2019/03/28 - 分享人:Sulian Thual、戴国锟
发布时间: 2019-03-26



Title: Identifying the ENSO harmonic oscillator in models and observations

Sulian Thual

Abstract: The El Niño-Southern Oscillation (ENSO) is a major climate signal on Earth with dramatic worldwide ecological and social impacts. It consists of an irregular cycle of alternating warm El Niño conditions and cold La Niña conditions in the equatorial Pacific that repeat around every 2 to 7 years and involve a strong ocean-atmosphere coupling. The simplest models that describe this cyclic nature are harmonic oscillators. Typically, these models are constructed from ordinary differential equations with basic modifications (nonlinearities, multiplicative noise, seasonal modulation...) and are able to reproduce the observed timeseries of common ENSO indices.

We propose here new types of ENSO harmonic oscillators that are derived directly as reduced versions of more complete models of the equatorial Pacific. The method takes advantages of the fact that in many of these models the ENSO variability arises as an oscillating eigenmode pair (or two) that is dominant in terms of growth/decay rate. The present harmonic oscillators can be easily adapted to represent the timeseries of any pair of ENSO indices and have the important advantages of conserving dynamical and spatial features consistent with the ones of their corresponding complete model. They can also be used to assess a model's strengths and deficiencies in representing the ENSO in nature through the identification of dynamical and spatial model biases that may otherwise be missed by common statistical methods. 


Title: A Brief Introduction of TIGGE Dataset and Its Application (in Chinese)

TIGGE数据集及其应用的介绍

Guokun Dai

 Abstract: The TIGGE (THORPEX Interactive Grand Global Ensemble) dataset consists of ensemble forecast data from ten global numerical weather prediction centres, starting from October 2006, which has been made available for scientific research. TIGGE was established as a key component of THORPEX: a World Weather Research Programme to accelerate the improvements in the accuracy of 1-day to 2 week high-impact weather forecasts for the benefit of humanity. In this talk, a brief introduction of the TIGGE dataset will be given first. After that, several investigations about ensemble forecasting, predictability, prediction of severe weather and forecast skill evaluation with TIGGE dataset will be reviewed.