Role of regional bioproductivity in atmospheric CO 2 changes

GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 13, NO 2, PAGES 531-553, JUNE 1999

Role of regional bioproductivity in atmospheric CO2 changes

Jonathan J. Rich, David Hollander, and G. Edward Birchfield Department of Geological Sciences, Northwestern University, Evanston, Illinois

Abstract. One process that has been proposed to explain the glacial-interglacial change in atmospheric CO2 is a change in the strength of the ocean's biological pump, that is, changes in ocean export bioproductivity. Using a coupled ocean-atmosphere box model that incorporates carbon chemistry and a nutrient budget, we investigate the effect on atmospheric CO2 levels of changes to ocean export productivity in different regions. The primary discovery is the role of water column stratification in controlling the extent to which atmospheric CO2 is affected by regional changes in the biological pump. Stratification manifests itself in two competing ways. On the one hand, increasing stratification causes an increase in atmospheric CO2 sensitivity to export bioproductivity. This results in modeled CO2 being almost an order of magnitude more sensitive to low-latitude bioproductivity changes than it is to high-latitude changes. On the other hand, increasing stratification decreases the rate at which nutrients mix up into the surface layer, thereby setting a limit on the overall magnitude of bioproductivity increase and atmosphere CO2 decrease that is possible in stratified ocean regions. One of the implications of this work is to point to the potential importance of changes in the strength of low-latitude ocean bioproductivity as a cause of the glacial-interglacial atmospheric CO2 change.

1. Introduction

One of the persistent challenges confronting paleocli- matologists is the question of what caused the glacial to interglacial increase in atmospheric CO2 of •85 /•mol/mol, from 195 to 280/•mol/mol, documented in ice core records [e.g., Neftel et al., 1982; Barnola et al., 1987]. Interest in this question arises due both to the postindustrial CO2 increase of comparable magnitude and to the important role that the greenhouse gas CO2 is thought to have in climate change [e.g., $magorin- sky, 1981; Peixoto and Oort, 1992]. Recent evidence that the glacial-interglacial CO2 increase preceded the decrease in ice volume [Saltzman and Verbitsky, 1994; Broecker and Henderson, 1998] that raised sea level 120m further highlights the importance of understanding the mechanisms which control changes in atmospheric CO2 concentration.

Numerous mechanisms have been proposed to explain the Xco2 change accompanying glacial-interglacial tran- sitions, including a shift in the partitioning of carbon between the ocean-atmosphere reservoir and the terrestrial forest-humus reservoir, as well as changes in

Copyright 1998 by the American Geophysical Union.

Paper number 1998GB900023. 0886-6236 / 99 / 1998G B900023 $12.00

531

ocean circulation, ocean salinity, and ocean temperature among others (see Broecker [1995] for a partial summary and Broecker and Henderson [1998] for a more recent list). (Here we adopt the recommendation of Tel- lus [Rodhe and Sundqvist, 1996] in accord with the recommendations of the International Union of Pure and

Applied Chemistry (IUPAC) [Schwartz and Warneck, 1995] that SI units be used in atmospheric chemistry. Fundamentally, what is being referred to by the symbol Pco2, rather than pressure, is a molar mixing ratio. So we follow IUPAC's recommendation in using the letter x as the symbol to denote "the mole fraction of atmospheric trace constituents". So pco• becomes Xco•, which has SI units of •umol/mol rather than ppm or ppmv, though the magnitude is unchanged. Also throughout this paper we have replaced the liter by its SI equivalent, the cubic decimeter, dm 3, and when using the time unit of year we abbreviate with the recom- mended symbol "a" .) Certainly multiple processes were involved. However one important mechanism, the "biological pump", is a prime candidate because of the evidence that glacial bioproductivities were different from today's. This provides the motivation for developing a thorough understanding of the relationship between the biological pump and the atmospheric CO2 level.

The "biological pump" refers to the process whereby photoautotrophs and heterotrophs in the surface layer

532 RICH ET AL.' ROLE OF REGIONAL BIOPRODUCTIVITY IN CO2 CHANGE

of the ocean assimilate dissolved carbon from the sur-

rounding water and convert it to organic materials necessary for cellular function and inorganic minerats for- skeletal support. Much of this organic and mineral carbon is degraded, respired, and recycled within the surface layer, but some fraction of this biomass, in such forms as fecal pellets or the particulate organic detritus known as "marine snow", gravitationally settles out of the surface mixed layer of the ocean, thereby "export- ing" carbon from the surface to the deeper ocean. This creates a further flux of CO2 from the atmosphere to the carbon-depleted surface ocean. Thus a more vigorous functioning of the biological pump results in a lower atmospheric CO2 level, while a less vigorous pump gives rise to a higher level.

A growing body of sedimentological, geochemical, and paleontological evidence indicates that rates of ocean bioproductivity varied significantly in the past, particularly from glacial to interglacial times. (We use the term "bioproductivity" as a convenient shorthand for what is more precisely called "export bioproductivity". In this paper we are not concerned with what is termed "primary productivity" (of which the export productivity is some fraction), so there should be no ambiguity about our use of the term in this way. Whenever we need to refer to primary productivity, we do so explicitly.) This raises the question of whether such bioproductivity changes were responsible for the glacial-interglacial atmospheric CO2 change. Further- more, because the paleodata indicate that the glacial- interglacial changes in bioproductivity varied regionally (see survey in section 2), it is important to understand the role that regional bioproductivity changes may have played in atmospheric CO2 change. The constraints imposed by various paleodata records, such as 513C and 5180, indicate that changes in bioproductivity could not have been wholly responsible for the glacial-interglacial shift [e.g., Curry and Crowley, 1987; Leuenberger et al., 1992; Marino et al., 1992; Bender et al., 1994], but probably accounted for approximately 20-80% of the shift (15-70/•mol/mol, section 2.3).

Prior modeling studies have examined the role of changes in bioproductivity on atmospheric CO2. Per- haps the most influential of these has been the •tudy of $arrniento and Toggweiler [1984]. The significance of this box model study was to emphasize high-latitude regions, since some 75% of the ocean's volume has den- sities that outcrop in high-latitude regions with _• 4% of the ocean's surface area. This study examined the effect on atmospheric CO2 of changes in high-latitude bioproductivity (as well as other processes: the rate of thermohaline circulation and the rate of high-latitude overturning). It ignored the potential role of changes in low-latitude productivity. There have been other modeling studies since [e.g., Broecker and Peng, 1986; Keir,

1988], including general circulation model (GCM) studies [e.g., Kurz and Maier-Reirner, 1993], but the legacy of the Sarmiento and Toggweiler [1984] study has been an emphasis on high-latitude processes at the expense of low-latitude processes. The sedimentary record of paleoproductivity data, on the other hand (section 2.1 and Table 1), has tended to indicate just the opposite, that glacial equatorial productivities were enhanced [e.g., Pedersen, 1983] while high-latitude glacial productivities may have been reduced [e.g., Mortlock et al., 1991].

In this paper we examine the role that changes in regional ocean export bioproductivity might have played in the observed glacial-interglacial shift in atmospheric CO2 concentration. Using a coupled ocean-atmosphere model which incorporates a carbon cycle and nutrient budget, we examine the role of changes in bioproductivity in different ocean regions both at high latitudes and at low latitudes on atmospheric CO2 changes. We examine the sensitivity of atmospheric CO2 changes to regional changes in bioproductivity and explain the mechanisms responsible for the differences in regional sensitivity that we find. The model is then applied to an understanding of the glacial-interglacial CO• change by identifying possible combinations of regional bioproductivity changes that could produce the necessary amount of CO• drawdown. Finally, we reevaluate the role of low-latitude bioproductivity in the glacial-interglacial CO2 change.

2. Background

2.1. Survey of Paleoproductivity Estimates

There is a rich and diverse literature concerned with

estimating past ocean bioproductivities and aimed at discerning glacial-interglacial patterns of change. In this section we present a review from the literature of regional glacial-interglacial productivity changes (Ta- ble 1). A concise and unambiguous synthesis of this in- formation is difficult due to its diversity and complexity. Some of the complexity is related to problems associated with the proxies [e.g., Arrhenius, 1988; Archer, 1996] (see Berger et al. [1994] for a review). A further complication is that although much of the literature is concerned with estimating past values of primary productivity, what is more important to understanding CO• change is to estimate past values of export productivity, since it is the export productivity rather than the primary productivity that regulates CO• levels. Despite these complications, a synthesis of paleoproductivity in- formation is worth attempting due to the linkage between atmospheric CO2 levels and ocean productivity and the bearing the productivity changes could have on our understanding of the glacial-interglacial CO• change.

Table 1. Estimates From Literature of Last Glacial Maximum Ocean Export Paleoproductivities Relative to Present-Day Values

(Pexp)LGM Type of Data Reference Location (peXp)p ..... t

533

High Latitudes Southern Ocean

45-50 ø S • 2 radionuclides N of 50øS -•2 opal AR 43 ø S -• 1-2 radionuclides

45øS -•1 opal, CaCO3, 515N, 23øTh Atl -•1 Ba AR S of 50øS •1-1 radionuclides

S of APF < 1 opal, Corg, barium S of 50øS 0.2-0.5 opal AR

Atlantic

Subpolar (N, S) 1.2 planktonic forams 45-60ø N •1 diatom assemblage NE (50ø,58øN) < i benthic forams NE (47-60øN) ? %CaCO3, %TOC, MAR

Pacific

45-60øN 10 diatom assemblage Bering Sea < I 51aC, 515N

Atlantic Equatorial Region 23øW 4-10 Ca7 alkenone AR 12øW 6 diatom AR

15ø,8øW 3.5-4 barium 36-10øW •1-4 opal, Corg MAR 45øW-10øE 1.9 planktonic forams 23øW-5øE • I diatom assemblages 44øW •1 CaCO3 20øW 0.75 CaCOa

Pacific

90øW • 3-4

140ø,110øW 2 160øW 1.7-1.9 115-100øW 1.5-1.8 107øW 1.5 135øW > 1 96øW ?

Atlantic

Congo Fan 2 NE Atl (39øN) Angola Basin 1.4'

Pacific

E Pac (22øN) -•0.5 NE Pac (42øN) 0.5

Indian

Western Australia 1.5-2 Arabian Sea 0.3-0.5

Kumar et al. [1995] Mortlock et al. [1991] Kumar et al. [1995] Francois et al. [1992, 1993] Niirnberg et al. [1997] Kumar et al. [1995] Shimmield et al. [1994] Mortlock et al. [1991]

Mix [1989] $ancetta [1992] Thomas et al. [1995] Manighetti and McCave [1995]

Sancetta [1992] Nakatsuka et al. [1995]

Sikes and Keigwin [1994] Abrantes et al. [1994] Gingele and Dahmke [1994] Verardo and Mcintyre [1994] Mix [1989] Pokras [1987] Curry and Lohmann [1990] Curry and Lohrnann [1990]

%CaCO3, %Corg barite MAR BFAR

mn conc, Corg %CaCOa A1/Ti ratio opal, CaCO3, Corg MAR

Continental Margins

barium

faunal assemblage Corg

Pedersen [1983] Paytan et al. [1996] Herguera [1994] Berger et al. [1983] Adelseck and Anderson [1978] Murray et al. [1993] Reaet al. [1986]

Gingele and Dahmke [1994] Lebreiro et al. [1997] Holmes et al. [1997]

Corg MAR Corg, Ba, opal MAR

Ganeshram et al. [1995] Lyle et al. [1992a]; Dymond et al. [1992]

BSMAR, BFAR, authigenic U Ba/A1 ratio

McCorkle et al. [1994] Hermelin and Shimmield [1995]

Low-Latitude to Midlatitude Open Ocean Atlantic

South Atl gyre 13 barium Gingele and Dahrnke [1994] subtropical 1.5 planktonic forams Mix [1989]

Global Ocean

(65 cores world-wide) 1.3-5 Corg, MAR, p Sarnthein et al. [1988]

Entries are organized by productivity province, by ocean basin, and in decreasing order of glacial age bioproductivity enhancement. SO is Southern Ocean, TOC is total organic carbon, AR is accumulation rate, MAR is mass AR, BSMAR is biogenic sediment MAR, BFAR is benthic foraminifera AR, and LGM is Last Glacial Maximum. (•[•exp)LGM/(•[)exp)p ..... t is the estimated ratio of LGM glacial export production to present-day export production. A "?" indicates studies which found no clear glacial-interglacial pattern, due to contradictory results from multiple paleoproductivity estimates.

*Calculated from primary productivity estimates using the formula •[)exp = (•[)T) TM [Berger and Herguera, 1992] where •[)exp is export bioproductivity and PT is primary (or total) productivity.

534 RICH ET AL.: ROLE OF REGIONAL BIOPRODUCTIVITY IN CO2 CHANGE

Paleoproductivity Estimates G<I G=I G>I

s.o.(8) Atl (3)

i Pac (2) High latitudes

Atl (8)

Pac (6)

Equatorial

Atl (3)

Continental

Pac (2) margins

IInd (2)

(2) Gyres

Figure 1. Frequency histograms from the estimates of Table i comparing the number of estimates in which glacial (G) productivities were less than interglacial (I) (left column) with the number in which glacial productivities were greater than interglacial (right column). The middle column shows estimates in which there was

no change. The number of estimates that went into each histogram is shown in parentheses. In order to avoid visual distortion, each histogram was normalized by the number of estimates involved so that all histograms have the same area. Ind denotes Indian Ocean, Pac denotes Pacific Ocean, Atl denotes Atlantic Ocean, and S.O. denotes Southern Ocean.

For the purpose of this survey we categorize the ocean into four productivity provinces: high latitudes (pole- ward of 45øN or 45øS); equatorial upwelling regions; the oligotrophic open ocean pelagic region (e.g., subtropical gyres); and low-latitude to midlatitude continental margins. Figure 1 presents the estimates from the survey in terms of histograms for each region showing the numbers of estimates in which glacial productivities were higher than interglacial versus the numbers in which glacial productivities were lower. The clearest picture of paleoproductivity changes is from the equatorial up-

welling regions. Here the data support a strong consensus for enhanced glacial productivities with estimates ranging anywhere from 50% larger to as much as 600% larger or more (Table 1) [e.g., Adelseck and Anderson, 1978; Sikes and Keigwin, 1994].

Bioproductivity changes at continental margins are more complicated and appear to vary according to the ocean basin. Though small in area, these regions are some of the most productive in the ocean, and there is some evidence they may be at least twice as important to the global productivity budget as the much larger pelagic regions [Walsh et al., 1981; Bienfang and Zie- mann, 1992; Bauer and Druffel, 1998]. For the Atlantic continental margins there is evidence of higher glacial productivity [e.g., Lebreiro et al., 1997], while the Pa- cific continental margins show evidence of having had reduced glacial productivities [e.g., Lyle et al., 1992b; Ganeshrarn et al., 1995]. More research on these margins will be needed to clarify this emerging pattern and to determine from it the net effect of these areas on the

global productivity budget. The most difficult locations to characterize with re-

spect to glacial-interglacial productivity changes are the high latitudes. Here there are conflicting estimates as to whether glacial productivity was increased [Ku- mar et al., 1995], decreased [Mortlock et al., 1991], or remained the same [Francois et al., 1993], and no consensus has yet emerged [Berger et al., 1994]. Be- sides the proxy problems mentioned earlier, common to all regions, part of this complication stems from features unique to the high latitudes. Glacial-interglacial changes in sea ice extent and the migration of ocean frontal systems such as the Antarctic Polar Front can result in substantial changes in bioproductivity [Fis- cher et al., 1988; Mortlock et al., 1991]. These factors make interpretation of high-latitude paleoproductivity data problematic. Yet it is important to try to de- velop an understanding of glacial productivities in these ß

high-latitude regions, since influential modeling results [Sarmiento and Toggweiler, 1984; Knox and McElroy, 1984; Wenk and Siegenthaler, 1985] have suggested that it was productivity changes in the high latitudes that were responsible for the glacial-interglacial shift in atmospheric CO2 levels.

With respect to subtropical gyres and open ocean pelagic regions, the problem of estimating paleoproductivities is not so much conflicting data as it is a con- spicuous and almost complete lack of data. Mix [1989] included some of these areas in his analysis, and the extensive compilation by Sarnthein et al. [1988] of numerous workers' results (published and unpublished) includes a handful of data points from these areas. This dearth of data is unfortunate because although the current paradigm is that these regions are oligotrophic and therefore unimportant to global carbon fluxes, there is

RICH ET AL- ROLE OF REGIONAL BIOPRODUCTIVITY IN CO2 CHANGE 535

evidence that this may be a misconception [Jenkins, 1982; Platt and Harrison, 1985; Tr•guer et al., 1995]. Certainly, it becomes more tenuous to assume that they remained oligotrophic under a different climate regime such as the glacial [Falkowski, 1997].

The picture we have accrued then is of glacial productivities which were increased relative to today's in zones of equatorial upwelling and in Atlantic continental margins while being reduced in Pacific continental margins. The changes at high latitudes and over the vast pelagic open ocean regions remain unclear. Even further removed from complete understanding is the question of how the various regional changes in ocean bioproductivity summed to produce the globally integrated glacial value.

2.2. Theories of Enhanced Productivity

Despite the lack of a clear picture of glacial age global bioproductivity, there are several theoretical arguments for the idea that it was enhanced relative to today's. One is John Martin's iron hypothesis. The modern Southern Ocean has excess nutrients in its surface wa-

ters which indicate that the biological pump there is not working to maximum capacity. Martin's discovery that these waters may be iron-limited [Martin et al., 1990] led to his hypothesis that high-nitrate low-chlorophyll (HNLC) regions of the oceans (such as the Southern Ocean, the tropical eastern Pacific and the North Pa- cific) are iron-limited and hence that stronger glacial winds would have increased aeolian delivery of iron via dust to HNLC regions with the possibility that South- ern Ocean bioproductivity could have been increased by as much as 40 times, from < 0.1PgC/a to as much as 3 Pg C / a [ Martin, 1990].

The iron hypothesis has had indirect support from ice core evidence that atmospheric dust levels were significantly higher during glacial periods [Petit et al., 1981; Hammer et al., 1985; Legrand et al., 1988; Dansgaard et al., 1989; Petit et al., 1990; Edwards et al., 1997], as much as 30 times present-day [Yung et al., 1996]. If, as Martin argued, iron is the limiting factor of productivity in large areas of the ocean, the ice core dust record has important implications for glacial productivity since the major source of iron in pelagic environments is aeolian dust [Duce and Tindale, 1991]. Figure 2 shows atmospheric CO2 plotted alongside atmospheric dust flux, iron accumulation, and a midlatitude paleoproductivity record. It shows the approximate coincidence of changes in the four records and is meant to visually make the case for a connection between changes in dust, iron, bioproductivity, and atmospheric CO2.

The recent IronEx II results [Coale et al., 1996; Behrenfeld et al., 1996; Cooper et al., 1996] provided direct and clear support for the iron hypothesis with a mesoscale in situ fertilization experiment in the equato-

SIMMAX.28

transfer

function

d Paleoproductivity proxy (Atlantic Ocean: 39'N, 13'W)

4OO

200

A Fe accumulation /• c 30 15 mg

c•-•-ka

10 15

m2a flux 5 (Vost 0

b 0 200

gmol

250

-- v- (Vostok ice core) a 300

ß ' ..... 5'o' ' o ka BP

Figure 2. Comparison of climate records of the past 190 ka. (a) Atmosphere CO2 level as recorded in the Vostok ice core [Barnola et al., 1994] (plotted on an inverted scale to facilitate visual comparison). (b) Dust flux, also from the Vostok ice core [Petit et al., 1990]. (c) Iron accumulation in a Southern Ocean sediment core [Kumar et al., 1995]. Note that this record is less highly resolved than the others shown and so is likely hiding higher-frequency events. (d) Midlatitude At- lantic Ocean paleoproductivity proxy based on faunal data using a transfer function [Lebreiro et al., 1997]

rial Pacific's HNLC region. Results showed as much as a 14-fold increase in primary productivity [Coale et al., 1996] and a drop in (Xco2)aq of 90/•mol/mol, corresponding to a 60% decrease in the ocean-to-atmosphere CO2 flux[Cooper et al., 1996].

Another theoretical argument for enhanced glacial age productivities has been presented by Falkowski [1997]. His argument is also tied to aeolian delivery of iron but is based on iron's essential role in nitrogen fixation. He argues that glacial age aeolian delivery of iron to the ocean could have greatly stimulated nitrogen fixation, thereby lifting the nitrogen limitation and increasing productivity. As he states, this has the potential for causing significant changes not only in HNLC regions (as with the iron hypothesis) but also could turn low-nutrient, low-chlorophyll regions such as subtropical ocean gyres into productive areas, thereby greatly affecting atmosphere-ocean CO2 fluxes in these locations as well.

A third argument for enhanced glacial bioproductivity, that of Ganeshram et al. [1995], also deals with ni-

5:]6 RICH ET AL.: ROLE OF REGIONAL BIOPRODUCTIVITY IN CO2 CHANGE

trogen as the limiting nutrient in the ocean but focuses attention on the process of denitrification (a sink) rather than nitrogen fixation (a source). They present data showing that in the equatorial Pacific HNLC region, the currently high rate of denitrification "was greatly diminished during glacial periods" [Ganeshram et al., 1995, p. 755] and speculate that this might have been caused by changes in oxygen delivery via wind-driven upwelling. This change in the denitrification rate is significant because nitrogen is the limiting nutrient in most of the ocean (based on our analysis of data of Conkright et al. [1994]; also see Falkowski [1997] for discussion). Thus, reduced denitrification enhances nitrate availability and bioproductivity.

2.3. Constraints on Biopump Involvement in Deglacial Atmospheric CO2 Increase

In the discussion above we have identified at least

three reasons for thinking that stronger glacial winds might have increased global glacial productivity, thereby strengthening the biological pump giving rise to the lower CO2 levels seen in the glacial atmosphere. On the other hand, with multiple processes available to explain lower CO2 levels, there is no reason to assume that the biological pump alone was responsible for all of the change. In fact, there are good arguments to be made against that position. One argument often made is that there is no evidence for the reduced deep ocean oxygen levels and anoxia that would be expected to accom- pany a biopump strong enough to reduce atmospheric CO• to glacial levels. This argument grew out of box model studies which seemed to show that deep ocean anoxia would have resulted if the biological pump had been responsible for all of the atmosphere CO• change [Knox and McElroy, 1984; Toggweiler and $armiento, 1985; Sarmiento et al., 1988a]. Anoxia resulted because the greater export production to the deep ocean enhanced rates of remineralization which depleted deep ocean oxygen. No evidence of any such glacial age deep ocean anoxia has been found.

However, there is evidence that the deep ocean oxygen issue has been overrated as a problem for enhanced biopump scenarios. $armiento and Orr [1991], using a three-dimensional (3-D) model (i.e., a general circulation model or GCM) found substantially different oxygen results from those given by the box models. The 3-D model lost only 40 •umol/kg of oxygen compared to the three-box model loss of 144 •umol/kg out of an av- erage ocean oxygen content of 168 •umol/kg [Sarrniento and Orr, 1991]. They explained that the difference resulted from a lack of resolution of the three-box model

that gave rise to the unrealistic results. This argues that the emphasis on deep ocean anoxia as a necessary accompaniment to increased export productivity has been misguided by box model results and needs to be reassessed in light of results from 3-D simulations.

Various types of (•13C data (ice core CO•, terrestrial organic matter, and A51•C measuring the planktic- benthic 51sC contrast in foraminifera) provide another constraint on the extent to which the biological pump could have been involved in the glacial-interglacial CO• change. One of the consequences of the operation of the biopump is the fractionation of the isotopes of carbon as lighter 12C is preferentially incorporated into biomass, leaving the surface water reservoir enriched in 1•C. This tends to create a surface-deep gradient in the ocean of (•13C with lighter values in the deep ocean and heav- ier values in the surface. The stronger this gradient as measured by the difference in 513C between planktic and benthic foraminifera, the stronger the biological pump must have been. Curry and Crowley [1987] concluded from A513C data that the biopump was responsible for only about one third (• 30 •umol/mol) of the glacial-interglacial CO• shift. However, more re- cently $pero et al. [1997] have presented evidence that carbonate ion concentration affects 51•C in foraminifera which "confounds the interpretation of A51sC between planktic and benthic foraminifera" and from which they concluded there was even a "stronger glacial biological pump than previously indicated" [$pero et al., 1997, p. 499].

Determination of the glacial value for atmospheric 51•C provides another way to assess the magnitude of glacial ocean productivity. However, the terrestrial organic matter 51•C data [Marino et al., 1992] and the ice core 51•C data [Leuenberger et al., 1992] are not in full agreement, but leave the biopump responsible either for a A51:•C of 0.15%0 or 0.70%0, respectively (see Broecker [1995] for discussion). Combined with model results showing that each A51•C of 0.10%0 results in an Xco• change of 10 •umol/mol [ Toggweiler and $arrniento, 1985; Wenk and $iegenthaler, 1985; Broecker and Peng, 1986], the result is that 15-70 •umol/mol (• 20-80% of the glacial-interglacial CO• change) is left to be explained by stronger glacial bioproductivities. In section 5.4 we use this range as the target in considering what combinations of regional change might have been responsible for the glacial-interglacial CO• change.

3. Model Synopsis and Nutrient Parameterizat ion

Presented in Appendix A is a detailed description of the coupled ocean-atmosphere model used in this study. Here we present our method for parameterizing bioproductivity and the carbon cycle. The model is a global coverage coupled ocean-mixed-layer-atmosphere box model which incorporates a number of feedbacks thought to be important in the climate system, for example, the temperature-albedo feedback and the CO•- temperature-water vapor-radiation feedback. Sketches of the model geometry and configuration are shown

RICH ET AL.- ROLE OF REGIONAL BIOPRODUCTIVITY IN C02 CHANGE 537

Atm 2N

Figure 3. Sketches of the model geometry and configuration. (a) Plan view of the model. The ocean box identifiers in the lower right corner of each box are for the purpose of following the model description in the appendix. (b) Schematic diagram (not to scale) showing cross section of the model's Pacific basin together with the accompanying carbon cycle fluxes. The atmosphere- mixed-layer component of the model is shown with dotted borders, while the ocean component is shown with solid borders. Each box is characterized by its mean values of temperature, salinity, total dissolved carbon C, alkalinity ,4, and nutrient concentration [PO43-]. Jc and JA are the downward fluxes of carbon and alkalinity from the biological pump as determined by the mixed layer nutrient concentration (see text); FAD are the advective-diffusive fluxes transported by the processes of circulation and ocean mixing; FB is the flux brought into the mixed layer from below via mixing processes; and Fsfc,CO2 is the flux of carbon across the air-sea interface. For clarity only the biological pump particulate fluxes are shown in the Southern Hemisphere, and only the nonparticulate fluxes are shown in the Northern Hemisphere.

in Figure 3. Box boundaries are at 45øN, the equator, and 35øS. The variables modeled are temperature, salinity, total dissolved carbon (C), alkalinity (,4), and limiting nutrient. Following the lead of earlier workers we take phosphate as the limiting nutrient [Sarmiento and Toggweiler, 1984; $iegenthaler and Wenk, 1984; Bacastow and Maier-Reimer, 1990], though we could just as easily have used nitrate. Though the atmosphere-mixed-layer component has a seasonal cycle, extrapolation of the chemical variables takes place in the ocean component, which operates on the basis of. annual mean values.

Our approach to parameterizing the biological pump is appropriately simplified for our model. We make no attempt to invoke Michaelis-Menten kinetics as this would result in unrealistically high bioproductivities since the model has no provision for zooplankton graz- ers. Rkther, since the ocean component modeling is on large temporal (i.e., annual) and spatial scales over which much smoothing has occurred, we adopt an approach used by others [Baes and Killough, 1986; Peng et al., 1987] and parallel to that seen in satellite primary productivity algorithms based on chlorophyll concentration, that there is a linear relation between productivity and nutrient concentration [Falkowski et al.,

1998]. What is new with our approach is the use of an assumed nutrient profile. Figure 4 presents data from Conkright et al. [1994] of the nutrient profiles corresponding to our boxes. On the basis of these profiles we have assumed a nutrient profile which is constant for a surface layer of thickness dsfc and then increases linearly (with slope m) to the bottom of the model's 200-m-thick mixed layer. For high latitudes we take dsfc - 0m (m = 0.0025 •umol dm -3 m -•) and for low latitudes dsfc = 50m (m = 0.0053 •umol dm -3 m -1). Knowing the nutrient profile and mean 200m mixed layer concentration, the mean nutrient concentration of the upper 100m euphotic zone can be calculated. The general relation is

_dM (dM) m(dM+de_2ds)) Ce -•ee CM -- •ee-1 (Cs+•- m ds 2 (1)

with Cs--CM--•-dM (1--•M) where c are concentrations, d are layer thicknesses, and the subscripts s, e, and M denote the surface, euphotic zone, and mixed layer, respectively. For our specific values of dM, de, and ds the relations are


PO 4 nutrient profiles High-latitude Low-latitude

50 ß "•

E '. j::::: ,

',, 150

200 0 1 2 0 1

[PO41/(•mol dm -3) o

ds

50 .....

150

200

Figure 4. Nutrient profiles of data and model. Data, shown with symbols, are from analysis of Conkright et al.'s [1994] values for annual mean phosphate concentrations. For the high latitudes (HL) circles denote the North Atlantic, triangles denote the North Pacific, and squares denote the Southern Ocean. For the low latitudes (LL) squares denote the Northern Hemisphere (NH) Indian Ocean (IO), triangles denote the Southern Hemisphere (SH) IO, stars denote the LL NH Atlantic, circles denote the LL SH Atlantic, diamonds denote the LL NH Pacific, and inverted triangles denote the LL SH Pacific. The terms high latitude (HL) and low latitude (LL) are with respect to model geometry (Figure 3). The bottom panels show the shape of the assumed model profiles; the exact positions at each time step are determined by the mean model phosphate concentration of each box. Here, ds is the thickness of the surface layer of water over which the model profile assumes a constant nutrient concentration (equation (1)).

[PO}-]eu,HL,i = [PO}-]ML,i- (50m)(mHL)

[PO}-leu,LL,i = [PO}-]ML, i -- (43.75 m)(mLL) for the high latitudes and low latitudes, respectively. The assumption of a constant-depth euphotic zone is a simplification, but one common to a number of studies [e.g. $armiento et al., 1988b; Bacastow and Maier- Reimer, 1990] and reasonable in light of the fact that

the nutrient concentration is extrapolated on the basis of annual means. To incorporate the dependence of photosynthesis on light availability, each box is as- signed a dimensionless light factor that is large near the equator and diminishes toward the poles [Bacas- tow and Maier-Reimer, 1990] (for example, 0.98 for the low-latitude Northern Hemisphere Indian Ocean, 0.64 for the Southern Ocean; see Table A1 for the full set of values).

RICH ET AL.- ROLE OF REGIONAL BIOPRODUCTIVITY IN CO2 CHANGE 539

As stated above, we adopt a linear relation between 't•e export production of organic carbon, Pexp, and the mean euphotic zone nutrient concentration, [PO•-]e u. So the relation used to predict organic carbon export production is

•Pexp,box -- /•C:P lfbox k:•exp [PO4 •-] .... box areabox 100m (2)

where /•c:P is the Redfield ratio of carbon to phos- phorus characteristic of marine organic matter, If is the dimensionless light factor, and kpexp is the proportionality factor relating nutrient concentration to bio- production and having dimension (time) -1 One can think of k•>exp as proportional to an uptake rate with (kpexp) -1 as a residence time. As is common, we assume a constant ratio of production of biogenic CaC03 to organic C and a constant Redfield ratio of C:N:P [e.g., $armiento and Toggweiler, 1984; $iegenthaler and Wenk, 1984; Walker, 1991]; see Table A1 for model values. Knowing the organic carbon export production Pexp, these allow calculation of the fluxes of alkalinity (,4) and total dissolved carbon (C) associated with the biological pump (see section A3 for details). Of these fluxes, 75% are assumed degraded and remineralized in the top 800m of the water column while the remaining 25% are remineralized and recycled in the deep ocean below 800m, in accord with sediment-trap- based scaling for particle fluxes [Martin et al., 1987]. Return fluxes are via the processes of ocean mixing and circulation. There is no provision for sediment fluxes nor for dissolved organic carbon. Imposing conservation of mass allows calculation of C and ,4 in the mixed

layer. It is these values, together with atmospheric CO2 level and mixed layer temperature (and to a lesser degree, mixed layer salinity) that determine the exchange of CO2 between ocean and atmosphere. Henry's law and Fickian diffusion are the processes by which this exchange is modeled [Broecker and Peng, 1982] (solu- bilities are from Weiss [1974], carbonic acid dissociation constants from Dickson and Millero [1987], and an exchange time constant of 8 years is used [Bacastow and Maier-Reimer, 1990]).

Modern ocean total alkalinity [Takahashi et al., 1981] and total PO4 [Conkright et al., 1994] were used together with the total carbon of the preindustrial ocean [Toggweiler and Sarmiento, 1985]. The value of k7•exp in (2) was tuned until the equilibrium CO2 value of the preindustrial atmosphere of 280/•mol/mol was achieved. For this case, global export production was 8.5 PgC/a. This is within the range of present-day estimates of 5- 10PgC/a [Eppley, 1989]. For this standard case the value of (k7•exp) -1 was 6.1 years. By comparison, Peng et al. [1987] showed that the phosphate residence time varies from 3 months in May to tens of years in the winter, so an annual mean value of 6.1 years is within this range. We note that the model's control experi-

I I

2O

low lat

E

latitudes

-10

lTemps Air

Ocean

Mixed-layer

1 '0 2'0 Observed Temperatures/øC

Figure 5. Comparison of model with data for annual mean values of temperature. Air temperatures are from Schutz and Gates [1971, 1972a, b, 1973a, b, 1974a, b]; ocean and mixed layer temperatures are from Levitus and Boyer [1994].

ment euphotic zone nutrient concentrations (Table 2) are generally about twice that of data. This could be improved by tuning the model's ocean circulation and mixing, but we feel that it should not affect the results presented here since the main effect would be a scaling of k7•exp by a factor of 2 to produce an annual mean phosphate residence time of 3 years.

4. Model-Data Comparison

A comparison of various model output with observed climate data (annual mean temperatures of the atmosphere, mixed layers, and ocean; and ocean thermohaline circulation) is used to show that the model can usefully replicate the operation and function of aspects of the climate system. Figure 5 plots model temperatures against the corresponding observed annual mean temperatures for the air, mixed layer, and ocean. The boxes with the most thermal inertia, the cold deep ocean, are quite close to observed values, clustering around 4øC. Low-latitude boxes, both ocean and mixed layer, are warmer than observed, while air temperatures and high- latitude mixed layer boxes are colder than observed but not significantly so. The important feature of the climate system, the equator to pole temperature gradient, is well represented.

With regard to ocean circulation the strength of the simulated North Atlantic Deep Water formation is 13 Sv

540 RICH ET AL- ROLE OF REGIONAL BIOPRODUCTIVITY IN CO2 CHANGE

Table 2. Regional ABioproductivity Experiments

Exp k7>exp

global N Atl N Pac S.O.

•exp AXco2 PO4 •l•exp AXco2PO4 •exp AXco2PO4 •l•exp AXco2 PO4

Low Lat

•Pexp AXco2 PO4

0x o• -8.5 61.0 1.9 -0.5 0.7 1.8 lx 6.1a 0 0 1.2 0 0 1.8 2x 3.1a 4.8 -21.5 1.0 0.5 -0.7 1.8 4x 1.6a 12.3 -44.5 0.8 1.4 -1.9 1.7 8x 0.8a 23.8 -70.2 0.7 2.9 -4.1 1.5

16x 0.4a 40.4 -98.0 0.5 5.4 -7.3 1.3

-0.3 1.2 2.0 0 0 1.9 0.3-1.1 1.9 0.8-3.1 1.8

1.8-6.2 1.6 3.2-10.4 1.3

-3.0 7.2 2.0 0 0 1.9

2.8 -6.4 1.8 7.8 -17.6 1.7

16.3 -34.9 1.6 29.0 -57.2 1.3

-4.6 50.6 1.9

0 0 0.8 1.3 -13.6 0.5 2.3 -23.7 0.3 2.9 -30.0 0.2 3.3 -33.4 0.1

Results from investigation of effects on atmospheric CO2 of regional changes in export bioproductivity, /•Pexp. •l•exp is the change in export bioproductivity for the given region in PgC/a (also the global change since < 1% of the global change took place outside the perturbed region). Ax½o2 is the global change in atmospheric CO2 concentration for each experiment in units of/zmol/mol. PO4 is the mean phosphate concentration of the region's surface 100-m euphotic zone in/zmol/dm a. The experiment (Exp) labels are the factors by which the standard value of k7>exp was scaled for the given region and experiment. North Atlantic (NAtl), North Pacific (N Pac), and Southern Ocean (S.O.) are the high-latitude oceans, that is, north of 45øN in the Northern Hemisphere and south of 35øS in the Southern Hemisphere (Figure 3). Low Lat denotes low-latitude oceans. Equilibrium results are presented in all cases. There was little change in model output after 100 years, but all experiments were run for at least 500 years.

(1 Sv = 106m 3/s). This is similar to the 12Sv of Manabe and Stouffer's [1988] ocean GCM simulation, though both are on the low side of Broecker's [1991] estimate of 15-25Sv. The simulation also has a well

developed global thermohaline circulation, with a deep flux of 15 Sv from the Atlantic to the Southern Ocean

(S.O.) and with a 15 Sv inflow to the deep Pacific Ocean.

5. Results and Discussion

5.1. Atmospheric CO2 Response to Regional Changes in Export Productivity

To explore the effect of regional changes in the strength of the biopump on the atmospheric CO2 level, we performed suites of experiments in which we changed regional values of the bioproductivity proportionality constant (kpexp) in (2). The results of our experiments are presented in Table 2 and plotted in Figure 6.

An important result to come out of our modeling ex- perirnents is that the sensitivity of atmospheric C'O2 to changes in the strength of export production is not a constant, but varies regionally. The low latitudes have the greatest sensitivity, while the high latitudes have the least. This can be seen from noting that the lines in Fig- ure 6, relating AXco2 to changes in regional bioproduc- tion, have different slopes. The slopes are the sensitivity of the atmosphere's CO2 level to changes in the strength of export production. What is striking is that the CO2 level is much more sensitive to low-latitude changes in export production than to high-latitude changes. A small change taking place in the low latitudes can have as much as 7 times the effect on atmosphere C02 as the same change taking place in the North Atlantic. For example, taking $arnthein et al.'s [1988] estimate that

glacial productivity was from 2-5 Pg C/a larger than today's, one can see that a 2 PgC/a increase in the North Atlantic would reduce Xco2 by only • 3 ttmol/mol, while the same increase in low latitudes would reduce Xco2 by • 21/umol/mol.

To more precisely examine the regional sensitivity, we quantify it by calculating the change in atmospheric CO2 scaled by the change in bioproductivity. We term this quantity pex•qxco2 and define it as

(Xco2)ne w --(Xc02)01 d •[;>expSX CO2 • (•[Pexp)new -- (•[Pexp)old

Change in

Atm CO 2 (gmol/mol)

0•

-20

-40

-60 10 20

ABioproductivity(Pg C a -•)

30

Figure 6. Atmospheric CO2 change (Axco•.) versus regional changes in export bioproductivity, Pexp-

RICH ET AL- ROLE OF REGIONAL BIOPRODUCTIVITY IN CO:• CHANGE 541

, , ! • ,,,• ......... I , , I

-1.5 N Atlantic

-2 3 Southern Ocean

-3.7 N Pacific ....... -' .,. '--.' .;..: :::'::•:--..-:::-:::.:•: :5 •:;:::.. :::.:.< .......... . ................................

Io TM latitudes -10. 3' .,::..• .:...

-':4.4 ...... global ..

" ; i ......... ," , i ' , , ' i , ......

0 -3 -6 -9

PexpSxc02 Figure ?. Sensitivity of atmospheric CO2 to regional changes in export bioproductivity, pexpSX½o2. Units are (pmol/mol)/(eg C a-l).

This quantity expresses the degree to which atmospheric CO2 is affected by changes in bioproductivity. We expect it to be negative since given our understanding of the role of the biological pump the changes to CO2 and bioproductivity should be in opposite directions: increases in bioproductivity should cause decreases in X½o•_ and vice versa. Small values indicate climates or regions in which atmospheric CO2 level is little affected by changes in ocean bioproductivity. Large values are indicative of regions where bioproductivity does have an important influence on CO2 levels.

Figure 7 shows the regional sensitivities we obtain from our model experiments. The major difference is between the high-latitude oceans, which have sensitivities between -1 and -4, and the low-latitude oceans with a sensitivity of about -10. In the most dramatic contrast the low-latitude ocean has a sensitivity as much as 7 times that of the high-latitude North Atlantic. However, we also see individual differences among the high-latitude oceans, as they have sensitivities that vary among themselves by more than a factor of 2, from -1.5 for the North Atlantic to -3.7 for the North Pacific.

5.2. Why the Regional Sensitivity Difference?

What is the explanation for these regional differences in sensitivity? The primary answer lies in regional differences in water column stratification. This manifests

itself in the model via the different values that are used

for the vertical eddy mixing coefficient, k•aL, that ap- pears in (A3) and which controls the extent of mixing across the bottom of the mixed layer. A robust and well-known physical characteristic of the ocean is the

well-developed pycnocline and stable stratification of the water column in low latitudes versus the weaker pycnocline and less stably stratified column in high-latitude oceans, a consequence of the differences in surface forcing [Peixoto and Oort, 1992]. This feature of the oceans motivated the use of two different mixing coefficients in the model, one f6r high latitudes and one for low latitudes. The low-latitude value corresponds to a vertical eddy diffusive constant of 2 x 10 -6 m2/s, while the high- latitude value corresponds to a value 20 times larger of 40 x 10 -6 m2/s [Broecker, 1966; $armiento et al., 1976; Kratz et al., 1983]. The result is that where stratification is greater, the carbon that is removed from the surface ocean by the action of the biological pump is more effectively sequestered in the deeper ocean (i.e., its residence time there lengthens).

To test this idea that sensitivity is controlled by the degree of water column stratification, we did some additional experiments in which the degree of high-latitude stratification was altered. We changed the high-latitude value of the vertical eddy mixing coefficient from 20 times the low-latitude value to only 6 times and then to only twice the low-latitude value. If our understanding is correct, we would expect the magnitude of high- latitude sensitivity to increase as a consequence of these reductions in high-latitude mixing (increases in high- latitude stratification). Indeed, model results for the Southern Ocean show an increase in sensitivity from its value of -2.3 for the control run to values of -6.0

and -12.0 for the experiments with kML,hilat reduced to 6 times and twice kML,lolat, respectively. This con- firms our idea that water column stratification plays an important role in controlling the effectiveness of the biological pump in regulating atmospheric CO2 levels.

Earlier box model experiments actually showed this same result, though they were not interpreted in this context. For example, Figure 3b of $armiento and Tog- gweiler [1984] shows that for a fixed level of bioproductivity, as the high-latitude overturning rate (their fhd) is reduced, the CO2 level drops. That is, as stratification increases, the existing biopump becomes more effective at lowering CO2 levels. In a similar experiment, Wenk and $iegenthaler [1985] using a different

1 high-latitude model showed a similar result; in their • overturning experiment, atmosphere CO2 dropped by 45pmol/mol even as low-latitude bioproductivity decreased slightly (with high-latitude bioproductivity held constant).

Observational data alsO) support the idea of the role of stratification in regulating the biopump's capability to draw down atmosphere CO2. Watson et al. [1991] presented Joint Global Ocean Flux Study (JGOFS) data from the North Atlantic on temperature, total dissolved carbon and (Xco2)aq that led them to conclude "Our ob- Servations are consistent with models in which the onset


of seasonal stratification ... is the key event leading to summer drawdown ofp½o2" [Watson et el., 1991, p. 52].

The differences in regional sensitivities among the three high-latitude oceans can be explained in terms of the differences between the regional deep ocean residence times that depend on the differences in the mag- nitudes of the thermohaline circulation fluxes. In the model the North Atlantic has a thermohaline circula-

tion of 13Sv, while the North Pacific has a circulation of only 3Sv, and the Southern Ocean has 35Sv. As the volumes are in the approximate ratio of 2:1:10, this results in deep ocean residence times with respect to the thermohaline fluxes that flush the boxes that are

1 10 We expect in the approximate ratios that longer deep ocean residence times will correspond to greater sensitivities, and this is what we find. The North Atlantic, relative to its volume, has the most vigorous thermohaline circulation and the shortest deep ocean residence time and, as a result, is least affected by changes in the strength of the biopump. In the model the Southern Ocean has a large thermohaline circulation, but relative to its volume it is less vigorous than the North Atlantic and, consequently, has a greater sensitivity. The North Pacific has the smallest ratio of thermohaline circulation to volume and exhibits the greatest sensitivity of the model's three high-latitude oceans.

15.3. Effect of Stratification on Regional Differences in Total CO2 Drawdown

Another significant result from our study is to find that different regions have different limits on the amount of atmosphere CO2 drawdown of which they are capable. Table 2 and Figure 6 show that though the low latitudes are more capable of CO2 drawdown per unit of bioproductivity, they are less capable than the South- ern Ocean at effecting a large change (> 40/•mol/mol) in atmosphere Xco2. As before, this can be explained by the regional differences in water column stratification. Because the model has a nutrient budget, the changes in bioproductivity possible are constrained by the rates at which nutrients enter the upper ocean via either the thermohaline circulation or eddy mixing. As the uptake rate is increased, these other processes be- come the rate-limiting steps in determining the amount by which export production can be enhanced. For the low latitudes with their higher degree of stratification and lower mixing rates, this nutrient limitation is reached fairly quickly. One can see in Table 2 that despite successive doublings in the uptake rate, the in- cremental increases in low-latitude export productivity began to dwindle. For the first doubling, low-latitude productivity increased by 1.3PgC/a, but for the fi- nal doubling the increase was only 0.4PgC/a. Thus though the low-latitude fourfold efficiency experiment draws down atmosphere CO2 by almost 25/•mol/mol, a

further fourfold increase in low-latitude efficiency adds only an additional 10/•mol/mol to the atmosphere CO2 drawdown. Consequently, the absolute amount of CO2 drawdown that can be brought about by greater effi- ciencies in low-latitude bioproductivity is limited. The Southern Ocean, on the other hand, with its higher mixing rates, experienced no such nutrient limitation and so always had available sufficient nutrients to increase Southern Ocean export productivity and drawdown CO2. As the uptake rate doubled, productivity generally did the same. The North Atlantic and North Pacific Oceans, because of their smaller areas, could cause only smaller changes in atmosphere CO2. For our eightfold experiment, the Southern Ocean caused CO2 to decrease by m 35/•mol/mol, while the low latitudes caused a change of 30/•mol/mol, and the North Pacific and North Atlantic caused smaller changes of 6.2 and 4.1 ttmol/mol, respectively (Table 2).

15.4. Role of Regional Bioproductivity Changes in the Glacial Atmospheric CO2 Shift

How can these model results on regional capabilities for Xco2 drawdown be applied to further our understanding of the glacial-interglacial atmospheric Xco2 shift? In section 2.3 we discussed the considerations that led to

our conclusion that we would aim at finding bioproductivity changes capable of producing a CO2 drawdown of from 15 to 70 pmol/mol. Table 2 shows that the experiments in which the nutrient uptake rate was globally doubled or quadrupled, producing bioproductivity changes of from 5 to 12 Pg C/a, resulted in decreases in atmosphere CO2 of from m 20 to 45 pmol/mol, so these would be candidate scenarios. A low-latitude uptake rate quadrupling by itself drew down CO2 by almost 25 pmol/mol, so this is another scenario that meets the criterion of 15-70 pmol/mol of drawdown. This experiment produced a bioproductivity change of 2.3 Pg C/a, which is a regional 50% increase over the control value of 4.6PgC/a. Such a 50% increase agrees well with the low-latitude Atlantic estimate of Mix [1989]. The Southern Ocean fourfold experiment drew down CO2 by 18pmol/mol, so this is another scenario that produces a drawdown of interest. Neither the North Atlantic nor

the North Pacific is individually capable of producing sufficiently large amounts of Xco2 change, but in combi- nation with other regions they would be candidates.

Shown in Figure 8 are results from experiments in which bioproductivity changes in the low latitudes were combined with changes in the Southern Ocean and North Pacific. As one example, Figure 8a shows that a low-latitude increase of 2PgC/a combined with a Southern Ocean increase of 15PgC/a together would drive atmospheric CO2 down by 50pmol/mol. As another example we see from Figure 8b that a drawdown of 36 pmol/mol can be accomplished with the combine-

RICH ET AL.: ROLE OF REGIONAL BIOPRODUCTIVITY IN CO2 CHANGE 543

o • 40

-0 1 2 3

a Low latitude/tBioproductivity (Pg C a -•)

•, 3 -7

:3 2

0

0

a3 1

0 z

-o

-o i 2 3

b Low latitude ABioproductivity (Pg C a -1)

Figure 8. Changes in model atmospheric CO2 as a function of increases in low-latitude export bioproductivity and (a) Southern Ocean (b) North Pacific export bioproductivity. The shaded regions are our target for CO2 drawdown caused by changes in ocean bioproductivity; see discussion in section 2.3.

tion of a 3PgC/a increase in the low latitudes and a 2PgC/a increase in the North Pacific. Compared to the control run, with a low-latitude bioproductivity of 4.6PgC/a, this is an increase in the low latitudes by 65%, which accords well with the estimate of Herguera [1994]. In the North Pacific the control run has a biopro-

ductivity of 0.3 Pg C/a, so the change required is much larger (• 600%) and might seem unrealistic, except that $ancetta [1992] found that glacial North Pacific biomass was as much as an order of magnitude higher than present-day. These then are possible scenarios of bioproductivity changes that can achieve the targeted (15-70 •umol/mol) drawdown in atmospheric CO2 while meeting the constraints imposed by 513C data or paleoproductivity data.

5.5. Reevaluating the Role of Low-Latitude Bioproductivity in Atmospheric CO2 Drawdown

In light of these results a reassessment of the question of the relative roles of high-latitude versus low- latitude bioproductivity changes on atmospheric CO2 levels is merited. Ever since the seminal modeling studies of $armiento and Toggweiler [1984], Knox and McE1- roy [1984], and Wenk and $iegenthaler [1985], attention, particularly of modelers, has been focused primar- ily on the role of the high-latitude biopump, while the low-latitude pump was largely ignored as inconsequen- tial. This was the result of an important assumption. The assumption was based on the observation that low- latitude surface nutrient concentrations are significantly lower than the corresponding high-latitude concentrations. Figure 4 shows that this is indeed the case with low-latitude surface phosphate concentrations of about 0.25/zmol/dm 3 and high-latitude concentrations about 4 times larger at •1/zmol/dm 3. From this observation the more debatable inference was drawn that the low-

latitude biological pump was already working at near maximum capacity. Consequently, in two of the studies [$armiento and Toggweiler, 1984; Wenk and $iegenthaler, 1985] the low-latitude surface nutrient concentration for all experiments (both standard and glacial simulation) was prescribed to be 0, and any nutrients that arrived in the box were fully consumed by the biopump. This model prescription shifted the focus to the high-latitude biopump and omitted the possibility of experimentation on the role of changes in the strength of the low-latitude pump.

Since these earlier studies, new ideas have emerged such as the iron hypothesis and the ideas of how rates of nitrogen fixation or denitrification might change in a different climate, thereby changing the nutrient supply and functioning of the low-latitude biopump [Martin, 1990; Ganeshram et al., 1995; Falkowski, 1997]. In ad- dition, a recent compilation of data [Conkright et al, 1994] shows (Figure 4) that low-latitude surface nutrient concentrations are not zero (though seasonal values are probably more critical than annual means, and such seasonal data would be useful to further study of this matter).


Further support for a reassessment of the relative importances of low- versus high-latitude bioproductivities comes from a number of sources. Beaufort et al. [1997] presented coccolithophore assemblage data of the past 910ka from the equatorial Indian Ocean which showed variations in primary productivity by a factor of 5 and which, when subjected to spectral analysis, showed control by local insolation. Their phase analysis also indicated that productivity maxima led ice volume maxima. This shows a strong connection between climate change, as reflected by ice volume, and low-latitude productivity. Westerhausen et al. [1994] showed with their reconstruction of a 330 ka equatorial record of (Xco2)aq that local Xco2 varied from 230 to 440/tmol/mot. They also showed that even at times of maximum upwelling and outgassing, biological productivity caused local (Xco2)aq values to reach their mini- mum . This, they said, suggests that "the 'biological pump' dominates over the role of physical outgassing" [Westerhausen et al., 1994, p. 379] and they concluded that "atmospheric pco2 changes resulted mainly from a global integration of the various local Pco2 variations in the low-latitude surface ocean" (p. 377).

Thus we feel that evidence is growing that a new paradigm needs to be considered in which low-latitude bioproductivity is at least equally as important as high- latitude bioproductivity at regulating atmospheric CO2 levels on glacial-interglacial timescales.

6. Summary

In this paper we have explored the hypothesis that regional changes in the strength of ocean export productivity were partly responsible for the lowered glacial atmospheric CO2 level. An important result was that the low-latitude oceans were found to have an effect

on atmospheric CO2, per unit of export bioproductivity, that is almost an order of magnitude larger than the high-latitude oceans. This difference in sensitivities was found to result from differences in water column strati-

fication. Where the water column is more stratified, the effect of bioproductivity on atmosphere CO2 is greater. However, stratification also plays a dual role by limiting the rate at which nutrients return from the deep ocean to the euphotic zone, hence settin• a limit on the absolute amount of regional bioproductivity change, and associated atmospheric CO2 drawdown, that is possible. Our results challenge the idea that high-latitude changes in the biopump hold the key to understanding the glacial-interglacial Axco2 puzzle. Instead we find that low-latitude changes potentially have an important role to play, since smaller changes in low-latitude export productivity are capable of a much larger C02 drawdown.

Although other processes were surely also important and much data remains to be sorted through and recon-

ciled, we feel that the evidence is growing that changes in export productivity, and particularly changes at low latitudes, will eventually turn out to have been one of the important processes involved in the glacial-interglacial C02 change.

Appendix A' Model Description

A1. Geometry and Overview

The model used in this study is a global box model representation of the coupled ocean-mixed-layer-atmosphere system (see Figure 3). It has two major components: the atmosphere-mixed-layer (atm-ML) component with a seasonal cycle and the ocean component which operates on an annual-mean basis. Conceptually, the atmosphere, mixed layer, and land surfaces have latitudinal boundaries at 45øN, the equator, and 35øS. This establishes four zonally averaged single-layer atmosphere boxes extending from Earth's surface to the top of the atmosphere. Underlying each atmosphere box are land and ocean (i.e., mixed layer) sectors, which may in turn have snow-covered and sea ice-covered subsectors, respectively. As with the atmosphere, land surfaces are represented by a single mean zonal value except in the high-latitude Northern Hemisphere where the North American and Eurasian land masses are separately represented.

Earth's ocean is considered to consist of three basins

(Atlantic, Indian, and Pacific) connected via the cir- cumpolar Southern Ocean with latitudinal ocean boundaries at 45øN and 35øS but not at the equator. In each of the three high-latitude oceans, a single box extends from sea level to the ocean floor. The low-latitude

oceans are represented by six boxes: upper boxes extending from the surface down through the permanent thermocline (taken to be at 800 m depth) and deep boxes from 800 m to the ocean bottom.

In the model as a whole, the mixed layer is concep- tualized in two different ways. From the perspective of the atmosphere-mixed-layer component, mixed layers are distinct from the underlying oceans. They extend from the mixed-layer-atmosphere interface down to a depth of hML. However, from the perspective of the ocean component, they have no distinct identity and are considered an integral part of those ocean boxes which adjoin the atmosphere.

Key features of the model are that the ocean circulation and atmospheric Xco• are internally determined and so available to participate in climate feedbacks [e.g., Birchfield et al., 1994]. The albedo-temperature feedback is incorporated via the provision for changes in sea ice and land snow coverage as functions of temperature. The COl.-temperature-water vapor-radiation feedback is included via the dependence of the atmospheric radiation calculations on Xco2, the temperatures, and the

RICH ET AL.- ROLE OF REGIONAL BIOPRODUCTIVITY IN CO2 CHANGE 545

Table A1. Model parameters and constants

Parameter

Surface albedos

O/O,LL

O/O,HL

C•L,LL

O/L,HL C•seaice

O•snow

Sea ice, land snow h•

(•T)ML 1 (dT)ML 4 (

Hydrological cycle e2s ,4

e5s,4

ess ,4

eSN,7

e2N,1

wvF5.s e2N,8N

El,c

Cloud properties 5LC

5HC

frLL,HC frHL,HC frLL,LC f•HL, LC ;)•o ,HC

•)(•L, HC

)•O,LL,LC •)(•O,HL,LC

)•L,LL,LC

)•L,HL,LC

Miscellaneous

h0

LW

ffSW

ErLH

CD D

•HL

Table A1. (continued) Parameter Value

Value • kML,HL 10 -7 m/s

Atmosphere kML,LL 5 X 10 -ø m/s

hML Mixed Layer

0.08

0.15

0.20

0.25

0.55

0.7O

2m

øc/'

øc/'

0.45 'c/'

o.7oc/.

0.08

0.12

0.08

0.07

0.12

0.1 Sv

0.15

1.5

13.3 Sv

1.00

0.75

0.11

0.24

0.22

0.48

5.0

6.0

6.0

12.0

7.0

13.0

0.80

1367W/m 2 0.40

0.40

0.40

0.04 PW/K 0.1PW

0.01

0.012 kgm -2 s -• 3.4 x 106 m 2/s

49.57 øC/In hPa 34.76 øC/In hPa

200 m

920 kg/ma

Ocean and Chemistry fCorg 0.25 f½inorg 0.25 JCinorg / JCorg 0.25 RC:N 6.7 Rc:p 133

/•H 104 m2/s nV 10-4m2/s A 2374 peq/kg C 2240 pmol/kg kt (8.0 a) -• atmkco2 1.78 10 TM mol C (pmol mol-X) -•

Bioproductivity

gfML1 0.47 /fML4 0.64 IfML7 0.62 /fM L•N 0.90 lfML•.s 0.94 /fMLsN 0.98 /fML•s 0.94 /fMLsN 0.93 /fMLss 0.95 roLL 0.0053 pmol dm-a/m mill 0.0025 pmol dm-a/m kpexp (6.1 a)- x

Here, • is cloud emissivity; fr is cloud fraction; X is cloud optical thickness; $½ is the solar constant; and 7 is the atmospheric lapse rate. For subscripts, O is ocean, L is land, LL is low latitude, HL is high latitude, HC is high cloud, and LC is low cloud. The parameters fCorg and fCinorg are the fractions of organic C and biogenic CaCOs that are remineralized in the deep boxes. Jc•no•g/Jco•s is the ratio of production of inorganic C (CaCOs) to organic C. RC:N and Rc:r are the Redfield ratios of C to N and C to P, respectively. A (½) is the global modern (preindustrial) ocean mean value of alkalinity (total dissolved carbon). The If are the light factors that incorporate the dependence of photosynthesis on light availability (equation (2)).

temperature-dependent atmospheric water vapor content (see Appendix B). The model also incorporates the effect of changing orbital parameters on the seasonal cycle of insolation, although only present-day orbital values were used in this study. Table A1 lists values of parameters and constants used in the model.

A2. Atmosphere-Mixed-Layer Component

A2.1. Atmosphere. The atmosphere component conserves energy and water vapor. The energy conservation equation integrated vertically, zonally, and lati-


tudinally over each atmosphere box can be written

OOk --FsH,½N,k -}- FSH,½s,k = +

0t sin (0N,k) -- sin (0S,k)

where 0• is the mean potential temperature in box k; Fsn,O is the northward meridional flux of sensible heat at latitude ½; ½•,• and ½s,• are the latitudes of the north and south boundaries of box k; Q•,j is the diabatic heating in box k, and j ranges over the contributions from short wave heating SW, long wave heating LW, latent heating LH, and sensible heat flux SH from the underlying surface; 0 and L represent contributions from the ocean and land sectors, respectively; IC and IF represent contributions from the ice-covered (snow- covered) and ice-free (snow-free) subsectors of the ocean (land) sectors; and t is time.

For each atmosphere box the diabatic heating of the volume overlying surface sector k of energy type j can be written

• Q•,• •,•= p•o I•,• with I•,•- da c0 A• Ps,•

where p00 - 1000 hPa; rr = pips, where p is the pressure and ps is the surface pressure; co = Cp/g, where Cp is the specific heat of dry air and g is the acceleration of gravity; A• is the area of surface sector k; Ps,• is the mean surface air pressure over sector k; n = Rd/Cp, where Rd is the dry gas constant. Introducing explicit expressions for the four Q•,j contributions, the Ik,j can be written

where Fsw is the short wave flux, Fnw is the long wave flux, B and T are the bottom and top of the atmosphere, respectively; Fsw and Fnw depend on Earth's orbital parameters, time of year, air and surface temperatures, Xco2, surface humidity, surface air pressure, surface albedos, lapse rate, and cloud properties and are known from best fit polynomials found from a detailed analysis based on the radiation code of an atmospheric GCM (Appendix B); P• is the precipitation over sector k; Lc is the latent heat of condensation; ATk is the temperature difference between Earth's surface and the overlying air for sector k; CD is a drag coefficient; a; is a weighted a value appropriate to each j (see Table A1 for the values of these parameters). There is no sensible

heat exchange with mixed layer subsectors covered by sea ice.

Meridional northward fluxes of sensible heat are taken

to be

cos(•r/4) FSH,45øN -- -D (01 -- 02N)

(•r/4) re 2 FSH,0øN -- --aSH (1 + 5) (Ts,2N -- Ts,2S) q- •)SH

cos(357r/180) FSH,35os -- +D (04 -- 02S)

(•r/4) re 2

where D is an eddy diffusion coefficient; re is Earth's radius; and Ts,• are mean surface air temperatures for the respective boxes. It is assumed that through the operation of the Hadley cell, cross-equatorial transports of sensible and latent heat are linked and move in opposite directions, with latent heat moving into the warmer hemisphere. So ash and/3si• are given by

• •E• •3 •E• t•SH -- •SH •

s E•,c s E•,c

where s is a scale factor equal to re2(Cp/g)Poo; y]tEt/El,½ is the total model-calculated low latitude evaporation scaled.by its present-day climatological value, El,c; and •, 3, and 5 have been determined empirically on the basis of the data of Oort [1971].

Sea ice coverage is treated in three mixed layer boxes' the North Atlantic (ML1), North Pacific (ML7), and Southern Ocean (ML4). For each box, sea ice coverage is found from a function giving sea ice extent as a function of mixed layer temperature. These functions have been determined empirically using the data of ocean area versus latitude of Levitus and Boyer [1994], assuming the mixed layer temperature under sea ice is at the freezing point of seawater Tr, and that the rate of change of mixed layer temperature equatorward of the edge of sea ice dT/dc) is constant. The latent heat exchanges with the mixed layer resulting from changes in sea ice extent are included in the mixed layer energy balance, assuming a uniform sea ice thickness, h•. Land snow coverage is treated similarly, except that the surface air temperature is used instead of the mixed layer temperature and no latent heat exchanges are calculated.

A2.2. Hydrological cycle. The model hydrological cycle includes meridional basinal and zonal interbasin transports of water vapor. The meridional basinal fluxes from low to high latitudes outside of the North Atlantic are a fixed fraction of the low-latitude evaporation and are given by

where I is the index of a low-latitude box, h is the index of an adjoining high-latitude box, and Et is the evaporation from the low-latitude box. The et,h are constants

RICH ET AL.' ROLE OF REGIONAL BIOPRODUCTIVITY IN CO2 CHANGE 547

based on the data of Baumgavtnev and Reichel [1975]. For the North Atlantic the flux is

__ wvF2N,1 ½2N,1 (oTs,2N -oTs,1 E2N

with oTs,k the mean surface air temperature over ML box k and r a constant based on the work of Stone

and Miller [1980]. The inclusion of the power law temperature gradient term is a way of incorporating more explicitly the effect of baroclinicity on transient eddy transport in the mid-latitudes. Cross-equatorial flux of water vapor is

wvF0øN -- CtLH (1 - •) (Ts,2N -- Ts,2S ) - •LH

with

• Y']IEI flLH- • Y•tEI C•LH -- L½ El,c Lc El,c The freshwater fluxes to individual low-latitude ocean

basins coming from the cross-equatorial flux are made proportional to the width of the basin at the equator. Two zonal interbasin fluxes are included. The flux from

the Indian to Pacific Oceans wvF5,8 is a constant while the Atlantic to Pacific flux is given by

lated in the ocean component (described below) as annual mean values; salinity is not separately calculated for mixed layer boxes.

Mixed lay0rs are of a constant thickness, hML. In the atm-ML component, mixed layers exchange energy only across their upper and lower boundaries; that is, there is no horizontal exchange across lateral boundaries between adjacent mixed layers. Energy exchanges between the mixed layers and underlying ocean boxes are calculated on the seasonal cycle as diffusive fluxes assuming constant underlying ocean temperatures, determined in the ocean component, and temporally constant though latitudinally dependent thermocline diffusive constants, kMn. Energy fluxes at the upper surface are known from the radiation polynomials and the bulk aerodynamic formulas for latent and sensible heats given earlier. From the sea ice parameterization the latent heat flux associated with changes in sea ice area is known.

The energy conservation equations for the mixed layers on the seasonal timescale are written

OTMLu Y•.jFTMt•,sfcu,j q- FTMt•,B,k 0t CMLu -- CI,k(TMLu)

(A2)

wvF2N,SN -- e2N,SN E2N

with e2N,SN a constant and E2N the evaporation from ML 2N (the Northern Hemisphere low-latitude Atlantic; see Figure 3a).

Evaporation over land or ocean sectors is found using the standard bulk aerodynamic method:

Ek - CD (qs(Ps,k, T.,k ) - ho qs(Ps,k, Ts,k ) )

where qs is the saturation specific humidity, a function of pressure and temperature; T.,• is the mean temperature of the surface (for land surfaces it is determined by assuming land has zero heat capacity and solving the resultant implicit equation); Ts,• is the mean surface air temperature; Ps,• is the mean surface air pressure; and h0 is the relative humidity at the top of the atmospheric boundary layer and taken to be constant. The model has no evaporation from snow- or ice-covered surfaces. Above ocean surfaces, conservation of water vapor is imposed by setting precipitation over each ocean sector to be the residual of the evaporation from that ocean sector minus the divergence of the horizontal fluxes of water vapor, ignoring the storage term. Above land surfaces, conservation is maintained by setting precipitation equal to evaporation, so PL = EL as there is no provision for runoff.

A2.3. Mixed Layer. Though atmosphere variables are extrapolated over the seasonal cycle, only the mixed layer temperatures are also seasonally extrapolated. The mixed layer chemical variables are extrapo-

where FTMt.,sfcu,j is the net flux of energy of type j into the surface of ML box k; FTM•,,B,k is the flux of energy in at the bottom of the mixed-layer; CML• is the heat capacity of ML k; and CI,k(TML• ) is the effective heat capacity associated with change of sea ice extent in box k. FTML,B,k and CI,k(TML• ) have the forms

FTM L , B , k -- CpO/90 kMLu AML• (To• - TML• ) (A3)

dAI,ML,• (A4) - Lf PI hi dTML• where %o is the heat capacity of seawater; po is the density of seawater; kmL• is the thermocline eddy diffusive constant for box k; AML• is the area of ML box k; To• is the temperature of the upper ocean underlying ML box k; TML• is the temperature of ML k; Lf is the latent heat of ice formation; pI is the density of ice; hi is the thickness of the sea ice; and Ai,ML• is the areal coverage of sea ice for box k.

A3. Ocean Component

In the low-latitude Atlantic, Indian, and Pacific O- ceans the model has an upper layer of depth 800m overlying a deep ocean box (Figure 3b). Each of these upper ocean boxes includes, as an integral part, two mixed layers, one in the Northern Hemisphere and one in the Southern Hemisphere. Each of the high-latitude oceans (the North Atlantic, the North Pacific, and the Southern Ocean) is represented by a single box extending from the surface to the bottom; each includes a surface mixed layer. No direct connection exists between

548 RICH ET AL.' ROLE OF REGIONAL BIOPRODUCTIVITY IN CO•. CHANGE

the Indian Ocean and the Pacific Ocean or the North

Pacific and the Arctic (Figure 3a). The surface fluxes of heat and fresh water from the atmosphere which force the mixed layer, averaged over the year, force the large ocean boxes.

The ocean variables, with the exception of the mixed layer temperatures, represent mean annual values; exchange of carbon between the atmosphere and ocean is calculated on the mean annual timescale, so Xco2 rep- resents an annual mean. Bioproductivity is a function of the 100-m-thick mean euphotic zone nutrient concentration which is calculated from the mixed layer concentration by assuming a specified nutrient profile, one for high latitudes and another for low latitudes as described in section 3 and shown in Figure 4. The bioproductivity removes carbon and alkalinity from the mixed layer. Prescribed fractions of these fluxes are oxidized and dis-

solved in the upper ocean boxes, while the remainder is oxidized and dissolved in the deep boxes. Ocean circulation is responsible for the return of carbon and alkalinity to the surface layer [e.g., Walker, 1991]. There is currently no provision for sediments, land biota, or dissolved organic carbon.

Conservation equations for ocean box k for energy, salt, total dissolved carbon (tdc), and alkalinity all have the form

(•9k __ F•,sfc,k q-S•,k q- ZiF•,AD,i,k (A5) __

Ot c• V• V•

where 9 takes on the values of To, the mean box temperature for the energy conservation equation; s, the mean box salinity for the salt conservation equation; C, the total dissolved carbon (= [HCO•] + [CO]-] + [C02,aq] where brackets denote chemical concentration); and A, the alkalinity (-- [HCOj] + 2[C0]-]). F•,sfc,k is the flux of 9 across the ocean-air interface; $9, k is the internal source of 9; c• is the specific weighting factor for conservation of 9; Vk is the volume of box k; F•,^D,i,• is the net advective-diffusive flux into box k from adjoining box i.

Net advective-diffusive fluxes, horizontal and vertical, are calculated using the scheme of Fiadeiro.[1975] which uses a weighted mixture of an advective "up- stream" term and a diffusive term. For example, the horizontal advective-diffusive flux from box i to box k

of 9 is

rtli,k (kHl,k,i •k q- kH2,i,k F•'AD'i'k ---- (kHl,k,i q- kH2,i,k )

2 (9 i - 9 • ) kH• ,k,i kH2,i,• kH•,k,i q- kH2,i,k

, , _ I - •/ H,i,k q- k,i kHlik • (KH,i,k mk,i q- K 2 m 2 ) -- 1 (KH,i,kq-mk, q-•/K2 2 ) kH2,k,i • i H,i,k q- mk,i

nH Ai,k with KH,i,k =

Ari,k

where nH is the horizontal eddy diffusive coefficient; Ai,k is the area of the interface between boxes i and k; and Ari,k is the distance between the centers of boxes i and k. Similar equations exist for the vertical advective- diffusive fluxes with nH replaced by nv, the vertical eddy diffusive coefficient. The vertical components of the thermohaline circulation, mi, are parameterized in terms of horizontal density gradients, and the horizontal components, rrli,k are parameterized by imposing conservation of mass [Wang and Birchfield, 1992].

The air-water interface fluxes are

where overbars denote the annual mean of the season-

ally calculated values; Q, P, and E are energy, precipitation, and evaporation terms, respectively, and are known from the atm-ML component; k• -1 is the exchange time constant for C02 between atmosphere and ocean; Air,• is the area of ice-free ocean in box k as no C02 exchange is assumed to occur through sea ice; Ao is the total ocean area; Xco2 is the molar mixing ratio of C02 in the atmosphere and Xco2,ML• is the mixing ratio of C02 that would be in equilibrium with the concentration of dissolved C02 in the mixed layer; and atmkco 2 is a constant for converting an atmospheric C02 molar mixing ratio to mol C.

Source terms for the ocean boxes are

Sro ,• -- 0

Ss,• - 0

Sc,• = - Jc,• SA,• = - J.4,•

where Jc,k is the sink of carbon in ocean box k due to the biological pump and JA,i is the corresponding sink of alkalinity A. For the three high-latitude ocean boxes which extend all the way from atmosphere interface to ocean bottom, these J are identically 0. For the three upper low-latitude boxes, we have

where mi,k is the thermohaline circulation from box i to box k; and

Zc,k (Jcorg,ML•N q- JCorg,ML•s) fCorg q- (Jcinorg ,ML•N q- JCinorg,ML•s ) fcinorg

RICH ET AL' ROLE OF REGIONAL BIOPRODUCTIVITY IN CO2 CHANGE 549

JCorg,MLi is the export production of organic carbon (termed Pexp in equation (2) in section 3). The JCinorg,MLi are obtained from JCo,.,,MLi by assuming a fixed ratio of production of biogenic carbonate carbon to organic carbon, Cinorg/Corg , and fCo• (fci,•o•,) is the fraction of bioproduced organic (inorganic) carbon which leaves the upper box unrespired (undissolved) (the remainder is assumed respired (dissolved) and re- turned to the tdc (alkalinity) pool in the upper box). For the three deep low-latitude boxes, since there are no sediment fluxes, each J is known from the constraint

0- JC,upperbox q- JC,lowerbox

The JA,k are handled similarly, except that for the three upper low-latitude boxes, the equations are

- 2 + RN:C (JCos,MLN + JCos.MLs) fCors

where RN:C is the Redfield ratio of N to C in the production of marine organic matter. This equation takes account of the changes to alkalinity that result from (1) the inorganic (carbonate)component of bioproductivity's adding 2 equivalents per mole in the form of Ca 2+ and (2) the fact that the organic component has associated with it a certain amount of N which will be-

come NO•, thereby reducing the alkalinity. The conservation equation for atmospheric C02 is

•Xco2 = - Z Fc,sfc,k (A7) Ot

k

with k ranging over all the surface ML boxes and Fc,sfc,• as given in (A6c).

Mixed layer conservation equations for the variables other than the temperature are

Ot = c. 14 t 14 (AS)

where • is C or A; the surface fluxes, F•,sfc,k, are as given in (A6c) and (A6d); F•,B.•, the fluxes into the mixed layer from the underlying ocean, are defined in a fashion that parallels (A3).

For the carbon chemistry the following equation is used to calculate ML X½o2 values given the ML C and A in box k:

Xco2 ,ML• ---- OZsol(TML•) kt(rML• 8ML•,)[HCO•] 2 ' k

where [HCO•]• and [COl-]k, the respective concentrations of HCO• and COl- in ML k, are [e.g., Walker,

[HCO•]• =

v/c 2 - Ak (2Ok - Ak)(1 - 4 k'(TML• 8ML•,)) Ck- k ,

[col-I, =

I - 4 k'(TML•, 8ML•, )

Ak -[HCO•]• 2

where k'(TML,, $ML•) is the equilibrium constant (_= k2/k•, the ratio of the second and first dissociation constants for carbonic acid), a function of temperature and salinity, which determines the concentrations of HCOj, COl-, and CO2 and is calculated based on the work of Dickson and Millero [1987] and asol(TML,) is the Henry's law solubility of CO2 in seawater and is determined on the basis of the data of Weiss [1974].

The standard simulation has an equilibrium Xco2 equal to that of the preindustrial atmosphere of 280 ymol/mol [Broecker, 1995]. This value is controlled, among other things, by the bioproductivity levels and by the total alkalinity and carbon in the system. To- tal alkalinity and total PO•- were set to that of the modern ocean [Takahashi et al., 1981; Conkright et al., 1994] and total carbon to that of the preindustrial ocean [Toggweiler and $armiento, 1985]. The nutrient uptake rate parameter, kpexp in (2), was tuned until the desired calibration of atmospheric Xco= was achieved.

A4. Time-Stepping Procedure

Extrapolations of this two-component model are made in a two-step process. Given initial conditions for the surface air, mixed layer, and ocean box temperatures and Xco=, the energy conservation equations for the four atmospheric and nine mixed layer boxes, equations (A1) and (A2), are extrapolated with a Runge-Kutta scheme over the seasonal cycle for /ktoce (10 years). Atmospheric Xco2 and the ocean temperatures underlying the mixed layers, To•, are considered constant during this extrapolation. For each of the seasonal cycles of Atoce, the mean annual mixed layer temperatures and the heat and freshwater fluxes from the atmosphere into the mixed layer are calculated. These mean annual values provide the external forcing for the ocean component.

After the atm-ML component has been extrapolated /ktoce, the ocean component is extrapolated for the same length of time using the following procedure: The conservation equations for energy, salt, carbon (½), and alkalinity (A) for the nine large ocean boxes (equation (A5)), the conservation equations for C and A for the nine mixed layer boxes (equation (AS)), and the conservation equation for the atmospheric Xco• (equation (A7)) are integrated simultaneously with a Runge- Kutta scheme for /ktoce years. In this integration the mean annual mixed layer temperatures found in the atmosphere-mixed-layer component are used to evaluate


k•(TML, $ML) and O•soI(TML) which appear in the carbon equations and also to determine the energy exchange between the mixed layers and the underlying ocean, FTML ,B .

Using the new values of X½o•. and Tok, the extrapolation cycle is repeated as the atmosphere-mixed-layer energy equations are integrated for the next Atoce years. The seasonal cycle of insolation is recalculated from the orbital parameters once every /ktorb. For the seasonal extrapolations the insolation is area weighted over the latitudinal extent of each of the underlying land snow- covered and snow-free, and ocean ice-covered and ice- free, sectors of each of the atmosphere boxes.

Appendix B' Construction of the Radiation Polynomials

The radiation polynomials were constructed based on radiation code of the Laboratoire de M6t6orologie Dy- namique (LMD) GCM model [Le Treut, 1985]. For our single-layer atmosphere boxes, four polynomials were required' one for the incoming short wave (SW) fluxes at the top of the atmosphere; one for the outgoing SW fluxes at the bottom of the atmosphere; one for the incoming long wave (LW) fluxes at the bottom of the atmosphere; and one for the outgoing LW fluxes at the top of the atmosphere. A fixed vertical relative humidity profile was used [Manabe and Wetheraid, 1967]. The SW fluxes were considered functions of surface air tem-

perature Ts, surface air pressure ps, surface albedo a, surface relative humidity h0, lapse rate 7, low-cloud fraction fr,.c, high-cloud fraction frac , low-cloud optical thickness XLc, high-cloud optical thickness Xac, and the cos of the mean daylight solar zenith angle cos (. The value of cos( is, in turn, dependent on the latitude, time of year, and the orbital parameters.

Similarly, the LW fluxes were considered dependent on surface air temperature, surface air pressure, surface relative humidity, air-surface temperature difference AT, lapse rate, low-cloud fraction, high-cloud fraction, low-cloud emissivity SLc , high-cloud emissivity Sac , low-cloud optical thickness, high-cloud optical thickness, and atmospheric Xco•.. For each dependent variable, approximately four representative values spanning a reasonable domain were selected. For each of the re-

suiting million or so possible combinations of variables or scenarios , the LMD GCM code was ilsed to calculate the required four fluxes. Then in lieu of using the "lookup table" approach, we chose to construct polynomials. Least squares fits to the tables of values were made for polynomials of various degrees. It was determined that a reasonable fit could be had using polynomials that were linear in each variable except for a cubic dependence on Ts for the LW polynomials.

Acknowledgments. We thank Zhao-Xin Li and Hervd Le Treut for their generous assistance in developing the radi-

ation polynomials, Abe Lerman and Huaxiao Wang for valu- able discussions, and two anonymous reviewers for sugges- tions and comments that improved the paper. GEB thanks the Laboratoire de M•t•orologie Dynamique of CNRS, Paris and Elf Aquitaine for financial support during a sabbati- cal leave. This work was supported by NSF grant ATM- 9311962.

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G. E. Birchfield, D. Hollander, and J. Rich, Department of Geological Sciences, Northwestern University, Evanston, IL 60208. ([email protected])

(Received February 3, 1998; revised November 16, 1998; accepted November 24, 1998.)

Role of regional bioproductivity in atmospheric CO 2 changes

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