Potential Feedbacks Between Pacific Ocean Ecosystems and Interdecadal Climate Variations
Miller, Alexander, Boer, Chai, Erickson, Gabric, Laws, Nakamoto,
Neilson, Norris, Perry, Schneider, Shell, and other future contributors
To be submitted to Bulletin of the American Meteorological Society
Rough Draft, June 26, 2001
Abstract: [NOT WRITTEN YET]
1. Introduction
The mechanisms responsible for interdecadal 1 climate variations of the Pacific Ocean are unclear. Many types of feedbacks loops in the physical ocean-atmosphere system have been proposed to explain some aspects of these climate variations(e.g.,
Latif, 1998; Miller and Schneider, 2000). However, stochastic theories can reproduce the dominant characteristics of interdecadal climate variations in the Pacific. There is still no convincing evidence that Pacific Ocean to atmosphere feedbacks can close a feedback loop on interdecadal timescales in observations or in full-physics coupled models.
Oceanic ecosystems clearly respond to changing climate conditions, although the specific ways that biology responds to physical forcings are obscure. Oceanic ecosystems, in turn, have been suggested to exert an influence on the physical climate system. This influence is thought to be either immediate(e.g., through radiative effects on the upper ocean)or very gradual
(e.g., through changes in CO
2
in the global atmosphere)and so its possible role in controlling interdecadal climate variations has not been thoroughly studied.
Here we investigate whether the oceanic biological response to Pacific interdecadal climate forcing can conceivably organize a significant feedback process to the physical climate system. The ocean biology may either sensitize or de-sensitize a proposed physical climate loop, or it may close an interdecadal feedback loop that could not exist without the biology. We therefore address the following questions:
1 We use the term `interdecadal' to loosely refer to timescales that are longer than interannual (ENSO)and shorter than centennial (greenhouse gas forcing)

 What mechanisms might allow Pacific Ocean biological response to influence variations of the physical climate system on interdecadal timescales?
 Do these oceanic biological feedback mechanisms increase or decrease the Pacific Ocean/atmosphere sensitivity in physical climate loops?
 What are the key regions in the Pacific Ocean where these biological influences might be active in interdecadal climate variations?
 How can these possible biological feedbacks be tested with models and observations?
The overarching goal of this paper is to suggest a coordinated modeling and observational effort to elucidate mechanisms of interdecadal climate variability in the truly coupled physicalbiological Pacific climate system.
2. Background a. Physical modes of interdecadal climate variability
There are many ideas about how feedback loops may control
Pacific interdecadal climate variations (Miller and Schneider,
2000,summarize the bulk of these ideas). Many of these mechanisms rely on positing simplified processes that establish delayed timescales and organize structures to explain key aspects of the observations. Since the mechanisms cannot be tested directly with the limited observations, investigators have used full-physics coupled models to determine if the simple theories are in fact active.
Two of the most celebrated theories that are promising ideas to explain Pacific interdecadal climate variations are the midlatitude atmosphere ocean gyre mode hypothesis (Latif and
Barnett)and the teleconnected atmosphere oceanic subduction mode hypothesis(Gu and Philander, 1996).
[Niklas, Axel, Liu: Paragraph explaining LB mode]
[Liu, Niklas, Axel: Paragraph explaining GP mode]
The full-physics coupled models that have been analyzed to date, however, fail to generate either of these two broad categories of feedback loops (Schneider et al., 2001; Pierce et al., 2001; + Others). In a nutshell, stochastic forcing of the ocean by the atmosphere explains the bulk of interdecadal Pacific

Ocean climate variability found in full-physics models. The hints of ocean to atmosphere feedbacks that have been detected do not seem to be consistent with the two leading hypotheses (at least for the Pacific Ocean).
[Liu, Niklas, Axel, Joel, Mike, George: AM I WRONG HERE? IF SO,
CALL ME OUT!]
Other processes may also contribute to enhanced variance at low frequencies in the Pacific. For example, the seasonal cycle of
MLD has the potential to influence upper ocean temperatures from one winter to the next. Temperature anomalies of the deep winter mixed layer remain beneath the mixed layer when it shoals in spring. The thermal anomalies are then incorporated into the stable summer seasonal thermocline (30-100 m) and thereby insulated from surface fluxes that generally act to damp the original SST anomalies. When the mixed layer deepens again in the following fall, the anomalies are re-entrained into the surface layer and influence SST. This "reemergence mechanism" has been documented to occur over much of the North Pacific and Atlantic
(Namias and Born 1974; Alexander and Deser 1995; Alexander et al.
1999) and can influence SSTs for several additional years where the wintertime mixed layers are very deep (Alexander et al. 2000;
Watanabe and Kimoto 2000). and Caribbean Sea. Model results also indicate that a significant fraction of the dominant pattern of low frequency (> 10 years) SST variability in the North Pacific is associated with tropical forcing by the atmospheric bridge
(Alexander et al. 2001).
Observations suggest that there is enhanced physical climate variability on with periods of 10 years, 20 years and 50-
80 years in the Pacific Ocean (lots of paper can be cited here).
Those peaks, however, are generally only marginally significant.
Longer records would be needed to clarify if there are truly preferred timescales in the Pacific. b. Interdecadal ecosystem changes
[Ian, Ken, Fei: PLEASE ADD LITTLE MORE RELEVANT DETAIL]
Biological systems in the Pacific Ocean also change on interdecadal timescales. In some case, like the anchovy and sardine regimes that last 10 to 50 years (e.g., Schwartzlose et al.), most of the variance is concentrated in the interdecadal band. In other cases, like zooplankton concentration along the
North American westcoast (e.g., Bograd et al), interannual variance dominates the signal.

The ecosystem variations may be explained by theories that involve intrinsic biological interactions in the presence of a steady physical environment. On the other hand, many biological populations have been linked to specific physical environmental changes. Often, the environmental change involves sea-surface temperature since that is the most heavily sampled physical variable in the ocean. But since SST is often correlated with other environmental changes (such as upwelling, mixing, and horizontal currents),the specific mechanisms linking physics to ocean ecosystem changes have remained difficult to isolate.
Variations of oceanic physical variables have a series of implications on marine ecosystems (Mantua et al., 1997; Sugimoto and Tadoroko, 1997; McGowan et al., 1998). Widespread ecological changes associated with the 1976-77 climatic shift were observed throughout the North Pacific Ocean, ranging from plankton to the higher trophic levels(Venrick et al., 1987; Polovina et al., 1994;
Francis and Hare, 1994). In the northwestern subtropical gyre region, chlorophyll a in spring showed a steady increase from the mid 1970s to the mid 1980s (Limsakul et al., 2001). Temporal increases of phosphate and apparent oxygen utilization (AOU) in the western subarctic Pacific from 1968 to 1998 have been reported by Ono et al. (2001). Emerson et al. (2001) have reported similar increases in the northeast subtropical Pacific between 1980 and
1997.
Chai et al. (2001) modeled Spring-time (March-April) phytoplankton biomass in the Northwest Pacific (35 o N-45 o N, 160 o E-
160 o W)before and after the 1976-77 climate shift. The change in the modeled nitrate has impact on the modeled phytoplankton biomass. Figure Chai1 shows the spring-time (March-April)vertical integrated (0-60m) total phytoplankton biomass for the North West
Pacific (35N-45N, 160E-160W). The modeled total phytoplankton biomass includes both diatoms and small phytoplankton, the diatoms are dominated in the total biomass. In general, the phytoplankton blooms spring blooms are much stronger after 1975/76 compared to the period before, which is consistent with the nitrate conditions in the upper 100m. There is some interannual variability, and it seems the spring blooms are intensified in the northwest Pacific during El Nino years, for example, 1972/73, 1976/76, 1982/83,
1986/87, and 1991/92. The most dominant trend is between the period 1962-75 and 1976-1992, when the springtime integrated phytoplankton biomasses are 27.8 vs. 36 (mmol N m -2 ).
Many researchers have documented the changes of phytoplankton biomass in the Northwest Pacific. For example, a two-fold increase in the integrated chlorophyll a was observed during summer(Venrick et al., 1987). In the northwestern subtropical gyre region, chlorophyll a in spring showed a steady increase from

the mid 1970s to the mid 1980s (Limsakul et al., 2001). In the subarctic North Pacific, rapid changes in zooplankton abundance
(Brodeur & Ware, 1992) and a striking increase in combined salmon catches (Beamish & Bouillon, 1993) were reported. Overall, the modeled physical-biological results seem to support some limited observations in the North Pacific. c. Potential interdecadal ocean ecosystem feedback mechanisms
The oceans are active in modifying, and in some instances controlling, the energy and chemical budget of the atmosphere.
Air-sea chemical fluxes are a result of many biological and physical forcing functions. Many of the chemicals that cycle between the air and sea interact with the radiative balance of the atmosphere and ocean, thus altering the thermodynamic properties that control generalized Earth climate.
Ocean ecosystems may be able to fundamentally affect physical interdecadal feedback loops of the climate system in at least two ways. The first is through the effect of phytoplankton on upperocean absorption of solar radiation. The second is through the flux of dimethylsulfide (DMS) to the atmosphere, which affects cloud formation. We now discuss the specific ways these two mechanisms may alter the physical climate system. i. Phytoplankton effects on upper-ocean absorption of radiation
[Ragu, Carter, Sho, Marlon, Karen, et al.: PLEASE REVISE]
Solar radiation that penetrates downward into clear water is converted to internal energy that directly heats the ocean. The presence of phytoplankton in the water column changes the way that solar radiation is absorbed as a function of depth. The amount of visible energy that is absorbed by phytoplankton is termed
PUR(Photosynthetically Usable Radiation). PUR is related to
PAR (Photosynthetically Available Radiation) by PUR = PAR x (mean absorption coefficient per unit chlorophyll pigment)as described by Morel (1978) and Kishino et al. (1986). Morel (1988) used observations to show that the amount of solar energy converted into and stored as chemical energy in the form of organic matter
[coined photosynthetically stored radiation (PSR)] hardly exceeds more than 2% of the visible incident energy (PAR). This means that most of PUR is radiated as heat into the surrounding water.
Art – I think this conclusion does not follow. The 2% figure could reflect the fact that phytoplankton never absorb more than
2% of the PAR, i.e., most of the PAR is absorbed by the water itself or by substances other than phytoplankton. In other words, the efficiency of converting PUR into PSR could be virtually 100%,

but if phytoplankton never absorb more than 2% of the PAR, Morel’s observation would still follow.
It is true that from an energetic standpoint not all of the energy absorbed by the phytoplankton is converted into chemical energy. In the most productive microalgal mass culture systems, about 5% of PAR is stored as chemical energy in organic matter.
In those systems, virtually all incoming light is absorbed by phytoplankton. The inefficiency of use of absorbed energy is a result of the inherent inefficiencies of thermodynamic processes, the fact that some light is absorbed by photoprotective pigments that do not route the energy to the photosynthetic reaction centers, respiration (there are energetic costs associated with assembling proteins, carbohydrates, and lipids) and the fact that the photosynthetic process runs off photons, not energy. Hence a photon of red light is just as useful as a photon of violet light, although the latter is associated with substantially more energy.
Thus, the heat budget of the upper-ocean must include the effects of living phytoplankton on solar radiation.
Art – I think you need to go at this in two steps. First, it is true that most energy absorbed by phytoplankton does not wind up being stored chemically in organic matter. The efficiency of converting PUR into organic matter is less than 5%. How significant this is to the ocean’s heat budget depends on how much of the PAR phytoplankton actually absorb. So the second step is to argue that phytoplankton absorb and subsequently radiate as heat enough of the PAR to make a difference.
Recent in situ observations in the north Pacific demonstrate that living phytoplankton absorb three times more energy than the dead phytoplankton (Sasaki et al., 2001).
Morel and Antoine (1994) proposed a simple parameterization
(hereafter denoted as MA94) to account for this biological heating. It allows the vertical profile of heating rate to be prescribed from the phytoplankton pigment concentration, which can be remotely detected from space by using ocean color sensors.
Their parameterization, MA94, is developed for oceanic case I water, yielding the vertical profile of the solar radiation absorption and heating rate.
The strength of this effect has been assessed in some ocean model runs that include the tropical Pacific and the entire
Pacific Ocean. Murtugudde et al. (XX) show that including the effects of phytoplankton absorption in the tropical Pacific cause the cold tongue to warm by up to 2 o C. This may explain why physical ocean models without biology usually have a cold tongue

that is too cold compared to observations. [BRIEFLY ELABORATE]
Nakamoto et al. (2001) conducted numerical experiments to examine the effect of the MA94 parameterization in a three-dimensional ocean general circulation model. They found that plankton induced additional cooling of the cold tongue due to a feedback between the shallowing mixed layer(explicitly modeled as a bulk parameterization) and more energetic surface and subsurface currents. [BRIEFLY ELABORATE] The major effect of including phytoplankton in the ocean model is the amplification of the seasonal cycle. The seasonally-varying phytoplankton concentration increases the amplitude of the annual cycle of SST by about 0.3 K in both the Northern Hemisphere and Southern
Hemisphere, as shown in Figure Shell1. On the yearly average, the warming in the summer dominates the cooling in the winter; thus, the net effect of the phytoplankton is to warm the annually averaged SST by about 0.04 degrees C compared with the control run.
Shell et al. (2001) used the phytoplankton-driven SST anomal patterns obtained by Nakamoto et al. (2001) to drive an atmospheric GCM. The primary effect of incorporating the phytoplankton SST is an amplification of the seasonal cycle in the lowest layer temperature (Figure Shell2), similar to the amplification found in the SST. The seasonal cycle is amplified by about 0.3 K, the same as the SST seasonal cycle amplification. In addition, the phytoplankton run is slightly warmer overall than the control run, by about 0.05 K. While the air temperature anomalies over the ocean closely follow the SST anomalies, we also obtain significant temperature changes over land. In response to the amplification of the seasonal cycle in the phytoplankton run, the ITCZ moves closer to the summer hemisphere. ii. Phytoplankton control of DMS fluxes and effects on clouds
[Al, David, et al.: PLEASE REVISE, AND SHORTEN?]
Dimethylsulfide is the most abundant form of volatile sulfur
(S) in the ocean and is the main source of biogenic reduced S to the global atmosphere (Andreae and Crutzen, 1997). The sea-to-air flux of S due to DMS is currently estimated to be in the range of
15-33 Tg S/yr, which constitutes about 40% of the total atmospheric sulfate burden (Chin and Jacob, 1996). Once ventilated to the atmosphere, DMS is rapidly oxidized to form non-sea-salt sulfate (nss-SO42- ) and methanesulfonate (MSA) aerosols.
Various species of phytoplankton produce differing amounts of dimethylsulfoniopropionate (DMSP), the precursor to DMS. In general, coccolithophorids and small flagellates have higher intracellular concentrations of DMSP, which is thought to act as an osmolyte in the algal cell. Shaw (1983) and then Charlson et

al. (1987) postulated links between DMS, atmospheric sulfate aerosols and global climate. It was hypothesized that an increase in biogenicly produced sulfate aerosols would lead to formation of more cloud condensation nuclei (CCN), and brighter clouds. This change in cloud microphysics could cool the earth's surface and thus stabilize climate against perturbations due to greenhouse warming. While phytoplankton are protagonists in this feed-back loop, recent advances in understanding suggest that it is the entire food web that determines net DMS production and not just algal taxonomy (Simo, 2001).
The proposed DMS-climate link, later called the CLAW hypothesis after the authors of the Charlson et al. (1987) paper, stimulated a flurry of research in the 1990's and several hundred scientific publications, but is still to be verified. Attempts to assess the direction and magnitude of the DMS-climate feedback
(Foley et al., 1991; Lawrence, 1993; Gabric et al., 1998) in the context of global warming due to increased greenhouse gasses suggest the likelihood of a small, negative feedback
(stabilizing), with magnitude of order 10%, and considerable uncertainty. These studies have all concluded that a feedback would occur over multi-decadal time-scales. But they did not try to link the spatial structures of specific interdecadal climate loops to regional alterations of DMS production by the ecosystem.
Unfortunately, seawater DMS time series long enough to enable an evaluation of the CLAW hypothesis on interdecadal time scales are non-existent. Typically, oceanic data are collected over a short term (weeks) while the ship is under way. Blooms of marine phytoplankton are relatively short lived, so assessing seasonality brings problems with respect to spatial coverage (vertical and horizontal) and frequency of sampling.
Bates and Quinn (1997) collated data from 11 cruises in the
Equatorial Pacific undertaken from 1982 to 1996. They reported that mean DMS levels during El Nino periods were not significantly different from those in normal years. It should be noted that the cruise data were all short-term (< a month), so that a proper interannual comparison was not possible. Despite the major physical changes that occurred during the well-documented 1992 El
Nino, the chemical and biological variability was small (Murray et al. 1994). Even though primary production decreased during the
ENSO event, this appeared to be due to a reduction in the numbers of larger diatoms, which are not major DMS producers.
In contrast to the Bates and Quinn (1997) study, Legrand and
Feniet-Saigne (1991) found a good correlation betweenn El Nino events and high MSA concentrations in south polar snow layers deposited over the 1922-1984 time period presumably due to enhanced DMS concentrations at high southern latitudes during El
Nino years. Legrand and Feniet-Saigne (1991) suggest this could

have been due to higher sea surface wind speed (implying increased sea-to-air exchange), or variations in sea-ice cover, which can affect ocean salinity and hence the osmotic balance in the algal cell for which DMSP is thought to have a regulating role.
Analysis of an 8-year time series of atmospheric measurements at Cape Grim, Tasmania (40 41 S, 144 41 E), illustrates the strong seasonality in DMS, and has confirmed the connection between atmospheric DMS and aerosol sulfur species in this region (Ayers et al., 1991; Boers et al., 1994). A multi-decadal times series of
MSA observations at Cape Grim is shown in Figure Gabric1. Although there is considerable interannual variability in the magnitude of the MSA peak, the strong seasonality and early January timing of the MSA maximum is remarkably consistent.
In the absence of long-term oceanic time series, modeling can provide some insights into the potential for an interdecadal feedback. Gabric et al (in press) forced a regional DMS production model in the Subantarctic Southern Ocean with data on temperature, cloud, wind speed and mixed layer depth under enhanced greenhouse conditions derived from a coupled general circulation model. The
GCM and DMS models were run in transient mode over the time period
1961-2080. Interestingly, the results showed considerable interdecadal variability in the annual integrated DMS flux, suggesting the potential for a significant DMS response to changes in the physical forcings. iii. Additional considerations
[Shaoping, Fei, Cathy, Ken, Ed, David, et al.: PLEASE REVISE]
Nutrient cycling changes may modulate the above two mechanisms. Limiting nutrients include nitrate, phosphate, silicate and iron. Ocean physics can control the flux of these nutrients into the regions where radiation effects and DMS fluxes influence climate variability and must therefore be accounted for on interdecadal timescales. For example, the frequency and intensity of Asian dust storms (sources of iron-rich aerosols) may have some decadal signals and could potentially alter the ocean productivity on an interdecadal scale.
Other, more subtle, effects may also come into play. The transfer velocity (k w
) of gases between the ocean and atmosphere is a function of sea surface turbulence, so there are feedbacks with climate. [David, PLEASE EXPLAIN THAT] Ecosystems change the surfactants on the sea surface and hence modulate the wind stress magnitude. These effects, though, are probably much smaller than the ones already discussed. It is unlikely that changes in
CO
2
in the atmosphere(and its consequent effect on radiation) due to changing oceanic ecosystems is important on interdecadal timescales. The reservoir of CO
2
in the atmosphere is far too

large to be impacted by oceanic ecosystem CO
2
flux or sequestration. [Ken, Fei: IS THIS THE RIGHT WAY TO SAY THIS?].
The possible drawdown of macronutrients (N and P) in highnutrient, low-chlorophyll (HNLC) regions, particularly in the
Southern Ocean, and changes in the production of calcium carbonate are potentially major negative feedbacks on the accumulation of atmospheric CO
2
. Stratification of the Southern Ocean, whether due to temperature or salinity effects (e.g., partial melting of
Antarctic ice cap), would reduce the input of macronutrients to surface waters and give Aeolian iron inputs a chance to catch up with the input of N and P from upwelling. A complete drawdown via photosynthesis of excess N and P in the ocean could reduce atmospheric CO
2
concentrations to 100-140 ppm (U.S. Global Change
Research Program, 1999). Since atmospheric CO
2
concentrations are currently increasing at a rate of  3.3 ppm y -1 , such a drawdown of excess N and P would offset the current rate of accumulation of CO
2 in the atmosphere for 70-80 years.
Studies with both coral reef communities and coccolithophorids have shown that a reduction in the saturation state of calcium carbonate due to the accumulation of CO
2
in the surface water of the ocean will very likely decrease the rate of calcium carbonate production (Gattuso et al. 1998; Kleypas et al.
1999; Leclercq et al. 2000). Riebesell et al. (2000), for example, have shown that increasing the atmospheric CO
2 concentration from 280 to 750 ppm reduces the calcification rates of the coccolithophorids Emiliania huxleyi and Gephyrocapsa oceanica by 16-83%. However, the increase in seawater CO
2 concentrations will also reduce the buffer capacity of seawater, causing more CO
2
to be released per CaCO
3
precipitated.
Furthermore, any reduction in pelagic CaCO
3
production will likely lead to less efficient ballasting and ultimately burial of exported organic carbon. While a reduction in CaCO
3
production will exert a negative feedback on the accumulation of CO
2
in the atmosphere, the concomitant reduction in buffer capacity of surface seawater and ballasting of exported organics will reduce the impact of this feedback to an extent that is unclear at this time.
A major uncertainty in quantifying the feedback between biological systems and their environment is the resiliency and adaptability of biological communities due to their genetic diversity, phenotypic plasticity and evolutionary potential.
Efforts to develop a theoretical understanding of the manner in which ecosystems adapt to environmental change date from the work of Lotka (1922), who postulated that natural selection tends to maximize the energy flux through a system, at least within the constraints to which the system is subject. Odum (1983) expanded

on Lotka's theory. He argued that natural systems tend to maximize power, and that theories and corollaries derived from the maximum power principle could explain much about the structure and processes of these systems. In rationalizing the application of the maximum power principle to ecosystems, Odum drew analogies between ecosystem behavior and the laws of thermodynamics. A number of authors have explored the application of analogues of thermodynamic principles to the behavior of natural systems
(Jorgensen, 2000; Jorgensen and Straskraba, 2000), and in many cases these thermodynamic approaches have met with considerable success in estimating parameters to describe real ecosystems.
In a recent paper Cropp and Gabric (2001) employed a genetic algorithm to simulate the evolutionary response of the biota of a model ecosystem. The model ecosystem consisted of a simple autotroph-herbivore-nutrient oceanic mixed-layer. One of the interesting results of the simulations was that the optimum parameter values proved to be very insensitive to the choice of selection pressure. In particular, the simulations suggested the hypothesis that within the constraints of the external environment and the genetic potential of their constituent biota, ecosystems evolve to the state most resilient to perturbation.
Laws et al. (2000) have applied the hypothesis of maximum resilience to a more complex food web model of an open-ocean pelagic ecosystem, similar to that of Cropp and Grabric (2001).
Most of the parameter values were chosen from information in the literature or were otherwise constrained in a deterministic manner. Two parameters, however, were allowed to adapt so as to maximize the resiliency of the steady state solution. These two adaptive parameters were the relative growth rate(sensu Goldman,
1980) of the large phytoplankton and the biomass of the filter feeders. In this case, it was possible to compare the predictions of the model with results of field studies carried out as a part of the Joint Global Ocean Flux Study and related work.
Some results are shown in Figure GabricLaws1. Because the system was assumed to be in steady state, the export ratio equals the f ratio (Eppley and Peterson, 1979) and was designated the ef ratio. The predicted ef ratios based on the principle of maximum resiliency are in remarkable agreement with observed ef ratios, and there is likewise remarkable agreement between predicted and observed heterotrophic bacterial biomass. These comparisons clearly support the assumption that pelagic marine ecosystems tend to evolve toward a condition of maximum resiliency, as predicted by the results of the Cropp and Gabric (2001) simulations.
Relevant to climate feedback is the fact that the model ef ratio is negatively correlated with temperature. Hence an increase in the temperature of the surface waters of the ocean due to global warming would lead to less efficient export of organic matter to

the interior of the ocean. More of the organic matter that was exported would probably take the form of dissolved organic carbon, and the efficiency of the biological pump would be reduced. Thus the response of the ef ratio to global warming would amount to a positive feedback on the climate system.
3. Coupled effects of Pacific interdecadal climate and ocean ecosystems a. Stochastic excitation
[Niklas, Liu, Joel, Axel: FLESH THIS OUT]
Even in the context of stochastic excitation of oceanic decadal variability, biological feedbacks may be important. For example, in the simplest framework of thermal forcing of SST anomalies (e.g., Hasselmann, 1976), the strength of SST response may be modulated by the ecosystem if it exerts a positive or negative feedback on the SST (even without considering SST influencing the atmosphere). b. The subduction/teleconnection mode
[Art, Doug: NEED A SKETCH OF THE GU-PHILANDER MODE INCLUDING
BIOLOGY IN IT]
The subduction/teleconnections mode could be profoundly influenced by biological processes in two ways. First, the effect of upwelling on tropical Pacific SST should include a response to the changing ecosystem. Hence, the amplitude of tropical SST anomalies would be changed in the presense of biology.
Consequently, the strength of the atmospheric teleconnections to the midlatitude would altered. Likewise, the midlatitude SST anomalies would be altered by biological feedbacks including radiation effects and cloud formation. Second, if water masses that are subducted from the midlatitudes or subtropics into the tropical upwelling zones contain nutrients that limit the growth of tropical ecosystems, then the strength of tropical biological response can be modulated as well. c. The midlatitude gyre circulation mode
[Art, Doug: NEED A SKETCH OF THE LATIF-BARNETT
MODE INCLUDING BIOLOGY IN IT]
The midlatitude gyre mode may be profoundly influenced by biological processes as well. The Kuroshio-Oyashio Extension region is where the atmosphere is most sensitive to SST anomalies in uncoupled atmospheric models (Peng et al.) and in the full-

physics coupled model (Schneider et al., 2001). Since ocean dynamics control the SST in the KOE on interdecadal timescales in the coupled model,changes in the ecosystem could influence the coupled feedback loop in two ways. First, the amplitude of the
SST anomaly may be altered by the radiation effect. This may be a negative feedback if upwelling (which drives the cold phase of
SST) increases phytoplankton absorption effects. Second, the flux of DMS into the atmosphere may change the stability properties of the storm track and alter the large-scale structure of the atmospheric response. Without such a feedback, the coupled mode does not exhibit an atmospheric response to KOE SST that reverses the phase of the gyre mode. The effects of including such a feedback, including its seasonality, are unknown and need to be explored in this context. d. The re-emergence mechanism
[Mike, et al.: SHOULD THIS BE A SEPARATE SECTION OR INCLUDED
ABOVE?]
There are several ways in which biological processes could interact with the reemergence mechanism and the atmospheric bridge. Both physical processes influence SST and MLD, which affect primary productivity and thus the amount of light absorbed in the water column. The latter, can in turn, feed back upon the temperature profile of the upper ocean. In addition to its influence on temperature, the reemergence process may alter the seasonal evolution of other quantities such as nutrients or plankton. For example, nutrient rich (or poor) water sequestered below the mixed layer at the end of one winter may return in the subsequent fall/winter. Biological processes could also affect the atmospheric bridge by changing the amplitude and/or frequency of SST anomalies in the equatorial Pacific. While it is likely that feedbacks between marine biology and the reemergence and bridge processes influence climate variability, it is unlikely that the result would lead to oscillations with a preferred decadal period. e. Synopsis
Oceanic ecosystems may profoundly influence previously hypothesized interdecadal climate mode loops in the Pacific. The biology does not appear capable of setting a timescale in the modes. Instead, it is likely to sensitize or de-sensitize the strength of physical ocean-atmosphere interaction. Determining whether positive or negative feedbacks occur requires further study.

The equatorial region is a key area requiring particular attention due to the strong atmospheric response to SST anomalies there(and its role in the subduction/teleconnection mode theory).
The Kuroshio-Oyashio Extension region (the heart of the midlatitude gyre mode theory) is another hot spot where midlatitude ocean to atmosphere feedback is most likely. In both regions, the effects of phytoplankton on upper-ocean absorption and the changes in the atmospheric response due to DMS effects on cloudiness require study.
[George, Chick: HOW DO WEAVE THIS THIS IN? AND SHORTEN?]
Our current understanding of the climate system is based on a somewhat artificial separation into an external component, which is assumed to be known or given, and an internal component, the statistics of which are determined by the external component and the physics of the system. This separation is clear in the case of modern climate models in which the external system is comprised of the shape and size of the earth, its rotation rate, the location and topography of the oceans and continents, the composition of the atmosphere and so on; those aspects of the climate system that are specified for the model. Given this external information, the model climate is simulated by integrating the governing equations. The internal component of the climate is embodied in the prognostic variables in the model, such as temperatures, winds, precipitation rates and so forth, namely those that are mutually determined and internally computed as the model is integrated in time.
For the collection of global coupled climate models used to study climate and climate change (e.g. Table 9.1, Chapter 9,
IPCC2001) biological processes are not part of the internal system and so do not interact with other climate variables to determine the variability of climate nor its change under external forcing changes. In fact, biological processes are notably lacking as part of the external system as well except perhaps as specified features of the land surface (e.g. such as the roughness and albedo associated with differing vegetation cover). For the oceans, current CGCMs typically do not include biological processes as part of either the internal or external system.
A perturbation to the climate system may be generated through some externally imposed forcing (i.e. a change in some aspect of the external system such as a change in solar constant or GHG concentration of the atmosphere) or it may be internally generated through the natural working of the complex climate system.

Biological 'feedback' processes may act to enhance, transform, and/or suppress externally forced changes or they may act to engender, enhance, transform, and/or suppress internally generated variations. The energy density of the physical climate system and the flow of energy through it is large. Biology can affect climate mainly by modifying or redirecting this flow of energy. The most direct way this may be accomplished, and the basis for most climate forcing mechanisms, is by altering the flow of radiation from the sun or back to space. Modification of the solar radiative input to the climate system may be accomplished by changing the absorptivity or reflectivity of the system via aerosols and cloud effects in the atmosphere, by modification of the surface albedo, and/or the absorptivity, and hence the storage of energy, in the ocean. Modification to the long wave output may be accomplished via the greenhouse gas concentration of the atmosphere and/or cloud processes in the infrared.
Considering the nature of the response of the climate system to an arbitrary perturbation may focus the search for important biological climate processes. Because the climate system rapidly distributes and dilutes energy, even a locally strong biologically mediated perturbation may have a weak local and unimportant global effect. This is because in general, it is the overall magnitude of the forcing change which is important to the perturbed system not the details of its distribution. To first order, the physical feedbacks in the system determine the magnitude and distribution of the response to change, biological or otherwise. The implication is that biological mechanisms will be effective if they provide a 'strong' forcing perturbation or if they operate directly in conjunction with a powerful local feedback mechanism.
The latter consideration suggests that 'weak' biological effects may nevertheless be important provided they occur in preferred regions.
A variety of climate model experiments illustrate this feature of the climate system response to forcing changes which are spatially distributed or localized. Examples of distributed forcing changes are changes in atmospheric composition, solar constant or orbital parameters (i.e. as for paleoclimate) or volcanic aerosols which are rapidly distributed by the winds. More local forcing is seen in conjunction with land use changes and anthropogenic aerosols which have relatively concentrated source and removal processes which limit their distribution.
Figure Boer1 illustrates how the first order response of the climate system depends on the magnitude rather than the geographical distribution of a forcing change. The result is for an idealized 2xCO
2
climate change simulation with and without the effect of sulfate aerosols (Reader and Boer, 19xx). The aerosols are comparatively localized, in this simulation, over southern

Asia (Figure Boer1a). They reflect sunlight and so act to cool the system but the resulting cooling pattern (1b) does not resemble the forcing pattern (1a) but is widely distributed and attains some of its largest values at high latitudes and over North
America. The aerosol cooling pattern looks more like the CO2 warming pattern (1c) which is the response to a distributed forcing change.
This illustrates how, under relatively general circumstances and to first order, the response to both distributed and localized forcing is the same 'generic' response pattern with the amplitude of the response determined by the magnitude, rather than the pattern, of the forcing. If aerosols, biology, or another process disturbs the flow of energy, the climate system distributes and dilutes this change over the globe. The feedback processes operating in the system rather than the forcing pattern itself localize the system response. In Figure Boer1, for instance, strong high latitude feedback processes involving the snow/ice albedo and the stability of the atmospheric column localize the cooling effect of the low latitude aerosols. Analogous results may be seen in other model simulations (e.g. Mitchell et al, 1995,
Roeckner et al., 1995, Boer et al, 2000 for sulfate aerosols) and for other local and distributed forcing (e.g. Tett et al., 1999,
Chapter 12, IPCC2001).
The implications are that if bioclimatic processes result in large perturbations to the energy flow then the climate response will be important globally and will be largely manifest in terms of a 'generic' response. However, if perturbations are modest then they will be expected to be unimportant both locally and globally.
The exception is a bioclimatic process that is modest in amplitude but which occurs in a region characterized by strong feedback processes so that modest perturbations are magnified. In broad terms we might expect biological perturbations of large magnitude to be possible where energy flows are largest, namely in the tropics where solar input is strong. Otherwise we might search for important climate effects due modest biological perturbations to the energy flow if they occur in conjunction with strong local feedback mechanisms such as the ice/snow albedo feedback at high latitudes and/or the modification of the stability of the air or water column.
5. Research Challenges
[EVERYONE'S HELP NEEDED HERE....]
Given the complexity of the physical-biological feedback problem, we now discuss what lines of research are best followed to better understand these effects in interdecadal Pacific climate variations.

a. Modeling strategies
One of the most direct ways to address the issue is to fully couple an ocean ecosystem model in a full-physics coupled oceanatmosphere model and execute long runs to determine if feedbacks occur on interdecadal timescales. The coupling should at least include a proper treatment of solar absorption in the upper ocean.
Analogous runs without oceanic biology must assess the signal-tonoise ratio of the bio-ocean-atmosphere effects. Follow-up runs should include successively more sophisticated biology to determine the importance of refinements and the conditions for importance. An important goal of the coupled biological/physical models will be to assess the potential of HNLC regions to draw down excess N and P in response to changes in physical circulation and to quantify the impact on the biological and solubility pumps of the combined effect of reduced calcification, reduced buffer capacity, and reduced ballasting of exported organics caused by increases in upper ocean CO
2
concentrations. Biological models targeting changes that may occur on an interdecadal time scale must allow for the evolutionary potential of biological communities, and particularly of organisms such as plankton with short generation times. As noted by Hutchinson (1967, p. 376),
“The annual cycle [of a plankton community] is thus, in terms of generation time, the equivalent of perhaps up to ten thousand years in the successional history of . . . some forest trees.”
Resilience of biological communities appears to be a common consequence of evolutionary pressures.
[Fei, Chick: PLEASE REVISE]
One could also include or exclude greenhouse gas forcing to identify what part of the biological signal is natural interdecadal variability versus anthropogenic. In order to estimate the anthropogenic CO
2
invasion into the ocean, one has to consider variation of the total CO
2
concentration caused by the natural variability. In the North Pacific, the TCO
2
can vary on the order of 50 umol/kg on interdecadal time scale, and there is a gradually anthropogenic CO
2
invasion in the region. Separating these two processes may not be an easy task, and need more attention and research.
Determining the sensitivity of the atmosphere to changes in the DMS fluxes due to ocean biology is much more subtle. The first experiment that needs to be done is to make an estimate of the observed DMS flux by ocean biology. Then it should be used as a specified forcing of an uncoupled atmospheric model. The results should be compared against a model without the forcing.

Oceanic biological models now range from simple single currency nitrate-phytoplankton-zooplankton model to multiple currency models that have scores of degrees of freedom. It is difficult to validate the more complicated models due to a lack of observations. But is would be ideal to intercompare these models in uncoupled mode to assess the usefulness of including large numbers of dependent variables insofar that they affect the structure of radiative and DMS response.
A more sophisticated comparison of ecosystem model would involve running them in hindcast mode (using observed atmospheric forcing over decadal timescales) fully coupled to an ocean model.
The models should then be qualitatively and quantitatively verified against available observations in the key regions of interdecadal interaction (the tropical Pacific and the KOE region).
Assessing sensitivity of the atmosphere to radiation and DMS flux can be handled with a cloud-ML-biology model.
[Joel, others: PLEASE EXPLAIN BETTER] b. Observational strategies
Two key hot spots of biological response and physical sensitivity have been identified. These are the tropical Pacific and the KOE region. Long-term measurements of biology [ WHAT
VARIABLES?
] need to be measured there. New satellite strategies need to be devised [TO MEASURE WHAT?] Commitments to measure these variables for long periods of time are needed by the international community. For example, CalCOFI has been measuring biology and physics for 50 years, but it turns out not be be a key region of ocean-atmosphere interaction. Our knowledge now is advance to the point that we can make a good guess where the hot spots are.
6. Discussions and Conclusion
For the collection of global coupled climate models used to study climate and climate change (e.g. Table 9.1, Chapter 9,
IPCC2001) biological processes are not part of the internal climate system and so do not interact with other climate variables to determine the variability of climate nor its change under specified forcing changes. For the oceans, current CGCMs typically do not include biological processes as part of either the internal or external system. We here have begun to assess the potential role of ecosystems in interdecadal climate variations.
In particular, we have asked if biological processes are important forinfluencing climate variability (how biological processes

engender, enhance, and/or suppress internally generated climate variability or externally forced change). We concentrated our attention here on the Pacific Ocean ecosystem and on interdecadal climate variation and change.
In summary, Figure Norris1 displays the key processes that need to be understood....[Joel: Can you give a nice simple description of your figure?]
Acknowledgments
This synopsis was conceived during the Surfside Climate Workshop on ``Climate Forcing of Oceanic Ecosystems: Are Significant
Biological Feedbacks Possible on Interdecadal Timescales?" held
April 18-20, 2001, in La Jolla, California. We are grateful to the
Director of the Scripps Institution of Oceanography for generous funding, as well as to NSF (OCE00-82543) and NASA (NAG5-9788) for additional funding.
References
Gattuso, J-P., M. Frankignoulle, I. Bourge, S. Romaine, and R. W.
Buddemeier. 1998. Effect of calcium carbonate saturation of seawater on coral calcification. Global and Planetary Change
18 : 37-46.
Hutchinson, G. E. 1967. A Treatise on Limnology. Vol II. Wiley,
New York. 1115 pages.
Kleypas, J. A., R. W. Buddemeier, D. Archer, J-P. Gattuso, C.
Langdon, and B. N. Opdyke. 1999. Geochemical consequences of increased atmospheric carbon dioxide on coral reefs. Science
284 : 118-120.
Leclercq, N., J. Gattuso, and J. Jaubert. 2000. CO
2
partial pressure controls the calcification rate of a coral community. Global Change Biology 6 : 329-334.
Riebesell, U., I. Zondervan, B. Rost, P. D. Tortell, R. E. Zeebe, and F. M. M. Morel. 2000. Reduced calcification of marine plankton in response to increased atmospheric CO
2
. Nature 407 :
364-367.
U.S. Global Change Research Program. 1999. A U.S. Carbon Cycle
Science Plan. A Report of the Carbon and Climate Working
Group, J. L. Sarmiento and S. C. Wofsy (co-chairs).
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Legends
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