Coastal Ocean Data Analysis Product in North America (CODAP-NA) - An internally consistent data product for discrete inorganic carbon, oxygen, and nutrients on the U.S. North American ocean margins

. Internally-consistent, quality-controlled data products play a very important role in promoting regional to global research efforts to understand societal vulnerabilities to ocean acidification (OA). However, there are currently no such data products for the coastal ocean where most of the OA-susceptible commercial and recreational fisheries and aquaculture industries are located. In this collaborative effort, we compiled, quality controlled (QC), and synthesized two decades of discrete measurements of inorganic carbon system parameters, oxygen, and nutrient chemistry data from the U.S. North 40 American continental shelves, to generate a data product called the Coastal Ocean Data Analysis Product for North America (CODAP-NA). There are few deep-water (>1500m) sampling locations in the current data product. As a result, cross-over analyses, which rely on comparisons between measurements on different cruises in the stable deep ocean, could not form the basis for cruise-to-cruise adjustments. For this reason, care was taken in the selection of data sets to include in this initial release of CODAP-NA, and only data sets from laboratories with known quality assurance practices were included. New 45 consistency checks and outlier detections were used to QC the data. Future releases of this CODAP-NA product will use this core data product as the basis for secondary QC. We worked closely with the investigators who collected and measured these data during the QC process. This version of the CODAP-NA is comprised of 3,292 oceanographic profiles from 61 research cruises covering all continental shelves of North America, from Alaska to Mexico in the west and from Canada to the Caribbean in the east. Data for 14 variables (temperature; salinity; dissolved oxygen concentration; dissolved inorganic 50 carbon concentration; total alkalinity; pH on the Total Scale; carbonate ion concentration; fugacity of carbon dioxide; and concentrations of silicate, phosphate, nitrate, nitrite, nitrate plus nitrite, and ammonium) have been subjected to extensive QC. CODAP-NA is available as a merged data product (Excel, CSV, MATLAB, and NetCDF, 2 ), and concentrations of silicic acid, phosphate, nitrate, nitrite, nitrate plus nitrite, and ammonium. For discrete pH on the Total Scale, [CO 32- ], and f CO 2 , both measured and calculated values were presented. Saturation states of aragonite ( W arag ) and 165 calcite ( W calc ) could only be calculated. The carbonate system calculations were conducted using the MATLAB version 3.01 (Sharp et al., 2020) of the CO2SYS program (Lewis and Wallace, 1998), with the dissociation constants for carbonic acid of this data and Y-YX (ranked alphabetically their last data his group measured nutrients data for the ECOA2 and all of the Northeast Fisheries Science Center (NOAA/NFSC)'s Ecosystem Monitoring Program (EcoMon) cruises.


Introduction
Anthropogenic ocean acidification (OA) refers to the process by which the ocean's uptake of excess anthropogenic atmospheric carbon dioxide (CO2) reduces ocean pH and calcium carbonate mineral saturation states (Feely et al., 2004;Orr 60 et al., 2005;Jiang et al., 2019;IPCC, 2011). OA is making it more difficult for marine calcifiers to build a shell and/or skeletal structure, endangering coral reefs and other marine ecosystems (Doney et al., 2009;Gattuso and Hanson, 2011).
Despite only covering ~20% of Earth's land surface, coastal regions (from the coastline up to 200 km inland) host over 50% of the entire human population (Small and Nicholls, 2003;Hugo, 2011;Neumann et al., 2015). Coastal ecosystems account for most of the economic activities related to commercial and recreational fisheries and aquaculture industries, supporting about 90% of the global fisheries yield and 80% of known species of marine fish (Cicin-Sain et al., 2002). Studies have shown that OA has the potential to significantly impact both the fisheries and aquaculture industries, and change the way humans make their living, run their communities, and live their lives in coastal regions around the world (Cooley and Doney, 2009; Barton et al., 2012Barton et al., , 2015.

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The Global Ocean Data Analysis Project (GLODAPv2) offers an internally consistent data product for discrete samplingbased, open-ocean carbonate chemistry, nutrient chemistry, isotopes, and transient tracer data (Olsen et al., 2016;Olsen et al., 2020), allowing for a slew of new research products related to OA and its temporal trends in the global ocean (e.g., Jiang et al., 2015a;Gruber et al., 2019;. While there are several coastal surface water partial pressure of CO2 (pCO2) data products and climatologies (e.g., Bakker et al., 2016;Laruelle et al., 2017;Roobaert et al., 2019; 75 Takahashi et al., 2020), internally consistent data products for water column carbonate and nutrient chemistry data in the coastal ocean currently do not exist. Such products would contribute significantly to our understanding of the current status of OA and its temporal trends, and help guide OA mitigation and adaptation efforts in coastal oceans where most of the global fisheries and aquaculture industries are focused.

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The impact of OA on North American ocean margins is expected to vary significantly from region to region, with distinct regional drivers amplifying or mitigating overall coastal acidification. Anthropogenic carbon dioxide (CO2) invasion has been identified as the primary driver of open ocean acidification over decadal time scales, but coastal ocean acidification is influenced by many other physical, biological, and anthropogenic processes that can oppose or amplify the anthropogenic CO2 uptake. The U.S. continental West Coast (WC) and East Coast (EC) are in two vastly different ocean basins (Pacific vs.

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Atlantic) with different amounts of net organic matter remineralization in deeper waters flowing along the path of the Global Thermohaline Circulation (Broecker, 1991;Feely et al., 2008;Jiang et al., 2010;Wanninkhof et al., 2015). In the surface ocean, latitudinal variation of sea surface temperature (SST) and the ratio of dissolved inorganic carbon (DIC) to total alkalinity (TA) result in significantly different pH and calcium carbonate mineral saturation states between the Alaska Coast and Gulf of Mexico (Jiang et al., 2019;Cai et al., 2020). Upwelling can bring deep waters with corrosive OA chemistry 90 (resulting from large respiratory CO2 loads) to the surface, while onshore surface flow can bring less-corrosive open ocean waters to the coastline (Hales et al., 2005;Feely et al., 2008Feely et al., , 2016. Riverine input of low-pH water is found to intensify OA shoreward of the shelf break on the EC (Hunt et al., 2011;Xue et al., 2016). However, riverine water composition also varies significantly and the Mississippi River is a source of high-TA water to the Gulf of Mexico Stets et al., 2014;Gomez et al., 2020). Eutrophication (enhancement of biological production of organic matter through addition of nutrients) 95 causes high pH and calcium carbonate mineral saturation states in surface waters of the coastal ocean, and can lead to subsurface hypoxia (via subsequent respiration of that production), which is associated with low pH and calcium carbonate mineral saturation (Borges and Gypens, 2010;Cai et al., 2011;Laurent el al., 2017;Feely et al., 2016Feely et al., , 2018. The lack of OA https://doi.org/10.5194/essd-2020-402 synthesis efforts on North American ocean margins hampers our understanding of the geographic pattern and relative regional progression rates of OA along these coastlines (Cai et al., 2020).

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Carbonate data in the coastal ocean are often collected by multiple laboratories with different methods and instruments.
Many of the data sets may have never been shared with any major data centers, nor have these data sets gone through rigorous quality control (QC) and inter-comparison analyses. The lack of observations in intermediate and deep water (water depth >1500 m) makes it challenging to adjust the data based on constancy of parameters in deep water (i.e., cross-over 105 analyses) as is done for the open ocean (Lauvset and Tanhua, 2015). All these factors contribute to the lack of internally consistent data products for these important coastal environments. In this study, we compiled and QCed discrete samplingbased data for inorganic carbon, oxygen, and nutrient chemistry, and hydrographic parameters collected from the entire U.S.
North American continental shelves. We serve both the internally-consistent climate quality data product, as well as the QCed original cruise data through the NOAA National Centers for Environmental Information (NCEI). This effort will 110 promote future OA research, modeling, and data synthesis in critically important coastal regions to help advance the OA adaptation, mitigation, and planning efforts of U.S. coastal communities. While we only partially address these limitations in this study, we do produce a data product that can be used as the basis to address these limitations and incorporate additional coastal cruises going forward. We hope this release will be considered analogous to GLODAPv2 (Olsen et al. 2016), in the sense that the new data sets added in the subsequent GLODAPv2.2019 and .2020 updates (Olsen et al., 2019;2020) were 115 brought to be internally consistent with the fully quality-controlled data in the original GLODAPv2 product.

Study area
From a geopolitical perspective, the term "continental shelf" is defined as the region between the coastline (excluding estuaries) and a distance of 200 nautical miles (~370 km) offshore. While this definition is not as mechanistic as one based on a change in bathymetric gradient or a hydrographic condition such as chlorophyll or salinity levels, it is regionally and 120 seasonally invariant, and captures the full extent of coastal influences . This version of the data product is Bering-Chukchi Seas, and Beaufort Sea (see Sherman et al., 2009 for more information on the LMEs).
-U.S. West Coast (WC) -covering the LMEs of California Current and Gulf of California.
-Gulf of Mexico (GMx) Data beyond continental shelves will be included if they are collected from a cruise that predominately covers parts of the U.S. North American ocean margins.

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CODAP-NA was focused on chemical oceanographic data (inorganic carbon system parameters, oxygen, and nutrients) collected from discrete sampling-based observations. This also included discrete samples taken from shipboard flow-through systems rather than solely water collected in sampling rosette bottles. Carbon parameters recorded from continuous underway measurements by inline analytical instruments were excluded, as they had been QCed and included within the Surface Ocean CO2 Atlas (SOCAT) (Bakker et al., 2016). The same was true for carbon parameters from time-series 140 moorings. Data from large open estuaries (e.g., Salish Sea, Chesapeake Bay, Bay of Fundy) were also excluded during this first round of analysis, but these are among the data that may be able to benefit from secondary QC against CODAP.
We started with the highest quality coastal data sets to define a protocol for consistent QC and inter-comparison, which will subsequently be applied to other compiled coastal data sets. As a first step, only climate-quality discrete measurements (core 145 data sets) with known quality and metadata from the Atlantic Oceanographic and Meteorological Laboratory (AOML),

Parameters / variables
For the current version of the CODAP-NA, inorganic carbon system parameters, oxygen, nutrients, and related hydrographic parameters were included (Table 2). CTDPRES, CTDTEMP, CTDSAL, and CTDOXY were commonly measured with pressure, temperature, conductivity, and oxygen sensors, respectively, mounted on a CTD rosette. In some cruises with surface samples collected from flow-through systems, temperature and salinity were also provided in columns reserved for 160 CTDTEMP and CTDSAL, respectively. Water samples were collected and measured onboard or later in a shore-based laboratory for discrete salinity, discrete dissolved oxygen concentration (DO), dissolved inorganic carbon concentration  Lueker et al. (2000), bisulfate (HSO4 -) of Dickson (1990), hydrofluoric acid (HF) of Perez and Fraga (1987), and with the total borate equations of Lee et al., (2010).

Technical Approach and Methodology
Quality control often involves two steps: primary QC and secondary QC (Tanhua et al., 2010). Primary QC is the process of identifying outliers and obvious errors within an individual cruise data set using measurement metadata or approaches like property-to-property plots. It should largely be done by the investigators responsible for the measurements. However, it is 175 advisable to provide additional uniform primary QC to all cruises within a data product using common tools and common thresholds to help identify any issues that have been missed by the data producers. These issues are communicated back to the investigators so that the issues could be reviewed and, if necessary, addressed. This additional layer of primary QC is often performed by the data product synthesis community. Secondary QC is a process in which data from one cruise are objectively compared against data from another cruise or a previously synthesized dataset in order to quantify systematic 180 differences in the reported values. The secondary QC process often entails cross-over analysis (Lauvset and Tanhua, 2015), and increasingly regional Multiple Linear Regression (MLR) and inversions (Olsen et al., 2019;2020).
Due to the scarcity of cross-over stations at depths where parameters were not influenced by temporal variations (sampling depth >1500 m, Olsen et al., 2020) on coastal cruises, secondary QC was not conducted for this version of the CODAP-NA 185 and no cruise-wide offsets or multiplicative adjustments were applied. Instead, the QC relied on (a) stringent criteria for the selection of data sources, and (b) an enhanced primary QC procedure with rigorous consistency checks. This version of the CODAP-NA only accepted data from laboratories with direct involvement in the CODAP effort and with a track record of producing high-quality data and following best practices, making secondary quality control less essential. It is likely that there are other very high-quality coastal cruise data sets that are not yet included in this version of CODAP-NA.

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We worked directly with the data providers who knew their data best to conduct these primary QC procedures in order to leverage all of the resources related to a measurement: details related to the methods, instrumentation, reference standards, access to the raw data, and the analysts' recollection of the measurements. A new suite of QC tools was developed by this team of authors to satisfy the requirements of enhanced consistency checks. These tools will be made available to the public soon, with a separate paper dedicated to their rationales, development details, and instructions (Jiang et al., in prep.). The plan is to make it available through a web interface, so that no MATLAB license is required to use the tool. Below are the major steps of the QC procedures: Step One was to ensure all of the cruise data files were ingested into NCEI's archives and documented with a rich metadata 200 record (Jiang et al., 2015b). Maintaining a cruise data
Step Two was to load the measurement values from the original cruise data files into MATLAB. All missing values were replaced with "-999" during this process. Variables without a QC flag from the original cruise data file were assigned an initial flag of 2 (good values, Table 4). Variables that were clearly out of range (e.g., a DIC value of < 0) were automatically 210 assigned with a QC flag of "4" (bad values). The QC flags for all "-999" values or missing values were replaced with "9" (missing values).
Some surface samples from a few coastal cruises were collected from flow-through systems onboard research vessels, instead of Niskin bottles on sampling rosettes. In such cases, the temperature and salinity values were stored under the 215 CTDTEMP and CTDSAL columns, respectively, although they were not measured from sensors mounted on a CTD rosette.
Similarly, their sampling depth values were extracted from the metadata as the depth of the water inlet and stored under CTDPRES (Table 2). When water inlet depth information was not available, its sampling depth was set to be 5 dbar. There is a column named "Observation_type" in the CODAP data product file to indicate whether a sample is from a "Flow-through" system or a "Niskin" bottle.

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Step Three was to conduct several key calculations. The QC tool automatically calculated or assigned the below parameters: (c) recommended_Salinity_PSS78 ( (h) calculated pH, carbonate ion, and fCO2 at in-situ conditions using CO2SYS from DIC and TALK, along with temperature, salinity, pressure, and nutrients; and (i) in-situ pH, carbonate ion, and fCO2 from their respective values at their measurement conditions. Sample_IDs were calculated from STATION_ID (station identification number), CAST_NO (cast number) and NISKIN_ID (Niskin identification) based on equation (1), if they were not already available: 235 Sample_ID = Station_ID × 10000 + Cast_number × 100 + Niskin_ID (1) For example, at station 15, the 2nd cast, a Niskin_ID of 3 will have a Sample_ID of 150203. In cases when they could not be calculated (e.g., Station_ID is non-numerical), Sample_ID was assigned as 1, 2, 3, … from the first row to the last row of the The "recommended_salinity_PSS78" column was created by merging the discrete salinity and CTDSAL columns. Data were preferentially chosen from the discrete measurements provided their QC flags were equal to 2 or 6. If these values were not available, CTDSAL values with QC flags of 2 or 6 were chosen. In the absence of these two, discrete salinity measures with 250 QC flags other than 2 or 6 were chosen. Lastly, the CTDSAL values with other QC flags were chosen. The same principles were applied to merge the oxygen data. The merged discrete oxygen and CTDOXY data were stored in the column named "recommended_Oxygen. (Table 2).
Conservative temperature (Θ) is proportional to the potential enthalpy and is recommended as a replacement for potential 255 temperature (q), as it more accurately represents the heat content (IOC et al., 2010). Absolute Salinity (SA) is the mass fraction of salt in seawater (unit: g/kg) based on conductivity ratio plus a regional correction term as opposed to the practical salinity scale (SP, Practical Salinity Scale 1978, or PSS-78, unitless, based solely on the conductivity ratio) (Le Menn et al., 2018). Conservative temperature, absolute salinity, and sigma-theta were calculated using the equations of "gsw_CT_from_t", "gsw_SA_from_SP", and "gsw_sigma0", respectively, from the TEOS-10 (IOC et al., 2010). Apparent 260 oxygen utilization (AOU) was calculated based on absolute salinity, conservative temperature, latitude, longitude, CTDPRES, and recommended_Oxygen variable using the function "gsw_O2sol" as described in the TEOS-10 (IOC et al., Garcia and Gordon (1992).

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Carbonate_insitu_measured, pH_TS_insitu_measured, and fCO2_insitu_measured (Table 2) were recalculated from their respective values at measurement conditions (i.e., pH_TS_measured, Carbonate_measured, and fCO2_insitu_measured) with the CO2SYS program, using the dissociation constants as described above. TALK was preferentially used as the second carbon parameter. When it was not available, DIC was used. If neither of them was available, TALK derived from salinity with the locally interpolated alkalinity regression (LIARv2) method was used for the adjustment from measurement to in-situ 280 conditions (Carter et al., 2018). Carbonate_insitu_calculated, pH_TS_insitu_calculated, fCO2_insitu_calculated, aragonite saturation state, calcite saturation state, and Revelle_Factor were calculated from DIC and TALK, along with insitu temperature, salinity, pressure, silicate, and phosphate using the same dissociation constants as above (Table 2). When either silicate or phosphate data were unavailable, their mean values during the cruise were used for the calculation. Samples with a salinity of less than 15 were excluded from this calculation, due to the potentially large uncertainties.

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Step Four was to identify outliers. Outliers were determined by visual inspection. Two types of outlier identification were used for this effort: (a) a broad-scale outlier identification by visually examining the plot of a variable against its sampling depth and other property-to-property plots, and (b) a fine-scale outlier identification based on consistency checks. Here, consistency checks refer to both the "internal consistency checks", i.e., the comparison of a measurement with its calculated 290 value (e.g., spectrophotometrically-measured pH vs. pH calculated from other carbon parameters using CO2SYS), as well as a measurement with one method against that with a different method (e.g., oxygen measured from Winkler vs. a sensor Step five was to append all of the individual cruise data files one after another into one data product file with all of the variables as listed in Table 2. For surface samples collected from flow-through systems, their Cast_numbers and Niskin_IDs 310 were all set to "-999", and their Niskin_flags were all set to "9". The contents of Observation_type were standardized to be either "Niskin" or "Flow-through". Data values with QC flags that were not 2 (good), 3 (questionable), or 6 (average of duplicate measurements) were replaced with "-999", and their corresponding QC flags were changed to "9". The merged data product file was further QCed by plotting all of the non-missing values for each variable. These plots were examined further, with focus on the outliers falling out of 2.5 times their respective standard deviations.

6 Data products
The data product is available in Excel, CSV, MATLAB, and NetCDF formats at NOAA/NCEI with a DOI of [10.25921/531n-c230] and NCEI Accession Number of [0219960] (Jiang et al., 2020). All parameters in Table 2, along with their primary level QC flags (Table 4) and Cruise_flags (Table 3) are presented. The chosen primary level QC flag convention is the same as the GLODAPv2 project (Olsen et al., 2020). Note the difference between the WOCE primary level 320 QC flags (e.g., 2, 3, 4, 9, etc.) and the Secondary QC flags as used by the GLODAPv2 (a choice of either 0 or 1). The "cruise flags" were newly minted to indicate the overall quality of a cruise data set (Table 3). In the current version (v2020) of the  Table 3. Cruise flags used for this product.

Flag value Meaning
A These were dedicated OA cruises that were executed following Best Practices for global ocean work as outlined in Hood et al. (2011) and other documents as can be found on GO-SHIP site * . Colloquially these are referred to as GO-SHIP quality. Traceable standards and certified reference materials were used, and deep stations (> 2500 m) were sampled to allow using near-constant deep-water concentrations as anchor points. A third inorganic carbon system parameter, such as pH or carbonate ion concentration were often measured, allowing consistency checks.

B
These are dedicated OA cruises that had onboard inorganic carbon measurements performed according to Best Practices (Dickson et al. 2007), and many other parameters to highest accuracy through use of standards and certified reference materials. However, the cruises did not necessarily have all other parameters analyzed to highest standards, such as freezing nutrients for shoreside analyses; not taking oxygen and nutrients samples on most Niskins; not normalizing CTD/O2 trace to Winkler oxygen values, insufficient metadata etc. There often are insufficient deep stations to compare data with open ocean data.
C These were opportunistic cruises where OA parameters were measured in the water column. They include standard hydrographic, carbon, and OA parameters; T, S, O2, nutrients, TALK, DIC, pH. Many parameters, including carbon and OA parameters were measured shoreside; CTD oxygen data were not adjusted to Winkler oxygen values. Generally, no dedicated OA personnel were onboard.
D Underway samples only. These cruises had no CTD casts, and only had samples taken from the seawater supply line, with often a limited amount of other hydrographic parameters. T and S were obtained from thermosalinographs with limited or no salinity check samples.

Flag value Meaning
Missing value   Table 5. The minimum, maximum, mean, and data point counts of the parameters that are included in the final product.

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Refer to Table 2 for their full parameter names and units.  Figure 2). In addition,

Figure 2. Sampling profiles for certain parameters. A profile is plotted if it has at least one measured value. Panel (a) only includes profiles that have both dissolved inorganic carbon (DIC) and total alkalinity (TA) measured. Panel (b)
is for profiles with discrete pH measurements from a spectrophotometer. Panel ( Table 2 for more details). Panels (f-i) are for profiles with nutrient measurements.
One major difference between the CODAP-NA and the GLODAPv2 is the shallower sampling depths of the former ( Figure   3). About 80% of the 3,292 profiles have a maximum sampling depth of < 300 m, and 30% of them have maximum sampling depth of < 25m, with a lot of them being surface-only measurements. Only 193 profiles (< 6% of the total 3,292 355 profiles) have at least one sampling depth level below 1500 m, which has commonly been used as a threshold for subsurface cross-over analyses (Figure 3)  and winter having 677, 1538, 975, and 102 profiles, respectively ( Figure 4). All coasts have good summer data coverage, but the only area with meaningful winter data coverage is the northeastern U.S. coast (Figure 4, Table 6).

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To demonstrate the large seasonal amplitude (defined here as the difference between the maximum and minimum values of a variable on an annual cycle) in the study area, an analysis was conducted to group surface stations (with at least one To present a rough estimate of the measurement uncertainties of these variables, a similar approach was used to group deep water stations with a maximum sampling depth of >1500 m. Due to the scarcity of deep-water stations, a radius of 10 km and 200 m depth difference were used to find the comparison pairs. This analysis is limited to certain cruises with deep water 395 sampling (~5% of the data) only, thus the uncertainty estimates only hold true for these "reference" cruises, mostly with a cruise flag of A (Table 3). They do not apply to the rest of the cruises. Results show that the DIC and TA uncertainties (0.1% and 0.2%, respectively) are about the same as previously reported by the GLODAPv2 group ( Figure 6, 2020). Note the measurement uncertainties could be overestimated, because this analysis includes natural gradients due to the large radius and depth differences, as well as any temporal changes within the 1 to 10 years (average 6 years) period.   For aragonite and calcite saturation states, their uncertainty comes primarily from the use of an empirical equation to approximate the real-world apparent solubility product (Ksp'). Despite the 3% number shown in Table 7, the real uncertainty of aragonite and calcite saturation states is likely >5% (Mucci, 1983;Jiang et al., 2015a;Orr et al., 2018). Best practices for  (Sulpis et al., 2020). This applies to a lot of Alaska coast stations. In brackish water (salinity < 20), the relative uncertainty in carbonate ion concentration is worse than that in open ocean water (Dickson et al., 2007; (Jiang et al., 2020). The original cruise data files have also been updated with data providers' consent and summarized in a table with the link: 435 https://www.ncei.noaa.gov/access/ocean-acidification-data-stewardship-oads/synthesis/NAcruises.html.

Summary and conclusions
In this study, we relied on consistency checks performed in direct collaboration with the data providers who originally 440 collected and measured the samples to QC and synthesize two decades of discrete measurements of inorganic carbon system parameters, oxygen, and nutrient chemistry data from North America's coastal oceans. The generated data product is called It is strongly recommended to measure a third carbon-related variable for consistency check purposes. The large majority of coastal OA cruises have already measured DIC and TALK, with a lot of them also measuring pH using high-precision spectrophotometric methods (Byrne and Breland, 1989;Clayton and Byrne, 1993;Dickson 1993;Liu et al., 2011;Douglas and Byrne 2017). Recently, laboratories have increasingly begun to include carbonate ion concentration ([CO3 2-]) as an 450 additional measurable parameter of the seawater CO2 system (Byrne and Yao, 2008;Sharp and Byrne, 2019). Uncertainty analyses suggest that cross-over adjustments could be applied to future coastal data QC. All major coastal cruises in the future are recommended to take deep water samples (>1500 m) when feasible, ideally at agreed-upon reference stations for QC purposes.

Author contribution:
flags that were used by this data product. JH, CM, NM, JS, SS, and Y-YX (ranked alphabetically based on their last names) 460 participated in the QC process. AK provided data management support to this product development. RHB, W-JC, JC, GCJ, BH, CL, JM, and JS (ranked alphabetically based on their last names) contributed data to this data product. DWT and his group measured nutrients data for the ECOA2 cruise and all of the Northeast Fisheries Science Center (NOAA/NFSC)'s Ecosystem Monitoring Program (EcoMon) cruises.

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The authors declare that they have no conflict of interest.

Acknowledgements
Funding for this work comes from the National Oceanic and Atmospheric Administration (NOAA) Ocean Acidification Program (OAP, Project #: OAP 1903-1903