Abstract
 Top of page
 Abstract
 1. Introduction
 2. Algorithm Development
 3. Model Evaluation and Caveats
 4. Seasonal Evolution of Ω_{arag}^{e} on the Oregon Coast
 5. Implications
 Acknowledgments
 References
 Supporting Information
[1] We developed a multiple linear regression model to robustly determine aragonite saturation state (Ω_{arag}) from observations of temperature and oxygen (R^{2} = 0.987, RMS error 0.053), using data collected in the Pacific Northwest region in late May 2007. The seasonal evolution of Ω_{arag} near central Oregon was evaluated by applying the regression model to a monthly (winter)/biweekly (summer) watercolumn hydrographic timeseries collected over the shelf and slope in 2007. The Ω_{arag} predicted by the regression model was less than 1, the thermodynamic calcification/dissolution threshold, over shelf/slope bottom waters throughout the entire 2007 upwelling season (May–November), with the Ω_{arag} = 1 horizon shoaling to 30 m by late summer. The persistence of water with Ω_{arag} < 1 on the continental shelf has not been previously noted and could have notable ecological consequences for benthic and pelagic calcifying organisms such as mussels, oysters, abalone, echinoderms, and pteropods.
1. Introduction
 Top of page
 Abstract
 1. Introduction
 2. Algorithm Development
 3. Model Evaluation and Caveats
 4. Seasonal Evolution of Ω_{arag}^{e} on the Oregon Coast
 5. Implications
 Acknowledgments
 References
 Supporting Information
[2] Since the preindustrial, atmospheric loading of CO_{2} from fossil fuel combustion and land use changes has driven an anthropogenic ocean uptake of 146 ± 20 Pg C (updated from Sabine and Feely [2007]) and a corresponding average surface water pH change of 0.1 units [Feely et al., 2004]. Accelerating emission rates and reduced buffering capacity will decrease pH by as much as 0.3–0.4 units by the end of this century under businessasusual scenarios [Orr et al., 2005]. Effects of these “ocean acidification” changes on marine organisms are still under intense study [Kleypas et al., 2006; Fabry et al., 2008; Doney et al., 2009], but increased ocean CO_{2} content will result in a reduced saturation state for calcium carbonate minerals and potentially deleterious impacts for organisms that form CaCO_{3} shells, including corals, pteropods, foraminifera, and commercially important shellfish and their larvae.
[3] The saturation state (Ω) of CaCO_{3} minerals is determined by the relationship:
where K′_{sp}, the stoichiometric solubility product, is a function of temperature, salinity, pressure, and the particular mineral phase (aragonite or calcite). In a thermodynamic sense, Ω > 1 indicates mineral precipitation is favored and Ω < 1 indicates dissolution is favored, although biogenic calcification is subject to “vital effects” such as organic shell coatings and speciesspecific calcification mechanisms, and calcification/dissolution can occur when ambientwater Ω values indicate opposing thermodynamic effects [Langdon et al., 2003; Tunnicliffe et al., 2009]. However, recent experiments indicate that Ω < 1 adversely impacts some organisms; Fabry et al. [2008] reported net dissolution in live pteropods within 48 hours of exposure to undersaturated water. Because aragonitic CaCO_{3} has a metastable crystalline structure and is ≈50% more soluble than calcite [Mucci, 1983], organisms that form aragonitic shells will likely be affected first, and perhaps most severely, by ocean acidification.
[4] Transient episodes of reduced aragonite Ω (Ω_{arag}) have already been noted in productive eastern boundary upwelling systems such as the California current system [Feely et al., 2008a]. Understanding the duration, intensity, and overall ecological impact of these events is a key need in economically and socially important coastal fisheries regions. Here we present an approach, updated from Feely et al. [2008b], to determine Ω_{arag} from temperature and O_{2}, using data collected on a 2007 survey of North American Pacific coastal waters. We justify the approach with a statistical evaluation, and apply it to a hydrographic timeseries from the central Oregon coast to evaluate seasonal changes in Ω_{arag}.
2. Algorithm Development
 Top of page
 Abstract
 1. Introduction
 2. Algorithm Development
 3. Model Evaluation and Caveats
 4. Seasonal Evolution of Ω_{arag}^{e} on the Oregon Coast
 5. Implications
 Acknowledgments
 References
 Supporting Information
[5] Ω_{arag} is a function of temperature (T), salinity (S), pressure (P), and the [Ca^{2+}] and [CO_{3}^{2−}] of seawater (equation (1)). Because [Ca^{2+}] changes are proportionally small in seawater, variations in Ω_{arag} are largely determined by changes in [CO_{3}^{2−}], which can be predicted from observations of dissolved inorganic carbon (DIC) and total alkalinity (TA). DIC concentrations are governed by physics (solubility, surface gas exchange) and biology (photosynthesis/respiration) and therefore should be a function of T, S, and either O_{2} or NO_{3}^{−} [Anderson and Sarmiento, 1994; Lee et al., 2000]. TA can also be modeled as a function of T and S [Lee et al., 2006]. We would therefore expect a predictive relationship for Ω_{arag} as a function of T, S, P, O_{2}, NO_{3}^{−}, or a subset of these parameters.
[6] A hydrographic survey of the U.S. west coast in 2007 [Feely et al., 2008a] allowed an opportunity to develop predictive relationships for Ω_{arag} based on contemporaneous T, S, P, O_{2}, and NO_{3}^{−} measurements. We first evaluated a linear additive model of the following form:
where Ω_{arag}^{e} is the empirically predicted aragonite saturation state, and the coefficients β_{i} are empirical constants. We determined coefficients for equation (2) using an ordinary leastsquares regression of Ω_{arag} observations collected in the Pacific Northwest (PNW) region (transects of Washington, Oregon, and N. California coastal waters, Figure 1a), using only data in the 30–300 m depth range to minimize localized effects of surface warming, gas exchange and riverine inputs and to include only relevant source water masses for the shelf/slope region. Although all resulting regression coefficients were significant, tests of collinearity among the independent variables (via pair wise regression and the variance inflation factor test, see Table 1) indicated that S, O_{2}, and NO_{3}^{−} were too closely related, leading to potential errors in leastsquares regression coefficients [Kutner et al., 2004]. Stepwise regression and regression statistics (R^{2}, RMS error) subsequently identified O_{2} as the most robust predictor of the three collinear variables.
Table 1. Summary of Model Parameters, Coefficients, and Indicators Used in Model Selection^{a}Parameters  VIF^{b}  R^{2}  RMS Error  Coefficients ± STD Error^{c}  Comments 


T, S, P, O_{2}, NO_{3}^{−}  3.9, 24, 2.9, 35, 9.3  0.966  0.090  β_{0} = 6.3 ± 1.7 β_{1} = 9.5·10^{−2} ± 1.0·10^{−2}β_{2} = −1.94·10^{−1} ± 5.0·10^{−2}β_{3} = 8.6·10^{−4} ± 1.5·10^{−4}β_{4} = 2.82·10^{−3} ± 4.5·10^{−4}β_{5} = −3.7·10^{−3} ± 1.7·10^{−3}  O_{2}, S, and NO_{3}^{−} collinear (VIF > 5) 
T, O_{2}, P  2.8, 3.8, 3.0  0.965  0.084  β_{0} = −0.521 ± 7.0·10^{−2}β_{1} = 7.74·10^{−2} ± 8.3·10^{−3}β_{2} = 5.18·10^{−3} ± 1.3·10^{−4}β_{3} = 1.16·10^{−3} ± 1.3·10^{−4}  Residuals show bias at high/low O_{2} and T (see Figure S1) 
O_{2}  N/A  0.946  0.088  β_{0} = 1.145 ± 6·10^{−3}β_{1} = 4.99·10^{−3} ± 7·10^{−5}  Residuals show bias, as above 
(O_{2}–O_{2,r}), (T–T_{r})·(O_{2}–O_{2,r})  1.5, 1.5  0.987  0.053  α_{0} = 9.242·10^{−1} ± 4.4·10^{−3}α_{1} = 4.492·10^{−3} ± 5.0·10^{−5}α_{2} = 9.40·10^{−4} ± 3.4·10^{−5}  T_{r} = 8°C; O_{2,r} = 140 μmol/kg; 
(T–T_{r}), (O_{2}–O_{2},_{r}), (T–T_{r})·(O_{2}–O_{2},_{r}), (S–S_{r})·(O_{2}–O_{2},_{r}), (P–P_{r})·(O_{2}–O_{2},_{r})  9, 33, 7, 23, 19  0.990  0.043  α_{0} = 9.079·10^{−1} ± 4.6·10^{−3}α_{1} = 3.37·10^{−2} ± 7.0·10^{−3}α_{2} = 3.4710^{−3} ± 1.8·10^{−4}α_{3} = 7.49·10^{−4} ± 5.9·10^{−5}α_{4} = −1.32·10^{−3} ± 1.3·10^{−4}α_{5} = 5.8·10^{−6} ± 1.2·10^{−6}  T_{r} = 8°C; O_{2,r} = 140 μmol/kg; P_{r} = 200 dbar S_{r} = 34 
[7] A multiple linear regression of T, P, and O_{2} yielded significant regression coefficients and reasonable regression statistics (Table 1). However, residuals for this relationship showed a strong bias, i.e., overestimation of Ω_{arag}^{e} at minimum and maximum T and O_{2} (see Figure S1 of the auxiliary material). This bias is likely the result of the nonlinear dependence of CO_{3}^{2−} on TA and DIC, which arises in coastal waters with high pCO_{2} and significant contributions to TA from noncarbon species. We examined several possible nonlinear terms and found that the bias could be minimized through the addition of an interaction term between T and O_{2} (Figure S1); when this term is added, P and T are no longer significant as predictor variables. To reduce large magnitudes of the product of T · O_{2} and subsequent errors in the leastsquares regression analysis (Table 1) [Kutner et al., 2004], we normalized each term by subtracting a reference value for each variable, i.e.,
Where α's indicate regression coefficients and T_{r} and O_{2,r} are values representative of upwelling source water in the PNW region (T_{r} = 8°C and O_{2,r} = 140 μmol/kg, see Figure 2 and Table 1). The resulting model had improved regression statistics (Table 1) and resulted in Ω_{arag}^{e} predictions that correctly reproduce both the magnitude and depthdistribution of Ω_{arag} observations for the effective range experienced over the shelf/slope areas (≈0.6 to 2.2) of the PNW region (Figure 1).
3. Model Evaluation and Caveats
 Top of page
 Abstract
 1. Introduction
 2. Algorithm Development
 3. Model Evaluation and Caveats
 4. Seasonal Evolution of Ω_{arag}^{e} on the Oregon Coast
 5. Implications
 Acknowledgments
 References
 Supporting Information
[8] We evaluated the skill of the model described by equation (3) by comparison of the unexplained error in Ω_{arag}^{e} and the ability to constrain Ω_{arag} given analytical uncertainties in DIC and TA (2 and 3 μmol/kg, respectively [Feely et al., 2008a]). Uncertainty in Ω_{arag} was determined by a Monte Carlo approach, in which DIC and TA inputs into the Matlab^{®} program CO2SYS [van Heuven et al., 2009] were varied randomly about chosen values for the PNW data, with standard deviations equal to analytical uncertainties. The 1σ values of 1000 individually calculated Ω_{arag} determinations, 0.017/0.034 for minimum/maximum Ω_{arag} values in the PNW data (0.61/2.22, respectively), represent the theoretical lower limit for unexplained random error, ɛ, in any model used to predict Ω_{arag}^{e}. The RMS error determined for the equation (3) model is close to, but still slightly higher than, the limit calculated for analytical uncertainties alone (ɛ). Although adding new terms to the regression model causes the RMS error to approach ɛ, the contribution of these additional terms to the explained variance is marginal (Table 1). To avoid overfitting, we rejected these models. A simple model based only on O_{2}, which was the strongest predictor variable of Ω_{arag}^{e} (R^{2} = 0.946, RMS error 0.088; Table 1) was also considered. However, the O_{2} model had a higher RMS error and a strong bias in residuals similar to that observed for the multiple linear regression of T, P, and O_{2} (Figure S1). Based on these observations, we chose the equation (3) model.
[9] Because the only Ω_{arag} data available for algorithm development in this region are from late May 2007, we note there may be important caveats to a seasonal application of equation (3). However, three lines of evidence indicate seasonal application is justified. First, biologicallydriven changes in Ω_{arag} for the 30–300 m depth range (i.e., due to remineralization of organic matter over the productive summer months) are to a first order driven by changes in DIC rather than TA, since diatoms typically dominate coastal upwelling systems [Lassiter et al., 2006]. DIC and O_{2} changes are expected to be proportional in remineralization zones that are not anoxic [Hales et al., 2005; Anderson and Sarmiento, 1994], and therefore changes in DIC should be inherently captured in an algorithm involving O_{2}. Second, the TS (and TO_{2}) range experienced spatially in the PNW data is similar to the range observed seasonally near Newport (see Figure S2), suggesting that the water masses present in the seasonal data are present in the regional PNW data. Finally, algorithm developments for the Southern California Bight region suggest no significant bias of algorithm development using only late May data (i.e., difference of measured and predicted values for August 2008 was 0.075 (S. Alin, unpublished data, 2009)). As more Ω_{arag} data become available, the algorithm for this region can be tested and refined. Nevertheless, these arguments point toward the ability to model the seasonal Ω_{arag} dynamics near Newport with the data in hand.
[10] One potential time frame when algorithm predictions could deviate from observations is between February and May. PNW coastal waters experience intense river inputs during the rainy winter months, and the TA:DIC signature of these freshwaters is often different than in the open ocean [Park et al., 1969]. Proportionality of [Ca^{2+}] to salinity, an assumption used in calculating Ω_{arag}, may also change during these months. Consequently, we do not present predictions for this time period.
4. Seasonal Evolution of Ω_{arag}^{e} on the Oregon Coast
 Top of page
 Abstract
 1. Introduction
 2. Algorithm Development
 3. Model Evaluation and Caveats
 4. Seasonal Evolution of Ω_{arag}^{e} on the Oregon Coast
 5. Implications
 Acknowledgments
 References
 Supporting Information
[11] We calculated the seasonal evolution of Ω_{arag}^{e} on the shelf and slope near Newport, Oregon with the model described by equation (3) and a timeseries of T and O_{2} data (described by Peterson and Keister [2003]) collected on biweekly to monthly intervals in 2007. The central Oregon coast is located in the northern end of the California Current system and experiences seasonal upwelling during spring and summer months. The region has been wellstudied with regard to the physical forcing driving seasonal and interannual variability in water properties (cf. the 2006 Geophysical Research Letters special issue devoted to this region). Selected sections of Ω_{arag}^{e} (Figure 2) show a distinct seasonal cycle that is tightly coupled to upwelling dynamics near Newport. In January, the Ω_{arag}^{e} = 1 saturation horizon sits near the shelf break (≈125 m), roughly at the depth horizon of the 140 μmol/kg O_{2} contour and the 9°C isotherm. The 1.5 Ω_{arag}^{e} horizon is 25 m shallower, at ≈100 m. The onset of upwelling season begins in early May with the physical spring transition [Huyer et al., 1979], during which wind forcing becomes predominantly equatorward, and offshore transport becomes positive. The offshore transport is compensated by the upwelling of cold, dense waters that are rich in DIC and nutrients, and poor in O_{2}. The strong upwelling event in midMay (strongly negative N wind stress, blue lines in Figure 2 (top)) results in sharply upwarped isosurfaces, and the outcropping of the 0.8 Ω_{arag}^{e} horizon to the upper 30 m from the midshelf (80 m isobath) to the coast. After the spring transition, persistent upwellingfavorable winds pull the 1.0 Ω_{arag}^{e} and the 140 μmol/kg O_{2} contour onto the shelf where they remain through midNovember (Figure 2). Occasional poleward wind stress events (Figure 2, top) result in relaxation from upwelling, but the source water remains over shelf/slope regions. The late May transect used to formulate the algorithm (Figure 1b) occurred during one of these relaxation events.
[12] Throughout the remainder of the season Ω_{arag}^{e} and O_{2} distribution show depletion on similar hydrographic surfaces, presumably as a result of biological activity (e.g., 1.0/1.5 Ω_{arag}^{e} and 140/220 μmol/kg O_{2} contours retain similar behavior). Between May and November the 1.0 Ω_{arag}^{e} contour reaches 30 m nearcontinuously over the inner shelf (i.e., from the 80 m isobath shoreward), with the exception of early October, when a strong downwelling event confines the lowΩ_{arag}^{e} water to the shelfbottom (not shown). Over the outer shelf and slope, the 1.5 Ω_{arag}^{e} horizon shoals to less than 30 m by midJuly and the 1.0 horizon shoals to 50 m by midAugust (Figure 2). After the onset of persistent downwellingfavorable winds in midNovember the 1.0 Ω_{arag}^{e} and 140 μmol/kg contours retreat back to the shelfbreak/slope region, similar to conditions predicted for January 2007.
[13] The coupling of low Ω_{arag} state and physicallydriven upwelling dynamics would be expected, given the high DIC (low pH) signature associated with upwelling source waters [Hales et al., 2005]. The absolute magnitude of Ω_{arag} over the coastal shelf regions, however, is largely unknown, due to a lack of depthresolved DIC and TA measurements. This model therefore provides previously unattainable insight into both the magnitude of Ω_{arag} and how it relates to seasonal hydrography changes on the central Oregon shelf. The range in Ω_{arag}^{e} experienced seasonally over the shelf (e.g., 0.5–1.4 and 0.8–1.8 for the midshelf at 80 and 30 m, respectively) is also much greater than the uncertainty in model predictions (0.053). This favorable signal to noise ratio makes the region particularly amenable to this approach, compared to open ocean subtropical regions where the seasonal range is considerably less [Doney et al., 2009].
[14] An obvious question to ask is: What is the anthropogenic contribution to Ω_{arag} on the central Oregon shelf? We used the densityanthropogenic CO_{2} relationship presented by Feely et al. [2008a, supplement] to correct observed DIC in PNW waters for anthropogenic CO_{2} input (20–40 μmol/kg) and calculated a “preindustrial” Ω_{arag}^{e} for our data. A parallel algorithm with the same form as equation (3) was fitted to the data (R^{2} = 0.989) and used to predict the preindustrial Ω_{arag}^{e} = 1 horizon for the timeseries data (Figure 2). This preindustrial Ω_{arag}^{e} = 1 threshold very closely follows the 2007 Ω_{arag}^{e} 0.8 isoline. Therefore, within the ability to estimate anthropogenic CO_{2} content in coastal waters (±50% [Feely et al., 2008a]), undersaturation over shelf/slope bottom waters is likely a natural phenomena, but an anthropogenic reduction in Ω_{arag} by 0.2 units has caused a shoaling of the 1.0 horizon by ≈25m (shelf/slope) to ≈40m (offshore). Exposure of pelagic communities to undersaturated water may therefore be lengthened or intensified by anthropogenic CO_{2} input.
5. Implications
 Top of page
 Abstract
 1. Introduction
 2. Algorithm Development
 3. Model Evaluation and Caveats
 4. Seasonal Evolution of Ω_{arag}^{e} on the Oregon Coast
 5. Implications
 Acknowledgments
 References
 Supporting Information
[15] The persistence of water with Ω_{arag} < 1 over the shelf throughout the May–November upwelling season has not been previously noted. Although it is unclear how organisms on the central Oregon coast are directly affected by these conditions, laboratory experiments have indicated potentially deleterious impacts for organisms exposed to waters with Ω_{arag} < 1 [Kleypas et al., 2006; Fabry et al., 2008; Doney et al., 2009]. A clear application of the regression model presented here is to explore effects of low Ω_{arag} on shelf communities when DIC and TA data are unavailable. Preliminary examination of historical pteropod abundance data from the Oregon coast from the last 20 years (B. Peterson, unpublished data, 2009) indicates that pteropods are generally found where upwelling water is not; their abundances are maximum in offshore waters outside of the upwelling region and peak over the shelf only during winter or El Nino events, when upwelling is suppressed. Indepth examination of these data and other historical records may provide insight into adaptations organisms use to cope with low Ω_{arag} conditions.
[16] Bakun [1990] and Snyder et al. [2003] have suggested that upwelling intensity is likely to increase under future warming climate scenarios. Because the transit time of upwelling source waters from last atmospheric exposure to the sites of local upwelling are on the order of decades [Feely et al., 2008a], additional anthropogenic CO_{2} is already “in the pipeline” in the ocean interior, and will continue to decrease coastal Ω_{arag} well into this century, regardless of atmospheric CO_{2} rise scenarios. Impacts of these changes will be better understood as studies of the seasonality in Ω_{arag} and effects on coastal organisms emerge. The Ω_{arag}^{e} relationship presented here (equation (3)) will need to be adjusted on 5–10 year intervals to account for the additional anthropogenic CO_{2} input.
[17] A key advantage of the ability to estimate Ω_{arag} using commonly available hydrographic parameters (T, O_{2}) is the capability to hindcast Ω_{arag} from historical datasets to explore relationships with previously documented ecological/physical observations, provided corrections for reduced anthropogenic CO_{2} in prior data, if significant, can be taken into account. For example, regression model development efforts by T. Kim et al. (Prediction of East/Japan Sea acidification over the past 40 years using a multipleparameter regression model, submitted to Global Biogeochemical Cycles, 2009) highlight the importance of ventilation events for determining subsurface (50–500 m) Ω_{arag} in a 50year hydrographic timeseries in the East/Japan Sea. Continued refinement of Ω_{arag}^{e} regression models for the PNW and other coastal regions (Kim et al., submitted manuscript, 2009; S. R. Alin et al., manuscript in preparation, 2009) as more Ω_{arag} data become available will significantly enhance our understanding of the sensitivity of coastal regions to future CO_{2}chemistry changes and warming.
Supporting Information
 Top of page
 Abstract
 1. Introduction
 2. Algorithm Development
 3. Model Evaluation and Caveats
 4. Seasonal Evolution of Ω_{arag}^{e} on the Oregon Coast
 5. Implications
 Acknowledgments
 References
 Supporting Information
Auxiliary material for this article contains figures used to select the best algorithm for predicting aragonite saturation state on the central Oregon Coast and evaluate the algorithm ability to apply to a seasonal timeseries.
Auxiliary material files may require downloading to a local drive depending on platform, browser, configuration, and size. To open auxiliary materials in a browser, click on the label. To download, Rightclick and select “Save Target As…” (PC) or CTRLclick and select “Download Link to Disk” (Mac).
See Plugins for a list of applications and supported file formats.
Additional file information is provided in the readme.txt.
Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.