Different SST responses in the SPG

We investigated projections from 40 climate models participating in the fifth Coupled Model Intercomparison Project (CMIP5) under different emission scenarios (RCPs)42. We systematically scanned 27 RCP2.6 simulations, 39 RCP4.5 simulations and 40 RCP8.5 simulations for a total of 106 experiments. In each case, the analysis also included the preceding historical simulation capturing all known radiative changes since 1860. For all projections a rise in the ensemble mean global SST was found (Fig. 2). The global SST trends are, respectively, 0.46±0.30 °C per century for the RCP2.6 ensemble, 1.27±0.39 °C per century for the RCP4.5 ensemble and 3.01±0.58 °C per century for the RCP8.5, thus continuing the warming trend observed over the last decades43,44. By normalizing the ensemble mean SST trend by the globally averaged value, the resulting pattern clearly reveals that the warming signal is not uniform in space (Fig. 2). Some regions experience an amplified SST increase, for example, the Nordic Seas, while other regions are characterized by a subdued warming trend, for example, the SPG. Moreover, the uncertainty in SST projections peaks over the NA convection regions (black contour in Fig. 2). In the SPG, 70% of the experiments feature an increase in SST, while the remaining 30% show a reversed trend (Supplementary Table 1). The area of the NA that for each RCP scenario is characterized by a subdued SST warming and a significant model uncertainty (Methods) roughly matches the SPG (red contour in Fig. 1). Our analysis focuses on this region to identify possible abrupt cooling events and to evaluate whether the models exhibiting such abrupt cooling are reliable.

Figure 2: Patterns of SST response in RCP scenarios. Ensemble mean of the 21st century SST trend normalized by its own global mean (dimensionless quantity) for (a) RCP2.6 simulations, (b) RCP4.5 simulations and (c) RCP8.5 simulations. The globally averaged SST trend ensemble mean is indicated for each scenario, that is, 0.46 10−2 oC year−1 for the RCP2.6 experiments, 1.27 10−2 oC year−1 for the RCP4.5 experiments and 3.01 10−2 oC year−1 for the RCP8.5 experiments. Since the globally averaged SST trend ensemble mean is positive for all scenarios, the non-dimensional value in each grid point is >1 when characterized by amplified warming, <1 when characterized by a subdued warming and <0 when characterized by cooling. The black contour shows regions with maximum ensemble spread (see Methods). Full size image

Abrupt cooling events in the SPG

We define here as ‘abrupt’ those cooling events in the SPG for which the 10-year SST decrease is at least three times larger than the standard deviation of its annual data in the pre-industrial simulation (Methods). We detected a total of 15 cases (14% of the available projections) satisfying our definition (Supplementary Fig. 1), involving nine different models (22.5% of the models). We identified two main processes driving an abrupt SPG cooling. In seven models (17.5% of the total) a rapid SST decrease in the SPG is driven by a sudden local MLD contraction, that is, a convection collapse, affecting but not completely disrupting the AMOC. In two models (5% of the total) the temperature drop involves the entire northern NA and is caused by a massive AMOC reduction and its associated change in meridional heat transport. Thus, although deep convection and AMOC are strictly connected, abrupt shifts in SPG convection may not necessarily imply similar AMOC shifts. Because deep convection in the Greenland-Iceland-Norwegian Sea and in the Labrador-Irminger Sea, as well as the overflows from Denmark Strait and the Scotland-Faroe channel are all integral parts of the AMOC deep-water formation system, a collapse in one part of this system may occur without an equally abrupt response in the AMOC. This supports the distinction between two separated climatic tipping points for the NA abrupt cooling, namely one associated with a local SPG convection collapse and one associated with a large-scale AMOC disruption.

Three different types of SST response in the North Atlantic

Based on their different SPG projections, we discerned three different subsets of models (Table 1), namely those not showing any kind of abrupt cooling (the ‘non-abrupt’ ensemble, 31 models), those characterized by a convection collapse in the SPG (the ‘SPG convection collapse’ ensemble, seven models) and those simulating a collapse of the AMOC before year 2100 (the ‘AMOC disruption’ ensemble, two models). For the models projecting a rapid SST cooling over the SPG, the corresponding level of global warming and the year in which the abrupt change starts are also displayed (Table 1). All but three abrupt cooling events occur for global mean temperature increases below the often invoked 2 °C limit, in line with a recent study showing the high occurrence of oceanic transitions for moderate levels of global warming45.

Table 1 Classification of CMIP5 models in the three sub-ensembles. Full size table

In Fig. 3, an example of the different features characterizing each subset of models is shown for the RCP2.6 scenario, while a more comprehensive illustration and discussion is provided in Supplementary Figs 2–6 and Supplementary Notes 1–2. In order to clearly identify the abrupt signal, we smoothed all time series by applying a 10-year running mean, thus removing the higher-frequency internal variability. In the non-abrupt model, the SPG is characterized by a warming trend (Fig. 3a), and the AMOC and the MLD in the last decade (2091-2100) appear reduced by about 10% compared with their value over the decade 2006-2015 (Fig. 3d).

Figure 3: Characterization of the different SST responses in the SPG. Examples of the SST, MLD and AMOC evolutions over the SPG in the three model subsets (non-abrupt, SPG convection collapse and AMOC disruption) for the RCP2.6 scenario. Only one example for each sub-ensemble is shown while the Supplementary Figs 2–6 provides a more comprehensive illustration. All time series were smoothed using a 10-year running mean to remove the high-frequency variability. (a–c) SST anomaly (oC) with respect to its initial magnitude, that is, the mean over the decade 2006–2015, in (a) NorESM1-M, that is, non-abrupt model, (b) GISS-E2-R, that is, SPG convection collapse model, (c) FIO-ESM, that is, AMOC disruption model. Values in brackets indicate SST magnitudes at the beginning of the RCP2.6 experiments (2006–2015). (d–f) Relative changes (%) of AMOC (red lines) and MLD (blue lines) in d NorESM1-M, (e) GISS-E2-R, (f) FIO-ESM with respect to their initial values (2006–2015). Absolute magnitudes of AMOC (Sv) and MLD (m) averaged over the period 2006–2015 are, respectively, displayed in red and blue brackets. It is worth noticing that the strong AMOC reduction in the FIO-ESM model already takes place during the historical period (Supplementary Fig. 6), yielding a low absolute value over the 2006–2015 period. Full size image

A sudden SST decrease of around 3 °C in 10 years typifies the SST response in the SPG convection collapse model (Fig. 3b). It occurs in combination with a sudden contraction of the MLD, which appears more than halved in 2091–2100 (Fig. 3e). The SST drop is also preceded by a rapid SPG freshening, which leads to an abrupt sea surface density decline responsible for the local MLD reduction and the associated decrease in vertical heat fluxes (Supplementary Figs 2 and 3). Although an SPG convection collapse weakens the AMOC, the latter does not collapse but experience a relatively limited and linear reduction, contrary to the non-linear response of both the MLD and the SST (Fig. 3e and Supplementary Fig. 4). The AMOC strength (maximum index) remains always higher than 13 Sv for all the experiments performed with SPG convection collapse models, consistent with an active deep convection in the Nordic Seas (Supplementary Fig. 5), which still sustains the overturning circulation46. Moreover, the AMOC change at the time of the abrupt cooling event is comparable to former AMOC variations that do not coincide with any rapid cooling (Fig. 3e and Supplementary Fig. 4), thus suggesting that the associated decrease in northward heat transport24,25 is not decisive in driving the temperature drop. Rather, the rapid cooling is mainly caused by a suddenly reduced vertical heat transfer from the deep to the upper ocean due to a collapsed convective mixing36.

The strong cooling observed in the AMOC disruption model exceeds 4 °C at the end of the 21st century (Fig. 3c). This subset of models exhibits a massive AMOC decline of 60% (80% if compared with its pre-industrial strength), which strongly differs from the characteristic AMOC reduction in both SPG convection collapse and non-abrupt models (Fig. 3 and Supplementary Fig. 6). The resulting negative SST anomaly involves both the SPG and Nordic Seas, spanning the entire region of deep-water formation, which is, however, unrealistically reproduced in AMOC disruption models (Supplementary Fig. 5).

The three different SST responses over the SPG strongly characterize three different climatic impacts. Figure 4 shows the ensemble mean surface air temperature (SAT) trend for the RCP4.5 scenario in the different subsets of models. For the non-abrupt sub-ensemble, the increase in SAT covers the whole globe (Fig. 4a), causing a global mean air temperature (GMT) trend of about 2 °C per century. The SPG convection collapse sub-ensemble shows an atmospheric ‘warming hole’ over the NA, which strongly influences the temperature response over highly populated areas such as the eastern North American coast and Western Europe (Fig. 4b), where the global warming trend is suddenly halted. The resulting GMT trend is about 1.5 °C per century. For the two models projecting a massive AMOC reduction (Fig. 4c), the northern hemisphere cools while the southern hemisphere strongly warms, consistent with the so-called bipolar seesaw47,48. The altered hemispheric temperature gradient also affects the precipitation patterns by shifting the position of the intertropical convergence zone, in line with previous findings49,50. The GMT rise in the AMOC disruption sub-ensemble is about 1 °C. However, the different levels of global warming among the models might also depend on the climate sensitivity of the CMIP5 models, for example, cloud parameterizations51. Qualitatively similar results were found for RCP2.6 and RCP8.5 scenarios (Supplementary Figs 7 and 8).

Figure 4: Different climate impacts. Patterns of the 21st century SAT trend (oC 10−2 year−1) under the RCP4.5 scenario for: (a) non-abrupt ensemble (27 members), (b) SPG convection collapse ensemble (7 members) and (c) AMOC disruption ensemble (2 members). The GMT trend is also displayed for each subset. The light grey and dark grey contours define regions where the ensemble mean precipitation trend, respectively, exceeds 300 mm per century and is lower than −300 mm per century. Results for the RCP2.6 and RCP8.5 scenarios can be found in Supplementary Fig. 7 and Supplementary Fig. 8. Full size image

The SPG stratification as a constraint for SST projections

The model spread in SST projections over the SPG stems from different dynamical responses in the convective regions. Excluding the two AMOC disruption models (FGOALS-s2 and FIO-ESM), for which an extended analysis including MLD changes over Nordic Seas would be required, the MLD response in the SPG is crucial in determining the SST evolution in the subpolar NA. The panels on the left in Fig. 5 show the linear correlation between the simulated SST trend and winter MLD trend, which ranges from 0.63 to 0.75 for the different scenarios. A shallower MLD causes the surface heat loss to the atmosphere to be less well counterbalanced by upward mixing of heat from deeper layers37. This implies that changes in the vertical density profile have a key role. In particular, the importance of the modelled present-day SPG winter density stratification (hereafter named background stratification, see Methods) in constraining the future SST evolution in the SPG is evidenced in the panels on the right in Fig. 5. The relation between background stratification and SST projection is non-linear and becomes more robust for more severe warming scenarios. The non-linear correlation (Methods), significant at the 95% confidence level, ranges from 0.63 for RCP2.6 simulations to 0.79 for RCP8.5 simulations. Models projecting an abrupt SPG cooling are characterized by weaker background stratification, while models featuring a more stratified SPG are more prone to project a continuous warming trend. This is physically robust since stronger (weaker) stratification is symptomatic of weaker (stronger) convective activity, and, therefore, the potential for cooling effects due to a MLD reduction is lower (higher). The model uncertainty in projecting SST over the SPG is, therefore, significantly related to the spread in simulating the winter vertical density profile for present-day conditions. This makes the background stratification a promising ‘emerging constraint’52 for the future SST evolution in the SPG. These conclusions do not change if a more restricted area for the calculation of the MLD and the stratification is used (Supplementary Fig. 9).

Figure 5: The role of stratification in SPG projections. Scatterplot of simulated SST trends (oC 10−2 year−1) over the SPG versus (a,c,e) the relevant MLD-trend (m 10−2 year−1) and (b,d,f) the present-day stratification indicator (Kg m−3). Non-abrupt models are indicated with red circles and SPG convection collapse models with blue circles, for (a,b) the RCP2.6, (c,d) the RCP4.5, (e,f) the RCP8.5 scenario. In a,c,e the value r l indicates the linear correlation between the SST and MLD trends, whose significance above the 95% confidence level was evaluated with a two-tailed Student’s t-test. The crosses indicate the linear best-fit of the SST trends against the MLD trend, that is,. the linear regression using the least squares method. In b,d,f the value r nl indicates the non-linear correlation between SST-trend and the stratification indicator, statistically significant at the 95% confidence level (see Methods). The crosses indicate the logarithmic best-fit of the SST trends against the stratification index, that is, the logarithmic regression using the least squares method. The dashed vertical black line centred on 0 indicates the observationally based stratification index, calculated as the average of GLORYS Reanalysis (1993–2012) data and EN3 analysis data (1950–2012). The arrows at the bottom indicate the areas in the panels for which the simulated SPG stratification is either more, or less stable than in the observational data. Full size image

The right panels of Fig. 5 also show that the CMIP5 ensemble is biased towards a too stratified SPG for present-day conditions, and that, on average, the background stratification in SPG convection collapse models compares better with the observations than that in non-abrupt models. The difference in mean background stratification between the non-abrupt sub-ensemble and the SPG convection collapse sub-ensemble is significant at the 95% according to a Monte Carlo test (Supplementary Fig. 10). This difference is further detailed in Fig. 6 where the vertical profiles of winter density, temperature and salinity for present-day conditions in non-abrupt and SPG convection collapse sub-ensembles are compared with observational data. The non-abrupt ensemble is on average excessively stratified in the SPG. This is a direct consequence of an excessively low density in the upper ocean, which is mainly due to a negative salinity bias, only partly compensated by a cold SST bias. The too strong stratification limits the MLD and deep-water formation. By contrast, the SPG convection collapse ensemble is, on average, much less stratified in the SPG, rendering the region more suitable for a deep winter mixed layer. This makes the cooling effect due to a MLD reduction potentially more effective than in the non-abrupt ensemble. The fact that a large number of non-abrupt models significantly overestimate the present-day SPG stratification (Fig. 5) suggests that a tipping point for the local convection collapse cannot exist for them, as an already too weak present-day convective activity prevents any future abrupt shift to a collapsed state. This implies that the chance of future abrupt cooling events in the NA may be underestimated when considering the whole CMIP5 model ensemble.

Figure 6: Different SPG stratifications in the model sub-ensembles and their comparison with the observational data. Present-day vertical profiles of (a) winter density (kg m−3), (c) temperature (oC) and (e) salinity (psu) in the SPG region for observational data (black lines), for ensemble-mean of non-abrupt models (red lines) and for ensemble-mean of the SPG convection collapse models (blue lines). Right panels show the difference between the modelled present-day winter conditions in the SPG and the observation-based data (b) for density, (d) for temperature and (f) salinity. Horizontal dashed lines are drawn at every 1,000 metre. A zoom has been made for the first 1,000 metres. Full size image

The reliability of the different SST projections over the SPG

Since the spread in SST responses over the SPG can be linked to different model biases, it follows that not all future climate projections are equally plausible, bringing model reliability into question. Our approach to evaluate this reliability consists of assessing the model’s capability to reproduce a relevant observable metric52. Here we use the background stratification as a ‘performance metric’, given its relevance in constraining SST projections over the SPG, proven in Fig. 5. We also tested whether the present-day AMOC may act as an emerging constraint. However, it turns out that there is no robust statistical relation between the simulated present-day AMOC and future SST trends over the SPG across the CMIP5 ensemble (Supplementary Fig. 11).

For each model, we computed a skill score S that measures the model’s accuracy in reproducing the observed present-day winter density profile over the SPG (Methods). The values of S across the CMIP5 models (Supplementary Table 1) range between 0, which corresponds to an extremely unrealistic reproduction of the background stratification, and 1, which corresponds to a simulated background stratification perfectly matching the observations. The skill score allows a selection of models for a more reliable analysis. By setting S=0.8 (0.9) as an acceptable limit for model credibility in its representation of the SPG stratification, the ensemble of models surpassing this limit consists of 18 (11) members. Such a model selection reduces the spread in SST projections over the SPG originally exhibited by the 40 CMIP5 models (Fig. 7). Moreover, the most skilled models clearly produce a more moderate SPG warming trend for the RCP8.5 scenario and a cooling trend for the RCP2.6 scenario and the RCP4.5 scenario (Fig. 7). This is linked to the strong MLD reduction under the RCP scenarios evidenced by all the most skilled models, independent of the occurrence of an SPG convection collapse. The MLD reduction induces a local cooling opposing to the global warming, which may regionally result in a subdued warming or even cooling.

Figure 7: Reduction of model uncertainty over the SPG. Model ensemble mean and spread of the 21st century SST trend (oC 10−2 year−1) over the SPG in the RCP scenarios for different subsets of models: (black) all the 40 CMIP5 models; (red) CMIP5 models possessing a skill score S>0.8; (blue) CMIP5 models possessing a skill score S>0.9. Error bars indicate the standard deviation of the SST trend ensemble mean for the different subsets of models. Full size image

The analysis of the most reliable models also highlights that the likelihood of an SPG convection collapse increases for models featuring a better background stratification. The probability of occurrence of a SPG convection collapse is 17.5% when all models are considered, that is, seven models over 40. However, the probability becomes 33.3% (45.5%) if only the 18 (11) models possessing a skill score S>0.8 (0.9) are considered (Table 2). Similarly, by weighting the CMIP5 models by their skill scores (Method), the likelihood of a future SPG convection collapse becomes 26.6%. This is a direct consequence of the fact that the SPG convection collapse ensemble features the best skill score among the three subsets of models.

Table 2 Implications of models’ evaluation. Full size table

These results highlight that the potential occurrence of an SPG convection collapse in CMIP5 models is conditional on a realistic representation of the local background stratification. However, this does not imply that there exists a deterministic relation between background stratification and convection collapse, since 12 (6) of the 18 (11) most skilled models do not project any abrupt event in the SPG. Convection generally depends on the stratification, but details in the particular configuration of temperature and salinity are also important, notably for SST and Sea Surface Salinity (SSS)53. A common feature of the 12 (6) non-abrupt models possessing a skill score S>0.8 (0.9) is that they simulate, on average, too warm and salty SPG surface water masses for present-day conditions, that is, SST=6.9±1.2 °C (6.4±1.2 °C) and SSS=35.1±0.4 psu (35.1±0.3 psu), as compared to observations, that is, SST=5.4±0.3 °C and SSS=34.8±0.0 psu. This configuration differs strongly from that in the remaining non-abrupt models, which are, on the contrary, too cold and fresh, that is, SST=3.4±1.9 °C (4.4±2.4 °C) and SSS=34.1±0.6 psu (34.4±0.7 psu). The SPG convection collapse ensemble features the smallest bias in SST and SSS, that is, SST=5.9±0.9 °C (5.8±0.9 °C) and SSS=35.0±0.1 psu (35.0±0.2 psu), and their models would have been estimated the most reliable also by using a multi-parameter skill score based on SST and SSS. These different configurations, consistent with the density compensating SST and SSS biases already evidenced across the CMIP5 models54, further explain the different responses among the models. Indeed, the deep-water formation is sensible to SST and SSS, with a warmer/saltier configuration being more favourable for deep convection than a colder/fresher configuration53. Hence, in models reproducing too cold and fresh SPG surface water masses for present-day conditions (that is, non-abrupt models with S<0.8), the convective activity may already be unrealistically inhibited before global warming, suggesting the non-existence of a climatic threshold for an abrupt convection collapse. On the contrary, in models that are biased towards too warm and salty SPG surface water masses for present-day conditions (that is, non-abrupt models with S>0.8), the climatic threshold for a transition to a collapsed convection potentially exists, but its achievement might be unrealistically long postponed.