홍콩시립대의 Dr. Chi-Yung Francis Tam이 오는 1월 11일 APEC 기후센터를 방문한다. Dr. Tam은 “Forecasting the Seasonal Mean East Asian Rainfall during Decaying ENSO/ENSO Modoki-Connection with SST Predictability”라는 제목으로 오전 11시, 부산에 위치한 APEC 기후센터의 로터스 국제회의장에서 세미나 발표를 진행한다.

이어서, 같은 날 오후 2시, 3층 중회의실에서는 APEC 기후센터 기후변화팀의 신선희 박사, 김옥연 박사와 기후분석팀의 양유빈 박사가 Dr. Tam과 함께 “Dynamical Downscaling/TC Seasonal Prediction Collaboration”에 관해 정보 및 의견을 교환할 예정이다. 또한 오후 4시에는 기후예측팀의 손수진 박사와 “ENSO-ENSO Modoki Seasonal Prediction Project Using the APCC Coupled MME”에 대해 논의한다.

APEC 기후센터와 홍콩시립대학교의 에너지환경대학교(School of Energy and Environment of the City University of Hong Kong)는 지난 2011년, 기후학과 환경학 분야의 지역 기후 예측 및 열대성 사이클론 계절 예측 시스템 분야를 위한 기본연구 및 응용연구에 관한 상호 협력과 공동 프로그램 개발에 대한 필요성을 인정하고 2011년 9월, 환경적 변동성, 변화, 예측 연구 분야를 위한 협력에 관한 협약(MoU)을 체결한 바 있다.

이번 회의는 양 기관의 최신 지식과 정보를 공유하고 APCC의 기후 정보를 활용을 논하는 자리가 될 것으로 보인다. Dr. Tam의 발표 세미나의 구체적인 내용은 아래와 같다.

1. Presenter: Dr. Chi-Yung Francis Tam (Assistant Professor of School of Energy and Environment, City University of Hong Kong)
 

2. Title: Forecasting the Seasonal Mean East Asian Rainfall during Decaying ENSO/ENSO Modoki-Connection with SST Predictability
 

3. Time: 11:00 a.m. on January 11, 2013
 

4. Place: The Lotus room on the second floor, APCC
 

5. Abstract
Hindcast experiments from the 6-month lead APCC coupled MME prediction were analyzed, focusing on the prediction of ENSO and ENSO Modoki, as well as their impacts on East Asian (EA) climate, during their decay phase from March to August (MAMJJA). Overall, models perform better in predicting the Nino3 index than the ENSO-Modoki index (EMI) during the MAMJJA period. The MME mean outperforms all individual models in forecasting the Nino3 index in its 1- to 6- month lead predictions. However, this is not the case for EMI forecast, probably due to the poor performance of one to two models. Auto-correlation analyses show that, from observations, Modoki events are more persistent than canonical ENSO events. On the other hand, it seems difficult for model to reproduce such strong persistence of Modoki signals, and this is especially the case for 4-to-6-month lead hindcast predictions. Also, composite analyses and the pattern projection method reveal that, for some models, there is a strong "degree of mixing" between canonical El Nino and El Nino Modoki sea surface temperature (SST) patterns, and that the mixing tends to be more severe as forecast lead time increases. Despite the difficulties in capturing the SST variations, models have skills in reproducing the large-scale wind circulation over East Asian region during the two types of ENSO in their decaying phases. However, regional details of the EA rainfall anomalies are not well reproduced in the hindcast experiments. Finally, inspection of individual model performance suggests that a correct model basis state of the EA monsoon is probably the crucial factor in determining model's skill of the EA rainfall in the JJA season after the peak of the ENSO or ENSO Modoki.
 

6. Brief CV

1) Education
ㆍPh. D in Atmospheric and Oceanic Sciences – 2004: Princeton University
 

2) Professional Experience
ㆍNovember 2010 - present: Assistant Professor, School of Energy and Environment, City University of Hong Kong
ㆍ2008-2010, Lecturer, School of Energy and Environment, City University of Hong Kong
ㆍ2006-2008, Research Scientist, APEC Climate Center, Busan, Korea
ㆍ2003-2006, Postdoctoral Fellow, International Pacific Research Center, U of Hawaii
ㆍ1998-2003, Research Assistant, Program in Atmospheric and Oceanic Sciences, Princeton U
ㆍ1997-1998, Scientific Officer, Hong Kong Observatory
ㆍ1996-1997, Graduate Master, St. Paul's Co-educational College, Hong Kong
ㆍ1993-1996, Teaching Assistant, Physics Department, Chinese University of Hong Kong



* About the School of Energy and Environment at the City University of Hong Kong
The School of Energy and Environment (SEE) was established in 2009 at the City University of Hong Kong with the mission to perform cutting-edge research and provide professional education in energy and environment related issues. SEE is at the forefront of fundamental and application-oriented research, as well as professional training, in the areas of energy and the environment.