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Over the last century, the global mean surface air temperature (SAT) has experienced two periods of warming slowdown (hiatuses), namely 1940–1975 and 1998–2012, as well as showing well-defined interdecadal oscillations. Previous studies have focused mainly on the most recent hiatus, and little is known about the period between 1940 and 1975. From the point of view of the sea surface temperature (SST), there are two aspects of interest; i.e., the climatological SST and SST variability. In this paper, observational and modelling evidence is used to show that, compared with the climatological SST, SST variability has been the main cause of the slowdown in rate of increase in SAT since 1940. In addition, the observational data and simulation results show that SST variability had a greater impact on the slowdown in rate of increase in SAT from 1940 to 1975 (− 1.2 × 10 −3  °C/year) than from 1998 to 2012 (− 5.7 × 10 −3  °C/year). The SAT change over the period 1940–1975 (1.0 × 10 −4  °C/year) was less affected by the climatological SST forcing experiment than that over the period 1998–2012 (− 5.0 × 10 −4  °C/year). Comparing with 1940–1975, the SAT change over the period 1998–2012 was much affected by the global SAT long-term warming. The distributions of wind stress and atmospheric pressure both indicate that, although the eastern Pacific Ocean played an important role in influencing the global SAT trend between 1998 and 2012, it made little contribution to changes in global SAT between 1940 and 1975. In addition, from the perspective of seasonality, the interdecadal variation of SAT over these two periods was a seasonally dependent phenomenon. Over the period 1940–1975, the annual SAT trend essentially followed the summer SAT trend, whereas between 1998 and 2012, winter was the dominant season of annual SAT change.

Decadal climate prediction presumes there are decadal-timescale processes and mechanisms that, if initialized properly in models, potentially provide predictive skill more than one or two years into the future. Candidate mechanisms involve Pacific decadal variability and Atlantic multidecadal variability, elements of which involve slow fluctuations of tropical Pacific and Atlantic sea surface temperatures (SSTs) from positive anomalies (positive phase) to negative anomalies (negative phase). Here we use model experiments to show that there tends to be a weak opposite-sign SST response in the tropical Pacific when observed SSTs are specified in the Atlantic, while there is a weak same-sign SST response in the tropical Atlantic when observed SSTs are specified in the tropical Pacific. Net surface heat flux in the Atlantic and ocean dynamics in the Pacific play contrasting roles in the ocean response to specified SSTs in the respective basins. We propose that processes in the Pacific and Atlantic are sequentially interactive through the atmospheric Walker circulation along with contributions from midlatitude teleconnections for the Atlantic response to the Pacific. Atmospheric Walker circulation results in a two-way interaction between decadal-scale sea surface temperature variability in the Atlantic and Pacific, according to pacemaker climate modelling experiments.

Sea surface temperature (SST) variability of El Niño–Southern Oscillation (ENSO) underpins its global impact, and its future change is a long-standing science issue. In its sixth assessment, the IPCC reports no systematic change in ENSO SST variability under any emission scenarios considered. However, comparison between the 20th and 21st century shows a robust increase in century-long ENSO SST variability under four IPCC plausible emission scenarios.Sea surface temperature variability of the equatorial Pacific Ocean dictates the strength of El Niño–Southern Oscillation events. CMIP6 models under four IPCC emission scenarios show increased variability in the 21st century from the 20th century.

The North Atlantic has shown large multidecadal temperature shifts during the twentieth century. There is ongoing debate about whether this variability arises primarily through the influence of atmospheric internal variability, through changes in ocean circulation, or as a response to anthropogenic forcing. This study isolates the mechanisms driving Atlantic sea surface temperature variability on multidecadal time scales by using low-frequency component analysis (LFCA) to separate the influences of high-frequency variability, multidecadal variability, and long-term global warming. This analysis objectively identifies the North Atlantic subpolar gyre as the dominant region of Atlantic multidecadal variability. In unforced control runs of coupled climate models, warm subpolar temperatures are associated with a strengthened Atlantic meridional overturning circulation (AMOC) and anomalous local heat fluxes from the ocean into the atmosphere. Atmospheric variability plays a role in the intensification and subsequent weakening of ocean overturning and helps to communicate warming into the tropical Atlantic. These findings suggest that dynamical coupling between atmospheric and oceanic circulations is fundamental to the Atlantic multidecadal oscillation (AMO) and motivate approaching decadal prediction with a focus on ocean circulation.

The Pacific Decadal Oscillation (PDO), the leading mode of Pacific decadal sea surface temperature variability, arises mainly from combinations of regional air-sea interaction within the North Pacific Ocean and remote forcing, such as from the tropical Pacific and the Atlantic. Because of such a combination of mechanisms, a question remains as to how much PDO variability originates from these regions. To better understand PDO variability, the equatorial Pacific and the Atlantic impacts on the PDO are examined using several 3-dimensional partial ocean data assimilation experiments conducted with two global climate models: the CESM1.0 and MIROC3.2m. In these partial assimilation experiments, the climate models are constrained by observed temperature and salinity anomalies, one solely in the Atlantic basin and the other solely in the equatorial Pacific basin, but are allowed to evolve freely in other regions. These experiments demonstrate that, in addition to the tropical Pacific’s role in driving PDO variability, the Atlantic can affect PDO variability by modulating the tropical Pacific climate through two proposed processes. One is the equatorial pathway, in which tropical Atlantic sea surface temperature (SST) variability causes an El Niño-like SST response in the equatorial Pacific through the reorganization of the global Walker circulation. The other is the north tropical pathway, where low-frequency SST variability associated with the Atlantic Multidecadal Oscillation induces a Matsuno-Gill type atmospheric response in the tropical Atlantic-Pacific sectors north of the equator. These results provide a quantitative assessment suggesting that 12–29% of PDO variance originates from the Atlantic Ocean and 40–44% from the tropical Pacific. The remaining 27–48% of the variance is inferred to arise from other processes such as regional ocean-atmosphere interactions in the North Pacific and possibly teleconnections from the Indian Ocean.

The relative contributions of external forcing and internal processes to the observed spatial and temporal characteristics of “Atlantic Multidecadal Variability” (AMV) are still under debate. Here, the efficacy of the commonly‐used “global temperature residual method” for isolating the internal component of AMV is investigated by means of model Large Ensembles where the truth is known a priori. In this method, local sea surface temperature variability associated with global‐mean temperature (G) is removed via linear regression, and the residuals regressed upon the North Atlantic residual timeseries. We show that this method introduces spurious connections over the Indo‐Pacific due to the fact that G in any single realization includes both external and internal components: the latter dominated by influences from Pacific Decadal Variability independent of AMV. This methodological shortcoming can be overcome by using the forced component of G in the residual method applied to individual model realizations and to observations. Plain Language Summary The phenomenon known as “Atlantic Multidecadal Variability” (AMV) has been widely studied and has important climate effects over Africa, Asia, Europe, and eastern North America. Recently, there has been considerable debate regarding the relative contributions of natural processes within the coupled ocean‐atmosphere system versus human influences related to aerosol emissions on the spatial and temporal character of AMV. An accurate separation of these natural and anthropogenic contributions has proven challenging due to limitations of the data record. Statistical techniques aimed at isolating the natural component of AMV often yield conflicting results. Here, we investigate one widely‐used approach based on removing variability associated with global‐mean temperatures. We show that the conventional application of this method aliases another well‐known phenomenon, “Interdecadal Pacific Variability,” onto the pattern of natural AMV. However, with a simple modification to the method, the true pattern of natural AMV can be recovered. Key Points A common method for isolating the internal component of Atlantic Multidecadal Variability is to remove fluctuations associated with global‐mean temperature This method introduces spurious Indo‐Pacific connections in model Large Ensembles and observations due to internal variations in global‐mean temperature A revised method based on removing fluctuations associated with the forced component of global‐mean temperature mitigates this issue

Previous studies suggested that Atlantic Multidecadal Variability (AMV) modulations on pan‐Pacific sea surface temperature (SST) variability and prediction are model‐dependent. These results were mainly based on SST forcing experiments in which AMV‐related Atlantic SST anomalies were prescribed. However, the AMV itself is also model‐dependent, but its influences on the Pacific remain unclear. Here, we use multi‐model fully coupled experiments from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with observations, to study the model‐dependent AMV trans‐basin effects. We found that AMV strength is a key factor: Stronger (Weaker) model AMV than observations overestimates (underestimates) SST response and decadal prediction skills, mainly in the North Pacific. The reason is that stronger positive phased AMV, for example, leads to higher sea level pressure anomalies over the North Pacific, which lifts sea surface height and deepens thermocline to warm SST. Our study highlights the necessity to improve simulations of AMV strength. Plain Language Summary Pacific sea surface temperature (SST) decadal variability and prediction are important for economy and environment. The Atlantic Multidecadal Variability (AMV) was thought to significantly influence Pacific SST variability and prediction. However, the results of previous studies were model‐dependent. In this paper, we use fully coupled experiments of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to study what contributes to the model dependency. We found that the strength of the AMV is a key factor influencing AMV trans‐basin modulations on SST variability and decadal prediction, mainly in the North Pacific Ocean. The related mechanisms are discussed. Our paper sheds light on the way to improve North Pacific SST decadal prediction. Key Points Climate models show a strong inter‐model spread of Atlantic Multidecadal Variability (AMV) strength Models with stronger (weaker) AMV than observations overestimate (underestimate) North Pacific sea surface temperature (SST) response and decadal prediction The North Pacific SST response is primarily forced by AMV‐induced wind stress curl and ocean dynamics

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. Plain Language Summary North Atlantic sea surface temperatures, particularly in the subpolar region, are among the most predictable locations in the world's oceans. However, it remains uncertain if processes in the atmosphere or ocean are more important for driving temperature fluctuations in this region occurring over multiple decades. We use a machine learning approach to predict the sea surface temperature state from climate model outputs, given snapshots of atmospheric or oceanic variables. Ocean variables lead to more accurate predictions relative to atmospheric variables and standard prediction baselines out to 25 years ahead if processes that drive the trends in climate, such as human‐induced warming, are present in the data. These successful predictions arise consistently from the same region near the Gulf Stream‐North Atlantic Current region. Despite being trained on climate models, the neural networks can predict the timing of observed positive and negative states of real‐world sea surface temperatures, suggesting that there is potential for using model output to train neural networks at predicting the actual North Atlantic sea surface variability. Key Points Neural networks outperform persistence forecasts in predicting extreme states of North Atlantic sea surface temperature out to 25 years An explainable neural network technique reveals successful predictions rely consistently on the Transition Zone region Neural networks trained on climate model output predict the phasing of multidecadal variability on an observation‐based data set

Interannual sea surface temperature (SST) variability in the central equatorial Pacific consists of a component related to eastern Pacific SST variations (called Type-1 SST variability) and a component not related to them (called Type-2 SST variability). Lead–lagged regression and ocean surface-layer temperature balance analyses were performed to contrast their control mechanisms. Type-1 variability is part of the canonical, which is characterized by SST anomalies extending from the South American coast to the central Pacific, is coupled with the Southern Oscillation, and is associated with basinwide subsurface ocean variations. This type of variability is dominated by a major 4–5-yr periodicity and a minor biennial (2–2.5 yr) periodicity. In contrast, Type-2 variability is dominated by a biennial periodicity, is associated with local air–sea interactions, and lacks a basinwide anomaly structure. In addition, Type-2 SST variability exhibits a strong connection to the subtropics of both hemispheres, particularly the Northern Hemisphere. Type-2 SST anomalies appear first in the northeastern subtropical Pacific and later spread toward the central equatorial Pacific, being generated in both regions by anomalous surface heat flux forcing associated with wind anomalies. The SST anomalies undergo rapid intensification in the central equatorial Pacific through ocean advection processes, and eventually decay as a result of surface heat flux damping and zonal advection. The southward spreading of trade wind anomalies within the northeastern subtropics-to-central tropics pathway of Type-2 variability is associated with intensity variations of the subtropical high. Type-2 variability is found to become stronger after 1990, associated with a concurrent increase in the subtropical variability. It is concluded that Type-2 interannual variability represents a subtropical-excited phenomenon that is different from the conventional ENSO Type-1 variability.

In a recent paper, we argued that ocean dynamics increase the variability of midlatitude sea surface temperatures (SSTs) on monthly to interannual time scales, but act to damp lower-frequency SST variability over broad midlatitude regions. Here, we use two configurations of a simple stochastic climate model to provide new insights into this important aspect of climate variability. The simplest configuration includes the forcing and damping of SST variability by observed surface heat fluxes only, and the more complex configuration includes forcing and damping by ocean processes, which are estimated indirectly from monthly observations. It is found that the simple model driven only by the observed surface heat fluxes generally produces midlatitude SST power spectra that are too red compared to observations. Including ocean processes in the model reduces this discrepancy by whitening the midlatitude SST spectra. In particular, ocean processes generally increase the SST variance on <2-yr time scales and decrease it on >2-yr time scales. This happens because oceanic forcing increases the midlatitude SST variance across many time scales, but oceanic damping outweighs oceanic forcing on >2-yr time scales, particularly away from the western boundary currents. The whitening of midlatitude SST variability by ocean processes also operates in NCAR’s Community Earth System Model (CESM). That is, midlatitude SST spectra are generally redder when the same atmospheric model is coupled to a slab rather than dynamically active ocean model. Overall, the results suggest that forcing and damping by ocean processes play essential roles in driving midlatitude SST variability.