ENSO asymmetry: the search for extreme El Niño events in HadGEM

<p>Analysis of a long control run of the Hadley Centre coupled model shows that ENSO asymmetry is weak. We use the same model in our seasonal and decadal prediction systems, and while on seasonal timescales the initialised prediction realistically captures the amplitude of extreme El Niño events, on longer timescales the predictions revert to the control behaviour i.e. there are no very large El Niño events. This may impact on our ability evaluate the risk of extreme regional events. Here we show results exploring asymmetry in both the control model, and also from a number of perturbed parameter experiments, each a plausible realisation of the control.</p>

Dynamics for El Niño-La Niña asymmetry constrain equatorial-Pacific warming pattern

The El Niño-Southern Oscillation (ENSO) results from the instability of and also modulates the strength of the tropical-Pacific cold tongue. While climate models reproduce observed ENSO amplitude relatively well, the majority still simulates its asymmetry between warm (El Niño) and cold (La Niña) phases very poorly. The causes of this major deficiency and consequences thereof are so far not well understood. Analysing both reanalyses and climate models, we here show that simulated ENSO asymmetry is largely proportional to subsurface nonlinear dynamical heating (NDH) along the equatorial Pacific thermocline. Most climate models suffer from too-weak NDH and too-weak linear dynamical ocean-atmosphere coupling. Nevertheless, a sizeable subset (about 1/3) having relatively realistic NDH shows that El Niño-likeness of the equatorial-Pacific warming pattern is linearly related to ENSO amplitude change in response to greenhouse warming. Therefore, better simulating the dynamics of ENSO asymmetry potentially reduces uncertainty in future projections.

Impacts of Atmospheric Processes on ENSO Asymmetry: A Comparison between CESM1 and CCSM4

An evaluation of El Niño–La Niña asymmetry is conducted in the two recent NCAR coupled models (CCSM4 and CESM1) sharing the same ocean component. Results show that two coupled models generally underestimate observed ENSO asymmetry, mainly owing to an overestimate of the cold SST anomaly during the La Niña phase. The weaker ENSO asymmetry corresponds to a cold bias in mean SST climatology that is more severe in CESM1 than in CCSM4, despite a better performance in simulating ENSO asymmetry in the former. Corresponding AMIP (CAM4 and CAM5) runs are examined to probe the origin of the weaker ENSO asymmetry in coupled models. The analysis reveals a stronger time mean zonal wind in AMIP models, favoring a cold bias in mean SST. The bias of the stronger mean wind, associated with changes in mean precipitation, is more significant in CAM5 than in CAM4. The simulated skewness of the interannual variability of zonal winds is weaker than observations but somewhat improved in CAM5 compared to CAM4, primarily resulting from a more westward shift of easterly wind anomalies tied to the displacement of precipitation anomalies during the cold phase. Wind-forced ocean GCM experiments confirm that the bias in AMIP model winds can weaken ENSO asymmetry, with the contribution from the wind interannual variability being larger than from the mean winds. This demonstrates that the bias in ENSO asymmetry in coupled models can be traced back to the bias in the stand-alone atmosphere models to a large extent. The results pinpoint a pathway to reduce the bias in ENSO asymmetry in coupled models.

Factors Determining the Asymmetry of ENSO

A fundamental aspect of the observed ENSO is the positive asymmetry between its two phases: the strongest El Niño is stronger than the strongest La Niña. The nonlinear term in the equation for the surface ocean heat budget has been theorized as a cause of the asymmetry. This theory is challenged by the diversity of asymmetry among the CMIP5 models: these models all employ primitive equations and thus have the nonlinear term in the heat budget equation for the ocean surface, yet the asymmetry simulated by these models ranges from significantly negative to significantly positive. Here, the authors employ an analytical but nonlinear model—a model that simulates the observed ENSO asymmetry—to show that the nonlinear heating term does not guarantee the oscillation in the system to possess positive asymmetry. Rather, the system can have regimes with negative, zero, and positive asymmetry. The regime in which the system finds itself depends on a multitude of physical parameters. Moreover, the range of certain physical parameters for the system to fall in the regime with positive asymmetry in the oscillation is rather narrow, underscoring the difficulty of simulating the observed ENSO asymmetry by CMIP5 models. Moreover, stronger positive asymmetry is found to be associated with a more complicated oscillation pattern: the two adjacent strongest warm events are spaced farther apart and more small events occur in between. These results deepen the understanding of factors that are behind the asymmetry of ENSO and offer paths to take to improve model-simulated ENSO asymmetry.