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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 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

Tree‐ring width (RW) records primarily capture low‐frequency temperature variability, yet resolving high‐frequency signals is critical for testing climate models and contextualizing modern extremes. Blue rings (BRs)—bands of unlignified cells revealed by micro‐anatomical staining—capture short‐lived cooling events. Using 83 Pinus longaeva cores, we present the first millennial‐length (900–2014 CE) BR chronology and explore its paleoclimatic significance. BRs are largely decoupled from growth reductions: they often occur in rings of normal or above‐average width, preceding RW minima by one year, recording abrupt late‐season cooling that does not immediately suppress growth but frequently triggers reduced growth the following year. Events include anomalies associated with major volcanic eruptions, whose impacts are delayed and smoothed in RW chronologies. By capturing transient temperature declines with annual precision, BRs provide a novel dendroclimatic proxy that bridges low‐frequency and sub‐seasonal climate signals, offering benchmarks for climate‐model evaluation and attribution under past and contemporary climate change.

Climate variability is typically amplified towards polar regions. The underlying causes, notably albedo and humidity changes, are challenging to accurately quantify with observations or models, thus hampering projections of future polar amplification. Polar amplification reconstructions from the ice-free early Eocene (∼56–48 Ma) can exclude ice albedo effects, but the required tropical temperature records for resolving timescales shorter than multi-million years are lacking. Here, we reconstruct early Eocene tropical sea surface temperature variability by presenting an up to ∼4 kyr resolution biomarker-based temperature record from Ocean Drilling Program (ODP) Site 959, located in the tropical Atlantic Ocean. This record shows warming across multiple orbitally paced carbon cycle perturbations, coeval with high-latitude-derived deep-ocean bottom waters, showing that these events represent transient global warming events (hyperthermals). This implies that orbital forcing caused global temperature variability through carbon cycle feedbacks. Importantly, deep-ocean temperature variability was amplified by a factor of 1.7–2.3 compared to the tropical surface ocean, corroborating available long-term estimates. This implies that fast atmospheric feedback processes controlled meridional temperature gradients on multi-million year, as well as orbital, timescales during the early Eocene. Our combined records have several other implications. First, our amplification factor is somewhat larger than the same metric in fully coupled simulations of the early Eocene (1.1–1.3), suggesting that models slightly underestimate the non-ice-related – notably hydrological – feedbacks that cause polar amplification of climate change. Second, even outside the hyperthermals, we find synchronous eccentricity-forced temperature variability in the tropics and deep ocean that represent global mean sea surface temperature variability of up to 0.7 °C, which requires significant variability in atmospheric pCO2. We hypothesize that the responsible carbon cycle feedbacks that are independent of ice, snow, and frost-related processes might play an important role in Phanerozoic orbital-scale climate variability throughout geological time, including Pleistocene glacial–interglacial climate variability.

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.

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

A key question in climate modeling is to what extent sea surface temperature and upper-ocean heat content are driven passively by air–sea heat fluxes, as opposed to forcing by ocean dynamics. This paper investigates the question using a climate model at different resolutions, and observations, for monthly variability. At the grid scale in a high-resolution climate model with resolved mesoscale ocean eddies, ocean dynamics (i.e., ocean heat flux convergence) dominates upper 50m heat content variability over most of the globe. For deeper depths of integration to 400m, the heat content variability at the grid scale is almost totally controlled by ocean heat flux convergence. However, a strong dependence on spatial scale is found—for the upper 50m of ocean, after smoothing the data to around 78, air–sea heat fluxes, augmented by Ekman heat transports, dominate. For deeper depths of integration to 400m, the transition scale becomes larger and is above 108 in western boundary currents. Comparison of climate model results with observations show that the small-scale influence of ocean intrinsic variability is well captured by the highresolution model but is missing from a comparable model with parameterized ocean-eddy effects. In the deep tropics, ocean dynamics dominates in all cases and all scales. In the subtropical gyres at large scales, air–sea heat fluxes play the biggest role. In the midlatitudes, at large scales .108, atmosphere-driven air– sea heat fluxes and Ekman heat transport variability are the dominant processes except in the western boundary currents for the 400m heat content.

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.