Search for: All records

The North Atlantic Oscillation (NAO) is widely recognized as a primary driver of the Gulf Stream (GS) path variability on interannual to decadal timescales. While the canonical view holds that the GS path responds to the NAO in a temporally stationary and approximately linear manner, we show that the sign and strength of the NAO-GS relationship vary markedly through time. Using satellite altimetry, atmospheric reanalysis, and a wind-driven, two-layer ocean model, we investigate the temporal evolution and dynamical origins of this non-stationarity in the relationship between the NAO and the meridional position of the separated GS throughout the altimetry era (1993-2023). Lead-lag analysis of the full record shows a significant, positive correlation between the NAO and GS position when the NAO leads by 3–20 months, with the maximum correlation occurring at a lag of 8 months. However, moving-window correlations demonstrate pronounced modulation of this relationship through time, with transitions from strong positive correlations to weak or reversed correlations. This behavior is reproduced by the wind-driven model, indicating that wind forcing plays a dominant role in driving the observed non-stationarity in the NAO-GS relationship. Analysis of sea level pressure variability reveals substantial shifts in the spatial structure of the NAO. The transition from positive to weakly negative NAO–GS correlation coincides with southwestward shifts of the subtropical high and subpolar low, consistent with changes in the geometry of the associated wind stress curl forcing over the GS region and intergyre boundary. These results suggest that the sensitivity of the GS to atmospheric variability depends strongly on the spatial structure of the forcing, rather than solely its magnitude, and highlight the importance of accounting for non-stationarity when interpreting and predicting GS variability and its impacts on regional climate, sea level, and marine ecosystems. 

Abstract We investigate changes in the vertical structure of the ocean temperature annual cycle amplitude (TEMP_AC) down to a depth of 300 m, providing important insights into the relative contributions of anthropogenic and natural influences. Using observations and phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we perform a detection and attribution analysis by applying a standard pattern-based “fingerprint” method to zonal-mean TEMP_ACanomalies for three major ocean basins. In all model historical simulations and observational datasets, TEMP_ACincreases significantly in the surface layer, except in the Southern Ocean, and weakens within the subsurface ocean. There is a decrease in TEMP_ACbelow the annual-mean mixed layer depth, mainly due to a deep-reaching winter warming signal. The temporal evolution of signal-to-noise (S/N) ratios in observations indicates an identifiable anthropogenic fingerprint in both surface and interior ocean annual temperature cycles. These findings are consistent across three different observational datasets, with variations in fingerprint detection time likely related to differences in dataset coverage, interpolation method, and accuracy. Analysis of CMIP6 single-forcing simulations reveals the dominant influence of greenhouse gases and anthropogenic aerosols on TEMP_ACchanges. Our identification of an anthropogenic TEMP_ACfingerprint is robust to the selection of different analysis periods. S/N ratios derived with model data only are consistently larger than ratios calculated with observational signals, primarily due to model versus observed TEMP_ACdifferences in the Atlantic. Human influence on the seasonality of surface and subsurface ocean temperature may have profound consequences for fisheries, marine ecosystems, and ocean chemistry. Significance StatementThe seasonal cycle is a fundamental aspect of our climate, and gaining insight into how anthropogenic forcing has impacted seasonality is of scientific, economic, and societal importance. Using observations and CMIP6 model simulations, this research applies a pattern-based detection and attribution method to ocean temperature annual cycle amplitude (TEMP_AC) down to 300 m across three major ocean basins. Key findings reveal significant increases in surface layer TEMP_ACexcept in the Southern Ocean and a weakening of TEMP_ACwithin the subsurface ocean. Importantly, the analysis confirms human influence on TEMP_AC. These findings underscore the profound influence of human-caused climate change on the world’s oceans and have important implications for marine ecosystems, fisheries, and ocean chemistry. 

Abstract The role of ocean dynamics in Atlantic climate variability and predictability is often studied through the lens of sea surface temperature (SST). Unlike SST, sea surface salinity (SSS) is not directly damped by surface fluxes, and its low-frequency variability responds primarily to oceanic processes. This study investigates the drivers of SSS variability using a stochastic model hierarchy to disentangle oceanic and atmospheric contributions to Atlantic climate variability, in particular, the role of local vertical processes. Representation of SST and SSS persistence and variance is especially improved by the introduction of damping of anomalies below the mixed layer, though SSS anomalies remain too persistent. The effect of SST–evaporation feedback on SSS is comparatively smaller except in regions with strong SST–SSS correlation. Despite the lack of representation of geostrophic advection, the stochastic model successfully reproduces spatial patterns of recurring SST/SSS anomalies in the Community Earth System Model 1 (CESM1) Large Ensemble at monthly to interannual time scales. At multidecadal time scales, the stochastic model is unable to simulate the amplitude of SST/SSS variability, but their spatial patterns are broadly reproduced, suggesting that direct atmospheric forcing and local vertical processes are important for capturing these features. Further analysis of the processes missing from the stochastic model suggests that the lack of geostrophic advection is largely responsible for too persistent SSS in the stochastic model, while the lack of interannual mixed-layer depth variability explains the underestimated persistence and variance in some regions for both SST and SSS. 

Abstract The Oyashio Extension (OE) frontal zone in the northwest Pacific Ocean is associated with strong gradients of sea surface temperature (SST) and salinity. The OE front enhances baroclinicity and anchors the storm tracks; changes in its position and strength may impact atmospheric variability. North–south shifts in the OE front are often defined using the leading principal component for the latitude of the absolute maximum SST gradient in the northwest Pacific (145°–170°E), the so-called Oyashio Extension index (OEI). We show that the OEI is sensitive to the choice of SST dataset used in its construction, and that the significance of regressions of atmospheric fields onto the OEI also depends on the choice of SST datasets, leading to nonrobust results. This sensitivity primarily stems from the longitudinal domain used to define the OEI including a region with parallel or indistinct frontal zones in its central section (155°–164°E), leading to divergent results across datasets. We introduce a new index that considers the extent to which the SST front across this central section departs from climatology, the frontal disturbance index (FDI). For the months considered and over short time lags, the FDI produces more consistent results on air–sea interactions and associated high-frequency storm-track metrics than the conventional OEI, with a southward shift of the storm track for a more positive FDI. The FDI appears to be related to oceanic mesoscale eddy activity in the central OE region. There are significant asymmetric associations between the FDI and storm-track metrics dependent on the sign of the FDI. Significance StatementIn this study, we aim to understand how the choice of dataset may influence the interpretation of interactions between the ocean and the overlying atmosphere near sea surface temperature (SST) fronts. We find that using different SST datasets affects the results, due to slight differences in the representation of the location of the maximum SST gradient. To understand this, we develop a new index which relates to the degree of disturbance of the SST front. The new index produces regression results that are more consistent across the different datasets. We also identify some possible links between the frontal disturbance and the presence of ocean eddies. We advise that the sensitivity to dataset choice is given due consideration in regions near SST fronts. 

Abstract The Oyashio Extension (OE) frontal zone in the northwest Pacific Ocean is associated with strong gradients of sea surface temperature (SST) and salinity. The OE front enhances baroclinicity and anchors the storm tracks; changes in its position and strength may impact atmospheric variability. North–south shifts in the OE front are often defined using the leading principal component for the latitude of the absolute maximum SST gradient in the northwest Pacific (145°–170°E), the so-called Oyashio Extension index (OEI). We show that the OEI is sensitive to the choice of SST dataset used in its construction, and that the significance of regressions of atmospheric fields onto the OEI also depends on the choice of SST datasets, leading to nonrobust results. This sensitivity primarily stems from the longitudinal domain used to define the OEI including a region with parallel or indistinct frontal zones in its central section (155°–164°E), leading to divergent results across datasets. We introduce a new index that considers the extent to which the SST front across this central section departs from climatology, the frontal disturbance index (FDI). For the months considered and over short time lags, the FDI produces more consistent results on air–sea interactions and associated high-frequency storm-track metrics than the conventional OEI, with a southward shift of the storm track for a more positive FDI. The FDI appears to be related to oceanic mesoscale eddy activity in the central OE region. There are significant asymmetric associations between the FDI and storm-track metrics dependent on the sign of the FDI. Significance StatementIn this study, we aim to understand how the choice of dataset may influence the interpretation of interactions between the ocean and the overlying atmosphere near sea surface temperature (SST) fronts. We find that using different SST datasets affects the results, due to slight differences in the representation of the location of the maximum SST gradient. To understand this, we develop a new index which relates to the degree of disturbance of the SST front. The new index produces regression results that are more consistent across the different datasets. We also identify some possible links between the frontal disturbance and the presence of ocean eddies. We advise that the sensitivity to dataset choice is given due consideration in regions near SST fronts. 

Using a regional atmospheric model at 27-km resolution over the western North Pacific, we explore the sensitivity of the atmospheric response to sea surface temperature (SST) anomalies associated with meridional shifts of the Oyashio Extension (OE) front. Twenty different SST conditions are prescribed by adding SST anomalies in different years of 1979–2016 to a base SST state taken on a February 2013 day with a relatively neutral SST distribution. The model is integrated for a single storm duration and the entire February 2013, using identical initial and lateral boundary conditions. The differences between the responses to the 10 highest and lowest SST anomalies are highly significant and baroclinic. Simulations with the extreme positive and negative SST anomalies and their decomposition into spatial mean and residuals indicate that the large-scale and storm-track responses are determined by the spatial mean SST anomalies and are insensitive to the residual mesoscale SSTs. This holds at 10-km atmospheric resolution, where the responses remain similar and are dominated by the impact of the spatial mean SST anomalies, except for slight differences in the storm-track response and stronger vertical motions. We also investigate the influence of the central region (155°–164°E) of the OE that often has parallel or indistinct frontal zones in part linked to mesoscale eddy activity, as described by a frontal disturbance index (FDI). Differences between the response to the 10 highest and lowest FDI cases are small and lack statistical significance. Mesoscale SST variations thus had very little impact on the large-scale atmospheric response. Significance StatementSeveral modeling studies have suggested that mesoscale eddy variability in western boundary current regions has a strong influence on the storm track and the atmospheric circulation. However, these studies were generally based on simulations using spatially smoothed SSTs so that the mean SST gradients are weakened. Using a regional model without such spatial smoothing, we show that the atmospheric response to SST anomalies in the Oyashio Extension region is determined by their spatial mean and is largely insensitive to residual mesoscale SST variations. These results have to be mitigated against the smallness of the spatial domain simulated and the fact that we are only considering one single February month. 

The rapid loss of Arctic sea ice is a striking consequence of anthropogenic global warming. Its remote impacts on mid‐latitude weather and climate have attracted scientific and media attention. In this study, we use a hybrid (dynamical plus machine‐learning) atmospheric model—Google's NeuralGCM—to investigate the mid‐latitude atmospheric circulation responses to Arctic sea‐ice loss for the first time. We conduct experiments in which NeuralGCM is forced with pre‐industrial and future sea‐ice concentrations following the protocol of the Polar Amplification Model Intercomparisom Project. To assess the performance of NeuralGCM, we compare the results with those simulated by two physics‐based climate models. NeuralGCM produces a comparable response of near‐surface warming to sea‐ice loss and the subsequent weakened zonal wind in mid‐latitudes. However, there is a substantial discrepancy between the two models' stratospheric responses, where different temperature responses in these models are associated with different zonal wind and geopotential height responses. Further investigation of North Atlantic blocking shows that NeuralGCM produces stronger, more frequent, and more realistic blocking events. Our results demonstrate the capability of NeuralGCM in simulating the tropospheric responses to Arctic sea‐ice loss, but improvements may be needed for the stratospheric representation. 

The models that participated in the Coupled Model Intercomparison Project (CMIP) exhibit large biases in Arctic sea ice climatology that seem related to biases in seasonal atmospheric and oceanic circulations. Using historical runs of 34 CMIP6 models from 1979 to 2014, we investigate the links between the climatological sea ice concentration (SIC) biases in September and atmospheric and oceanic model climatologies. The main intermodel spread of September SIC is well described by two leading EOFs, which together explain ∼65% of its variance. The first EOF represents an underestimation or overestimation of SIC in the whole Arctic, while the second EOF describes opposite SIC biases in the Atlantic and Pacific sectors. Regression analysis indicates that the two SIC modes are closely related to departures from the multimodel mean of Arctic surface heat fluxes during summer, primarily shortwave and longwave radiation, with incoming Atlantic Water playing a role in the Atlantic sector. Local and global links with summer cloud cover, low-level humidity, upper or lower troposphere temperature/circulation, and oceanic variables are also found. As illustrated for three climate models, the local relationships with the SIC biases are mostly similar in the Arctic across the models but show varying degrees of Atlantic inflow influence. On a global scale, a strong influence of the summer atmospheric circulation on September SIC is suggested for one of the three models, while the atmospheric influence is primarily via thermodynamics in the other two. Clear links to the North Atlantic oceanic circulation are seen in one of the models. 

Abstract The storage of anthropogenic heat in oceans is geographically inhomogeneous, leading to differential warming rates among major ocean basins with notable regional climate impacts. Our analyses of observation-based datasets show that the average warming rate of 0–2000-m Atlantic Ocean since 1960 is nearly threefold stronger than that of the Indo-Pacific Oceans. This feature is robustly captured by historical simulations of phase 6 of Coupled Model Intercomparison Project (CMIP6) and is projected to persist into the future. In CMIP6 simulations, the ocean heat uptake through surface heat fluxes plays a central role in shaping the interbasin warming contrasts. In addition to the slowdown of the Atlantic meridional overturning circulation as stressed in some existing studies, alterations of atmospheric conditions under greenhouse warming are also essential for the increased surface heat flux into the North Atlantic. Specifically, the reduced anthropogenic aerosol concentration in the North Atlantic since the 1980s has been favorable for the enhanced Atlantic Ocean heat uptake in CMIP6 models. Another previously overlooked factor is the geographic shape of the Atlantic Ocean which is relatively wide in midlatitudes and narrow in low latitudes, in contrast to that of the Indo-Pacific Oceans. Combined with the poleward migration of atmospheric circulations, which leads to the meridional pattern of surface heat uptake with broadly enhanced heat uptake in midlatitude oceans due to reduced surface wind speed and cloud cover, the geographic shape effect renders a higher basin-average heat uptake in the Atlantic.