Decreased Indian Ocean Dipole variability under prolonged greenhouse warming.

The Indian Ocean Dipole (IOD) is a major climate variability mode that substantially influences weather extremes and climate patterns worldwide. However, the response of IOD variability to anthropogenic global warming remains highly uncertain. The latest IPCC Sixth Assessment Report concluded that human influences on IOD variability are not robustly detected in observations and twenty-first century climate-model projections. Here, using millennial-length climate simulations, we disentangle forced response and internal variability in IOD change and show that greenhouse warming robustly suppresses IOD variability. On a century time scale, internal variability overwhelms the forced change in IOD, leading to a widespread response in IOD variability. This masking effect is mainly caused by a remote influence of the El Niño-Southern Oscillation. However, on a millennial time scale, nearly all climate models show a long-term weakening trend in IOD variability by greenhouse warming. Our results provide compelling evidence for a human influence on the IOD.

Hysteresis of the El Niño-Southern Oscillation to CO2 forcing.

El Niño-Southern Oscillation (ENSO) is the strongest interannual climate variability with far-reaching socioeconomic consequences. Many studies have investigated ENSO-projected changes under future greenhouse warming, but its responses to plausible mitigation behaviors remain unknown. We show that ENSO sea surface temperature (SST) variability and associated global teleconnection patterns exhibit strong hysteretic responses to carbon dioxide (CO2) reduction based on the 28-member ensemble simulations of the CESM1.2 model under an idealized CO2 ramp-up and ramp-down scenario. There is a substantial increase in the ensemble-averaged eastern Pacific SST anomaly variance during the ramp-down period compared to the ramp-up period. Such ENSO hysteresis is mainly attributed to the hysteretic response of the tropical Pacific Intertropical Convergence Zone meridional position to CO2 removal and is further supported by several selected single-member Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations. The presence of ENSO hysteresis leads to its amplified and prolonged impact in a warming climate, depending on the details of future mitigation pathways.

Probabilistic projections of El Niño Southern Oscillation properties accounting for model dependence and skill.

The El Niño - Southern Oscillation (ENSO) is a dominant mode of global climate variability. Nevertheless, future multi-model probabilistic projections of ENSO properties have not yet been made. Main roadblocks that have been hindering making these projections are climate model dependence and difficulty in quantifying historical model performance. Dependence is broadly defined as similarity between climate model output, assumptions, or physical parameterizations. Here, we propose a unifying metric of relative model performance, based on the probability density function (PDF) of ENSO paths. This metric is applied to assess the overall skill of Climate Model Intercomparison Project phase 6 (CMIP6) climate models at capturing ENSO. We then perform future multi-model probabilistic projections of changes in ENSO properties (from years 1850-1949 to 2040-2099) under the shared socioeconomic pathway scenario SSP585, accounting for model skill and dependence. We find that future ENSO will likely be more seasonally locked (89% chance), and have a longer period (67% chance). Yet, the jury is still out on future ENSO amplification. Our method reduces uncertainty by up to 37% compared to a simple approach ignoring model dependence and skill.

A flexible data-driven cyclostationary model for the probability density of El Niño-Southern Oscillation.

Model simulations of El Niño-Southern Oscillation (ENSO) are usually evaluated by comparing them to observations using a multitude of metrics. However, this approach cannot provide an objective summary metric of model performance. Here, we propose that such an objective model evaluation should involve comparing the full joint probability density functions (pdf's) of ENSO. For simplicity, ENSO state is defined here as sea surface temperature anomalies over the Niño 3 region and equatorial Pacific thermocline depth anomalies. We argue that all ENSO metrics are a function of the joint pdf, the latter fully specifying the underlying stochastic process. Unfortunately, there is a lack of methods to recover the joint ENSO pdf from climate models or observations. Here, we develop a data-driven stochastic model for ENSO that allows for an analytic solution of the non-Markov non-Gaussian cyclostationary ENSO pdf. We show that the model can explain relevant ENSO features found in the observations and can serve as an ENSO simulator. We demonstrate that the model can reasonably approximate ENSO in most GCMs and is useful at exploring the internal ENSO variability. The general approach is not limited to ENSO and could be applied to other cyclostationary processes.

A novel approach for discovering stochastic models behind data applied to El Niño-Southern Oscillation.

Stochastic differential equations (SDEs) are ubiquitous across disciplines, and uncovering SDEs driving observed time series data is a key scientific challenge. Most previous work on this topic has relied on restrictive assumptions, undermining the generality of these approaches. We present a novel technique to uncover driving probabilistic models that is based on kernel density estimation. The approach relies on few assumptions, does not restrict underlying functional forms, and can be used even on non-Markov systems. When applied to El Niño-Southern Oscillation (ENSO), the fitted empirical model simulations can almost perfectly capture key time series properties of ENSO. This confirms that ENSO could be represented as a two-variable stochastic dynamical system. Our experiments provide insights into ENSO dynamics and suggest that state-dependent noise does not play a major role in ENSO skewness. Our method is general and can be used across disciplines for inverse and forward modeling, to shed light on structure of system dynamics and noise, to evaluate system predictability, and to generate synthetic datasets with realistic properties.

Fokker-Planck dynamics of the El Niño-Southern Oscillation.

The asymmetric nature of the El Niño-Southern Oscillation (ENSO) is explored by using a probabilistic model (PROM) for ENSO. Based on a Fokker-Planck Equation (FPE), PROM describes the dynamics of a nonlinear stochastic ENSO recharge oscillator model for eastern equatorial Pacific temperature anomalies and equatorial Pacific basin-averaged thermocline depth changes. Eigen analyses of PROM provide new insights into the stationary and oscillatory solutions of the stochastic dynamical system. The first probabilistic eigenmode represents a stationary mode, which exhibits the asymmetric features of ENSO, in case deterministic nonlinearities or multiplicative noises are included. The second mode is linked to the oscillatory nature of ENSO and represents a cyclic asymmetric probability distribution, which emerges from the key dynamical processes. Other eigenmodes are associated with the temporal evolution of higher order statistical moments of the ENSO system. The model solutions demonstrate that the deterministic nonlinearity plays a stronger role in establishing the observed asymmetry of ENSO as compared to the multiplicative stochastic part.