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The Variability of Air-sea O2 Flux in CMIP6: Implications for Estimating Terrestrial and Oceanic Carbon Sinks


doi: 10.1007/s00376-021-1273-x

  • The measurement of atmospheric O2 concentrations and related oxygen budget have been used to estimate terrestrial and oceanic carbon uptake. However, a discrepancy remains in assessments of O2 exchange between ocean and atmosphere (i.e. air-sea O2 flux), which is one of the major contributors to uncertainties in the O2-based estimations of the carbon uptake. Here, we explore the variability of air-sea O2 flux with the use of outputs from Coupled Model Intercomparison Project phase 6 (CMIP6). The simulated air-sea O2 flux exhibits an obvious warming-induced upward trend (~1.49 Tmol yr−2) since the mid-1980s, accompanied by a strong decadal variability dominated by oceanic climate modes. We subsequently revise the O2-based carbon uptakes in response to this changing air-sea O2 flux. Our results show that, for the 1990−2000 period, the averaged net ocean and land sinks are 2.10±0.43 and 1.14±0.52 GtC yr−1 respectively, overall consistent with estimates derived by the Global Carbon Project (GCP). An enhanced carbon uptake is found in both land and ocean after year 2000, reflecting the modification of carbon cycle under human activities. Results derived from CMIP5 simulations also investigated in the study allow for comparisons from which we can see the vital importance of oxygen dataset on carbon uptake estimations.
    摘要: 目前对大气氧浓度及相关氧收支的估算已经被用于反推海洋和陆地的碳汇。然而,在这种基于氧的碳汇估计方法中,如何衡量海气之间氧通量至今仍存在着较大的分歧,这已经成为了影响碳汇估算的主要不确定因素之一。在这种背景下,本文利用国际耦合模式比较计划第六阶段(CMIP6)的模式数据探索了海气间氧通量的时空变率。结果表明,从19世纪80年代中期开始,全球海气氧通量呈现出了非常明显的上升趋势(约1.49 Tmol yr−2)。这种增暖导致的上升趋势与海洋气候模态主导的年代际振荡共同作用,最终形成了海气氧通量的时间变化序列。我们根据上述结果,对基于氧收支的海洋和陆地的碳汇估算进行了订正:在1900−2000年期间,海洋和陆地的平均碳沉降速率分别为2.10±0.43 Gt C yr−1以及1.14±0.52 Gt C yr−1,这与全球碳计划(GCP)得出的结果大致相符。海洋和陆地对于碳的吸收在2000年后有着加强的迹象,这反映了碳循环对人类活动不断增强的响应。此外,我们还将该结果与以CMIP5为基础得到的结果进行了对比,二者之间的差异反映出了氧数据集对碳沉降估计的重要影响。
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  • Figure 1.  The spatial distributions of annual mean air-sea O2 flux (a) averaged from 1985 to 2014 in CMIP6 historical simulations, and (b) compared with two other studies. Positive flux in Fig. 1a means O2 outgassing from ocean to the atmosphere. For sake of comparisons, the ocean is partitioned into 13 regions as shown in Fig. S3 in the ESM. The results from Li et al (2020) are similar with Resplandy et al 2015, which are not shown here.

    Figure 2.  Time series in the historical period (1950−2014) of (a) air-sea O2 flux and (b) its EEMD decomposition. The red dashed line in (a) represents linear regression from 1980 to 2014, significant at the 0.01 level. Shaded area is the uncertainty of the flux represented by the standard deviation of these models. The decadal variability in (b) (the blue solid line) is the sum of IMF2-5 from the EEMD and the long-term trend (the red solid line) is the IMF6. Positive values in both panels indicate oceanic O2 outgassing to the atmosphere.

    Figure 3.  EOF analysis of de-trended global air-sea O2 flux over the 1985−2014 period. The spatial patterns of the first and second EOF mode are presented in panel (a) and (b), respectively. The black and blue lines in (a) represent the temporal coefficient of the two modes. Note that the original timeseries is pre-processed with a pentad running average to remove the influence of the high-frequency oscillations.

    Figure 4.  15-year changes in the long-term trend of air-sea O2 flux since 1985. The error bars in panel (b) represent the uncertainty of flux change.

    Figure 5.  Differences of air-sea O2 flux between CMIP6 and CMIP5 during period 1975−2005 (i.e. FLUXCMIP6 minus FLUXCMIP5). The black line in (a) is the time series of the difference and (b) shows the spatial distribution of the difference averaged from 1975−2005.

    Figure 6.  Changes in observed atmospheric concentrations of O2/N2 and CO2 from 1990 to 2014. The blue dots represent the annual averaged O2 and CO2 anomaly (here we choose the concentrations in 1990 as the reference value). The vectors in the diagram schematically illustrate the contribution of each process related to the changes in O2 (vertical axis) and CO2 (horizontal axis) during this period. The effect of air-sea O2 flux is highlighted in red.

    Figure 7.  The observed time series of atmospheric O2/N2 and CO2 concentrations. The blue, green and red lines represents observations in La Jolla (32.9°N, 277.3°W), Alert (82.5°N, 62.3°W), and Cape Grim (40.7°S, 144.7°E), respectively. The black line is the annual mean concentrations averaged among the three stations with a weight of 0.25, 0.25 and 0.5.

    Figure 8.  Role of air-sea O2 flux in O2-based carbon sinks estimations. The diagram is same as Fig. 6, except for no air-sea O2 flux considered in the calculation. The bar charts in the top right show the comparisons between estimated ocean/land carbon sink with and without O2 flux correction.

    Figure 9.  Estimated ocean and land carbon sinks in different studies. The asterisks and triangles are seven-year averaged carbon sinks in this study and Li et al 2021, with error bars representing uncertainties of the estimations. The time series of carbon sinks derived from Global Carbon Project 2019, Landschützer et al 2016 and Carbon Tracker 2019 are colored in red, green and blue, respectively. The thin dashed lines and the thick solid lines are annual and seven-year running averaged carbon sinks, respectively.

    Table 1.  Typical oxidative ratio for each fuel type

    Fuel TypeOxidative ratio (αF)
    Solid fuel (coal)1.17±0.03
    Liquid fuel (oil)1.44±0.03
    Gas fuel (natural gas)1.95±0.04
    Cement production0.00±0.00
    Biofuel1.07±0.03
    DownLoad: CSV

    Table 2.  The CMIP6 models used in this study to obtain the air-sea O2 fluxa

    Model NameInstitute
    IPSL-CM5A2-INCAInstitut Pierre-Simon Laplace, France
    GFDL-CM4Geophysical Fluid Dynamics Laboratory, USA
    GFDL-ESM4Geophysical Fluid Dynamics Laboratory, USA
    MPI-ESM-1-2-HAMMax Planck Institute for Meteorology, Germany
    NorESM2-LMNorwegian Climate Centre, Norway
    NorESM2-MMNorwegian Climate Centre, Norway
    a The air-sea O2 flux was calculated by the model in mol m−2 s−1, so we converted this value to mol of oxygen per year by converting from seconds to year (×31 536 000).
    DownLoad: CSV

    Table 3.  Estimations of O2-based carbon sinks in different periods

    Period∆δ (O2/N2)a,b
    (per meg yr−1)
    ∆CO2a,b
    (ppm yr−1)
    Feffa,c
    (Tmol yr−1)
    Ffossila
    (GtC yr−1)
    Ocean sinka
    (GtC yr−1)
    Land sinka
    (GtC yr−1)
    Our results1990−00−15.81 (0.52)1.46 (0.08)45.7 (30.6)6.37 (0.24)2.10 (0.43)1.14 (0.52)
    2000−10−20.14 (0.34)1.94 (0.07)58.7 (31.3)7.93 (0.83)2.66 (0.41)1.15 (0.50)
    2004−08−19.62 (1.33)1.79 (0.27)50.4 (30.1)8.28 (0.40)2.64 (0.66)1.84 (0.79)
    Keeling et al., 20141990−2000−15.771.52 (0.02)44 (45)6.39 (0.38)1.94 (0.62)1.22 (0.80)
    2000−10−20.391.90 (0.02)44 (45)7.81 (0.47)2.72 (0.60)1.05 (0.84)
    Tohjima et al., 20192004−08−19.291.92 (0.09)27.5 (27.5)8.21 (0.41)1.97 (0.62)2.17 (0.82)
    a Estimated uncertainties are shown in parentheses. These uncertainties are propagated to the ocean and land sink uncertainties during calculation. b The linear trend of the observations during the selected period. Uncertainties shown in parentheses are the standard error of the regression coefficient. c Ensemble mean of the CMIP6 models. Uncertainties shown in parentheses are standard deviation among the models.
    DownLoad: CSV
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Manuscript received: 27 July 2021
Manuscript revised: 24 September 2021
Manuscript accepted: 08 October 2021
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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The Variability of Air-sea O2 Flux in CMIP6: Implications for Estimating Terrestrial and Oceanic Carbon Sinks

    Corresponding author: Jianping HUANG, hjp@lzu.edu.cn
  • 1. College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
  • 2. Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China
  • 3. Enlightening Bioscience Research Center, Mississauga, L4X 2X7, Canada

Abstract: The measurement of atmospheric O2 concentrations and related oxygen budget have been used to estimate terrestrial and oceanic carbon uptake. However, a discrepancy remains in assessments of O2 exchange between ocean and atmosphere (i.e. air-sea O2 flux), which is one of the major contributors to uncertainties in the O2-based estimations of the carbon uptake. Here, we explore the variability of air-sea O2 flux with the use of outputs from Coupled Model Intercomparison Project phase 6 (CMIP6). The simulated air-sea O2 flux exhibits an obvious warming-induced upward trend (~1.49 Tmol yr−2) since the mid-1980s, accompanied by a strong decadal variability dominated by oceanic climate modes. We subsequently revise the O2-based carbon uptakes in response to this changing air-sea O2 flux. Our results show that, for the 1990−2000 period, the averaged net ocean and land sinks are 2.10±0.43 and 1.14±0.52 GtC yr−1 respectively, overall consistent with estimates derived by the Global Carbon Project (GCP). An enhanced carbon uptake is found in both land and ocean after year 2000, reflecting the modification of carbon cycle under human activities. Results derived from CMIP5 simulations also investigated in the study allow for comparisons from which we can see the vital importance of oxygen dataset on carbon uptake estimations.

摘要: 目前对大气氧浓度及相关氧收支的估算已经被用于反推海洋和陆地的碳汇。然而,在这种基于氧的碳汇估计方法中,如何衡量海气之间氧通量至今仍存在着较大的分歧,这已经成为了影响碳汇估算的主要不确定因素之一。在这种背景下,本文利用国际耦合模式比较计划第六阶段(CMIP6)的模式数据探索了海气间氧通量的时空变率。结果表明,从19世纪80年代中期开始,全球海气氧通量呈现出了非常明显的上升趋势(约1.49 Tmol yr−2)。这种增暖导致的上升趋势与海洋气候模态主导的年代际振荡共同作用,最终形成了海气氧通量的时间变化序列。我们根据上述结果,对基于氧收支的海洋和陆地的碳汇估算进行了订正:在1900−2000年期间,海洋和陆地的平均碳沉降速率分别为2.10±0.43 Gt C yr−1以及1.14±0.52 Gt C yr−1,这与全球碳计划(GCP)得出的结果大致相符。海洋和陆地对于碳的吸收在2000年后有着加强的迹象,这反映了碳循环对人类活动不断增强的响应。此外,我们还将该结果与以CMIP5为基础得到的结果进行了对比,二者之间的差异反映出了氧数据集对碳沉降估计的重要影响。

    • Human beings are now faced with continuous growth of the climate risk in the warming world. The climate change, occurring mainly as a consequence of anthropogenic CO2 emissions, is already wielding its influences on ecosystems, economic sectors and people's health (Bopp et al., 2013; Huang et al., 2016; Frölicher et al., 2018; Wei et al., 2021). An increasing number of evidence warns us that actions should be taken urgently to minimize dangerous anthropogenic interference with the climate system, limiting global warming to 2 degrees – a threshold laid down by the Paris Agreement (Seneviratne et al., 2016; Huang et al., 2017b). Under this circumstance, the carbon neutrality, which refers to the balance of emissions of carbon dioxide with its removal, has become one of the most essential things human society needs to achieve in the mid-late 21st century (Dhanda and Hartman, 2011; Niu et al., 2021).

      The land and ocean play an important role in the storage of atmospheric CO2 (Dai et al., 2013; DeVries et al., 2019). It has been reported that the land and ocean have sequestered approximately half of the anthropogenic CO2 emitted to the atmosphere in the past decades, which helps greatly buffer climate change (Friedlingstein et al., 2019; Gao et al., 2019, 2020). Thus, for a reasonable design of global warming mitigation and carbon neutrality strategies, there is a pressing need to address the effectiveness of terrestrial and oceanic carbon uptake and their susceptibility to climate change. According to this view, the measurement of atmospheric O2 concentrations and related oxygen budget could provide us a concise and effective method to estimate carbon-uptake capacity of land and ocean on the basis of the close relationship between oxygen and carbon (Huang et al., 2018, 2021; Han et al., 2021; Li et al., 2021).

      The accuracy of this O2-based carbon uptake estimation largely depends on how the oxygen data, especially the air-sea O2 exchange, is processed in the calculation. Early studies used to assume that there was no long-term oceanic effect of O2 on the atmosphere (Keeling and Shertz, 1992; Battle et al., 2000). However, a number of indications have revealed the huge oceanic heat uptake under climate change (Willis et al., 2004; Cheng et al., 2018; Cheng and Zhu, 2018; Li et al., 2019), which implies the air-sea O2 exchange could vary as a consequence of warming-induced solubility and circulation changes (Bopp et al., 2002; Li et al., 2020). Later studies have thus taken air-sea O2 flux into consideration (Manning and Keeling, 2006; Tohjima et al., 2019), where the oceanic O2 outgassing to the atmosphere is approximately estimated by a linear regression with ocean heat content, assuming the relationship between gas flux and heat flux bears a proportional relationship at the air–sea interface. In fact, mechanisms that control the variability of air-sea O2 flux are rather complicated. Its temporal and spatial variations could be affected by changes in ocean primary production, ventilation and stratification, as well as oceanic internal modes such as El Niño-Southern Oscillation(ENSO) (Resplandy et al., 2015; Yang et al., 2017). The intensified ocean heat uptake in the past few decades (Trenberth et al., 2014; Cheng et al., 2017) also wields its influences in the long-term period. How to accurately quantify the air-sea O2 flux has therefore been one of the most important questions in the field of O2-based carbon uptake estimations.

      Here, based on recent CMIP6 model simulations, we systematically investigate the characteristics of air-sea O2 flux and from it, we subsequently calculate the terrestrial and oceanic carbon sinks. We hope to provide a better understanding of air-sea O2 flux under ongoing climate change. We also hope the applications of process-based air-sea O2 flux from CMIP6 model simulations can provide a more comprehensive and reliable carbon sink estimation, compared with results from previous studies where the air-sea O2 flux is not considered or simply approximated by a linear relationship between O2 outgassing and heat content.

      The paper is arranged as follows. Section 2 describes the detailed method of O2-based carbon sink estimations and the datasets, especially air-sea O2 flux, used in this study. The climatology characteristics of air-sea O2 flux and its variability under climate change in CMIP6 are shown in section 3.1. Section 3.2 provides our estimations of terrestrial and oceanic carbon sinks with the use of this air-sea O2 flux. Discussion and conclusion are presented in section 4.

    2.   Data and methods
    • The assessments of land and ocean carbon sinks in this study are based on the strong relationship between oxygen and carbon, which can be written as follows (Keeling and Manning, 2014; Li et al., 2021):

      where ∆CO2 and ∆O2 represent changes in atmospheric CO2 and O2; Ffossil is the industrial CO2 emissions, which mainly comes from fossil fuel combustion; Fair-sea represents the air-sea O2 flux; αF and αB are dimensionless parameters which represent the globally averaged O2: CO2 mole exchange ratios for fossil fuel burning and biological process; Sland and Socean represent the net land carbon sink and ocean carbon sink, respectively. These two equations briefly describe the human impacts on the oxygen and carbon cycles. All variables in the equations mentioned above use the units of mole.

    • The concentrations of CO2 in the atmosphere ($X_{{\rm{CO}}_2} $) are measured using the unit of “ppm” (parts per million). Its change can be expressed as

      where Mair represents the global total number of moles of dry air (Mair=1.769×1020). The change of atmospheric O2 concentrations, however, is typically measured as the mole ratio changes of O2/N2 rather than the mole fraction such as ppm, due to its high abundance in the atmosphere. Following Keeling and Shertz (1992), the O2 content of an air sample can be defined as

      where (O2/N2)sample is the mole ratio of O2 to N2 in the sample air and (O2/N2)ref is the ratio in an arbitrary reference gas. Note that δ(O2/N2) is typically multiplied by 106 and expressed as “per meg” unit. The observed changes of δ(O2/N2) in the atmosphere could thus be written as

      where ∆O2 and ∆N2 are changes in moles of atmospheric O2 and N2; $X_{{\rm{O}}_2} $ and $X_{{\rm{N}}_2} $ are the standard mole fraction of O2 and N2 in the atmosphere ($X_{{\rm{O}}_2} $ = 0.2094 and $X_{{\rm{N}}_2} $ = 0.7808).

      According to Eqs. (1)−(5), the land and ocean carbon sink can be written as

      The observed timeseries of atmospheric CO2 and O2 concentrations [i.e. $X_{{\rm{CO}}_2} $ and δ(O2/N2)] can be downloaded from Scripps O2 Program (https://scrippso2.ucsd.edu/), which provides records of both CO2 and O2 concentrations at 12 stations. In this study, we choose the longest three timeseries, at Alert (82.5°N, 62.3°W), La Jolla (32.9°N, 277.3°W), and Cape Grim (40.7°S, 144.7°E), respectively, and calculate the average with weights of 0.25, 0.25, 0.5 (given the equal weight in both hemispheres).

    • The global CO2 emissions (Ffossil) are derived from Carbon Dioxide Information Analysis Center (CDIAC, Andres et al., 2016), which counts the consumptions of each type of fossil fuel. It should be noted that each fuel type has its own combustion ratio (αF), as shown in Table 1 (Liu et al., 2020). The global averaged αF therefore slightly varies with time due to changes of global energy sources [Fig. S1 in the Electronic Supplementary Material, (ESM)]. The oxidative ratio αB also exhibits temporal variations due to modifications to global vegetation cover by human activities, however, it is generally believed the decrease of αB is less than 0.01 over 100 years (Randerson et al., 2006). We thus set the typical value of αB as 1.10 according to previous studies (Keeling and Manning, 2014; Battle et al., 2019).

      Fuel TypeOxidative ratio (αF)
      Solid fuel (coal)1.17±0.03
      Liquid fuel (oil)1.44±0.03
      Gas fuel (natural gas)1.95±0.04
      Cement production0.00±0.00
      Biofuel1.07±0.03

      Table 1.  Typical oxidative ratio for each fuel type

    • Due to the importance of O2 flux (Fair-sea) in estimating the carbon uptake, here we discuss it in greater detail. The air-sea O2 flux evaluated in this study builds on the process-based ocean physical and biochemical models developed as part of Coupled Model Intercomparison Project phase 6 (CMIP6), which can be downloaded from https://esgf-node.llnl.gov/search/cmip6/. The detailed descriptions of these models are presented in Table 2. Here we choose the historical experiments of these models to match the timeseries of O2 observations. Note that the air-sea O2 flux is calculated by the model in mol m−2 s−1, so we convert to mol of oxygen per year (mol m−2 yr−1). For sake of comparisons and analysis, all the model results are gridded to 1°×1° resolution.

      Model NameInstitute
      IPSL-CM5A2-INCAInstitut Pierre-Simon Laplace, France
      GFDL-CM4Geophysical Fluid Dynamics Laboratory, USA
      GFDL-ESM4Geophysical Fluid Dynamics Laboratory, USA
      MPI-ESM-1-2-HAMMax Planck Institute for Meteorology, Germany
      NorESM2-LMNorwegian Climate Centre, Norway
      NorESM2-MMNorwegian Climate Centre, Norway
      a The air-sea O2 flux was calculated by the model in mol m−2 s−1, so we converted this value to mol of oxygen per year by converting from seconds to year (×31 536 000).

      Table 2.  The CMIP6 models used in this study to obtain the air-sea O2 fluxa

      Furthermore, it should be noted that, due to import of N2 in the atmospheric O2 observations, oceanic N2 outgassing must be considered in the calculations. The total effect of the ocean on carbon sinks could thus be expressed as Eq. (8). Here we apply the tuning parameter β=0.88 to represent the negative effect of N2 outgassing (Keeling and Manning, 2014); it can be shown that the equation can be written as

      The related ocean physics variables such as sea temperature, salinity, and mixed layer depth in CMIP6 are also used in this study to analyze mechanisms of O2 flux change.

    • We use the ensemble empirical mode decomposition (EEMD) method to separate the human-induced long-term signals from natural decadal variability in the time series of air-sea O2 flux. This noise-assisted method can separates scales naturally without any prior subjective criterion (Ji et al., 2014; Huang et al., 2017a). EEMD performs operations that partition a series into different “modes” (Intrinsic Mode Functions, IMFs), which are expressed by the following equation:

      where IMFi(t) is the ith IMF, and rn(t) is the residual of data X(t). The detailed descriptions of the steps on how to execute EEMD method can be found in Text S1 in the ESM. In this study, the noise added to the data has an amplitude that is 0.2 times the standard deviation of the raw data, and the ensemble number is 400. The number of IMFs is 6. A python version of EEMD is available at https://www.github.com/laszukdawid/PyEMD (Laszuk, 2017).

    3.   Results
    • The transfer of gases across the air-sea interface is controlled by several physical, biological and chemical processes in the atmosphere and ocean, which could influence not only the partial pressure differences but also the efficiency of transfer processes (Wanninkhof, 1992; Liang et al., 2013). The air-sea O2 flux thus varies considerably among the ocean regions. Figure 1a presents the model-ensemble-mean of annual air-sea O2 flux averaged from 1985 to 2014 in CMIP6 historical experiments (positive means a flux to the atmosphere). Spatial distributions of O2 flux in each individual model can be found in Fig. S2 in the ESM. The results show an overall net O2 outgassing from ocean to the atmosphere at low latitudes, while a significant influx of O2 occurs at high latitudes. The tropical and subtropical ocean (30°S−30°N) emits approximately 250.8±38.4 Tmol O2 per year (1 Tmol = 1012 mol), which is partly compensated by O2 absorption in the high-latitude ocean, about −105.2±24.8 and −87.2±41.4 Tmol yr−1 in the Northern (>30°N) and Southern Hemisphere (>30°S), respectively, eventually leading to a net O2 outgassing of ~58.5±9.6 Tmol yr−1 over the global ocean. This pattern highlights the solubility effect driven by meridional temperature gradients, as well as combinations of the dynamical and biological effects, which lead to a surplus of oceanic O2 production in low latitudes (Bopp et al., 2002).

      Figure 1.  The spatial distributions of annual mean air-sea O2 flux (a) averaged from 1985 to 2014 in CMIP6 historical simulations, and (b) compared with two other studies. Positive flux in Fig. 1a means O2 outgassing from ocean to the atmosphere. For sake of comparisons, the ocean is partitioned into 13 regions as shown in Fig. S3 in the ESM. The results from Li et al (2020) are similar with Resplandy et al 2015, which are not shown here.

      Furthermore, the simulated O2 flux is evaluated against results derived from previous studies (Gruber et al., 2001; Resplandy et al., 2015), which are found in Fig. 1b . The ocean is divided into 13 regions for sake of comparison (Fig. S3 in the ESM). The patterns presented by the ensemble-mean of the suite of models in CMIP6 correspond well with estimations based on ocean inversions (Gruber et al., 2001), except for the Sothern Ocean. The results derived from Gruber et al. (2001) exhibit a much stronger O2 outgassing in the subpolar South Atlantic [95.0 Tmol yr−1 differences between this study and Gruber et al. (2001)]. However, this difference could roughly cancel out when we integrate the whole Southern Ocean regions, as it also exists a larger O2 influx in subpolar Indian-Pacific Ocean and Oceans >58°S (differences of −58.1 and −26.2 Tmol yr−1, respectively). Besides, the spatial distribution shows a remarkable consistency with preindustrial experiments presented by Resplandy et al. (2015), indicating the robust of models in simulating O2 flux.

    • Temporal evolution of the air-sea O2 flux reveals that significant modifications have been occurring in response to ongoing climate change (Fig. 2). In Fig. 2a, we can see sizable oscillations of air-sea O2 flux during the period 1950−85. Also obvious is the increase of oceanic O2 outgassing found since the mid-1980s, with an upward trend of ~1.49 Tmol yr−2 (significant at 0.01 level). Based on EEMD method, here we split the evolution of air-sea O2 flux into decadal variability (i.e. sum of IMFs 2−5 from EEMD) and the long-term trend (i.e. IMF 6). As shown in Fig. 2b, the time series of air-sea O2 flux from 1950 to 1985 is primarily dominated by natural decadal variability, while the human-induced long-term changes gradually wields its influence after 1985. The combination of the two terms eventually lead to an overall upward trend since the 1980s, with natural variability modulating the long-term trend.

      Figure 2.  Time series in the historical period (1950−2014) of (a) air-sea O2 flux and (b) its EEMD decomposition. The red dashed line in (a) represents linear regression from 1980 to 2014, significant at the 0.01 level. Shaded area is the uncertainty of the flux represented by the standard deviation of these models. The decadal variability in (b) (the blue solid line) is the sum of IMF2-5 from the EEMD and the long-term trend (the red solid line) is the IMF6. Positive values in both panels indicate oceanic O2 outgassing to the atmosphere.

      The EOF analysis was applied to the de-trended global air-sea O2 flux over the 1985−2014 period to explore the spatio-temporal distributions of decadal variability (Fig. 3). The first two modes explain approximately 58% of the total variance. The highest decadal variability of O2 flux is found in the North Pacific, the North Atlantic and the Southern Ocean (Figs. 3a, 3b). The most significant changes in the Atlantic are mainly in the high-latitude areas where the sinking branch of the Atlantic Meridional Overturning Circulation (AMOC) is located, the changes of which could significantly influence climate (Yang et al., 2016; Wen et al., 2018; Yang and Wen, 2020). In the Southern Ocean, the spatial pattern exhibits opposite phase between 40°S and 65°S, suggesting the potential relationship with the Southern Annular Mode (SAM). Time series associated with EOF modes reveal a cycle of ~15 years with different phases in PC1 and PC2 (Fig. 3c). The standard deviation of the decadal variability derived from EEMD also shows a similar spatial distribution compared with the EOF analysis (Fig. S4 in the ESM).

      Figure 3.  EOF analysis of de-trended global air-sea O2 flux over the 1985−2014 period. The spatial patterns of the first and second EOF mode are presented in panel (a) and (b), respectively. The black and blue lines in (a) represent the temporal coefficient of the two modes. Note that the original timeseries is pre-processed with a pentad running average to remove the influence of the high-frequency oscillations.

      The long-term changes of air-sea O2 flux, which are generally considered as modifications to anthropogenic forcing, is presented in Fig. 4. Positive values are mainly found in the high latitude areas (Fig. 4a), where strong O2 uptake in the climatological state is seen (Fig. 1a), revealing the weakening of the oceanic O2 absorption capacity from the atmosphere. The maximum increase of the flux occurs in the Southern Ocean (SO>58°S), where it reaches 5.39±0.34 Tmol yr−1. The next two highest increases occur in the North Pacific (Temp NPac) and North Atlantic (N NAtl), with an increase about 4.39±0.17 and 3.25±0.11 Tmol yr−1, respectively (Fig. 4b). This long-term change could be attributed to human-induced solubility and circulation changes. The solubility of dissolved O2 has been decreasing in the warming ocean. This effect could be written as:

      Figure 4.  15-year changes in the long-term trend of air-sea O2 flux since 1985. The error bars in panel (b) represent the uncertainty of flux change.

      where Q is the total sea-surface downward heat flux; Cp represents the heat capacity of sea water; ∂O2/∂T is the temperature dependence of O2 solubility which could be derived from Garcia and Gordon (1992). Our calculations reveal that roughly one quarter of the increase is directly associated with reduced solubility in the warming ocean, which is consistent with results found by Li et al. (2020) and Plattner et al. (2002). Warming-induced ocean stratification also plays an important role in the modifications of air-sea O2 flux. Strong shoaling of the mixed layer is found in the North Atlantic and widespread areas in the Southern Ocean (Fig. S5 in the ESM), which prevents oxygen supplies from reaching the deeper layers and eventually result in a positive contribution to the air-sea O2 flux.

    • In Li et al. (2020), the air-sea O2 flux derived from CMIP5 is applied to investigate the terrestrial and oceanic carbon sinks. It is therefore necessary to clarify the difference of the flux between the CMIP5 and CMIP6 as well as its influences on carbon sink estimations.

      For a simulated historical period from 1975 to 2005, the comparisons between CMIP6 (this study) and CMIP5 [derived from Li et al. (2021)] reveal pronounced temporally varying differences of air-sea O2 flux (Fig. 5). Except for a short period of time around year 1990, the ocean in CMIP6 exhibits an overall smaller oceanic O2 outgassing, up to −22 Tmol yr−1, than in CMIP5. Spatial patterns shown in Fig. 5b reveal that this difference is mainly caused by the intensified high-latitude oceanic O2 uptake in CMIP6, especially in the North Atlantic and Southern Ocean. Although there still exists relatively large uncertainties, this intensified uptake in CMIP6 is more consistent with the regional observations in the Southern Ocean (Bushinsky et al., 2017), reflecting the improvement of simulations in CMIP6. Furthermore, slight difference also exists in the long-term trend of air-sea O2 flux. An upward linear trend of ~1.52 Tmol yr−2 has been found in CMIP6 during the period 1985 to 2005, while the trend is approximately 1.12 Tmol yr−2 in CMIP5. This indicates an accelerated oceanic O2 outgassing in CMIP6, which is tightly associated with ocean deoxygenation (Bopp et al., 2013; Palter and Trossman, 2018; Li et al., 2020).

      Figure 5.  Differences of air-sea O2 flux between CMIP6 and CMIP5 during period 1975−2005 (i.e. FLUXCMIP6 minus FLUXCMIP5). The black line in (a) is the time series of the difference and (b) shows the spatial distribution of the difference averaged from 1975−2005.

      According to Eqs. (6)−(8), this difference in O2 flux could lead to a total fluctuation as large as 0.4 GtC yr−1 in the estimated carbon sink. It should be noted that, besides the air-sea O2 flux, the estimated carbon sink could also be influenced by the choice of other oxygen datasets in the study, which is therefore rather complicated. Comparisons of O2-based carbon sinks between this study and Li et al. (2021), as well as other previous studies, will be discussed in detail in the following section.

    • Simulations of the air-sea O2 flux in CMIP6 provide a valuable complement for the O2-based carbon uptake estimations. With the use of air-sea O2 flux as well as other O2-related variables, the global terrestrial and oceanic carbon sinks could be calculated based on Eqs. (1)−(9). The processes are briefly diagrammed in Fig. 6.

      Figure 6.  Changes in observed atmospheric concentrations of O2/N2 and CO2 from 1990 to 2014. The blue dots represent the annual averaged O2 and CO2 anomaly (here we choose the concentrations in 1990 as the reference value). The vectors in the diagram schematically illustrate the contribution of each process related to the changes in O2 (vertical axis) and CO2 (horizontal axis) during this period. The effect of air-sea O2 flux is highlighted in red.

      The dots in Fig. 6 are the observed anomalies of global atmospheric CO2 (horizontal axis) and O2/N2 concentrations (vertical axis) from 1990 to 2014. Here we set the concentrations in year 1990 as the base point (0 ppm, 0 per meg). These dots show an increase of CO2 concentration and a simultaneous decline in O2/N2 concentration with time. For example, the concentrations in 2014 could be written as (44 ppm, −465 per meg) in this coordinate system, which means a 44 ppm increase of CO2 concentration and a 465 per meg decrease of O2/N2 concentration in the atmosphere since year 1990. The arrows in Fig. 6 reveal the effect of related processes on atmospheric CO2 and O2/N2 concentration changes. For example, the fossil fuel combustion is marked by the black arrow in Fig. 6, starting at (0, 0) and ending at (89.0, −584.7), meaning that the fossil fuel burning would have contributed to a total 89.0 ppm increase of CO2 (that is, a release of 189.0 GtC CO2, 1 Gt = 1015 g, 1 ppm = 2.12 GtC) and 584.7 per meg decrease of O2/N2 concentration during 1990−2014, if no other processes were involved. This is to say, the observed decline of O2/N2 (~465.1 per meg) is a bit smaller compared with the decline directly derived from fossil fuel combustion (584.7 per meg) during 1990−2014. More importantly, the observed atmospheric CO2 concentration only increases by about half of the value derived from fossil fuel combustion (that is, ~44 ppm, as shown in Fig. 6 and Fig. 7), from which we can thus infer huge land and ocean carbon sinks, absorbing a total of 96.6 GtC carbon. The projections of these arrows on the x- axis are also drawn in Fig. 6, which reflect how the atmospheric CO2 concentrations are influenced by the related processes. The land and ocean carbon sinks can be separated from the total carbon uptake according to Eq. (6) and Eq. (7), as 33.5 GtC and 63.2 GtC, respectively, during this period.

      Figure 7.  The observed time series of atmospheric O2/N2 and CO2 concentrations. The blue, green and red lines represents observations in La Jolla (32.9°N, 277.3°W), Alert (82.5°N, 62.3°W), and Cape Grim (40.7°S, 144.7°E), respectively. The black line is the annual mean concentrations averaged among the three stations with a weight of 0.25, 0.25 and 0.5.

      It should be especially noted that the air-sea O2 flux plays an important role in the carbon uptake estimations. The ocean emits ~1.54 Pmol O2 (1 Pmol = 1015 mol) to the atmosphere (sum of the air-sea O2 flux from 1990 to 2014 in Fig. 2a), making a positive contribution of about 36.7 per meg to the atmospheric O2/N2 concentration (red vector in Fig. 6). Despite this air-sea O2 flux being relatively small, it plays an important role in the estimation of land and ocean carbon sinks. Figure 8 describes the situation assuming that the air-sea O2 flux is negligible on a multiannual-to-decadal timescale, as proposed in the early studies (Bender and Battle, 1999; Battle et al., 2000). If the air-sea O2 flux is not considered in the O2 budget, the ocean carbon sink would be apparently underestimated by approximately 14.8 GtC during 1990−2014, while the land carbon uptake would be largely overestimated (bar charts in the top right of Fig. 8).

      Figure 8.  Role of air-sea O2 flux in O2-based carbon sinks estimations. The diagram is same as Fig. 6, except for no air-sea O2 flux considered in the calculation. The bar charts in the top right show the comparisons between estimated ocean/land carbon sink with and without O2 flux correction.

    • We subsequently calculated the averaged terrestrial and oceanic carbon uptake over several different periods and compared them with previous O2-based carbon uptake estimations (Table 3). Here, we use the linear trend of atmospheric O2/N2 and CO2 concentrations in the period to represent the O2/N2 and CO2 changes in Eqs. (6)−(7) (∆δ(O2/N2) and ∆CO2). For observed atmospheric concentration changes and fossil fuel consumption (Ffossil), our results are relatively consistent with Keeling et al. (2014) (differences less than 0.06 ppm yr−1 in ∆CO2 and 0.12 GtC yr−1 in Ffossil). The effect of air-sea flux in our study (which are derived from process-based CMIP6 model simulations, as described above) shows a relatively large discrepancy with that in Keeling et al. (2014) (which is calculated based on the linear regression between O2 flux and net changes of ocean heat content). Our results show an averaged ocean and land carbon sink of 2.10±0.43 and 1.14±0.52 GtC yr−1, respectively, during 1990−2000. An increase is found in both ocean and land carbon sinks during 2000−10, while results from Keeling et al. (2014) show an increase in ocean sink but a decline in land sink. Furthermore, the averaged carbon sinks from 2004 to 2008 in our study (2.64±0.66 GtC yr−1 for ocean and 1.84±0.79 GtC yr−1 for land) are generally larger than that in Tohjima et al. (2019) (1.97±0.62 GtC yr−1 for ocean and 2.17±0.82 GtC yr−1 for land), which could also be partly attributed to the discrepancy in the air-sea flux (Table 3).

      Period∆δ (O2/N2)a,b
      (per meg yr−1)
      ∆CO2a,b
      (ppm yr−1)
      Feffa,c
      (Tmol yr−1)
      Ffossila
      (GtC yr−1)
      Ocean sinka
      (GtC yr−1)
      Land sinka
      (GtC yr−1)
      Our results1990−00−15.81 (0.52)1.46 (0.08)45.7 (30.6)6.37 (0.24)2.10 (0.43)1.14 (0.52)
      2000−10−20.14 (0.34)1.94 (0.07)58.7 (31.3)7.93 (0.83)2.66 (0.41)1.15 (0.50)
      2004−08−19.62 (1.33)1.79 (0.27)50.4 (30.1)8.28 (0.40)2.64 (0.66)1.84 (0.79)
      Keeling et al., 20141990−2000−15.771.52 (0.02)44 (45)6.39 (0.38)1.94 (0.62)1.22 (0.80)
      2000−10−20.391.90 (0.02)44 (45)7.81 (0.47)2.72 (0.60)1.05 (0.84)
      Tohjima et al., 20192004−08−19.291.92 (0.09)27.5 (27.5)8.21 (0.41)1.97 (0.62)2.17 (0.82)
      a Estimated uncertainties are shown in parentheses. These uncertainties are propagated to the ocean and land sink uncertainties during calculation. b The linear trend of the observations during the selected period. Uncertainties shown in parentheses are the standard error of the regression coefficient. c Ensemble mean of the CMIP6 models. Uncertainties shown in parentheses are standard deviation among the models.

      Table 3.  Estimations of O2-based carbon sinks in different periods

      To further explore the temporal changes of ocean and land carbon sinks over the past two decades, the averaged ocean and land carbon sinks were calculated for several representative periods: 1991−97, 1994−2000 and 2004−10 were selected for the estimates of averaged ocean sinks; meanwhile, 1994−2000, 2002−08 and 2008−14 were selected for the estimates of averaged land sinks. These results are shown as the asterisks in Fig. 9, accompanied by time-continuous estimations from the Global Carbon Project (GCP, Friedlingstein et al., 2019), Landschützer et al 2016 and Carbon Tracker (CT, Jacobson et al., 2020). The estimates by GCP clearly show a quasi-monotonous increase of the oceanic carbon sink over the past few decades (Fig. 9a, red line). However, the oceanic uptake in our results show a decline from 2.04±0.47 GtC yr−1 in 1991−97 to 1.85±0.45 GtC yr−1 in 1994−2000. A significant upward trend is subsequently found in the 21st century, with ocean uptake increasing to 2.87±0.47 GtC yr−1 in 2004−10. This temporal pattern is generally consistent with results derived from observed surface partial pressure of CO2 in Landschützer et al. (2016) (Fig. 9a, green line), which may occur as consequences of the combined influence of anthropogenic forcing and oceanic internal modes. The net terrestrial carbon uptake estimated in this study corresponds well with the results derived from GCP. An increase of land carbon uptake (from 1.23±0.60 GtC yr−1 to 1.91±0.50 GtC yr−1 according to our estimations) could be found in the 2000s (Fig. 9b) which has been reported by several atmospheric inversion and model-based studies (Keenan et al., 2016; Ballantyne et al., 2017; Piao et al., 2018). Despite the fact that the mechanisms behind this increase are still under discussion, it is generally believed that the changes in land use, modifications of terrestrial productivity and respiration, as well as climatic variations of temperature and moisture are responsible for changes in terrestrial carbon uptake (Chen et al., 2020; Piao et al., 2020a, b; Yue et al., 2020).

      Figure 9.  Estimated ocean and land carbon sinks in different studies. The asterisks and triangles are seven-year averaged carbon sinks in this study and Li et al 2021, with error bars representing uncertainties of the estimations. The time series of carbon sinks derived from Global Carbon Project 2019, Landschützer et al 2016 and Carbon Tracker 2019 are colored in red, green and blue, respectively. The thin dashed lines and the thick solid lines are annual and seven-year running averaged carbon sinks, respectively.

    • In this section, we specifically investigate the differences of the carbon sinks from that in Li et al. (2021). As mentioned in section 3.1.3, the air-sea O2 flux used in Li et al. (2021) is derived from CMIP5, while CMIP6 simulation of the flux is used in this study. Meanwhile, the other O2-related variables (such as atmospheric O2 decline) in Li et al. (2021) are derived from the oxygen budget proposed by Huang et al. (2018), which is also different from this study. Terrestrial and oceanic carbon uptakes estimated by Li et al. (2021) are depicted by the triangles in Fig. 9. From the comparisons between this study and Li et al. (2021), we can discern the role of oxygen data in carbon sink estimations.

      For the terrestrial carbon sink, both of the two studies corresponds well with GCP in the 21st century, which exhibit an enhanced uptake mentioned in section 3.2.2. However, the result from Li et al. (2021) seems to present an unrealistically high land carbon uptake (1.50 GtC yr−1) in the 1990s, while the current study behaves in good agreement with GCP during this period (1.06 GtC yr−1). The oceanic carbon uptake in both this study and Li et al. (2021) exhibits a similar variability with that in Landschützer et al. (2016) (that is, a downward trend in the 1990s subsequently followed by an upward trend in the 2000s). Despite this, discrepancy occurs around year 2010, as shown in Fig. 9a. The estimated oceanic carbon uptake in this study (2.87 GtC yr−1) is relatively larger than it in Li et al. (2021) (2.45 GtC yr−1) and GCP (2.36 GtC yr−1).

      Overall, both of the two studies reveal an enhanced carbon uptake in the 21st century. This study provides a more reliable estimate of the terrestrial carbon uptake in the 1990s, while the oceanic carbon sink in Li et al. (2021) is more consistent with the Global Carbon Project after year 2010. Our calculations show that the differences in air-sea O2 flux ($ {F}_{\mathrm{a}\mathrm{i}\mathrm{r}-\mathrm{s}\mathrm{e}\mathrm{a}} $) and atmospheric O2 change ($ \Delta {\mathrm{O}}_{2} $) are the main contributors to the discrepancies. If the difference in O2 flux is expressed as $\Delta {F}_{{\rm{air-sea}}}$ (the other variables remain unchanged), its influence on the terrestrial and oceanic carbon uptake could then be respectively expressed as $\Delta B=-{\beta }/{{\alpha }_{{\rm B}}}{\Delta F}_{\mathrm{a}\mathrm{i}\mathrm{r}-\mathrm{s}\mathrm{e}\mathrm{a}}$ and $\Delta O={\beta }/{{\alpha }_{{\rm B}}}{\Delta F}_{\mathrm{a}\mathrm{i}\mathrm{r}-\mathrm{s}\mathrm{e}\mathrm{a}}$, according to Equations 6−8. This implies that a weakened oceanic O2 outgassing, approximately −22 Tmol O2 yr−1, would lead to an increase of 0.21 GtC yr−1 land carbon sink and a simultaneous opposite effect on ocean carbon sink. For the period 1990−95, Li et al. (2021) shows a smaller declining trend of atmospheric O2 and oceanic outgassing in 1990−95, which could eventually lead to a larger land uptake in Li et al. (2021) during this period. These results highlight the vital importance of oxygen datasets on carbon sink estimations.

    4.   Summary and discussion
    • We use the coupled ocean biogeochemistry models in CMIP6 to investigate the modifications of air-sea O2 flux under climate change and its influences on the estimations of global terrestrial and ocean carbon uptake. Our results show an enhanced global oceanic O2 outgassing to the atmosphere since the 1980s, accompanied by a strong decadal variability dominated by oceanic internal modes. Consistent with Li et al. (2020), this study shows maximum changes of flux mainly occurring in the high latitudes, with roughly one quarter of the outgassing directly associated with reduced solubility in the warming ocean, and the rest mainly linked with circulation changes and ocean stratification. This modification of air-sea O2 flux plays an important role in estimating carbon uptake, as described in section 3.2.

      The application of air-sea O2 flux in CMIP6 provides a valuable complement for studies of O2-based global carbon sinks estimations under climate change. Our results reveal the significant increases of terrestrial and oceanic carbon sinks in the 21st century, reflecting the human impacts on the carbon cycle and Earth’s environments. The model biases of air-sea O2 flux between CMIP5 and CMIP6 are also investigated in this study, which could lead to a total discrepancy up to 0.4 GtC yr−1 in the estimations, indicating the importance of improvement of air-sea O2 flux parameterizations in the model.

      Some limitations should also be acknowledged. Our estimation of carbon sinks still suffers from relatively large uncertainties (0.4−0.8 GtC yr−1) due to the accumulations of uncertainty of each term in the calculations. Furthermore, the earliest observations of O2/N2 we could obtain are from the late 1980s, which greatly limits the lengths of estimated time series. The comparisons between this study and Li et al. (2021) also reveal the importance of the accuracy of oxygen datasets on the carbon uptake estimations. Presently, we are working on structuring the global oxygen budget (Huang et al., 2018) under the constrain of O2/N2 observations, from which we hope to extend the time series of atmospheric O2 changes back to the 1900s as well as provide a more reliable oxygen dataset. Further explorations and investigations of the O2-based carbon uptake estimations should be done in the future.

      Acknowledgements. The authors acknowledge the Scripps O2 Program for providing the observations of atmospheric O2 and CO2 data. The authors also acknowledge the World Climate Recruitment Programme’s (WCRP) Working Group on Coupled Modelling (WGCM), and the Global Organization for Earth System Science Portals (GO-ESSP) for producing outputs of CMIP6 model simulations. This work was jointly supported by the National Science Foundation of China (Grant Nos. 41991231, 91937302) and the China 111 project (Grant No. B13045). The data processes and analysis are supported by Supercomputing Center of Lanzhou University.

      Electronic supplementary material: Supplementary material is available in the online version of this article at https://doi.org/10.1007/s00376-021-1273-x.

      Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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