Spatial variations in sea surface temperature (SST) and rainfall changes over the tropics are investigated based on ensemble simulations for the first half of the twenty-first century under the greenhouse gas (GHG) emission scenario A1B with coupled ocean–atmosphere general circulation models of the Geophysical Fluid Dynamics Laboratory (GFDL) and National Center for Atmospheric Research (NCAR). Despite a GHG increase that is nearly uniform in space, pronounced patterns emerge in both SST and precipitation. Regional differences in SST warming can be as large as the tropical-mean warming. Specifically, the tropical Pacific warming features a conspicuous maximum along the equator and a minimum in the southeast subtropics. The former is associated with westerly wind anomalies whereas the latter is linked to intensified southeast trade winds, suggestive of wind–evaporation–SST feedback. There is a tendency for a greater warming in the northern subtropics than in the southern subtropics in accordance ...

Global Warming Pattern Formation: Sea Surface Temperature and Rainfall

We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Nino-Southern Oscillation (ENSO). The CMIP5 multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.

ENSO representation in climate models: from CMIP3 to CMIP5

A comprehensive statistical analysis of how an index of the Southern Oscillation changed from 1882 to 1995 was given by Trenberth and Hoar [1996], with a focus on the unusual nature of the 1990–1995 El Nino-Southern Oscillation (ENSO) warm event in the context of an observed trend for more El Nino and fewer La Nina events after the late 1970s. The conclusions of that study have been challenged by two studies which deal with only the part of our results pertaining to the length of runs of anomalies of one sign in the Southern Oscillation Index. They therefore neglect the essence of Trenberth and Hoar, which focussed on the magnitude of anomalies for certain periods and showed that anomalies during both the post-1976 and 1990-mid-1995 periods were highly unlikely given the previous record. With updated data through mid 1997, we have performed additional tests using a regression model with autoregressive-moving average (ARMA) errors that simultaneously estimates the appropriate ARMA model to fit the data and assesses the statistical significance of how unusual the two periods of interest are. The mean SOI for the post-1976 period is statistically different from the overall mean at <0.05% and so is the 1990-mid-1995 period. The recent evolution of ENSO, with a major new El Nino event underway in 1997, reinforces the evidence that the tendency for more El Nino and fewer La Nina events since the late 1970s is highly unusual and very unlikely to be accounted for solely by natural variability.

El Nino and climate change

The change in the zonal mean atmospheric circulation under global warming is studied in comparison with the response to El Nino forcing, by examining the model simulations conducted for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In contrast to the strengthening and contraction of the Hadley cell and the equatorward shift of the tropospheric zonal jets in response to El Nino, the Hadley cell weakens and expands poleward, and the jets move poleward in a warmed climate, despite the projected “El Nino–like” enhanced warming over the equatorial central and eastern Pacific. The hydrological impacts of global warming also exhibit distinct patterns over the subtropics and midlatitudes in comparison to the El Nino. Two feasible mechanisms are proposed for the zonal mean circulation response to global warming: 1) The increase in static stability of the subtropical and midlatitude troposphere, a robust result of the quasi-moist adiabatic adjustment to the surface warming, may stabilize the baroclinic eddy growth on the equatorward side of the storm tracks and push the eddy activity and the associated eddy-driven wind and subsidence poleward, leading to the poleward expansion of the Hadley cell and the shift of midlatitude jets; 2) the strengthening of the midlatitude wind at the upper troposphere and lower stratosphere, arguably a consequence of increases in the meridional temperature gradient near the tropopause level due to the tropospheric warming and tropopause slope, may increase the eastward propagation of the eddies emanating from the midlatitudes, and thus the subtropical region of wave breaking displaces poleward together with the eddy-driven circulation. Both mechanisms are somewhat, if not completely, distinct from those in response to the El Nino condition.

Response of the zonal mean atmospheric circulation to El Nino versus global warming

Originating in the equatorial Pacific, the El Nino–Southern Oscillation (ENSO) has highly consequential global impacts, motivating the need to understand its responses to anthropogenic warming. In this Review, we synthesize advances in observed and projected changes of multiple aspects of ENSO, including the processes behind such changes. As in previous syntheses, there is an inter-model consensus of an increase in future ENSO rainfall variability. Now, however, it is apparent that models that best capture key ENSO dynamics also tend to project an increase in future ENSO sea surface temperature variability and, thereby, ENSO magnitude under greenhouse warming, as well as an eastward shift and intensification of ENSO-related atmospheric teleconnections — the Pacific–North American and Pacific–South American patterns. Such projected changes are consistent with palaeoclimate evidence of stronger ENSO variability since the 1950s compared with past centuries. The increase in ENSO variability, though underpinned by increased equatorial Pacific upper-ocean stratification, is strongly influenced by internal variability, raising issues about its quantifiability and detectability. Yet, ongoing coordinated community efforts and computational advances are enabling long-simulation, large-ensemble experiments and high-resolution modelling, offering encouraging prospects for alleviating model biases, incorporating fundamental dynamical processes and reducing uncertainties in projections. The El Nino–Southern Oscillation (ENSO) has global climatic implications, necessitating understanding of its observed and projected changes. This Review brings together knowledge of ENSO in a warming climate, revealing projected increases in ENSO magnitude, as well as ENSO-related rainfall and sea surface temperature variability.

Changing El Niño–Southern Oscillation in a warming climate

El Nino–Southern Oscillation (ENSO) is the strongest mode of interannual climate variability in the current climate, influencing ecosystems, agriculture, and weather systems across the globe, but future projections of ENSO frequency and amplitude remain highly uncertain. A comparison of changes in ENSO in a range of past and future climate simulations can provide insights into the sensitivity of ENSO to changes in the mean state, including changes in the seasonality of incoming solar radiation, global average temperatures, and spatial patterns of sea surface temperatures. As a comprehensive set of coupled model simulations is now available for both palaeoclimate time slices (the Last Glacial Maximum, mid-Holocene, and last interglacial) and idealised future warming scenarios (1 % per year CO2 increase, abrupt four-time CO2 increase), this allows a detailed evaluation of ENSO changes in this wide range of climates. Such a comparison can assist in constraining uncertainty in future projections, providing insights into model agreement and the sensitivity of ENSO to a range of factors. The majority of models simulate a consistent weakening of ENSO activity in the last interglacial and mid-Holocene experiments, and there is an ensemble mean reduction of variability in the western equatorial Pacific in the Last Glacial Maximum experiments. Changes in global temperature produce a weaker precipitation response to ENSO in the cold Last Glacial Maximum experiments and an enhanced precipitation response to ENSO in the warm increased CO2 experiments. No consistent relationship between changes in ENSO amplitude and annual cycle was identified across experiments.

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Zhongshi Zhang

Papers

Comparison of past and future simulations of ENSO in CMIP5/PMIP3 and CMIP6/PMIP4 models