LANS Informal Seminar: Chen Chen

Seminar Title: ENSO diversity: future change, modeling and predictability
Speaker: Chen Chen,

Date/Time: 2016-07-21 11:00
Location: Bldg 240 rm 1407

Description:
El Niño Southern Oscillation (ENSO) is one atmosphere-ocean coupled variability that takes place in the tropical Pacific roughly every two to seven years. Its occurrence has far-reaching impacts on seasonal precipitation and temperature patterns in many areas of the globe.

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 the 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. EPD applied to 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 of ENSO are largely model dependent and generally within the range of natural variation. The change favoring eastward propagating El Niños is the most robust response seen in the SST anomaly field.

GCMs and ESMs in CMIP5 show a large spread of ENSO performance, while a data-driven model called Empirical Model Reduction (EMR; a low-dimensional nonlinear model fit from the observation with a representation of memory effect and seasonality) is able to reproduce reasonable realistic ENSO statistics. So a suite of EMR control experiments are conducted to investigate the key model ingredients for ENSO simulation and prediction. Appropriate level of model nonlinearity is found necessary to reconstruct the skewed distribution of SST anomalies and reproduce a realistic ENSO diversity by being able to simulate the extreme El Niños. The nonlinear model also improves the prediction of the El Niño-La Niña transition compared to the linear model. Models with seasonal periodic terms reproduce ENSO winter phase locking feature but do not improve the prediction skill 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.

El Niños peaking in the central Pacific (CP) or the eastern Pacific (EP) are associated with varying global impacts. So one new target is to predict not only the occurrence and amplitude of El Niño (EN) but also its peak location. The fact that many prediction models have difficulties with it motivates the investigation on whether such ENSO diversity has an intrinsically limited predictability. The estimate is carried out using linear inverse modeling, singular vector analysis and probabilistic measures. The results show that two similar initial conditions with western Pacific SST warming anomalies may develop to either CPEN or EPEN. The equatorial Pacific subsurface evolution is important to tell the final outcome. The prediction horizon in SST field appears to be four months for CPEN and seven months for EPEN. A benchmark using transition probabilities is introduced for the probabilistic El Niño flavor prediction.

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Chen Chen is a new postdoctoral scholar in the department of the geophysical sciences at University of Chicago. She is currently involved in both the center for robust decision making on climate and energy policy at Computation Institute and the research network of statistical methods for atmospheric and oceanic sciences. She received her PhD of Climatology from the department of earth and environmental sciences at Columbia University. Her research interests include: climate variabilities and impacts, climate change sensitivity, modeling and diagnostics, predictability and forecast, geophysical fluid dynamics (rotating horizontal convection).