1. Introduction
The concentration of atmospheric CO2 increased by about 45% from 277 ppm in 1750 (Joos and Spahni 2008) to 402.8 ± 0.1 ppm in 2016 (Dlugokencky and Tans 2018). Meanwhile, the ocean has absorbed 160 ± 20 Pg C (1 Pg = 1015 g), which is about one-third of the anthropogenic CO2 emissions (Le Quéré et al. 2016), from the atmosphere via air–sea CO2 exchanges. Based on about 3 million measurements of surface-water CO2 partial pressure (
The mechanism of interannual variability of the air–sea CO2 flux over the tropical Pacific Ocean associated with ENSO has been well established through previous research on both observations and models. In the El Niño phase, the warmer sea surface temperature (SST) helps increase the air–sea CO2 flux, whereas the reduction in dissolved inorganic carbon (DIC) due to weakened upwelling acts to suppress the flux (Feely et al. 2006; Wang et al. 2006, 2015; Long et al. 2013). Among these two competing processes, the reduction in DIC is a dominant factor in controlling the interannual variability of air–sea CO2 flux related to ENSO (McKinley et al. 2004; Li and Xu 2013; Jin et al. 2017).
Earth system models (ESMs) are the latest generation of the state-of-the-art climate models, in which marine and terrestrial biogeochemical processes are added into coupled atmosphere–ocean general circulation models (AOGCMs). Compared with offline OBGCMs, which are driven by prescribed atmospheric data, ESMs characterize the interaction between the carbon cycle and the physical climate system. Owing to these features, the ESMs can simulate the physical, chemical, and biological processes on Earth, and hence ESMs are powerful tools for understanding and projecting changes in the global carbon cycle (Wang et al. 2016; Li et al. 2016; Li and Ilyina 2018; Kwiatkowski and Orr 2018). Because of the nonlinear carbon cycle and climate feedbacks in ESMs, the complex coupling between biogeochemical processes and the physical processes may also cause some unknown biases in the air–sea CO2 flux in ESMs (Boer and Arora 2009; Zhou et al. 2014; Schwinger et al. 2014). Therefore, before using ESMs to perform climate projections, including the study of carbon cycle and climate feedbacks, their accuracy in reproducing the air–sea CO2 fluxes for the present climate should be assessed.
Evaluation of the ocean carbon cycle in 18 ESMs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) showed that models agree on the sign and magnitude of the CO2 flux, but they show weaker year-to-year variability than the observations (Anav et al. 2013). Dong et al. (2016) examined the interannual variability of global air–sea CO2 flux in 18 CMIP5 models and found that 12 models fail to represent the observed ENSO-related pattern over the tropical Pacific owing to the stronger interannual variability in the Southern Ocean and inconsistent period of air–sea CO2 flux with the period range of ENSO events. However, it is unclear whether the mechanism of ENSO-related air–sea CO2 flux interannual variations can be reproduced by CMIP5 ESMs. As a key component of the global carbon cycle, the air–sea CO2 flux over the tropical Pacific Ocean, especially the prominent relation with ENSO, is necessary for system evaluation. In this study, the physical and biogeochemical processes of the interannual variations in the air–sea CO2 flux over the tropical Pacific associated to ENSO were evaluated quantitatively by decomposing the anomalous 
The remainder of the paper is organized as following. Section 2 introduces the model, data, and analysis methods. Major results are presented in section 3. Section 4 summarizes our major findings.
2. Model, data, and analysis method
a. CMIP5 models
Fourteen ESMs from the CMIP5 were used based on the availability of monthly outputs of the emission-driven historical experiment (esmhistorical). Different from traditional concentration-driven historical experiment, forced by the historical atmospheric CO2 concentration, esmhistorical is forced by spatially distributed CO2 emissions reconstructed using estimated fossil fuel consumption from 1850 to 2005, which is more like the processes of global carbon cycle in nature. The atmospheric CO2 concentration freely varies with human emission and is modulated by carbon exchange between air, ocean, and the terrestrial biosphere (Taylor et al. 2012). Table 1 summarizes the model names, ocean carbon cycle components, horizontal and vertical resolutions of ocean models, and corresponding references. Only one ensemble member for each ESM is used. Seven (bold in Table 1) out of these 14 models are used to analyze sources of model bias depending on whether the data are available.
Names, ocean resolutions, and ocean carbon cycle components of the 14 CMIP5 ESMs used in this study. Bold type indicates models used to analyze sources of model bias (see section 2a). OCMIP2 = Ocean Carbon-Cycle Model Intercomparison Project version 2; iBGC = idealized ocean biochemistry; BEC = Biogeochemical Elemental Cycling model; TOPAZ2 = Tracers of Ocean Phytoplankton with Allometric Zooplankton code version 2.0; Diat-HadOCC = diatom version of the Hadley Centre Ocean Carbon Cycle model; PISCES = Pelagic Interactions Scheme for Carbon and Ecosystem Studies; NPZD-type = nutrients (N), phytoplankton (P), zooplankton (Z), and detritus (D); HAMOCC = Hamburg ocean carbon cycle model; other acronyms are available online at http://www.ametsoc.org/PubsAcronymList.
b. Data
Observational and reanalysis datasets include 1) a well-structured climatological mean distribution of the air–sea CO2 fluxes with a resolution of 4° × 5° (Takahashi et al. 2009); 2) monthly mean air–sea CO2 fluxes and 
Although the observation-based products are used as “observational metrics” to gauge the performance of the models, these types of products rely heavily on data interpolation; for example, the product of Takahashi et al. (2009) employed an advection-based algorithm, whereas Landschützer et al. (2015) used a neural network interpolation.
For evaluating the climatology, since the observation-based products employ the reference year of 2000, the modeled results are averaged from 1990 to 2005 to match with the observation-based product. In the study of interannual variability, we focus on the boreal winter [December–February (DJF)] from 1982 to 2005, which is available in both the observation-based and model results. To focus on interannual variability, we filtered out variations longer than eight years from the original datasets using a Lanczos filter.
The upward ocean mass transport (kg s−1), a standard output of CMIP5 models, is used to analyze physical processes related to model biases. Its unit is different from that of the upward velocity (m s−1) in CMIP3. To make the upward ocean mass transport with different spatial resolutions in CMIP5 ESMs comparable, we transformed the upward ocean mass transport (kg s−1) to upward velocity (m s−1) by dividing the ocean gridcell area (m2) and seawater density (1025 kg m−3) for each model.
c. Analysis method
The term 
The terms on the right side represent the effect of variability in SST, salinity-normalized DIC (nDIC), salinity-normalized alkalinity (nAlk), and sea surface salinity (SSS). The partial derivatives are estimated from the variation of 
Range of the factors for calculating partial derivatives.
3. Results
a. Annual mean
The annual-mean air–sea CO2 fluxes over the tropical Pacific Ocean in the observation-based products and multimodel ensemble (MME) are shown in Fig. 1. Positive values indicate that CO2 fluxes go upward from ocean to the atmosphere. The modeled air–sea CO2 fluxes are averaged in the period 1990–2005, matching with the observation-based product in reference year 2000 in Takahashi et al. (2009). In the observation-based product, the spatial pattern of air–sea CO2 fluxes shows a large contrast between the domains of the eastern and western tropical Pacific Ocean (Fig. 1a). Evidently, the eastern Pacific is characterized by larger efflux than that in the western Pacific. This feature is reproduced reasonably by MME (Fig. 1b). The spatial correlation coefficient between observation-based product and MME is 0.84, which is statistically significant at the 1% level.
Annual-mean air–sea CO2 fluxes (g C m−2 yr−1) in the period 1990–2005 over the tropical Pacific based on (a) an observation-based product (Takahashi et al. 2009) and (b) the MME. (c) Annual-mean air–sea CO2 fluxes (Pg C yr−1) over the tropical Pacific (TP), western tropical Pacific (WP; west of 160°W), and eastern tropical Pacific (EP; east of 160°W). Blue (brown) bars represent the MME (observed results). The MME is constructed by using the first realizations from 14 CMIP5 ESMs (black dots). Positive fluxes indicate that CO2 fluxes go upward from ocean to the atmosphere.
Citation: Journal of Climate 32, 8; 10.1175/JCLI-D-18-0131.1
In the observation-based product, the climatological annual-mean air–sea CO2 flux over the tropical Pacific (18°S–18°N, 120°E–80°W) is 0.47 Pg C yr−1 (Fig. 1c, brown bar), which mainly originates from the eastern tropical Pacific (0.43 Pg C yr−1). The annual CO2 flux (0.35 Pg C yr−1) simulated by the MME is about 25% less than the observation-based products, owing to less efflux over the eastern tropical Pacific in the MME (0.32 Pg C yr−1). However, the intermodel spread is large. Among the CMIP5 models, some models (BCC-CSM1.1, BCC-CSM1.1-m, BNU-ESM, CanESM2, IPSL-CM5A-LR, MPI-ESM-LR, and NorESM1-ME) reasonably reproduce the observed magnitude, while others cannot.
The Taylor diagram is presented in Fig. 2 to quantitatively assess the individual performance of CMIP5 models in the simulation of annual-mean air–sea CO2 flux. Better model performance is indicated by a higher pattern correlation coefficient and a normalized standard deviation nearer to 1.0. For the tropical Pacific (Fig. 2, black dot), the pattern correlation coefficients of most models with the observation-based product are statistically significant at the 5% level (correlation coefficient > 0.5), except for MIROC-ESM (0.15) and HadGEM2-ES (0.31), indicating that the overall characteristics of the spatial distribution of climatological air–sea CO2 flux over the tropical Pacific Ocean are reasonably reproduced by most of the ESMs; the details, however, are not well captured. Although the ratios of the standard deviations between model and observation-based product show large spread (from 0.55 for HadGEM2-ES to 2.64 for MRI-ESM), they are distributed symmetrically around 1.0. The pattern correlation coefficient (0.84) and the ratio of standard deviation (1.02) in MME are better than the individual ESM, which implies that the ratio of noise-to-signal in MME is lower than that for the individual ESM. For the western tropical Pacific (Fig. 2, red dot), the spatial correlation coefficients exceed 0.65 except for MIROC-ESM (0.30). However, the ratios of the standard deviations are generally higher than 1.0, indicating a stronger spatial variation than the observation-based product. The pattern correlation coefficients in the 14 ESMs of the eastern tropical Pacific (Fig. 2, blue dot) are less than those of the western tropical Pacific, and the MME yields better performances in terms of both the spatial distribution (0.75) and standard deviation (0.97) over the eastern tropical Pacific.
Taylor diagram for displaying the pattern statistics during annual-mean air–sea CO2 fluxes averaged from 1990 to 2005. The observation-based product is considered as the reference (REF), and the angular coordinate indicates the pattern correlation between the model and observation-based product. The vertical coordinate represents the ratio of the standard deviations of the model and observation-based product. The smaller the distance between a number and REF, the better is the performance of the corresponding model. Numbers represent 14 CMIP5 models and the MME. As in Fig. 1, the WP is west of 160°W, and the EP is east of 160°W.
Citation: Journal of Climate 32, 8; 10.1175/JCLI-D-18-0131.1
b. Interannual variability
Previous modeling studies have indicated that the interannual variability of the global air–sea CO2 flux is primarily determined by the interannual variability over the tropical Pacific (account for approximately 70% of the global variance; Le Quéré et al. 2000; McKinley et al. 2004; Obata and Kitamura 2003). However, an offline biogeochemical model indicates that the contribution of the tropical Pacific variability is approximately 40% of the global variance (Valsala et al. 2014). Here the averaged contribution from the 14 ESMs is 25%, ranging from 11% in MPI-ESM-LR to 46% in CESM1(BGC), showing large intermodel spread (figure not shown).
The monthly interannual variability intensity of the air–sea CO2 flux over the tropical Pacific Ocean simulated by CMIP5 MME is shown in Fig. 3. The interannual standard deviation for the period 1982–2005 is calculated as the anomalies relative to the mean annual cycle for each month of the year. Two peaks were observed (one each in the winter and summer); they are associated with the peak phase and developing phase of ENSO, respectively. Since the highest interannual variability intensity of the air–sea CO2 flux occurs in ENSO mature winter, we focus on the first leading mode of the air–sea CO2 flux in DJF.
Monthly interannual standard deviation of the air–sea CO2 flux in the period from 1982 to 2005 over the tropical Pacific Ocean simulated by CMIP5 MME. The shaded area indicates one standard deviation across CMIP5 ESMs. The datasets have been filtered through an 8-yr high-pass filter.
Citation: Journal of Climate 32, 8; 10.1175/JCLI-D-18-0131.1
The EOF analysis was performed to examine whether the ESMs can reproduce the leading mode of interannual variability of the air–sea CO2 flux in winter over the tropical Pacific Ocean (20°S–20°N, 120°E–80°W). In the observation-based product, the first leading mode (EOF1) exhibits negative anomalies over the central tropical Pacific, and this explains 49.8% of the variance (Fig. 4a). The first principal component (PC1) correlates with the Niño-3.4 index (the area-averaged SSTA over 5°S–5°N, 120°–170°W) with a high correlation coefficient of 0.92 (Fig. 5a), indicating the dominant influence of ENSO. In the CMIP5 ESMs, the correlation coefficients between the PC1 and Niño-3.4 index calculated based on the model SST range from 0.59 (IPSL-CM5A-LR) to 0.96 (CanESM2) (Fig. 5), and all values are statistically significant at the 1% level. The correlations between PC1 and the Niño-3.4 index are intended to be positive to ensure that the corresponding EOF1 patterns are comparable across models. Hence, the high correlations between PC1 and the Niño-3.4 index calculated based on the model SST show that ENSO also plays dominant role in determining the air–sea CO2 flux variability in the ESMs.
Spatial pattern of the first EOF of the winter mean (DJF) air–sea CO2 flux (g C m−2 yr−1) in the period from 1982 to 2005 over the tropical Pacific. The percent variance (%) captured by EOF1 is noted. The datasets have been filtered through an 8-yr high-pass filter.
Citation: Journal of Climate 32, 8; 10.1175/JCLI-D-18-0131.1
The principal component of the first EOF modes of the winter mean air–sea CO2 flux (red line) and Niño-3.4 index (black line). The correlation of PC1 and the Niño-3.4 index is shown.
Citation: Journal of Climate 32, 8; 10.1175/JCLI-D-18-0131.1
Biases in the simulated atmospheric CO2 concentration in CMIP5 ESMs were reported in several recent studies (Hoffman et al. 2014; Friedlingstein et al. 2014). To exclude the possible influence of biases in atmospheric CO2 on the interannual variability of air–sea CO2 flux, we repeated the EOF analysis on concentration-driven historical simulations in the corresponding 13 models; FIO-ESM was excluded as it did not have any air–sea CO2 flux output (see the online supplemental material, Figs. S1 and S2 therein). The EOF results of emission-driven and concentration-driven simulations differ only slightly. Hence, the influence of biases in simulated atmospheric CO2 concentration in ESMs can be neglected in this study.
The observed negative anomalies in the central Pacific are well captured by several ESMs [CESM1(BGC), FIO-ESM, GFDL-ESM2M, and MRI-ESM], but not so by some other ESMs, and some models (BNU-ESM, HadGEM2-ES, and MPI-ESM-LR) even exhibit positive anomalies in the spatial pattern of EOF1 (Fig. 4). The key question is what causes the biases in response of the air–sea CO2 flux to El Niño in some ESMs. Therefore, based on the availability of the model output fields related to the ocean carbon cycle, seven models are selected to examine the related physical processes and potential causes of biases. The high-skill models [CESM1(BGC) and GFDL-ESM2M], low-skill models (HadGEM2-ES and MPI-ESM-LR), and medium-skill models (CanESM2, GFDL-ESM2G, and NorESM1-ME) are distinguished by the pattern correlation coefficients shown in Fig. 4 between each model and observation-based product (Table 3).
Pattern correlation coefficients of the spatial pattern of EOF1 between each model and observation-based result shown in Fig. 4.
Based on Eq. (1), the variations in the air–sea CO2 flux can be attributed to the gas transfer coefficient k, CO2 solubility α, and 
Regression coefficients of the (a1)–(h1) air–sea CO2 flux (g C m−2 yr−1), (a2)–(h2) gas transfer coefficient (cm h−1), (a3)–(h3) CO2 solubility (mmol m−3 atm−1), and (a4)–(h4) surface ocean pCO2 (ppm) with respect to the normalized Niño-3.4 index in the period 1982–2005. The dotted regions are statistically significant at the 5% level based on the Student’s t test. The datasets have been filtered through an 8-yr high-pass filter.
Citation: Journal of Climate 32, 8; 10.1175/JCLI-D-18-0131.1
To further understand the causes of biases of the 
Regression of the contributions of surface ocean pCO2 anomalies from (b1)–(h1) sea surface salinity, (b2)–(h2) SST, (b3)–(h3) surface-water salinity-normalized dissolved inorganic carbon, and (b4)–(h4) salinity-normalized alkalinity with respect to the Niño-3.4 index in the period 1982–2005. The dotted regions are statistically significant at the 5% level based on the Student’s t test. The datasets have been filtered through an 8-yr high-pass filter.
Citation: Journal of Climate 32, 8; 10.1175/JCLI-D-18-0131.1
Regression coefficients in Fig. 7 averaged in the central-eastern tropical Pacific (10°S–10°N, 180°–70°W).
Citation: Journal of Climate 32, 8; 10.1175/JCLI-D-18-0131.1
Based on previous studies, the suppressed upwelling in the equatorial Pacific during El Niño is an important process that reduces upward transport of high-concentration DIC from deeper layers (McKinley et al. 2004; Long et al. 2013; Jin et al. 2017). This process can be expressed as 
Equatorial anomaly profiles (2°S–2°N) of (a1)–(f1) DIC (shadings; mmol m−3), (a2)–(f2) upward velocity (10−3 m s−1), and (a3)–(f3) seawater temperature (°C) over the tropical Pacific Ocean with respect to the Niño-3.4 index in the period 1982–2005. The dotted regions are statistically significant at the 5% level based on the Student’s t test. (a4)–(f4) Equatorial profile (2°S–2°N) of the climatological DIC (mmol m−3) and its vertical gradient (mmol m−3 m−1) in the period 1990–2005 over the tropical Pacific Ocean. The green line in (a3)–(f3) is the climatological thermocline depth, represented by the 20°C isotherm. The datasets have been filtered through an 8-yr high-pass filter.
Citation: Journal of Climate 32, 8; 10.1175/JCLI-D-18-0131.1
The anomalous upward velocity of seawater (x axis; 10−3 m s−1), the vertical gradient of climatological DIC (y axis; mmol m−3 m−1), and their product (
Citation: Journal of Climate 32, 8; 10.1175/JCLI-D-18-0131.1
4. Conclusions and discussion
The annual-mean climatology and interannual variability of the air–sea CO2 fluxes over the tropical Pacific Ocean in the 14 CMIP5 ESMs were evaluated in this study. We found that more than half of the models cannot reproduce well the spatial pattern of the tropical Pacific CO2 flux response to ENSO, despite the reasonable performances in the simulation of the mean states. The underlying reason was analyzed by comparing the models with different skills.
The spatial pattern of the observed annual-mean air–sea CO2 fluxes in the tropical Pacific can be reasonably reproduced in CMIP5 MME, despite large spreads in the magnitude and spatial gradient across the ESMs. The models also consistently show the controlling influence of ENSO on the interannual variability of the tropical Pacific air–sea CO2 fluxes in DJF. However, the dominant interannual modes (i.e., the spatial pattern) exhibit even stronger outgassing during an El Niño year in several models, such as HadGEM2-ES and MPI-ESM-LR, which is in contrast with the result obtained using observation-based data.
Further analysis show that the positive anomalies of air–sea CO2 fluxes during the warm events of ENSO result from the positive response of surface-water partial pressure (
In terms of the seasonal cycle, the equatorial Pacific 
In the observation-based product, the spatial pattern of the first leading EOF of the air–sea CO2 flux clearly exhibits negative anomalies over the central tropical Pacific (Fig. 4a), which appears like the pattern of El Niño Modoki (Ashok et al. 2007). However, we speculate that the physical mechanism of air–sea CO2 flux anomalies over the central tropical Pacific is different from that of El Niño Modoki. The air–sea CO2 flux anomalies over the central tropical Pacific result from the gas transfer coefficient anomalies (Fig. 6a2) and 
Acknowledgments
We wish to thank the modeling groups joined in the CMIP5 and the Program for Climate Model Diagnosis and Intercomparison (PCMDI), which provide the model data. This research was supported by the National Natural Science Foundation of China (Grants 41330423 and 41605057) and National Key Research and Development Program of China (2018YFF0300105).
REFERENCES
Adachi, Y., and Coauthors, 2013: Basic performance of a new Earth system model of the Meteorological Research Institute (MRI-ESM1). Pap. Meteor. Geophys., 64, 1–19, https://doi.org/10.2467/mripapers.64.1.
Anav, A., and Coauthors, 2013: Evaluating the land and ocean components of the global carbon cycle in the CMIP5 Earth system models. J. Climate, 26, 6801–6843, https://doi.org/10.1175/JCLI-D-12-00417.1.
Arora, V. K., and Coauthors, 2011: Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys. Res. Lett., 38, L05805, https://doi.org/10.1029/2010GL046270.
Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, https://doi.org/10.1029/2006JC003798.
Assmann, K. M., M. Bentsen, J. Segschneider, and C. Heinze, 2010: An isopycnic ocean carbon cycle model. Geosci. Model Dev., 3, 143–167, https://doi.org/10.5194/gmd-3-143-2010.
Boer, G. J., and V. K. Arora, 2009: Temperature and concentration feedbacks in the carbon cycle. Geophys. Res. Lett., 36, L02704, https://doi.org/10.1029/2008GL036220.
Chavez, F. P., P. G. Strutton, G. E. Friederich, R. A. Feely, G. C. Feldman, D. G. Foley, and M. J. McPhaden, 1999: Biological and chemical response of the equatorial Pacific Ocean to the 1997–98 El Niño. Science, 286, 2126–2131, https://doi.org/10.1126/science.286.5447.2126.
Christian, J. R., M. A. Verschell, R. Murtugudde, A. J. Busalacchi, and C. R. McClain, 2001: Biogeochemical modelling of the tropical Pacific Ocean. I: Seasonal and interannual variability. Deep-Sea Res. II, 49, 509–543, https://doi.org/10.1016/S0967-0645(01)00110-2.
Collins, W. J., and Coauthors, 2011: Development and evaluation of an Earth-system model—HadGEM2. Geosci. Model Dev., 4, 1051–1075, https://doi.org/10.5194/gmd-4-1051-2011.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.
Dlugokencky, E., and P. Tans, 2018: Trends in atmospheric carbon dioxide. NOAA/ESRL, accessed 21 February 2018, http://www.esrl.noaa.gov/gmd/ccgg/trends/global.html.
Doney, S. C., I. Lima, R. A. Feely, D. M. Glover, K. Lindsay, N. Mahowald, J. K. Moore, and R. Wanninkhof, 2009: Mechanisms governing interannual variability in upper-ocean inorganic carbon system and air–sea CO2 fluxes: Physical climate and atmospheric dust. Deep-Sea Res. II, 56, 640–655, https://doi.org/10.1016/j.dsr2.2008.12.006.
Dong, F., Y. C. Li, B. Wang, W. Y. Huang, Y. Y. Shi, and W. H. Dong, 2016: Global air–sea CO2 flux in 22 CMIP5 models: Multiyear mean and interannual variability. J. Climate, 29, 2407–2431, https://doi.org/10.1175/JCLI-D-14-00788.1.
Dufresne, J. L., and Coauthors, 2013: Climate change projections using the IPSL-CM5 Earth system model: From CMIP3 to CMIP5. Climate Dyn., 40, 2123–2165, https://doi.org/10.1007/s00382-012-1636-1.
Dunne, J. P., and Coauthors, 2012: GFDL’s ESM2 global coupled climate–carbon Earth system models. Part I: Physical formulation and baseline simulation characteristics. J. Climate, 25, 6646–6665, https://doi.org/10.1175/JCLI-D-11-00560.1.
Feely, R. A., R. Wanninkhof, T. Takahashi, and P. Tans, 1999: Influence of El Niño on the equatorial Pacific contribution of atmospheric CO2 accumulation. Nature, 398, 597–601, https://doi.org/10.1038/19273.
Feely, R. A., and Coauthors, 2002: Seasonal and interannual variability of CO2 in the equatorial Pacific. Deep-Sea Res. II, 49, 2443–2469, https://doi.org/10.1016/S0967-0645(02)00044-9.
Feely, R. A., T. Takahashi, R. Wanninkhof, M. J. McPhaden, C. E. Cosca, S. C. Sutherland, and M. E. Carr, 2006: Decadal variability of the air–sea CO2 fluxes in the equatorial Pacific Ocean. J. Geophys. Res., 111, C08S90, https://doi.org/10.1029/2005JC003129.
Friedlingstein, P., M. Meinshausen, V. K. Arora, C. D. Jones, A. Anav, S. K. Liddicoat, and R. Knutti, 2014: Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Climate, 27, 511–526, https://doi.org/10.1175/JCLI-D-12-00579.1.
Hoffman, F. M., and Coauthors, 2014: Causes and implications of persistent atmospheric carbon dioxide biases in Earth system models. J. Geophys. Res. Biogeosci., 119, 141–162, https://doi.org/10.1002/2013JG002381.
Ilyina, T., K. D. Six, J. Segschneider, E. Maier-Reimer, H. M. Li, and I. Núñez-Riboni, 2013: Global ocean biogeochemistry model HAMOCC: Model architecture and performance as component of the MPI-Earth System Model in different CMIP5 experimental realizations. J. Adv. Model. Earth Syst., 5, 287–315, https://doi.org/10.1029/2012MS000178.
Ishii, M., S. Saito, T. Tokieda, T. Kavano, K. Matsumoto, and H. Y. Inoue, 2004: Variability of surface layer CO2 parameters in the western and central equatorial Pacific. Global Environmental Change in the Ocean and on Land, M. Shiyomi et al., Eds., Terrapub, 59–94.
Ishii, M., and Coauthors, 2014: Air–sea CO2 flux in the Pacific Ocean for the period 1990–2009. Biogeosciences, 11, 709–734, https://doi.org/10.5194/bg-11-709-2014.
Ji, D., and Coauthors, 2014: Description and basic evaluation of BNU-ESM version 1. Geosci. Model Dev., 7, 2039–2064, https://doi.org/10.5194/gmdd-7-1601-2014.
Jiang, M. S., and F. Chai, 2006: Physical control on the seasonal cycle of surface pCO2 in the equatorial Pacific. Geophys. Res. Lett., 33, L23608, https://doi.org/10.1029/2006GL027195.
Jin, C. X., T. J. Zhou, X. L. Chen, and B. Wu, 2017: Seasonally evolving dominant interannual variability mode of air–sea CO2 flux over the western North Pacific simulated by CESM1-BGC. Sci. China Earth Sci., 60, 1854–1865, https://doi.org/10.1007/s11430-015-9085-4.
Joos, F., and R. Spahni, 2008: Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years. Proc. Natl. Acad. Sci. USA, 105, 1425–1430, https://doi.org/10.1073/pnas.0707386105.
Kwiatkowski, L., and J. C. Orr, 2018: Diverging seasonal extremes for ocean acidification during the twenty-first century. Nat. Climate Change, 8, 141–145, https://doi.org/10.1038/s41558-017-0054-0.
Landschützer, P., N. Gruber, D. C. E. Bakker, and U. Schuster, 2014: Recent variability of the global ocean carbon sink. Global Biogeochem. Cycles, 28, 927–949, https://doi.org/10.1002/2014GB004853.
Landschützer, P., N. Gruber, and D. C. E. Bakker, 2015: A 30 years observation-based global monthly gridded sea surface pCO2 product from 1982 through 2011. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Dept. of Energy, http://cdiac.ornl.gov/ftp/oceans/SPCO2_1982_2011_ETH_SOM_FFN.
Le Quéré, C., J. C. Orr, P. Monfray, O. Aumont, and G. Madec, 2000: Interannual variability of the oceanic sink of CO2 from 1979 through 1997. Global Biogeochem. Cycles, 14, 1247–1265, https://doi.org/10.1029/1999GB900049.
Le Quéré, C., and Coauthors, 2016: The global carbon budget 2016. Earth Syst. Sci. Data, 8, 605–649, https://doi.org/10.5194/essd-8-605-2016.
Li, H. M., and T. Ilyina, 2018: Current and future decadal trends in the oceanic carbon uptake are dominated by internal variability. Geophys. Res. Lett., 45, 916–925, https://doi.org/10.1002/2017GL075370.
Li, H. M., T. Ilyina, W. A. Müller, and F. Sienz, 2016: Decadal predictions of the North Atlantic CO2 uptake. Nat. Commun., 7, 11076, https://doi.org/10.1038/ncomms11076.
Li, Y. C., and Y. F. Xu, 2013: Interannual variations of the air–sea carbon dioxide exchange in the different regions of the Pacific Ocean. Acta Oceanol. Sin., 32, 71–79, https://doi.org/10.1007/s13131-013-0291-7.
Long, M. C., K. Lindsay, and S. Peacock, 2013: Twentieth-century oceanic carbon uptake and storage in CESM1(BGC). J. Climate, 26, 6775–6800, https://doi.org/10.1175/JCLI-D-12-00184.1.
Lovenduski, N. S., N. Gruber, S. C. Doney, and I. D. Lima, 2007: Enhanced CO2 outgassing in the Southern Ocean from a positive phase of the southern annular mode. Global Biogeochem. Cycles, 21, GB2026, https://doi.org/10.1029/2006GB002900.
McKinley, G. A., M. J. Follows, and J. Marshall, 2004: Mechanisms of air–sea CO2 flux variability in the equatorial Pacific and the North Atlantic. Global Biogeochem. Cycles, 18, GB2011, https://doi.org/10.1029/2003GB002179.
Obata, A., and Y. Kitamura, 2003: Interannual variability of the air–sea exchange of CO2 from 1961 to 1998 simulated with a global ocean circulation–biogeochemistry model. J. Geophys. Res., 108, 3337, https://doi.org/10.1029/2001JC001088.
Qiao, F. L., Z. Y. Song, Y. Bao, Y. J. Song, Q. Shu, C. J. Huang, and W. Zhao, 2013: Development and evaluation of an Earth system model with surface gravity waves. J. Geophys. Res., 118, 4514–4524, https://doi.org/10.1002/jgrc.20327.
Schwinger, J., and Coauthors, 2014: Nonlinearity of ocean carbon cycle feedbacks in CMIP5 Earth system models. J. Climate, 27, 3869–3888, https://doi.org/10.1175/JCLI-D-13-00452.1.
Smith, T., R. Reynolds, T. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J. Climate, 21, 2283–2296, https://doi.org/10.1175/2007JCLI2100.1.
Takahashi, T., and Coauthors, 2009: Climatological mean and decadal change in surface ocean pCO2, and net sea-air CO2 flux over the global oceans. Deep-Sea Res. II, 56, 554–577, https://doi.org/10.1016/j.dsr2.2008.12.009.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1.
Tjiputra, J. F., C. Roelandt, M. Bentsen, D. M. Lawrence, T. Lorentzen, J. Schwinger, Ø. Seland, and C. Heinze, 2013: Evaluation of the carbon cycle components in the Norwegian Earth System Model (NorESM). Geosci. Model Dev., 6, 301–325, https://doi.org/10.5194/gmd-6-301-2013.
Valsala, V. K., M. K. Roxy, K. Ashok, and R. Murtugudde, 2014: Spatiotemporal characteristics of seasonal to multidecadal variability of pCO2 and air–sea CO2 fluxes in the equatorial Pacific Ocean. J. Geophys. Res. Oceans, 119, 8987–9012, https://doi.org/10.1002/2014JC010212.
Wang, L., J. B. Huang, Y. Luo, and Z. C. Zhao, 2016: Narrowing the spread in CMIP5 model projections of air–sea CO2 fluxes. Sci. Rep., 6, 37548, https://doi.org/10.1038/srep37548.
Wang, X. J., J. R. Christian, R. Murtugudde, and A. J. Busalacchi, 2006: Spatial and temporal variability of the surface water pCO2 and air–sea CO2 flux in the equatorial Pacific during 1980–2003: A basin-scale cycle model. J. Geophys. Res., 111, C07S04, https://doi.org/10.1029/2005JC002972.
Wang, X. J., R. Murtugudde, E. Hackert, J. Wang, and J. Beauchamp, 2015: Seasonal to decadal variations of sea surface pCO2 and sea-air CO2 flux in the equatorial oceans over 1984–2013: A basin-scale comparison of the Pacific and Atlantic Oceans. Global Biogeochem. Cycles, 29, 597–609, https://doi.org/10.1002/2014GB005031.
Wanninkhof, R., 1992: Relationship between wind speed and gas exchange over the ocean. J. Geophys. Res., 97, 7373–7382, https://doi.org/10.1029/92JC00188.
Wanninkhof, R., and Coauthors, 2013: Global ocean carbon uptake: Magnitude, variability and trends. Biogeosciences, 10, 1983–2000, https://doi.org/10.5194/bg-10-1983-2013.
Watanabe, R., and Coauthors, 2011: MIROC-ESM2010: Model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev., 4, 845–872, https://doi.org/10.5194/gmd-4-845-2011.
Weiss, R. F., 1974: Carbon dioxide in water and seawater: The solubility of a non-ideal gas. Mar. Chem., 2, 203–215, https://doi.org/10.1016/0304-4203(74)90015-2.
Wu, T. W., and Coauthors, 2013: Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century. J. Geophys. Res. Atmos., 118, 4326–4347, https://doi.org/10.1002/jgrd.50320.
Wu, T. W., and Coauthors, 2014: An overview of BCC climate system model development and application for climate change studies. J. Meteor. Res., 28, 34–56, https://doi.org/10.1007/s13351-014-3041-7.
Zhou, T. J., L. W. Zou, B. Wu, C. X. Jin, F. F. Song, X. L. Chen, and L. X. Zhang, 2014: Development of Earth/climate system models in China: A review from the Coupled Model Intercomparison Project perspective. J. Meteor. Res., 28, 762–779, https://doi.org/10.1007/s13351-014-4501-9.