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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.

 

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.

 

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.

 

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.

 

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 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.

 

El Niño-Southern Oscillation (ENSO) sea surface temperature (SST) anomaly skewness encapsulates the nonlinear processes of strong ENSO events and affects future climate projections. Yet, its response to CO2 forcing remains not well understood. Here, we find ENSO skewness hysteresis in a large ensemble CO2 removal simulation. The positive SST skewness in the central-to-eastern tropical Pacific gradually weakens (most pronounced near the dateline) in response to increasing CO2, but weakens even further once CO2 is ramped down. Further analyses reveal that hysteresis of the Intertropical Convergence Zone migration leads to more active and farther eastward-located strong eastern Pacific El Niño events, thus decreasing central Pacific ENSO skewness by reducing the amplitude of the central Pacific positive SST anomalies and increasing the scaling effect of the eastern Pacific skewness denominator, i.e., ENSO intensity, respectively. The reduction of eastern Pacific El Niño maximum intensity, which is constrained by the SST zonal gradient of the projected background El Niño-like warming pattern, also contributes to a reduction of eastern Pacific SST skewness around the CO2 peak phase. This study highlights the divergent responses of different strong El Niño regimes in response to climate change. 

The Pacific–North American (PNA) teleconnection pattern is one of the prominent atmospheric circulation modes in the extratropical Northern Hemisphere, and its seasonal to interannual predictability is suggested to originate from El Niño–Southern Oscillation (ENSO). Intriguingly, the PNA teleconnection pattern exhibits variance at near-annual frequencies, which is related to a rapid phase reversal of the PNA pattern during ENSO years, whereas the ENSO sea surface temperature (SST) anomalies in the tropical Pacific are evolving much slower in time. This distinct seasonal feature of the PNA pattern can be explained by an amplitude modulation of the interannual ENSO signal by the annual cycle (i.e., the ENSO combination mode). The ENSO-related seasonal phase transition of the PNA pattern is reproduced well in an atmospheric general circulation model when both the background SST annual cycle and ENSO SST anomalies are prescribed. In contrast, this characteristic seasonal evolution of the PNA pattern is absent when the tropical Pacific background SST annual cycle is not considered in the modeling experiments. The background SST annual cycle in the tropical Pacific modulates the ENSO-associated tropical Pacific convection response, leading to a rapid enhancement of convection anomalies in winter. The enhanced convection results in a fast establishment of the large-scale PNA teleconnection during ENSO years. The dynamics of this ENSO–annual cycle interaction fills an important gap in our understanding of the seasonally modulated PNA teleconnection pattern during ENSO years.