□ 추진 배경
○ 한․러 기상협력 약정(1999년 5월)이후 2001년부터 러시아 베이코프 중앙지구물리연구소와 국립기상연구소간 공동워크
숍 개최
○ 지난 2007년까지 4회에 걸쳐 2년을 주기로 상호개최
- 우리나라 개최 : 2001년 1차, 2005년 3차
- 러시아 개최 : 2003년 2차, 2007년 4차
○ 제 5차 한·러 공동 기후변화워크숍은 2010년 한·러 수교 20주년 기념행사의 일환으로 추진
- Workshop title : The 5th Korea-Russia Joint Workshop on Climate Change and Variability in Eurasian Continent

□ 목적
○ 국가간 기후변화 및 기후변동성 연구에 관한 기술정보 및 전문가 교류를 통하여 기후관련 연구협력 강화

□ 회의기간 및 장소
○ 2010년 5월 26(수)~28(금) / 제주 KAL호텔

□ 주요내용
○ Observations of Climate System
○ Climate and Weather extremes
○ Global and regional climate change projections
○ Detection and attribution of climate change
○ Impact and vulnerability assessment of climate change
○ Development and evaluation of climate model
○ Towards seamless prediction: from NWP to climate change projection
○ Applied meteorology including air quality and weather modification
○ Biogeochemical cycle on climate change

 

We examined 1) the causes of inter-model variations of surface energy partitioning (SEP) and 2) the effects of model grid size on the simulated SEP. In particular, we focus on the nonlinear effect of spatial heterogeneity in atmospheric conditions on the simulation of surface fluxes in the mesoscale model by testing their scale-invariance from a tower footprint to regional scales. The test domain was a homogeneous shortgrass prairie in the central part of the Tibetan Plateau with an eddy-covariance flux tower at the center. We found that 1) soil evaporation controls the model differences of the SEP and 2) the spatial variability resulting from changing distribution of clouds and precipitation in the model domain affected radiative forcing at the ground surface, thereby altering the partitioning of surface fluxes. Consequently, due to increasing spatial variability in atmospheric conditions, the results of the mesoscale model did not produce convergent estimates of surface fluxes with increasing grid sizes. Our finding demonstrates that an atmospheric model can underestimate surface fluxes in regional scale not necessarily due to intrinsic model inaccuracy (e.g., inaccurate parameterization) but due to scale-dependent nonlinear effect of spatial variability in atmospheric conditions.