This thesis aims to improve the understanding of El Niño Southern Oscillation (ENSO) diversity, in its future change, modeling and predictability. How might ENSO change in the warming climate? To reach a comprehensive understanding, a set of empirical probabilistic diagnoses (EPD) is introduced to measure the ENSO behaviors as to tropical Pacific sea surface temperature (SST) climatology, annual cycle, ENSO amplitude, seasonal phase locking, diversity in peak location and propagation direction, as well as the El Niño-La Niña asymmetry in amplitude, duration and transition. This diagnosis is applied to the observations, and consistency with previous studies indicates it is valid. Analysis of 37 CMIP5 model simulations for the 20th century and the 21st century shows that, other than the projected increase in SST climatology, changes in other aspects are largely model dependent and generally within the range of natural variation. The change favoring eastward propagating El Niños is the most robust seen in the SST anomaly field. To what extent can we trust the future projection? CMIP5 models show large spreads in terms of 20th century ENSO performance. So a data-driven approach called Empirical Model Reduction (EMR) is carried out, by fitting a low-dimensional nonlinear model from the observation with a representation of memory effect and seasonality. The stochastic simulation of EMR is able to reproduce a realistic ENSO diversity statistics and a reasonable range of natural variation, thus provides an additional benchmark to evaluate the CMIP5 model biases. What are the key model components leading to a good performance to simulate and predict ENSO? Using a suite of models under the aforementioned framework of EMR, control experiments are conducted to advance the understanding of ENSO diversity, nonlinearity, seasonality and the memory effects. Nonlinearity is found necessary to reproduce the ENSO diversity features by simulating the extreme El Niños. Nonlinear models reconstruct the skewed distribution of SST anomalies and improve the prediction of the El Niño-La Niña transition. Models with periodic terms reproduce the SST seasonal phase locking but do not improve the prediction appreciably. Models representing the ENSO memory effect, based on either the recharge oscillator (multivariate model with tropical Pacific subsurface information) or the time-delayed oscillator (multilevel model with SST history information), both improve the prediction skill dramatically. Models with multiple ingredients capture several ENSO characteristics simultaneously and exhibit overall better prediction skill. In particular, models with a memory effect show an alleviated skill drop during the spring barrier and a reduced prediction timing delay. One new ENSO prediction target is to predict not only the occurrence and amplitude of El Niño (EN) but also the peak location is at the central Pacific (CP) or the eastern Pacific (EP). Many prediction models have difficulty with it, which motivates the investigation on whether such ENSO diversity has intrinsically limited predictability. Here three aspects are addressed including the source/limit of predictability, time range and uncertainty. Approaches are combined including linear inverse modeling, singular vector analysis and probabilistic measure. The results show that two similar initial conditions with western Pacific SST warming anomalies may finally develop to either CPEN or EPEN. The equatorial Pacific subsurface evolution is important to tell the final outcome. Restricted by the chaotic property, the prediction horizon appears to be ~4 months before CPEN and ~7 months before EPEN. A flavor prediction model using data's transition probabilities is introduced as a new benchmark for probabilistic prediction.

The El Niño – Southern Oscillation (ENSO) has far-reaching impacts across the globe and provides the most reliable source of seasonal to interannual climate prediction over North America. Though numerous studies have discussed the impacts of ENSO teleconnections on North America during boreal winter, it is becoming more and more apparent that the regional impacts of ENSO teleconnections are highly sensitive to the seasonal evolution of ENSO events. Also, the significant impacts of ENSO are not limited to the boreal winter seasons. To address these knowledge gaps, this thesis examines the seasonal dependence of ENSO teleconnections and impacts on North American surface climate, focusing on two examples. Chapter 1 examines the relationship between El Niño – California winter precipitation. Results show that the probability of the anomalous statewide-wetness increases as El Niño intensity increases. Also, the influences of El Niño on California winter precipitation are statistically significant in late winter (Feb-Apr), but not in early winter even though that is when El Niño usually reaches its peak intensity. Chapter 2 further investigates why the strong 2015/16 El Niño failed to bring above normal winter precipitation to California, focusing on the role of westward shifted equatorial Pacific sea surface temperature anomalies (SSTAs) based on two reasons: the maximum equatorial Pacific SSTAs was located westward during the 2015/16 winter compared to those during the 1982/83 and 1997/98 winters, both of which brought extremely wet late winters to California. Also, the North American Multi-Model Ensemble (NMME) forecasts overestimated the eastern tropical Pacific SSTAs and California precipitation in the 2015/16 late winter, compared to observations. The Atmospheric General Circulation Model (AGCM) experiments suggested that the SST forecast error in NMME contributed partially to the wet bias in California precipitation forecast in the 2015/16 late winter. However, the atmospheric internal variability could have also played a large role in the dry California winter during the event. ENSO also exerts significant impacts on agricultural production over the Midwest during boreal summer. Chapter 3 examines the physical processes of the ENSO summer teleconnection, focusing on the summer when a La Niña is either transitioning from an earlier El Niño winter or persisting from an existing La Niña winter. The results demonstrate that the impacts are most significant during the summer when El Niño is transitioning to La Niña compared to that when La Niña is persisting, even though both can loosely be defined as developing La Niña summer. During the transitioning summer, both the decaying El Niño and the developing La Niña induce suppressed deep convection over the tropical Pacific and thereby the corresponding Rossby wave propagations toward North America, resulting in a statistically significant anomalous anticyclone over northeastern North America and, therefore, a robust warming signal over the Midwest. These features are unique to the developing La Niña transitioning from El Niño, but not the persistent La Niña. In Chapter 4, we further evaluate the performance of NCAR CAM5 forced with historical SSTA in terms of the La Niña summer teleconnections. Though the model ensemble mean well reproduces the features in the preceding El Niño/La Niña winters, the model ensemble mean has very limited skill in simulating the tropical convection and extratropical teleconnections during both the transitioning and persisting summers. The weak responses in the model ensemble mean are attributed to large variability in both the tropical precipitation, especially over the western Pacific, and atmospheric circulation during summer season. This thesis synthesizes the physical processes and assessments of climate models in different seasons to establish the sensitivity of regional climate to the seasonal dependence of ENSO teleconnections. We demonstrate that the strongest impacts of ENSO on North American regional climate might not be necessarily simultaneous with maximum tropical Pacific SST anomalies. We also emphasize the importance of the multi-year ENSO evolutions when addressing the seasonal impacts on North American summertime climate. The findings in this thesis could benefit the improvement of seasonal hydroclimate forecasting skills in the future.