Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean



[1] We investigate the functioning of the ocean’s biological pump by analyzing the vertical transfer efficiency of particulate organic carbon (POC). Data evaluated include globally distributed time series of sediment trap POC flux, and remotely sensed estimates of net primary production (NPP) and sea surface temperature (SST). Mathematical techniques are developed to compare these temporally discordant time series using NPP and POC flux climatologies. The seasonal variation of NPP is mapped and shows regional- and basin-scale biogeographic patterns reflecting solar, climatic, and oceanographic controls. Patterns of flux are similar, with more high-frequency variability and a subtropical-subpolar pattern of maximum flux delayed by about 5 days per degree latitude increase, coherent across multiple sediment trap time series. Seasonal production-to-flux analyses indicate during intervals of bloom production, the sinking fraction of NPP is typically half that of other seasons. This globally synchronous pattern may result from seasonally varying biodegradability or multiseasonal retention of POC. The relationship between NPP variability and flux variability reverses with latitude, and may reflect dominance by the large-amplitude seasonal NPP signal at higher latitudes. We construct algorithms describing labile and refractory flux components as a function of remotely sensed NPP rates, NPP variability, and SST, which predict POC flux with accuracies greater than equations typically employed by global climate models. Globally mapped predictions of POC export, flux to depth, and sedimentation are supplied. Results indicate improved ocean carbon cycle forecasts may be obtained by combining satellite-based observations and more mechanistic representations taking into account factors such as mineral ballasting and ecosystem structure.

1. Introduction

[2] The marine biosphere is a major component of the global carbon cycle, responsible for roughly half of the annual photosynthetic absorption of CO2 from the atmosphere [Field et al., 1998]. However, the influence of marine carbon cycle variability on atmospheric CO2 concentrations is poorly understood [Sarmiento and Le Quéré, 1996; Le Quéré et al., 2005]. In particular, there is little basis to predict how potential alterations in upper ocean ecosystems may influence the capacity of the ocean to store carbon [Sarmiento et al., 1998]. Significant variability in the vertical flux of particulate organic carbon between oceanographic regions [Lutz et al., 2002; Neuer et al., 2002] implies the potential for substantial biogeochemical consequences resulting from alterations to upper ocean dynamics. Changes in the efficiency of the marine organic carbon cycle may contribute to variability of atmospheric CO2 on glacial-interglacial timescales [Sigman and Boyle, 2000]. Understanding of the fate of biogenic carbon within the ocean is essential for improved forecasts of future atmospheric CO2 concentrations and rates of global warming, especially in the context of changing ocean circulation [Sarmiento et al., 1998; Schmittner, 2005].

[3] The transport of biogenic elements from surface waters to the deep ocean and sediments occurs through a variety of processes collectively known as the “biological pump” [Volk and Hoffert, 1985]. This biogeochemical and physical system may be conceptually divided into four interrelated components: production, export, flux to depth, and sedimentation. During primary production, phytoplankton incorporate dissolved nutrients and inorganic carbon into particulate organic matter (POM), lowering the partial pressure of CO2 in surface waters, thus enhancing the ocean’s ability to absorb CO2 from the atmosphere. Much POM is recycled within surface waters where associated nutrients support regenerated primary production [Eppley and Peterson, 1979]. A fraction this POM is exported beneath surface waters and fluxes to depth within the ocean’s interior where it is predominantly consumed and regenerated by subsurface heterotrophs. The minor portion of POM that reaches the seafloor is mostly remineralized in support of benthic biological activity. Flux to the seafloor escaping benthic regeneration is buried in the sediments.

[4] In general, the greater the depth at which sinking organic carbon is regenerated, the longer time it takes to return to the photic zone as dissolved CO2, where it may reenter atmospheric carbon cycle. Organic carbon reaching the deep ocean is entrained in water masses with longer flow pathways back to the surface and smaller advective velocities than in the upper ocean. Ventilation of subsurface waters occurs on timescales ranging from annual to hundreds of years in the upper ocean (∼100–1000 m) to thousands of years in the deep ocean (>1500 m). Carbon entering sediments may be geologically stored for hundreds of millions of years. Thus to describe the storage of biogenic carbon in the ocean, the depth of remineralization must be known.

[5] This paper explores how global-scale environmental parameters, sea surface temperature (SST) and the seasonality of net primary production (NPP), relate to the efficiency of the biological pump. The ability to infer SST and NPP from remote observations allows for their synoptic comparison to sediment trap data. This investigation introduces methodology to facilitate the synthesis of these time series. Our metadata analysis is used to describe the seasonal efficiency of production-to-flux relationships, evaluate hypotheses regarding the functioning of the biological pump, assemble new particle flux algorithms, and test parameterization accuracy.

2. Surface Temperature and Seasonality of NPP

[6] Recent evidence indicates water temperature may be a key environmental parameter influencing the proportion of NPP exported from surface waters to the deep ocean [Laws et al., 2000; Rivkin and Legendre, 2001]. This hypothesis suggests the rate of organic matter recycling is influenced by SST. Specifically, lower-water temperatures limit the activity of microbial heterotrophic decomposers more than the activity of autotrophic producers. This scenario implies that in colder regions of the ocean, a greater fraction of production is not remineralized and is therefore available for export. Furthermore, due to the lack of surface recycling where SST is colder, exported detritus may be more labile than where SST is warmer. Laws et al. [2000] propose a euphotic zone food web model describing biogenic export relying heavily on this premise. In support of this rationale, the authors demonstrate a strong near-linear correlation between observed f ratios, the ratio of new to total production [Eppley and Peterson, 1979], and those predicted by their model. Water temperature alone explains most of the f ratio variability found in the model by Laws et al. [2000].

[7] Another proposed control on biogenic carbon export is variability in the efficiency of trophic transfer within the upper ocean food web [Berger et al., 1989; Berger and Wefer, 1990; Lampitt and Antia, 1997; Rühlemann et al., 1999; Fischer et al., 2000]. This hypothesis suggests that organic matter recycling is influenced by controls related to variability of upper ocean ecosystem structure. In particular, the fraction and biodegradability of production exported from more variable ecosystems may be greater than from stable ones. Where the supply of solar irradiation and nutrients are relatively constant, environments are stable throughout the year and activities of autotrophs and heterotrophs are balanced. Trophic transfer is efficient and recycling dominates. The fraction of production exported is minimal. Conversely, seasonal environments reflect time-varying physical conditions, solar irradiation, and nutrients, and are characterized by “leaky” food webs with temporally imbalanced components. New production dominates and rapid autotrophic growth outpaces consumer feeding and decompositional activity. Particulate matter has a greater opportunity to avoid surface heterotrophy and be exported in a more labile or “fresh” state. Thus in variable environments particulate matter has a greater opportunity to escape the euphotic zone and be more biodegradable than in stable environments.

3. Previous Flux Algorithms

[8] The empirical algorithms of Suess [1980], equation (1) [Six and Maier-Reimer, 1996; Dymond et al., 1997; Tyrell, 1999], and Martin et al. [1987], equation (2) [Sarmiento et al., 1998; Hedges et al., 1999; Buesseler et al., 2000; Fischer et al., 2000] are commonly used to describe oceanic particulate organic carbon flux:

equation image
equation image

Particulate organic carbon flux (Cflux(z); mg Corg m−2 d−1) is described as a function of the production of organic carbon in surface waters (Cnpp) or the export of organic carbon (Cexport) from the base of the euphotic zone (z0), scaled to depth below the sea-surface (z). The Suess [1980] equation was determined from field-based production and sediment trap flux measurements, collected from the subtropical eastern Pacific and northwestern Atlantic at depths between 50 and 5400 m. The Martin et al. [1987] normalized power function is a “best fit” based on sediment trap data collected in the low to midlatitude east Pacific from depths between 100 and 2000 m. Global carbon models including marine biology commonly describe flux to depth using the fixed parameters shown in equations (1) and (2).

4. Methods

[9] Our approach builds on the relationships of Suess [1980] and Martin et al. [1987] [equations (1) and (2)] by using the greater amount of recently available data to constrain more of the variability between production and flux to depth. In particular, we compare globally distributed annual and time series sediment trap flux measurements, and remotely sensed estimates of NPP and SST. Sediment traps and satellites allow for the characterization of marine biogeochemical variability on timescales longer than the data sets collected through traditional ship-based oceanography.

[10] The global sediment trap data set assembled for this analysis contains estimates of flux collected during the past 25 years. Satellite-based estimates of production are employed because field-based observations of production are not available at the same locations and durations as the sediment trap experiments. Annual climatologies are used to compare the seasonality of these temporally discordant time series. Interannual variability is not focused upon in this study.

4.1. Sediment Traps

[11] Moored sediment traps are funnel-shaped devices attached at varying depths to line that is fixed in location to the seafloor [Honjo, 1978]. Settling detritus intercepted by these funnels collects in sample bottles opened and closed at the funnel base. Each sediment trap measurement reflects the total mass of flux collected during each bottle open-close interval. Sampling frequency and interval duration typically varies within sediment trap experiments. Experiments often include interruptions produced during collection interval malfunctions and between trap deployments. The result of a sediment trap experiment is typically a discontinuous time series of average flux rates representing bottle open-close intervals of variable durations.

[12] The following criteria are used in assembling sediment trap data that best reflect surface production (Table 1 and Figures 1 and 2) :

Figure 1.

Geographic locations of sediment trap particulate organic carbon flux to depth measurements. Observations include annual estimates (○) and subannual time series (•). Arrows indicate locations of the meridional transects of net primary production time series shown in Figure 5.

Figure 2.

Depth versus latitude of sediment trap particulate organic carbon flux to depth measurements. Observations include annual estimates (○) and subannual time series (•).

Table 1. Sediment Trap Particulate Organic Carbon Flux to Depth (Corg) Observations
RegionTrap IDLat.Lon.Water depth (m)Trap depth (m)Collection intervalCorg (mg m−2 d−1)LabelaReference
  • a

    Labels refer to flux time-series in Figure 8.

  • b

    Data available online (; S. Honjo and R. Francois.

  • c

    Multiple trap deployment flux estimates are combined.

  • d

    Discrete open-close interval flux to depth data not available in numerical format. Records of flux to depth are digitized from data presented in graphs.

  • e

    Climatology open and close dates are estimated as the first days of the month of reported monthly averages.

  • f

    Organic carbon estimated as (1.8 * OC = OM) following Kawahata et al. [1998].

  • g

    Due to local cloud cover, remote sensing data used are from ∼2700 m north of location.

  • h

    Flux corrected for a component potentially associated with pteropod ‘swimmers' following Collier et al. [2000].

  • i

    Excluding 10 Feb. 1990 to 8 Feb. 1992 interval of overlap between traps to avoid attenuating seasonality.

  • j

    Data available online (; M. Conte.

  • k

    Data available online (; F. Müller-Karger, R.Varela, R. Thunell, M. Scranton, and others.

  • l

    Excluding KG2 10 Dec. 1985 to 24 Dec 1985 interval of overlap between KG2 and KG3 trap deployments to avoid attenuating seasonality.

  • m

    Data available online (; P. Tréguer.

Pacific Ocean
   Central Bering Sea 581793783313718 Jul 199112 Jun 19925.53 Honjo [1996]b
   Bering Sea, Aluetian BasinAB53.5−177378831987 Aug 199025 Jul 19956.961-DTakahashi et al. [2000]c
   Central Okhotsk SeaOS53149117025812 Aug 199012 Aug 19918.58 Honjo [1996]b
   Central Okhotsk SeaOS531491170106112 Aug 199012 Aug 19914.081-IHonjo [1996]b
   S of Kamchatka PeninsulaGD51.516549604500Jul 1991Jun 19924.382-DWong et al. [1994]d,e
   W Pacific Subarctic Gyre50N50165550012271 Dec 199718 May 20004.62-IHonda [2001]; Honda et al. [2002]b,c
   W Pacific Subarctic Gyre50N50165550032601 Dec 199718 May 20003.13-DHonda [2001]; Honda et al. [2002]b,c
   W Pacific Subarctic Gyre50N50165550050901 Dec 199718 May 20002.34-DHonda [2001]; Honda et al. [2002]b,c
   Subarctic PacifcOSP50−14542402008 May 198916 May 199418.21-SWong et al. [1999]c,d,e
   Subarctic PacifcOSP50−1454240100027 Mar 198316 May 19947.422-SWong et al. [1999]c,d,e
   Subarctic PacifcOSP50−1454240380023 Sep 198216 May 19943.105-DWong et al. [1999]c,d,e
   Subarctic PacifcSA49−173.9540648069 Aug 19902 Aug 19953.936-DTakahashi et al. [2000]c
   Juan de Fuca RidgeJDF48.0−128.1na22009 Sep 198410 Aug 19852.99 Dymond and Lyle [1994]
   N Central PacificNP-B46.8162.15670700Aug 19851 Jun 198633.7 Tsunogai and Noriki [1991]d
   N Central PacificNP-B46.8162.156705200Aug 19851 Jun 19865.1 Tsunogai and Noriki [1991]d
   Subarctic FrontSite 846.1175.05435141215 Jun 199316 Apr 19948.113-IKawahata [2002]b
   E of Kurile Is.GA4516558305300Jul 1991Jun 19923.017-DWong et al. [1994]d,e
   S of Aleutian Is.GB45−17761005600Jul 1991Jun 19921.378-DWong et al. [1994]d,e
   W Pacific Subarctic GyreKNOT4415555009241 Dec 199713 May 20009.13-SHonda et al. [2002]c,d
   W Pacific Subarctic GyreKNOT44155550029601 Dec 199713 May 20006.1 Honda et al. [2002]c
   W Pacific Subarctic GyreKNOT44155550049891 Dec 19975 Mar 20004.2 Honda et al. [2002]c
   California Current, MidwayMW42.2−127.6na283022 Sep 198716 Sep 19886.02 Dymond and Lyle [1994]
   California Current, NearshoreNS42.1−125.8na282922 Sep 198716 Sep 198813.4 Dymond and Lyle [1994]
   California Current, GyreG41.6−132na366428 Sep 198714 Sep 19882.46 Dymond and Lyle [1994]
   NW PacificEM40.9142.0537050001 Sep 19881 May 198912.8 Tsunogai and Noriki [1991]d
   Subarctic boundary40N4016555009531 Dec 199720 Jan 20004.94-SHonda [2001]; Honda et al. [2002]b,c
   Subarctic boundary40N40165550029861 Dec 199720 Jan 20003.99-DHonda [2001]; Honda et al. [2002]b,c
   Subarctic boundary40N40165550050161 Dec 199720 Jan 20003.410-DHonda [2001]; Honda et al. [2002]b,c
   E Japan SeaSta. T39.7132.43300280019 Jul 199418 Jul 19958.111-DHong et al. [1996]
   California CurrentMFZ39.5−127.7na42309 Sep 19831 Sep 19843.48 Dymond and Lyle [1994]
   Subarctic FrontSite 737.4174.9510514821 Jun 19939 Apr 19946.444-IKawahata [2002]b
   Subarctic FrontSite 737.4174.9510545881 Jun 19939 Apr 19943.0612-DKawahata [2002]b
   Monterey BayS136.8−122.0700450Aug 19891 Nov 199239.5 Pilskaln et al. [1996]c
   Kuroshio ExtensionWCT-7-s36.7154.95578119119 Aug 199929 Aug 20004.445-IMohiuddin et al. [2004]
   Kuroshio ExtensionWCT-7-d36.7154.95578503419 Aug 199929 Aug 20003.8313-DMohiuddin et al. [2004]
   Kuroshio ExtensionWCT-3-s36.0147.05615110830 Aug 199810 Aug 19992.506-IMohiuddin et al. [2004]
   Kuroshio ExtensionWCT-3-d36.0147.05615508130 Aug 199810 Aug 19994.0614-DMohiuddin et al. [2004]
   Subtropical FrontSite 534.4177.73365134215 Jun 199316 Apr 19942.897-IKawahata [2002]b
   Subtropical FrontSite 534.4177.73365284815 Jun 19931 Jun 19944.2815-DKawahata [2002]b
   Santa Barbara Basin 34.2−120.059054012 Aug 199310 Sep 199612.3 Thunell [1998a]c
   Subtropical FrontSite 630.0175.05390387315 Jun 19931 Jun 19942.6716-DKawahata [2002]b
   Gulf of CaliforniaGuaymas Basin27.9−111.77005008 Jul 19905 Jan 199718.95-SThunell [1998b]c
   Okinawa TroughJAST0127.2126.41650101019 Jan 199310 Aug 19956.238-IHonda [2001]b,c
   Okinawa TroughJAST0127.2126.41650154719 Jan 200330 Dec 20034.119-IHonda [2001]b
   Ryukyu TrenchJAST0325.1127.4377131578 Oct 199410 Aug 19951.2317-DHonda [2001]b,c
   South China SeaSCS-N18.51163750100010 Sep 198721 Oct 19883.92 Wiesner et al. [1996]; Jianfang et al. [1998]
   South China SeaSCS-N18.51163750335010 Sep 198721 Oct 19882.02 Wiesner et al. [1996]; Jianfang et al. [1998]
   South China SeaSCS-C14.6115.1431012001 Dec 199029 May 19954.20 Wiesner et al. [1996]; Jianfang et al. [1998]c
   South China SeaSCS-C14.6115.1431022401 Dec 199029 May 19953.51 Wiesner et al. [1996]; Jianfang et al. [1998]c
   South China SeaSCS-C14.6115.1431037701 Dec 199029 May 19952.52 Wiesner et al. [1996]; Jianfang et al. [1998]c
   N Equatorial CurrentNEC12.0134.35300120021 Nov 198816 Dec 19890.3810-IKemp and Knaack [1996]
   N Equatorial CurrentNEC12.0134.35300430021 Nov 198816 Dec 19890.4318-DKemp and Knaack [1996]
   N Equatorial Counter CurrentMANOP-S11−140na70029 Dec 198214 Feb 19843.126-SDymond and Collier [1988]c
   N Equatorial Counter CurrentMANOP-S11−140na160029 Dec 198214 Feb 19842.3311-IDymond and Collier [1988]c
   N Equatorial Counter CurrentMANOP-S11−140na340029 Dec 198214 Feb 19841.6119-DDymond and Collier [1988]c
   N Equatorial Counter CurrentMANOP-S11.1−140.1na462029 Dec 198214 Feb 19840.82 Dymond and Lyle [1994]
   Equatorial PacificJGOFS-EqPac-9N9−140510021502 Feb 199224 Jan 19931.5112-IHonjo et al. [1995]
   Equatorial PacificJGOFS-EqPac-9N9−140510022502 Feb 199224 Jan 19931.5213-IHonjo et al. [1995]
   Equatorial PacificJGOFS-EqPac-9N9−140510044002 Feb 19924 Dec 19921.0920-DHonjo et al. [1995]
   E Tropical PacificMANOP-M8.8−104.0na315012 Sep 198023 Oct 19813.78 Dymond and Lyle [1985]; Dymond and Lyle [1994]
   Equatorial Counter CurrentSite 47.9175.05260474325 Sep 199213 Apr 19930.78 Kawahata et al. [2000]f
   E Tropical PacificMANOP-H6.6−92.8na356520 Sep 198017 Oct 19812.44 Dymond and Lyle [1985]; Dymond and Lyle [1994]
   Panama BasinPB25.4−85.638608903 Dec 19792 Dec 198097-SHonjo [1982]d
   Panama BasinPB25.4−85.6386025903 Dec 19792 Dec 19801121-DHonjo [1982]d
   Panama BasinPB25.4−85.6386035603 Dec 19792 Dec 19801422-DHonjo [1982]d
   Equatorial PacificJGOFS-EqPac-5N5−140449311912 Feb 199224 Jan 19936.0214-IHonjo et al. [1995]
   Equatorial PacificJGOFS-EqPac-5N5−140449320912 Feb 199224 Jan 19934.5015-IHonjo et al. [1995]
   Equatorial PacificJGOFS-EqPac-5N5−140449337932 Feb 19927 Jan 19933.6923-DHonjo et al. [1995]
   Equatorial Counter CurrentECC5138.84130113021 Nov 198816 Dec 19891.7816-IKemp and Knaack [1996]
   Equatorial Counter CurrentECC5138.84130313021 Nov 198816 Dec 19890.6724-DKemp and Knaack [1996]
   Equatorial Counter CurrentSite 24.1136.3488817694 Jun 199115 Apr 19926.7217-IKawahata et al. [2000]; Kawahata et al. [2002]f
   Equatorial Counter CurrentSite 24.1136.3488845744 Jun 199115 Apr 19926.0625-DKawahata et al. [2000]f
   Equatorial Counter CurrentSite 13.0135.0440215924 Jun 199127 Apr 19929.3318-IKawahata et al. [1998]; Kawahata et al. [2002]f
   Equatorial Counter CurrentSite 13.0135.0440239024 Jun 199127 Apr 19927.8326-DKawahata et al. [1998]f
   Equatorial PacificJGOFS-EqPAC-2N2−140439722032 Feb 199224 Jan 19934.0219-IHonjo et al. [1995]
   S Equatorial CurrentSite 101.22160.6318111641 Oct 199416 Apr 19951.9420-IKawahata et al. [2000]
   Equatorial PacificC1.04−138.9na444523 Dec 19823 May 19853.56 Dymond and Lyle [1994]c
   S Equatorial CurrentMANOP-C1−139na108912 Dec 19821 May 19854.1721-IDymond and Collier [1988]c
   S Equatorial CurrentMANOP-C1−139na188912 Dec 19821 May 19855.5222-IDymond and Collier [1988]c
   S Equatorial CurrentMANOP-C1−139na290812 Dec 198225 Feb 19845.1827-DDymond and Collier [1988]
   S Equatorial CurrentMANOP-C1−139na349525 Feb 19841 May 19853.2628-DDymond and Collier [1988]
   Equatorial PacificJGOFS-EqPac-Eq0.0−14043588802 Feb 199224 Jan 19934.658-SHonjo et al. [1995]
   Equatorial PacificJGOFS-EqPac-Eq0.0−140435822842 Feb 199224 Jan 19934.4923-IHonjo et al. [1995]
   Equatorial PacificJGOFS-EqPac-Eq0.0−140435836182 Feb 199224 Jan 19934.3729-DHonjo et al. [1995]
   S Equatorial CurrentSite 30.0175.2488013571 Jun 199216 Apr 19932.4424-IGupta and Kawahata [2000]; Kawahata et al. [2000]f
   S Equatorial CurrentSite 30.0175.2488043631 Jun 199216 Apr 19931.5030-DGupta and Kawahata [2000]; Kawahata et al. [2000]f
   Equatorial PacificJGOFS-EqPac-2S−2−140429335932 Feb 199224 Jan 19933.6131-DHonjo et al. [1995]
   Equatorial PacificJGOFS-EqPac-5S−5−140419820992 Feb 199224 Jan 19932.7325-IHonjo et al. [1995]
   Equatorial PacificJGOFS-EqPac-5S−5−140419822092 Feb 199224 Jan 19932.7226-IHonjo et al. [1995]
   Equatorial PacificJGOFS-EqPac-5S−5−140419823162 Feb 199224 Jan 19932.7927-IHonjo et al. [1995]
   Equatorial PacificJGOFS-EqPac-12S−12−135429412922 Feb 199224 Jan 19931.5228-IHonjo et al. [1995]
   Equatorial PacificJGOFS-EqPac-12S−12−135429435942 Feb 199224 Jan 19930.7132-DHonjo et al. [1995]
   Tropical Converg., Coral SeaSite 11−13.0156.01832131516 May 19951 Apr 19961.5029-IKawahata and Ohta [2000]f
   Tropical Converg., Coral SeaSite 12−17.8154.82821230416 May 199516 Mar 19960.5630-IKawahata and Ohta [2000]f
   Peru-Chile currentCH3, CH4−29.5−73.24360232321 Jul 199323 Sep 19947.6931-IHebbeln et al. [2000]c
   Peru-Chile currentCH1, CH3−29.5−73.2436036872 Nov 199223 Jan 19947.5633-DHebbeln et al. [2000]c
   Tasman FrontSite 13−35.5161317411611 Jun 19951 Mar 19962.5032-IKawahata and Ohta [2000]f
   Subtrop. Front, N Chatham RiseNCR−42.7178.615003009 Jun 199615 May 199710.1 Nodder and Northcote [2001]
   Subtrop. Front, NChatham RiseNCR−42.7178.6150010009 Jun 199615 May 199720.6 Nodder and Northcote [2001]
   Subtrop. Front, S Chatham RiseSCR−44.6178.615003009 Jun 199615 May 19974.119-SNodder and Northcote [2001]
   Subtrop. Front, S Chatham RiseSCR−44.6178.6150010009 Jun 199615 May 19974.9310-SNodder and Northcote [2001]
   Subantarctic ZoneJGOFS-AESOPS-MS1−53.0−174.7544198628 Nov 199624 Dec 19971.2511-SHonjo et al. [2000]b
   Polar Frontal ZoneJGOFS-AESOPS-MS2−56.9−170.2492498228 Nov 199627 Jan 19984.9212-SHonjo et al. [2000]b
   Polar Frontal ZoneJGOFS-AESOPS-MS2−56.9−170.24924422428 Nov 199627 Jan 19981.7134-DHonjo et al. [2000]b
   Antarctic Circumpolar CurrentJGOFS-AESOPS-MS3−60.3−170.13958100328 Nov 199627 Jan 19986.4033-IHonjo et al. [2000]b
   Antarctic Circumpolar CurrentJGOFS-AESOPS-MS4−63.1−169.92886103128 Nov 199627 Jan 19986.0334-IHonjo et al. [2000]b
   Antartic ZoneJGOFS-AESOPS-MS5−66.2−168.73016184228 Nov 19963 Nov 19972.8235-IHonjo et al. [2000]b
   Antartic ZoneJGOFS-AESOPS-MS5−66.2−168.7301693728 Nov 199627 Jan 19985.3113-SHonjo et al. [2000]b
   Ross Sea polynyaC top−72.5−172.549323022 Jan 199022 Feb 199210.8 Dunbar et al. [1998]c
   Ross Sea polynyaC bot−72.5−172.549344322 Jan 199022 Feb 19922.78 Dunbar et al. [1998]c
   Ross Sea polynyaJGOFS-AESOPS-MS6−73.5176.956520028 Nov 199627 Jan 19985.2314-SCollier et al. [2000]
   Ross Sea polynyaJGOFS-AESOPS-MS7b−76.5−178.058120628 Nov 199627 Jan 199813.615-SCollier et al. [2000]g
   Ross Sea polynyaJGOFS-AESOPS-MS7a−76.5−178.058146528 Nov 199627 Jan 199828.916-SCollier et al. [2000]g
   Ross Sea polynyaJGOFS-AESOPS-MS7b−76.5−178.058148128 Nov 199627 Jan 199833.417-SCollier et al. [2000]g,h
   Ross Sea polynyaA top−76.5167.571923014 Jan 199024 Apr 199113.7 Dunbar et al. [1998]
   Ross Sea polynyaA bot−76.5167.571966913 Jan 19907 Feb 199211.118-SDunbar et al. [1998]c
   Ross Sea polynyaB top−76.5−175.051923017 Jan 19901 Jan 199219.219-SDunbar et al. [1998]c,g,i
   Ross Sea polynyaB bot−76.5−175.051946917 Jan 19909 Feb 199211.220-SDunbar et al. [1998]c,g,i
Atlantic Ocean
   Central Fram StraitFS-178.91.42823244220 Aug 198415 Aug 19850.6536-IHonjo et al. [1987]
   E Fram StraitSP-2, SP-378.96.7166911181 Jul 19885 Jul 199019.737-IHebbeln [2000]c
   N Water polynya, Baffin BayS5A76−78365258Aug 1997Jun 199926.9 Hargrave et al. [2002]c
   N Water polynya, Baffin BayS2B76−73561511Sep 1997Jul 199930 Hargrave et al. [2002]c
   N Norwegian SeaBI-175.911.52123170012 Aug 198410 Aug 19854.6038-IHonjo et al. [1987]
   Greenland BasinGB-2375.6−6.7344528234 Aug 198520 Jul 19860.8835-DHonjo et al. [1987]
   E Greenland SeaOG7 75N75.0−10.6307350023 Jul 199415 Aug 199510.7 Ramseier et al. [1999]
   Greenland SeaOG72.5−7.72700500Sep 1988Jul 19917.8321-SBodungen et al. [1995]c,d
   Greenland SeaOG72.5−7.727001000Sep 1988Jul 19913.16 Bodungen et al. [1995]c
   Greenland SeaOG72.5−7.727002200Sep 1988Jul 19910.95 Bodungen et al. [1995]c
   Norwegian BasinNB700.4335050019 Feb 199219 Feb 19939.81 Bodungen et al. [1995]
   Norwegian BasinNB700.43350100019 Feb 199219 Feb 19936.03 Bodungen et al. [1995]
   E Lofoten BasinLB-169.5103161276015 Aug 19831 Aug 19842.2136-DHonjo et al. [1987]
   Voering PlateauVP67.85.51300525Jun 1986Oct 19876.6622-SBathmann et al. [1990]d
   Aegir RidgeNA-165.513058263021 Aug 198516 Jun 19861.6237-DHonjo et al. [1987]
   E Gotland Sea 57.320na1401 Sep 199630 Jul 199716.823-SSchneider et al. [2000]d
   NE AtlanticL354.6−21.23027220010 Jun 199224 May 19942.15 Kuss and Kremling [1999]c
   NE AtlanticL354.5−21.12979288010 Jun 199212 May 19932.03 Kuss and Kremling [1999]
   Mid-European cont. mar.OMEX-249.2−12.814506001 Jul 19933 Sep 19945.75 Antia et al. [1999]
   Mid-European cont. mar.OMEX-249.2−12.8145010501 Jul 19933 Sep 19946.30 Antia et al. [1999]
   Mid-European cont. mar.OMEX-349.1−13.436605801 Jul 199312 May 19946.03 Antia et al. [1999]
   Mid-European cont. mar.OMEX-349.1−13.4366014501 Jul 19933 Sep 199410.1 Antia et al. [1999]
   Mid-European cont. mar.OMEX-349.1−13.4366032601 Jul 19933 Sep 19945.75 Antia et al. [1999]
   Madeira Abyssal PlainPAP48.8−16.54850100012 Oct 19958 Mar 19982.6024-SLampitt et al. [2001]c
   Madeira Abyssal PlainPAP48.8−16.54850300018 Apr 198926 Sep 19993.3238-DLampitt et al. [2001]c
   Madeira Abyssal PlainPAP48.8−16.54850470018 Apr 198929 Aug 19993.1839-DLampitt et al. [2001]c
   NE AtlanticL247.8−19.7455350027 Mar 199212 Jun 19942.96 Scholten et al. [2001]
   NE AtlanticL247.8−19.8455110334 Apr 199328 Jun 19953.56 Kuss and Kremling [1999]; Scholten et al. [2001]c
   NE AtlanticL247.8−19.84551201710 Jun 199228 Jun 19955.11 Kuss and Kremling [1999]; Scholten et al. [2001]c
   NE AtlanticL247.8−19.84553351510 Jun 199228 Jun 19952.35 Kuss and Kremling [1999]; Scholten et al. [2001]c
   North Atlantic DriftJGOFS-NABE-48N47.7−20.9443511103 Apr 198916 Apr 19904.0539-IHonjo and Manganini [1993]c
   North Atlantic DriftJGOFS-NABE-48N47.7−20.9443521093 Apr 198916 Apr 19903.7840-IHonjo and Manganini [1993]c
   North Atlantic DriftJGOFS-NABE-48N47.7−20.9443537343 Apr 198916 Apr 19902.74 Honjo and Manganini [1993]
   North Atlantic DriftBOFS-48N47.3−19.54555310018 Apr 198916 Sep 19905.19 Newton et al. [1994]; Jickells et al. [1996]
   NE AtlanticPOMME-NE43.5−17.3376040014 Feb 200125 Jun 20023.29 Guieu et al. [2005]
   NE AtlanticPOMME-NE43.5−17.33760100014 Feb 200125 Jun 20024.93 Guieu et al. [2005]
   NE AtlanticPOMME-SE39.5−17.3494040014 Feb 200125 Jun 20026.30 Guieu et al. [2005]
   NE AtlanticPOMME-SE39.5−17.34940100014 Feb 200125 Jun 20023.29 Guieu et al. [2005]
   E Alboan GyreALB-4-S36.3−1.522406451 Jul 199722 May 199826.4 Sanchez-Vidal et al. [2004]
   E Alboan GyreALB-4-I36.3−1.5224011701 Jul 199722 May 199816.6 Sanchez-Vidal et al. [2004]
   W Alboan GyreALB-1-S36.2−4.2610044717 Jan 199722 May 199826.9 Fabres et al. [2002]
   W Alboan GyreALB-2-S36.0−4.2913373967 Jan 199722 May 199846.6 Fabres et al. [2002]
   E Subtropical GyreJGOFS-NABE-34N33.8−21.0517211593 Apr 198916 Apr 19902.7441-IHonjo and Manganini [1993]c
   E Subtropical GyreJGOFS-NABE-34N33.8−21.0517219813 Apr 198916 Apr 19902.8242-IHonjo and Manganini [1993]c
   E Subtropical GyreJGOFS-NABE-34N33.8−21.0517244783 Apr 198916 Apr 19902.0340-DHonjo and Manganini [1993]c
   NE AtlanticL133.1−225269201020 Sep 199325 Sep 19951.82 Kuss and Kremling [1999]; Scholten et al. [2001]c
   NE AtlanticL133.1−225269407520 Sep 199314 Oct 19951.59 Kuss and Kremling [1999]; Scholten et al. [2001]c
   Hatteras Abyssal PlainHAP32.7−70.8na54007 Jun 198228 Apr 19832.09 Dymond and Lyle [1994]
   W Sargasso SeaOFP31.8−64.2450050029 Mar 198425 Jun 19984.125-SConte et al. [2001]c,j
   W Sargasso SeaOFP31.8−64.24500150029 Mar 198425 Jun 19982.443-IConte et al. [2001]c,j
   W Sargasso SeaOFP31.8−64.2450032006 Apr 197825 Jun 19981.741-DDeuser et al. [1981]; Conte et al. [2001]c,j
   Madeira Abyssal PlainBOFS-31N31.6−24.75440444010 Dec 198527 Oct 19861.44 Lampitt [1992]; Jickells et al. [1996]
   Canary CurrentESTOC-CI29.1−15.5360090025 Nov 19912 Sep 19941.3726-SFischer et al. [1996a]; Neuer et al. [1997]c,d
   Canary CurrentESTOC-CI29.1−15.53600307725 Nov 19912 Sep 19942.3042-DNeuer et al. [1997]c,d
   Subtropical Gyre EBOFS-28N28−22.0482036007 Oct 199028 Jul 19911.34 Jickells et al. [1996]
   Subtropical Gyre EBOFS-25N24.6−22.84860387014 Oct 199027 Sep 19911.99 Jickells et al. [1996]
   Nares Abyssal PlainNAP23.2−64.0na584721 Aug 198327 Sep 19841.34 Dymond and Lyle [1994]
   Cabe BlancCB3, CB421.2−20.741007328 Apr 199019 Nov 19917.127-SFischer et al. [1996b]c
   Cabe BlancCB121.2−20.73646219522 Mar 19888 Mar 19893.344-IFischer et al. [1996b]; Fischer et al. [2000]
   Cabe BlancCB2, CB3, CB421.2−20.74100354015 Mar 198919 Nov 19914.843-DFischer et al. [1996b]; Fischer et al. [2000]c
   Mauritanian upwelling zoneEUMELI-O21−314600100019 Feb 19912 Dec 19921.828-SBory et al. [2001]
   Mauritanian upwelling zoneEUMELI-O21−314600250023 Feb 19912 Dec 19921.545-IBory et al. [2001]
   E Atlantic Coastal-BoundaryBOFS-19N19−20.23295219021 Oct 19902 Jun 199121.3 Jickells et al. [1996]
   Mauritanian upwelling zoneEUMELI-M18.5−213100100012 Feb 19912 Dec 199220.229-SBory et al. [2001]
   Mauritanian upwelling zoneEUMELI-M18.5−213100250012 Feb 19912 Dec 199216.246-IBory et al. [2001]
   Mauritanian upwelling zoneEUMELI-M18.5−21310030009 Oct 19912 Dec 19925.344-DBory et al. [2001]
   Cabe VerdeCV1, CV211.5−2149719895 Oct 19925 Apr 19944.52 Fischer et al. [2000]c
   Cariaco BasinA10.5−64.714002758 Nov 19959 May 200572.130-SThunell et al. [2000]; Müller-Karger et al. [2004]c,k
   Cariaco BasinB10.5−64.714004408 Nov 19959 May 200553.631-SThunell et al. [2000]; Müller-Karger et al. [2004]c,k
   Cariaco BasinC10.5−64.714008408 Nov 19959 May 200537.332-SThunell et al. [2000]; Müller-Karger et al. [2004]c,k
   Cariaco BasinD10.5−64.7140012558 Nov 19959 May 200530.647-IThunell et al. [2000]; Müller-Karger et al. [2004]c,k
   E Equatorial AtlanticGB2, GBN3, GBN6, EA21.8−11.2431485315 Mar 198813 Apr 19915.9733-SFischer et al. [2000]c
   Guinea BasinGBN3-l, GBN6-l1.8−11.1450239211 Mar 19897 Apr 19915.4845-DFischer and Wefer [1996]c
   W Equatorial AtlanticWA80.0−23.5374471825 Aug 199417 Aug 19956.82 Fischer et al. [2000]
   E Equatorial AtlanticEA3c, EA7, EA9, EA100.0−10.84385113813 Apr 199127 May 19944.35 Fischer et al. [2000]c
   E Equatorial AtlanticGBS4, GBS5−2.2−9.939166961 Mar 198930 Mar 19915.6234-SFischer et al. [2000]c
   Guinea BasinGBZ5-l−2.2−9.9392033821 Apr 199030 Mar 19915.75 Fischer and Wefer [1996]
   W Equatorial AtlanticWA1, WA4, WA7−4−25.7555277117 Oct 199217 Aug 19954.61 Fischer et al. [2000]
   E Equatorial AtlanticEA5−4.3−10.3349094713 Apr 199129 Nov 19912.16 Fischer et al. [2000]
   E Equatorial AtlanticEA8−5.8−9.4345059815 Dec 19916 Oct 19926.05 Fischer et al. [2000]
   W Equatorial AtlanticWA2, WA3, WA6−7.5−28536360219 Oct 199217 Aug 19952.57 Fischer et al. [2000]c
   Walvis RidgeWR1, WR2, WR3, WR4-l−20.09.2222116634 Mar 198817 Dec 199110.1 Fischer et al. [2000]c
   Walvis RidgeWR2-u−20.09.2219659918 Mar 198913 Mar 199014.035-SWefer and Fischer [1993]
   Namibia UpwellingNU2−28.914.63055251620 Jan 19923 Feb 19934.41 Fischer et al. [2000]
   Polar FrontPF3, PF5, PF7, PF8−50.15.873795314510 Nov 198915 Jan 19964.93 Fischer et al. [2002]c
   Polar FrontPF1, PF3, PF5, PF7, PF8−50.15.85378765815 Jan 198715 Jan 19963.84 Fischer et al. [2002]c
   Bovert IslandBO1, BO2, BO3, BO4, BO5−54.3−3.34272848728 Dec 199015 Jan 19962.19 Fischer et al. [2002]c
   Bovert IslandBO1, BO2, BO5−54.3−3.342729220928 Dec 199015 Jan 19961.10 Fischer et al. [2002]c
   N Weddell Sea GyreVIII-u−62.1−40.632802453199019920.61 Pudsey and King [1997]
   Bransfield St., King George Is.KG1−62.3−57.5195249431 Dec 198325 Nov 198421.136-SWefer et al. [1988]; Wefer and Fischer, [1991]
   Bransfield St., King George Is.KG1−62.3−57.5195215881 Dec 198325 Nov 19848.2048-IWefer et al. [1988]; Wefer and Fischer, [1991]
   Bransfield St., King George Is.KG2, KG3−62.4−57.719926874 Dec 19847 May 19863.0137-SWefer et al. [1988]; Wefer and Fischer, [1991]c,l
   N. Weddell Sea GyreI-u−63.2−42.737982971199019920.24 Pudsey and King [1997]
   Weddell SeaWS3−64.9−2.6505336016 Jan 19884 Feb 19896.2538-SWefer and Fischer [1991]
   Weddell SeaWS2−64.9−2.55000445620 Jan 198720 Nov 19870.4746-DWefer et al. [1990]; Wefer and Fischer [1991]
Indian Ocean
   Arabian SeaMS-117.757.9144780811 Nov 199424 Dec 199510.539-SHonjo et al. [1999]b,c
   Arabian SeaMS-117.757.9144799911 Nov 199424 Dec 199510.540-SHonjo et al. [1999]b,c
   Bay of BengalNorth-s17.489.622638091 Oct 19871 Oct 19889.84 Ittekkot et al. [1991]
   Bay of BengalNorth-d17.489.6226317271 Oct 19871 Oct 19887.26 Ittekkot et al. [1991]
   Arabian SeaMS-217.458.8144783911 Nov 199424 Dec 199513.541-SHonjo et al. [1999]b,c
   Arabian SeaMS-217.458.8144791411 Nov 199413 Sep 199517.242-SHonjo et al. [1999]b,c
   Arabian SeaMS-217.458.83642198511 Nov 199424 Dec 199517.449-IHonjo et al. [1999]b,c
   Arabian SeaMS-217.458.83642315011 Nov 199424 Dec 199513.247-DHonjo et al. [1999]b,c
   Arabian SeaMS-317.259.6346577811 Nov 199424 Dec 199513.243-SHonjo et al. [1999]b,c
   Arabian SeaMS-317.259.6346587311 Nov 199424 Dec 199517.544-SHonjo et al. [1999]b,c
   Arabian SeaMS-317.259.63465187011 Nov 199424 Dec 199516.350-IHonjo et al. [1999]b,c
   Arabian SeaMS-317.259.63465297911 Nov 199424 Dec 199512.848-DHonjo et al. [1999]b,c
   Arabian SeaWAST16.560.54016302710 May 198615 Oct 19908.77 Haake et al. [1993]
   Arabian SeaEAST15.568.73785283010 May 198623 Oct 19905.75 Haake et al. [1993]
   Arabian SeaMS-415.361.5397481419 Nov 199424 Dec 19958.945-SHonjo et al. [1999]b,c
   Arabian SeaMS-415.361.53974222211 Nov 199427 Aug 199511.151-IHonjo et al. [1999]b,c
   Arabian SeaMS-415.361.53974348411 Nov 19947 Dec 19958.949-DHonjo et al. [1999]b,c
   Arabian SeaCAST14.964.13904295410 May 198624 Feb 19895.21 Haake et al. [1993]
   Bay of BengalCentral-s13.284.43259906Oct 1987Oct 19887.23 Ittekkot et al. [1991]
   Bay of BengalCentral-d13.284.432592282Oct 1987Oct 19887.15 Ittekkot et al. [1991]
   Arabian SeaMS-510.065.04411236328 Nov 199424 Dec 19953.852-IHonjo et al. [1999]b
   Arabian SeaMS-510.065.04411391528 Nov 199424 Dec 19953.350-DHonjo et al. [1999]b
   Bay of BengalSouth-s4.4387.340171040Oct 1987Oct 19886.49 Ittekkot et al. [1991]
   Bay of BengalSouth-d4.4387.340173006Oct 1987Oct 19885.59 Ittekkot et al. [1991]
   Southern OceanANTARES-M2−5261.54600130013 Feb 199431 Jan 19952.8853-ITréguer, unpub.m
   Southern OceanANTARES-M2−5261.54600402513 Feb 199431 Jan 19951.5651-DTréguer, unpub.m
   Polar Frontal ZonePFZ54−53.8141.8228083021 Sep 199721 Feb 19982.1946-STrull et al. [2001]
   Polar Frontal ZonePFZ54−53.8141.82280158021 Sep 199721 Feb 19981.3754-ITrull et al. [2001]
   Prydz BayPZB-1−62.473.04000140030 Dec 199830 Dec 19992.4755-IPilskaln et al. [2004]
   Southern OceanANTARES-M3−63714020130020 Feb 19948 Feb 19950.30 Tréguer, unpub.m
   Southern OceanANTARES-M3−63714020344520 Feb 19948 Feb 19950.4452-DTréguer, unpub.m

[13] (1) Values are from depths greater than the local export depth (ze). The depth of export herein describes maximum of the euphotic zone and mixed layer depths during flux formation. Sediment traps located above this depth may not reflect the flux of particulate matter to the deep ocean. On the basis of the geographic and depth distribution of sediment trap observations, we apply a first-order approximation of the export zone depth as 100 m at equatorial latitudes (<35°) increasing linearly to 400 m at high-latitudes (>50°).

[14] (2) Observations are minimally influenced by resuspension of benthic surface sediment, typically those from depths above the nepheloid layer.

[15] (3) Terrestrial sources of detritus are minimal.

[16] (4) Observations must describe at least one entire year of flux to depth. This includes trap experiments of less than a year in duration if the seasonal cycle of flux may be well approximated. The sediment trap data set assembled for this study includes 244 annual flux estimates and 153 subannually resolved flux time series used to create flux climatologies.

[17] Flux climatology construction involves multiple steps depending on the temporal coverage of trap measurements. First, time series are made continuous for the entire duration of the experiment. Gaps produced during sampling bottle failures or between trap deployments are filled with the linear integral of temporally adjacent observations. Next, to account for timing inconstancies between trap experiments, time series are temporally standardized. Temporal standardization involves a method of describing sediment trap measurements that is a combination of the regularly applied techniques.

[18] Authors typically depict sediment trap experiments using linear connections between observation midpoints [Bathmann et al., 1990; Neuer et al., 1997; Honjo et al., 1999; Hargrave et al., 2002]. Flux is also frequently reported in bar-graph format as the average rate during the open-close intervals of each bottle [Newton et al., 1994; Honjo et al., 2000; Lampitt et al., 2001; Honda et al., 2002]. Our approach, demonstrated in Figure 3, is a smoothing of the linear style held equal in mass collected per bottle interval to the bar-graph style. Dashed lines connecting bottle midpoints (closed circles) and grey columns indicate the linear and bar-graph data representation styles. Open circles designate temporal intersections of the linear style with sample bottle openings (xo) and closings (xc). The solid thin line l connects these intersections. The curve D indicates the “stretching” of line l such that the integral of D equals the area represented by the bar-graph style for each bottle:

equation image

where equation image denotes the average flux rate. The stretch function D is accomplished by multiplying line l by a daily cubic spline interpolation of normal distribution constrained so that the endpoints (open circles) remain constant. The direction line l stretches to become curve D, up, down, or not at all, depends on the flux rate measured in each bottle relative to the rates of neighboring bottles. Minimum daily flux rates estimated by the stretch function D are constrained not to be negative. Finally, sediment trap measurements including more than 1 year of observations are averaged into one climatological year.

Figure 3.

Demonstration of the stretch function ((D)dx) curve fit technique developed to create sediment trap particulate organic carbon flux to depth climatologies. Time series standardization involves a combination of the regularly applied description techniques: a smoothing of the linear midpoint connection style (dashed lines and •) held equal in mass to the bar-graph format (grey columns). Open circles (○) designate temporal intersections of the linear style with sample bottle openings (xo) and closings (xc). The solid thin line l shows linear connects these intersections. The curve D indicates “stretching” of line l so that the integral of D equals the area represented by the bar-graph style for each bottle. The direction line l stretches to become curve D, up, down, or not at all, depends on the flux rate measured in each bottle relative to that of its neighboring bottles.

4.2. NPP and SST

[19] NPP is estimated following Behrenfeld and Falkowski [1997a] using 7 years of 8-day satellite images available between 19 August 1997 and 24 June 2004. Data sets applied include the NOAA/NASA AVHRR Oceans Pathfinder SST (, NASA SeaWiFs surface chlorophyll concentrations, and photosynthetically active radiation ( Images applied display global coverage with an equal-area grid of 9-km resolution. NPP climatologies are constructed in manner identical to that of flux climatologies. During climatology construction, missing production estimates, for example, those obscured by cloud cover, are substituted with the linear interpolation of adjacent values.

4.3. Potential Error

[20] Errors associated with methods used to estimate marine biogeochemical processes are difficult to assess. Field-based oceanography is far from a controlled well-calibrated laboratory experiment. The ability of sediment traps to accurately measure flux has received much attention in the scientific literature. Possible trap biases include inadvertent “swimmer” capture [Lee et al., 1988], uncertain preservation of trapped particulate matter [Lee et al., 1992], hydrodynamic interactions with traps of various designs [Gust et al., 1994; Buesseler et al., 2000], and influences in brine and poison addition [Lee and Cronin, 1982; Knauer et al., 1984]. A standard trap design and methodological protocol is not adopted by the oceanographic community. The magnitude of these and other trap biases may be highly variable and as large as a factor of two or more [Gardner, 2000]. Calibration of sediment trap results have been attempted [Buesseler et al., 2000; Yu et al., 2001; Scholten et al., 2001]. In defense of sediment trap technology, we note that sediment trap results: (1) are interannually consistent with respect to timing, variability, and magnitude of particle fluxes [Deuser, 1986; Conte et al., 2001]; and (2) reflect euphotic zone biological variability [Deuser et al., 1981; Honjo, 1982; Deuser et al., 1990]. We use sediment trap data sets here because they represent the only available direct measurements of annual subsurface particle fluxes through the water column.

[21] To address potential error of sediment trap observations, estimates global flux to depth include the calibration rationale suggested by radionuclide studies. The validity of sediment traps to measure flux to the deep ocean (>1.5 km) is validated by 230Th and 231Pa calibration studies [Scholten et al., 2001; Yu et al., 2001]. Within the upper ocean (<1500 km) these radionuclide studies suggest sediment traps may often underestimate fluxes. With the exception of the California margin, Yu et al. [2001] found a trapping efficiency of 40% is a typical minimum value for the pelagic upper ocean. To account for the potential undertrapping error, we report global estimates of flux to the upper ocean with (observed flux divided by 0.4) and without radiochemical calibration. This trapping efficiency estimate may be uncertain in part because of the variable incorporation of radionuclides on particles of different sizes [Buesseler et al., 1995; Yu et al., 2001].

[22] Satellite-based estimates of NPP also include significant uncertainties [Platt and Sathyendranath, 1993; Behrenfeld et al., 2002a]. These uncertainties largely stem from errors in relating surface chlorophyll biomass to phytoplankton carbon biomass [Behrenfeld et al., 2005]. Furthermore, empirical models used to estimate NPP from ocean color may not correctly simulate relevant phytoplankton physiological variability [Behrenfeld and Falkowski, 1997b; Behrenfeld et al., 2002b; Banse and Postel, 2003]. Ocean color is a property of the uppermost portion of euphotic zone and additional factors are needed to estimate deeper ocean production. Finally, field-based radiocarbon measurements of production used to calibrate satellite-derived algorithms include error [Sakshaug et al., 1997]. A recent performance analysis indicates various NPP algorithms yield significantly dissimilar results [Campbell et al., 2002]. We acknowledge algorithms describing production using satellite data may have significant systematic biases.

[23] Specific estimates of error regarding satellite-based NPP and sediment trap flux are not easily quantified and such an exercise is generally avoided in the literature. There is no a priori reason to believe errors associated with sediment trap observations covary with errors associated with satellite-based observations. Thus we infer that statistical significance in the results described below is found in spite of, and not because of, error in the data sets used.

4.4. Seasonal Variation Index

[24] We use the seasonal variation index (SVI) to describe temporal variability of the time series. SVI is defined as the coefficient of variation, the standard derivation (σ(X)) normalized to the average (equation image), of either the NPP or flux climatologies:

equation image

[25] SVI is dimensionless. This description of variability is statistically similar to the seasonality index, the number of months required to accumulate one half of the total annual primary production [Berger et al., 1989; Berger and Wefer, 1990], and the flux stability index, the minimum time taken for 50% of the annual flux to be collected [Lampitt and Antia, 1997].

4.5. Annual and Seasonal Production Ratios

[26] Previous research has compared e ratios, the ratio of flux to export production, to examine the transfer efficiency of POC [Laws et al., 2000; Francois et al., 2002]. A recent study shows transfer efficiency described by flux normalized to either production or export yield results of similar accuracy [Lutz et al., 2002]. Here following the approach of Suess [1980], we compare the transfer efficiency between regions using annual production (p) ratios, defined as the annual particle flux at depth (z) normalized to annual NPP in the overlying surface waters:

equation image

This study also uses seasonal p ratios to examine subannual production-to-flux dynamics. Seasonal p ratio creation involves several steps and is conducted at locations where an entire year of flux time series is available. First, seasons of production are delineated by dividing NPP climatologies into autumn, winter, spring, and bloom intervals. The bloom production interval describes the average production during the 30 continuous days of an annual record with the maximum average production rates. Similarly, the winter production interval describes the average production during the 30 continuous days with the minimum average production rates. Autumn and spring production intervals describe average productions during the intervening time periods between bloom and winter seasons. The durations of autumn and spring production intervals varies depending on the timing of the bloom and winter seasons, and are constrained to be of at least one month in duration. By this constraint, 134 time series of production are included in the seasonal p ratio analysis.

[27] Next, the timings of corresponding sediment trap flux seasons are determined by adding a temporal lag to the timing of the production seasons. The temporal lag is defined by dividing the trap depth (m) by a sinking rate (m d−1). Here we use a sinking rate of 70 m d−1 based on the 1.5-month lag between surface events observed by satellite and arrival of the consequences of those events in sediment traps at 3.2 km shown by Deuser et al. [1990]. Finally, seasonal p ratios are determined by normalizing seasons of flux to corresponding seasons of production. We acknowledge this technique allows for a first-order description of production and flux dynamics, as sinking rates may differ between seasons, regions, and depths [Berelson, 2002]. Due to the lack of polar wintertime production, winter season p ratios are absent at latitudes >60°.

4.6. Flux Algorithm With Labile and Refractory Components

[28] To quantify relationships between satellite-based NPP and SST data, and sediment trap flux observations, annual production ratios are estimated using the following exponential algorithm:

equation image

[29] This algorithm was previously derived from an empirical fit between regional production and flux measurements [Lutz et al., 2002] and is applied here to determine if flux can be estimated using environmental parameters derived from satellite observations. This function describes the labile and refractory components of flux to depth below the export zone depth ze [depth (z) minus export zone depth]. The prd and rld coefficients measure flux that is available to decay and sinks more slowly. The rld coefficient describes the e-folding remineralization length/depth scale and measures the rate of sinking detritus degradation. The prr coefficient describes the more refractive and more rapidly sinking portions of flux. Larger prd and prr values indicate the export of a greater fraction of NPP. Larger rld values indicate the labile fraction of export sinks deeper before regeneration. For simplicity, this equation treats sinking and remineralization rates as constants, although we acknowledge these factors vary as particles are transformed during their descent through the ocean [Berelson, 2002]. For further derivation of equation (6) see Banse [1990] and Lutz et al. [2002].

[30] Annual p ratio estimates are used to determine the coefficients of equation (6) grouped by either SST or SVI. Coefficient approximation requires the p ratio data be divided into groups so that the entire water column is described for each SVI and SST value evaluated. To best characterize SVI and SST variability, groups used herein are of equal spacing on a logarithmic-scale for SVI and a linear-scale for SST. The prd, rld, and prr coefficients are determined for each group separately. Within each group the prr coefficient describes the median of the deepest 25% of p ratios, the depth of which vary slightly between groups. Thus for the purpose of this comparison it is assumed that on an annual basis flux to the deepest traps is refractory relative to upper ocean traps. The prd coefficient describes the maximum shallow p ratio minus the prr coefficient. The rld coefficient is determined by fitting equation (6) to the p ratio data with the previously determined prd and prr coefficients. For each coefficient the maximum number of groups is used that yields a statistically significant relationship to either SVI or SST. Hence the number of groups for the prd and prr parameters differs depending on the availability of the shallow and deep trap data within each group. Similarly the grouping for the rld parameter requires a sufficient number of observations to characterize the entire water column. Due to the infrequency of sediment trap observations near the local export zone depth, prd values are deemed minimal approximations.

5. Results

5.1. Patterns of Remotely Sensed Parameters, NPP, and SST

[31] The results of our NPP climatology generally agree with those described by Behrenfeld and Falkowski [1997b]. Here we briefly describe the first-order characteristics of production temporal variability limited to those relating to patterns shown by our flux to depth climatology.

[32] The NPP climatology, as described by the SVI (Figure 4), displays regional- and basin-scale geographic patterns. Temporal trends of the NPP climatology (Figure 5) are similar to those found in the literature [Yoder et al., 1993; Longhurst, 1995] and include the well-known open ocean equatorial near-constant low productivity, the multiple blooms of temperate and monsoonal regions, and the intense summer bloom of polar regions. A primary component of global distribution of NPP SVI is the increase of values poleward of roughly 45°S and N, reflecting the seasonal availability of photosynthetically available radiation and distribution of sea ice.

Figure 4.

The geographic distribution of the seasonal variation index (annual standard deviation divided by average) of net primary production (NPP). This estimate is derived from NPP climatology modeled following Behrenfeld and Falkowski [1997a] and constructed from 7 years of satellite-based estimates of sea surface temperature and chlorophyll. Arrows indicate locations of the meridional transects of NPP time series shown in Figure 5. Missing data (dark grey) indicates landmasses and permanent ice cover.

Figure 5.

Climatologies of net primary production (NPP; g Corg m−2 yr−1) versus latitude. Shown are time series of NPP located along meridional transects, indicated in Figures 1 and 4, within the (a) central Pacific (134°W), (b) Atlantic (29°W), and (c) Indian (65°E) Oceans. This estimate uses NPP modeled following Behrenfeld and Falkowski [1997a] and constructed from 7 years of satellite-based estimates of sea surface temperature and chlorophyll. Missing data (dark grey) indicates landmasses and permanent ice cover.

[33] The heterogeneous distribution of SVI values at lower-latitudes indicates provinces where interactions of annual climatic phenomenon with the density structure of upper ocean waters permit seasonal upwelling. In open ocean waters, diminished SVI values characterize subtropical regions of year-round high surface atmospheric pressure and the seasonal limits of the intertropical convergence zone. Reduced SVI values characterize the oligotrophic subtropical ocean gyres and equatorial western Pacific and Atlantic Oceans, where production is relatively constant year-round. Narrow longitudinal bands of moderate SVI values resulting from seasonal water mass divergence characterize the equatorial upwelling regions of the Pacific and Atlantic Oceans. Elevated SVI values approximate the influence of the circumpolar westerlies. Monsoonally influenced regions of the western Indian Ocean exhibit enhanced SVI values. Larger SVI values typically characterize the centers of eastern boundary seasonal coastal upwelling and the associated offshore areas where upwelled nutrients are advected. Additionally, SVI values reflect the seasonal input of riverine and aeolian nutrients, as well as limitations to production, such as mixing of surface waters beneath the photic zone, phytoplankton self-shading, and herbivore activity [Parsons et al., 1977; Broecker and Peng, 1982; Chester, 1990; Duce and Tindale, 1991].

[34] The Southern Hemisphere of the Pacific Ocean indicated by transect A of Figures 4 and 5 epitomizes the transition from low-amplitude seasonality and diminished production of equatorial regions to high-amplitude seasonality and strong summer blooms of polar regions. Heading south from the equator, between 5° and 15°S, SVI values decrease as the strength of equatorial upwelling diminishes. Continuing south, a latitudinal ribbon of elevated SVI values, between 25° and 35°S, indicates where mixing of subsurface waters increases surface nutrient levels sufficient to generate a summer bloom. Adjacently poleward, a narrow latitudinal band of minimum SVI values, between 35° and 40°S, indicates where upper water column mixing sustains elevated production year-round, without distinct spring and fall blooms. Farther poleward SVI values increase as bloom intensity amplifies. The latitude-dependent SVI fluctuations and the northeast trending “sustained-bloom” band of diminished SVI values distinguish the influence of the westerlies, and are less distinctly repeated at similar latitudes in the western and eastern portions of the Northern and Southern Hemispheres, respectively.

[35] For much of the global ocean the SVI of production and SST show similar trends (Figures 6 and 7) . Poleward of 35° latitude, where waters are cooler than approximately 18°C, SVI, and SST generally covary. At lower-latitudes the distribution of SVI and SST varies depending on regional oceanographic circumstances and differs significantly between ocean basins. Globally, neither variability of production nor SST correlates significantly with rates of NPP.

Figure 6.

Latitude versus (a) the Seasonal Variation Index (annual standard deviation divided by average) of net primary production and (b) sea surface temperature (SST) for the Pacific, Atlantic, and Indian Oceans. Circles represent data at 1.5° latitude and longitude intervals.

Figure 7.

The seasonal variation index (annual standard deviation divided by average) of net primary production versus seas surface temperature (SST) for the Pacific, Atlantic, and Indian Oceans. Circles represent data at 1.5° latitude and longitude intervals.

5.2. Patterns of Sediment Trap Flux

[36] The results of the flux to depth climatology are shown in Figure 8. Here we focus on regional- and basin-scale patterns of flux rather than restate findings of original sources. Global sediment trap sampling is heterogeneous in depth and geographic distribution. Often, the water column is not entirely measured, leaving either shallow, intermediate, or deep waters uncharacterized. Sediment trap observations are heavily weighted, by 70%, toward the Northern Hemisphere. Major portions of the Southern Hemisphere are uncharacterized, including the deep Antarctic circumpolar waters of Atlantic and Indian Oceans. The Indian Ocean has received particularly little attention, with less than an eighth of all observations. Large segments of continental shelf regions are unknown, such as surrounding South America and much of Antarctica, and bordering the Indian Ocean. Meagerly characterized open ocean regions include the equatorial upwelling of the Atlantic and Indian Oceans, and the moderately productive Southern Hemisphere subtropical convergence. Regions of mesotrophic productivity have received the majority of attention. The most productive centers of upwelling regions are not well characterized. The modestly productive oligotrophic central ocean gyres are similarly not frequently sampled. Hence regions characterizing minimum and maximum limits of global productivity rates largely await description.

Figure 8.

Climatologies of particulate organic carbon flux to depth (mg Corg m−2 d−1) derived using sediment traps versus latitude. Horizontal contours indicate each sediment trap time series of flux to various depth ranges (shallow ≤1 km; intermediate >1 and ≤2.5 km; deep >2.5 km) within the Atlantic, Pacific, and Indian Oceans. Circles represent the climatological year-day of each sediment trap bottle observation. Sediment trap experiments are identified in Table 1 by labels located on the right of each time series.

[37] Recognition of basin-scale seasonal cycles of flux to depth is limited by the geographic heterogeneity of sampling and intermittent high-frequency temporal variability of measurements. Nevertheless, the flux climatology compilation includes five major patterns of seasonality (Figure 8) similar to those of NPP: polar, equatorial and coastal upwelling, monsoonal, and subtropical-subpolar. Cycles of enhanced summertime flux and diminished wintertime flux characterize the poles [e.g., Honjo et al., 1987; Bathmann et al., 1990; Wefer and Fischer, 1991; Bodungen et al, 1995; Dunbar et al., 1998; Honjo et al., 2000; Schneider et al., 2000; Takahashi et al., 2000; Wong et al., 1999; Honda et al., 2002; Collier et al., 2000; Pilskaln et al., 2004; and additional references in Table 1 and Figure 8]. Polar seasonality is greater than of other regions, with fluxes ranging from <0.5 to >100 mg Corg m−2 d−1 during winter production hiatus and summer blooms, respectively. In general, summertime fluxes to shallow, intermediate, and deep-water depths are 20, 10, and 5 times wintertime fluxes. Atypical wintertime flux peaks of some polar observations (15 and 19 s) may be due to sediment resuspension [Dunbar et al., 1998; Collier et al., 2000].

[38] Equatorial seasonality is low and varies little with depth [e.g., Honjo et al., 1995; Dymond and Collier, 1988; Kemp and Knaack, 1996; Kawahata, 2002]. Seasons of maximum flux are generally double the rate of seasons of minimum flux. Monsoonal seasonality is moderate and characterized by the multiple flux maxima of the southwest and northeast monsoons [Honjo et al., 1999]. The range of rates between seasons of maximum and minimum flux is typically a factor of four, with minor attenuation of seasonality with increasing depth. Regions influenced by coastal upwelling show relatively enhanced seasonality [e.g., Neuer et al., 1997; Fischer and Wefer, 1996; Hebbeln et al., 2000; Bory et al., 2001]. Maximum fluxes to shallow, intermediate, and deep-water depths are typically 13, 9, and 4 times minimum fluxes.

[39] Flux seasonality of subtropical-subpolar open ocean waters is characterized by a latitude trending pattern, whereby maximum fluxes occur later in the year at higher-latitudes. This pattern is coherent across multiple sediment trap time series, recognizable in the Atlantic Ocean in intermediate [e.g., Figure 8: 40-i, 41-i, and 42-i, Honjo and Manganini, 1993] and deep waters [e.g., Figure 8: 36-d, Honjo et al., 1987; 38-d and 39-d, Lampitt et al., 2001; 40-d, Honjo and Manganini, 1993; 41-d, Deuser et al., 1981; Conte et al., 2001; 42-d, Neuer et al., 1997]. This pattern is also apparent in deep waters of the Pacific Ocean [e.g., Figure 8: 9-d and 10-d, Honda et al., 2002; 12-d, 15-d, and 16-d, Kawahata et al., 2002; 13-d and 14-d, Mohiuddin et al., 2004]. Overall, flux peaks are delayed by approximately five days per degree latitude increase. Maximum flux timings are generally synchronous with the timing of temperate spring blooms of lower latitudes and summertime polar blooms of higher latitudes, with a production-to-flux lag typically between 40 and 80 days. The range of flux rates between season’s maximum and minimum flux is roughly a factor of six.

6. Discussion

6.1. Limitations Using Seasonal Production Ratios

[40] Sediment trap measurements of flux to depth reflect variability of surface water production whereby periods of enhanced production generally correspond to periods of enhanced flux to depth [Deuser et al., 1981; Honjo, 1982; Deuser et al., 1990]. We use seasonal production ratios (section 4.5) to characterize global-scale subannual variability of particle transport efficiency. Inferences using seasonal production ratios are limited due to methodological and natural factors. The seasonal interval technique does not completely describe subannual variability of production and flux to depth. For example, dual production blooms and flux events of monsoonal regions [Honjo et al., 1999] are not completely characterized. Furthermore, the technique is limited by interannual differences between the timing of production and flux to depth time series used to compile the climatologies. For example, where production and flux are influenced by long-term climate phenomenon, such as ENSO [Kawahata and Gupta, 2004], some bloom production and flux intervals may be mismatched. Differences in particulate matter sinking rates between seasons and depths may further obscure interpretations. Furthermore, the potential inaccuracy of shallow sediment traps may limit the interpretation of shallow p ratio estimates. This analysis represents an initial step toward characterizing subannual production-to-flux variability.

6.2. The Seasonal Sinking Fraction of NPP

[41] Our results indicate the sinking fraction of production is not seasonally constant. The global range of seasonal production ratios (Figures 9a and 10a) spans four orders of magnitude, from 0.0003 to 0.93, with much overlap between seasons. This seasonal range is almost two orders of magnitude greater than the range of annual p ratios between different ocean regions (from about 0.001 to 0.1) [Lutz et al., 2002]. A trend toward lower p ratios during the bloom seasonal interval is indicated; however, an overlap between seasonal estimates makes differentiation between seasons difficult. Much of the global variability in seasonal p ratios reflects differences between regions that exhibit different ranges of NPP and different efficiencies of the biological pump [Lutz et al., 2002; Neuer et al., 2002]. In order to reduce the influence of variability between locations and changes between depths, seasonal p ratios are normalized to local annual p ratios (Figures 9b and 10b). Normalized p ratios during bloom intervals are on average half of those during the rest of the year. The greatest range of normalized p ratios and the bulk of the lowest normalized p ratios occur during the bloom season at <1 km.

Figure 9.

(a) Seasonal production ratios describing the particulate organic carbon fraction of production that sinks beneath surface waters associated with each season of production. (b) Seasonal production ratios normalized to the annual production ratio at each location. Values to the left and right of the dashed vertical line indicate the factor to which the seasonal p ratios are less or greater than, respectively, the annual p ratio.

Figure 10.

(a) Statistical distribution of seasonal production ratios grouped by depth. Horizontal black lines designate median values and shaded boxes designate the distribution range 25 and 75% quartiles. (b) Statistical distribution of seasonal production ratios normalized to the annual production ratio at each location. Values above and below the dashed horizontal lines indicate the factor to which the seasonal p ratios are greater or less than, respectively, the annual p ratio. Seasonal p ratio estimates shown are grouped into the following depth ranges: 1 km wide groups between 0 and ≤4 km, and >4 km. Quartile distributions are derived using data shown in Figure 9.

[42] The production ratio results indicate a global-scale synchronicity in the seasonal functioning of the biological pump. This pattern may reflect a seasonal change in the biodegradability of sinking particulate matter. Diminished bloom p ratios are consistent with a greater proportion of particulate matter remineralized during enhanced production. Increases in flux lability in response to increased production is suggested by previous flux studies involving sediment traps [Lee and Cronin, 1984; Lohrenz et al., 1992; Haake et al., 1993; Newton et al., 1994; Lampitt and Antia, 1997]. Changes of biodegradability may be due to the seasonal manufacture of skeletal material by phytoplankton. For example, growth forms of many Antarctic diatoms progress seasonally from long, lightly silicified chains (more labile) in the austral spring, to heavily silicified valves (more dense and refractory) in the autumn and winter [El-Sayed and Fryxell, 1993].

[43] The pattern of seasonal production ratio may additionally reflect particulate matter retention or the delay of sinking particulate matter to reach deeper waters. Diminished p ratios during bloom production relative to other seasons is consistent with a portion of bloom-derived particulate matter being retained to be recycled or fluxed during latter seasons. Multiseasonal retention and recycling of detritus within the deep ocean is suggested to explain seasonal patterns of suspended particulate matter concentrations in the northeast Pacific [Bishop et al., 1999].

6.3. Seasonality of NPP and Flux to Depth

[44] The balance between seasonality of production and seasonality of flux reverses with latitude (Figure 11). At higher-latitudes seasonality of production is generally greater than seasonality of flux. At lower-latitudes seasonality of production is generally less than seasonality of flux. To account for this latitudinal difference we suggest that processes influence flux seasonality discernable at lower-latitudes, where variability of production is diminished, are masked by the larger-amplitude production seasonality signal of higher latitudes. Processes that may enhance low-latitude flux seasonality relative to production include phytoplankton mass sedimentation events [Kemp et al., 2000] and transient meteorological forcing of pulsed export [Conte et al., 2003]. Processes that may attenuate seasonality between production and flux at high-latitudes include the regeneration of detritus retained within surface waters, delayed herbivore growth, and seasonal mixed-layer deepening associated with polar winter onset. Overall, the production-to-flux seasonality signal may be influenced by horizontal water mass advection [Siegel and Deuser, 1997], which may covary with rates of production [Lampitt and Antia, 1997].

Figure 11.

Latitude versus the seasonal variation index (annual standard deviation divided by average) of particulate organic carbon flux to various depth ranges [≤1 km (○), >1 and ≤2.5 km (+), and >2.5 km (△)] and the seasonal variation index of net primary production (•) at sediment trap locations.

6.4. Parameterization of the Annual Sinking Fraction of NPP

[45] Our analysis of SST- and SVI-associated hypotheses includes determining relationships between the remotely sensed parameters and production ratio data, and comparing the ability of algorithms derived to predict flux. Flux predictions are based on algorithms that describe the coefficients of equation (6) as a function of the remotely sensed parameters, as outlined in section 4.6. Coefficient parameterization involves testing different equation functional forms (polynomial and exponential), various orders of equations, and different numbers of data groups to find the most accurate predictions. Algorithm curve fits were constrained to not allow negative coefficient parameterizations. Figure 12 and Table 2 show the data groupings and coefficient algorithms that produced the most accurate flux predictions. Curve fits shown do not imply complex relationships, but rather show the simplest and most accurate coefficient algorithms attained.

Figure 12.

Coefficients used to forecast the annual average particulate organic carbon flux to depth normalized to overlying production [p ratio(ze)] as a function of satellite-derived parameters, the seasonal variation index of production (SVI; annual standard deviation divided by average) and sea-surface temperature (SST; °C). Coefficients indicate the components of flux using equation (6): the labile fraction of export (prd; a and b), remineralization length scale (rld; c and d), and more refractory and rapidly sinking fraction of export (prr; e and f). Curve fit coefficient algorithms are reported in Table 2.

Table 2. Curve Fit Algorithms Describing Coefficients Used to Estimate Annual Particulate Organic Carbon Flux to Depth Normalized to Overlying Production (p ratio(ze)) as a Function of Satellite-derived Parameters, the Seasonal Variation Index of Production (SVI; Annual Standard Deviation Divided by Average) and Sea-surface Temperature (SST; °C)
Parameter (x)Coefficient algorithmbNcR2
  • a

    Coefficients describe flux using equation (6)a: the labile fraction of export (prd; Figure 12, A and B), remineralization length scale (rld; Figure 12, C and D), and more refractory and rapidly sinking fraction of export (prr; Figure 12, E and F).

  • a

    Equation (6): p ratio(ze) = prd exp(equation image) + prr.

  • b

    The functional forms of coefficient algorithms selected are either polynomial or exponential fits of lowest order constrained to not yield negative values and produce the most significant curve fits.

  • c

    The number of data groups used are selected to produce the most significant curve fits.

SVIprd = (31 x2 + 49 x + 7.8) 10−3100.92
rld = 1400 exp(−0.54 x)100.68
prr = (2.6 x2 − 4.2 x + 4.8) 10−380.96
SSTprd = (0.0060 x4 − 0.42 x3 + 10 x2 − 100 x + 340) 10−371.0
rld = −2.0 x2 + 60 x + 68080.39
prr = (0.010 x2 − 0.34 x + 6.0) 10−370.62

[46] Comparison of SST and p ratio data groups shown in Figure 12 indicates little significant variation in p ratios for much of the range of SST. Dissimilar coefficient behavior is generally confined to minimal SST values. This discontinuous distribution may imply that SST influences remineralization in a stepwise manner with little influence until a certain low threshold temperature is reached, where flux is more labile as indicated by the prd and rld coefficients. Alternately, this behavior may reflect SST and SVI covariance, characteristic of lower temperatures (Figure 7). SVI coefficients and p ratio data groups show somewhat more continuous distributions and include a larger range of rld and prr coefficient values. SVI coefficient distributions are consistent with the suggestion that where production is more variable, sinking PM is more labile and decays more rapidly.

[47] Flux to depth forecast performance abilities of the SST- and SVI-associated parameterizations and the relationship presented by Suess [1980] are compared in Figure 13. The most accurate forecast performance is found using the SVI-related algorithm (Figure 13). All equations overestimate flux to depth and p ratios in the upper ocean (≤1500 m), although to degrees varying depending on latitude range evaluated. Overestimation of flux and p ratios is largest using the equation of Suess [1980], especially at shallow depths. The SVI- and SST-algorithms perform similarly in the deep ocean (≥1500 m). A portion of the improved accuracy derived using equation (6) may be due to our incorporation of a larger range flux and production data available to Suess [1980]. In the upper ocean the SST algorithm overestimates high-latitude flux and p ratios at all latitudes more than the SVI algorithm. This depth dependant discrepancy suggests the exponential form of equation (6) and the depth scaling of the equation of Suess [1980] do not always account for the most rapid degradation of detritus in the upper water column.

Figure 13.

Residuals describing the accuracy of algorithms used to forecast annual average particulate organic carbon flux to depth relative to sediment trap observations grouped by latitude. Residuals indicate (a) predicted minus observed flux rates (mg Corg m−2 d−1) and (b) the ratio of predicted to observed p ratios (flux normalized to production). Flux is estimated as a function of satellite-derived data using the seasonal variation index (annual standard deviation divided by average) of production- (SVI; ○) and SST-associated (▴) using equation (6), and the equation of Suess [1980] (▪).

6.5. Predictions of Annual Flux to Depth

[48] Coupling between surface and subsurface biogeochemical process has been proposed by comparing satellite-derived estimates of production and sediment trap-derived flux to depth [Lampitt and Antia, 1997; Fischer et al., 2000; Antia et al., 2001; Müller-Karger et al., 2005]. We build on this insight by using parameters in addition to the rate of NPP to further constrain flux to depth. Algorithms developed using the remotely sensed parameters, the SVI of production and SST, and equation (6) allow for predictions of flux and p ratios and the assessment of forecast accuracy relative to the equation of Suess [1980].

[49] The accuracy of SVI-associated annual flux and p ratio predictions allows for prediction of global particulate organic carbon flux using satellite-derived NPP (Figure 14 and Tables 3 and 4). For much of the global ocean, the geographic pattern of flux is similar at different depths. Enhanced export and flux characterize regions where rates of production and production seasonality are enhanced. However, in the central northern Atlantic and Pacific Oceans and in the Southern Ocean where export is enhanced, flux to the seafloor is diminished. Flux to the seafloor is greatest on continental shelves and within centers of coastal upwelling. At depths greater than the continental shelves the geographic distribution of flux to the seafloor is similar to that of flux within the water column. While absolute flux rates differ, the geographic patterns of flux and export predicted by the SVI algorithm are generally similar to other satellite-derived global estimates [Falkowski et al., 1998; Laws et al., 2000; Müller-Karger et al., 2005], with proportionally larger fluxes in regions of enhanced seasonality (for example, higher latitudes and regions of seasonal upwelling).

Figure 14.

Global ocean forecasts of annual average particulate organic carbon (a) export from surface waters, (b) flux to the base of the mesopelagic zone (1 km), (c) flux to the center of the bathypelagic zone (2.5 km), and (d) flux to the seafloor (g Corg m−2 yr−1). Flux is estimated as a function of a satellite-derived net primary production (NPP) and the seasonal variation index (annual standard deviation divided by average) of NPP using equation (6). Flux to the seafloor employs the TerrainBase 5-min global bathymetry available from the National Geophysical Data Center.

Table 3. Annual Average Particulate Organic Carbon Export, Flux to Depth, and Flux to the Seafloor in the Global Ocean
DepthFlux (Pg Corg yr−1)MethodsReference
Export1.8; 4.6aSVI-algorithm using sediment trap flux and remotely sensed productionthis study
1 km0.88; 2.2aSVI-algorithm using sediment trap flux and remotely sensed productionthis study
2.5 km0.31SVI-algorithm using sediment trap flux and remotely sensed productionthis study
Seafloor0.76; 1.6aSVI-algorithm using sediment trap flux and remotely sensed productionthis study
Export16f ratio algorithmb using remotely sensed productionFalkowski et al. [1998]
Export10Global ocean circulation/biogeochemical inverse model using hydrographic, oxygen, nutrient, and carbon dataSchlitzer [2000]
Export11–13Pelagic food web models using remotely sensed productionLaws et al. [2000]
Seafloor0.886‘Mineral ballast’ sediment trap derived modelKlaas and Archer [2002]
Seafloor0.93Sediment trap derived flux algorithmc and remotely sensed productionMüller-Karger et al. [2005]
Seafloor0.40Apparent oxygen utilization of surface sedimentsJahnke [1996]
Table 4. Annual Average Particulate Organic Carbon Export, Flux to Depth, and Flux to the Seafloor (Pg Corg yr−1) Divided Into Ocean Basins
RegionaArea (%)ExportFlux to 1 kmFlux to 2.5 kmFlux to seafloor
  • a

    Estimates are derived as a function of a satellite-derived net primary production (NPP) and the Index of Seasonal Variation (annual standard deviation divided by average) of NPP using equation (6).

  • a

    Ocean basins are divided such that: the Southern Ocean is delineated south of 40°S; Arctic waters are delineated north of 70°N; the Indian Ocean is delineated east of the Cape of Good Hope (20°E) and west of Sumatra and Australia from Cape Londonderry (127°E) to Melbourne (147°E); the Atlantic Ocean includes the Mediterranean Sea, which has minor a contribution to global flux.

  • b

    Radiogenic calibration at depths <1.5 km.

Pacific Ocean430.60, 1.5b0.30, 0.76b0.120.27, 0.55b
Atlantic Ocean210.52, 1.3b0.24, 0.60b0.0780.27, 0.60b
Indian Ocean150.25, 0.62b0.12, 0.29b0.0440.085, 0.16b
Arctic waters10.026, 0.065b0.0082, 0.020b0.000810.045, 0.11b
Southern Ocean210.42, 1.0b0.20, 0.51b0.0610.079, 0.15b

[50] Sediment trap-derived estimates of global export are significantly less than those developed using other methods. Multiple causes may influence this discrepancy. Accuracy of sediment trap-derived estimates of export is limited by the lack of measurements characterizing the upper mesopelagic where greatest rates of subsurface recycling occur. As noted in section 4.3, underestimation of upper ocean fluxes by sediment traps is suspected for a variety of reasons. While radiogenic calibration narrows the discrepancy between estimates, the difference remains significant. Variability associated with the radiogenic technique may be responsible [Yu et al., 2001]. Finally, a portion of this discrepancy may be due to export of dissolved organic carbon [Carlson et al., 1994] not measurable using sediment traps.

[51] Sediment trap estimates of flux to the seafloor, although not significantly dissimilar from one another, are significantly greater than flux estimated using apparent oxygen utilization (AOU) in surface sediments [Jahnke, 1996]. This discrepancy may be due to errors associated with sediment trap and AOU-associated flux methods. The accuracy of traps within the deep ocean may be influenced by some of the hydrodynamic limitations associated with traps in shallow waters. Resuspension of benthic detritus into deep-water traps may be greater than anticipated. Multiple factors may limit the accuracy of the AOU-associated flux estimations. The flux and subsequent accumulation of settled detritus on the seafloor may be geographically heterogeneous (for example, favoring topographic low points) and thus not well characterized by spatial distribution of coring techniques typically used to collect benthic surface sediments. Benthic biological activity and bioturbation may further enhance the geographic heterogeneity of benthic remineralization [Tedesco and Wanless, 1991; Meadows and Meadows, 1994]. Finally, the remineralization of flux arriving on the seafloor may occur on timescales not assessed by the methods reported in the work of Jahnke [1996].

7. Conclusions

[52] Theoretical connections have been proposed between variability of upper ocean dynamics and pelagic biogeography [Longhurst, 1995], and between biogeography and biogeochemical cycling within the ocean [Longhurst and Harrison, 1989; Lampitt and Antia, 1997]. In particular, Lampitt and Antia [1997] suggest biogeography, as described by the plankton climate categories of Longhurst [1995], influences marine biogeochemical cycling. Results presented in this study further establish these findings. Our analysis suggests variability of production, as characterized by the seasonal variation index, reflects ecosystem-scale biogeochemical processes. This connection may be because, by measuring environmental change, SVI of production reflects pelagic ecosystem structure [Longhurst, 1995].

[53] The following conclusions are based on our study of the biological pump: The NPP climatology displays seasonal patterns coherent over large geographic provinces, indicating the relative dominance of solar, climatic, and oceanographic controls on the annual variability of NPP. In general, seasonal patterns of flux reflect those of production. Prominent patterns of flux include polar, equatorial upwelling, coastal upwelling, monsoonal, and within subtropical-subpolar regions. Notably, within subtropical-subpolar open ocean regions the timing of maximum flux is delayed by approximately 5 days per degree latitude increase. Coherence of flux patterns between multiple widely dispersed locations demonstrates the ability of sediment traps to characterize sinking particulate matter. Seasonal production-to-flux analyses indicate POC vertical transfer efficiency is significantly seasonally variable. In particular, the ratio of flux to production during bloom production is typically half that of the remaining year. Comparison of production and flux variability shows a latitudinal dependant relationship. At lower-latitudes seasonality of flux is typically greater than that of production, while at higher latitudes, seasonality of production is typically greater than that of flux. This reversal of variability may describe a biogeographic distinction in the controls of production-to-flux relationships.

[54] By analyzing a globally distributed data set of NPP and flux to depth, we show the accuracy of algorithms describing flux is improved by incorporating SVI- and SST-related controls and describing labile and refractory sinking fractions of production. The use of the Behrenfeld and Falkowski [1997a] global NPP model with equation (6), parameterized as a function of the SVI of production and SST, yields annual satellite-based estimates of deep-sea particulate fluxes with greater skill than the commonly applied equation of Suess [1980]. Global-scale coherence between seasonal patterns of production and flux to depth demonstrate the influence of annual climatic variability on the marine carbon cycle. Results suggest atmospheric CO2 variability is influenced by changes in ecosystem structure as well as the rate of production.

[55] Differentiation between SVI- and SST-associated controls is limited because of their covariance. Correlations found do not prove causality. Furthermore, the potential for systematic error in either the NPP or sediment trap flux data may influence the accuracy of predictions. Greater confidence in flux to depth forecasts may require consideration of additional NPP model estimates [Campbell et al., 2002], flux to depth techniques [Buesseler et al., 2000], or other marine carbon cycle methodologies [e.g., Aydin et al., 2004; Bishop et al., 2004]. This study is an initial step toward characterizing the efficiency of the biological pump.

[56] Evaluation of additional flux mechanisms may help refine remotely sensed flux relationships and further improve simulation of the marine biogenic carbon cycle. For example, microbial heterotrophic activity responds to the rate of NPP [Lucas et al., 1986] and thus may influence the SVI-derived relationship. Differences in the mineral ballast of phytoplankton may contribute to seasonal variability of particulate organic carbon flux and remineralizaiton [Armstrong et al., 2002; Klaas and Archer, 2002]. Additional factors that may influence the efficiency of the biological pump include plankton community structure variability [Boyd and Newton, 1995; Arrigo et al., 1999], phytoplankton taxa-specific particulate matter biodegradability [Dunbar et al., 2003], the vertical migration of zooplankton [Bishop et al., 1986; Bishop, 1989], physical forcings of surface waters [Conte et al., 2001; Fischer et al., 1996b], multiseasonal recycling and retention of subsurface particulate matter [Bishop et al., 1999], and mass sedimentation events [Kemp et al., 2000].


[57] We thank the following researchers for their inspiration and advice: James Bishop (Laurence Berkeley National Laboratory), Kevin Arrigo, Gert van Dijken (for advice on satellite image processing), Pamela Matson, Alexandria Boehm, Alessandro Tagliabue, Rochelle Labiosa, and Tasha Reddy (Stanford University). This research was supported by a number of sources, including the NSF ROAVERRS program, Lawrence Livermore National Laboratory DOE Center for Research on Ocean Carbon Sequestration, Stanford University McGee and A. W. Mellon Foundations, and International JGOFS Program (Ocean Biogeochemical Modeling Course, Bangalore, India). Finally, we thank the J. Geophys. Res. reviewers for their recommendations.