The El Niño-Southern Oscillation (ENSO) drives substantial variability in rainfall, severe weather, agricultural production, ecosystems and disease in many parts of the world. Given that further human-forced changes in the Earth's climate system seem inevitable, the possibility exists that the character of ENSO and its impacts might change over the coming century. Although this issue has been investigated many times during the past 20 years, there is very little consensus on future changes in ENSO, apart from an expectation that ENSO will continue to be a dominant source of year-to-year variability. Here we show that there are in fact robust projected changes in the spatial patterns of year-to-year ENSO-driven variability in both surface temperature and precipitation. These changes are evident in the two most recent generations of climate models, using four different scenarios for CO2 and other radiatively active gases. By the mid- to late twenty-first century, the projections include an intensification of both El-Niño-driven drying in the western Pacific Ocean and rainfall increases in the central and eastern equatorial Pacific. Experiments with an Atmospheric General Circulation Model reveal that robust projected changes in precipitation anomalies during El Niño years are primarily determined by a nonlinear response to surface global warming. Uncertain projected changes in the amplitude of ENSO-driven surface temperature variability have only a secondary role. Projected changes in key characteristics of ENSO are consequently much clearer than previously realized.

[1] The ability to reliably estimate CO 2 fluxes from current in situ atmospheric CO 2 measurements and future satellite CO 2 measurements is dependent on transport model performance at synoptic and shorter timescales. The TransCom continuous experiment was designed to evaluate the performance of forward transport model simulations at hourly, daily, and synoptic timescales, and we focus on the latter two in this paper. Twenty-five transport models or model variants submitted hourly time series of nine predetermined tracers (seven for CO 2 ) at 280 locations. We extracted synoptic-scale variability from daily averaged CO 2 time series using a digital filter and analyzed the results by comparing them to atmospheric measurements at 35 locations. The correlations between modeled and observed synoptic CO 2 variabilities were almost always largest with zero time lag and statistically significant for most models and most locations. Generally, the model results using diurnally varying land fluxes were closer to the observations compared to those obtained using monthly mean or daily average fluxes, and winter was often better simulated than summer. Model results at higher spatial resolution compared better with observations, mostly because these models were able to sample closer to the measurement site location. The amplitude and correlation of model-data variability is strongly model and season dependent. Overall similarity in modeled synoptic CO 2 variability suggests that the first-order transport mechanisms are fairly well parameterized in the models, and no clear distinction was found between the meteorological analyses in capturing the synoptic-scale dynamics.