Temporal and spatial variability of noble gas tracers in the North Pacific

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116,, doi: /2010jc006828, 2011 Temporal and spatial variability of noble gas tracers in the North Pacific Taka Ito, 1,2 Roberta C. Hamme, 3 and Steve Emerson 4 Received 19 November 2010; revised 17 June 2011; accepted 6 July 2011; published 30 August [1] We develop a numerical model of dissolved argon and neon in the global ocean as a tool to investigate the physical processes controlling their saturation states in the upper ocean of the North Pacific. The distribution of argon and neon is simulated using the time varying, three dimensional circulation fields determined by the Estimating the Circulation and Climate of the Oceans (ECCO) project from 1992 to The model is in overall agreement with limited observational data from the subpolar and subtropical North Pacific using a relatively low vertical diffusivity. Temporal variability in argon saturation is enhanced in the surface ocean, dominated by diffusive gas exchange coupled to air sea heat fluxes. This variability in surface argon saturation is significantly correlated to the Southern Oscillation Index (El Niño) in the tropics and to the North Pacific Index in midlatitudes. Using sensitivity experiments, we find that the mean state of argon saturation in the ventilated thermocline is characterized by a mutual compensation between mixing induced supersaturation and sea level pressure and heat flux induced undersaturation. Neon distributions exhibit a stronger influence from bubble mediated gas fluxes that is partially compensated by the effect of sea level pressure variation. Our result demonstrates the important role of air sea interaction and ocean mixing in controlling the mean state of the dissolved noble gases and highlights the importance of diffusive gas exchange coupled to air sea heat fluxes in controlling temporal variability, with implications for using noble gas measurements to derive estimates of diapycnal diffusivity in the subtropical thermocline. Citation: Ito, T., R. C. Hamme, and S. Emerson (2011), Temporal and spatial variability of noble gas tracers in the North Pacific, J. Geophys. Res., 116,, doi: /2010jc Introduction [2] The distribution of dissolved noble gases in the ocean can provide useful information about physical oceanic processes that play important roles in the climate system. Unlike gases such as carbon dioxide or oxygen, inert gases are not affected by biology and have proven useful tracers of physical ocean processes alone [Stanley et al., 2006; Hamme and Severinghaus, 2007; Stanley et al., 2009]. Perhaps the simplest inert gas tracers are the noble gases, which have no internal oceanic sources or sinks and a uniform atmospheric mixing ratio, yielding steady state oceanic distributions driven solely by air sea gas exchange and ocean transport. 1 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA. 2 Now at School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA. 3 School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada. 4 School of Oceanography, University of Washington, Seattle, Washington, USA. Copyright 2011 by the American Geophysical Union /11/2010JC Noble gases have been widely used as tracers of local air sea gas exchange processes [Emerson et al., 1995; Battle et al., 2003]. [3] More recently, modeling studies have shown that Ar distributions in the ocean interior are sensitive to diapycnal mixing [Henning et al., 2006], and can be used to infer the rate of diapycnal tracer mixing in the thermocline [Ito et al., 2007], with implications for our understanding of regional biogeochemical processes. Vertical exchanges of nutrients and carbon between the abyss and sunlit euphotic layer driven by three dimensional ocean currents and mixing are the limiting factors for biological carbon uptake and export from the surface ocean to the abyss, a primary control on oceanic uptake of atmospheric carbon dioxide [Siegenthaler and Sarmiento, 1993]. Because diapycnal mixing in the interior ocean thermocline is much slower than the rate of nutrient uptake in the upper ocean, the biological carbon uptake and export may critically depend on the rate at which the nutrients are replenished through diapycnal mixing in the thermocline. Detailed mechanisms and pathways by which nutrients are supplied to the surface ocean in oligotrophic subtropical gyres are still unresolved [Hayward, 1987; Glessmer et al., 2008]. Improved estimates of diapycnal 1of16

2 paper, the saturation states of noble gases are expressed in terms of normalized values. c Dc ¼ c sat ð; SÞ ð% Þ Figure 1. Schematic diagram of the processes controlling the air sea fluxes and the distribution of noble gases: diffusive gas exchange, air sea heat exchange, bubble mediated gas exchange, and lateral and vertical transport and mixing. mixing based on noble gases could constrain the role of diapycnal mixing in sustaining low latitude productivity. [4] Recent analytical advances allow several noble gases to be more widely and accurately sampled [Emerson et al.,1999; Hamme and Emerson, 2004a; Hamme and Severinghaus, 2007], and precise measurements of noble gas tracers are beginning to emerge from time series stations and a few ocean transects [Hamme and Emerson, 2002; Stanley et al., 2009]. Both air sea gas transfer and ocean mixing processes are likely to play important roles in noble gas distributions, but the relative importance of these processes has not yet been quantified. With increasing constraints from in situ observational data, it has become feasible to develop and validate a numerical model of noble gas tracers that includes key physical processes in both air sea gas transfer and ocean transport. In this paper, we present a numerical simulation of noble gas tracers in the North Pacific, where simulated fields are tested against observational data from the region. Then the model fields are used to diagnose the processes controlling the distribution of noble gases in the region with the following questions in mind. [5] 1. How do air sea gas exchange, ocean circulation and mixing combine to determine the distribution of the saturation state of noble gases? [6] 2. Can we explain the observed distribution of noble gases in the North Pacific? [7] 3. What is the expected scale of temporal variability of noble gas tracers and what controls it? [8] The solubility of noble gases in seawater is a decreasing function of both temperature and salinity, so that the saturated concentration at equilibrium, c sat, is greater in cold/fresh waters than in warm/salty waters. Temperature is the most important controlling variable; saturated argon concentrations vary by 50% over the full range of oceanic temperatures but only by 2 3% over the range of oceanic salinity [Hamme and Emerson, 2004a]. Throughout this where and S are potential temperature and salinity, and c sat is calculated relative to standard atmospheric pressure. Supersaturations (undersaturations) are represented by positive (negative) values of Dc. Observed noble gas concentrations are within a few percent of their saturated values [Hamme and Emerson, 2002] indicating that air sea gas transfer is generally efficient at keeping surface concentrations close to their thermodynamic equilibrium. Processes that decouple the gas concentration from equilibrium are, however, of great interest. Detailed descriptions of individual processes are provided elsewhere [Hamme and Severinghaus, 2007; Ito and Deutsch, 2006], so we provide only a brief overview here. [9] In the surface ocean mixed layer, where both heat and gas tracers can be changed through interaction with the atmosphere, several processes are able to produce air sea disequilibria (see Figure 1). First, because the solubility of noble gases is a decreasing function of temperature, heating (or cooling) of surface waters alters the solubility. Heating decreases c sat, driving the surface water toward supersaturation (c sat < c), while cooling tends to produce undersaturation (c sat > c). The magnitude of the resulting disequilibrium depends on the degree to which the gases can be exchanged across the air sea interface in response to that disequilibrium. Second, breaking waves push bubbles of air beneath the surface to a higher pressure. This drives gases out of the bubble and into the water, tending to supersaturate the gases. Third, gas saturation is usually calculated relative to saturated concentrations at standard atmospheric pressure, so variations in atmospheric pressure can create apparent supersaturation or undersaturation. [10] Finally, because the solubility of noble gases is a nonlinear function of temperature, ocean mixing processes can raise the saturation state of these gases. Bieri et al. [1966] were among the first to suggest that the saturation state of argon might reflect diapycnal mixing in the water column, and this mechanism has been demonstrated in numerical simulations [Henning et al., 2006] and in field measurements [Gehrie et al., 2006]. Ito and Deutsch [2006] derived a mathematical relationship between the saturation state of argon and diapycnal diffusivity, predicting that the magnitude of argon saturation is sensitive to the rate of diapycnal mixing in the subtropical thermocline. Ito et al. [2007] applied the theory to limited argon observations in the subtropical North Pacific and inferred a regional rate of diapycnal mixing, but with important uncertainties from potential spatial and temporal variability as well as sparse sampling. Therefore, to use noble gases to determine diapycnal mixing requires us to determine the spatial temporal variability of the saturation state of noble gases and its driving mechanisms. [11] The distribution of argon saturation results from the imprint of air sea gas exchange and interior ocean mixing as well as temporal variability in circulation. A main objective of this paper is to quantitatively separate these effects through the systematic application of sensitivity experi- 2of16

3 ments. Several experiments are performed to isolate and quantify the effect of individual processes, and the results are used to interpret the existing observational data as well as the simulated pattern of gas saturation in the thermocline. [12] The structure of the paper is as follows. In section 2, we describe the formulation of the model and the parameterization of air sea gas exchange processes. The design of the model experiments is discussed in section 3 including the initialization and sensitivity runs. The model fields from the control simulation are compared to in situ observations from the North Pacific in section 4. The mechanisms driving the distribution of noble gas saturation are evaluated, including the relative importance of air sea gas exchange and ocean mixing, in section 5. Section 6 illustrates the relationships between climate variability and noble gases, and then section 7 describes along isopycnal distributions of noble gases in the thermocline. We discuss the implication of our results to the estimation of diapycnal diffusivity in the thermocline in section Model Formulation [13] We developed a numerical model of dissolved noble gas tracers in the global oceans using a previously developed ocean circulation model with a lateral resolution of a degree in latitude and longitude and with 23 vertical layers in the z coordinate. We employed the circulation field from the Estimating the Climate and Circulation of the Ocean (ECCO) project (version 3, iteration 73, provided by P. Heimbach of MIT) where the Massachusetts Institute of Technology ocean general circulation model (MITgcm) [Marshall et al., 1997b, 1997a] has been optimized to physical observations in a weighted least squares sense [Wunsch and Heimbach, 2007]. Briefly, this model obeys the Navier Stokes equations and conserves mass, heat and salt. Since the model does not explicitly resolve small scale motions such as mesoscale eddies and convection, these subgrid scale processes are parameterized using an isopycnal thickness diffusion scheme [Gent and McWilliams, 1990] and the nonlocal K profile parameterization mixed layer scheme [Large et al., 1994]. During original model development, a cost function was used to compare the model to in situ observations (ARGO, CTD, and XBTs), altimetric observations (e.g., TOPEX, Jason), and other data sets (e.g., sea surface temperature). Reduction of the modelobservation misfit was then achieved by systematically adjusting the control variables (prescribed atmospheric state and initial conditions) using the adjoint method. Costs associated with control variable perturbations ensure a physically realistic solution [Wunsch and Heimbach, 2007]. [14] We simulated dissolved noble gases in the offline mode using the defined ECCO circulation fields. The model transports two noble gas tracers, neon and argon. Passive tracers that mimic temperature and salinity are also included where the surface distributions are restored to the ECCO fields at every time step, and are used to determine the saturation state of the gases. This process means that an exactly identical circulation field transports temperature, salinity, and noble gases. Comparison of passive temperature and salinity to the corresponding ECCO fields showed that these tracers did not significantly deviate from the ECCO fields. [15] Air sea exchange of noble gases was parameterized as two distinct physical processes; bubble injection and diffusive gas exchange [Hamme and Emerson, 2002] (see schematic diagram in Figure 1). Air bubbles formed by surface breaking waves inject air into the surface waters. For bubbles that completely dissolve, the gas flux across the air sea interface (F inj ) is proportional to the atmospheric mixing ratio of the specific gas and the total volume of air bubbles collapsing per unit time, and is known as bubble injection. Bubble injection increases the saturation of low solubility gases most, favoring Ne over Ar in this model. For simplicity, we did not explicitly parameterize larger bubbles that only partially dissolve since there are not yet enough measurements to constrain two different bubble fluxes separately. [16] Molecular diffusion at the air water interface and subsequent turbulent mixing transfer gases between the ocean and atmosphere. This diffusive gas exchange is downgradient, and the diffusive gas flux (F dif ) across the air sea interface depends on the concentration gradient, molecular diffusivity of the gas, and the turbulence in the surface mixed layer. [17] The full governing equation for a noble gas tracer in this þ u rc F inj þ F dif where c is the local concentration of the gas tracer in units of [mol m 3 ]. The large scale circulation field (u) and the subgrid scale diffusion tensor (K) were prescribed from the ECCO product. A third order upwind advection scheme is used throughout this study. The value of K varies spatially, and is determined by the subgrid scale eddy parameterization and the surface mixed layer scheme. In the interior ocean below the surface mixed layer, along isopycnal eddy diffusivity is set to m 2 s 1 throughout this study. The last term on the left hand side represents the diapycnal turbulent diffusion in the interior ocean, with the diapycnal diffusivity of set to m 2 s 1 throughout this study. [18] The gas fluxes, F inj and F dif, in units of mol m 2 s 1 were defined downward positive. Bubble injection flux was parameterized following Hamme and Severinghaus [2007] with the bubble flux magnitude proportional to the cubic power of surface wind speed [Asher and Wanninkhof, 1998]. 8 F inj ¼ V injð1 f ice Þ P atm c u 10 2:27 3 >< u 10 2:27 RT U 0 2:27 ð2þ >: 0 u 10 < 2:27 where V inj is a closure coefficient ms 1, f ice is the fractional ice cover averaged over the grid box, p atm is the atmospheric pressure in units of Nm 2, c c is the atmospheric mixing ratio of a specific gas, R is the universal gas constant, 8.31 Jmol 1 K 1, T is the local sea surface temperature [K], u 10 is the wind speed at 10 m above the surface in units of ms 1, and U 0, is a reference wind speed set to 10.0 ms 1. Bubble flux is assumed to be zero when the wind speed is below the empirically determined threshold of 2.27 ms 1 for the formation of white caps [Monahan and Torgersen, 1991]. The closure coefficient, V inj, is directly proportional to the volume of air bubbles injected per unit area at ð1þ 3of16

4 the reference wind speed. The value of V inj is set to ms 1, empirically determined using a quasi steady state model (Appendix A). [19] Diffusive gas exchange was parameterized as proportional to a gas exchange coefficient and the air sea concentration gradient, F dif ¼ G p atm p 0 c sat c ¼ ð1 f ice Þ u 10 U 0 2 Sc 0:5 p atm 660 p 0 c sat c where G, the gas transfer velocity, is further parameterized as proportional to the square of the 10 m wind speed and a function of sea ice cover and the Schmidt number of the gas [Wanninkhof, 1992; Nightingale et al., 2000; Ho et al., 2006]. Here p 0 is standard atmospheric pressure in units of Nm 2 ; c sat is the saturated concentration of the specific gas at the standard atmospheric pressure, and local temperature and salinity [Hamme and Emerson, 2004a]; a is a closure coefficient that can be interpreted as the normalized gas transfer velocity at 10 m reference wind speed and a Schmidt number of 660; Sc is the Schmidt number for the specific gas at the local temperature and salinity. The closure coefficient, a, is set to ms 1, as determined using the quasi steady state model (Appendix A). This specific value is 10 20% greater than previous estimates by Wanninkhof [1992] and Ho et al. [2006]. [20] The model must be integrated over many centuries to spin up the noble gas tracers in the deep ocean due to the slow ventilation of abyssal waters, however the ECCO circulation field is calculated for the time period of 1992 through 2008 only. Thus, we developed a monthly climatology of the ECCO circulation field. First, the noble gas tracer fields are spun up using this climatological circulation field including mean seasonal cycles. Then, starting from the equilibrium state, a 17 year simulation is performed including the interannual variability using a time step of an hour. At each time step, the circulation, sea ice and atmospheric forcing fields are interpolated from monthly mean fields. This allows us to evaluate the temporal variability of noble gas tracers, and to compare the model fields against concurrent observational data. 3. Experimental Design [21] Considering the form of equation (1), several processes can modify the distribution of noble gas tracers in this model: physical transport, air sea heat fluxes, sea level pressure variation, bubble injection, and diffusive gas exchange. The left hand side of (1) represents the physical transport by ocean currents and turbulent mixing. The righthand side of (1) represents the sources and sinks of noble gases due to air sea gas transfer processes. Atmospheric pressure variation affects diffusive and, to a lesser extent, bubble injection fluxes. Air sea heat fluxes coupled to the diffusive gas exchange can generate surface disequilibrium. In order to evaluate each process, we present results from four simulations: a control run that includes all terms and three sensitivity experiments in which the air sea gas exchange are modified. The three sensitivity runs are the uniform sea level pressure run, the fast gas exchange run, and the no bubble injection run. The uniform sea level pressure run prescribes the sea level pressure to be equal to the standard atmospheric pressure at all times (p atm = p 0 ). The fast gas exchange run ð3þ prescribes the surface distribution of noble gases to be exactly at saturation with local temperature and salinity and with standard atmospheric pressure. Therefore, this run effectively removes air sea disequilibrium from any source, including surface heat fluxes, such that the resulting noble gas tracer distributions only reflect the effect of ocean transport and turbulent mixing. In the no bubble injection run, we suppress the effect of bubble injection by setting F inj =0andalsosetting the sea level pressure to the standard atmospheric pressure. The result from the no bubble injection run reflects the effects of ocean mixing and diffusive gas exchange driven by the air sea heat fluxes. We can separately determine each component of the gas saturation from the four experiments. The mixing component is determined by the fast gas exchange run (Dc mix = Dc 2 ). The difference between the fast gas exchange and no bubble injection runs isolates the effect of diffusive gas exchange driven by air sea heat fluxes (Dc hf = Dc 3 Dc 2 ). The difference between the uniform sea level pressure and no bubble injection runs isolates the effect of bubble injection (Dc bub = Dc 1 Dc 3 ). Finally, the effect of sea level pressure can be determined by taking the difference between the control and the uniform sea level pressure runs (Dc slp = Dc Dc 1 ). The following list summarizes the components of noble gas saturation that are determined from each simulation. Control run Dc ¼ Dc mix þ Dc slp þ Dc hf þ Dc bub Uniform sea level pressure run Dc 1 ¼ Dc mix þ Dc hf þ Dc bub Fast gas exchange run Dc 2 ¼ Dc mix No bubble injection run Dc 3 ¼ Dc mix þ Dc hf [22] The sum of all four components exactly recovers the control simulation. Given that neon and argon saturations are always within a few percent of equilibrium, the assumption that each process affects the total gas saturation in a linear manner is a reasonable one. We performed additional simulations to test the linearity assumption for the different gas saturation components, in particular, separating the mixing and heat flux component. To achieve this, Dc hf must be calculated independently, not as a residual between Dc 3 and Dc 2. In this additional simulation, we use the distribution of surface disequilibrium as a boundary condition of passive tracers, which are then transported and mixed into the interior ocean. The tracer carries only the information from surface disequilibrium, providing an independent test for the linearity assumption between heat flux and mixing components. Comparing DAr hf and (DAr 3 DAr 2 ), the RMS error is % over the dynamic range of ±2%. Nonlinear coupling of other components is likely less significant as their dynamic range is smaller. Thus the errors associated with the linearization are about two orders of magnitude smaller than each individual component. We next present an extensive modeldata comparison, because this is the first time that noble gas tracers have been simulated using data constrained physical ocean circulation fields with realistic temporal variability. 4. Model Validation: Control Simulation [23] The simulated noble gas tracer fields are first compared to available observations from the subtropical and 4of16

5 subpolar North Pacific time series stations: Hawaii Ocean Time series (HOT) and Kyodo North Pacific Ocean Time series (KNOT). These two stations reflect distinct regimes in the North Pacific. Station HOT is located at N, 158 W in the subtropical gyre, while KNOT is located at 44 N, 155 E in the western subpolar region. Argon and neon concentrations have been measured at both sites [Hamme and Emerson, 2002; Hamme and Severinghaus, 2007], using two different measurement techniques. The majority of available Ar measurements at these two stations were analyzed using the ONAr method where the Ar/O 2 ratio is determined using mass spectrometry and then multiplied by the absolute O 2 concentration determined from Winkler titration [Emerson et al., 1999]. This method is only accurate where the water is well oxygenated, so we use only ONAr data where the dissolved oxygen concentration exceeds 100 mmol kg 1 in the model data comparison. Second, all the neon measurements and one depth profile of Ar measurements at HOT were analyzed using an isotope dilution method where measurements are calibrated by the addition of an isotopic spike ( 22 Ne or 38 Ar) to the samples and then the isotope ratio is determined by mass spectrometry [Hamme and Emerson, 2004b; Hamme and Severinghaus, 2007]. The isotope dilution method is not expected to be biased in low oxygen regions, as it does not rely on absolute oxygen measurements. [24] The model was integrated from January 1992 through December 2008, and the simulated fields archived as monthly averages with 204 time slices (12 months times 17 years). The saturation states of argon and neon (DAr, DNe) were calculated for each time slice, and then the values from the grid points closest to the locations of HOT and KNOT were extracted. [25] The comparison is not perfect but the model indeed captures the vertical structure of argon at HOT (Figures 2a and 2b). Temporal variability in the model is pronounced near the surface and significantly overlaps with the observational data. Comparing the model and observation for the wintertime mixed layer, DAr is less saturated in the model than the observation. December January February (DJF) mean mixed layer DAr is 0.28% in the model and is 0.09% ± 0.26% in the observation. The model also underestimates argon saturation slightly below the mixed layer between about 50 m and 200 m depth (above s = 25.0) by %. Relatively high observed supersaturations at this depth are likely generated by subsurface heating as has been demonstrated in the North Atlantic subtropics at BATS [Spitzer and Jenkins, 1989; Dietze and Oschlies, 2005]. In subtropical gyres where surface water is relatively clear, significant amounts of short wave radiation can reach below the base of the surface mixed layer, which generates supersaturation below the base of the mixed layer. The attenuation of shortwave radiation is crudely parameterized in this model, and so this subsurface heating may not be adequately represented in the upper water column. [26] The model also captures the distribution of argon at station KNOT (Figures 2c and 2d). The temporal variability is significantly greater at KNOT than at HOT, reflecting the greater seasonal variation at this location. At this location, wintertime mixed layer DAr is more undersaturated in the observations with a mean DJF surface DAr of 0.28% in the model compared with 0.64 ± 0.50% in the observations (based on the measurements from a single cruise). While the variability is strongest near the surface, significant temporal variability extends down to 500 m. Observational data points are mostly captured within the range of the model s variability. However, low oxygen concentrations, and the lack of isotope dilution data at this location, limited our model data comparison to the upper 250 m of the water column. More observations using the isotope dilution method are necessary to assess the model s performance in the deep ocean. [27] The model captures the upper ocean distribution of neon at the HOT (Figures 3a and 3b). Temporal variability in the model exists near the surface, similar to and overlapping with the observations. Comparing the winter mixed layer values between the model and observation, DJF mean surface DNe is 0.9% in the model and 1.40 ± 0.10% in the observation. The model matches the neon data better than argon in the near subsurface ( m), because neon saturations are less temperature dependent and therefore less affected by subsurface heating. At the KNOT (Figures 3c and 3d), DJF mean surface DNe is 1.2% in the model and 1.50 ± 0.31% in the observation. [28] Observational data is sparse below the thermocline, making it difficult to evaluate the model data misfit. There is a significant uncertainty in the model simulation in the deep ocean since gas exchange processes near the region of deep water formation are not well resolved in the model. Because neon is much less soluble, neon saturation is more sensitive to bubble injection than is argon [Hamme and Severinghaus, 2007]. Melting of glacial ice shelves at depth can inject air into the ocean, causing a significant increase in neon saturation in the polar ocean [Schlosser et al., 1990; Hohmann et al., 2002]. These processes are not included in our model and are likely to impact on the noble gas distribution in the deep ocean. However, because our main interest is in simulating the upper ocean, the impact of unresolved deep water formation processes is less of a concern. In summary, the model captures the distribution of argon in overall agreement with the in situ data from the time series stations HOT and KNOT. The modeled neon saturation is in reasonable agreement in the surface and thermocline. 5. Sensitivity to Air Sea Gas Exchange and Mixing [29] We next investigate the contributions of each physical process. Throughout the water column, the mixing (DAr mix ) and bubble injection (DAr bub ) components tend to cause argon supersaturation, while the diffusive gas exchange driven by air sea heat fluxes (DAr hf ) and sea level pressure (DAr slp ) components tend to cause undersaturation (Figure 4). While the mixing and bubble injection components reinforce one another, the mixing component is much greater in magnitude. At HOT, the mixing component dominates the net effect in the upper ocean above 800 m, and the diffusive gas exchange and the lower sea level pressure at middle and high latitudes dominate the argon undersaturation below 800 m. The strong supersaturation in the upper ocean subtropical thermocline is consistent with 5of16

6 Figure 2. Comparison of modeled and observed argon saturation at the two ocean time series stations in the Pacific Ocean. The comparison is carried out in density coordinates (Figures 2a and 2c) and depth coordinates (Figures 2b and 2d) at station HOT (Figures 2a and 2b) and at station KNOT (Figures 2c and 2d). Triangles indicate the Ar/O 2 measurements, and circles indicate the isotope dilution measurements. Crosses indicate the monthly model output, and the solid black line indicates the climatological mean of the model output. the theory of Ito et al. [2007], which suggested that the rate of increase in argon saturation by turbulent mixing should be proportional to the square of thermal stratification. The magnitude of dt/dz is approximately 0.05 K/m at 250 m, and gradually decreases down to K/m at 1000 m. Since the abyss has much smaller stratification relative to the upper ocean thermocline, the deep argon saturation is dominated by the effect of slow air sea gas exchange that cannot keep up with cooling driven undersaturation during deep water formation [Hamme and Severinghaus, 2007]. At KNOT, modeled argon in the upper 150 m is supersaturated, and the rest of water column is undersaturated (Figure 4b). The undersaturation is driven by both low sea level pressure and slow diffusive gas exchange that allows thermal dis- 6of16

7 Figure 3. Comparison of modeled and observed neon saturation at the two ocean time series stations in the Pacific Ocean. The comparison is carried out in density coordinates (Figures 3a and 3c) and depth coordinates (Figures 3b and 3d) at station HOT (Figures 3a and 3b) and at station KNOT (Figures 3c and 3d). Triangles indicate data, all by the isotope dilution method. Crosses indicate the monthly mean model output, and the solid black line indicates the climatological mean of the numerical model output. equilibria to persist, only partially compensated close to the outcrop location by the effect of mixing and bubble injection. The transition from the supersaturated upper ocean to the undersaturated deep ocean occurs at a much shallower depth at KNOT, reflecting a relatively weak mixing component and the relatively shallow depth of the thermocline. The magnitude of dt/dz is approximately K/m at 300 m, and further decreases down to K/m at 600 m. [30] Similar to argon, the mixing (DNe mix ) and bubble injection (DNe bub ) components tend to cause neon supersaturation, while the sea level pressure variation (DNe slp ) and diffusive gas exchange driven by air sea heat flux (DNe hf ) 7of16

8 Figure 4. Four components of the simulated climatological mean argon saturation at the two ocean time series stations in the Pacific Ocean. The comparison is carried out in depth coordinates at the (a) HOT station and (b) KNOT station. The solid black line shows the net effect of all processes. The green dashed line with circles indicates the effect of sea level pressure, and the blue dashed line with triangles indicates the mixing component. The magenta dashed line with crosses indicates the diffusive gas exchange driven by the air sea heat flux component, and the red dashed line with dots indicates the bubble injection component. components tend to cause undersaturatation (Figure 5). Relative magnitudes of the mixing and bubble injection components are different from that of argon. Neon supersaturation is more strongly influenced by bubble injection and less influenced by mixing because of neon s lower solubility and the smaller temperature dependence of its solubility. As expected, the effect of sea level pressure is identical for both neon and argon. Focusing on the upper ocean, the bubble injection component dominates the net effect at HOT. The bubble injection and mixing component together supersaturate the neon concentration, partially compensated by the effect of low sea level pressure. The bubble component at HOT shows a slight maximum at 300 m, which is likely driven by higher wind speeds in the region of water mass formation at the midlatitude isopycnal outcrop. At KNOT, the bubble injection and mixing components together drive neon supersaturation in the upper 1000 m. [31] The vertical structures of argon and neon saturation have a common pattern. The effect of mixing and bubble injection tends to drive upper ocean supersaturation, compensated by the effect of diffusive gas exchange and sea level pressure variation. This finding is consistent with previous modeling studies [Ito and Deutsch, 2006]. The magnitudes of these different components are similar indicating significant mutual compensation. Therefore, observed argon and neon concentrations reflect a residual that is significantly smaller than the effect of individual processes. This paper is partly motivated by the need to understand the importance of ocean mixing in controlling noble gas saturation, relative to air sea interaction. As discussed above, argon saturation is more sensitive to the effect of ocean mixing, so we focus on the temporal and spatial variability of argon saturation in sections 6 and Temporal Variability of Argon Saturation [32] At the two time series stations, temporal variability in argon and neon saturations is pronounced at the surface and decreases with depth. This indicates the importance of surface processes in controlling the variability of noble gas saturation in the oceans. To further investigate the driving mechanism of the observed variability, we calculate the temporal mean and standard deviation of wintertime surface DAr distributions, focusing on Northern Hemisphere winter (December, January, and February). We investigate the wintertime statistics, because the surface anomaly enters into the interior thermocline during winter. The equatorial upwelling region is strongly supersaturated by up to 6% due to the intense heating of recently upwelled waters. Midlatitude regions are generally undersaturated, especially near the western boundary of the basin (Figure 6a). The standard deviation of wintertime DAr is enhanced in the equatorial upwelling (equatorial Pacific) region and in the Kuroshio extension (KOE) region (Figure 6b). [33] We next investigate these two regions of enhanced variability and attempt to determine the mechanisms driving 8of16

9 Figure 5. Same as Figure 4 but for neon saturation. the variability in DAr. First, a time series of the DAr anomaly is constructed by taking the area weighted average of surface DAr within the two boxes in Figure 6b and subtracting its time mean value. Anomaly time series (including seasonal cycles) are also constructed for the three components of the gas saturation associated with air sea gas transfer. In the equatorial Pacific, the components of variability driven by sea level pressure and bubble fluxes are much weaker than the heat flux component (Figure 7a). In the Kuroshio extension, seasonal variability dominates the time series with DAr undersaturated during winter and supersaturated during summer months (Figure 8a). All three air sea flux components have significant variability in this region. However, the components of DAr driven by sea level pressure and by bubble fluxes tend to oppose one another. During winter, sea level pressure is lower, leading to apparent undersaturation, while wind speeds are higher, leading to stronger bubble fluxes and a similar magnitude supersaturation. Consequently, the net effect is dominated by the air sea heat flux component. [34] Variability in DAr is dominated by the air sea heat flux in both regions, and closely related to climate variability. In the equatorial Pacific, interannual variability is dominated by the El Niño Southern Oscillation. During an El Niño, tropical Pacific SST is higher than in normal years due to weakened trade winds and the suppression of cold upwelling. The warm SST anomaly leads to a local cooling due to stronger evaporation, driving a negative anomaly in DAr in the region. The amplitude of the DAr anomaly is greater in the central Pacific than in the eastern Pacific (Figure 6b), because of the effect of low clouds on shortwave heating. In normal years, low clouds over the eastern Pacific help maintain the cold SST by reflecting incoming short wave radiation [Norris and Leovy, 1994]. In an El Niño, low clouds diminish over the eastern Pacific, which increases short wave heating, partially compensating for the evaporative cooling there. The correlation coefficient between monthly DAr anomaly and the wintertime (DJF mean) Southern Oscillation Index (SOI) as defined by the difference in the sea level pressure between Tahiti and Darwin [Trenberth, 1984] is 0.85 (statistically significant within the 99% confidence interval). [35] In the Kuroshio extension, interannual variability is much weaker than the amplitude of the seasonal cycle. However, the interannual variability in this region is of great interest as this is the region where thermocline waters are formed during winter. In this region, wintertime conditions are strongly influenced by the strength of the Aleutian Low. When the Aleutian Low is strong, intense heat loss from the surface ocean causes a more negative DAr anomaly. We find a significant correlation between wintertime DAr anomaly and the NPI defined as the area weighted sea level pressure over the region 30 N 65 N, 160 E 140 W [Trenberth and Hurrell, 1994] (Figure 8b). The strongest correlation is found when the NPI anomaly leads the DAr anomaly by a month (correlation coefficient of 0.67%, statistically significant within the 99% confidence interval), which can be explained by the lag time scale of diffusive gas exchange. Temporal standard deviation of surface DAr anomaly is approximately 0.5% in the Kuroshio extension, which can subduct into the thermocline and potentially complicate the interpretation of gas saturations in the inte- 9of16

10 Figure 6. Surface distribution and variability of wintertime argon saturation. (a) Wintertime mean surface argon saturation (DJF mean). (b) Standard deviation of the wintertime surface argon saturation. Color scale indicates the degree of saturation in percent. The two boxes indicate the regions of enhanced variability in the equatorial Pacific and in the Kuroshio extension investigated in the time series analysis. rior thermocline. In section 7, spatial and temporal variability of DAr in the thermocline is analyzed along constant density (isopycnal) layers to assess this effect. 7. Along Isopycnal Distribution of Argon Saturation [36] Pathways of water masses are primarily oriented along constant density surfaces, so the along isopycnal distribution of noble gases can provide useful information about the effect of surface outcrop conditions as well as ocean mixing and transport. [37] The distribution of DAr has similar features between s = 26.0 and 26.8 (Figure 9). The model is not perfect in simulating the North Pacific Intermediate Water. The deeper thermocline (s > 26.6) is observed to ventilate in the northwestern Pacific [Talley, 1988] but this simulation also includes waters ventilated from the northeastern Pacific. While these differences are unlikely to alter the main conclusions, care must be taken in the interpretation of Figure 9 as the location of the isopycnal outcrop may be biased eastward in the model. [38] A tongue of thermocline water with relatively lower saturation fills the central subtropical gyre from the northeastern to the central Pacific. This feature is surrounded by waters with relatively high saturation from the poorly ventilated tropical thermocline and from the western Pacific region. Newly ventilated waters in the northeastern Pacific are strongly undersaturated due to intense cooling during water mass formation and are transported southwestward by the gyre circulation. What causes the supersaturation in the eastern tropical thermocline and in the Kuroshio region? To better understand the mechanisms controlling the structure of argon saturation along isopycnals, the four components of 10 of 16

11 and in an idealized model [Ito and Deutsch, 2006]. This supersaturation is due to the combination of northward transport of supersaturated, tropical thermocline waters and mixing across the strong thermal gradient of the western boundary current. A budget calculation using the idealized model of Ito and Deutsch [2006] suggests that roughly half the supersaturation is locally generated by mixing, while the rest is transported from low latitudes. Again, the magnitude of DAr in this region is set by the regional temperature gradient and the saturation state of mixing end members. [40] Ocean mixing tends to supersaturate argon in the thermocline, which is partially compensated by the effect of air sea heat fluxes as discussed in section 5 (compare Figures 10a and 10c). The components driven by sea level pressure and bubble fluxes are relatively constant along the isopyncals. The undersaturation induced by rapid cooling is Figure 7. Time series of surface argon saturation anomaly in the equatorial Pacific and its driving mechanisms. Plotted quantities are anomalies from the temporal mean values. (a) Black solid line indicates the area weighted average of the surface argon saturation anomaly in the region bounded by 150 E, 110 W and 10 S, 10 N. Blue dashed line with circles indicates the area weighted average of the component of surface argon saturation driven by air sea heat flux. Red dashed dotted line indicates the sum of all other components. (b) Black solid line indicates the area weighted average of the surface argon saturation anomaly normalized by its standard deviation. Blue dashed line with triangles indicates the normalized Southern Oscillation Index. argon saturation are diagnosed for the isopycnal layer s = 26.2 (Figure 10). [39] The mixing induced supersaturation controls the overall variation in argon saturation along isopycnals in the main thermocline of the North Pacific. DAr in the tropical upwelling region is significantly supersaturated due to vertical mixing acting on the strong thermal stratification there [Gehrie et al., 2006]. This mixing induced supersaturation mainly reflects the vertical temperature gradient and the preformed saturation states of mixing end members. Thus the supersaturation in the tropics is decoupled from the rate of diapycnal mixing [Ito and Deutsch, 2006]. DAr at the midlatitude outcrop is also supersaturated in the Kuroshio extension region. This feature is identified in previous studies in the North Atlantic basin [Henning et al., 2006] Figure 8. Time series of surface argon saturation anomaly in the Kuroshio extension and its driving mechanisms. (a) Black solid line indicates the area weighted average of the surface argon saturation anomaly in the region bounded by 150 E, 180 W and 30 N, 45 N. Blue dashed line with circles indicates the area weighted average of the component of surface argon saturation anomaly driven by air sea heat flux. Red dasheddotted line indicates the component driven by sea level pressure, and green dots indicate the component driven by bubble fluxes. (b) Black solid line indicates the area weighted average of the surface argon saturation anomaly (DJF mean) normalized by its standard deviation. Blue dashed line with triangles indicates the normalized North Pacific Index (NDJ mean). 11 of 16

12 Figure 9. Along isopycnal distribution of simulated argon saturation interpolated onto potential density surfaces: (a) 26.0, (b) 26.2, (c) 26.5, and (d) Color shading is the saturation state in percent. Gray dashed line in Figure 9c represents the ventilation pathway used for the estimation of diapycnal diffusivity. most pronounced at the midlatitude isopycnal outcrop. The magnitude of the undersaturation gradually decreases away from the midlatitude isopycnal outcrop due to the effect of mixing with waters that are ventilated in other regions. This gradient is relatively weak, approximately 0.2% across the entire subtropical gyre from 15 N to 45 N, but can introduce a moderate, positive bias in an estimate of diapycnal diffusivity from DAr. [41] Temporal variability is highest at the outcrop and decays along isopycnals into the interior ocean (Figure 11). While the temporal standard deviation of surface DAr can be as large as 0.5% in the Kuroshio extension (Figure 6b), the variability in DAr on the isopycnal layer is less than 0.3%. Variability of DAr in the thermocline is generated at the surface by fluctuations in wintertime air sea heat fluxes (as shown in section 6), and those anomalies propagate into the interior thermocline. However, as those anomalies propagate into the thermocline, their amplitude rapidly decreases due to along isopycnal eddy stirring, which tends to average out rapidly fluctuating anomalies. Within km from the isopycnal outcrop, the temporal standard deviation diminishes to the order of 0.15%, which is close to the sampling error of in situ observations. This calculation therefore suggests that argon saturation within 300 km of the isopycnal outcrop should not be considered for evaluating the diapycnal diffusivity. 8. Discussion and Conclusions [42] Recent advances in the ability to accurately measure noble gas concentrations offer the potential to provide new insights into physical ocean processes underlying biogeochemical cycles [Emerson et al., 1999; Gehrie et al., 2006]. As in situ measurements are beginning to reveal the basinscale distributions of noble gas tracers, an effective modeling framework is required to interpret the processes controlling the distribution of these gas tracers. We have presented a global model of noble gas tracers driven by a data constrained, time varying ocean circulation to simulate the distribution and sensitivity of noble gases to four key governing processes: mixing, bubble injection, diffusive gas exchange driven by heat fluxes, and sea level pressure variation. The model simulation is in reasonable agreement with in situ measurements from the time series stations in the North Pacific [Hamme and Emerson, 2002; Hamme and Severinghaus, 2007]. The key findings of this study can be summarized as follows: [43] 1. The mean state of thermocline DAr distribution is characterized by the competition between cooling induced undersaturation and mixing induced supersaturation. [44] 2. Variability in surface DAr is controlled by the dominant modes of climate variability through fluctuations in air sea heat flux and gas exchange. [45] 3. In the thermocline, DAr is supersaturated in the tropical shadow zone and the Kuroshio extension region, and its magnitude in those regions mainly reflects the background thermal gradient. [46] 4. In the subtropical thermocline, a tongue of relatively low DAr exists due to the balance between the mixing induced supersaturation and thermocline ventilation. [47] We developed a method to separate noble gas saturation into components driven by different physical processes. Based on our analysis the basin scale distribution of argon saturation depends on the pattern of air sea heat flux, 12 of 16

13 Figure 10. Four components of simulated argon saturation interpolated onto potential density surface 26.2: (a) air sea heat fluxes, (b) sea level pressure, (c) mixing, and (d) bubble injection. Color shading is the saturation state in percent. ventilation pathways and diapycnal mixing. The western Pacific region near the Kuroshio extension and the southeastern unventilated shadow zone consistently exhibit strong supersaturation due to the effect of mixing consistent with previous studies in the tropical Pacific [Gehrie et al., 2006] and North Atlantic [Henning et al., 2006]. In these regions, the magnitude of DAr is decoupled from the rate of diapycnal mixing in the thermocline. Figure 11. percent. Temporal standard deviation of argon saturation on the potential density surface 26.2 in 13 of 16

14 [48] Between these two regions, we find a relatively low saturation in the tongue region, where the saturation state of argon increases from the northeastern to the central Pacific. In this region, the dynamical balance between ventilation and mixing sets the magnitude of argon saturation [Ito and Deutsch, 2006]. Thus, it is this tongue region where measurements of argon saturation may be used to infer the rate of diapycnal tracer diffusion. Our analyses are based on the linear independence of different gas saturation components, which has been tested against direct numerical simulations. Errors associated with the nonlinear coupling between different processes are typically two orders of magnitude smaller than the signal itself. [49] How well can we estimate the known diffusivity ( m 2 s 1 throughout this study) in the model based on simulated DAr distribution? To address this question, we consider the isopycnal layer s = 26.5 in Figure 9c, the alongisopycnal difference in DAr along the tongue region is approximately 0.3% between 20 N and 40 N (marked by the dash line in Figure 9c) where we avoid the region of enhanced variability near the isopycnal outcrop. Based on the simulated temporal variability, uncertainty in DAr is less than 0.15%. Using the method of Ito et al. [2007], we can combine the change in DAr, water mass age, and the background stratification to estimate the diapycnal mixing rate averaged over the ventilation pathway. To determine water mass age, we performed a simulation of CFC 12 using the same circulation model, and based on the simulated pcfc 12 age, the corresponding water mass age difference is approximately 13 years. Combining this information, the DAr based estimate of the diapycnal diffusivity rate is 1.6 ± m 2 s 1. While this method can capture the overall magnitude of the prescribed diffusivity, it tends overestimate the known diffusivity for several reasons [Ito and Deutsch, 2006]. Because DAr responds to any mixing of waters across isotherms, the estimated diffusivity can include additional effects from numerical diffusion and along isopycnal eddy stirring in the presence of strong T S compensation. The uncertainty in our estimate is based on the upper bound of the temporal variability and the spatial variability of heat flux induced argon saturation. [50] Since the noble gas tracers naturally integrate the effects of diapycnal mixing over the thermocline ventilation time scale, these tracers can provide unique estimates of diapycnal diffusivity averaged over decadal ventilation time scales. The inferred diffusivity is relevant to biogeochemical tracers including nutrients and carbon. The mixing of waters between the abyss and sunlit euphotic layer limits the supply of nutrients that maintain biological carbon uptake. Improved estimates of diapycnal mixing based on noble gases could constrain this pathway of nutrient supply. The results of this study have several implications for estimating diapycnal tracer diffusivity using noble gas tracers. First, temporal variability of argon saturation can be a source of error in the interior thermocline DAr of the order of ±0.15%. Second, mean heat flux induced DAr can vary spatially up to 0.2% over the subtropical thermocline, potentially leading to a positive bias in diapycnal tracer diffusivity. Finally, alongisopycnal eddy stirring with the tropical shadow zone or Kuroshio extension region can increase the saturation state of the tongue region, leading to a positive bias in diapycnal tracer diffusivity. Quantifying the effects of along isopycnal eddy stirring requires additional sensitivity experiments perturbing the eddy closure coefficients, which is left for the future study. The next step in investigating the large scale response of argon saturation to ocean mixing will be to rerun the model with varying isopycnal and diapycnal diffusivities. Those sensitivity experiments are currently being performed. In addition to further modeling studies, we believe that more complete observational mapping of noble gas tracers will provide important avenues for determining the basin scale mean diapycnal diffusivity based on noble gas saturation. Appendix A: Specification of Closure Coefficients [51] The rate of bubble injection is proportional to the closure coefficient (V inj ) that reflects the volume of air injected into the surface water due to collapsing bubbles per unit time and unit area at the reference wind speed 10 ms 1. Similarly, diffusive gas exchange is proportional to the closure coefficient (a). In this study, these coefficients are assumed to be globally uniform constants, and are adjusted using a quasi steady state model such that the spatial and temporal mean surface neon supersaturation is approximately 2%, as suggested by available observations [Hamme and Emerson, 2002]. The tracer equation for neon in a water parcel within the surface mixed layer [Follows and Williams, 2004] is D Dt Ne ¼ G p atm Ne Ne sat þ F inj ða1þ h p 0 h where F inj is the Ne flux from bubbles in units of mol Ne m 2 s 1, and h is the mixed layer depth. The rate of change in the neon concentration of the water parcel is determined by the effect of air sea gas exchange only. The equation for the saturated concentration of neon at equilibrium can be derived from the thermodynamic equation, assuming a linear relationship between Ne sat and temperature, D Dt Ne sat ¼ H 0 c p ða2þ where H is the air sea heat flux (positive into the oceans), c p is the specific heat of seawater, and r 0 is the density of seawater. By taking the difference between (A1) and (A2) we obtain the equation for the saturation state of neon, dne = Ne Ne sat D Dt Ne ¼ G h p atm Ne Ne sat 1 p 0 þ F inj h H 0 c p ða3þ [52] The quasi steady state model assumes that the saturation state is in dynamic equilibrium by setting D/Dt = 0. Thus the Ne concentration may change but the saturation state stays constant. This leads to the expression for the local saturation state of neon in the quasi steady state condition. Ne Ne sat ¼ p atm 1 p 0 þ V injg H GNe sat 0 c p ln Ne ða4þ [53] In this theoretical calculation, the saturation state of neon depends on three processes as represented by the three terms on the right hand side of equation (A4) including (1) atmospheric pressure variation, (2) a balance between 14 of 16