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We present new climate field reconstructions (CFR) of tropical Pacific ENSO sea surface temperatures (HadISST) for the boreal winter season using a circum‐Pacific tree‐ring network from known El Niño rainfall impact regions. We use two different CFR methods: Point‐by‐Point Regression (PPR) and reduced‐space Orthogonal Spatial Regression (OSR). Both methods produce reconstructions with high validation skill, but OSR is preferred because it has less spatial noise and is more efficient. Only the leading EOF of the SST field (EOF1) can be skillfully reconstructed by either method; EOF2 does not validate. The success of EOF1 reflects its importance for ENSO rainfall impacts over land; the failure with EOF2 is from the lack of these impacts. EOF1 allows for the reconstruction of many ENSO indices, including the ENSO Longitudinal Index (ELI). We also find evidence in our reconstructions for a recent increase in ENSO activity.

 

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. 

Most current climate models predict that the equatorial Pacific will evolve under greenhouse gas–induced warming to a more El Niño-like state over the next several decades, with a reduced zonal sea surface temperature gradient and weakened atmospheric Walker circulation. Yet, observations over the last 50 y show the opposite trend, toward a more La Niña-like state. Recent research provides evidence that the discrepancy cannot be dismissed as due to internal variability but rather that the models are incorrectly simulating the equatorial Pacific response to greenhouse gas warming. This implies that projections of regional tropical cyclone activity may be incorrect as well, perhaps even in the direction of change, in ways that can be understood by analogy to historical El Niño and La Niña events: North Pacific tropical cyclone projections will be too active, North Atlantic ones not active enough, for example. Other perils, including severe convective storms and droughts, will also be projected erroneously. While it can be argued that these errors are transient, such that the models’ responses to greenhouse gases may be correct in equilibrium, the transient response is relevant for climate adaptation in the next several decades. Given the urgency of understanding regional patterns of climate risk in the near term, it would be desirable to develop projections that represent a broader range of possible future tropical Pacific warming scenarios—including some in which recent historical trends continue—even if such projections cannot currently be produced using existing coupled earth system models. 

Five out of six La Niña events since 1998 have lasted two to three years. Why so many long-lasting multiyear La Niña events have emerged recently and whether they will become more common remains unknown. Here we show that ten multiyear La Niña events over the past century had an accelerated trend, with eight of these occurring after 1970. The two types of multiyear La Niña events over this time period followed either a super El Niño or a central Pacific El Niño. We find that multiyear La Niña events differ from single-year La Niñas by a prominent onset rate, which is rooted in the western Pacific warming-enhanced zonal advective feedback for the central Pacific multiyear La Niña events type and thermocline feedback for the super El Niño multiyear La Niña events type. The results from large ensemble climate simulations support the observed multiyear La Niña events–western Pacific warming link. More multiyear La Niña events will exacerbate adverse socioeconomic impacts if the western Pacific continues to warm relative to the central Pacific.

 

The trends over recent decades in tropical Pacific sea surface and upper ocean temperature are examined in observations-based products, an ocean reanalysis and the latest models from the Coupled Model Intercomparison Project phase six and the Multimodel Large Ensembles Archive. Comparison is made using three metrics of sea surface temperature (SST) trend—the east–west and north–south SST gradients and a pattern correlation for the equatorial region—as well as change in thermocline depth. It is shown that the latest generation of models persist in not reproducing the observations-based SST trends as a response to radiative forcing and that the latter are at the far edge or beyond the range of modeled internal variability. The observed combination of thermocline shoaling and lack of warming in the equatorial cold tongue upwelling region is similarly at the extreme limit of modeled behavior. The persistence over the last century and a half of the observed trend toward an enhanced east–west SST gradient and, in four of five observed gridded datasets, to an enhanced equatorial north–south SST gradient, is also at the limit of model behavior. It is concluded that it is extremely unlikely that the observed trends are consistent with modeled internal variability. Instead, the results support the argument that the observed trends are a response to radiative forcing in which an enhanced east–west SST gradient and thermocline shoaling are key and that the latest generation of climate models continue to be unable to simulate this aspect of climate change.

 

Abstract This paper attempts to enhance our understanding of the causes of Atlantic Multidecadal Variability, the AMV. Following the literature, we define the AMV as the SST averaged over the North Atlantic basin, linearly detrended and low-pass filtered. There is an ongoing debate about the drivers of the AMV, which include internal variability generated from the ocean or atmosphere (or both), and external radiative forcing. We test the role of these factors in explaining the time history, variance, and spatial pattern of the AMV using a 41-member ensemble from a fully coupled version of CESM and a 10-member ensemble of the CESM atmosphere coupled to a slab ocean. The large ensemble allows us to isolate the role of external forcing versus internal variability, and the model differences allow us to isolate the role of coupled ocean circulation. Both with and without coupled ocean circulation, external forcing explains more than half of the variance of the observed AMV time series, indicating its important role in simulating the 20 th century AMV phases. In this model the net effect of ocean processes is to reduce the variance of the AMV. Dynamical ocean coupling also reduces the ability of the model to simulate the characteristic spatial pattern of the AMV, but forcing has little impact on the pattern. Historical forcing improves the time history and variance of the AMV simulation, whilst the more realistic ocean representation reduces the variance below that observed and lowers the correlation with observations. 

The North Atlantic Oscillation (NAO) is predictable in climate models at near-decadal timescales. Predictive skill derives from ocean initialization, which can capture variability internal to the climate system, and from external radiative forcing. Herein, we show that predictive skill for the NAO in a very large uninitialized multi-model ensemble is commensurate with previously reported skill from a state-of-the-art initialized prediction system. The uninitialized ensemble and initialized prediction system produce similar levels of skill for northern European precipitation and North Atlantic SSTs. Identifying these predictable components becomes possible in a very large ensemble, confirming the erroneously low signal-to-noise ratio previously identified in both initialized and uninitialized climate models. Though the results here imply that external radiative forcing is a major source of predictive skill for the NAO, they also indicate that ocean initialization may be important for particular NAO events (the mid-1990s strong positive NAO), and, as previously suggested, in certain ocean regions such as the subpolar North Atlantic ocean. Overall, we suggest that improving climate models’ response to external radiative forcing may help resolve the known signal-to-noise error in climate models.