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ORIGINAL RESEARCH article

Front. Mar. Sci., 22 March 2021 | https://doi.org/10.3389/fmars.2021.636881

Quantifying the Atmospheric CO2 Forcing Effect on Surface Ocean pCO2 in the North Pacific Subtropical Gyre in the Past Two Decades

  • 1State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
  • 2National Oceanic and Atmospheric Admistration (NOAA) Pacific Marine Environmental Laboratory, Seattle, WA, United States
  • 3College of Marine Science, University of South Florida, Tampa, FL, United States
  • 4School of Marine Sciences, University of Maine, Orono, ME, United States

Despite the well-recognized importance in understanding the long term impact of anthropogenic release of atmospheric CO2 (its partial pressure named as pCO2air) on surface seawater pCO2 (pCO2sw), it has been difficult to quantify the trends or changing rates of pCO2sw driven by increasing atmospheric CO2 forcing (pCO2swatm_forced) due to its combination with the natural variability of pCO2sw (pCO2swnat_forced) and the requirement of long time series data records. Here, using a novel satellite-based pCO2sw model with inputs of ocean color and other ancillary data between 2002 and 2019, we address this challenge for a mooring station at the Hawaii Ocean Time-series Station in the North Pacific subtropical gyre. Specifically, using the developed pCO2sw model, we differentiated and separately quantified the interannual-decadal trends of pCO2swnat_forced and pCO2swatm_forced. Between 2002 and 2019, both pCO2sw and pCO2air show significant increases at rates of 1.7 ± 0.1 μatm yr–1 and 2.2 ± 0.1 μatm yr–1, respectively. Correspondingly, the changing rate in pCO2swnat_forced is mainly driven by large scale forcing such as Pacific Decadal Oscillation, with a negative rate (-0.5 ± 0.2 μatm yr–1) and a positive rate (0.6 ± 0.3 μatm yr–1) before and after 2013. The pCO2swatm_forced shows a smaller increasing rate of 1.4 ± 0.1 μatm yr–1 than that of the modeled pCO2sw, varying in different time intervals in response to the variations in atmospheric pCO2. The findings of decoupled trends in pCO2swatm_forced and pCO2swnat_forced highlight the necessity to differentiate the two toward a better understanding of the long term oceanic absorption of anthropogenic CO2 and the anthropogenic impact on the changing surface ocean carbonic chemistry.

Introduction

Since industrialization, the global ocean has been a major sink of the increasing atmospheric CO2, absorbing ∼25% of anthropogenic CO2 in recent years (Sabine et al., 2004a; Friedlingstein et al., 2019; Gruber et al., 2019). On one hand, the continuous ocean sink of atmospheric CO2 (its partial pressure is named as pCO2air) is changing ocean carbonic chemistry and the ocean carbon cycle (Borges et al., 2005; Cai et al., 2006; Fujii et al., 2009; Landshützer et al., 2013; Wanninkhof et al., 2013; Xiu and Chai, 2014). On the other hand, the resulting ocean acidification has great potential to degrade marine ecosystems and marine biota, particularly the calcifying organisms such as shellfish and corals (Widdicombe and Spicer, 2008; Doney, 2010; Fabricius et al., 2011; Dickinson et al., 2012; Chan and Connolly, 2013; Davis et al., 2017). Both impacts are closely related to the sustainable development of the marine biota and ecology. Therefore, the anthropogenic effect on surface seawater carbonic chemistry and the potential of the ocean in absorbing anthropogenic CO2 in the changing world are pressing concerns of the environmental research community.

At present, the study on anthropogenic CO2 at the sea surface is quite limited. Instead, there are many studies on anthropogenic CO2 in the ocean interior. The anthropogenic CO2 stored in the ocean exists in various forms of carbon, originating from the cumulative CO2 emissions from human activities (e.g., fossil fuel combustion, cement production, etc.) since the beginning of the Industrial Revolution. Several chemical and isotopic tracer approaches have been attempted to estimate the size of this pool of anthropogenic CO2 (e.g., Sabine et al., 2002, 2004b; Lee et al., 2003; Quay et al., 2017; Gruber et al., 2019). However, due to the sparse measurements of chemical tracers in space and time, there is still significant uncertainty in the long-term accumulation rates of anthropogenic CO2 and the potential of the ocean in continued absorption of anthropogenic CO2, making it important to investigate the anthropogenic CO2 variabilities at the sea surface.

One approach for tracking changes in surface pCO2sw is through the collection of autonomous underway and mooring observations over the global ocean (e.g., Surface Ocean CO2 Atlas (SOCAT, Bakker et al., 2016; Sutton et al., 2019). Many studies focused on the overall variabilities in surface pCO2sw and CO2 flux (e.g., Rödenbeck et al., 2015; Landshützer et al., 2016, 2019; Gregor et al., 2019; Denvil-Sommer et al., 2019; Iida et al., 2020 among others). However, because of the absence of isotope tracers in the autonomous observing systems of surface pCO2sw, it is very challenging to estimate how much anthropogenic CO2 emissions is driving measured pCO2sw. Alternatively, it is known that surface pCO2sw is affected by both increasing atmospheric CO2 forcing (mainly caused by anthropogenic CO2 emissions) and natural oceanic forcing (e.g., driven by oceanic physical and biogeochemical dynamics) (Fennel et al., 2008; Ikawa et al., 2013; Xue et al., 2016). The effect of atmospheric CO2 forcing on surface pCO2sw (named as pCO2swatm_forced hereafter) actually refers to the changes of surface pCO2sw driven by the increase of atmospheric CO2. Since the increase of atmospheric CO2 is due to anthropogenic emissions, the changing rates of pCO2swatm_forced in the past decades can be used to infer the interannual-decadal variations of the anthropogenic signals in surface pCO2sw. Yet the pCO2swatm_forced should be differentiated from the total observed pCO2sw because of the combination of the natural variability in surface pCO2sw (pCO2swnat_forced). Here pCO2swnat_forced refers to the remaining pCO2sw component without atmospheric CO2 forcing effect, which could be influenced by different physical and biogeochemical processes in the ocean, including the biological activities (i.e., photosynthesis and respiration), ocean warming driven by climate change and anthropogenic CO2 emissions, and ocean mixing, etc. The effect of all these different processes was regarded as the overall natural oceanic forcing effect on surface pCO2sw, It should be clarified that, although we regard all these different oceanic processes to be natural, their changes can still not be completely due to “natural” forcing because these changes in 2002–2019 inherently and implicitly contain atmospheric forcing.

Long time data records are needed to quantify the interannual-decadal trends of pCO2swatm_forced and pCO2swnat_forced in the ocean. Indeed, Sutton et al. (2019) analyzed the time scale of trend detection using 40 autonomous mooring time series of total observed surface pCO2sw over the globe, and found that anthropogenic trend detection requires a minimum 8 and 16 years of data records for the sites studies in open ocean and coastal regions, respectively. However, the current global time series observation network of surface pCO2sw just starts to approach these time scales, which has made it difficult to track the atmospheric forcing effect for most oceanic environments where the moorings are deployed. Several recent studies attempted to examine the anthropogenic trend in pCO2sw based on underway measurements in the past decades (Takahashi et al., 2009, 2014; McKinley et al., 2011). For example, Takahashi et al. (2009, 2014) found that pCO2sw is increasing at varying rates of 1.2 ± 0.5∼2.1 ± 0.5 μatm yr–1 in different ocean basins. However, the ship-based measurements are quite limited in both spatial and temporal coverage, leading to many uncertainties in the derived rates. More importantly, these rates are not exactly referring to the atmospheric forcing rates of surface pCO2sw, because of the combination of natural variability (i.e., pCO2swnat_forced) as mentioned above and the difficulty to differentiate and quantify both pCO2swatm_forced and pCO2swnat_forced using in situ observations of surface pCO2sw alone.

When combined with in situ surface pCO2sw observations, satellite remote sensing has become an important tool for synoptic estimation of surface pCO2sw (e.g., Lohrenz et al., 2010, 2018; Hales et al., 2012; Signorini et al., 2013; Bai et al., 2015; Chen et al., 2019). Without a spectroscopic method for direct measurements of surface pCO2sw from space, it is possible to develop satellite-based pCO2sw models through correlations with other related environmental variables. A satellite-based surface pCO2sw model also makes it possible to differentiate pCO2swatm_forced from pCO2swnat_forced. Indeed, satellite data accumulated in the past 20 years show great potential to quantify the interannual-decadal trends of the atmospheric forcing effect on pCO2sw. However, the past remote sensing studies mainly focused on the retrieval of seasonal surface pCO2sw (e.g., Lefèvre et al., 2005; Chierici et al., 2009; Zhu et al., 2009; Borges et al., 2010; Jo et al., 2012; Tao et al., 2012; Marrec et al., 2015; Parard et al., 2015; Le et al., 2019), and are quite limited in predicting interannual variability because of their insufficient parameterization of increasing atmospheric CO2 forcing (Shadwick et al., 2010; Chen et al., 2019). Therefore, the satellite-based pCO2sw algorithms need to be refined to enable their capabilities in assessing the interannual trends of pCO2swatm_forced and pCO2swnat_forced.

The Woods Hole Oceanographic Institution Hawaii Ocean Time-series Station (WHOTS) near Hawaii in the North Pacific Subtropical Gyre (NPSG) maintains high resolution surface pCO2sw observations. It provides an important open ocean reference for Hawaiian coral reefs (Dore et al., 2003; Sutton et al., 2017; Terlouw et al., 2019), thus is important to know the interannual-decadal trends of the atmospheric forcing effect on surface pCO2sw for a better understanding of the long-term ocean acidification and oceanic absorption of anthropogenic CO2. The WHOTS station was selected mainly because it has sufficient field data records for anthropogenic trend detection as mentioned above. WHOTS is located at station ALOHA (A Long-term Oligotrophic Habitat Assessment) (Karl and Church, 2018) in the NPSG (Figure 1), under the large-scale climate forcing of Pacific Decadal Oscillation (PDO). Several published studies investigated the interannual variability of the upper ocean carbon cycle at this station (Dore et al., 2003, 2009; Keeling et al., 2004; Palevsky and Quay, 2017). For example, based on a 14-year time series (1988–2002) at ALOHA, Brix et al. (2004) found that surface pCO2sw and isotopic 13C/12C showed long-term increase and decrease (yet no rates were provided), respectively, and they attributed it to the uptake of isotopically light anthropogenic CO2 from the atmosphere. Using the same data time series of pCO2sw at ALOHA, Dore et al. (2003) found that the significant decrease in CO2 sink in 1989–2001 was driven by the climate variability in salinity (Lukas and Santiago-Mandujano, 2008). Later based on a longer data record of 19 years (1988–2007) at ALOHA, Dore et al. (2009) presented a pCO2sw increasing rate of 1.88 μatm yr–1. In contrast, with a synthesis of 35 years of observations in the North Pacific, Takahashi et al. (2006) found the interannual-decadal change in surface pCO2sw is mostly correlated with the increases of sea surface temperature (SST) and anthropogenic CO2. Therefore, it is necessary to further investigate the effects of both anthropogenic CO2 emissions and the climate-driven natural variability in the ocean on surface pCO2sw. However, to date, no studies have differentiated these two forcing effects.

FIGURE 1
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Figure 1. The geolocation of the study site WHOTS (annotated in black star), and the general climate mode of the North Pacific in terms of PDO based on the HadISST data set (Rayner et al., 2003) for the period 1870–2019.

Considering the importance of addressing this knowledge gap to promote our understanding of the ocean capability in absorbing anthropogenic CO2 in the long run, here we for the first time differentiate the atmospheric forcing and natural forcing effects on surface pCO2sw, that’s, pCO2swatm_forced and pCO2swnat_forced, based on a novel satellite-based pCO2sw model developed in this study. Specifically, the objectives of this study include: (1) develop a satellite-based surface pCO2sw model at WHOTS, which should be able to capture the interannual-decadal variabilities in pCO2sw and differentiate pCO2swatm_forced and pCO2swnat_forced, and (2) quantify the interannual-decadal trends of both terms in the past 2 decades, and understand its implications for ocean acidification and long term oceanic uptake of anthropogenic CO2. Although the study was conducted at the WHOTS station, the findings in this study may provide insight on the interannual-decadal trends of pCO2sw driven by atmospheric and natural forcing effects, respectively, in other global subtropical open ocean regions. More importantly, the approach developed in this study can be extended to other regions with sufficient data available.

Data and Methods

Data

The WHOTS station (22.7°N, 158°W) is located in the subtropical oligotrophic region of the North Pacific and is operated by the Woods Hole Oceanographic Institution (WHOI). Field data time series [including surface pCO2sw, and pCO2air, SST, and sea surface salinity (SSS)] at this station collected between 2004 and 2018 at led by NOAA’s Pacific Marine Environmental Laboratory and were obtained from the National Centers for Environmental information (NCEI)1 (Sutton et al., 2012). Specifically, the pCO2 data were measured by a non-dispersive infrared gas analyzer (LI-CORTM, model LI-820), which has a sampling frequency of every 3 h, with an accuracy of 2 μatm (or better) (Sutton et al., 2014; Sabine et al., 2020). Surface pCO2sw data were collected at a water depth of <0.5 m, and the pCO2air data were collected at 1.2 m above the sea surface. SST and SSS were obtained from a CTD (SBE16) integrated in the autonomous CO2 mooring system. The details of data collection, processing, and quality control can be found in Sutton et al. (2014). These data were binned to daily time series to remove the diurnal variations (i.e., 0.4∼3.4 μatm), which are not considered in this study. The data time series were then averaged at monthly scales as presented in in Figures 2A,B,D. The Hawaii Ocean Time-series (HOT) program also maintains ship-based monthly sampling of surface pCO2sw calculated from dissolved inorganic carbon (DIC) and total alkalinity (TA) at this location (Figure 2D). We chose to use the high-frequency data from the WHOTS buoy mainly to assure that there are sufficient data available to develop the machine learning pCO2sw model and the monthly averages of the modeled pCO2sw should have lower bias than the monthly observed pCO2sw at HOT.

FIGURE 2
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Figure 2. Interannual variations of the monthly SST (A), SSS (B), Chla (C), pCO2air, and pCO2sw (D) at the WHOTS station in the period of 2002–2019. Note that the ship-based monthly pCO2sw time series at HOT calculated from DIC and TA measurements was overlaid in (D) for reference, and the calculated pCO2air in (D) is based on the data of atmospheric CO2 measured at MLO.

NASA standard daily SST (Figure 2A) and 8 day Chlorophyll-a (Chla, mg m–3) (Figure 2C) Level-3 data products (version R2018.0) covering the study region for the period of July 2002–December 2019 with a spatial resolution of ∼4 km were downloaded from the NASA Goddard Space Flight Center (GSFC)2. These Level-3 data products were derived from measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite.

Clearly there are lots of data gaps in the field measurements (e.g., SST, pCO2air, pCO2sw, Figures 2A,C). Full record of SST is obtained from MODIS. For a full data record of pCO2air at WHOTS between 2002 and 2019, daily time series of atmospheric xCO2 (in unit of ppm) at Mauna Loa Observatory (MLO) in Hawaii between 2002 and 2019 were obtained from the NOAA ESRL Global Monitoring Laboratory (2019), and this data was regarded as the atmospheric xCO2 at WHOTS over the study period considering the close distance between Mauna Loa and WHOTS. To calculate the corresponding pCO2air at WHOTS from the atmospheric xCO2 following the standard operating procedures (Weiss, 1974; Dickson et al., 2007), ancillary daily data of sea surface air pressure (in unit of atm) and specific humidity (in unit of%) were obtained from the National Centers for Environmental Prediction (NCEP), with a spatial resolution of 2.5°. The derived pCO2air (Figure 2D) together with the MODIS data (Figures 2A,C) were used to estimate pCO2sw between 2002 and 2019 based on the developed pCO2sw model. It should be clarified that, for broader impact, one main reason in choosing MODIS SST and NCEP ancillary data instead of other in situ data at the WHOTS mooring was to demonstrate our model capability in dealing with the uncertainties in each parameter, particularly when extending our method to other locations or regions where field measurements could be limited.

Methods

Surface pCO2sw is mainly controlled by four oceanic processes – the thermodynamic effect, biological activity, physical mixing, and air-sea CO2 exchange (Fennel et al., 2008; Ikawa et al., 2013; Xue et al., 2016). Accordingly, satellite-derived variables of SST, SSS, and Chla are commonly used to estimate surface pCO2sw from remote sensing in past studies (Olsen et al., 2004; Ono et al., 2004; Lohrenz and Cai, 2006; Sarma et al., 2006; Lohrenz et al., 2010, 2018; Nakaoka et al., 2013; Chen et al., 2016, 2017, 2019). However, these algorithms are quite limited in capturing the long-term trend in pCO2sw, mainly because of the insufficient parameterization of the anthropogenic or atmospheric CO2 forcing effect on pCO2sw. Feely et al. (2006), and Landshützer et al. (2013, 2016) have investigated the interannual and decadal variations of pCO2sw and CO2 flux under the anthropogenic CO2 forcing, yet to better quantify this effect, further studies are needed to differentiate the warming effect of SST from the atmospheric effect on surface pCO2sw and quantify both effects separately.

Dore et al. (2003) found that the significant increase of pCO2sw at ALOHA in 1989–2001 was mainly caused by the increase of SSS due to excess evaporation over this period, suggesting that the physical changes in the subtropical North Pacific may affect the ocean biogeochemistry including surface pCO2sw. Yet in this study, SSS was found to have little effect on pCO2sw (R = 0.102 at p > 0.05, which explains 1% nges in pCO2sw) at the WHOTS station over the period of 2004∼2018, as also found by Sutton et al. (2017) which shows a small effect (<5%) of salinity changes on pCO2sw increase. The SMOS satellite maintains the longest SSS data record since 2009 (Font et al., 2009, 2013), however, a comparison between the field SSS and SMOS-derived SSS shows a very large uncertainty of 1.1 for SSS ranging between 34.5 and 35.5 at WHOTS. As such, SSS was not used in the model. The mixed layer depth (MLD) could drive the interannual dynamics of surface pH at ALOHA (Dore et al., 2009), yet considering the lack of MLD data from remote sensing and the covariations of SST and MLD dynamics, we chose to use SST alone to indicate the effect of warming and mixing on surface pCO2sw. Therefore, the inputs of the satellite pCO2sw algorithm included observed SST and pCO2air, and concurrent MODIS-derived Chla, as well as Julian day (Jday) normalized sinusoidally to “tune” the seasonal cycles of pCO2sw (Friedrich and Oschlies, 2009; Signorini et al., 2013; Chen et al., 2016, 2017), and the output was modeled pCO2sw (Eq. 1). In total, there were 3074 matched data samples between 2004 and 2017. Within this dataset, data samples collected in 2016 (N = 311) were kept for independent validation considering its near full coverage in each month (other years do not); the remaining were randomly divided into two groups: one for model training (N = 1,934), and the other for model validation (N = 829).

Various approaches have been used to model pCO2sw from remote sensing, such as polynomial regression, mechanistic semi-analytical approach, machine-learning approaches (Friedrich and Oschlies, 2009; Jo et al., 2012; Landshützer et al., 2013; Bai et al., 2015; Moussa et al., 2016; Lohrenz et al., 2018). Chen et al. (2019) did extensive comparisons of these approaches and found that, the Random Forest based Regression Ensemble (RFRE) was the most robust one in modeling pCO2sw. Therefore, this approach was used in this study with model parameters locally tuned for the WHOTS station (Eq. 1). RFRE is one type of machine learning technique, which ensembles many weighted regression trees to implement the random forest algorithm (Breiman, 1996, 2001; James et al., 2013) in Matlab (R2017a). For better model generalization, the RFRE takes advantage of each regression tree via bootstrap aggregation (or bagging) (Breiman, 1996; James et al., 2013) in model parameterization. In the model training phase, the ensemble regression trees grow independently on a drawn bootstrap replica of the training dataset. That’s, each regression tree can randomly select a subset of predictors at each split and can involve many splits in the algorithm. This manipulation greatly reduces the correlations among the developed regression trees, resulting in improved independency among the regression trees. The mean square error was used as loss function to adjust the model performance in each iteration. Briefly, there are two important parameters to define the RFRE model structure: the minimum leaf size and the number of regression trees. Leaf size refers to the number of data samples used in each node of a regression tree, and its minimum thus determines the splits and depth of a regression tree. By trial and error, these two parameters were optimized to be 8 and 28, respectively. With these settings, the RFRE model became stable and had the best model statistics, thus it was used to predict pCO2sw. See Chen et al. (2019) for more details of the RFRE approach.

M o d e l e d p C O 2 s w = f R F R E [ S S T , l o g 10 ( C h l a ) , p C O 2 a i r , c o s ( 2 π × J d a y / 365 ) ] (1)

Standard statistical measures, including root mean square difference (RMSD, both absolute and relative), coefficient of determination (R2), mean bias (MB), mean ratio (MR), unbiased percent difference (UPD), and mean relative difference (MRD) (Barnes and Hu, 2015), were used to quantify the accuracy of the modeled pCO2sw.

We varied SST, Chla, and pCO2air by ± 1°C, and ± 20%, and ± 5 μatm, respectively, to examine the sensitivity of the model to changes in each variable. The changes are based on the uncertainties in the MODIS-derived SST and Chla (Gregg and Casey, 2004; Mélin et al., 2007; Hu et al., 2009) as well as on the seasonal variations in pCO2air.

The modeled pCO2sw is the sum of pCO2swatm_forced and pCO2swnat_forced. Just as its name implies, the pCO2swnat_forced refers to the pCO2sw without atmospheric CO2 forcing, thus based on the model developed following Eq. 1, the pCO2swnat_forced was calculated by assuming that the pCO2air remained at the same level as in in the start year (i.e., 2002) of the study period (Eq. 2). The pCO2swatm_forced was defined as the difference between the modeled pCO2sw (Eq. 1) and pCO2swnat_forced (Eq. 3). To quantify the natural forcing effect, the net atmospheric CO2 forcing effect over the study period (2002–2017) remained at exactly zero by keeping the pCO2air values in the model at the same level as in 2002. By doing so, both the derived pCO2swnat_forced and pCO2swatm_forced are relative quantities to the year of 2002, which should be higher than those derived by referring to pre-industrialization. However, either referring to 2002 or other years only affects the absolute values of these quantities, and they would affect the changing rates of trends in both pCO2swnat_forced and pCO2swatm_forced in the past two decades that we are interested in.

p C O 2 s w n a t _ f o r c e d = f R F R E [ S S T , l o g 10 ( C h l a ) , p C O 2 a i r @ 2002 , c o s ( 2 π × J d a y / 365 ) ] (2)

where the pCO2air@2002 means the pCO2air data in 2002–2019 remained at the same level as in 2002 by assuming that there is no additional atmospheric effect referred to 2002.

p C O 2 s w a t m p _ f o r c e d = p C O 2 s w - p C O 2 s w n a t _ f o r c e d (3)

Trends in pCO2sw, pCO2swatm_forced, pCO2swnat_forced, pCO2air, SST, and Chla were quantified based on their monthly anomalies, which were derived by subtracting the monthly climatologies from the monthly averages between 2002 and 2019 using least-square technique.

Results

Figure 3 shows the performance of the RFRE-based pCO2sw algorithm in both model training and validation. Clearly, most of the data pairs of the observed and modeled pCO2sw followed closely along the 1:1 line, with a RMSD of 2.2 μatm (0.6%) and R2 of 0.98. The additional independent validation (Figure 4) using the data time series in 2016 also shows good consistency between the observed pCO2sw and modeled pCO2sw, with a RMSD of 4.3 μatm (1.1%) and R2 of 0.87.

FIGURE 3
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Figure 3. The RFRE model performance in estimating surface pCO2sw in both model training (A) and model validation (B).

FIGURE 4
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Figure 4. The RFRE model performance in reconstructing the surface pCO2sw data time series in 2016, in comparison with the corresponding mooring-observed surface pCO2sw. Note that none of the field observations in 2016 was used in the model development. The error bar represents one standard deviation of the diurnal changes of pCO2sw time series.

The RFRE model is more sensitive to changes in SST and pCO2air than to changes in Chla (Figure 5). Statistically, with + 1°C (-1°C) added to SST, the modeled pCO2sw was higher (lower) than the original pCO2sw, with RMSD of 9.7 μatm (2.6%) [8.0 μatm (2.1%)], R2 of 0.89 (0.93), and MB of 8.5 μatm (-6.8 μatm). The resulting pCO2sw shows slight underestimation and overestimation in cases of 20% increase and 20% decrease in Chla, with MB of 1.3 and −1.3 μatm, respectively. With + 5 μatm in pCO2air, the new pCO2sw was estimated higher than the original pCO2sw, with RMSD of 5.7 μatm (1.6%), R2 of 0.90, and MB of 3.7 μatm. With −5 μatm in pCO2air, the new pCO2sw was underestimated compared to the original pCO2sw with RMSD of 6.1 μatm (1.6%), R2 of 0.89 and MB of −4.2 μatm.

FIGURE 5
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Figure 5. Sensitivity of the RFRE pCO2sw algorithm to uncertainties in satellite-derived SST (A,B) and Chla (C,D) and to the natural variability of pCO2air (E,F).

In the North Pacific subtropical gyre at the WHOTS station, time series of pCO2sw between 2002 and 2019 was obtained using this RRFE-based pCO2sw algorithm, with good consistency to the observed pCO2sw in the overlapped time periods (Figure 6). Overall, the pCO2sw follows the same seasonal pattern as SST from high values in summer to low values in winter, with a seasonal magnitude of ∼50 μatm, in the opposite phase of pCO2air (Figure 6). In addition, the pCO2sw was lower than the pCO2air most of the time over the years, suggesting a continuous CO2 flux from the atmosphere to the ocean.

FIGURE 6
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Figure 6. Modeled pCO2sw in the full time period between 2002 and 2019, in comparison with the mooring-observed surface pCO2sw and calculated pCO2air at WHOTS. Note that the calculated pCO2air is based on the data of atmospheric CO2 measured at MLO.

Both pCO2sw and pCO2air show significant increase between 2002 and 2019 (Figure 6). After removing the seasonality signals, statistically, the pCO2sw had a mean rate of 1.7 ± 0.1 μatm yr–1 (R2 = 0.80, at p < 0.05), lower than the rate of pCO2air (2.2 ± 0.1 μatm yr–1, R2 = 0.99, at p < 0.05), as shown in Figure 7. The pCO2swnat_forced shows a significant increasing rate of 0.2 ± 0.1 μatm yr–1 (R2 = 0.07, at p < 0.05) on average in the study period. In contrast, the pCO2swatm_forced, which is just driven by the atmospheric CO2 forcing, had a mean rate of 1.4 ± 0.1 μatm yr–1 (R2 = 0.84, at p < 0.05), but tended to plateau since 2016. Indeed, the pCO2sw without the thermodynamic effect (i.e., pCO2nonT, Chen and Hu, 2019) had similar interannual patterns as pCO2ant at a mean rate of 1.2 ± 0.1 μatm yr–1. Correspondingly, the Chla time series did not show any trends over the years while the SST was increasing at an overall rate of 0.03 ± 0.01°C yr–1 (R2 = 0.07, at p < 0.05). This warming trend could be influencing the pCO2natural trend.

FIGURE 7
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Figure 7. Interannual variations of the monthly anomalies in SST (A), Chla (B), pCO2air and modeled pCO2sw (C), modeled pCO2swnat_forced and pCO2swatm_forced (D), and PDO index (E) in the period of 2002–2019.

Clearly, there are some visible trends (e.g., <10 years) particularly in SST and pCO2swnat_forced different from those over the 20-year time frame (Figure 7). To further investigate the trends in each variable, we quantified the rates of each for a variety of periods starting between 2002 and 2015, ending between 2006 and 2019, with durations ranging from 5 to 18 years (Figure 8). It is found that, at confidence level of >95%, the SST had a negative and positive rate of −0.1 ± 0.02°C yr–1 and 0.1 ± 0.05°C yr–1 for periods ending in ≤2013 and >2013, respectively (Figure 8A). Again, the Chla did not show any trend over the years. Correspondingly, pCO2swnat_forced shows a very similar pattern as the rates in SST, with a negative rate of −0.5 ± 0.2 μatm yr–1 for periods ending in ≤2013, and a positive rate of 0.6 ± 0.3 μatm yr–1 for periods ending in >2013. The anthropogenic forcing on atmospheric pCO2 tends to accelerate over the study period consistent with the published studies (Canadell et al., 2007), with a rate of 1.7 ± 0.1 μatm yr–1 for periods ending in ≤2011, and a rate of 2.3 ± 0.2 μatm yr–1 for periods ending in beyond 2011, and the acceleration is getting even stronger (2.4 ± 0.1 μatm yr–1) after 2016. As a result, the pCO2sw shows a lower rate (1.5 ± 0.4 μatm yr–1) for periods starting in 2002–2005, ending in 2006–2019; a higher rate (2.2 ± 0.3 μatm yr–1) for periods starting in 2006–2013, ending in 2010–2017; and a lower rate (1.5 ± 0.4 μatm yr–1) again for periods starting in 2006–2013, ending in 2018–2019. Correspondingly, the pCO2swatm_forced shows similar but significantly weaken signals (at p < 0.05) in these three time frames, with rates of 1.6 ± 0.3 μatm yr–1, 1.8 ± 0.5μatm yr–1, and 0.9 ± 0.5 μatm yr–1, respectively.

FIGURE 8
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Figure 8. Interannual changing rates of SST (A), Chla (B), pCO2air (C), modeled pCO2sw (D), modeled pCO2swnat_forced (E), and pCO2swatm_forced (F) for a variety of periods starting between 2002 and 2015, and ending between 2006 and 2019 at Station WHOTS. The X-axis and Y-axis represent the start year and end year, respectively, of each rate analyzed. The diagonal lines (i.e., 5, 10, and 15 years) indicate the length of trend periods. A white cross is superposed on the plot when the p value was >0.05.

Discussion

Model Uncertainty

The satellite-based RFRE pCO2sw model developed in this study had a RMSD of 4.3 μatm (1.1%), significantly smaller than most of the published pCO2sw algorithms in open ocean waters (Olsen et al., 2004; Feely et al., 2006; Nakaoka et al., 2013; Moussa et al., 2016). This uncertainty is reasonably acceptable considering the diurnal variations (i.e., 0.4∼3.4 μatm) in surface pCO2sw at WHOTS.

The sensitivity of the pCO2sw model to each input variable indicates not only the model’s capacity in tolerating the uncertainty of each variable, but also the model’s response to real changes in each variable. Specifically, the positive feedback of modeled pCO2sw to changes in SST are consistent with the thermodynamic effect on pCO2sw (increased SST leads to an increase in pCO2sw and vice versa). The negative response of the pCO2sw model to Chla suggests that the increase (decrease) in Chla indicates stronger (weaker) biological uptake of oceanic CO2, therefore, the resulting modeled pCO2sw was lower (higher) than without the Chla perturbation. Although the Chla level at the WHOTS station is consistently low (Figure 2C), the sensitivity analysis here suggests the necessity of including Chla in the model to better modulate the seasonal variations of surface pCO2sw. Yet it should be noted that, Chla is only a proxy to indicate the overall biological activities that could affect surface pCO2sw. Although there is no visible change in surface Chla, still there could be possible changes in the phytoplankton community and net community production. The insignificant responses of the pCO2sw model to the 20% change in Chla suggest the model is insensitive to uncertainties in the satellite Chla. For the same reason, the biological uptake of CO2 tends to have a quite limited effect on pCO2sw in the oligotrophic ocean, consistent with previous studies (Chen and Hu, 2019). For regions where satellite Chla is not available due to severe cloud coverage (e.g., some tropical and high latitude zones), a first examination of the Chla effect on surface pCO2sw using field observations (if there are) is suggested to determine the potential bias that would be resulted in pCO2sw if Chla is not included in the model. The changes of pCO2air directly affect the gradient between pCO2air and pCO2sw, which drives the air-sea CO2 exchange, thus, it is reasonable to see a positive response of the pCO2sw model to changes in pCO2air. The resulting increase (MB = 3.7 μatm) in pCO2sw was slightly weaker than the assigned increase of 5 μatm in pCO2air, which may be due to the ocean’s increasing Revelle Factor and reduced buffering capacity of seawater (Fassbender et al., 2017).

Interannual Changes of pCO2sw Driven by Natural and Atmospheric Forcing

In response to the accelerating rates of pCO2air, the modeled surface pCO2sw shows different rates at various time intervals. Specifically, the 5 year pCO2sw trends we derived for the periods of 2007–2011, 2008–2012, and 2009–2013 are high at rates of 2.5, 2.1, and 2.5 μatm yr–1, respectively, which are higher than the relatively low rates in period of 2003–2007 visually interpreted from Figure 1 in Dore et al. (2009). To further examine the trends in pCO2sw, we analyzed the ship-based monthly pCO2sw datasets at ALOHA from HOT program (used in Dore et al., 2009). Indeed, the 5 year HOT-based pCO2sw trends starting in 2007–2008 did show low values, but these low values are insignificant at p > 0.05, yet no such statistics was available in Dore et al. (2009). For the 5 year pCO2sw trend starting in 2009, the HOT-based pCO2sw and our modeled pCO2sw show close trends of 2.5 and 2.2 μatm yr–1, respectively, at p < 0.05. Meanwhile, the overall trend we detected in surface pCO2sw (i.e., 1.7 ± 0.1μatm yr–1) in period of 2002–2019 was a bit smaller than that (i.e., 1.88 μatm yr–1) in period of 1988–2007 found in Dore et al. (2009) and that (i.e., 2.4 μatm yr–1) in period of 2003–2014 presented in Sutton et al. (2017). This could be reasonable considering the different physical and biogeochemical dynamics on decadal time scales and the acceleration of ocean acidification in the western North Pacific (Ono et al., 2019). Besides, it should be noted that the ship-based monthly pCO2sw dataset is derived from measurements of DIC and TA collected approximately once a month to compose this monthly dataset. In contrast, our monthly pCO2sw is based on the daily modeled pCO2sw and is validated thoroughly with daily-averaged in situ measurements at WHOTS. Therefore, the trends in the modeled pCO2sw we derived here should be reliable with high confidence. Also, the mooring measures pCO2sw at surface of <0.5 m, while the ship-based HOT data were based on the mean measurements within 0–30 m, which could be another potential source for the discrepancy. In the North Pacific subtropical gyre (represented by the WHOTS station), the interannual changes of surface pCO2sw is mainly driven by both SST and pCO2air (Figures 7, 8 and Table 1), consistent with the published studies (Takahashi et al., 2006). Despite the little impact of SSS on pCO2sw shown in our study period (2002–2019), a further experiment with SSS added into our model was conducted. It shows that the inclusion of SSS did not result in any significant difference in the modeled pCO2sw and pCO2swnat_forced. Considering the important impact of SSS on pCO2sw in 1989–2007 presented in Dore et al. (2003), it seems that the effect of SSS depends on the specific study periods. Here we prefer to exclude SSS from our model mainly considering the large error (i.e., 1.1) in the SMOS SSS at present. With more accurate SSS data available from satellites in the future, it could be possible to include SSS to better model the variations of pCO2sw, particularly the effect of rainfall minus precipitation on pCO2sw in any time periods. However, most of the published studies directly regarded the interannual trend of pCO2sw as the trend of anthropogenic pCO2sw. It should be noted that the anthropogenic pCO2sw refers to the pCO2sw impacted by atmospheric CO2 increases, thus most of the reported anthropogenic trend of pCO2sw actually refers to the total rate of pCO2sw (Takahashi et al., 2009, 2014; McKinley et al., 2011; Sutton et al., 2019), which also includes the natural variability of pCO2sw driven by the general oceanic processes (e.g., thermodynamics, ocean mixing, biological activities).

TABLE 1
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Table 1. Correlation coefficients among the monthly anomalies of pCO2sw, pCO2sw nat_forced, pCO2swatm_forced, SST, Chla, pCO2air, and PDO index, with insignificant correlation (i.e., p > 0.05) annotated in italic.

In this study, both the natural and atmospheric CO2 forcing effects on pCO2sw were separately quantified. The rates in pCO2swnat_forced over the study period follow a similar pattern as those in SST with a correlation coefficient (R) of 0.82, indicating that the interannual trend signals in pCO2swnat_forced are mainly driven by SST, at least over the study period of 2002–2019. The cooling characteristics in SST between 2002 and 2012 resulted in a significant negative rate in pCO2swnat_forced, and the warming effect since 2013, which were also reported in previous studies (Sutton et al., 2017; Terlouw et al., 2019), leads to a significant positive rate in pCO2swnat_forced. In addition to the global warming effect on SST, the interannual SST dynamics could also be attributed to the changes in MLD because of the ocean mixing effect on SST. As such, the interannual variations in pCO2swnat_forced could also be driven by the MLD changes, and more DIC enriched waters would be entrained into the surface when MLD deepens and SST decreases (Dore et al., 2009). Overall, it seems that the rate of pCO2swnat_forced tends to correspond to decadal oscillations in SST between cooling and warming periods associated with PDO (Yasunaka et al., 2014; Newman et al., 2016; Landshützer et al., 2019). Indeed, the interannual PDO (Figure 7E) shows very similar variation patterns to the SST (Figure 7A) with a significant correlation of R = 0.53 (Table 1). Specifically, the PDO decreased progressively from 2004 to 2012, was low in 2011–2012, reached a maximum in 2015, and then decreased from 2015 to 2019. As a result, the pCO2swnat_forced also shows a significant correlation (R = 0.41, see Table 1) with PDO, suggesting the large scale climate forcing also contribute to the natural oceanic forcing effect on surface pCO2sw.

With the exclusion of pCO2swnat_forced, the pCO2swatm_forced rates were significantly smaller than the corresponding pCO2sw rates in various time intervals (Figure 8). Although pCO2swatm_forced is mainly driven by the oceanic uptake of increasing atmospheric CO2 (R = 0.91), it shows distinctively different patterns in changing rates from that of the pCO2air over various time intervals in 2002–2019. This different response of pCO2swatm_forced toward pCO2air seems mainly caused by the buffering effect of dissolved CO2 in seawater (Egleston et al., 2010). However, for the tendency of pCO2swatm_forced to plateau after 2016, there could be several potential explanations depending on the condition of air-sea CO2 fluxes. Specifically, it would be reasonable to observe a plateau signal in pCO2swatm_forced if there is little change in air-sea CO2 fluxes after 2016; yet if the dissolved CO2 keeps increasing after 2016, the little response in pCO2swatm_forced would tend to suggest that a larger fraction of dissolved CO2 stays in forms of other carbonate species (i.e., HCO3–, CO32–), significantly lowering the Revelle factor and enhancing the ocean’s buffering capacity in recent years; and if there is a decrease in air-sea CO2 fluxes after 2016, it would be likely that a fration of bicarbonate and carbonate species are converted to dissolved CO2, which would lower the ocean’s buffering capacity and promote ocean acidification. Xue and Cai (2020) found that TA minus DIC can be used as a proxy for deciphering ocean acidification. Here using the ship-based monthly TA and DIC data in the study period, we found a significant decreasing trend in TA minus DIC over the years (Figure 9A), which suggests a strong ocean acidification in the study period. However, the changing rates of TA minus DIC is distinctively higher in recent years since 2014 (Figure 9B), suggesting a stronger ocean acidification and weaker buffering capacity in the past few years. Indeed, ocean acidification has shifted the carbonate chemistry speciation and lowered the CaCO3 saturation state (Orr et al., 2005; Doney et al., 2009; Krug et al., 2011), yet further studies are needed to investigate and quantify the changing patterns of the air-sea CO2 flux and the carbonate species over the past decades. In general, the oceanic uptake of anthropogenic CO2 is resulting in more rapid changes in carbonic chemistry in the surface ocean and accelerating ocean acidification (Feely et al., 2009; Ono et al., 2019), yet a revisit of such phenomenon is needed when more satellite/field data are available in the coming years.

FIGURE 9
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Figure 9. The interannual variations of the monthly anomalies in TA minus DIC, which was a proxy of ocean acidification, based on the ship-based monthly TA and DIC measurements at ALOHA in surface waters (A), and the corresponding interannual changing rates for a variety of periods starting between 2002 and 2015, and ending between 2006 and 2019 (B). In (B), the X-axis and Y-axis represent the start year and end year, respectively, of each rate analyzed, the diagonal lines (i.e., 5, 10, and 15 years) indicate the length of trend periods, and a white cross is superposed on the plot when the p-value was >0.05.

Implications

Long time series data are required to investigate the anthropogenic effect on surface pCO2sw. However, the field data are always limited in both spatial and temporal coverage. For example, few of the 40 global pCO2sw mooring stations have data coverage of >10 years (Sutton et al., 2019), and the global field pCO2sw database (i.e., SOCAT or LDEO, Bakker et al., 2016; Takahashi et al., 2019), although greatly accumulated in recent years, still has data gaps in some regions and at some time intervals. More importantly, it is impossible or difficult to separate the pCO2swatm_forced and pCO2swnat_forced signals apart based on purely field measurements to better quantify the anthropogenic forcing impact on surface pCO2sw. Instead, with the related environmental variables observed from satellites, surface pCO2sw models using satellite data and other ancillary data can be developed and applied to the full satellite data record over the past ∼20 years. Besides, SSS measurements from SMOS and SMAP satellite have been available since 2009 and 2015, respectively, with longer and accurate data records available, the interannual and decadal trends in surface pCO2sw as well as the natural forcing and atmospheric CO2 forcing components can be further studied. The recovered long time series of pCO2sw can be used to quantify both pCO2swatm_forced and pCO2swnat_forced accordingly. The findings of decoupled changing rates in pCO2swatm_forced and pCO2swnat_forced in this study highlight the necessity of differentiating the two, in order to have a better understanding of the long term oceanic absorption of anthropogenic CO2 and its buffering capacity in the long term. Therefore, this study sets a template for future study to examine both natural and anthropogenic or atmospheric CO2 forcing effects on pCO2sw in various oceanic systems over the past decades, toward an improved understanding of anthropogenic forcing on surface pCO2sw.

Specifically, the pCO2sw in the North Pacific subtropical gyre shows various increase rates in response to the increasing pCO2air between 2002 and 2019. The accelerating increase rates in pCO2air and the weaker rates in pCO2sw indicate stronger gradients between pCO2air and pCO2sw, which implies an accelerated oceanic CO2 uptake and ocean acidification. If the warming effect continues following the decadal pattern in SST in recent years since 2010, a steady rate of ∼0.8 ± 0.1 μatm yr–1 in pCO2swnat_forced (see Figure 8E) would be expected in the coming few years. The weaker rate in pCO2swatm_forced in recent years in response to the accelerating rate in pCO2air implies a lower ocean buffering capacity leading to more rapidly changing oceanic carbon chemistry and ocean acidification, yet further study in this field is needed to promote our knowledge and understanding.

Based on observations at WHOTS, the present work demonstrated the necessity in differentiating the atmospheric forcing and natural forcing effects on surface pCO2sw, and show unprecedented information on their interannual-decadal trends over both short and long time scales. The WHOTS station is located in the North Pacific Subtropical Gyre, therefore, the results and findings should be referential to understand the overall surface pCO2sw dynamics for a broader impact of the ocean in absorbing anthropogenic CO2, particularly under both anthropogenic CO2 forcing and natural oceanic forcing (Henson et al., 2016).

More importantly, the pCO2sw model was developed using satellite-derived environmental data and other ancillary data, thus the model is capable to tolerate the uncertainties involved in each variable as demonstrated in the sensitivity analysis. This is of great importance and significance to locations or areas where very limited data are available. Specifically, with these limited field observations of surface pCO2sw, it would be possible to develop a surface pCO2sw model with related environmental variables from satellite and ancillary data from NCEP to differentiate the two forcing effects following Eqs 1 and 2. With nearly 20 years of satellite data records, it would be straightforward to extend the current study to other oceanic regions to investigate the interannual-decadal surface pCO2sw dynamics by differentiating the atmospheric forcing and natural forcing effects toward a better understanding of the ocean in absorbing anthropogenic CO2 and its impact on the surface ocean carbonate chemistry.

Conclusion

The rate of anthropogenic or atmospheric CO2 forcing pCO2sw in surface seawater has been difficult to characterize because of the interaction of natural variability in pCO2sw and the requirement of long time series data records. In this study, we show that a remote sensing algorithm applied to the WHOTS station in the North Pacific subtropical gyre can reveal the interannual-decadal variability of surface pCO2sw between 2002 and 2019. Such an ability enables the separation of atmospheric CO2 forced pCO2sw (pCO2swatm_forced) from natural variability in pCO2sw (pCO2swnat_forced). We believe that this is the first time such atmospheric CO2 forced pCO2sw and natural oceanic processes driven pCO2sw are mathematically differentiated and their interannual-decadal changing rates are statistically quantified. Results show unprecedented information on their interannual-decadal rates over both short and long time scales at the WHOTS site. With the availability of ocean color data and other ancillary data globally, it is straightforward to extend the current study to other oceanic regions.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: The WHOTS mooring dataset analyzed in this study is available at National Centers for Environmental information (NCEI) under https://www.nodc.noaa.gov/ocads/oceans/Moorings/. The MODIS ocean color data are available at the NASA Goddard Space Flight Center (GSFC) https://oceancolor.gsfc.nasa.gov/. The atmospheric xCO2 data at Mauna Loa is available at the NOAA ESRL Global Monitoring Laboratory under https://www.esrl.noaa.gov/gmd/dv/data/index.php.

Author Contributions

SC designed the study, processed, analyzed the data, and wrote the manuscript. AS contributed the mooring data and main concept definition. CH and FC contributed data analysis and manuscript writing. All authors commented on the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) projects (42030708, 41906159, and 41730536). The pCO2 observations at WHOTS were supported by the Office of Oceanic and Atmospheric Research of the NOAA, U.S. Department of Commerce, including resources from the Global Ocean Monitoring and Observation program.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We gratefully acknowledge the support of National Natural Science Foundation of China (NSFC) and the contribution of NOAA PMEL. The MODIS data were maintained by the NASA Goddard Space Flight Center, the daily xCO2 data was provided by the NOAA’s Global Monitoring Laboratory. We thank NASA and NOAA for providing these data. This is PMEL contribution #5131.

Footnotes

  1. ^ https://www.nodc.noaa.gov/ocads/oceans/Moorings/
  2. ^ https://oceancolor.gsfc.nasa.gov/

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Keywords: surface pCO2, remote sensing, anthropogenic CO2, sea surface temperature, North Pacific

Citation: Chen S, Sutton AJ, Hu C and Chai F (2021) Quantifying the Atmospheric CO2 Forcing Effect on Surface Ocean pCO2 in the North Pacific Subtropical Gyre in the Past Two Decades. Front. Mar. Sci. 8:636881. doi: 10.3389/fmars.2021.636881

Received: 02 December 2020; Accepted: 01 March 2021;
Published: 22 March 2021.

Edited by:

Masao Ishii, Meteorological Research Institute (MRI), Japan

Reviewed by:

Nicolas Metzl, Centre National de la Recherche Scientifique (CNRS), France
Shin-ichiro Nakaoka, National Institute for Environmental Studies (NIES), Japan

Copyright © 2021 Chen, Sutton, Hu and Chai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Shuangling Chen, slchen19@126.com; slchen@sio.org.cn