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Abstract Pacific decadal variability (PDV), reflected in low‐frequency Pacific sea surface temperature (SST) changes, impacts global climate. Disentangling anthropogenic effects upon PDV is challenging because PDV and anthropogenic forcing vary on similar time scales. Using single‐forcing climate model large ensembles, we find that anthropogenic forcing drives a spatially varying pattern of mean‐state change in Pacific SST that projects onto PDV patterns, principally the Pacific Decadal Oscillation (PDO) and the North Pacific Gyre Oscillation (NPGO). When the trend is removed by subtracting the ensemble mean, there is no forced change of either PDV mode. However, analysis of individual ensemble members, where the mean‐state trend cannot be cleanly removed, yields apparent anthropogenic changes in PDO and NPGO decadal variability. This suggests that observed PDV responses to anthropogenic forcing may be erroneously convolved with the background trend pattern. Therefore, correctly determining the mean‐state trend is a necessary precursor for identifying possible forced changes to PDV. 

Abstract Stratospheric aerosol injection (SAI) and marine cloud brightening (MCB) are two proposed methods of compensating for greenhouse gas‐induced warming by reflecting incoming solar radiation. However, their effects on the El Niño–Southern Oscillation (ENSO), a critical mode of climate variability, are poorly understood. Here we use ensembles of climate model simulations to show that deploying MCB in the subtropical eastern Pacific dramatically reduces ENSO amplitude by approximately 61%, while SAI has a negligible impact. MCB increases cloud albedo, which cools the subtropical eastern Pacific and triggers a loss of moist static energy. This cooling promotes atmospheric subsidence, dries the tropical Pacific, and intensifies the trade winds. The ultimate effect is a dramatic reduction in all air‐sea feedback processes operating during ENSO, which we demonstrate using a mixed‐layer heat budget. This contrast between the MCB and SAI impacts on ENSO shows that the choice of climate intervention strategy used to mitigate global warming has drastic regional implications. 

Abstract The recharge oscillator (RO) is a simple mathematical model of the El Niño Southern Oscillation (ENSO). In its original form, it is based on two ordinary differential equations that describe the evolution of equatorial Pacific sea surface temperature and oceanic heat content. These equations make use of physical principles that operate in nature: (a) the air‐sea interaction loop known as the Bjerknes feedback, (b) a delayed oceanic feedback arising from the slow oceanic response to winds within the equatorial band, (c) state‐dependent stochastic forcing from fast wind variations known as westerly wind bursts (WWBs), and (d) nonlinearities such as those related to deep atmospheric convection and oceanic advection. These elements can be combined at different levels of RO complexity. The RO reproduces ENSO key properties in observations and climate models: its amplitude, dominant timescale, seasonality, and warm/cold phases amplitude asymmetry. We discuss the RO in the context of timely research questions. First, the RO can be extended to account for ENSO pattern diversity (with events that either peak in the central or eastern Pacific). Second, the core RO hypothesis that ENSO is governed by tropical Pacific dynamics is discussed from the perspective of influences from other basins. Finally, we discuss the RO relevance for studying ENSO response to climate change, and underline that accounting for ENSO diversity, nonlinearities, and better links of RO parameters to the long term mean state are important research avenues. We end by proposing important RO‐based research problems. 

Abstract Understanding and forecasting Tropical Pacific Decadal‐scale Variability (TPDV) strongly rely on climate model simulations. Using a Linear Inverse Modeling (LIM) diagnostic approach, we reveal Coupled Model Intercomparison Project Phase 6 models have significant challenges in reproducing the spatial structure and dominant mechanisms of TPDV. Specifically, while the models' ensemble mean pattern of TPDV resembles that of observations, the spread across models is very large and most models show significant differences from observations. In observations, removing the coupling between extratropics and tropics reduces TPDV by ∼60%–70%, and removing the tropical thermocline variability makes the central tropical Pacific a key center of action for TPDV and El Niño Southern Oscillation variability. These characteristics are only confirmed in a subset of models. Differences between observations and simulations are outside the range of natural internal TPDV noise and pose important questions regarding our ability to model the impacts of natural internal low‐frequency variability superimposed on long‐term climate change. 

Abstract Assessing uncertainty in future climate projections requires understanding both internal climate variability and external forcing. For this reason, single‐model initial condition large ensembles (SMILEs) run with Earth System Models (ESMs) have recently become popular. Here we present a new 20‐member SMILE with the Energy Exascale Earth System Model version 1 (E3SMv1‐LE), which uses a “macro” initialization strategy choosing coupled atmosphere/ocean states based on inter‐basin contrasts in ocean heat content (OHC). The E3SMv1‐LE simulates tropical climate variability well, albeit with a muted warming trend over the twentieth century due to overly strong aerosol forcing. The E3SMv1‐LE's initial climate spread is comparable to other (larger) SMILEs, suggesting that maximizing inter‐basin ocean heat contrasts may be an efficient method of generating ensemble spread. We also compare different ensemble spread across multiple SMILEs, using surface air temperature and OHC. The Community Earth system Model version 1, the only ensemble which utilizes a “micro” initialization approach perturbing only atmospheric initial conditions, yields lower spread in the first ∼30 years. The E3SMv1‐LE exhibits a relatively large spread, with some evidence for anthropogenic forcing influencing spread in the late twentieth century. However, systematic effects of differing “macro” initialization strategies are difficult to detect, possibly resulting from differing model physics or responses to external forcing. Notably, the method of standardizing results affects ensemble spread: control simulations for most models have either large background trends or multi‐centennial variability in OHC. This spurious disequlibrium behavior is a substantial roadblock to understanding both internal climate variability and its response to forcing. 

Tropical Instability Waves (TIWs) dominate intraseasonal variability in the tropical Pacific Ocean, strongly impacting climate variability and marine ecosystems. However, their response to greenhouse warming remains uncertain because most current climate models cannot resolve them well. Using a suite of high-resolution climate model simulations capable of representing TIWs, we identify two consistent and distinct mechanisms driving the response of TIWs to CO₂increases. North of the equator, TIW activity intensifies under higher CO₂due to enhanced meridional shear of the prevailing zonal currents during boreal fall. Along the equator, TIW activity weakens and shifts slightly westward, driven by a reduced meridional temperature gradient and shoaling of the Equatorial Undercurrent. These changes result in a robust decrease in TIW-driven temperature variability and associated eddy dynamical heating along the equator, underscoring their importance for constraining both the magnitude and spatial pattern of future tropical Pacific warming. 

The equatorial Pacific sea surface temperature (SST) zonal gradient has worldwide impacts and is expected to be highly sensitive to future climate change. However, biases in climate models call the reliability of future SST gradient projections into question. Here, we combine multiple climate model Large Ensembles to show that equatorial precipitation and cloud feedbacks have a controlling influence on the future Pacific SST gradient. An “SST gradient sensitivity” parameter is computed for each model, which shows that models with stronger historical equatorial precipitation have systematically higher sensitivities (more El Nino-like changes). This arises from the stronger negative SST-shortwave radiation feedback, which then creates a wind response that favors El Nino–like warming. Notably, when simulated historical deep convection is sufficiently strong, a “saturation” effect occurs that tends to inhibit this effect. These results imply that models likely underestimate future El Nino–like changes but that the “true” magnitude of changes may be predictable. 

Abstract. This work assesses a recently produced 21-member climate model large ensemble (LE) based on the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 2 (E3SM2). The ensemble spans the historical era (1850 to 2014) and 21st century (2015 to 2100), using the SSP370 pathway, allowing for an evaluation of the model's forced response. A companion 500-year preindustrial control simulation is used to initialize the ensemble and estimate drift. Characteristics of the LE are documented and compared against other recently produced ensembles using the E3SM version 1 (E3SM1) and Community Earth System Model (CESM) versions 1 and 2. Simulation drift is found to be smaller, and model agreement with observations is higher in versions 2 of E3SM and CESM versus their version 1 counterparts. Shortcomings in E3SM2 include a lack of warming from the mid to late 20th century, likely due to excessive cooling influence of anthropogenic sulfate aerosols, an issue also evident in E3SM1. Associated impacts on the water cycle and energy budgets are also identified. Considerable model dependence in the response to both aerosols and greenhouse gases is documented and E3SM2's sensitivity to variable prescribed biomass burning emissions is demonstrated. Various E3SM2 and CESM2 model benchmarks are found to be on par with the highest-performing recent generation of climate models, establishing the E3SM2 LE as an important resource for estimating climate variability and responses, though with various caveats as discussed herein. As an illustration of the usefulness of LEs in estimating the potential influence of internal variability, the observed CERES-era trend in net top-of-atmosphere flux is compared to simulated trends and found to be much larger than the forced response in all LEs, with only a few members exhibiting trends as large as observed, thus motivating further study.