| 31 Jul 2026
Boreal autumn to winter North Atlantic atmosphere-ocean coupling in large ensemble climate simulations
Abstract. Recent studies have suggested an observed causal relationship between autumn North Atlantic sea surface temperature (SST) anomalies and the phase of the winter North Atlantic Oscillation (NAO). This autumn SST-winter NAO link, which is mediated by turbulent heat fluxes (THF) and baroclinicity, appears to be underestimated in seasonal prediction models, suggesting possible model limitations in representing air-sea coupling. However, strong atmospheric driving of North Atlantic THF and SST variability at seasonal timescales presents a challenge in establishing a causal ocean feedback onto the large-scale atmosphere. This study examines the representation of autumn-winter North Atlantic atmosphere-ocean variability in ERA5 reanalysis data and historical large ensembles from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Models adequately capture concurrent North Atlantic atmosphere-SST variability within the autumn and winter seasons, when THF and SST tendencies are mainly atmosphere-driven. However, the leading mode of covariance between autumn SST–winter mean sea level pressure (MSLP) in models differs from ERA5. In ERA5, warm SST anomalies in the central North Atlantic in autumn precede a positive winter NAO anomaly. Conversely, the CMIP6 models show, on average, weak cool autumn SST anomalies precede a positive winter NAO with large model spread. Using an atmospheric analogue method, we separate atmosphere- and ocean-driven components of autumn THF variability and assess their respective relationships with winter MSLP variability. In ERA5, the winter MSLP signal associated with ocean-driven autumn THF anomalies is near zero; in contrast, the winter NAO is linked to atmosphere-driven autumn THF variability. This suggests that atmospheric processes, such as tropical-extratropical teleconnections, could explain the autumn THF–winter MSLP relationship in ERA5 described in a previous study, or that SST anomalies arising from autumn atmospheric forcing persist into winter and contribute to a lagged feedback on the atmosphere. CMIP6 models generally underestimate the winter NAO signal associated with atmosphere-driven autumn THF anomalies, which could reflect biases in feedbacks, atmospheric teleconnections or underpersistence of inter-seasonal North Atlantic SST anomalies.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Weather and Climate Dynamics.
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This paper examines the winter atmosphere-autumn SST relationship in ERA5 and CMIP6 models to assess the representation of atmospheric response to SST over the North Atlantic region. It starts with a MCA analysis between SLP and SST in simultaneous and lagged analysis, finding largely consistent concurrent MCA but not for lagged MCA in CMIP6 against ERA5. This underestimation of lagged response in MCA is then assessed, and seems to be confirmed, in an independent approach of atmospheric circulation analogue on the autumn surface heat flux. This is a comprehensive analysis of the CMIP6 models against observations and provides a reference that can be compared against using other methods. The results are generally interesting. Nevertheless, the complex analysis, with multiple data sets and multiple methods (MCA, EOF, concurrent and lead-lag analysis, and analogue method) makes it a great challenge to follow. I recommend a minor revision before the publication of the paper.
Major comments:
My major comments concerns with the presentation of the results, instead of the result.
The paper uses a comprehensive analysis strategy that employes multiple methods, but didn’t give a guideline of this complex strategy of analysis. For example, the lead-lag MCA analysis has been used to isolate atmospheric driving of and response to SST in the first half of the paper. Then, it uses the analogue method on surface heat flux to re-examine the feedback. This is my understanding after some long-time contemplation, because the paper didn’t lay out this strategy clearly. Similarly, the paper also uses MCA analysis and then compares with the EOFs of single variables to check if the coupled mode explains the dominant variability in each field…
It will be useful to readers to state clearly the strategy of the paper. For example, why using the lead-lag MCA is not sufficient and the analogue method need to be used? I suggest the authors to add a subsection explicitly in 2.3 Method to describe the overall strategy, and the reason why so many different methods have to be used.
Also, it will be very helpful for readers, after the application of a method, specify what has been learned and what are the remaining problems, and how this problem can be resolved using another method. This naturally introduces another method, making the paper flow more smoothly. For example, after the lead-lag MCA analysis, what has been learned on the response and feedback, and what is the remaining question. How this remaining question is to be addressed, using another method, such as the analogue method.
It is perhaps also useful to highlight that this paper concerns more on the atmospheric response to SST (or the feedback of SST on the atmosphere in models against observations), since the atmospheric driving of SST seems to be a robust feature in models as in observations. It is the feedback part that show diverse results in the models, consistent with GCM simulations (Kushnir et al 2002) and the signal-noise paradox. Accordingly, the discussion on the feedback part should be given higher priority than the driving part, than in the current version of the paper.