our results could also be explained by a uniform transfer efficiency and satellite-derived export production systematically overestimated in low latitude productive carbonate-dominated regions (eastern equatorial Pacific; the Arabian Sea), and/or systematically underestimated in high latitude productive regions (Subarctic Pacific; Southern Ocean). Recent revisions of satellite chlorophyll algorithms [e.g., Moore and Abbott, 2000] and results from inverse modeling [Schlitzer, 2002] make it increasingly clear that satellite-derived estimates of net and export production are much too low in the Southern Ocean, and we have tried to address this problem by using mean annual

7 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX 34-7 Figure 4. Linear regression between F Corg /EP and the accompanying carbonate flux. EP and f-ratio values derived from direct measurements made in this region [Nelson et al., 2002]. The agreement between inverse modeling and satellite-derived algorithms appears to be better in other oceanic regions [Schlitzer, 2000], but a systematic comparison of the results obtained by inverse modeling and the algorithm of Laws et al. [2000] is still needed to identify possible systematic biases, which could affect the equations derived in the present study. 4. Effect of Seasonality on the Transfer Efficiency of Organic Carbon to Deep Sea [12] One possibility to explain the negative k 2 obtained for biogenic silica in equation (2) is the difference in biodegradability of organic matter exported from different planktonic ecosystems, possibly in response to seasonality. Warm, oligotrophic, carbonate-dominated systems are less seasonal. In these regions, organic matter is more thoroughly processed by more complex food webs and effective microbial loops [Laws et al., 2000], before being exported from the euphotic zone. This results in lower f- ratios, but the more refractory nature of the exported organic matter could enhance the efficiency of its transfer through the mesopelagic zone and to the deep sea. In contrast, colder regions are more seasonal, have higher f- ratios, and are often dominated by diatoms [Honjo, 1997; Buesseler, 1998]. Regions with a high opal flux (or with high seasonality, as in the Nordic Seas) could thus export more labile organic matter that has been less extensively processed by the food web in the mixed layer. As a result, this exported material could be more effectively remineralized during its transit through intermediate depths, reducing its transfer efficiency from the base of the euphotic zone to the deep sea. We have thus added a simple linear term to equation (1) to explore the importance of seasonality (DPP, primary production) in controlling transfer efficiency T eff ¼ k1 ball CaCo 3 þ k2 ball Opal þ k3 ball Lithog Fballast þ k 4 Z 1 þ ½ k5 DPPŠ ð3þ [13] Seasonality was quantified by calculating seasonal (January March; April June; July September; October December) net primary production (g C m 2 season 1 ; [Behrenfeld and Falkowski, 1997]) and by taking the difference between maximum and minimum seasonal production (Table 1). [14] Multiple linear regressions with the data reported in Table 1 (excluding the samples from the Bay of Bengal, the West Caroline Basin, and the Northern Bering Sea) gives T eff ¼ 3: ball CaCO 3 0: ball Opal þ 0: ball Lithog ŠFballast þ 56:1Z 1 0: DPP þ 0:003 ð4þ [15] Adding seasonality improves the fit (Figure 5a; r 2 = 0.85 versus Figure 3; r 2 = 0.67). As anticipated, the transfer efficiency of organic carbon is negatively correlated to seasonality. Again, the influence of clay and opal can largely be neglected (Figure 5b; r 2 = 0.84). Multiple linear regression without opal and clay fluxes gives T eff ¼ 3: F CaCO3 0:002 þ 68:4Z 1 0: DPP [16] These results indicate that the transfer efficiency of organic carbon to the deep sea could to a large degree be ð5þ

8 34-8 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX Figure 5. Regressions between F Corg /EP and T eff calculated with (a) equation (4) and (b) equation (5). Open squares are sites probably affected by lateral transport of continental or shelf organic carbon. predicted from the flux of carbonate, seasonality, and water depth. The standard deviation of the difference between F Corg /EP and calculated T eff is 0.015, which is 8% of the measured F Corg /EP range (0.18). At the 95% confidence level, this simple model thus explains 84% of the transfer efficiency variability in the world ocean. In the database reported in Table 1, F CaCO3 ranges from 1.4 (Fram Strait) to 44 g m 2 yr 1 (Panama Basin), DPP ranges from 1.9 (North Figure 6. Regressions between F Corg /EP and T eff calculated with (a) SST-based equation (6) and (b) f-atio-based equation (7).

9 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX 34-9 Figure 7. Transfer efficiency in the Southern Ocean (170 W). Crosses are the data from the regression reported in Figure 5b. Grey circles and black diamonds are transfer efficiencies to the seafloor calculated with the f-ratio or SST-based algorithm (equation (6) or (7)) for (1) Northern ACC, (2) Polar Frontal Region, (3) Southern ACC, and (4) Ross Gyre. Open triangle is the transfer efficiency for the AESOPS-2 trap (4224 m) calculated with the f-ratio and SST-based algorithms. Pacific Gyre) to 169 g C m 2 yr 1 (Greenland Sea), and 1/Z from to m 1. The relative influence of these three variables on the variability in transfer efficiency is thus 65, 25, and 10%, respectively. [17] Equation (5) is only a statistical relationship and other variables can be used to replace seasonality. For instance, slightly weaker but similar correlations are obtained with sea-surface temperature (SST; Figure 6a; r 2 = 0.77) T eff ¼ 2: F CaCO3 0:029 þ 55:7Z 1 or with the f-ratio (Figure 6b; r 2 = 0.78) T eff ¼ 2: F CaCO3 þ 0:009 þ 102Z 1 þ 1: SST ½0:096 f ratioš [18] These relationships indicate that the transfer efficiency of organic carbon to the deep sea increases with SST and decreases with the f-ratio. SST and f-ratio are only weakly linearly correlated with DPP (r 2 = 0.24 and 0.30, respectively), confirming that the carbonate flux is the dominant factor in controlling the transfer efficiency. 5. Transfer Efficiency of Organic Carbon to the Deep Southern Ocean [19] Most of the sediment trap flux that were collected during the US-JGOFS AESOPS program in the Southern Ocean were obtained at 1000 m depth [Honjo et al., ð6þ ð7þ 2000]. They were not included in this study, but for one sediment trap deployed at 4224 m (AESOPS-2; S, W), where relatively low transfer efficiency ( ) was both calculated and measured (Table 1). Because ocean color-based algorithms seem to underestimate productivity in this region [Moore and Abbot, 2000; Schlitzer, 2002], we used mean annual productivity and export values derived from direct measurements during the US JGOFS program [Nelson et al., 2002]. For the same reason, we cannot use equation (5) to calculate T eff, because satellite data underestimate DPP. Instead, we calculated T eff using the SST-based and f-ratio-based algorithms (Figure 6). [20] To further assess the general applicability of our predictive equations in the Southern Ocean, we used the estimates of carbon fluxes reaching the seafloor, which have been derived from pore water profiles of nitrate, alkalinity, and TCO 2 [Sayles et al., 2001]. The transfer efficiency of organic carbon to the seafloor in the Northern Antarctic Circumpolar Current (Northern ACC), the Polar Frontal region (PFZ), the Southern ACC, and the Ross Gyre, as defined by Nelson et al. [2002], was calculated by dividing export production estimated from 15 N incubations, nitrate drawdown, and 234 Th deficit [see Nelson et al., 2002] by the carbon flux reaching the seafloor estimated by Sayles et al. [2001]. Using carbonate fluxes measured with sediment traps collected at 1000 m [Honjo et al., 2000], T eff to the seabed was calculated using the mean annual f-ratio [Nelson et al., 2002] or satellite-derived SST (Figure 6). The results confirm the low transfer efficiency in this region (Table 2 and Figure 7), which is also consistent with the inverse

10 34-10 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX Figure 8. Predicted (from equation (8)) versus measured fluxes of organic carbon. modeling study of Schlitzer [2002]. We obtain a reasonable agreement between F Corg /EP and T eff calculated at the three northernmost regions. At the Ross Gyre station, however, the pore water data indicate a much higher flux than predicted by the algorithms. The 230 Th-normalized flux of lithogenic particles reaching the sediment at this site is also much higher than the flux collected by the sediment trap at 1000 m [Chase, 2001], suggesting downslope transport and lateral input of clay and organic matter from the nearby slope of the Antarctic continent. 6. Flux of Organic Carbon to Deep Sea [21] By re-arranging equation (5), we can predict the flux of organic carbon at depth >2000 m from satellite-derived EP and DPP, from water depth, and from the flux of carbonate F Corg ¼ EP 3: F CaCO3 þ 68:4Z 1 0: DPP 0:002Þ ð8þ [22] The calculated C org fluxes agree well with the fluxes measured (Figure 8), but generally not in regions with low transfer efficiency (<0.03) and high export production (>30 gcm 2 yr 1 ). This stems from the high relative error on T eff in regions with low transfer efficiency (Figure 5). It is therefore more difficult to predict the absolute flux of organic carbon reaching the deep sea in high latitude productive regions such as the Nordic Seas, the Southern Ocean, and the North Pacific. 7. Preliminary Interpretation of Equations (5) (7) [23] Equations (5) (7) are just statistical relationships. They are not an explicit proof that the flux of carbon to the deep sea is directly and linearly controlled by the flux of carbonate, seasonality as defined here, SST, and f-ratio, but they provide clues to possible underpinning mechanisms that may control the flux of organic carbon to the deep sea. The fact that the flux of carbonate accounts for 65% of the transfer efficiency variability suggests that the ballasting effect of this comparatively dense biogenic mineral (2.7 g cm 3 ) may play an important role, presumably by increasing the sinking rates of settling particles. However, density cannot be the only factor coming into play, since biogenic opal is also denser than organic matter, and yet does not show a strong ballasting capacity. One possibility may be the intervention of a packaging factor that would be correlated to carbonate flux, i.e., particles sinking from carbonate-dominated ecosystems with complex food webs would be more tightly packaged in fecal pellets, while particles sinking from seasonal, opal-dominated systems would be looser, less hydrodynamic aggregates, produced in large parts by aggregation of senescent diatoms. Thus in opal-dominated regions with comparatively high f-ratio and seasonality, and low SST (e.g., in the northwest Pacific or the Southern Ocean), the transfer efficiency of organic carbon to the deep sea is low because the exported organic matter would be more biodegradable and packaged into looser aggregates that could disaggregate easily. In contrast, in warm productive regions dominated by carbonate-producing plankton with low f-ratios (e.g., equatorial upwelling regions, Honjo et al. [1995]; Arabian Sea, Honjo et al. [1999]), transfer efficiency is higher because most of the highly biodegradable organic matter would have been consumed by higher trophic levels before export from the euphotic zone, and because particles would be sinking rapidly through the water column as a result of carbonate ballast and tight packaging into fecal pellets. The validity of this initial interpretation of equa-

11 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX tions (5) (7) could be directly tested by contrasting the mean sinking rates and the morphology of particles in the mesopelagic zone of different oceanic regions (carbonate versus opal-dominated regions, high versus low latitude; mesotrophic versus oligotrophic), and by developing means of assessing the biodegradability of sinking organic matter in relation to f-ratio, seasonality, and mineral composition of settling particles. [24] The Nordic Seas have high seasonality and f-ratios [Laws et al., 2000], but are not dominated by opal [Honjo, 1990]. Here, as in opal-dominated regions, we suggest that transfer efficiency is very low, also in large part because of the high biodegradability of the exported organic matter and loose packaging. Both these characteristics may be promoted by the highly seasonal nature of primary production at high latitudes. Due to the brevity of the high latitude summers, much of the organic matter produced during the annual phytoplankton bloom is not grazed [Honjo et al., 1988; Honjo, 1990; Grebmeier et al., 1989; Wassmann, 1991; Walsh et al., 1997]. Climatic conditions deteriorate rapidly after the bloom, preventing full development of the grazer population. As a result, instead of being grazed and recycled, a large fraction of the primary production is exported (resulting in high f-ratios). Seasonality has been suggested as a parameter that may enhance export [Berger and Wefer, 1990]. While seasonality is likely to increase the f-ratio and export production, equation (5) suggests that much of the exported material will be remineralized in the mesopelagic zone, resulting in little additional export to abyssal depths. [25] If confirmed, this observation has also implications for the effect of Fe-fertilization of the ocean on glacial-interglacial timescale. Since Fe-fertilization promotes mainly diatom production [Coale et al., 1996; Watson et al., 2000], the resulting enhanced export production may not necessarily translate into higher sequestration of CO 2 in the deep-sea. 8. Comparison With Martin et al. s [1987] Power Function [26] Martin et al. [1987] proposed a power function to describe the decreasing flux of sinking organic carbon (F Corg (Z)) with depth (Z) F Corg ðzþ ¼ EPðZ=Z 0 Þ b ð9þ where EP is the flux at depth Z 0 (base of the layer from which organic C is exported) and b is a (dimensionless) negative constant. In the original equation, Z 0 = 100m and b = 0.86 [Martin et al., 1987]. [27] This equation is often used in biogeochemistry models [Najjar et al., 1992; Yamanaka and Tajika, 1996; Lampitt and Antia, 1997; Marchal et al., 1998], largely because it permits a simple parameterization of the remineralization of nutrients and nutrient-like tracers. However, it is based on organic carbon flux measurements that were made during short-term deployments (6 34 days) in the upper 2000 m in the northeast Pacific only, and its global and depth range applicability still need to be confirmed. A recurring problem in circulation-biogeochemistry models using a constant remineralization profile is the simulation of large nutrient excesses relative to observations at the subsurface in the tropical Pacific and Indian Ocean [Najjar et al., 1992; Bacastow and Maier-Reimer, 1991; Marchal et al., 1998]. This nutrient trapping was ascribed to a positive feedback peculiar to equatorial circulation regimes, whereby upwelling of nutrients increases export production, remineralization at depth, and the nutrient content of the upwelling water [Najjar et al., 1992]. It may be due to not only different deficiencies in the models such as the omission of dissolved organic matter [Bacastow and Maier-Reimer, 1991; Najjar et al., 1992; Marchal et al., 1998] and the poor representation of the equatorial circulation [Matear and Holloway, 1995; Aumont et al., 1999], but also due to the inadequate parameterization of organic carbon remineralization. The annually averaged sediment trap flux data that are now available provide a means of further assessing the general applicability of Martin et al. s [1987] equation below 2000 m and to all oceanic regions Bathypelagic Zone [28] If we apply equation (9) to satellite-derived export production data reported in Table 1 and compare the calculated and measured fluxes of organic carbon reaching the sediment traps below 2000 m, we find systematic deviations (Figure 9a). In productive regions with high seasonality (e.g., at high latitudes such as in the Nordic Seas, the North Pacific, and the Southern Ocean), Martin et al. s [1987] equation generally overestimates the carbon fluxes. This could be either because Laws et al. s [2000] algorithm systematically overestimates export production in these regions or because the general exponent (0.86) in equation (9) is too high. In contrast, Martin et al. s equation underestimates the organic carbon fluxes in low latitude productive regions (Arabian Sea, equatorial Pacific, Guinea Basin). In this case, either the productivity algorithm of Laws et al. systematically underestimates export production, or the exponent in equation (9) must be greater than If we accept the export production estimates, we can adjust the exponent b to match the measured fluxes of carbon at each station (Table 1) b ¼ 1n F Corg= EP =1nðZ=100Þ ð10þ where F Corg is the flux of organic carbon measured by sediment traps deployed at depth Z. Like T eff, b varies systematically with the flux of carbonate and seasonality. Multiple linear regressions between e b (where b is calculated with equation (10); Table 1), F carb, and DPP or SST give e b ¼ 0:35 þ 8: F carb 1: DPP r 2 ¼ 0:73 ð11þ e b ¼ 0:24 þ 5: F carb þ 5: SST r 2 ¼ 0:49 ð12þ [29] Equation (11) gives a better fit but cannot be applied to the Southern Ocean. The exponent b is more negative than the value (0.86) proposed by Martin et al. [1987] in

12 34-12 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX Figure 9. Comparison between F Corg measured with sediment traps and F Corg calculated (a) with Martin et al. s [1987] original equation (F Corg = EP(Z/100) 0.86 ), and (b) calculated with Martin et al. s equation adjusted according to equation (11). high latitude productive and seasonal regions as in the Nordic Seas and the northwest Pacific, and less negative in low latitude productive regions, as in the upwelling regions of the Arabian Sea, the equatorial Pacific, and the Guinea Basin (Figure 10). The other areas, however, cluster around Martin et al. s original value of Decreasing the exponent b toward more negative values in equation (9) amounts to increasing the rate of remineralization at shallower depths, i.e., decreasing the transfer efficiency of carbon to the deep sea. With b values typical of the Nordic Seas, nearly all the organic carbon exported is oxidized before reaching 2000 m depth, while b values characteristic of low latitude high productivity regions produce a transfer efficiency of 2000 m approaching 20%. Applying equation (12), we find a very negative value in the Southern Ocean (b = 1.11), consistent with other high latitude productive regions. When applying exponent b calculated from equation (11) to Martin et al. s equation (Figure 9b), we obtain a Figure 10. (a) Regression between e b calculated with equation (11) and equation (10). (b) Correlation between b calculated with the same two equations.

13 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX Figure 11. Organic carbon fluxes predicted at depth shallower than 2000 m by equation (5) and the power function of Martin et al. [1987] adjusted as explained in the text. (a) North Pacific; (b) Arabian Sea; (c) Norwegian Sea. much better fit between the calculated flux of carbon and the measured flux reported in Table 1. [30] Alternatively, since the depth domain of calibration of the present algorithm and that of Martin et al. s equation overlap at 2000 m, we could also adjust exponent b by matching the flux predicted at 2000 m by our algorithms (Figure 11) b ¼ 1n 3: F Carb þ ð68:4=2000þ 0: DPP 0:002Þ=1nð2000=100Þ ð13þ

14 34-14 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX b ¼ 1n 2: F Carb þ ð55:7=2000þ þ 1: SST 0:029Þ=1nð2000=100Þ ð14þ 8.2. Mesopelagic Zone [31] Considering the underlying database, equation (5) is broadly applicable, but, in principle, should only be used to estimate organic carbon fluxes below 2000 m. As a first step toward evaluating the degree to which we can constrain mesopelagic carbon fluxes, we can verify whether equation (5) produces mesopelagic fluxes that deviate from those predicted with the equation of Martin et al. [1987] after adjustment of the b exponent. [32] The adjusted Martin algorithm and equation (5) diverge significantly at mesopelagic depths in low latitude productive regions (Figure 11). This cautions from using equation (5) at depth shallower than 2000 m. Further progress in developing a truly global depth-integral algorithm requires accurate (i.e., trapping efficiency corrected; Yu et al. [2001]; Scholten et al. [2001]) and globally distributed flux measurements to further assess the effect of mineral flux and organic matter biodegradability on the regeneration length scales of organic matter between the base of the euphotic zone and 2000 m depth. 9. Comparison With Armstrong et al. s [2002] Ballast Algorithm [33] Recently, Armstrong et al. [2002] have criticized the use of power functions to describe the depth dependency of the flux of organic carbon in the deep sea on the ground that these functions do not reproduce the asymptotic trend that would be conferred to the flux of organic carbon by the intimate association of organic matter with minerals. Instead, they proposed a model where the settling flux of organic carbon is subdivided into a protected fraction ( 1 F Corg ), intimately associated with minerals, and an unprotected fraction ( xs F Corg ), which decreases exponentially with depth 1 F Corg ¼ r 1 F ballast ð15þ z F ballast ¼ 1 F ballast þ z0 F ballast 1 zz0 ð F ballast Þe ð Þ=db ð16þ xs zz0 F Corg ¼ EPe ð Þ=de ð17þ where z F ballast is the flux of minerals (or ballast ) at depth z, 1 F ballast is the asymptotic value of the flux of ballast, db and de are the remineralization depth scales for ballast and xs C org, EP is the flux of organic carbon exported from surface water, z 0 is a reference depth, and z is the water depth. [34] Unlike Martin et al. s [1987] power function, Armstrong et al. s [2002] equation decreases asymptotically with depth toward a value ( 1 F Corg ) dictated by the asymptotic flux of ballast (i.e., the flux of all minerals, including biogenic opal and lithogenic particles) and the ballast s transport ratio (r). Armstrong et al. suggested that r would likely vary with ballast composition. [35] Equation (5) also gives an asymptotic value for F Corg with depth, but the asymptote ( 1 F Corg ) is independent of the flux of biogenic opal and lithogenic particles, and is a function of EP, the flux of carbonate, and seasonality. For z!1 1 F Corg ¼ EP 3: F CaCO3 0: DPP 0:002 ð18þ [36] Unlike the asymptote in Armstrong et al. s [2002] algorithm, the asymptote obtained from equation (18) can be negative (in regions with low carbonate flux and high DPP) and has no clear physical meaning. To illustrate the difference between the Armstrong et al s algorithm and equation (5), we have compared their description of the flux of carbon at 5 N, 140 W. The fitted value for de and r in the equatorial Pacific are 480 m and 0.054, respectively [Armstrong et al., 2002; Case 3]. At station EqPac-5N (Table 1), a flux of 1.64 g C m 2 yr 1 was measured at 2100 m [Honjo et al., 1995], and a good fit with the measured fluxes can be obtained with Armstrong et al. s algorithm, if we fix 1 F ballast ¼ z 0 F ballast. Then we obtain F Corg ¼ 0:054 24:6 þ 24:6e þ ð15:6 ð0:054 24:6ÞÞe ð z100 Þ=480 ð z100 Þ=480 [37] At 2100 m, Armstrong et al. s [2002] algorithm thus predicts a flux of 1.55 g C m 2 yr 1. Equation (5) predicts F Corg ¼ ðð0: :96Þþ ð68:4=2100þ ð0: :5Þ0:002Þ15:6 ¼ 1:45 g C m 2 yr 1 [38] The two algorithms agree well (Figure 12) with the organic carbon fluxes measured with sediment traps below 2000 m and predicted from sediment pore water oxygen profiles [Hammond et al., 1996]. There is, however, a significant divergence in the mesopelagic zone, which further highlights the need for better flux measurements at intermediate depths. [39] Although the two algorithms are based on very different mechanistic premises, they predict similar fluxes below 2000 m. They become increasingly divergent with increasing depth, but the differences do not exceed 15%. In view of the small decrease in organic carbon fluxes below 2000 m and the uncertainties associated with sediment trap and benthic flux measurements, it is unlikely that the validity of the physical underpinning of either of these algorithms can be supported or revoked by further statistical treatments of deep sea fluxes. Whether the flux of carbon to the deep sea is controlled purely by the ballasting and protecting effect of minerals, as advocated by Armstrong et al. [2002], or whether systematic differences in the biodegradability of organic matter and hydrodynamic properties of sinking particles play a role, as proposed here, will have to be established by specific process studies. 10. Export of Organic Carbon to Deep Sea and Water Column Remineralization Profiles [40] Buesseler [1998] has compiled export fluxes of organic carbon estimated from the deficit of 234 Th activity

15 FRANCOIS ET AL.: FACTORS CONTROLLING ORGANIC CARBON FLUX Figure 12. (a) Organic carbon fluxes predicted by Armstrong et al. s [2002] algorithm and equation (5) at 5 N; 140 W. Measured fluxes are from sediment trap measurements [Honjo et al., 1995; Wakeham et al., 1997] and modeling of organic carbon oxidation on the seafloor [Hammond et al., 1996]. (b) Percent difference between the organic carbon flux predicted by the two algorithms. in surface waters and defined an export ratio ThE as the ratio of 234 Th-based export production to net primary production. ThE ratios are often higher in regions that are dominated by biogenic opal, suggesting that diatoms, and possibly seasonality [Berger and Wefer, 1990] promote export of organic matter from surface waters. Boyd and Newton [1999] derived a similar conclusion from a food web model. On the other hand, in oligotrophic carbonatedominated ecosystems, ThE ratios are invariably low, as most of the primary production is recycled through the microbial loop. The present study provides an interesting counter-point to these earlier observations. In this study, biogenic opal is found to be much less efficient at transferring organic carbon to deeper water than carbonate. Therefore, while a higher fraction of the organic carbon produced in diatom-dominated regions is exported below the euphotic zone, it must be more efficiently remineralized within the mesopelagic zone before reaching 2000 m depth. In contrast, while a smaller fraction of organic matter produced in calcite-dominated systems is exported from the euphotic zone, a larger fraction of the exported organic carbon is transferred to the deep sea for long-term removal. [41] This is best illustrated by comparing the organic carbon fluxes measured with sediment traps and predicted with equation (5) at three contrasting stations (Figure 13); KNOT, from the productive, diatom-dominated northwest Pacific [Honda, 2001]; AS-3 from the productive, carbonate-dominated Arabian Sea [Honjo et al., 1999], and CP-6 from the oligotrophic central gyre of the North Pacific [Kawahata et al., 1998]. The transfer efficiency predicted at 2000 m (Figure 13a) is about three times higher in the low latitude (AS-3) versus high latitude (KNOT) productive region (T eff = 0.12 versus 0.04), and intermediate in the oligotrophic ocean (T eff = 0.07). A higher fraction of the exported organic matter is remineralized in the mesopelagic zone of productive high latitude regions (0.96 versus 0.89). Even though export production is nearly twice higher in the diatom-dominated northwest Pacific (78.1 g C m 2 yr 1 ; Table 1) than in the Arabian Sea upwelling region (44.3 g C m 2 yr 1 ; Table 1), the flux of organic carbon reaching below 2000 m is lower in the northwest Pacific (Figure 13b). As a result, the flux of organic carbon reaching the sediment water interface biases the surface productivity signal. It accentuates the productivity signal in low latitude productive regions compared to high latitude productive regions (Figure 13c). It also accentuates the contrast between low latitude productive and oligotrophic regions, but decreases the contrast between low latitude oligotrophic and high latitude productive regions. These trends must be taken into account when interpreting benthic oxygen demand maps [Jahnke, 1996] in terms of surface productivity patterns. 11. Relationship Between the Flux of Organic Carbon to Deep Sea and Primary Production in Surface Waters [42] Several algorithms have been proposed that attempt to relate the flux of organic carbon to net primary production instead of export production [Suess, 1980; Betzer et al., 1984; Pace et al., 1987; Berger et al., 1990; Lampitt and Antia, 1997]. This approach adds one level of complexity to the relationship, which then includes not only factors controlling remineralization in the water column, but also factors controlling the f-ratio. Figure 6b suggests that although regions with higher f-ratios export a larger fraction of net primary production, a larger fraction of the settling organic carbon flux is remineralized in the mesopelagic zone. These two opposing effects could largely cancel each other and result in a roughly linear increase in the flux of organic carbon to the deep sea with net primary production.