Absorption of Radiation by Phytoplankton in the Upper Ocean

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The effect of the absorption of solar energy by phytoplankton on upper-ocean thermal properties has been the subject of research for the past 20 years. While absorption of solar energy is dominated by absorption from seawater itself in many open ocean regions, the variability in the absorption and distribution of solar energy into the upper layers of the open ocean is controlled primarily by phytoplankton pigment concentrations (Platt, 1969; Smith and Baker, 1978). Lewis et al. (1983) were the first to demonstrate that nonuniform vertical distributions of phytoplankton pigments cause variations in local heating, and, under certain vertical chlorophyll profile conditions, could support the development of a thermal instability within the water column.

Initial attempts (Paulson and Simpson, 1977) at addressing the effects of varying water quality types [as described by Jerlov (1968)] on the attenuation of irradiance in the ocean lead to a simple parameterization that characterizes absorption between the longwave and shortwave (visible) bands of solar energy using different e-folding scales, and set the e-folding scale of the shortwave band dependent on the water quality type. This parameterization has commonly been used to provide buoyancy forcing in one-dimensional, ocean surface mixed-layer models (Price et al., 1986; Schudlich and Price, 1992). More sophisticated methods to estimate the solar energy flux into the ocean resolve the depth- and wavelength-dependent spectral diffuse attenuation coefficients (Siegel and Dickey, 1987; Morel and Antoine, 1994), and Siegel and Dickey (1987) have shown that this method greatly improves the ability to compare observed irradiance fields to model estimates.

The first work to directly address the link between ocean thermodynamics and bio-optical processes (Simonot et al., 1988) coupled the bulk mixed-layer model of Gaspar (1985, 1988) to a simple, nonspectral, diffuse attenuation model for solar energy attenuation and a six-component ecosystem model (Agoumi et al., 1985). Results on simulations of the seasonal cycles at Ocean Weather Station Romeo show that the phytoplankton seasonal cycle has a significant impact on sea surface temperature evolution.

While early modeling studies all agreed that chlorophyll attenuation plays an important role in ocean physics, few direct observations had been available to confirm this. However, during the coastal transition zone field study along the California coast, Ramp et al. (1991) interpreted observations of a surface warming feature during a wind relaxation event to be caused by patchiness in the near surface chlorophyll distribution. The first notion that this biological-physical process acted on equatorial ocean regions was presented by Sathyendranath et al. (1991), who argued that chlorophyll patches were important driving mechanisms for variations in sea surface temperature. Such sea surface temperatures (SST) anomalies have been shown (Kershaw, 1985) to influence the evolution of Arabian Sea monsoons. Additionally, Kahru et al. (1993) presented evidence from AVHRR satellite analysis and in situ observation to show that cyanobacteria blooms in the Baltic Sea were responsible for elevating the SST to 1.5°C. Global analysis of the ocean color fields in the tropical Pacific Ocean (McClain et al., 2002) also verified that enhanced chlorophyll regions were linked with enhanced surface-layer heating. Further evidence of the impact of phytoplankton on the evolution of mixed layers is presented by Stramska and Dickey (1993), who used bio-optical observations from a mooring off Iceland in conjunction with a version of the Mellor-Yamada two-and-a-half layer mixed layer model (Mellor and Yamada, 1982) to show that the importance of this coupling is most significant in regions of high chlorophyll and weak vertical mixing. One such region is the equatorial region of the ocean, where high solar fluxes are collocated with low wind speed "doldrums" (Fig. 2a) and high chlorophyll equatorial upwelling regions (Fig. 2b).

In the western warm pool (WWP) region of the Pacific Ocean, Siegel et al. (1995) demonstrated that the amount of solar radiation penetrating through the bottom of the mixed layer (~23Wm-2 at 30 m) is a large fraction of the net air-sea heat flux (^40 W m~2). Following a period of sustained westerly wind burst and a corresponding near 300% increase in mixed-layer chlorophyll concentrations, the resulting biologically mediated increase in solar energy attenuation created a decrease in energy flux across the mixed layer (5.6 W m~2 at 30 m) and supported a mixed-layer heating rate of 0.13 °C per month. In the same year, Ramanathan et al. (1995) believed that a discrepancy occurred within the computed heat balance of the ocean-atmosphere energy budget in the western equatorial Pacific and that this discrepancy was due to "A Missing Physics'' which would modify the manner and importance of cloud absorption of solar energy. Arguments were presented that this Missing Physics was in fact related to the manner in which solar radiation penetrates through the bottom of the mixed layer in this clear water region (M. R. Lewis, private communication). Further evidence has largely dismissed the claims of Ramanathan et al. (1995), and the importance of properly attenuating solar energy into the water column is now widely accepted.

The importance of characterizing the penetrative fluxes of solar energy through the upper-ocean mixed layer and into the permanent pycnocline prompted Ohlmann et al. (1996) to carry out a global analysis of the magnitude of this flux. The global map of these fluxes (Fig. 3) shows high net solar fluxes (10-25 Wm-2) in the equatorial Pacific and Indian Oceans regions. The values are highest at the eastern regions of the equatorial Pacific Ocean, where heat below the pycnocline is transported west to the Pacific arm pool regions. How variations in these fluxes are linked to El Nino southern oscillations (ENSO) dynamics is still unknown, but storage of heat below the mixed layer can tie up heat energy until winter ventilation/mixing

0.01 [mg Chlorophyll a m-3] 67.00

NASA EOS-IDS Modeling project John R. Moisan NASA/GSFC NTF

Figure 2: (a) January mean of Oberhuber atlas surface wind field. Note the low wind speeds near the equatorial Pacific and Indian Ocean regions. (b) SeaWiFS annual mean 2 x 2 degree binned climatology. Note the high chlorophyll values in the eastern equatorial Pacific (For colour version, see Colour Plate Section).

NASA EOS-IDS Modeling project John R. Moisan NASA/GSFC NTF

Figure 2: (a) January mean of Oberhuber atlas surface wind field. Note the low wind speeds near the equatorial Pacific and Indian Ocean regions. (b) SeaWiFS annual mean 2 x 2 degree binned climatology. Note the high chlorophyll values in the eastern equatorial Pacific (For colour version, see Colour Plate Section).

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Figure 3: Modeled climatological values of the net solar flux at the base of the deepest monthly mixed layer (W m-1 m-1). Values correspond to solar fluxes entering the permanent pycnocline. Largest values exist where the deepest monthly mixed layer and chlorophyll concentration are low and solar flux is high. From Ohlmann et al. (1996) (For colour version, see Colour Plate Section).

processes entrain it back into the mixed layer. Further demonstrations of the importance of bio-optical forcing (Ohlmann et al., 1998), using data collected from the western Pacific warm pool during TOGA-COARE and mixed-layer model simulations, noted also that increases in the penetrative heat loss to below the mixed layer resulted in a destabilization of the thermocline and a deepening of the mixed layer - creating a feedback mechanism for ocean heat flux and mixed-layer depths (MLDs) that are modified through chlorophyll concentrations.

An additional link was made between clouds and ocean heat flux processes by Siegel et al. (1999) who show that under cloudy sky conditions the near UV to green fraction of the solar spectrum is less absorbed than the rest of the solar energy spectrum. This allows a greater fraction of the total energy to penetrate further into the water column. At 0.1 m depth, this relative increase can be as high as a factor of 2 and likely influences the diurnal heat balance by altering the upper most ocean layer daily heat balances, and could alter the local heat budgets on longer timescales when taking into account the effects on the ocean-atmosphere latent, sensible and back radiation terms. A more recent effort using observations from the hyperspectral ocean dynamics experiment (HYCODE) and a radiative transfer model shows that the rate of heating in a coastal region water column can increase by ^0.2 °C (13 h)-1 during high chlorophyll conditions (Chang and Dickey, 2004).

The majority of the research in the 1990s focused on demonstrating the importance of bio-optics in modifying the vertical flux of heat in the upper ocean. Links between bio-optical forcing of the upper-ocean thermal structure and horizontal momentum forcing only began to appear in the early part of this millennium. Edwards et al. (2001, 2004) used steady-state forms of the

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Figure 3: Modeled climatological values of the net solar flux at the base of the deepest monthly mixed layer (W m-1 m-1). Values correspond to solar fluxes entering the permanent pycnocline. Largest values exist where the deepest monthly mixed layer and chlorophyll concentration are low and solar flux is high. From Ohlmann et al. (1996) (For colour version, see Colour Plate Section).

momentum equation in conjunction with an analytical description of a high concentration band of phytoplankton biomass (chlorophyll) to ascertain the effect of chlorophyll on ocean circulation patterns. The results demonstrated that the presence of chlorophyll in the water has an impact on ocean circulation, creating both horizontal currents and bands of upwelling and down-welling in regions near the chlorophyll/biomass front. Gildor et al. (2003), in another modeling study, used a simple atmospheric model for climate coupled to a nitrogen-phytoplankton-zooplankton (NPZ) model (Edwards and Brindley, 1999) to demonstrate that intraseasonal variations in SST and precipitation could be forced by inherent oscillations of an ecosystem.

Recent results from coupled circulation/bio-optical models have demonstrated the significance of biological feedbacks with the ocean climate. Phytoplankton pigment concentrations derived from the coastal zone color scanner (CZCS) were used by Nakamoto et al. (2000) to force an isopycnal ocean circulation model coupled to a mixed-layer model to show that the higher chlorophyll concentrations in October versus May increased the amount of solar energy absorption and the rate of heating in the upper ocean. These changes lead to a decrease in MLDs, a decrease in water temperatures beneath the mixed layer, and an increase in surface mixed-layer temperature. Comparison simulations of the equatorial Pacific (Nakamoto et al., 2001) using a similar coupled isopycnal-mixed layer ocean circulation model and forced with and without chlorophyll (CZCS-derived pigments) demonstrated that the presence of the chlorophyll leads to shallower mixed layer in the equatorial Pacific, which generates anomalous westward geo-strophic currents north and south of the equator. In the western equatorial Pacific, the anomalous currents enhance the equatorial undercurrent (EUC). The biologically enhanced EUC leads to anomalous upwelling in the eastern equatorial Pacific, while the spatially averaged SST over the Pacific increases due to heat trapped by phytoplankton in the upper ocean. Using sea-viewing wide field-of-view sensor (SeaWiFS)-derived chlorophyll pigment data in the MIT global ocean model, Ueyoshi et al. (2003) confirmed the process described by Nakamoto et al. (2001) whereby chlorophyll modulates oceanic heat uptake by radiation and subsequently generates biologically induced currents in the equatorial Pacific.

Shell et al. (2003) forced an atmospheric general circulation models (GCM) with the SST pattern that arises from this phytoplankton effect and showed that the amplitude of the global surface-layer atmospheric temperature seasonal cycle increases by roughly 0.5°C. Frouin and Iacobellis (2002) estimated that phytoplankton serves to warm the global atmosphere by up to 0.25 °C, supporting the idea that phytoplankton exerts a significant influence on large-scale climate variability. Oschlies (2004) showed that surface heat fluxes act as a negative feedback to reduce the absorptive warming effects of phytoplankton in the upper ocean of a fully interactive physical-biological model. Manizza et al. (2005) used a fully coupled physical-biological ocean model to show that phytoplankton biomass amplifies the seasonal cycles of SST, MLDs, and ice cover by roughly 10%.

Attenuation of solar energy into the ocean using diffuse attenuation coefficients has been used for a variety of ocean modeling studies. Rochford et al. (2001) developed a global field for the diffuse attenuation kPARof pho-tosynthetically available radiation (PAR) - the visible portion of the solar energy spectrum that is not absorbed in the first several centimeter of water column - using data from the SeaWiFS. The diffuse attenuation field was used in the finite depth version of the NLOM global ocean circulation model with an embedded mixed layer to determine the sensitivity of the model solutions to the diffuse attenuation fields. The results demonstrated that using the derived SST prediction improved in the low latitude regions but the MLD predictions showed no significant improvement. In addition, using a constant clear ocean kPARvalue of 0.06 m_1 produces reasonable results for much of the global ocean regions.

In a similar study using a primitive equation, global ocean circulation/ mixed layer model forced with spatially varying radiation attenuation coefficients derived from CZCS data, Murtugudde et al. (2003) show that the results from such coupled models can be counterintuitive. For instance, in the eastern equatorial Pacific, where the presence of high chlorophyll leads to strong attenuation of solar energy, realistic solar energy attenuation leads to increased subsurface loss of solar energy, increased SST, deeper mixed layers, reduced stratification, and horizontal divergence (upwelling/ downwelling). Timmermann and Jin (2002), using a dynamic ENSO model, point out that eastern equatorial Pacific ocean chlorophyll blooms during La Nina periods create a temperature regulating negative feedback that redistributes heat into the surface layer and the associated results from the air-sea coupling dampens the La Nina conditions. This mechanism is thought to counter the positive Bjerknes atmosphere-ocean feedback that links La Nina events with stronger trade winds that force stronger upwell-ing leading to the intensification of La Nina conditions.

More sophisticated attempts to link the role of ocean biological feedback mechanisms are just beginning to emerge and support the notion that biological effects enhance ENSO variability. Marzeion et al. (2005) used a primitive equation ocean model with a dynamic ocean mixed layer and a nine-component ecosystem model coupled to an atmospheric mixed-layer model and a statistical atmospheric model to investigate the feedback between chlorophyll concentrations and the ocean heat budget in the tropical Pacific. The results from this study supported the earlier conclusions by Timmermann and Jin (2002) of a bioclimate feedback mechanism and earlier results describing the possible effects on the surface ocean currents (Murtugudde et al., 2003). The results present a scenario where subsurface

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Figure 4: The 2 x 2 degree annual mean SeaWiFS-derived diffuse attenuation coefficient [m-1] field for PAR (For colour version, see Colour Plate Section).

chlorophyll concentrations force changes in subsurface heating rates and leading to changes in subsurface heating, mixed-layer deepening, alterations in surface ocean currents, and ultimately supporting an eastern Pacific surface warming.

The most recent version of the community climate system model under development at NCAR and the NASA MOM4 model is making use of the observed spatially varying diffuse attenuation obtained from ocean color estimates (Fig. 4). Future global climate simulations will be taking this physical-biological feedback mechanism into account (Ohlmann, 2003).

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