Spatial variations of CO 2 flux in the North Atlantic

Spatial distributions of trends in CO 2 flux during the 25 years before the mid-1990s SPG abrupt warming (that is, from 1970 to 1995) show a remarkably different pattern between the uninitialized and assimilation runs (Fig. 1). The oceanic CO 2 uptake in the uninitialized simulations increases at about 0.2 gC m−2 per year2, following the increase in atmospheric CO 2 due to rising fossil fuel carbon emissions, with a somewhat larger increase in the eastern part of the North Atlantic. The CO 2 flux in the assimilation run does not increase uniformly in the North Atlantic; the largest increase is found in the western SPG region (Fig. 1b), and decreasing trends are found in the eastern SPG region. A zonal dipole distribution in delta pCO 2 anomalies was also found in a previous study based on the simulations with an ocean only model6. This dipole trend pattern of CO 2 uptake can be attributed to the NAO-related western–eastern heat loss gradient19 and the related ocean-mixing strength changes (Supplementary Fig. 1 and Supplementary Note 1). Regarding the mid-1990s warming, further decomposition of the SPG region by a previous study20 suggests a longer predictive skill in the western SPG region, which involves AMOC and meridional heat transport changes in response to persistent positive NAO phase from 1988 to 1995, and a shorter predictive skill in the eastern SPG region, which involves gyre circulation adjustment in response to NAO phase switch from positive to negative in 1995. Here we focus on the western SPG region with the largest trend of CO 2 uptake and with longer prediction skill of the ocean physical state.

Figure 1: Trends of annual mean CO 2 flux into the ocean from 1970 to 1995. Ensemble mean of uninitialized simulations (a) and assimilation simulation (b) (units: gC m−2 per year2). The trends are calculated based on linear regression. The dots show grid points where the trends are significant at 95% level, based on a two-sided t-test. The green box in a denotes the SPG region where the mid-1990s abrupt warming occurred11, and the green box in b denotes the western SPG region where the CO 2 flux increases the most. Full size image

Temporal evolution of CO 2 uptake and related processes

To further explore the temporal variability of the oceanic carbon uptake and how it is related to prominent local hydrodynamic processes in the SPG region, we compare the CO 2 flux calculated in the assimilation run with the observed NAO index21, observed SST22 and mixed layer depth (MLD) in the assimilation run (Fig. 2). The western SPG MLD and CO 2 flux from our model results are highly correlated (with a correlation coefficient of 0.78), and both are highly correlated (with correlation coefficients of 0.69 and 0.56, respectively) with the observed December–January–February–March (DJFM) NAO index. The correlations are significantly different from zero with P values below 0.01. Consistent with previous model studies6, the close connection between the NAO and the North Atlantic CO 2 uptake at interannual and decadal timescales is also reproduced in the assimilation run. On the one hand, the heat loss related to a positive NAO leads to an enhancement of the ocean mixing and deep-water formation in the North Atlantic19; this is indicated by the relatively high correlation (0.69) between NAO and MLD. Moreover, the SST anomalies in the SPG region with deep convection recur from winter to winter through a re-emergence process23,24, which works as follows. The winter thermal anomalies related to positive NAO remain at depth below the shallow summer mixed layer, these anomalies persist through summer and are partially re-entrained into the following winter mixed layer. On the other hand, the North Atlantic atmospheric circulation changes can also affect CO 2 uptake through the associated changes in AMOC and the corresponding meridional heat transport changes. Enhanced northward heat transport leads to increased SST thereby affecting the oceanic carbon uptake in the SPG. These processes lag the NAO changes by several years14. Accordingly, given all the processes, the correlation between CO 2 flux and SST is not significant in our simulations.

Figure 2: Normalized time series of CO 2 flux and related physical variables. The observed (Obs.) DJFM NAO21, observed JFM SST22 in SPG region, western SPG (WSPG) MLD and CO 2 flux calculated with the assimilation run are shown with black solid line, black dashed line, blue solid line and red solid line, respectively. The location of SPG and WSPG refers to green box in Fig. 1a,b, respectively. The correlation (Cor.) coefficients between time series and NAO (CO 2 flux) are shown on the top (bottom) of the figure. The normalized time series are calculated by dividing individual variable anomalies with their respective standard deviation. Full size image

Potential predictive skill of CO 2 flux and SST

The state-of-the-art decadal prediction systems consistently identify the North Atlantic as a key region with pronounced forecast skill for different parameters of the climate system25,26,27,28,29,30,31. Several abrupt climate events in the twentieth century have been shown to be predictable several years ahead10,11,12,13. The SPG abrupt warming and cooling events are also well captured in our initialized simulations, whereas the uninitialized simulations only capture the long-term warming trend and display large spread among ensemble members (Supplementary Fig. 2 and Supplementary Note 2). Given the robust predictive skill of the physical state of the ocean, can variations in the North Atlantic CO 2 uptake be predicted as well? A previous study explored multi-year predictability of tropical marine productivity and found a predictive skill of 3 years, whereas the predictive skill of SST there is only 1 year32. In our analysis, as indicated by the initialized time series at different lead times (Supplementary Fig. 3 and Supplementary Note 3), the variability of CO 2 flux, which is absent in the uninitialized simulations, can be reproduced by the model with initialization several years in advance and implies predictability of CO 2 uptake.

Owing to lack of observational data, we first use model fields calculated in the assimilation run as a proxy to quantify the potential predictive skill of SST and of the CO 2 flux into the ocean (Fig. 3, see the Methods section). The correlation coefficients of the initialized 4-year mean SST exceed the uninitialized correlations for lead times of 1–4 and 2–5 years. The correlation goes down in the intermediate lead time, however, it recovers from a lead time of 5–8 years. The results suggest improved potential predictive skills of the background ocean physical fields. The correlation coefficient of initialized CO 2 flux is significantly higher than the uninitialized correlation until a lead time of 4–7 years. The P values are lower than 0.05 until the lead time of 4–7 years suggesting significant improvement of potential predictive skill due to initialization of the physical states constrained by observations.

Figure 3: Potential predictive skill of CO 2 flux and related physical variables. Correlation skill of ensemble mean of 4-year mean SST (a), 4-year mean CO 2 flux into the ocean (b), seasonally stratified 4-year mean CO 2 flux into the ocean (late winter, that is, January–February–March) (c) and monthly stratified 4-year mean CO 2 flux, SST and MLD (d) in the western SPG region. Shown are uninitialized (blue dot in a–c) and initialized (red dots in a–c) simulations at different lead time verified against assimilation. The correlations are calculated from 4-year mean predictions (that is, years 1–4, 2–5 and so on) of initialized simulations, and 4-year running mean of the corresponding assimilation and uninitialized time series. To ensure that the number of validation years is the same for both initialized and uninitialized simulations, we use the common time period from 1967–1970 mean to 2008–2011 mean. The blue dashed line in a–c extends the uninitialized correlation for easy comparison. The vertical lines in a–c provide 90% confidence intervals based on a bootstrap approach46. The numbers on the top of the bars in a–c show the P values based on the hypothesis that the difference of correlations between the initialized and uninitialized simulations is smaller or equal to zero based on 1,000 bootstrapped resamples. The potential predictive skills of monthly stratified 4-year mean CO 2 flux, SST and MLD in d are shown with red, black and blue curves, respectively. Three-month running mean is applied before monthly correlation estimation in d. The horizontal dashed grey line in d shows the 95% level of significance based on a two-sided t-test. The vertical dashed grey lines in d are added to better distinguish different lead year results. Full size image

Further investigation of the potential predictive skill with seasonal and monthly time series (Fig. 3c,d) reveals that the high predictive skill of CO 2 flux in the initialized simulations is mainly maintained during winter months. This is related to the seasonal shift of processes in regulating the CO 2 uptake and the fact of assimilation for the decadal prediction system. In winter, the CO 2 uptake in western SPG region is regulated primarily by physical process such as the ocean-mixing strength and ocean circulation33,34,35. The variations of ocean physical fields are well represented in the initialized simulations, as the corresponding initial states are constrained by observations through assimilation. However, in spring and early summer, when the ocean warms, the biological primary production draws down seawater pCO 2 and regulates the oceanic CO 2 uptake in the western SPG region33,34,35; the correlation of the initialized simulations goes down from May, as no ocean biological observations are assimilated into the system.

We further explore possible causes of the potential predictive skill of CO 2 uptake in comparison with that of the SST and MLD (Fig. 3d). The SST correlations peak in December and go down from late winter, which is coherent with that of the MLD and the CO 2 uptake. It suggests that both the evolution of the ocean thermal state and the local mixing strength contribute to the predictive skill of CO 2 uptake in winter. Although the correlation skill of MLD is generally lower than that of CO 2 uptake, both share similar seasonal cycle of predictive skill, indicating effects of MLD changes in maintaining the predictive skill of CO 2 uptake. Moreover, the potential predictive skill of CO 2 uptake is related to the AMOC variability. As revealed by previous studies, the predictive skill of SPG SST is assured by initialization of the AMOC variability27, and the AMOC shows predictive skill up to 4 years17. The potential predictive skill of AMOC in our system is up to 2–5 years (figure omitted). The AMOC at lead time of 1 year is highly correlated with CO 2 flux at lead time of 1 year and onwards (Supplementary Fig. 4 and Supplementary Note 4). An observational study also suggested a close connection between the AMOC and the CO 2 uptake in SPG region3.

From these our results suggest that the potential predictive skill of CO 2 uptake in the western SPG is up to 4–7 years. The predictive skill is mainly attributed to the combination of improved ocean physical states and circulation variability, primarily in winter.

Evaluation of model predictions against observations

The high potential predictability of CO 2 uptake provides a basis for assessing our predictions against observations using the surface ocean CO 2 atlas (SOCAT) measurement36. Although observational data from SOCAT are sparse in the SPG region, these are the best ocean surface observations we can get for this region. The ocean surface pCO 2 in the SPG region peaks in winter as a result of the enhanced vertical supply of carbon from the intermediate waters by deep convection; surface pCO 2 values reach a minimum in summer due to biological draw down33,34,35 (Fig. 4). The initialized predictions produce ocean surface pCO 2 closer to SOCAT observation than the uninitialized simulations as indicated by the correlations and root mean squared errors. The correlations of assimilation (0.60) and initialized simulation at a lead time of 3 years (0.44) are significantly larger than the correlation of initialized simulation (0.29) at 95% significance level. The root mean squared error is lower in the assimilation and initialized simulations than in the uninitialized simulation. As we use monthly data due to lack of continuous observations, the better performances of initialized simulations are partially due to better representation of the seasonal cycle in the initialized simulations. We further separate the time series seasonally, and find that in addition to the seasonal cycle there is an improvement of the initialized run against the uninitialized run particularly in the winter months when the pCO 2 is high. The root mean squared error of ocean surface pCO 2 is much smaller in the assimilation (7.3 p.p.m.) and initialized simulations at a lead time of 3 years (13.0 p.p.m.) than in the uninitialized simulations (24.0 p.p.m.; Fig. 4b). The coherences between model simulations and observations are generally lower in spring months (Fig. 4c). The improvement of prediction in individual seasons further demonstrates that the interannual variations of oceanic carbon cycle are improved in the initialized simulations. The higher correlations between SOCAT and initialized simulations and the lower root mean squared error of the initialized simulations against SOCAT confirm that the oceanic carbon cycle can be predicted several years ahead by initialization of the ESM (Supplementary Fig. 5 and Supplementary Note 5). However, owing to temporal and spatial gaps in observations, the precise prediction skill with respect to observations cannot be estimated. Comparison of simulations against SOCAT observations only confirm that prediction of the state of the oceanic carbon cycle is improved by initialization of the physical ocean state with observations.