Testing an ocean carbon model with observed ... - Wiley Online Library

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, C07047, doi:10.1029/2007JC004428, 2008

Testing an ocean carbon model with observed sea surface pCO2 and dissolved inorganic carbon in the tropical Pacific Ocean James R. Christian,1 Richard A. Feely,2 Masao Ishii,3 Ragu Murtugudde,4 and Xiujun Wang4 Received 29 June 2007; revised 6 March 2008; accepted 26 March 2008; published 30 July 2008.

[1] A basin-scale carbon model for the tropical Pacific has been tested against in situ

observations of CO2 partial pressure (pCO2) and dissolved inorganic carbon. Best agreement between model and observations occurs when gas exchange is enhanced at low wind speeds and when frictional smoothing as a parameterization of mesoscale eddy stirring/mixing is minimal. However, different realizations of the biological pump are not equally sensitive to the friction parameters, and it is not possible to completely isolate the effects of physics and biology. The model ocean shows substantial interannual variability in pCO2 and CO2 flux which is strongly correlated with the Multivariate ENSO Index. Interannual variability is similar to other models and suggests a relatively small role for the ocean in interannual variability of atmospheric CO2 growth. There are significant areas where CO2 remained supersaturated throughout the 1997–1998 El Ninõ and there was net outgassing from the ‘‘Wyrtki Box’’ at all times, but the net flux from the full model domain was near zero at the peak of the event. Testing the model against ship-based observations produces a credible four-dimensional field of the tropical ocean carbon system. Sampling this field with methods analogous to those used in empirical reconstructions of CO2 flux suggests that those methods can underestimate the interannual variability by up to a factor of 2.5 depending on the grid resolution used. Models and observations are not currently adequate to state with confidence that undersampled mesoscale variability does not affect the variability of the regional aggregate flux. Citation: Christian, J. R., R. A. Feely, M. Ishii, R. Murtugudde, and X. Wang (2008), Testing an ocean carbon model with observed sea surface pCO2 and dissolved inorganic carbon in the tropical Pacific Ocean, J. Geophys. Res., 113, C07047, doi:10.1029/2007JC004428.

1. Introduction [2] The tropical Pacific Ocean plays a dominant role in the Earth’s climate and biogeochemical cycles, and especially in the generation of interannual variability. The historical record of atmospheric CO2 shows significant interannual variability in the rate of accumulation in the atmosphere, which is closely related to the El Ninõ – Southern Oscillation (ENSO) cycle [Bacastow, 1976; Elliott et al., 1991; Joos et al., 1999; Rayner et al., 1999; Rayner and Law, 1999; Watanabe et al., 2000]. While the source of much of the interannual variability appears to be ENSO, the relative contributions of the ocean and the terrestrial biosphere remain uncertain, and several studies have suggested

1

Fisheries and Oceans Canada, Canadian Centre for Climate Modelling and Analysis, University of Victoria, Victoria, British Columbia, Canada. 2 Pacific Marine Environmental Laboratory, National Oceanic and Atmospheric Administration, Seattle, Washington, USA. 3 Geochemical Research Department, Meteorological Research Institute, Tsukuba, Japan. 4 Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA.

that the ocean contribution is relatively small [Lee et al., 1998; Le Que´re´ et al., 2000; Obata and Kitamura, 2003; McKinley et al., 2004; Park et al., 2006]. However, direct observations of the partial pressure of dissolved CO2 (pCO2) in the tropical Pacific Ocean suggest that the ocean contributes about one third of the observed interannual variability in the atmospheric growth rate [Feely et al., 2002, 2004, 2006]. Large changes in pCO2 are observed in association with El Ninõ events [Feely et al., 1999, 2002; Chavez et al., 1999], and the tropical Pacific accounts for the majority of the ocean contribution [Le Que´re´ et al., 2000; Feely et al., 2002; Park et al., 2006]. [3] A number of modeling studies have examined the airsea exchange of CO2 in the tropical Pacific [e.g., Winguth et al., 1994; Le Que´re´ et al., 2000, 2003; Obata and Kitamura, 2003; McKinley et al., 2004; Wetzel et al., 2005; Jiang and Chai, 2006; Wang et al., 2006]. Other modeling studies have looked at biogeochemical cycles in the tropical Pacific but did not specifically address air-sea exchange of CO2 [e.g., Toggweiler and Carson, 1995; Chai et al., 1996; Christian et al., 2002; Jiang et al., 2003]. Here we examine the effects of climate variability (e.g., ENSO) and biogeochemical processes on the carbon cycle in the tropical Pacific using a model with high horizontal and vertical

Published in 2008 by the American Geophysical Union.

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CHRISTIAN ET AL.: TROPICAL PACIFIC CARBON CYCLE

resolution and fully prognostic biology including iron [Christian et al., 2002; Wang et al., 2005], and more than 10 years of direct observations of DIC and pCO2 in surface seawater [Cosca et al., 2003; Ishii et al., 2004]. [4] Underway pCO2 data are among the most spatially extensive ship-based observations available in ocean biogeochemistry [Takahashi et al., 2002; Feely et al., 2006]. Although they lack the synoptic coverage of satellite ocean color data, these data have a spatial and temporal coverage much greater than is available for DIC or other variables measured from discrete bottle samples. To construct synoptic fields of sea surface pCO2 and CO2 flux the possible approaches are (1) interpolation of sparse, irregularly spaced data, (2) extrapolation using empirical relationships with synoptically observed fields like sea surface temperature (SST), or (3) models. Our approach in this paper is to test the model directly against the observations at the time and place where the observations were made, thereby creating a synoptic four-dimensional field with extensive sea-truth. This field can be used to evaluate empirical extrapolation methods that are otherwise largely unconstrained. In the process, we have identified areas of sensitivity of the modeldata agreement to details of model formulation including biology, gas exchange, and horizontal mixing.

2. Methods 2.1. Model Description [5] The biogeochemical ocean model has been described previously [Christian et al., 2002; Wang et al., 2005, 2006] and is discussed only briefly here. The ocean circulation model is a 20-layer reduced-gravity s-coordinate model, with the first layer corresponding to the ocean’s mixed layer [Chen et al., 1994]. The grid employed here stretches from 124°E to 76°W with uniform 1° resolution in longitude, and from 30°S to 30°N with maximal latitudinal resolution in the equatorial band (1/3° at latitudes < 10°). The model was run using NCEP weekly (6 d) mean wind stress computed from 10 m winds with a constant drag coefficient of 0.0015, and GPCP (monthly) interannual precipitation [Huffman et al., 1997]. Climatological solar radiation was used as by Christian et al. [2002]. A simple atmospheric boundary layer model is employed so that heat fluxes and evaporation are freely evolving [Murtugudde et al., 1996]. [6] The N and Fe based ecosystem model was employed as described previously, with a few minor modifications. The biogeochemical model contains a nine-component ecosystem with two size classes each of phytoplankton, zooplankton, and detritus, and the nutrients nitrate, ammonium, and iron [Leonard et al., 1999; Christian et al., 2002]. Phytoplankton growth is simultaneously limited by irradiance and nitrogen and iron availability, with fixed Fe/N ratios so that iron in all compartments except the dissolved pool is not modeled explicitly. Modifications from the original version include a hyperbolic tangent formulation for the carbon-to-chlorophyll ratio, giving stronger dependence of chlorophyll on irradiance at low irradiance [Christian et al., 2006], nitrification as a function of irradiance rather than depth [Wang et al., 2006], and temperature-dependent remineralization of detritus [Wang et al., 2006]. The fractional solubility of aeolian Fe was reduced to 1% following correction of a scaling error in the original [Christian et al.,

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2002] code. All other biogeochemical model parameters are the same as in our previously published simulations [Christian et al., 2002; Wang et al., 2005]. [7] The carbon cycle model is coupled to the ecosystem model using the Redfield C:N ratio of 6.625. The carbon chemistry model is as described by Signorini et al. [2001] and Wang et al. [2006]. Air-sea gas exchange is calculated using pseudostress (u2) calculated from the wind stress (linearly interpolated from 6-day products). The atmospheric CO2 boundary condition was constructed from ship-of-opportunity flask sampling data (NOAA-CMDL). Six years of data (1987– 1993) were used to construct a monthly climatology interpolated to 2° latitude from the original sampling resolution of 5° (longitude invariant) for the base year 1990. Temporal trends taken from the Mauna Loa time series were superimposed on this. Alkalinity was modeled as a linear function of salinity, TA ¼ 65:8881S 4:124

(r = 0.98, n = 2281) using bottle data compiled by the Global Ocean Data Analysis Project (GLODAP; Key et al., 2004). The GLODAP gridded (1°) data set was used for initialization of DIC. 2.2. Data [8] Semicontinuous underway observations of seawater CO2 partial pressure (pCO2(sw)) have been collected over many years by NOAA-PMEL and JMA-MRI personnel [e.g., Cosca et al., 2003; Ishii et al., 2004; Feely et al., 2006]. These data have been mapped to our model grid by a simple averaging procedure: if any data are present the mean is used. Because the data are clustered along the ship tracks, the number of data is therefore much reduced (most grid points that contain any data contain a substantial number). This mapping was carried out monthly from 1992 to 2003, and for the 12 months of the ‘climatological’ annual cycle. The number of climatological data points decreases with the threshold (Nmin) specified for a climatology to be calculated (the minimum number of years with data in a particular month at a given grid point). There are a large number of model grid points that were sampled in only 1 year, so anomaly comparisons are based on a smaller number of data than the raw data comparison. The total number of data is 3263 for Nmin = 3. Nmin = 3 was chosen as the standard value; correlation and error statistics were not sensitive to the value chosen. 2.3. Model Experiments [9] A series of experiments were conducted to test the sensitivity of the model to the different ecosystem formulations, horizontal mixing (‘‘friction’’), gas exchange, and precipitation/wind variability. These are summarized in Table 1. The different ecosystems have been described in previous publications [Christian et al., 2002; Wang et al., 2005, 2006] and are used here as presented in those papers. The gas exchange formula derived from the GASEX 2001 cruise data [Feely et al., 2004; McGillis et al., 2004], k660 ¼ 8:2 þ 0:014u310 ;

where k660 (cm h1) is the gas exchange coefficient for CO2 at 20°C (Schmidt number of 660) is used as the default, as

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Table 1. List of Model Experimentsa Experiment

Description

1 2 3 4 5 6 7 8

high friction, original biology low friction, original biology high friction, modified biology low friction, modified biology high friction, W92 gas exchange low friction, W92 gas exchange climatological precipitation climatological wind

a

All experiments use GASEX gas exchange formulation except where Wanninkhof [1992] (W92) specified, and interannual NCEP wind and GPCP precipitation except where climatology specified. ‘‘Original’’ biology signifies work by Christian et al. [2002]; ‘‘modified’’ biology is work by Wang et al. [2005]. Original biology and low friction used except where specified. High friction indicates fourth-order Shapiro filter, low friction indicates eighth order.

its enhanced gas exchange at low wind speed improves agreement with observed pCO2. Sensitivity experiments were conducted to compare the performance of the widely used formula of Wanninkhof [1992]. [10] Horizontal stirring and mixing is represented by a Shapiro filter [Shapiro, 1970; Gent and Cane, 1989]. We modified the intensity of mixing by altering the order and frequency of application of the filter. Our standard simulations [Christian et al., 2002; Wang et al., 2005, 2006] have employed a fourth-order filter applied every 12 time steps (‘‘high friction’’); our ‘‘low friction’’ runs employ an eighth-order filter applied every 4 steps. Increased frequency of application is necessary for numerical stability with the higher-order filter, but the overall effect is significantly reduced smoothing in the horizontal direction. The Shapiro filter is a form of low-pass filter and the order is proportional to the wave number cutoff; the order 8 filter permits a greater fraction of the intermediate frequency (mesoscale) variability that the order 4 filter excludes. The model has previously been shown to give more realistic circulation at enhanced resolution and reduced friction by reducing spurious horizontal (primarily meridional) mixing of heat into

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the equatorial zone [Jochum et al., 2005]. We will show below that even without altering the resolution, reduced friction can significantly improve agreement with shipbased observations of DIC and pCO2.

3. Results 3.1. Data Distribution 3.1.1. Temporal and Spatial Distribution [11] The spatial pattern of pCO2 for the data mapped to the model grid is shown in Figure 1. The basic spatial pattern is well known: supersaturation in the cold tongue and values near atmospheric equilibrium in the warm pool, the convergence zones, and the subtropical gyres. Similar plots for individual months show the same general pattern, with allowance for seasonal and interannual variability, but will show much more white space where no data are available (not shown). [12] The temporal distribution of the data is illustrated in Figure 2. The merged data set includes extensive coverage of both seasonal and interannual variability from 1992 to 2003 (Figure 2). There are some biases in that particular regions tend to be sampled in particular seasons, especially in the western tropical Pacific where the MRI conducts regular cruises each year during boreal winter in order to document the ENSO-related interannual variability (Figure 2d). The PMEL data set has more complete seasonal coverage, and extensive interannual coverage, with the notable exception of 2001 (Figure 2a). The merged data set covers both aspects of variability well in the spatial aggregate, although there are still seasonal biases in some regions. By contrast, the temporal coverage available in the GLODAP database [Key et al., 2004] is extremely limited: 1655 observations of DIC and alkalinity at depths less than 50 m span only the years 1991 through 1996, with 47% in 1992, 31% in 1994, and none in 1995. 3.1.2. Data Comparison Across Laboratories [13] The spatial overlap between the two data sets permits a limited comparison of the methodologies used. There are no overlap points where the same model grid box was occupied by both institutions in the same month of the same

Figure 1. Spatial distribution of underway pCO2. Data were mapped to the model grid, and mean values are shown for model grid boxes with at least three data points. 3 of 17

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Figure 2. Temporal distribution of underway pCO2 data. Number of model grid boxes sampled (left) by year and (right) by month of year (1 is January, etc.).

year. However, there are a number of cases where the same grid boxes were occupied by both institutions in the same month of different years, and a larger number where a ‘‘temporally agnostic’’ comparison (occupation of the same grid box at any time during the 12-year period) is possible. [14] For the years 1992 – 2003 there are 136 occupations by the U.S. and Japanese ships of the same model grid boxes, which range from 10°S to 5°N and 160°E to 160°W. There are 36 occupations in the same month. For the temporally agnostic comparison, the correlation coefficient is 0.33 (P < 0.01, n = 136). For the monthly climatological comparison it is 0.17 (NS). The lower values attest to the importance of interannual variability relative to the annual cycle at these locations, and to the strong spatial gradients in water properties that exist at all times of the year. The monthly matchups cover a fairly extensive area of the overall region of overlap, but with very incomplete spatial coverage, with most of the data occurring on transects north of the equator along 165°E and south of the equator along 170°W (not shown). [15] The relative magnitudes of the measurements are similar. The values are on average about 10% larger in the MRI data (Figure 3). The discrepancy is slightly smaller when time of year is taken into account (9.8% versus 11.0%), and the median offset is slightly smaller than the mean in each case. These numbers change only slightly when a uniform 1° or 2° grid is used in place of the model grid (not shown). At coarser resolution, the correlation coefficients for the temporally agnostic comparison decline

and become insignificant, while those for the monthly climatological comparison increase. [16] Given the relatively small difference in pCO2 and the differences in time of sampling, there is no compelling reason to conclude that the differences are due to analytical methods. Plotting the ratio against SST or sea surface salinity (SSS) can give some idea of the regional distribution and the relationship of differences between the two laboratories to spatial and temporal gradients in water properties. For a uniform 2° grid, with time of year taken into account, the discrepancy is largest at lower temperatures (not shown); that is, it appears that the differences are largest in the eastern parts of the region where spatial and temporal variability is largest, suggesting a sampling rather than an analytical explanation. However, in the temporally agnostic comparison the opposite is true, with ratios near 1 for temperatures 1 (Figures 7 and 8), and may permit the model to generate extreme values that are not observed (e.g., Figure 4). This problem may be further enhanced at eddy-resolving resolution. [38] Jiang and Chai [2006] assert that our model overestimates the role of biology and underestimates that of temperature in determining the variability of ocean surface pCO2 in the NINO3 region. They conducted experiments where either the temperature or the net community production (NCP) were fixed to their annual mean values, and found that most of the variability in pCO2 is driven by temperature rather than DIC. Because our model underestimates the amplitude of the annual cycle of SST, they conclude that it is biased toward the conclusion that DIC is the dominant control. However, the annual cycle accounts for a fairly large fraction of the total SST variance in the Eastern Equatorial Pacific, but much less in the Central Equatorial Pacific, and our model reproduces the amplitude of the interannual variability quite well [Wang et al., 2006, and references therein]. In addition, observed SST is affected by surface skin temperature effects especially during the warm season when the wind is weak. While skin temperature is an important control on CO2 flux [Robertson and Watson, 1992], it is important to compare analogous quantities when evaluating model errors. [39] We cannot repeat the experiment of Jiang and Chai [2006] exactly with our layer model, but we can do a reasonably analogous offline calculation with modeled DIC and SST. Over the period 1990 – 2003, modeled CO2 flux with annual mean SST is correlated with the ‘true’ flux (offline calculation with varying SST) at r = 0.97 over the NINO3 region (not shown); with annual mean DIC it is anticorrelated (r = 0.75). This result is robust to the replacement of modeled with observed SST. In other words, most of the seasonal and interannual variability of pCO2 is driven by changes in DIC, and SST effects act largely to moderate the extremes (higher pCO2 and increased outgassing during El Ninõ when the efflux is low, and lower pCO2 under strong upwelling conditions). In the constant DIC scenario, the largest outgassing occurs during the 1997 – 98 El Ninõ owing to elevated SST (not shown), opposite to what actually occurs [e.g., Feely et al., 2002, 2006; Ishii et al., 2004]. While Jiang and Chai [2006] held only NCP

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constant rather than DIC and so include dynamical effects on DIC, this experiment shows that DIC variability plays a very important role in regulating CO2 flux, and that this result is robust with respect to errors in modeled SST. [40] The relative roles of dynamics, temperature, and biology are difficult to separate because of strong temporal correlation. Le Que´re´ et al. [2003] also minimize the importance of biological variability, noting that in their model biologically induced changes in pCO2 are much smaller than those associated with temperature and ocean dynamics. They observe that in the subtropics there is a consistently positive correlation between pCO2 and SST (solubility control) while in the equatorial zone there is a negative correlation (ocean dynamics control). However, in upwelling systems variations in biological production are systematically related to dynamically induced variations in surface DIC (Table 5); upwelling of DIC-enriched subsurface waters also brings nutrients to the surface resulting in enhanced export production. This does not affect the sign of the pCO2-SST relationship, but it does determine the magnitude of outgassing. This systematic and consistent relationship, along with the additional moderating influence of SST noted above, may explain in part why the interannual variability of ocean-atmosphere CO2 exchange appears to be small (see section 4.4). However, it is likely that the model responds to upwelling with increased export in a more consistent and linear fashion than do real ocean ecosystems. The modified [Wang et al., 2005] ecosystem model has a less linear response due to the removal of multiplicative grazing, and at least in the high-friction case, this model (experiment 3) performs much better than the original one (experiment 1) for identical circulation. Increased variance at small to intermediate scales via modifications to either the physical or biological model appears to enhance predictive skill with respect to pCO2, and the variability of the biological response to upwelling is probably underestimated in models generally. 4.3. Role of Precipitation [41] Precipitation observations have been significantly improved in recent years. The results of this study suggest an important role for precipitation observations in the further refinement of ocean carbon cycle modeling. Errors in nDIC are systematically smaller than those in DIC (Figures 6 and 8), suggesting that the circulation and biology parts of the model are performing relatively well and that much of the error in DIC results from errors in salinity arising from the precipitation boundary condition. [42] In the climatological wind + interannual precipitation case (experiment 8), the anomaly correlation for nDIC is 0.03, whereas for DIC it is 0.36 (Table 4). So there is a significant fraction of interannual variability in DIC that is reproduced by the interannual precipitation forcing. In the climatological precipitation + interannual wind case (experiment 7), the anomaly correlation is 0.76 for nDIC and 0.66 for DIC. With interannual precipitation (experiment 2) this increases to 0.83 and 0.76 respectively (Table 4). Similarly, the anomaly correlation for pCO2 in the core ENSO region increases from 0.52 to 0.65 (Table 2). So it is clear that climatological precipitation can give a reasonable first approximation, and the gains in skill with interannual precipitation are incremental. However, while nDIC is a

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useful index of the simulation of circulation and biology, it is important to note that all processes affecting DIC affect pCO2 and CO2 flux, and our correlations are generally better for nDIC than for pCO2. [43] These results also indicate the limitations of the common practice of relaxing SSS to climatology as a parameterized freshwater flux boundary condition. Several previous models that found low interannual variability in CO2 flux applied this method [Obata and Kitamura, 2003; McKinley et al., 2004; Wetzel et al., 2005]. SSS is a freely evolving quantity in our model, and forcing with interannual precipitation provides a modest but significant increase in predictive skill with respect to observed pCO2 in regions of high interannual variability (Figure 5 and Table 2). 4.4. Ocean Role in Atmospheric CO2 Growth [44] There has been substantial effort expended in determining the causes of interannual variability in atmospheric CO2 growth rate; the emerging consensus is that terrestrial rather than ocean processes account for the majority of the variability [e.g., Bousquet et al., 2000; Patra et al., 2005]. However, it is not clear that either ocean models or ocean observations give definitive estimates of the ocean contribution. Observation-based estimates to date have been based on empirical relationships between pCO2 and quantities like temperature and salinity that are more broadly observed [Lee et al., 1998; Loukos et al., 2000; Feely et al., 2002, 2006; Ishii et al., 2004; Park et al., 2006], and the models have various errors and limitations. The pCO2 data coverage is quite extensive in both space and time, but still far from synoptic in its coverage of (x, y, t) space (Figures 1 and 2). By testing the model directly against the observations at the time and place where they were collected, we create a synoptic 4D data set that is as credible as possible, and identify model configurations that show either too little variability (e.g., experiments 1 and 5), or too much (experiment 4). [45] Interannual variability in the total ocean-atmosphere CO2 flux is consistent with other studies, and does not suggest that the ocean contribution to CO2 growth has been significantly underestimated. For the original biology, interannual variability is significantly greater in low-friction (experiments 2 and 6) than high-friction runs (experiments 1 and 5) within the Wyrtki Box, where much of the variability is associated with mesoscale processes (Figure 10). For the modified biology, it is substantially larger in the low-friction case (Figure 10), but the direct model-data comparisons do not suggest that this enhanced variability reflects real processes (Figures 7 and 8). Whether by changes to the friction parameters or to the ecosystem model, it is clear that enhanced variability, relative to experiment 1 (high friction + original biology), is required for maximal fidelity to the observations (Figures 5 – 8). However, this enhanced variability appears to have relatively little effect on the domainintegrated interannual variability. Further experiments need to be conducted to determine whether this result holds at higher (eddy-resolving) resolution. [46] The interannual variability is similar in experiments 2, 3, and 6 (Figures 10 and 11); experiment 6 has a stronger seasonal cycle due to weaker gas exchange at low wind speed. The mean efflux is larger in experiment 3, which shows consistent outgassing even at the peak of the 1997–

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98 El Ninõ (Figure 11). This experiment appears to have a high bias in the equatorial zone (Figure 9) but has among the lowest RMS error overall (Figure 7 and Table 2). Experiment 3 is the most consistent with the estimates of Feely et al. [2002] for non– El Ninõ conditions, which are at the high end of our results. Low-friction runs (experiments 2 and 6), by contrast, show a flux near zero in early 1998 (Figure 11), even with the enhanced flux at low wind speed provided by the GASEX algorithm (experiment 2). The 1997 – 1998 El Ninõ was notable for the near disappearance of CO2 supersaturation in the tropical Pacific [Chavez et al., 1999; Feely et al., 2002]. Substantial areas remained supersaturated, but only slightly, whereas more usual values are in excess of 70 matm [Feely et al., 2002, 2006]. Because El Ninõ also implies a sharp reduction in the wind speed, the outgassing was extremely small even where slight supersaturation persisted. Feely et al. [2002] estimate an outgassing of 0.9 PgC a1 for 1996 and 0.4 PgC a1 for 1997 – 1998. The fluxes for the same periods in our model are 0.75 and 0.49 PgC a1 for experiment 3 and 0.40 and 0.16 PgC a1 for experiment 2. So relative to these databased estimates experiment 3 slightly underestimates the variability while experiments 2 and 6 underestimate both the mean and the variability. For the peak El Ninõ period from spring 1997 through spring 1998, Feely et al. [2002] estimate 0.2 PgC a1 while experiment 3 gives 0.43 PgC a1 and experiment 2 gives 0.08 PgC a1. So experiment 3 does not have a strong El Ninõ state, although in absolute terms the difference between El Ninõ and non– El Ninõ states is similar to experiments 2 and 6 (Figure 11). [47] Our analysis of published empirical methods for estimating the interannual variability suggests that these methods have their own intrinsic biases. The Lee et al. [1998] method estimates the mean efflux quite well, but underestimates the interannual variability especially at the coarse resolution of the pCO2 climatology (Table 6). The Loukos et al. [2000] method does not underestimate the interannual variability in absolute terms, but the coefficient of variation is very low (Table 6) because the mean is high; that is, the method significantly overestimates surface pCO2 and ocean outgassing (Figure 12). Both methods produce impossible values locally and should only be used for broad regional averages. The method of Cosca et al. [2003] creates nonphysical ’jumps’ at the boundaries between the seasons within which its coefficients are defined, even within the limited domain where it was applied by Park et al. [2006]. The combined method of Park et al. [2006] has larger interannual variability than Lee et al. but also has a high bias in the mean efflux (Table 6 and Figure 12). [48] By creating a ‘known’ continuous four-dimensional field we can evaluate these methods independently of data limitations. The results suggest that the underestimation of interannual variability is as much a function of data limitations as of the methods themselves. In particular, the Lee et al. [1998] method loses variability rapidly as the grid resolution decreases (Table 6 and Figure 12), and the threshold for this degradation appears to be somewhat finer than the resolution of the Takahashi climatology. Even within the deep tropics, the 1997 – 1998 El Ninõ is almost entirely invisible in a time series constructed with this method at 4° resolution, whereas at 2° its amplitude is similar to the original model resolution (not shown). So the

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question remains whether either the models or the observations actually resolve the relevant scales of variability. Models in general do not, and new dynamics are introduced at eddy-resolving resolution [e.g., Jochum et al., 2005]. The data product generates a mean for each grid box that is necessarily based on the observations available, and mesoscale processes are probably undersampled. While it is not certain that resolving these processes will significantly affect the temporal variance of the regional aggregate flux, by modeling and sampling the finer spatial scales we are likely to learn unexpected new things about how tropical oceans function in the climate system. [49] Acknowledgments. We are very grateful to all of the MRI and PMEL staff who have collected and processed these data over many years. The investigators who collected the data on EqPac 1992 provided an invaluable data set for the development and testing of our model. Atmospheric CO2 flask data from NOAA-CMDL were also invaluable in creating the boundary conditions for the model. The Taylor diagrams were drawn with scripts modified from a version written by Kos Zahariev.

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R. A. Feely, Pacific Marine Environmental Laboratory, National Oceanic and Atmospheric Administration, Seattle, WA 98115, USA. M. Ishii, Geochemical Research Department, Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan. R. Murtugudde and X. Wang, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USA.

J. R. Christian, Fisheries and Oceans Canada, Canadian Centre for Climate Modelling and Analysis, University of Victoria, P.O. Box 1700 STN CSC, Victoria, BC V8W 2Y2, Canada. ([email protected])

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