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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 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 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. 

We provide the first scientific evidence that a human-caused signal in the seasonal cycle of sea surface temperature (SST) has emerged from the background noise of natural variability. Geographical patterns of changes in SST seasonal cycle amplitude (SSTAC) reveal two distinctive features: an increase at mid-latitudes in the Northern Hemisphere related to mixed-layer depth changes, and a robust dipole pattern between 40˚S and 55˚S in the Southern Hemisphere which is mainly driven by surface wind changes. The model-predicted pattern of SSTAC change is identifiable with high statistical confidence in four observed SST products and in 51 individual model realizations of historical climate evolution. Simulations with individual forcing reveal that greenhouse gas increases drive most of the change in SSTAC, with smaller but distinct contributions from anthropogenic aerosol and ozone forcing. The robust human influence identified here on the seasonality of SST is likely to have wide-ranging impacts on marine ecosystems. 

Abstract North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable in the world's oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks (NNs) are trained to examine the relative importance of oceanic and atmospheric predictors in predicting the NASST state in the Community Earth System Model 1 (CESM1). In the presence of external forcings, oceanic predictors outperform atmospheric predictors, persistence, and random chance baselines out to 25‐year leadtimes. Layer‐wise relevance propagation is used to unveil the sources of predictability, and reveal that NNs consistently rely upon the Gulf Stream‐North Atlantic Current region for accurate predictions. Additionally, CESM1‐trained NNs successfully predict the phasing of multidecadal variability in an observational data set, suggesting consistency in physical processes driving NASST variability between CESM1 and observations. 

Modeled water-mass changes in the North Pacific thermocline, both in the subsurface and at the surface, reveal the impact of the competition between anthropogenic aerosols (AAs) and greenhouse gases (GHGs) over the past 6 decades. The AA effect overwhelms the GHG effect during 1950–1985 in driving salinity changes on density surfaces, while after 1985 the GHG effect dominates. These subsurface water-mass changes are traced back to changes at the surface, of which ~70% stems from the migration of density surface outcrops, equatorward due to regional cooling by AAs and subsequent poleward due to warming by GHGs. Ocean subduction connects these surface outcrop changes to the main thermocline. Both observations and models reveal this transition in climate forcing around 1985 and highlight the important role of AA climate forcing on our oceans’ water masses. 

Abstract 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 observed winter Barents-Kara Sea (BKS) sea ice concentration (SIC) has shown a close association with the second empirical orthogonal function (EOF) mode of Eurasian winter surface air temperature (SAT) variability, known as Warm Arctic Cold Eurasia (WACE) pattern. However, the potential role of BKS SIC on this WACE pattern of variability and on its long-term trend remains elusive. Here, we show that from 1979 to 2022, the winter BKS SIC and WACE association is most prominent and statistically significant for the variability at the sub-decadal time scale for 5–6 years. We also show the critical role of the multi-decadal trend in the principal component of the WACE mode of variability for explaining the overall Eurasian winter temperature trend over the same period. Furthermore, a large multi-model ensemble of atmosphere-only experiments from 1979 to 2014, with and without the observed Arctic SIC forcing, suggests that the BKS SIC variations induce this observed sub-decadal variability and the multi-decadal trend in the WACE. Additionally, we analyse the model simulated first or the leading EOF mode of Eurasian winter SAT variability, which in observations, closely relates to the Arctic Oscillation (AO). We find a weaker association of this mode to AO and a statistically significant positive trend in our ensemble simulation, opposite to that found in observation. This contrasting nature reflects excessive hemispheric warming in the models, partly contributed by the modelled Arctic Sea ice loss.