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Abstract Under anthropogenic warming, future changes to climate variability beyond specific modes such as the El Niño-Southern Oscillation (ENSO) have not been well-characterized. In the Community Earth System Model version 2 Large Ensemble (CESM2-LE) climate model, the future change to sea surface temperature (SST) variability (and correspondingly marine heatwave intensity) on monthly timescales and longer is spatially heterogeneous. We examined these projected changes (between 1960–2000 and 2060–2100) in the North Pacific using a local linear stochastic-deterministic model, which allowed us to quantify the effect of changes to three drivers on SST variability: ocean “memory” (the SST damping timescale), ENSO teleconnections, and stochastic noise forcing. The ocean memory declines in most areas, but lengthens in the central North Pacific. This change is primarily due to changes in air-sea feedbacks and ocean damping, with the shallowing mixed layer depth playing a secondary role. An eastward shift of the ENSO teleconnection pattern is primarily responsible for the pattern of SST variance change. 

Abstract Recent marine heatwaves in the Gulf of Alaska have had devastating impacts on species from various trophic levels. Due to climate change, total heat exposure in the upper ocean has become longer, more intense, more frequent, and more likely to happen at the same time as other environmental extremes. The combination of multiple environmental extremes can exacerbate the response of sensitive marine organisms. Our hindcast simulation provides the first indication that more than 20% of the bottom water of the Gulf of Alaska continental shelf was exposed to quadruple heat, positive hydrogen ion concentration [H⁺], negative aragonite saturation state (Ω_arag), and negative oxygen concentration [O₂] compound extreme events during the 2018–2020 marine heat wave. Natural intrusion of deep and acidified water combined with the marine heat wave triggered the first occurrence of these events in 2019. During the 2013–2016 marine heat wave, surface waters were already exposed to widespread marine heat and positive [H⁺] compound extreme events due to the temperature effect on the [H⁺]. We introduce a new Gulf of Alaska Downwelling Index (GOADI) with short‐term predictive skill, which can serve as indicator of past and near‐future positive [H⁺], negative Ω_arag, and negative [O₂] compound extreme events near the shelf seafloor. Our results suggest that the marine heat waves may have not been the sole environmental stressor that led to the observed ecosystem impacts and warrant a closer look at existing in situ inorganic carbon and other environmental data in combination with biological observations and model output. 

Abstract Climate variability has distinct spatial patterns with the strongest signal of sea surface temperature (SST) variance residing in the tropical Pacific. This interannual climate phenomenon, the El Niño-Southern Oscillation (ENSO), impacts weather patterns across the globe via atmospheric teleconnections. Pronounced SST variability, albeit of smaller amplitude, also exists in the other tropical basins as well as in the extratropical regions. To improve our physical understanding of internal climate variability across the global oceans, we here make the case for a conceptual model hierarchy that captures the essence of observed SST variability from subseasonal to decadal timescales. The building blocks consist of the classic stochastic climate model formulated by Klaus Hasselmann, a deterministic low-order model for ENSO variability, and the effect of the seasonal cycle on both of these models. This model hierarchy allows us to trace the impacts of seasonal processes on the statistics of observed and simulated climate variability. One of the important outcomes of ENSO’s interaction with the seasonal cycle is the generation of a frequency cascade leading to deterministic climate variability on a wide range of timescales, including the near-annual ENSO Combination Mode. Using the aforementioned building blocks, we arrive at a succinct conceptual model that delineates ENSO’s ubiquitous climate impacts and allows us to revisit ENSO’s observed statistical relationships with other coherent spatio-temporal patterns of climate variability—so called empirical modes of variability. We demonstrate the importance of correctly accounting for different seasonal phasing in the linear growth/damping rates of different climate phenomena, as well as the seasonal phasing of ENSO teleconnections and of atmospheric noise forcings. We discuss how previously some of ENSO’s relationships with other modes of variability have been misinterpreted due to non-intuitive seasonal cycle effects on both power spectra and lead/lag correlations. Furthermore, it is evident that ENSO’s impacts on climate variability outside the tropical Pacific are oftentimes larger than previously recognized and that accurately accounting for them has important implications. For instance, it has been shown that improved seasonal prediction skill can be achieved in the Indian Ocean by fully accounting for ENSO’s seasonally modulated and temporally integrated remote impacts. These results move us to refocus our attention to the tropical Pacific for understanding global patterns of climate variability and their predictability. 

Abstract Biomass burning aerosol (BBA) emissions in the Coupled Model Intercomparison Project phase 6 (CMIP6) historical forcing fields have enhanced temporal variability during the years 1997–2014 compared to earlier periods. Recent studies document that the corresponding inhomogeneous shortwave forcing over this period can cause changes in clouds, permafrost, and soil moisture, which contribute to a net terrestrial Northern Hemisphere warming relative to earlier periods. Here, we investigate the ocean response to the hemispherically asymmetric warming, using a 100-member ensemble of the Community Earth System Model version 2 Large Ensemble forced by two different BBA emissions (CMIP6 default and temporally smoothed over 1990–2020). Differences between the two subensemble means show that ocean temperature anomalies occur during periods of high BBA variability and subsequently persist over multiple decades. In the North Atlantic, surface warming is efficiently compensated for by decreased northward oceanic heat transport due to a slowdown of the Atlantic meridional overturning circulation. In the North Pacific, surface warming is compensated for by an anomalous cross-equatorial cell (CEC) that reduces northward oceanic heat transport. The heat that converges in the South Pacific through the anomalous CEC is shunted into the subsurface and contributes to formation of long-lasting ocean temperature anomalies. The anomalous CEC is maintained through latitude-dependent contributions from narrow western boundary currents and basinwide near-surface Ekman transport. These results indicate that interannual variability in forcing fields may significantly change the background climate state over long time scales, presenting a potential uncertainty in CMIP6-class climate projections forced without interannual variability. 

Abstract Variations of sea-surface temperature (SST) in the subtropical North Pacific have received considerable attention due to their potential role as a precursor of El Niño-Southern Oscillation (ENSO) events in the tropical Pacific as well as their role in regional climate impacts. These subtropical SST variations, known as the North Pacific Meridional Mode (PMM), are thought to be triggered by extratropical atmospheric forcing and amplified by air-sea coupling involving surface winds, evaporation, and SST. The PMM is often defined through a statistical technique called maximum covariance analysis (MCA) that identifies patterns of maximum covariability between SST and surface winds. Here we show that SST alone is sufficient to reproduce the MCA-based PMM index with near-perfect correlation. This dominance of the SST suggests that the MCA-based definition of the PMM may not be ideally suited for capturing two-way wind-SST interaction or, alternatively, that this interaction is relatively weak. We further show that the MCA-based PMM definition conflates intrinsic subtropical and remote ENSO variability, thereby undermining its interpretation as an ENSO precursor. Our findings indicate that, while air-sea coupling may be important for variability in the subtropical North Pacific, it cannot be reliably identified by the MCA-based definition of the PMM. This highlights the need for refined tools to diagnose variability in the subtropical North Pacific. 

Abstract Feedbacks from tropical instability waves (TIWs) on the seasonal cycle of the eastern Pacific Ocean are studied using two eddy‐rich ocean simulations, with and without TIWs. By warming the equatorial waters by up to 0.4°C through nonlinear advection in boreal summer and fall, TIWs reduce the amplitude of the seasonal cycle in upper ocean temperatures. In addition, TIWs stabilize the upper part of the Equatorial Undercurrent (EUC) through enhanced barotropic energy conversion, leading to a year‐round weakening by −0.15 m s⁻¹and preventing an unrealistic re‐intensification in boreal fall usually found in non‐eddy resolving models. A coarser simulation at 1‐degree horizontal resolution fails to reproduce the TIW‐induced nonlinear warming of equatorial waters, but succeeds in inhibiting the EUC re‐intensification. This suggests a threshold effect in TIW strength, associated with the model's ability to simulate eddies, which may be responsible for long‐standing biases displayed by global climate models in this region. 

The El Niño–Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill, yet, quantifying the sources of skilful predictions is a long-standing challenge. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible, limiting our understanding of the dynamics underpinning the advancements. Here we show that an extended nonlinear recharge oscillator (XRO) model shows skilful ENSO forecasts at lead times up to 16–18 months, better than global climate models and comparable to the most skilful artificial intelligence forecasts. The XRO parsimoniously incorporates the core ENSO dynamics and ENSO’s seasonally modulated interactions with other modes of variability in the global oceans. The intrinsic enhancement of ENSO’s long-range forecast skill is traceable to the initial conditions of other climate modes by means of their memory and interactions with ENSO and is quantifiable in terms of these modes’ contributions to ENSO amplitude. Reforecasts using the XRO trained on climate model output show that reduced biases in both model ENSO dynamics and in climate mode interactions can lead to more skilful ENSO forecasts. The XRO framework’s holistic treatment of ENSO’s global multi-timescale interactions highlights promising targets for improving ENSO simulations and forecasts. 

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)—key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO ${}_2$forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.