APEC 기후센터(APCC) 기후예측팀의 손수진 박사와 기후변화연구팀의 Rohini Lakshman Bhawar 박사가 오는 4월 7일 ~ 12일에 오스트리아 빈에서 열리는 유럽 최대 학회 European Geosciences Union (EGU) General Assembly 2013에 참석해 연구 내용을 발표한다.

이번 학회에서 손수진 박사는 “Six-month lead downscaling prediction of winter-spring drought in South Korea based on a multi-model ensemble”라는 제목으로 발표를 진행한다. Dr. Rohini Lakshman Bhawar 박사는 “Cloud types and their radiative forcing during summer monsoon season over South-East Asia”의 제목으로 연구 내용을 전달할 예정이다.

손박사와 Rohini 박사의 발표 제목과 초록은 아래와 같다.

* Dr. Rohini Lakshman Bhawar’s Presentation:
Cloud types and their radiative forcing during summer monsoon season over South-East Asia
Rohini Bhawar¹, Rahul P.R.C²., Ali Behrangi³ and Mahesh Shinde^2,4
1 APEC Climate Center, Busan, South Korea
² Indian Institute of Tropical Meteorology, Pune, India
³ Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109
⁴ Catalan Institute of Climate Sciences, Barcelona, Spain
 

Clouds play a very important role in sustaining one of the largest monsoon systems, the Asian monsoon, in terms of formation, occurrence and feedback. Most of the previous studies about cloud variability have been concentrated over oceans. Though different cloud types drive the mesoscale circulation, there is still a lack of understanding of the cloud types and their role in the feedback processes which involve microphysical effects that eventually affect precipitation patterns. Thus, the dominance of cloud types, in terms of occurrence frequency and radiative forcing estimates, are a crucial aspect and are being investigated in this study. We use the NASA A-train satellite CloudSat data which provides unprecedented 3-dimensional measurements of cloud profiles. CloudSat data is used to study the cloud types (cirrus, deep convective, cumulus, etc.), in conjunction with the International Satellite Cloud Climatology Project (ISCCP) dataset, which also gives diurnal variability. This study focuses on the South-East Asia region during the summer monsoon season, June-September of 2008. We also examine clouds by large-scale parameters ω500, OLR, temperature, SST, etc. from these datasets. The impacts of clouds on radiation will also be estimated.

* Dr. Soo-Jin Sohn’s Presentation:
Six-month lead downscaling prediction of winter-spring drought in South Korea based on a multi-model ensemble
Soo-Jin Sohn¹, Joong-Bae Ahn², and Chi-Yung Tam^3
1 Climate Prediction Team, Climate Research Department, APEC Climate Center, Busan, Republic of Korea
² Division of Earth Environmental System, Pusan National University, Busan, Republic of Korea
³ Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China

Given the changing climate, advance information on hydrological extremes such as droughts will help in planning for disaster mitigation and facilitate better decision making for water availability management. A deficit of precipitation for long-term time scales beyond 6 months has impacts on the hydrological sectors such as ground water, streamflow, and reservoir storage. The potential of using a dynamical-statistical method for long-lead drought prediction was investigated. In particular, the APEC Climate Center (APCC) 1-Tier multi-model ensemble (MME) was downscaled for predicting the standardized precipitation evapotranspiration index (SPEI) over 60 stations in South Korea. SPEI depends on both of precipitation and temperature, and can incorporate the impact of global warming on the balance between precipitation and evapotranspiration. It was found that 1-Tier MME has difficulties in capturing the local temperature and rainfall variations over extratropical land areas, and has no skill in predicting SPEI during boreal winter and spring. On the other hand, temperature and precipitation predictions were substantially improved in the downscaled MME (DMME). In conjunction with variance inflation, DMME can give reasonably skillful six-month-lead forecasts of SPEI for the winter-to-spring period. The results could potentially improve hydrological extreme predictions using meteorological forecasts for policymaker and stakeholders in water management sector for better climate adaption.