The 40 fixed time series stations are located in the Pacific (29), Atlantic (9), Indian (1), and Southern (1) ocean basins in open ocean (17), coastal (13), and coral reef (10) ecosystems (Table 1; Fig. 1). All surface ocean pCO2 and pH time series were established between 2004 and 2013. Thirty-three of these stations are active, whereas three have been moved to nearby locations better representing regional biogeochemical processes and four have been discontinued due to the lack of sustained funding. The range of support and partnerships for maintaining these moored time series is extensive (see Acknowledgements for details). Many of these 40 moored time series stations also make physical oceanographic and marine boundary layer meteorological measurements, and subsequently enable multi-disciplinary studies involving carbon cycle dynamics.

A Moored Autonomous pCO2 (MAPCO2) system measuring marine boundary layer air at a height of 0.5–1 m and seawater at a depth of <0.5 m is deployed at each fixed time series site (Sutton et al., 2014b). The MAPCO2 systems measure xCO2 in equilibrium with surface seawater using a nondispersive infrared gas analyzer (LI-COR, model LI-820) calibrated prior to each measurement with a reference gas traceable to World Meteorological Organization standards. Seawater xCO2 equilibration occurs by cycling a closed loop of air through a floating bubble equilibrator at the sea surface for 10 min, which is described in detail by Sutton et al. (2014b). Each time series site has either a Sea-Bird Electronics (SBE) 16plus V2 SeaCAT or a SBE 37 MicroCAT deployed at approximately 0.5 m measuring sea surface temperature (SST) and salinity (SSS). These measurements are used to calculate pCO2 and the fugacity of CO2 (fCO2) consistent with standard operating procedures (Dickson et al., 2007; Weiss, 1974). Total estimated uncertainties of the resulting pCO2 measurements are <2 µatm for seawater pCO2 and <1 µatm for air pCO2. For a detailed description of the MAPCO2 methodology, calculations, data reduction, and data quality control, see Sutton et al. (2014b).

In addition to pCO2, SST, and SSS, 17 of the time series also include seawater pH measurements at a depth of 0.5 m (Table 1). These measurements are made by either the spectrophotometric-based Sunburst SAMI-pH sensors (Seidel et al., 2008) or ion sensitive field effect transistor-based SeaFET pH sensors (Bresnahan et al., 2014; Martz et al., 2010). Field-based sensor validation suggests that these sensors (once calibrated and adjusted in the case of the SeaFET) have a total uncertainty of <0.02 in this surface buoy application (Sutton et al., 2016). Data quality control of these pH time series, including calibration, comparison with discrete samples, and assessment of drift due to sensor performance and biofouling, are described in detail by Sutton et al. (2016). All seawater pH data are expressed in the total scale and reported at the in situ SST. At 3-hourly sampling intervals, this configuration of MAPCO2 and the associated sensors is typically deployed for 1 year before recovery, maintenance, and redeployment of the buoy and sensors.

All post-calibrated and quality-controlled data are archived at NCEI: https://www.nodc.noaa.gov/ocads/oceans/time_ series_moorings.html (last access: 11 March 2019). For each site, an annual deployment has data and quality control descriptors at the data archive, including the following: (1) 3-hourly MAPCO2 and associated data, including measured parameters such as xCO2, humidity, and atmospheric pressure so data users can recalculate pCO2 if desired; (2) a data quality flag (QF) log that identifies and describes likely bad (QF = 3) or bad (QF = 4) CO2 and pH data included in the data set; and (3) a metadata file with deployment-level information such as reference gas value and MAPCO2 air value comparisons to the GLOBALVIEW-CO2 marine boundary layer (MBL) product (GLOBALVIEW-CO2, 2013). The reader is referred to Sutton et al. (2014b) for a detailed description of this deployment-level archived information. In addition to data archived at NCEI, these deployment-level mooring data sets are also included in the annual Surface Ocean CO2 Atlas data product (Bakker et al., 2016). Future data management plans include integrating the pCO2 and pH data into OceanSITES, which would provide a single access point to open ocean biogeochemical, physical oceanographic, and marine boundary layer meteorological measurements in a common, self-documented format.

The data product presented here is a compiled and simplified time series developed from these deployment-level archived files. Each fixed moored location has one file with a header including the following basic metadata: (1) data source and contact information; (2) data use request; (3) data product citation; (4) time series name, time range, and coordinates; (5) description of variables; (6) methodology references; and (7) links to deployment-level archived data and metadata at NCEI. Following the header, each fixed moored time series file includes the entire time series of SST, SSS, seawater pCO2, air pCO2, air xCO2, and pH with an associated time stamp.

The time series data product only includes data from the original deployment-level data files assigned QF = 2 (good data). Any missing values or values assigned QF of 3 or 4 in the original deployment-level data are replaced with “NaN” in the time series product. Of the data assigned QF of 2, 3, or 4, the good data (QF = 2) retained in this data product comprise 96% of all seawater xCO2 measurements and 88 % of all seawater pH measurements. Missing or bad SST or SSS data further reduce the quantity of seawater pCO2 values to 85 % compared with the archived deployment-level data. Data users interested in all available xCO2 and pH data should continue to retrieve deployment-level data from the NCEI archive.

Two time-series locations are exceptions to the abovementioned details. Because 3-hourly SST and SSS are not available for the Twanoh and Dabob sites, the data archived at NCEI for these two sites includes xCO2 (dry) air and seawater values but not calculated pCO2. In order to calculate pCO2 for those sites, the data user can incorporate atmospheric pressure, SST, and SSS from other sources. Atmospheric pressure at 3-hourly intervals can be found in the deployment-level archived data files at NCEI. Other data sources, including 2-hourly SST and SSS data at both Twanoh and Dabob, can also be located through the data portal of the Northwest Association of Networked Ocean Observing Systems: http://nvs.nanoos.org/. As interpolating 2-hourly data with the 3-hourly MAPCO2 data requires making assumptions about temporal variability that may differ according to the research interests of the data user, data from these two locations are only available in the deployment-level data files archived at NCEI.

This data product has been developed to provide easier access to quality-assured seawater pCO2 and pH data and broaden the user base of these data. This data product is ideal for modelers interested in using fixed time series data to validate Earth system model output or other data users accustomed to working with ship-based time series data. It also makes the time series more accessible to students, researchers from other disciplines, and marine resource managers who may not have a seawater CO2–carbonate chemistry background or the resources necessary to process and interpret the more detailed deployment-level data.

Descriptive statistics from these time series products are presented here to compare the variability in seawater pCO2 and pH across the 40 locations. Seasonal amplitude is the difference in the mean of all observations during winter and summer. For Northern Hemisphere sites, winter is defined as December, January, and February, and summer is June, July, and August (vice versa for Southern Hemisphere sites).

The climatological mean is derived by averaging means for each of the 12 months over the composite, multiyear time series. Interannual variability (IAV) is presented as the standard deviation of individual yearly means throughout the time series. In the case of missing observations, climatological monthly means are substituted to calculate yearly means for IAV. This approach seeks to minimize the impact of data gaps on the IAV estimates. Because long-term trends in pCO2 and pH are not well constrained at all locations, data are not detrended before calculating the IAV. At Woods Hole Oceanographic Institution Hawaii Ocean Time-series Station (WHOTS), for example, removing a trend of 2 µatmyr-1 changes the IAV estimate by 12 %. Therefore, IAV likely has high uncertainty due to the lack of detrending, data gaps, and the relatively short time series lengths (≤12 years). Future efforts to improve these IAV estimates will be able to rely on future assessment of longer time series (moored or observations from other platforms) and regional models that better characterize all modes of temporal variability.

The seasonal cycle is removed from the data using the approaches described in detail in Bates (2001) and Takahashi et al. (2009). This method results in a time series of seasonally detrended monthly anomalies, which are monthly residuals after removing the climatological monthly means.

When applied to environmental data, ToE is a statistical method that estimates the number of years necessary in a time series to detect an anthropogenic signal over the natural variability. This method has been used to determine ToE from, for example, chlorophyll satellite records (Henson et al., 2010) and ocean biogeochemical models (Lovenduski et al., 2015). ToEts (in years) of each time series is derived using the method of Weatherhead et al. (1998): ToEts=3.3σNω01+∅1-∅2/3, where σN and ∅ are the standard deviation and autocorrelation (at lag 1) of monthly anomalies, respectively, and ω0 is the anthropogenic signal of 2 µatm pCO2 or 0.002 pH per year, assuming surface seawater is in equilibrium with the global mean rate of atmospheric CO2 increase. This method results in a 90 % probability (dictated by the factor of 3.3 in Eq. 1) of trend detection by the estimated ToEts at the 95 % confidence interval. Uncertainty in ToEts, uToE, is calculated as follows: uToE=ToEts×eB, where B is the uncertainty factor calculated using the method of Weatherhead et al. (1998). Uncertainty is based on the number of months (m) in the time series and autocorrelation of monthly anomalies (∅): B=43m1+∅1-∅. With time series lengths of ≤12 years, most of the moored time series characterize diurnal to interannual variability of surface ocean pCO2; however, low-frequency decadal variability may not yet be fully captured. Decadal variability of surface ocean carbon is poorly quantified by observations in general (Keller et al., 2012; McKinley et al., 2011; Schuster and Watson, 2007; Séférian et al., 2013). In the absence of the constraint of decadal variability at each of these locations, we consider an example in the tropical Pacific to estimate the impact of decadal variability on ToEts. For this example, we assume the decadal-scale forcing (i.e., primarily the Pacific Decadal Oscillation; Newman et al., 2016) leads to a 27 % change in CO2 flux in the tropical Pacific (Feely et al., 2006). We take a conservative approach and assume this forcing is driven primarily by decadal changes in surface seawater pCO2 of as much as 15 % and determine the impact that added decadal variability has to the ToE estimates at the seven sites on the Tropical Atmosphere Ocean (TAO) array (McPhaden et al, 1998). This is done by repeating the existing pCO2 time series until the time series length is 50 years and applying a 15 % offset in the data on 10-year intervals at random. This simulated 50-year time series is then used to recalculate ToE. The simulation with added low-frequency decadal signals increases ToE by an average of 40 %, with significant variance across the TAO sites. Decadal forcing has less impact at the eastern Pacific TAO sites where subseasonal to interannual variability controlled by equatorial upwelling, tropical instability waves, and biological productivity is dominant, and more impact in the central and western Pacific where these higher-frequency modes of variability are less pronounced.

Decadal forcing may be particularly strong in the tropical Pacific due to the influence of the Pacific Decadal Oscillation on equatorial upwelling of CO2-rich water (Feely et al., 2006; Sutton et al., 2014a) compared with other subtropical sites (Keller et al., 2012; Landschützer et al., 2016; Lovenduski et al., 2015; Schuster and Watson, 2007). However, we apply this 40 % increase in ToEts to all 40 time series in order to provide a conservative estimate of when an anthropogenic signal can be detected using these moored time series data. The reported ToE for each moored time series is the result from Eq. (1) multiplied by 1.4: ToE=ToEts×1.4. For the data sets with a time series length greater than these ToE estimates, monthly anomalies are linearly regressed against time to determine the long-term rate of change. Linear regression statistics, including uncertainty in rate and r2, are calculated using standard methods described in Glover et al. (2011).

Across the 40 moored stations, climatological means of surface ocean pCO2 range from 255 to 490 µatm (Fig. 1). The seasonal amplitude of seawater pCO2 varies from 8 to 337 µatm. With more recent establishment of seawater pH observations, only 10 of the 17 sites with pH sensors have the seasonally distributed pH data necessary to determine the climatological mean and seasonal amplitude. At these 10 locations, the climatological mean and seasonal amplitude of seawater pH vary from 8.00 to 8.21 and 0.01 to 0.14, respectively (Fig. 2). All of the sites with a seasonal amplitude reported in Figs. 1 and 2 have observations distributed across all seasons (Fig. 3). The seasonal amplitude of surface seawater pCO2 is highest at the coastal sites (60 to 337 µatm) compared with the open ocean (8 to 71 µatm) and coral reef sites (11 to 178 µatm). While seasonal pH variation is only constrained at 10 of the 40 sites, these patterns also hold for pH with ranges of 0.08 to 0.14, 0.01 to 0.07, and 0.02 to 0.07 at the coastal, open ocean, and coral sites, respectively.

The IAV of seawater pCO2, which is the standard deviation of yearly means, range from 2 to 29 µatm. The largest IAV is found at the coastal and coral sites with values at Coastal MS, Twanoh, and CRIMP2 of 29, 27, and 25 µatm, respectively. With a large IAV of 25 µatm, CRIMP2 tends to be an anomaly among coral sites, with most tropical coral locations exhibiting an IAV similar to open ocean sites of ≤5 µatm (Fig. 1). Surface seawater pH time series are not yet long enough to determine a robust estimate of IAV.

These descriptive statistics show higher seawater pCO2 values throughout the year in the tropical Pacific where equatorial upwelling of CO2-rich water dominates. Seasonal forcing of pCO2 values in this region is low, but IAV, driven by the El Niño–Southern Oscillation (Feely et al., 2006), is the highest of open ocean time series stations (Fig. 1). The coastal time series stations suggest annual CO2 uptake with climatological means of seawater pCO2 less than atmospheric CO2 levels. Seasonal changes of SST and biological productivity drive the large seasonal amplitudes in pCO2 and pH at the US coastal locations (Fassbender et al., 2018; Reimer et al., 2017; Sutton et al., 2016; Xue et al., 2016). The coastal stations Twanoh and Coastal MS exhibit the highest IAV of seawater pCO2 (reported as seawater xCO2 for Twanoh) due to large variability from year to year in circulation, freshwater input, and biological productivity (Fig. 1). Most coral reef time series stations suggest net annual calcification with positive ΔpCO2 (seawater–air) values. Net calcification has been confirmed by independent assessments at some of these coral reef time series stations (Bates et al., 2010; Courtney et al., 2016; Drupp et al., 2011; Shamberger et al., 2011).

Clusters of fixed time series stations in Washington and California State waters, the Hawaiian island of Oahu, and Bermuda provide examples of how different processes drive ocean carbon chemistry. Seasonal amplitude and IAV are almost twice as large at the time series stations within the freshwater-influenced Puget Sound (Dabob and Twanoh) compared with the stations on the outer coast of Washington (Chá bă and Cape Elizabeth; Fig. 1b). Dabob is closer to ocean source waters and is deeper compared to Twanoh, which experiences greater water residence time and more persistent stratification; these factors result in increased influence of biological production and respiration on seawater xCO2 at Twanoh (Fassbender et al., 2018; Lindquist et al., 2017). These processes can cause subsurface hypoxia and low pH (<7.4) and aragonite saturation (<0.6) conditions in this region of Puget Sound (Feely et al., 2010), which likely contribute to the elevated surface seawater xCO2 levels observed at Dabob and Twanoh. The paired CCE1 and CCE2 moorings in coastal California provide the contrast of open ocean and upwelling regimes, respectively. The climatological mean and seasonal amplitude of pCO2 are both higher at CCE2 where summer upwelling supplies CO2-rich water to the surface. The IAV is similar at both sites, suggesting interannual drivers of pCO2, such as the El Niño–Southern Oscillation (Nam et al., 2011), likely have an influence throughout the southern California Current Ecosystem.

In both Hawaii and Bermuda, coral reef time series stations are paired with offshore, open ocean pCO2 observatories, although the offshore Bermuda Testbed Mooring (BTM) station was discontinued before the Bermuda reef sites were established. In both cases, the offshore stations of WHOTS and BTM both exhibit climatological mean seawater pCO2 slightly below atmospheric values (Fig. 1c), with previous studies indicating that these locations are net annual CO2 sinks (Bates et al., 2014; Dore et al., 2003, 2009; Sutton et al., 2017). The fringing or outer reef sites in Oahu (Kilo Nalu, Ala Wai, Kaneohe) tend to exhibit seawater pCO2 values closer to these open ocean background levels. The lagoonal Oahu reefs (CRIMP1 and CRIMP2) reflect increased water retention time paired with coral reef photosynthesis/respiration and calcification/dissolution, which elevate both annual mean and daily to interannual variability in seawater pCO2 values (Fig. 1c; Courtney et al., 2017; Drupp et al., 2011, 2013). One exception is the similar (almost as large) IAV at the fringing reef Ala Wai site, which is impacted by a nearby urban canal/estuary with high nutrient and organic matter input during storm events (Drupp et al., 2013). Positive ΔpCO2 values at the lagoonal reef sites also suggest that these sites are a net source of CO2 to the atmosphere contrary to the annual net CO2 uptake at the nearby open ocean sites (Fig. 1c).

In contrast, the outer reef site in Bermuda (Hog Reef) has a higher seasonal amplitude and mean pCO2 than the inner reef (Crescent Reef) despite having a shorter water residence time (Fig. 1). This is due to the greater biomass at Hog Reef, reflecting the influence of short-term (∼1–2 days) carbonate chemistry variability of the local active reef community, whereas Crescent Reef reflects the integrated signal of multiple habitats and days (∼6 days; Takeshita et al., 2018). Another caveat is that the coral reef time series in this data product have an inherent spatial bias, as 80 % of the coral reef moorings are located >20∘ latitude. The patterns for cooler, high-latitude reefs (e.g., Oahu and Bermuda) may differ from lower latitude reef sites (e.g., La Parguera and Chuuk), which would generally have less pronounced seasonality.

Atmospheric CO2 observations at the 40 time series sites all show a positive long-term trend (Fig. 4a). The mean trend at the open ocean sites are not significantly different from the global average rate of change of 2 ppm yr-1 (Sutton et al., 2014b). Figure 4a shows all 40 time series of atmospheric xCO2 with a rate of change of approximately 20 µmolmol-1 (or ppm) over a decade; that is, from 380 µmolmol-1 in January 2006 to 400 µmolmol-1 in January 2016.

Although the global observing network of atmospheric CO2 that tracks anthropogenic CO2 increase requires higher measurement quality (≤0.1 ppm) than the measurement quality of the MAPCO2 system (≤1 ppm), the MAPCO2 air data may be valuable for regional air CO2 studies in coastal regions where land-based activities cause larger hourly to interannual variability in atmospheric CO2 (Bender et al., 2002). In general, the coastal stations exhibit higher annual mean and seasonal amplitude compared to GLOBALVIEW-CO2 MBL values, which is a product based on interpolating high-quality atmospheric measurements around the globe to latitudinal distributions of biweekly CO2 (Fig. 4b, c). Open ocean and coral reef sites do not show this overall pattern compared with GLOBALVIEW-CO2 MBL values, although there is variability across the sites with some time series exhibiting higher means and seasonal amplitudes compared with the data product and vice versa (Fig. 4b, c).

Estimated length of time for an anthropogenic trend in seawater pCO2 to emerge from natural variability in the 40 time series varies from 8 to 41 years (Fig. 5). This range is 8 to 15 years at the open ocean sites, 16 to 41 years at the coastal sites, and 9 to 22 years at the coral reef sites. For the pH data sets with long enough time series to calculate ToE (i.e., the circles in Fig. 2), there is no significant difference between ToE of pCO2 and pH (ToE calculated using hydrogen ion concentration, [H+], not -log⁡[H+]); therefore, it is likely that ToE presented in Fig. 5 signifies both surface seawater pCO2 and pH. However, as the pH time series lengthen and variability is better constrained, future work should focus on a more thorough assessment of the ToE of seawater pH.

In this application ToE is dependent on the variability in the data, resulting in a pattern where sites that exhibit larger seasonal to interannual variability (Figs. 1 and 2) tend to have longer ToE estimates (Fig. 5). The fringing and outer reef sites of south shore Oahu (Kilo Nalu and Ala Wai) and Kaneohe Bay, respectively, have a shorter ToE compared with the lagoonal sites (CRIMP1 and CRIMP2) with larger seasonal to interannual variability. Similarly, the freshwater-influenced, highly productive Puget Sound sites (Dabob and Twanoh) have the longest ToE of all 40 sites and are approximately twice as long as the nearby time series on the outer coast of Washington (Chá bă and Cape Elizabeth). In the southern California Current, the ToE of the upwelling-influenced CCE2 is 50 % longer than the offshore CCE1 site.

These data also suggest that removing seasonal variability from the times series is essential to reduce the ToE and determine accurate long-term trends. The ToE estimates presented in Fig. 5 are based on seasonally detrended monthly anomalies, which are the residuals of the climatological monthly means. These ToE estimates are on average 55 % shorter than ToE estimated using raw time series data. This reduction in ToE due to seasonally detrending has a larger impact at higher latitudes where the seasonal amplitude of surface seawater pCO2 is larger compared with tropical sites. Using anomalies of climatological monthly means also minimizes the impact of the start and end month of the time series on the resulting trend estimation.

Of the 40 seawater pCO2 time series, ToE estimates suggest that only the WHOTS and Stratus time series are currently long enough to detect an anthropogenic trend. KEO, Papa, Kilo Nalu, and some TAO time series are approaching ToE, but at this time final data are not yet available through 2017. Data available at the time of publication suggest the anthropogenic trend in surface seawater pCO2 at WHOTS from 2004 to 2014 is 1.9±0.3 µatmyr-1 (Fig. 6). In this trend analysis we do not include data from the 2014–2015 anomalous event that warmed the North Pacific Ocean surface water (Bond et al., 2015) and elevated seawater pCO2 values (Feely et al., 2017). This WHOTS trend is not significantly different from the seawater pCO2 trend observed from 1988 to 2013 at the collocated ship-based Station ALOHA of 2.0±0.1 µatmyr-1 (Sutton et al., 2017). Both WHOTS and Station ALOHA trends are not significantly different from the trend expected if surface seawater is in equilibrium with the global average atmospheric CO2 increase.

The long-term trend at Stratus from 2006 to 2015 is 1.6±0.3 µatmyr-1 (Fig. 6). This trend is slightly lower than expected if the seawater pCO2 change is in equilibrium with the atmosphere. Considering the uncertainty in the ToEts estimate (Table 2) and the added uncertainty around unconstrained decadal variability at each of these locations, continued observations will be necessary at this site to confirm whether this lower rate of change persists. In addition to uptake of atmospheric CO2, the seawater pCO2 trend may be impacted by surface meteorological or upper ocean changes in this region. Significant trends in wind speed, wind stress, and the air–sea exchange of heat, freshwater, and momentum were observed from meteorological and surface ocean measurements on Stratus from 2000 to 2009 (Weller, 2015). These trends are related to the intensification of Pacific trade winds over the last 2 decades across the entire basin (England et al., 2014) and are likely to impact surface ocean pCO2 and CO2 flux in other regions of the Pacific. Sustained, continuous time series such as Stratus can contribute to constraining the physical and biogeochemical processes controlling long-term change.