Ship-based time series, some now approaching over 3 decades long, are
critical climate records that have dramatically improved our ability to
characterize natural and anthropogenic drivers of ocean carbon dioxide
(CO2) uptake and biogeochemical processes. Advancements in autonomous
marine carbon sensors and technologies over the last 2 decades have led to
the expansion of observations at fixed time series sites, thereby improving
the capability of characterizing sub-seasonal variability in the ocean. Here,
we present a data product of 40 individual autonomous moored surface ocean
pCO2 (partial pressure of CO2) time series
established between 2004 and 2013, 17 also include autonomous pH
measurements. These time series characterize a wide range of surface ocean
carbonate conditions in different oceanic (17 sites), coastal (13 sites), and
coral reef (10 sites) regimes. A time of trend emergence (ToE) methodology
applied to the time series that exhibit well-constrained daily to interannual
variability and an estimate of decadal variability indicates that the length
of sustained observations necessary to detect statistically significant
anthropogenic trends varies by marine environment. The ToE estimates for
seawater pCO2 and pH range from 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. Only two open ocean pCO2 time series,
Woods Hole Oceanographic Institution Hawaii Ocean Time-series Station (WHOTS)
in the subtropical North Pacific and Stratus in the South Pacific gyre, have
been deployed longer than the estimated trend detection time and, for these,
deseasoned monthly means show estimated anthropogenic trends of 1.9±0.3
and 1.6±0.3µatmyr-1, respectively. In the future, it
is possible that updates to this product will allow for the estimation of
anthropogenic trends at more sites; however, the product currently provides a
valuable tool in an accessible format for evaluating climatology and natural
variability of surface ocean carbonate chemistry in a variety of regions.
Data are available at 10.7289/V5DB8043 and
https://www.nodc.noaa.gov/ocads/oceans/Moorings/ndp097.html (Sutton et
al., 2018).
Introduction
Biogeochemical cycling leads to remarkable temporal and spatial
variability of carbon in the mixed layer of the global ocean and particularly
in coastal seas. The ocean carbon cycle, specifically surface ocean
CO2–carbonate chemistry, is primarily influenced by local physical
conditions and biological processes, basin-wide circulation patterns, and
fluxes between the ocean and land/atmosphere. Since the industrial period,
increasing atmospheric CO2 has been an additional forcing on ocean
biogeochemistry, with the ocean absorbing roughly 30 % of anthropogenic
CO2 (Khatiwala et al., 2013; Le Quéré et al., 2018). The
resulting decrease of the seawater pH and carbonate ion concentration, referred
to as ocean acidification, has the potential to impact marine life such as
calcifying organisms (Bednaršek et al., 2017b; Chan and Connolly, 2013;
Davis et al., 2017; Fabricius et al., 2011; Gattuso et al., 2015). Shellfish,
shallow-water tropical corals, and calcareous plankton are a few examples of
economically and ecologically important marine calcifiers potentially
affected by ocean acidification.
Open ocean observations have shown that the inorganic carbon chemistry of the
surface ocean is changing globally at a mean rate consistent with atmospheric
CO2 increases of approximately 2.0 µatmyr-1 (Bates
et al., 2014; Takahashi et al., 2009; Wanninkhof et al., 2013). However,
natural and anthropogenic processes can magnify temporal and spatial
variability in some regions, especially coastal systems through
eutrophication, freshwater input, exchange with tidal wetlands and the sea
floor, seasonal biological productivity, and coastal upwelling (Bauer et
al., 2013). This enhanced variability can complicate and at times obscure
detection and attribution of longer-scale ocean carbon changes. There are
also processes that can act in the opposite direction; for example, riverine
and estuarine sources of alkalinity increase the buffering capacity of coastal
waters and reduce the variability of other carbon parameters.
Efforts to observe and predict the impact of ocean acidification on marine
ecosystems must be integrated with an understanding of both the natural and
anthropogenic processes that control the ocean carbonate system. Marine
organisms experience highly heterogeneous seawater carbonate chemistry
conditions, and it is unclear what exact conditions in the natural
environment will lead to physiological responses (Hofmann et al., 2010).
However, responses associated with exposure to corrosive carbonate conditions
such as low values of the aragonite saturation state (Ωaragonite) have been observed (e.g., Barton et al., 2012, 2015;
Bednaršek et al., 2014, 2016, 2017a; Reum et al., 2015). Observations
show that present-day surface seawater pH and Ωaragonite
conditions throughout most of the open ocean exceed the natural range of
preindustrial variability, and in some coastal ecosystems known biological
thresholds for shellfish larvae are exceeded during certain times of the
seasonal cycle (Sutton et al., 2016). Are these present-day conditions
significantly impacting marine life in the natural environment? How will the
intensity, frequency, and duration of corrosive carbonate conditions change
as surface seawater pH and Ωaragonite continue to decline
and influence other processes of the biogeochemical cycle in the coastal
zone? Paired chemical and biological observations at timescales relevant to
biological processes, such as food availability, seasonal spawning, larval
growth, and recruitment, are one tool for identifying and tracking the
response of marine life to ocean acidification.
Long-term, sustained time-series observations resolving diurnal to seasonal
conditions encompass many timescales relevant to biological processes and
can help to characterize both natural variability and anthropogenic change
in ocean carbon. Fixed time-series observations fill a unique niche in ocean
observing as they can serve as sites of multidisciplinary observations and
process studies, high-quality reference stations for validating and
assessing satellite measurements and Earth system models, and test beds for
developing and evaluating new ocean sensing technology. If of sufficient
length and measurement quality to detect the anthropogenic signal above the
noise (i.e., in this case the natural variability of the ocean carbon
system), these observations can also serve as critical climate records.
Here, we introduce time-series data from 40 moored stations in open ocean,
coastal, and coral reef environments. These time series include 3-hourly
autonomous measurements of surface seawater temperature (SST), salinity
(SSS), mole fraction of atmospheric CO2 (xCO2),
partial pressure of atmospheric and seawater CO2
(pCO2), and seawater pH. This data product was developed to
provide easy access to uninterrupted time series of high-quality
pCO2 and pH data for those who do not require the detailed
deployment-level information archived at the National Centers for
Environmental Information (NCEI;
https://www.nodc.noaa.gov/ocads/oceans/time_series_moorings.html, last
access: 11 March 2019).
We also present an overview of the seasonal variability to long-term trends
revealed in the pCO2 and pH observations, as well as an
estimate of the length of time series required to detect an anthropogenic
signal at each location. We use a statistical method described by Tiao et
al. (1990) and further applied to environmental data by Weatherhead et
al. (1998) to estimate the number of years of observations needed to detect a
statistically significant trend over variability, which we refer to here as
time of emergence (ToE). An input required in this statistical model is an
estimate of the trend. We adopt a trend in seawater pCO2 of
2 µatmyr-1, which assumes surface seawater changes track the
current rate of globally averaged atmospheric CO2 increase. This
assumption allows for the comparison of the trend-to-variance pattern across the
network of 40 time series locations. The ToE methodology does not allow for
the identification of actual long-term trends that may be different from
2 µatmyr-1 due to other long-term changes in, for example,
biological production/respiration or coastal carbon sources/sinks. Nor does
it address the point in time at which a system may cross the envelope of
preindustrial variability or biological thresholds (e.g., Pacella et
al., 2018; Sutton et al., 2016). It indicates the time at which the imposed
signal of 2 µatmyr-1 emerges from the variance, and not
necessarily when the actual anthropogenic signal may emerge or when organisms
may be impacted.
Another caveat of this methodology is that the results apply to present-day
conditions, and these estimates will change as the time series lengthen due
to continued anthropogenic forcing. For example, even if using seasonally
detrended monthly anomalies (i.e., when the mean seasonality of ocean
carbonate chemistry is accounted for), magnification of the seasonal
amplitude of pCO2 due to warming, reduction in buffering
capacity, and/or other carbon cycle feedbacks could add variance to the
monthly anomalies, resulting in increased detection time (Kwiatkowski and
Orr, 2018; Landschützer et al., 2018). Changes in circulation,
stratification, and meltwater inputs in the Arctic cryosphere due to
anthropogenic warming could also influence these estimates over time. For
regions where the drivers of anthropogenic forcing and natural variability
are well constrained, the methodology could be modified to provide more
accurate estimates of trend detection time. However, ToE estimates presented
here use monthly anomalies of present-day observations and a fixed
anthropogenic pCO2 trend of 2 µatmyr-1 to
compare the trend-to-variance patterns across the network of 40 moored time
series. These estimates provide a starting point for trend calculations using
this data product.
MethodsSite and sensor description
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.
Region, coordinates, surface ocean carbon parameters measured, year
carbon time series established, and current status of the 40 fixed moored
time series stations. All time series also include atmospheric CO2,
SST, and SSS.
AbbreviationDescriptive nameRegionLatitudeLongitudeCarbonStartStatusparametersyearNH-10Newport Hydrographic LineUS west coast44.904-124.778pCO2, pH2014Moved to newStation 10 Oceanlocation in 2017dAcidification MooringTwanohORCA buoy at TwanohUS west coast47.375-123.008xCO2c2009Activein Hood CanalAla WaiAla Wai Water Quality BuoyPacific island21.280-157.850pCO22008Activeat South Shore Oahucoral reefChuukChuuk Lagoon OceanPacific island7.460151.900pCO2, pH2011ActiveAcidification Mooringcoral reefCRIMP1Coral Reef InstrumentedPacific island21.428-157.788pCO22005Moved toMonitoring Platform 1coral reefCRIMP2 in2008CRIMP2Coral Reef InstrumentedPacific island21.458-157.798pCO22008ActiveMonitoring Platform 2coral reefKaneoheKaneohe Bay Ocean AcidificationPacific island21.480-157.780pCO2, pH2011ActiveOffshore Observatorycoral reefKilo NaluKilo Nalu Water Quality BuoyPacific island21.288-157.865pCO22008Activeat South Shore Oahucoral reefGray's ReefNDBC Buoy 41008 in Gray's ReefUS east coast31.400-80.870pCO2, pH2006ActiveNational Marine SanctuaryGulf of MaineCoastal Western GulfUS east coast43.023-70.542pCO2, pH2006Activeof Maine MooringCrescent ReefCrescent Reef Bermuda BuoyAtlantic coral reef32.400-64.790pCO22010ActiveHog ReefHog Reef Bermuda BuoyAtlantic coral reef32.460-64.830pCO22010ActiveCoastal MSCentral Gulf of Mexico OceanGulf of Mexico coast30.000-88.600pCO2, pH2009Moved to newObserving System Station 01location in 2017eCheeca RocksCheeca Rocks Ocean AcidificationCaribbean coral reef24.910-80.624pCO2, pH2011ActiveMooring in Florida Keys NationalMarine SanctuaryLa PargueraLa Parguera Ocean AcidificationCaribbean coral reef17.954-67.051pCO2, pH2009ActiveMooring
Notes: a data from December 2004 to July 2007 in the
WHOTS time series are from the Multi-disciplinary Ocean Sensors for
Environmental Analyses and Networks (MOSEAN) station at 22.80∘ N,
158.10∘ W (20 km from the WHOTS location). Previous studies have
shown that the MOSEAN and WHOTS locations have similar surface seawater
pCO2 conditions (Sutton et al., 2014b, 2017); therefore, they are
combined in this data product as one time series location.
b Measurements of pH to be included in future updates of the time
series data product.
c SST and SSS data are collected
on the Dabob and Twanoh buoys at 2-hourly intervals. Because combining these
data with the 3-hourly MAPCO2 data requires making assumptions about
temporal variability that reflect the research interests of the data user,
only the direct measurements of CO2 (i.e., the mole fraction of
CO2 in equilibrium with surface seawater – xCO2) are
available in the NCEI archived data sets.
d The NH-10 buoy and
carbon sensors were moved approximately 75 nmi south to Cape Arago, Oregon,
following establishment of an Ocean Observatories Initiative buoy at NH-10
with redundant pCO2
and pH sensors: https://www.pmel.noaa.gov/co2/story/CB-06 (last access: 11 March 2019).
e The Coastal MS
buoy and carbon sensors were moved approximately 115 nmi southwest to
coastal Louisiana waters:
https://www.pmel.noaa.gov/co2/story/Coastal+LA (last access: 11 March
2019).
Location of (a) 40 moored pCO2 time
series with insets enlarged for the (b) US west coast and
(c) Hawaiian island of Oahu. Circle color represents climatological
mean seawater pCO2 (µatm), size of circle
represents seasonal amplitude, and thickness of circle outline represents
interannual variability (IAV). Gray squares show the locations of JKEO, M2,
and NH-10 where insufficient winter observations prevent the calculation of
climatological mean or seasonal amplitude. The IAV is not shown for sites with
less than 3 years of observations (Kaneohe, Iceland, BOBOA, SEAK, M2, SOFS,
BTM, TAO165E, TAO155W, NH-10, and JKEO). Dabob and Twanoh data shown here are
xCO2 (µmolmol-1). Moored time series
locations and names are detailed in Table 1.
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 <2µatm for seawater pCO2 and <1µatm 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.
Data product description
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.
Statistical analyses
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 µatmpCO2 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).
Results and discussionClimatology and natural variability
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.
Location of 17 moored pH time series. Circle color represents
climatological mean seawater pH and size of circle represents seasonal
amplitude. Gray squares show the locations of moored pH time series where the
lack of seasonal distribution of measurements prevent the calculation of
climatological mean or seasonal amplitude. No pH time series are of
sufficient length to estimate the IAV as presented for seawater
pCO2 in Fig. 1.
Number of surface seawater (a)pCO2 and
(b) pH observations by season in each of the 40 moored time series.
For Northern Hemisphere sites, winter is defined as December, January, and
February; spring is March, April, and May; summer is June, July, and August; and fall
is September, October, and November (seasons reversed for Southern Hemisphere
sites). The number of observations for Dabob and Twanoh shown here are seawater
xCO2.
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
≤5µatm (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.
Marine boundary layer atmospheric CO2
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.
(a) Weekly averaged air xCO2 observations
from the 40 time series. Different colors represent different time series.
Dates are MM/YY. (b) Climatological means and (c) seasonal
amplitudes of air xCO2 from the MAPCO2 measurements
compared to the GLOBALVIEW-CO2 MBL data product (GLOBALVIEW-CO2, 2013) for
open ocean (blue), coastal (orange), and coral reef (gray) time series
locations.
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).
Detection of anthropogenic trends in surface seawater
pCO2 and pH
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.
(a) Time of trend emergence (ToE) estimates, i.e., number
of years of observations necessary to detect an anthropogenic trend, with
insets enlarged for (b) the US west coast and
(c) the Hawaiian island of Oahu. ToE is not shown for sites with less
than 3 years of observations (Kaneohe, Iceland, BOBOA, M2, SEAK, SOFS, BTM,
TAO165E, TAO155W, NH-10, and JKEO). Years shown are the earliest dates of
seawater pCO2 trend detection for each time series, which is
the ToE estimate plus the time series start year (Table 1). These years of
trend detection and the associated uncertainty are also shown in Table 2. 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 the ToE of
pCO2 and 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±0.3µatmyr-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±0.1µatmyr-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.
Surface seawater pCO2 (µatm) 3-hourly
observations (dots), deseasoned monthly anomalies (squares), and trends
(lines) for the Stratus (dark gray) and WHOTS (brown) time
series. The time series in red is monthly averaged atmospheric
xCO2 (µmolmol-1) from Mauna Loa, Hawaii
(NOAA ESRL Global Monitoring Division, 2016). Dates are MM/YY.
The long-term trend at Stratus from 2006 to 2015 is 1.6±0.3µatmyr-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.
Data access to deployment-level archived data files at NCEI and the
time series data product for each moored buoy location. The earliest date of
seawater pCO2 trend detection is based on time series
product data and calculated by adding the ToE estimate (Eqs. 1–4) to the
time series start year (Table 1). The uncertainty presented here (in years)
is the result of Eqs. (2)–(3), which is based on ToEts and does
not include any additional uncertainty due to the decadal estimate from
Eq. (4). NA denotes sites with less than 3 years of observations where
interannual variability is likely not represented in a time series, and ToE
is consequently not calculated.
Buoy nameNCEI archived data filesTime series data productEarliest date of(https://www.nodc.noaa.gov/...)(https://www.pmel.noaa.gov/co2/...)seawater pCO2trend detectionCCE1ocads/data/0144245.xml (Sutton et al., 2016l)timeseries/CCE1.txt2020±2Papaocads/data/0100074.xml (Sutton et al., 2012c)timeseries/PAPA.txt2017±2KEOocads/data/0100071.xml (Sutton et al., 2012a)timeseries/KEO.txt2018±2JKEOocads/data/0100070.xml (Sabine et al., 2012c)timeseries/JKEO.txtNAaWHOTSocads/data/0100073.xmlb (Sabine et al., 2012d),timeseries/WHOTS.txt2013±1ocads/data/0100080.xml (Sutton et al., 2012f)TAO110Wocads/data/0112885.xml (Sutton et al., 2013b)timeseries/TAO110W.txt2024±4TAO125Wocads/data/0100076.xml (Sutton et al., 2012e)timeseries/TAO125W.txt2017±4TAO140Wocads/data/0100077.xml (Sutton et al., 2012b)timeseries/TAO140W.txt2018±2TAO155Wocads/data/0100084.xml (Sutton et al., 2012h)timeseries/TAO155W.txtNATAO170Wocads/data/0100078.xml (Sutton et al., 2012g)timeseries/TAO170W.txt2016±4TAO165Eocads/data/0113238.xml (Sutton et al., 2013c)timeseries/TAO165E.txtNATAO8S165Eocads/data/0117073.xml (Sutton et al., 2014d)timeseries/TAO8S165E.txt2021±2Stratusocads/data/0100075.xml (Sutton et al., 2012d)timeseries/STRATUS.txt2015±1BTMocads/data/0100065.xml (Sabine et al., 2012a)timeseries/BTM.txtNAaIcelandocads/data/0157396.xml (Sutton et al., 2016d)timeseries/ICELAND.txtNABOBOAocads/data/0162473.xml (Sutton et al., 2016g)timeseries/BOBOA.txtNASOFSocads/data/0118546.xml (Sutton et al., 2014i)timeseries/SOFS.txtNAGAKOAocads/data/0116714.xml (Cross et al., 2014)timeseries/GAKOA.txt2027±3Kodiakocads/data/0157347.xml (Cross et al., 2016b)timeseries/KODIAK.txt2028±3aSEAKocads/data/0157601.xml (Cross et al., 2016a)timeseries/SEAK.txtNAaM2ocads/data/0157599.xml (Cross et al., 2016c)timeseries/M2.txtNACape Elizabethocads/data/0115322.xml (Sutton et al., 2013d)timeseries/CAPEELIZABETH.txt2030±4Chá băocads/data/0100072.xml (Sutton et al., 2012j)timeseries/CHABA.txt2033±4CCE2ocads/data/0084099.xml (Sutton et al., 2012k)timeseries/CCE2.txt2028±3Dabobocads/data/0116715.xml (Sutton et al., 2014g)use NCEI files2050±6NH-10ocads/data/0157247.xml (Sutton et al., 2016h)timeseries/NH10.txtNAaTwanohocads/data/0157600.xml (Sutton et al., 2016j)use NCEI files2050±6Ala Waiocads/data/0157360.xml (Sutton et al., 2016b)timeseries/ALAWAI.txt2024±3Chuukocads/data/0157443.xml (Sutton et al., 2016k)timeseries/CHUUK.txt2021±2CRIMP1ocads/data/0100069.xml (Sabine et al., 2012b)timeseries/CRIMP1.txt2022±4aCRIMP2ocads/data/0157415.xml (Sutton et al., 2016c)timeseries/CRIMP2.txt2030±3Kaneoheocads/data/0157297.xml (Sutton et al., 2013d)timeseries/KANEOHE.txtNAKilo Naluocads/data/0157251.xml (Sutton et al., 2016f)timeseries/KILONALU.txt2017±2Gray's Reefocads/data/0109904.xml (Sutton et al., 2013a)timeseries/GRAYSREEF.txt2027±3Gulf of Maineocads/data/0115402.xml (Sutton et al., 2014h)timeseries/GULFOFMAINE.txt2023±3Crescent Reefocads/data/0117059.xml (Sutton et al., 2014b)timeseries/CRESCENTREEF.txt2020±2Hog Reefocads/data/0117060.xml (Sutton et al., 2014c)timeseries/HOGREEF.txt2023±3Coastal MSocads/data/0100068.xml (Sutton et al., 2012i)timeseries/COASTALMS.txt2046±7Cheeca Rocksocads/data/0157417.xml (Sutton et al., 2016i)timeseries/CHEECAROCKS.txt2020±2La Pargueraocads/data/0117354.xml (Sutton et al., 2014f)timeseries/LAPARGUERA.txt2019±2
Notes: a discontinued sites where a long-term trend
cannot be quantified solely from this time series data product.
b Links to NCEI archived deployment-level data files are provided
for both MOSEAN and WHOTS; however, these time series are combined in the
time series data product.
Data availability
Locations of deployment-level archived data at NCEI and the
time series data product for each mooring site are listed in Table 2. The
digital object identifier (DOI) for this data product is
10.7289/V5DB8043 (Sutton et al., 2018). Data users looking for easier
access to quality-assured seawater pCO2 and pH data that has
been designated as good (QF = 2; see Sect. 2.2) should consider using this time
series data product. The time series data files will be updated each time new
deployment-level data are submitted to the NCEI archive. Data users
interested in all available MAPCO2 and pH data should retrieve
deployment-level data from NCEI (links also provided in Table 2).
These data are made freely available to the public and the scientific
community in the belief that their wide dissemination will lead to greater
understanding and new scientific insights. Users of these time series data
products should reference this paper and acknowledge the major funding
organizations of this work: NOAA's Ocean Observing and Monitoring Division
and Ocean Acidification Program.
Conclusions
This product provides a unique data set for a range of users
including providing a more accessible format for non-carbon chemists
interested in surface ocean pCO2 and pH time series data.
These 40 time series locations represent a range of ocean, coastal, and coral
reef regimes that exhibit a broad spectrum of daily to interannual
variability. These time series can be used as a tool for estimating
climatologies, assessing natural variability, and constraining models to
improve predictions of trends in these regions. However, at this time, only
two time series data sets (WHOTS and Stratus) are long enough to estimate
long-term anthropogenic trends. ToE estimates show that at all but these two
sites, an anthropogenic signal cannot be discerned at a statistically
significant level from the natural variability of surface seawater
pCO2 and pH. If and when that date of trend detection is
attained, it is essential to seasonally detrend data prior to any trend
analyses. Even though the ToE provided are conservative estimates, data users
should still use caution in interpreting that an anthropogenic trend is
distinct from decadal-scale ocean forcing that is not well characterized.
Future work should be directed at improving upon these ToE estimates in
regions where other data, proxies, or knowledge about decadal forcing are
more complete.
Author contributions
AJS conducted the analysis and wrote the manuscript. RAF
contributed to the analysis. SMJ, SM, CD, NM, and RB are responsible for
quality assurance and control of the pCO2 and pH time series
data. JO is responsible for data management of the pCO2 and
pH time series data. AK is responsible for data archival of the moored time
series data at NCEI. JC, AJA, NRB, WJC, MFC, EHDC, BH, SDH, CML, DPM, MJM,
MM, JBM, JAN, SEN, JHN, SRO, JES, US, TWT, DCV, and RAW lead the projects
that maintain the surface buoy platforms, making the pCO2
and pH time series possible. All coauthors read the manuscript and
contributed edits.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
We gratefully acknowledge the major funders of the pCO2 and
pH observations: the Office of Oceanic and Atmospheric Research of the
National Oceanic and Atmospheric Administration, US Department of Commerce,
including resources from the Ocean Observing and Monitoring Division of the
Climate Program Office (fund reference number 100007298) and the Ocean
Acidification Program. We rely on a long list of scientific partners and
technical staff who carry out buoy maintenance, sensor deployment, and
ancillary measurements at sea. We thank these partners and their funders for
their continued efforts in sustaining the platforms that support these
long-term pCO2 and pH observations, including the following institutions: the Australian
Integrated Marine Observing System, the Caribbean Coastal Ocean Observing System,
Gray's Reef National Marine Sanctuary, the Marine and Freshwater Research
Institute, the Murdock Charitable Trust, the National Data Buoy Center, the National
Science Foundation Division of Ocean Sciences, NOAA–Korean Ministry of
Oceans and Fisheries Joint Project Agreement, the Northwest Association of
Networked Ocean Observing Systems, the Research Moored Array for
African-Asian-Australian Monsoon Analysis and Prediction (i.e., RAMA),
the University of Washington, the US Integrated Ocean Observing System, and the
Washington Ocean Acidification Center. The open ocean sites are part of the
OceanSITES program of the Global Ocean Observing System and the Surface Ocean
CO2 Observing Network. All sites are also part of the Global Ocean
Acidification Observing Network. This paper is PMEL contribution number 4797.
Review statement
This paper was edited by David Carlson and reviewed by three anonymous referees.
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