On The Global Oxygen Anomaly and Air-Sea Flux Hernan Garcia and Ralph Keeling
We present a new climatology of monthly air-sea oxygen fluxes throughout the ice-free surface global ocean. The climatology is based on weighted linear least-squares regressions using heat flux monthly anomalies as a template for spatial and temporal interpolation of historical O_{2} data. The seasonal oceanic variations show that the equatorial belt (20°S to 20°N) is characterized by relatively small air-sea fluxes when compared to the middle to high latitudes (40°-70°). The largest and lowest seasonal fluxes occur during summer and winter in both hemispheres. By means of an atmospheric transport model, we show that the model simulated amplitude and phasing of the variations in atmospheric O_{2}/N_{2} ratios due to seasonal air-sea exchanges is in good agreement with observations at base-line stations in the Pacific Ocean; better than previous climatologies. The contribution of each major oceanic basin to the atmospheric observations is described. The global ocean seasonal net oxygen outgassing (SNO) is estimated to be about 0.87 Pmol O_{2} (1 Pmol = 10^{15} mol). The extra-tropical regions (³30° latitude) account for most (>80%) of the SNO in both hemispheres. The seasonal net thermal (SNO_{T}) and biological (SNO_{B}) outgasing components of the flux are examined in relation to latitudinal bands, basin-wide, and hemispheric contributions. The Southern Hemisphere's SNO_{B} (~0.26 Pmol) and SNO_{T} (~0.29 Pmol) values are larger than the Northern Hemisphere's SNO_{B} (~0.15 Pmol) and SNO_{T} (~0.16 Pmol) values. We estimate a seasonal extra-tropical carbon production of 3.4-3.5 Pg C (1 Pg = 10^{15} g) based on hemispheric-averaged SNO_{B} values poleward of 30° latitude and an O_{2}/C ratio of 1.45, lower than previously estimated from air-sea O_{2} climatologies.
1. Introduction (top of document) The oceanic O_{2} flux contribution to the atmospheric O_{2}/N_{2} ratio is primarily through seasonal-scale variability caused by a range of biochemical and physical processes across the air-sea interface. Biochemical processes include sources and sinks of O_{2} due to marine production, respiration, and remineralization of organic matter. Physical processes include sources and sinks caused by water mass renewal or ventilation, air-sea gas exchange, solubility changes driven by seawater warming and cooling, near-surface turbulence and mixing whether induced by wind, waves, or small-scale breaking (i.e., bubble injection and spray), and aeolian inputs of labile substances. The seasonal distribution of surface O_{2} anomalies and air-sea fluxes are useful to study the interplay between biochemical and physical processes that affect the O_{2} concentration in seawater and its ultimate effect on the atmosphere. Two important aspects of studying the oceanic annual cycle of O_{2} are to delimit sources and sinks of photosynthetic carbon production and air-sea exchange of CO_{2} [i.e., Keeling et al., 1993].
Najjar and Keeling [2000] presented a global climatology of monthly air-sea O_{2} fluxes based on rescaling the O_{2} anomaly values of Najjar and Keeling [1997] using the sea-surface temperature (SST) climatology of Shea et al. [1992] and adding a correction for O_{2} anomalies caused by air bubble injection. Keeling et al. [1998] described the SST rescaled climatology. Briefly, Keeling et al. [1998] averaged the SST climatology of Shea et al. [1992] into 12° latitudinal bands for each major ocean basin and calculated the fundamental harmonic amplitude of the averaged SST data. They then computed averaged SST values where concurrent O_{2} anomaly values were available and computed the fundamental harmonic amplitude for this reduced SST data set in each 12° latitudinal band. They then calculated a scaling factor between the fundamental harmonic amplitudes of the averaged SST climatology and the reduced SST data. They multiplied the O_{2} anomaly values of the Najjar and Keeling [1997] climatology by the scaling factor after subtracting the annual mean O_{2} anomaly at each grid point. The SST-rescaled O_{2} anomaly climatology was obtained after adding back the annual mean to each monthly value in the original O_{2} anomaly value climatology. The SST-rescaled climatology improved the difference between the model simulated and observed variations in the atmosphere when compared to the climatology without rescaling. But the rescaled climatology still underestimated the amplitude of the atmospheric variations by about 20% at baseline stations located poleward of 30° latitude in the Northern and Southern Hemisphere.
Here we revisit the task of computing a representative global air-sea O_{2} flux monthly climatology. The new climatology reduces excessive smoothing in O_{2} data sparse regions. We outline an alternative method to that of Najjar and Keeling [1997, 2000], and Keeling et al. [1998] for estimating a global monthly climatology of sea-surface O_{2} anomaly and air-sea flux using selected historical O_{2} data at observed depth levels. Our approach is based on binning the O_{2} data by coarse spatial increments in latitude and, instead of binning the data by time of collection, we bin the O_{2} data by fine increments in air-sea seasonal heat flux anomalies independent of the time of year. Ocean heat flux is an objective, conservative variable. We use the DQ_{sea} data as a spatial and temporal template for interpolation of O_{2} anomaly and flux values throughout the ice-free oceanic domain. Seasonal heat flux anomalies were chosen for interpolating the O_{2} data because, as shown by Keeling et al. [1993], we expect a relationship between seasonal heat flux and air-sea O_{2} flux regardless of whether the O_{2} exchange is mediated by biological or physical processes. We compare our results to the Keeling et al. [1998] and the Najjar and Keeling [1997; 2000] data without air bubble injection correction. We find that the new flux climatology yields improved simulations of atmospheric O_{2}/N_{2} variations. Since the solubility of O_{2} in seawater is primarily temperature dependent, oceanic regions of net heating degas O_{2} to the atmosphere and oceanic regions of net cooling absorb atmospheric O_{2}. In this sense, air-sea O_{2} flux is out of phase with temperature but in phase with heat flux. A similar relationship is expected for biologically mediated O_{2} air-sea exchange whenever there is a strong correlation between primary production, oxygen production, and stratification. Increased vertical mixing during fall and winter driven by surface cooling brings nutrient-rich, carbon-rich, but relatively O_{2}-poor water (less recently ventilated waters) to the mixed layer. Surface heat loss is thus associated with oceanic O_{2} uptake. During spring and summer, vertical stratification reduces vertical mixing between surface (nutrient-poor, O_{2}-rich) and deeper (nutrient-rich, O_{2}-poor) waters. Biologically O_{2} production then increases due to increases in primary production that result from a combination of increased solar irradiance and available nutrients and O_{2} is released to the atmosphere. Surface heating is thus associated with biologically mediated O_{2} outgassing. As the season progresses and vertical stratification occurs, nutrient concentrations and primary production generally decrease. The seasonal trends of surface heating and cooling and biological O_{2} production suggest a correlation between heat flux anomalies and air-sea oxygen flux.
2. Data
sources and data quality The primary data used in this study are discrete water samples with measured O_{2} concentrations, in-situ temperature (T, °C), and salinity from selected near sea-surface historical oceanographic data. The hydrographic data are from the World Ocean Circulation Experiment (WOCE), NODC archived data as of 1998, and the data compilations of Reid [1989; 1994], Garcia [1996], and Garcia et al. [1998]. The NODC data corresponds to the world ocean database 1998. Typically, the bottle data consists of 12 or 24 depth discrete observations between the surface and the bottom. The O_{2} data in volumetric units were converted to mass units (΅mol kg^{-1}) using the molar volume of O_{2} as a real gas and a density (r, kg m^{-3}) corresponding to T and S [Millero and Poisson, 1981]. The historical bottle data (918489 observations) provide good coverage in the Northern Hemisphere and some parts of the Southern Hemisphere. We recognize that there is variability (i.e., diurnal, inter-annual or longer time-scale) inherent in the historical data but this potential temporal bias is not treated here. In this study, we take O_{2} measured data in the upper 15-m of the water column to represent the distribution of O_{2} in the surface layer. The historical hydrographic data were collected over several years on different cruises. The precision of historical T, S, and pressure data are estimated to be ±0.01°C, ±0.01, ±5 dbar respectively [Saunders, 1986; Garcia, 1996]. We carried out a data quality control on the historical O_{2} on several steps. We first rejected all measured O_{2} data collected prior to 1960 because we believe that about this time more precise O_{2} data began to be routinely collected. The hydrographic data were then sorted into 10° latitude by 10° longitude areas. We rejected spurious observations by means of variance and range checks on the historical data contained within these areas using O_{2} against T and S plots. Then for each area, we calculated the spatially averaged value for T, S, and O_{2} and the standard error using all the data at each interpolated level of density. About 10% of the historical O_{2} data were excluded from this data quality step. The precision of the historical O_{2} data retained in this study is about ±3 ΅mol kg^{-1}. 3. Computation of O_{2}
anomalies and air-sea fluxes The initial data processing step is the calculation of oxygen anomalies (D[O_{2}], ΅mol kg^{-1}) at every location where there were historical O_{2}, T, and S data. D[O_{2}] was evaluated as the difference between the measured O_{2} concentration ([O_{2}]_{obs}) and the O_{2} solubility ([O_{2}]^{*}) in seawater, D[O_{2}]= [O_{2}] - [O_{2}]^{* }+ δ_{skin}, where d_{skin} is the skin temperature correction. The [O_{2}]^{*} values were computed as a function of T and S from the bottle data and sea level atmospheric pressure (P_{a}, at) using the O_{2} solubility equations of Garcia and Gordon [1992], the atmospheric pressure equation of Benson and Krause [1984], and the monthly atmospheric pressure climatology of Oberhuber [1988]. The precision of the O_{2} solubility values is about 0.1-0.2% [Garcia and Gordon, 1992]. We assume complete O_{2} equilibration with the atmosphere. The average atmospheric pressure corrections relative to mean sea-level atmospheric pressure (1013 mbar) are typically about ±4 ΅mol kg^{‑1}. For d_{skin}, we used the equation of Hasse [1971] with a depth for the bulk in-situ temperature data from the hydrographic data of 2.5 m and the European Center for Medium-range Weather Forecasting (ECMWF) heat flux monthly climatology [Gibson et al., 1997]. The d_{skin} corrections are typically about ±1 ΅mol kg^{-1}. The d_{skin} and P_{a} corrections combined are ~±4 ΅mol kg^{-1}. The combined corrections are small when compared to the magnitude of the seasonal variations in D[O_{2}]. We do not treat bubble injection corrections. There are no adequate constraints on the net effect of bubble injection on the global ocean gas flux and the net seasonal effect is likely small [Schudlich and Emerson, 1996; Keeling et al., 1998; Emerson et al. 1993]. We evaluate air-sea O_{2} fluxes (_{O2}, mol m^{-2} month^{-1}) using _{O2}= ρk_{O2}D[O_{2}], where k_{O2} is the gas transfer velocity for O_{2} (k_{O2}, m s^{-1}). To calculate k_{O2}, we use the formulation of Wanninkhof [1992] for long-term winds [k_{O2}=0.39u^{2}(Sc_{O2}/660)^{-1/2}, where u (m s^{-1}) is wind speed and Sc_{O2} is Schmidt number for O_{2}]. For calculating the Schmidt number we use the relation Sc_{O2}=1638-81.83T+1.483T^{2}-0.008004T^{3} [Keeling et al., 1998] using SST values from Shea et al. [1992]. For wind speeds we use ECMWF monthly values [Gibson et al., 1997]. We use the k_{O2} formulation of Wanninkhof [1992] because it appears to be applicable to large-scale studies [Keeling et al. 1998; Najjar and Keeling, 2000]. We computed _{O2} values for all locations with retained historical O_{2} data. 4. Regression
analysis The objective of the weighted linear least-squares regression model is to estimate _{O2} as a function of monthly heat flux anomalies in 10° latitudinal bands covering the ice-free global ocean surface (80°S to 80°N). For evaluating the statistical weights for the _{O2} data in each 10° latitudinal band, both the _{O2} and the monthly heat flux anomaly values were processed through a three-step data processing scheme. First, we computed monthly heat flux anomalies ([DQ]_{sea}, Watt m^{-2}) at each point of the ECMWF grid by subtracting the annual mean heat flux value from each monthly heat flux value. Second, a monthly [DQ]_{sea} value corresponding to the geographical location and month of collection of every _{O2} data value was calculated as follows. The [DQ]_{sea} value corresponding to each of the _{O2} data value was found by first locating the monthly heat flux anomaly value in the ECMWF climatology equal to the month of each O_{2} observation, and then we used spatial interpolation between adjacent [DQ]_{sea} grid points to assign a [DQ]_{sea} value to each _{O2} data value according to its geographical position. Third, we divided the _{O2} data set into 10° latitudinal bands between 80°N and 80°S without overlapping and binned the _{O2} values within monthly [DQ]_{sea} increments of 50-watts m^{-2} in each 10° latitudinal band. The choice of 50-watts m^{-2} increments in [DQ]_{sea} is a qualitative compromise between having a relatively large number of _{O2} data points and low resolution in [DQ]_{sea} and a high resolution in [DQ]_{sea} with small number of _{O2} data values in any particular latitudinal band. We then computed the mean and standard errors of the _{O2} values regardless of month of collection and geographic location within each DQ_{sea} increment. We use the mid-point value of the 50-watts m^{-2} DQ_{sea} increments to bin the _{O2 }values. Values of _{O2 }that differed by more than four standard deviations from their mean values in each DQ_{sea} increment were rejected from further analysis. This data quality control step eliminated about 5% of the O_{2} data. We then evaluated the mean and standard errors for the retained _{O2} values at every 50-watts m^{-2} DQ_{sea} increment within each 10° latitudinal band. Thus, all bins with data were independently evaluated according to the statistical information in each bin. The data processing described above yields the mean and standard errors of _{O2} values binned in each DQ_{sea} increment for all the data geographically located in each 10° latitudinal band. We performed weighted linear least-squares regressions between DQ_{sea} as the independent variable and _{O2 }as the dependent variable for all the data in 10° latitudinal bands of the general form _{O2}=a_{0}+a_{1}[DQ]_{sea}, where a_{0} and a_{1} are the intercept and slope regression coefficients. We assume that the variance in the regressions is solely due to the _{O2} data. Thus, in calculating the regression coefficients for each 10° latitudinal band, we used weights based on the standard error of the _{O2 }data binned in each DQ_{sea} increment. The purpose of the weights in the calculation of the regression coefficients is to provide more importance to _{O2} values whose variance factors are smaller and less importance to _{O2} values whose variance factors are larger at every DQ_{sea} increment. Thus, the regression coefficients are sensitive to the statistical information in the _{O2} values. Appendix A and Appendix B show examples of the linear least-regressions between oxygen anomaly and heat flux and oxygen flux and heat flux for different latitudinal bands in the Northern and Southern Hemispheres. Appendix C lists the _{O2}=a_{0}+a_{1}[DQ]_{sea} regression coefficients. Figure 1 shows the magnitude of the slope regression coefficients (a_{1}) for each 10° latitudinal band. The coefficients show large north-south gradients within the northern and southern hemispheres and are generally asymmetrical about the equator. The a_{1} coefficients increase poleward reaching maximum values between 40° and 70°. The Southern Hemisphere shows higher a_{1} values with a somewhat narrower latitudinal peak centered near 45°S than at comparable latitudes in the Northern Hemisphere. Poleward of 70° latitude, the a_{1} values decrease because of increased seasonal sea-ice coverage and decreases in the wind gas exchange and wind speeds. Between 10°N and 10°S, the coefficients are near zero or even slightly negative. The equatorial waters show large D[O_{2}] values that can be attributed to upwelling of waters highly under-saturated with respect to O_{2}. But the surface waters exhibit relatively small seasonal changes in _{O2 }and DQ_{sea} when compared to surface waters at middle to high latitudes. For statistical comparison, the coefficient of linear determination (r^{2}) is greater than 0.6 poleward of 20° reaching maximum values (≥ 0.8) poleward of about 40° latitude. The r^{2} values are greater than 0.4 between 20°S and 20°N. We also carried out separate regression analysis between _{O2 }and DQ_{sea} for data located in the Atlantic, Pacific, and Indian Oceans in each 10° latitudinal band (Figure 1). The main reason for evaluating separate regression analysis in each major ocean basin is because differing sampling density between basins could bias the magnitude of the global regression coefficients towards values appropriate for the basins with the largest number of observations. We adopt subjective definitions for the geographical boundaries of the major ocean basins. The latitudinal patterns of the a_{1} coefficients in each basin show similar features as the global a_{1} coefficients, although some differences are notable (Figure 1). The North Atlantic shows the largest a_{1} values except north of 70°N and south of 50°S. The Indian Ocean slope coefficients show that largest positive values centered near 50°S. What is the effect of k_{O2} on the computed air-sea O_{2} fluxes as a function of latitude? To examine the effect of k_{O2} alone, we carried out a weighted regression analysis between D[O_{2}] and DQ_{sea} of the form D[O_{2}]=b_{0}+b_{1}[DQ]_{sea}, where b_{0} and b_{1} are the intercept and slope regression coefficients. We followed the same procedure described earlier for obtaining the a_{0} and a_{1} coefficients in each 10° latitudinal band. Any latitudinal differences between b_{1} (D[O_{2}]/[DQ]_{sea}) and a_{1} (_{O2}/[DQ]_{sea}) reflect the effect of k_{O2} differences because this is the only free parameter. Figure 2 shows the latitudinal variation of the b_{1} slope coefficients. The b_{1} coefficients increase poleward nearly monotonically (Figure 2), while the a_{1} coefficients increase poleward reaching maximum values between 40° and 70° and decrease poleward of 70° latitude as described earlier (Figure 1). Thus, the decrease in the a_{1} values observed at high and low latitudes reflect latitudinal decreases in wind speed values. Appendix D lists the D[O_{2}]=b_{0}+b_{1}[DQ]_{sea} regression coefficients. The latitudinal distribution of the regression coefficients is subject to uncertainty resulting from sampling errors, data variability, the effect of differing sampling density between latitudinal bands and between basins, and covariance effects between _{O2}, k_{O2}, and DQ_{sea}. It is difficult to quantify these effects. We believe that the a_{1} regression coefficients are representative of the mean large-scale air-sea O_{2} flux variability on seasonal time scales across each 10° latitudinal band, and demonstrate this later. First, the regression coefficients obtained for the Atlantic, Pacific, and Indian Oceans closely follow the latitudinal trends of the global regression coefficients (Figures 1, 2). Second, evaluating linear regressions over 5° latitudinal bands did not yield significantly different regression coefficients that those obtained using 10° bands. Third, the latitudinal distribution of the a_{1} coefficients indicates real north-south trends. Fourth, as shown below, the correlation between _{O2} and DQ_{sea} as a function of latitude captures most of the variation in O_{2}/N_{2} as seen in the atmosphere. The sensitivity of the regressions coefficients to covariance between _{O2}, k_{O2}, and DQ_{sea} is difficult to quantify. We believe that the most important sources of error in the calculation of _{O2} values are the variability in the O_{2} data. 5.
Global distribution of seasonal air-sea O_{2} fluxes To obtain a global distribution of monthly _{O2} values, we use the regression coefficients (a_{0} and a_{1}) as linear scale functions of DQ_{sea}. We calculated _{O2} values at every grid point of the DQ_{sea} climatology both spatially and temporally within the 10° latitudinal bands. We used a linear interpolation scheme to obtain _{O2} values between the mid-point of the 10° degree latitudinal bands. We calculated seasonal anomalies in air-sea fluxes (_{O2})_{sea} at every grid point according to (_{O2})_{sea}=a_{1}DQ_{sea}. The annual mean of (_{O2})_{sea} computed this way is zero because DQ_{sea} also has zero annual mean. The mean annual _{O2} does not contribute measurably to seasonal variation in the atmospheric O_{2}/N_{2} ratio [Keeling et al., 1998]. Only the seasonal component of the air-sea O_{2} flux is needed in our atmospheric simulation as shown below. We set the (_{O2})_{sea} values to zero poleward of 80° latitude and in ice-covered regions using the SST data of Shea et al. [1992]. Maps of the distribution of the air-sea O_{2} fluxes monthly anomalies show strong meridional gradients reflecting the seasonal variation of the distribution of winds (exchange velocity), seasonal heat fluxes, and biological production. For brevity, we show maps for December and June, the months with the largest seasonal fluxes (Figure 3). The seasonal pattern of O_{2} flux anomalies in both hemispheres is characterized by sea-to-air fluxes during summer and air-to-sea fluxes during winter. This hemispheric pattern is consistent with warming, vertical stratification, and high rates of primary production during summer, and cooling, enhanced vertical mixing, and low rates of primary production during winter throughout most of the open surface ocean. In the Northern Hemisphere fluxes generally increase polewards to about 60°N and decrease north of this latitude. In contrast, in the Southern Hemisphere the fluxes are largest over a relatively narrow latitudinal belt centered near 50°S. 6.
Simulated and observed O_{2}/N_{2} ratios in the atmosphere We use our estimated (_{O2})_{sea} values to simulate the seasonal variability in the air O_{2}/N_{2} ratio by means of the TM2 atmospheric transport model [Heimann, 1995]. Briefly, the TM2 model is a 3-D advection model with a spatial resolution of about 7.5°x7.5° and 9 vertical levels. We initialize the atmospheric transport model using the (_{O2})_{sea} values and the ECMWF wind climatology for the year 1986. Steady state results are typically reached after the fourth year after initialization of the TM2 model. The model results are then compared to inter-annually de-trended O_{2}/N_{2} observations collected at time-series stations located mainly in the Pacific (Table 1). The oceanic component of the de-trended data is isolated correcting the data for land photosynthesis and respiration using CO_{2} data following Keeling et al. [1998]. To predict changes in the oceanic O_{2}/N_{2} ratio, it is necessary also to account for the small seasonal N_{2} variation in the atmosphere due to air-sea N_{2} exchange (_{N2}). The _{N2} values were computed as the product of ECMWF seasonal heat flux anomaly times the temperature derivative of the N_{2} solubility ([N_{2}]^{*}). The ([N_{2}]^{* }values were computed using the solubility equation of Weiss [1974] as a function of SST [Shea et al., 1992]. We subtracted the annual mean value from the monthly N_{2} flux to get the seasonal component (_{N2})_{sea}. The seasonal cycles for the simulated and observed O_{2}/N_{2} cycles are fitted using a four-harmonic seasonal cycle. We adopt the per meg unit to compare the oceanic and atmospheric O_{2}/N_{2} variations [Keeling et al., 1993]. We do not separately simulate the contribution of the annual-mean O_{2} and N_{2} fluxes to the atmospheric O_{2}/N_{2} changes. Our interest here is the seasonal cycles in O_{2}/N_{2} for which the annual-mean fluxes appear to be insignificant as shown by Keeling et al. [1998]. Figure 4a shows a comparison between the model simulated and the measured O_{2}/N_{2} cycles. The agreement between the simulated and observed O_{2}/N_{2} variations is very good, particularly at the middle to high-latitude time-series stations where the largest seasonal variations are found. The timing of the air cycle in both hemispheres generally follows the timing of the cycle of oceanic ingassing in winter and outgassing in summer. The simulated O_{2}/N_{2} variations based on the global regression coefficients lead the atmospheric observations by slightly less than a month. Except for La Jolla, the simulated values of the peak-to-peak amplitude are within 10% of the observed amplitude. At La Jolla, the simulated amplitude is too low by about a factor 0f 0.6. The Kumukahi station has the largest phase difference, about a month. Even neglecting the biological component, some phase lag is expected based on mixed layer equilibration time for O_{2} of a few weeks that our model simulations neglect. Also, a phase lag in the biological component is expected due to the lagged response of photosynthesis to seasonal stratification and nutrient availability. The contribution of the major ocean basins to the observed O_{2}/N_{2} variation is shown in Figure 4b. The amplitude and phasing of the seasonal oceanic cycles at the baseline stations is dominated by the O_{2}/N_{2} contributions from the Pacific and Atlantic basins. The contribution of the Indian Ocean to the Pacific O_{2}/N_{2} ratio plays a smaller but not insignificant role. The results suggest that the large-scale contribution of each basin to the O_{2}/N_{2} variations is most sensitive to the north-south distribution of O_{2} surface fluxes, and to a lesser extent, to zonal distribution because the atmosphere smoothes out east-west trends. Simulations based on the O_{2} anomaly climatology of Najjar and Keeling [1997] are shown for comparison purposes in Figure 4c. Results are shown using the Najjar and Keeling [1997] climatology both with and without SST-rescaling. The model simulations based on the Najjar and Keeling [1997] climatology underestimate the observed O_{2}/N_{2} cycles poleward of 31° latitude on average by about 15% in the Northern Hemisphere and by 23% in the Southern Hemisphere. A particularly large discrepancy is seen at Samoa (SMO) station (14°S), a result not explained by Keeling et al. [1998]. Model simulations based on our climatology are generally in better agreement with observations, particularly at Samoa, suggesting that the Najjar and Keeling [1997] climatology may be inaccurate in the tropical South Pacific. On the other hand, the lack of atmospheric observations at additional baseline locations in the Tropical Pacific makes it difficult to draw firm conclusions, and the discrepancy could be caused by other errors such as uncertain atmospheric transports, in which case our model simulations are in agreement fortuitously. 7.
Sensitivity analysis of the simulated O_{2}/N_{2} variations To assess the significance of the hemispheric (_{O2})_{sea} values, we compared the effect of the (_{O2})_{sea} latitudinal gradients in each hemisphere on the observed O_{2}/N_{2} ratio in the atmosphere by means of the TM2 model. We calculated (_{O2})_{sea} values in the southern hemisphere using the a_{1} regression coefficients for the northern hemisphere. We calculated also (_{O2})_{sea} values in the northern hemisphere using the a_{1} regression coefficients for the southern hemisphere. We then initialized the TM2 model with the new set of (_{O2})_{sea} values. The results of the simulations indicate that the amplitude of the atmospheric cycles at the baseline stations are too low or too high by as much as 40% depending on the base-line station. This suggests that the magnitude and latitudinal gradient of the a_{1} coefficients shown in Figure 1 are significant and reflect real features in amplitude and phasing of the cycles in each hemisphere. The results also indicate that there is little inter-hemispheric mixing on seasonal time scales with respect to O_{2}. We also assessed the sensitivity of the simulated O_{2}/N_{2} variations to the choice of global and basin-wide regression coefficients. We initialized the TM2 model using (_{O2})_{sea} that we obtained from the a_{1} regression coefficients estimated independently in the Atlantic, Pacific, and Indian basins. Using these basin a_{1} coefficients produced changes in the amplitude of the oceanic cycles at the baseline stations of about ±10% depending on location when compared to the fluxes obtained using the global a_{1} coefficients. This means that the magnitude and latitudinal gradient of the global a_{1} coefficients capture the essential large-scale features of the air-sea O_{2} flux. We cannot discard the possibility that finer spatial and temporal resolution of the a_{0} and a_{1} values might improve the simulated O_{2}/N_{2} cycles. In summary, the results indicate that the regression approach provide a representative prediction of the seasonal air-sea O_{2} flux and the oceanic O_{2}/N_{2} variations when compared to the atmospheric observations. 8. Wind exchange coefficient
calibration One approach to achieve a better quantitative agreement between the simulated and observed atmospheric O_{2}/N_{2} variations is to scale the global oceanic O_{2} flux field by a constant, dimensionless correction or calibration factor while fixing the seasonal N_{2} flux. This factor can be viewed as a multiplicative correction to the air-sea O_{2} gas exchange coefficient. Keeling et al [1998] optimized the gas-exchange velocity for O_{2} based on the Najjar and Keeling [1997] climatology using the algorithm of Heimann and Keeling [1989]. The algorithm yields scaling factors that minimize the sum of squared residuals between the observed and simulated atmospheric O_{2}/N_{2} cycles in the least-squares sense. Here we used the same algorithm to estimate scaling factors for the O_{2} exchange velocities based on the present climatology. We refer to this scaling factor as atmospheric calibration. Table 3 lists atmospheric calibrations based on the present O_{2} flux climatology as well as correction factors computed previously by Keeling et al. [1998] using the Najjar and Keeling [1997] climatology. Except for the La Jolla station, all of the atmospheric calibrations are very close to 1. We find that the atmospheric calibration for simultaneous fitting of all the stations is 1.01±0.05. The atmospheric calibrations for the high-latitude stations range between 1.09±0.09 in the Northern Hemisphere (ALT, CBA, NWR) and 0.97±0.08 in the Southern Hemisphere (CGO, SPO). In contrast, Keeling et al. [1998] obtained larger atmospheric calibrations for the same grouping of stations: 1.14±0.05 for all the stations while for the high-latitude stations the scaling factors ranged between 1.15±0.05 (ALT, CBA, NWR) and 1.23±0.06 (CGO, SPO) as shown in Table 3. The results indicate that the regression approach provides a representative simulated atmospheric O_{2}/N_{2} variation without the need for atmospheric calibrations factors. The combined use of the surface O_{2} anomaly data of this work and the k_{O2} formulation of Wanninkhof [1992] yield simulated O_{2}/N_{2} variations in good agreement with the atmospheric O_{2}/N_{2} observations. The results substantiate the use of the Wanninkhof [1992] relation for computing air-sea gas exchange rates at large spatial and monthly time scales. 9. Biological and thermal
components of the air-sea flux We represent _{O2} as the sum of thermal (f_{T}) and biological (f_{B}) fluxes. Following Keeling et al., [1993], we compute the f_{T} component using,
Where Q is heat flux, c_{p} is heat capacity of seawater (3992 Joule kg^{-1} °C^{-1}), and T is temperature (°C). We evaluate f_{T} at every grid point of the _{O2} climatology and calculate the seasonal thermal component (f_{T})_{sea} by subtracting the annual mean from each monthly value. We evaluate the seasonal biological component (f_{B})_{sea} as the difference bettwen the total seasonal flux and the thermal component using (_{B})_{sea}=(_{O2})_{sea}- (_{T})_{sea}. Because f_{T} assumes complete O_{2} equilibration with the air, its seasonal amplitude might be overestimated and (f_{B})_{sea} might be underestimated. Thus, the biological component of the flux represents a conservative lower limit when there is no O_{2} equilibration of surface waters with the atmosphere. As described earlier, we set all fluxes equal to zero in ice-covered waters. 10.
Seasonal net outgassing One index of the seasonal air-sea O_{2} flux exchange is seasonal net outgassing (SNO) defined by Keeling and Shertz [1992] as the spatially and temporally integrated oxygen flux over the annual periods when the spatially integrated flux is positive (e.g., sea to air). Najjar and Keeling [2000] adopted a slightly different definition of SNO than Keeling and Shertz [1992] in which the annual-mean O_{2} flux is subtracted before integration. The difference in the definition is insignificant at the hemispheric scale, and the latter definition is perhaps more appropriate on smaller spatial scales as a measure of the contribution to seasonal variations in atmospheric O_{2}/N_{2}. Here we follow the Najjar and Keeling [2000] definition of SNO and compute also both the thermal (SNO_{T}), and biological (SNO_{B}) seasonal net outgassing. To compute SNO, we first subtracted the annual mean flux from each monthly flux value and then we integrated over the months when the fluxes are positive. We carried this calculation on hemispheric and latitudinal bands for the global ocean and major ocean basins. We computed SNO_{T} and SNO_{B} in a similar manner as SNO using the f_{T} and f_{B} monthly climatology. For simplicity, SNO values are reported in units of 10^{14} mol O_{2}. Table 2 lists SNO_{T} and SNO_{B} values without atmospheric calibrations for selected latitudinal ranges in the global ocean and ocean basins. We computed the global SNO_{B} (4.2) and SNO_{T} (4.5) values by summing the thermal and biological hemispheric results (Table 2). The SNO_{B}:SNO_{T} ratio provides insight into the contribution of thermal and biological sources. The SNO_{B}:SNO_{T} ratio in the Southern Hemisphere is 0.9, while in the Northern Hemisphere the ratio is 1.0. This suggests that the thermal and biological hemispheric averaged contributions to SNO are roughly equal. The extra-tropical (>30°) SNO_{B}:SNO_{T} ratios are 1.2 in the Northern Hemisphere and 1.1 in the Southern Hemisphere indicating a slightly greater biological contribution to SNO. Computing SNO_{B} and SNO_{T} values between 30° and 60° latitude yielded SNO_{B}:SNO_{T} ratios of 1.3 and 1.2 in the Northern and Southern Hemispheres suggesting a greater biological than thermal contribution to SNO in the temperate regions. In general, different latitudinal bands have distinct biological and thermal contributions to SNO. The global pattern of SNO is similar in each major basin. The Atlantic, Pacific and Indian Basins have SNO_{B}/SNO_{T} ratios in each hemisphere ranging between 0.9 and 1.1. The Southern Hemisphere's SNO_{B} and SNO_{T} values are greater than in the Northern Hemisphere by about ~1.7 and ~1.8. The difference can be explained in part due to the greater mean ice-free ocean area of the Southern Hemisphere compared to the mean ice-free ocean area of the Northern Hemisphere (about 1.4 larger). Our calculated thermal and biological SNO values for the extra-tropical regions (≥ 30° latitude) account for about 77% of the SNO_{T} and 92% of the SNO_{B} hemispheric results. Our estimate essentially omits equatorial and other oceanic areas where the seasonal O_{2} flux variation is not well resolved. We summed the Northern and Southern Hemispheric SNO values without atmospheric calibrations to estimate a global SNO of about 8.6 (Table 4). The Northern and Southern Hemispheres contribute 36% and 64% of the global SNO. On basin scales, the Pacific is the largest contributor (50%) to the global SNO, followed by the Atlantic (30%) and the Indian Basins (20%). A basin-wide SNO value is difficult to establish because the value depends on the choice of geographical boundaries of each basin. To gain insight into how representative our simulated oxygen fluxes are without atmospheric calibrations when compared to the atmospheric observations, we compared the calculated SNO against values estimated using the calibration coefficients listed in Table 3. We multiplied the fluxes poleward of 30° latitude by 1.09 in the Northern Hemisphere and by 0.97 in the Southern Hemisphere. The choice of calibration factors is to some extent arbitrary because the optimized fluxes are sensitive to the atmospheric observations in the Pacific stations. Nevertheless, we used these calibration factors because most of the oceanic contribution to the atmospheric O_{2}/N_{2} variations at the Pacific base-line stations appears to be caused by seasonal air-sea variations poleward of 30° latitude [Keeling et al., 1998]. We show that the difference between the uncalibrated (8.6) and calibrated (8.7) sum of hemispheric SNO values is small, about 1 % (Table 4). The small difference is not surprising because the high-latitude calibration values and the averaged calibration factors for all the base-line stations combined is about 1 (Table 3). For comparison purposes, we have also calculated SNO values from the Najjar and Keeling [2000] climatology. Using the Najjar and Keeling [2000] climatology without atmospheric calibrations, we obtain hemispheric SNO values of 3.22 and 5.34 and we obtain extra-tropical (>30° latitude) SNO values of 2.72 and 3.84 in the Northern (N.H.) and Southern (S.H.) Hemispheres respectively. Although hemispheric SNO estimates using our climatology (without atmospheric calibration) are quite similar (3% smaller in the N.H. and 3% larger in the S.H.) the distribution is different. Whereas the Najjar and Keeling [2000] climatology yields 85% (N.H.) and 72% (S.H.) of the hemispheric SNO in the extra-tropics, our climatology yields 82% (N.H.) and 86% (S.H.) of hemispheric SNO in the extra-tropics. Particularly in the Southern Hemisphere, our climatology has larger seasonal fluxes at higher latitudes and lower fluxes at lower latitudes than the Najjar and Keeling [2000] climatology. The present O_{2} flux climatology is probably more realistic on the basis of the comparison with the atmospheric observations at Samoa (14°S), Cape Grin (41°S), and the South Pole (90°S) (Figure 4c). 11.
Carbon production estimates Following Keeling and Shertz [2000], Najjar and Keeling [2000] used the biological SNO values computed from their O_{2} flux climatology to estimate extra-tropical (> 20° latitude) biological new production in the mixed layer during the shoaling period. First, they calculated SNO_{B} for each 10° latitudinal band poleward of 20°N and 20°S and excluded from their calculations the North Indian Ocean. They then added up the SNO_{B} values for individual 10° latitudinal bands and reported a spring-summer mixed layer new production of 4.5 Pg C (without atmospheric calibrations) and 5.6 Pg C (with atmospheric calibrations) assuming an O_{2}:C ratio of 1.45 (1 Pg= 10^{15} g). If we add up individual 10° latitude bands poleward of 20° (excluding the North Indian Ocean) following Najjar and Keeling [2000], we calculated a global SNO_{B} value equivalent to 3.71 Pg C (without atmospheric calibrations) and 3.75 Pg C (using atmospheric calibrations) based on the present O_{2} flux climatology and an O_{2}:C ratio of 1.45. Our carbon production estimates are smaller than those of Najjar and Keeling [2000]. The discrepancies between the estimates include differences between the O_{2} anomaly climatologies, calculation of thermal fluxes, and the choice of geographic boundaries of the oceanic basins and non-ice covered oceanic regions. The calculation of SNO_{B} is sensitive to the area of integration as well as the phasing of the flux fields. For example, the sum of hemispheric-averaged SNO_{B} values (without atmospheric calibrations) is equivalent to 3.45 Pg C for the present climatology and 4.25 Pg C for the Najjar and Keeling [2000] climatology. If we use the atmospheric calibrated O_{2} fluxes to calculate hemispheric SNO_{B} values, then the values increase to 3.51 Pg C for the present climatology and to 5.18 Pg C for the Najjar and Keeling [2000] climatology. Following Najjar and Keeling [2000], we can compare our estimates of extra-tropical carbon production based on SNO_{B} with satellite estimates of carbon primary production. Najjar and Keeling [2000] computed an extra-tropical seasonal primary production of 11.8 Pg C in the mixed layer based on the primary production data of Antoine et al. [1996] based on CZCS chlorophyll observations, and computed an f-ratio (the ratio of new to total primary production) of 0.38-0.46. Using their 11.8 Pg C estimate of seasonal primary production and our estimate of extra-tropical carbon production (3.71-3.75 Pg C), we derive an f-ratio of approximately 0.3. Some caution must be exercised in interpreting SNO_{B} values strictly in relation to carbon new production. The assumption that the seasonal O_{2} outgassing approximates net community O_{2} production during the seasonal period of shoaling mixed layer, has been shown to yield reasonable results locally (Jenkins and Goldman, 1985; Emerson, 1987), although it is only approximately correct. Oxygen outgassing could be smaller than carbon production to the extent that some of the seasonal O_{2} production is stored in the mixed layer, or it could be either larger or smaller than carbon production to the extent that the air-sea O_{2} flux is also influenced by exchanges of O_{2} with deeper waters. Here we present an improved global monthly climatology of surface O_{2} anomalies and air-sea fluxes based on a weighted linear least-squares approach. The regressions use seasonal heat flux anomalies for spatial and temporal interpolation of selected historical O_{2} data. The method gives a robust relation for estimating the large-scale mean seasonal distribution of O_{2} anomalies and fluxes. We concentrate primarily on the distribution of the global air-sea seasonal O_{2} flux. Model simulated seasonal oceanic air-sea O_{2}/N_{2} contributions to the atmosphere compare well with seasonal variations in atmospheric O_{2}/N_{2} ratios at base-line stations in the Pacific Ocean. Optimization of the gas-exchange velocity for O_{2} by means of scaling factors shows that little adjustment is necessary between the simulated and observed O_{2}/N_{2} variations. This substantiates that the present O_{2} anomaly climatology together with the wind exchange velocity formulation of Wanninkhof [1992] for steady wind conditions provide a robust measure of the air-sea O_{2} flux. Our study suggests that the component of the air-sea O_{2} flux that correlates with heat flux dominates the large-scale air-sea O_{2} exchange on seasonal time scales. The results are useful as constraints on global seasonally resolved models. The global seasonal net oxygen outgassing is estimated to be about 0.9 Pmol O_{2} (1 Pmol= 10^{15} mol). Evaluation of the seasonal air-sea O_{2} flux attributable to thermal and biological sources indicates that the two components make roughly equal contributions to the seasonal air-sea exchange on hemispheric scales. The extra-tropical biological seasonal net outgassing is equivalent to a seasonal carbon production of about 3.8 Pg C based on an O_{2}:C ratio of 1.45. |